# Optimized Usage of Multi-User Diversity to Enhance Spectral Efficiency in (5G) Wireless Networks

Guillaume Terrier, Cedric Gueguen, Yassine Hadjadj-Aoul

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Guillaume Terrier, Cedric Gueguen, Yassine Hadjadj-Aoul. Optimized Usage of Multi-User Diversity to Enhance Spectral Efficiency in (5G) Wireless Networks. ISNCC 2025 - International Symposium on Networks, Computers and Communications, Oct 2025, Paris, France. pp.1-7, 10.1109/ISNCC66965.2025.11250421. hal-05402495

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# Optimized Usage of Multi-User Diversity to Enhance Spectral Efficiency in (5G) Wireless Networks

Guillaume Terrier

Univ Rennes, IRISA

Rennes, France

guillaume.terrier@irisa.fr

Cedric Gueguen

Univ Rennes, IRISA

Rennes, France

cedric.gueguen@irisa.fr

Yassine Hadjadj-Aoul

Univ Rennes, INRIA, CNRS, IRISA

Rennes, France

yassine.hadjadj-aoul@irisa.fr

**Abstract**—Wireless networks keep evolving to handle an increase of the number of users equipment and services. One of the main challenge is to enhance spectral efficiency as it lead to higher throughput and system capacity. Traditional scheduler, such as Maximum Signal-to-Noise Ratio (MaxSNR), Proportional Fair (PF) perform well in specific context, but can be outperformed in others. Moreover, there is many possible scheduler to try in order to find the best ones for diverse contexts. An approach that uses Artificial Intelligence (AI) becomes a conceivable solution. In this paper we introduce a new AI tool that selects and evaluates schedulers that enhance spectral efficiency for different contexts. Our approach is to train the AI model on several traffic load, to evaluate and select schedulers based on a general scheduling formula, resulting in an explainable model for traffic scheduling. After the training, the appropriate scheduler is used for each traffic load conditions. In addition, our tool detects terms of the formula that does not have a positive impact on enhancing spectral efficiency. Simulation results show that the obtained scheduler outperform state of the art algorithms such as RR, MaxSNR and PF.

**Index Terms**—Wireless Network, Artificial Intelligence, Scheduling, Machine Learning, Reinforcement Learning

## I. INTRODUCTION

In wireless networks, the number of User Equipments (UEs) and services is rising. Resource Blocks (RBs) obtained through the division of the bandwidth are limited and impacted by physical phenomena such as: shadowing, path-loss and multipath fading. They are allocated to UEs to access services. To face the increase of UEs and services in a 5G cell, solutions that enhance Spectral Efficiency (SE) have emerged. This means increasing the mean number of bit per RB, which allows enhancing the system capacity, hence supporting more users and services. Radio resource allocation is one of the main keys to reduce delay, buffer occupancy and ensuring fairness. To guarantee an efficient scheduler (resource allocation algorithm) with enhanced SE is a crucial issue which has been well investigated. Traditional schedulers are often based on heuristics. When the context changes, the heuristic may not be suited for the new situation. Furthermore, there is a lot of available schedulers to try on different contexts. The usage of

pioneering solutions, such as Artificial Intelligence (AI) could provide new tools to explore and evaluate great number of schedulers in various contexts.

Originally created for wired networks, Round Robin (RR) [1] and Random Access (RA) [2] allocate Resource Units (RUs) equitably between UEs. They are non-opportunistic algorithm, which means that they do not take into account the Channel State Information (CSI) when allocating a RU to a UE. They are not suited for wireless networks and results in a poor SE. Regardless of their efficiency, they are great algorithms for benchmark purposes [3].

Opportunistic schedulers are resource allocation algorithms that take the CSI of UEs into account for the RB allocation. As the signal quality for a RB varies between users and changes frequently, allocating a RB to a user will impact the spectral efficiency of the scheduler. With more users in the system that need to access data, the scheduler can access to more combination for the RBs allocation. It gives better probabilities to make an efficient allocation in terms of spectral efficiency. This phenomena is called Multi-user Diversity and it allows opportunistic algorithm to enhance their SE when the number of users grows. It can be represented with the mean number of users available for a RB allocation.

