In today’s intelligent transportation systems, emerging vehicular applications, such as multi-vehicle data fusion, platoon management, and high-definition video processing, demand a new generation of communication architectures to meet stringent processing and latency requirements. NOMA-based Vehicular Edge Computing (VEC) architectures have gained prominence as a solution, offering efficient spectrum reuse and reduced processing delays by leveraging distributed edge resources. While decentralized management of these architectures promises real-time, adaptive task offloading, existing approaches are limited by their reliance on local observations and insufficient integration of comprehensive global network state information. To address these challenges, we propose ROLAM, a novel framework that fuses LSTM-based state estimation with Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to optimize resource allocation under dynamic conditions. Our evaluation demonstrates that ROLAM significantly improves energy efficiency and overall system performance, underscoring its potential to robustly meet the evolving needs of next-generation vehicular applications.

The advent of cooperative intelligent transport systems (C-ITS) and connected vehicles has brought with it a raft of novel services, including autonomous driving and dynamic traffic management, which necessitate both real-time processing and substantial data processing capacity. These technological advancements have imposed significant network constraints, requiring the development of robust network infrastructures to guarantee the reliable transmission of critical information in real time. In consequence, numerous studies have concentrated on NOMA-assisted Multi-Access Edge Computing (NOMA-MEC) to facilitate the provision of new services and, in particular, on their management through the utilisation of Multi-Agent Deep Reinforcement Learning (MADRL) algorithms. Nevertheless, the absence of a unified framework for the rigorous evaluation and comparison of these different approaches represents a significant issue. For this reason, we have established a unified, adaptable, and extensible framework, NedgeSIM-RL, with the objective of evaluating decentralised offloading strategies within the context of NOMA-VEC architectures. In this paper, we illustrate the functionality of the framework, the potential configurations, and the areas of research that can be pursued. We also present a basic simulation that can be executed on the framework to manage decentralised offloading via NOMA on a single MEC server by multiple vehicles.

The optimal transmit power that maximizes energy efficiency (EE) in Longe Range (LoRa) networks is investigated by using the deep learning (DL) approach. Particularly, the proposed artificial neural network (ANN) is trained two times; in the first phase, the ANN is trained by the model-based data which are generated from the simplified system model while in the second phase, the pre-trained ANN is re-trained by the practical data. Numerical results show that the proposed approach outperforms the conventional one which directly trains with the practical data. Moreover, the performance of the proposed ANN under both partial and full optimum architecture are studied. The results depict that the gap between these architectures is negligible. Finally, our findings also illustrate that instead of fully re-trained the ANN in the second training phase, freezing some layers is also feasible since it does not significantly decrease the performance of the ANN.

The system-level performance of multi-gateway downlink long-range (LoRa) networks is investigated in the present paper. Specifically, we first compute the active probability of a channel and the selection probability of an active end-device (ED) in the closed-form expressions. We then derive the coverage probability (Pcov) and the area spectral efficiency (ASE) under the impact of the capture effects and different spreading factor (SF) allocation schemes. Our findings show that both the Pcov and the ASE of the considered networks can be enhanced significantly by increasing both the duty cycle and the transmit power. Finally, Monte-Carlo simulations are provided to verify the accuracy of the proposed mathematical frameworks.

In the present paper a closed-form framework is derived for the analysis and optimization of the coverage probability (Pcov) and of the area spectral efficiency (ASE) in long-range (LoRa) networks. The proposed framework exploits stochastic geometry tools to associate the Pcov and the ASE to the end device (ED) transmit power and to the ED density. The analysis reveals the trends of the Pcov and of the ASE curves, with respect to both of the two parameters, while the robustness of the framework holds even at the asymptotic cases. Building upon the derived framework, the analysis demonstrates that no joint global optimum exists that jointly maximizes the Pcov over both parameters, suggesting that the optimization of the Pcov must be performed separately, for the two key network parameters considered. As opposed to that, the analysis demonstrates that a set of global optima exists that jointly maximize the ASE over both parameters, and these global maxima are subsequently derived in closed form. Thus, the derived framework fully characterizes the performance of LoRa networks, while defining in closed form the optimal points of operation that can be proven of significant value, for the transceiver and network design, of practical LoRa networks.

In this work, coverage probability (Pcov) and area spectral efficiency (ASE) of LoRa networks with multiple classes of quality-of-service (QoS) of end-devices (EDs) are investigated. In particular, an approximated but tractable mathematical framework is proposed to compute a recent definition of the Pcov. Based on the proposed framework, the closed-form expressions of both Pcov and ASE are provided. Moreover, the trends of both metrics are unveiled that respect to some key parameters such as the density of the EDs, the transmit power, and the spreading factor (SF). Our findings show that depending on networks parameters, the ASE is either a unimodal function or monotonically increasing as a function of EDs' density. In addition, the optimal value of EDs' density that maximizes the ASE are computed in closed-form expression too. Finally, Monte Carlo simulations are provided to verify the correctness of our framework.

