The emergence of 6G-enabled Vehicle-to-Everything (V2X) networks has created unprecedented demand for ultra-reliable, low-latency spectrum allocation across heterogeneous entities including vehicles, IoT devices, and industrial systems. Current spectrum allocation methods suffer from exponential computational complexity, extensive information sharing requirements, and poor scalability in dense networks. This paper proposes AMADRL (Attention-based Multi-Agent Deep Reinforcement Learning), a novel framework employing dual critic networks with multi-head self-attention mechanisms for intelligent spectrum allocation. The dual critic architecture resolves individual-collective optimization conflicts through local critics for independent entity optimization and a global critic with attention-based coordination. Our approach significantly reduces information sharing requirements while handling heterogeneous QoS demands across diverse entity types. Comprehensive experimental evaluation comparing AMADRL against state-of-the-art baselines including MADDPG, MAAC, QMIX, attention-based methods (A-DDPG, MHA-DQN), and game-theoretic approaches reveals that AMADRL achieves superior performance across multiple metrics including spectrum utilization efficiency, interference mitigation, and network scalability, while preserving user privacy and satisfying strict latency constraints required by safety-critical and industrial use cases.

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have emerged as a promising technology for enabling full-space signal manipulation and enhancing wireless network coverage and capacity. In this article, we present a comprehensive analytical comparison of STAR-RIS-assisted systems with single-input single-output (SISO), conventional RISs, and decode-and-forward (DF) relaying schemes, including both half-duplex (HD) and full-duplex (FD) modes. Closed-form expressions are derived for the achievable secrecy rates of STAR-RIS-aided communications under both the absence and presence of eavesdroppers. Unlike most existing works, the direct source–destination link is incorporated in all considered schemes, and optimal transmit power allocation is investigated for HD- and FD-DF relaying. Furthermore, we provide the conditions under which STAR-RIS outperforms HD- and FD-DF relaying and quantify the minimum number of STAR-RIS elements required to achieve superior rates. The impacts of key system parameters—including transmit power, number of elements, reflection and transmission amplitude coefficients, element-splitting factor, and deployment positions—on both achievable and secrecy performance are investigated. The results reveal that STAR-RIS systems can achieve superior rates and secrecy rates compared to all benchmark schemes.

Unmanned aerial vehicles (UAV)-mounted simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) systems can provide full-dimensional coverage and flexible deployment opportunities in future 6G-enabled IoT networks. However, practical imperfections such as jittering and airflow of UAV could affect the phase shift of STAR-RIS, and consequently degrade network security. In this respect, this paper investigates the impact of phase shift errors on the secrecy performance of UAV-mounted STAR-RIS-assisted IoT systems. More specifically, we consider a UAV-mounted STAR-RIS-assisted non-orthogonal multiple access (NOMA) system where IoT devices are grouped into two groups: one group on each side of the STAR-RIS. The nodes in each group are considered as potential Malicious nodes for the ones on the other side. By modeling phase estimation errors using a von Mises distribution, an analytical closed-form expressions for the ergodic secrecy rates under imperfect phase adjustment are derived. An optimization problem to maximize the weighted sum secrecy rate (WSSR) by optimizing the UAV placement is formulated and is then solved using a linear grid-based algorithm. Monte Carlo simulations are provided to validate the analytical derivations. The impact of phase estimation errors on system’s secrecy performance is analyzed, providing critical insights for the practical realisation of STAR-RIS deployments for secure UAV-enabled IoT networks.

This study investigates the synergy between ratesplitting multiple access (RSMA) and simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) as a unified framework to realize ubiquitous, intelligent, and resilient connectivity in future sixth-generation networks, while enhancing both spectral and energy efficiency. Specifically, in the STAR-RIS-assisted multi-user RSMA network under consideration, we develop an intelligent optimization strategy that jointly designs the active beamforming at the transmitter, the allocated transmission rate for the common stream, and the passive beamforming vectors for both transmission and reflection regions of the STAR-RIS, while accounting for transceiver hardware impairments and imperfect channel state information (CSI). In addition, system robustness is ensured by incorporating a bounded channel estimation error model that rigorously reflects CSI imperfections and ensures resilience against worst-case estimation errors. To tackle the highly non-convex problem, we propose an intelligent optimization algorithm that decouples the original problem into two sub-problems, which are then solved iteratively. Firstly, the active beamforming vectors for both the common and private signals are obtained by reformulating the original non-convex problem into a tractable convex semi-definite programming (SDP) framework, leveraging successive convex approximation (SCA) and semi-definite relaxation (SDR) for enhanced computational efficiency. Secondly, the passive beamforming vectors for the transmission and reflection regions of the STAR-RIS are optimized through a convex SDP reformulation by exploiting SCA and SDR techniques. Additionally, when the resulting active or passive beamforming solutions are of higher rank, Gaussian randomization is employed to construct rank-one solutions. Finally, the effectiveness of the proposed optimization strategy is demonstrated through numerical simulations, which reveal significant performance gains over benchmark schemes and confirm rapid convergence.

This paper investigates the secure communication capabilities of a non-orthogonal multiple access (NOMA) network supported by a STAR-RIS (simultaneously transmitting and reflecting reconfigurable intelligent surface) deployed on an unmanned aerial vehicle (UAV), in the presence of passive eavesdroppers. The STAR-RIS facilitates concurrent signal reflection and transmission, allowing multiple legitimate users-grouped via NOMA-to be served efficiently, thereby improving spectral utilization. Each user contends with an associated eavesdropper, creating a stringent security scenario. Under Nakagami fading conditions and accounting for phase shift inaccuracies in the STAR-RIS, closed-form expressions for the ergodic secrecy rates of users in both transmission and reflection paths are derived. An optimization framework is then developed to jointly adjust the UAV's positioning and the STAR-RIS power splitting coefficient, aiming to maximize the system's secrecy rate. The proposed approach enhances secure transmission in STAR-RIS-NOMA configurations under realistic hardware constraints and offers valuable guidance for the design of future 6G wireless networks.

Retinopathy of Prematurity (ROP) is a vision-threatening condition in premature infants requiring timely and accurate diagnosis to prevent blindness. While electronic health (eHealth) technologies promise to improve neonatal care, automating ROP diagnosis faces challenges such as limited labeled datasets, architectural complexity, and high computational demands. This study introduces LightEyeNet, a lightweight deep-learning architecture optimized for eHealth applications in ROP severity classification. By integrating DenseNet121 and a channel-wise residual attention network block, LightEyeNet enhances diagnostic accuracy and efficiency. Explainable AI techniques, including Grad-CAM and LIME, further improve transparency and clinical interpretability. LightEyeNet achieves 96.28% testing accuracy, outperforming state-of-the-art pre-trained networks, including DenseNet201 (95.78%, + 0.5%), Inception-V3 (93.80%, + 2.48%), Xception (94.54%, + 1.74%), and EfficientDense (87.10%, + 9.18%). Furthermore, LightEyeNet is the most compact architecture among these, with a size of 2.45 MB, compared to Efficient-Dense (6.88 MB), Inception-V3 (3.56 MB), Xception (21.48 MB), and DenseNet201 (5.05 MB). With a specificity of 0.99, a sensitivity of 0.95, and an AUC score of 0.99 across five ROP severity classes, LightEyeNet demonstrates a balance of superior performance and efficiency.

Efficient UAV navigation in large-scale six-generation (6G)-enabled green internet of things (GIoT) systems presents significant challenges due to stringent energy constraints, dynamic network conditions, and the need for robust connectivity and high coverage. Existing methods often neglect critical factors such as real-time adaptability to network variations, scalable multi-agent coordination, and integrated communication-energy optimization. To address these gaps, this paper proposes a novel multi-agent UAV path-planning framework based on the proximal policy optimization (PPO) algorithm, explicitly designed for connectivity-aware navigation. The approach integrates simultaneous wireless information and power transfer (SWIPT)-based energy harvesting, dynamic obstacle avoidance, and communication-driven reward shaping to enable sustainable, efficient UAV operations. Extensive simulations in a custom Gymnasium environment, modeled on precision agriculture, validate the framework. Experimental results with three UAV agents demonstrate 100% mission success, up to 0.0096 J/bit energy efficiency, average communication latency below 11 ms, and improved coverage with zero collisions for two agents. Compared to classical planners (A*, Dijkstra) and baseline PPO methods, the proposed model achieves a 15% higher cumulative reward and superior energy-latency trade-offs. Fully decentralized decision-making further enables scalable, practical deployment. This framework enables real-time UAV coordination by integrating 6G communication, energy, and dynamic environment adaptation for Green IoT.

