# Modeling and Optimization of Energy Efficiency in 5G mmWave Networks With JT-CoMP Under Different Deployment Conditions

Mohamad Younes, Laurent Quibus

► To cite this version:

Mohamad Younes, Laurent Quibus. Modeling and Optimization of Energy Efficiency in 5G mmWave Networks With JT-CoMP Under Different Deployment Conditions. IEEE Access, 2025, 13, pp.115804-115827. 10.1109/ACCESS.2025.3584304. hal-05245045

HAL Id: hal-05245045

https://hal.science/hal-05245045v1

Submitted on 14 Nov 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Received 5 June 2025, accepted 22 June 2025, date of publication 30 June 2025, date of current version 10 July 2025.

Digital Object Identifier 10.1109/ACCESS.2025.3584304

RESEARCH ARTICLE

# Modeling and Optimization of Energy Efficiency in 5G mmWave Networks With JT-CoMP Under Different Deployment Conditions

MOHAMAD YOUNES<sup>1</sup> AND LAURENT QUIBUS<sup>1,2</sup>

Corresponding author: Mohamad Younes (mohamad.younes@st-cyr.terre-net.defense.gouv.fr)

**ABSTRACT** In the face of exponential growth in mobile traffic and network densification, reducing energy consumption is a strategic challenge for the sustainability of 5G networks, particularly in the millimeter wave (mmWave) bands, where high propagation losses and inter-cell interference make optimizing Energy Efficiency (EE) a complex task. The urgent need to develop reliable models to quantify and improve EE under these conditions is all the more crucial given that operators have to reconcile performance and energy sobriety. Against this backdrop, this article proposes an accurate and flexible analytical model to assess the EE of two transmission modes: Joint Transmission-Coordinated Multi-Point (JT-CoMP), which exploits inter-cell interference as useful signals, and the uncoordinated No-CoMP mode. The analysis covers several typical scenarios for 5G mmWave networks, including Rural Macro (RMa), Urban Macro (UMa), and Urban Micro (UMi) environments. The model features the joint integration of complex propagation parameters (line-of-sight probabilities, path losses, shadow fading, three-dimensional distances, and breakpoints), topological factors (type of base stations and Inter-Site Distances – ISDs), as well as the main sources of energy consumption (transmission, circuits, and backhaul), while taking into account the spatial distribution of users. Simulation results demonstrate: 1) a clear superiority of JT-CoMP in terms of EE over No-CoMP in densified environments, with maximum gains observed in UMi, particularly at high mmWave frequencies; 2) the specific challenges of UMa, linked to larger ISDs in urban environments, although it offers higher data rates; and 3) the importance of adapting ISD and transmission power to maintain optimal EE. Finally, we identify critical thresholds beyond which No-CoMP becomes more efficient, underlining the importance of adaptive transmission strategies depending on the environment.

**INDEX TERMS** Energy efficiency (EE), inter-cell interference (ICI), inter-site distances (ISDs), joint transmission-coordinated multi-point (JT-CoMP), millimeter wave (mmWave), rural macro (RMa), urban macro (UMa), urban micro (UMi).

## I. INTRODUCTION

Fifth-Generation (5G) wireless networks mark a major evolution in communications technologies, meeting growing demands for data rate, ultra-low latency and ubiquitous connectivity [1]. One of the key advances of 5G lies in the exploitation of millimeter waves (mmWave, 30-300 GHz), which, thanks to their wide bandwidths, enable ultra-high

The associate editor coordinating the review of this manuscript and approving it for publication was Chih-Min Yu.

data rates and improved communications in complex environments [2]. However, these benefits come with major challenges: high attenuation, limited diffraction, and an increased sensitivity to obstacles, leading to an unstable coverage, particularly in dense urban environments [3]. These constraints are even more pronounced when the same content, such as an emergency alert, needs to be transmitted simultaneously on a single carrier frequency to a large number of User Equipments (UEs) [4], [5]. Such transmissions require efficient resource management to guarantee the necessary

<footnote n="1">¹CReC Saint-Cyr, Saint-Cyr Coëtquidan Military Academy, 56381 Guer, France</footnote>

<footnote n="2">²IETR-UMR CNRS 6164, University of Rennes, 35708 Rennes, France</footnote>
Quality of Service (QoS), while avoiding network saturation, which is the main challenge of this study.

To overcome these limitations, network densification appears to be a promising solution, making it possible to improve both coverage and capacity [4], [6]. However, this approach intensifies Inter-Cell Interference (ICI), which can degrade QoS, an issue that is all the more critical during mass transmissions, as in emergency situations [7]. In this context, multicast/broadcast [8], [9], [10] and Joint Transmission-Coordinated Multi-Point (JT-CoMP) [11], [12], [13], [14] modes are emerging as alternatives to the Unicast (UC) mode (also known as No-CoMP).

Multicast/broadcast modes, such as Point-To-Multipoint (PTM), within a single Base Station (BS) and Single Frequency Network (SFN), over multiple BSs, guarantee a constant capacity for all UEs, based on the worst-case Signal-to-Interference-plus-Noise Ratio (SINR) [8], [9], [10], [15]. On the other hand, JT-CoMP, although it involves the cooperation between BSs like SFN, exhibits a variable capacity depending on the position of the UEs, due to variations in SINR induced by propagation conditions and dynamic interference [11], [12], [13], [14]. This particularity raises energy optimization issues, especially with regard to the trade-offs between network performance and energy consumption, a central issue we will explore in detail in this article.

Our previous research [8] investigated the performance of UC and SFN modes at low frequencies, assessing their efficiency in allocating spectral resources. That work was extended in [9] to incorporate a wider range of transmission modes, such as UC, UC with beamforming, PTM and SFN, exploring various scenarios. In those works, we introduced a novel method for determining the optimal threshold of UEs per BS at which broadcast modes outperform UC in terms of spectral allocation. Following on from those works, the present study focuses on the potential of JT-CoMP mode for the large-scale transmission of identical content, with a particular emphasis on the energy challenges involved in this cooperative approach.

Initially designed for frequencies below 6 GHz, JT-CoMP is finding increased relevance in the mmWave bands, where it can reduce ICI and improve connectivity, particularly at the cell edge [16], [17], [18], [19]. Its principle is based on the cooperation of several BSs transmitting data simultaneously to a given user, removing the most unfavorable SINR constraint and improving the overall Energy Efficiency (EE) [20], [21], [22], [23], [24]. However, its integration into mmWave densified networks raises major challenges, particularly in terms of time synchronization between BSs to avoid mismatches in signal arrival times [8], [9], [25]. In addition, simultaneous transmission between BSs increases the energy load on the backhaul, which can adversely affect the EE [25], [26], [27]. This challenge is further complicated by the fact that the EE of JT-CoMP is strongly influenced by several interdependent parameters, such as the density of BSs, their type (macro or micro) and the specifics of the deployment

environment: Rural Macro (RMa), Urban Macro (UMa) and Urban Micro (UMi).

With this in mind, our study proposes an EE model specifically adapted to mmWave networks, in RMa, UMa and UMi environments. Unlike traditional approaches, this model takes into account parameters often neglected, such as Line-of-Sight (LOS) and NLOS (Non-LOS) propagation conditions, Shadow Fading (SF) effects, and environment-adjusted propagation models, in line with the 3rd Generation Partnership Project (3GPP) [28], 5G Channel Model (5GCM) [29] and Millimeter-Wave Based Mobile Radio Access Network for 5G Integrated Communications (mmMAGIC) [30] standards. It also incorporates key factors such as three-dimensional distances, BreakPoint (BP) distances, ISDs, ICI, and the energy consumption of macro- and micro-BSs (including transmission, Radio Frequency (RF) circuits and backhaul), enabling a more accurate EE assessment.

To deepen our understanding of these complex interactions, our study evaluates several key indicators beyond the EE, such as the coverage probability, the average and worst-case SINR, the backhaul power consumption, and the data rate. The comparative analysis of No-CoMP and JT-CoMP modes, in various scenarios, highlights the necessary trade-offs between energy consumption and network performance. Our work thus paves the way for more efficient and energy-saving 5G mmWave networks, capable of supporting critical uses such as the dissemination of emergency alerts, thus helping to enhance public safety.

The remainder of this article is structured as follows: Section II provides a review of the state-of-the-art on EE management with JT-CoMP in low-frequency networks, before examining its application to 5G mmWave networks. Here, we highlight the main limitations of existing approaches and present our contributions. Section III describes the system model and SINR calculation in No-CoMP and JT-CoMP modes, while proposing expressions for the EE. Section IV is devoted to the LOS probability and Path Loss (PL) models developed by the 3GPP, 5GCM and mmMAGIC standards, adapted to RMa, UMa and UMi environments in mmWave frequencies. Section V presents simulation results and analyzes the impact of various parameters on key performance indicators, with a particular focus on the EE. Finally, Section VI summarizes the main results.

## II. RELATED WORKS AND OUR CONTRIBUTIONS

In this section, we first present related work, focusing on studies addressing JT-CoMP transmission. Table 1 provides a comparative overview, highlighting the methodologies used, key contributions, and the main limitations that our approach aims to overcome. After a more detailed discussion of each reference, we then describe our own contributions, emphasizing how they address the remaining challenges in the existing literature.

The bibliographic search was carried out using databases such as IEEE Xplore, SpringerLink, and Google Scholar, following the following main selection criteria:
<table><caption>TABLE 1. Comparative analysis of existing JT-CoMP and mmWave studies: Methodologies, contributions, and limitations relative to our work.</caption><thead><tr><td>Ref.</td><td>Methodology Used</td><td>Main Contributions</td><td>Identified Limitations Compared to Our Contribution</td></tr></thead><tbody><tr><td>[11], [12]</td><td>Joint optimization of JT-CoMP deployment and Energy and Spectral Efficiency (ESE), activation of BSs in JT-CoMP</td><td>Interference management, energy optimization, improvement of ESE, EE, SINR, and coverage</td><td>- Simplified propagation model, not adapted to UMa, UMi, and RMa environments</td></tr><tr><td>[13], [14]</td><td>Radio Resource Management (RRM) with JT-CoMP in Heterogeneous Networks (HetNets) and Orthogonal Frequency Division Multiple Access (OFDMA)</td><td>RRM-centered optimization, EE improvement under QoS constraints</td><td>- No dedicated analysis of mmWave-specific features (LOS/NLOS, SF, and BP)</td></tr><tr><td>[31]–[34]</td><td>JT-CoMP with Dynamic Point Selection (DPS)</td><td>EE gains, transmission power optimization, and performance improvement</td><td>- Incomplete energy consumption models (RF circuits and/or backhaul ignored)</td></tr><tr><td>[35]–[37]</td><td>JT-CoMP with integration of green energy</td><td>EE optimization, SINR improvement, and carbon emission reduction</td><td>- No consideration of power variations according to BS type/environment</td></tr><tr><td>[38]–[41]</td><td>JT-CoMP combined with Non-Orthogonal Multiple Access (NOMA)/Orthogonal Multiple Access (OMA), multi-connectivity, NOMA clustering</td><td>EE optimization, throughput increase, interference management, and computational complexity reduction</td><td>- No analysis of the impact of BS density and ISD</td></tr><tr><td>[42]–[44], [25]</td><td>JT-CoMP with heterogeneous BSs (macro/pico/femto)</td><td>Joint improvement of EE and coverage</td><td>- No analysis of dynamic switching between No-CoMP and JT-CoMP</td></tr><tr><td>[16]</td><td>mmWave measurement campaign (73 GHz), UMi environment</td><td>Large-scale measurements, interference reduction, fading modeling, human blockage, and macrodiversity efficiency</td><td>- No treatment of EE in 5G mmWave</td></tr><tr><td>[17]</td><td>Analysis and comparison of three JT-CoMP strategies</td><td>Selection of the optimal scheme, interference reduction, and downlink precoding</td><td>- Incomplete energy consumption models (RF circuits and/or backhaul ignored)</td></tr><tr><td>[18], [19], [45]</td><td>mmWave JT-CoMP, random blockage, robust hybrid beamforming, imperfect CSI, and stochastic learning</td><td>Capacity and reliability enhancement of mmWave JT-CoMP links under blockages and imperfections</td><td>- No integration of variable power by environment</td></tr><tr><td>[46]–[48]</td><td>DPS / Non-Coherent JT-CoMP / Hybrid JT-CoMP, mmWave, JT clustering, BS caching, and dynamic routing</td><td>Performance optimization with JT-CoMP (throughput and QoS) in mmWave, backhaul traffic reduction, dynamic ICI management, and simulations of various scenarios (indoor/macro)</td><td>- Absence of realistic multi-environment modeling (UMa, UMi, and RMa)</td></tr><tr><td>[20]–[24]</td><td>JT-CoMP with massive MIMO/NOMA/Simultaneous Wireless Information and</td><td>EE optimization in JT-CoMP under mmWave</td><td>- Simplified propagation models without realistic mmWave integration (LOS/NLOS, SF, BP)</td></tr><tr><td></td><td></td><td></td><td>- No consideration of different BS types and ISD impact</td></tr><tr><td></td><td></td><td></td><td>- Often simplified propagation models, not specific to UMi/UMa/RMa environments</td></tr></tbody></table>
<table><caption>TABLE 1. (Continued.) Comparative analysis of existing JT-CoMP and mmWave studies: Methodologies, contributions, and limitations relative to our work.</caption><tr><td>Power Transfer (SWIPT), power control, hybrid energies (renewable/grid), Device-to-Device (D2D) communications, and new metrics</td></tr></table>

- Backhaul not modeled

- Macro/micro BS differences ignored

- No analysis of ISD-power joint optimization

- No analysis of dynamic switching between No-CoMP and JT-CoMP

(i) explicit use of the JT-CoMP technique;

(ii) direct contribution to EE optimization in cellular networks;

(iii) consideration of heterogeneous networks, including low-frequency systems, LTE-Advanced (LTE-A) and 5G mmWave networks;

(iv) integration of realistic modeling elements, such as backhaul energy cost and specific propagation characteristics depending on the environment;

(v) the originality and technical depth of the proposed approaches.

The studies identified have been grouped into two main categories:

* Subsection II-A covers work related to low-frequency networks, with a state-of-the-art on EE optimization in the JT-CoMP context.

* Subsection II-B is dedicated to 5G mmWave networks, with a state-of-the-art of recent research on JT-CoMP in this specific context.

In II.B, we highlight the scarcity of studies combining JT-CoMP and EE in mmWave environments, underlining a strategic area that has yet to be fully explored. Our contribution addresses this gap.

## A. STATE-OF-THE-ART EE MANAGEMENT WITH JT-COMP IN LOW-FREQUENCY NETWORKS

EE optimization in Heterogeneous Networks (HetNets) incorporating JT-CoMP has been the subject of a great deal of research, with a common goal: reducing the energy consumption while effectively managing interference. For example, in the work conducted in [11], the authors evaluate the Energy and Spectral Efficiency (ESE) of a dense network by simultaneously optimizing the deployment of BSs and the activation of JT-CoMP. They formulate an optimization problem to maximize ESE while respecting the probability of UEs failure. A similar approach, combining the K-means algorithm and energy optimization strategies, was explored in [12], where the positioning of BSs and the use of sleep mode effectively reduced energy consumption in two-tier networks. These studies show that joint optimization of BSs deployment and management plays a key role in improving the EE.

The work of [13] and [14] also highlights the importance of resource allocation for optimizing EE with JT-CoMP. In particular, the study conducted in [13] proposes the EE optimization in HetNets using JT-CoMP to reduce ICI and optimize Radio Resource Management (RRM). Furthermore, the algorithm introduced in [14] enables resource allocation in Orthogonal Frequency Division Multiple Access (OFDMA) networks, by transforming a non-convex problem into a convex one, enabling fast convergence and efficient numerical results while respecting QoS constraints.

