In this article, we investigate the joint task offload for Device-to-Device (D2D) communications with energy harvesting and power control requirements. Utilizing D2D communication for data offloading effectively decreases the load on cellular Base Stations (BSs). Thus, we investigate the challenge of optimizing connection density in D2D networks with multiple connections and investigate scenarios involving delays and energy harvesting, aiming for simultaneous transmissions and efficient resource utilization accordingly. The BS assigns each device a task, and the under-resourced devices aim to build a connection with one helper and offload part of the task. This scheduling policy includes optimal task assignment and practical helper choice. We formulate this problem as finding a semi-matching over a bipartite graph derived from multiple connections and distinct constraints, i.e., power budget and time constraint. Furthermore, we compared our proposed solution, Energy Harvesting Task Offloading Semi-Matching (EHTO-SM), with predefined baselines. Numerical results confirm that the proposed task allocation scheme provides users with high-quality services and demonstrates the effectiveness and dynamic resource adaptability in multiuser settings in various scenarios.

Recently, robust technologies such as unmanned aerial vehicles (UAVs) and intelligent reconfigurable surfaces (IRSs) have demonstrated remarkable abilities to boost the coverage of limited-range millimeter wave (mmWave) communications. This paper handles the use of UAV-mounted IRS (U-IRS) to aid the mmWave base station (mWBS) in assisting clients/mobile users (MUs) positioned within hotspot regions. The UAV should wrap large-capacity hotspots while minimizing its aviation/hovering energy consumption. Hence, an energy-efficient advanced multi-armed bandit (MAB) scheme, named explore then commit based minimax optimal stochastic strategy (ETC-MOSS), is proposed as a practical self-learning technique to handle such issues intelligently. In this framework, the UAV (MAB player) selects hotspots (arms) to serve, aiming to maximize communication rate (reward) and minimize energy use (budget). Simulations demonstrated the superior performance of the envisioned ETC-MOSS scheme over classical MOSS and formal heuristic solutions according to mean rate and energy efficiency (EE), enabling future applications in the Metaverse, IoT, VANETs, and beyond.

The escalating integration of deep neural networks (DNNs) in autonomous vehicles underscores the urgency of fortifying them against adversarial attacks. This paper presents a novel approach to enhance the robustness of convolutional neural networks (CNNs) in self-driving cars through a combination of adversarial mitigation techniques: they are randomization, image padding, and, most uniquely, the addition of random Gaussian noise after convolution layers. Our specialized neural network demonstrates consistent steering control under various attack scenarios, avoiding the over-steering or under-steering issues observed in standard models. As 6 G networks emerge with their ultra-reliable low-latency communication capabilities, our research contributes to the security foundation necessary for autonomous vehicles in this coming era, where resilience against adversarial manipulation will be crucial for maintaining safety in increasingly connected transportation ecosystems. Our open-sourced model provides a benchmark for real-time attackresistant systems applicable to 6G-enabled autonomous driving technologies.

Autonomous vehicles generate massive sensor data daily but operate as isolated intelligence units due to privacy constraints and network limitations. Current centralized machine learning approaches face critical barriers including compliance issues, high bandwidth costs, and latency constraints preventing real-time safety decisions. While Federated Learning (FL) enables collaborative training without raw data sharing and 6G networks promise ultra-low latency, a fundamental mismatch exists between FL's dynamic computational demands and 6G's static resource allocation mechanisms. This paper presents Predictive FL-Aware Network Slicing (PFANS), a novel framework that integrates real-time convergence modeling with proactive 6 G slice reconfiguration for autonomous vehicle networks. PFANS predicts FL computational demands multiple training rounds in advance and automatically reconfigures network slices before bottlenecks occur. Experimental results demonstrate superior resource utilization efficiency, significantly faster convergence compared to baseline approaches, and excellent handover success rates with minimal context migration times. The framework achieves state-of-theart prediction accuracy while introducing negligible network overhead, establishing effective adaptive resource management for next-generation vehicular networks.

Human Activity Recognition (HAR) systems hold great potential in aiding disabled and elderly individuals to live independently. Various approaches have been suggested for identifying human activities, including sensors, cameras, wearables, and contactless microwave sensing. This latter method is gaining significant attention due to its ability to address privacy concerns arising from cameras and alleviate discomfort caused by wearables. However, current microwave sensing techniques have a key limitation, requiring controlled and ideal conditions to achieve accurate activity detection. In this paper, we propose RISense, a deep learning-aided system for HAR using Re-configurable Intelligent Surface (RIS). RISense introduces novel modules designed to generate a human activity representational space that ensures separability between activity classes, even in the presence of noisy and distorted Channel State Information (CSI) measurements. These representations are then fed into a Recurrent Neural Network (RNN), which learns the sequential changes in features to accurately estimate the user activity. The evaluation of RISense across two realistic settings, encompassing both non-line-of-sight and multi-floor scenarios, showcases its efficacy. Specifically, RISense achieves an activity recognition accuracy of 98.7%. This performance overcomes the accuracy of state-of-the-art systems by over 15%. These findings highlight the superior efficacy of the proposed methodologies, including integrating RIS and advanced learning techniques.