One of the most renowned opportunistic schedulers is MaxSNR [4], widely acknowledged and referenced in numerous papers. Its fundamental principle is to leverage multi-user diversity. In MaxSNR the allocation of RUs goes to the mobile with the highest Signal-to-Noise Ratio (SNR). It allocate the RU n to the mobile $k \in \{1, 2, 3, ..., k_{max}\}$, such as:

$$mobile = \underset{k}{\operatorname{argmax}}(SNR_{k,n}). \qquad (1)$$

In congestion or when dealing with full buffer sources, MaxSNR offers optimal spectral efficiency because of its usage of Multi-user Diversity. However, under typical traffic loads within the system's capacity, a limitation arises from its inherent unfairness characteristic. MaxSNR prioritizes users close to the base station with low path loss, effectively clearing their buffers rapidly. Consequently, it diminishes the system's inherent Multi-user Diversity and reduces the number of pos-

This work has been partially supported by grant ANR-21-CE25-0005 from the Agence Nationale de la Recherche, France for the SAFE project.
sible combinations available for further allocation. As a result, this leads to sub-optimal system throughput performance.

The Proportional Fair (PF) scheduler and PF-based schedulers serve two primary objectives: spectral efficiency and fairness [5], [6], [7]. PF specifically allocates RU n to a mobile k that possesses the highest ratio between instantaneous Signal-to-Noise Ratio and Mean Signal-to-Noise Ratio (MSNR) such as:

$$mobile = \underset{k}{\operatorname{argmax}} \left( \frac{ISNR_{k,n}}{MSNR_k} \right). \quad (2)$$

In contrast to MaxSNR's unfair approach, PF relies on a better utilization of multi-user diversity. It delivers better throughput performance than MaxSNR when the system's load remains within sustainable limits, primarily due to its superior fairness which induces a better conservation of multi-user diversity and allows for further combinations.

New solution that uses AI has emerged. They often are build with Neural Network. They can be used for traffic prediction [8] and handling solution with large dimensions [9]. As powerful as they may be, a scheduler should be safe to use and interpretable [10], [11]. Moreover, solution that use Neural Network technologies may have high computational costs [12]. Such solutions may consume more energy compared to traditional algorithms. The complexity and size of neural networks contribute to their substantial energy demands.

In this paper, we present a new safe and interpretable Artificial Intelligence Tool for Exploring Schedulers (called AITES) and a new scheduler preserving the multi-user diversity in the system (EMUDS), which obtain a better Spectral Efficiency than traditional state of the art algorithms, such as MaxSNR or PF in non full-buffer context. This scheduler has been confirmed with the help of our AI tool which differ from other AI as it has been built to explore and evaluate numerous schedulers in varying contexts.

The paper is organized as follows: Section II describes the AI tool. Section III details the system description. Section IV presents the EMUD results, and the final section concludes the paper.

## II. AITES & EMUDS

### A. AITES Overview

The usage of AI solution in wireless networks, raises concerns about safety and interpretability. In [10], the authors emphasise that “AI safety is a critical factor in ensuring that systems behave as expected and do not take unintended actions that could lead to catastrophic failures”. In wireless networks, conditions change quickly, and consequences of actions taken by the solution can have negative impact. AI solutions used in wireless networks should ensure that performed actions should not degrade network performances.

Interpretability of AI solution in wireless networks is another important subject. It means to understand how decisions are made and to be able to check if the AI works as intended. In [11], the authors emphasize that beyond accuracy, AI systems must prioritize interpretability to foster trust and ensure

accountability. In resource allocation for wireless networks, it means to be able to understand and explain process behind resource allocation. Hence, it should be transparent and comprehensible to network administrators. Without interpretability, struggle to troubleshoot issues or adjust parameters when the system's behavior is unexpected may arise. Solution such as deep learning, with high performances, lack of interpretability which make them less suitable for critical application.