Other studies explore the application of Dynamic Point Selection (DPS) in JT-CoMP mode in various types of networks, highlighting its impact on the EE. For example, the DPS schemes proposed in [31] and [32] for LTE-A networks show significant gains in EE. These approaches reduce cluster size and BS power consumption while maintaining high performance. In Cloud-Radio Access Networks (C-RAN), the work in [33] shows that integrating DPS with JT-CoMP significantly improves the EE and throughput. Furthermore, the strategy presented in [34] aims to maximize the EE by dynamically selecting nodes and optimizing power allocation, while guaranteeing a minimum data rate.

In addition, the integration of green energy sources into cellular networks brings significant energy benefits. For example, the hybrid model proposed in [35] combines renewable-powered BSs and storage batteries, delivering significant savings through JT-CoMP while ensuring continuous coverage. Similarly, as shown in [36], the use of hybrid sources, such as solar power and diesel generators, can improve the EE by 26% in off-grid networks compared with conventional configurations. Moreover, the hybrid model developed in [37], which also combines renewable energies and diesel generators, enabling a 20% reduction in overall energy consumption through BS cooperation using JT-CoMP.

Advanced techniques such as Non-Orthogonal Multiple Access (NOMA) and virtualization have also been studied to improve the EE. For instance, the study in [38] explores the combination of JT-CoMP and NOMA in Wireless Network Virtualization (WNV) to improve the balance between energy resources and data transmission. The impact of imperfect Channel State Information (ipCSI) and imperfect Successful Interference Cancellation (ipSIC) is analyzed in [39], highlighting their negative effect on the EE of a cooperative
network based on JT-CoMP and NOMA. A comparison between joint-transmission NOMA and NOMA in [40] shows that joint-transmission NOMA offers better EE results thanks to efficient power allocation. Furthermore, a hybrid method combining Orthogonal Multiple Access (OMA) and NOMA with JT-CoMP is proposed in [41], maximizing the EE by exploiting the spectral efficiency of NOMA while reducing interference with JT-CoMP.

Finally, small cells play a central role in EE optimization. For example, the solution described in [42] combines JT-CoMP with Simultaneous Wireless Information and Power Transfer (SWIPT) in HetNets, which reduces small-cell energy consumption while maintaining high performance. Furthermore, the deployment strategy outlined in [43] shows that deploying femto and pico BSs under the umbrella of macro-BSs improves both the EE and network coverage. This approach, using two- and three-tier configurations, optimizes resource management while reducing energy costs. Additionally, the spatial fluid model studied in [44] indicates that EE increases with the number of coordinated BSs, with femto-cells outperforming macro-cells. Furthermore, the analysis presented in [25] examines the impact of backhaul and demonstrates that increased coordination of BSs further improves EE, particularly in femto-cell networks.

## B. STATE-OF-THE-ART ON THE USE OF JT-COMP IN 5G mmWAVE NETWORKS

Originally designed to overcome the coverage limitations of 4G networks at the edge of BSs, JT-CoMP transmissions are now proving to be an essential solution for the growing requirements of 5G networks and beyond. Thanks to their cooperative approach, they address the challenges specific to mmWave bands, including blocking and interference. This subsection first explores the major work on the integration of JT-CoMP in mmWave networks, before highlighting the challenges and opportunities associated with improving the EE in these contexts.

The study presented in [16] evaluated the performance of macrodiversity and JT-CoMP in 5G mmWave networks based on measurements at 73 GHz in urban microcells. The study shows that SF between BSs is independent and that JT-CoMP improves resilience to outages caused by human blocking, while evaluating precoding techniques suitable for mmWave. Furthermore, as demonstrated in [17], three cooperation strategies between BSs are compared: Fixed-Number Cooperation (FNC), Fixed-Region Cooperation (FRC) and Interference-Aware Cooperation (IAC). Simulations based on a Poisson Point Process (PPP) model reveal that FRC improves reliability in networks with a low density of BSs, while IAC optimizes performance in denser networks.

The impact of random blocking on the capacity of mmWave networks is also key. The study in [18] analyzes this effect on the ergodic capacity of an uplink JT-CoMP transmission, showing that JT-CoMP mitigates blocking and increases network capacity. In parallel, the work in [19] proposes a robust JT-CoMP scheme to counter blockages via stochastic

learning, avoiding complex combinatorial methods, while the contribution in [45] introduces a hybrid beamforming model taking into account blockages and ipCSI, optimized by a solution based on Mini-batch Stochastic Gradient Descent (MSGD).

In the context of JT-CoMP optimization for mmWave, the investigation presented in [46] analyzes DPS and Non-Coherent JT-CoMP (NC-JT-CoMP) schemes. Furthermore, the work in [47] proposes the Hybrid JT-CoMP (H-JT-CoMP) algorithm, which dynamically adjusts joint transmission according to CSI to reduce ICI, especially at the cell edges. A different approach is found in [48], which introduces an algorithm to reduce return traffic by optimizing JT-CoMP grouping and using caches in BSs.

Despite these advances, EE optimization for JT-CoMP in mmWave remains little explored, underscoring the relevance of our approach, which aims to fill this gap. To address this gap, the research in [20] combines SWIPT with a JT-CoMP-coordinated UE-BS association algorithm, optimizing the EE in ultra-dense mmWave HetNets and achieving superior performance and convergence. In the same context, the study in [21] presents an energy-efficient scheme for JT-CoMP where a hybrid power supply, combining renewable and grid energy, optimizes BS participation, reducing energy consumption by 80% while maintaining an acceptable outage probability. In mmWave NOMA networks, the study in [22] shows that cooperation between BSs via JT-CoMP significantly improves the EE, while reducing the outage probability for cell-edge UEs. In addition, the strategy described in [23] optimizes EE by adjusting power and transmission duration of small cells, integrating Device-to-Device (D2D) relays to maintain the EE in the event of blockages, with Stackelberg game modeling. Finally, the work in [24] introduces metrics such as the Energy Wastage Factor (EWF) and the Consumption Efficiency Factor (CEF) to optimize the EE in mmWave and sub-THz networks, highlighting the efficiency of smaller cells and wide bands, while optimizing energy management for resource-constrained devices.

## C. OUR CONTRIBUTIONS

Despite the advances made in optimizing the EE in JT-CoMP mode for 5G mmWave networks, several limitations remain. In particular, the majority of existing work is based on simplified path loss models, often far removed from the realities of UMa, UMi, and RMa environments. Furthermore, the differences in energy consumption between macro- and micro-BSs — covering transmission, RF circuits, and backhaul — are frequently ignored. Moreover, this work is often limited to fixed transmission powers, without taking into account variations specific to different environments or the energy requirements of the backhaul, particularly in JT-CoMP. Finally, much of the literature focuses on low frequencies or relies on static assumptions about BSs density, ISD, and deployment schemes, limiting their applicability to mmWave networks where propagation conditions differ
significantly. In order to overcome these constraints, the main
contributions of our work are summarized as follows:

*   **Realistic propagation modeling and EE evaluation in mmWave:** We perform realistic propagation modeling in mmWave, integrating LOS/NLOS conditions, SF, and BP distances. Based on 3GPP [28], 5GCM [29], and mmMAGIC [30] standards specific to UMi, UMa, and RMa environments, we define a SINR model specific to each transmission mode, in a context where all BSs simultaneously downlink (DL) the same content on a single carrier frequency. The EE is then calculated as the ratio between the cell's data rate, a function of the SINR, and the power consumption, including transmission power, RF circuits and backhaul.

*   **ISD optimization and interference management:** Our system model is based on a ring architecture, where the BSs are organized in concentric rings around a central BS (Fig. 1). We study the impact of the number of rings on performance, showing that beyond a certain threshold, the effect of ICI becomes negligible. Finally, we examine the trade-off between the BSs density and EE, identifying optimal ISD values for maximizing the EE in RMa, UMa, and UMi environments.

*   **Power consumption analysis of cooperative backhauling:** We carry out an in-depth analysis of the power consumption of cooperative backhauling, taking into account the type of BSs (macro or micro) and environmental characteristics. Our results show that, although JT-CoMP improves intercellular coordination, its effectiveness varies according to the environment and is accompanied by an increase in backhaul power costs. We thus identify critical thresholds beyond which JT-CoMP becomes less advantageous than No-CoMP in terms of EE.

*   **Transmission power optimization:** We analyze the impact of variations in BS transmission power (macro and micro) on the data rate and EE. Our approach enables us to identify the optimal power calibration to maximize the EE in UMi and UMa environments.

*   **Optimization strategies adapted to various environments:** We propose practical recommendations for optimizing the deployment of mmWave 5G networks in heterogeneous environments (RMa, UMa, UMi). These strategies include adjusting the ISD and transmission power based on network characteristics such as frequency and UEs distribution, with a particular focus on performance optimization. The goal is to enhance the coverage probability, average and minimum SINR, as well as the total data rate, while reducing the energy consumption.

### III. SYSTEM MODEL AND PERFORMANCE INDICATORS IN No-CoMP AND JT-CoMP MODES

In this section, we present the adopted system model (see Fig. 1) and the key elements for analyzing performance in

No-CoMP and JT-CoMP modes. We begin by describing the network configuration, which includes the hexagonal structure, the distribution of BSs and UEs, and the main deployment parameters. We then detail the SINR models for each transmission mode, and conclude by analyzing the corresponding EE models.

#### A. NETWORK CONFIGURATION

Fig. 1 illustrates a homogeneous hexagonal cellular network presented in [49], where each cell has a uniform radius $R$. The network is structured in rings with $n_{rings}$ around a central BS, whose overall coverage radius defined by $R_{nc} = (2n_{rings} + 1)R_c$, where $R_c$ corresponds to half the ISD between two adjacent BSs, i.e. $R_c = \text{ISD}/2$. Since each hexagonal cell is made up of six equilateral triangles, the length of their sides is given by: $R = \frac{2\sqrt{3}}{\sqrt{3}}R_c$. Furthermore, the equivalent radius $R_e$, defined as that of a circle having the same area as a hexagonal cell, is expressed by $R_e = \sqrt{\frac{2\sqrt{3}}{\pi}}R_c$.

Two DL transmission modes are studied: No-CoMP and JT-CoMP. Their performance is evaluated in various scenarios, where all BSs transmit the same content with identical frequency $f_c$, bandwidth $B$, and transmission power $P_{tx}$. The evaluation is based on Monte Carlo simulations, each corresponding to a random distribution of UEs in the central cell. At each iteration, the allocation of LOS and NLOS links is performed randomly, in accordance with the LOS probability models defined by the three standards for the RMa, UMa, and UMi scenarios (detailed in Section IV, which also presents the corresponding PL models).

<figure><img src="image_2.png" /><figcaption>FIGURE 1. Network geometry.</figcaption></figure>

The system model assumes that $N_{BS}$ BSs are uniformly distributed over $n_{rings}$, and that $N_u$ UEs are randomly distributed within a circle of radius $R_e$ centered on the central BS. To refine this distribution, we introduce the parameter $\rho_{UE}$, which distinguishes scenarios where the UEs are primarily located near the center of the cell from those where they are concentrated at the periphery. In the first scenario, the UEs are distributed between $\rho_{UE}R_e$ and $R_e$, with $0 < \rho_{UE} \le 1$. A high value indicates a concentration of UEs at the periphery. A special case is $\rho_{UE} = 0$, which corresponds
to a uniform distribution of UEs over the entire disk of radius $R_e$, ranging from the vicinity of the origin (at a minimum distance defined by the PL models of each standard) to $R_e$. Although we denote this case as $\rho_{UE} = 0$ for simplicity, in our simulations, the closest UEs are not strictly located at the origin.

## B. SINR MODELS IN No-CoMP AND JT-CoMP MODES

### 1) SINR MODEL IN NO-CoMP MODE

In No-CoMP mode, only the service BS (central BS) ensures transmission of the wanted signal to the UE, while the other BSs introduce ICIs. The received signal power, taking into account the path loss $PL_s$ in dB between the UE and the service BS, is given by:

$$P_{s, No-CoMP} = P_{tx} G_t G_r 10^{-PL_s/10}, \quad (1)$$

where $G_t$ and $G_r$ are the transmit and receive antenna gains, and index $s$ denotes the service BS.

In this work, the receiver antenna gains are assumed to be unitary, while the antenna gains at the BSs are modeled as fixed values, representative of the directivity induced by beamforming, without an explicit modeling of radiation patterns (beampatterns). In other words, detailed antenna characteristics—such as the main lobe, side lobes, beamwidth, or front-to-back ratio—are not considered explicitly. This choice aims to limit the complexity of the model while ensuring consistent comparisons between different network configurations.

However, it is acknowledged that a more accurate modeling of beampatterns could improve SINR evaluation, particularly in dense environments or at high frequencies, where directivity plays a key role. Although this approach lies beyond the scope of the present study, it has been addressed in our previous work [8], [9], in which BSs transmit using beamforming based on antenna arrays compliant with 3GPP specifications.

Thus, in addition to the useful signal power, it is necessary to account for the interference introduced by the non-cooperative BSs. The total interference power received by the UE, originating from these BSs, is expressed as follows:

$$I_{i \in \psi / i \neq s, No-CoMP} = P_{tx} G_t G_r \sum_{i \in \psi / i \neq s} 10^{-PL_i/10}, \quad (2)$$

where $\psi$ represents the set of all BSs and $PL_i$ the PL between the UE and the $i$-th BS ($i \in \psi / i \neq s$).

Thus, the SINR in No-CoMP is defined by:

$$\gamma_{No-CoMP} = \frac{P_{s, No-CoMP}}{I_{i \in \psi / i \neq s, No-CoMP} + P_N}, \quad (3)$$

with $P_N$ representing the noise power at the receiver, calculated as follows:

$$P_N = 10^{(N+10\log10(kTB))/10}, \quad (4)$$

where $N$ is the noise figure (in dB), $k$ is Boltzmann's constant and $T$ is the receiver system temperature (in kelvins).

### 2) SINR MODEL IN JT-CoMP MODE

Unlike No-CoMP mode, where only the central BS is responsible for data transmission, JT-CoMP relies on the coordination of several cooperative BSs, grouped together in a coordination group. These BSs synchronize their transmissions to improve the signal received by the UE, complementing the central BS.

In this study, JT-CoMP is mainly deployed among the BSs closest to the central BS, i.e. those in the first ring. Their total number, including the central BS, noted $N_{co}$, varies from 2 to 7. When a user receives a signal from a BS different from his central BS, this signal undergoes a certain delay, directly influencing the rate of contribution to the useful power received ($\delta_j$), whose value is between 0 and 1 [9]. The total power received in JT-CoMP mode is therefore the sum of the contributions of the cooperative $N_{co}$ BSs, expressed by:

$$P_{JT-CoMP} = P_{tx} G_t G_r \sum_{j=1}^{N_{co}} \delta_j \cdot 10^{-PL_j/10}, \quad (5)$$

where $\delta_j$ the weighting factor of the $j$-th BS in the cooperative area, calculated as in [9], and $PL_j$ represents the PL in dB between the UE and the $j$-th cooperative BS.

In JT-CoMP mode, the interference power is divided into two components. The first comes from the BSs in the first ring, likely to generate interference due to delayed signals. This component, denoted $I_{i \in \psi / i \le N_{co}}$, is given by:

$$I_{i \in \psi / i \le N_{co}} = P_{tx} G_t G_r \sum_{j=1}^{N_{co}} (1 - \delta_i) \cdot 10^{-PL_j/10}. \quad (6)$$

The second component comes from the BSs located outside the coordination group, i.e., those that do not participate in JT-CoMP and are denoted as ($j \in \psi / j \ge N_{co} + 1$). It is expressed as follows:

$$I_{j \in \psi / j \ge N_{co} + 1, JT-CoMP} = P_{tx} G_t G_r \sum_{j=N_{co}+1}^{N_{BS}} 10^{-PL_j/10}. \quad (7)$$

Thus, the SINR in JT-CoMP mode is defined by:

$$\gamma_{JT-CoMP} = \frac{P_{JT-CoMP}}{I_{i \in \psi / i \le N_{co}} + I_{j \in \psi / j \ge N_{co} + 1, JT-CoMP} + P_N}. \quad (8)$$

It should be noted that although $\gamma_{JT-CoMP}$ (8) takes into account the contribution of cooperative BSs, integrating their impact on received useful power ($P_{JT-CoMP}$) and interference power ($I_{i \in \psi / i \le N_{co}}$), this formula remains applicable to scenarios where cooperative BSs may belong to several rings. However, in our particular case, where only the BSs in the first ring transmit in JT-CoMP, the weighting factor $\delta_i$ is equal to 1, which cancels out the interference generated by these BSs ($I_{i \in \psi / i \le N_{co}} = 0$). Nevertheless, we emphasize that in more complex configurations, involving more distant cooperative BSs, synchronization between BSs becomes a major technical challenge. The impact of these phenomena has been explored in our previous work [8], [9], to which we refer the reader for further analysis.
## C. EE MODELS IN No-CoMP AND JT-CoMP MODES

### 1) EE MODEL IN NO-CoMP MODE

Having presented the calculation of SINR in No-CoMP and JT-CoMP modes, we continue with the analysis of the total data rate, spectral efficiency (SE), and EE in these two transmission modes.

