This paper presents a distributed beamforming framework for a constellation of airborne platform stations (APSs) in a massive Multiple-Input and Multiple-Output (MIMO) non-terrestrial network (NTN) that targets the downlink sum-rate maximization under imperfect local channel state information (CSI). We propose a novel entropy-based multi-agent deep reinforcement learning (DRL) approach where each non-terrestrial base station (NTBS) independently computes its beamforming vector using a Fourier Neural Operator (FNO) to capture long-range dependencies in the frequency domain. To ensure scalability and robustness, the proposed framework integrates transfer learning based on a conjugate prior mechanism and a low-rank decomposition (LRD) technique, thus enabling efficient support for large-scale user deployments and aerial layers. Our simulation results demonstrate the superiority of the proposed method over baseline schemes including WMMSE, ZF, MRT, CNN-based DRL, and the deep deterministic policy gradient (DDPG) method in terms of average sum rate, robustness to CSI imperfection, user mobility, and scalability across varying network sizes and user densities. Furthermore, we show that the proposed method achieves significant computational efficiency compared to CNN-based and WMMSE methods, while reducing communication overhead in comparison with shared-critic DRL approaches.

In recent years, the demand for seamless connectivity and highly efficient, reliable network services for low earth orbit (LEO) satellites has escalated. To meet these expectations, a critical issue that must be addressed is combating malicious jamming attacks on satellite networks, which occur due to the open nature of satellite-ground connections. Moreover, in the era of artificial intelligence (AI), AI-based jamming poses a severe threat to the security of satellite networks and disrupts secure communications, particularly given the dynamic movements of LEO satellites and the time-sequential complexity of such attacks. Accordingly, this paper proposes a quantum adversarial deep reinforcement learning (QADRL) approach to mitigate AI-based jamming attacks while enhancing the quality-of-service (QoS) for LEO satellite networks. Specifically, the proposed QADRL approach is based on a zero-sum Markov game utilizing two opposing learning networks: one optimizing satellite routing links to avoid jamming and improve QoS, while the other, focuses on the jammer, optimizes the trajectory, jamming nodes, and power of unmanned aerial vehicles (UAVs) to maximize jamming success. The results demonstrate that the proposed QADRL outperforms classical adversarial DRL (CADRL) by reducing the jamming success rate by 33.33% and increasing the average QoS of the satellite network by 18.4975%.

The challenge of handling missing data in time series is critical for maintaining the accuracy and reliability of machine learning (ML) models in applications like fifth generation mobile communication (5G) network management. Traditional methods for validating imputation rely on ground truth data, which is inherently unavailable. This paper addresses this limitation by introducing two statistical metrics, the wasserstein distance (WD) and jensen-shannon divergence (JSD), to evaluate imputation quality without requiring ground truth. These metrics assess the alignment between the distributions of imputed and original data, providing a robust method for evaluating imputation performance based on internal structure and data consistency. We apply and test these metrics across several imputation techniques. Results demonstrate that WD and JSD are effective metrics for assessing the quality of missing data imputation, particularly in scenarios where ground truth data is unavailable.

Non-terrestrial base stations (NTBSs), including high-altitude platform stations (HAPSs) and hot-air balloons (HABs), are integral to next-generation wireless networks, offering coverage in remote areas and enhancing capacity in dense regions. In this paper, we propose a distributed beamforming framework for a massive MIMO network with a constellation of aerial platform stations (APSs). Our approach leverages an entropy-based multi-agent deep reinforcement learning (DRL) model, where each APS operates as an independent agent using imperfect channel state information (CSI) in both training and testing phases. Unlike conventional methods, our model does not require CSI sharing among APSs, significantly reducing overhead. Simulations results demonstrate that our method outperforms zero forcing (ZF) and maximum ratio transmission (MRT) techniques, particularly in high-interference scenarios, while remaining robust to CSI imperfections. Additionally, our framework exhibits scalability, maintaining stable performance over an increasing number of users and various cluster configurations. Therefore, the proposed method holds promise for dynamic and interference-rich NTBS networks, advancing scalable and robust wireless solutions.

