Integrated sensing and communication (ISAC) has been recognized as one of the key technologies capable of simultaneously improving communication and sensing services in future wireless networks. Moreover, the introduction of recently developed movable antennas (MAs) has the potential to further increase the performance gains of ISAC systems. Achieving these gains can pose a significant challenge for MA-enabled ISAC systems operating in the near-field due to the corresponding spherical wave propagation. Motivated by this, in this paper we maximize the weighted sum rate (WSR) for communication users while maintaining a minimal sensing requirement in an MA-enabled near-field ISAC system. To achieve this goal, we propose an algorithm that optimizes the sensing receive combiner, the communication precoding matrices, the sensing transmit beamformer and the positions of the users' MAs in an alternating manner. Simulation results show that using MAs in near-field ISAC systems provides a substantial performance advantage compared to near-field ISAC systems with only fixed antennas. Additionally, we demonstrate that the highest WSR is obtained when larger weights are allocated to the users placed closer to the BS, and that the sensing performance is significantly more affected by the minimum sensing signal-to-interference-plus-noise ratio (SINR) threshold compared to the communication performance.

In this paper, we investigate the performance of a non-orthogonal multiple access (NOMA)-enhanced active reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) system under a non-terrestrial network (NTN) framework. The performance is analyzed in terms of outage probability, ergodic capacity, beampattern gain, and probability of detection (PoD). To this end, the end-to-end equivalent channel distribution under maximum ratio transmission beamforming is derived and modeled as a Gamma distribution. Based on this, a closed-form expression for the outage probability (OP) is obtained in terms of Meijer G-functions. The accuracy of the analytical results is validated through Monte Carlo simulations. To provide further insight, we derive the asymptotic expression of the OP and the corresponding diversity order. Additionally, an approximate expression for the ergodic capacity is derived using the method of moments, along with its upper and lower bounds. To evaluate the impact of the active RIS in mitigating the impact of multiplicative fading through amplification of the incident signal, its performance is compared against a baseline passive RIS scheme, revealing significant performance improvements. In particular, the outage probability is improved by approximately 72.94% and 73.33% for the weak user (D1) and strong user (D2), respectively, when employing an active RIS compared to a passive RIS at a transmit SNR of 22 dBm. The influence of practical impairments, including hardware impairments, imperfect channel state information, and imperfect successive interference cancellation, is also examined. The results show that increasing the number of RIS elements and the number of antennas at the satellite can effectively alleviate the adverse effects of these impairments. On the sensing side, the proposed ISAC system is compared with a conventional radar system in terms of the PoD for aerial targets located at various positions, and it additionally demonstrates beampattern gain. The results are obtained under the assumptions of ideal RIS phase configuration and line-of-sight (LoS) between the satellite and targets, representing key limitations of the analysis.

We propose a novel integrated sensing and communication (ISAC) framework that combines active reconfigurable intelligent surfaces (RIS) with a movable antenna (MA) array at the base station to jointly enhance the communication throughput and the radar sensing resolution. Unlike conventional architectures that employ passive RIS or fixed antenna arrays, the proposed framework leverages dual-domain reconfigurability, electromagnetic and geometric, by jointly optimizing transmit beamforming, RIS reflection coefficients with amplification constraints, and the spatial positions of the mobile antennas. The system is modeled under a practical RIS noise amplification model and subject to the constraints of stringent signal-to-interference-plus-noise ratio (SINR), radar beampattern, and transmission power. A unified optimization problem is formulated and decomposed into tractable subproblems using an alternating optimization approach based on semidefinite relaxation (SDR), successive convex approximation (SCA), and convex programming. Numerical results confirm that the proposed design significantly outperforms conventional passive RIS and fixed array systems in terms of both radar and communication metrics, particularly under dynamic channel conditions and constrained power budgets.

The novel flexible-antenna technology, known as pinching antennas, has recently attracted significant research interest. By embedding discrete dielectric materials, a pinching antenna can be dynamically activated at arbitrary points along a dielectric waveguide, enabling flexible control of large-scale path loss and on-demand coverage extension. This paper investigates the performance analysis of a downlink rate-splitting multiple access (RSMA) enhanced pinching-antenna assisted integrated sensing and communication (ISAC) system. Specifically, we consider a sensing-centric scenario in which a single pinching antenna simultaneously serves two communication users and performs target sensing, where the sensing target and one user are co-located in the same room and are served via a line-of-sight (LoS) link, while the other user resides in a separate room and is connected through a non-LoS (NLoS) link. We derive novel closed-form expressions for the outage probabilities (OPs) of both communication and sensing tasks. The communication OP is obtained through an information-theoretic approach, whereas the sensing OP is evaluated using a mean square error (MSE) based approach. In addition, the system throughput for the communication task and average sensing MSE for the sensing task are also evaluated. To gain further insights, asymptotic OP expressions are also derived for both communication and sensing tasks in the high signal-to-noise ratio (SNR) regime. Monte Carlo simulations are performed to validate the analytical results. Numerical results demonstrate that the proposed pinching-antenna-assisted RSMA–ISAC system outperforms pinching-antenna-assisted non-orthogonal multiple access (NOMA)–ISAC and space-division multiple access (SDMA)–ISAC in terms of communication reliability and sensing accuracy. Furthermore, compared with a conventional antenna-assisted RSMA–ISAC benchmark, the proposed framework achieves significant sensing performance gains due to reduced effective sensing distance and near-field spatial focusing enabled by pinching antennas, while exhibiting a controllable sensing–communication trade-off governed by power allocation and antenna positioning. Finally, we analyze the impact of pinching-antenna height, room size, and other key system parameters on the performance of both communication and sensing tasks.

This work addresses the problem of joint robust transmission, reflection, and reception strategy design in an active reconfigurable intelligent surface (ARIS)-assisted multiuser multiple-input multiple-output (MIMO) system. Specifically, a signal-to-interference-noise (SINR) maximization problem has been formulated by jointly optimizing the transmit beamforming matrix at the base station (BS), the linear reception filters at the users, and the reflection coefficient matrix at the ARIS. The optimization has been performed under constraints on the BS transmit power, the maximum amplification power of the ARIS, and the maximum amplitude coefficients of the ARIS. To jointly optimize RIS-assisted systems, this paper proposes an efficient deep learning (DL) model. Specifically, a multi-layer perceptron (MLP)-based deep neural network (DNN) has been designed to effectively approximate the optimal solution. Further, to handle channel state information (CSI) uncertainties arising from estimation errors and environmental variations, a novel uncertainty injection scheme has been proposed for training DL models. The output of the solution is perturbed through uncertainty injection. The model learns a robust beamforming matrix, linear reception filters, and reflection configurations that maintain high SINR under worst-case channel conditions. Simulation results demonstrate that, for the optimized phase and ARIS configuration, the proposed DL trained with the UI scheme achieves a 26.23% SINR improvement compared to the model trained without UI (WUI). In addition to the ARIS, the performance of the passive reconfigurable intelligent surface has also been analyzed. Further, the time complexity and robustness of the proposed model have been evaluated.

This paper investigates an integrated sensing and communication (ISAC) system incorporating backscattering radio frequency identification (RFID) tag. In this configuration, a base station (BS) simultaneously serves multiple users through a communication beam while utilizing a sensing beam to detect the presence of an RFID tag. A joint beamforming design problem is formulated to maximize the sum-rate for the users while ensuring the minimum quality of service (QoS) for the tag detection and the minimum QoS of all communication users. To tackle the non-convex nature of objective function the Lagrangian dual transform technique is employed. Due to the coupling of variables, an alternating optimization (AO) based algorithm is proposed with guaranteed convergence. Through numerical simulations, we validate the effectiveness of our proposed algorithm. Additionally, we assess the impact of several key parameters on system performance, including the number of transmitting antennas at the BS, the available transmit power at the BS, the minimum QoS for the communication users, and the number of communication users.

This paper presents an optimization framework aimed at enhancing communication throughput for electric vehicles (EVs) operating on electrified roads (eROADs) while simultaneously ensuring quality of service (QoS). By integrating inductive coil-based wireless power transfer (WPT) with multiuser vehicular networks, our framework facilitates continuous inmotion charging alongside efficient communication management. To address transmission power and data rate constraints in a non-orthogonal multiple access (NOMA) uplink scenario, we tackle challenges posed by realistic mobility models, Doppler effects, and variability in network coverage. We develop a hybrid deep reinforcement learning (DRL) algorithm that integrates deep deterministic policy gradient (DDPG) and proximal policy optimization (PPO) techniques to maximize throughput. Simulation results indicate that our hybrid DRL algorithm surpasses traditional DDPG and PPO methods, achieving up to 13.74% higher throughput under low transmission power limits. Our approach manages WPT and communications efficiently, promoting EV operations in intelligent transport systems and aligning with green communication goals.

The integration of the Internet of Things (IoT) with meta-materials advances both communication and sensing technologies. Using Meta-IoT sensors, environmental data can be acquired by analyzing the frequency response from reflected signals. In this study, we introduce a dual-purpose Meta-IoT sensor capable of simultaneously sensing environmental conditions and enabling communication. Machine learning (ML) plays a key role in optimizing sensor design and power management within these meta-material IoT systems. Hence this work leverages machine learning (ML) to optimize sensor design and power management in Meta-material IoT (Meta-IoT) systems. Specifically, Gaussian process regression (GPR) is used to explore and optimize sensor structures, while reinforcement learning adapts power management strategies dynamically. Simulation results highlight that the ML-driven methods enable adaptable and efficient Meta-IoT systems.

This study analyzes the performance of a two-hop communication system that integrates free-space optical (FSO) transmission with intelligent reflecting surface (IRS)-aided radio frequency (RF) transmission. This analysis focuses on the transmission within a non-orthogonal multiple access (NOMA) system over heterogeneous channels: the source-to-relay FSO channel follows a Fisher-Snedecor$F$distribution, the relay-to-users RF channels are modeled as Rayleigh distributions, the relay-to-IRS and IRS-to-users RF channels are characterized by Rician distributions. A closed-form expression for the outage probability has been derived and approximated as a Gamma distribution using the moment-generating function under imperfect successive interference cancellation. Notably, the outage probability expressions and their approximation closely align with our simulation results, thus validating the accuracy of our findings. Additionally, an asymptotic expression for the outage probability has been derived, particularly in the FSO transmit SNR regime, and the corresponding diversity order is evaluated. The impact of various system parameters, such as turbulence, on the rate of decay of the outage probability is explained through the diversity order.

Smart wireless communications enabled by reconfigurable intelligent surfaces (RISs) have gained significant research interest in the areas of localization and sensing over the past few years. This paper investigates an unconventional approach for 3D direction-of-arrival (DoA) estimation of multiple aerial user targets using an RIS-based communication architecture. In particular, the measurements required for DOA estimation at the receivers are optimized through a deep reinforcement learning framework. The core of the proposed method lies in formulating the DoA estimation problem as a Markov decision process (MDP), which is optimized via a proximal policy optimization algorithm for its optimization. Considering a practical RIS setup with 2-bit states at each unit element, we demonstrate significant improvements in DoA estimation accuracy, in terms of reduced root mean squared error (RMSE) for various simulation scenarios of the system.

Visible light communication (VLC) is an energy-efficient, green, and low-cost technology for high-speed next-generation communication systems. However, it has been observed that the performance of a VLC-based system is limited due to the light-emitting diode's (LED) nonlinear characteristics, low coverage area, and loss of the VLC signal due to the absence of a direct link between the transmitter and receiver caused by blockages present in the environment. In addition, the secrecy sum rate (SSR) of a VLC system is compromised due to the presence of an eavesdropper. To address the problem of dead zones and low coverage area, an optical simultaneously transmitting and reflecting intelligent surface (OSTAR-RIS) is proposed in the literature. For SSR maximization, the particle swarm optimization (PSO) method is employed and compared with the benchmark exhaustive search method. For implicit channel estimation and direct symbol detection, an LSTM-based algorithm for a nonlinear OSTAR-RIS VLC system is proposed. Simulations indicate superiority of the proposed optimization and implicit channel estimation technique over benchmark schemes.

Backscatter communication (BackCom) holds immense potential for enhancing the capabilities of energy-constrained Internet of Things (IoT) devices and supporting a wide range of applications. Nonorthogonal multiple-access (NOMA) schemes are advantageous for meeting the demands of massive connectivity in next-generation communication networks, and NOMA-aided BackCom can leverage the benefits of both BackCom and NOMA. This article covers the basics of BackCom, explores different types of BackCom systems, and explains how NOMA can be integrated with BackCom, where various crucial issues, including design principles, channel state information (CSI) estimation, node pairing schemes, and reflection coefficient (RC) designs, are illustrated. Furthermore, this survey evaluates the effectiveness of different network architectures for NOMA-aided BackCom, especially in IoT networks, and identifies potential research directions and future trends in this area. The key considerations for designing and optimizing NOMA-aided BackCom systems in real-world scenarios are also highlighted in this survey. In addition, by reviewing the existing literature and research on NOMA-aided BackCom, this article provides valuable insights for researchers and practitioners interested in this emerging wireless communication technique.

This article characterizes the performance of mixed free-space optical (FSO)/active intelligent reflecting surface (IRS)-aided nonorthogonal multiple access (NOMA) communication in the presence of imperfect channel state information (CSI) and imperfect successive interference cancelation (SIC). The detrimental effects of atmospheric turbulence on the FSO communication link, which affects end-to-end communication, are also presented. Considering practical implementation, we illustrate the impact of phase quantization error, imperfect SIC, and imperfect CSI on the outage performance. Our numerical analysis indicates that the outage performance can be significantly improved by utilizing multiple transmit apertures through transmit aperture selection at the base station and employing multiple transmit antennas via maximum ratio transmission at the relay. In addition, the numerical results demonstrate that performance improvements are achieved by mitigating double path loss through an active IRS compared to a passive IRS. The closed-form expression for the outage probability is derived using methods of moments and then verified through Monte Carlo simulations. To gain more valuable insights, asymptotic expressions are also provided for high RF transmit power and high FSO transmit power scenarios. Consequently, diversity orders are derived for an asymptotic study of the outage performance of the considered NOMA network.

This work proposes a quantum-aided deep reinforcement learning (DRL) framework designed to enhance the accuracy of direction-of-arrival (DoA) estimation and the efficiency of computational task offloading in integrated sensing and communication systems. Traditional DRL approaches face challenges in handling high-dimensional state spaces and ensuring convergence to optimal policies within complex operational environments. The proposed quantum-aided DRL framework that operates in a military surveillance system exploits quantum computing’s parallel processing capabilities to encode operational states and actions into quantum states, significantly reducing the dimensionality of the decision space. For the very first time in literature, we propose a quantum-enhanced actor-critic method, utilizing quantum circuits for policy representation and optimization. Through comprehensive simulations, we demonstrate that our framework improves DoA estimation accuracy by 91.66% and 82.61% over existing DRL algorithms with faster convergence rate, and effectively manages the trade-off between sensing and communication and by optimizing task offloading decisions under stringent ultra-reliable low-latency communication requirements. Comparative analysis also reveals that our approach reduces the overall task offloading latency by 43.09% and 32.35% compared to the DRL-based deep deterministic policy gradient and proximal policy optimization algorithms, respectively.