According to Shannon's formula, the SE of a UE $u$, expressed in bits/s/Hz, depends on its SINR $\gamma_u$ and is defined by:

$$SE_u = \log_2 (1 + \gamma_u), \quad (9)$$

where $\gamma_u$ can correspond to $\gamma_{No-CoMP}$ or $\gamma_{JT-CoMP}$, depending on the transmission mode used. The maximum achievable data rate for a UE $u$, noted $D_u(r)$, expressed in bits/s, is calculated by multiplying the allocated bandwidth $B_u = B/N_u$ by $SE_u$ i.e.:

$$D_u(r) = B_u SE_u. \quad (10)$$

Since the number of users per BS $N_u$ is constant and each UE has the same bandwidth $B_u$, the total data rate of the cell, denoted $D_{total}$, depends mainly on the SINR $\gamma_u$ of the $N_u$ UEs. This total data rate is therefore expressed by:

$$D_{total} = N_u \mathbb{E}(D_u) = B \cdot \mathbb{E}(\log_2 (1 + \gamma_u)), \quad (11)$$

where $\mathbb{E}[\cdot]$ represents the mathematical expectation.

<table><caption>TABLE 2. Power consumption for macro and micro BSs [50].</caption><thead><tr><th>Parameters</th><th>η</th><th>P<sub>tx</sub> [W]</th><th>P<sub>c</sub> [W]</th><th>P<sub>0</sub> [W]</th></tr></thead><tbody><tr><td>Macro BSs</td><td>0.14</td><td>80</td><td>244</td><td>255</td></tr><tr><td>Micro BSs</td><td>0.32</td><td>6.31</td><td>35</td><td>34</td></tr></tbody></table>

The EE, expressed in bits/J, is then defined as the ratio between the total data rate $D_{total}$ and the total network power consumption. Thus, in No-CoMP, the EE is calculated by:

$$EE_{No-CoMP} = \frac{D_{total}^{No-CoMP}}{N_{BS} \times P_{BS}^{tot}}, \quad (12)$$

where $P_{BS}^{tot}$ represents the total power expenditure per BS, calculated as follows [50]:

$$P_{BS}^{tot} = \frac{1}{\eta} P_{tx} + P_{c} + P_{0}, \quad (13)$$

where $\eta$ represents the efficiency of the Power Amplifier (PA), $P_c$ the power required to operate certain RF circuits, and $P_0$ the power consumption of Direct Current (DC) and Alternating Current (AC) converters. Table 2 shows the specific values of $\eta$, $P_{tx}$, $P_c$, and $P_0$ for macro-BSs and micro-BSs, highlighting notable differences between these two types of BSs.

In our modeling, we have assumed ideal PAs with fixed efficiencies ($\eta$), a simplifying assumption commonly adopted in the early phases of EE analysis. We recognize, however, that under real-world conditions, PAs exhibit nonlinear behavior, with efficiency decreasing as output power increases [51]. This non-linearity can affect EE, particularly

in macro-cellular scenarios where the transmitted powers are higher. In the specific context of this study, however, we have chosen to retain a fixed-efficiency model in order to focus the analysis on the comparative effects of transmission modes (No-CoMP vs. JT-CoMP), radio environments (UMi, UMa, RMa), and BS types, on EE. The aim is to highlight the dominant mechanisms influencing EE in the different configurations, while maintaining sufficient analytical readability.

Finally, replacing $D_{total}^{No-CoMP}$ with its expression $D_{total}^{No-CoMP} = B \cdot \mathbb{E}(\log_2 (1 + \gamma_{No-CoMP}))$ in (11), then replacing $P_{BS}^{tot}$ with its expression from (12), we obtain:

$$EE_{No-CoMP} = \frac{B \cdot \mathbb{E}(\log_2 (1 + \gamma_{No-CoMP}))}{N_{BS} \left( \frac{1}{\eta} P_{tx} + P_c + P_0 \right)}. \quad (14)$$

### 2) EE MODEL IN JT-CoMP MODE

Although JT-CoMP improves the data rate of 5G mmWave networks, it places an additional load on the backhaul, as data must be simultaneously distributed to UEs by multiple BSs, increasing energy consumption. Thus, in JT-CoMP mode, the EE must integrate the power consumption linked to backhauling, noted $P_{CoMP}$. This corresponds to the power required to transport and share data between the BSs in a cooperative network. It depends directly on backhaul traffic in JT-CoMP, i.e. the total data rate exchanged ($D_{total}^{JT-CoMP}$) between UEs connected to a coordinated BS. More precisely, the backhauling power for a coordinated BS is expressed as follows [27], [52]:

$$P_{CoMP} = \alpha_{bh} D_{total}^{JT-CoMP}, \quad (15)$$

where $\alpha_{bh}$ represents the power coefficient of the backhaul, generally about ten times higher for a macro-BS than for a small-BS [27].

Although it was introduced earlier, the distinction between cooperative and non-cooperative BSs deserves to be reiterated, as it is a central element of our energy modeling. Only cooperative BSs, typically those located in the first ring around the central BS, actually participate in joint transmission in JT-CoMP and therefore bear the consumption load linked to the backhaul ($P_{CoMP}$). Conversely, non-cooperative BSs, operating in No-CoMP mode, generate no coordination traffic and consume only their own power ($P_{BS}^{tot}$). This functional differentiation makes it possible to accurately assess the energy impact of each transmission strategy in the deployment scenarios considered.

Note that in the section dedicated to simulation results, we first carry out a comparative analysis of the different deployment conditions, setting $\alpha_{bh}$ at $4 \times 10^{-9}$ for macro-BSs in RMa and UMa, and at $4 \times 10^{-10}$ for micro-BSs in UMi. We then extend this analysis by examining the impact of this parameter in urban environments, enabling us to identify critical thresholds beyond which the JT-CoMP mode becomes less efficient than the No-CoMP mode. Thus, this study highlights the limits of the JT-CoMP EE as a function of deployment conditions, and underlines the extent to which backhaul costs can compromise the overall system EE.
In order to remain generic while capturing real-world behaviors, we do not detail specific backhaul technologies (fiber, radio links, etc.), but group their energetic effects in the parameter $\alpha_{bh}$. This implicitly reflects infrastructure availability, multi-hop architectures, link saturation, and the unavailability of physical resources. For example, in the absence of direct access to the fiber, multi-hop architectures can be envisaged, with each hop entailing an additional energy cost.

We also point out that backhaul requirements are linked to dynamic traffic management on the fronthaul: a radio overload can trigger a dynamic reallocation of JT-CoMP flows to less stressed BSs, thus modifying cooperation patterns and the volume of data to be exchanged via the backhaul. Although these complex interactions are not modeled explicitly, they are encapsulated in the $\alpha_{bh}$ coefficient. Their detailed modeling would thus be a natural prospect for further refining our approach.

Let us now focus on calculating the EE in JT-CoMP mode, defined as the ratio between the total data rate and the mode-specific power consumption. The latter can be broken down into two main components:

* The power consumed by all BSs, excluding backhaul, noted $N_{BS}P_{BS}^{tot}$.

* The backhauling power $P_{CoMP}$ consumed by the $N_{co}$ cooperative BSs involved in JT-CoMP transmission, i.e. $N_{co}P_{CoMP}$.

Thus, the EE in JT-CoMP mode, is expressed as follows:

$$EE_{JT-CoMP} = \frac{D_{total}^{JT-CoMP}}{N_{co}P_{CoMP} + N_{BS}P_{BS}^{tot}} \quad (16)$$

Substituting the expression for $D_{total}^{JT-CoMP}$ by its formula, $D_{total}^{JT-CoMP} = B.E(\log_2(1 + \gamma_{JT-CoMP}))$, and integrating the equation for $P_{BS}^{tot}$, we obtain:

$$EE_{JT-CoMP} = \frac{B.E(\log_2(1 + \gamma_{JT-CoMP}))}{N_{co}P_{CoMP} + N_{BS}(\frac{1}{\eta}P_{tx} + P_c + P_0)} \quad (17)$$

This formulation highlights the interdependence between backhauling parameters, BS powers, and the signal quality, enabling a more accurate evaluation of the EE in JT-CoMP mode. It is essential for determining the optimal deployment strategy in 5G mmWave networks, where the coverage, ICI management and energy consumption are critical. However, for full EE optimization, it is also necessary to take into account the specifics of the deployment environments. With this in mind, the following section explores LOS probability and PL models in RMA, UMa, and UMi environments, which directly influence JT-CoMP performance.

## IV. LOS PROBABILITY AND PL MODELS IN RMA, UMi, AND UMa ENVIRONMENTS

This section first examines the LOS probabilities, then presents the PL models for RMA, UMi, and UMa environments, as proposed by 3GPP [28], 5GCM [29], and mmMAGIC [30].

### A. LOS PROBABILITY FOR UMi AND UMa SCENARIOS

The UMi and UMa environments differ mainly in the height and density of the BSs, as follows [53], [54]:

* **UMa scenario:** BSs are positioned above building roofs, generally at a height of between 25 m and 30 m. UEs, on the other hand, are located at ground level, at a height of around 1.5 m. Moreover, ISDs in this type of environment generally do not exceed 500 m ($ISD_{max}^{UMa} = 500$ m).

* **UMi scenario:** This context is characterized by a high concentration of UEs and BSs in dense urban areas, such as Street Canyons (SC). BSs are installed below roof level, at heights ranging from 3 m to 20 m. As in the UMa scenario, the UEs remain 1.5 m above ground level, but in this case, the ISDs are smaller, generally not exceeding 200 m ($ISD_{max}^{UMi} = 200$ m).

In the rest of this study, carrier frequencies are expressed in GHz, while heights and distances are in m and PL in dB.

#### 1) LOS PROBABILITY ACCORDING TO 3GPP TR 38.901

The LOS probability model described in 3GPP Technical Report (TR) 38.901 (noted 3GPP TR 38.901) [28], applicable to UMi and UMa scenarios, is commonly referred to as the $d_1/d_2$ model. Note that the parameters $d_1$ and $d_2$ are specifically optimized to match the LOS probability characteristics of the UMi and UMa environments. We will also refer to $d_{2D}$ as the BS-UE separation distance.

##### a: LOS PROBABILITY IN UMi

In a UMi scenario, where the typical BS height is $h_{BS} = 10$ m, the LOS probability is expressed as [28]:

$$P_{LOS}^{UMi} = \min \left( \frac{d_1}{d_{2D}}, 1 \right) \left( 1 - \exp \left( -\frac{d_{2D}}{d_2} \right) \right) + \exp \left( -\frac{d_{2D}}{d_2} \right), \quad (18)$$

with the following parameters: $d_1 = 18$ m and $d_2 = 36$ m.

##### b: LOS PROBABILITY IN UMa

In a UMa environment, the typical BS height is $h_{BS} = 25$ m, and the LOS probability also depends on the UE height, introducing a corrective factor. This probability is given by [28]:

$$P_{LOS}^{UMa} = \left( \min \left( \frac{d_1}{d_{2D}}, 1 \right) \left( 1 - \exp \left( -\frac{d_{2D}}{d_2} \right) \right) + \exp \left( -\frac{d_{2D}}{d_2} \right) \right) \left( 1 + C(d_{2D}, h_{UE}) \right), \quad (19)$$

with $d_1 = 18$ m, $d_2 = 63$ m, and $C(d_{2D}, h_{UE})$ expressed by:

$$C(d_{2D}, h_{UE}) = \begin{cases} 0, & h_{UE} < 13 \text{ m} \\ \left( \frac{h_{UE} - 13}{10} \right)^{1.5} g(d_{2D}), & 13 \text{ m} \le h_{UE} \le 23 \text{ m}, \end{cases} \quad (20)$$
where:

$$g(d_{2D}) = \begin{cases} 0, & d_{2D} < 18 \text{ m} \\ 1.25 \left(\frac{d_{2D}}{100}\right)^3 \exp\left(-\frac{d_{2D}}{150}\right), & d_{2D} \ge 18 \text{ m.} \end{cases} \quad (21)$$

## 2) LOS PROBABILITY ACCORDING TO 5GCM

The 5GCM [29], developed by a consortium of universities and companies, proposes a dual approach to LOS probability modeling, applied to UMi and UMa scenarios.

### a: LOS PROBABILITY IN UMi

Two methods are used to determine the LOS probability in UMi:

* **Model $d_1/d_2$:** Variant of the 3GPP TR 38.901 model (18), with modified parameters $d_1 = 20$ m and $d_2 = 39$ m [29]. This model is applied in our work to assess the probability of LOS in the UMi scenario.

* **NYU squared model:** Derived from empirical measurements in New York [55], this model aims to improve accuracy by applying a square to the initial 3GPP equation, i.e. [29]:

$$P_{LOS}^{UMi} = \left( \min \left( \frac{d_1}{d_{2D}}, 1 \right) \left( 1 - \exp \left( -\frac{d_{2D}}{d_2} \right) \right) + \exp \left( -\frac{d_{2D}}{d_2} \right) \right)^2, \quad (22)$$

with modified parameters: $d_1 = 22$ m and $d_2 = 100$ m.

### b: LOS PROBABILITY IN UMa

Two similar approaches are used for the UMa case:

* **Model $d_1/d_2$:** A variant of the UMa model in 3GPP TR 38.901 (19), with adjusted values: $d_1 = 20$ m and $d_2 = 66$ m [29]. This model is used in our work to assess the probability of LOS in the UMa scenario.

* **NYU squared model:** This model applies a square to the full expression of the 3GPP model, i.e. [29]:

$$P_{LOS}^{UMa} = \left( \left( \min \left( \frac{d_1}{d_{2D}}, 1 \right) \left( 1 - \exp \left( -\frac{d_{2D}}{d_2} \right) \right) + \exp \left( -\frac{d_{2D}}{d_2} \right) \right) (1 + C(d_{2D}, h_{UE})) \right)^2, \quad (23)$$

with modified parameters: with $d_1 = 20$ m and $d_2 = 160$ m.

## 3) LOS PROBABILITY ACCORDING TO mmMAGIC

The mmMAGIC project [30], supported by the European Union, adopts an approach similar to that of 3GPP TR 38.901 [28] to model LOS probability in UMi, as defined in (18). Thus, for the UMi scenario, the LOS probability and the parameters $d_1$ and $d_2$ used in mmMAGIC are identical to those in the 3GPP model. However, it should be noted that the mmMAGIC project does not propose LOS probability modeling in UMa, unlike the 3GPP and 5GCM models.

## B. LOS PROBABILITY FOR THE RMa SCENARIO

In RMa scenarios, BSs are generally located at $h_{BS}$ varying between 10 m and 150 m, while UEs remain close to the ground, typically at a $h_{UE}$ of around 1.5 m, with ISDs of up to 5 km ($ISD_{max}^{RMa} = 5$ km) [53], [54]. It should also be noted that the LOS probability model described in 3GPP TR 38.901 [28] is based on the recommendation of the International Telecommunications Union-Radiocommunication (ITU-R) M.2135 [54], itself derived from WINNER's LOS model for rural macro-cells. This probability is expressed for distances $d_{2D}$ greater than 10 m as follows [28]:

$$P_{LOS}^{RMa} = \exp \left( - \frac{d_{2D} - 10}{1000} \right). \quad (24)$$

Finally, it should be noted that the modeling of LOS probability and PL models in RMa have not been specified by the other standards, namely 5GCM and mmMAGIC.