The AI-based strategy described in this paper aims to find the adequate scheduler among a set of large possibilities to ensure safety and enhance spectral efficiency. For one defined objective, the Artificial Intelligence Tool for Exploring Schedulers (AITES) should select and evaluate different resource allocation solutions (likewise for different focused objectives in a same context). The AI does not directly perform resource allocation but try to find the efficient scheduler formula that should be used. All along the offline training (in a simulation), AITES dynamically testes policies in order to enhance its targeted score. To do so, once a scheduler has been chosen, resource allocation is performed with the selected policy. This results in impacts that can be measured and evaluated on the system. As AITES has for objective to enhance the SE of the system, it is measured after the resource allocation process. If the selected policy increases the SE of the system, a good reward is delivered to it and a bad one otherwise. The policies (schedulers) are expressed as formulas based on inputs that are collected by AI. The generic scheduler formula consists in allocating the RU n to the mobile k that has the greatest value, such as:

$$mobile = \underset{k}{\operatorname{argmax}}(Input_1^\gamma \times Input_2^\delta \times \dots \times Input_i^\omega). \quad (3)$$

where each term of the formula is an input retrieved by the AI. The number of terms in the formula is defined by the number of inputs retrieved. An exponent is given to each term. It has V possible values. The total number of available policies (schedulers) is:

$$\text{Number of schedulers} = V^I \quad (4)$$

### B. AITES Setup

The AITES solution has been built with a Sequential Collaborative Multi Armed Bandit (SCMAB) with delayed reward architecture. The AI algorithm is described in Alg. 1. The number of agent is equal to the number of terms in the scheduling equation (3). Sequentially, agents select a pair of exponent/value to set for the next resource allocation. The different selected pairs define the scheduling formula.

The concept of state is used to take a sequence of actions, transitioning an agent from one state to another. The set of available states for an agent is determined by the previously available actions. The agent initiating a sequence always starts in the initial state. Subsequent agents arrive in a state defined by the exponent/value combination chosen by the preceding agent. The current agent cannot select a value for the exponent contained in the current state. As AITES is trained on a fixed number of users, their mean input and buffer occupancy are
**Algorithm 1 Thompson Sampling with Beta Priors**

1: **Input:** Initialize $\alpha_a = 1, \beta_a = 1$ for all $a \in \mathcal{A}$

2: **for** each round $t = 1, 2, \dots, \mathbf{do}$

3: **for** each agent **do**

4: **for** each action $a \in \mathcal{A}$ **do**

5: Sample $\theta_a \sim \text{Beta}(\alpha_a, \beta_a)$

6: **end for**

7: Select action $a_t = \arg\max_a \theta_a$

8: Check if exponent already set

9: **end for**

10: Perform resource allocation

11: Observe reward $r_t \in \{0, 1\}$

12: **for** each agent **do**

13: Update:

$$ \alpha_{a_t} \leftarrow \alpha_{a_t} + r_t, \quad \beta_{a_t} \leftarrow \beta_{a_t} + (1 - r_t) $$

14: **end for**

15: **end for**

16: Perform last scheduler selection

17: End Simulation

fixed, hence, we do not include them in the state representation (redundant information).

The total number of possible actions is in a state MV for the first agent and $(MV) - V$ for the following ones. Let each action $a \in \mathcal{A}$ be associated with a Beta prior $\text{Beta}(\alpha_a, \beta_a)$, where:

* $\alpha_a$: number of successes observed for action a

* $\beta_a$: number of failures observed for action a.

To select its exponent/value pair, each agent uses Thompson Sampling. At the start, each agent has a prior belief about the expected reward for each exponent/value pair. At each time step $t$, agent $m$ samples a parameter vector $\theta_i^t$ from the posterior distribution for each pair $(e_m, v_n)$, and selects the pair that maximizes the expected reward:

$$ a_i^t = \arg \max_{(e_m, v_n)} \mathbb{E}[r_i | \theta_i^t] $$

The posterior distribution is updated after each selection based on the observed reward:

$$ \alpha_{a_t} \leftarrow \alpha_{a_t} + r_t, \quad \beta_{a_t} \leftarrow \beta_{a_t} + (1 - r_t) $$

Through this mechanism, the agent improves its knowledge of the reward function. It allows a balance between exploration of new action with exploitation of already observed action. The scheduler selection process stops when an agent selects an exponent/value pair $(e_m, v_n)$ that has already been set by a previous agent (listing 1).

Once a policy has been selected by the agents, the resource allocation process is performed with the new scheduler. It results in a spectral efficiency (bit per RB) that can be measured and used as a score. The score is compared to the one obtained with the previous policy. If the score exceeds the previous one, a positive delayed reward is given; otherwise, a negative reward is given.