This paper investigates a cooperative Internet of Things (IoT) non-orthogonal multiple access (NOMA) network comprising of a multi-antenna base station (BS), a near IoT user (NU) with full-duplex capabilities, and multiple far IoT users (FU). The NU utilizes the power-splitting (PS) energy harvesting (EH) protocol and augments the harvested energy with a little energy from its battery to assist the FU. Considering the novel cooperative NOMA/non-cooperative (C-NM/NC) switching, the impact of successive interference cancellation (SIC), channel state information (CSI) errors, and nonlinear EH, closed-form expressions are derived for average blocklength error rates (BLER) of both users in finite blocklength regime. Utilizing the derived BLER expressions, the Goodput, Reliability, Latency, and battery power efficiency are expressed in closed form. We then demonstrate that the CSI errors severely degrades the performance of both the users. Monte Carlo simulations validate the accuracy of the derived analytical expressions. We also establish that a significantly better maximum performance is attained at the FU, given a target NU Goodput by the careful choice of NOMA and EH parameters. C-NM/NC switching offers a higher EE and better self-interference immunity. Results indicate that the choice of blocklength is crucial for efficient system performance.

In the rapidly evolving domain of vehicular metaverse, this study introduces a cutting-edge quantum-based decentralized and heterogeneity-aware federated learning framework for vehicular metaverse named QV-FEDCOM, which stands as a testament to the innovative fusion of quantum computing principles with federated learning (FL). This framework is ingeniously tailored to address the challenges in a vehicular metaverse, offering a cost-efficient and adaptive solution for the dynamic vehicular landscape. QV-FEDCOM is strengthened by key components like quantum sequential-training-program, with reinforcement learning-based dynamic mode switching to reduce communication costs and manage vehicle states adaptively, and the quantum vehicle-context-grouping utilizing hierarchical clustering and simulated annealing for effective vehicle grouping based on contextual data similarity, addressing the complexities of data heterogeneity. Additionally, the integration of quantum-inspired principal component analysis (Q-PCA) enhances memory efficiency, further optimizing the framework. These elements converge in the QV-FEDCOM algorithm, establishing a decentralized, efficient, and context-aware quantum federated learning (QFL) process that redefines learning dynamics in the vehicular metaverse. Our study also introduces an innovative quantum trajectory loss (QTL) function, specifically designed for trajectory prediction tasks, which combines the Huber loss with an angular deviation penalty to robustly handle errors and penalize large deviations in the predicted trajectory angle. The effectiveness of the QV-FEDCOM framework is rigorously validated through comprehensive simulations, with its performance meticulously compared against various adaptations, showcasing its transformative capabilities within the vehicular metaverse ecosystem.

The increasing complexity of real-time healthcare necessitates intelligent systems for dynamic data management and personalized assistance. This paper proposes a novel dual-LLM framework that integrates large language models (LLMs) into wireless healthcare networks. The first LLM powers an interactive artificial intelligence module (IAIM) embedded within a mobile edge computing (MEC) environment, which dynamically optimizes user-specific data routing and reconfigurable intelligent surface (RIS) configurations via a modified proximal policy optimization (PPO) algorithm. A novel Greedy Look-Ahead Algorithm (GLAA) is introduced for real-time path selection based on signal strength, emergency factors, and user-specific parameters. The second LLM, utilizing a retrieval-augmented generation (RAG) approach, serves as a personalized healthcare chat assistant that delivers context-aware patient support using real-time and historical data. Simulation results demonstrate that the proposed IAIM achieves a 9.6% reduction in network overhead compared to manual modeling and reduces latency by up to 52.5% over baseline PPO approaches, thus enabling enhanced user experience and responsiveness in healthcare systems.

One of the disruptive communication technologies for sixth-generation (6G) wireless networks is cell-free massive multiple-input multiple-output (CF-mMIMO), which is capable to control inter-cell interference in MIMO systems. This paper investigates the performance of a full-duplex (FD) CF-mMIMO systems with practical limited-capacity fronthaul links. The proposed system employs a large number of M distributed FD APs, arbitrarily distributed$K_{d}$downlink (DL) and$K_{u}$uplink (UL) half-duplex (HD) single-antenna equipped user terminals (UEs), and a central processing unit (CPU). To exploit the energy efficiency and potential throughput gains of FD systems, each AP is linked to the CPU through a fronthaul link with limited capacity that handles the quantized UL/DL data to/from the CPU. Each AP is expected to support K HD UEs on the same spectrum resource, where$K = (K_{u} + K_{d})$. Imperfect channel state information and the mobility of the UEs are also considered. A closed-form expression for the outage probability is derived using the optimal uniform quantization and maximum-ratio combining/maximum-ratio transmission considering the Welch-Satterthwaite approximation. Additionally, the asymptotic and infinite-M outage expressions for the proposed system are analytically studied and verified via Monte Carlo simulation. Simulation results demonstrate the relationship between the improved outage performance and uniform quality of service (QoS) for all UEs. Moreover, this analysis provides valuable insights into the behavior of FD-CF-mMIMO system and underscores the importance of providing a uniform QoS to all UEs in improving the overall performance of the system.

The paper evaluates the ergodic sum capacity, outage performance for communication users, and the detection probability, beampattern gain for sensing targets in a simultaneous transmitting and reflecting intelligent reflecting surfaces (STAR-IRS) aided integrated sensing and communication (ISAC) system. The rate splitting multiple access (RSMA) technique and maximal ratio transmit beamforming at the multi-antenna base station have been explored. Closed-form expressions for the ergodic sum capacity and outage probability of the STAR-IRS-aided RSMA ISAC system are derived through moment methods. The derived expressions are validated through Monte Carlo simulations. Additionally, to provide deeper insights into the diversity orders of the RSMA ISAC system, we conduct an asymptotic outage probability analysis in the high signal-to-noise ratio regime. The effect of the number of base station antennas and STAR-IRS elements on outage performance has been demonstrated, along with an explanation of the underlying reasons through diversity gain. Furthermore, it shows that the implementation of STAR-IRS significantly boosts the system’s ergodic sum capacity compared to traditional reflecting-only IRS. Additionally, the RSMA technique delivers more substantial performance improvements than the non-orthogonal multiple access (NOMA) in high transmit SNR conditions while demonstrating comparable performance in low transmit SNR scenarios. A comparison between energy splitting and mode switching STAR-IRS has been conducted under both ideal and random phase shift conditions. A trade-off analysis between communication and sensing rates is presented. Additionally, the accuracy of target sensing is evaluated by measuring the mean square error (MSE) in beampattern gain matching. The impact of quantization levels for phase shift of STAR-IRS on outage probability has also been addressed. Finally, the effects of power allocation for sensing on detection probability and beam pattern gain are also presented.

In this work, we consider a cooperative communication network wherein a base station (BS) utilizes a simultaneous wireless power and information transfer (SWIPT) energized full duplex-decode and forward relay to communicate with a downlink user. The relay augments the harvested energy with a bit of energy from its battery. Assuming a practical nonlinear energy harvesting (EH) model, we derive approximated closed-form expressions for the end-to-end blocklength error rate (BLER) under the finite blocklength (FBL) regime for both power splitting (PS) and time switching (TS) protocols. We then derive a high-SNR approximated expression for end-to-end BLER to demonstrate the interplay of the battery energy, blocklength, and EH parameters on the system’s performance. We also analytically establish the convexity of the BLER with respect to the relay’s battery energy. Using the expression for the BLER, we derive expressions for the goodput and battery energy efficiency in approximated closed-form. Moreover, for a desired target BLER requirement, we show that, by carefully choosing the TS/PS parameter, the required relay’s battery energy can be minimized. The accuracy of the derived analytical expressions is validated using Monte Carlo simulations. Finally, we discuss the impact of key parameters such as transmit power, total available blocklength, nonlinear EH, TS, and PS parameters, and battery energy on the network’s performance.

This paper introduces a novel multi-agent federated deep reinforcement learning (MA-FDRL) framework designed to minimize vehicular task offloading latency in electrified road (eROAD) environments. The solution integrates inductive coil-based wireless power transfer (WPT) systems with full duplex multiple input and multiple output (MIMO) vehicular networks, enabling continuous charging and reducing computational delays for electric vehicles (EVs) on eROADs. The MA-FDRL framework optimizes the offloading of vehicular tasks, with support from base stations (BS), unmanned aerial vehicles (UAVs), and satellites, while ensuring data privacy through differential privacy techniques. By intelligently distributing resources across these supporting entities, the framework enhances the efficiency of task processing. Key challenges such as dynamic wireless charging, intermittent BS coverage, and privacy-preserving task offloading are addressed using a comprehensive WPT framework, a mobility model, and a differential privacy-enhanced MA-FDRL algorithm. The proposed MA-FDRL solution effectively reduces the latency of vehicle task offloading by 17.05% over proximal policy optimization (PPO) algorithm, ensures balanced task distribution between edge servers, and offers a scalable and privacy-preserving approach for future autonomous electric vehicles and connected wireless environments.

This paper presents a state-of-the-art analytical framework aimed to enhance spectral efficiency in satellite and terrestrial integrated networks (STINs), utilizing reconfigurable intelligent surface (RIS) within the realm of integrated sensing and communication (ISAC). Our methodology pivots on a pioneering federated deep reinforcement learning strategy that introduces new ground beyond conventional optimization techniques to tackle the intricate problem of non-convex resource allocation. The approach leverages federated learning to dynamically adapt to network changes, enabling efficient resource management and ensuring compliance with beamforming designs, multiple target signal-to-interference-plus-noise ratio thresholds, and RIS phase-shift requirements through an effective feedback loop. In particular, we propose a federated deep deterministic policy gradient (F-DDPG) algorithm across multi-agent systems that outperforms existing federated deep Q-network (F-DQN), centralized, and traditional DDPG and DQN methods. The empirical findings underscore the efficiency of the federated algorithms, which closely align with the performance of centralized models while markedly reducing execution time, thus achieving an optimal synergy between operational efficiency and system performance. Simulation results highlight the remarkable advantages of optimal RIS configurations, showcasing a performance increase of 54.2% over random RIS setups and a remarkable 76.8% enhancement compared to scenarios without RIS, underscoring the transformative impact of our federated learning approach. Additionally, our study evaluates the impact of channel estimation errors and interference, confirming the robustness of our approach and its potential to optimize ISAC-enabled STINs.

The paper explores hybrid free-space optical (FSO) and active intelligent reflecting Surface (IRS)-aided radio frequency (RF) transmission in non-orthogonal multiple access (NOMA) networks to improve outage performance of internet of things (IoT) devices under imperfect channel state information (imCSI) and imperfect successive interference cancellation (imSIC). The superposed signal is transmitted from the source to the relay over FSO link exploiting the line of sight (LOS) path modeled as Gamma-Gamma distribution. Whereas RF signals transmission from the relay to IoT devices are boosted through an active-IRS. The multiplication factor of path-loss is prominent in the presence of a direct path from the relay to IoT devices. The active-IRS can mitigate the issue. In this paper, the direct path is modeled using a Rayleigh distribution, while the indirect paths via the Active-IRS are modeled using a Rician distribution. The paper demonstrates the impact of active noise introduced by the Active-IRS on outage performance. The closed-form expression for outage probability is derived under imCSI and imSIC, taking into account active-IRS noise. Additionally, it conducts an asymptotic analysis in a high signal-to-noise ratio (SNR) regime. The analytical results closely align with Monte Carlo simulation results, validating the accuracy of the findings.

This paper presents a novel analytical framework for minimizing transmit power in satellite and terrestrial integrated networks using reconfigurable intelligent surface (RIS) technology within integrated sensing and communication systems. We employ a cutting-edge federated deep reinforcement learning approach, utilizing a federated deep deterministic policy gradient (F-DDPG) algorithm, to tackle the complex non-convex power minimization problem effectively. The proposed F- DDPG approach surpasses the federated deep Q-network (DQN), traditional DDPG, and DQN techniques by dynamically adapting to network changes, enabling efficient resource management and compliance with beamforming designs, multiple target and user signal-to-interference-plus-noise ratio thresholds, and RIS phase-shift requirements. Simulation results confirm that the use of RIS can significantly lower power requirements at the base station and maintain a critical balance between efficient power management and strategic resource allocation.

This paper investigates the deployment of a full-duplex integrated sensing and communication (ISAC) system for task offloading service to serve ultra-reliable low-latency communications (URLLC), significantly enhanced by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). Utilizing a non-orthogonal multiple access frame-work, this study extensively addresses the challenges associated with latency-sensitive task offloading from information receivers (IRs) to a mobile edge computing platform. We introduce a novel and sophisticated multi-agent deep reinforcement learning (MA-DRL) approach aimed at minimizing latency in task offloading under a variety of stringent ISAC-URLLC network constraints, including imperfect channel state information. The proposed MA-DRL operates on decentralized execution while maintaining centralized training, using multi-actor-critic networks to enhance learning and performance. The innovative reward decentralization framework in the present MA-DRL optimizes downlink and uplink communications through dynamic power allocation, precise beamforming, and intelligent phase shift management facilitated by the STAR-RIS. The proposed MA-DRL framework significantly outperforms existing multi-agent DRL algorithms, demonstrating substantial gain in reward maximization (i.e., linked to offloading latency minimization) by 27.46% and 52.73%.

Vehicular crowdsensing (VCS) encounters challenges within social Internet of Vehicles networks, including interdependent behaviors and the necessity for long-term sensing strategies that balance energy efficiency and delay tolerance in dynamic settings. To tackle these obstacles, this study explores a VCS model tailored for social IoV networks, considering dynamic environmental parameters. We further develop a utility model that seamlessly integrates data-quality aware functional and social incentives for each vehicle, ensuring optimal task payoff, efficient energy usage, and minimized processing time within the dynamic social IoV environment. Additionally, we introduce a non-cooperative game between vehicles and propose a multi-agent deep reinforcement learning (DRL)-based solution for the dynamic VCS framework. This enables vehicles to autonomously adjust sensing levels, maximizing both individual and collective utility. Finally, through comparative simulations, we demonstrate the effectiveness of our approach in comparison to baseline methods.

In this study, we present a hybrid deep reinforcement learning (DRL) algorithm, trained using vehicular federated learning (VFL), specifically tailored for dynamic vehicular networks with historical data. Our approach utilizes VFL-based DRL to refine the caching scheme in these networks, focusing on predicting and storing the most effective content nearby to enhance cache efficiency and reduce content request delays. We propose a modified proximal policy optimization (mPPO) based approach for the DRL-based decision-making for caching management, which combines the advantages of proximal policy optimization (PPO) and double deep Q-network (DDQN). Our study encompasses a vehicular framework that includes a central edge node (CEN), roadside units (RSUs), unmanned aerial vehicles (UAVs), and vehicles equipped with historical data. We tackle the challenges posed by varying vehicle density and mobility, non-uniform RSU coverage, and constrained caching capacity. Through comprehensive simulations, we demonstrate that mPPO outperforms conventional DRL methods like PPO and DDQN, as well as heuristic approaches. These results underscore the efficacy of the VFL-based mPPO in dynamic vehicular networks, confirming its potential for real-world applications.

In this paper, we consider rate splitting multiple access (RSMA) based simultaneous refracting/transmitting and reflecting (STAR)-reconfigurable intelligent surface (RIS) aided downlink wireless network for the data transmission from an access point (AP) to two Internet-of-Things devices (IoDs) over Nakagami fading channel. AP executes the integrated sensing and communication (ISAC) principle to eliminate the issue of undesired interference between a communication system and a target. RIS association with RSMA is used in the system to enhance the quality of signal at a higher sum rate. To evaluate the performance of the proposed system, we analyze the outage probability and ergodic sum rate. Simulation results show the impact of the diversity order of Nakagami parameter, and configurable elements of RIS on the system performance along with the sensing performance of AP. Almost 10% rate enhancement is achieved through RSMA compared with non-orthogonal multiple access (NOMA) technique at 10 dBm transmit power.