## C. CATEGORIES OF LARGE-SCALE PL MODELS SCENARIO

To estimate distance-dependent signal attenuation in the wide mmWave frequency range, several large-scale PL models are used. The distance between the BS and UE in 3D, denoted $d_{3D}$, is a key parameter of these models, taking into account both the 2D distance ($d_{2D}$) and the respective heights of the BSs and UEs ($h_{BS}$ and $h_{UE}$), as follows:

$$d_{3D} = \sqrt{d_{2D}^2 + (h_{BS} - h_{UE})^2}. \quad (25)$$

### 1) PL MODEL WITH REFERENCE DISTANCE IN FREE SPACE CLOSE-IN (CI)

The CI PL model is widely used due to its simplicity and efficiency [28], [29], [30]. It is based on a free-space reference distance fixed at 1 m, linking the PL at any frequency to the Free Space PL (FSPL) calculated at 1 m, according to the Friis equation. The CI model's general formula for propagation loss is expressed as follows:

$$PL^{CI} (f_c, d_{3D}) = FSPL (f_c, 1m) + 10n\log_{10} (d_{3D}) + \chi_{\sigma}^{CI}, \quad (26)$$

where $f_c$ is the carrier frequency, and $\chi_{\sigma}^{CI}$ represents the attenuation due to the SF, modeled as a random variable (r. v.) following a Gaussian distribution of mean zero and standard deviation $\sigma_{SF}^{CI}$ in dB. The parameter n designates the PL exponent, which is determined by fitting the model to the measured data, so as to minimize the deviation between the model and experimental observations. The FSPL in dB is calculated as a function of $f_c$ by:

$$FSPL (f_c, 1m) = 20\log_{10} \left( \frac{4\pi f_c \cdot 10^9}{c} \right) = 32.4 + 20\log_{10} (f_c), \quad (27)$$

where c is the speed of light.
## 2) FLOATING INTERCEPT (FI) PL MODEL

The PL model with Floating Interception (FI), also known as the ABG (Alpha-Beta-Gamma) model, is based on three main parameters: $\alpha$, $\beta$, and $\gamma$ [28], [29], [30]. The FI/ABG model formula for propagation loss is expressed as follows:

$$PL^{ABG}(f_c, d_{3D}) = 10\alpha\log_{10}(d_{3D}) + \beta + 10\gamma\log_{10}(f_c) + \chi_{\sigma}^{ABG}, \quad (28)$$

where $\alpha$ represents the slope of the PL as a function of logarithmic distance, $\beta$ is the floating offset in dB, and $\gamma$ models the dependence of the PL on frequency $f_c$. In addition, $\chi_{\sigma}^{ABG}$ describes the attenuation due to SF, modeled as a Gaussian r. v. with zero mean and standard deviation $\sigma_{SF}^{ABG}$ expressed in dB.

# D. LARGE-SCALE PL FOR UMi AND UMa

We now present various PL models developed by the 3GPP, 5GCM and mmMAGIC standards for UMi and UMa environments. It should be noted that in UMi, although all three standards deal mainly with the UMi-SC model, only 5GCM also offers PL models for the UMi-Open Square (UMi-OS) environment.

## 1) 3GPP TR 38.901

The 3GPP TR 38.901 [28] proposes PL models valid at carrier frequencies ($f_c$) ranging from 0.5 to 100 GHz, under LOS and NLOS conditions, for UMi-SC and UMa environments, detailed below.

### a: PL IN THE UMi-SC CASE

To correctly apply PL models in the UMi environment, certain constraints must be respected. Under LOS and NLOS conditions, the 2D BS-UE distance ($d_{2D}$) must be at least 10 m. In addition, $h_{UE}$ must be between 1.5 m and 22.5 m, while $h_{BS}$ is fixed at 10 m.

The PL model for UMi-SC under LOS conditions is expressed by a BP model, whose expression is as follows:

$$PL_{UMi-LOS}^{BP} = \begin{cases} PL_1, & 10 \text{ m} \le d_{2D} \le d_{BP} \\ PL_2, & d_{BP} \le d_{2D} \le 5 \text{ km}, \end{cases} \quad (29)$$

where $PL_1$ and $PL_2$ represent the PL before and after the BP, respectively, and are defined as follows:

$$PL_1 = 32.4 + 21\log_{10}(d_{3D}) + 20\log_{10}(f_c) + \chi_{\sigma}^{BP}, \quad (30)$$

$$PL_2 = 32.4 + 40\log_{10}(d_{3D}) + 20\log_{10}(f_c) - 9.5\log_{10}(d_{BP}^2 + (h_{BS} - h_{UE})^2) + \chi_{\sigma}^{BP}, \quad (31)$$

where $\chi_{\sigma}^{BP}$ represents SF attenuation, modeled as a Gaussian r. v. of zero mean and standard deviation $\sigma_{SF}^{BP} = 4$ dB.

The BP distance, noted $d_{BP}$, depends on the heights of the BS and UE, as well as the frequency, and is given by:

$$d_{BP} = 4(h_{BS} - 1)(h_{UE} - 1)f_c \times 10^9/c. \quad (32)$$

In the UMi-SC scenario under NLOS conditions, two models are used: the CI model and the ABG model. The ABG

model, in addition to taking $d_{3D}$ into account, includes a frequency-dependent term $f_c$, which reflects the increase in PL with frequency. It also incorporates a correction for UE height, and a mathematical adjustment to ensure that signal loss in NLOS is never less than that observed in LOS. This ABG model is expressed as:

$$PL_{UMi-NLOS}^{ABG} = \max \left( PL_{UMi-LOS}^{BP}, PL_{UMi-NLOS}^{ABG} \right), \quad (33)$$

where:

$$PL_{UMi-NLOS}^{ABG} = 22.4 + 35.3\log_{10}(d_{3D}) + 21.3\log_{10}(f_c) - 0.3(h_{UE} - 1.5) + \chi_{\sigma}^{ABG}, \quad (34)$$

with $\sigma_{SF}^{ABG} = 7.82$ dB.

An alternative CI model for PL in NLOS is defined as follows:

$$PL_{UMi-NLOS}^{CI} = 32.4 + 31.9\log_{10}(d_{3D}) + 20\log_{10}(f_c) + \chi_{\sigma}^{CI}, \quad (35)$$

with $\sigma_{SF}^{CI} = 8.2$ dB.

### b: PL IN THE UMa CASE

For the UMa environment, the constraints on 2D BS-UE distance ($d_{2D} > 10$ m), UE height ($1.5 \text{ m} \le h_{UE} \le 22.5 \text{ m}$), and BS height ($h_{BS} = 25 \text{ m}$) are identical under LOS and NLOS conditions.

Under LOS conditions, the UMa PL model is also based on a BP model whose expression has already been given in (29) but with $PL_1$ and $PL_2$ defined by:

$$PL_1 = 28 + 22\log_{10}(d_{3D}) + 20\log_{10}(f_c) + \chi_{\sigma}^{BP}, \quad (36)$$

$$PL_2 = 28 + 40\log_{10}(d_{3D}) + 20\log_{10}(f_c) - 9\log_{10}(d_{BP}^2 + (h_{BS} - h_{UE})^2) + \chi_{\sigma}^{BP}, \quad (37)$$

with $\sigma_{SF}^{BP} = 4$ dB.

In the case of NLOS in UMa, the CI and ABG models are used. The ABG model is written:

$$PL_{UMa-NLOS}^{ABG} = \max \left( PL_{UMa-LOS}^{BP}, PL_{UMa-NLOS}^{ABG} \right), \quad (38)$$

where:

$$PL_{UMa-NLOS}^{ABG} = 13.54 + 39.08\log_{10}(d_{3D}) + 20\log_{10}(f_c) - 0.6(h_{UE} - 1.5) + \chi_{\sigma}^{ABG}, \quad (39)$$

with $\sigma_{SF}^{ABG} = 6$ dB.

Finally, the CI model for PL in NLOS in the UMa environment is formulated as follows:

$$PL_{UMa-NLOS}^{CI} = 32.4 + 30\log_{10}(d_{3D}) + 20\log_{10}(f_c) + \chi_{\sigma}^{CI}, \quad (40)$$

with $\sigma_{SF}^{CI} = 7.8$ dB.

## 2) 5GCM

The 5GCM [29] applies to frequencies $f_c$ between 6 and 100 GHz, under LOS and NLOS conditions, and covers UMi environments, including UMi-SC and UMi-OS, as well as the UMa environment, presented below.
### a: PL IN THE UMi-SC CASE

For the UMi-SC scenario under **LOS** conditions, the **CI** model is used, expressed as follows:

$$PL_{UMi-LOS}^{CI} = 32.4 + 21\log_{10}(d_{3D}) + 20\log_{10}(f_c) + \chi_{\sigma}^{CI}, \quad (41)$$

with $\sigma_{SF}^{CI} = 3.76$ dB.

In the **NLOS** case, two models are used: the **CI** model and the **ABG** model. The **CI** model for NLOS is expressed as:

$$PL_{UMi-NLOS}^{CI} = 32.4 + 31.7\log_{10}(d_{3D}) + 20\log_{10}(f_c) + \chi_{\sigma}^{CI}, \quad (42)$$

with $\sigma_{SF}^{CI} = 8.09$ dB.

The **ABG** model for NLOS is formulated as follows:

$$PL_{UMi-NLOS}^{ABG} = 22.4 + 35.3\log_{10}(d_{3D}) + 21.3\log_{10}(f_c) + \chi_{\sigma}^{ABG}, \quad (43)$$

with $\sigma_{SF}^{ABG} = 7.82$ dB.

### b: PL IN THE UMi-OS CASE

For the UMi-OS scenario under **LOS** conditions, the **CI** model is used, expressed as follows:

$$PL_{UMi-LOS}^{CI} = 32.4 + 18.5\log_{10}(d_{3D}) + 20\log_{10}(f_c) + \chi_{\sigma}^{CI}, \quad (44)$$

with $\sigma_{SF}^{CI} = 4.2$ dB.

In the **NLOS** case, two models are used: the **CI** model and the **ABG** model. The **CI** model for NLOS is expressed as:

$$PL_{UMi-NLOS}^{CI} = 32.4 + 28.9\log_{10}(d_{3D}) + 20\log_{10}(f_c) + \chi_{\sigma}^{CI}, \quad (45)$$

with $\sigma_{SF}^{CI} = 7.1$ dB.

The **ABG** model for NLOS is formulated as follows:

$$PL_{UMi-NLOS}^{ABG} = 3.66 + 41.4\log_{10}(d_{3D}) + 24.3\log_{10}(f_c) + \chi_{\sigma}^{ABG}, \quad (46)$$

with $\sigma_{SF}^{ABG} = 7$ dB.

### c: PL IN THE UMa CASE

In the UMa case, for the **LOS** scenario, the **CI** model is also used and is written:

$$PL_{UMa-LOS}^{CI} = 32.4 + 20\log_{10}(d_{3D}) + 20\log_{10}(f_c) + \chi_{\sigma}^{CI}, \quad (47)$$

with $\sigma_{SF}^{CI} = 4.1$ dB.

For the **NLOS** scenario, the same models are applied. The **CI** model for NLOS is written:

$$PL_{UMa-NLOS}^{CI} = 32.4 + 30\log_{10}(d_{3D}) + 20\log_{10}(f_c) + \chi_{\sigma}^{CI}, \quad (48)$$

with $\sigma_{SF}^{CI} = 6.8$ dB.

Finally, the **ABG** model for NLOS is formulated as follows:

$$PL_{UMa-NLOS}^{ABG} = 19.2 + 34\log_{10}(d_{3D}) + 23\log_{10}(f_c) + \chi_{\sigma}^{ABG}, \quad (49)$$

with $\sigma_{SF}^{ABG} = 6.5$ dB.

### 3) mmMAGIC

The mmMAGIC model [30] is applicable for $f_c$ between 6 and 100 GHz, under LOS and NLOS conditions, but exclusively for the UMi-SC environment, described below.

#### a: PL IN THE UMi-SC CASE

For the UMi-SC scenario under **LOS** conditions, the **ABG** model is used, and is expressed as follows:

$$PL_{UMi-LOS}^{ABG} = 32.9 + 19.2\log_{10}(d_{3D}) + 20.8\log_{10}(f_c) + \chi_{\sigma}^{ABG}, \quad (50)$$

with $\sigma_{SF}^{ABG} = 2$ dB.

In the **NLOS** case, the **ABG** model is also used and is expressed as follows:

$$PL_{UMi-NLOS}^{ABG} = 31 + 45\log_{10}(d_{3D}) + 20\log_{10}(f_c) + \chi_{\sigma}^{ABG}, \quad (51)$$

with $\sigma_{SF}^{ABG} = 7.82$ dB.

### E. LARGE-SCALE PL FOR RMa

The PL model for RMa environments, as defined by 3GPP TR 38.901 [28], is largely based on the recommendations of ITU-R M.2135 [54]. The latter establishes guidelines for the evaluation of IMT-Advanced radio interface technologies over a wide frequency range, from 0.5 GHz to 100 GHz.

Under LOS conditions, the model introduces a BP distance and a maximum $d_{2D}$ distance of 10 km. Under NLOS conditions, however, this maximum distance is reduced to 5 km, without any specific BP distance being defined.

The PL model in LOS, as specified by the ITU-R for RMa environments, features a relatively complex formulation, incorporating variations in building height ($h$). This **LOS** PL model is expressed before and after the BP respectively by [28] and [54]:

$$PL_{RMa-LOS}^{BP} = \begin{cases} PL'_{1}, & 10 \text{ m} \le d_{2D} \le d'_{BP} \\ PL'_{2}, & d'_{BP} \le d_{2D} \le 10 \text{ km}, \end{cases} \quad (52)$$

with

$$PL'_{1} = 20\log(40\pi d_{3D}f_c/3) + \min(0.03h^{1.72}, 10)\log(d_{3D}) - \min(0.044h^{1.72}, 14.77) + 0.002\log_{10}(h) d_{3D}, \quad (53)$$

$$PL'_{2} = PL_{1}(d'_{BP}) + 40\log_{10}(d_{3D}/d'_{BP}), \quad (54)$$

where the BP distance $d'_{BP}$ is defined by:

$$d'_{BP} = 2\pi h_{BS}h_{UE}f_c/c. \quad (55)$$
In NLOS, the PL model in RMa zones, as adopted in ITU-R M.2135, incorporates several empirical coefficients to take into account environmental variations specific to rural areas, such as street width (W), in addition to building height (h) already included in LOS conditions. The general formula for PL in NLOS is obtained by taking the maximum value between PL in LOS and PL in NLOS, as shown in the following expression [28], [54]:

$$PL_{RMa-NLOS}^{ABG} = \max (PL_{RMa-LOS}^{BP}, PL_{RMa-NLOS}^{ABG}), \quad (56)$$

with:

$$PL_{RMa-NLOS}^{ABG} = 161.04 - 7.1\log_{10}(W) + 7.5\log_{10}(h) - \left(24.37 - 3.7\left(\frac{h}{h_{BS}}\right)^2\right)\log_{10}(h_{BS}) + (43.42 - 3.1\log_{10}(h_{BS}))\left(\log_{10}(d_{3D}) - 3\right) + 20\log_{10}(f_c) - (3.2(11.75 h_{UE})^2 - 4.97). \quad (57)$$

Finally, the values of the parameters $h_{BS}$, $h_{UE}$, W and h and rural environment according to LOS/NLOS conditions are defined as follows [28], [54]:

1) In the rural LOS environment, the default values for the main parameters are: $h_{BS} = 35$ m, $h_{UE} = 1.5$ m, $h = 5$ m. Also adding that this model is applicable in the following ranges: $10 m < h_{BS} < 150$ m, $1 m < h_{UE} < 10$ m, $5 m < h < 50$ m.