Vehicle recognition approaches have recently gained remarkable interest in improving intelligent transportation systems, especially in harsh weather or insufficient illumination conditions. Vehicle recognition using seismic waves is a novel technique that records the vibrations of vehicles using geophones. Hence, SeismicSense is introduced, a deep learning-based system that is trained using vehicle vibrations. Moreover, the proposed system includes different modules that ensure its robustness against noise. A real data set is collected and leveraged to implement our proposed SeismicSense. Evaluation Results ensure its remarkable accuracy in precise vehicle recognition.

Recently, vehicular over-the-air (OTA) updates have been raised as an intelligent solution for the autonomous vehicle revolution. It is critical to download important vehicular safety and stability updates onboard. The forthcoming six-generation (6G) systems might consider OTA a cach service delivered by base stations or roadside units (RSUs). This paper introduces the practical implementation of OTA updates using roadside units and dual-band WiGig/Wi-Fi communications. The system's performance is investigated against different blocking vehicles (small, medium, and large), simulating realistic scenarios. Experimental results ensure the 60 GHz WiGig performance efficiency regarding download speed and response time.

Recently, Narrowband Internet of Things (NB-IoT) systems gained a significant focus as a promising direction for massive connectivity issues in forthcoming wireless communication systems. Thus, this paper investigates the challenge of maximizing connection density in NB-IoT networks, considering a downlink Non-Orthogonal Multiple Access (NOMA) scenario for simultaneous transmissions and efficient resource utilization. The base station assigns each device to one of the accessible Physical Resource Blocks (PRBs). This scheduling policy includes effective device clustering and optimal NOMA power assignment. We formulate this problem as finding semi-matching over a bipartite graph derived from multiple PRBs and distinct constraints, i.e., power budget, admitted PRB, interference, and quality of service constraints. Furthermore, we compared our solutions NOMA-Semi Matching 1 (NOMA-SM1) and NOMA-Semi Matching 2 (NOMA-SM2) with previous solutions. Numerical Simulations confirm that the proposed semi-matching aided approaches attain a better theoretical bound and superior performance.

This article presents accurate approximation expressions for the outage and secrecy outage probabilities of relay-assisted free-space optical (FSO) communication links utilizing unmanned aerial vehicles (UAVs). We consider the effects of weather attenuation and random fluctuations of UAVs’ orientations and positions. The obtained expressions are applicable to different types of intensity modulation direct detection techniques. We utilized$L$-ary multipulse pulse-position modulation$(L$MPPM) scheme and optimized link performance by adjusting the$L$MPPM settings using our derived expressions. We conduct numerical analyses to evaluate the impact of system settings, including beam divergence and modulation settings, on outage and secrecy outage probabilities. Our results demonstrate that adaptive$L$MPPM can improve average spectral efficiency, particularly in time-varying UAV-based FSO channels, while maintaining the same outage and secrecy outage probabilities as ordinary multipulse pulse-position modulation. Moreover, we investigate the effect of an eavesdropper’s location on the probability of a secure channel between legitimate users. We validate the accuracy of our expressions by comparing them with Monte Carlo (MC) simulation results under different channel conditions. Derived expressions provide efficient and accurate evaluation results to optimize the performance of UAV-based relay-assisted FSO communication systems.

This paper uses an aerial mounted RIS (A-RIS) to assist WiGig base station (BS) in serving mobile equipments (MEs) located within hotspot zones. The aerial should cover numerous large-capacity hotspots in this context while anticipating its flying/hovering energy expenditures. Hence, two advanced multi armed bandit (MAB) approaches, i.e. perturbed history exploration (PHE) and mini-max optimal Thompson sampling (MOTS), are envisioned as applicable self-learning methodologies to deal with such a problem effectively. Simulation results ensure the excellent performance of the envisioned schemes over naive upper confidence bound (UCB) and Thompson sampling (TS) algorithms and traditional heuristic solutions.