In the ever-evolving landscape of next-generation wireless communication systems, the need for high data rates, seamless connectivity, and energy efficiency continues to increase. To meet these demands, the integration of emerging technologies such as cell-free massive multiple-input multiple-output (CFm-MIMO) and reconfigurable intelligent surfaces (RIS) has gained significant attention. This paper presents a comprehensive performance analysis of a downlink active RIS-assisted CFm-MIMO system in the context of user terminal (UT) mobility, emphasizing the critical aspect of constrained fronthaul capacity. The paper employs a rigorous analytical framework to determine the outage performance of the proposed system considering imperfections in the channel state information (CSI). The findings presented in this paper demonstrate the impact of the number of RIS elements ($N$), quantization parameters, UT mobility, scattering models, and imperfect CSI on the outage probability. It is revealed that by increasing the$N$from 8 to 32 will significantly decreases the OP by 99.75%. Moreover, it is interesting to observe that the choice and optimization of quantization parameters remain consistent regardless of RIS presence.

This paper explores a cutting-edge terahertz (THz) integrated sensing and communication system (ISAC) that utilizes active simultaneously transmitting and reflecting reconfigurable intelligent surfaces (A-STAR-RIS). The system incorporates a novel dynamic delay alignment (DDA) modulation technique, allowing signals from different paths to reach the receiver simultaneously, eliminating the need for complex channel equalization and mitigating inter-symbol interference, while considering the dynamic movement of the vehicles, and accounting for time-selective fading and uniform Doppler power spectra (DPS) model. Our system features a dual-function radar and communication multiple antenna base station (BS), serving both communication and target sensing functions concurrently through an A-STAR-RIS. The objective is to maximize the sum rate by jointly optimizing BS transmit beamforming, A-STAR-RIS reflection and transmission beamforming matrices, vehicular unit (VU) mobility correlation parameters, and radar receive filter. Given the intricate nature of this non-convex optimization problem, owing to dynamic changes in communication links and the interplay of multiple variables, traditional optimization methods prove challenging. To overcome this, we propose a machine learning (ML) based deep deterministic policy gradient (DDPG) algorithm. Our simulations validate the substantial benefits of A-STAR-RIS over conventional benchmark scenarios.

In this paper, we present an innovative approach to spectrum management in cognitive radio networks (CRNs) aimed at serving ultra-reliable low-latency communication (URLLC) enabled secondary users (SUs). Unmanned aerial vehicles (UAVs) are deployed for accurate and reliable spectrum sensing (SS), enhancing cooperative spectrum sensing (CSS) effectiveness. A distinctive aspect of our methodology is the integration of digital twin (DT) technology, which, to our knowledge, has not been explored previously in the context of CRNs for bandwidth assignment to URLLC-enabled SUs. This integration facilitates more sophisticated and adaptive management of spectrum resources. Moreover, we propose a deep reinforcement learning (DRL) framework incorporating a modified proximal policy optimization (MPPO) algorithm. This algorithm is designed for better stability and convergence, outperforming the standard PPO in terms of faster convergence in the present URLLC transmission latency minimization process. Simulation results indicate that our proposed DT-based spectrum management and MPPO in CRNs result in a 27.89% increase in CRN's average throughput and a 39.94% reduction in transmission latency compared to the conventional equal resource allocation scheme.

This paper explores simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted multi-user downlink communications, focusing on maximizing information secrecy despite channel state information (CSI) errors. Perfect CSI is hard to achieve due to limited radio frequency chains at the STAR-RIS, noise, interference, and non-collaborative eavesdroppers. The study addresses the worst-case robust beamforming design problem to maximize the sum secrecy rate, considering transmit power limits, quality of service requirements, and practical constraints on the STAR-RIS phase shifter array. The S-procedure is used to estimate semi-infinite inequality constraints, followed by alternating optimization with a line search to iteratively update the precoder and phase shift matrix. Simulation results highlight STAR-RIS’s superior secrecy performance over conventional RIS and the algorithm’s efficiency across various scenarios.

With the imminent arrival of 6G communication, the relevance of advanced technologies, such as multi-input-multioutput (MIMO), nonorthogonal multiple access (NOMA), reconfigurable intelligent surfaces (RISs), and integrated sensing and communication (ISAC), has become prominent for plethora of Internet of Things (IoT) applications. However, integrating ISAC into a MIMO heterogeneous network (HetNets) necessitates reevaluating network performance in terms of outage probability and ergodic rates. This article introduces a novel analytical framework for evaluating downlink transmissions in MIMO HetNets. The proposed framework considers independent homogeneous Poisson point processes (PPP) for spatial arrangement of the NOMA-enabled base stations (BSs) and users. BS in the tth tier exploits superimposed NOMA signal for target sensing. Active RISs are considered to be distributed with homogeneous PPP and are used to mitigate blockage for user equipments when the direct link from the BSs does not exist. The approximated and asymptotic outage probability expressions are derived for two distinct scenarios: one involving direct transmission from the BS to the typical blocked user and the other entailing transmission via active RIS. Moreover, a practical case of imperfect successive interference cancelation (i-SIC) is considered. The analysis emphasizes the benefits of the proposed active RIS-NOMA compared to conventional orthogonal multiple access HetNets, and valuable insights are drawn by varying the number of RIS elements. Additionally, an increase in the RIS elements significantly improves the proposed active RIS-NOMA outage performance. The approximated expressions of ergodic rates, system throughput and beampattern for the sensing performance are also derived.

Vehicular crowdsensing (VCS) within the social Internet of Vehicles (IoV) significantly advances urban transportation management by enhancing road safety, traffic efficiency, and the overall driving experience. This article presents an intelligent multiagent deep reinforcement learning (DRL) framework for augmented dynamic task prioritization in a multi-incentive VCS system. Our framework, named intelligent multiagent reinforcement learning (IMARL), leverages augmented intelligence to integrate human-like decision-making processes with autonomous vehicle operations, ensuring more adaptive and robust task management. The proposed IMARL framework offers several key advantages: it dynamically adjusts the sensing levels of each vehicle, ensuring efficient energy usage and minimized processing times, and employs a data-quality aware multi-incentive utility model to capture both functional and social incentives. Additionally, our framework incorporates a layered server architecture, enhancing system resilience and scalability. Simulation results demonstrate the superiority of our approach. IMARL achieves significant improvements in task completion rates, energy consumption, and processing delays compared to other DRL and non-DRL benchmark methods. Furthermore, our approach exhibits strong adaptability to changing environmental conditions, maintaining high performance even in high-density traffic scenarios. These quantified results validate the effectiveness of the proposed framework, highlighting its potential to significantly enhance VCS systems in real-world applications.

In this study, we present a hybrid deep reinforcement learning (DRL) algorithm, trained using vehicular federated learning (VFL), specifically tailored for dynamic vehicular networks with historical data. Our approach utilizes VFL-based DRL to refine the caching scheme in these networks, focusing on predicting and storing the most effective content nearby to enhance cache efficiency and reduce content request delays. We propose a modified proximal policy optimization (mPPO)-based approach for the DRL-based decision making for caching management, which combines the advantages of proximal policy optimization (PPO) and double deep Q-network (DDQN). Our study encompasses a vehicular framework that includes a central edge node (CEN), roadside units (RSUs), unmanned aerial vehicles (UAVs), and vehicles equipped with historical data. We tackle the challenges posed by varying vehicle density and mobility, nonuniform RSU coverage, and constrained caching capacity. Through comprehensive simulations, we demonstrate that the mPPO outperforms the conventional DRL methods like PPO and DDQN, as well as heuristic approaches. These results underscore the efficacy of the VFL-based mPPO in dynamic vehicular networks, confirming its potential as a viable solution for real-world applications.

This paper investigates a digital twin (DT) and reconfigurable intelligent surface (RIS)-aided mobile edge computing (MEC) system under given constraints on ultra-reliable low latency communication (URLLC). In particular, we focus on the problem of total end-to-end (E2E) latency minimization for the considered system under the joint optimization of beamforming design at the RIS, power, bandwidth allocation, processing rates, and task offloading parameters using DT architecture. To tackle the formulated non-convex optimization problem, we first model it as a Markov decision process (MDP). Later, we adopt deep deterministic policy gradient (DDPG) based deep reinforcement learning (DRL) algorithm to solve it effectively. We have compared the DDPG results with proximal policy optimization (PPO), modified PPO (M-PPO), and conventional alternating optimization (AO) algorithms. Simulation results depict that the proposed DT-enabled resource allocation scheme for the RIS-empowered MEC network using DDPG algorithm achieves up to 60% lower transmission delay and 20% lower energy consumption compared to the scheme without an RIS. This confirms the practical advantages of leveraging RIS technology in MEC systems. Results demonstrate that DDPG outperforms M-PPO and PPO in terms of higher reward value and better learning efficiency, while M-PPO and PPO exhibit lower execution time than DDPG and AO due to their advanced policy optimization techniques. Thus, the results validate the effectiveness of the DRL solutions over AO for dynamic resource allocation w.r.t. reduced execution time.

This paper investigates an unconventional superposition scheme, i.e., rate-splitting multiple access (RSMA) to maximize the overall user fairness and high system performance gain for ultra-reliable low-latency communication (URLLC), enhanced mobile-broadband (eMBB) traffic coexistence in uplink scenarios. In particular, we focus on maximizing the worst-case performance of uplink eMBB and URLLC users when multiplexed in a given resource block using an effective rate-splitting approach among multiple sub-messages. Subsequently, a multi-objective optimization problem (MOOP) is formulated to jointly maximize the worst-case rate and minimize the worst-case packet-error probability (PEP) for eMBB and URLLC users, respectively, using effective power splitting and successive interference cancellation (SIC) decoding of the sub-messages. To solve the non-convexity of the formulated MOOP, we adopt a priori articulation scheme combined with the weighted product approach to transforming the MOOP into a single objective optimization problem (SOOP) and later, solve it using a low complex differential evolution (DE)-based meta-heuristic algorithm. We derive an optimal decoding strategy for sub-messages to ensure better user fairness among eMBB-URLLC traffic. Numerical simulations demonstrate the superiority of the considered RSMA-based superposition for hybrid eMBB-URLLC traffic over conventional slicing and superposition techniques. Moreover, the adopted weighted product method-based DE algorithm outperforms the state-of-art solutions.

Re-configurable intelligent surface (RIS)-aided communication has been envisaged as a frontier scheme to enable ultra-high spectral efficiency (SE) and energy efficiency (EE) for next-generation communication. This paper investigates an unconventional framework of RIS-aided full-duplex (FD) multi-user multiple-input multiple-output (MIMO) communication and analyzes its resource efficiency (RE), a preferable performance metric for realizing trade-off between SE and EE maximization. In particular, we focus on the RE maximization problem via a joint optimization of transmit covariance, optimal receive covariance, and phase-shift matrices for each RIS subject to the given constraint on the power budget. To solve the formulated non-convex problem, we propose two optimization approaches: a) policy gradient-based deep-reinforcement learning (DRL) algorithm based on a Markov decision process formulation for a stochastic-time varying channel and b) alternate optimization (AO) algorithm based on general approximations and majorization-minimization (MM) for static channel conditions. Simulation results validate the out-performance of the considered RIS-aided FD-MIMO system compared to the counterpart system with half-duplex (HD) mode and without RIS case. The proposed DRL algorithm achieves comparable RE performance with reduced computational complexity and running time compared to the traditional AO-based algorithm.

This paper explores the state-of-the-art terahertz (THz) integrated sensing and communication system (ISAC) that uses active reconfigurable intelligent surfaces (ASRIS) that can transmit and reflect signals at the same time. The system incorporates a novel dynamic delay alignment (DDA) modulation technique, allowing signals from different paths to reach the receiver simultaneously, eliminating the need for complex channel equalization and mitigating inter-symbol interference, while considering the dynamic movement of the vehicular units (VUs), and accounting for time-selective fading and uniform Doppler power spectra (DPS) model. Our system is equipped with a dual-function radar and communication multiple-antenna base station (BS), which simultaneously serves both communication and target sensing functions through an ASRIS. The objective is to maximize the sum rate by jointly optimizing BS transmit beamforming, ASRIS reflection and transmission beamforming matrices, VU mobility correlation parameters, and radar receive filter. Traditional optimization methods prove challenging given the intricate nature of this non-convex optimization problem, owing to dynamic changes in communication links and the interplay of multiple variables. To overcome this, we propose a machine learning (ML)-based multi-agent deep deterministic policy gradient (MADDPG) algorithm. MADDPG enables collaborative learning, adapts to the dynamic communication environment, and excels in optimizing interdependent parameters in the proposed THz system. Deep deterministic policy gradient (DDPG), proximal policy optimization (PPO), and modified-PPO (MPPO) algorithms serve as benchmarks, showcasing the distinctive advantages of the ML-based MADDPG solution for the proposed system’s complexities. Our simulations validate the substantial benefits of ASRIS over conventional RIS benchmark scenarios.

In this study, we investigate the performance of a hybrid reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA) network, augmented with backscattering capabilities, designed to facilitate integrated sensing and communication (ISAC). Our primary objective is two-fold: first, to enhance the overall communication throughput, and second, to strengthen the sensing power for target detection. To achieve these goals, we introduce two novel dynamic user clustering algorithms namely composite distance and angle-based (CDA) and channel-oriented adaptive (COA) algorithm for grouping users into clusters with fixed base station and RIS positions, where the successive interference cancellation (SIC) is employed for effective communication within each pair. Moreover, we present a comprehensive optimization problem that jointly maximizes the sum rate and sensing power. This problem involves optimizing the transmit beamformer at the base station, the power allocation factors within each cluster, and the phase shifts at the RIS. This methodology not only adheres to strict power constraints and quality of service requirements at each receiving node but also ensures equitable resource allocation among the targets and enforces unit modulus phase shifts at each RIS element. To tackle the complex interdependencies and non-convex nature of the optimization problem, we introduce an advanced iterative algorithm based on alternative optimization (AO). This state-of-the-art technique employs successive convex approximation (SCA) to systematically address this multifaceted problem. Finally, the simulation results empirically validate the proposed algorithm’s effectiveness, considering the number of RIS elements, maximum power budget, number of targets, and imperfect channel state information (CSI) while showing the trade-off between communication and sensing performance.

Next-generation wireless applications are expected to enable extended ultra-reliable low latency communication (xURLLC) to support high data rates along with ultra-high reliability and low end-to-end latency features beyond the capabilities of existing core services. These consolidated data rates and URLLC requirements in resource-constrained systems necessitate the shift from conventional architectures to more powerful and robust multiple access schemes. This paper investigates a multi-reconfigurable intelligent surface (RIS)-assisted rate-splitting multiple access (RSMA) to prompt an unconventional xURLLC service called mobile broadband reliable low latency communication (mBRLLC) for high spectral efficiency under finite block-length (FBL) transmission constraints. To enable spectral-efficient resource allocation, we formulate a sum throughput maximization problem for joint optimization of precoder design at the base-station (BS), block-length of common and private symbols of each user, and passive beamforming at each RIS. To solve the NP-hardness and non-convexity of the formulated problem, we use an alternating optimization technique to decouple the original problem into three sub-problems: active beamforming at the BS, block-length optimization, and passive beamforming at each RIS which are solved using general convex approximations. Simulations demonstrate the effectiveness of the proposed resource allocation algorithm over conventional schemes. The considered RSMA system achieves high data rates even with lower latency and higher reliability. Additionally, the investigation encompasses the evaluation of RIS deployment implications, the analysis of the worst-case latency scenario, and the assessment of the influence of channel estimation errors.