2) In the NLOS rural environment, the standard parameters remain similar, but also include a street width set by default at $W = 20$ m. The application range extends to the same intervals for $h_{BS}$ and $h_{UE}$, with the addition of $5 m < W < 50$ m.

## F. SELECTION OF CHANNEL MODELS

Based on the PL models defined in the three standards, we detail here the configurations selected for simulations in the different environments studied.

### 1) UMa AND UMi ENVIRONNEMENTS

In the UMa and UMi-SC environments, Table 3 summarizes the propagation configurations considered, distinguishing between LOS and NLOS conditions and the PL models associated with the corresponding equations (shown in brackets).

For the 3GPP standard, two configurations are considered. The LOS PL is identical in both cases, based on the BP model, but they differ in the NLOS configuration, with the CI model in the 3GPP-1 configuration and the ABG model in the 3GPP-2 configuration.

For the 5GCM standard, two configurations are also considered. The LOS PL remains identical in both cases, based on the CI model, but they differ in NLOS configuration: the CI model in the 5GCM-1 configuration and the ABG model in the 5GCM-2 configuration.

<table><caption>TABLE 3. LOS and NLOS configurations for UMi-SC and UMA SCENARIOS, with associated equations in brackets.</caption><thead><tr><th rowspan="2">Conf.</th><th colspan="2">PL UMi-SC</th><th colspan="2">PL UMa</th></tr><tr><th>LOS</th><th>NLOS</th><th>LOS</th><th>NLOS</th></tr></thead><tbody><tr><td>3GPP-1</td><td>BP<br>(30) (31)</td><td>CI<br>(35)</td><td>BP<br>(36) (37)</td><td>CI<br>(40)</td></tr><tr><td>3GPP-2</td><td>BP<br>(30) (31)</td><td>ABG<br>(33) (34)</td><td>BP<br>(36) (37)</td><td>ABG<br>(38) (39)</td></tr><tr><td>5GCM-1</td><td>CI<br>(41)</td><td>CI<br>(42)</td><td>CI<br>(47)</td><td>CI<br>(48)</td></tr><tr><td>5GCM-2</td><td>CI<br>(41)</td><td>ABG<br>(43)</td><td>CI<br>(47)</td><td>ABG<br>(49)</td></tr><tr><td>mmMAGIC</td><td>ABG<br>(50)</td><td>ABG<br>(51)</td><td>-</td><td>-</td></tr></tbody></table>

Finally, for the mmMAGIC standard, a single configuration is adopted, defined only in UMi-SC, with ABG models used in both LOS and NLOS conditions.

On the other hand, for the UMi-OS environment (not included in Table 3), the PL model is derived exclusively from the 5GCM standard, with two configurations similar to those in UMi-SC. In LOS, the PL model is based on the CI model, represented by (44). In NLOS, the configurations adopted are the CI model for 5GCM-1 and the ABG model for 5GCM-2, defined by (45) and (46) respectively.

### 2) RMa ENVIRONNEMENTS

For the RMa environments, the PL model is based exclusively on the 3GPP standard, with LOS conditions represented by (53) and (54), and NLOS conditions by (56) and (57).

## V. SIMULATION RESULTS

After establishing a detailed theoretical framework in the previous sections, we now focus on the numerical evaluation of the performance of No-CoMP and JT-CoMP transmission modes in the RMa, UMa, and UMi environments of 5G mmWave networks.

To this end, we evaluate several key performance indicators, including the coverage probability, average SINR, worst-case SINR, backhaul power consumption, total data rate, and EE. These evaluations are based on three reference standards—3GPP, 5GCM, and mmMAGIC—as detailed in Subsection IV-F.

## A. SIMULATION PARAMETERS

Simulations are designed to provide reliable results by incorporating realistic parameters adapted to the environments studied. Three frequencies are considered ($f_c = 30$ GHz, 50 GHz, and 100 GHz) with a fixed bandwidth ($B = 2$ GHz). Receiver noise power ($P_N$) is calculated taking $N = 10$ dB and $T = 300$ K. Antenna gains are set at $G_{t,dBi} = 10\log_{10}(G_t) = 20$ dBi for transmit and $G_{r,dBi} = 10\log_{10}(G_r) = 0$ dBi for receive.

The backhaul power consumption coefficient is initially set to $\alpha_{bh} = 4 \times 10^{-9}$ for macro-BSs and $\alpha_{bh} = 4 \times 10^{-10}$ for
micro-BSs, before being varied over a wide range in order to
analyze the sensitivity of the JT-CoMP to this parameter.

Our analysis is based on 1000 Monte Carlo simulations,
where each iteration corresponds to a random distribution
of $N_u = 300$ UEs in the central cell. In addition, we con-
sider $\rho_{UE} = 0$, corresponding to a scenario where UEs are
distributed between a minimum distance to the origin and
radius $R_e$.

ISDs are defined as half the maximum values for each
environment: $ISD_{max}^{RMa}/2$ for RMa, $ISD_{max}^{UMa}/2$ for UMa, and
$ISD_{max}^{UMi}/2$ for UMi, with $ISD_{max}^{RMa} = 5$ km, $ISD_{max}^{UMa} = 500$ m,
and $ISD_{max}^{UMi} = 200$ m. In the remainder of this study, UMi
refers by default to UMi-SC. Note also that the network
configuration around the central BS is fixed $n_{rings} = 5$ rings,
ensuring a uniform network coverage across all simulations.

Finally, unless otherwise specified, these parameters
remain constant, and any modification highlights their impact
on energy and operational performance.

B. COVERAGE PROBABILITY ANALYSIS FOR DIFFERENT ENVIRONMENTS AND FREQUENCIES

We begin our study by analyzing the coverage probability of UEs ($p_c$), equivalent to the CCDF of the SINR, by comparing the No-CoMP and JT-CoMP modes across different frequencies, in accordance with 3GPP channel configurations. Specifically, we focus on the 3GPP configuration in RMa (Fig. 2), as well as the 3GPP-1 configurations for UMa (Fig. 3) and UMi (Fig. 4). In these scenarios, the JT-CoMP mode is evaluated with a fixed number of cooperative BSs ($N_{co} = 7$ BSs), in order to analyze its impact on coverage. Figs. 2, 3 and 4 illustrate the evolution of $p_c$ as a function of SINR for the two transmission modes in the three environments (RMa, UMa, and UMi) and for the three frequencies ($f_c = 30$ GHz, 50 GHz, and 100 GHz).

In all environments, a common trend emerges: $p_c$ systematically decreases with increasing SINR, a direct consequence of the challenges associated with signal propagation, particularly marked at high frequencies ($f_c = 50$ GHz and 100 GHz). These challenges are exacerbated by propagation losses and ICI, but the introduction of the JT-CoMP mode proves essential in mitigating these limitations thanks to its intercell coordination, which enhances useful signals. The beneficial effects of JT-CoMP are particularly visible in urban environments (UMa and UMi), where propagation conditions are the most complex. Nevertheless, performance varies significantly according to the specific features of each environment, as detailed below.

1) RMa ENVIRONMENT

In the RMa environment (Fig. 2), characterized by large
distances between BSs and UEs and a low density of BSs, the
results reveal distinct behaviors depending on the frequencies
studied. At low frequencies ($f_c = 30$ GHz), propagation con-
ditions are more favorable due to a better obstacle penetration
and a reduced sensitivity to losses. These properties enable
the No-CoMP mode to achieve a relatively high SINR, while

<figure><img src="image_3.png" /><figcaption>FIGURE 2. $p_c$ as a function of SINR for the RMa scenario, comparing No-CoMP and JT-CoMP (with $N_{co} = 7$ BSs) modes, using the 3GPP RMa channel model with three frequencies ($f_c = 30$ GHz, 50 GHz and 100 GHz).</figcaption></figure>

the contribution of JT-CoMP remains limited, as ICIs are
already low.

At high frequencies ($f_c = 100$ GHz), however, the situation
changes dramatically. The frequent absence of LOS and high
attenuation over long distances lead to a marked drop in
SINR in No-CoMP mode. Counter-intuitively, the JT-CoMP
mode shows inferior performance in this case, underlining
the limits of intercell coordination over long distances when
$f_c$ is high. These observations show that high mmWave fre-
quencies, even when combined with intercellular cooperation
techniques such as JT-CoMP, are poorly suited to rural envi-
ronments with low BSs density.

2) UMa ENVIRONMENT

In the UMa environment (Fig. 3), where the density of BSs
is moderate and obstacles more numerous than in RMa,
performance is influenced by the complexity of propagation
conditions. At low frequencies ($f_c = 30$ GHz), urban obsta-
cles increase losses, resulting in lower SINR in No-CoMP
mode than in the RMa environment. However, JT-CoMP
mode manages to effectively reduce ICI thanks to better coor-
dination, improving coverage probability even in these more
complex conditions. At high frequencies ($f_c = 100$ GHz),
JT-CoMP mode reveals its full potential. Signals from neigh-
boring BSs, often a source of interference in No-CoMP mode,
are exploited here to reinforce the useful signals thanks to
efficient intercell coordination. This mechanism compensates
for high losses due to obstacles and significantly improves
$p_c$, demonstrating the importance of JT-CoMP in overcoming
propagation challenges in UMa environments.

3) UMi ENVIRONMENT

In the UMi environment (Fig. 4), characterized by a high den-
sity of BSs and proximity between UEs and BSs, ICIs reach
critical levels. In No-CoMP mode, this interference severely
limits the SINR, even at low frequencies ($f_c = 30$ GHz).
<figure><img src="image_4.png" /><figcaption>**FIGURE 3.** *p*<sub>c</sub> as a function of SINR for the UMa scenario, comparing No-CoMP and JT-CoMP (with *N*<sub>co</sub> = 7 BSs) modes, using the 3GPP-1 UMa channel model at three frequencies (*f*<sub>c</sub> = 30 GHz, 50 GHz and 100 GHz).</figcaption></figure>

However, the JT-CoMP mode stands out for its efficient intercell coordination, resulting in a significant improvement in coverage probability. At high frequencies (*f*<sub>c</sub> = 100 GHz), JT-CoMP takes advantage of the proximity between BSs and UEs to maximize transmission diversity and boost useful signals. In this environment, its performance far exceeds that observed with the No-CoMP mode, and also outperforms the gains observed in other environments.

<figure><img src="image_5.png" /><figcaption>**FIGURE 4.** *p*<sub>c</sub> as a function of SINR for the UMi scenario, comparing No-CoMP and JT-CoMP (with *N*<sub>co</sub> = 7 BSs) modes, using the 3GPP-1 UMi channel model at three frequencies (*f*<sub>c</sub> = 30 GHz, 50 GHz and 100 GHz).</figcaption></figure>

### C. ANALYSIS OF AVERAGE SINR ACCORDING TO ENVIRONMENT AND FREQUENCY

Fig. 5 illustrates the evolution of the average SINR ($\gamma_{mean}$) with the No-CoMP and JT-CoMP modes across the three environments, while maintaining the same frequencies. The complex environments, particularly urban ones (UMa and UMi), confirm the consistent advantage of the JT-CoMP mode observed earlier. The ability of JT-CoMP to reduce

<figure><img src="image_6.png" /><figcaption>**FIGURE 5.** $\gamma_{mean}$ for No-CoMP and JT-CoMP (with $N_{co}$ = 7 BSs) modes in RMa, UMa and UMi environments, at three frequencies ($f_c$ = 30 GHz, 50 GHz and 100 GHz).</figcaption></figure>

ICI and leverage signals from neighboring BSs results in significantly superior $\gamma_{mean}$ performance. These results further support the findings from Figs. 3 and 4, highlighting that the effectiveness of the intercell coordination is strongly influenced by the BSs density and the presence of favorable conditions such as LOS. In this regard, JT-CoMP stands out for its ability to transform propagation structural limitations into optimization opportunities.

The impact of frequencies on $\gamma_{mean}$ is another dimension highlighted by Fig. 5. Although increasing $f_c$ inevitably leads to a degradation of $\gamma_{mean}$ due to increased signal attenuation, the urban environments (UMa and UMi) show better resilience thanks to their density of BSs. These observations, in conjunction with those in Figs. 3 and 4, confirm that JT-CoMP manages to limit propagation losses even under demanding conditions, provided that the density of BSs is sufficient to enable efficient coordination.

### D. IMPACT OF CHANNEL CONFIGURATION ON COVERAGE PROBABILITY

After comparing the performance of $p_c$ and $\gamma_{mean}$ for the two transmission modes in the same channel model configuration, this section deepens the analysis by examining the impact of channel models on $p_c$. Focusing on the LOS and NLOS configurations in UMa and UMi urban environments presented in Table 2, Figs. 6, and 7 assess the effect of models specifically adapted to mmWave frequencies on No-CoMP and JT-CoMP (with $N_{co}$ = 7 BSs) modes at $f_c$ = 100 GHz.

#### 1) UMa ENVIRONMENT

In the UMa environment (Fig. 6), the 5GCM models (5GCM-1 and 5GCM-2) stand out clearly in No-CoMP and JT-CoMP mode. In particular, 5GCM-2 maintains a high coverage probability with target SINRs several decibels higher than the other models. For example, for $p_c$ = 0.9, in No-CoMP mode, 5GCM-2 requires a SINR about 1.5 dB higher than 5GCM-1,
<figure><img src="image_7.png" /><figcaption>FIGURE 6. $p_c$ as a function of SINR in the UMa scenario, comparing No-CoMP and JT-CoMP (with $N_{co} = 7$ BSs) modes at $f_c = 100$ GHz, with different channel configurations.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>FIGURE 7. $p_c$ as a function of SINR in the UMi scenario, comparing No-CoMP and JT-CoMP (with $N_{co} = 7$ BSs) modes at $f_c = 100$ GHz, with different channel configurations.</figcaption></figure>

3.9 dB higher than 3GPP-1, and 5.2 dB higher than 3GPP-2. In JT-CoMP mode, these differences widen to 1.2 dB, 4.4 dB, and 5.5 dB respectively. These results confirm the consistent superiority of the 5GCM models in terms of performance.

## 2) UMi ENVIRONMENT

In the UMi environment (Fig. 7), characterized by a high BSs density and increased ICI intensity, the JT-CoMP mode stands out for its significant coverage gains. These gains are manifested by a substantial shift of the curves towards higher SINRs compared with the No-CoMP mode. The 5GCM-1 and 5GCM-2 models, combined with JT-CoMP, almost achieve coverage probability $p_c = 1$ for SINRs of around 19 dB, demonstrating effective ICI management and efficient exploitation of BSs density. In contrast, the mmMAGIC and 3GPP models struggle to achieve similar performance.

Overall, analysis of Figs. 6 and 7 highlights the crucial importance of channel model selection and coordination

strategies for optimizing mmWave network performance. Although performance differences are limited to a few decibels, 5GCM models offer consistently superior performance in complex scenarios, in No-CoMP and JT-CoMP. However, these results do not guarantee greater fidelity of 5GCM models, but rather their ability to provide optimistic network predictions.

## E. IMPACT OF NUMBER OF RINGS ($N_{RINGS}$) AND COOPERATIVE BSs ($N_{CO}$) ON TOTAL DATA RATE

Analysis of Figs. 2 to 7 has highlighted the effects of frequencies and channel model configurations under LOS and NLOS conditions across various environments, focusing on fixed scenarios with $N_{co} = 7$ BSs and $n_{rings} = 5$. To complete this study, in Fig. 8 we now investigate the impact of varying $N_{co}$ (from 2 to 7) and $n_{rings}$ (from 1 to 13) on performance, in order to better understand the effectiveness of JT-CoMP mode in wide-area networks, where network coverage ($R_{nc}$) is directly related to $n_{rings}$. It should be noted that, for the sake of clarity, in the rest of the study, when we compare different environments with the same performance metric on the ordinate, as in Fig. 8, it is mentioned only once, on the leftmost sub-figure. In addition, a single legend is used for all sub-figures.