Simultaneous wireless information and power transfer (SWIPT) is a promising technology to prolong the lifetime of energy-constrained networks by enabling wireless nodes to harvest man-made RF signals. Unlike all studies in the open literature, this paper investigates the performance of a dual-hop full-duplex decode-and-forward relay network over$\alpha$–Lomax fading channels. The relay node is energy-constrained and relies entirely on harvesting energy from the signal transmitted by the source. The system performance is evaluated in terms of ergodic outage probability. In this regard, the cumulative distribution function of the product of two$\alpha$–Lomax random variables (RVs) is derived. This was then employed to derive a closed-form analytical expression for the ergodic outage probability. Monte-Carlo simulations are also provided throughout to verify the accuracy of the analysis. The derived expression is used to investigate the impact of various system parameters on the performance. The results indicated that a good selection of the energy harvesting time is crucial to minimize the ergodic outage probability.

In order to bolster future wireless networks, there has been a great deal of interest in non-terrestrial networks, especially aerial platforms including high-altitude platform stations (HAPS) and uncrewed aerial vehicles (UAVs). These platforms can integrate advanced technologies such as reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA). In this regard, this paper proposes a multi-layer network architecture consisting of HAPS and UAV, where the former acts as a HAPS super macro base station (HAPS-SMBS), while the latter serves as a relay node for the ground Internet of Things (IoT) devices. The UAV is equipped with active transmissive RIS, which is a novel technology with promising benefits. We also utilize multiple-input single-output (MISO) technology, i.e., multiple antennas at the HAPS-SMBS and a single antenna at the IoT devices. Additionally, we consider NOMA as the multiple access technology as well as the existence of hardware impairments as a practical limitation. We compare the proposed system model with various scenarios, all involving the HAPS-SMBS and RIS-equipped UAV relay combination, but with different types of RIS, antenna configurations, and access technologies. Sum rate and energy efficiency are used as performance metrics, and the findings demonstrate that, in comparison to all benchmarks, the proposed system yields significant performance gains. Moreover, hardware impairment limits the system performance at high transmit power levels.

The convergence of Intelligent Reflecting Surfaces (IRS) and Terahertz (THz) communications represents a transformative advancement for sixth-generation (6G) wireless networks, yet presents unprecedented challenges in system optimization. This paper addresses the critical challenge of joint optimization between IRS phase shifts and THz resource allocation in dynamic Internet of Things (IoT) environments, focusing on real-time adaptation to rapidly changing channel conditions. We propose a novel Adaptive Online Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework that leverages dynamic experience weighting to automatically adjust learning based on detected environmental changes. Our approach incorporates a multi-resolution buffer structure that balances recent observations with historical patterns, enabling both rapid adaptation and long-term optimization while considering the unique characteristics of THz-band propagation and IRS reflection patterns. The framework employs explicit coordination protocols between IRS controllers and resource managers, significantly improving convergence in non-stationary environments. Comprehensive simulations using realistic THz channel models and practical IRS configurations demonstrate that our proposed framework achieves a 45% improvement in system throughput, a 38% reduction in end-to-end latency, and a 30% enhancement in energy efficiency compared to conventional optimization approaches. More significantly, our solution demonstrates unprecedented adaptation capabilities, recovering 90% of optimal performance within 5 ms after abrupt environmental changes a critical requirement for future 6G networks. The framework maintains robust performance under diverse conditions, including high user mobility scenarios and adverse atmospheric conditions, while exhibiting linear computational scaling with increasing IRS elements (tested up to 512 elements). These results establish the viability of Adaptive Online MADDPG-based joint IRS-THz optimization for practical 6G deployments, particularly in dynamic IoT environments where traditional communication approaches face significant limitations.

Low Earth orbiting (LEO) satellite-assisted wireless communication is increasingly vital for future communication networks due to the significant spectrum scarcity in radio frequency channels, presenting a critical bottleneck. Thus, optimizing the utilization of available radio frequency spectrum has become imperative. Advanced techniques like underlay communication and Rate Split Multiple Access (RSMA) have proven effective in enhancing spectrum utilization. When LEO satellites are applied to tasks such as agricultural assistance, search and rescue operations, and military defense, LEO-to-ground communication can leverage underlay fashion using RSMA to transmit messages to multiple users simultaneously on the same channel. However, conventional underlay communication setups necessitate transmitter cooperation to manage system interference. Enabling non-cooperative systems to communicate in an underlay fashion unlocks the untapped potential of these advanced transmission techniques. This study addresses the challenge of maximizing the RSMA rate of the LEO-to-ground communication system (secondary system) operating in an underlay mode without cooperation with the ground-to-ground communication system (primary system), where the primary network operates in a time-division multiple-access fashion. We propose a dueling-based double deep Q-learning solution to optimize the allowed transmission power at the LEO satellite, ensuring no outage in the primary system. Additionally, we introduce an optimal solution framework to distribute the allowed transmission power among all signals of the secondary devices, maximizing the RSMA rate while meeting the rate requirements of all underlay secondary devices. Simulation results demonstrate that this hybrid solution framework provides excellent performance while ensuring no outage at the primary network.

A cascaded multi-intelligent reflecting surface (IRS)-assisted signed quadrature spatial modulation (SQSM) system is proposed in this paper with intelligently integrating single and multiple IRS into the SQSM scheme. In this system, the signal is transmitted via the reflection of single or cascaded multiple IRSs. The proposed system models, however, benefit from the spatial multiplexing gain and the signal-to-noise ratio (SNR) gain of SQSM and IRSs, respectively. The bit error rate (BER) performance of the proposed systems is evaluated and compared to the performance of the conventional SQSM system without IRS for the sake of fair comparison. The simulation results show that a considerable performance gain is achieved by the proposed cascaded multi-IRS-assisted SQSM system over the conventional SQSM scheme, and this gain increases with increasing the number of cascaded IRSs and the number of their passive elements. The close match is also noticed between the analytical and simulated BER performances of the proposed systems with high SNR values.

Reconfigurable intelligent surface (RIS)-assisted generalized spatial modulation (GSM) systems have recently been regarded as prospective candidates for the upcoming 6G wireless networks. This is primarily because GSM scheme utilizes the indices of the active antennas to convey extra spatial symbols, and RIS can intelligently adjust the wireless propagation environment, leading to a significant improvement in both network coverage and energy efficiency. This paper explores the potential of integrating multiple distributed RISs into GSM scheme and proposes a norm-based RIS selection algorithm to aid the transmission between the transmitter and receiver by selecting a number of RISs with the largest channel norm values. Simulation results demonstrate that the proposed multi-RIS-assisted GSM system with norm-based RIS selection algorithm exhibits superior bit error rate (BER) performance compared to conventional GSM system and multi-RIS-assisted GSM system without RIS selection. Moreover, the proposed system achieves higher power efficiency compared to multi-RIS-assisted GSM system without the selection strategy.

Emerging technologies such as artificial intelligence and big data have made numerous Internet of things (IoT) applications possible. In particular, the Artificial Intelligence of Things (AIoT) has the potential to promote the digitization and intelligent connection of all things. However, the openness and diversity of AIoT makes data information vulnerable to security attacks which can lead to a disruption of mobile communication networks. The complexity of real-time data security events requires accurate prediction of AIoT security performance. In this paper, a secure communication system model based on decode-and-forward (DF) relaying is proposed and its security performance is analyzed. Expressions for the secrecy outage probability (SOP) are derived, and these are used to evaluate the security performance. For this purpose, an intelligent SOP prediction algorithm based on MS-Net is proposed. MobileNet and SqueezeNet networks are used to design an improved lightweight MS-Net model, which is composed of a depth separable convolution block and a fire module in parallel. The fire module is used to reduce the number of parameters in the first branch, and the depth-separable convolution block is employed in the second branch instead of the standard convolution. This can adapt to nonlinear characteristic in the AIoT safety data and reduce energy consumption. Afterwards, the convolutional block attention module(CBAM) attention mechanism is used to improve the model’s ability to capture features. The proposed algorithm provides better AIoT security performance than other algorithms. In particular, the mean squared error (MSE) is 68.1% better than that of RegNet.

