Drug-induced liver injury (DILI) constitutes a critical challenge in pharmaceutical development and accounts for over 50% of acute liver failure cases. This study employs deep learning (DL) methodologies for DILI prediction utilizing convolutional neural networks (CNNs) and multi-representation molecular imaging. We employ ResNet (18, 34, 50) and EfficientNet (B0-B3) architectures on three molecular image representations, such as normal images (NM), heatmap 1 (HM1) derived from Crippen logP contributions, and heatmap 2 (HM2) extracted from bioconcentration factor analysis. Experimental outcomes on 475 compounds using 4-fold cross-validation demonstrate that heatmap representations substantially outperform conventional molecular images. ResNet-34 attains optimal performance with HM2, achieving an AUC of 0.8853 and a Recall of 0.8410. This significantly enhances performance compared to conventional images (AUC: 0.8604, Recall: 0.8295). ResNet-34 and EfficientNet-B0 with HM1 exhibits superior functional group identification capabilities with a Recall of 0.8499 and 0.8682. Grad-CAM visualization illustrates that HM1 effectively emphasizes specific functional groups while HM2 facilitates comprehensive molecular analysis. Functional group analysis indicates that nitro groups (93.75%), sulfones (92.31%), and urea derivatives (91.67%) are associated with the highest DILI risk, while sulfur-containing moieties broadly serve as toxicity indicators. In contrast, iodine-containing compounds and ketones exhibit notably lower toxicity rates. Our multi-representation methodology exhibits competitive performance while delivering enhanced interpretability through explainable AI techniques. This framework presents considerable potential for pharmaceutical toxicity assessment and diminishes dependence on animal testing protocols.

The growing complexity of urban transportation networks demands intelligent, data-driven systems capable of real-time perception, optimization, and prediction. Within the Internet of intelligent Vehicles (IoIV), optimal sensor placement is essential for enhancing distributed edge intelligence, as it significantly improves traffic observability, ensures accurate data collection, and enables dynamic decision-making. In this context, we propose a dual approach for hybrid quantum-classical (HQC) optimization—classical for quantum and quantum for classical. First, we formulate the optimal sensor placement problem within the quantum approximate optimization algorithm (QAOA) framework using a minimum vertex cover approach to address its NP-hard nature. Moreover, we enhance the QAOA by integrating long short-term memory (LSTM) networks to adaptively optimize its parameters, thereby achieving faster convergence and improved surveillance coverage in urban transport networks. Second, we employ quantum LSTM (QLSTM) networks to predict traffic flow from data collected by the optimally placed sensors. The QLSTM model reduces the number of learnable parameters while maintaining the expressive capability of classical LSTM architectures. This semantic information is then leveraged to manage traffic flow more effectively, predicting traffic patterns and supporting proactive decision-making. Experimental results demonstrate that the LSTM-guided QAOA outperforms conventional optimization methods such as stochastic gradient descent, achieving faster and more reliable convergence. Likewise, the QLSTM model attains superior predictive accuracy, as evidenced by significant improvements in the explained variance score and root mean squared error. Collectively, these advancements represent a substantial step forward in intelligent traffic management and highlight the practical potential of HQC machine intelligence in IoIV systems.

This paper addresses the problem of reliable task offloading in space-air-ground integrated network (SAGIN)-assisted edge computing systems, with the goal of maximising the ratio of tasks successfully offloaded and executed within quality-of-service (QoS) constraints. In the considered system, ground users offload computation tasks to a satellite-mounted edge server via unmanned aerial vehicles (UAVs) acting as relays. The formulated optimisation problem jointly considers task offloading portions and bandwidth allocations across ground-to-air and air-to-space links, subject to constraints on transmission rates, total bandwidth, energy budgets, and the satellite’s computational capacity. The resulting problem is non-linear, non-convex, and mixed-integer, making it challenging to solve with traditional optimisation techniques. To this end, we propose a deep reinforcement learning (DRL)-based solution to learn optimal offloading and resource allocation policies in dynamic environments. Furthermore, to enhance scalability and decentralised coordination, we develop a multi-agent DRL framework that enables cooperative decision-making across UAVs. Simulation results demonstrate that both the single-agent and multi-agent approaches achieve stable training performance, and the proposed method improves the reliable task offloading ratio by up to two times compared to benchmark schemes, while also achieving more efficient resource utilisation in complex SAGIN scenarios.

Respiratory rate is an important vital sign that can be used to determine human physiological state. In recent years, Wi-Fi-based contactless respiration monitoring has drawn significant attention due to the prevalence of wireless local area network (WLAN) infrastructure. Most existing approaches to respiration monitoring perform well in controlled environments, without the presence of additional moving individuals in the area of interest. A few recent studies have attempted to reduce the impact of other people moving in the vicinity of the target individual. However, these approaches exhibit notable limitations, such as restricting the number of interfering individuals to one, or requiring a direct wired connection between the Wi-Fi transmitter and receiver for synchronization. To address these issues, in this study, we develop a contactless respiration monitoring system using commodity Wi-Fi devices, which we name RoSense. Through a series of empirical studies, we observe that the channel state information (CSI) for subcarriers is significantly affected by the presence of interfering individuals, but a small subset retain relatively clear signal patterns linked to the target’s respiration. Leveraging these findings, RoSense employs a signal power-based subcarrier selection strategy to identify high-quality subcarriers. The selected subcarriers are then aligned to enhance signal gain and fused to complement the weaker periodic parts. Additionally, RoSense periodically detects the quality of subcarriers, selecting the most effective subcarriers to maximize the contribution of high-quality ones. Extensive experiments were performed in real-world settings with 10 volunteers to verify the feasibility and effectiveness of RoSense. Our results demonstrate that RoSense is able to achieve robust respiration monitoring by suppressing the impact of interfering individuals.

This study realizes secure near-field location division multiple access through employing both a variational quantumcircuit and a nature-inspired algorithm. With the escalating demands of the sixth generation (6G) and beyond, there is growing interest in using large numbers of antenna elements,making near-field communications more practical Seizing this opportunity, we leverage near-field communications for a distinct multiple access technique, termed location division multipleaccess (LDMA), which capitalizes on spatial orthogonality to distinguish users by both angle and distance. Nevertheless, relying on beamforming to direct signals to distinct users poses a clear physical-layer security risk: adversaries might eavesdrop on messages intended for legitimate users, prompting the need tooptimize beamforming to enhance security while adhering towireless systems’ constraints. To make matters worse, conventional approaches typically entail multiple matrix inversions, and the optimization problem is far from trivial to solve due toits non-convexity and NP-hardness. To this end, our solutionleverages variational quantum circuits (VQC), motivated bythe potential benefits offered by quantum computing. On topof that, we improve upon the existing VQC workflows by integrating a classical algorithm, particularly, the differential evolution algorithm, thereby reducing quantum computational resource demands while maintaining high exploration efficiency. We further investigate how different quantum circuit depths influence the balance between expressibility and convergence. Simulation results reveal that our proposed scheme consistently outperforms conventional benchmarks in terms of secrecyrates, while simultaneously satisfying quality-of-service (QoS)constraints and power allocation requirements.

Simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has received significant attention as a potential technology for the sixth generation (6G) of wireless network due to its ability to boost signal coverage and enhance system efficiency. In this paper, we investigate the potential of a near-optimal hybrid quantum-classical optimization approach to jointly optimize beamforming and the discrete phase shifts of the STAR-RIS assisted wireless network. In particular, we formulate a discrete optimization problem to maximize the total power transmitted to the ground users. This is achieved by optimizing the beamforming at the base station (BS) and the phase shift of the STAR-RIS under minimal power allocation for each user and the maximum power budget at the BS. Since the addressed problem is NP-hard, we propose a quantum approximate optimization algorithm with alternating optimization (QAOA-AO) method that iteratively addresses beamforming components and discrete phase shifts to search for the near-optimal solutions for the problem. Numerical results validate the effectiveness and robustness of the proposed QAOA-AO compared to the classical benchmarks in terms of runtime and system power, and highlight its potential for practical deployment when solving medium-to-large-scale networks.

In this paper, we explore a maritime 6G-enhanced satellite-air-ground integrated network (SAGIN) that incorporates a UAV-carried reconfigurable intelligent surface (UCR) relay, and low Earth orbit (LEO) satellites equipped with mobile edge computing (MEC) facilities. The system captures dynamic maritime conditions, including ultra-reliable low-latency communication (URLLC) user mobility and UCR movements across harbor environments. The primary objective is to minimize the total system cost by jointly optimizing task offloading decisions, bandwidth allocation, local computational resource distribution, transmission power control, and caching management, while satisfying strict latency and resource constraints. To address this, we formulate a mixed-integer nonlinear programming (MINLP) problem that captures the complexity of resource optimization in the maritime 6G-enhanced SAGIN. Two quantum-enhanced deep reinforcement learning algorithms, namely quantum-enhanced deep deterministic policy gradient (QEDDPG) and quantum-enhanced proximal policy optimization (QEPPO), are proposed to solve the formulated MINLP problem. Moreover, higher-order quantum feature encoding and quantum neural networks are utilized to accelerate learning and enhance decision-making. Simulation results demonstrate that QEDDPG and QEPPO significantly outperform conventional deep reinforcement learning methods by achieving lower system costs and more efficient resource allocation. These findings shows that the potential of quantum-driven reinforcement learning for enabling scalable, efficient, and intelligent resource management in future 6G-enhanced SAGINs.

Breast cancer remains a leading cause of death among women worldwide. Early detection of breast cancer is a crucial step towards improving survival rates for patients affected by the disease and is typically performed with the help of ultrasound imaging. Current rapid advancements in artificial intelligence (AI) research have produced a plethora of machine learning methods that aid in building automated diagnostic assistance systems for early cancer detection, including breast cancer detection. While deep learning has shown promise in medical image analysis, most existing approaches rely on single models or simple ensemble methods that fail to fully exploit complementary feature representations across architectures. This paper introduces a novel feature-enhanced stacking ensemble framework that combines state-of-the-art global context vision transformer (GCViT) with well-established convolutional neural network (CNN) architectures (ResNet-50V2, ConvNeXt-Tiny, and EfficientNetV2-B3) for automated breast cancer classification from ultrasound images. Unlike conventional ensembles that aggregate only prediction probabilities, our approach extracts deep feature embeddings from a dedicated CNN branch and concatenates them with base model predictions as input to a meta-learner, a multi-layer perceptron (MLP), enabling the ensemble to leverage both decision-level and feature-level information. When incorporating a meta model with feature representations from a CNN-based feature extractor, we are able to produce superior performance across multiple metrics compared to prior works. We accomplish top performance of 94.23% accuracy, 95.47% AUC-ROC. To further evaluate the robustness and generalizability of our approach, we conduct additional experiments on the melanoma cancer image dataset and achieve 95.4% accuracy. We provide comprehensive explainability analysis through shapley additive explanations (SHAP) values for feature attribution, permutation importance for model contribution quantification, and saliency maps for visual interpretation from base models and the end-to-end ensemble model to explain their contributions to final predictions.

This paper aims to tackle the complex challenge of channel assignment and joint power-energy allocation within a cellular-vehicle-to-everything (C-V2X) network, which is deployed to manage vehicular dynamics at an urban traffic intersection. The primary function of the C-V2X network is to facilitate the coordination of multiple vehicle platoons formed by closely spaced same-lane vehicles. This coordination involves two critical communication tasks including the timely update of platoon states to a roadside unit (RSU) and the reliable exchange of cooperative awareness messages (CAMs) among vehicles within the same platoon. The main objective of this paper is to minimise the average age of information (AoI) to ensure the timely update between vehicle platoons and the RSU, maximise the CAM delivery probability (CDP) to guarantee the successful exchange of CAMs among vehicles, and promote sustainable, green communication practices through the implementation of our optimal power-energy management strategies. Recognising the intricate and dynamic nature of this challenge, we adopt a multi-agent deep reinforcement learning (MADRL) approach based on the Markov decision process (MDP). Two innovative algorithms based on the multi-agent deep deterministic policy gradient (MADDPG) and twin delayed deep deterministic policy gradient (TD3) algorithms are proposed to address this optimisation problem effectively. Finally, comprehensive simulation results are presented, which demonstrate the remarkable performance of our proposed schemes, particularly in terms of energy efficiency when compared to existing research. Importantly, these gains in energy efficiency are achieved while maintaining competitive algorithm convergence speeds, low AoI levels, and high CDP, showcasing the practical viability of the developed methods.

Radio frequency fingerprint identification (RFFI) offers a promising physical-layer method to authenticate devices based on unique hardware impairments. However, existing RFFI systems use deep learning (DL) models that are resource- intensive. Training is particularly demanding, requiring repeated updates to a large number of parameters. In this paper, we introduce a quantum-assisted training (QAST) framework to address training inefficiencies in RFFI. QAST integrates a quantum neural network (QNN) with a mapping network to generate parameters for a classical DL model. This indirect training strategy substantially reduces the number of trainable parameters and the overall memory requirements compared to direct training of the DL model. We achieve this by introducing a multimodal mapping network that effectively learns the QNN output. This network generates multiple classical parameters from a shared quantum representation, thereby reducing qubit requirements and lowering the risk of barren-plateau-related trainability degradation. We also propose a new embedding method that reduces the size of the embedding matrix and yields a 15% to 30% reduction in training time. The tailored QAST framework trains the RFFI model while requiring only 10% of the original number of parameters while maintaining comparable accuracy, thereby substantially reducing memory and computational overhead and enabling efficient training or retraining in resource-limited environments.

This paper investigates a drone (aka UAV)-assisted physical layer security framework for space–air–ground integrated networks (SAGINs) in the presence of multiple eavesdroppers. A single full-duplex UAV is deployed to support satellite-to-ground communications by simultaneously relaying desired signals to legitimate users and transmitting artificial noise to degrade the reception quality of eavesdroppers. To enhance secure connectivity, we formulate a max–min secrecy rate optimization problem that jointly considers sub-channel allocation and power distribution. The sub-channel allocation is optimized using a constrained genetic algorithm, which efficiently handles the combinatorial nature of the problem. Additionally, power allocation is optimized through a nested-loop approach, in which the outer loop employs Bayesian optimization to address complex objective functions, while the inner loop makes the allocation tractable using variable substitutions and approximation methods to overcome non-convexity. The simulation results demonstrate that the proposed method outperforms the benchmark schemes in terms of secrecy performance, particularly under stringent resource and security constraints in SAGINs.

Energy-efficient space–air–ground integrated networks (SAGINs) are vital for sustainable communications. This study presents an energy-aware SAGIN framework that utilizes a uncrewed aerial vehicle (UAV)-mounted mobile edge computing (MEC) platform enhanced by digital-twin technology, UAV energy harvesting via wireless power transfer, and a nano-satellite constellation with MEC facilities. We formulate a joint optimization problem for UAV trajectory planning, task offloading, computational resource allocation, and satellite load balancing as a mixed-integer nonlinear programming (MINLP) problem that minimizes the weighted system cost while satisfying energy and latency constraints. To address this complex problem, two quantum-driven deep reinforcement learning (QD-DRL) algorithms namely quantum-driven cost-effective advantage actor–critic (QD-CE-A2C) and quantum-driven cost-effective proximal policy optimization (QD-CE-PPO) are proposed. These algorithms employ angle encoding with learnable parameters and variational quantum neural networks to enhance policy exploration and accelerate convergence. Simulation results demonstrate that the proposed QD-DRL approaches achieve superior cost efficiency and ensure effective service to all access points within the defined mission duration. Moreover, QD-DRL approaches achieved higher cumulative rewards and faster convergence compared to classical DRL baselines. Consequently, the proposed frameworks provide a scalable and intelligent paradigm for cost-efficient resource management in future 6G-enabled SAGINs.

This paper considers the cell-free integrated sensing and communication (CF-ISAC) networks utilizing reconfigurable intelligent surface (RIS)-mounted uncrewed aerial vehicles (UAVs). We aim to maximize the sum of weighted sum rate within the whole ISAC period by jointly optimizing access points (APs)’ transmit beamformings, RISs’ phase shifts, user-RIS association, and UAVs’ locations. To deal with a highly complex non-convex optimization problem, we propose an alternating optimization solutions by decomposing the original problem into three subproblems. In particular, for optimizing APs’ transmit beamformings, RISs’ phase shifts, and user-RIS association, we convert the log-sum problem into a quadratically constrained quadratic programming problem using the Lagrangian dual principle and multi-ratio fractional programming. For optimizing UAVs’ locations, the successive convex approximation technique is used to transform it into a convex problem. Simulation results highlight the considerable performance advantage of the proposed network compared to benchmark schemes employing fixed RISs, without RIS-mounted UAVs (URISs), and collocated network with URISs.

This paper propose a two-stage quantum neural network (QNN) framework for cell-free multiple-input and multiple-output (MIMO) wireless communication systems. Cell-free MIMO, which has been regarded as a key technology for enhancing the performance of the next-generation wireless communication systems, leverages the collective capability of multiple distributed access points (APs), allowing collaboration between them. However, optimizing cell-free MIMO can pose challenges for centralized optimization schemes. In particular, complexities associated with the joint optimizations of user-transmission assignment and transmission precoding, two factors which are of much importance for determining the quality-of-service, grow with the number of APs and served users. To this end, a unified scheme employing distributed QNNs is used to optimize downlink transmitter-user assignment and transmit precoding with the goal of maximizing the achieved sum rate. Firstly, the cloud processing unit, which holds holistic information about the particular wireless communication network, employs QNN to assign each AP to its designated mobile terminal. Secondly, the edge processing units, which are computed in proximity relative to the AP in order to reduce latency, estimate transmission precoding for their corresponding APs. Moreover, numerical results are presented to showcase the performance of the proposed protocol.

The Network Digital Twins (NDTs) have become a frontier thanks to their real-time monitoring, analysis, prediction, and optimisation capabilities. However, their architecture has not been designed for the scale of next-generation networks that will ensure ubiquitous connectivity. At this, different NDT applications may require distinct levels of quality of service requirements to be met. With increasing size and heterogeneity in the networks, the processing load at the NDTs escalates, and these requirements may not be met. Therefore, we propose a microservice-based architecture with application-oriented slicing. Here, we present the scaling methodology, which enables scaling at both microservice and slice levels. Then, we evaluate the throughput, delay, and quality of service requirement violation rate metrics under two scenarios. Thanks to the slicing approach with microservice-based implementation, the end-to-end delay and the QoS requirement violation rate are reduced while having higher throughput performance.

In vehicle-to-everything (V2X) networks, real-time telemetry is essential for enabling predictive analytics and fault detection in intelligent transportation systems. However, frequent wireless disruptions due to interference, mobility, and congestion lead to telemetry gaps that degrade downstream decision-making. To address this challenge, we propose a framework that enhances wireless telemetry robustness using large language models (LLMs) guided by digital twin-based context. Our system combines retrieval-augmented generation with environmental priors to recover high-dimensional, time-correlated telemetry streams lost during communication outages. We also integrate federated continual learning to maintain fault classification performance across non-i.i.d. V2X conditions without centralized data exchange. Extensive evaluations on real-world driving datasets with simulated wireless impairments show that our method significantly improves reconstruction fidelity, reduces degradation from multi-step gaps, and sustains long-term classifier stability. This work demonstrates how AI-driven semantic recovery mechanisms can improve the functional reliability of wireless V2X telemetry under dynamic and lossy network conditions.

Quantum sensing networks (QSNs) are expected to play a critical role in quantum networks by achieving measurement precision unattainable with classical methods, leveraging quantum properties such as superposition and entanglement. Distributed quantum sensing, a key application of QSNs, can reach Heisenberg-limited precision scaling with the number of sensors involved. However, practical implementation faces significant challenges due to noise effects, complicating the optimal selection of sensor configurations. In this paper, we propose applying a variational quantum algorithm (VQA) combined with a genetic algorithm to efficiently mitigate noise in quantum sensing protocols and to identify high-quality sensor configurations. Performance analysis demonstrates that our approach outperforms traditional sensor configuration methods in single- and multi-parameter sensing scenarios under dephasing and amplitude damping noises, significantly improving quantum sensing accuracy and scalability.

In this paper, we explore a 6G-enabled space-air-ground integrated network (SAGIN) framework that integrates ground communication hubs (CHs), a UAV with mobile edge computing (MEC) capabilities, and a constellation of low Earth orbit (LEO) nano-satellites. We formulate a joint optimization problem for UAV trajectory, task offloading, and satellite load balancing, modeled as a mixed-integer nonlinear programming (MINLP) problem. To solve this, we propose a quantum-enhanced advantage actor-critic (QEA2C) reinforcement learning algorithm that employs quantum neural networks and two quantum state encoding methods: amplitude encoding (AE) and higher-order encoding (HOE). Simulation results show that HOE achieves superior performance in terms of convergence speed, cumulative rewards, and learning efficiency, successfully serving all CHs with a well-optimized UAV trajectory. Meanwhile, AE achieves better cost minimization with lower resource consumption, making it a more practical option when computational efficiency is a priority. Moreover, these results highlight the trade-offs between learning performance and cost efficiency in quantum-enhanced decision-making for managing 6G-enabled SAGINs.

In this work, the benefits of employing quantum neural networks (QNNs) in reinforcement learning (RL)-based methods used in vehicular networks are explored. We substitute the classical-bit-based neural networks (NNs) in the multi-agent deep RL (MADRL) with QNNs and propose a QNN-based quantum MADRL (QMADRL) framework to solve a resource allocation (RA) problem in a cellular-vehicle-to-everything (C-V2X) network. The objective of the optimisation is to minimise the age of information (AoI) for vehicle-to-infrastructure (V2I) communications, maximise the delivery probability of the cooperative awareness messages (CAMs) for the vehicle-to-vehicle (V2V) communications, and jointly minimise the power and energy consumption to promote green communication practices. Compared to classical MADRL methods, the proposed QMADRL framework delivers substantially faster convergence while achieving comparable performance after convergence.

This study presents a dynamic UAV swarm framework to support ground networks in disaster zones. The framework leverages Generative AI (GenAI) for real-time hover point generation to guide waypoint-based UAV navigation and realistic task modeling, integrated with graph neural networks (GNN) for safe navigation and obstacle avoidance. A multi-agent graph reinforcement learning (MAGRL) mechanism optimizes UAV coordination, enhancing energy efficiency, task completion, and load balancing in response to environmental changes. The framework's graph attention mechanism further improves inter-UAV communication, enabling adaptive task allocation and efficient coverage of high-risk zones. Extensive simulations show that the integrated GenAI-GNN and MAGRL approach achieves superior performance in task completion, energy savings, and system utility, outperforming benchmarks including MADDPG, GCRL, PSO, and Greedy strategies in dynamic disaster scenarios.

This paper addresses the problem of reliable task offloading in space-air-ground integrated network (SAGIN) based edge computing systems. Specifically, we aim to maximise the successful task offloading ratio for ground users communicating with a satellite's edge server. In our network topology, end-to-end communications are facilitated by relay unmanned aerial vehicles (UAVs). The formulated problem jointly optimises task offloading portions and bandwidth allocations for both ground-to-air and air-to-space links, subject to quality-of-service (QoS) requirements, transmission rates, system bandwidth, and the computing capacity of the satellite's edge server. To solve the formulated complex non-linear, non-convex, and mixed-integer problem, we propose an efficient solution underpinned by a deep reinforcement learning (DRL). Simulation results demonstrate the effectiveness of the proposed method, which achieves stable training performance and an optimised reliable offloading ratio compared to benchmark schemes.

This paper introduces a network architecture that integrates the space-air-ground integrated network with mobile edge computing (MEC) and orbital edge computing to advance sixth-generation communication systems. The proposed system employs unmanned aerial vehicles equipped with reconfigurable intelligent surfaces and satellite-based MEC to optimize resource management in complex, dynamic environments. By efficiently managing resources such as bandwidth and computational power at both base stations and low Earth orbit satellites, while making offloading decisions, the system aims to minimize utility costs while meeting stringent performance requirements. We utilize a long short-term memory (LSTM)-enhanced deep deterministic policy gradient (DDPG) algorithm to solve the formulated nonlinear programming problem, enabling dynamic and adaptive resource management. The LSTM-enhanced DDPG improves convergence speed by 44.44 % compared to conventional DDPG, significantly enhancing cost efficiency. Simulation results validate the robustness of the proposed method against state-of-the-art approaches.

In this study, we propose SCF6, a novel semantic communication framework for 6 G -enabled vehicular networks tailored to ultra-reliable low-latency communication (URLLC) scenarios. SCF6 integrates semantic encoding/decoding with conventional channel processing, optimizing transmission by focusing on data meaning. Leveraging BERT (bidirectional encoder representations from transformers)-based natural language processing, it ensures high semantic similarity between transmitted and received messages. To maximize semantic spectrum efficiency (SSEE) and success rate (SR) for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications under strict URLLC constraints, we design a multi-agent hierarchical attention-based semantic deep reinforcement learning (MAHAS-DRL) framework. MAHASDRL coordinates resource allocation and spectrum sharing, embedding hierarchical attention at both semantic and channel levels to enhance decision-making, optimize power control, and reduce interference. Simulations demonstrate SCF6's superiority over traditional DRL methods in spectrum efficiency, reliability, and latency, proving effective for dynamic urban vehicular networks.

This work proposes the integration of the holographic active reconfigurable intelligent surface (HARIS) into a multi-user uplink near-field-driven wireless communication system. In order to provide efficient resource utilization, a sumrate maximization problem is formulated, where the equalizer design, the power allocation at each user, and the HARIS phase profile are jointly optimized under the strict constraint of QoS requirement and limited power budget at each user and HARIS. In order to tackle the non-convex nature of the formulated problem, we propose an alternating optimization (AO)-based algorithm that adopts an iterative approach and uses optimization techniques such as minimum mean square error (MMSE), convex upper bound approximation, and semidefinite relaxation (SDR) to simultaneously optimize the equalizer at the BS, beamforming at the HARIS, and power allocation at each user. Then, extensive simulations are performed to validate the efficacy and convergence of the proposed algorithm. Furthermore, we also demonstrate the impact of key system parameters, such as HARIS elements, minimum quality of service (QoS) constraint corresponding to each user, maximum receive power at the base station (BS), and maximum amplification factor.

In this paper, we address the complex mixed-integer nonlinear programming problem associated with channel assignment and joint power-energy allocation in urban platoon-based cellular-vehicle-to-everything (C-V2X) networks. In this context, the potential advantages of integrating quantum neural networks (QNNs) with classical multi-agent deep reinforcement learning (MADRL) approaches are investigated. Specifically, we combine a variational quantum circuit (VQC) with traditional neural networks to develop a hybrid quantum-classical neural network for the MADRL training process. Our goal is to employ this hybrid quantum-classical approach to simultaneously minimise the average age of information (AoI) which quantifies the freshness of information exchange between vehicle platoons and the roadside unit (RSU), maximise the cooperative awareness message (CAM) exchange probability among vehicles within the same platoon, and foster sustainable, green communication strategies through efficient management for both power and energy. We introduce the innovative decomposed multi-agent deep deterministic policy gradient (DE-MADDPG) algorithm, which is integrated with the twin delayed deep deterministic policy gradient (TD3) technique and advanced quantum computing technologies, resulting in our proposed hybrid quantum-classical decomposed multi-agent TD3 (DE-MATD3) algorithm. Compared with classical approaches, our numerical results reveal that the proposed algorithm achieves exceptional energy efficiency performance, while maintaining the algorithm convergence rate and AoI levels.