Fig. 8 illustrates the evolution of total data rate ($D_{total}$) as a function of $n_{rings}$ for UMa and UMi environments at $f_c = 100$ GHz, according to the 3GPP-1 channel model. It reveals that increasing $N_{co}$ in JT-CoMP mode significantly improves $D_{total}$, thanks to enhanced coordination between a greater number of BSs, which improves the signal received by UEs. For example, in UMa, increasing $N_{co}$ from 2 to 7 BSs increases $D_{total}$ from 12.7 Gbits/s to 14.9 Gbits/s for $n_{rings} = 5$, and from 15.9 Gbits/s to 19.3 Gbits/s for $n_{rings} = 1$, corresponding to EE gains of 14.8% and 17.6%. Similarly, in UMi, $D_{total}$ increases from 7.5 Gbits/s to 10.1 Gbits/s for $n_{rings} = 5$, and from 10.5 Gbits/s to 15.2 Gbits/s for $n_{rings} = 1$, resulting in EE gains of 25.7% and 30.9%. This positive effect of $N_{co}$ is particularly marked in UMi, where the increase in the number of cooperative BSs and their proximity to UEs favors efficient ICI management.

However, in both environments, the improvement in $D_{total}$ is counterbalanced by a progressive decrease with increasing $n_{rings}$, due to the intensification of ICI caused by the increase in the total number of BSs in the network ($N_{BS}$). Beyond $n_{rings} = 5$, $D_{total}$ stabilizes, signaling benefit saturation in larger networks. Although the addition of further rings leads to a slight drop in $D_{total}$, it also increases network complexity. This justifies the choice of $n_{rings} = 5$, corresponding to a network coverage ($R_{nc}$) of $\frac{11}{2}$ ISD, as the optimal compromise between performance and complexity.

Finally, a key distinction between UMa and UMi lies in their respective performances: $D_{total}$ remains systematically higher in UMa. This is due to the higher transmission power ($P_{tx}$) and more favorable propagation conditions, linked to
<figure><img src="image_9.png" /><figcaption>**FIGURE 8.** $D_{total}$ as a function of $n_{rings}$ for the UMa and UMi scenarios, at $f_c$ = 100 GHz, with the 3GPP-1 channel and different $N_{co}$ values.</figcaption></figure>

the increased height of the BSs, which reduces obstructions and improves link quality.

## F. ANALYSIS OF WORST CASE SINR ACCORDING TO UES DISTRIBUTION

Analysis of the previous results, based on $ρ_{UE} = 0$, reveals limitations in performance evaluation, particularly for UEs on the periphery, which suffer increased propagation losses and intensified interference. To better understand these disparities, we vary $ρ_{UE}$ between 0.05 and 0.95 (Fig. 9) to distinguish scenarios where UEs are concentrated near the center of the cell from those where they cluster on the periphery. Fig. 9 illustrates the evolution of worst-case SINR ($SINR_0$) as a function of $ρ_{UE}$ for RMa, UMa, UMi environments, based on two frequencies (50 GHz and 100 GHz) and two JT-CoMP configurations ($N_{co}$ = 4 BSs and 7 BSs). For these simulations, we use 3GPP channel configurations in RMa and 3GPP-1 in UMa and UMi.

The results reveal distinct behaviors depending on the environment:

* UMa stands out for a high $SINR_0$, favored by moderate ISDs and BSs heights, ensuring an efficient propagation even for distant UEs on the periphery, where $ρ_{UE}$ is high.

* UMi shows inferior performance, due to the low height of the BSs and their reduced transmission power, which amplifies the impact of urban obstacles, especially for UEs distant from the BSs, where $ρ_{UE}$ is high.

* RMa has the lowest performance, due to large ISDs and low cell density, which amplify propagation losses for UEs on the periphery, particularly at high frequencies.

In terms of frequencies, at $f_c$ = 50 GHz, the reduction in propagation losses favors edge UEs, particularly in RMa. On the other hand, $f_c$ = 100 GHz becomes more competitive in urban environments. Finally, increasing $N_{co}$ systematically improves $SINR_0$, especially in the periphery, confirming the effectiveness of intercell coordination against ICI.

<figure><img src="image_10.png" /><figcaption>**FIGURE 9.** $SINR_0$ as a function of $ρ_{UE}$ for the RMa, UMa, and UMi scenarios, with two frequencies (50 GHz and 100 GHz), two JT-CoMP cooperation configurations ($N_{co}$ = 4 BSs and 7 BSs) and the 3GPP channel configurations in RMa and 3GPP-1 in UMa and UMi.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>**FIGURE 10.** $P_{CoMP}$ as a function of $N_{co}$ for the UMa and UMi scenarios, under different channel configurations, for $f_c$ = 100 GHz.</figcaption></figure>

## G. IMPACT OF $N_{CO}$ AND CHANNEL CONFIGURATION ON BACKHAULING POWER ($P_{COMP}$)

SSSWe now turn to power consumption required to coordinate the $N_{co}$ BSs in the first ring. The addition of a cooperative BS induces a direct increase in power consumption, mainly attributed to backhauling. Fig. 10 highlights these issues by examining the impact of different channel models (3GPP-1, 3GPP-2, 5GCM-1, 5GCM-2) in UMa and UMi environments, complemented by the mmMAGIC configuration for UMi.

Analysis of the variation in $P_{CoMP}$ reveals contrasting dynamics depending on the environment. In UMi, an increase in $N_{co}$ leads to a moderate rise in $P_{CoMP}$ per cooperative BS, regardless of the channel model used. In UMa, on the other hand, the addition of coordinated BSs generates a more significant increase in power consumption linked to backhauling. These results show that UMi, characterized by lower overall power dissipation than UMa, presents reduced $P_{CoMP}$ values, highlighting environment-specific energy challenges.
## H. IMPACT OF ISD ON $P_{COMP}$ AND EE UNDER VARIOUS CHANNEL CONFIGURATIONS

The analysis in Fig. 10 has highlighted the challenges associated with increasing $N_{co}$, particularly with regard to $P_{COMP}$, especially when ISD is set at half its maximum value according to the permitted limits of each environment. This highlights the need to integrate these factors into the evaluation of EE, defined as the ratio between total data rate ($D_{total}$) and total power consumption (including transmission, RF circuits, and backhaul). To extend this analysis, Figs. 11 to 13 examine the influence of intercell cooperation (with $N_{co} = 7$ BSs in JT-CoMP mode) on $P_{COMP}$ and EE as a function of ISD, in the three environments studied: RMa, UMa, and UMi. These analyses, carried out at a frequency of 100 GHz, are based on 3GPP, 5GCM, and mmMAGIC channel models, offering a global dimension on the balance between intercell cooperation intensity and EE in 5G mmWave networks under different inter-BS distances.

Figs. 11, 12 and 13 show that, in all three environments, JT-CoMP mode systematically improves EE over No-CoMP mode, irrespective of channel models. However, the extent of this improvement varies with ISD. Indeed, as ISD increases, $P_{COMP}$ and EE follow a concave curve: EE initially increases, reaches a maximum, then decreases after a critical threshold. This phenomenon suggests that excessive densification or distances between BSs can impair EE and reduce gains, although the impact of these effects is modulated by the particularities of each environment, as explained below.

### 1) RMa ENVIRONMENT

In RMa (Fig. 11), $P_{COMP}$ peaks at a moderate ISD (~1.4 km), a point that coincides with the EE peak in JT-CoMP mode. Although JT-CoMP improves EE for ISDs below 2 km, its performance progressively degrades beyond this distance, becoming even inferior to that of No-CoMP mode for ISDs above 4 km. This suggests that, despite the advantages of intercell coordination at short distances, EE gains become limited as ISD increases.

### 2) UMa ENVIRONMENT

In UMa (Fig. 12), $P_{COMP}$ and EE increase more strongly with ISD, mainly due to a greater reduction in propagation losses compared to RMa at 100 GHz. This reduction in losses promotes a significant increase in $D_{total}$, to which $P_{COMP}$ is directly proportional. However, although $P_{COMP}$ is higher in UMa than in RMa, the increase in $D_{total}$ far exceeds the increase in total power consumption, giving UMa a better EE than RMa. Additionally, the trends observed in UMa reveal a certain complexity, with several peaks in $P_{COMP}$ and EE as a function of ISD, resulting from variations in channel configurations.

### 3) UMi ENVIRONMENT

In UMi (Fig. 13), $P_{COMP}$ is significantly lower than that observed in RMa and UMa, mainly due to the factors already

<figure><img src="image_12.png" /><figcaption>**FIGURE 11.** $P_{COMP}$ and EE as a function of ISD in No-CoMP and JT-CoMP (with $N_{co} = 7$ BSs) modes, using the 3GPP channel in the RMa scenario, for $f_c = 100$ GHz.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>**FIGURE 12.** $P_{COMP}$ and EE as a function of ISD in No-CoMP and JT-CoMP (with $N_{co} = 7$ BSs) modes, under different channel configurations in the UMa scenario (3GPP-1, 3GPP-2, 5GCM-1, 5GCM-2), for $f_c = 100$ GHz.</figcaption></figure>

discussed in the analysis of Fig. 10. This low power consumption, due to less demanding backhaul, combined with lower transmission power and reduced RF circuit consumption, is accompanied by a total data rate comparable to that of UMa and significantly higher than that of RMa at $f_c = 100$ GHz. These features give UMi the best EE of the three environments. In addition, the high density of BSs and shorter ISDs accentuate the advantages of the JT-CoMP mode, maximizing its benefits in this dense urban context.

In terms of channel model performance in urban environments, 5GCM configurations significantly outperform 3GPP models in UMa, in both No-CoMP and JT-CoMP modes, with a particularly marked advantage for 5GCM-2. In UMi, although the performance of 5GCM-1 and 5GCM-2 models is similar, they remain overall more efficient than 3GPP and mmMAGIC models.

In summary, Figs. 12, 13, and 14 highlight that the EE of RMa, UMa, and UMi environments is strongly influenced by ISD. In urban environments (UMa and UMi),
a shorter ISD reduces propagation losses and improves the EE, unlike in RMa, where large inter-BS distances represent a major limitation. However, an excessive reduction in ISD can increase ICI, especially in No-CoMP mode. Among these environments, UMi stands out for its better utilization of the JT-CoMP mode, where the short ISD facilitates efficient BS coordination, further enhancing the overall EE.

## I. ANALYSIS OF EE AS A FUNCTION OF ISD IN UMi IN THE SPECIFIC SCENARIOS OF SC AND OS

Fig. 14 refines the analysis of the UMi environment, focusing on the SC and OS scenarios. Unlike Fig. 13, which offers an overview of UMi environments in SC with various channel models, Fig. 14 explores in greater detail the specific dynamics of SC and OS in 5GCM-1 and 5GCM-2 configurations.

<figure><img src="image_14.png" /><figcaption>FIGURE 13. $P_{CoMP}$ and EE as a function of ISD in No-CoMP and JT-CoMP (with $N_{co} = 7$ BSs) modes, under different channel configurations in the UMi scenario (3GPP-1, 3GPP-2, 5GCM-1, 5GCM-2, mmMAGIC), for $f_c = 100$ GHz.</figcaption></figure>

In the SC scenario, characterized by a confined urban environment, obstructions and ICI limit signal propagation, thus slowing down the increase in $D_{total}$ with ISD and reducing EE gains, even in JT-CoMP. Conversely, the OS scenario, with its smoother propagation, improves $D_{total}$, leading to higher EE above a certain ISD threshold. These thresholds vary according to channel model and transmission mode: around 110 m (JT-CoMP) and 140 m (No-CoMP) for 5GCM-1, versus 70 m and 100 m for 5GCM-2, underlining the advantage of the latter in open environments.

## J. ANALYSIS OF EE AS A FUNCTION OF BACKHAUL POWER COEFFICIENT ($\alpha_{BH}$) IN URBAN ENVIRONMENTS

In the previous analyses, a simplifying assumption was adopted: a constant backhaul power coefficient ($\alpha_{bh}$) in JT-CoMP mode. Although this approach identified global trends, it masks the complex interactions between $\alpha_{bh}$ and other critical factors influencing EE. Power consumption related to backhauling ($P_{CoMP}$), proportional to $\alpha_{bh}$, is a major constraint for JT-CoMP mode, unlike No-CoMP, which avoids it thanks to independent transmissions. To explore this further, Fig. 15 examines the effect of $\alpha_{bh}$, multiplying it by

<figure><img src="image_15.png" /><figcaption>FIGURE 14. EE as a function of ISD for No-CoMP and JT-CoMP (with $N_{co} = 7$ BSs) modes in UMi environment, according to 5GCM-1 and 5GCM-2 channel models in SC and OS scenarios at $f_c = 100$ GHz.</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>FIGURE 15. EE as a function of $\rho_{\alpha}$ for the UMa and UMi scenarios in JT-CoMP (with $N_{co} = 7$ BSs) mode, with two channel configurations (3GPP-1, 5GCM-1) and two frequencies ($f_{c1} = 30$ GHz and $f_{c2} = 100$ GHz).</figcaption></figure>

a coefficient $\rho_{\alpha}$ ranging from 0.1 to 100, in relation to frequency ($f_{c1} = 30$ GHz and $f_{c2} = 100$ GHz) and channel models (3GPP-1 and 5GCM-1).

Fig. 15 shows that EE in JT-CoMP decreases with increasing $\rho_{\alpha}$, while No-CoMP, insensitive to $\alpha_{bh}$, maintains constant performance. In UMi, the low frequency ($f_{c1}$) associated with 5GCM-1 offers a better EE in the face of increasing $\rho_{\alpha}$ thanks to more efficient ICI management. In contrast, the high frequency ($f_{c2}$) is more sensitive to energy costs, reducing its efficiency. In UMa, although the trends are similar, EE gains remain limited by macro-cell constraints.

Analysis of Fig. 15 also highlights a key point: the existence of a critical $\rho_{\alpha}$ threshold, beyond which JT-CoMP loses its EE advantage over No-CoMP. This threshold varies greatly according to environment, frequency and channel model. In UMa, it lies around $\rho_{\alpha} = 40$ for $f_{c1}$ (3GPP-1) and drops to $\rho_{\alpha} = 30$ for $f_{c2}$ (5GCM-1). In UMi, this threshold is significantly higher, reaching $\rho_{\alpha} = 100$ for $f_{c1}$ (3GPP-1) and $\rho_{\alpha} = 65$ for $f_{c2}$ (5GCM-1).
<figure><img src="image_17.png" /><figcaption>**FIGURE 16.** EE as a function of $\rho_{\alpha}$ for the UMa and UMi scenarios, with 3GPP-1 channel, $f_c = 100$ GHz, two user distributions ($\rho_{UE} = 0$ and 0.8), and three levels of intercell cooperation ($N_{co} = 2$ BSs, 4 BSs, and 7 BSs).</figcaption></figure>

Fig. 16 refines the analysis by integrating the spatial distribution of UEs ($\rho_{UE}$) and different levels of intercellular cooperation ($N_{co} = 2$ BSs, 4 BSs, 7 BSs). It shows that EE in JT-CoMP improves markedly when UEs are concentrated in the periphery ($\rho_{UE} = 0.8$), especially in UMi, where the density of BSs and ICI require better coordination. Conversely, for a uniform distribution of UEs in the central BS ($\rho_{UE} = 0$), the gap between No-CoMP and JT-CoMP narrows, reflecting less dependence on cooperation.

Fig. 16 also shows that the critical $\rho_{\alpha}$ thresholds, beyond which JT-CoMP loses its energy advantage, depend strongly on $\rho_{UE}$ and $N_{co}$. For example, in UMa, with $\rho_{UE} = 0$, these thresholds vary between 30 and 35 depending on $N_{co}$, while they exceed 100 for $\rho_{UE} = 0.8$ and $N_{co} = 1$, increasing with increased cooperation.

