Received 30 September 2023, accepted 8 November 2023, date of publication 13 November 2023, date of current version 22 November 2023.

Digital Object Identifier 10.1109/ACCESS.2023.3332225

## RESEARCH ARTICLE

# An LSTM-Based Network Slicing Classification
## Future Predictive Framework for Optimized Resource Allocation in C-V2X

MOHAMMED SALAH ABOOD<fnref n="1" />, HUA WANG<fnref n="1" />, DONGXUAN HE<fnref n="1" />, MAHA FATHY<fnref n="1" />, SAMI ABDULJABBAR RASHID<fnref n="2" />, MOHAMMAD ALIBAKHSHIKENARI<fnref n="1" />,<fnref n="3" />, (Member, IEEE), BAL S. VIRDEE<fnref n="1" />,<fnref n="4" />, SALAHUDDIN KHAN<fnref n="5" />, GIOVANNI PAU<fnref n="1" />,<fnref n="6" />, (Member, IEEE), IYAD DAYOUB<fnref n="1" />,<fnref n="7" />,<fnref n="8" />, (Senior Member, IEEE), PATRIZIA LIVRERI<fnref n="1" />,<fnref n="9" />, (Senior Member, IEEE), AND TAHA A. ELWI<fnref n="1" />,<fnref n="10" />

Corresponding authors: Mohammed Salah Abood (mohammedsalah@bit.edu.cn), Hua Wang (wanghua@bit.edu.cn), Mohammad Alibakhshikenari (mohammad.alibakhshikenari@uc3m.es), and Giovanni Pau (giovanni.pau@unikore.it)

Authors Acknowledge support from the CONEX-Plus program funded by Universidad Carlos III de Madrid and the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 801538. The authors also sincerely appreciate funding from Researchers Supporting Project number (RSP2023R58), King Saud University, Riyadh, Saudi Arabia. Additionally, this work was supported in part by the National Key Research and Development Program of China under Grant 2020YFB1807900, in part by the National Natural Science Foundation of China under Grant 62101306.

**ABSTRACT** With the advent of 5G communication networks, many novel areas of research have emerged and the spectrum of communicating objects has been diversified. Network Function Virtualization (NFV), and Software Defined Networking (SDN), are the two broader areas that are tremendously being explored to optimize the network performance parameters. Cellular Vehicle-to-Everything (C-V2X) is one such example of where end-to-end communication is developed with the aid of intervening network slices. Adoption of these technologies enables a shift towards Ultra-Reliable Low-Latency Communication (URLLC) across various domains including autonomous vehicles that demand a hundred percent Quality of Service (QoS) and extremely low latency rates. Due to the limitation of resources to ensure such communication requirements, telecom operators are profoundly researching software solutions for network resource allocation optimally. The concept of Network Slicing (NS) emerged from such end-to-end network resource allocation where connecting devices are routed toward the suitable resources to meet their requirements. Nevertheless, the bias, in terms of finding the best slice, observed in the network slices renders a non-optimal distribution of resources. To cater to such issues, a Deep Learning approach has been developed in this paper. The incoming traffic has been allocated network slices based on data-driven decisions as well as predictive network analysis for the future. A Long Short Term Memory (LSTM) time series prediction approach has been adopted that renders optimal resource utilization, lower latency rates, and high reliability across the network. The model will further ensure packet prioritization and will retain resource margin for crucial ones.

**INDEX TERMS** Cellular vehicles to everything (C-V2X), deep learning, latency, long short term memory (LSTM), machine learning, network slicing, optimization, reliability.

The associate editor coordinating the review of this manuscript and approving it for publication was Claudio Loconsole.

<footnote n="1">¹School of Information and Electronics, Beijing Institute of Technology, Haidian, Beijing 100081, China</footnote>

<footnote n="2">²Department of Computer Engineering Technique, Al-Maarif University College, Al-Anbar, Iraq</footnote>

<footnote n="3">³Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Leganés, 28911 Madrid, Spain</footnote>

<footnote n="4">⁴Center for Communications Technology, London Metropolitan University, N7 8DB London, U.K.</footnote>

<footnote n="5">⁵College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia</footnote>

<footnote n="6">⁶Faculty of Engineering and Architecture, Kore University of Enna, 94100 Enna, Italy</footnote>

<footnote n="7">⁷Université Polytechnique Hauts-de-France, Institut d'Électronique de Microélectronique et de Nanotechnologie (IEMN) CNRS UMR 8520, ISEN, Centrale Lille, University of Lille, 59313 Valenciennes, France</footnote>

<footnote n="8">⁸INSA Hauts-de-France, 59313 Valenciennes, France</footnote>

<footnote n="9">⁹Department of Engineering, University of Palermo, Palermo, 90128 Sicily, Italy</footnote>

<footnote n="10">¹⁰International Applied and Theoretical Research Center (IATRC), Baghdad, Iraq</footnote>
# I. INTRODUCTION

In the modern era of the Internet of Things, the spectrum of communication between objects is being diversified at a rapid pace. This mobility era has witnessed an increase in communication demands manifolds along with emerging requirements for high reliability and low latency rates. Vehicle to Everything (V2X) [1], [2], [3] is a relatively new rising concept that is seen as an important contour of making autonomous vehicles ubiquitous and also making the conditions of traffic more secure. Such a mode involves the establishment of communication between vehicles, pedestrians, road infrastructure, and other ad-hoc networks. To cater to such rising demands of communication where objects communicate with each other, an increase in network resources is becoming inevitable. The reliance on vehicular sensors or Direct Short Range Communication (DSRC) [4], [5] alone is insufficient for automated mobility where vehicles can work autonomously with the driver's intervention. The inclusion of cellular bands and technologies is essential to maintain perceptibility and enhanced capacity for staying abreast of the surroundings and ensuring highly secure maneuvering. This renders a non-line of sight cooperative driving and accurately detect the presence of objects in the surroundings. However, to ensure these factors, the limited recourse of the cellular networks are causing many challenges especially in terms of network resource allocation while keeping the latency low and higher reliability [6].

Software Defined Networking (SDN) has been seen as a key enabling technology in 5G communication networks [7]. Centralized resource management enables the division of network resources in an optimized manner while also ensuring accurate network monitoring and control. Some of the Key Performance Indicators (KPIs) ensure that the network conditions are kept at par with the service level agreements developed with the connecting objects. Nevertheless, the traditional approach involved in such networks makes use of the network address information in the form of IP and leads to the involvement of large overheads which renders poor dynamics, especially during the instances of URLLC requirements [8]. Thus, there is a need for a focus shift toward advanced tools having lower reliance on such overheads and instead allocating resources directly based on demand and network conditions.