Internet of Medical Things (IoMT) has surfaced as the emerging era of the Internet of Things (IoT), drawing the attention of researchers given its broad operations in Smart Healthcare Systems (SHS) [1]. Since it is extremely risky for an individual to communicate with doctors in the hospital for every minor issue in the present pandemic scenario, we may check our day-to-day health records using IoMT devices and take precautionary measures on our own. In order to improve the delicacy, thickness, and outturn of electronic outfits, IoMT is essential to the healthcare sectors [2], [3].

Reconfigurability and innovative design approaches to radio frequency components and network infrastructure are critical for the development of future communication networks, particularly beyond 5G (B5G), which aim to support the proliferation of Internet of Things (IoT) devices. Leveraging its favorable performance characteristics and potentially low cost, the fluid antenna system (FAS) has emerged as a compelling solution, garnering significant interest due to its reconfigurability, small form factor, flexibility, and transparency. This paper presents a comprehensive analysis of FAS in the context of B5G networks, with a focus on its theoretical performance and practical implementations. By deriving formulas for the semi-infinite outage probability and ergodic capacity of FAS receivers in equally correlated Nakagami-m channels, we showcase the remarkable diversity performance exhibited by FAS receivers, even with a half-wavelength antenna size. Monte Carlo simulations are employed to validate our theoretical findings in terms of the number of antenna ports and transmission power.

Emerging Internet of Things (IoT) applications, such as sensor-based Human Activity Recognition (HAR) systems, require efficient machine learning solutions due to their resource-constrained nature which raises the need to design heterogeneous model architectures. Federated Learning (FL) has been used to train distributed deep learning models. However, standard federated learning (fedAvg) does not allow the training of heterogeneous models. Our work addresses the model and statistical heterogeneities of distributed HAR systems. We propose a Federated Learning via Augmented Knowledge Distillation (FedAKD) algorithm for heterogeneous HAR systems and evaluate it on a self-collected sensor-based HAR dataset. Then, Kullback-Leibler (KL) divergence loss is compared with Mean Squared Error (MSE) loss for the Knowledge Distillation (KD) mechanism. Our experiments demonstrate that MSE contributes to a better KD loss than KL. Experiments show that FedAKD is communication-efficient compared with model-dependent FL algorithms and outperforms other KD-based FL methods under the i.i.d. and non-i.i.d. scenarios.

Millimeter-wave networks have already been successfully rolled out in many countries and now the research direction heads toward new technologies and standards to enable Tbps rates for future sixth-generation (6G) wireless communication systems. This work studies a point-to-point terahertz (THz) communication network exploiting the concept of a fluid antenna system (FAS) over correlated alpha-mu fading channels, nicely fitting the THz communication. Furthermore, the considered system is expanded to the selection-combining-FAS (SC-FAS) and maximum-gain-combining-FAS (MGC-FAS) diversity variates at the receiver side. The proposed FAS and its diversity configuration techniques are aimed to combat the high path loss, blockages, and molecular absorption effect related to the THz band. Our contribution includes comprehensive outage probability (OP) performance analysis for the THz band given the non-diversity and diversity FAS receivers. Moreover, the derived outage probability formulas are verified via Monte Carlo simulations. Numerical results have confirmed the superior performance of the MGC-FAS scheme in terms of OP. Finally, this work justifies that a higher number of antenna ports dramatically improves the system performance, even in the presence of correlation.

The study aims to leverage the vending machine's historic product sales data to predict future demand using machine learning (ML) techniques with the goal of strategic inventory planning, reducing stock waste, and increasing operational efficiency and revenue. The study deploys different ML algorithms including XGBoost, FB Prophet, Autoregressive Integrated Moving Average and Support Vector Regression. This is among the first studies of its kind to explore ML for predictive restocking in the vending industry. The experiment is divided into two parts. In the first part, we used common historic sales data variables to make the prediction where XGBoost emerged as the best-performing model in terms of the mean absolute error. In the second part, we used additional features such as public holidays, day of the week, and sales deviation flags to make the prediction. Using additional variables slightly improved the performance of the ML algorithms with XGBoost having the lowest mean absolute error of 8. Overall, the study demonstrates the efficacy of utilising ML techniques and additional features in accurately predicting future product demand for vending machines, thus enabling strategic inventory planning, minimised stock waste, improved operational efficiency and increased revenue for the vending industry.

In the constantly developing realm of the Internet of Things (IoT), guaranteeing fast data transfer and a smooth user experience is critical. In IoT contexts with limited resources, congestion control is crucial for sustaining network performance. This study suggests a new strategy for improving congestion control by deploying Long Short-Term Memory (LSTM) networks. LSTMs are recurrent neural networks (RNN), that excel at capturing temporal relationships and patterns in data. IoT-specific data such as network traffic patterns, device interactions, and congestion occurrences are gathered and analyzed. The gathered data is used to create and train an LSTM network architecture specific to the IoT environment. Then, the LSTM model’s predictive skills are incorporated into the congestion control methods. This work intends to optimize congestion management methods using LSTM networks, which results in increased user satisfaction and dependable IoT connectivity. Utilizing metrics like throughput, latency, packet loss, and user satisfaction, the success of the suggested strategy is evaluated. Evaluation of performance includes rigorous testing and comparison to conventional congestion control methods. The findings illustrate the concrete advantages of LSTM-enhanced congestion control in IoT, highlighting its potential to reduce network congestion and improve the overall user experience.

An intelligent reflecting surface (IRS)-assisted millimeter-wave (mmWave) massive multiple input multiple output (MIMO) system with transmit antenna selection (TAS) using orthogonal space-time block codes (OSTBC) scheme is proposed in this paper. This system combines TAS and IRS with hybrid analog-digital beamforming (HBF) for 60 GHz mmWave communications in order to exploit the benefits of TAS, OSTBC, analog beamforming (ABF), and transmit digital precoding techniques. The proposed system, however, benefits from the transmit diversity gain of OSTBC scheme as well as from the signal-to-noise ratio (SNR) gains of both the beamformer and the IRS technology. The simulation results show that TAS-OSTBC system with zero-forcing precoding outperforms the conventional TAS-OSTBC scheme. Furthermore, the bit error rate (BER) performance significantly improves as the number of antenna array elements increases due to providing a beamforming gain. In addition, increasing the number of reflecting elements further enhances the BER performance. It is also found from the simulation results that the TAS-OSTBC system with hybrid precoding has better performance than that of TAS-OSTBC with ABF, and IRS-assisted systems significantly outperform the conventional systems without the IRS technology. This makes the proposed IRS-assisted system an appealing solution for internet-of-things (IoT) networks.

Towards a future of fully autonomous and intelligent systems, sixth-generation (6G) wireless communication networks are expected to transform client services and apps via the Internet of Things (IoT). Together, the infrastructure and apps should facilitate and enhance intelligent administration of city services, workplaces, and everyday lives. In particular, mission-critical IoT (MC IoT), which is the connection of critical objects to the IoT network that are critical to human life and safety, is of utmost significance. This paper covers MC IoT visions, services, and applications, as well as networking infrastructure. First, visions toward 6G MC IoT are presented in the context of fundamental technological needs, trends, and use cases. Second, the emerging 6G MC IoT services and applications are described. Finally, a networking architecture for 6G MC IoT is provided. There are challenges to the implementation of robust and effective 6G MC-IoT services and applications which are mentioned and future research directions are presented.

Smart cities intelligently control the functionalities of the city through the use of various electronic methods, sensors, and advanced communication techniques. An intelligent transport system (ITS) is the backbone of smart cities and refers to a system in which numerous vehicles utilize a communication infrastructure to exchange vital information, such as traffic, congestion, and road conditions. One of the key elements of ITS is vehicle tracking or localization, which is the monitoring of a moving vehicle’s location using a Global Positioning System (GPS). Accurate vehicle localization is required because many safety and traffic management applications depend on the precise positioning of the vehicles. Intelligent reflecting surface (IRS) is a new technology that offers attractive features like improved performance gains, enhanced network coverage, and flexible deployment, and is considered a key enabler for the 6G-driven vehicle-to-everything (V2X) systems that provide a programmable wireless environment. In this paper, a comprehensive view of a 6G-driven vehicle localization mechanism utilizing IRS is provided. The work also lists the benefits of IRS-enabled sensing in 6G vehicular networks including enhanced security, overcoming blockages, and improved localization. A case study to highlight the advantages of the IRS in vehicle tracking is presented. Finally, we also present the research challenges and future work directions in IRS-enabled vehicle tracking.