Each agent receives the shared delayed reward. After training, the AI returns a new scheduler described in the next subsection.

### C. EMUDS: Enhance Multi-User Diversity Scheduler

By design, the Thompson Sampling algorithm used for training gives a safe and interpretable exploration and exploitation of the actions' space. Then, AITES returns a scheduler written as a mathematical formula which is interpretable. This solution can be studied before being implemented which increases safety. This tool is designed for an offline training, which increases the safety of the solution. In the next section, we described the system used for the AI training.

1) **Scheduler's formula:** In this section, we present the new scheduler return by our AI tool. Results in Section IV show that our solution preserves multi-user diversity in order to enhance SE. The scheduler is based on a heuristic provided by the AI tool that we presented previously, which is trained on a single point of load before congestion (due to simulation duration). We also study the impact of scheduling on QoS KPIs: system's Delay and Buffer Occupancy. For each point of load, we reached a steady state and retrieved the required data. Once all point of load have been simulated, results are plotted and compared. We use state of the art algorithms designed for SE optimization (MaxSNR/BestCQI and PF) for the benchmark. This new scheduling algorithm called EMUD uses the following formula:

$$ mobile = \underset{k}{\operatorname{argmax}} \left( \frac{\text{ISNR}_{k,n}^2}{\text{MSNR}_{k,n}^{-2}} \right) * D_k^0 * R_k^0 * T_k^0 * BO_k^3. \quad (5) $$

It is designed to enhance the system's Spectral Efficiency (ES) in order to optimize the system Throughput and capacity to handle more users and services. It manages to enhance Spectral Efficiency of the system through a better management of its multi-user diversity. The formula used for resource allocation is composed of the following terms:

* $\text{ISNR}_{k,n}$ is the SNR of user $k$ for the RB $n$

* $\text{MSNR}_{k,n}$ is the mean SNR of user $k$ for the RB $n$

* $D_k$ is the delay of user $k$

* $R_k$ is the type of traffic load (heavy or light) of user $k$

* $T_k$ is the time constraint of user $k$

* $BO_k$ is the buffer occupancy of user $k$

The first two terms of the formula allow the AI to use opportunistic scheduler with fairness between users with different distances. The following three terms are metrics linked to the QoS (delay, traffic type and time constraint). For this scheduler, the linked exponents are set to zero. It means that they have no impact on the resource allocation process. The last term of the formula, make the scheduler allocate the RB to users with higher buffer occupancy. It allows the scheduler to preserve the multi-user diversity.

2) **Discussion on Bias detection:** In our opinion, the stop condition of the algorithm (in listing 1) allows the AI to discard part of the formula. The terms that have not been selected before the AI meet the stop condition are left with an
exponent with a zero value. As seen in the previous section, the scheduling formula provided by the AI has several terms with their exponent to the power of zero. It can be reduced to:

$$mobile = \underset{k}{\operatorname{argmax}} \left( \frac{\mathrm{ISNR}_{k,n}^2}{\mathrm{MSNR}_{k,n}^{-2}} \right) * BO_k^3. \quad (6)$$

This means that the terms which are unset terms do not have a positive impact on the score. In this context (enhancing spectral efficiency), the AI found that the following input can be deleted from the scheduling formula: $D_k, R_k, T_k$. Detecting bias in the scheduling formula could be use in order to reduce the computational cost of the solution. If another objective is given to the AI (QoS, energy management...), there is no assurance that they will be discarded.

### III. SYSTEM DESCRIPTION

This study focuses on the radio resource allocation problem for the set of mobiles located in the coverage zone of an access point. A centralized approach is assumed since this allows efficient opportunistic scheduling approaches. The physical layer operates in Time Division Duplex (TDD) mode, which offers excellent compatibility with the Orthogonal Frequency Division Multiplexing (OFDM) based transmission mode [13]. The bandwidth is divided by frequency range in sub-carriers. Time is also divided in frame, each one being composed of Time Slots (TS) of constant length (which is an integer multiple of the OFDM symbol duration. A Resource Unit (RU) is defined as a (subcarrier, time slot) pair that can be allocated to any mobile with a specific modulation order.