As vehicular communication networks embrace metaverse beyond 5G/6G systems, the rich content update via the least interfered subchannel of the optimal frequency band in a hybrid band vehicle to everything (V2X) setting emerges as a challenging optimization problem. We model this problem as a tradeoff between multi-band VR/AR devices attempting to perform metaverse scenes and environmental updates to metaverse roadside units (MRSUs) while minimizing energy consumption. Due to the computational hardness of this optimization, we formulate an opportunistic band selection problem using a multi-armed bandit (MAB) that provides a good quality solution in real-time without computationally burdening the already stretched augmented/virtual reality (AR/VR) units acting as transmitting nodes. The opportunistic use of scheduling rich content updates at traffic signals and stand-still scenarios maps well with the formulated bandit problem. We propose a Dual-Objective Minimax Optimal Stochastic Strategy (DOMOSS) as a natural solution to this problem. Through extensive computer-based simulations, we demonstrate the effectiveness of our proposal in contrast to baselines and comparable solutions. We also verify the quality of our solution and the convergence of the proposed strategy.

Intelligent transportation systems have become increasingly important for efficient traffic management and road safety. Vehicle classification is a fundamental task in these systems, enabling various applications such as traffic monitoring, congestion management, and accident prevention. Traditional methods for vehicle classification heavily rely on visual or sensor-based data, such as images or radar signals. However, these methods may encounter limitations in adverse weather conditions, poor lighting, or occlusion scenarios. To address these limitations, this paper introduces a novel approach for vehicle classification using seismic data, which captures the vibrations generated by vehicles and is less susceptible to environmental factors. The proposed approach leverages the fractional wavelet domain to extract both time and frequency signatures of vehicles from the seismic data effectively. These signatures are then used to train a recurrent neural network that captures the temporal features of the input, thereby facilitating vehicle classification. Additionally, the proposed approach incorporates various modules to improve the generalization and robustness of the deep model against noise. The implementation of the proposed approach on realistic data demonstrated its ability to classify vehicles with an accuracy of 98%, improving upon the state-of-the-art techniques.

Recently emerging WiGig systems experience limited coverage and signal strength fluctuations due to strict line-of-sight (LoS) connectivity requirements. In this paper, we address these shortcomings of WiGig communication by exploiting two emerging technologies in tandem, namely the reconfigurable intelligent surface (RIS) and unmanned aerial vehicles (UAVs). In ultra-dense traffic sites (referred to as hotspots) where WiGig nodes or User Devices (UDs) experience complex propagation and non-line-of-sight (non-LoS) environment, we envision the deployment of a UAV-mounted RIS system to complement the WiGig base station (WGBS) to deliver services to the UDs. However, commercially available UAVs have limited energy (i.e., constrained flight time). Therefore, the trajectory of our considered UAV needs to be locally estimated to enable it to serve multiple hotspots while minimizing its energy consumption within the WGBS coverage boundaries. Since this tradeoff problem is computationally expensive for the resource-constrained UAV, we argue that sequential learning can be a lightweight yet effective solution to locally solve the problem with a low impact on the available energy on the UAV. We formally formulate this problem as a contextual multi-armed bandit (CMAB) game. Then, we develop the linear randomized upper confidence bound (Lin-RUCB) algorithm to solve the problem effectively. We regard the UAV as the bandit learner, which attempts to maximize its attainable rate (i.e., the reward) by serving distinct hotspots in its trajectory that we treat as the arms of the considered bandit. The context is defined as the hotspots’ locations provided using GPS (global positioning system) service and the reward history of each hotspot. Our proposal accounts for the energy expenditure of the UAV in moving from one hotspot to another within its battery charge lifetime. We evaluate the performance of our proposal via extensive simulations that exhibit the superiority of our proposed Lin-RUCB algorithm over benchmarking methods.

This paper proposes reinforcement learning (RL)-based solutions for unmanned aerial vehicle (UAV) data offloading in B5G mmWave-enabled communications. This is particularly useful for ad-hoc transmission scenarios within environments experiencing connectivity issues with the main servicing network as in disaster-stricken areas. Double deep Q-network and multiarmed bandit-based algorithms are proposed to tackle the joint problem of UAV-positioning and Rx-node activation and power allocation for data offloading in downlink NOMA transmissions. Numerical simulations are performed to ensure the proposed RL-based algorithms can adequately provide high data transfer rates, along with random and exhaustive search solutions as benchmarks for lower and upper bounds on the achievable sum-rate levels.

Abstract Due to the importance of both rate‐less digital fountain codes and Multi‐Input Multi‐Output (MIMO) schemes in Fifth and Sixth Generation (5G/6G) wireless networks, this paper investigates enhancing the performance of MIMO‐Orthogonal Frequency Division Multiplexing (MIMO‐OFDM) based system by employing the rate‐less fountain codes. The rate‐less codes, such as the Tornado codes and Raptor codes, have a flexible code rate that differs from the classical channel coding schemes. This paper uses Raptor code to study the MIMO system's performance with the rate‐less codes over different wireless communications channels. The proposed MIMO‐fountain code‐based system has been tested over the various wireless communication channel conditions concerning the varied Frequency Doppler (FD). In the proposed rate‐less wireless communication system, multiple interleaving techniques and equalizers are used to improve its performance. Computer‐based simulation experiments are assigned to evaluate the MIMO‐fountain code system using the different data transmission scenarios. Simulation results confirm the superiority of the presented wireless rate‐less system compared to fixed‐rate wireless systems in terms of Bit Error Rate (BER) and throughput.