Cellular Vehicle to Everything (C-V2X) is a pivotal and modern communication technology that allows vehicles to interact with each other as well as the infrastructure by leveraging the existing 4G or 5G network resources [1]. In this context, it is imperative to adopt mechanisms for efficient resource allocation. This is because the transmission of data in a real-time and reliable manner is essential to allow collision avoidance between vehicles. Stringent QoS shall be maintained to guarantee reliable data transmission to allow network congestion management under dense urban environments. Efficient resource allocation helps prevent the network from going into saturation and allows communication in a reliable manner. Therefore, scalability and spectrum

efficiency are of paramount importance, which help accommodate the growing number of vehicles [9]. Finally, it plays a critical role in realizing the full potential of C-V2X for intelligent transportation systems and automated driving.

Under the influence of such costs and least optimized performance resources, a focus has been steered toward the network slicing concepts along with the inclusion of Machine Learning [10], [11], [12]. The higher heterogeneity of the service requirements including the enhanced mobile broadband (eMBB), massive Machine Type Communication (mMTC), and URLLC [13], [14] has all been made part of C-V2X as well to meet various service demands including higher packet delivery rate, lower retransmission requirements, and bandwidth allocation, etc. These differ in the data rate requirements, capacity, latency rates, and QoS. The vehicular communication following this approach reaches the 5G networks through different devices including smartphones, and Internet of Things (IoT) sensing devices to exchange packets of information ranging from being less critical to more. Thus, the one-size-fits-all architecture that is generally employed in LTE communication does not meet such diverse requirements set and a focus is on the segmentation of network elements into slices [14]. Instead of relying on address-centered division, the increased focus is on the dynamic slice allocation with the aid of Machine Learning tools, where resources for the slices are allocated depending upon the specific context or network conditions in general. Such tools are trained with large datasets representing the network conditions and correct sets of decisions and are later used for making predictions on the incoming sets of requests. For instance, based on the Packet Delivery Ratio (PDR) requirements, a suitable slice is allocated to the device based on the ML predictive analysis framework.

Although Machine Learning serves as the right tool for making predictive analyses on slice types, however, bias control remains an important consideration. In networks, objects demanding a singular slice category likely render overload in a certain area of the network while leaving poor utilization of other more optimized resources available. The use of ML for slice allocation ignores this factor and relies on the trained set of conditions for resource allocation. Thus, a need for predictive analysis of future network conditions emerges to re-route the traffic toward another set of slices and retain overall high-performing networks.

This study focuses on the employment of Artificial Neural Networks (ANN) and LSTM Deep learning models for making predictive and future analyses of the network conditions. In the existing ML-based network slicing, the algorithms focus on the existing network conditions to create slices [10], [15], [16], [17]. However, the models only make predictions based on the provided feature sets (bandwidth required by a particular application, etc.). Nevertheless, certain trends do exist in traffic conditions depending on the day and time of the day. This type of data can be predicted by an LSTM model in advance and thus optimize the network resources. For instance, URLLC network slices are the most reliable
with low Packet Delay Budget (PDB). Therefore, if vacant slots are expected to remain available in a given timeframe, the application requested can be diverted toward it safely.

The model focuses on (1) making a predictive analysis of selecting the right network slice based on the service request requirements (2) analyzing the current network conditions and future predictive analysis using LSTM to analyze slice load conditions (3) rerouting traffic towards more optimized slices without depriving the existing network requests intended solely for those slices. The overall impact of this study will be the enhancement of reliability and latency conditions of networks and keeping the utilization at a better ratio.

The proposed Deep Learning model focuses primarily on predicting network conditions based on two key input features: the type of network slice (URLLC, mMTC, eMBB) and the anticipated number of devices that will demand these slices in the future. This approach allows the model to make predictions regarding resource allocation and network optimization by taking into account the critical factors of slice type and expected demand. By analyzing historical data and learning from patterns, the model can offer valuable insights into how to efficiently allocate resources, such as bandwidth and latency, to meet the forecasted demands of different network slices. While this approach simplifies the input features, it lays the foundation for optimizing network performance and improving the QoS for specific use cases.

This framework's applications and benefits in real-world scenarios include enhancing C-V2X communication. The model proposed as part of this simulation framework holds potential application in real-world scenarios. In the context of 5G networks and beyond, the model can serve as an enabler to optimize traffic management, cost-efficiency, and dynamic resource allocations based on the predicted demands. This can help reduce the latency rates, and packet loss rate, and improve the reliability of the network. Such adoptions make it well-suited for various sectors like transportation, healthcare, smart cities, industrial automation, etc. Such functionalities will help explore the full potential of the 5G networks. Thus creating a more sustainable and environmentally friendly C-V2X network infrastructure.

In Section II of this article, the theoretical framework of LSTM and ANN has been provided, Section III focuses on the model proposed for this study. Section IV focuses on the research findings and analysis. Section V concludes the work by providing future directions.

## II. RELATED WORK

To fully realize the methodology and techniques that can be employed for network slicing in C-V2X communication, various research articles from 5G and beyond have been explored targeting similar scenarios. Reference [15] focuses on the abstraction of network resources to render virtual slices with the aid of SDN and Network Functional Virtualization (NFV) concepts. A theoretical framework leading toward high-speed communication has been developed under

this frame of reference. Reference [16] makes virtualization in the network slicing using machine learning tools in a smart seaport environment. Although these articles serve as a good model for the employment of ML, however, the scope of the study is limited to the environment they have been tested. Reference [10] steps ahead by proposing deep learning tools for the accurate classification of an incoming request and allocation of the correct network slice as mMTC, eMBB, or URLLC. The predictions are based on the service requirements including the packet delay response and loss rate. The more effectively predicts the right slice for the network, however, does not incorporate a future network conditional prospect and thus can be subject to limitations. Reference [17] devises the network slicing techniques based on the operational management, control management, and constructional management. Later a set of machine learning operations are applied to infer useful characteristics and classifications. A comprehensive review of intelligent network slicing techniques has been made in [3].

Having established some of the relevant approaches adopted in the 5G network slicing in general, these approaches can find they are based on CV2X communication as well. Reference [18] considers the end-to-end approach in vehicular communication across various applications and develops a resource-centric framework for slicing. Reference [19] establishes a virtualized framework for network slicing in Vehicular ad hoc networks (VANETs) by considering a guaranteed QoS framework. Reference [20] provides an elaborate view of various machine learning and AI techniques that can be employed in VANETs across various domains of applications and performance architectures. For instance, the network requirements for infotainment and security applications, etc. Reference [21] provide the general requirements for SDN and virtualization in VANETs in the context of LTE, 5G, and beyond. In article [22], the concept of cross-network slicing has been introduced which somehow serves as a base framework for the idea proposed in this model. The cross-framing has been proposed in the domains of the internet of vehicles.