5G enabled maritime unmanned aerial vehicle (UAV) communication is one of the important applications of 5G wireless network which requires minimum latency and higher reliability to support mission-critical applications. Therefore, lossless reliable communication with a high data rate is the key requirement in modern wireless communication systems. These all factors highly depend upon channel conditions. In this work, a channel model is proposed for air-to-surface link exploiting millimeter wave (mmWave) for 5G enabled maritime unmanned aerial vehicle (UAV) communication. Firstly, we will present the formulated channel estimation method which directly aims to adopt channel state information (CSI) of mmWave from the channel model inculcated by UAV operating within the Long Short Term Memory (LSTM)-Distributed Conditional generative adversarial network (DCGAN) i.e. (LSTM-DCGAN) for each beamforming direction. Secondly, to enhance the applications for the proposed trained channel model for the spatial domain, we have designed an LSTM-DCGAN based UAV network, where each one will learn mmWave CSI for all the distributions. Lastly, we have categorized the most favorable LSTM-DCGAN training method and emanated certain conditions for our UAV network to increase the channel model learning rate. Simulation results have shown that the proposed LSTM-DCGAN based network is vigorous to the error generated through local training. A detailed comparison has been done with the other available state-of-the-art CGAN network architectures i.e. stand-alone CGAN (without CSI sharing), Simple CGAN (with CSI sharing), multi-discriminator CGAN, federated learning CGAN and DCGAN. Simulation results have shown that the proposed LSTM-DCGAN structure demonstrates higher accuracy during the learning process and attained more data rate for downlink transmission as compared to the previous state of artworks.

Non-orthogonal multiple access (NOMA) and ambient backscatter communication (AmBC) play major roles to enhance spectrum efficiency in wireless communication systems. Besides, the AmBC provides good reinforcement for the current trend towards dispensing batteries for battery-free Internet-of-Things (IoT) devices. In this paper, we propose a symbiotic battery-free IoT system, that exploits the downlink transmission of a NOMA multiplexing enabled cellular network, to permit an IoT spectrum-efficient uplink communication. The IoT backscatter device (BD) performs a symbiotic radio (SR) relation with the cellular source to power its communication by intelligently reflecting the received power. We derive a closed-form expression of the ergodic capacity (EC) of the BD transmission and tight approximations of the ECs of the cellular source transmission, where all channels undergo Nakagami-m fading. Additionally, we validate the analytical results obtained using Monte-Carlo simulations. The influences of several system parameters such as power allocation factor, reflection coefficient, and channels’ severity factors have been investigated. Finally, the performance of the proposed system is compared with a benchmark OMA-based system to highlight the achievable performance improvement.

This paper reports on findings on the analysis of the fast Fourier transform (FFT) hybrid power line communication (PLC) - visible light communication (VLC) system (PLC- VLC) based on ACO-OFDM in impulsive noise, when the perfect nulling technique is utilized to combat noise in the system. In this work, we develop theoretical expressions that describe the error floors realized in the system due to perfect nulling. Perfect nulling refers to a noise mitigation technique that perfectly identifies impulsive noise locations in a string of data samples and clips them to zero, leading to an error floor at a high Signal to Noise (SNR) ratio. Thus, the principal contribution of this paper is to analyze and derive theoretical Bit Error Rate (BER) expressions for the error floor achieved by a BPSK OFDM signal in the FFT hybrid PLC-VLC system based on asymmetrically clipped optical - orthogonal frequency division multiplexing (ACO-OFDM), in a channel corrupted with Added White Gaussian Noise (AWGN) and impulsive noise. The derived theoretical BER results will be compared to the error floors obtained through simulations.

Communications over millimeter-wave (mmWave) frequencies is a key technology for the fifth generation (5G) cellular networks due to the large bandwidth available at mmWave bands. The short wavelength of mmWave bands enables large antenna arrays to be placed on the transceivers which forms massive multiple-input multiple-output (MIMO). Massive MIMO with conventional fully-digital (FD) beamforming is difficult to be implemented due to high power consumption and hardware cost. One of the most effective solutions to this problem is hybrid beamforming which can be used to balance the beamforming gain, hardware implementation cost, and the power consumption. However, due to the non-convex constraints imposed by phase shifters, finding the global optima for the hybrid beamforming system is very challenging with high computational complexity. To address this issue, deep learning (DL)-based hybrid precoding with geometric mean decomposition (GMD) algorithm for narrowband mmWave massive MIMO system is proposed in this paper, where it can directly estimate the hybrid analog and digital precoders (combiners) from a given optimal FD precoder (combiner). Simulation results demonstrated that the proposed hybrid precoding model can more accurately approximate the FD precoding performance.

Intelligent reflecting surface (IRS)-assisted beamspace millimeter-wave (mmWave) multiuser massive multiple input multiple output (MIMO) with interference-aware (IA) beam selection scheme is proposed in this paper. This proposed scheme is capable of intelligently reconfiguring the radio environment and utilizing the beam selection for the sake of reducing the number of required radio frequency $( R$F) chains without any noticeable performance degradation. To ensure a fair comparison, the achievable sum-rate and energy efficiency (EE) performance metrics of the proposed scheme are evaluated and compared to that of IRS-assisted fully-digital systems with zero-forcing $(Z$F) precoding and the conventional systems without the IRS technology. Simulation results demonstrate that the proposed IRS-assisted beamspace mmWave massive MIMO system with IA beam selection algorithm outperforms the conventional system without IRS. It is also shown that the performance improves when the number of reflecting elements is more than the total number of mobile users. Moreover, the proposed scheme can potentially offer higher EE than the conventional schemes. Therefore, this shows that the proposed system can be considered as an alternative solution for the future generation of wireless systems.

Quantum Annealing (QA) uses quantum fluctuations to search for a global minimum of an optimization-type problem faster than classical computer. To meet the demand for future internet traffic and mitigate the spectrum scarcity, this work presents the QA-aided maximum likelihood (ML) decoder for multi-user non-orthogonal multiple access (NOMA) networks as an alternative to the successive interference cancellation (SIC) method. The practical system parameters such as channel randomness and possible transmit power levels are taken into account for all individual signals of all involved users. The brute force (BF) and SIC signal detection methods are taken as benchmarks in the analysis. The QA-assisted ML decoder results in the same BER performance as the BF method outperforming the SIC technique, but the execution of QA takes more time than BF and SIC. The parallelization technique can be a potential aid to fasten the execution process. This will pave the way to fully realize the potential of QA decoders in NOMA systems.

Vehicle-to-everything (V2X) connectivity in 5G-andbeyond communication networks supports the futuristic intelligent transportation system (ITS) by allowing vehicles to intelligently connect with everything. The advent of reconfigurable intelligent surfaces (RISs) has led to realizing the true potential of V2X communication. In this work, we propose a dual RIS-based vehicle-to-infrastructure (V2I) communication scheme. Following that, the performance of the proposed communication scheme is evaluated in terms of deriving the closed-form expressions for outage probability, spectral efficiency and energy efficiency. Finally, the analytical findings are corroborated with simulations which illustrate the superiority of the RIS-assisted vehicular networks.

The spectrum efficiency can be greatly enhanced by the deployment of machine-to-machine (M2M) communications through cellular networks. Existing resource allocation approaches allocate maximum resource blocks (RBs) for cellular user equipments (CUEs). However, M2M user equipments (MUEs) share the same frequency among themselves within the same tier. This results in generating co-tier interference, which may deteriorate the MUE’s quality-of-service (QoS). To tackle this problem and improve the user experience, in this paper, we propose a novel resource utilization policy, which exploits reinforcement learning (RL) algorithm considering the pointer network (PN). In particular, we design an optimization problem that determines the optimal frequency and power allocation needed to maximize the achievable rate performance of all M2M pairs and CUEs in the network subject to the co-tier interference and QoS constraints. The proposed scheme enables the user equipment (UE) to autonomously select an available channel and optimal power to maximize the network capacity and spectrum efficiency while minimizing co-tier interference. Moreover, the proposed scheme is compared with traditional spectrum allocation schemes. Simulation results demonstrate the superiority of the proposed scheme than that of the traditional schemes. Moreover, the convergence of the proposed scheme is investigated which reduces the computational complexity (CC).