The Δ-Σ analog-to-digital converter (ADC), with the modulator as its core component, has posed considerable challenges to the designers, due to the complex topologies and instability issues. Thanks to reinforcement learning (RL), appropriate models can be trained to automatically generate efficient modulator structures without the need for prior datasets. Proximal policy optimization (PPO), one of the latest and most promising branches of RL, can be optimally used by virtue of its simplicity and less hyperparameter tuning. This study focuses on multi-agent PPO (MAPPO) for the design of high-order Δ-Σ modulators, with two agents handling topology and parameter optimization respectively in a cooperative way. We address the challenge of both efficiency and stability through proper mathematical formulation and effective integration of weighted objectives. Through extensive simulations and iterative processes of MAPPO, our proposed methodology demonstrates effectiveness in maximizing the efficiency and stability objectives of the desired Δ-Σ modulator via a reward mechanism.

In analog and mixed-signal integrated circuits (ICs), low-dropout regulators (LDOs) are crucial for maintaining a stable power supply throughout the IC. As such, designing LDOs with both time and quality efficiency has attracted substantial research interest. This paper presents an implementation of multi-agent proximal policy optimization (MAPPO) in both separated-parameter and parameter-sharing configurations to address the challenges of multi-objective, multi-variable LDO design. Our experiments show that parameter-sharing MAPPO outperforms both separated-parameter MAPPO and single-agent PPO in exploration and convergence, benefiting from cooperative learning via parameter sharing, which accelerates the identification of optimal design configurations. In summary, our findings indicate that parameter-sharing MAPPO efficiently manages complex specifications and variables.

Effective maritime search and rescue (SAR) requires fast, coordinated action from Internet of Maritime Things (IoMT) nodes operating under extreme communication, energy, and environmental constraints. Existing solutions treat semantic sensing, digital twin modeling, and decentralized control in isolation, limiting their responsiveness and scalability. We propose SEMADT-RL, a unified framework that integrates semantic-driven communication, predictive digital twin forecasting, and decentralized multi-agent deep reinforcement learning with graph attention networks (MADRL-GNN). The semantic layer enables lightweight, anomaly-triggered updates, significantly reducing bandwidth while preserving critical detection cues. The digital twin assimilates these updates using an extended Kalman filter to forecast survivor drift and node dynamics. These forecasts guide decentralized agents that collaboratively optimize mobility, processing, and transmission policies under dynamic and constrained maritime conditions. Simulation results demonstrate that SEMADT-RL achieves faster survivor detection, lower communication overhead, and higher energy efficiency than state-of-the-art baselines, providing a scalable solution for next-generation IoMT-assisted SAR operations.

We investigate the secrecy performance of integrated satellite and terrestrial networks (ISTNs) with the support of a drone (aka UAV). An optimisation problem is formulated to maximise the secrecy rate while guaranteeing the quality of service, including the minimum secrecy rate of the legitimate user, the minimum data rate of normal users, and power consumption. A nested-loop algorithm including outer and inner loops is proposed to convert the initial non-convex problem into multiple convex problems, which are solved by the Dinkelbach algorithm. Simulation results prove the efficiency of our methods in terms of secrecy rate compared to traditional benchmarks.

Task offloading management in 6G vehicular net-works is crucial for maintaining network efficiency, particularly as vehicles generate substantial data. Integrating secure communication through authentication introduces additional computational and communication overhead, significantly impacting offloading efficiency and latency. This paper presents a unified framework incorporating lightweight Identity-Based Cryptographic (IBC) authentication into task offloading within cloud-based 6G Vehicular Twin Networks (VTNs). Utilizing Proximal Policy Optimization (PPO) in Deep Reinforcement Learning (DRL), our approach optimizes authenticated offloading decisions to minimize latency and enhance resource allocation. Performance evaluation under varying network sizes, task sizes, and data rates reveals that IBC authentication can reduce offloading efficiency by up to 50 % due to the added overhead. Besides, increasing network size and task size can further reduce offloading efficiency by up to 91.7%. As a countermeasure, increasing the transmission data rate can improve the offloading performance by as much as 63%, even in the presence of authentication overhead. The code for the simulations and experiments detailed in this paper is available on GitHub for further reference and reproducibility [1].

In this study, we introduce a digital twin (DT)-assisted IoV framework that combines a semi-synchronous adaptive federated learning (AdFL) method with multi-agent deep reinforcement learning, enhanced by generative artificial intelligence (GenAI) techniques, specifically conditional variational autoencoders (CVAE). This framework optimizes partial task offloading across distributed mobile edge computing (MEC) servers, ensuring scalable and efficient decision-making in diverse vehicular networks. By continuously reflecting the real-time conditions of vehicles and roadside units (RSUs), the DT framework ensures precise resource distribution and adaptive task handling. To handle the complexity of dynamic environments, we develop a global model that includes transformer layers in the federated learning (FL) process, which captures long-range dependencies. A semi-synchronous aggregation mechanism is introduced to maintain a balance between timely updates and model quality. The adaptive federated multi-agent reinforcement learning (AF-MARL) algorithm enables decentralized, collaborative learning among vehicles and RSUs, optimizing overall cost and energy use, reducing delays, and improving task completion rates. Comprehensive simulations show the framework's effectiveness compared to existing methods, emphasizing its potential to revolutionize real-time decision-making in IoV networks.

The stability of aircraft remains vulnerable to sudden external disturbances and unpredictable vortices. The aircraft's attitude angles undergo rapid changes due to random turbulence. Consequently, to ensure safety, it is essential to control the aircraft's control surfaces, i.e., ailerons, elevators, and rudder angles, to maintain its static stability. Although classical closed-loop control methods have been widely adopted, their limited adaptability to changing dynamics calls for more robust solutions. Reinforcement learning (RL) offers adaptive capabilities but often demands a large number of training parameters and substantial computational resources, which may be impractical for real-time lightweight aircraft applications. To overcome these limitations, this paper introduces a quantum aircraft with the quantum actorcritic networks-based aircraft control (QACN-AC) algorithm. By utilizing quantum neural networks (QNN), QACN-AC significantly reduces the number of parameters required for training, thus mitigating computational overhead while preserving robust control performance. The QACN-AC's effectiveness is validated through realistic simulations leveraging Boeing's B777 specifications. The results highlight QACN-AC's superiority over conventional RL, evidenced by a$1.25 \times$higher control performance and a$760 \times$reduction in the number of required parameters.

This paper investigates the integration of drone (aka UAV)-assisted networks with a reconfigurable intelligent surface (RIS) to enhance energy efficiency in near-and far-field communication scenarios. The coexistence of near-field and far-field communications introduces unique challenges in ensuring efficient resource allocation, managing interference, and meeting quality of service requirements for users. Primary users in the near-field areas have stronger signal links, while secondary users and primary far-field users face increased path loss and interference, necessitating sophisticated optimisation strategies to balance their performance. To address these challenges, we propose a joint optimisation framework for transmission power allocation and RIS phase-shift design. The framework aims to maximise energy efficiency while maintaining reliable communication for all user groups, leveraging the complementary characteristics of UAV and RIS technologies. The low-complexity optimisation approach is developed, leveraging advanced successive convex approximation techniques and iterative algorithms. The framework consists of the Dinkelbach algorithm for the outer loop and a combination of linear and convex optimisation algorithms for the inner loop. Linear programming is employed to handle the large number of variables, such as phase-reflecting coefficients, while convex programming is used to optimise power allocation in UAVs, with convergence guaranteed. Simulation results reveal significant energy efficiency gains compared to baseline methods, demonstrating the effectiveness of the proposed framework in managing the coexistence of near-and far-field communications. The findings underscore the importance of energy-efficient design in enabling scalable and sustainable UAV-assisted networks, offering valuable insights for the development of high-performance next-generation communication systems.

Uncrewed aerial vehicles (UAVs) are essential for providing communication and computation services in disaster recovery scenarios where traditional infrastructure is compromised. However, challenges related to energy efficiency, real-time adaptability, coverage, load balancing, and safe navigation persist, particularly in dynamic disaster environments. In this study, we propose a comprehensive framework that integrates generative AI (GenAI) with graph neural networks (GNNs) to dynamically generate hover points for waypoint-based UAV navigation and realistic task generation based on environmental conditions. The GNN-based collision avoidance mechanism further ensures safe navigation by allowing UAVs to avoid obstacles and no-fly zones while coordinating with neighboring UAVs in real time. To optimize UAV swarm operations, we introduce a multiagent graph reinforcement learning (MAGRL) framework, enabling UAVs to maximize overall system utility by refining hover point selection, task allocation, and load balancing in response to environmental changes. A graph attention mechanism enhances UAV coordination, improving communication efficiency and decision-making. Extensive simulations show that the proposed GenAI-GNN and MAGRL framework significantly outperforms existing methods in task completion, energy efficiency, and overall system utility in disaster recovery scenarios.

The rapid growth of edge computing has enabled low-latency and high-efficiency processing for a wide range of applications; however, it also leads to significant energy consumption and carbon emissions. In this context, this study investigates a CO2 emission minimisation problem in a digital twin-aided edge computing system, aiming to optimise task offloading decisions, transmit power, and processing rates of Internet of Things (IoT) devices. To address the formulated mixed-integer non-linear programming problem, we propose two solutions: an alternating optimisation method based on the successive convex approximation framework and a deep reinforcement learning (DRL) approach. Extensive simulations validate the effectiveness of the proposed solutions, demonstrating significant reductions in CO2 emissions, robust optimisation performance, and superior results compared to benchmark schemes. The findings highlight the feasibility of integrating advanced optimisation and artificial intelligence-driven techniques to achieve environmentally sustainable and high-performance edge computing systems, paving the way for greener technological innovation.

This paper investigates the use of the newly opened, and relatively unexplored, 6 GHz band for office occupancy detection using Wi-Fi sensing. To deliver accurate and efficient occupancy detection, we develop a novel channel model-informed feature engineering method combined with a random forest optimization strategy. Specifically, physically interpretable channel state information (CSI) amplitude-based features, such as the RicianK-factor and channel coherence time, are employed to capture channel variations induced by human presence and movement. A dual sliding window approach is introduced to effectively extract temporally relevant channel parameters, significantly improving computational efficiency and classification accuracy. Experimental validation conducted in a realistic office environment demonstrates that the proposed method achieves an average occupancy classification accuracy of 98.28%, outperforming existing methods while substantially reducing computational complexity. These findings suggest that integrating this Wi-Fi sensing approach into next-generation networks (e.g., IEEE 802.11bf) can enhance real-time responsiveness and reliability in smart building applications such as security and energy management.

Maritime transportation is essential for global trade but presents significant environmental challenges due to its greenhouse gas emissions. Existing studies have addressed these challenges through integrated routing and speed optimization frameworks, yet frequently lack explicit quantification of environmental impacts and exhibit limited scalability for large-scale ship routing operations. Conversely, existing quantum optimization research in vehicle routing predominantly targets land-based transportation scenarios, restricting its direct applicability to maritime logistics. Maritime logistics inherently involve distinct operational complexities, such as nonlinear interactions among speed, payload, fuel consumption, and numerous operational uncertainties. These combined limitations underscore the critical need for quantum optimization methods explicitly designed for green maritime supply chains. To bridge this gap, this paper proposes an efficient quantum-centric optimization framework that uses the quantum approximate optimization algorithm (QAOA) to jointly optimize ship routing and speed management within sustainable maritime supply chains. Specifically, we formulate an NP-hard cost minimization problem integrating critical maritime parameters, including fuel consumption, payload constraints, and operational speeds. We further develop a hybrid quantum-classical alternating optimization approach that iteratively addresses routing decisions through quantum computing techniques and optimizes ship speed using an analytical solution. Simulation results and real quantum hardware experiments demonstrate that our quantum-centric methodology achieves substantial cost reductions and highlights the potential for practical applicability in realistic maritime operations, significantly outperforming classical optimization benchmarks.

Achieving real-time decision-making and efficient resource management in dynamic, large-scale Internet-of-Vehicles (IoV) networks is a significant challenge due to their inherent complexity and scale. To address this, we propose a digital twin (DT)-assisted IoV framework that integrates a novel semi-synchronous adaptive federated learning (AdFL) approach with multiagent deep reinforcement learning, enhanced by generative artificial intelligence (GenAI) techniques, specifically conditional variational autoencoders (CVAEs). The framework optimizes partial task offloading across distributed mobile-edge computing (MEC) servers, ensuring scalable, efficient, and accurate decision-making in heterogeneous vehicular networks. By continuously mirroring the real-time states of vehicles and roadside units (RSUs), the DT framework enables precise resource allocation and adaptive task management. To tackle the complexities of dynamic environments, we design a global model with transformer layers embedded in the federated learning (FL) process, capturing long-range dependencies. A novel semi-synchronous aggregation mechanism is introduced to balance timely updates with model quality. The proposed adaptive federated multiagent reinforcement learning (AF-MARL) algorithm facilitates decentralized, collaborative learning among vehicles and RSUs, optimizing overall cost, and energy efficiency, reducing delay, and improving task completion rates. Extensive simulations demonstrate the effectiveness of the proposed framework against other existing approaches, highlighting its potential to transform real-time decision-making in IoV networks.

Holographically driven active reconfigurable intelligent surface (HARIS), leveraging densely packed subwavelength elements, overcomes the limitations of conventional RIS in signal processing, unlocking advanced capabilities for next-generation networks. Thus, to exploit its full potential, this work proposes the integration of HARIS into an Internet of Things (IoT) multiuser uplink near-field-driven wireless communication system. A sum-rate maximization problem is formulated to provide efficient resource utilization by jointly optimizing the equalizer design, power allocation at each IoT user, and the HARIS phase shift, while satisfying strict constraints of Quality-of-Service (QoS) requirement and limited power budget at each IoT user and HARIS. Due to the nonconvex nature of the problem, we propose an alternating optimization (AO)-based algorithm, incorporating techniques, such as minimum-mean-square error (MMSE), convex upper bound approximation, and semidefinite relaxation (SDR). Then, extensive simulations validate the algorithm’s efficacy and convergence, demonstrating up to 63% higher performance with HARIS than passive RIS. Additionally, we highlight that near-field communication yields up to 90% higher sum-rate than hybrid 76% and far-field model 73%. Moreover, we demonstrate the impact of imperfect channel state information (iCSI) on the system performance.

This work investigates the performance of rate splitting multiple access (RSMA) in a holographic reconfigurable intelligent surface (HRIS)-aided downlink network for efficient near-field communication. We formulate a sum-rate maximization problem that jointly optimizes the transmit beamforming at the base station (BS), the common rate of each receiving internet of things (IoT) node, and beamforming at the HRIS transmission design to ensure a minimum quality of service (QoS) at each node under the available resource constraints, such as the total power budget at the BS. Since the optimization problem is non-convex due to the coupling of the variables, we propose an iterative algorithm based on alternating optimization (AO) that efficiently solves the joint optimization problem utilizing analytical tools such as the successive convex approximation (SCA). Various numerical results are shown to validate the effectiveness and convergence of the proposed algorithm. Furthermore, we also discuss the impact of the key system parameters, such as reflecting elements, minimum QoS constraint, transmit power budget, and number of IoT nodes. The dominance of RSMA over the counterpart, non-orthogonal multiple access (NOMA), is also demonstrated. It is shown that the use of RSMA can achieve up to 96% higher performance compared to NOMA. It is also highlighted that with near-field assumptions, the average sum rate increases around 69% compared to hybrid 66% and far-field model 64%.

Physical activity energy expenditure (PAEE) offers significant benefits for general healthcare monitoring and has the potential to promote healthy and active aging for elderly individuals. With recent advancements in quantum information and computation, quantum machine learning (QML) has emerged as a tool capable of improving upon the measurement of PAEE. In this paper, we propose a hybrid QML model to predict PAEE which consists of a classical long short-term memory (LSTM) model integrated with a variational quantum circuit (VQC). This model, which we refer to as the enhanced quantum long short-term memory linear (eQLSTML), was subsequently trained and tested using the publicly available GOTOV Human Physical Activity and Energy Expenditure Dataset for Older Individuals. In particular, we study the proposed eQLSTML model with different gate choices in the quantum circuit along with various embedding and layering techniques. Our results indicate our model to be superior in both performance comparisons and prediction when compared to traditional machine learning methods currently employed. Our findings indicate that combining QML approaches with wearable IoT healthcare devices provides a new avenue for personalized healthcare monitoring and an effective method for promoting healthy aging.

In the shift toward the quantum computing era, the foundational principles of classical cybersecurity, particularly in the realm of cryptographic algorithms, are facing unprecedented challenges. This demands comprehensive reevaluation and redesign of cryptographic infrastructures to withstand quantum adversarial attacks. With the emergence of the quantum Internet, a new approach to secure communication is possible, utilizing quantum properties that have no counterpart in classical systems. As the quantum Internet facilitates the exchange of quantum information, data publication protocols become essential in anonymizing and protecting privacy-sensitive data in quantum communication networks. This paper proposes two controlled quantum anonymous communication (QAC) protocols for publishing classical and quantum information on an Internet server (IS) with the assistance of a communication service provider. The first protocol allows for the controlled publication of classical information without revealing the publisher’s identity such that an adversary, even with access to all network resources, cannot trace the publication source—i.e., achieving perfect untraceability. The second protocol enables anonymous publication of quantum information on an IS in a controlled and untraceable manner. These protocols serve as essential building blocks for advancing the quantum Internet, which has the potential to transform communication and information exchange methods. We provide a detailed anonymity analysis of these QAC protocols for data publication, ensuring that the published symbol or qudit information remains untraceable to its publisher. Moreover, the performance analysis in terms of publication error probability, fidelity, and degree of anonymity in noisy environments demonstrates the robustness of the protocols against noise and adversarial attacks.

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.

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 learning of fundamental quantum properties—namely coherence, discord, and entanglement—benchmarks the security, computational, and metrological capability of noisy intermediate-scale quantum (NISQ) communication, computing, and sensing networks. The current learning techniques vary widely for these fundamental quantum properties, including standard tomographic procedures that involve exhaustive optimization. Fortunately, the fundamentally distinct quantum properties feature an intricate connection. In this paper, we put forth the concept of universal quantum witness machines (UQWMs) to develop a unified framework for quantum property learning (QPL) of a quantum system. We first formulate the certification and quantification of quantum properties based on quantum witnesses. The witness-based certification method is experimentally accessible and resource-efficient but lacks reliability and generality. To universalize the scope and circumvent the unreliability, we transform the certification task into a classification task by employing UQWMs with classical machine learning to construct quantum property classifiers. This formalism offers a unifying perspective on the certification, quantification, and classification of these enigmatically linked fundamental quantum properties. To demonstrate our UQWM approach, we provide a comparative numerical analysis of quantum property quantification with quantum witnesses and classification performance analysis of quantum property classification with convolutional neural networks, specifically for$4 \times 4$quantum systems.

The emerging quantum technology has highlighted the necessity for secure and efficient secret sharing in quantum networks. In this paper, we introduce a verifiable multiparty counterfactual quantum secret sharing (QSS) protocol, enhancing security and efficiency. This QSS protocol utilizes a low-depth quantum circuit to encrypt and decrypt information, which comprises a unitary operator constructed using a preshared secret key. To ensure the robustness and verifiability of the shared secret key, the protocol imposes constraints on the participants with the Chinese remainder theorem. The most significant advantage of our proposed QSS protocol is incorporating counterfactual communication, which considerably enhances the scheme’s security by enabling exchange-free information sharing among participants, thereby minimizing the risk of eavesdropping or intercept-and-resend attacks. Furthermore, we incorporate a weighted-threshold mechanism that provides flexibility, enabling diverse use cases to design security protocols for quantum networks. The security analysis of the counterfactual QSS protocol and its implementation on IBM Quantum computers reveals strong resilience to internal and external attacks, along with high efficiency and robustness, making it effective for quantum encryption in the noisy intermediate-scale quantum era.

In the context of ultra-reliable low-latency communication (URLLC) in Internet-of-Things (IoT) networks, conventional half-space coverage limits the flexibility of reconfigurable intelligent surface (RIS) deployment. To overcome these constraints, this paper makes use of active simultaneously transmitting and reflecting RIS (STAR-RIS), which is seamlessly integrated into digital twin (DT) and mobile edge computing (MEC) frameworks. Our primary research objective is to achieve full-space coverage by enabling simultaneous transmission and reflection of the signals while improving uplink data transmission from IoT URLLC user nodes (UNs) to the base station (BS) with the assistance of active STAR-RIS, even in the presence of imperfect channel state information (CSI). We formulate the problem of minimizing total end-to-end (e2e) latency, computed using the alternating optimization (AO) algorithm. Subsequently, we have evaluated the performance of the AO algorithm against the stochastic gradient descent (SGD) algorithm, which serves as the benchmark solution. The simulation outcomes delineate a performance evaluation under perfect and imperfect CSI scenarios. The AO algorithm outperforms SGD with latency reductions of 19.7% at$N=32$and 20.4% at$N=64$. Increasing N from 32 to 64 results in a 39.3% latency reduction for AO, surpassing SGD’s 38.8%. However, the SGD algorithm consistently exhibits lower computational complexity compared to the AO algorithm. Additionally, the energy splitting mode achieves the system’s total e2e latency reductions of 28.4% over the mode switching mode and 11.04% over time switching mode. Furthermore, active STAR-RIS optimal beamforming (ARO) achieves$\approx 10$% latency reduction over the predictive optimal beamforming (PRO), which itself surpasses active STAR-RIS with random beamforming (ARR) by$\approx 9$%. This comparison considers key factors such as the power budget, the number of RIS elements, the caching capacity of the edge computing server (ECS), the number of IoT UNs, the minimum transmission rate, and maximum transmit power at BS of active STAR-RIS.

Counterfactual quantum communication enables communication between remote parties without transmitting any information-carrying particle. In this paper, we propose four protocols for secure quantum communication networks utilizing such communication. The first protocol, counterfactual quantum secure direct communication (CQSDC), enables a sender to securely and counterfactually communicate a secret message. The second protocol, counterfactual quantum secure dialogue (CQSD), allows legitimate parties to transmit secret messages in each direction simultaneously, securely and counterfactually. The third protocol, counterfactual controlled quantum teleportation (CCQT), facilitates a sender to counterfactually teleport a quantum state to a receiver under the supervision of a controller. Finally, the fourth protocol, counterfactual quantum private comparison (CQPC), capacitates a third party to compare the private states of the end parties without the actual knowledge of the counterfactually transmitted states. We devise the CQSDC and CQSD protocols by exploiting the counterfactual Swap, dual chained quantum Zeno (CQZ), and distributed controlled NOT gates. For CCQT and CQPC protocols, we utilize CQZ gates with a horizontally polarized photon input. We show that the security of CQSDC and CQSD relies on counterfactual entanglement swapping, while that of CCQT and CQPC depends on establishing secure counterfactual communication channels and security validation with decoy particles, respectively.

This paper investigates simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted multi-user downlink (dl) communications with a primary focus on maximizing information secrecy by considering the channel state information (CSI) error. Acquiring perfect CSI is particularly challenging due to the unavailability of radio frequency chains at the STAR-RIS, the inherent impact of noise and interference on the CSI estimation, as well as non-collaborative nature of the eavesdroppers. In particular, we tackle the worst-case robust beamforming design problem to maximize the sum secrecy rate of the system while considering transmit power limitations, quality of service requirements, and practical constraints on the STAR-RIS phase shifter array. To tackle the resulting non-convex problem, we employ the S-procedure as an initial step to approximate semi-infinite inequality constraints. Subsequently, we leverage the alternating optimization with a line search framework to update the precoder and phase shift matrix iteratively. Furthermore, we extend our solution to address the non-convexity by leveraging a deep reinforcement learning (DRL) multi-agent (MA) framework based on Markov decision process. We also analyze practical phase shifts and the effect of direct links to showcase the practicality of our approach. Simulation results confirm STAR-RIS’s significant performance edge, exhibiting approximately 27.1% higher secrecy in conventional optimization and around 35.4% in the MA-DRL context compared over the conventional RIS. Moreover, our proposed MA-DRL approach surpasses single-agent schemes by about 8.6% in the case of proximal policy optimization and 19.9% in the case of deep deterministic policy gradient, emphasizing the benefits of the MA framework with STAR-RIS.

Physical-layer security (PLS) is a new paradigm for secure communication without requiring secret key exchange and management. Moreover, PLS with frequency diverse subarray (FDSA) can better control information leakage in the angle-range domain, which mitigates the security weakness of the phased array caused by its lack of range resolution. In this paper, we propose a three-dimensional (3D) directional modulation (DM) using randomized radiation with FDSA for enhanced PLS, employing a planar array. In addition, nonuniform frequency offsets (FOs) are considered as FO configurations (FOCs) for FDSA to concentrate on the mainlobe and suppress the undesired sidelobes in 3D space, where logarithmically increasing FOC (L-FOC), Hamming window-based FOC (H-FOC), and piecewise trigonometric FOC (P-FOC) are introduced. Characterizing the process of selecting the random subsets for randomized radiation, we provide the exact analysis of the secrecy rate of the proposed scheme. Moreover, FOs applied to FDSA and the number of random subsets are optimized with a genetic algorithm (GA)-based optimization strategy. We evaluate the proposed schemes in terms of secrecy rate and vulnerable volume, where the simulation results verify our analysis and show that nonuniform FOCs are a more favorable choice for FDSA compared to uniform FOC (U-FOC).

Extremely large-scale antenna arrays (ELAA) require near-field spherical wave modeling due to the substantial increase in the number of antennas, which introduces new spatial dimensions to physical layer key generation (PLKG). We investigate multi-user PLKG in near-field environments, where a base station with an ELAA simultaneously generates secret keys with multiple users. We derive an analytical expression for the key rate (KR). By utilizing spatial dimensions of distance and angle in near-field environments, we apply eigenvalue decomposition and singular value decomposition to design precoding matrices to reduce interference among user equipments (UEs) and extract uncorrelated subchannels. Given that the KR is non-convex, we approximate it and optimize the precoding matrix to increase the KR. After precoding design, the KR depends on the transmit power allocated to the subchannels. Two optimization problems are formulated to further optimize transmit power allocation. The first problem focuses on maximizing the sum KR. We apply the Lagrange multiplier method to determine the optimal power allocation variables by searching the Lagrange multiplier. To reduce computational complexity, a supervised feedforward neural network (FNN) is designed to capture the relationship between the power allocation variables and the Lagrange multiplier. The second optimization problem focuses on KR fairness. By introducing a slack variable that is smaller than the KRs of all users, we use the CVX toolbox to find optimal power allocation variables that maximize this slack variable. To further reduce complexity, the Lagrange multiplier method offers an analytical solution for power allocation variables in terms of Lagrange multipliers determined by the slack variable in the high-power case. We employ a bisection algorithm to find the slack variable. Furthermore, we propose an FNN to map transmit power to the slack variable. Simulations demonstrate that the proposed methods efficiently leverage near-field effects for multi-user PLKG, reducing pilot overhead.