This paper investigates the use of beyond diagonal reconfigurable intelligent surface (BD-RIS) with$N$elements to advance integrated sensing and communication (ISAC). We address a key gap in the statistical characterizations of the radar signal-to-noise ratio (SNR) and the communication signal-to-interference-plus-noise ratio (SINR) by deriving tractable closedform cumulative distribution functions (CDFs) for these metrics. Our approach maximizes the radar SNR by jointly configuring radar beamforming and BD-RIS phase shifts. Subsequently, zeroforcing is adopted to mitigate user interference, enhancing the communication SINR. To meet ISAC outage requirements, we propose an analytically-driven successive non-inversion sampling (SNIS) algorithm for estimating network parameters satisfying network outage constraints. Numerical results illustrate the accuracy of the derived CDFs and demonstrate the effectiveness of the proposed SNIS algorithm.

The Internet of Vehicles (IoV) generates massive data traffic and demands reliable end-to-end connectivity to achieve multi-Gbps throughput between vehicles and roadside units. Millimeter-wave (mmWave) bands, with their abundant bandwidth, are promising for high-throughput IoV networks. However, in this context, significant propagation losses, intermittent line-of-sight availability, and dynamic topology changes due to vehicle mobility present critical challenges. This article introduces resource allocation for vehicular networks ($\textsf {RAVEN}$), a centralized resource management framework designed to address these challenges effectively.$\textsf {RAVEN}$leverages a digital twin network (DTN) to optimize the end-to-end system capacity of multihop mmWave IoV networks by effectively managing co-channel interference among vehicles.$\textsf {RAVEN}$comprises the following three steps: 1) a channel prediction step that utilizes DTN’s awareness of vehicular mobility and environmental contexts to predict site-specific channel gains for vehicular communication links; 2) a clustering step that partitions vehicles into nonoverlapping clusters, allowing vehicles within each cluster to share the same mmWave channel for data transmission, while simultaneously reducing co-channel interference; and 3) a multihop connectivity optimization step that provides a connected vehicular networking topology by jointly optimizing vehicle-to-vehicle and vehicle-to-infrastructure connectivity using a graph theory approach. A proof-of-concept of$\textsf {RAVEN}$is developed by implementing a DTN on the Microsoft Azure Digital Twins platform while integrating real-world vehicular mobility traces, edge-cloud collaboration, and parallel computing. Extensive simulations demonstrate that$\textsf {RAVEN}$outperforms several benchmark schemes, and offers scalability and near real-time decision-making capabilities for managing interference in large-scale IoV networks.

This paper develops a deep reinforcement learning solution to simultaneously optimize the multi-UAV cell-association decisions and their moving velocity decisions on a given 3D aerial highway. The objective is to improve both the transportation and communication performances, e.g., collisions, connectivity, and HOs. We cast this problem as a Markov decision process (MDP) where the UAVs' states are defined based on their velocities and communication data rates. We have a 2D transportation-communication action space with decisions like UAV acceleration/deceleration, lane-changes, and UAV-base station (BS) assignments for a given UAV's state. To deal with the multi-dimensional action space, we propose a neural architecture having a shared decision module with multiple network branches, one for each action dimension. A linear increase of the number of network outputs with the number of degrees of freedom can be achieved by allowing a level of independence for each individual action dimension. To illustrate the approach, we develop Branching Dueling Q-Network (BDQ) and Branching Dueling Double Deep Q-Network (Dueling DDQN). Simulation results demonstrate the efficacy of the proposed approach, i.e., 18.32% improvement compared to the existing benchmarks.

Wireless sensor networks (WSNs) in smart grid (SG) applications are greatly affected by the detrimental impact of bursty impulsive noise (IN) generated by various partial discharges from aging power equipments in Smart Grid’s power substations. Additionally, the deleterious impact of fading on the radio frequency (RF) links between the sensor nodes further deteriorates the performance. To alleviate the problem of fading on the RF links, in this paper, we propose to exploit reconfigurable intelligent surfaces (RISs). In addition, to deal with the bursty IN, we consider the maximal a posteriori (MAP) decoding of the received symbols using the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm. The performance of the considered system is evaluated by deriving a novel closed-form expression for the bit error rate (BER). Furthermore, an asymptotic BER analysis is presented in order to highlight the diversity order achieved by the considered RIS-aided system. Finally, extensive numerical results are provided to demonstrate the significant gain that can be achieved through the proposed RIS-aided architecture for WSNs.