Intelligent reflecting surfaces (IRS)-aided multiuser millimeter-wave (mmWave) massive multiple input multiple output (mMIMO) systems have recently attracted a great interest as an attractive paradigm for the future 6G wireless communications. The main benefits of these systems include achieving a transmission in a cost-effective, energy-efficient and intelligent manner. In this paper, multi-IRS-aided systems are introduced as a promising technique to assist the adaptive cross entropy (ACE)-based hybrid precoding, which is a machine learning (ML) inspired energy-efficient hybrid precoding scheme that requires a small number of low-cost and energy-efficient switches and inverters. Extensive computer simulations have been conducted to evaluate the achievable sum-rate performance and the energy efficiency (EE) of the proposed multi-IRS-assisted mMIMO systems with ACE-based hybrid precoding architecture. For the sake of fair comparisons, the conventional precoding schemes are also evaluated. The simulation results demonstrate the remarkable advantages of the proposed scheme compared with the conventional state-of-the-art schemes. It is also shown that the EE can be further improved as the number of cascaded IRSs and their elements increase. This makes the proposed system a promising candidate for 6G wireless communications.

In this paper, we study a cognitive hybrid satellite-terrestrial relay network with imposed practical limitations, such as channel state information mismatch and transceiver-induced hardware impairments. Moreover, it is assumed that the network undergoes multiple independent and non-identically distributed interference noises arising from neighboring transmitters. Generalized closed-form expressions of the outage probability for a terrestrial user are obtained while taking into account the effect of an interference temperature constraint. Finally, analytical derivations are verified through Monte Carlo simulation and the impact of impairments is examined.

Multidimensional generalized quadrature index modulation scheme is proposed in this paper for conveying extra digital information with the aid of the space, radio frequency (RF) mirrors, and time indices. Explicitly, this proposed scheme cleverly combines another proposed time-indexed generalized quadrature spatial modulation (TI-GQSM) system with media-based modulation (MBM) transmission principle using RF mirrors, and it is referred to as TI-GQSM-MBM scheme. This scheme is attractive because of both the high data rate and the significant performance improvements that can be achieved. The system performance of the proposed schemes in terms of the bit error rate (BER) is evaluated and compared to the performance of the conventional schemes. Simulation results showed that a significant improvement is achieved by the TI-GQSM-MBM scheme as compared to that of TI-GQSM, time-indexed media-based modulation (TI-MBM) and the conventional generalized quadrature spatial modulation (GQSM) schemes for the same rate. It is also demonstrated that the proposed schemes are robust to channel estimation errors (CEEs) as compared to multidimensional generalized spatial modulation (GSM) schemes. Therefore, the proposed schemes can be effectively used as an alternative solution for various 5G and beyond wireless networks.

A novel space-time parallel index modulation scheme is proposed in this paper for conveying extra digital information with the aid of space and time indices. In this proposed scheme, time-indexing is cleverly performed in parallel spatial modulation (PSM) schemes using transmit antenna grouping, and referred to as time-indexed parallel spatial modulation (TI-PSM). In this scheme, transmit antennas are divided into groups to adopt TI-SM scheme in large-scale multiple input multiple output (MIMO) systems with inter-channel interference (ICI) avoidance. This scheme is attractive due to both the high data rate and good performance improvement that can be achieved. The bit error rate (BER) performance of the TI-PSM scheme is evaluated and compared to that of the conventional schemes. Simulation results demonstrated that a significant improvement is achieved by the TI-PSM scheme as compared to the TI-SM and PSM schemes for the same achieved rate. It is also shown that TI-SM and TI-PSM schemes provide higher performance drop with channel estimation errors (CEEs) than the conventional SM and PSM systems. Therefore, due to the high performance improvements achieved in TI-PSM scheme, it can be effectively regarded as a promising solution for various 5G and beyond wireless networks.

This paper investigates a non-orthogonal multiple access (NOMA) assisted cognitive satellite-terrestrial network which is practically limited by interference noises, transceiver hardware impairments, imperfect successive interference cancellation, and channel state information mismatch. Generalized outage probability expressions for NOMA users in both primary and secondary networks are derived considering the impact of interference temperature constraint. Finally, obtained results are corroborated by Monte Carlo simulations and compared with the orthogonal multiple access to show the superior performance of the proposed network model.

Abstract The next generation of mobile networks (5G) is expected to achieve high data rates, reduce latency, as well as improve the spectral and energy efficiency of wireless communication systems. Several technologies are being explored to be used in 5G systems. One of the main promising technologies that is seen to be the enabler of 5G is massive multiple‐input multiple‐output (mMIMO) systems. Numerous studies have indicated the utility of mMIMO in upcoming wireless networks. However, there are several challenges that needs to be unravelled. In this paper, the latest progress of research on challenges in mMIMO systems is tracked, in the context of mutual coupling, antenna selection, pilot contamination and feedback overhead. The results of a systematic mapping study performed on 63 selected primary studies, published between the year 2017 till the second quarter of 2020, are presented. The main objective of this secondary study is to identify the challenges regarding antenna design and channel estimation, give an overview on the state‐of‐the‐art solutions proposed in the literature, and finally, discuss emerging open research issues that need to be considered before the implementation of mMIMO systems in 5G networks.

The physical-layer security (PLS) of wireless networks has witnessed significant attention in next-generation communication systems due to its potential toward enabling protection at the signal level in dense network environments. The growing trends toward smart mobility via sensor-enabled vehicles are transforming today's traffic environment into Internet of Vehicles (IoVs). Enabling PLS for IoVs would be a significant development considering the dense vehicular network environment in the near future. In this context, this article presents a PLS framework for a vehicular network consisting a legitimate receiver and an eavesdropper, both under the effect of interfering vehicles. The double-Rayleigh fading channel is used to capture the effect of mobility within the communication channel. The performance is analyzed in terms of the average secrecy capacity (ASC) and secrecy outage probability (SOP). We present the standard expressions for the ASC and SOP in alternative forms, to facilitate analysis in terms of the respective moment generating function (MGF) and characteristic function of the joint fading and interferer statistics. Closed-form expressions for the MGFs and characteristic functions were obtained and Monte Carlo simulations were provided to validate the results. Approximate expressions for the ASC and SOP were also provided, for easier analysis and insight into the effect of the network parameters. The results attest that the performance of the considered system was affected by the number of interfering vehicles as well as their distances. It was also demonstrated that the system performance closely correlates with the uncertainty in the eavesdropper's vehicle location.

This paper studies an Internet-of-Things (IoT) network employing a reconfigurable intelligent surface (RIS) over generalized fading channels. Inspired by the promising potential of RIS-based transmission, we investigate a RIS-enabled IoT network with the source node employing a RIS-based access point. The system is modelled with reference to a receiver-transmitter pair and the Fisher-Snedecor F model is adopted to analyse the composite fading and shadowing channel. Closed-form expressions are derived for the system with regards to the average capacity, average bit error rate (BER) and outage probability. Monte-Carlo simulations are provided throughout to validate the results. The results investigated and reported in this study extend early results reported in the emerging literature on RIS-enabled technologies and provides a framework for the evaluation of a basic RIS-enabled IoT network over the most common multipath fading channels. The results indicate the clear benefit of employing a RIS-enabled access point, as well as the versatility of the derived expressions in analysing the effects of fading and shadowing on the network. The results further demonstrate that for a RIS-enabled IoT network, there is the need to balance between the cost and benefit of increasing the RIS cells against other parameters such as increasing transmit power, especially at low SNR and/or high to moderate fading/shadowing severity.