The integration of terrestrial and non-terrestrial networks with mobile edge computing (MEC) and orbital edge computing (OEC) technologies is essential for advancing 6G communication networks. This paper introduces a network architecture that combines terrestrial and non-terrestrial networks by integrating drones (also known as UAV)-carried reconfigurable intelligent surfaces (RIS) and satellite-based MEC to optimize resource allocation in intelligent autonomous transportation systems (IATS). The primary objective is to minimize total system utility costs through the optimal allocation of bandwidth, computational power at the base station and low Earth orbit (LEO) satellite, and offloading decisions, all while adhering to strict performance and delay constraints. We address the complex resource optimization challenge by formulating a nonlinear programming (NLP) problem. To solve this problem, we employ long short-term memory (LSTM)-enhanced deep deterministic policy gradient (DDPG) and LSTM-enhanced twin delayed deep deterministic policy gradient (TD3) algorithms, which enable dynamic and adaptive resource management. These LSTM-enhanced algorithms improve convergence speed by 44.44% and 73.81%, respectively, compared to their conventional counterparts, while significantly enhancing cost efficiency. Our simulation results demonstrate substantial improvements in system performance, with effective resource allocation and minimal utility costs, providing a robust solution for ensuring high-quality, low-latency communication in diverse 6G IATS environments.

Network backbone black holes(BH) pose significant challenges in the Internet by causing disruptions and data loss as routers silently drop packets without notification. These silent BH failures, stemming from issues like hardware malfunctions or misconfigurations, uniquely affect point-to-point packet flows without disrupting the entire network. Unlike cyber attacks and network intrusions, BHs are often untraceable, making early detection vital and challenging. This study addresses the need for an effective forecasting solution for BH occurrences, especially in environments with unlabeled traffic data where traditional anomaly detection methods fall short. The Type-Independent Black Hole Forecasting Model is introduced to predict BH occurrences with high precision across various anomalies, including contextual and collective anomaly types. The three-stage methodology processes unlabeled time-series network data, where the data is not pre-labeled as anomaly or normal, using machine learning and deep learning techniques to identify and forecast potential BH occurrences. The ’Point BH Identification and Segregation’ stage segregates point BH traffic using Density-Based Spatial Clustering of Applications with Noise(DBSCAN), followed by Reintegration and Time Series Smoothing. The final stage, Advanced Contextual and Collective BH Detection leverages Convolutional AutoEncoder(Conv-AE) with window sliding for advanced anomaly detection. Evaluation using a dual-dataset approach, including real backbone network traffic and a time-series adapted public dataset, demonstrates the adaptability of the model to real backbone BH detection systems. Experimental results show superior performance compared to state-of-the-art unsupervised anomaly forecasting models, with a 98% detection rate and 90% F-1 score, outperforming models like MultiHeadSelfAttention, which is the main building block of Transformers.

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.

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.

The advancement of sophisticated communication technologies and robust computing systems has unlocked opportunities for new applications across various domains. While these applications promise enhanced convenience and improved living standards, they also raise a critical concern regarding the trade-off between convenience and environmental sustainability. This paper addresses this concern by investigating sustainable resource management, employing a digital twin approach to minimise CO2emissions in edge computing systems. Specifically, our aim is to reduce the amount of CO2emissions by optimising the allocation of computing and communication resources. This includes optimising transmit power, adjusting the clock speed for task processing, and making optimal decisions regarding task offloading. To tackle this complex optimisation problem, we employ an iteratively alternating optimisation algorithm. Through extensive simulations, we illustrate the efficacy of our proposed solution in not only mitigating CO2emissions but also optimising resource allocation, thereby contributing to both environmental sustainability and technological efficiency.

The continuous monitoring of activities of daily living (ADLs) can play a vital role in assessing an individuals capability to live independently and enable the possibility for early disease detection. This paper introduces a novel hybrid model, called quantum-based gated recurrent unit - multiclass classifier (QGRU-MC), to enhance ADL classification from wear-able sensor data. Using statistical feature extraction from the raw accelerometer sensor signals, the QGRU-MC model demonstrates good performance in activity recognition. Preliminary findings suggest that our model has good potential in healthcare applications, and in particular, can contribute to the advancement of future intelligent systems centered on daily activity monitoring and the promotion of healthy aging.

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-MetaFL, 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-MetaFL is strengthened by the quantum sequential-training-program (Q-STP) algorithm, a quantum-based sequential training program that transforms model training, reducing communication costs and adeptly managing vehicle states. Complementing this, the quantum vehicle-context-grouping (Q-VCG) mechanism groups vehicles based on contextual data similarity, effectively tackling the complexities of data heterogeneity. The synergy of Q-STP and Q-VCG culminates in the QV-MetaFL algorithm, a decentralized, efficient, and context-aware quantum federated learning (QFL) process that redefines learning dynamics in the vehicular metaverse. Additionally, our research introduces an innovative composite loss function that amalgamates classical loss metrics with quantum parameter regularization, deftly addressing quantum sensitivity to noise. The effectiveness of the QV-MetaFL framework is rigorously validated through comprehensive simulations, with its performance meticulously compared against various adaptations, showcasing its transformative capabilities within the vehicular metaverse ecosystem.

This paper presents a novel design for a mobile edge computing (MEC) service that integrates digital twin technology with an active simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). This configuration leverages edge intelligence, aiming to strengthen ultra-reliable and low-latency communications (URLLC) within Internet-of-Things (IoT) frameworks. We explore the uplink data transmission path from singular-antenna IoT URLLC nodes (UNs) to a multi-antenna base station (BS) facilitated by an active STAR-RIS. Our focus is on framing an end-to-end (e2e) latency reduction strategy for the presented system. Due to the inherent non-convexity of this problem, we propose an efficient alternating optimization (AO) algorithm to get a solution. This algorithm decomposes the main problem into five distinct sub-problems: transmit beamforming design, optimization of caching and offloading policies, joint communication and computation optimization, and enhancement of active STAR-RIS beamforming. An extensive set of simulation outcomes indicates that our DT-enhanced optimal-phase STARRIS approach consistently surpasses benchmark methods, particularly when accounting for variables such as power constraints, the number of RIS elements, the caching capacity of the edge computing server (ECS), and the number of IoT UNs.

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 study explores implementing a digital twin network (DTN) for efficient 6 G wireless network management, aligning with the fault, configuration, accounting, performance, and security (FCAPS) model. The DTN architecture comprises the Physical Twin Layer, implemented using NS-3, and the Service Layer, featuring machine learning and reinforcement learning for optimizing carrier sensitivity threshold and transmit power control in wireless networks. We introduce a robust “What-if Analysis” module, utilizing conditional tabular generative adversarial network for synthetic data generation to mimic various network scenarios. These scenarios assess four network performance metrics: throughput, latency, packet loss, and coverage. Our findings demonstrate the efficiency of the proposed what-if analysis framework in managing complex network conditions, highlighting the importance of the scenario-maker and the impact of twinning intervals on network performance.

In this paper, we present the results of a set of channel measurements conducted within the 6 GHz band used in IEEE 802.11be based wireless local area networks (WLANs). A range of indoor and outdoor client to access point (AP) communication scenarios were considered for both line-of-sight (LOS) and non-LOS (NLOS) channel conditions. We have investigated the path loss, large-scale, and small-scale fading across 256 frequency points between 6.425 and 6.445 GHz. To model the large-scale fading we have utilized the lognormal and gamma distributions, while for the small-scale fading this was the Rayleigh, Rician, and Nakagami-m distributions. The information loss incurred when encoding the empirical distributions with the aforementioned theoretical ones was determined using the resistor-average distance (RAD). It was found that the gamma distribution provided a better fit to the large-scale fading, while the Rician and Nakagami-m distributions observed the lowest RAD values for the small-scale fading. To ascertain the temporal stability of the considered channels, the coherence time was inferred using an analysis of the autocorrelation. Our results indicate that the coherence time for the large-scale fading was typically longer than for small-scale fading.

Radio frequency fingerprint identification is a promising technique for device authentication that relies on the unique radio frequency fingerprint features caused by hardware impairments. Existing radio frequency fingerprint identification models usually contain a significant number of trainable parameters, making them undesirable for Internet of Things applications. In this paper, we augment a classical neural network by introducing an intermediary quantum neural network stage to enhance the authentication of Internet of Things devices using radio frequency fingerprint features. The model is based on the combination of quantum and classical machine learning and benefits from a significantly smaller number of trainable parameters. Empirical results show that our proposed model not only achieves a much smaller footprint (in terms of device memory) but also delivers competitive accuracy to conventional deep learning approaches. It therefore shows much promise as a solution for securing networks which feature resource-constrained Internet of Things devices.

With the rapid development of communication applications, the integration of reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) techniques has emerged as a promising approach to enhance connectivity and data transmission rate in future wireless networks. To successfully deploy RIS-NOMA aided 6G network, an accurate channel estimation is a crucial task. Quantum machine learning (QML) is a novel approach showing potential computational advantages in various problems of 6G wireless communications. However, its application, particularly in channel estimation, remains largely theoretical rather than adopted in practice. We propose a hybrid quantum-classical neural network model based on convolutional neural network (CNN) and quantum long short-term memory (QLSTM) for channel estimation in RIS-aided 6G NOMA system. Our results show that the proposed CNN-QLSTM model has a better channel prediction compared to its classical counterpart with regard to root mean square error (RMSE) and mean absolute error (MAE).

Federated learning (FL) is a promising solution to overcome data island and privacy issues in intrusion detection systems (IDSs) for the Industrial Internet of Things (IIoT). However, the heterogeneity of various IIoT devices poses formidable challenges to FL-based intrusion detection, especially the training cost relating to delay and energy consumption. In this article, we propose a delay and energy-efficient asynchronous FL (AFL) framework for intrusion detection (DEAFL-ID) in heterogeneous IIoT. Specifically, we address the shortcomings of low efficiency and high energy consumption in existing FL-based solutions involving all idle IIoT devices. To do so, we formulate an AFL-based optimal device selection problem which aims to select high-quality training devices in advance by exploring the device advantages in detection accuracy, delay reduction, and energy saving. Subsequently, a deep Q-network (DQN)-based learning algorithm is developed to quickly solve the above high-dimensional problem. In addition, to further improve the detection performance, we build a hybrid sampling-assisted convolutional neural network (CNN)-based IDS model, which can eliminate the imbalance of IIoT data and enable the selected devices to fully extract data features. Through simulations, we demonstrate that DEAFL-ID achieves a significant improvement in training cost and detection performance compared with existing IDS schemes.

Network intrusion detection systems (NIDSs) are vital for identifying security attacks and predicting early invasion attempts, which is essential for protecting the Internet. Recently, deep learning (DL) has made significant achievements in enhancing intrusion detection accuracy. Nevertheless, the practical implementation of high-complexity DL models is limited by the constrained computational capabilities of the Internet of Things (IoT) devices, e.g., home routers and IoT gateways. This article introduces a novel NIDS approach explicitly tailored for IoT networks, leveraging a lightweight DL model. During the data preprocessing phase, we use a spatially enriched data conversion technique to decrease the dimensionality of high-dimensional raw traffic variables. This helps to offset the problem of increased model complexity. Furthermore, when spatial relationships often exist in the data, we can simplify the learning architecture by utilizing state-of-the-art vision transformer techniques in the computer vision field that can substantially reduce model complexity. The experimental results indicate that the proposed method achieves outstanding accuracy up to 99.57% with high-volume traffic input. Moreover, the proposed method reaches substantial reductions in learnable parameters by 55.35% and 82.07%, along with a remarkable decrease in floating point operations (FLOPs) by 93.56% and 99.28% compared to existing studies. The outstanding achievement highlights the proposed method’s ability to balance model complexity and accuracy performance, making it extremely appropriate for deployment on IoT gateways with limited resources.

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.

In this article, we propose a novel framework that combines simultaneous localization and communication (SLAC) using a reconfigurable intelligent surface (RIS) aided integrated sensing and communication (ISAC) systems. Our primary focus is on enhancing resource efficiency in such systems. We introduce Cloud Radio Access Networks (C-RAN) that facilitate collaboration between multiple base stations (BSs), enhancing cooperation benefits for both communication and sensing capabilities. To evaluate localization performance, we formulate an optimization problem to minimize the squared position error bound (SPEB) that reflects the system functional performance by optimizing the transmit beamformer, phase shift and subcarrier assignment under certain constraints. Moreover, in order to adjust the phase shift of the RIS, we propose a RIS-aided cooperative ISAC SLAC protocol. This approach utilizes the measurements collected to refine the location and velocity estimates of the agent, as well as to reconstruct the environmental map with enhanced accuracy. However, the high dimensionality of the decision space makes the problem computationally intensive and challenging to navigate using gradient-based or exhaustive search methods. To efficiently tackle these issues, we construct a framework based on Markov decision processes (MDPs) and address it by introducing a novel algorithm called hybrid deep reinforcement learning (HDRL) algorithm. We validate our proposed algorithm through various simulations, demonstrating its effectiveness in improving system performance by comparing with the baseline schemes.

This work investigates the performance of simultaneous wireless information and power transfer (SWIPT) in a reconfigurable intelligent surface (RIS)-aided internet of things (IoT) communications under imperfect channel state information (CSI). We formulate a multi-objective optimization problem (MOOP) to design transmit precoding vector (TPV) at the base station (BS) and phase shift matrix (PSM) at the RIS that jointly maximizes energy efficiency (EE) and harvested power (HP) under the norm bounded CSI error model. Due to the conflicting objective functions and non-convex nature of the above optimization problem, the MOOP is simplified using the.-constraint method and subsequently adopting advanced optimization tools, such as Dinkelbach method, S-procedure, general sign-definiteness, semidefinite programming and convex-concave procedure. Thereafter, we propose an alternating optimization-based algorithm which determines optimal TPV and PSM iteratively that jointly maximizes the EE and HP of the considered system. Through numerical simulations, we validate the robustness, optimality, convergence, accuracy and effectiveness of our proposed algorithm. Furthermore, we assess the impact of several key parameters such as the number of RIS elements, available transmit power at BS and the minimum HP on the performance of the considered system.

In this article, we propose hybrid deep quantum-transformer networks (QTNs) to predict the optimal beam in integrated sensing and communication (ISAC) systems employing millimeter-wave (mmWave) band. In mobile applications, vehicle-to-infrastructure (V2I) communications at high frequency require large antenna arrays and narrow beams, which is associated with high-beam training overhead. In such a scenario, selecting an optimal beam to maximize the signal power at the receiver can be learned from the sensory data collected at the base station and guided by the position-based data provided by the user equipment. Such multimodal sensory data can be utilized by deep learning frameworks to create situational awareness for intelligently predicting optimal beams. We evaluate the proposed learning models in real-world V2I scenarios provided by the multimodal deepsense sixth generation data set and compare them with the existing works. The experimental results show a distance-based accuracy (DBA) score of 0.9124 for multimodal and 0.8832 for position-based data, respectively. Moreover, the hybrid QTN achieve the best DBA scores and the highest accuracy compared to other models on zero-shot testing. These QTN models exhibit low complexity and high performance, demonstrating their potential to address the challenges of beam management in mmWave ISAC systems.

The convergence of advanced communication technologies and powerful computing architecture has unlocked a plethora of opportunities for Internet-of-Things applications. To fully realize this potential, a synergistic design encompassing sensing, computing, and communication is crucial. This article investigates these critical technologies to facilitate service-oriented systems by minimizing end-to-end latency and the number of deployed services at edge servers in mobile edge computing, all within the confines of stringent ultrareliable and low-latency communication requirements and system budget constraints. The addressed optimization problem takes into account variables, such as service placement strategies, task offloading portions, and bandwidth allocation. Simulation results validate the effectiveness of our solution and highlight the impact of key parameters on system performance.

Digital twin technology is crucial to the development of the sixth-generation (6G) Internet of Vehicles (IoV) as it allows the monitoring and assessment of the dynamic and complicated vehicular environment. However, 6G IoV networks have critical challenges in network security and computational efficiency, which need to be addressed. Existing digital twin technologies in 6G IoV networks often suffer from limitations, such as reliance on static models and high computational demands, leading to unstable attack detection and inefficiencies. Their results for attack detection performance metrics, precision, detection rate, and F1-Score are insufficient for 6G IoV. Moreover, these systems concentrate all computational processes within the digital twin’s service layer, leading to inefficiencies. To address these challenges, we introduce a novel artificial intelligence (AI) enhanced digital twin framework designed to significantly improve 6G IoV network security and computational efficiency under dynamic conditions. Our framework employs an advanced feature engineering module that uses feature selection methods and stacked sparse autoencoders (ssAE) to reduce feature dimensions within the cyber twin layer, effectively distributing the overall computational load. It also utilizes an online learning module which enables a network-aware attack detection mechanism for precise attack detection. The proposed solution exhibits a stable performance of around 98% success rate regarding attack detection metrics against two data sets. Specifically, our solution reduces system latency by 12%, energy consumption by 15%, RAM usage by 20%, and improves packet delivery rates by 6.1%. These findings underscore the potential of our framework to enhance the robustness and responsiveness of 6G IoV systems, offering a significant contribution to vehicular network security and management.

Quantum Byzantine fault tolerance (BFT) consensus is a secure and reliable mechanism that enables network nodes to reach an agreement even in the presence of faulty nodes, by using distributed private correlated lists. It plays a crucial role in developing the blockchain-based Metaverse to ensure its integrity and security. In this paper, we propose a counterfactual quantum BFT (CQ-BFT) protocol for a multipartite network using counterfactual unitary telecomputation with the chained quantum Zeno gates. This consensus protocol achieves an agreement among the parties without the passage of any physical particles through the quantum channel. Due to the unique properties of counterfactual communication, we demonstrate that the CQ-BFT protocol can operate in the absence of a shared phase reference and provide a quantum layer of security and robustness against dephasing noise, fulfilling the stringent requirements of blockchain technology. In addition, we analyze the performance tradeoff of the CQ-BFT protocol in terms of the three pillars of blockchain—i.e., security, scalability, and decentralization. The human-centric Metaverse could leverage high degrees of security, noise resilience, and fault tolerance of the CQ-BFT protocol to enhance its underlying network infrastructure. This protocol leads to more robust and immersive virtual environments that prioritize the needs and experiences of Metaverse users.

QSNs (QSNs) incorporate quantum sensing and quantum communication to achieve Heisenberg precision and unconditional security by leveraging quantum properties such as superposition and entanglement. However, the QSNs deploying noisy intermediate-scale quantum (NISQ) devices face near-term practical challenges. In this paper, we employ variational quantum sensing (VQS) to optimize sensing configurations in noisy environments for the physical quantity of interest, e.g., magnetic-field sensing for navigation, localization, or detection. The VQS algorithm is variationally and evolutionarily optimized using a genetic algorithm for tailoring a variational or parameterized quantum circuit (PQC) structure that effectively mitigates quantum noise effects. This genetic VQS algorithm designs the PQC structure possessing the capability to create a variational probe state that metrologically outperforms the maximally entangled or product quantum state under bit-flip, dephasing, and amplitude-damping quantum noise for both single-parameter and multiparameter NISQ sensing, specifically as quantified by the quantum Fisher information. Furthermore, the quantum anonymous broadcast (QAB) shares the sensing information in the VQS network, ensuring anonymity and untraceability of sensing data. The broadcast bit error probability (BEP) is further analyzed for the QAB protocol under quantum noise, showing its robustness—i.e., error-free resilience—against bit-flip noise as well as the low-noise BEP behavior. This work provides a scalable framework for integrated quantum anonymous sensing and communication, particularly in a variational and untraceable manner.

Next-generation wireless applications are expected to enable extended ultra-reliable low-latency communication (URLLC) to support high data rates along with ultra-reliability and low-latency features beyond the capabilities of existing core services. There is a need to transition from conventional architectures to more efficient and robust multiple-access schemes to meet these consolidated requirements in resource-constrained systems. This study explores the utilization of simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in non-orthogonal multiple access (NOMA) systems to enable spectrally efficient URLLC, even under the imperfect channel state information. In particular, we focus on maximizing spectral efficiency by jointly designing robust beamforming at the base station and STAR-RIS subject to given URLLC requirements. Due to the non-convexity of the formulated problem, we propose an alternating optimization framework that obtains sub-optimal solutions to the problems of beamforming design at the BS and STAR-RIS, respectively by exploiting$\mathcal {S}-$procedure and successive convex approximation. Simulation results confirm that the STAR-RIS-NOMA system can significantly boost the spectral efficiency by 10-15% compared to conventional reflecting-only RIS while guaranteeing the strict URLLC requirements. Specifically, among all the possible modes of STAR-RIS, the time-splitting mode provides better spectral efficiency than other modes owing to its better interference management.

We consider the problem of estimating time-varying quantum noise. Specifically, we focus on Pauli qubit noise with time-variations and attempt to construct the most accurate instantaneous channel description. To this end, we propose an adaptive framework of simultaneous communication and parameter estimation (SCAPE) that efficiently and accurately estimates the time-varying Pauli channel while communicating reliably over the channel being estimated. This adaptive framework gives the informed control of communication rate–parameter estimation tradeoff to communicating parties. Interestingly, this adaptive SCAPE requires post-processing entirely on the receiver’s end and minimal feedback to the sender to increase, decrease, or continue with the same code rate of employed error correcting code. This procedure can be particularly useful in time-varying quantum channels with natural periodic deviations in channel conditions, e.g., in satellite communication channels.

Physical layer key generation (PLKG) leverages wireless channels to produce secret keys for legitimate users. However, in millimetre-wave (mmWave) frequency bands, the presence of blockage significantly reduces the key rate (KR) of a PLKG system. To address this issue, we introduce reconfigurable intelligent surfaces (RISs) as a potential solution for constructing RIS-reflected channels, thereby enhancing the KR. Our study focuses on the beam-domain channel model and exploits the sparsity of mmWave bands to enhance the randomness of secret keys. To relieve pilot overhead in multi-user systems, we employ a compressed sensing (CS) algorithm to estimate angular information and propose a channel probing protocol with the full-array configuration for acquiring the beam-domain channel. We derive the analytical expressions for the KR in the case of full-array configuration. To optimize the KR, we design the phase shift and precoding vectors based on the obtained angular information. Furthermore, we employ a water-filling algorithm that relies on the Karush-Kuhn-Tucker (KKT) conditions to optimize power allocation for estimating the beam-domain channel with the same channel variance. When channel variances of the beam-domain channel differ, we design a deep-learning-based power allocation method for a more complex problem. What is more, we design a sub-array configuration scheme that exploits the difference in spatial angles between users to reduce pilot overhead and derive the analytical expression for the KR. Through extensive simulations, we demonstrate that our proposed PLKG schemes outperform existing methods.

In this paper, we consider an Internet-of-Things network supported by several satellites and multiple cache-assisted unmanned aerial vehicles (UAVs). Due to the long-distance transmission and detrimental effects from the transmission environment, the latency can be extremely high, especially in the presence of backhaul congestion. Therefore, we formulate an optimisation problem with the aim of minimising the total network latency. To reduce the complexity of the original problem, it is divided into three sub-problems, namely, clustering ground users associated with UAVs, cache placement in UAVs (to support the network in avoiding backhaul congestion), and power allocation for satellites and UAVs. We propose a distributed optimisation method consisting of: a non-cooperative game is designed to obtain the solution to the clustering problem; a genetic algorithm, which is powerful in the scenario of many variables, is employed to obtain the optimal solution to the high-complexity caching problem; and a quick estimation technique is used for power allocation. Additionally, a centralised optimisation method is presented as a benchmark. Simulation results show that although the distributed method leads to network latency of approximately 30% higher than the centralised method, it takes significantly less time to execute and is suitable for systems requiring strict real-time computing constraints. Furthermore, the numerical results prove the efficiency of our methods compared with other conventional ones.

Mobile edge computing (MEC) brings computational resources to the edge of network that triggers the paradigm shift of centralized machine learning towards federated learning. Federated learning enables edge nodes to collaboratively train a shared prediction model without sharing data. In MEC, heterogeneous edge nodes may join or leave the training phase during the federated learning process, resulting in slow convergence of dynamic communities and federated learning. In this paper, we propose a fine-grained training strategy for federated learning to accelerate its convergence rate in MEC with dynamic community. Based on multi-agent reinforcement learning, the proposed scheme enables each edge node to adaptively adjust its training strategy (aggregation timing and frequency) according to the network dynamics, while compromising with each other to improve the convergence of federated learning. To further adapt to the dynamic community in MEC, we propose a meta-learning-based scheme where new nodes can learn from other nodes and quickly perform scene migration to further accelerate the convergence of federated learning. Numerical results show that the proposed framework outperforms the benchmarks in terms of convergence speed, learning accuracy, and resource consumption.

This paper exploits a quantum-empowered machine learning algorithm to enhance computation learning speed. We leverage quantum phenomena such as superposition and entanglement to work on large-scale multi-dimensional data represented by quantum states. Under stochastic behaviors and quantum uncertainty, we examine the offloading problem to maximize the computational task processing efficiency, considering the computation latency, energy consumption, and quantum network adaptability. From the Markov decision process, the paper proposes a novel quantum-empowered deep reinforcement learning (Qe-DRL) approach, combining quantum computing theory and machine learning to achieve exploration and exploitation trade-off via quantum parallelism significantly. Furthermore, we develop a modified Grover’s algorithm with exponential convergence speed to provide a searching strategy for transition quantum states probabilities. Simulation results establish the effectiveness of the proposed Qe-DRL algorithm and its superior computational learning speed. Our proposed Qe-DRL algorithm outperforms other benchmarks in terms of energy efficiency performance.

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 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.

In this paper, we propose a novel approach for optimal resource management in ultra-reliable low-latency communication (URLLC)-enabled Internet of Vehicles (IoV) networks. The framework includes mobile edge computing (MEC) servers integrated into roadside units (RSUs), unmanned aerial vehicles (UAVs), and base stations (BSs) for hybrid vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. We utilize asynchronous federated learning (AFL) approach to enhance the accuracy of the global model by considering the mobility characteristics of vehicles. The problem of optimal resource allocation is formulated to achieve the best allocation of frequency, computation, and caching resources while complying with the delay restrictions. To solve the non-convex problem, a multi-agent actor-critic type deep reinforcement learning algorithm called D-MAAC algorithm is introduced. Extensive simulations show the effectiveness of the proposed framework and algorithms compared to existing schemes.

Recently, the advances of low-latency communication technologies and edge intelligence have enabled a wide range of task-oriented time-sensitive applications. This paper aims at designing adaptive service placement, task offloading, and bandwidth allocation for ultra-reliable and low-latency communication (URLLC)-aided edge networks. The main objective is to minimise both the total end-to-end (e2e) latency and number of installed services at edge servers. The optimal solutions are obtained by jointly optimising service placement decisions, task offloading portions and bandwidth allocation at dynamic timescales subject to network budgets and application requirements under uncertain environment. Selective simulation results are provided to validate the effectiveness of the proposed solution in term of reducing the latency as well as optimising service placement decisions.

This paper exploits a quantum-empowered machine learning algorithm to enhance computation learning speed. Under stochastic behaviours and quantum uncertainty, we examine the offloading problem to maximize the computational task processing efficiency, considering the computation latency, energy consumption, and quantum network adaptability. From the Markov decision process, the paper proposes a novel quantumempowered deep reinforcement learning (Qe-DRL) approach, combining quantum computing theory and machine learning to achieve exploration and exploitation trade-off via quantum parallelism significantly. Furthermore, we develop a modified Grover’s algorithm with exponential convergence speed to provide a searching strategy for transition quantum states probabilities. Simulation results establish the effectiveness of the proposed QeDRL algorithm and its superior computational learning speed.