The spectrum of machine learning in the domain of V2X is diverse and many problems have been addressed through it. Although this paper focuses on network slicing and resource allocation, however, some other mediums of ML and their convergence toward the slicing problem can be found and made. For instance, [23] provides a novel terminology based on machine learning to serve multi-hop searching and enhanced reachability in the domain of CV2X and DSRC. The framework proposed loosely relates to the Channel State Information or Network Slices future predictive analysis. Reference [24] adopts another view of predicting QoS based on the traffic and other conditions in the network using machine learning. This article directly can relate to the proposed frame of work where future classification and object request predictions have been made.

Before delving into the actual details of the proposed design parameters, the use of LSTM and its applicability
to the current model shall be explored. Across the various uses of this novel deep learning unsupervised scheme including the autocorrelation for anomaly detection or time series predictive analysis. Although the spectrum is highly diverse including the stock exchange predictive analysis or indoor localization [25], etc. this method can directly be employed for the current problem under review.

Article [26] proposes a deep learning network slicing based on the dataset using the data generated for a week where 7-8 different devices send communication requests and are allocated network slices based on the predictive settings using ANN. The study further makes use of a main slice to avoid network disbalance and congestion of slices. However, the consideration of network optimization has been ignored. The slices having lower connection requests can be configured for the devices biased towards a single type, thus the overall network conditions can be optimized. However, this demands a future predictive approach to stay abreast of the expected network conditions to keep the overall approach pragmatic. This work is a direct continuation of the said model using the same dataset and making network-level enhancements that will be rendered suitable for CV2X and 5G in general.

### III. PROPOSED SYSTEM MODEL

The model under consideration is based on future predictions and classification norms. Artificial Neural Networks have been employed to make predictions on the network slice classification (eMBB, mMTC, or URLLC) based on the service requirement request demand, while a future predictive model based on LSTM keeps analyzing the upcoming network conditions, slice utilization, and thus overall network optimization by rerouting traffic towards the underutilized nodes.

The frequency of this learning process can be adapted to the specific operational needs of the network. Typically, the learning phase will occur periodically or based on the arrival of new data, depending on the network's dynamic nature and requirements. The system that feeds data to the model is part of the network's infrastructure for data collection and analytics. It collects historical network performance data, including traffic patterns, latency, packet loss rates, and other relevant metrics. This data is then preprocessed and used to train the LSTM model. Additionally, the model can receive real-time input data to make predictions for future demands. This system operates at both the network and operational levels. At the network level, the model is deemed to be integrated into the core network infrastructure and works with network performance data to make predictions for resource allocation. It helps optimize network resources based on predicted demand, improving overall network conditions. At the operational level, the model provides insights and recommendations to network administrators and operators. It guides resource allocation decisions, such as rerouting traffic from lower-resource slices (e.g., mMTC) to higher-resource slices (e.g., URLLC) during periods of low predicted demand. This operational aspect allows for real-time adjustments to optimize network performance.

### A. DATASET

The dataset employed in this study has been taken from [27] and offers a comprehensive insight into the QoS within cellular networks, encompassing LTE and 5G technologies, across a wide array of use cases. The dataset comprises several crucial input features and an output feature that collectively shed light on network performance, reliability, and latency characteristics across diverse scenarios. These input features encompass the type of use case or application, LTE/5G User Equipment (UE) categories, supported cellular technologies, days of the week, times of data collection, and the differentiation between Guaranteed Bit Rate (GBR) and non-GBR services. Moreover, the dataset captures essential metrics for assessing network reliability, such as Packet Loss Rate (PLR), as well as latency metrics, notably the Packet Delay Budget (PDB), specifying acceptable latency thresholds in milliseconds. The output feature denotes the network slice type corresponding to each use case, with categories encompassing Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communication (URLLC), and Massive Machine Type Communication (mMTC). This dataset proves invaluable for the evaluation and optimization of cellular network performance, especially in the context of diverse use cases and their specific QoS requirements.

<table><caption>TABLE 1. Dataset features and description.</caption><thead><tr><th>Feature</th><th>Description</th><th>Example</th></tr></thead><tbody><tr><td>Use Case Type</td><td>Application category</td><td>AR/VR/Gaming, Healthcare Industry 4.0, IoT Devices, Public Safety/E911, Smart City & Home, Smart Transportation</td></tr><tr><td>LTE/5G UE Category</td><td>UE category</td><td>-</td></tr><tr><td>Technology Supported</td><td>Cellular technology</td><td>LTE, 5G</td></tr><tr><td>Day</td><td>Day of the week for data collection</td><td>Monday to Sunday</td></tr><tr><td>Time</td><td>The time interval for data collection</td><td>24 Hours</td></tr><tr><td>PLR</td><td>Packet loss rate</td><td>0.001, 0.000001</td></tr><tr><td>PDB</td><td>Packet delay budget</td><td>50ms, 100ms, 10ms, 300ms</td></tr><tr><td>Slice Type</td><td>Network slice type</td><td>eMBB, URLLC, mMTC</td></tr><tr><td>GBR</td><td>Guaranteed Bit Rate (GBR) or non-GBR service</td><td>-</td></tr><tr><td>QCI</td><td>Quality of Service Class Identifier</td><td>-</td></tr></tbody></table>

### B. CLASSIFICATION FRAMEWORK

The classification of network frames is based on the service requirement requests that involve the use of the QoS class, Packet delay budget, and PLR. The models take the data as the input at a given time frame and render two parallel operations: future predictive analysis to predict upcoming network conditions and predictive load on slices, and the classification of the incoming request into the desired slice class. The sampling rate of the rata is recorded at approximately 338 requests per second, and the LSTM model has been trained to predict the upcoming 10,000 requests which constitute around 30 seconds of data. The slice classification
has been based on the similar norms presented in this dataset and reference article [26], however, the use of the main slice has been discarded and all routing is based on the network conditions and load preferences.

The network requirements along the individual slices and incoming data requests have been taken from Table 2 in [26]. The allocation is based on the PLR, PDB, and QoS class and category of the device. Devices mimic the general spectrum of 5G communication devices, however, also serve as founding blocks in V2X communication for different services including infotainment, security data exchange, etc. The proposed scheme and network architecture have been illustrated in Figure 1.

<figure><img src="image_1.png" /><figcaption>FIGURE 1. Network Slice Allocation based on Proposed Scheme.</figcaption></figure>

The algorithm of the underlying approach is given as follows:

**Algorithm 1** Predictive and Classification Analysis for Slicing and Network Optimization

**Inputs:** User Request Equipment, PDB, PLR, QCI, Slice, D (data)

**Outputs:** Network Slice Allocation

**Process Starts:**

**Learning Phase**

1. D ← train_test_split()

2. train_ANN (D)

3. train_LSTM ← linear split(), loss_func, learning_rate

4. model_fit ← validation_loss(), training_loss()

5. make Judgments()

**Prediction Phase**

6. Incoming Request()

7. ANNPredict()

8. LSTMPredict() → 30 seconds future predictions

9. Analysis of slice congestion level

10. Allocation and Reallocation of network slices

**Network Efficiency Measures**

11. Network Latency Rate

12. Network Reliability Comparison

Thus, the overall efficacy of the model has been illustrated in the form of network performance measures including the impact on latency rate, and the reliability enhancements during the processing of the packets.