The massive growth of the Internet of Things (IoT) poses important challenges on providing IoT devices with specific quality of service (QoS) requirements in terms of urgency and reliability over long distances and Long Range Wide Area Network (LoRaWAN). Due to the diversity of these services and the increasing complexity in large scale IoT networks, software defined networking (SDN) alongside network slicing are needed to increase flexibility in managing network slices and providing IoT networks with an optimized parameters configuration. Therefore, we propose in this paper a SDN-based network slicing architecture for LoRaWAN where network slices are virtually deployed and isolated over LoRa physical gateways. Moreover, we aim to improve large scale network configuration by proposing TOPG, a slice-based optimization that improves LoRa parameters configuration based on QoS thresholds of each slice. Simulation results performed over NS3, highlight the utility of the proposed optimization in improving the network performance of LoRa slices in terms of reliability and respecting QoS thresholds in IoT dense deployments.

The massive growth of the Internet of Things (IoT) poses important challenges on network operators to support billions of IoT devices connected through the cloud with each having constrained battery life and computational capacity. To support these requirements over long distances, Long Range Wide Area Network (LoRaWAN), is now widely being deployed with the promise to support an all-connected world with numerous IoT applications. In large scale access networks, supporting urgent and reliable communications with their QoS demands becomes more challenging. Hence, network slicing within an SDN-based architecture brings numerous advantages to solve this problem by easily managing network resources and reserving part of the latter for urgent traffic and avoiding its performance degradation due to congestion. In this paper, we tackle the raised questions regarding scalability limitations by proposing a distributed slicing strategy based on coalitional game and matching theory over an SDN-based LoRaWAN architecture. In this context, resource reservation for LoRa slices and configuration optimization are performed closer to the edge at the gateway level. Simulation results performed over NS3 highlight the utility of the distributed slicing strategy in respecting quality of service (QoS) thresholds in terms of delay, throughput, energy consumption and improving reliability while providing complete isolation between LoRa slices.

The Internet of Things (IoT) represents the next wave in networking and communication which will bring by 2020 tens of billions of Machine-to-Machine (M2M) devices connected through the internet. Hence, this rapid increase in Machine Type Communications (MTC) poses a challenge on cellular operators to support M2M communications without hindering the existing Quality of Service for already established Human-to-Human (H2H) communications. LTE-M is one of the candidates to support M2M communications in Long Term Evolution (LTE) cellular networks. In this paper, we appraise and present an in depth performance evaluation of LTE-M based on cross-layer network metrics. Compared with LTE Category 0 previously released by 3GPP for MTC, simulation results show that LTE-M offers additional advantages to meet M2M communication needs in terms of wider coverage, lower throughput, and a larger number of machines connected through LTE network. However, we show that LTE-M is not yet up to the level to meet future applications requirements regarding a near-zero latency and an advanced Quality of Service (QoS) for this massive number of connected Machine Type devices (MTDs).

This paper presents a new cross-layer (PHY/MAC) resource allocation scheme based on Multiple Input Multiple Output (MIMO) and Orthogonal Frequency Division Multiple Access (OFDMA) system with a multi-service (MS) and a multiuser (MU) configuration. It is about a downlink transmission chain based on the IEEE 802.16m specifications, in which the available resources (power and bandwidth) are dynamically allocated according to the system parameters such as Channel State Information (CSI), spectral efficiency, error code corrector rate, Quality of Service (QoS) requirements and services scheduling. The specificity of our system is the joint parameterization of these elements according to the total power and threshold rate constraints in order to guarantee a good trade-off between users QoS requirements and sub-carriers distribution. Simulation results show that the proposed scheme offers better performances in terms of average throughput and users satisfaction than previous resource allocation schemes.

This paper proposes a global approach of JPWL (ISO/IEC 15444-11) image transmission over a realistic wireless channel able to ensure the best Quality of Service (QoS). In order to exploit the channel diversity, we consider a Closed-Loop MIMO-OFDM scheme with different precoder designs. In particular, the high flexibility of QoS precoder allows taking into account the scalability of JPWL jointly with the instantaneous MIMO channel status. This increases the visual quality of received images. The monitoring of the quality is made by a reduced-reference metric (QIP) based on object's saliency and interest point, both linked to human perception. It is performed in association with a robust JPWL decoder to determine the optimal decoding configuration in terms of PSNR. The proposed scheme provides very good results and its performance is shown through a realistic wireless channel.