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.

Physical layer key generation (PLKG) exploits the distributed entropy source of wireless channels to generate secret keys for legitimate users. When the millimeter wave (mmWave) channel is blocked, reconfigurable intelligent surfaces (RISs) have emerged as a prospective approach to constructing reflected channels and improving the secret key rate (SKR). This paper investigates the key generation scheme for the RIS-aided mmWave system. We study the beam domain channel model and exploit the sparsity of mmWave bands to reduce the pilot overhead. We propose a channel probing method to acquire the reciprocal angular information and channel gains. To analyze the SKR, we investigate the channel covariance matrix of beam domain channels. We find that the channel gains of beams are uncorrelated which increases the randomness of secret keys. Considering an eavesdropper, we derive the analytical expressions of SKR when the eavesdropping channel has overlapping clusters with the legitimate channel. Simulations validate that the proposed PLKG scheme outperforms existing schemes.

The physical-layer security (PLS) of a space–ground communication system is examined. To improve the security performance, a pair of reconfigurable intelligent surfaces (RISs) is integrated into the system and benchmarked against a scheme, where there is only a single RIS close to the ground station. As for the double-RIS scenario, we formulate a secrecy rate maximization problem, and then propose an alternating optimization (AO) algorithm for jointly optimizing three vectors, namely, the beamformer of the ground station and the reflecting vectors of two different RISs. Similarly, as for the single-RIS case, we also propose another AO algorithm for optimizing a pair of vectors, namely, the beamformer of the ground station and the reflecting vector of the single RIS. Both the double-RIS and the single-RIS AO algorithms are developed on the basis of the first-order Taylor expansion and Dinkelbach’s method, which allow us to approximate nonconvex optimization problems by convex ones. Our results demonstrate that the proposed double-RIS scheme outperforms the single-RIS benchmark scheme in terms of its security.

AS ONE of the new communication scenarios in 5th-generation (5G) mobile communication systems, ultra-reliable and low-latency communications (URLLC) have stringent requirements on latency (around 1 ms) and reliability (up to 99.99999%). Nevertheless, existing 5G URLLC alone cannot fulfill all the Key Performance Indicators (KPIs) in emerging mission-critical applications like industrial automation, intelligent transportation, telemedicine, Tactile Internet, and Virtual/Augmented Reality (VR/AR). The 6th generation (6G) communication systems need to meet additional requirements on some of the following KPIs in combination with URLLC: high spectrum efficiency (SE)/throughput/energy efficiency (EE)/network availability/security as well as low Age of Information (AoI)/jitter/round-trip delay. These new requirements pose unprecedented challenges in terms of design methodologies and enabling technologies in 6G. To fill the gap between 5G URLLC and the diverse KPI requirements of the neXt generation URLLC (xURLLC), novel methodologies and innovative technologies are much needed.

In this paper, we study joint communication and computation offloading (JCCO) for hierarchical edge-cloud systems with ultra-reliable and low latency communications (URLLC). We aim to minimize the end-to-end (e2e) latency of computational tasks among multiple industrial Internet of Things (IIoT) devices by jointly optimizing offloading probabilities, processing rates, user association policies and power control subject to their service delay and energy consumption requirements as well as queueing stability conditions. The formulated JCCO problem belongs to a difficult class of mixed-integer non-convex optimization problem, making it computationally intractable. In addition, a strong coupling between binary and continuous variables and the large size of hierarchical edge-cloud systems make the problem even more challenging to solve optimally. To address these challenges, we first decompose the original problem into two subproblems based on the unique structure of the underlying problem and leverage the alternating optimization (AO) approach to solve them in an iterative fashion by developing newly convex approximate functions. To speed up optimal user association searching, we incorporate a penalty function into the objective function to resolve uncertainties of a binary nature. Two sub-optimal designs for given user association policies based on channel conditions and random user associations are also investigated to serve as state-of-the-art benchmarks. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the e2e latency and convergence speed.

For future networks, it is highly demanding to satisfy a wide range of time-sensitive and computation-intensive services. This is a very challenging task, since it requires a combination of aspects from information, communication and computation in order to establish a digital representation of the real network environment. This paper introduces a fairness-aware latency minimisation (FALM) framework in the digital twin (DT) aided edge computing with ultra-reliable and low latency communications (URLLC), which jointly optimises various communication and computation parameters, namely, bandwidth allocation, transmission power, task offloading portions, and processing rate of user equipments (UEs) and edge servers (ESs). The formulated problem is highly complicated, due to non-convex constraints and strong coupling among optimisation variables. To deal with this problem, we develop both centralised and distributed optimisation approaches. In particular, we first resort to successive convex approximation (SCA) method to develop a low-complexity iterative algorithm and solve the problem in a centralised manner. Combining tools from SCA and alternating direction method of multipliers (ADMM), we develop an efficient distributed solution with parallel computation processing at ESs under global consensus in each iteration and strong theoretical performance guaranteed. Numerical results are provided to validate the proposed solutions in terms of convergence speed and overall latency as well as improving fairness among all UEs.

Integrating the full-duplex capability with quantum communication potentially equips emerging wireless networks with a quantum layer of security for the stringent communication efficiency and security requirements. This paper proposes two new full-duplex quantum communication protocols to exchange classical or quantum information between two remote parties simultaneously without transferring a physical particle over the quantum channel. The first protocol, called quantum duplex coding, enables the exchange of a classical bit using a preshared maximally entangled pair of qubits by means of counterfactual disentanglement. The second protocol, called quantum telexchanging, enables the exchange of an arbitrary unknown qubit without using preshared entanglement by means of counterfactual entanglement and disentanglement. We demonstrate that quantum duplex coding and quantum telexchanging can be achieved by exploiting counterfactual electron-photon interaction gates. It is shown that these tasks can be viewed as full-duplex transmission of bits and qubits via binary erasure channels and quantum erasure channels, respectively.

In this paper, we study a relay-assisted federated edge learning (FEEL) network under latency and bandwidth constraints. In this network,$N$users collaboratively train a global model assisted by$M$intermediate relays and one edge server. We firstly propose partial aggregation and spectrum resource multiplexing at the relays in order to improve the communication of the relay-assisted FEEL system. Furthermore, we derive analytical and asymptotic expressions of the system outage probability and convergence rate. For the purpose of improving the system performance, we further optimize the relay-assisted FEEL network by maximizing the number of users who participate in each round of federated learning, through allocation of the wireless bandwidth among users and relays. Specifically, two bandwidth allocation (BA) schemes have been proposed, assuming either instantaneous or statistical channel state information (CSI). Simulations show the advantages of the proposed BA schemes over other benchmarks, regarding the accuracy and convergence rate of the considered relay-assisted FEEL network.

In this paper, we propose a joint large-scale resource allocation and optimal multibeam design for satellite-enabled Internet-of-Things (IoT) networks. To overcome the long latency issue in satellite communications, a new gaming optimisation framework is proposed, which is solved in real-time scenario. Firstly, IoT devices are clustered using coalition game that is designed for considering simultaneously transmission time minimisation and channel gain maximisation. Then, bisection search which is very low-complexity procedure is used for maximising the network energy efficiency in closed-form power allocation. The numerical results prove that our method outperforms conventional approaches and is applicable to large-scale networks with real-time IoT scenario.

In this paper, the potential benefit of employing quantum neural networks (QNNs) for cell-free MIMO is explored. In particular, QNN-based scheme are used to optimize transmitter-user assignment in cell-free MIMO. The objective of the optimization is to maximize the minimum achieved sum rate. Although QNN has received increasing research attention owing to the potential benefit of quantum computation, its utilization for multi-transmitter scenario is still limited. As such, in this paper, we consider the QNN-based algorithm for optimal resourcea allocation in cell-free MIMO systems. We also demonstrate the advantage of our proposed QNN-based algorithm through the numerical results.

We propose a new spectrum allocation strategy, aided by unsupervised learning, for multiuser terahertz communication systems. In this strategy, adaptive sub-band bandwidth is considered such that the spectrum of interest can be divided into sub-bands with unequal bandwidths. This strategy reduces the variation in molecular absorption loss among the users, leading to the improved data rate performance. We first formulate an optimization problem to determine the optimal sub-band bandwidth and transmit power, and then propose the unsupervised learning-based approach to obtaining the near-optimal solution to this problem. In the proposed approach, we first train a deep neural network (DNN) while utilizing a loss function that is inspired by the Lagrangian of the formulated problem. Then using the trained DNN, we approximate the near-optimal solutions. Numerical results demonstrate that comparing to existing approaches, our proposed unsupervised learning-based approach achieves a higher data rate, especially when the molecular absorption coefficient within the spectrum of interest varies in a highly non-linear manner.

We address the problem of minimising latency with computation offloading in digital twin wireless edge networks in industrial Internet-of-Things environment via ultra-reliable and low latency communications links. The minimised latency is obtained by jointly optimising both communication and computation variables, namely transmit power, user association of IoT devices, offloading portions, the processing rate of users and edge servers. To deal with this challenging problem, we propose an iterative algorithm based on alternating optimisation approach combined with inner convex approximation framework. Simulation results demonstrate the proposed algorithm’s effectiveness in reducing the latency compared with other benchmark schemes.

We study a joint communication and computation offloading (JCCO) for hierarchical edge-cloud systems with ultra-reliable and low latency communications (URLLC). We aim to minimize the worst-case end-to-end (e2e) latency of computational tasks among multiple industrial Internet of Things (IIoT) devices by jointly optimizing offloading probabilities, processing rates, user association policies and power control subject to their service delay and energy consumption requirements as well as queueing stability conditions. To tackle the problem, we first decompose the original problem into two subproblems and then leverage the alternating optimization (AO) approach to solve them in an iterative fashion by developing newly convex approximate functions. The numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the e2e latency and convergence speed.

In this paper, we propose and assess the performance of a downlink non-orthogonal multiple access (NOMA)based full-duplex (FD) unmanned aerial vehicle (UAV) network for disaster scenarios. The K-means algorithm is used to partition the user equipments (UEs) residing in the disaster region into a number of clusters. The UAVs are located at the cluster centers and act as decode-and-forward relays to secure coverage from an operational base station (BS) into the disaster region. The power-domain NOMA used at the BS and the UAVs operating in FD mode improve the system performance in terms of outage probability and sum rate compared to orthogonal multiple access and half-duplex mode. In particular, analytical expressions for the outage probability and sum rate are derived. Numerical results are provided to reveal the impact of system parameters on the performance of the NOMA-based FD UAV network over Nakagami-m fading which in turn illustrate design options for applications in disaster scenarios.

We investigate the optimal resource allocation of mobile edge computing (MEC) with multiple Internet-of-Thing (IoT) devices in digital twin applications. Based on Markov decision process and model-free deep reinforcement learning (DRL) approach, we propose double deep RL-based online computation offloading method to implement the deep neural network that learns from interactions to solve the computation offloading and transmission latency problem in the dynamic MEC-aided IoT environments. In particular, we design an adaptive method for continuous action-state spaces to minimize the completion time and total energy consumption of the IoT devices for stochastic computation offloading task. The proposed real-time Lyapunov optimization and DRL algorithms achieve a low computational complexity and optimal processing time. Simulation results demonstrate that the proposed method can achieve near-optimal control performance with an enhanced energy consumption and significantly minimize the computation time.

This paper investigates the impact of unreliable backhaul and channel estimation error on the performance of small-cell networks over independent and identically distributed Nakagami-m fading channels. To overcome the impact of these practical constraints, we propose an optimal selection scheme where the best small cell with respect to the maximal secrecy capacity is selected. The secrecy outage probability for the considered scheme is derived and compared with Monte-Carlo simulations. To gain additional insights on the impact of unreli-able backhaul and imperfect channel estimation, the asymptotic behaviour of secrecy outage probability is also obtained.

Abstract A digital twin (DT) framework for Internet‐of‐thing (IoT) networks is proposed where unmanned aerial vehicles (UAVs) acting as flying mobile edge computing (MEC) servers support the task offloading on the fly. The considered DT model is very well suitable for industrial automation with the strict constraints of mission‐critical services' ultra‐reliable low‐latency communication (URLLC) links. To support low‐latency IoT devices, we formulate the end‐to‐end (e2e) latency minimisation problem of digital twin‐aided offloading UAV‐URLLC. Specifically, the minimised latency is obtained by jointly optimising both communication and computation parameters, namely power, offloading factors, and the processing rate of IoT devices and MEC‐UAV servers. Due to the highly non‐convex optimisation problem, we first consider the K‐means clustering algorithm to optimally deploy the on‐demand UAVs. Then, an alternative optimisation approach combined with appropriate inner approximations is effectively exploited to tackle this challenge. We demonstrate the effectiveness of the proposed DT framework through representative numerical results.

Unmanned aerial vehicles (UAVs) are now beginning to be deployed for enhancing the network performance and coverage in wireless communication. However, due to the limitation of their on- board power and flight time, it is challenging to obtain an optimal resource allocation scheme for the UAV-assisted Internet of Things (IoT). In this paper, we design a new UAV-assisted IoT system relying on the shortest flight path of the UAVs while maximising the amount of data collected from IoT devices. Then, a deep reinforcement learning-based technique is conceived for finding the optimal trajectory and throughput in a specific coverage area. After training, the UAV has the ability to autonomously collect all the data from user nodes at a significant total sum-rate improvement while minimising the associated resources used. Numerical results are provided to highlight how our techniques strike a balance between the throughput attained, trajectory, and the time spent. More explicitly, we characterise the attainable performance in terms of the UAV trajectory, the expected reward and the total sum-rate.

This paper addresses the problem of minimising latency in computation offloading with digital twin (DT) wireless edge networks for industrial Internet-of-Things (IoT) environment via ultra-reliable and low latency communications (URLLC) links. The considered DT-aided edge networks provide a powerful computing framework to enable computation-intensive services, where the DT is used to model the computing capacity of edge servers and optimise the resource allocation of the entire system. The objective function is comprised of local processing latency, URLLC-based transmission latency and edge processing latency, subject to both communication and computation resources budgets. In this regard, the minimum latency is obtained by jointly optimising the transmit power, user association, offloading portions, the processing rate of users and edge servers. The formulated problem is highly complicated due to complex non-convex constraints and strong coupling variables. To deal with this computationally intractable problem, we propose an iterative algorithm which decomposes the original problem into three sub-problems and resolve this problem in the fashion of alternating optimisation approach combined with an inner convex approximation framework. Simulation results demonstrate the effectiveness of the proposed method in reducing the latency compared with other benchmark schemes.

In this article, we consider a new unmanned ariel vehicles (UAV)-aided intelligent wireless sensing scheme, where the UAVs are deployed for smart sensing and collecting data from Internet-of-Things (IoT) devices. In particular, we propose optimal UAVs’ path planing approaches for minimizing the completion time and total energy consumption of the UAVs’ deployment for data collection. Two optimal schemes, namely, optimal energy consumption by peer-to-peer UAV-IoT sensing networks and optimal energy consumption by clustering UAV-IoT sensing networks, are considered. The low-complexity procedures of our advanced optimization techniques are suitably applied to disaster relief networks when the solving time must be strictly adhered to. Our real-time optimization algorithms result in low computational complexity with fast deployment and low processing time for solving the problem of tracking and gathering sensor data, i.e., in very short time (milliseconds). Through simulations results we demonstrate that our proposed approaches in UAV-aided intelligent IoT wireless sensing are suitable for time-critical mission applications such as emergency communications, public safety, and disaster relief networks.

In this paper, we consider a mobile edge computing (MEC)-based secure computation offloading system, and design a practical multi-eavesdropper model including two specific scenarios of non-colluding and colluding eavesdropping. Furthermore, we design a requirement satisfaction model by exploring practical variations in user request patterns for security provisioning, delay reduction and energy saving. Based on these, we propose a satisfaction-maximized secure computation offloading (SMax-SCO) scheme, and then formulate an optimization problem aiming at maximizing users’ requirement satisfactions subject to secrecy offloading rate, tolerable delay, task workload and maximum power constraints. Since the optimization problem is nonconvex, we present an efficient successive convex approximation (SCA)-based algorithm to obtain suboptimal solutions. We demonstrate that the proposed SMax-SCO scheme achieves a significant improvement in security performance and requirement satisfaction compared with existing schemes. Moreover, we conclude that SMax-SCO can resist eavesdropping attacks of multiple eavesdroppers and even colluding eavesdroppers.

Federated learning (FL) is capable of performing large distributed machine learning tasks across multiple edge users by periodically aggregating trained local parameters. To address key challenges of enabling FL over a wireless fog-cloud system (e.g., non-i.i.d. data, users’ heterogeneity), we first propose an efficient FL algorithm based on Federated Averaging (called$\mathsf {FedFog}$) to perform the local aggregation of gradient parameters at fog servers and global training update at the cloud. Next, we employ$\mathsf {FedFog}$in wireless fog-cloud systems by investigating a novel network-aware FL optimization problem that strikes the balance between the global loss and completion time. An iterative algorithm is then developed to obtain a precise measurement of the system performance, which helps design an efficient stopping criteria to output an appropriate number of global rounds. To mitigate the straggler effect, we propose a flexible user aggregation strategy that trains fast users first to obtain a certain level of accuracy before allowing slow users to join the global training updates. Extensive numerical results using several real-world FL tasks are provided to verify the theoretical convergence of$\mathsf {FedFog}$. We also show that the proposed co-design of FL and communication is essential to substantially improve resource utilization while achieving comparable accuracy of the learning model.

In this paper, we propose to use multiple mobile robots as relay terminals to assist the wireless connectivity between the base stations and industrial Internet-of-Things (IIoT) devices. Under the strict latency constraint via short blocklength, we propose an optimal resource allocation scheme to minimise the error probability at the IIoT devices. For fast deployment, we propose a deep neural network to optimise the positions of the mobile robots. Then, a joint blocklength and power allocation optimisation of the base stations and relay robots is considered. Due to non-convexity of such optimization problem, we propose a sub-problem with an effective iterative algorithm for solving the reliability maximisation. Representative numerical results are provided to demonstrate the advantages of our proposed scheme over the conventional approach.

In this work, we consider a joint optimisation of real-time deployment and resource allocation scheme for UAV-aided relay systems in emergency scenarios such as disaster relief and public safety missions. In particular, to recover the network within a disaster area, we propose a fast K-means-based user clustering model and jointly optimal power and time transferring allocation which can be applied in the real system by using UAVs as flying base stations for real-time recovering and maintaining network connectivity during and after disasters. Under the stringent QoS constraints, we then provide centralised and distributed models to maximise the energy efficiency of the considered network. Numerical results are provided to illustrate the effectiveness of the proposed computational approaches in terms of network energy efficiency and execution time for solving the resource allocation problem in real-time scenarios. We demonstrate that our proposed algorithm outperforms other benchmark schemes.

The capability of massive multiple-input multiple-output (mMIMO) systems supporting the throughput requirement of as many users as possible is investigated. The bottleneck of serving small numbers of users by a large number of transmit antennas in conventional mMIMO is unblocked by a new time-fraction-wise beamforming technique, which focuses signal transmission in fractions of a time slot. Based on this time-fraction-wise signal transmission, a new user service scheduling scheme for multi-cell mMIMO, whose cell-edge users suffer not only poor channel conditions but also multi-cell interference, is proposed to support a large user-population. We demonstrate that the numbers of users served by our multi-cell mMIMO within a time-slot may be as high as twice the number of its transmit antennas.

Unmanned aerial vehicle (UAV)-aided two-way relaying networks are designed, where a UAV is deployed to assist multiple pairs of users in their information exchange. There are two basic approaches for the user pairs' information exchange within a single time slot via the UAV relay. The first approach is based on full-duplex, where all participants operate in the full-duplex mode to transmit and receive signals simultaneously. However, all transceivers have to operate in the face of severe self-interference, which cannot be completely suppressed. The second approach is based on conventional half-duplex, where the users send their information to the UAV within a certain fraction of the time slot, and the UAV relays them within the remaining fraction to avoid the self-interference. In either approach, the joint bandwidth and power allocation maximizing the sum information exchange throughput under realistic resource and user throughput constraints poses a complex nonconvex problem. New inner approximations are proposed for developing path-following algorithms for their computation. Our numerical results show that the time-fraction-based half-duplex approach clearly outperforms the high-complexity full-duplex approach.

This paper considers cell-free massive MIMO (cfm-MIMO) for downlink ultra reliable and low-latency communication (URLLC). At the time of writing, cfm-MIMO has only been considered for communication in the long blocklength regime (LBR), whose throughput is determined by the Shannon capacity with the interference treated as Gaussian noise. Conjugate beamforming (CB) is often used as it requires only local channel state information (CSI) for implementation but its design is based on a large-scale nonconvex problem, which is computationally intractable. The rate function in URLLC is much more complex than the Shannon rate function. The paper proposes a special class of CB, which admits a low-scale optimization formulation for computational tractability. Accordingly, a new path-following algorithm, which generates a sequence of better feasible points and converges at least to a locally optimal solution, is developed for optimizing URLLC rates and cfm-MIMO energy efficiency. Furthermore, the paper also develops improper Gaussian signaling to improve both the Shannon rate and URLLC rate.

In this paper, we investigate a mobile edge computing (MEC) network with two computational access points (CAPs), where the source is equipped with multiple antennas and it has some computational tasks to be accomplished by the CAPs through Nakagami-m distributed wireless links. Since the MEC network involves both communication and computation, we first define the outage probability by taking into account the joint impact of latency and energy consumption. From this new definition, we then employ receiver antenna selection (RAS) or maximal ratio combining (MRC) at the receiver, and apply selection combining (SC) or switch-and-stay combining (SSC) protocol to choose a CAP to accomplish the computational task from the source. For both protocols along with the RAS and MRC, we further analyze the network performance by deriving new and easy-to-use analytical expressions for the outage probability over Nakagami-m fading channels, and study the impact of the network parameters on the outage performance. Furthermore, we provide the asymptotic outage probability in the low regime of noise power, from which we obtain some important insights on the system design. Finally, simulations and numerical results are demonstrated to verify the effectiveness of the proposed approach. It is shown that the number of transmit antenna and Nakagami parameter can help reduce the latency and energy consumption effectively, and the SSC protocol can achieve the same performance as the SC protocol with proper switching thresholds of latency and energy consumption.

In this article, we address the social-awareness property and unmanned-aerial-vehicle (UAV)-assisted information diffusion in emergency scenarios, where UAVs can disseminate alert messages to a set of terrestrial users within their coverage, and then these users can continuously disseminate the received data packets to their socially connected users in a device-to-device (D2D) multicast manner. In this regard, we have to solve both the dynamic cluster formation and spectrum sharing problems in stochastic environments, since both UAVs and terrestrial users may arrive or depart suddenly. For the cluster formation problem, considering that the data rate of a multicast cluster is determined by the member with the worst link condition, we formulate it as a many-to-one matching game and adopt the rotation-swap algorithm to maximize the expected number of users receiving the alerting messages in each time slot. For the dynamic spectrum sharing problem, aiming at eliminating the interference while minimizing the channel switching cost, we propose a dynamic hypergraph coloring approach to model the cumulative interference and maintain the mutual interference at a low level by exploring a small number of vertices, when the graph is dynamically updated, i.e., the insertion/deletion of vertex/edge. Moreover, we prove some crucial properties, including global stability, convergence, and complexity. Finally, simulation results show that our proposed approach can achieve a better tradeoff among the information diffusion speed, channel switch cost, and complexity.

This paper investigates phasor measurement unit (PMU) placement for informative state estimation in smart grid by incorporating various constraints for observability. Observability constitutes an important property for PMU placement to characterize the depth of the buses' reachability by the placed PMUs, but addressing it solely by binary linear programming as in many works still does not guarantee a good estimate for the grid state. Some existing works have considered optimization of some estimation indices by ignoring the observability requirements for computational ease and thus potentially lead to trivial results such as acceptance of the estimate for an unobserved state component as its unconditional mean. In this work, the PMU placement optimization problem is considered by minimizing the mean squared error or maximizing the mutual information between the measurement output and grid state subject to observability constraints, which incorporate operating conditions such as presence of zero injection buses, contingency of measurement loss, and limitation of communication channels per PMU. The proposed design is thus free from the fundamental shortcomings in the existing PMU placement designs. The problems are posed as large scale binary nonlinear optimization problems involving thousands of binary variables, for which this paper develops efficient algorithms for computational solutions. Their performance is analyzed in detail through numerical examples on large scale IEEE power networks. The solution method is also shown to be extendable to AC power flow models, which are formulated by nonlinear equations.

This paper considers a network consisting of a multiple antenna array access point serving multiple single antenna downlink users with the assistance of a reconfigurable intelligent surface (RIS). The reflecting coefficients of the RIS can be programmed to ensure that the signals reflected from the RIS elements add coherently at the users. The joint design of these programmable reflecting coefficients and transmit beamforming to maximize the users' worst rate is addressed. Under either proper Gaussian signaling (PGS) or improper Gaussian signaling (IGS), the design poses a very computationally challenging nonconvex problem. Based on their exactly penalized optimization reformulation, which incorporates the computationally intractable unit-modulus constraints on the reflecting coefficients into the optimization objectives, new iterative algorithms of low computational complexity, which converge at least to a locally optimal solution, are developed. The provided simulations show not only the benefit of using the RIS, but also the advantage of IGS over PGS in delivering higher rates to users.

We consider a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) network in which a source node, Alice, communicates with an energy-harvesting destination node, Bob, in the presence of a passive eavesdropper. To secure the wireless transmission, Alice generates a hybrid artificial noise (AN) in both frequency and time domains. Moreover, in order to collect more energy, Bob splits the received signal power of the cyclic prefix of each OFDM block. We then propose two non-convex optimization problems to balance both the need for security and the need for harvesting energy at Bob. While one considers maximizing the secrecy rate, the other approach aims at maximizing the harvested energy. Path-following algorithms of low computational complexity are developed and evaluated. Our numerical results show the gain of our proposed scheme and the effectiveness of our proposed algorithms.

This paper studies a cell-free massive multiple-input multiple-output (MIMO) system where its access points (APs) and users are equipped with multiple antennas. Two transmission protocols are considered. In the first transmission protocol, there are no downlink pilots, while in the second transmission protocol, downlink pilots are proposed in order to improve the system performance. In both transmission protocols, the users use the minimum mean-squared error-based successive interference cancellation (MMSE-SIC) scheme to detect the desired signals. For the analysis, we first derive a general spectral efficiency formula with arbitrary side information at the users. Then analytical expressions for the spectral efficiency of different transmission protocols are derived. To improve the spectral efficiency (SE) of the system, max-min fairness power control (PC) is applied for the first protocol by using the closed-form expression of its SE. Due to the computation complexity of deriving the closed-form performance expression of SE for the second protocol, we apply the optimal power coefficients of the first protocol to the second protocol. Numerical results show that two protocols combining with multi-antenna users are prerequisites to achieve the sub-optimal SE regardless of the number of user in the system.