The algorithm presented above entails two main parts. The first part involves predictive modeling of the network

to analyze the future demands of the slice type. This can mathematically be modeled as:

$$D_{i,t+1} = f(S_{l_i}, H_i(t)) \quad (1)$$

Here $D_{i,t+1}$ represents the depicted demand for the network slice $i$ at time $t+1$, $f$ is the deep learning model that predicts the future demands based on the slice type $S_i$ and historical data $H_i(t)$.

The second aspect is resource allocation, where network resources like bandwidth, PLR, etc. associated with each slice are allocated based on the predictive demands. This has been modeled as below:

$$R_i(t+1) = \frac{D_{i,t+1}}{\sum_{j=1}^{N} D_{j,t+1}} \cdot QoS_i \cdot R_T(i+1) \quad (2)$$

Here $R_i(t+1)$ represents the resource allocation for the network slice $i$ at time $t+1$. $QoS_i$ represents the quality of service associated with slice $i$ that influences the resource allocation, and $R_T$ represents the total resources available.

The system level and data parameters adopted in this study are provided in Table 2.

<table><caption>TABLE 2. The simulation and dataset parameters.</caption><thead><tr><th>Input Application</th><th>Technology</th><th>PLR</th><th>PDB (ms)</th></tr></thead><tbody><tr><td>Smartphones</td><td>LTE/5G</td><td>Up to 10-2</td><td>Up to 300</td></tr><tr><td>IoT Sensors</td><td>IoT</td><td>10-2</td><td>Up to 300</td></tr><tr><td>Autonomous Vehicles</td><td>LTE/5G</td><td>10-6</td><td>10</td></tr><tr><td>Industry 4.0</td><td>LTE/5G</td><td>Up to 10-3</td><td>Up to 50</td></tr><tr><td>Healthcare</td><td>LTE/5G or IoT</td><td>10-6</td><td>10</td></tr><tr><td>Public Safety / E911</td><td>LTE/5G</td><td>10-6</td><td>10</td></tr><tr><td>Smarties Homes</td><td>LTE/5G or IoT</td><td>10-2</td><td>Up to 300</td></tr></tbody></table>

The architecture of the LSTM model employed in this study is designed for predictive purposes, specifically to forecast the future requirements of users for a particular network slice (URLLC, mMTC, eMBB) based on historical time series data. The model begins by preparing the training and testing datasets, to specify the number of past and future time steps considered in the prediction. The input layer implicitly takes sequences of historical data, with each sequence consisting of a certain number of past time steps and a single input feature, representing the type of network slice transformed into integer values. The model then comprises two LSTM layers, with the first having 64 units and the second having 32 units, both employing the ‘relu’ activation function. The first LSTM layer returns sequences as output, capturing temporal dependencies in the data, while the second LSTM layer produces a fixed-length output. To prevent overfitting, a dropout layer with a 0.2 dropout rate is introduced after the second LSTM layer. The model concludes with a dense output layer, which predicts a single integer value corresponding to one of the network slice types (e.g., 0 for eMBB, 1 for URLLC, and 2 for mMTC) for each input sequence, making it suitable for multi-class classification tasks. During training, the model minimizes the Mean
<figure><img src="image_2.png" /><figcaption>FIGURE 2. Network architecture of the proposed network slicing and predictive model.</figcaption></figure>

Squared Error loss using the Adam optimizer. This architecture enables the model to learn from historical data and provide predictive information, facilitating resource reallocation decisions in the context of network management. By doing so, it empowers real-time optimization of resource allocation, ultimately improving the efficiency and performance of C-V2X communication networks. This architecture enables the model to learn from historical data and provide predictive information, facilitating resource reallocation decisions in the context of network management.

The LSTM model has been trained on 95 percent of the data while considering 30 past values and a single value for future validation during the training phase. Additionally, a further split of 10 percent has been made as validation incorporation. The details of model accuracy rates and suitability have been made part of the results section. The overall architecture that has been developed for both ANN and LSTM has been presented in Figure 2.

Within the context of 5G networks and the predictive system's role in re-routing, it's essential to elucidate several critical facets regarding identifier reassignment and re-routing strategies. In cases where devices rely on a 5G system employing identifiers such as Service Set Type (SST) and Service Data (SD), the process of reassigning identifiers to users is deemed to be facilitated through the network's control and management mechanisms. These mechanisms entail mapping user-profiles and their specific requirements to the relevant SST/SD identifiers. As for re-routing, it can occur between slices of the same type or between slices exhibiting compatibility in terms of characteristics and resource availability. The precise re-routing strategy hinges on network policies and optimization objectives; for example, if the anticipated demand for a lower-priority slice, such as mMTC, is low, the traffic can be redirected to a higher-priority slice like URLLC or eMBB, provided they share compatible attributes. Furthermore, the re-routing

process is adaptable to both slices employing the 5G identifier system and internal slices managed by the network operator. Internal slices, which may not directly use the 5G identifier system, could be tailored to operator-specific services or optimization strategies. Thus, the re-routing decisions encompass both internal and standard 5G slices, contingent on the network's design and optimization objectives.

## IV. RESULTS

This section illustrated the results generated across various stages of the model development and an overall impact on the network conditions.

### A. SLICE CLASSIFICATION USING ANN

Slice classification using ANN has been performed by training a neural network having 3 dense layers to intrigue into the feature set of the network slicing data. The train and test datasets have been generated to identify the accuracy of the model which is found to be highly accurate as depicted in Table 3. The model fits perfectly with the data while bringing in a significant reduction in both the validation loss (10 percent of the training data) and training loss. 04 different performance metrics have been selected namely Precision, Recall, Accuracy, and F1 Score. Additionally, a confusion matrix has been generated to identify the probability of true and false predictive measures. The training accuracy and loss model for both Validation and Training sets has been depicted in Figures 3, and 4, respectively.