Each frame duration is assumed equal to a value inferior to the coherence time of the channel, allowing the transmission on each sub-frequency to experience flat fading during each frame. Therefore, channel's state is assumed static for the frame's duration for a mobile on a subcarrier. The channel state varies not only for a single mobile device across different sub-carriers but also between different mobiles devices on the same RU. Thanks to SNR measurement of the signal sent by each mobile (for instance during the uplink contention sub frame), the access point is thus, able to estimate their channel state attenuation at a given TS on each sub-carrier. According to [14], channel state can be assumed stable on a scale of 50 ms allowing to consider a full knowledge of radio conditions available at the receiver [15]. This requires that mobiles have to transmit their control information alternatively on each subcarrier once for several frames (in order for the access point to successfully refresh the channel state information).

On each RU, a modulation order adapted to the channel state (between the access point and the selected mobile) is assumed. This provides the flexible resources allocations allowing opportunistic scheduling. The channel gain between the access point and the mobile k on the subcarrier n is given by:

$$G_{k,n} = h \cdot 10^{\frac{x\sigma}{10}} \left( \frac{d_{\text{ref}}}{d_k} \right)^{\tau}, \quad (7)$$

where h represents the Rayleigh multi-path fading, which is modeled by an exponential distribution, X is a standard Gaussian random variable, σ is the standard derivation of shadowing in dB, $d_{\text{ref}}$ is the reference distance, $d_k$ is the distance between the mobile k and the access point while τ is the path loss exponent. The SNR computation of mobile k on subcarrier n associated to the access point is given by:

$$\gamma_{k,n} = \frac{P_n G_{k,n}}{B_{sub} N_0}, \quad (8)$$

where the parameter $P_n$ represents the transmitted power on subcarrier n of the access point. Parameter $N_0$ is the thermal noise power density and parameter $B_{sub}$ is the subcarrier spacing. To compute the spectral efficiency $\eta_{k,n}$ of mobile k on subcarrier n associated to access point, the Shannon's formula is used such as:

$$\eta_{k,n} = \log_2 \left( 1 + \frac{\gamma_{k,n}^i}{\Gamma} \right), \quad (9)$$

where parameter $\Gamma$ is an SNR correction factor that takes into account the difference between the information-theoretic performances and the practical implementation of the MCS [16] defined as follows:

$$\Gamma = - \frac{\ln(5E)}{1.5}, \quad (10)$$

where E is a Bit Error Rate (BER) Target.

Simulation parameters given in I enable the design of a 5G antenna simulator aligned with 3GPP specifications and literature. The Cell radius of 500 meters align with the definition of urban micro-cells [17]. The number of sub-carriers allows a manageable complexity and is suitable for use cases within 5G spectrum. In [18], the concept of adapting transmission parameters, such as bandwidth, to align with traffic demands as a mean to achieve energy savings is discussed. The simulator bandwidth division aligns with the principles outlined in [18], where resource allocation is tailored to meet current traffic demands, thereby optimizing energy consumption. The Antenna Transmit Power is also in accordance with [17] for macro and micro base. Parameters for signal attenuation are also described in [17]. A duration of 500 000 time slots for a simulation was the length required for the simulator to return representative results (steady state).
<figure><img src="image_2.png" /><figcaption>(a) Spectral Efficiency.</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>(c) Active user per Resource Block allocation.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>(b) Bandwidth usage.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>(d) Spectral Efficiency of user based on distance.</figcaption></figure>

<caption>Fig. 1: Spectral Efficiency metrics</caption>

## IV. RESULTS OBTAINED WITH EMUD

<table><caption>TABLE I: Simulations parameters.</caption><thead><tr><td>Parameters</td><td>Value</td></tr></thead><tbody><tr><td>Cell Radius</td><td>500 m</td></tr><tr><td>Number of subcarriers</td><td>128</td></tr><tr><td>Number of Time Slots</td><td>10</td></tr><tr><td>RRH transmit power</td><td>20 W (43 dBm)</td></tr><tr><td>Standard deviation of shadowing</td><td>σ = 8 dB</td></tr><tr><td>Path-loss exponent (τ)</td><td>3.5 (urban context)</td></tr><tr><td>Target BER</td><td>5 × 10<sup>-5</sup></td></tr><tr><td>Subcarrier spacing</td><td>15 kHz</td></tr><tr><td>Thermal noise power density (N<sub>0</sub>)</td><td>- 174 dBm/Hz</td></tr><tr><td>Simulation duration</td><td>500 000 TS</td></tr><tr><td>Possible value for exponents</td><td>{-3, -2, -1, -0.5, 0, 0.5, 1, 2, 3}</td></tr><tr><td>Number of allocation performed by AI before drawing new formula</td><td>2 frames</td></tr></tbody></table>