## K. ANALYSIS OF $D_{TOTAL}$ AND EE AS A FUNCTION OF TRANSMISSION POWER IN URBAN ENVIRONMENTS

Previous analyses, based on a fixed value of $P_{tx}$ for macro and micro-BSs, provided a global view of energy trends. However, this simplification masks the crucial role of $P_{tx}$ which, far from being a constant, influences both $D_{total}$ and EE. To explore this issue further, Fig. 17 and 18 examine the impact of variations in $P_{tx}$ power, multiplying it by a coefficient $\tau_{P_{tx}}$ ranging from 0.01 to 1, and analyze the performance of No-CoMP and JT-CoMP modes in the UMa and UMi scenarios.

Fig. 17 shows a direct relationship between increasing $\tau_{P_{tx}}$ and increasing $D_{total}$, with a marked advantage for the JT-CoMP mode, particularly when $\rho_{UE} = 0.8$. In UMa, for example, a change from $\tau_{P_{tx}} = 0.1$ to 1 increases $D_{total}$ from 0.88 Gbits/s to 4.33 Gbits/s in No-CoMP and from 1.73 Gbits/s to 8.57 Gbits/s in JT-CoMP, translating into an EE gain of almost 49% for the two extreme values of $\tau_{P_{tx}}$. In UMi, although data rates are lower overall, JT-CoMP retains its superiority, with gains of up to 62% for $\tau_{P_{tx}} = 1$.

<figure><img src="image_18.png" /><figcaption>**FIGURE 17.** $D_{total}$ as a function of $\tau_{P_{tx}}$ in the UMa and UMi scenarios for the No-CoMP and JT-CoMP (with $N_{co} = 7$ BSs) modes, with the 3GPP-1 channel, and $\rho_{UE} = 0$ and 0.8.</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>**FIGURE 18.** EE as a function of $\tau_{P_{tx}}$ in the UMa and UMi scenarios for the No-CoMP and JT-CoMP (with $N_{co} = 7$ BSs) modes, with the 3GPP-1 channel, and $\rho_{UE} = 0$ and 0.8.</figcaption></figure>

It should be noted that this increased efficiency of JT-CoMP with increasing $P_{tx}$ is explained by its advanced ICI management. Unlike No-CoMP, where increasing $\tau_{P_{tx}}$ also amplifies ICI, JT-CoMP keeps them at a lower level, thus improving SINR and promoting an increase in $D_{total}$, even in dense environments.

Fig. 18 provides additional insight by examining the impact of $\tau_{P_{tx}}$ on EE. In contrast to the linear growth of $D_{total}$ observed in Fig. 17, EE follows a concave curve, with a global optimum for intermediate values of $\tau_{P_{tx}}$ (typically between 0.1 and 0.2 in UMa and between 0.2 and 0.4 in UMi). This point marks an optimum balance between increasing data rate and controlling energy consumption. Beyond this point, EE decreases, as data rate gains are outweighed by a disproportionate increase in power consumption.

Finally, Fig. 19 takes a closer look at the interactions between EE and $\tau_{P_{tx}}$, examining the effect of $f_c$. The results show a decrease in optimum EE with increasing $f_c$. For example, in JT-CoMP mode in UMa, the optimum EE drops from 296.34 kbits/J at 30 GHz to 228.02 kbits/J at 100 GHz,
<figure><img src="image_20.png" /><figcaption>FIGURE 19. EE as a function of $\tau_{P_{tx}}$ in the UMa and UMi scenarios for the No-CoMP and JT-CoMP (with $N_{co} = 7$ BSs) modes, with the 3GPP-1 channel and three frequency values ($f_c = 30$ GHz, 50 GHz, and 100 GHz).</figcaption></figure>

a reduction of 23.05%. In UMi, although the trend is similar, the impact is mitigated by the increased cell density: optimal EE decreases from 1593.88 kbits/J (for $f_c = 30$ GHz) to 1341.50 kbits/J (for $f_c = 100$ GHz), a reduction of 15.83%.

Another key aspect of Fig. 19 is the variation of $\tau_{P_{tx}}$, which optimizes EE as a function of frequency $f_c$, reflecting an increase in energy requirements. For example, in JT-CoMP mode, the optimal value of $\tau_{P_{tx}}$ increases from 0.05 to 0.2 in UMa when $f_c$ increases from 30 GHz to 100 GHz. In UMi, on the other hand, this value increases from 0.2 to 0.4 at 100 GHz. This result highlights the fact that dense urban environments require a more pronounced adjustment of $\tau_{P_{tx}}$ to optimize EE, especially at high frequencies, due to the initially lower $P_{tx}$ for micro-BSs (in UMi) than for macro-BSs (in UMa).

## L. SUMMARY OF SIMULATION RESULTS

In a context where EE optimization is a central issue for 5G mmWave networks, this study proposes a realistic and adaptable analytical framework to capture the complex interactions between energy performance and propagation conditions. Unlike many previous approaches, which often focus on throughput optimization or interference management based on specific assumptions or partially representative models, our model jointly integrates propagation characteristics (LOS probability, path loss, shadow fading, 3D distances, BP distances, etc.), diversity of environments (RMa, UMa, UMi), and fine modeling of power consumption — including transmission, RF circuits, and backhaul — while explicitly distinguishing macro- and micro-BSs. To assess the robustness of our approach, we conducted an in-depth comparative analysis between JT-CoMP and No-CoMP modes, across a wide range of configurations (ISD, number of rings, carrier frequency, transmission power, and number of cooperative BSs), based on reference propagation models (3GPP, 5GCM, and mmMAGIC), thus ensuring a rigorous and representative evaluation in various contexts.

The results show that JT-CoMP delivers significant gains in SINR, coverage probability ($p_c$), total data rate ($D_{total}$), and EE in various deployment scenarios. However, these benefits are offset by increased power consumption, mainly linked to inter-BS coordination via backhaul. This energy trade-off leads to the emergence of a transition point: beyond a certain threshold — depending on the ISD, the energy cost of backhaul, and the environment — the No-CoMP mode becomes more advantageous.

This trend reversal can be explained by two combined mechanisms. On the one hand, as ISD increases above a certain threshold, ICI naturally decreases. The main benefit of JT-CoMP — namely the reduction of these interferences — then becomes less relevant. Under these conditions, the gains in coordination no longer compensate for the associated additional energy costs. On the other hand, as the backhaul power coefficient increases, the consumption linked to inter-BS coordination becomes progressively dominant, leading to a marked degradation of EE in JT-CoMP mode. Thus, the superiority of No-CoMP can be explained by a combination of lower cooperative benefits in low-interference environments and an increasing energy cost for JT-CoMP.

The spatial distribution of UEs also appears to be a determining factor in JT-CoMP efficiency. When the majority of UEs are located at the edge of the cell, inter-BS coordination becomes more relevant, and the gains in data rate justify the additional energy cost. Conversely, a more uniform distribution increases the load on the backhaul without significant benefits, which, in cases of high ISD and high backhaul power coefficient, can make No-CoMP more efficient. On the other hand, the overall density of UEs does not influence the transition point, as the results are averaged over a large number of Monte Carlo simulations. A higher density simply improves the statistical robustness of the results, without altering the fundamental trade-off between gain and energy consumption.

The analysis also reveals that EE varies significantly depending on the deployment environment and carrier frequency ($f_c$). In UMi, JT-CoMP performance remains high even at frequencies up to 100 GHz, thanks to the proximity between BSs, low ISD, and reduced overall power consumption. These conditions make it possible to maintain a satisfactory SINR while achieving better EE values. Conversely, in UMa and RMa environments, greater inter-site distances reduce the efficiency of intercellular coordination. EE is defined as the ratio of total data rate to power consumption. In UMi, the latter remains low (thanks to low backhaul power, low transmission power, and low-power RF circuits), enabling a good compromise to be maintained: competitive data rates for minimum power consumption, thus explaining the better EE performance of JT-CoMP at 100 GHz in this environment compared with other deployment scenarios.

Finally, transmission power ($P_{tx}$) also plays a differentiated role: its increase benefits more the JT-CoMP, which efficiently exploits the data rate gain, while the No-CoMP suffers an intensification of ICI without a proportional improvement in EE.
Taken together, these results highlight the complexity of interactions between the key parameters of 5G mmWave networks. The proposed model offers an integrated, flexible, and real-world framework designed to optimize EE while balancing QoS requirements (SINR, coverage probability, and total data rate) and consumption constraints. It enables fine-tuning of deployment parameters, and provides a predictive tool to identify, according to contextual specifics, scenarios in which No-CoMP mode becomes more energy-efficient than JT-CoMP.

## M. LIMITATIONS AND FUTURE RESEARCH DIRECTIONS

This study focused on outdoor scenarios, using propagation models derived from 3GPP, 5GCM, and mmMAGIC standards. These models, based on common parameters (LOS probability, path loss, shadow fading, 3D distances, BP distances, etc.), guarantee a consistent comparison between RMa, UMa, and UMi environments. However, they do not fully cover certain dynamic effects, such as moving obstructions or specific penetration losses. Future work could therefore integrate urban mobility models, to better simulate the impact of dynamic obstacles (vehicles, pedestrians, etc.) on SINR and EE. In addition, the exploration of indoor scenarios — using the models proposed by 3GPP TR 38.901 [28] and 5GCM [29], which integrate losses linked to structures (walls, glazing, etc.) — would make it possible to assess the robustness of JT-CoMP under mixed propagation conditions, more representative of real-world deployments.

In the same spirit, the integration of user dynamics, such as mobility and traffic variations, is another key area. The current, deliberately static, framework aims to isolate the impact of structuring parameters (ISD, number of rings, carrier frequency, transmission power, and number of cooperative BSs). Although some spatial variability is introduced via the $ρ_{UE}$ parameter, mobility is not explicitly modeled. In dense urban environments, mobility can lead to link instability, frequent handovers, and increased latency — all factors that can impair the efficiency of JT-CoMP. Similarly, traffic fluctuations can make systematic cooperation sub-optimal from an energy point of view. The use of realistic mobility and traffic models would enable us to design adaptive activation strategies that are more efficient in real-world conditions.

Furthermore, although our model proposes EE optimization solutions in different environments and configurations for No-CoMP and JT-CoMP modes, it is based on the assumption that all BSs transmit the same downlink content to UEs. This assumption, although simplifying, is relevant in specific cases such as the dissemination of warning messages or critical content in areas at risk with high user density — as illustrated in our previous work [8], [9]. We recognize, however, that this framework does not reflect the diversity of flows observed in current 5G networks. Extending our approach to differentiated traffic scenarios between BSs is thus a natural prospect for future work.

Moreover, the adopted approach relies on offline simulations, which are effective for analyzing global trends but

require simplification for real-time implementation. Integration into a distributed architecture or the adoption of heuristic solutions and decentralized cooperation mechanisms may prove necessary to reconcile energy efficiency and practical feasibility. Additionally, future work could further explore the integration of artificial intelligence, particularly through machine learning and deep reinforcement learning, to optimize EE in 5G mmWave networks with JT-CoMP. These techniques would enable adaptive resource management and dynamic decision-making in complex environments [56], [57].

The 7-24 GHz band is another area of concern. Although included in the 3GPP TR 38.901 model [28], this range is based on theoretical extrapolations. Recent experimental measurements presented in [58], however, provide empirical data from measurement campaigns at 6.75 GHz and 16.95 GHz in a dense urban environment (Brooklyn), which is a useful advance for refining the accuracy of propagation models in 5G mmWave scenarios.

In addition, certain assumptions need to be revisited to enhance the model's realism. In particular, the use of fixed-efficiency power amplifiers simplifies the calculations, but does not reflect the real variation in efficiency as a function of output power. A more refined model would enable more accurate energy estimation. Similarly, the assumption of a direct backhaul between each cooperative BS and the central unit simplifies the analysis, but departs from the multi-hop architectures encountered in constrained networks. The latter, while introducing additional energy costs, may also offer advantages in terms of coverage or deployment flexibility. Incorporating these dimensions into the model would contribute to a better representation of the diversity of mmWave 5G architectures, particularly in extended rural environments or dense urban areas.

Finally, in this study, we assumed a fixed antenna gain and a single antenna per BS, regardless of the carrier frequency. This simplification is intended to ensure a consistent comparison framework across different frequency bands and scenarios, but it deviates from practical configurations, especially in the millimeter-wave band, where the reduced wavelength allows for the integration of compact and high-gain antenna arrays. These architectures enable the full exploitation of massive beamforming (or massive MIMO), through frequency-dependent adaptation of the number of elements and radiation pattern characteristics. As an extension of this work, a more detailed modeling of these aspects, in accordance with 3GPP specifications, will be integrated to better reflect the actual system performance in both No-CoMP and JT-CoMP modes, particularly in terms of SINR and EE.

## VI. CONCLUSION

This study contributes to the optimization of the EE in 5G mmWave networks by proposing strategies adapted to Rural Macro (RMa), Urban Macro (UMa) and Urban Micro (UMi)
environments, as well as to the specificities of mmWave frequencies. Our model takes into account elements often neglected in previous work, such as LOS/NLOS propagation conditions and their associated probabilities, Shadow Fading effects, three-dimensional distances, BreakPoint distances, inter-BS distance (ISD), and total macro and micro-BS energy consumption. It is also based on path loss models standardized by 3GPP for the rural environment, and on 3GPP, 5GCM, and mmMAGIC standards for rural and urban environments (presented in Subsection IV-F). The results show that in urban environments, 5GCM configurations significantly outperform 3GPP models in UMa, with a marked advantage for the 5GCM-2 model. In UMi, the performance of the 5GCM-1 and 5GCM-2 models is close, but overall superior to that of the 3GPP and mmMAGIC models. Beyond the analysis of propagation models, our study highlights the necessary balance between network performance and energy constraints for No-CoMP and JT-CoMP transmission modes, depending on key parameters such as the ISD, number of rings, and number of BSs. In UMi, the high density of BSs favors efficient coordination, generating significant gains in EE, particularly at high mmWave frequencies. In UMa, although gains are observed, they remain limited by more extensive ISDs, while in RMa, the JT-CoMP, although less efficient, remains useful for mitigating the effects of uncoordinated propagation at low frequencies. This analysis also highlights the importance of optimal ISD calibration to maximize the EE in these three environments, avoiding both excessive interference and inefficient coordination. Furthermore, our study identifies critical thresholds beyond which JT-CoMP mode becomes less efficient than No-CoMP mode, due to the energy costs associated with cooperative processing and backhauling, particularly impacting UEs at cell edges. Finally, optimizing transmission power in UMa and UMi is essential to balance data rate and energy consumption. Thus, this research proposes adaptive strategies to maximize the efficiency of No-CoMP and JT-CoMP modes in 5G mmWave networks, while anticipating the challenges of 6G networks and responding to critical needs, such as the mass dissemination of emergency alerts to the general public, thus helping to enhance public safety.

## REFERENCES

[1] A. Dogra, R. K. Jha, and S. Jain, "A survey on beyond 5G network with the advent of 6G: Architecture and emerging technologies," *IEEE Access*, vol. 9, pp. 67512-67547, 2021.

[2] A. Ahmed, M. Zakarya, X. Liu, R. Khan, A. Ali, and A. A. Khan, "Quality enhancement in a mm-wave multi-hop, multi-tier heterogeneous 5G network architecture," *Telecommun. Syst.*, vol. 80, no. 2, pp. 169-187, Jun. 2022.

[3] R. Poorzare and A. C. Augé, "How sufficient is TCP when deployed in 5G mmWave networks over the urban deployment?" *IEEE Access*, vol. 9, pp. 36342-36355, 2021.

[4] M. A. Adedoyin and O. E. Falowo, "Combination of ultra-dense networks and other 5G enabling technologies: A survey," *IEEE Access*, vol. 8, pp. 22893-22932, 2020.

[5] A. Gotsis, S. Stefanatos, and A. Alexiou, "UltraDense networks: The new wireless frontier for enabling 5G access," *IEEE Veh. Technol. Mag.*, vol. 11, no. 2, pp. 71-78, Jun. 2016.

[6] J. Liu, M. Sheng, L. Liu, and J. Li, "Network densification in 5G: From the short-range communications perspective," *IEEE Commun. Mag.*, vol. 55, no. 12, pp. 96-102, Dec. 2017.