The low-resolution reality of the hardware elements associated with massive mmWave antenna or reflector arrays is associated with the performance degradation of the wireless link when it is not properly controlled. In particular, the unintended angular radiations of the transmission or reflection arrays (e.g., transmission in non-intended directions) would invalidate the usual assumptions of information secrecy, even with perfect channel state information (CSI) knowledge at the transmitter, in the presence of low-resolution hardware. In this paper, we study a hybrid beamforming design for reconfigurable intelligent surface (RIS)-assisted multi-user multiple-input multiple-output (MU-MIMO) downlink (DL) communication, from the prospect of information secrecy maximization, wherein the array element phase rotations belong to the known discrete space. To address the NP-hard and non-convex nature of the problem at hand, we propose an iterative procedure by re-structuring the obtained discrete-domain problem into a tractable form which solves the problem numerically and guarantees the convergence to a stationary point. Further, we confirm the accuracy of the proposed optimization algorithm by an exhaustive search method based on graphical simulations. The minimal performance disparity that exists between the proposed algorithm and the considered digital beamforming (DBF) scheme as the upper bound validates the hybrid beamforming design. Moreover, the proposed work highlights the superiority of discrete-aware design over various existing baseline schemes, demonstrating the significant gains attainable by adopting discrete space design from the outset. Additionally, the proposed solution discusses the improvement in secrecy system performance by deploying RIS with an increased number of reflecting elements and thereby restricting the effect of eavesdroppers on secure communication.

This work proposes a new model of reconfigurable intelligent surface (RIS) called cognizable RIS (CRIS) that is specifically designed to meet the unique demands of users who require extreme-ultra-reliable and low-latency Communication (xURLLC) in the sixth generation (6G) wireless networks. The programmable elements in the proposed CRIS unit can adapt to different modes of operation to provide significant performance gain. To improve reliability at the receiver, we integrate unmanned aerial vehicles with the CRIS module, which enhances network performance through beamforming and mobility. Our study focuses on maximizing the sum throughput in a multiple-input multiple-output scenario using the rate-splitting multiple access communication system. To achieve this, we introduce a novel hybridized multi-agent-based deep reinforcement learning (DRL) algorithm for optimal resource allocation that maximizes the sum throughput. We incorporate long-short-term memory (LSTM) networks into our proposed DRL to address the temporal dependencies due to stochastic channel conditions. By utilizing the proposed LSTM-based multi-agent DRL (MA-DRL) algorithm, we achieve notable gains of 11.7% and 26.9% in sum throughput over widely recognized DRL benchmark algorithms, all while adhering to xURLLC’s stringent maximum packet error probability constraint of 10−9.

The proliferation of multiple devices and acceleration of spectral efficiency has become a pivotal requirement for the unprecedented connectivity and performance of vehicle-to-everything (V2X) networks. This paper investigates an unconventional framework of reconfigurable intelligent surface (RIS)-integrated full-duplex (FD) rate-splitting multiple access (RSMA) communication systems, which aims to maximize the spectral efficiency of uplink (UL) and downlink (DL) vehicles in V2X network. In particular, a robust spectral-efficient design for the considered RIS-integrated FD-RSMA system via joint beamforming design and power allocation at UL vehicles under imperfect channel state information is investigated. To tackle the non-convexity of the original sum-rate maximization problem, we adopt a deep reinforcement learning (DRL)-based proximal policy optimization (PPO) algorithm which leverages Markov decision process formulation. Simulation results demonstrate the effectiveness of the integration of RIS, RSMA, and FD schemes for V2X networks over half-duplex (HD) and multi-user linear precoding schemes. Furthermore, the superiority of the proposed PPO algorithm is validated over the counterpart deep deterministic policy gradient algorithm (DDPG).

This paper investigates the problem of spectral efficiency maximization in an underlay cognitive radio network (CRN) utilizing rate-splitting multiple access (RSMA) transmission for MISO downlink under short packet communications and imperfect channel estimation information. In particular, we focus on an effective transmit beamforming design at the cognitive base station while satisfying the requirements of ultra-reliable and low-latency communication (URLLC), interference temperature, power budget, and minimum throughput. We model the dynamic resource allocation problem as a Markov decision process (MDP) and employ deep reinforcement learning techniques, specifically the deep deterministic policy gradient (DDPG) and proximal policy optimization (PPO) algorithms, while taking into account the time-varying channel conditions. Simulation results demonstrate that the DDPG algorithm outperforms PPO at low interference temperatures for the primary receiver, while the opposite holds at high interference temperatures. Moreover, the considered RSMA system for CRN outperforms traditional multi-user linear precoding and power-domain multiple access schemes while maintaining small packet sizes and high reliability.

The cell-free massive multiple-input and multiple-output (CF-mMIMO) communication technology has the ability to handle inter-cell interference in MIMO systems, making it a potential candidate for sixth-generation (6G) wireless communication. A CF-mMIMO system is investigated in this paper for mission-critical ultra-reliable low latency communication (URLLC) applications involving a central processing unit (CPU), many distributed access points (APs), each with multiple antennas, and multiple single-antenna user equipment (UEs). In order to maximize energy efficiency (EE) and throughput gains, each AP is linked to the CPU through a fronthaul link with limited capacity, which handles the quantized uplink data to the CPU. We assume that each AP serves fewer UEs. Our approach has a minimal signal processing complexity and offers UEs uniform quality of service (QoS) as well as improved EE. Closed-form expression for outage probability (OP) in the uplink of the CF-mMIMO system considering Welch-Satterthwaite approximation is derived using a variety of Doppler power spectra (DPS) models that consider imperfect channel state information (CSI) and mobility of UEs. Our numerical simulations validate the correctness of the derived expressions.

This paper investigates an unconventional framework of rate-splitting multiple access (RSMA)-integrated full-duplex (FD) system to attain spectral-efficient multi-user communication. The considered FD-RSMA system divides and encodes the original messages of each downlink (DL) and uplink (UL) into two different sub-messages, and later transmits them at the same resource block, resulting in strong inter-user interference and cross-link interference, i.e., self-interference (SI) and co-channel interference (CCI). Specifically, we focus on maximizing the sum rate of the considered FD-RSMA system via joint power allocation for simultaneous UL and DL communication, subject to transmit power constraints and given quality of service (QoS) requirements. To tackle the non-convexity of the formulated problem, we adopt an iterative algorithm that employs semidefnite programming (SDP), majorization minimization (MM), and inner approximation (IA) techniques to attain near-optimal resource allocation with effective interference management. Simulation results validate that the FD-RSMA scheme outperforms conventional half-duplex, multi-user linear precoding, and non-orthogonal multiple access schemes.

In order to support the need for higher spectral and energy efficiencies with a wider coverage area, simultaneous refracting/transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and rate splitting multiple access (RSMA) have emerged as the potential technologies required for architectural advancement in the next-generation wireless communication networks. In this work, we propose a novel analytical framework of an RSMA-enhanced STAR-RIS-aided downlink multi-user communication system. First, we discuss the statistical characteristics of the different channels involved in the transmission and derive their probability density function (PDF). Using the derived PDF, we analyze the performance of the system and derive the analytical closed-form expressions of the outage probability at each reflecting and refracting downlink user for two different STAR-RIS operational protocols namely i) energy splitting (ES) and ii) mode switching (MS). Furthermore, we validate the accurateness of the all analytical expressions through Monte-Carlo (MC) simulations. We also highlight the impact of some important parameters of the system such as transmit power at the BS, elements in the STAR-RIS, imperfect channel state information (CSI) on the outage probability of each user. Finally, we demonstrate the dominance of RSMA over non-orthogonal multiple access (NOMA) on the system performance.

Reconfigurable intelligent surface (RIS) is a break-through technology that enhances both energy efficiency (EE) and spectrum efficiency (SE) by artificial reconfiguration of the electromagnetic waves utilizing the reflective property of the metasurface elements. This work studies the optimization of the SE-EE trade-off using the deep reinforcement learning (DRL) algorithm in a RIS-assisted full-duplex multi-user multiple-input multiple-output (MIMO) communication system. We use partial channel state information to control the overhead signaling requirement and demand for energy supply to the system. We consider resource efficiency (RE), in which the RIS’s phase-shift design and power allocation at the nodes (i.e., node in BS in downlink (DL) and user in uplink (UL)) are jointly optimized, with the goal of investigating the SE-EE trade-off of the considered system using an appropriate performance metric. We adopt a DRL-based approach for the proposed system to tackle the challenges involved in optimization due to time-varying channels and exploitation in real-time applications. Additionally, simulation outcomes exemplify the efficiency and swift conver-gence rate of the proposed algorithm and demonstrate how different system characteristics, including co-channel interference (CCI), residual self-interference (RSI), and the number of RIS reflecting elements, affect the system’s performance.

In this work, we consider a novel reconfigurable intelligent surface (RIS)-assisted full duplex (FD) sixth generation (6G)-vehicle-to-everything (V2X) communication network having a FD base station (BS) simultaneously communicating with an uplink (UL) and a downlink (DL) mobile vehicles with the aide of two RISs, one for each link. We provide an analytical framework to investigate the performance of this network and, consequently, formulate an optimization problem to jointly optimize the phase-shift matrices at both the RISs that maximizes the achievable sum-rate. Thereafter, we propose a successive refinement algorithm which uses an iterative approach to solve the problem and provide optimum values of phase-shift matrix at each RIS. We validate the accuracy of the proposed algorithm by exhaustive simulation based graphical results. Accordingly, we demonstrate the dominance of the considered FD system over its half-duplex (HD) counterpart. Moreover, we also highlight the impact of imperfect self interference cancellation and discuss the trade-off between the UL and DL performances due to this imperfection.

Digital twin (DT) has emerged as a promising technology for improving resource allocation decisions in Internet of Vehicles (IoV) networks. In this paper, we consider an IoV network where mobile edge computing (MEC) servers are deployed at the roadside units (RSUs). The IoV network provides ubiquitous connections even in areas uncovered by RSUs with the assistance of unmanned aerial vehicles (UAVs) which can act as a relay between RSUs and task vehicles. A virtual representation of the IoV network is established in the aerial network as DT which captures the dynamics of the entities of the physical network in real-time in order to perform efficient resource allocation for delay-intolerant tasks. We investigate an intelligent delay-sensitive task offloading scheme for the dynamic vehicular environment which provides computation resources via local execution, vehicle-to-vehicle (V2V), and vehicle-to-roadside-unit (V2I) offloading modes based on the energy consumption of the system. Moreover, we also propose a multi-network deep reinforcement learning (DRL)-based resource allocation algorithm (RADiT) in the DT-assisted network for maximizing the utility of the IoV network while optimizing the task offloading strategy. Further, we compare the performance of the proposed algorithm with and without the presence of V2V computation mode. RADiT is further evaluated by comparing it with another benchmark DRL algorithm called soft actor-critic (SAC) and a non-DRL approach called greedy. Finally, simulations are performed to demonstrate that the utility of the proposed RADiT algorithm is higher under every condition compared to its respective conditions in SAC and greedy approach. Consequently, the proposed framework jointly improves energy efficiency and reduces the overall delay of the network. The proposed algorithm with UAV relay further increases the efficiency of the network by increasing the task completion rate.

In this paper, we explore the use of reconfigurable intelligent surface (RIS) in unmanned aerial vehicle (UAV) based multiuser downlink communications, where a flying UAV serves multiple single antenna users through multiple RISs mounted on various buildings. More specifically, we consider the selection of RISs based on the outdated and imperfect channel state information (CSI) of the composite UAV-RIS-User channels at the UAV. After selection process, the UAV communicates to the user via the selected RISs and also with the direct link. Particularly, we derive an infinite series based expression for selection probability of RISs under both the outdated and imperfect CSI of composite channels based selection scheme. We also derive the statistical distribution of instantaneously received signal-to-noise ratio (SNR) under outdated and imperfect CSI conditions of both the direct and composite links at the user. Next, using the derived statistics, we analyze the network’s performance in terms of the average coverage probability (ACP) and average bit error rate (ABER) over the complete UAV flight time. Moreover, we discuss the behavior of ACP and ABER for very small and very large values of UAV transmit power, respectively. It is depicted through numerical results that selecting more RISs from a group of small-sized RISs may not be as advantageous as selecting fewer RISs from a group of large-sized RISs. Moreover, we also demonstrate the effect of several system parameters such as number of RIS reflecting elements, number of selected RISs, the severity of UAV-RIS and RIS-User links, and the severity of imperfect and outdated CSI on the network’s performance. The analytical results are corroborated with Monte-Carlo simulations.

The efficient and effective framework for next-generation (5G and beyond 5G) wireless networks should include mission-critical aspects such as ultralow latency ($\leq \!\!1$ms), ultrahigh reliability (99.999%), and enhanced data rate. Billions of ubiquitously connected devices are expected to serve various industrial applications in upcoming industry standards such as Industry 5.0. These industrial applications include mission-critical tasks such as smart grids, remote surgery, and intelligent transportation systems. This article considers an industrial Internet of Things (IIoT) environment in mission-critical ultrareliable low latency communication (URLLC) application where the main industrial unit or industrial control node (CN) sends messages to the target device (TD) with the aid of a cooperative device (CD). We investigate a novel transmission protocol and analyze the network’s performance. Considering the nonlinear energy harvesting (EH) mechanism at power-constrained nodes and direct and cooperative phase transmissions, the outage probability (OP) and block error rate (BLER) performances are evaluated for Rayleigh distributed fading channels. The analytical results are validated through Monte–Carlo simulations.

In this paper, an intelligent reflecting surface (IRS) aided uplink (UL) rate-splitting multiple access (RSMA) system is investigated for dead-zone users where the direct link between the users and the base station (BS) is unavailable and the UL transmission is carried out only through IRS. In the considered RSMA system, a message of each user is split into several sub-messages and each part contributes to the rate of that user and depending upon split proportions BS decodes them using appropriate decoding order. The problem of sum-rate maximization is formulated to jointly design the optimal power allocation at each UL user, passive beamforming at the IRS under optimal decoding order of sub-messages. Due to non-convexity and discrete non-linear programming of the formulated problem, the original problem is intractable and hence, we decouple the problem into different sub-problems in which the problems of power allocation and passive beamforming are alternatively solved under using successive convex approximation and Riemaniann conjugate gradient algorithms, respectively. Moreover, the decoding order strategy is analytically derived which confirm that the optimal decoding order strategy depend upon decreasing order of channel gain of users and increasing order of split proportions of sub-messages. Later, the unified solution based on block-coordinate descent (BCD) algorithm is proposed. Simulation results validate that the proposed decoding order scheme attains performance closer to the optimal solution with low computational complexity. Moreover, the proposed IRS aided RMSA system outperforms the system with non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes in terms of achievable sum-rate throughput.

In this paper, a phase-shift coupled simultaneous transmitting/refracting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided uplink (UL) rate-splitting multiple access (RSMA) system is investigated to achieve improved spectral efficiency. The considered UL RSMA system splits the rate for each user by dividing their message into multiple sub-messages and these sub-messages are transmitted to the base station (BS) via STAR-RIS as direct link between BS and user is absent. In particular, we formulate a resource allocation design problem which aim to maximize the overall rate-throughput of the considered system under the joint optimization of power allocation, decoding order, user-fairness and beamforming design at STAR-RIS for various operating modes of STAR-RIS modes, which is mixed- integer non-linear programming (MINLP). To solve the formulated non-convex complex problem, we first transform the original sum-rate maximization into its simplified form and then solved it using an alternating optimization algorithm where the sub-problems of power allocation and beamforming design under given decoding order are solved alternatively using general convex approximation and fractional programming approaches. Numerical simulation and computational complexity analysis validate that the proposed solution attains fast convergence. Moreover, the proposed RSMA scheme in STAR-RIS aided UL system outperforms the conventional nonorthogonal multiple access and orthogonal multiple access schemes in terms of overall rate-throughput and user-fairness.