This article proposes an optimization framework for power and time resource allocation during time sharing non-orthogonal multiple access (TS-NOMA) transmissions performed by an unmanned aerial vehicle (UAV) in the context of a large-scale scenario. The objective of the proposed UAV-TS-NOMA system and optimization framework is to jointly maximize the energy efficiency (EE) and the downlink throughput fairness among users within the UAV communication range. The idea behind is to propose a communication system that: i) merges the advantages of UAV communications with the ones offered by the TS-NOMA paradigm and ii) maximizes the EE and the downlink fairness among users. The resulting model finds applicability in performing energy efficient and throughput fair transmissions into power-constrained communication scenarios. Performance investigations regarding the proposed framework in finding the optimal set of resources which maximizes jointly the above mentioned network metrics, have shown the advantage of the proposed two-step optimization framework in finding the optimal configuration of both power and time resources, respecting both the power constraints at the transmitter and the quality-of-service requirement of the users. In addition, it is shown how under particular conditions the proposed framework jointly optimizes the aforementioned network metrics in only one step.

This paper considers wireless-powered relaying multiple-input-multiple-output (MIMO) communication, where all four nodes (information source, energy source, relay, and destination) are equipped with multiple antennas. Orthogonal frequency division multiplexing (OFDM) is applied for information processing to compensate the frequency selectivity of communication channels between the information source and the relay and between the relay and the destination as these nodes are assumed to be located far apart from each. The relay is equipped with a full-duplexing interface for harvesting energy not only from the wireless transmission of the dedicated energy source but also from its own transmission while relaying the source information to the destination. The problem of designing the optimal power allocation over OFDM subcarriers and transmit antennas to maximize the overall spectral efficiency is addressed. Due to a very large number of subcarriers, this design problem poses a large-scale nonconvex optimization problem involving a few thousand variables of power allocation, which is very computationally challenging. A novel path-following algorithm is proposed for computation. Based on the developed closed-form calculation of linear computational complexity at each iteration, the proposed algorithm rapidly converges to an optimal solution. Compared to the best existing solvers, the computational complexity of the proposed algorithm is reduced at least 105times, making it very efficient and practical for online computation while existing solvers are ineffective. Numerical results for a practical simulation setting show promising results by achieving high spectral efficiency.

Recent studies of single-cell two-user networks have shown that a higher network throughput is achieved by using a common message to be decoded by both users and conveying partial information for both users, rather than using the common message to convey the entire information for one of the two users. The latter is essentially the conventional non-orthogonal multiple access (NOMA), which performs better than orthogonal multiple access (OMA) only under users' dissimilar channel conditions. Unlike NOMA, the former performs consistently better than OMA. This paper generalizes such a signaling strategy to a general multi-cell multiuser network, which leads to a new NOMA approach (called n-NOMA) in which each pair of users decodes a message that conveys partial information for one of them only. Unlike the conventional NOMA, whose performance is dependent on the users' pairing strategy, the proposed n-NOMA consistently outperforms both NOMA and OMA schemes. Both proper and improper Gaussian signaling is considered for all the concerned schemes and it is shown that the latter is clearly more advantageous than the former.

As the Internet of Things (IoT) is maturing and acquires its social flavor, the Social IoT enables smart devices to build inter-thing social networks without human intervention. As a new form of smart devices, unmanned aerial vehicles (UAVs) are finding their way into IoT applications. The integrated Social Internet of Flying Things (SIoFT) can provide the social-aware UAV-assisted services. However, the broadcast nature of air-to-ground (A2G) channels makes them vulnerable to being eavesdropped by terrestrial malicious users due to their strong line-of-sight (LoS) links. In this paper, we investigate to ensure the security of A2G communications when the location information of multiple potential eavesdroppers cannot be perfectly estimated. Following the “no pain no gain” principle, the terrestrial users who reuse the UAV cellular spectrum will act as friendly jammers to realize “win-win” situation. Hence, joint trajectory design, power control, and channel allocation optimization problem is formulated to maximize the average secrecy rate of UAVs in worst case. In the first stage, we utilize the block coordinate descent method and successive convex optimization method to solve the trajectory design and power control problems in an iterative manner. In the second stage, we convert the user pairing problem into a popular matching problem with externalities. Two distributed algorithms are proposed to maintain the popular matching under dynamics. Moreover, we conduct detailed analysis of the popularity, convergence, and computational complexity. Simulation results demonstrate the superiority of our proposed method in terms of different performance metrics.

The paper considers the problem of beamforming design for a multi-cell network of downlink users, who either harvest energy or decode information or do both by receiving signals from the multi-antenna base station (BS) within a time slot and over the same frequency band. Our previous contributions have showed that the time-fraction based energy and information transmission, under which first the energy is transferred within the initial fraction of time and then the information is transferred within the remaining fraction, is the most efficient design alternative both in terms of its practical implementation and network performance. However, at the time of writing, both energy and information beamforming has only been implemented for proper Gaussian signaling (PGS), which has limited the network's throughput. Although the network throughput could be improved in some specific scenarios by using non-orthogonal multi-access (NOMA), this may compromise the user secrecy. In order to circumvent the above implementations, we conceive improper Gaussian signaling (IGS) for information beamforming, which enables the network to substantially improve its throughput in any scenario without jeopardizing the user secrecy despite its low-complexity signal processing at the user end. A simpler subclass of IGS is also considered, which also outperforms NOMA PGS and works under any arbitrary scenario.

Internet-of-things (IoT) applications continue to drive advancements in serving as many heterogeneous low-latency downlinks and uplinks as possible within a constrained communication bandwidth. Full-duplexing (FD) transceivers have been introduced to implement simultaneous signal transmission and reception (STR) over the entire available frequency band. However, both inter-link interference and FD loop-interference are hardly suppressed to a necessary level for the effectiveness of FD-based STR even for microcells. This paper proposes an alternative STR technique per one time-slot for macrocells, where a fraction of a time-slot is used for downlinks and the remaining complementary fraction of the time-slot is used for uplinks. Thus, STR over the entire available bandwidth can be implemented in a way with no loop interference. Furthermore, another approach of using a fraction of the available bandwidth for downlinks and the remaining complementary fraction of the bandwidth for uplinks over the whole time-slot is also proposed. The problem of both downlink and uplink beamforming to maximize the energy efficiency of such heterogeneous networks subject to the quality-of-service in terms of downlink and uplink throughput is examined for all three possible STRs. Numerical results demonstrate the advantages of the time-fraction-wise STR and bandwidth-fraction-wise STR over the FD-based STR, where the time-fraction-wise STR is not only the best in serving the same numbers of downlinks and uplinks but also is capable of serving many more downlinks and uplinks with a higher energy efficiency.

As a new Internet of Things (IoT) paradigm where smart devices work socially by exploiting social ties with adjacent devices, the Social IoT can effectively meet the real-time data sharing demands in Industrial IoT scenario, with the inter-device social relations being incentives. Besides, precaching on device level can potentially combat the backhaul capacity bottlenecks. Considering the limited cache memory, we may not use the whole capacity for caching, but leave a fraction for buffering data packets. In this article, we investigate how to maximize the quality of experience while minimizing the energy consumption. First, we design a proactive cache placement scheme for cost minimization. Next, we conceive the content sharing procedure with the framework of tripartite graph and propose a ternary stable matching algorithm to let devices self-organize the content sharing. Finally, we prove that inconspicuous manipulation with domino effect can further improve the system performance.

This paper proposes Han-Kobayashi signaling (HKS), under which each pair of users decodes a common message to improve their throughput, for UAV-enabled multi-user communication. Given that only a single transmit antenna is used and thus there is no null space of users' channels for inserting an artificial noise that would effectively help to jam an eavesdropper without interfering the users' desired signals, a new information and artificial noise transfer scheme to address physical layer security (PLS) for the considered networks is investigated. Under this scheme, the UAV sends the confidential information to its users within a fraction of the time slot and sends the artificial noise within the remaining fraction. Accordingly, the problem of jointly optimizing the time-fraction, bandwidth and power allocation to maximize the users' worst secrecy throughput is formulated. New inner approximations are proposed for developing path-following algorithms for its computation. Simulation shows that the proposed information and artificial noise transfer enables not only HKS but also orthogonal multi-access and nonorthogonal multi-access to provide PLS for UAV-enabled communication even when the eavesdropper is in the best channel condition. HKS outperforms the other two schemes in terms of users' worst secrecy throughput.

In this work, we propose efficient optimisation methods for relay-assisted unmanned aerial vehicles (UAVs) in cognitive radio networks (CRNs) to cope with the network destruction in the event of a natural disaster. Our model considers real- time optimisation in embedded UAV-CRN communication involved in recovering wireless communication services. Particularly, by conceiving advanced optimisation techniques and training deep neural networks, our solutions become capable of supporting real-time applications in disaster recovery scenarios. Our algorithms impose low computational complexity, hence, have a low execution time in solving real- time optimisation problems. Numerical results demonstrate the benefits of our approaches proposed for UAV-CRN.

In this work, we propose efficient optimisation methods for embedded relay-assisted unmanned ariel vehicles (UAVs) in wireless sensor networks (WSNs) to cope with the hazardous effect of natural disaster. Particularly, by using advanced optimisation techniques, our low-complexity procedures are suitable applied to internet-of-things (IoT) applications when the execution time is strictly governed in disaster scenarios. Our model considers real-time optimisation in embedded UAV-WSN communication for tracking and gathering sensor data. Our algorithms are low computational complexity with fast deployment and low execution time for solving our problem in milliseconds. Numerical results are shown to demonstrate the benefit of our proposed approaches for UAV-WSN.

For a multi-user multi-cell network, which suffers both intra-cell and inter-cell interference, this letter considers improper Gaussian signaling (IGS) as a means to improve the achievable rate. The problem of interest is designing of improper Gaussian signals' augmented covariance matrices to maximize the users' minimum rate subject to transmit power constraints. This problem is seen as a nonconvex matrix optimization problem, which cannot be solved by conventional techniques, such as weighted minimum mean square error minimization or alternating optimization. A path-following algorithm, which iterates a sequence of improved feasible points, is proposed for its computation. The provided simulation results for three cells serving 18 users show that IGS offers a much better max-min rate compared with that achieved by conventional proper Gaussian signaling. Another problem of maximizing the energy efficiency in IGS is also considered.

An energy harvesting (EH) enabled network is capable of delivering energy to users, who are located sufficiently close to the base stations. However, wireless energy delivery requires much more transmit power than what the normal information delivery does. It is very challenging to provide the quality of wireless information and power delivery simultaneously. It is of practical interest to employ non-orthogonal multiple access (NOMA) to improve the network throughput, while fulfilling the EH requirements. To realize both the EH and information decoding, this paper considers a transmit time-switching (transmit-TS) protocol. Two important problems of users' max-min throughput optimization and energy efficiency maximization under power constraint and EH thresholds, which are non-convex in beamforming vectors, are addressed by efficient path-following algorithms. In addition, the conventional power splitting (PS)-based EH receiver is also considered. The provided numerical results confirm that the proposed transmit-TS-based algorithms clearly outperform the PS-based algorithms in terms of throughput and energy efficiency.

Unmanned aerial vehicles (UAVs) can be deployed as flying base stations (BSs) to leverage the strength of line-of-sight connections and effectively support the coverage and throughput of wireless communication. This paper considers a multiuser communication system, in which a single-antenna UAV-BS serves a large number of ground users by employing non-orthogonal multiple access (NOMA). The max-min rate optimization problem is formulated under total power, total bandwidth, UAV altitude, and antenna beamwidth constraints. The objective of max-min rate optimization is non-convex in all optimization variables, i.e., UAV altitude, transmit antenna beamwidth, power allocation, and bandwidth allocation for multiple users. A path-following algorithm is proposed to solve the formulated problem. Next, orthogonal multiple access (OMA) and dirty paper coding (DPC)-based max-min rate optimization problems are formulated and respective path-following algorithms are developed to solve them. The numerical results show that NOMA outperforms OMA and achieves rates similar to those attained by DPC. In addition, a clear rate gain is observed by jointly optimizing all the parameters rather than optimizing a subset of parameters, which confirms the desirability of their joint optimization.

Caching and multicast have surged as effective tools to alleviate the heavy load from the backhaul links while enabling content-centric delivery in communication networks. The main focus of work in this area has been on the cache placements to manage the network delay and backhaul transmission cost. An important issue of optimizing the cost efficiency in content delivery has not been addressed. This paper tackles this issue by proposing collaborative multicast beamforming in cache-enabled ultra-dense networks. The objective is to maximize the cost efficiency, which is defined as the ratio of the content throughput to the sum of power consumption and backhaul cost, in providing quality-of-service for content delivery. Zero-forcing beamforming and generalized zero-forcing beamforming are employed to force the multi-content interference to zero or mitigate it while amplifying the desired signals for users. These problems of collaborative multicast beamforming design are computationally difficult. Path-following algorithms, which invoke a simple convex quadratic program at each iteration, are developed for their solution. Numerical results are provided to demonstrate the computational efficiency of the proposed algorithms and also give insights into the impact of caching on the cost efficiency.

Key update and residence management have been investigated as an effective solution to cope with desynchronization attacks in mobility management entity (MME) handovers. In this paper, we first analyze the impacts of the key update interval (KUI) and MME residence interval (MRI) on handover processes and their secrecy performance in terms of the number of exposed packets (NEP), signaling overhead rate (SOR), and outage probability of vulnerability (OPV). Specifically, the bounds of the derived NEP and SOR not only capture their behaviors at the boundary of the KUI and MRI, but also show the tradeoff between the NEP and SOR. Additionally, through the analysis of the OPV, it is shown that the handover security can be enhanced by shortening the KUI and the desynchronization attacks can be avoided with high-mobility users. The above facts accordingly motivate us to propose a multi-objective optimization (MO) problem to find the optimal KUI and MRI that minimize both the NEP and SOR subject to the constraint on the OPV. To this end, two scalarization techniques are adapted to transform the proposed MO problem into single-objective optimization problems, i.e., an achievement-function method via fractional programming (FP) and a weighted-sum method. Based on the derived bounds on NEP and SOR, the FP approach can be optimally solved via a simple numerical method. For the weighted-sum method, the firefly algorithm (FA) is utilized to find the optimal solution. The results show that both techniques can solve the proposed MO problem with a significantly reduced searching complexity compared to the conventional heuristic iterative search technique.

A key challenge in nanonetworking is to develop a means of coordinating a large number of nanoscale devices. Molecular communication has emerged as a promising technique to assist in the coordination problem. Devices in molecular communication systems-once information molecules are released-are typically viewed as passive, not reacting chemically with the information molecules. While this is an accurate model in diffusion-limited links, it is not the only scenario. In particular, the dynamics of molecular communication systems are more generally governed by reaction-diffusion, where the reaction dynamics can also dominate. This leads to the notion of reaction-limited molecular communication systems, where the concentration profiles of information molecules and other chemical species depends largely on reaction kinetics. In this regime, the system can be approximated by a chemical reaction network. In this paper, we exploit this observation to design new protocols for both point-to-point links with feedback and networks for event detection. In particular, using connections between consensus and advection theory and reaction networks lead to simple characterizations of equilibrium concentrations, which yield simple-but accurate-design rules even for networks with a large number of devices.

A multi-input multi-output (MIMO) orthogonal frequency-division multiplexing (OFDM) network in the presence of a passive eavesdropper is considered. The deployment of radio frequency power transfer at the receiver and the use of hybrid artificial noise at the transmitter are simultaneously taken into account. At the legal receiver, the cyclic prefix of each OFDM block is used for the purpose of harvesting energy. In parallel, the power-splitting SWIPT technique is additionally used. We then propose a trade-off problem to maximize the secrecy rate of the network while keeping the harvested energy above a given threshold. Throughout the numerical results, the performance of our proposed secure scheme is evaluated.

The demand for increased connectivity and reliability of devices in the fifth generation (5G) of wireless communications requires new technology for ensuring massive connectivity and high spectral efficiency. In addition, wireless backhauls with guaranteed reliability are being considered to improve the overall system performance. In this paper, we investigate an opportunistic non-orthogonal multiple access (NOMA) system with unreliable wireless backhauls. In particular, we develop two opportunistic selection rules which allow the selection of the best among either near or far-away group transmitters, considering both the unreliability of wireless backhauls and fading effects of fronthauls. In order to analyze the performance, new exact and approximated closed-form expressions for the outage probabilities of the grouped receivers are derived, thus providing an insight into the impact of unreliable random backhauls and opportunistic NOMA. We show that the proposed scheme gives an outage performance gain of more than 3dB gains to a dominant receiver in the selection rules and improvement in receiver fairness when compared to the orthogonal multiple access (OMA) with an unreliable wireless backhaul. In addition, our results clearly reveal that unreliability levels of wireless backhaul links are responsible for the outage floors.

In this study, a cooperative non‐orthogonal multiple access (NOMA) scheme is investigated in an underlay cognitive radio network. With this aim, a number of secondary users are concerned in the cooperative NOMA, in which a user with strong channel gains is properly selected (to act as a relay) by a multi‐antenna base station ( ) for assisting another user with poor channel gains in the presence of a primary network. Closed‐form expressions for the outage probability of the secondary network are derived, based on which the asymptotic outage behaviours of each secondary user assuming various realistic cases are discussed. Our analytical results clearly reveal that an outage floor may exist in the outage probability of the secondary users, being determined by the interference constraint and the number of antennas at the . The impact of the multiple antennas and the number of cooperative NOMA users on the system performance is examined, and it shows that the cell‐edge user under poor channel gains can benefit from both the cooperative NOMA and opportunistic relay transmission.

This paper considers a massive MIMO network that includes one multiple-antenna base station, one multiple-antenna eavesdropper, and K single-antenna users. The eavesdropper operates in passive mode and tries to overhear the confidential information from one of the users in the down-link transmission. In order to secure the confidential information, two artificial noise (AN)-aiding schemes are proposed. In the first scheme, AN is injected into the downlink training signals to prevent the eavesdropper from obtaining the correct channel state information of the eavesdropping link. In the second scheme, AN is deployed in both downlink training phase and payload data transmission phase to further degrade the eavesdropping channel. Analytical expressions and tight approximations of the achievable secrecy rate of the considered systems are derived with taking imperfect channel estimation and two types of precoding, i.e., maximum-ratio-transmission and zero-forcing, into consideration. Optimization algorithms for power allocation are proposed to enhance the secrecy performance of the proposed AN-aiding schemes. The results reveal that deploying AN in the downlink training phase of massive MIMO networks does not affect the downlink channel estimation process at users while enabling the system to suppress the downlink channel estimation process at eavesdropper. As a consequence, the proposed AN-aided schemes improve the system performance significantly. Furthermore, implementing AN in both phases allows the considered system having a flexible solution to maximize its secrecy performance at the price of higher complexity.

Based on the knowledge of the channel distributions of a multi-input single-output wireless network of multiple transmitter-user pairs overheard by an eavesdropper, this letter develops an outage-aware beamforming design to optimize the users' quality-of-service (QoS) in terms of their secrecy rates. This is a very computationally difficult problem with a nonconcave objective function and nonlinear equality constraints in beamforming vectors. A path-following algorithm of low-complexity and rapid convergence is proposed for computation, which is also extended to solving the problem of maximizing the network's secure energy efficiency under users' QoS constraints. Numerical examples are provided to verify the efficiency of the proposed algorithms.

In this paper, we study the performance improvement methods for a cell-edge user of two-user non-orthogonal multiple access (NOMA) systems in downlink scenarios. To this end, we propose two cooperative relaying schemes, namely ON/OFF-full-duplex relaying (ON/OFF-FDR) and ON/OFF-half-duplex relaying (ON/OFF-HDR) schemes. More specifically, in order to improve the performance of the cell-edge user, we consider a cell-center user as a relay, where either FDR or HDR can be employed to assist the direct NOMA transmission from a base station (BS) to the cell-edge user. An ON/OFF mechanism is proposed to decide whether the cooperative relaying transmission is necessary or not. The ON/OFF relaying decision is made based on the quality of the direct and relaying links from the BS to the cell-edge user. The performance of the two proposed schemes is investigated in terms of outage probability and sum throughput. Numerical results reveal that the proposed schemes not only provide essential outage performance improvements for the cell-edge user, but also are able to improve the sum throughput of the two-user NOMA systems. The advantages and drawbacks of each proposed scheme are highlighted and insightful discussions are provided.

This paper studies the security aspect of a recently introduced “cell-free massive MIMO” network under a pilot spoofing attack. First, a simple method to recognize the presence of this type of an active eavesdropping attack to a particular user is shown. In order to deal with this attack, we consider the problem of maximizing the achievable data rate of the attacked user or its achievable secrecy rate. The corresponding problems of minimizing the power consumption subject to security constraints are also considered in parallel. Path-following algorithms are developed to solve the posed optimization problems under different power allocation to access points (APs). Under equip-power allocation to APs, these optimization problems admit closed-form solutions. Numerical results show their efficiency.

A heterogeneous network (HetNet) of a macrocell base station equipped with a large-scale massive multi-in multi-out (MIMO) antenna array overlaying a number of small cell base stations (small cells) can provide high quality of service (QoS) to multiple users under low transmit power budget. However, the circuit power for operating such a network, which is proportional to the number of transmit antennas, poses a problem in terms of its energy efficiency (EE). This paper addresses the beamforming design at the base stations to optimize the network EE under the QoS constraints and a transmit power budget. Beamforming tailored for weak, strong, and medium cross-tier interference HetNets is proposed. In contrast to the conventional transmit strategy for power efficiency in meeting the users' QoS requirements, which suggest the use of a few hundred antennas, it is found out that the overall network EE quickly drops if this number exceeds 50. It is found that, for a given number of antennas, HetNet is more energy efficient than massive MIMO when considering the overall energy consumption.

Considering a multi-user interference network with an eavesdropper, this paper aims at the power allocation to optimize the worst secrecy throughput among the network links or the secure energy efficiency in terms of achieved secrecy throughput per Joule under link security requirements. Three scenarios for the access of channel state information are considered: the perfect channel state information; partial channel state information with channels from the transmitters to the eavesdropper exponentially distributed; and not perfectly known channels between the transmitters and the users with exponentially distributed errors. The paper develops various path-following procedures of low complexity and rapid convergence for the optimal power allocation. Their effectiveness and viability are illustrated through numerical examples. The power allocation schemes are shown to achieve both high secrecy throughput and energy efficiency.

In this paper, we study performance and reliability improvement for a cell-edge user in downlink scenarios of two-user non-orthogonal multiple access (NOMA) networks. To this end, we propose a full-duplex (FD) cooperative scheme, in which a near user acts as a FD relay to forward source's signals to a far user, while the far user employs distributed switch-and-stay combining (DSSC) technique to process the incoming signals. We then investigate the performance of the far user in terms of outage probability (OP). In particular, we obtain a closed-form expression for the OP of the far user as well as its asymptotic OP. The developed analysis is corroborated through Monte-Carlo simulation. Numerical results reveal that the proposed scheme achieves better outage performance in comparison with conventional NOMA systems. It is also showed that the choice of the switching threshold used in DSSC technique and/or the target data rate of the system sensitively affects the outage performance of the proposed scheme.

This paper aims to design linear precoders for signal superposition at the base stations of non- orthogonal multiple access multiple-input multiple-output multi-cellular systems to maximize the overall sum throughput subject to the users' quality-of-service requirements, which are imposed independently on the users' channel conditions. This design problem is formulated as the maximization of a highly nonlinear and nonsmooth function subject to nonconvex constraints, which is very computationally challenging. A path- following algorithm for its solution, which invokes only a simple convex problem of moderate dimension at each iteration, is developed. Generating a sequence of improved points, this algorithm converges at least to a local optimum. Numerical results are then provided to demonstrate its merit.

In this paper, we investigate outage performance and diversity gain of transmit antenna selection (TAS) schemes in two-user multiple-input single-output non-orthogonal multiple access (MISO-NOMA) cooperative downlink transmissions. To this end, two TAS criteria, namely Criterion I and Criterion II, are proposed, which select an antenna that experiences the best fading condition of the channel from the source to the far user and to the near user, respectively. Additionally, considering the near user as a relay to help improve the reliability of the far user, hybrid simultaneous wireless information and power transfer (SWIPT) architecture is adopted to power the near user's relaying operation. Tight closed-form approximate expressions for the outage probability (OP) of both users are derived. Numerical results reveal that Criterion I and II achieve, respectively, the diversity order of K + 1 and 2 at the far user, and 1 and K at the near user, where K denotes the number of transmit antennas at the base station.

In this paper, we propose a cooperative approach to improve the security of both primary and secondary systems in cognitive radio multicast communications. During their access to the frequency spectrum licensed to the primary users, the secondary unlicensed users assist the primary system in fortifying security by sending a jamming noise to the eavesdroppers, while simultaneously protect themselves from eavesdropping. The main objective of this work is to maximize the secrecy rate of the secondary system, while adhering to all individual primary users' secrecy rate constraints. In the case of passive eavesdroppers and imperfect channel state information knowledge at the transceivers, the utility function of interest is nonconcave and involved constraints are nonconvex, and thus, the optimal solutions are troublesome. To address this problem, we propose an iterative algorithm to arrive at a local optimum of the considered problem. The proposed iterative algorithm is guaranteed to achieve a Karush-Kuhn-Tucker solution.

This work studies the secrecy outage performance of buffer-aided dual-hop multi-antenna relay systems without eavesdropper's channel state information (CSI). By modeling the dynamic buffer state transitions with the Markov chain, the secrecy outage probability at each state is investigated and the stationary distribution probabilities of all states are subsequently derived. Using the total probability theorem, the closed-form expression of the secrecy outage probability of the system is finally obtained. It demonstrates that due to the fully exploitation of the available channels, the buffer-aided relay selection yields to better performance than Best Relay Selection (BRS), even when less relays and antennas are utilized. It is also shown that the buffer-aided relaying only results in a small performance degradation when the buffers are constrained to finite size, thus can be well applied to practical relaying cooperative networks. Simulation results are given to verify the theoretical analysis.

This paper investigates the security at the physical layer in cooperative wireless networks (CWNs) where the data transmission between nodes can be realised via either direct transmission (DT) or relaying transmission (RT) schemes. Inspired by the concept of physical-layer network coding (PNC), a secure PNC-based modify-and-forward (SPMF) is developed to cope with the imperfect shared knowledge of the message modification between relay and destination in the conventional modify-and-forward (MF). In this paper, we first derive the secrecy outage probability (SOP) of the SPMF scheme, which is shown to be a general expression for deriving the SOP of any MF schemes. By comparing the SOPs of various schemes, the usage of the relay is shown to be not always necessary and even causes a poorer performance depending on target secrecy rate and quality of channel links. To this extent, we then propose an opportunistic secure transmission protocol to minimise the SOP of the CWNs. In particular, an optimisation problem is developed in which secrecy rate thresholds (SRTs) are determined to find an optimal scheme among various DT and RT schemes for achieving the lowest SOP. Furthermore, the conditions for the existence of SRTs are derived with respect to various channel conditions to determine if the relay could be relied on in practice.

We consider a cooperative relay network in the presence of many eavesdroppers whose locations are distributed according to a homogeneous Poisson point process. The relay, which operates in amplify-and-forward protocol, has very large transmit and receive antenna arrays, while other nodes are equipped with a single antenna. We assume that the relay exploits maximum ratio combing (MRC) in the uplink and maximum ration transmission in the downlink, while all eavesdroppers are able to exploit MRC to maximize the received signals. In addition, there is no perfect channel state information of any eavesdroppers since all eavesdroppers in practice tend to hide from the legitimate users. Furthermore, we suppose that there are direct links between source and eavesdroppers, while a direct link between source and destination does not exist. Under such assumptions, which are totally biased towards eavesdroppers, we examine the security performance of the proposed system throughout secrecy outage probability and connection outage probability.