[7] V. M. Nguyen and M. Kountouris, "Performance limits of network densification," *IEEE J. Sel. Areas Commun.*, vol. 35, no. 6, pp. 1294-1308, Jun. 2017.

[8] M. Younes and Y. Louet, "Analysis of unicast/broadcast switch over with regard to resource allocation for future cellular networks," in *Proc. 4th Global Power, Energy Commun. Conf. (GPECOM)*, Cappadocia, Turkey, Jun. 2022, pp. 605-610.

[9] M. Younes and Y. Louet, "Enhancing public safety through dynamic switching between unicast and broadcast transmission modes in cellular networks," *IEEE Access*, vol. 13, pp. 1496-1510, 2025.

[10] A. Al-Asadi, M. Al-Amidie, A. C. Micheas, R. G. McGarvey, and N. E. Islam, "Worst case fair beamforming for multiple multicast groups in multicell networks," *IET Commun.*, vol. 13, no. 6, pp. 664-671, Apr. 2019.

[11] G. Zhao, S. Chen, L. Zhao, and L. Hanzo, "Joint energy-spectral-efficiency optimization of CoMP and BS deployment in dense large-scale cellular networks," *IEEE Trans. Wireless Commun.*, vol. 16, no. 7, pp. 4832-4847, Jul. 2017.

[12] S. K. Dastoor, U. Dalal, and J. Sarvaiya, "Cellular planning for next generation wireless mobile network using novel energy efficient CoMP," *Cluster Comput.*, vol. 22, no. S2, pp. 4611-4623, Mar. 2019.

[13] J. Yu, G. Yu, Y. Wang, and Q. Zhang, "Energy-efficient radio resource management in a HetNet downlink system with JT CoMP," in *Proc. IEEE/CIC Int. Conf. Commun. China-Workshops (CIC/ICCC)*, Nov. 2015, pp. 142-147.

[14] K. Mohammed Saidul Huq, S. Mumtaz, and J. Rodriguez, "QoS aware energy-efficient resource scheduling for HetNet CoMP," in *Proc. IEEE Int. Conf. Commun. (ICC)*, Jun. 2015, pp. 5954-5960.

[15] J. Vargas, C. Thienot, and X. Lagrange, "MBSFN and SC-PTM as solutions to reduce energy consumption in cellular networks," in *Proc. IEEE Symp. Comput. Commun. (ISCC)*, Jun. 2022, pp. 1-6.

[16] G. R. MacCartney and T. S. Rappaport, "Millimeter-wave base station diversity for 5G coordinated multipoint (CoMP) applications," *IEEE Trans. Wireless Commun.*, vol. 18, no. 7, pp. 3395-3410, Jul. 2019.

[17] J. Zhao, L. Yang, M. Xia, and M. Motani, "Unified analysis of coordinated multipoint transmissions in mmWave cellular networks," *IEEE Internet Things J.*, vol. 9, no. 14, pp. 12166-12180, Jul. 2022.

[18] B. Maham and P. Popovski, "Capacity analysis of coordinated multipoint reception for mmWave uplink with blockages," *IEEE Trans. Veh. Technol.*, vol. 69, no. 12, pp. 16299-16303, Dec. 2020.

[19] H. Iimori, G. T. F. de Abreu, O. Taghizadeh, R. A. Stoica, T. Hara, and K. Ishibashi, "Stochastic learning robust beamforming for millimeter-wave systems with path blockage," *IEEE Wireless Commun. Lett.*, vol. 9, no. 9, pp. 1557-1561, Sep. 2020.

[20] B. Li, Y. Dai, Z. Dong, E. Panayirci, H. Jiang, and H. Jiang, "Energy-efficient resources allocation with millimeter-wave massive MIMO in ultra dense HetNets by SWIPT and CoMP," *IEEE Trans. Wireless Commun.*, vol. 20, no. 7, pp. 4435-4451, Jul. 2021.

[21] S.-Y. Kim and H. Ko, "Energy-efficient CoMP joint transmission in hybrid-powered mmWave networks," *IEEE Access*, vol. 10, pp. 104793-104800, 2022.

[22] Y. Zhang, J. Mu, and J. Xiaojun, "Performance of multi-cell mmWave NOMA networks with base station cooperation," *IEEE Commun. Lett.*, vol. 25, no. 2, pp. 442-445, Feb. 2021.

[23] J. Gui, X. Dai, and N. N. Xiong, "Design and analysis of network behaviors for optimizing network energy efficiency in 5G mmWave systems," *IEEE Trans. Netw. Sci. Eng.*, vol. 8, no. 2, pp. 1842-1861, Apr. 2021.

[24] O. Kanhere, H. Poddar, Y. Xing, D. Shakya, S. Ju, and T. S. Rappaport, "A power efficiency metric for comparing energy consumption in future wireless networks in the millimeter-wave and terahertz bands," *IEEE Wireless Commun.*, vol. 29, no. 6, pp. 56-63, Dec. 2022.

[25] J. Chen, X. Ge, Y. Zhong, and Y. Li, "A novel JT-CoMP scheme in 5G fractal small cell networks," in *Proc. IEEE Wireless Commun. Netw. Conf. (WCNC)*, Marrakech, Morocco, Apr. 2019, pp. 1-7.

[26] A. Askri and G. R. Othman, "Priority-resource-limited JT-CoMP scheme for small dense networks," in *Proc. IEEE Int. Medit. Conf. Commun. Netw. (MeditCom)*, Athens, Greece, Sep. 2021, pp. 306-311.

[27] R. Hou, J. Cai, and K. S. Lui, "Distributed cache-aware CoMP transmission scheme in dense small cell networks with limited backhaul," *Comput. Commun.*, vol. 138, pp. 11-19, Apr. 2019.
[28] Study on Channel Model for Frequencies From 0.5 to 100 GHz, document TR 38.901 V18.0.0, 3GPP, 2024. [Online]. Available: https://www.3gpp.org/ftp/Specs/archive/38_series/38.901/38901-i00.zip

[29] 5GCM. (2016). 5G Channel Model for Bands Up to 100 GHz, White Paper V2.3. [Online]. Available: http://www.5gworkshops.com/5GCMSIG_White%20Paper_r2dot3.pdf

[30] T. S. Rappaport, Y. Xing, G. R. MacCartney, A. F. Molisch, E. Mellios, and J. Zhang, “Overview of millimeter wave communications for fifth-generation (5G) wireless networks—With a focus on propagation models,” *IEEE Trans. Antennas Propag.*, vol. 65, no. 12, pp. 6213–6230, Dec. 2017.

[31] V. I. Tatsis, M. Karavolos, D. N. Skoutas, N. Nomikos, D. Vouyioukas, and C. Skianis, “Energy-aware clustering of CoMP-DPS transmission points,” *Comput. Commun.*, vol. 135, pp. 28–39, Feb. 2019.

[32] M. F. Hossain, M. J. Huque, A. S. Ahmad, K. S. Munasinghe, and A. Jamalipour, “Energy efficiency of combined DPS and JT CoMP technique in downlink LTE-A cellular networks,” in *Proc. IEEE Int. Conf. Commun. (ICC)*, Kuala Lumpur, Malaysia, May 2016, pp. 1–6.

[33] A. Jahid, M. H. Alsharif, P. Uthansakul, J. Nebhen, and A. A. Aly, “Energy efficient throughput aware traffic load balancing in green cellular networks,” *IEEE Access*, vol. 9, pp. 90587–90602, 2021.

[34] Y. Zhang, Q. Cui, and N. Wang, “Energy efficiency maximization for CoMP joint transmission with non-ideal power amplifiers,” in *Proc. IEEE 28th Annu. Int. Symp. Pers., Indoor, Mobile Radio Commun. (PIMRC)*, Oct. 2017, pp. 1–6.

[35] A. Jahid, A. B. Shams, and M. F. Hossain, “Energy efficiency of JT CoMP based green powered LTE-A cellular networks,” in *Proc. Int. Conf. Wireless Commun., Signal Process. Netw. (WiSPNET)*, Chennai, India, Mar. 2017, pp. 1739–1745.

[36] A. Jahid, M. S. Islam, M. S. Hossain, M. E. Hossain, M. K. H. Monju, and M. F. Hossain, “Toward energy efficiency aware renewable energy management in green cellular networks with joint coordination,” *IEEE Access*, vol. 7, pp. 75782–75797, 2019.

[37] S. Euttamarajah, Y. H. Ng, and C. K. Tan, “Energy-efficient joint power allocation and energy cooperation for hybrid-powered comp-enabled Het-Net,” *IEEE Access*, vol. 8, pp. 29169–29175, 2020.

[38] S. Rezvani, N. M. Yamchi, M. R. Javan, and E. A. Jorswieck, “Resource allocation in virtualized CoMP-NOMA HetNets: Multi-connectivity for joint transmission,” *IEEE Trans. Commun.*, vol. 69, no. 6, pp. 4172–4185, Jun. 2021.

[39] W. E. Philip and A. K. Mishra, “Performance analysis of JT-CoMP-CNOMA networks with imperfect CSI and SIC: A SWIPT-empowered approach,” in *Proc. IEEE Recent Adv. Intell. Comput. Syst. (RAICS)*, Kerala, India, May 2024, pp. 1–6.

[40] S. R. C. Magalhães, S. Bayhan, and G. Heijenk, “Power allocation for multi-cell non-orthogonal multiple access networks: Energy efficiency vs. throughput vs. power consumption,” in *Proc. Joint Eur. Conf. Netw. Commun. 6G Summit (EuCNC/6G Summit)*, Gothenburg, Sweden, Jun. 2023, pp. 287–292.

[41] A. Akbar, A. Ahmed, A. Zafar, and S. Jangsher, “An energy-efficient JT-CoMP enabled framework with adaptive OMA/NOMA in HetNets,” *Comput. Netw.*, vol. 254, Dec. 2024, Art. no. 110733.

[42] J. Tang, A. Shojaeifard, D. K. C. So, K. Wong, and N. Zhao, “Energy efficiency optimization for CoMP-SWIPT heterogeneous networks,” *IEEE Trans. Commun.*, vol. 66, no. 12, pp. 6368–6383, Dec. 2018.

[43] S. Rizvi, A. Aziz, M. T. Jilani, N. Armi, G. Muhammad, and S. H. Butt, “An investigation of energy efficiency in 5G wireless networks,” in *Proc. Int. Conf. Circuits, Syst. Simulation (ICCSS)*, Jul. 2017, pp. 142–145.

[44] Y. Hou, L. Zitoune, and V. Vèque, “Energy efficiency analysis of JT-CoMP scheme in macro/femto cellular networks,” in *Proc. IEEE Global Commun. Conf. (GLOBECOM)*, Waikoloa, HI, USA, Dec. 2019, pp. 1–6.

[45] H. Iimori, G. T. F. de Abreu, O. Taghizadeh, R. Stoica, T. Hara, and K. Ishibashi, “A stochastic gradient descent approach for hybrid mmWave beamforming with blockage and CSI-error robustness,” *IEEE Access*, vol. 9, pp. 74471–74487, 2021.

[46] S. Muruganathan, S.Faxer, S. Jarmyr, S. Gao, and M. Frenne, “On the system-level performance of coordinated multi-point transmission schemes in 5G NR deployment scenarios,” in *Proc. IEEE 90th Veh. Technol. Conf. (VTC-Fall)*, Sep. 2019, pp. 1–5.

[47] C. Merlhe, C. Gueguen, and X. Lagrange, “Hybrid joint-transmission multi-point coordination for inter-cell interference management,” in *Proc. IEEE 93rd Veh. Technol. Conf. (VTC-Spring)*, Helsinki, Finland, Apr. 2021, pp. 1–7.

[48] Y.-J. Yu, T.-Y. Hsieh, and A.-C. Pang, “Millimeter-wave backhaul traffic minimization for CoMP over 5G cellular networks,” *IEEE Trans. Veh. Technol.*, vol. 68, no. 4, pp. 4003–4015, Apr. 2019.

[49] M. Maqbool, P. Godlewski, M. Coupechoux, and J. Kélif, “Analytical performance evaluation of various frequency reuse and scheduling schemes in cellular OFDMA networks,” *Perform. Eval.*, vol. 67, no. 4, pp. 318–337, Apr. 2010.

[50] C. Li, J. Zhang, and K. B. Letaief, “Energy efficiency analysis of small cell networks,” in *Proc. IEEE Int. Conf. Commun. (ICC)*, Jun. 2013, pp. 4404–4408.

[51] A. Bazzi and M. Chafii, “On integrated sensing and communication wave-forms with tunable PAPR,” *IEEE Trans. Wireless Commun.*, vol. 22, no. 11, pp. 7345–7360, Nov. 2023.

[52] A. J. Fehske, P. Marsch, and G. P. Fettweis, “Bit per Joule efficiency of cooperating base stations in cellular networks,” in *Proc. IEEE Globecom Workshops*, Miami, ML, USA, Dec. 2010, pp. 1406–1411.

[53] Technical Specification Group Radio Access Network; Study on 3D Channel Model for LTE, document TR 36.873 V12.7.0, 2017. [Online]. Available: https://www.3gpp.org/ftp/Specs/archive/36_series/36.873/36873-c70.zip

[54] Guidelines for Evaluation of Radio Interface Technologies for IMT-Advanced, document ITU-R M.2135-1, 2009.

[55] M. K. Samimi, T. S. Rappaport, and G. R. MacCartney, “Probabilistic omnidirectional path loss models for millimeter-wave outdoor communications,” *IEEE Wireless Commun. Lett.*, vol. 4, no. 4, pp. 357–360, Aug. 2015.

[56] A. Iqbal, M. Tham, Y. J. Wong, A. Al-Habashna, G. A. Wainer, Y. X. Zhu, and T. Dagiuklas, “Empowering non-terrestrial networks with artificial intelligence: A survey,” *IEEE Access*, vol. 11, pp. 100986–101006, 2023.

[57] F. Ezzahra Achamrah, “Leveraging transfer learning in deep reinforcement learning for solving combinatorial optimization problems under uncertainty,” *IEEE Access*, vol. 12, pp. 181477–181497, 2024.

[58] D. Shakya, “Urban outdoor propagation measurements and channel models at 6.75 GHz FR1(C) and 16.95 GHz FR3 upper mid-band spectrum for 5G and 6G,” in *Proc. IEEE Int. Conf. Commun. (ICC)*, Montreal, QC, Canada, Apr. 2025, pp. 1–6.

<figure><img src="image_21.png" /></figure>

**MOHAMAD YOUNES** received the M.S. degree in electronics engineering and the M.S. degree in telecommunications research from the Brest National School of Engineering (ENIB), Brest, France, in 2015, and the Ph.D. degree in digital communications from Université de Bretagne-Occidentale (UBO), Brest, in 2019.

From 2019 to 2020, he was an Associate Professor with École Normale Supérieure (ENS), Rennes, France. From 2020 to 2022, he was a

Postdoctoral Researcher with CentraleSupélec, Rennes. Since 2022, he has been an Associate Professor with Saint-Cyr Coëtquidan Military Academy (AMSCC) and a Researcher with Coëtquidan Research Center. His research interests include digital communications, signal processing, wireless communications, and resource allocation for 5G/6G and LoRaWAN networks.

<figure><img src="image_22.png" /></figure>

**LAURENT QUIBUS** received the M.S. degree in physical engineering and the Ph.D. degree in engineering and technology from Université catholique de Louvain, Louvain-la-Neuve, Belgium, in 2015 and 2020, respectively.

From 2020 to 2022, he was a Postdoctoral Researcher with the Office National d'Etudes et de Recherches Aérospatiales (ONERA), Toulouse, France. Since 2022, he has been with Saint-Cyr Coëtquidan Military Academy (AMSCC), and

Coëtquidan Research Center (CReC), Guer, France. Since 2024, he has been also an Associate Professor with the University of Rennes, and Institut d'Electronique et des Technologies du numérique (IETR), Rennes, France. He is mostly interested in electromagnetic propagation, especially through the atmosphere, and its applications, principally to Earth-space radio links, with an emphasis on numerical weather prediction (NWP).

<figure><img src="image_23.png" /></figure>