In this work, we investigate rate-splitting multiple access (RSMA) for a multiuser downlink wireless network consisting of an unmanned aerial vehicle (UAV)-assisted base station (BS) that serves multiple ground users (GUs) simultaneously. Considering two different transmission regimes, namely infinite blocklength (IBL), and finite blocklength (FBL), we analyze the performance of the considered network under the effect of imperfections in channel state information (CSI) estimation and successive interference cancellation (SIC) with the probabilistic line of sight fading channels. For IBL transmission, we derive the closed-form expressions of the outage probability, throughput, and achievable ergodic rate at each GU. Furthermore, for short packet communication, the closed-form expressions of block error rate (BLER), goodput, and achievable ergodic rate are determined with FBL transmission. Moreover, for the FBL regime we also formulate an optimization problem that jointly optimizes the 3D-position of the UAV, power allocated to each user, and common rate distribution at each user to maximize the ergodic sum rate subject to the practical constraints such as maximum tolerable BLER, minimum private and total rate at each user. We then propose an alternating optimization-based iterative algorithm to solve the problem. Monte-Carlo simulations are used to verify the accuracy of derived analytical results and demonstrate the trade-off between transmit power and achievable BLER. In addition to this, the effectiveness of RSMA in UAV-assisted communication under IBL and FBL transmission regimes is also observed compared to non-orthogonal multiple access.

In this work, we consider a hybrid aerial full-duplex (FD) relaying consisting of a reconfigurable intelligent surface (RIS) mounted over an FD unmanned aerial vehicle (UAV) relay operating in decode and forward mode to assist the information transfer between the base station and multiple users. For better spectral efficiency, we investigate the use of rate splitting multiple access (RSMA) in such networks and focus on joint optimization of RSMA parameters, 3D-coordinates of the UAV/RIS, and phase shift matrix at the RIS along with analyzing the outage probability, block error rate (BLER) and achievable weighted sum rate for finite blocklength (FBL) and infinite blocklength (IBL) codes under imperfect successive interference cancellation (SIC) at each user and residual-self interference (RSI) at the UAV. We first formulate the weighted sum rate maximization problem and adopt the block coordinate descent (BCD) method to deal with the non-convex nature of the problem. Thereafter, we propose a BCD-based algorithm that jointly optimizes these parameters using a heuristic approach for optimum power allocation, a Riemannian conjugate gradient-based algorithm to get the optimal phase shift at the RIS, and an iterative algorithm to obtain the optimal UAV/RIS position. It also distributes the common rate among the users optimally. Next, with obtained optimal parameters, we further analyze the performance of the network and derive the closed-form expressions of BLER, outage probability, and average weighted sum rate. We present Monte Carlo simulation-based results to validate the accuracy of the proposed algorithms and derived expressions, and demonstrate the superiority of RSMA over non-orthogonal multiple access (NOMA) and conventional orthogonal multiple access (OMA) schemes.

Reconfigurable intelligent surface (RIS) is an emerging next-generation technology that can improve the energy efficiency using multiple low-power passive metasurfaces which reflect the desired signal to the users. In this work, we consider a RIS-assisted underlay multiuser multiple-input multiple-output cognitive radio network and formulate a weighted energy efficiency maximization problem in order to jointly optimize the active precoding matrix (APM) at the secondary transmitter and passive precoding matrix (PPM) at the RIS subject to the constraints of available transmission power at the secondary transmitter and maximum allowable interference towards the primary user/receiver. However, due to the coupling of APM and PPM variables, the problem becomes non-convex, and conventional optimization methods cannot be used to solve it. Therefore, by adopting the weighted minimum mean-square error method we first transform the non-convex objective function into a convex one. Next, based on the block coordinate descent method, we propose an iterative algorithm that determines the optimal APM and PPM using Lagrange dual decomposition method and inner approximation method, respectively. Finally, the optimality and efficacy of the proposed algorithm are validated using numerical simulations. The impact of the channel state information (CSI) error has been studied via numerical simulations on the proposed network and it shows that the proposed design is relatively robust against the CSI imperfections, especially at low SNR conditions.

Reconfigurable intelligent surface (RIS) has emerged as an important transmission technology for numerous applications in Internet of Things (IoT) systems. Thus, in this paper, we investigate the application of RIS in energy harvesting (EH) based unmanned aerial vehicle (UAV) communication network with finite block length (BL) codes, where a rotary wing type flying UAV communicates with the multiple single antenna IoT users with the aid of multiple RISs mounted on several skyscraper buildings. To transmit the signal to a particular IoT user, the UAV selects an RIS on the basis of either UAV-RIS (i.e., partial) or UAV-RIS-IoT (i.e., full) channel state information (CSI) and then transmits the signal through the selected RIS along with the direct link transmission. In particular, we derive (i) the expression for probability of RIS selection, (ii) the statistical distribution of instantaneously received information signal-to-noise ratio (SNR) at the IoT user. Based on the derived statistics, we analyze the performance of the considered system under finite BL codes in terms of the average outage probability, average block error rate (ABLER) and goodput averaged over entire flying duration. Moreover, the BLER performance with finite BL codes is also compared with the infinite BL codes scenario. Additionally, we also investigate the impact of various channel and system parameters like imperfect CSI, number of RISs and the number of reflecting elements at each RIS, location of IoT users, variable altitude of the UAV, and the severity of channel fading of UAV-RIS link on the system performance. Furthermore, we have obtained the optimum UAV location in each time slot which minimizes the ABLER per time slot over all the users in the network. The analytical results are corroborated with Monte Carlo simulations.

In this paper, a reconfigurable intelligent surfaces (RISs)-aided millimeter wave (mmWave) uplink (UL) rate-splitting multiple access (RSMA) system is investigated which targets to achieve better rate performance and enhanced coverage capability for multiple users. The considered UL RSMA model splits the rate for each user by dividing their message into multiple parts and hence exploits all the necessary degrees of freedom to achieve maximum capacity region and high user fairness. In particular, we focus on the sum-rate maximization for considered UL RSMA system subject to joint optimization of power allocation to the UL users and beamforming design, i.e., active receive beamforming at the base-station (BS) and passive beamforming at multiple RISs. To efficiently mitigate high inter-node interference in multi-user scenario, we first provided a low-complex user pairing scheme based on k-means clustering and then develop an effective low-cost alternating optimization framework to solve the joint optimization problem sub-optimally by decoupling the problem into different sub-problems of power allocation and beamforming design. Specifically, the sub-problems of power allocation and beamforming design are solved using successive convex approximation, Riemannian manifold and fractional programming techniques. Later, the unified solution based on block coordinate descent (BCD) algorithm is proposed. Extensive numerical simulations validate that the user-clustering effectively significantly improves the performance gain and the considered RSMA system outperforms the conventional multiple schemes in terms rate and user-fairness. Also, the exploitation of spatial correlation among each RIS elements i.e., non-diagonal phase-matrices at each RIS achieve better performance that conventional diagonal phase-matrices setting.

Owing to the factors such as controllable mobility, ready-to-use technology, low cost, easy implementation, and so on, unmanned aerial vehicle (UAV) possesses tremendous potential to be one of the primary candidates for next-generation (6G) wireless networks. This paper presents a UAV-assisted multiuser communication network where a multiple antenna UAV base station (BS) serves multiple single antenna ground users (GUs). UAV-BS uses a laser source-based charging mechanism to fulfill its power requirement and applies simultaneous wireless information and power transfer (SWIPT) in the downlink in order to provide desired power to energy-constrained GUs. Also, a clustering-based hybrid multiple access technique is used that combines both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) signaling to transmit the information to all GUs, simultaneously. Due to the involved analytical complexity corresponding to the multiple antennas and users, we use a hybrid beamforming method for efficient communication. Next, we analyze the performance of the proposed framework in terms of user outage probabilities, their respective throughput, and average power harvested considering non-linear energy harvesting and derive expressions of these performance metrics. Moreover, we formulate an optimization problem where the throughput of one GU is maximized by optimally choosing the power allocation parameter while ensuring the desired target throughput at other GU in each cluster. We also illustrate how crucial is the optimal selection of the target rates to maximize the network performance. Simulation results are provided to validate the accuracy of derived expressions and to highlight the dominance of hybrid beamforming and hybrid NOMA compared to conventional methods on the performance of the considered network.

In this work, we consider a hybrid aerial full-duplex (FD) relaying protocol consisting of a reconfigurable intelligent surface (RIS) mounted over an FD unmanned aerial vehicle (UAV) relay operating in the decode and forward mode to assist the information transfer between the base station and multiple users. For better spectral efficiency, we investigate the use of non-orthogonal multiple access (NOMA) in such networks and focus on both the performance analysis and design optimization of the considered RIS-NOMA network under imperfect channel state information (CSI) and successive interference cancellation (SIC) at each user, and residual-self interference (RSI) at UAV. We first formulate the sum rate maximization problem and adopt the block coordinate descent method to deal with the non-convex nature of the problem. Thereafter, we propose an algorithm based on the Riemannian conjugate gradient method to get the optimal phase shifts at the RIS, an iterative algorithm to obtain the optimal UAV/RIS position and the exhaustive method to obtain the optimum power allocation coefficients. Next, with obtained optimal position, phase shift and power coefficients, we further analyze the performance of the network and derive the closed-form expressions of outage probability, achievable throughput and ergodic capacity. We present Monte Carlo simulation-based results to validate the accuracy of the proposed algorithms and derived expressions and demonstrate the superiority of NOMA over OMA.

Due to the dynamic nature of a vehicular fog computing environment, efficient real-time resource allocation in an Internet of Vehicles (IoV) network without affecting the quality of service of any of the onboard vehicles can be challenging. This article proposes a priority-sensitive task offloading and resource allocation scheme in an IoV network, where vehicles periodically exchange beacon messages to inquire about available services and other important information necessary for making the offloading decisions. In the proposed methodology, the vehicles are stimulated to share their idle computation resources with the task vehicles, whereby a deep reinforcement learning algorithm based on soft actor–critic is designed to classify the tasks based on priority and computation size of each task for optimally allocating the power. Furthermore, we also design deep deterministic policy gradient (DDPG) and twin delayed DDPG (TD3) algorithms for the considered framework. In particular, the algorithms work toward achieving the optimal policy for task offloading by maximizing the mean utility of the considered network. Extensive numerical results under different network conditions, along with comparison among the three algorithms, are presented to validate the feasibility of distributed reinforcement learning for task offloading in future IoV networks.

Reconfigurable intelligent surface (RIS) has emerged as important transmission technology to improve the spectral/energy efficiency in the next-generation (beyond 5G (B5G) and 6G) wireless communication network and has numerous applications in the areas of Internet of Things (IoT) and vehicular communication systems. Thus, in this paper, we investigate the application of RIS in unmanned aerial vehicle (UAV) enabled vehicular communication system with infinite and finite block length codes, where UAV communicates with the single antenna ground vehicle in the presence of several interfering vehicles on the road. We have obtained the approximate closed-form statistics of received SINR at ground vehicle in the presence of multiple nonidentical interference links. Furthermore, we analyze the performance of the considered system in terms of the coverage probability, bit-error-rate, block error rate (BLER) and goodput. It has been shown through the numerical results that the deployment of RIS significantly improves the performance of UAV-enabled vehicular communication network, even in the presence of the direct link between the UAV and the ground vehicle. Additionally, we also investigate the impact of various channel and system parameters like practical reflection coefficients of RIS, number of RIS reflecting elements, and number of interfering vehicles on the system performance. The analytical results are corroborated with Monte Carlo simulations.

This paper investigates unmanned aerial vehicles (UAVs)-assisted full-duplex (FD) non-orthogonal multiple access (NOMA) system based cellular network, aiming to improve overall sum-rate throughput of the system through dynamic user clustering, optimal UAV placement and power allocation. Since each UAV operates in FD mode, self-interference (SI), co-channel interference (CCI), inter-UAV interference (IUI) and intra-node interference (INI) dominate the system’s performance. Consequently, we propose an unconventional two-stage dynamic user clustering for user nodes (UNs) to reduce the cross-interference in multi-UAV aided FD-NOMA system. Particularly, all UNs are initially clustered into$K$clusters using k-means clustering in the first stage where each cluster is served by an UAV. Furthermore, each cluster is further divided into sub-clusters and each sub-clusters are operated in FD-NOMA scheme. Finally, to control interferences, a sum-rate throughput maximization problem is formulated for each UAV to jointly optimize uplink and downlink power allocation and UAV placement. The joint optimization problem is non-convex and difficult to solve directly, for which we decoupled the original problem by addressing UAV placement and power allocation separately. We first fix the UAV position and then solve the problem iteratively using successive convex approximation (SCA) method. By utilizing brute-force search algorithm, an optimal UAV placement is later performed which corresponds to maximum possible sum-rate throughput. Simulation results demonstrate that the proposed solution for the considered FD-NOMA system outperforms the conventional schemes.

We consider a full-duplex (FD)-enabled multi-user two-way communication system with adaptive amplify-and-forward (AF)/decode-and-forward (DF) relaying protocol. All the users are assumed to be mobile and to have FD abilities. The effect of mobility, which results in time-selective fading, is modeled using a first-order autoregressive (AR1) process. The fading channel-based approach characterizes the residual self-interference (RSI) at the FD relay, user nodes and is modeled as a Rician distributed random variable. This paper constitutes the most critical use case of 5G, i.e., ultra-reliable low latency communication (URLLC), which adopts short-packets finite blocklength (FB) codes to spin out into the mission-critical applications where strict latency and reliability requirements are highly desirable. The outage performance of the system is studied over independent and non-identically distributed complex Gaussian (Rayleigh envelope) channels with imperfect channel state information (CSI) for with and without URLLC use cases. The closed-form expressions for the outage probability and block error rate (BLER) are derived for the absolute channel power-based scheduling scheme considering the different Doppler power spectra models and the effect of co-channel interference (CCI). The expressions for the asymptotic outage probability are also derived. The presented analysis is compared with baseline schemes, e.g., the results derived with adaptive AF/DF relaying are also compared with both AF and DF relaying, and the performance of the FD transmissions is compared to that of half-duplex (HD) transmissions. The impact of node mobility, RSI, FBL, number of user pairs, and imperfect CSI on the system performance is investigated. Moreover, essential insights are obtained related to the performance gain and region of the superiority of the adaptive AF/DF relaying scheme. The derived analytic results are validated through Monte Carlo simulations. Furthermore, at high transmit power, the outage performance for the adaptive AF/DF protocol approaches the derived asymptotic floor.

We consider a multi-hop cooperative network inside a factory environment with the number of devices which are placed uniformly in a strip-shaped manner. the randomly deployed multiple intermediate relay nodes serve source and destination using the opportunistic large array (OLA) cooperative communication protocol for packet transmission. However, the simultaneous transmission of packets results in interference when the multiple transmissions occur simultaneously in the multihop wireless network. This paper analyzes the impact of such interference in the considered factory automation scenario from the Industry 4.0 perspective. We analyze the system performance in terms of the outage probability, success rate and latency with various network parameters. Moreover, the key insights are obtained related to the impact of packet insertion rate and tiers of interference on the system performance. Specifically, it is observed that the success rate of considered cooperative OLA network significantly increases due to the spatial diversity gains. However, the interference of multiple packets transmission severely affects the network success rate.