### B. SLICE PREDICTIONS USING LSTM

Long Short-Term Memory (LSTM) has been trained on 95 percent of the dataset by tuning the past and future predictive parameters. The model has been trained on 30 past values constituting 0.625 seconds. The model performs an unsupervi-sed run on the complete training dataset and accuracy has been determined in terms of Training Loss,
<table><caption>**TABLE 3.** Accuracy parameters and performance metrics.</caption><thead><tr><td>Parameter</td><td>Value</td></tr></thead><tbody><tr><td>Training Accuracy</td><td>1.0</td></tr><tr><td>Validation Accuracy</td><td>1.0</td></tr><tr><td>Training Loss</td><td>0.000033</td></tr><tr><td>Validation Loss</td><td>0.0000078</td></tr><tr><td>Precision</td><td>1.0</td></tr><tr><td>Recall</td><td>1.0</td></tr><tr><td>Accuracy</td><td>1.0</td></tr><tr><td>F1 Score</td><td>1.0</td></tr></tbody></table>

<figure><img src="image_3.png" /><figcaption>**FIGURE 3.** Training and validation accuracy of the ANN on deep slicing dataset.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**FIGURE 4.** Training and validation loss of the ANN on deep slicing dataset.</figcaption></figure>

Validation Loss during the epoch run. The test dataset is passed through a similar shaping model and is predicted using the trained model set. Similar performance metrics have been used as proposed in the case of ANN and their respective values are depicted in Table 4.

<table><caption>**TABLE 4.** LSTM performance parameters.</caption><thead><tr><td>Parameter</td><td>Value</td></tr></thead><tbody><tr><td>Training Loss</td><td>0.0052</td></tr><tr><td>Validation Loss</td><td>0.0017</td></tr><tr><td>Precision</td><td>0.997</td></tr><tr><td>Recall</td><td>0.997</td></tr><tr><td>Accuracy</td><td>0.997</td></tr><tr><td>F1 Score</td><td>0.997</td></tr></tbody></table>

The model runs on 24,940 training parameters and predictive results are computed on 24 hours of data. The predictive class has been compared with the original class where numerical mapping has been made as follows:

<table><caption>**TABLE 5.** Integer mapping on network slicing class.</caption><thead><tr><td>Slice</td><td>Map</td><td>Slice</td><td>Map</td><td>Slice</td><td>Map</td></tr></thead><tbody><tr><td>URLLC</td><td>0</td><td>eMBB</td><td>1</td><td>mMTC</td><td>2</td></tr></tbody></table>

In Figure 5, the predictive results based on the LSTM model have been presented. The two lines reveal the original and predicted network slices against a chuck of time samples taken across the training dataset. A small bias of 0.1 has been added in the predicted class for ease of visualization. The model is found to accurately predict the future data network topology that has mainly been employed in this study to enhance the latency and reliability figures across the entire network.

<figure><img src="image_5.png" /><figcaption>**FIGURE 5.** LSTM network slicing predictive results.</figcaption></figure>

### C. REALLOCATION OF NETWORK SLICES

The model involves predictive analysis for the set of network slices across the 24-hour chunk of data and analysis of the bias and available capacity across them. Based on the PDB, and PLR figure, slices have been ranked as URLLC > eMBB > mMTC, and ideally, when URLLC is vacant, most of the traffic shall be diverted towards this slice. This differs from a standard statistical or Bayesian model where predictions are made on a static set of conditions, while LSTM ensures future predictions using a data-centric approach that leaves less space for error predictabilities.

Mathematically, the factor of available capacity has been found, and based on that relevant traffic is diverted toward the higher slice. This has been modeled as below:

$$C_{NS_i} = \frac{\left( \sum_{t=1}^{T} C - L_t \right)}{T} \quad (3)$$

where *i* refers to the classes of network slices (0, 1, 2 in the present case), *C* depicts the total capacity of a network slice
and is assumed to equal requests per minute to avoid congestion cases. $L_t$ refers to the capacity occupancy in the given case that is computed across the given frame of reference. $T$ is the total frame of time considered for the remapping process. The average factor across each slice $i$ is multiplied by the fleeting capacity of the network slice to compute the new network factors including latency and reliability.

The concept of rerouting the packet requests across the network slices involves the allocation of packets using ANN and then routing some of the packets based on the capacity variable.

<figure><img src="image_6.png" /><figcaption>**FIGURE 6.** PDB reduction in the network slicing based on LSTM predictions and rerouting.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**FIGURE 7.** PLR in the network slicing based on LSTM predictions and rerouting.</figcaption></figure>

## D. EFFECTS ON NETWORK LATENCY CONDITIONS

The existing ANN model involves a standard set of rules based on which the PDB is settled and packets are transmitted. For example, a standard request coming from a smartphone under a given QCI is directly mapped to the eMBB slice where the PLR is kept close to 0.01 while packet delay is controlled to be less than 50 ms. However, the dynamics of the network are not incorporated into this while better resources are available and can be fairly assigned. In the above capacity model, the LSTM can infer this flexibility and make the network keep using higher resources until the capacity levels have been met. This renders better performance observation from the rerouted packets and the overall impact on the network-level performance can be analyzed.

As illustrated in Figure 6, the PDB constraints under standard predictive conditions are high. The average values

<figure><img src="image_8.png" /><figcaption>(a)</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>(b)</figcaption></figure>

<caption>**FIGURE 8.** Network slices traffic conditions. a. Before rerouting. b. After rerouting.</caption>

recorded without incorporating the rerouting algorithm are found to be 102.26 ms, while the value falls at 55.23 ms after the application of the capacity factor. This is a tremendous improvement in the network conditions, especially in the context of CV2X. When the vehicles are moving at a high speed, re-transmissions and connectivity are required frequently and even those applications requiring less reliable conditions suffer from this factor. Utilization of the available source using these network parametrizations aids in the enhancement of the CV2X network conditions in general while ensuring the provision of URLLC frames to critical applications including autonomous driving.

## E. EFFECTS ON NETWORK RELIABILITY CONDITIONS

The traditional approach employed in the previous studies involves slice allocation based on reliability thresholding. The under requests demanding a reliability factor of 0.01 are allocated to the relevant slices without consideration of the better slice availability. This renders non-optimized network conditions similar to the case of network latency conditions. To cater to this effect, similar modeling has been performed based on the LSTM approach. The incoming requests are facilitated to higher network slices where enhanced reliability can be met. This effect has been illustrated in Figure 7. The average threshold value becomes 0.0040 compared to 0.0054 observed in the non-routing case.

Better to analyze the results based on ratio format, Ex: Our proposed model reduces the End-End delay by 3% compared
to the traditional method (better to name the method, traditional is not the accurate way in general).

## F. EFFECTS ON NETWORK SLICES AFTER REROUTING

Since the method resides on the rerouting of some of the traffic towards URLLC and eMBB the reliability is higher and packet delay rates are lower. The effect of these conditions for one such network instance has been elaborated in Figure 7. Figure 8 (a) depicts the network conditions before rerouting took place, and Figure 8 (b) provides the network conditions after rerouting has been performed. It can be analyzed that URLLC and eMBB after rerouting become denser, while the mMTC becomes less dense.