In this paper, we investigate the average throughput of a multi-antenna wireless powered communication network where an energy-constrained user harvests energy from a hybrid access-point (AP) equipped with multiple antennas in the downlink, and then transmits information to the AP in the uplink using the harvested energy. Specifically, we consider a more practical scenario, i.e., nonlinear energy harvester, as compared with the traditional linear model. In order to evaluate the key parameters, such as the transmit power, antenna numbers, time-splitting, channel fading severity, on the performance of the considered system, we derive closed-form expressions of the average throughput for both delay tolerant and delay intolerant transmission modes in Nakagami-m fading channel. In addition, to further exploit the insights on the application of the considered system, the asymptotic analysis for the achievable throughput are also provided in two special cases, i.e., high transmit power regime and high saturation threshold regime. Finally, our results demonstrate that the considered system exhibits the throughput saturation phenomenon, and the parameters of channel fading severity produce a different impact on the average throughput in the two transmission modes.

We consider a diffusion-based nano-network with N spatially distributed transmitters and one receiver. While the transmitters are linked in a unified entity to perform one transmission, the receiver is large enough to be viewed as a plane. Messages are encoded into the number of nano-scale molecules. Based on these assumptions, we analyze the signal-to-interference ratio, which is based on the average numbers of absorbed molecules. Moreover, we present an approach to interference alignment for molecular communications.

In this paper, we investigate the impacts of unreliable backhaul connections on cognitive heterogeneous networks with best relay selection. Since spectrum sharing is employed, the transmit powers of the small-cell transmitter and relays are constrained by the peak interference at the primary user, as well as their maximum transmit powers. The closed-form expressions of the outage probability, ergodic capacity and symbol error rate are derived along with the asymptotic performance to get full insights. Our results show that the backhaul reliability is a limiting factor of the system performance.

In LTE networks, key update and residence management have been investigated as an effective solution to cope with desynchronization attacks in mobility management entity (MME) handovers. In this paper, we first analyse the impacts of the key update interval (KUI) and MME residence interval (MRI) on the handover performance in terms of the number of exposed packets (NEP) and signaling overhead rate (SOR). By deriving the bounds of the NEP and SOR over the KUI and MRI, it is shown that there exists a tradeoff between the NEP and the SOR, while our aim is to minimise both of them simultaneously. This accordingly motivates us to propose a multiobjective optimisation problem to find the optimal KUI and MRI that minimise both the NEP and SOR. By introducing a relative importance factor between the SOR and NEP along with their derived bounds, we further transform the proposed optimisation problem into a single-objective optimisation problem which can be solved via a simple numerical method. In particular, the results show that a higher accuracy of up to 1 second is achieved with the proposed approach while requiring a lower complexity compared to the conventional approach employing iterative searches.

Non‐orthogonal multiple access (NOMA) in amplify‐and‐forward relay systems with partial relay selection (PRS) is investigated. More specifically, new exact closed‐form expressions for the outage probabilities at two users are derived, based on which an asymptotic analysis at high signal‐to‐noise ratio (SNR) is carried out. Additionally, to investigate the performance gap between the NOMA and orthogonal multiple access (OMA) schemes, a closed‐form approximate expression at high SNR for the sum rate is derived. Furthermore, relying on the results, the impact of the PRS on the sum rate and outage probability of the proposed NOMA scheme is examined. In particular, the derived asymptotic expressions show that the proposed scheme can improve over the traditional OMA not only the sum rate but also the user fairness. Finally, simulation results are presented to corroborate the analytical results.

Consider an underlay cognitive radio network where an eavesdropper (Eve) targets to intercept the information exchanging between the primary nodes. The secondary system is allowed to access the licensed spectrum as long as it does not violate the target quality‐of‐service (QoS) of the primary network. In return, the secondary network also assists the primary network against the malicious attack of the Eve. The authors aim at designing a resource allocation algorithm maximising the secrecy rate of the primary system while also satisfying the QoS requirement of the secondary system. To be more precise, a jamming noise accompanied by the information signal to degrade the Eve's channel and the information beamforming vector at the secondary transmitter is jointly optimised. The problem of interest is formulated as a non‐convex optimisation problem. For the case in which global channel state information (CSI) is available, the authors propose a path‐following algorithm which aims at locating a Karush‐Kuhn–Tucker solution to the original non‐convex program. By novel transformations and approximations, the authors arrive at only a simple convex problem of moderate dimension. For the case in which only statistics of the Eve's CSI are available, the authors reformulate the considered problem by replacing a non‐convex probabilistic constraint with a set of convex constraints. A worst‐case scenario for a secure communication, where an optimal linear decoder is used at the Eve, is also considered. The superior performance of the proposed design is revealed by numerically comparing it with other known solutions.

The throughput of users with poor channel conditions, such as those at a cell edge, is a bottleneck in wireless systems. A major part of the power budget must be allocated to serve these users in guaranteeing their quality-of-service (QoS) requirements, hampering QoS for other users, and thus compromising the system reliability. In non-orthogonal multiple access (NOMA), the message intended for a user with a poor channel condition is decoded by itself and by another user with a better channel condition. The message intended for the latter is then successively decoded by itself after canceling the interference of the former. The overall information throughput is thus improved by this particular successive decoding and interference cancellation. This paper aims to design linear precoders/beamformers for signal superposition at the base stations of NOMA multiple-input multiple-output multi-cellular systems to maximize the overall sum throughput subject to the users' QoS requirements, which are imposed independently on the users' channel conditions. This design problem is formulated as the maximization of a highly nonlinear and nonsmooth function subject to nonconvex constraints, which is very computationally challenging. Path-following algorithms for its solution, which invoke only a simple convex problem of moderate dimension at each iteration, are developed. Generating a sequence of improved points, these algorithms converge at least to a local optimum. Extensive numerical simulations are then provided to demonstrate their merit.

Considering a multiple-user multiple-input multiple-output channel with an eavesdropper, this letter develops a beamformer design to optimize the energy efficiency in terms of secrecy bits per Joule under secrecy quality-of-service constraints. This is a very difficult design problem with no available exact solution techniques. A path-following procedure, which iteratively improves its feasible points by using a simple quadratic program of moderate dimension, is proposed. Under any fixed computational tolerance, the procedure terminates after finitely many iterations, yielding at least a locally optimal solution. Simulation results show the superior performance of the obtained algorithm over other existing methods.

This paper investigates the impact of primary network interference on the performance of cognitive multihop secondary network under various multiple-input multiple-output (MIMO) approaches per hop. Specifically, the cognitive system involves a secondary network with MIMO relays that use the amplify-and-forward protocol, and each of which shares the same spectrum resources of multiple primary users (PUs) transmit and receive stations. Two different receive array conditions, and hence processing approaches, per hop in the secondary network are treated separately, which are maximal ratio combining for sufficiently spaced receive antennas to provide receive diversity gain and interference cancellation (IC) for insufficiently spaced antennas to reduce the effect of PUs interference. The latter approach involves two different algorithms that vary in terms of complexity and achieved performance, which are dominant receive IC and adaptive receive IC. Moreover, for both approaches, the transmit array gain is achieved per hop through the low-complexity transmit antenna selection. In doing so, new analytical results for multihop secondary network's end-to-end outage probability are developed. Moreover, simple asymptotic results for this outage performance in high SNR regime are provided, from which the achieved diversity and coding gains and the diversity-multiplexing tradeoff can be extracted. In addition, to further enhance the secondary network, optimal power allocation among hops is obtained based on the asymptotic outage performance under the constraints of transmit power of a secondary transmit station and interference limit on the primary network. The developed analytical results in this paper are validated through numerical and simulation results.

A cooperative relay network operating in the presence of eavesdroppers, whose locations are distributed according to a homogeneous Poisson point process, is considered. The relay is equipped with a very large antenna array and can exploit maximal ratio combing in the uplink and maximal ratio transmission in the downlink. A realistic model in which the channel state information of every eavesdropper is not known is considered, as eavesdroppers tend to hide themselves in practice. The destination is thus in a much weaker position than all the eavesdroppers because it only receives the retransmitted signal from the relay. Under this setting, the security performance is investigated for two relaying protocols: amplify-and-forward and decode-and-forward. The secrecy outage probability, the connection outage probability, and the tradeoff between them, which is controlled by the source power allocation, are examined. Finally, suitable solutions for the source power (such that once the transmission occurs with high reliability, the secure risk is below a given threshold) are proposed for a tradeoff between security and reliability.

Information and energy can be transferred over the same radio-frequency channel. In the power-splitting (PS) mode, they are simultaneously transmitted using the same signal by the base station (BS) and later separated at the user (UE)'s receiver by a power splitter. In the time-switching (TS) mode, they are either transmitted separately in time by the BS or received separately in time by the UE. In this paper, the BS transmit beamformers are jointly designed with either the receive PS ratios or the transmit TS ratios in a multicell network that implements wireless information and power transfer (WIPT). Imposing UE-harvested energy constraints, the design objectives include: 1) maximizing the minimum UE rate under the BS transmit power constraint, and 2) minimizing the maximum BS transmit power under the UE data rate constraint. New iterative algorithms of low computational complexity are proposed to efficiently solve the formulated difficult nonconvex optimization problems, where each iteration either solves one simple convex quadratic program or one simple second-order-cone-program. Simulation results show that these algorithms converge quickly after only a few iterations. Notably, the transmit TS-based WIPT system is not only more easily implemented but outperforms the receive PS-based WIPT system as it better exploits the beamforming design at the transmitter side.

A communication system is considered consisting of a full-duplex multiple-antenna base station (BS) and multiple single-antenna downlink users (DLUs) and single-antenna uplink users (ULUs), where the latter need to harvest energy for transmitting information to the BS. The communication is thus divided into two phases. In the first phase, the BS uses all available antennas for conveying information to DLUs and wireless energy to ULUs via information and energy beamforming, respectively. In the second phase, ULUs send their independent information to the BS using their harvested energy while the BS transmits the information to the DLUs. In both the phases, the communication is operated at the same time and over the same frequency band. The aim is to maximize the sum rate and energy efficiency under ULU achievable information throughput constraints by jointly optimizing beamforming and time allocation. The utility functions of interest are nonconcave and the involved constraints are nonconvex, so these problems are computationally troublesome. To address them, path-following algorithms are proposed to arrive at least at local optima. The proposed algorithms iteratively improve the objectives with convergence guaranteed. Simulation results demonstrate that they achieve rapid convergence and outperform conventional solutions.

In order to enhance secrecy performance of protecting scenarios, understanding the illegitimate side is crucial. In this paper, from the perspective of the illegitimate side, the security attack from a full-duplex cyber-weapon equipped with massive antenna arrays is considered. To evaluate the behavior of the proposed cyber-weapon, we develop a closed-form, a tight approximation, and asymptotic expressions of the achievable ergodic secrecy rate with taking into consideration imperfect channel estimation at the cyber-weapon. The results show that even under some disadvantage conditions, i.e., imperfect channel estimation and self-interference, the full-duplex massive array cyber-weapon can disable traditional physical layer protecting schemes, i.e., increasing the transmit power and the number of antennas at the legitimate transmitter. In addition, when a transmit power optimization scheme for maximizing the difference between the eavesdropping rate and the legitimate rate is applied at the full-duplex cyber-weapon, the malicious attack is even more dangerous. The results also reveal that when the legitimate side faces an advance adversary, it is essential to prevent important information in the training phases exposing to the illegitimate side.

It is known that superposition signaling in Gaussian interference networks is capable of improving the achievable rate region. However, the problem of maximizing the rate gain offered by superposition signaling is computationally prohibitive, even in the simplest case of two-user single-input single-output interference networks. This paper examines superposition signaling for the general multiple-input multiple-output broadcast Gaussian interference networks. The problem of maximizing either the sum rate or the minimal user's rate under superposition signaling and dirty paper coding is solved by a computationally efficient path-following procedure, which requires only a convex quadratic program for each iteration but ensures convergence at least to a locally optimal solution. Numerical results demonstrate the substantial performance advantage of the proposed approach.

In this paper, we investigate the secrecy performance of full-duplex multi-antenna spectrum-sharing wiretap networks in which a jamming signal is simultaneously transmitted by the full-duplex secondary receiver (Bob) based on the zero forcing beamforming (ZFB) algorithm. For the security enhancement, we propose the two antenna reception schemes: 1) random selection combining (RSC) where Bob selects LB antennas at random to combine the received signals and 2) generalized selection combining (GSC) where Bob selects LB strongest antennas to combine the received signals. We derive the exact closed-form expressions for the secrecy outage probability of full-duplex multi-antenna spectrum-sharing wiretap networks with ZFB algorithm. In order to explore a new design of the proposed schemes, we provide tractable asymptotic approximations for the secrecy outage probability in high signal-to-noise ratio regime under two distinct scenarios. From the analysis, we demonstrate that: 1) when the main channel is much better than the eavesdropper's channel, GSC/ZFB scheme achieves full diversity NB, while RSC/ZFB scheme only achieves partial diversity LB and 2) GSC/ZFB scheme achieves better secrecy performance than RSC/ZFB with different antenna numbers at Bob.

In this paper, we analyze the downlink outage performance of opportunistic scheduling in dual-hop cooperative networks consisting of one source, multiple radio-frequency energy harvesting relays, and multiple destinations. To this end, two low-complexity, suboptimal, yet efficient, relay-destination selection schemes are proposed, namely direct links plus opportunistic channel state information (CSI)-based selection (DOS) scheme and direct links plus partial CSI-based selection (DPS) scheme. Considering three relaying strategies, i.e., decode-and-forward (DF), variable-gain amplify-and-forward (VG-AF), and fixed-gain amplify-and-forward (FG-AF), the performance analysis in terms of outage probability (OP) is carried out for each selection scheme. For the DF and VG-AF strategies, exact analytical expressions and tight closed-form approximate expressions for the OP are derived. For the FG-AF strategy, an exact closed-form expression for the OP is provided. Additionally, we propose a gradient-based search method to find the optimal values of the power-splitting ratio that minimizes the attained OPs. The developed analysis is corroborated through Monte Carlo simulation. Comparisons with the optimal joint selection scheme are performed and it is shown that the proposed schemes significantly reduce the amount of channel estimations while achieving comparable outage performance. In addition, regardless of relaying strategy used, numerical results show that the DOS scheme achieves full diversity gain, i.e., M + K, and the DPS scheme achieves the diversity gain of M+1, where M and K are the numbers of destinations and relays, respectively.

Some next-generation wireless networks will likely involve the energy-efficient transfer of information and energy over the same wireless channel. Moreover, densification of such networks will make the physical layer more vulnerable to cyber attacks by potential multi-antenna eavesdroppers. To address these issues, this paper considers transmit time-switching (TS) mode, in which energy and information signals are transmitted separately in time by the base station (BS). This protocol is not only easy to implement but also delivers the opportunity for multi-purpose beamforming, in which energy beamformers can be used to jam eavesdroppers during wireless power transfer. In the presence of imperfect channel estimation and multiantenna eavesdroppers, the energy and information beamformers and the transmit TS ratio are jointly optimized to maximize the worst-case user secrecy rate subject to energy constrained users' harvested energy thresholds and a BS transmit power budget. New robust path-following algorithms, which involve one simple convex quadratic program at each iteration are proposed for computational solutions of this difficult optimization problem and also the problem of secure energy efficiency maximization. The latter adds further complexity due to additional optimization variables appearing in the denominator of the secrecy rate function. Numerical results confirm that the performance of the proposed computational solutions is robust against channel uncertainties.

This paper proposes a cross-layer design to enhance the security of a set of buffered legitimate source nodes wishing to communicate with a common destination node using a time-division multiple-access scheme with probabilistic time slot assignment. The users' assignment probabilities to the time slots are optimized to satisfy a certain quality-of-service (QoS) requirement for each of the legitimate source nodes. To further improve the system security, we propose beamforming-based cooperative jamming schemes subject to the availability of the channel state information (CSI) at the legitimate nodes. We assume that if a source node is not selected for data transmission, it is a cooperative jamming node. We impose an average transmit power constraint (averaged across time slots) on each source node. Hence, the source nodes should efficiently distribute their average transmit powers throughout the network operation between data and artificial noise transmissions to satisfy the QoS requirements. We investigate the two cases where a global CSI is assumed at the legitimate nodes and where there is no eavesdropper's CSI. The case where there is no CSI at the jamming nodes is also investigated and a new scheme is proposed. We derive closed-form expressions for the instantaneous secrecy rate for each scheme as well as the secrecy outage probability. Moreover, we derive the secrecy stable-throughput and delay-requirement regions of the network. Our proposed jamming schemes achieve significant increases in the secure throughput over existing schemes from the literature and over the no-jamming scheme.

This paper considers the joint design of user power allocation and relay beamforming in relaying communications, in which multiple pairs of single-antenna users exchange information with each other via multiple-antenna relays in two time slots. All users transmit their signals to the relays in the first time slot while the relays broadcast the beamformed signals to all users in the second time slot. The aim is to maximize the system's energy efficiency (EE) subject to quality-of-service (QoS) constraints in terms of exchange throughput requirements. The QoS constraints are nonconvex with many nonlinear cross-terms, so finding a feasible point is already computationally challenging. The sum throughput appears in the numerator while the total consumption power appears in the denominator of the EE objective function. The former is a nonconcave function and the latter is a nonconvex function, making fractional programming useless for EE optimization. Nevertheless, efficient iterations of low complexity to obtain its optimized solutions are developed. The performance of the multiple-user and multiple-relay networks under various scenarios is evaluated to show the merit of the proposed method.

In this paper, new strategies are devised for joint load balancing and interference management in the downlink of a heterogeneous network, where small cells are densely deployed within the coverage area of a traditional macrocell. Unlike existing work, the limited backhaul capacity at each base station (BS) is taken into account. Here, users (UEs) cannot be offloaded to any arbitrary BS, but only to ones with sufficient backhaul capacity remaining. Jointly designed with traffic offload, transmit power allocation mitigates the intercell interference to further support the quality of service of each UE. The objective here is either: 1) to maximize the network sum rate subject to minimum throughput requirements at individual UEs, or 2) to maximize the minimum UE throughput. Both formulated problems belong to the difficult class of mixed-integer nonconvex optimization problems. The inherently binary BS-UE association variables are strongly coupled with the transmit power variables, making the problems even more challenging to solve. New iterative algorithms are developed based on an exact penalty method combined with successive convex programming, where the binary BS-UE association problem and the nonconvex power allocation problem are dealt with one at a time. At each iteration of the proposed algorithms, only two simple convex problems need to be solved at the same time scale. It is proven that the algorithms improve the objective functions at each iteration and converge eventually. Numerical results demonstrate the efficiency of the proposed algorithms in both traffic offloading and interference mitigation.

This paper considers the efficient design of precoding matrices for sum throughput maximization under throughput quality of service (QoS) constraints and energy harvesting (EH) constraints for energy-constrained devices in a full-duplex (FD) multicell multi-user multiple-input-multiple-output network. Both time splitting (TS) and power splitting are considered to ensure practical EH and information decoding. These problems are quite complex due to non-concave objectives and nonconvex constraints. Especially, with TS, which is implementation-wise quite simple, the problem is even more challenging because the time splitting variable is not only coupled with the downlink throughput function but also coupled with the self-interference in the uplink throughput function. New path-following algorithms are developed for their solutions, which require only a single convex quadratic program for each iteration and ensure rapid convergence. Moreover, the FD EH maximization problem under throughput QoS constraints with TS is also considered. The performance of the proposed algorithms is compared with that of the modified problems assuming half-duplex systems. Finally, the merit of the proposed algorithms is demonstrated through extensive simulations.

In this paper, we investigate the secure communication of cognitive full-duplex relay networks in the presence of multiple eavesdroppers and multiple primary receivers. In the considered network, multiple full-duplex relays are deployed to transfer information in the secondary network, under the malicious attempts of non- colluding/colluding eavesdroppers. Meanwhile, the transmit powers of secondary transmitters are constrained by the quality-of-service of the primary network. The optimal relay selection scheme is proposed to enhance the secrecy performance of the considered system. We study the secrecy performance by providing the exact closed- form and asymptotic expressions of the proposed system secrecy outage probability. We have demonstrated that increasing the number of full- duplex relays can improve the security performance. At the illegitimate side, using colluding eavesdroppers and increasing the number of eavesdroppers put information confidentiality at a greater risk. Besides, the transmit power and the desired outage probability of the primary network have great influences on the secrecy outage probability of the secondary network.

Video streaming applications and services (VASs) consume an enormous amount of scarce resources in mobile devices and cellular wireless networks due to the demand for high data rates of video streaming. The limited resource of wireless media and unreliable nature of wireless channels in cellular networks make VASs challenging to deliver videos at high quality of experience (QoE). Therefore, in this paper, we propose a femtocaching framework of QoE-oriented resource efficiency optimization for high performance of cooperative VASs over 5G two-tier cellular networks, where the collaboration between macro base stations (BSs) and femtocells are exploited to efficiently deliver videos to mobile users (MUs). Our proposed framework aims at solving two problems. The first problem is how to cache the videos in femtocells to minimize the bandwidth resource consumed at the BSs and wasted at femtocells while guaranteeing high hit rate and utilizing the available storage resource of femtocells. The second one is how to encode the videos into descriptions and assign them to each femtocell for transmission, so as to minimize the reconstructed distortion of received videos for high playback quality at the MUs. The simulation results are further provided to demonstrate the benefits of the proposed framework.

In this paper, we consider beamforming design for an underlay cognitive radio multiple-input single-output broadcast channel, where a pair of secondary users coexists with multiple primary receivers. There exist multiple malicious eavesdroppers who attempt to overhear the confidential messages from the secondary system. When the channel state information of the eavesdroppers can only be obtained in the statistical sense, we transform the constraint which results from the statistical information of the passive eavesdroppers into a linear matrix inequality and convex constraint. To improve the secrecy rate of the secondary system, we aim to design a jamming noise to degrade the eavesdroppers. The main objective of the design is to maximize the secrecy rate of the secondary system while satisfying all the interference power constraints at the primary users and per-antenna power constraint at the secondary transmitter. The original problem is a nonconvex program, which can be reformulated to a convex program by applying the rank relaxation method. To this end, we prove that the rank relaxation is tight and it can be efficiently solved. Moreover, to develop an efficient resource allocation scheme we transform the relaxed problem into an equivalent problem based on a duality result.

In this paper, the outage performance of nonorthogonal multiple access (NOMA) schemes in amplify-and-forward (AF) relay systems with partial relay selection is investigated. To this end, closed-form expressions for the outage probabilities at two users are derived, based on which an asymptotic analysis at high signal-to-noise ratio (SNR) is carried out to provide additional insights into the system performance. In particular, the diversity order at each user is explicitly obtained. Numerical results illustrate the impact of relay selection on the performance of the considered NOMA scheme. Also, computer simulation results are presented to validate the accuracy of the attained analytical results.

In this paper, we investigate the secrecy performance of dual-hop amplify-and-forward (AF) multi-antenna relaying systems over Rayleigh fading channels by taking into account the direct link between the source and destination. To improve the secrecy performance, two linear processing schemes at relay and maximal ratio combining (MRC) at destination are proposed, namely, Zero-forcing/MRC (ZF/MRC) and Maximal ratio transmission/MRC (MRT/MRC). For these schemes, we present new tight analytical expressions of the secrecy outage probability. In addition, we examine the performance in high signal-to-noise ratio (SNR) regimes, and present simple secrecy outage approximations for all schemes. The results reveal that: 1) The MRT/MRC scheme achieves a full diversity order of M+1, while the ZF/MRC scheme achieves a diversity order ofM, where M is the number of antennas at relay. 2) The ZF/MRC scheme outperforms the MRT/MRC scheme in the low SNR regime, while becomes inferior to the MRT/MRC scheme in the high SNR regime.