The emerging intelligent reflecting surface (IRS) technology introduces the potential of controlled light propagation in visible light communication (VLC) systems. This concept opens the door for new applications in which the channel itself can be altered to achieve specific key performance indicators. In this paper, for the first time in the open literature, we investigate the role that IRSs can play in enhancing the link reliability in VLC systems employing non-orthogonal multiple access (NOMA). We propose a framework for the joint optimisation of the NOMA and IRS parameters and show that it provides significant enhancements in link reliability. For example, the bit-error-rate of the NOMA user in the first decoding order is reduced to the order of 10−6using the proposed framework compared to an error floor of 10−2under fixed power allocation and fixed IRS reflection coefficients. The enhancement is even more pronounced when the VLC channel is subject to blockage and random device orientation.

Providing high-quality matching between drivers and riders is imperative for sustaining the growth of ride-sharing platforms. A user-focused matching mechanism design plays a key role in terms of ensuring user satisfaction. In this paper, we consider the matching problem in the community ride-sharing setting, where drivers and riders have strong personal preferences over the matched counterparties. Obtaining high-quality solutions that accommodate drivers’ and riders’ preferences in such a setting is particularly challenging as drivers and riders maybe reluctant to share with the platform their personal preferences over their ride-sharing counterparties due to privacy and ethical concerns. To this end, we propose a VOting-based MAtching (VOMA) mechanism to compute near-optimal matching solutions for drivers and riders, while preserving their privacy. The mechanism is a distributed implementation of the simulated annealing meta-heuristic, which computes matching solutions by guiding drivers and riders in the distributed search process using an iterative voting protocol. We evaluate the performance of VOMA using test cases generated based on New York taxi data sets. The experiment results show that the proposed matching mechanism achieves on average 90.9% efficiency compared with optimal solutions. We also show that VOMA improves the vehicle miles traveled (VMT) savings by up to 35% compared to an alternative voting-based greedy matching mechanism. System scalability and other practical issues regarding the implementation of such a matching mechanism in community ride-sharing platforms are also discussed.

The fifth-generation wireless technology (5G) has been developed with an aim to provide ubiquitous and scalable connectivity for Internet-of-Things (IoT) nodes. Likewise, the cloud radio access network (C-RAN) architecture can be exploited to enable efficient network access to IoT nodes. Nevertheless, the 5G C-RAN architecture is based on large data centers geographically located far apart, which introduces an inevitable overhead. Therefore, to supply real-time data services near by the data terminals, fog computing emerges as a promising solution. However, constrained physical fog resources and delay-sensitive services hinder the application of new virtualization technologies in the baseband unit (BBU) task allocation management of the fog network. To tackle these challenges, a task allocation framework for hierarchical software-defined fog virtual radio access networks (v-RANs) is proposed in this article. Precisely, we apply an enhanced ant colony optimization (ACO) in combination with a max-min algorithm to efficiently determine the optimal path for BBU task allocation management, while minimizing the transmission time for parallel task execution scheduling. Experimental results demonstrate that the queue delay in our approach is 98.38% and 98.82% lower than the round-robin (RR) algorithm and least connection technique (LCT), respectively.

Impulsive noise is a common impediment in many wireless, power line communication (PLC), and smart grid communication systems that prevents the system from achieving error-free transmission. To overcome the detrimental effects of such impulsive interference, knowledge of impulsive noise parameters is generally required by the available mitigation techniques. This work considers a machine learning perspective for the estimation of the impulsive noise parameters in communication systems under the influence of Middleton class-A noise. Precisely, we consider a deep learning approach and design a deep neural network (DNN) that classifies a set of received symbols according to the parameters of the impulsive noise affecting them. It is sown that the classification accuracy greatly depends on the number of symbols fed into the neural network as well as the number of considered states in the classification, where the proposed approach can reach a testing accuracy of more than 99%.

In this paper, we consider full duplex amplify and forward (AF) relay networks in vehicle-to-vehicle (V2V) applications. In this context, taking into account the self-interference (SI) at the relay and assuming independent and not necessarily identically distributed (i.n.i.d) generalized L-Nakagami-m fading channels, we derive novel expressions for the probability density function (PDF) and cumulative distribution function (CDF) of the signal-to-interference-plus-noise ratio at the relay. Capitalizing on this, a lower bound to the end-to-end outage probability is derived. Monte-Carlo simulation results are presented to corroborate the derived analytical results. Our results show that the channel cascading significantly impacts the end-to-end outage probability of full duplex (FD) relaying V2V systems, where the cascading of the source-relay link is shown to be more significant than that of the relay-destination link.