## V. CONCLUSION

The study involves the incorporation of deep learning models for the segmentation of the network into slices thus rendering accurate resource allocation. A provision LSTM for future predictive modeling has been incorporated that makes data-driven decisions to project future network conditions and performs slice-request rerouting based on that. The study served to enhance two performance parameters of C-V2X and 5G networks in general: Latency and Reliability. Without changing and altering the network conditions and infrastructure requirements, an overall positive trend has been analyzed and the network becomes more optimized. It has been found that the average packet delay rate across the network reduces to 55.23 ms instead of 102.26 ms. Also, the packet loss rate has been lowered to 0.0040 from the 0.0054 threshold value.

In the future, a similar predictive model based on LSTM can be used to cater to various attacks that cause the network to move into congestion. By employing the adopted LSTM approach towards an autoencoder mode, the network can make predictive analysis for attach detection and could route those attacks to a virtual node without affecting the existing network topology.

Additionally, the Deep Learning model's predictive capabilities can be enhanced by incorporating additional features and considerations. These include considering the specific application types or services running on network slices, accounting for different communication protocols used by devices, analyzing device characteristics such as category and mobility patterns, assessing the network's physical and logical topology for factors like congestion and routing, integrating service-level agreements (SLAs) to align with predefined quality commitments, and utilizing real-time data streams for dynamic predictions and rapid adjustments. These expanded features would enable the model to offer more context-aware, granular predictions, leading to improved network optimization and resource allocation strategies. This comprehensive approach would result in a more robust solution for enhancing network conditions and QoS in 5G networks.

## REFERENCES

[1] V. Mannoni, V. Berg, S. Sesia, and E. Perraud, "A comparison of the V2X communication systems: ITS-G5 and C-V2X," in Proc. IEEE 89th Veh. Technol. Conf. (VTC-Spring), Apr. 2019, pp. 1-5.

[2] F. Arena, G. Pau, and A. Severino, "V2X communications applied to safety of pedestrians and vehicles," J. Sensor Actuator Netw., vol. 9, no. 1, p. 3, Dec. 2019.

[3] M. S. Abood, H. Wang, D. He, Z. Kang, and A. Kawoya, "Intelligent network slicing in V2X networks—A comprehensive review," J. Artif. Intell. Technol., vol. 3, no. 2, pp. 75–84, Mar. 2023.

[4] A. R. Khan, M. F. Jamlos, N. Osman, M. I. Ishak, F. Dzaharudin, Y. K. Yeow, and K. A. Khairi, "DSRC technology in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) IoT system for intelligent transportation system (ITS): A review," in Recent Trends in Mechatronics Towards Industry 4.0: Selected Articles From iM3F 2020, Malaysia, 2022, pp. 97–106.

[5] K. Z. Ghafoor, M. Guizani, L. Kong, H. S. Maghdid, and K. F. Jasim, "Enabling efficient coexistence of DSRC and C-V2X in vehicular networks," IEEE Wireless Commun., vol. 27, no. 2, pp. 134–140, Apr. 2020.

[6] A. Bazzi, A. O. Berthet, C. Campolo, B. M. Masini, A. Molinaro, and A. Zanella, "On the design of sidelink for cellular V2X: A literature review and outlook for future," IEEE Access, vol. 9, pp. 97953–97980, 2021.

[7] G. Kakkavas, A. Stamou, V. Karyotis, and S. Papavassiliou, "Network tomography for efficient monitoring in SDN-enabled 5G networks and beyond: Challenges and opportunities," IEEE Commun. Mag., vol. 59, no. 3, pp. 70–76, Mar. 2021.

[8] M. Ojo, D. Adami, and S. Giordano, "A SDN-IoT architecture with NFV implementation," in Proc. IEEE Globecom Workshops (GC Wkshps), Dec. 2016, pp. 1–6.

[9] K. Sehla, T. M. T. Nguyen, G. Pujolle, and P. B. Velloso, "Resource allocation modes in C-V2X: From LTE-V2X to 5G-V2X," IEEE Internet Things J., vol. 9, no. 11, pp. 8291–8314, Jun. 2022.

[10] M. H. Abidi, H. Alkhalefah, K. Moiduddin, M. Alazab, M. K. Mohammed, W. Ameen, and T. R. Gadekallu, "Optimal 5G network slicing using machine learning and deep learning concepts," Comput. Standards Interfaces, vol. 76, Jun. 2021, Art. no. 103518.

[11] M. Toscano, F. Grunwald, M. Richart, J. Baliosian, E. Grampín, and A. Castro, "Machine learning aided network slicing," in Proc. 21st Int. Conf. Transparent Opt. Netw. (ICTON), Jul. 2019, pp. 1–4.

[12] B. Han and H. D. Schotten, "Machine learning for network slicing resource management: A comprehensive survey," 2020, arXiv:2001.07974.

[13] G. Wang, G. Feng, W. Tan, S. Qin, R. Wen, and S. Sun, "Resource allocation for network slices in 5G with network resource pricing," in Proc. IEEE Global Commun. Conf. (GLOBECOM), Dec. 2017, pp. 1–6.

[14] K. Katsalis, N. Nikaein, E. Schiller, A. Ksentini, and T. Braun, "Network slices toward 5G communications: Slicing the LTE network," IEEE Commun. Mag., vol. 55, no. 8, pp. 146–154, Aug. 2017.

[15] P. Subedi, A. Alsadoon, P. W. C. Prasad, S. Rehman, N. Giweli, M. Imran, and S. Arif, "Network slicing: A next generation 5G perspective," EURASIP J. Wireless Commun. Netw., vol. 2021, no. 1, p. 102, Dec. 2021.

[16] R. M. Sohaib, O. Onireti, Y. Sambo, and M. A. Imran, "Network slicing for beyond 5G systems: An overview of the smart port use case," Electronics, vol. 10, no. 9, p. 1090, May 2021.

[17] V. P. Kafle, Y. Fukushima, P. Martinez-Julia, and T. Miyazawa, "Consideration on automation of 5G network slicing with machine learning," in Proc. ITU Kaleidoscope, Mach. Learn. 5G Future (ITU K), Nov. 2018, pp. 1–8.

[18] M. Afaq, J. Iqbal, T. Ahmed, I. Ul Islam, M. Khan, and M. S. Khan, "Towards 5G network slicing for vehicular ad-hoc networks: An end-to-end approach," Comput. Commun., vol. 149, pp. 252–258, Jan. 2020.

[19] H. Cao, S. Garg, G. Kaddoum, M. M. Hassan, and S. A. AlQahtani, "Intelligent virtual resource allocation of QoS-guaranteed slices in B5G-enabled VANETs for intelligent transportation systems," IEEE Trans. Intell. Transp. Syst., vol. 23, no. 10, pp. 19704–19713, Oct. 2022.

[20] A. Mchergui, T. Moulahi, and S. Zeadally, "Survey on artificial intelligence (AI) techniques for vehicular ad-hoc networks (VANETs)," Veh. Commun., vol. 34, Apr. 2022, Art. no. 100403.

[21] W. Ben Jaballah, M. Conti, and C. Lal, "A survey on software-defined VANETs: Benefits, challenges, and future directions," 2019, arXiv:1904.04577.