### A. Introduction to figures

1) *Spectral Efficiency metrics:* The figures showed in this paragraph display algorithm's behaviors in steady state for a given metric in ordinate over the traffic load (in number of users) in abscissa. Figure 1a shows the mean spectral efficiency in bit per resource block of the system. Figure 1d represents the same metric, but with a differentiation of users based on the user's distance from the antenna (close or far). An opportunistic algorithm will see its spectral efficiency increased when the traffic load increases. A non-opportunistic algorithm will have a flat spectral efficiency as it does not use the Multi-user Diversity of the system. The mean bandwidth usage in percentage is graphed on figure 1b. The highest the spectral efficiency, the lowest the bandwidth usage. The system enter congestion when the mean usage of the bandwidth per frame is one hundred percent. The mean number of active user per resource block allocation is represented by figure 1c. An active user, is a user that is electable by the algorithm

for the resource block allocation. It represents the Multi-User Diversity of the system. This metric is what allows opportunistic scheduler to improve spectral efficiency. An increase of the mean number of active user per resource block means that there is statistically more opportunities to have a user with a good spectral efficiency for a given resource block.

2) *Quality of Service and Fairness Metrics:* The mean buffer occupancy (in bits) is shown in figure 2a. The mean delay of packets in the system (in ms) is shown in figure 2b. The same metrics with the user differentiation are shown in figure 2c for buffer occupancy and figure 2d for the delay. Figure 3b shows the Jain's Index of Fairness for the mean delay of users in the system based on the distance. A value of one for the Jain's Index of Fairness represents the highest fairness that can be obtained, and zero the lowest. Figure 3a shows the mean buffer occupancy (in bits) for a specific traffic load (near congestion).

### B. Detailed results

1) *RR:* First of all, it is important to identify (on Fig. 1a) the traffic load key points where RR encounter congestion (120 for RR). This results (120) can be determined theoretically with calculation: number of RU in a frame (1280) multiplied by the mean spectral efficiency (17.9) then divided by the targeted throughput for a mobile during a frame duration (193 bits). RR is the first solution that experiment congestion since it has the lowest spectral efficiency (17.93 bit/ms/RU) due to its non-opportunistic behavior. Before congestion, the mean SE of RR remains flat. The rise in SE for the RR from 120 users is linked to the fact that before congestion, less RB are allocated to users close to the antenna. They need less RB to empty their buffers, Fig. 1b because of their better Spectral Efficiency, Fig. 1d, compared to users far from the antenna.
<figure><img src="image_6.png" /><figcaption>(a) Mean Buffer Occupancy.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>(c) Buffer Occupancy based on user's distance.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>(b) Mean Packets Delay.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>(d) Mean Packets Delay based on user's distance.</figcaption></figure>

<caption>Fig. 2: QoS metrics</caption>

When congested, users close to the antenna are not able to empty their buffers and retrieves RB previously allocated to others. The multi-user diversity of the system represented by the mean number of active users per resource block allocation seen on figure 1c is not used by RR algorithm due to its non-opportunistic nature. It is reflected by the absence of growth of the mean spectral efficiency when the mean number of active users grows. The lack of opportunistic capabilities results in a poor efficiency for QoS metrics. It is the first algorithm to reach congestion, with a rise of its mean buffer occupancy 2a and mean delay 2b. This is true for both the delay and the buffer occupancy for users close and far from the antenna 2d2c. It's Fairness characteristic results in an high Jain Index for Fairness based on the delay 3b.