This is a Special Issue of IET Communications: ‘Green Computing and Telecommunications Systems,’ published in association with the 3rd International Conference on Computing, Management and Telecommunications (ComManTel 2015). There were 30 submissions to the issue, ranging across the subject of green computing and networks. These spanned emerging technologies and methods for green computing, to green wireless and multimedia telecommunications networks, resource allocation, and test-beds for green telecommunication networks. This Special Issue comprises 9 papers (3 of which were extended from papers presented at ComManTel 2015 and invited to submit to the special issue) which were chosen for publication after a rigorous peer review and selection process. The accepted papers cover the energy-efficiency aspects of telecommunications systems, address the requirement for minimising consumed resources whenever possible, propose approaches for green connected energy as well as presenting designs for components of a green wireless system. The paper entitled “Uplink training for multicell massive multiple-input–multiple-output systems: A combination of time-shifted and time-aligned pilot approaches” by H. V. Nguyen, V.-D. Nguyen and O.-S. Shin proposes two schemes to mitigate the effect of pilot contamination with the aim to improve the achievable uplink and downlink rates in a multicell massive multiple-input–multiple-output (MIMO) system. Their results show that the two proposed strategies significantly improve both the achievable uplink and downlink rates when compared with those of a conventional time-aligned pilot strategy and a previously proposed time-shifted pilot strategy. Considering cellular networks having multi-tier heterogeneous architectures in which the channel conditions change rapidly, D.-A. Le, H.V. Vu, M. Ranjbar, N.H. Tran, T. Karacolak and T.-M. Hoang in the paper “On the capacity and energy efficiency of non-coherent Rayleigh fading channels with additive Gaussian mixture noise” examine the capacity and energy efficiency of non-coherent Rayleigh fading channels with Gaussian mixture noise where neither the transmitter nor the receiver has the knowledge of channel state information. The energy efficiency, which is related to the capacity and optimal input in low-power regimes, is examined by calculating the minimum bit energy and wideband slope of the spectral-efficiency curve. Q.-T. Vien, T.A. Le, B. Barn and C.V. Phan adopt non-orthogonal multiple access (NOMA) and propose power allocation for the wireless downlink of a cloud-based central station to multiple base stations in a heterogeneous cloud radio access network (HCRAN) sharing the same time and frequency resources. The energy efficiency of the practical HCRAN utilising NOMA is analysed taking into account practical channel modelling with power consumptions at base stations of different cell types (e.g. macrocell, microcell etc.) and backhauling power. These results are presented in the paper “Optimising energy efficiency of non-orthogonal multiple access for wireless backhaul in heterogeneous cloud radio access network.” For the potential of fully cooperative OFDM in the design of emerging low power applications, such as a wireless body area network comprising wearable computing devices, L.C. Tran and A. Mertins present an analysis of decode-and-forward, space-time coded, fully cooperative OFDM systems from both error performance and energy efficiency perspectives, in identically/non-identically distributed frequency selective Rayleigh fading channels. Results from their paper “Error performance and energy efficiency analyses of fully cooperative OFDM communication in frequency selective fading” demonstrate that the fully cooperative OFDM outperforms direct OFDM transmission with respect to error performance and energy efficiency in many cases. In their paper entitled ‘’Energy efficient medium access scheme for visible light communication system based on IEEE 802.15.7 with unsaturated traffic’’ H. Liu, L. Zhang and M. Jiang propose a medium access scheme for reducing power consumption and improving the random channel access mechanism for multiuser visible light communication (VLC) networks under unsaturated traffic conditions. The proposed scheme is shown to outperform the IEEE 802.15.7 CSMA/CA scheme in terms of average power consumption, system throughput and packet dropping probability. N.-S. Vo, D.-B. Ha, B. Canberk and J. Zhang propose an energy, bandwidth, and quality (EBQ) optimisation framework for green two-tiered wireless multimedia sensor systems, where the first tier contains the camera sensors and the second includes cluster heads selected from the camera sensors with higher available energy and processing capacity. These results are presented in the paper “Green two-tiered wireless multimedia sensor systems: An energy, bandwidth, and quality optimisation framework.” The next two papers in the issue consider energy harvesting techniques in cooperative and cognitive networks. N.T. Do, D.B. da Costa and B. An consider the joint impact of energy harvesting technique and transceiver hardware impairment on the outage performance of multirelay decode-and-forward cooperative networks in their paper “Performance analysis of multirelay RF energy harvesting cooperative networks with hardware impairments”. It is shown that the “harvested energy-based relay selection” scheme achieves a diversity order of 1 and is independent of the relay numbers, while the “channel quality-base relay selection” scheme achieves full diversity. The paper “Impact of primary networks on the performance of energy harvesting cognitive radio networks” by Jinghua Zhang, N.-P. Nguyen, Junqing Zhang, E. Garcia-Palacios and N.P. Le examines the influence of the primary transmitter's transmit power on the energy harvesting secondary network in the presence of multiple power beacons and multiple secondary transmitters. Results reveal that the influence is negative in both the near/far scenarios of the primary transmitter's location to the secondary network. However, it can be alleviated by increasing the number of power beacons and primary transmitters. The paper “Design of compact frequency reconfigurable planar invert-F antenna for green wireless communications” proposes a single frequency reconfigurable planar invert-F antenna (FRPIFA) using PIN diodes based on the changes of shorting pin positions. H.T.P. Thao, V.T. Luan and V.V. Yem also present a frequency reconfigurable multiple-input multiple-output (MIMO) antenna consisting of two single reconfigurable ones with a distance of half-wavelength, which is shown to achieve a high isolation in all operating bands. The proposed compact single and MIMO antennas are demonstrated as suitable for green wireless communication systems. Trung Q. Duong (S′05, M′12, SM′13) received his Ph.D. degree in Telecommunications Systems from Blekinge Institute of Technology (BTH), Sweden in 2012. Since 2013, he has joined Queen's University Belfast, UK as a Lecturer (Assistant Professor). His current research interests include physical layer security, energy-harvesting communications, and cognitive relay networks. He is the author or co-author of more than 230 technical papers published in scientific journals (116 articles) and presented at international conferences (114 papers). Dr. Duong currently serves as an Editor for IEEE Transactions on Wireless Communications, IEEE Transactions on Communications, IEEE Communications Letters and IET Communications. He was an editor of Wiley Transactions on Emerging Telecommunications Technologies, Electronics Letters and has also served as the Guest Editor of Special Issues in some major journals including IEEE Journal in Selected Areas on Communications, IET Communications, IEEE Access, IEEE Wireless Communications Magazine, IEEE Communications Magazine, EURASIP Journal on Wireless Communications and Networking and EURASIP Journal on Advances Signal Processing. He was awarded the Best Paper Award at the IEEE Vehicular Technology Conference (VTC-Spring) in 2013, and the IEEE International Conference on Communications (ICC) 2014. He is the recipient of the prestigious Royal Academy of Engineering Research Fellowship (2016–2021). Vo Nguyen Quoc Bao (SMIEEE) is an associate professor of Wireless Communications at the Posts and Telecommunications Institute of Technology (PTIT), Vietnam. He also served as Dean of the Faculty of Telecommunications and as Director of the Wireless Communication Laboratory (WCOMM). His research interests include wireless communications and information theory with current emphasis on MIMO systems, cooperative and cognitive communications, physical layer security, and energy harvesting. He has published more than 140 journal and conference articles that have 1300+ citations and H-index of 20. He is an Editor of Transactions on Emerging Telecommunications Technologies (Wiley ETT), VNU Journal of Computer Science and Communication Engineering, and REV Journal on Electronics and Communications. He is also a Guest Editor of a EURASIP Journal on Wireless Communications and Networking “Special Issue:Cooperative Cognitive Networks” and an IET Communications “Special Issue: Secure Physical Layer Communications”. He served as a Technical Program co-chair for ATC (2013, 2014), NAFOSTED-NICS (2014, 2015, 2016), REV-ECIT 2015, ComManTel (2014, 2015), and SigComTel 2017. He is a Member of the Executive Board of the Radio-Electronics Association of Vietnam (REV) and the Electronics Information and Communications Association Ho Chi Minh City (EIC). He is currently serving as a scientific secretary of the Vietnam National Foundation for Science and Technology Development (NAFOSTED) scientific Committee in Information Technology and Computer Science. Mohsen Guizani (S′85–M′89–SM′99–F′09) received B.S. (with distinction) and M.S. degrees in electrical engineering, M.S. and Ph.D. degrees in computer engineering from Syracuse University, Syracuse, NY, USA, in 1984, 1986, 1987, and 1990, respectively. He is currently a professor and Electrical and Communications Engineering Department Chair at the University of Idaho. Previously, he was Professor and Associate Vice President of Graduate Studies at Qatar University, Doha, Qatar, served as Chair of the Computer Science Department, Western Michigan University, Kalamazoo, MI, USA, from 2002 to 2006, and Chair of the Computer Science Department, University of West Florida, Pensacola, FL, USA, from 1999 to 2002. He also served in academic positions at the University of Missouri- Kansas City, Kansas City, MO, USA, University of Colorado-Boulder, Boulder, CO, USA, Syracuse University, and Kuwait University, Kuwait City, Kuwait. His research interests include wireless communications and mobile computing, computer networks, mobile cloud computing, and smart grid. He currently serves on the Editorial Boards of many international technical journals and is the Founder and Editor-in-Chief of Wireless Communications and Mobile Computing journal (Wiley). He is the author of nine books and more than 400 publications in refereed journals and conferences. He has guest edited a number of Special Issues in IEEE journals and magazines. He has also served as member, Chair, and General Chair of a number of conferences. He was selected as the Best Teaching Assistant for two consecutive years at Syracuse University (1988 and 1989). He was the Chair of the IEEE Communications Society Wireless Technical Committee and the Chair of the TAOS Technical Committee. He served as the IEEE Computer Society Distinguished Speaker from 2003 to 2005.

This study considers a dual‐hop cognitive inter‐vehicular relay‐assisted communication system where all communication links are non‐line of sight ones and their fading is modelled by the double Rayleigh fading distribution. Road‐side relays (or access points) implementing the decode‐and‐forward relaying protocol are employed and one of them is selected according to a predetermined policy to enable communication between vehicles. The performance of the considered cognitive cooperative system is investigated for K th best partial and full relay selection (RS) as well as for two distinct fading scenarios. In the first scenario, all channels are double Rayleigh distributed. In the second scenario, only the secondary source to relay and relay to destination channels are considered to be subject to double Rayleigh fading whereas, channels between the secondary transmitters and the primary user are modelled by the Rayleigh distribution. Exact and approximate expressions for the outage probability performance for all considered RS policies and fading scenarios are presented. In addition to the analytical results, complementary computer simulated performance evaluation results have been obtained by means of Monte Carlo simulations. The perfect match between these two sets of results has verified the accuracy of the proposed mathematical analysis.

This study considers amplify‐and‐forward two‐way relay networks, where an energy constrained relay node harvests energy from the received radio‐frequency signal. Based on time switching receiver, they separate the energy harvesting (EH) phase and the information processing (IP) phase in time. In the EH phase, three practical wireless power transfer policies are proposed: (i) dual‐source (DS) power transfer, where both sources transfer power to the relay; (ii) single‐fixed‐source power transfer, where a fixed source transfers power to the relay; and (iii) single‐best‐source (SBS) power transfer, where a source with the strongest channel transfers power to the relay. In the IP phase, a new comparative framework of the proposed wireless power transfer policies is presented in two bi‐directional relaying protocols, known as multiple access broadcast (MABC) and time division broadcast (TDBC). To characterise the performance of the proposed policies, new analytical expressions are derived for the outage probability, the throughput, and the system energy efficiency. Numerical results corroborate the authors’ analysis and show: (i) the DS policy performs the best in terms of both outage probability and throughput among the proposed policies, (ii) the TDBC protocol achieves lower outage probability than the MABC protocol, and (iii) there exits an optimal value of EH time fraction to maximise the throughput.

Two direct sampling correlator‐type receivers for differential chaos shift keying (DCSK) communication systems under frequency non‐selective fading channels are proposed. These receivers operate based on the same hardware platform with different architectures. In the first scheme, namely sum‐delay‐sum (SDS) receiver, the sum of all samples in a chip period is correlated with its delayed version. The correlation value obtained in each bit period is then compared with a fixed threshold to decide the binary value of recovered bit at the output. On the other hand, the second scheme, namely delay‐sum‐sum (DSS) receiver, calculates the correlation value of all samples with its delayed version in a chip period. The sum of correlation values in each bit period is then compared with the threshold to recover the data. The conventional DCSK transmitter, frequency non‐selective Rayleigh fading channel, and two proposed receivers are mathematically modelled in discrete‐time domain. The authors evaluated the bit error rate performance of the receivers by means of both theoretical analysis and numerical simulation. The performance comparison shows that the two proposed receivers can perform well under the studied channel, where the performances get better when the number of paths increases and the DSS receiver outperforms the SDS one.

In this letter, we consider wireless powered communication networks which could operate perpetually, as the base station (BS) broadcasts energy to the multiple energy harvesting (EH) information transmitters. These employ “harvest then transmit” mechanism, as they spend all of their energy harvested during the previous BS energy broadcast to transmit the information towards the BS. Assuming time division multiple access (TDMA), we propose a novel transmission scheme for jointly optimal allocation of the BS broadcasting power and time sharing among the wireless nodes, which maximizes the overall network throughput, under the constraint of average transmit power and maximum transmit power at the BS. The proposed scheme significantly outperforms “state of the art” schemes that employ only the optimal time allocation. If a single EH transmitter is considered, we generalize the optimal solutions for the case of fixed circuit power consumption, which refers to a much more practical scenario.

In this paper, we study a two-phase underlay cognitive relay network, where there exists an eavesdropper who can overhear the message. The secure data transmission from the secondary source to secondary destination is assisted by two decode-and-forward (DF) relays. Although the traditional opportunistic relaying technique can choose one relay to provide the best secure performance, it needs to continuously have the channel state information (CSI) of both relays, and may result in a high relay switching rate. To overcome these limitations, a secure switch-and-stay combining (SSSC) protocol is proposed where only one out of the two relays is activated to assist the secure data transmission, and the secure relay switching occurs when the relay cannot support the secure communication any longer. This security switching is assisted by either instantaneous or statistical eavesdropping CSI. For these two cases, we study the system secure performance of SSSC protocol, by deriving the analytical secrecy outage probability as well as an asymptotic expression for the high main-to-eavesdropper ratio (MER) region. We show that SSSC can substantially reduce the system complexity while achieving or approaching the full diversity order of opportunistic relaying in the presence of the instantaneous or statistical eavesdropping CSI.

This paper considers a wirelessly powered wiretap channel, where an energy constrained multi-antenna information source, powered by a dedicated power beacon, communicates with a legitimate user in the presence of a passive eavesdropper. Based on a simple time-switching protocol, where power transfer and information transmission are separated in time, we investigate two popular multi-antenna transmission schemes at the information source, namely, maximum ratio transmission and transmit antenna selection. Closed-form expressions are derived for the achievable secrecy outage probability and average secrecy rate for both schemes. In addition, simple approximations are obtained at the high signal-to-noise ratio (SNR) regime. Our results demonstrate that by exploiting the full knowledge of channel state information (CSI), we can achieve a better secrecy performance, e.g., with full CSI of the main channel, the system can achieve substantial secrecy diversity gain. On the other hand, without the CSI of the main channel, no diversity gain can be attained. Moreover, we show that the additional level of randomness induced by wireless power transfer does not affect the secrecy performance in the high SNR regime. Finally, our theoretical claims are validated by the numerical results.

In this paper, we investigate secure device-to-device (D2D) communication in energy harvesting large-scale cognitive cellular networks. The energy constrained D2D transmitter harvests energy from multiantenna equipped power beacons (PBs), and communicates with the corresponding receiver using the spectrum of the primary base stations (BSs). We introduce a power transfer model and an information signal model to enable wireless energy harvesting and secure information transmission. In the power transfer model, three wireless power transfer (WPT) policies are proposed: 1) co-operative power beacons (CPB) power transfer, 2) best power beacon (BPB) power transfer, and 3) nearest power beacon (NPB) power transfer. To characterize the power transfer reliability of the proposed three policies, we derive new expressions for the exact power outage probability. Moreover, the analysis of the power outage probability is extended to the case when PBs are equipped with large antenna arrays. In the information signal model, we present a new comparative framework with two receiver selection schemes: 1) best receiver selection (BRS), where the receiver with the strongest channel is selected; and 2) nearest receiver selection (NRS), where the nearest receiver is selected. To assess the secrecy performance, we derive new analytical expressions for the secrecy outage probability and the secrecy throughput considering the two receiver selection schemes using the proposed WPT policies. We presented Monte carlo simulation results to corroborate our analysis and show: 1) secrecy performance improves with increasing densities of PBs and D2D receivers due to larger multiuser diversity gain; 2) CPB achieves better secrecy performance than BPB and NPB but consumes more power; and 3) BRS achieves better secrecy performance than NRS but demands more instantaneous feedback and overhead. A pivotal conclusion is reached that with increasing number of antennas at PBs, NPB offers a comparable secrecy performance to that of BPB but with a lower complexity.

In this paper, we consider a multiuser downlink wiretap network consisting of one base station (BS) equipped with AA antennas, NB single-antenna legitimate users, and NE single-antenna eavesdroppers over Nakagami-m fading channels. In particular, we introduce a joint secure transmission scheme that adopts transmit antenna selection at the BS and explores threshold-based selection diversity scheduling over legitimate users to achieve a good secrecy performance while maintaining low implementation complexity. More specifically, in an effort to quantify the secrecy performance of the considered system, two practical scenarios are investigated, i.e.: in Scenario I, the eavesdropper's channel state information (CSI) is unavailable at the BS, and in Scenario II, the eavesdropper's CSI is available at the BS. For Scenario I, novel exact closed-form expressions for the secrecy outage probability are derived, which are valid for general networks with an arbitrary number of legitimate users, antenna configurations, number of eavesdroppers, and the switched threshold. For Scenario II, we take into account the ergodic secrecy rate as the principle performance metric, and derive novel exact closed-form expressions for the ergodic secrecy rate. In addition, we also provide simple and asymptotic expressions for secrecy outage probability and ergodic secrecy rate under two distinct cases, i.e.: in Case I, the legitimate user is located close to the BS, and in Case II, both the legitimate user and eavesdropper are located close to the BS. Our important findings reveal that the secrecy diversity order is AAmA and the slope of secrecy rate is one under Case I, while the secrecy diversity order and the slope of secrecy rate collapse to zero under Case II, where the secrecy performance floor occurs. Finally, when the switched threshold is carefully selected, the considered scheduling scheme outperforms other well-known existing schemes in terms of the secrecy performance and complexity tradeoff.

Key generation from the randomness of wireless channels is a promising technique to establish a secret cryptographic key securely between legitimate users. This paper proposes a new approach to extract keys efficiently from the channel responses of individual orthogonal frequency-division multiplexing (OFDM) subcarriers. The efficiency is achieved by: 1) fully exploiting randomness from time and frequency domains and 2) improving the cross-correlation of the channel measurements. Through the theoretical modeling of the time and frequency autocorrelation relationship of the OFDM subcarrier's channel responses, we can obtain the optimal probing rate and use multiple uncorrelated subcarriers as random sources. We also study the effects of non-simultaneous measurements and noise on the cross-correlation of the channel measurements. We find that the cross-correlation is mainly impacted by noise effects in a slow fading channel and use a low-pass filter to reduce the key disagreement rate and extend the system's working signal-to-noise ratio range. The system is evaluated in terms of randomness, key generation rate, and key disagreement rate, verifying that it is feasible to extract randomness from both time and frequency domains of the OFDM subcarrier's channel responses.

In this paper, we consider the secure beamforming design for an underlay cognitive radio multiple-input single-output broadcast channel in the presence of multiple passive eavesdroppers. Our goal is to design a jamming noise (JN) transmit strategy to maximize the secrecy rate of the secondary system. By utilizing the zero-forcing method to eliminate the interference caused by JN to the secondary user, we study the joint optimization of the information and JN beamforming for secrecy rate maximization of the secondary system while satisfying all the interference power constraints at the primary users, as well as the per-antenna power constraint at the secondary transmitter. For an optimal beamforming design, the original problem is a nonconvex program, which can be reformulated as a convex program by applying the rank relaxation method. To this end, we prove that the rank relaxation is tight and propose a barrier interior-point method to solve the resulting saddle point problem based on a duality result. To find the global optimal solution, we transform the considered problem into an unconstrained optimization problem. We then employ Broyden-Fletcher-Goldfarb-Shanno method to solve the resulting unconstrained problem, which helps reduce the complexity significantly, compared with the conventional methods. Simulation results show the fast convergence of the proposed algorithm and substantial performance improvements over the existing approaches.

We present two physical layer secure transmission schemes for multiuser multirelay networks, where the communication from M users to the base station is assisted by direct links and by N decode-and-forward relays. In this network, we consider that a passive eavesdropper exists to overhear the transmitted information, which entails exploiting the advantages of both direct and relay links for physical layer security enhancement. To fulfill this requirement, we investigate two criteria for user and relay selection and examine the achievable secrecy performance. Criterion I performs a joint user and relay selection, while Criterion II performs separate user and relay selections, with a lower implementation complexity. We derive a tight lower bound on the secrecy outage probability for Criterion I and an accurate analytical expression for the secrecy outage probability for Criterion II. We further derive the asymptotic secrecy outage probabilities at high transmit signal-to-noise ratios and high main-to-eavesdropper ratios for both criteria. We demonstrate that the secrecy diversity order is min (MN, M + N) for Criterion I, and N for Criterion II. Finally, we present numerical and simulation results to validate the proposed analysis, and show the occurrence condition of the secrecy outage probability floor.

We investigate the secrecy performance of dual-hop amplify-and-forward multi-antenna relaying systems over Rayleigh fading channels, considering the direct link between the source and the destination. In order to exploit the available direct link and the multiple antennas for secrecy improvement, different linear processing schemes at the relay and different diversity combining techniques at the destination are proposed, namely: 1) zero-forcing/maximal ratio combining (ZF/MRC); 2) ZF/selection combining (ZF/SC); 3) maximal ratio transmission/MRC (MRT/MRC); and 4) MRT/SC. For all these schemes, we present new closed-form approximations for the secrecy outage probability. Moreover, we investigate a benchmark scheme, i.e., cooperative jamming/ZF (CJ/ZF), where the secrecy outage probability is obtained in exact closed-form. In addition, we present asymptotic secrecy outage expressions for all the proposed schemes in the high signal-to-noise ratio (SNR) regime, in order to characterize key design parameters, such as secrecy diversity order and secrecy array gain. The outcomes of this paper can be summarized as follows: 1) MRT/MRC and MRT/SC achieve a full diversity order of M + 1, ZF/MRC and ZF/SC achieve a diversity order of M, while CJ/ZF only achieves unit diversity order, where M is the number of antennas at the relay; 2) ZF/MRC (ZF/SC) outperforms the corresponding MRT/MRC(MRT/SC) in the low SNR regime, while becomes inferior to the corresponding MRT/MRC (MRT/SC) in the high SNR; and 3) all the proposed schemes tend to outperform the CJ/ZF with moderate number of antennas, and linear processing schemes with MRC attain better performance than those with SC.

In this paper, we investigate the secrecy performance of multiuser dual-hop relay networks where a base station (BS) communicates with multiple legitimate users through the assistance of a trustful regenerative relay in the presence of multiple eavesdroppers. In particular, the maximal ratio transmission scheme is exploited at the BS and a threshold-based multiuser scheduling scheme is employed over the legitimate users, while concerning the imperfect decoding at the regenerative relay. To evaluate the secrecy performance of the considered system, two practical situations are addressed based on the availability of eavesdropper's channel state information (CSI), i.e., Scenario I, where the eavesdropper's CSI is not available at the relay, and Scenario II, where the eavesdropper's CSI is available at the relay. For both the scenarios, we further consider two eavesdropping modes, i.e., colluding eavesdropping and non-colluding eavesdropping. For Scenario I, new exact and asymptotic closed-form expressions for the secrecy outage probability (SOP) are derived. For Scenario II, we derive new exact and asymptotic closed-form expressions for the ergodic secrecy rate (ESR). The asymptotic SOPs demonstrate that the secrecy diversity order is independent of the number of legitimate users NB and eavesdroppers NE, the number of antennas equipped at eavesdroppers AE, as well as the fading factor of the wiretap channel mE. Furthermore, we also determine the secrecy multiplexing gain and the power cost to explicitly quantify the impact of the legitimate channel and wiretap channel on the ESR. Our findings demonstrate that increasing the switching threshold, the number of antennas at the BS, and the number of legitimate users has a positive impact on secrecy performance.

Abstract In this paper, we consider switch‐and‐stay combining (SSC) in two‐way relay systems with two amplify‐and‐forward relays, one of which is activated to assist the information exchange between the two sources. The system operates in either analog network coding (ANC) protocol where the communication is only achieved with the help of the active relay or time‐division broadcast (TDBC) protocol where the direct link between two sources can be utilized to exploit more diversity gain. In both cases, we study the outage probability and bit error rate (BER) for Rayleigh fading channels. In particular, we derive closed‐form lower bounds for the outage probability and the average BER, which remain tight for different fading conditions. We also present asymptotic analysis for both the outage probability and the average BER at high signal‐to‐noise ratio. It is shown that SSC can achieve the full diversity order in two‐way relay systems for both ANC and TDBC protocols with proper switching thresholds. Copyright © 2014 John Wiley & Sons, Ltd.

Opportunistic relaying scheme (ORS), where the best relay is selected for dual-hop communication, has been widely considered as the global optimum relaying technique. However, due to the requirement of acquiring the full channel state information (CSI) of all links, ORS has increased the system's complexity and might be harmful to the network stability, especially for the large-scale networks. In this paper, we therefore proposed an alternative scheme, namely, secure switch-and-stay combining (SSSC) protocol for providing the best secure performance. In particular, a two-phase underlay cognitive relay network, where one out of two decode-and-forward (DF) is activated to assist the secure data transmission. The secure relay switching occurs when the relay cannot support the secure communication any longer. We study the system secure performance of SSSC protocol by deriving an analytical secrecy outage probability as well as an asymptotic expression in the high main-to-eavesdropper ratio (MER) region. It is shown that SSSC can substantially reduce the switching rate with lower channel estimation complexity, and approach the full diversity meanwhile.

The development of 5G enabling technologies brings new challenges to the design of power amplifiers (PAs). In particular, there is a strong demand for low-cost, nonlinear PAs which, however, introduce nonlinear distortions. On the other hand, contemporary expensive PAs show great power efficiency in their nonlinear region. Inspired by this trade-off between nonlinearity distortions and efficiency, finding an optimal operating point is highly desirable. Hence, it is first necessary to fully understand how and how much the performance of multiple-input multiple-output (MIMO) systems deteriorates with PA nonlinearities. In this paper, we first reduce the ergodic achievable rate (EAR) optimization from a power allocation to a power control problem with only one optimization variable, i.e. total input power. Then, we develop a closed-form expression for the EAR, where this variable is fixed. Since this expression is complicated for further analysis, two simple lower bounds and one upper bound are proposed. These bounds enable us to find the best input power and approach the channel capacity. Finally, our simulation results evaluate the EAR of MIMO channels in the presence of nonlinearities. An important observation is that the MIMO performance can be significantly degraded if we utilize the whole power budget.

A cooperative network in which confidential messages are conveyed from a source to a legitimate destination with the help of decode-and-forward relays in the presence of a malicious eavesdropper is considered. Tight upper bounds on the ergodic secrecy rate are derived in the cases of i) cooperative beamforming and ii) multi-user selection. Further, a new concept of cooperative diversity gain, namely, adapted cooperative diversity gain (ACDG), is investigated. It is shown that the ACDG can be seen as an effective metric to evaluate the security level of a cooperative wireless network in the presence of eavesdroppers. Also, the ACDG obtained in the cooperative beamforming scenario is equal to the traditional cooperative diversity gain of traditional multiple-input single-output networks, while the ACDG obtained in the multiuser scenario is equal to that of traditional single-input multiple-output networks.

In this paper, a new mathematical framework to the analysis and optimization of wireless-powered cellular networks is introduced. The proposed approach leverages stochastic geometry for system-level analysis of cellular networks, by modeling base station locations as points of a Poisson point process. The trade-offs emerging from simultaneous wireless information and power transfer are characterized through the joint cumulative distribution function of average harvested energy and average rate, which is conveniently formulated in terms of an easy-to-compute two-fold integral. The analysis shows that an optimal operating point for system-level optimization exists, as well as that network densification and directional beamforming constitute essential enablers for enhancing the performance. Finally, the fundamental trade-offs emerging in wireless-powered cellular networks are quantified through the concept of feasibility regions.

In this paper, we proposed a symbiotic approach for a secure primary network by allowing the secondary users to send the jamming noise to degrade the wiretap ability of the eavesdropper. In particular, assuming that the global channel state information is perfectly known at tranceivers, we consider the case of the primary transmitter equipped with only one antenna, which implies that the primary transmitter does not have beamforming capability. As the reward of having access to the frequency spectrum which is licensed by the the primary user, the secondary transmitter will assist the primary systems in terms of security by sending the jamming noise to the eavesdropper. We propose an algorithm to find the optimal transmit power for maximizing the secrecy capacity of the primary system. Numerical results are presented to validate our proposed scheme.

During a disaster, communication systems are partially (or completely) interrupted with very limited resources due to infrastructures destruction and hence the lack of essential services. Meanwhile, the demand for communication reaches its highest peak ever since users need to contact loved ones and make sure they are safe, inform first responders and local governments about surrounding conditions, and receive urgent instructions for safety and resilience. Rather than only high quality of service (QoS), the affected users now require high quality of sustainability (QoSus) of communications that are characterized by high response, interoperable and robust connections, high hit rate and delivery capacity of contents, and resource savings. In this paper, we therefore design a QoSuS model for Mobile Ad Hoc Networks (MANET) to reduce as much harm as possible when a disaster strikes. To this end, we propose optimization problems that we solve for high QoSus of MANET in three-tier cellular networks (i.e., macrocells, femtocells, and mobile devices). Our optimal results include a number of replicas of each cached content, a set of femtocells to cache the replicas, a set of cellular users to share their sub-channels with other mobile devices, and a set of relay nodes for mobile device-to-device communications. We further take into account the constraints of co-channel interference due to many simultaneous channel accesses, storage resource of femtocells, and energy resource of mobile devices, to efficiently gain high performance of solution. Simulation results demonstrate that our proposed solutions provide the best possible QoSus under such severe conditions.