[22] A. Ibraheem, "Cross network slicing in vehicular networks," in Intelligent Technologies for Internet of Vehicles. Springer, 2021, pp. 151–189.

[23] M. E. Morocho-Cayamcela, H. Lee, and W. Lim, "Machine learning to improve multi-hop searching and extended wireless reachability in V2X," IEEE Commun. Lett., vol. 24, no. 7, pp. 1477–1481, Jul. 2020.

[24] D. C. Moreira, I. M. Guerreiro, W. Sun, C. C. Cavalcante, and D. A. Sousa, "QoS predictability in V2X communication with machine learning," in Proc. IEEE 91st Veh. Technol. Conf. (VTC-Spring), May 2020, pp. 1–5.
[25] Y. Zhang, C. Qu, and Y. Wang, "An indoor positioning method based on CSI by using features optimization mechanism with LSTM," *IEEE Sensors J.*, vol. 20, no. 9, pp. 4868-4878, May 2020.

[26] A. Thantharate, R. Paropkari, V. Walunj, and C. Beard, "DeepSlice: A deep learning approach towards an efficient and reliable network slicing in 5G networks," in *Proc. IEEE 10th Annu. Ubiquitous Comput., Electron. Mobile Commun. Conf. (UEMCON)*, Oct. 2019, pp. 0762-0767.

[27] A. Thantharate, C. Beard, R. Paropkari, and V. Walunj, "CRAWDAD umkc/networkslicing5g," *IEEEDataPort*, 2022.

<figure><img src="image_10.png" /></figure>

**MOHAMMED SALAH ABOOD** received the B.Sc. degree in computer engineering and the master's degree in communication and computer engineering from the University of Kebangsaan Malaysia (UKM), in 2004 and 2016, respectively. He is currently pursuing the Ph.D. degree in information and electronics engineering with the Beijing Institute of Technology (BIT), Beijing, China. His research interests include multi-RAT management and network slicing in 5G based on DL. He was a reviewer of some conferences and journals.

<figure><img src="image_11.png" /></figure>

**HUA WANG** received the Ph.D. degree from the Beijing Institute of Technology (BIT), Beijing, China, in 1999. From February 2009 to January 2010, he was a Visiting Professor with the Department of Electrical Engineering, Arizona State University, Tempe, AZ, USA. He is currently a Professor with the School of Information and Electronics, BIT. His research interests include communication theory and signal processing, wireless networking, modem designs, and implementation for satellite communication.

<figure><img src="image_12.png" /></figure>

**DONGXUAN HE** received the B.S. degree in automation and the Ph.D. degree in information and communication systems from the Beijing Institute of Technology (BIT), in 2013 and 2019, respectively. From 2017 to 2018, he was a Visiting Student with the Singapore University of Technology and Design (SUTD). From 2019 to 2022, he was a Postdoctoral Researcher with the Department of Electronic Engineering, Tsinghua University. He is currently an Assistant Professor with the School of Information and Electronics, BIT. His current research interests include terahertz communication, AI-empowered wireless communications, and physical layer security. He was an Exemplary Reviewer of IEEE WIRELESS COMMUNICATIONS LETTERS.

<figure><img src="image_13.png" /></figure>

**MAHA FATHY** received the B.Sc. degree (Hons.) from Pharos University in Alexandria, Egypt, in 2013, and the M.Sc. degree (Hons.) in electrical engineering from Alexandria University, Alexandria, Egypt, in 2018. She is currently pursuing the Ph.D. degree with the School of Information and Electronics Engineering, Beijing Institute of Technology, Beijing, China. Until 2019, she was an Assistant Lecturer of Pharos University in Alexandria. In 2018, she participated in staff mobility within Erasmus+, JAMK University of Applied Sciences, Jyväskylä, Finland. Her research interests include wireless communication, performance optimization, machine learning, deep learning, signal processing, and image processing.

<figure><img src="image_14.png" /></figure>

**SAMI ABDULJABBAR RASHID** was born in Al Anbar, Iraq. He received the B.Eng. degree in computer engineering technology from the Al-Maarif University College, Iraq, and the M.Sc. degree in communication and compute engineering from Universiti Kebangsaan Malaysia (UKM), Malaysia. He is currently pursuing the Ph.D. degree with the Department of Communication Engineering, University Tun Hussein Onn Malaysia (UTHM), Malaysia. His research interests include wireless and mobile communications and VANET.

<figure><img src="image_15.png" /></figure>

**MOHAMMAD ALIBAKHSHIKENARI** (Member, IEEE) was born in Mazandaran, Iran, in February 1988. He received the Ph.D. degree (Hons.) (European Label) in electronics engineering from the University of Rome "Tor Vergata," Italy, in February 2020. In 2018, he was a Ph.D. Visiting Researcher with the Chalmers University of Technology, Gothenburg, Sweden. His training during the Ph.D. research visit included a research stage with Swedish Company Gapwaves AB, Gothenburg. He is currently with the Department of Signal Theory and Communications, Universidad Carlos III de Madrid (uc3m), Spain, as a Principal Investigator of the CONnecting EXcellence (CONEX)-Plus Talent Training Program and Marie Skłodowska-Curie Actions. He was a Lecturer of electromagnetic fields with the Electromagnetic Laboratory, Department of Signal Theory and Communications, from 2021 to 2022. From 2022 to 2023, he spent three industrial and academic research visits with SARAS Technology Company Ltd., Leeds, England, U.K.; Edinburgh Napier University, Edinburgh, Scotland, U.K.; and the University of Bradford, West Yorkshire, England, which were defined by CONEX-Plus Talent Training Program and Marie Skłodowska-Curie Actions as the secondment research visit plans. His research interests include electromagnetic systems, antennas and wave-propagations, metamaterials and metasurfaces, sensors, synthetic aperture radars (SAR), 5G and beyond wireless communications, multiple input multiple output (MIMO) systems, RFID tag antennas, substrate integrated waveguides (SIWs), impedance matching circuits, microwave components, millimeter-waves and terahertz integrated circuits, gap waveguide technology, beamforming matrix, and reconfigurable intelligent surfaces (RIS), which led to achieve more than 5000 citations and H-index above 45 reported by Scopus, Google Scholar, and ResearchGate. He was a recipient of the three-year research grant funded by Universidad Carlos III de Madrid and the European Union's Horizon 2020 Research and Innovation Program under the Marie Skłodowska-Curie Grant, in July 2021; the two-year research grant funded by the University of Rome "Tor Vergata," in November 2019; and the three years Ph.D. Scholarship funded by the University of Rome "Tor Vergata," in November 2016; and the two Young Engineer Awards of the 47th European Microwave Conference, held in Nuremberg, Germany, in 2017, and the 48th European Microwave Conference, held in Madrid, Spain, in 2018. He received the "Teaching Excellent Acknowledgement" Certificate for the course of electromagnetic fields from the Vice-Rector of Studies of uc3m. His research article entitled "High-Gain Metasurface in Polyimide On-Chip Antenna Based on CRLH-TL for Sub Terahertz Integrated Circuits" (Scientific Reports) was awarded as the Best Month Paper from the University of Bradford, U.K., in April 2020. He also acts as a referee in several highly reputed journals and international conferences. He is serving as an Associate Editor for *Radio Science* and *IET Journal of Engineering*.
<figure><img src="image_16.png" /></figure>