This paper studies the physical layer security (PLS) of a vehicular network employing a reconfigurable intelligent surface (RIS). RIS technologies are emerging as an important paradigm for the realisation of smart radio environments, where large numbers of small, low-cost and passive elements, reflect the incident signal with an adjustable phase shift without requiring a dedicated energy source. Inspired by the promising potential of RIS-based transmission, we investigate two vehicular network system models: One with vehicle-to-vehicle communication with the source employing a RIS-based access point, and the other model in the form of a vehicular adhoc network (VANET), with a RIS-based relay deployed on a building. Both models assume the presence of an eavesdropper to investigate the average secrecy capacity of the considered systems. Monte-Carlo simulations are provided throughout to validate the results. The results show that performance of the system in terms of the secrecy capacity is affected by the location of the RIS-relay and the number of RIS cells. The effect of other system parameters such as source power and eavesdropper distances are also studied.

In this paper, we analyze the security capability of single-input multiple-output wireless transmission systems over k-μ shadowed fading channels in the presence of multiple eavesdroppers. Our security analysis relies on an important standard, i.e., average secrecy capacity which is more difficult and suitable for analyzing active eavesdropping scenario than secure outage probability and probability of strictly positive secrecy capacity. The novel expression of average secrecy capacity over k-μ shadowed fading channels with multiple eavesdroppers is deduced. The results of Monte Carlo simulation fully prove the correctness of our theoretical derivation. Through the obtained results, we observe that large antenna quantity in the highest signal-to-noise ratio regime, small number of the eavesdroppers, and small signal-to-noise-ratio of eavesdropping link will enhance confidentiality of the system under consideration.

In this study, the authors propose a hybrid power line communication (PLC)/visible light communication (VLC)/radio frequency (RF) fronthaul with a fibre‐based wired backhaul system to support massive number of smart devices (SDs). Since a signal‐to‐noise ratio‐based access point (AP) association and bandwidth (BW) allocation for each SD do not necessarily improve the system capacity, they propose novel and efficient AP association and BW allocation strategies to maximise the sum rate capacity (SRC) of the hybrid system under consideration. An optimisation problem is formulated for the SRC with the AP association and BW allocation as the optimisation parameters and a hierarchical decomposition method is used to convert the non‐linear optimisation problem into a set of convex optimisation problems. Then, the proposed strategies are used to solve the optimisation problem in an iterative manner until the SRC converges to an optimal value. Further, an analytical approximation for the BW allocated to each SD for a given AP association is derived using the Lagrangian multiplier method. The performance of the proposed system is evaluated through extensive numerical results. Moreover, the effect of the increased number of SDs on the optimal SRC is analysed.

This paper studies the physical layer security of a vehicular communication network in the presence of interference constraints by analysing its secrecy capacity. The system considers a legitimate receiver node and an eavesdropper node, within a shared network, both under the effect of interference from other users. The double-Rayleigh fading channel is used to capture the effects of the wireless communication channel for the vehicular network. We present the standard logarithmic expression for the system capacity in an alternate form, to facilitate analysis in terms of the joint moment generating functions (MGF) of the random variables representing the channel fading and interference. Closed-form expressions for the MGFs are obtained and Monte-Carlo simulations are provided throughout to validate the results. The results show that performance of the system in terms of the secrecy capacity is affected by the number of interferers and their distances. The results further demonstrate the effect of the uncertainty in eavesdropper location on the analysis.

Orthogonal frequency-division multiplexing (OFDM) is a popular multi-carrier technique used in many digital communication systems such as wireless fidelity (Wi-Fi), long term evolution (LTE) and power line communication systems. It can be designed using fast Fourier transform (FFT) or wavelet transform (WT). The major drawback in using WT is that it is computationally inefficient. In this study, we introduce a simple and computationally efficient WT, harmonic wavelet transform, for OFDM signal processing. The new WT uses the orthogonal basis functions of conventional FFT-OFDM except that it involves translation and dilation of the input signal; the new wavelets is referred to as harmonic wavelets (HW). When compared with pilot-assisted OFDM system in terms of reduction in the peak-to-average power ratio, the results show that HW-OFDM outperforms FFT-OFDM by 3 dB at 10-4CCDF (complementary cumulative distribution function). Over Rayleigh fading channel with additive white Gaussian noise (AWGN), the bit error ratio of both FFT-OFDM and HW-OFDM perfectly matched, showing that the proposed HW-OFDM is better in terms of peak-to-average power ratio reduction.

This paper investigates the secure performance of multi‐antenna decode‐and‐forward (DF) relaying networks where the Nakagami‐ m fading channel is taken into account. In practice, the joint impact of residual transceiver hardware impairments (HIs) and channel estimation errors (CEEs) on the outage probability (OP) and intercept probability (IP) are taken into account. Considering HIs and CEEs, an optimal transmit antenna selection scheme is proposed to enhance the secure performance and then a collaborative eavesdropping scheme is proposed. More specifically, they derive exact closed‐form expressions for the outage and intercept probabilities. To obtain more useful insights the asymptotic behaviours for the OP are examined in the high signal‐to‐noise ratio (SNR) regime and the diversity orders are obtained and discussed. Simulation results confirm the analytical derivations and demonstrate that: (i) As the power distribution coefficient increases, OP decreases, while IP increases; (ii) There exist error floors for the OP at high SNRs, which is determined by CEEs; (iii) The secure performance can be improved by increasing the number of source antennas and artificial noise quantisation coefficient, while as the number of eavesdropping increases, the security of the system is reduced; (iv) There is a trade‐off between the OP and IP.

The rapid growth of Internet-of-Things (IoT) in the current decade has led to the development of a multitude of new access technologies targeted at low-power, wide area networks (LP-WANs). However, this has also created another challenge pertaining to technology selection. This paper reviews the performance of LP-WAN technologies for IoT, including design choices and their implications. We consider Sigfox, LoRaWAN, WavIoT, random phase multiple access (RPMA), narrowband IoT (NB-IoT), as well as LTE-M and assess their performance in terms of signal propagation, coverage and energy conservation. The comparative analyses presented in this paper are based on available data sheets and simulation results. A sensitivity analysis is also conducted to evaluate network performance in response to variations in system design parameters. Results show that each of RPMA, NB-IoT, and LTE-M incurs at least 9 dB additional path loss relative to Sigfox and LoRaWAN. This paper further reveals that with a 10% improvement in receiver sensitivity, NB-IoT 882 MHz and LoRaWAN can increase coverage by up to 398% and 142%, respectively, without adverse effects on the energy requirements. Finally, extreme weather conditions can significantly reduce the active network life of LP-WANs. In particular, the results indicate that operating an IoT device in a temperature of -20 °C can shorten its life by about half; 53% (WavIoT, LoRaWAN, Sigfox, NB-IoT, and RPMA) and 48% in LTE-M compared with environmental temperature of 40 °C.

Reliable data transmissions and offering better mobility to the end user can be achieved by integrating different communication systems. In this paper, we investigate the performance of a cascaded indoor power line communication (PLC)/visible light communication (VLC) system with the presence of an amplify-and-forward (AF) relay. Using the pre-installed infrastructure of electricity wiring networks gives the advantage to use PLC as a backbone for VLCs. The performance of the proposed hybrid system is discussed in terms of the average capacity. A mathematical method is developed for this network to formulate the capacity by exploiting the statistical properties of both the PLC and VLC channels. The derived analytical expressions are validated by Monte Carlo simulations. The results showed that there is a considerable improvement in the performance of the hybrid system as the relay gain increases whereas it deteriorates with increasing the end-to-end distance. A comparison between the performance of a parallel hybrid/PLC and hybrid systems is also provided. It is found that the hybrid/PLC system outperforms the hybrid one. However, the user mobility offered by the latter system remains the main advantage over the former approach.

The IEEE 1901 powerline standard can be deployed using orthogonal frequency division multiplexing (OFDM) since it is robust over impulsive channels. However, the powerline channel picks up impulsive interference that the conventional OFDM driver cannot combat. Since the probability density function (PDF) of OFDM amplitudes follow the Rayleigh distribution, it becomes difficult to correctly predict the existence of impulsive noise (IN) in powerline systems. In this study, we use companding transforms to convert the PDF of the conventional OFDM system to a uniform distribution which avails the identification and mitigation of IN. Results show significant improvement in the output signal-to-noise ratio (SNR) when nonlinear optimization search is applied. We also show that the conventional PDF leads to false IN detection which diminishes the output SNR when nonlinear memoryless mitigation scheme such as clipping or blanking is applied. Thus, companding OFDM signals before transmission helps to correctly predict the optimal blanking or clipping threshold which in turn improves the output SNR performance.