In this paper we propose a novel scheme for SVC-based video transmission over MIMO channels using precoder solutions. On the one hand, H.264/SVC codec ensures spatial, temporal and quality scalabilities and provides an intrinsic hierarchy over transmitted bitstreams. On the other hand, precoder designs, which decouple a MIMO channel into parallel and independent SISOsub-channels, offer a high BER performance with a hierarchy over the sub-channels. The proposed scheme exploits the scalability of the H.264/SVC codec jointly with four precoder solutions providing an UEP without any surplus redundancy. Besides, some of these precoders such as QoS and E-dminallow a high flexibility on the power allocation across the sub-channels. This scalability is used to analytically draw the bandwidth allocation problem for H.264/SVC transmission over MIMO channels. The solution of this problem allows an optimal selection of each transmission bloc parameter approaching the optimal solution in term of Rate Distortion (RD) criterion. The simulation results show the performance of the proposed scheme over both statistical and realistic MIMO channels. Moreover, the accuracy of these precoder solutions against the Channel Estimation (CE) errors is investigated in different user mobility speeds.

In this paper, we study the problem of recognizing an unknown probability density function from one of its sample which is of interest in signal and image processing or telecommunication applications. By opposition with the classical Kolmogorov-Smirnov method based on empirical cumulative functions, we consider histogram estimators of the density itself built from our data. Those histograms are generated via model selection, more specifically via a codelength-based information criterion. From the histograms, we may compute a Kullback-Leibler distance to any theoretical law which is used to complete the recognition. We apply this histogram-based method for law recognition in a theoretical setup where the true density is known as well as in a real setup where data come from radio channel propagation experimentation.

In this paper we investigate the impact of a realistic physical layer on the H.264/AVC video transmission over Ad Hoc networks in urban environment. We propose a realistic Multiple Input Multiple Output (MIMO) physical layer which combines a determinist propagation model and a fine-grained model of wireless transmission errors. The determinist propagation model takes into account all the environmental characteristics (geometric and electric) and provides all the information of the multi-path channel (received power, complex impulse response). The wireless transmission errors model is based on a BER computation. The BER is calculated according to 802.11n standard to evaluate MIMO wireless links and, then, is compared to both SISO configuration and an existing wireless errors model using empirical propagation models. In the case of a SISO configuration, the BER is computed according to 802.11a standard. The simulation results show clearly a significant difference in term of QoS for the video transmission using realistic and empirical physical layer. In addition, the MIMO system, compared to a SISO one, improves the quality of links in the network and, thus, provides a better QoS for video transmission over Ad Hoc networks.

Mutual coupling effect between antennas on MIMO (multiple input multiple output) transmission is an important factor to take into account for an accurate modelling channel when antennas are closely spaced. The aim of this paper is to present the impact of mutual coupling on MIMO channel characteristics according to several propagation contexts. To achieve this purpose an original study, which has recourse to two complementary simulation methods, is set out. The first method simulates multipath propagation channel thanks to an optimized 3D ray-tracing. The second one is derived from rigorous electromagnetic analysis based on a method of moments. It models, in near field, mutual coupling between antennas. Results illustrate this impact on MIMO channel transfer matrix.

We investigate the spatio-temporal characterization of a radio mobile propagation channel in urban environment. This characterization is based on a 3D ray tracing software developed in our laboratory and on Bello's work. So, for the complex environment studied in the COST 231 project, we evaluate the dispersion and coherence properties of the radio channel and study the probability density functions (pdf) of the angle-of-arrival (AOA) in azimuth and elevation planes. We analyze the obtained results to show the difference existing between our approach and this stemming from statistical models of radio channel.

This paper presents a 3D simulation software of radiowave propagation for mobile systems in outdoor and indoor environments. The software allows us to predict the coverage zone of a base station and the behaviour of the transmission channel. After a brief recall of the radio channel characteristics for narrowband and wideband signals, the theoretical basis of our model and applications are described. Different results provided by our software are proposed : the evolution of the received power on a mobile route, the delay spread and the correlation bandwidth. To evaluate the capability of our software for the prediction of the previous parameters, measurements in outdoor and indoor environments are considered as a reference.

This paper presents a method which optimizes the computation time prediction of radio coverage. A solution is to reduce the number of application points of the used propagation model. For this, the method is based on a treatment by wavelet transform, on series of radio measurements realized around 2 GHz, and on a spatial analysis of the environment. This last tool allows us to define the evolution of the combination of electromagnetic phenomena (diffraction, reflection, line of sight) on an area. The proposed method is broken down into two stages called "learning" and "application" stages.

In this paper a statistical method for computation time optimization of the zone coverage prediction is presented. We compare it with complete simulation results for the UTD model.