Spatial multiplexing aided spatial modulation (SMx-SM) is a communication technology which essentially combines the concepts of Vertical Bell Labs Layered SpaceTime (V-BLAST) and spatial modulation (SM). In this paper, we construct a high-rate spatial multiplexing aided generalized spatial modulation (SMx-GSM) scheme which guarantees a very low detection complexity. Benefiting from the codebook structure, a tree-search based MIMO detector for the SMx-GSM scheme can be devised. Simulation results show that the detection complexity can be significantly reduced, which makes a highrate system feasible, and an error performance which is very close to that of the maximum-likelihood (ML) detector can be achieved, especially for the case of a high-rate large-scale SMx- GSM system with a large number of activated transmit antennas.

In this work, we investigate an unmanned aerial vehicle (UAV)-aided novel hybrid wireless communication network consisting of a cellular user and a small internet of things (IoT) network having one low-power IoT hub which serves a sensor node. During the first signalling phase, the UAV-assisted base station (BS) uses non-orthogonal multiple access signalling to serve the cellular user and the IoT-hub, simultaneously. In the second phase, the cellular user uplinks the control signal to UAV-BS, whereas, at the same time the IoT-hub communicates with the sensor node using the power harvested by applying simultaneous wireless information and power transfer (SWIPT) and nonlinear energy harvesting. We derive the closed-form expressions of the achievable ergodic capacity of the cellular user and the IoT-hub during the first signalling phase and the sensor node and the UAV-BS during the second phase considering nonlinear energy harvesting at the IoT hub. Further, we demonstrate the trade-off between available transmit power at UAV-BS, IoT-hub (harvested power), and cellular user to achieve desired capacity at the sensor node and UAV-BS. We validate the accuracy of derived expressions by using numerical simulations.

In this article, we investigate the nonlinear energy harvesting (EH)-based unmanned aerial vehicle (UAV)-assisted full-duplex (FD) Internet of Things (IoT) network with infinite and finite blocklength (FBL) codes. The reliability performance of the considered network, having two half-duplex UAVs and an FD IoT device, is analyzed in terms of the block error rate (BLER) with given ultrareliable and low-latency communication constraints. With the assumption of the combined effect of fading and shadowing, the closed-form expressions for BLER and network goodput are obtained over the Rician shadowed fading channels considering various shadowing scenarios, EH receiver architecture, IoT device mobility, inter-UAV interference, and self-interference (SI) cancelation capabilities at FD IoT device. The obtained results over the Rician shadowed fading for the nonlinear EH receiver architecture are also compared with the linear EH and over the Rician fading channels. The numerical results reveal important observations related to the impact of time-selective fading channels with imperfect channel state information, shadowing severity in the suburban areas, SI cancelation capabilities, blocklength, and the number of channel uses on the reliability performance of the UAV-assisted FD IoT network. Furthermore, the tightness of the approximation presented is verified through the Monte-Carlo simulations.

Intelligent reflecting surface (IRS) is being considered as a prospective candidate for next generation wireless communication due to its ability to significantly improve coverage and spectral efficiency by controlling the propagation environment. One of the ways IRS increases spectral efficiency is by adjusting phase shifts to perform passive beamforming. In this letter, we integrate the concept of IRS aided communication to the domain of multi-direction beamforming, whereby multiple receive antennas are selected to convey more information bits than existing spatial modulation (SM) techniques at any specific time. To complement this system, we also propose a successive signal detection (SSD) technique at the receiver. Numerical results show that the proposed design is able to improve the average successful bits transmitted (ASBT) by the system, which outperforms other state-of-the-art methods proposed in literature.

A reconfigurable intelligent surface (RIS)-assisted wireless communication system with non-linear energy harvesting (EH) and ultra-reliable low-latency constraints is considered for its possible applications in industrial automation. A distant data-center (DC) communicates with the multiple destination machines with the help of a full-duplex (FD) server machine (SM) and RIS. Assuming the deficiency of enough transmission power at the FD-SM, the SM is considered in the near vicinity of the destinations in the industry to forward the data received from the distant DC. The reception at SM is assisted by the RIS and a non-linear hybrid power-time splitting (PTS) based EH receiver architecture is adopted to extend the lifespan of SM, thus increasing network lifetime. The scheduling of multiple destinations is done by SM based on the considered selection criteria namely, random (RND) scheduling, absolute (ABS) channel-power-based (CPB) scheduling and normalized (NRM) CPB scheduling. The end-to-end performance of the considered FD RIS-assisted network is analyzed, and the expressions for the block error rate (BLER) for all scheduling schemes are derived. Moreover, the effects of number of RIS elements, packet size, channel uses on the system performance are analyzed for the considered ultra-reliable and low-latency communication (URLLC) network. The scheduling fairness of all the scheduling schemes is also analyzed to study the performance-fairness trade-off. The derived analytical results are verified through Monte-Carlo simulations.

In this paper, a multi-user full-duplex (FD) two-way relaying system with decode-and-forward protocol is considered where all the nodes are assumed to be mobile and to have FD abilities. The residual self-interference (RSI) at each node is modeled as a Rician distributed random variable and the effect of mobility is incorporated by adopting the first order auto-regressive (AR) model. Then, the signal-to-interference-plus-noise-ratio (SINR) based scheduling schemes namely random scheduling, absolute channel power-based (CPB) scheduling, hybrid scheduling, and normalized CPB scheduling, are modified and the user pair selection is carried out on the basis of instantaneous channel statistics. The performance of the considered framework is analyzed for the modified scheduling schemes in terms of error probability and average rate. Next, the closed-form expressions for the symbol error probability (SEP) and average rate are derived for independent and non-identically distributed Rayleigh fading channels. Also, the numerical analysis highlighting the impact of user mobility and imperfect channel estimation on the scheduling strategies, fairness is presented. Furthermore, the impacts of the degree of traffic asymmetry, scheduling fairness, user mobility, channel estimation errors and RSI on the system performance are investigated. Numerical results are validated through the Monte Carlo simulations.

In this paper, we focus on near-optimal designs for variable-length error-correcting (VLEC) codes, while considering the performance in terms of both the average codeword length (ACL) and the symbol-error rate (SER). The criteria for narrowing the search space and enhancing the SER performance are investigated. An efficient code construction algorithm is then devised based on a random search process. Taking advantage of the significantly reduced search complexity, we are able to construct near-optimal VLEC codes for large source alphabets. Numerical results obtained for various free distance values and alphabet sizes show that constructed VLEC codes have both reduced ACL values and enhanced SER performances. Performance improvement is more notable for large source alphabets.

A sequence is said to be perfect if it has an ideal periodic autocorrelation function. In addition, the degree of a sequence is defined as the number of distinct nonzero elements within each period of the sequence. This study presents a systematic method for constructing perfect sequences (PSs) of odd prime periods, where the general constraint equations for the sequence coefficients are derived, providing a solid theoretical foundation to the construction of PSs. The proposed scheme commences by partitioning a cyclic group ZP= {1, 2,⋯, P - 1} into K cosets of cardinality M, where P = K · M + 1 is an odd prime. Based on this partition, the degree-(K + 1) PSs are then constructed. Finally, case studies are presented to illustrate the proposed construction.

In this paper, a cooperative single-carrier transmission scheme considering a distributed quasi-orthogonal space-frequency block code (QOSFBC) that provides improved peak-to-average power ratio (PAPR) performance is constructed for uplink communications. Focused on reducing the size and computational complexity, the source node, i.e., the mobile devices, is equipped with two transmit antennas, while only one antenna is required at the relay node in order to collaboratively generate a distributed QOSFBC signal using four transmit antennas. To achieve full diversity, a phase rotation strategy is proposed at the relay node. In addition, since single-carrier schemes considering SFBCs re-permute the spectral components of the transmitted signal and induce high PAPR value, a modified SFBC coding technique is proposed that allows the high-PAPR problem to be mitigated. Consequently, the designed distributed single-carrier frequency-division multiple access (SC-FDMA) QOSFBC scheme is able to provide full transmit diversity over frequency-selective fading channels, with a lower PAPR value on mobile devices when compared with a conventional SC-FDMA QOSFBC scheme.

Serious multiple access interference (MAI) exists in uplink transmission of a multi-carrier code division multiple access (MC-CDMA) system in frequency-selective fading channels because orthogonality among codes cannot be restored. A novel MC-CDMA system is proposed in this paper to avoid MAI by employing Fourier duals of sparse perfect Gaussian integer sequences (SPGISs) as frequency-domain spreading codes. The SPGISs are obtained by linearly combining four base sequences or their cyclic-shift equivalents using nonzero Gaussian integer coefficients of equal magnitudes. The number of nonzero elements of SPGISs is 16 at most. The Fourier dual is the fast Fourier transform (FFT) of a SPGIS. Thus, when the Fourier duals of SPGISs are employed as frequency-domain spreading codes, the corresponding time-domain spreading codes are SPGISs. Furthermore, the modulated symbols of users should be properly allocated and the receiver architecture should be redesigned to avoid MAI. The computational complexity of the proposed MC-CDMA system is much lower than that of the traditional MC-CDMA system at the transmitter. Simulation results demonstrate both the bit error rate and peak-to-average power ratio performance of the proposed MC-CDMA system outperform those of the orthogonal frequency division multiple access, single-carrier frequency division multiple access, and the traditional MC-CDMA systems.

A complex number whose real and imaginary parts are both integers is called a Gaussian integer . A Gaussian integer sequence is said to be perfect if it has an ideal periodic autocorrelation function (PACF) where all out‐of‐phase values are zero. Further, the degree of a Gaussian integer sequence is defined as the number of distinct non‐zero Gaussian integers within one period of the sequence. Recently, the perfect Gaussian integer sequences have been found important practical applications as signal processing tools for orthogonal frequency‐division multiplexing systems. The present article generalises the authors’ earlier paper by Lee et al. (2015) related to the Gaussian integer sequences with ideal PACFs. By the applications of two‐tuple‐balanced binary sequences and cyclic difference sets, a number of new degree‐2 perfect Gaussian integer sequences with different periods are obtained.

A Gaussian integer is a complex number whose real and imaginary parts are both integers. Meanwhile, a sequence is defined as perfect if and only if it has an ideal periodic autocorrelation function. This paper proposes a method for constructing sparse perfect Gaussian integer sequences (SPGISs) in which most of the sequence elements are zero. The proposed SPGISs are obtained by linearly combining four base sequences or their cyclic-shift equivalents using nonzero Gaussian integer coefficients of equal magnitudes. Each base sequence contains four nonzero elements belonging to the set {±1, ±j}. The number of nonzero elements of the constructed SPGISs depends on the choice of complex coefficients and cyclic shifts. However, each SPGIS has at most 16 nonzero elements, irrespective of the sequence length. A systematic investigation is performed into the properties of the SPGISs and their Fourier dual equivalents. Finally, a general expression is derived for a perfect Gaussian integer sequence (PGIS) of length 4n, where n is any positive integer and most of the sequence elements are nonzero.

Multi-carrier code division multiple access (MC- CDMA) systems experience serious multiple access interference in uplink transmission since the orthogonality among codes is destroyed by the frequency-selective fading channels. The comb- spectrum (CS) CDMA system was proposed to overcome this drawback by orthogonally assigning subcarriers to various users. However, the CS-CDMA system has three main drawbacks: 1) the numbers of nonzero elements of various frequency-domain CS codes are different, 2) the nonzero elements of the frequency-domain CS codes have unequal magnitude, and 3) the computational complexity of the transmitter and receiver is extremely high. This study proposes a novel CS-CDMA system, wherein the perfect Gaussian integer sequences are adopted as the frequency-domain CS codes. The proposed CS-CDMA system inherits all the advantages of the traditional CS-CDMA system, and all three drawbacks are overcome. Simulation experiments demonstrate that the improvements of the proposed scheme for quadrature phase-shift keying and 16-quadrature amplitude modulation are approximately 7.5 dB and 4.5 dB, respectively.

Considering a multicell environment, a distributed user selection scheme for uplink multiuser multiple-input multiple-output (MU-MIMO) system is proposed. In the proposed scheme, each user determines their own transmit beamforming vector based on the locally available channel state information (CSI), and informs the associated base station (BS) of the amount of inter-cell interference (ICI) affecting adjacent cells and the resulting beamforming vector. With the identical first-order statistic between the ICI from adjacent cells and the ICI that affects adjacent cells, a set of users can be selected, which the BS will simultaneously serve based on either the achievable sum rate maximization or proportional fairness achievement. Simulation results show that compared with prior distributed user-selection schemes, the proposed method provides a significant improvement in terms of achievable sum rates.

In coordinated multi-point (CoMP) transmission systems, coordinated base stations (BSs) are geographically separated and have their own oscillators, thereby causing multiple carrier frequency offsets (CFOs) at the receiver. Therefore, perfect synchronization cannot be obtained at the mobile station (MS), resulting in severe interference. To resolve this problem, this study proposes a novel transceiver structure with improved robustness toward interference. With the knowledge of CFO and channel frequency response, a closed-form solution is derived for the sub-optimal CFO compensation value to minimize the approximated upper bound of the inter-carrier interference (ICI) plus noise power for environments with large-scale fading. The results reveal that the sub-optimal CFO compensation value is a function of both the degree of large-scale fading and the value of CFOs. Simulation results demonstrate that the proposed method significantly outperforms the existing structures and exhibits improved performance in terms of bit error rate.

A composite number can be factored into either N=mp or N=2n, where p is an odd prime and m, n ≥ 2 are integers. This paper proposes a method for constructing degree-3 and degree-4 perfect Gaussian integer sequences (PGISs) of an arbitrary composite length utilizing an upsampling technique and the base sequence concept proposed by Hu, Wang, and Li. In constructing the PGISs, the degree of the sequence is defined as the number of distinct nonzero elements within one period of the sequence. This paper commences by constructing degree-3 PGISs of odd prime length, followed by degree-2 PGISs of odd prime length. The proposed method is then extended to the construction of degree-3 and degree-4 PGISs of composite length N=mp. Finally, degree-3 and degree-4 PGISs of length N=4 are built to facilitate the construction of degree-3 and degree-4 PGISs of length N=2n, where n ≥ 3.

In this paper, an amplify-and-forward cooperative single carrier-frequency domain equalizer system for spectrum sharing is proposed to improve the performance of the primary user (PU) and allow the secondary user (SU) to transmit its information simultaneously. To achieve this purpose, a modified data-dependent superimposed training (DDST) scheme is employed. In the original DDST scheme, a part of the frequency bins of the information signal is discarded to insert pilots. In the proposed approach, after receiving PU's data SU firstly serves as a relay and amplifies PU's data. Following, the modified DDST scheme is utilized to discard a part of the subcarriers to insert SU's data. The overall signals are transmitted to the destination. At the destination, the signals from PU and SU are combined for detection by maximizing the signal-to-noise ratio in the frequency domain. Furthermore, the signal-to-interference plus noise ratio of the primary system is analyzed. If the BER of the primary system is given, the number of subcarriers shared to the SU can be determined. The simulation results reveal that the proposed approach is more suitable for spectrum sharing compared with cooperative orthogonal frequency division multiplexing system.

This paper investigates the performance of a dual-hop amplify-and-forward relaying system with co-channel interference at both the relay and destination nodes. The source node is equipped with multiple antennas and adopts the orthogonal space-time block code to increase the system performance. Both the outage probability and the average bit error rate under the presence of multiple Rayleigh fading interferers are obtained in closed form for the variable and fixed-gain relaying schemes. Moreover, the effect of imperfect channel estimation on the variable-gain relaying scheme is also discussed. The effect of co-channel interference at both the relay and destination nodes are discussed. Simulation experiments are also conducted to verify the theoretical derivations.