Wireless power transfer and energy harvesting have attracted a significant research attention in terms of their application in cooperative relaying systems. Most of existing works in this field focus on the half-duplex (HD) relaying mechanism over certain fading channels, however, in contrast, this paper considers a dual-hop full-duplex (FD) relaying system over a generalized independent but not identically distributed α-μ fading channel, where the relay node is energy-constrained and entirely depends on the energy signal from the source node. Three special cases of the α-μ model are investigated, namely, Rayleigh, Nakagami- m and Weibull fading. As the system performance, we investigate the outage probability (OP) for which we derive exact unified closed-form expressions. The provided Monte Carlo simulations validate the accuracy of our analysis. Moreover, the results obtained for the FD scenario are compared to the ones related to the HD. The results demonstrate that the decode-and-forward relaying outperforms the amplify-and-forward relaying for both HD and FD scenarios. It is also shown that the FD scenario performs better than the HD relaying systems. Finally, we analyzed the impact of the fading parameters α and μ on the achievable OP.

The Fisher-SnedecorFdistribution was recently proposed as an accurate and tractable composite fading model in the context of device-to-device communications. The present work derives the product of the Fisher-SnedecorFcomposite fading model, which is useful in characterizing fading effects in numerous realistic communication scenarios. To this end, novel analytic expressions are first derived for the probability density function, the cumulative distribution function and the moment of the product ofNstatistically independent, but not necessarily identically distributed, Fisher-SnedecorFrandom variables. Capitalizing on these expressions, we derive tractable closed-form expressions for channel quality estimation of the proposed model as well as the corresponding outage probability and average bit error probability for binary modulations. The offered results are corroborated by extensive Monte-Carlo simulation results, which verify the validity of the derived expressions. It is shown that the number of cascaded channels affects considerably the corresponding performance, as a variation of over an order of magnitude is observed across all signal-to-noise ratio regimes.

The performance of physical-layer security of the classic Wyner's wiretap model over Fisher-Snedecor F composite fading channels is considered in this work. Specifically, the main channel (i.e., between the source and the legitimate destination) and the eavesdropper's channel (i.e., between the source and the illegitimate destination) are assumed to experience independent quasi-static Fisher-Snedecor F fading conditions, which have been shown to be encountered in realistic wireless transmission scenarios in conventional and emerging communication systems. In this context, exact closed-form expressions for the average secrecy capacity (ASC) and the probability of non-zero secrecy capacity (PNSC) are derived. Additionally, an asymptotic analytical expression for the ASC is presented. The impact of shadowing and multipath fading on the secrecy performance is investigated. Our results show that increasing the fading parameter of the main channel and/or the shadowing parameter of the eavesdropper's channel improves the secrecy performance. The analytical results are compared with Monte-Carlo simulations to validate the analysis.

In this paper, we consider a two-hop amplify-and- forward (AF) relaying system, where the relay node is energy-constrained and harvests energy from the source node. In the literature, there are three main energy-harvesting (EH) protocols, namely, time-switching relaying (TSR), power-splitting (PS) relaying (PSR) and ideal relaying receiver (IRR). Unlike the existing studies, in this paper, we consider α-μ fading channels. In this respect, we derive accurate unified analytical expressions for the ergodic capacity for the aforementioned protocols over independent but not identically distributed (i.n.i.d) α-μ fading channels. Three special cases of the α-μ model, namely, Rayleigh, Nakagami-m and Weibull fading channels were investigated. Our analysis is verified through numerical and simulation results. It is shown that finding the optimal value of the PS factor for the PSR protocol and the EH time fraction for the TSR protocol is a crucial step in achieving the best network performance.

This paper analyzes the energy efficiency performance of cooperative and non-cooperative decode-and-forward (DF) relaying power line communication (PLC) systems. In order to further minimize the energy consumption of such systems, we propose incremental DF (IDF) relying over the impulsive noise PLC channel. For a more realistic scenario, the PLC modems power consumption profile is assumed to consist of both dynamic power and static power. For the sake of comparison and completeness as well as to quantify the achievable gains, we also analyze the performance of a single-hop PLC system. In this respect, accurate analytical expressions for the outage probability and energy efficiency are derived. Monte Carlo simulations are provided throughout the paper to validate the analysis. Results reveal that the cooperative relaying PLC systems can provide better energy efficiency performance compared to the non-cooperative ones. It is also shown that increasing the noise probability or the modems static power can negatively impact the system performance.

Energy-harvesting (EH) and wireless power transfer in cooperative relaying networks have recently attracted a considerable amount of research attention. Most of the existing work on this topic however focuses on Rayleigh fading channels which represents outdoor environments. Unlike these studies, in this paper we analyze the performance of wireless power transfer in two-hop decode-and- forward (DF) cooperative relaying systems in indoor channels characterized by log-normal fading. Three well-known EH protocols are considered in our evaluations: a) time switching relaying (TSR), b) power splitting relaying (PSR) and c) ideal relaying receiver (IRR). The performance is evaluated in terms of the ergodic outage probability for which we derive accurate analytical expressions for the three systems under consideration. Results reveal that careful selection of the EH time and power splitting factors in the TSR- and PSR-based system are important to optimize performance. It is also presented that the optimized PSR system has near- ideal performance and that increasing the source transmit power and/or the energy harvester efficiency can further improve performance.

Energy-harvesting (EH) and wireless power transfer are increasingly becoming a promising source of power in future wireless networks and have recently attracted a considerable amount of research, particularly on cooperative two-hop relay networks in Rayleigh fading channels. In contrast, this paper investigates the performance of wireless power transfer based two-hop cooperative relaying systems in indoor channels characterized by log-normal fading. Specifically, two EH protocols are considered here, namely, time switching relaying (TSR) and power splitting relaying (PSR). Our findings include accurate analytical expressions for the ergodic capacity and ergodic outage probability for the two aforementioned protocols. Monte Carlo simulations are used throughout to confirm the accuracy of our analysis. The results show that increasing the channel variance will always provide better ergodic capacity performance. It is also shown that a good selection of the EH time in the TSR protocol, and the power splitting factor in the PTS protocol, is the key to achieve the best system performance.

In this paper, we analyze the secrecy capacity of a half-duplex energy harvesting (EH)-based multi-antenna amplify-and-forward relay network in the presence of a passive eavesdropper. During the first phase, while the source is in the transmission mode, the legitimate destination transmits an auxiliary artificial noise (AN) signal which has two distinct purposes: 1) to transfer power to the relay and 2) to improve system security. Since the AN is known at the legitimate destination, it is easily cancelled at the intended destination, which is not the case at the eavesdropper. In this respect, we derive new exact analytical expressions for the ergodic secrecy capacity for various well-known EH relaying protocols, namely, time switching relaying (TSR), power splitting relaying (PSR), and ideal relaying receiver (IRR). Monte Carlo simulations are also provided throughout our investigations to validate the analysis. The impacts of some important system parameters, such as EH time, power splitting ratio, relay location, AN power, EH efficiency, and the number of relay antennas, on the system performance are investigated. The results reveal that the PSR protocol generally outperforms the TSR approach in terms of the secrecy capacity.

Wireless power transfer conveniently enables prolonging the lifetime of energy-constrained wireless nodes by means of scavenging the energy of radio-frequency signals. Most existing work on this topic assumes negligible background noise power and focuses only on harvesting the power signal transmitted by the access point (AP). In contract, in this paper we show that in Gaussian-Bernoulli (GB) impulsive noise channels such an assumption is invalid, especially in highly impulsive noise scenarios. In this respect, we study the performance of a multiple-antenna AP system with an energy- constrained single-antenna destination in various GB impulsive noise environments. The proposed system here adopts the harvest-then-transmit protocol where communication is accomplished over two distinct phases, namely, power transfer phase (down-link) and information transmission phase (up-link). To characterize system performance, we consider the ergodic capacity. An analytical expression for parameter is derived and then validated with Monte Carlo simulations. Results reveal that incorporating GB impulsive noise can considerably improve the performance of energy-harvesting based systems. It is also demonstrated that increasing the number of AP antennas will further enhance the ergodic capacity and that careful selection of the energy- harvesting time is crucial for achieving best performance.