In-phase/quadrature-phase Imbalance (IQI) is considered a major performance-limiting impairment in direct-conversion transceivers. Its effects become even more pronounced at higher carrier frequencies such as the millimeter-wave frequency bands considered for 5G systems. In this work, we quantify the effects of IQI on the performance of different modulations under multipath fading channels. This is realized by developing a comprehensive framework for the symbol error rate (SER) analysis of coherent phase shift keying (PSK), noncoherent differential phase shift keying (DPSK) and noncoherent frequency shift keying (FSK) under IQI effects. In this context, the moment generating function of the signal-to-interference-plus-noise-ratio is first derived for single-carrier systems suffering from transmitter (TX) IQI only, receiver (RX) IQI only and joint TX/RX IQI. Capitalizing on this, we derive analytic expressions for the SER of the different modulation schemes considered. These expressions are corroborated with simulation results and they provide insights into the dependence of IQI on the system parameters. We further demonstrate that, while in some cases, IQI can cause a slight degradation of the SER performance and, hence, it can be neglected, in other cases it should be compensated in order to achieve a reliable communication link.

Non-orthogonal multiple access (NOMA) has been recently proposed as a viable technology that has the potential to improve the spectral efficiency of fifth generation (5G) wireless networks and beyond. However, in practical communication scenarios, transceiver architectures inevitably suffer from radio-frequency (RF) front-end related impairments that can lead to non-negligible degradation of the overall system performance. In this context, in-phase/quadrature-phase imbalance (IQI) constitutes a major impairment in direct-conversion transceivers. Based on this, the present contribution quantifies the effects of IQI on the performance of NOMA based systems under multipath fading conditions. This is realized by first deriving novel analytic expressions for the signal-to-interference-plus-noise ratio and the outage probability of NOMA systems subject to IQI at the transmitter and/or the receiver sites. Capitalizing on these results, we demonstrate that the effects of IQI differ considerably between the different NOMA users and depending on the considered system's parameters.

Coherent detection requires exact knowledge of the channel state information, which is often a challenging task in demanding practical applications. Based on this, non-coherent detection of differentially modulated signals can be considered as an alternative method. The present paper investigates the effects of in-phase/quadrature-phase imbalance (IQI), which are known to degrade the performance of wireless communication systems. Specifically, we evaluate the effects of IQI on the bit error rate (BER) performance of differential quadrature phase shift keying (dQPSK) for ideal receiver (RX) with transmitter (TX) IQI, ideal TX with RX IQI and joint TX/RX IQI. Explicit analytic expressions are derived for the BER of both single-carrier and multi-carrier systems suffering from IQI at the TX and/or RX. Extensive Monte-Carlo simulation as well as offered analytic results show that realistic TX/RX IQI values can degrade the corresponding BER by over 30%. Likewise, it is shown that the detrimental effects of IQI are more considerable on DQPSK than on QPSK.

The analysis of composite fading channels, which are typically encountered in wireless channels due to multipath and shadowing is quite involved, as the underlying fading distributions do not lend themselves to analysis. An example of such channels are the Nakagami/Rayleigh-Lognormal fading channels. Several simplified expressions have been proposed in the literature. In this paper, a generalized fading model for composite and non-composite fading models, based on the so-called Mixture of Gaussians (MoG) distribution, is proposed. The well-known expectation-maximization algorithm is utilized to estimate the parameters of the MoG model. Furthermore, relying on the proposed MoG model, we derive closed form expressions for several performance metrics used in wireless communication systems, including the raw moments, the amount of fading, the outage probability, the average channel capacity, and the moment generating function. In addition, the symbol error rate of L-branch maximum ratio combining diversity receiver is studied for linear coherent signaling schemes. Monte Carlo simulations are presented to corroborate the analytical results and to assess the accuracy of the MoG model.

Pervasive healthcare systems, enabled by information and communication technology (ICT), can allow the elderly and chronically ill to stay at home while being constantly monitored. This work aims to present a framework for healthcare monitoring systems based on heterogeneous sensors. In addition to video cameras, the system makes use of a variety of heterogeneous sensors that allow adequate monitoring of each patient by fusing the data from the different sensors. The system described in this paper can be deployed into patients' homes sparing the healthcare costs of living in a health facility where constant monitoring by professionals is required and allowing the elder to live in his home for as long as possible. It can also be deployed in a hospital or a silver-care town environment to cut off the number of healthcare personnel needed, improve the monitoring of patients, and set alarms in case of critical situations.