This work presents a novel peak-to-average power ratio (PAPR) reduction scheme, namely extended selected mapping (eSLM), in space-frequency block coding (SFBC) MIMO-OFDM systems. By introducing phase rotations and amplitude extensions into so-called extension matrices, the candidate signals with lowest PAPR can be selected and the receiver is able to identify index of the selected candidate without transmission of side information. Thus, the proposed eSLM has higher spectrum efficiency compared to conventional SLM-based methods which demand side information. Furthermore, the extended matrices preserve orthogonality of the space-frequency encoded blocks, which allows low-complexity decoding at the receiver. It shows through simulations that the proposed eSLM outperforms existing blind SLM-based method, and has slight performance gap with costly oSLM scheme.

The symbol interleaving scheme is one of the peak-to-average power ratio (PAPR) reduction schemes for the orthogonal frequency division multiplexing (OFDM) system. Compared with the selected mapping (SLM) and the partial transmit sequence (PTS) schemes, it does not require any extra complex multiplications to generate candidate signals aside from the complex multiplications of inverse fast Fourier transform (IFFT) operations. However, the interleaving scheme still has high computational complexity since it also requires a bank of IFFT operations. In this paper, two time-domain properties of IFFT operations are used to construct the proposed low-complexity PAPR reduction scheme. Simulation results show that the complementary cumulative distribution function (CCDF) of the proposed scheme is close to that of the traditional interleaving scheme when the given threshold γ <; 7.2 dB and N = 256 subcarriers. The CCDF of the proposed scheme is lower than that of the traditional interleaving scheme when γ ≥ 7.2 dB and N = 256 subcarriers.

We propose a semi-blind channel estimation for two-way relay networks (TWRNs) where multiple relays employ amplify-and-forward (AF) protocol and the channel is frequency-selectively faded. The challenges of the channel estimation are resulted from inter-symbol interference (ISI), self-interference in two-way systems and difficulty to estimate each relay link separately. To deal with ISI and to obtain the effective channel impulse response (CIR) of each relay link individually, cyclic prefix is inserted in source messages and we propose a low-complexity and scalable design of precoding matrices at relays. Under the framework, effective CIR of each relay link is estimated through the second-order statistics of the received signals, while ambiguity resulted from the channel matrices of direct links between users are resolved by a small number of training blocks. It shows through simulations that NMSE of the proposed scheme exists error floor at high SNR, which can be mitigated by averaging over more blocks of received signals especially when the channel varies slowly.

ABSTRACT A joint data detection and channel estimation scheme is proposed for cooperative communications in cluster‐based networks. In the proposed approach, each relay detects the source information using its joint detection and estimation rules and then compresses this information and forwards it to the destination node. The source information received from all the relays in the network is combined at the destination node in accordance with a fusion decision rule in order to determine the corresponding symbol vector. The joint detection and estimation rule at the relays and the fusion rule at the destination node are both processed using an expectation maximization algorithm. In order to reliably avoid poor local maxima, we utilized one pilot symbol per signal block to obtain a better initial starting point for the expectation maximization algorithm. The simulation results show that the feedback mechanism accelerates the convergence of the local channel estimates toward the Cramer–Rao lower bound. Copyright © 2011 John Wiley & Sons, Ltd.

This paper investigates the performance of a dual-hop amplify-and-forward relaying system with co-channel interference at the relay. The source node is equipped with multiple antennas and adopts the space-time block code to increase the diversity gain. Both the outage probability and the bit error rate in the presence of multiple Rayleigh fading interferers are obtained in closed-form. Simulation experiments are conducted to verify the theoretical derivations.

In data-dependent superimposed training (DDST) scheme, the data-induced interference in channel estimation is eliminated at the sacrifice of data distortion. Unfortunately, data distortion causes data identification problem (DIP) for DDST scheme, which results in error floor phenomenon in bit error rate (BER). Although some literatures have analyzed the DIP, the DIP is still an open problem without solution. In this work, we firstly review the data identification problem from the view of sub-space. The analysis result inspires us to introduce a precoding matrix for resolving the DIP in DDST scheme. In order to prevent the advantages in DDST scheme being reduced by the precoding matrix, we introduce several constraints on the precoding matrix. Subsequently, we derive the requirement of the precoding matrix for solving the DIP in DDST system by using singular value decomposition. Furthermore, an appropriate precoding matrix is developed based on Zadoff-Chu sequence, which is shown to satisfy all conditions derived in this work. Finally, simulation results are conducted to verify that the precoding matrix removes the error floor in BER for DDST scheme.

A low-complexity blind data detector is proposed in this paper for orthogonal frequency division multiplexing (OFDM) systems, where the generalized likelihood ratio test (GLRT) approach is adopted. Traditional GLRT data detector (GDD) can be viewed as a combinatorial optimization problem, which suffers from prohibitively high computational complexity. The proposed scheme reduces the search space by exploiting the phase ambiguity. In addition, the computational complexity is further reduced by properly decoupling the GDD into several sub-group GDDs (SGDD). Mathematical analysis is adopted to evaluate the complexity of the proposed scheme. Simulation experiments are conducted to verify the system performance.

In wireless packet-forwarding networks, selfish nodes always want to maximize their utilities and do not like to help others forward data streams, thus causing severe network performance degradation. To solve the packet-forwarding problem, a novel coalitional game approach based on the selective decode-and-forward (SDF) relaying scheme is proposed. In the game-theoretic analysis, we study the properties and stability of the coalitions and prove that the cohesive behavior can be obtained by the aspect of outage probability. Simulation results show that the proposed SDF coalitional game can enforce cooperation and is beneficial in forming the cooperative groups.

Multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems with space-frequency block coding (SFBC) have a high computational complexity since the number of inverse fast Fourier transforms (IFFTs) required scales in direct proportion to the number of antennas at the transmitter. This paper proposes to generate the SFBC encoded signals of the various antennas in time domain by exploiting the time-domain signal properties and signal correlations among the various transmitter antennas, achieving a significant reduction in computational complexity. In particular, it is demonstrated that the time domain SFBC encoded signals of the various antennas can be obtained from the time domain signal of the first antenna. Therefore, the proposed scheme requires only one IFFT irrespective of the number of transmission antennas. In addition, a low-complexity peak-to-average power ratio (PAPR) reduction scheme is presented based on the proposed transmitter architectures.

Orthogonal frequency division multiplexing (OFDM) systems that adopt the cyclic-delay diversity (CDD) scheme require only one inverse discrete Fourier transform (IDFT) operation at the transmitter. Therefore, the CDD provides a low-complexity means of increasing the transmission diversity in multiple-input multiple-output (MIMO) OFDM systems. However, the optimal pilot sequences which minimize the mean square error (MSE) of the channel estimate in traditional MIMO-OFDM systems are inapplicable to CDD-OFDM systems. Accordingly, this paper commences by deriving the criteria which yield the minimum MSE of both the least square (LS) channel estimate and the minimum mean square error (MMSE) channel estimate in CDD-OFDM systems. The derived criteria are then used to develop a general methodology for determining the optimal pilot sequence. Significantly, the proposed design methodology enables the status of the channel to be estimated using just one OFDM symbol. The simulation results confirm that the proposed pilot design minimizes the MSE of both the LS and the MMSE channel estimates.

A Gaussian integer is a complex number whose real and imaginary parts are both integers. In addition, a sequence is defined to be perfect if and only if the out-of-phase value of the periodic autocorrelation function (PACF) equals zero. This paper commences by presenting a novel class of perfect sequences. The investigated perfect sequences are generated by two groups of base sequences with four base sequences in each group. The nonzero elements of these base sequences belong to the set {±1,±j}. A perfect sequence can be obtained by linearly combining these base sequences or their cyclic shift equivalents with arbitrary nonzero complex coefficients of equal magnitude. In particular, if the complex coefficients are Gaussian integers, the resulting perfect sequences are Gaussian integer sequences and are termed as the Gaussian integer perfect sequences (GIPSs).

Selected mapping (SLM) schemes are commonly employed to reduce the peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) systems. To decrease the number of inverse fast Fourier transform (IFFT) operations in traditional SLM schemes, the candidate signals can be generated in the time domain by linearly combining the original time-domain transmitted signal with multiple cyclic shift equivalents. However, the weighting coefficients and the number of cyclic shifts should be properly chosen to ensure that the elements of the corresponding frequency domain phase rotation vectors have an equal magnitude. This study presents a number of expressions for meeting this equal-gain-magnitude constraint in orthogonal frequency division multiple access (OFDMA) uplink systems. Of these various solutions, a low-complexity expression is selected to construct the proposed low-complexity scheme that requires only one IFFT. The proposed architecture is proven to be applicable to OFDMA uplink systems using either an interleaved or a sub-band sub-carrier assignment strategy.

Recently, multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system with space-frequency block coding (SFBC) has attracted substantial attention because of its robustness toward time selective fading channel. However, SFBC MIMO-OFDM system needs the same number of inverse fast Fourier transforms (IFFTs) as the number of antennas at the transmitter and the computational complexity is extremely high. In addition, it also inherits the disadvantage of a high peak-to-average power ratio (PAPR) from OFDM technique. In this paper, a low-complexity transmitter architecture is proposed for SFBC MIMO-OFDM systems with a general encoding matrix and an arbitrary number of transmitter antennas. The proposed scheme fully exploits the time-domain signal properties and requires only one IFFT. Then, a PRPR reduction scheme is presented by using the proposed transmitter architecture. The PAPR reduction performance of the proposed scheme is slightly worse than that of the traditional selected mapping (SLM) scheme. However, the proposed scheme achieves a substantially lower computational complexity.

The proposed scheme utilizes local information and a predictive model to provide feedback control commands for power adjustments. A sufficient condition that ensures system stability is obtained. In addition, the bound of the received carrier-to-interference ratio (CIR) is derived in the presence of short-term fading. The bound of the received CIR is shown to be a function of the power control step size and the target CIR. Simulation results were obtained to verify the theoretical derivations.

Orthogonal frequency division multiplexing (OFDM) and interleave-division multiple-access (IDMA) are both attractive technique in modern wireless communication. In this paper, we study a multi-user system, which combines OFDM and IDMA, and fortunately, inherits many advantages. In order to improve system performance, low-density parity-check (LDPC) coding is further applied. And moreover, a turbo-type iterative receiver structure is investigated to mitigate multi-use interference (MUI). Simulation results are conducted to illustrate performance improvement. It is demonstrated that MUI is canceling efficiently by means of iterative multi-user detection algorithm. The proposed scheme provides an efficient solution to high-rate multiuser communications over multipath fading channels.

The cell search procedure of the third generation partnership project long term evolution (3GPP LTE) includes the time synchronization, the frequency synchronization of frames, and the acquisition of cell identity (Cell ID). The traditional cell search scheme detects the Cell ID by computing the correlation between the received signal and all transmitted sequences. However, it causes high computational complexity and considerable processing time which effect the delay time of services power on, the standby time, the energy consumption, and the cost of manufacture. This paper firstly proposes a perfect sequence with special structure as preamble, and the sequence is linear combination of two base sequences. In addition, a low complexity cell search scheme, which utilizes the proposed perfect sequence, is then proposed in this paper.

Orthogonal frequency division multiplexing (OFDM) systems require the knowledge of signal-to-noise ratio (SNR) at the receiver in order to maximize the system performance. In general, the noise variance is required by the SNR estimator and the knowledge of noise variance also improves the performances of carrier frequency offset (CFO) and channel state estimations. This paper first investigates the maximum likelihood noise variance estimator (ML-NVE) in OFDM systems with imperfect frequency synchronization. Theoretical derivations show that both the mean and the variance of the ML-NVE are functions of the CFO. The mean and the variance of the SNR estimate are then derived by utilizing the derivation results of the ML-NVE. Finally, the validity of the theoretical observations is confirmed by simulation experiments.

This letter considers selection of the optimal peak reduction tone (PRT) set for the tone reservation (TR) scheme to reduce the peak-to-average power ratio (PAPR) of an orthogonal frequency division multiplexing (OFDM) signal. In the TR scheme, PAPR reduction performance achieved by a randomly generated PRT set is superior to that by a consecutive PRT set and that achieved by an interleaved tone set. However, the optimal PRT set requires an exhaustive search of all combinations of possible PRT sets, which is known to be a nondeterministic polynomial-time (NP)-hard and cannot be solved for the practical number of tones. Inspired by the efficiency of the cross-entropy (CE) method for finding near-optimal solutions in huge search spaces, this letter proposes the application of the CE method to search the optimal PRT set. Computer simulation results show that the proposed CE method obtains near-optimal PRT sets and provides better PAPR performance.

Orthogonal frequency division multiplexing (OFDM) systems require the knowledge of signal-to-noise ratio (SNR) at the receiver in order to optimize the system performance. In general, the noise variance is required by the SNR estimator and the knowledge of noise variance also improves the performance of carrier frequency offset (CFO) and channel state estimations. This paper commences by investigating the maximum likelihood noise variance estimator (ML-NVE) in OFDM systems with imperfect synchronization. Theoretical derivations show that both the mean and the variance of the ML-NVE are functions of the CFO and the advanced timing offset (ATO). In particular, it is demonstrated that the mean of the ML-NVE converges to a CFO-independent value as the ATO increases, while the variance converges to a CFO-dependent value. By exploiting the analytical results of the ML-NVE, we further investigate the SNR estimator. It is mathematically demonstrated that the SNR estimate is biased in the presence of CFO and ATO. Finally, the validity of the theoretical observations is confirmed by simulation experiments.

High peak-to-average power ratio (PAPR) of the transmitted signals is a major drawback of orthogonal frequency division multiple access (OFDMA) systems. The selected mapping (SLM) method is one of the major schemes employed for PAPR reduction. Unfortunately, the computational complexity of traditional SLM scheme is relatively high since a number of inverse fast Fourier transforms (IFFTs) are required. A low-complexity SLM PAPR reduction scheme is proposed in this paper for interleaved OFMDA uplink, where only one IFFT is needed. The proposed scheme exploits the interleaved mechanism and the time-domain signal property to construct a low-complexity architecture. The candidate signals are generated in time-domain by combining the original time-domain transmitted signal with its cyclic shift equivalents. The PAPR reduction performance of the proposed scheme is marginally poorer than that of the traditional SLM scheme. However, the proposed scheme achieves a substantially lower computational complexity.

This paper considers the joint symbol detection and channel estimation problem for cooperative communications in cluster-based networks. The proposed joint detection and estimation scheme is derived by using the expectation maximization (EM) algorithm. In addition, the cooperative communication between the relays and the destination is based on the concept of compress-and-forward (CF) scheme. Most importantly, the distribution detection theory is incorporated in developing the joint detection and estimation rules. Specifically, each relay makes its decision based on its joint detection and estimation rule and then forwards it to the destination node. The destination node makes a decision by fusing the decisions sent from the relays using its joint detection and estimation rule. In our considered structure, the feedback channels are also allowed from the destination node to all the relays to further improves the performance of cooperative communications. The simulation results show that the performance of cooperative communication can improve significantly by using the fusion technique and the feedback channels.