**BAL S. VIRDEE** received the B.Sc. degree in engineering from the University of Leeds, U.K., and the Ph.D. degree from the University of London, U.K. Since graduation, he has worked in industry for various high-tech companies, including Philips, U.K., as a Research and Development Engineer, and Teledyne Defence and Space, U.K., as a Future Products Developer of RF/microwave communications. He has taught in several academic institutions in the U.K. He is currently a Senior Professor of communications technology and the Director of the Center for Communications Technology, School of Computing and Digital Media, London Metropolitan University. He has supervised and examined numerous Ph.D. students and published extensive research papers at international conferences and peer-reviewed journals. His research, in collaboration with industry and academia, is in next-generation wireless communications systems. He is a Chartered Engineer (CEng) and a fellow of IET. He is also the Chair and an Executive Member of the Institution of Engineering and Technology's (IET) Technical and Professional Network Committee on RF/Microwave Technology.

**SALAHUDDIN KHAN**, photograph and biography not available at the time of publication.

<figure><img src="image_17.png" /></figure>

**GIOVANNI PAU** (Member, IEEE) received the bachelor's degree in telematic engineering from the University of Catania, Italy, and the master's (cum laude) and Ph.D. degrees in telematic engineering from the Kore University of Enna, Italy. He is currently an Associate Professor with the Faculty of Engineering and Architecture, Kore University of Enna. He is the author/coauthor of more than 80 refereed papers published in journals and conference proceedings. His research interests include wireless sensor networks, fuzzy logic controllers, intelligent transportation systems, the Internet of Things, smart homes, and network security. He has been involved in several international conferences as the session co-chair and a technical program committee member. He serves/served as a leading guest editor in special issues for several international journals. He is an Editorial Board Member and an Associate Editor of several journals, such as *IEEE Access*, *Wireless Networks* (Springer), *EURASIP Journal on Wireless Communications and Networking* (Springer), *Wireless Communications and Mobile Computing* (Hindawi), *Sensors* (MDPI), and *Future Internet* (MDPI).

<figure><img src="image_18.png" /></figure>

**IYAD DAYOUB** (Senior Member, IEEE) received the B.Eng. degree in telecommunications and electronics in Syria, in 1993, the M.A.Sc. degree in electrical engineering from the National Polytechnic Institute of Lorraine (INPL), and the Ph.D. degree from the University of Valenciennes and the Institute of Electronics, Microelectronics and Nanotechnology (IEMN), in 2001. He was a System Engineer with Siemens, Middle East; and a Researcher with Alcatel Business Systems, Alcatel, Colombes, Paris. He was an Adjunct Professor with Concordia University, Montreal, from 2010 to 2014. He is currently a Professor of communications engineering. His current research activities at IEMN, Université Polytechnique Hauts-de-France (UPHF), and INSA H-d-F are focused on wireless communications, high-speed communications, cognitive radio, and hybrid radio-optic technologies. From 2007 to 2014, he was a member of the National Council of Universities (CNU), France, in the areas of electrical engineering, electronics, photonics, and systems. He is a member of several international conference advisory committees, technical program committees, and organization committees, such as VTC, GLOBECOM, ICC, PIMRC, and WWC.

<figure><img src="image_19.png" /></figure>

**PATRIZIA LIVRERI** (Senior Member, IEEE) received the Laurea degree (Hons.) in electronics engineering and the Ph.D. degree in electronics and communications engineering from the University of Palermo, Italy, in 1986 and 1992, respectively. From 1993 to 1994, she was a Researcher with the National Council for Researches, CNR, Tome, Italy. Since 1995, she has been serving as the Scientific Director for the Microwave Instruments and Measurements Laboratory with the Department of Engineering, University of Palermo. In 2020, she joined the CNIT National Laboratory for Radar and Surveillance Systems RaSS, Pisa. She is currently a Professor with the Department of Engineering, University of Palermo, and a Visiting Professor with the San Diego State University. She is a Principal Investigator of the “Microwave Quantum Radar” project, funded by the Ministry of Defense, in 2021. She is a supervisor of many funded projects and the author of more than 200 published papers. Her research interests include microwave and millimeter vacuum high power (TWT, Klystron) and solid-state power amplifiers for radar applications, high-power microwave sources (virtual cathode oscillator and magnetically insulated transmission line oscillator), microwave and optical antennas, radar, and microwave quantum radar.

<figure><img src="image_20.png" /></figure>

**TAHA A. ELWI** received the B.Sc. degree (Hons.) from the Department of Electrical Engineering, Nahrain University, Baghdad, Iraq, in 2003, the M.Sc. degree (Hons.) from the Department of Laser and Optoelectronics Engineering, Nahrain University, in 2005, and the Ph.D. degree in system engineering and science from the University of Arkansas at Little Rock, in 2011. From April 2005 to August 2007, he was with Huawei Technologies Company, Baghdad. He is currently the Head of the International Applied and Theoretical Research Center (IATRC), Baghdad. He is the supervisor of many funded projects and Ph.D. theses with more than 150 published papers and holds ten patents. His research interests include wearable and implantable antennas for biomedical wireless systems, smart antennas, WiFi deployment, electromagnetic wave scattering by complex objects, design, modeling, and testing of metamaterial structures for microwave applications, design and analysis of microstrip antennas for mobile radio systems, precipitation effects on terrestrial and satellite frequency re-use communication systems, effects of the complex media on electromagnetic propagation and GPS, pattern recognition, signal and image processing, machine learning, deep learning, game theory, and medical image analysis-based artificial intelligence algorithms and classifications. His research is conducted to consider wireless sensor networks based on microwave terminals and laser optoelectronic devices. His work is extended to realize advancements in reconfigurable intelligent surfaces and control the channel performance. Nevertheless, the evaluation of modern physics phenomena in wireless communication networks including cognitive radio networks and squint effects is currently part of the research. The nanoscale structures in the entire electromagnetic spectrum are a part of the research interest. Since 2016, he has been a member of the Iraqi Scientific Research Consultant. He is leading three collaborations around the world regarding biomedical applications using microwave technology. He serves as an editor for many international journals and publishers, such as MDPI, IEEE, Springer, and Elsevier. He is considered one of the top 2% scientists by Stanford University, in 2022.