Abstract Millimetre wave (mmWave) communications, that is, 30 to 300 GHz, have intermittent short‐range transmissions, so the use of reconfigurable intelligent surface (RIS) seems to be a promising solution to extend its coverage. However, optimizing phase shifts (PSs) of both mmWave base station (BS) and RIS to maximize the received spectral efficiency at the intended receiver seems challenging due to massive antenna elements usage. In this paper, an online learning approach is proposed to address this problem, where it is considered a two‐phase multi‐armed bandit (MAB) game. In the first phase, the PS vector of the mmWave BS is adjusted, and based on it, the PS vector of the RIS is calibrated in the second phase and vice versa over the time horizon. The minimax optimal stochastic strategy (MOSS) MAB algorithm is utilized to implement the proposed two‐phase MAB approach efficiently. Furthermore, to relax the problem of estimating the channel state information (CSI) of both mmWave BS and RIS, codebook‐based PSs are considered. Finally, numerical analysis confirms the superior performance of the proposed scheme against the optimal performance under different scenarios.

To handle the exponentially growing service expectations from mobile users and circumvent the band switching slow rate, device-to-device (D2D) communication is receiving much research attention in the Internet of Things (IoT). While the emerging D2D nodes can support heterogeneous frequency bands [radio frequency (RF) including 2.4 GHz/5 GHz wireless local area network (WLAN), 38-GHz millimeter wave (mmWave), and visible light communication (VLC)], the physical constraints (e.g., blocking) require the user devices to dynamically switch between the bands in order to avoid the loss of connectivity and throughput degradation. In this article, we investigate an effective online link selection in hybrid RF-VLC scenarios for direct user data handling. First, we model the multiband selection issue as a multiarmed bandit (MAB) problem. The source/relay node acts as a player who gambles to maximize its long-term feedback/reward via selecting suitable arms, i.e., available bands (WLAN, mmWave, or VLC). Then, we propose an online, energy-aware band selection (EABS) methodology by leveraging three theoretically guaranteed MAB techniques [upper confidence bound (UCB), Thompson sampling (TS), and minimax optimal stochastic strategy (MOSS)] to derive optimal band selection policies. Based on these adopted policies, we propose three algorithms, namely, EABS-UCB, EABS-TS, and EABS-MOSS, to implement the EABS strategy, respectively. Extensive simulations demonstrate our proposed algorithms’ superior performance compared to the traditional link selection schemes regarding energy efficiency, average throughput, and convergence rate. In particular, EABS-MOSS emerges as the best algorithm as it exhibits near-optimal performance due to its flexibility to both stochastic and adversarial environments.

While hybrid-band wireless systems recently gained prominence to achieve high capacity, selecting the best channel in these systems in real-time is still a formidable research challenge that requires further investigations. In this paper, we address this challenge in terms of an optimization problem, which is reformu-lated as a stochastic multi-armed bandit (MAB). Then, we introduce online learning-based solutions to solve the MAB problem for the multi-band/channel selection (MBS). Improved variants of the upper confidence bound (UCB) scheme are investigated and modified to be energy-aware. Hence, we propose Energy-Aware Randomized UCB-MBS (EA-RUCB-MBS) and Energy-Aware Kullback-Leibler UCB-MBS (EA-KLUCB-MBS) methods, which demonstrate near-optimal results. Also, EA-KLUCB-MBS exhibits the fastest convergence, while the convergence of EA-RUCB-MBS is similar to that of the original UCB. Based on extensive simulation results, we evaluate the performance of our proposed algorithms against benchmark MBS schemes including UCB and Thompson sampling (TS).

Fractional Frequency Reuse (FFR) is an efficient method to mitigate Inter Cell Interference in multicellular Orthogonal Frequency Division Multiple Access (OFDMA) systems. In this paper, we analyze the downlink worst case Signal to Interference Ratio for FFR schemes. A closed form expression is derived analytically for the worst SIR, outage probability, and Spectral Efficiency (SE). The proposed analytical technique is used to configure a FFR solution for the downlink of OFDMA cellular system. The analysis is performed using two-tiers cellular network with uniform user density and for three different cases of FFR, namely, Frequency Reuse Factor (FRF) = 3, FRF=4 and sectored FFR. The inner radius configuration depends on equalizing the worst SIR for both inner and outer edges of the cell. Numerical results show that sectored FFR yields the highest SE and low outage probability. Sectored FFR highly balances the needs of interference reduction and resource efficiency.