In orthogonal frequency division multiplexing (OFDM) systems, the equally-powered pilots (EPP) scheme is known to provide the best channel estimation performance when the channel status is unknown. In this study, it is shown that if we assume the channel correlation function (CCF) of a quasistatic channel is perfectly known at the receiver, all of the available pilot power should be allocated to the pilot sub-carrier with the maximum channel amplitude. In addition, a practical water-filling-type power allocation scheme, designated as the gain-powered pilots (GPP) scheme, is proposed for the case in which the CCF is subject to a certain amount of error. The simulation results indicate that the GPP scheme consistently outperforms the EPP method in terms of improving the performance of the linear minimum mean square error (LMMSE) channel estimator.

Bandwidth efficiency is a critical concern in wireless communications. To fully utilize the available bandwidth, this paper adopts the superimposed training (ST) scheme in orthogonal frequency division multiplexing (OFDM) systems without using cyclic prefix (CP) and guard interval (GI). If the pilot pattern is fixed, it is shown that the performance of the channel estimation using the ST scheme is the same for both the proposed architecture, denoted as OFDM-ST, and the OFDM system with CP and ST scheme, denoted as CP-OFDM-ST. In addition, since the CP is not adopted in the proposed system, the performances of channel estimation and data detection are degraded because of the inter-symbol interference (ISI) and the inter-carrier interference (ICI). Therefore, a novel data detector, which includes ICI cancellation, is proposed to improve the channel estimation in OFDM-ST systems. The simulation results show that the proposed algorithm substantially enhances the systems performance.

One of the major drawbacks of the orthogonal frequency division multiple access (OFDMA) system is the multi-user interference (MUI) induced by the carrier frequency offsets (CFOs). In sub-band based OFDMA systems, users occupy consecutive and non-overlapping frequency sub-bands. The pilots are usually placed on the edges of a sub-band for channel estimation. However, the channel estimation performance is seriously degraded by the MUI. Traditionally, the MUI is alleviated by inserting a guard sub-carrier between adjacent users. In this paper, a novel pilot architecture is proposed, where three pilots are placed on each edge of a sub-band and pilots of adjacent users are overlapped. The proposed architecture has the same bandwidth efficiency as that of the conventional guard sub-carrier insertion scheme and provides better channel estimation performance and MUI reduction.

The multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system with space-frequency block coding (SFBC) is an attractive technique due to its robustness for time selective fading channels. However, the SFBC MIMO-OFDM system also inherits from OFDM systems the drawback of high peak-to-average power ratio (PAPR) of the transmitted signal. The selected mapping (SLM) method is a major scheme for PAPR reduction. Unfortunately, computational complexity of the traditional SLM scheme is relatively high since it requires a number of inverse fast Fourier transforms (IFFTs). In this letter, a low-complexity PAPR reduction scheme is proposed for SFBC MIMO-OFDM systems, needing only one IFFT. The proposed scheme exploits the time-domain signal properties of SFBC MIMO-OFDM systems to achieve a low-complexity architecture for candidate signal generation.

The selected mapping (SLM) is a major scheme for peak-to-average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM) systems. It has been shown that the complexity of the traditional SLM scheme can be substantially reduced by adopting the conversion vectors to replace the inverse fast Fourier transform (IFFT) operations. Each conversion vector is obtained by taking the IFFT of the phase rotation vector. Unfortunately, the corresponding phase rotation vectors of the conversion vectors in do not have equal magnitude, leading to significant degradation in bit error rate (BER) performance. This drawback can be remedied by adopting the perfect sequences as the conversion vectors. This paper presents two novel classes of perfect sequences, which are shown to be compositions of certain base vectors and their cyclic-shift versions. Then, two novel low-complexity SLM schemes are proposed by utilizing the special structures of the perfect sequences. The BER performances of both the proposed schemes are exactly the same as the traditional SLM scheme.

Fourier transformation is a powerful analytical tool with wide-ranging applications in many fields. In certain cases, some of the inputs to the transformation function are zero, while the others are real or complex. For the case, where the nonzero inputs are complex, the transform decomposition (TD) method enables a significant reduction in the computational complexity. This letter proposes a modified TD (MTD) algorithm to further reduce the complexity when the nonzero input data are consecutive and real-valued. The analytical and numerical results confirm that the complexity of the MTD scheme is not only significantly lower than that of the original TD method, but also lower than that of the traditional split-radix fast Fourier transform (FFT) method when the length of the input sequence is short.

This paper presents an architecture as well as circuit implementation of a ZigBee modulator/demodulator in the transceiver for personal area network, which is compliant with the physical layer of IEEE standard 802.15.4. A test prototype has been designed and fabricated using 0.18-mum single-poly six-metal CMOS process with core area of 0.16 mm2. The measurement results show that packet error rate (PER) is less than 1% given SNR = 5 dB. The total power consumption is merely 251 muW at a 2.4-MHz system clock.

An implantable system for long-term bladder urine pressure measurement system is presented. Not only is the design cost reduced, but also the reliability is enhanced by using a 1-atm canceling sensing IA (instrumentation amplifier). Because the urine pressure inside the bladder does not vary drastically, both the sleeping and working modes are required in order to save the battery power for long-term observation. The IA amplifies the signal sensed by the pressure sensor, which is then fed into the following ADC (analog-to-digital converter). Owing to the intrinsic 1-atm pressure (one atmospheric pressure) existing inside the bladder, the IA must be able to cancel such a pressure from the signal picked up by the pressure sensor to keep the required linearity and the resolution for pressure measurement of the bladder urine. The pressure range of the proposed system is found out to be 14.7 ~ 19.7 Psi, which covers the range of all of the known unusual bladder syndromes or complications.

In this paper, a super-imposed scheme for timing and frequency synchronization is proposed for orthogonal frequency division multiplexing (OFDM) systems. The proposed architecture includes a pre-defined pseudo-noise (PN) sequence, which is added to both the OFDM symbol and the cyclic prefix (CP) in time domain. In contrast to existing synchronization schemes, the proposed architecture has the advantage of better bandwidth utilization since it does not require extra pilot symbols. The peak-to-average power ratio (PAPR) is also significantly decreased. Both the timing and frequency offset estimators are derived using the maximum likelihood (ML) criterion. Simulation experiments are also conducted in frequency selective fading channels. Moreover, the optimal power allocation factor of the PN sequence is determined by minimizing the bit error rate using simulation experiments.

This paper presents a novel frequency offset estimator for inter-carrier-interference (ICI) self-cancellation coded orthogonal frequency division multiplexing (OFDM) systems, which map data symbols onto adjacent or symmetric subcarriers with weighting coefficients of +1 and -1. There are two major drawbacks for traditional ICI self-cancellation schemes. First, the common phase error (CPE) caused by the carrier frequency offset (CFO) can not be removed. Second, the performance is substantially degraded when CFO is large. To remedy these drawbacks, pilot sub-carriers are inserted and the maximum likelihood estimator (MLE) proposed by Moose (1994) is adopted to eliminate CFO. If residual CFO is small enough, traditional ICI self-cancellation scheme can operate efficiently. In this paper, a second order estimator (SOE) is proposed to exploit the second order statistical properties of the demodulated pilot signals at the output of ICI self-cancellation demodulation. The SOE is combined with MLE to estimate CFO and the combined estimator is termed second order with maximum likelihood estimator (SO-MLE). The optimal weighting coefficient is determined by conducting simulation experiments. It is demonstrated that the proposed SO-MLE has the advantage of tolerating larger CFO and CPE than traditional MLE, and the ICI can thus be significantly diminished.

This work considers the problem of collaborative detection in sensor networks with an unknown number of operating nodes. In wireless sensor networks, both the energy resource and the bandwidth of communication channel are limited. This work employs the sensor censoring scheme to achieve energy-efficiency and low communication rate on the design of distributed decision fusion when the number of operating sensors is unknown to the fusion center. Very surprisingly, in this work, we showed that the energy conservation does not necessary result in the degradation of fusion performance in both theoretical analysis and numerical simulations. Indeed, in many cases, utilizing more energy or bandwidth actually degrades the fusion performance, and the design of energy-efficient local detection rule should start from a nonzero censoring rate, which gives the optimal fusion performance

A novel structure of training symbol is proposed for orthogonal frequency division multiplexing (OFDM) systems. With the proposed training symbol, which has repeated sample blocks and a sign pattern on each block, both timing offset and frequency offset estimations can be obtained. In particular, the frequency synchronization is accomplished using two successive stages to obtain both the fractional and integral parts of frequency offset with an estimation range of plusmnN/4 sub-carrier spacing. Simulation experiments demonstrate that the performance of the proposed scheme is, in most of the investigated cases, substantially superior to the traditional schemes, or has negligible differences. In addition, the proposed synchronization mechanism has a relatively low system complexity, making a good comprise between performance and complexity.

In this paper, a complex matrix C consisting of a set of perfect sequences is studied. The matrix C is constructed by taking the inverse discrete Fourier transform (IDFT) of a diagonal matrix, in which the diagonal elements comprise an arbitrary periodically perfect sequence gamma. Properties of the matrix C are presented. In addition, the Fourier dual E of the matrix C is investigated. When gamma is a Zadoff-Chu sequence for the case of N even, M=1, and g=0, an explicit representation for the matrix E is derived.

A complex array for constructing perfect sequences is presented in this paper. The row sequences and their discrete Fourier transform form two sets of perfect sequences. The column sequences are orthogonal to each other for any cyclic shift. In addition, any combination of the column sequences with complex weighting coefficients of equal amplitude is also a perfect sequence. Applications of the constructed sequences are presented for OFDM-CDMA systems.

This paper presents architecture as well as the circuit implementation of a ZigBee transceiver using 868/915 MHz band, which is compliant to the physical layer of IEEE 802.15.4. Two low-power analog-to-digital converters (ADC) are integrated within the proposed transceiver. The ADC is a 5-bit SA-based (successive approximation) structure. The post-layout simulation shows the packet error rate (PER) is less than 1% given SNR = 5 dB. The overall power consumption is merely 6.57 mW (Tx power = 3.28 mW, Rx power= 3.29 mW)

A superimposed training scheme is proposed for channel estimation in OFDM systems. The generalized chirp-like (GCL) sequence is adopted since it has a constant magnitude in both the time domain and the frequency domain. Although the derived channel estimator has a slightly worse performance since the unknown data contributes extra noise, the effective data throughput is substantially increased. In addition, the proposed scheme is shown to have a much better peak-to-average power ratio (PAPR) because the added GCL sequence has a constant magnitude in the time domain.

A distributed multi-step power control algorithm is proposed for cellular networks. The proposed scheme utilizes local information to feedback control command for power adjustment. The sufficient condition that ensures system stability is obtained. In addition, general formula for the bound of received CIR is derived in the presence of short-term fading. The bound of received CIR is shown to be a function of number of power control steps, step size, and dead factor. Simulation results are obtained to verify the theoretical derivations

In this paper, we propose a real-time subcarrier, bit, and power allocation algorithm for OFDM-based multiuser communication systems in downlink transmission. Assuming that base stations know the channel gains of all subcarriers of all users, the proposed loading algorithm tries to minimize the required transmit power while satisfying the rate requirement and data error rate constraint of each user. The novel algorithm determines subcarrier, bit, and power allocation simultaneously by enhancing the suboptimal algorithm while having the same computational complexity. The proposed scheme offers better performance in terms of transmit power, as demonstrated in the simulation results, wherein the performance of the scheme was close to that of the optimal solution.

Since the back-off windows among competing users are not synchronized, the binary exponential back-off (BEB) algorithm results in a "capture effect" and, in the limit as the number of users approaches infinity, BEB is unstable for every arrival rate greater than 0. Both fixed collision rate (FCR) and quasi-FIFO algorithms have been proposed for collision resolution in wireless networks to remedy the drawbacks in BEB. A novel virtually-FIFO back-off algorithm is introduced to improve further the throughput and fairness in bandwidth utilization. In the virtually-FIFO scheme, packets generated in a given cycle period are guaranteed to be serviced within the next cycle and the order of services is virtually based on their times of arrival. With the virtually-FIFO scheme, the standard deviation of delay in BEB can be improved by more than two orders of magnitude and throughput is maintained at above 0.422.

Binary exponential back-off (BEB) scheme is widely adopted in both wire and wireless networks for collision resolution. Since the back-off windows among competing users arc not synchronized. BEB algorithm results in "capture effect" and, in the limit as the number of users approaches infinity. BEB is unstable for every arrival rate greater than 0. In wireless networks, a common back-off window size can be broadcast by the base-station or access point. Maximum throughput can he achieved when the back-off window size is equal to the number of competing users. A novel quasi-FIFO back-off scheme is proposed not only to improve the bandwidth usage, but also to maximize the throughput. In quasi-FIFO, packets generated in a given cycle period are guaranteed to be serviced in the next cycle. Newly arrived packets cannot join the contention until the current cycle has completed and all the packets generated in the previous cycle are transmitted successfully.

Ethernet adopts the well-known binary exponential back-off (BEB) scheme for collision resolution. Operation of the BEB algorithm leads to the last-come-first-served result among competing users (also known as the "capture effect", which allows a few "winning" users to occupy most of the available bandwidth); also, BEB is unstable for every arrival rate greater than 0. Taking advantage of the central control unit (access point or base station) in the wireless network, we propose a novel scheme, fixed collision rate (FCR) back-off algorithm, which repairs the drawbacks of BEB. FCR not only achieves a throughput of 1/e for an extremely busy channel, but also shares the limited bandwidth among competing users in a fairer way. Operation of the FCR scheme relies on estimating the number of users and maintains the collision rate at a constant level. A variety of estimation methods are provided. Simulation results with perfect knowledge of the number of users are obtained to find the performance upper bound of the proposed scheme.

A space-time RAKE (ST-RAKE) receiver with enhanced interference rejection is proposed for CDMA communications systems over a multipath fading channel. The proposed scheme involves three stages. An interference-blocking (IB) transformation is first developed, based on the received data (underspread data), for suppressing the strong interference. Optimum beamforming is then performed on the IB transformed despread data, consisting only of signal-of-interest (SOI) data and noise, to produce reception of the SOI and rejection of strong interference. Finally, a RAKE receiver employing the maximum ratio combining technique is used to collect the SOI energy constructively. Since strong interference has been removed, the RAKE receiver can effectively combine the SOIs, leading to performance enhancement as compared with the conventional RAKE receiver. Numerical results are given to demonstrate that the proposed IB-RAKE receiver exhibits robustness against strong interference.

In this paper, the outage probability of the "multiply-detected macrodiversity (MDM)" scheme is studied. The previously introduced MDM scheme is a non-selection based, postdetection combining scheme, which employs a maximum likelihood decision algorithm on signals received at a number of base-stations. Previously published performance results indicate that the MDM scheme considerably outperforms other conventional forms of macrodiversity throughout a large portion of the cell area. In this paper, we analyze and simulate the outage performance of the MDM scheme and compare those with the (S/I)-diversity scheme. As a point of reference, if the outage probability is defined as BER above 0.0001, the outage is eliminated at least 45% of the time as compared with (S/I)-diversity, for propagation attenuation exponent of 4 and shadowing standard deviation of 4 dB.

The stochastic nature of wireless communication that manifests itself by the fading processes leads the designers of wireless networks to over-engineer their designs. This random behavior can, however, be reduced by diversity methods. For example, Rayleigh fading can be mitigated by microdiversity and the effects of shadow fading can be reduced by macrodiversity techniques. In this paper, we advocate the use of a macrodiversity scheme based on signal-to-interference ratio, in combination with postdetection combining. This scheme has been termed multiply-detected macrodiversity (MDM). We discuss the sources of the improvement and demonstrate that the MDM scheme yields a considerable gain over the traditional macrodiversity scheme that is based on selection of the strongest signal path. Application of the MDM scheme in practical wireless networks results in reduction in the BER throughout over 50% of the system coverage.