This paper investigates the application of orthogonal poly-phase based multi-carrier code division multiple access (OPP-MC-CDMA) over multipath power-line channels perturbed by Middleton class-A noise. The proposed OPP-MC-CDMA system is aided with a minimum-mean square error equalizer combined with nonlinear preprocessing to overcome the effect of bursty noise and multipath fading. We study the performance of this system in terms of the output signal-to-noise ratio (SNR) and symbol error rate with various constellation sizes of OPP codes, different noise scenarios and non-linear processor's thresholds. For comparison-sake, the performance of a conventional orthogonal frequency-division multiple access (OFDM) scheme is included. The results reveal that the proposed approach always provides superior performance over the conventional OFDM system with a maximum output SNR gain of up to 5.25 dB. It is also shown that the performance of the OPP-MC-CDMA technique improves significantly with increasing the constellation size of the OPP codes.

Impulsive noise (IN) over power-lines can significantly corrupt communication signals. To diminish its effect, a nonlinear preprocessor is usually applied at the receiver's frond-end to blank or clip the incoming signal when it exceeds a certain threshold. Applying a combination of blanking and clipping in a hybrid fashion is characterized by two thresholds T1and T2(T2= αT1), where α is a scaling factor. Previous studies assumed a fixed value for the scaling factor and found that optimizing the threshold T1is the key to improve performance. In contrast to the existing work, in this paper we show that the performance of the hybrid technique is sensitive not only to the threshold but also to the scaling factor, and in light of this we propose to enhance the capability of this technique by optimizing the two parameters. System Performance is evaluated mathematically in terms of the probability of missed blanking/clipping (Pm), probability of IN identification (Pi) and the symbol error rate (SER) performance. In all our investigations, simulation results are provided to validate the analysis. Results reveal that the proposed scheme is superior in terms of minimizing Pmand maximizing Piwhich consequently results in improving SER performance.

In this paper, we analyze the secrecy capacity of a multiple-input multiple-out (MIMO) half duplex amplify-and-forward (AF) relay network in the presence of one passive eavesdropper. Zero forcing (ZF) processing is utilized at various locations to improve the capacity when the eavesdropper is equipped with a single antenna. The impact of the proposed ZF-based technique on the secrecy capacity is investigated for three different scenarios depending on where the ZF is applied, namely, 1) ZF at the relay and destination, 2) ZF at the source and relay, 3) ZF at the relay. For these configurations, analytical expressions for the ergodic-secrecy capacity are derived, and simulation results are provided throughout the paper to validate our analysis. Results reveal that reducing the number of source and/or destination antennas will enhance the ergodic-secrecy capacity and the significance of this enhancement is dependent on the particular scenario adopted. Furthermore, it will be shown that, in general, secrecy capacity improves with increasing the relay power.

In this paper we investigate the application of multi-carrier code division multiple access (MC- CDMA) in impulsive noise power-line channels. In order to improve system performance, impulsive noise blanking is utilized at the receiver's front-end. For comparison-sake, three families of spreading codes are considered here, namely, pseudonoise (PN), Walsh-Hadamard (WH) and poly- phase (PP) sequences. Probability of blanking error P_b and output signal-to-noise ratio (SNR) performances are adopted to characterize the achievable gains. The results reveal that the proposed scheme is able to achieve considerable improvements in terms of P_b and output SNR performance. It is also shown that PP-based MC- CDMA yields the best overall performance with a 1.2 dB SNR gain relative to blanking-based orthogonal frequency division multiple access systems.

Power-line communication (PLC) is considered as the backbone of smart grid. Impulsive noise (IN) over such channels, however, remains the main factor responsible for degrading communication signals. A simple method to mitigate IN over PLC channels is to precede the receiver with a nonlinear preprocessor to blank and/or clip the incoming signal when it exceeds a certain threshold. Applying a combination of blanking and clipping in a hybrid fashion was shown to provide the best performance. The hybrid scheme is characterized by two thresholds T1and T2(T1= α.T2), where a is a scaling factor. Previous studies assume a fixed value for the scaling factor and found that optimizing the threshold T2is the key to enhance performance. In this paper, we show that the performance of this scheme is sensitive not only to the threshold, but also to the scaling factor. With this in mind, a mathematical expression for the output signal-to-noise ratio as a function of the threshold and scaling factor is formulated and used to optimize the hybrid scheme performance. Simulation results are also provided to validate our analysis. The results reveal that using an adaptive hybrid scheme with an optimally selected threshold and scaling factor always outperforms other nonlinear schemes.

A very efficient method to mitigate impulsive noise (IN) over powerline channels is to precede the OFDM demodulator with a blanker to zero the incoming signal when it exceeds a certain threshold. Blanking the signal samples unaffected by IN exceeding this threshold, i.e. blanking errors, can cause severe performance degradation. For best performance, the optimal blanking threshold must be determined and this requires some prior and accurate knowledge about the characteristics of IN; this method is referred to as the unmodified method. In this paper, we propose an algorithm to enhance the capability of such methods by processing the OFDM signal at the transmitter to make the IN more easily identifiable at the receiver. This is done by simply deploying a peak to average power ratio (PAPR) reduction technique such as the selective mapping (SLM) scheme. A closed-form analytical expression for the probability of blanking error is derived and the problem of blanking threshold optimization is addressed under various IN environments. The results reveal that the proposed technique is able to minimize the probability of blanking error dramatically and can provide significant SNR improvement relative to the unmodified scheme. It will also be shown that when SLM is implemented with a large number of phase sequences, not only a considerable SNR enhancement is achieved but also, unlike the unmodified method, it becomes feasible to completely alleviate the need for any previous knowledge about the IN characteristics for optimal blanking.

Many IN mitigation techniques have been proposed to mitigate impulsive noise (IN) over powerlines, the most common of which is the blanking technique. The conventional way to implement this technique however requires prior knowledge about the IN characteristics to identify the optimal blanking threshold (OBT). When such knowledge cannot be obtained the performance deteriorates rapidly. To alleviate this, a look-up table (LUT) based algorithm with uniform quantization is deployed to utilize estimates of the peak to average power ratio at the receiver to determine the OBT. In this paper, we investigate the impact of quantization bits on the system performance as well as the performance loss due to the impact of IN on the side information. Two aspects of the achievable performance are considered namely, output signal-to-noise ratio (SNR) and symbol error rate under various IN scenarios. The results reveal that a 5 bit LUT is sufficient to achieve a gain of up to 3dB SNR improvement relative to the conventional blanking method. Furthermore, it will be shown that the loss due to the practical impact of IN on the side information is insignificant.

Powerline communication technology is a promising communication platform for smart grid and has nowadays become an attractive alternative for data transmission in the home. Impulsive noise (IN) over such channels, however, remains the main factor responsible for degrading communication signals. Many techniques for mitigating IN have been reported in the literature the most common of which is preceding the OFDM receiver with blanking, clipping or hybrid (combined blanking-clipping) nonlinear preprocessors. In this paper, we propose to enhance the capability of these techniques by preprocessing the signal at the transmitter. A closed-form analytical expression for the probability of IN detection error is derived and the problem of blanking/clipping threshold selection is also considered. The results reveal that the proposed is able to minimize the probability of IN detection error significantly and can provide up to 3dB SNR improvement relative to the conventional techniques.

Impulsive noise (IN) is the most dominant factor degrading the performance of communication systems over powerlines. Many IN mitigation techniques have been introduced in the literature such as the application of a blanker at the front-end of the receiver which sets the incoming signal to zero when it exceeds a certain threshold. Determining this threshold is the key for achieving best performance in this technique. Most studies dealing with threshold optimization require prior knowledge about the IN characteristics. However, if the peak to average power ratio (PAPR) of the OFDM symbols can be determined at the receiver, this will allow optimal blanking of IN without requiring any knowledge about the noise parameters. In this paper we propose a look-up table (LUT) based algorithm with uniform quantization to estimate the peak of OFDM symbols. We also investigate the impact of quantization bits on the system performance in terms of the signal-to-noise-ratio (SNR) at the output of the blanking device, in two different scenarios, weakly and heavily disturbed IN environments. Simulation results reveal that a 5 bit LUT is sufficient for reliable performance.