2) **MaxSNR**: Unlike RR, MaxSNR takes into account the CQI for the resource allocation process. It benefits of the system's multi-user diversity, represented in Fig. 1c to increase the spectral efficiency of the system (Fig. 1a and Fig. 1d). It has a lower bandwidth usage than RR (Fig. 1b). Users that are close to the antenna empty their buffers most of the time before users that are far from the antenna (Fig. 2c). This result in lower multi-user diversity usage and less users availability for the resource allocation process (1c). It results in the lowest mean number of users available for resource block allocation (1c). Before congestion MaxSNR has a lower mean spectral efficiency than PF (Fig. 1a) due to its unfairness between users at different distances as seen in Fig. 2c and Fig. 2d. Being unfair with user far away from the antenna leads the mean buffer occupancy and mean delay of the system to rise before PF and EMUDS schedulers, Fig. 2a and Fig. 2d. Being opportunistic and unfair for the resource allocation process before congestion leads to poorer results in buffer occupancy, shown on figure 3a. This also impact the mean delay and

fairness in delay between users and figure 3b.

3) **PF**: The fairness brought by PF preserve more the multi-user diversity of the system, seen on figure 1c. The mean number of active user per RB allocation is higher than one ones obtained by MaxSNR. This results in a higher mean SE, figure 1a, for close and far users, figure 1d. It outperforms MaxSNR algorithm for spectral efficiency before congestion, as seen on Fig. 1a. Therefore, it uses less bandwidth in percentage than MaxSNR, which is verified on figure 1b. This is true for users close or far from the antenna. With more users available for resource allocation, PF has statically over the time, more opportunities with higher spectral efficiency than MaxSNR. It reaches congestion under the same traffic load as MaxSNR, but it has a better mean buffer occupancy and mean delay (Fig. 2a and Fig. 2b). In opposition to MaxSNR, The fairness of the algorithm prevents the mean buffer occupancy (Fig. 2c, Fig. 3a) and mean delay to rise for users far from the antenna (Fig. 2d).

But even if the PF algorithm is fairer than MaxSNR, users who are close to the antenna have a better mean Spectral Efficiency, which results in close users becoming unavailable for resource allocation when far users still have data to download, as observed in figure 3b.

4) **EMUDS**: The new solution, EMUDS, preserves the multi-user diversity of the system more efficiently than other algorithms (Fig. 1c. To do so, when allocating a RB, the algorithm takes the Buffer Occupancy of users into account. This prevents emptying the buffer of users with a higher mean SE too fast. With more available users, EMUDS has more possibilities for the resource allocation, and obtains the best mean SE in figure 1a. Before congestion it also has the mean best SE for users close and far from the antenna 1d. This is verified by the mean bandwidth usage of the algorithm being
<figure><img src="image_10.png" /><figcaption>(a) Buffer Occupancy with 128 users.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>(b) Jain's Fairness Index of users' mean delay.</figcaption></figure>

the lowest as represented in Fig. 1b. It shows its ability to enhance SE and reduce the Bandwidth usage of the system due to a better management of the multi-user diversity of the system. The new scheduler reduce the mean buffer occupancy of the system (Fig. 2a), for both user far and close of the antenna (Fig. 2c), until congestion (Fig. 3a). Delay is also the lowest with EMUDS before congestion. the preservation of the Multi-user Diversity makes the scheduler delay's fairness (based on the Jain's Index for fairness) fair for more traffic loads (Fig. 3b).

The EMUD results show the good potential of an approach that preserves multi-user diversity when the system is not in congestion. In This work, the AI succeeded to find the adequate scheduler among a large panel of available strategies for the Spectral Efficiency optimization objective.

## V. CONCLUSION

To conclude this paper, we designed an Artificial Intelligence which can explore and exploit large set of schedulers in order to identify or confirm good solutions amongst them. We introduced an Artificial Intelligence able to identify and employ the adequate scheduler in order to reach a focused objective within various wireless networks cell contexts. Our innovative solution has demonstrated its ability to enhance spectral efficiency and has succeeded to adaptability find the adequate scheduler to use, in diverse traffic load contexts. Looking ahead, our future work will integrate and consider more environment inputs in order to enlarge the schedulers panel that could possibly lead to new innovative solution or confirms/corroborates the existing ones. In addition other objectives and system context will be studied.

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