In physical layer security systems there is a clear need to exploit the radio link characteristics to automatically generate an encryption key between two end points. The success of the key generation depends on the channel reciprocity, which is impacted by the non-simultaneous measurements and the white nature of the noise. In this paper, an OFDM subcarriers' channel responses based key generation system with enhanced channel reciprocity is proposed. By theoretically modelling the OFDM subcarriers' channel responses, the channel reciprocity is modelled and analyzed. A low pass filter is accordingly designed to improve the channel reciprocity by suppressing the noise. This feature is essential in low SNR environments in order to reduce the risk of the failure of the information reconciliation phase during key generation. The simulation results show that the low pass filter improves the channel reciprocity, decreases the key disagreement, and effectively increases the success of the key generation.

The proliferation problem of video streaming applications and mobile devices has prompted wireless network operators to put more efforts into improving quality of experience (QoE) while saving resources that are needed for high transmission rate and large size of video streaming. To deal with this problem, we propose an energy-aware rate and description allocation optimization method for video streaming in cellular network assisted device-to-device (D2D) communications. In particular, we allocate the optimal bit rate to each layer of video segments and packetize the segments into multiple descriptions with embedded forward error correction (FEC) for realtime streaming without retransmission. Simultaneously, the optimal number of descriptions is allocated to each D2D helper for transmission. The two allocation processes are done according to the access rate of segments, channel state information (CSI) of D2D requester, and remaining energy of helpers, to gain the highest optimization performance. Simulation results demonstrate that our proposed method (named OPT) significantly enhances the performance of video streaming in terms of high QoE and energy saving.

In this paper, we investigate secure device-to-device (D2D) communication in energy harvesting large-scale cognitive cellular networks. The energy constrained D2D transmitter harvests energy from multi-antenna equipped power beacons (PBs), and communicates with the corresponding receiver using the spectrum of the cellular base stations (BSs). We introduce a power transfer model and an information signal model to enable wireless energy harvesting and secure information transmission. In the power transfer model, we propose a new power transfer policy, namely, best power beacon (BPB) power transfer. To characterize the power transfer reliability of the proposed policy, we derive new closed-form expressions for the exact power outage probability and the asymptotic power outage probability with large antenna arrays at PBs. In the information signal model, we present a new comparative framework with two receiver selection schemes: 1) best receiver selection (BRS), and 2) nearest receiver selection (NRS). To assess the secrecy performance, we derive new expressions for the secrecy throughput considering the two receiver selection schemes using the BPB power transfer policies. We show that secrecy performance improves with increasing densities of PBs and D2D receivers because of a larger multiuser diversity gain. A pivotal conclusion is reached that BRS achieves better secrecy performance than NRS but demands more instantaneous feedback and overhead.

A multiuser scheduling multiple-input multiple-output (MIMO) cognitive radio network (CRN) with space-time block coding (STBC) is considered in this paper, where one secondary base station (BS) communicates with one secondary user (SU) selected from K candidates. The joint impact of imperfect channel state information (CSI) in BS → SUs and BS → PU due to channel estimation errors and feedback delay on the outage performance is firstly investigated. We obtain the exact outage probability expressions for the considered network under the peak interference power IPat PU and maximum transmit power Pmat BS which cover perfect/imperfect CSI scenarios in BS → SUs and BS → PU. In addition, asymptotic expressions of outage probability in high SNR region are also derived from which we obtain several important insights into the system design. For example, only with perfect CSIs in BS → SUs, i.e., without channel estimation errors and feedback delay, the multiuser diversity can be exploited. Finally, simulation results confirm the correctness of our analysis.

In this paper, a mathematical framework for evaluating the error probability of downlink Multiple-Input-Multiple-Output (MIMO) cellular networks is introduced. It is based on the Poisson Point Process (PPP)-based abstraction for modeling the spatial locations of the Base Stations (BSs) and it exploits results from stochastic geometry to characterize the distribution of the other-cell interference. The framework is applicable to spatial multiplexing MIMO systems with an arbitrary number of antennas at the transmitter (Nt) and at the receiver (Nr). It is shown that the proposed framework leads to easy-to-compute integral expressions, which provide insights for network design and optimization. The accuracy of the mathematical analysis is substantiated through extensive Monte Carlo simulations for various MIMO cellular network setups.

In this paper, we propose cyclic prefix single carrier (CP-SC) full-duplex transmission in cooperative spectrum sharing to achieve multipath diversity gain and full-duplex spectral efficiency. Integrating full-duplex transmission into cooperative spectrum sharing systems results in two intrinsic problems: 1) the peak interference power constraint at the PUs are concurrently inflicted on the transmit power at the secondary source (SS) and the secondary relays (SRs); and 2) the residual loop interference occurs between the transmit and the receive antennas at the secondary relays. Thus, examining the effects of residual loop interference under peak interference power constraint at the primary users and maximum transmit power constraints at the SS and the SRs is a particularly challenging problem in frequency selective fading channels. To do so, we derive and quantitatively evaluate the exact and the asymptotic outage probability for several relay selection policies in frequency selective fading channels. Our results manifest that a zero diversity gain is obtained with full-duplex.

In this study, the authors evaluate secrecy performance of cooperative protocols with relay selection methods under impact of co‐channel interference. In particular, the authors propose an optimal selection scheme to maximise the secrecy capacity obtained at the cooperative phase. In addition, the authors also consider suboptimal and random selection schemes which reduce the complexity of implementation, when compared with the optimal one. For performance evaluation, the authors derive exact and asymptotic closed‐form expressions of secrecy outage probability and probability of non‐zero secrecy capacity for the considered schemes over Rayleigh fading channels. Monte Carlo simulations are performed to validate the derivations.

In this study, the authors propose simple methods to evaluate the achievable rates and outage probability of a cognitive radio (CR) link that takes into account the imperfectness of spectrum sensing. In the considered system, the CR transmitter and receiver correlatively sense and dynamically exploit the spectrum pool via dynamic frequency hopping. Under imperfect spectrum sensing, false‐alarm and miss‐detection occur which cause impulsive interference emerged from collisions due to the simultaneous spectrum access of primary and cognitive users. That makes it very challenging to evaluate the achievable rates. By first examining the static link where the channel is assumed to be constant over time, they show that the achievable rate using a Gaussian input can be calculated accurately through a simple series representation. In the second part of this study, they extend the calculation of the achievable rate to wireless fading environments. To take into account the effect of fading, they introduce a piece‐wise linear curve fitting‐based method to approximate the instantaneous achievable rate curve as a combination of linear segments. It is then demonstrated that the ergodic achievable rate in fast fading and the outage probability in slow fading can be calculated to achieve any given accuracy level.

This study designs and analyses a new phase‐coded spread‐spectrum communication system where both phase‐coded carrier and spreading factor are varied based on a chaotic behaviour in the communication process. This design aims to reduce the probability of interception of the considered system. Discrete values generated by a chaotic map are exploited to create a non‐return‐to‐zero (NRZ)‐chaos sequence and simultaneously make bit duration variable. The NRZ‐chaos sequence is then modulated by binary phase‐shift keying technique to produce the phased‐coded carrier. Owing to chip duration being constant, the variation of bit duration also leads to the variation of spreading factor. Spectrum spreading in the transmitter is performed by multiplying directly the variable‐duration bits with the phase‐coded carrier. A coherent receiver relying on a direct correlator is used for recovering the data. Design of the transmitter and receiver as well as analysis of bit error probability for the proposed system in cases of single‐user and multi‐user under additive white Gaussian noise channel is presented. Simulation results are shown to confirm the operation of the designed structures and the obtained analytical performance.

The papers in this special issue focus on the topic of location awareness for radio and networks. Localization-awareness using radio signals stands to revolutionize the fields of navigation and communication engineering. It can be utilized to great effect in the next generation of cellular networks, mining applications, health-care monitoring, transportation and intelligent highways, multi-robot applications, first responders operations, military applications, factory automation, building and environmental controls, cognitive wireless networks, commercial and social network applications, and smart spaces. A multitude of technologies can be used in location-aware radios and networks, including GNSS, RFID, cellular, UWB, WLAN, Bluetooth, cooperative localization, indoor GPS, device-free localization, IR, Radar, and UHF. The performances of these technologies are measured by their accuracy, precision, complexity, robustness, scalability, and cost. Given the many application scenarios across different disciplines, there is a clear need for a broad, up-to-date and cogent treatment of radio-based location awareness. This special issue aims to provide a comprehensive overview of the state-of-the-art in technology, regulation, and theory. It also presents a holistic view of research challenges and opportunities in the emerging areas of localization.

The papers in this special issue on location awareness will continue with the state-of-the-art in technology, regulation, and theory for the emerging field of localization. This second part issue continues from the July 2015, Part I, issue which discusses location awareness for radio and networks.

In existing WiFi-based localization methods, smart mobile devices consume quite a lot of power as WiFi interfaces need to be used for frequent AP scanning during the localization process. In this work, we design an energy-efficient indoor localization system called ZigBee assisted indoor localization (ZIL) based on WiFi fingerprints via ZigBee interference signatures. ZIL uses ZigBee interfaces to collect mixed WiFi signals, which include non-periodic WiFi data and periodic beacon signals. However, WiFi APs cannot be identified from these WiFi signals by ZigBee interfaces directly. To address this issue, we propose a method for detecting WiFi APs to form WiFi fingerprints from the signals collected by ZigBee interfaces. We propose a novel fingerprint matching algorithm to align a pair of fingerprints effectively. To improve the localization accuracy, we design the K-nearest neighbor (KNN) method with three different weighted distances and find that the KNN algorithm with the Manhattan distance performs best. Experiments show that ZIL can achieve the localization accuracy of 87%, which is competitive compared to state-of-the-art WiFi fingerprint-based approaches, and save energy by 68% on average compared to the approach based on WiFi interface.

In this paper, we investigate the end-to-end performance of dual-hop proactive decode-and-forward relaying networks with Nth best relay selection in the presence of two practical deleterious effects: i) hardware impairment and ii) co-channel interference. In particular, we derive new exact and asymptotic closed-form expressions for the outage probability and average channel capacity of Nth best partial and opportunistic relay selection schemes over Rayleigh fading channels. Insightful discussions are provided. It is shown that, when the system cannot select the best relay for cooperation, the partial relay selection scheme outperforms the opportunistic method under the impact of the same co-channel interference (CCI) . In addition, without CCI but under the effect of hardware impairment, it is shown that both selection strategies have the same asymptotic channel capacity. Monte Carlo simulations are presented to corroborate our analysis.

A relay network in which a source wishes to convey a confidential message to a legitimate destination with the assistance of trusted relays is considered. In particular, cooperative beamforming and user selection techniques are applied to protect the confidential message. The secrecy rate (SR) and secrecy outage probability (SOP) of the network are investigated first, and a tight upper bound for the SR and an exact formula for the SOP are derived. Next, asymptotic approximations for the SR and SOP in the high signal-to-noise ratio (SNR) regime are derived for two different schemes: 1) cooperative beamforming and 2) multiuser selection. Furthermore, a new concept of cooperative diversity gain, namely, adapted cooperative diversity gain (ACDG), which can be used to evaluate the security level of a cooperative relaying network, is investigated. It is shown that the ACDG of cooperative beamforming is equal to the conventional cooperative diversity gain of traditional multiple-input single-output networks, while the ACDG of the multiuser scenario is equal to that of traditional single-input multiple-output networks.

In this paper, we propose general-order transmit antenna selection to enhance the secrecy performance of multiple-input-multiple-output multieavesdropper channels with outdated channel state information (CSI) at the transmitter. To evaluate the effect of the outdated CSI on the secure transmission of the system, we investigate the secrecy performance for two practical scenarios, i.e., Scenarios I and II, where the eavesdropper's CSI is not available at the transmitter and is available at the transmitter, respectively. For Scenario I, we derive exact and asymptotic closed-form expressions for the secrecy outage probability in Nakagami-m fading channels. In addition, we also derive the probability of nonzero secrecy capacity and the ε-outage secrecy capacity, respectively. Simple asymptotic expressions for the secrecy outage probability reveal that the secrecy diversity order is reduced when the CSI is outdated at the transmitter, and it is independent of the number of antennas at each eavesdropper NE, the fading parameter of the eavesdropper's channel mE, and the number of eavesdroppers M. For Scenario II, we make a comprehensive analysis of the average secrecy capacity obtained by the system. Specifically, new closed-form expressions for the exact and asymptotic average secrecy capacity are derived, which are valid for general systems with an arbitrary number of antennas, number of eavesdroppers, and fading severity parameters. Resorting to these results, we also determine a high signal-to-noise ratio power offset to explicitly quantify the impact of the main channel and the eavesdropper's channel on the average secrecy capacity.

In this paper, the impact of multiple active eavesdroppers on cooperative single carrier systems with multiple relays and multiple destinations is examined. To achieve the secrecy diversity gains in the form of opportunistic selection, a two-stage scheme is proposed for joint relay and destination selection, in which, after the selection of the relay with the minimum effective maximum signal-to-noise ratio (SNR) to a cluster of eavesdroppers, the destination that has the maximum SNR from the chosen relay is selected. To accurately assess the secrecy performance, exact and asymptotic expressions are obtained in closed form for several security metrics, including the secrecy outage probability, probability of nonzero secrecy rate, and ergodic secrecy rate in frequency selective fading. Based on the asymptotic analysis, key design parameters, such as secrecy diversity gain, secrecy array gain, secrecy multiplexing gain, and power cost, are characterized, from which new insights are drawn. In addition, it is concluded that secrecy performance limits occur when the average received power at the eavesdropper is proportional to the counterpart at the destination. In particular, for the secrecy outage probability, it is confirmed that the secrecy diversity gain collapses to zero with outage floor, whereas for the ergodic secrecy rate, it is confirmed that its slope collapses to zero with capacity ceiling.

Cooperative spatial multiplexing (CSM) system has played an important role in wireless networks by offering a substantial improvement in multiplexing gain compared with its cooperative diversity counterpart. However, there is a limited number of research works that consider the performance of CSM systems. As such, in this paper, we have derived exact performance of CSM with amplify-and-forward and decode-and-forward relays in terms of outage capacity and ergodic capacity. We have shown that CSM systems yield a unity diversity order regardless of the number of antennas at the destination and the number of relays in the networks, which is the direct result of diversity and multiplexing gain trade-off. Our analytical expressions are corroborated by Monte-Carlo simulations

In this paper, we analyze the performance of cognitive amplify-and-forward (AF) relay networks with beamforming under the peak interference power constraint of the primary user (PU). We focus on the scenario that beamforming is applied at the multi-antenna secondary transmitter and receiver. Also, the secondary relay network operates in channel state information-assisted AF mode, and the signals undergo independent Nakagami-m fading. In particular, closed-form expressions for the outage probability and symbol error rate (SER) of the considered network over Nakagami-m fading are presented. More importantly, asymptotic closed-form expressions for the outage probability and SER are derived. These tractable closed-form expressions for the network performance readily enable us to evaluate and examine the impact of network parameters on the system performance. Specifically, the impact of the number of antennas, the fading severity parameters, the channel mean powers, and the peak interference power is addressed. The asymptotic analysis manifests that the peak interference power constraint imposed on the secondary relay network has no effect on the diversity gain. However, the coding gain is affected by the fading parameters of the links from the primary receiver to the secondary relay network

In this paper, we propose physical layer security for cooperative cognitive radio networks (CCRNs) with relay selection in the presence of multiple primary users and multiple eavesdroppers. To be specific, we propose three relay selection schemes, namely, opportunistic relay selection (ORS), suboptimal relay selection (SoRS), and partial relay selection (PRS) for secured CCRNs, which are based on the availability of channel state information (CSI) at the receivers. For each approach, we derive exact and asymptotic expressions for the secrecy outage probability. Results show that under the assumption of perfect CSI, ORS outperforms both SoRS and PRS.

Molecular communication is set to play an important role in the design of complex biological and chemical systems. An important class of molecular communication systems is based on the timing channel, where information is encoded in the delay of the transmitted molecule - a synchronous approach. At present, a widely used modeling assumption is the perfect synchronization between the transmitter and the receiver. Unfortunately, this assumption is unlikely to hold in most practical molecular systems. To remedy this, we introduce a clock into the model - leading to the molecular timing channel with synchronization error. To quantify the behavior of this new system, we derive upper and lower bounds on the variance-constrained capacity, which we view as the step between the mean-delay and the peak-delay constrained capacity. By numerically evaluating our bounds, we obtain a key practical insight: the drift velocity of the clock links does not need to be significantly larger than the drift velocity of the information link, in order to achieve the variance-constrained capacity with perfect synchronization.

In this paper, we study the information-theoretical security of a downlink multiuser cooperative relaying network with multiple intermediate amplify-and-forward (AF) relays, where there exist multiple eavesdroppers which can overhear the message. To prevent the wiretap and strength the network security, we select one best relay and user pair, so that the selected user can receive the message from the base station assisted by the selected relay. The relay and user selection is performed by maximizing the ratio of the received signal-to-noise ratio (SNR) at the user to the eavesdroppers, which is based on both the main and eavesdropper links. For the considered system, we derive the closed-form expression of the secrecy outage probability, and provide the asymptotic expression in high main-to-eavesdropper ratio (MER) region. From the asymptotic analysis, we can find that the system diversity order is equivalent to the number of relays regardless of the number of users and eavesdroppers.

This paper exploits an amplify-and-forward (AF) two-way relaying network (TWRN), where an energy constrained relay node harvests energy with wireless power transfer. Two bidirectional protocols, multiple access broadcast (MABC) protocol and time division broadcast (TDBC) protocol, are considered. Three wireless power transfer policies, namely, dual-source (DS) power transfer; single-fixed-source (SFS) power transfer; and single-best-source (SBS) power transfer are proposed and well-designed based on time switching receiver architecture. We derive analytical expressions to determine the throughput both for delay-limited transmission and delay-tolerant transmission. Numerical results corroborate our analysis and show that MABC protocol achieves a higher throughput than TDBC protocol. An important observation is that SBS policy offers a good tradeoff between throughput and power.

In this paper, the impact of multiple eavesdroppers on cooperative single carrier systems with multiple relays and multiple destinations is examined. To achieve the secrecy diversity gains in the form of opportunistic selection, a two-stage scheme is proposed for joint relay and destination selection, in which, after the selection of the relay with the minimum effective maximum signal-to-noise ratio (SNR) to a cluster of eavesdroppers, the destination that has the maximum SNR from the chosen relay is selected. In order to accurately assess the secrecy performance, the exact and asymptotic expressions are obtained in closed-form for the ergodic secrecy rate in frequency selective fading. Based on the asymptotic analysis, key design parameters such as multiplexing gain, and power cost are characterized, from which new insights are drawn. Moreover, it is concluded that capacity ceiling occurs when the average received power at the eavesdropper is proportional to the counterpart at the destination.

We propose transmit antenna selection with receive generalized selection combining (TAS/GSC) in dual-hop cognitive decode-and-forward (DF) relay networks for reliability enhancement and interference relaxation. In this paradigm, a single antenna which maximizes the receive signal-to-noise ratio (SNR) is selected at the secondary transmitter and a subset of receive antennas with the highest SNRs are combined at the secondary receiver. To demonstrate the impact of multiple primary users on the cognitive relay network, we derive new closed-form expressions for the exact and asymptotic outage probability with TAS/GSC in the secondary network. Several important design insights are reached. We corroborate that the full diversity gain is achieved, which is entirely determined by the total number of antennas in the secondary network. The negative impact of the primary network on the secondary network is reflected in the SNR gain.

We examine the impact of transmit antenna selection with receive generalized selection combining (TAS/GSC) for cognitive decode-and-forward (DF) relaying in Nakagami-m fading channels. We select a single transmit antenna at the secondary transmitter which maximizes the receive signal-to-noise ratio (SNR) and combine a subset of receive antennas with the largest SNRs at the secondary receiver. In an effort to assess the performance, we first derive the probability density function and cumulative distribution function of the end-to-end SNR using the moment generating function. We then derive new exact closed-form expression for the ergodic capacity. More importantly, by deriving the asymptotic expression for the high SNR approximation of the ergodic capacity, we gather deep insights into the high SNR slope and the power offset. Our results show that the high SNR slope is 1/2 under the proportional interference power constraint. Under the fixed interference power constraint, the high SNR slope is zero.

In this paper, we investigate a multiuser cognitive relay network with direct source-destination links and multiple primary destinations. In this network, multiple secondary users compete to communicate with a secondary destination assisted by an amplify-and-forward (AF) relay. We take into account the availability of direct links from the secondary users to the primary and secondary destinations. For the considered system, we select one best secondary user to maximize the received signal-to-noise ratio (SNR) at the secondary destination. We first derive an accurate lower bound of the outage probability, and then provide an asymptotic expression of outage probability in high SNR region. From the lower bound and the asymptotic expressions, we obtain several insights into the system design. Numerical and simulation results are finally demonstrated to verify the proposed studies.

We examine the impact of primary and secondary interference on opportunistic relaying in cognitive spectrum sharing networks. In particular, new closed-form exact and asymptotic expressions for the outage probability of cognitive opportunistic relaying are derived over Rayleigh and Nakagami-m fading channels. Our analysis presents revealing insights into the diversity and array gains, diversity-multiplexing tradeoff, impact of primary transceivers' positions, and the optimal position of relays. We highlight that cognitive opportunistic relaying achieves the full diversity gain which is a product of the number of relays and the minimum Nakagami-m fading parameter in the secondary network. Furthermore, we confirm that the diversity gain reduces to zero when the peak interference constraint in the secondary network is proportional to the interference power from the primary network.

A wireless energy harvesting protocol is proposed for a decode-and-forward relay- assisted secondary user (SU) network in a cognitive spectrum sharing paradigm. An expression for the outage probability of the relay-assisted cognitive network is derived subject to the following power constraints: 1) the maximum power that the source and the relay in the SU network can transmit from the harvested energy, 2) the peak interference power from the source and the relay in the SU network at the primary user (PU) network, and 3) the interference power of the PU network at the relay-assisted SU network. The results show that as the energy harvesting conversion efficiency improves, the relay- assisted network with the proposed wireless energy harvesting protocol can operate with outage probabilities below 20% for some practical applications.

This work investigates the end-to-end performance of randomized distributed space-time codes with complex Gaussian distribution, when employed in a wireless relay network. The relaying nodes are assumed to adopt a decode-and-forward strategy and transmissions are affected by small and large scale fading phenomena. Extremely tight, analytical approximations of the end-to-end symbol error probability and of the end-to-end outage probability are derived and successfully validated through Monte-Carlo simulation. For the high signal-to-noise ratio regime, a simple, closed-form expression for the symbol error probability is further provided.

This paper investigates linear precoding designs for a cooperative amplify-and-forward (AF) network with a multi-antenna relay having complete channel state information (CSI). The focus is on both orthogonal AF (OAF) and non-orthogonal AF (NAF) protocols. The precoders at the relay are derived based on the maximum ratio combining (MRC) scheme, followed by an optimal power amplification factor to maximize the end-to-end achievable rate. For OAF, it is a concave optimization problem and the closed-form solution can be obtained using Karush-Kuhn-Tucker (KKT) conditions. However, the optimization problem for NAF is non-convex and getting globally optimal solution in closed-form is more challenging. Our approach is to investigate the achievable rate in different sub-domains of the channel matrix to upper-bound the original problem by a convex optimization problem. It is then shown that the optimal solution to the power amplification factor of the original optimization problem can be obtained in closed-form. The optimal MRC-based relay precoding vector is then established. Numerical results reveal that the proposed system achieves significant end-to-end rate gains over the conventional dual-hop AF multi-antenna as well as cooperative AF single-antenna systems.

In this paper, we investigate the performance of Nth best relay selection networks with secrecy constraints where several eavesdroppers try to overhear the source message. In order to enhance the network security, a single jammer is introduced to jam the eavesdroppers. In addition, sub-optimal selection strategy is introduced to combat the malicious attempt of eavesdroppers. We derive the exact secrecy performance in terms of probability of the non-zero secrecy capacity and the secrecy outage probability of the proposed relay selection approach. Based on these expressions, the effect of several important network parameters, i.e., the number of relays and the number of eavesdroppers, as well as the quality of the relay links, jammer links, and eavesdroppers links, on the proposed network are analytically characterized.

Abstract Traditional security schemes developed for sensor networks are not suitable for cluster‐based wireless sensor networks (WSNs) because of their susceptibility to Denial of Service (DoS) attacks. In this paper, we provide a security scheme against DoS attacks (SSAD) in cluster‐based WSNs. The scheme establishes trust management with energy character, which leads nodes to elect trusted cluster heads. Furthermore, a new type of vice cluster head node is proposed to detect betrayed cluster heads. Theoretical analyses and simulation results show that SSAD can prevent and detect malicious nodes with high probability of success. Copyright © 2011 John Wiley & Sons, Ltd.

This paper proposes relay selection to increase the physical layer security in multiuser cooperative relay networks with multiple amplify-and-forward relays, in the presence of multiple eavesdroppers. To strengthen the network security against eavesdropping attack, we present three criteria to select the best relay and user pair. Specifically, criteria I and II study the received signal-to-noise ratio (SNR) at the receivers, and perform the selection by maximizing the SNR ratio of the user to the eavesdroppers. To this end, criterion I relies on both the main and eavesdropper links, while criterion II relies on the main links only. Criterion III is the standard max-min selection criterion, which maximizes the minimum of the dual-hop channel gains of main links. For the three selection criteria, we examine the system secrecy performance by deriving the analytical expressions for the secrecy outage probability. We also derive the asymptotic analysis for the secrecy outage probability with high main-to-eavesdropper ratio. From the asymptotic analysis, an interesting observation is reached: for each criterion, the system diversity order is equivalent to the number of relays regardless of the number of users and eavesdroppers.

In this paper, we propose a multiuser cognitive relay network, where multiple secondary sources communicate with a secondary destination through the assistance of a secondary relay in the presence of secondary direct links and multiple primary receivers. We consider the two relaying protocols of amplify-and-forward (AF) and decode-and-forward (DF), and take into account the availability of direct links from the secondary sources to the secondary destination. With this in mind, we propose an optimal solution for cognitive multiuser scheduling by selecting the optimal secondary source, which maximizes the received signal-to-noise ratio (SNR) at the secondary destination using maximal ratio combining. This is done by taking into account both the direct link and the relay link in the multiuser selection criterion. For both AF and DF relaying protocols, we first derive closed-form expressions for the outage probability and then provide the asymptotic outage probability, which determines the diversity behavior of the multiuser cognitive relay network. Finally, this paper is corroborated by representative numerical examples.

In this paper, the impact of interference from multiple licensed transceivers on cognitive underlay single-carrier systems is examined. Specifically, the situation is considered in which the secondary network is limited by three key parameters: 1) maximum transmit power at the secondary transmitter, 2) peak interference power at the primary receivers, and 3) interference power from the primary transmitters. For this cognitive underlay single-carrier system, the signal-to-interference ratio (SIR) of the secondary network is obtained for transmission over frequency-selective fading channels. Based on this, a new closed-form expression for the cumulative distribution function of the SIR is evaluated, from which the outage probability and the ergodic capacity are derived. Further insights are established by analyzing the asymptotic outage probability and the asymptotic ergodic capacity in the high-transmission-power regime. In particular, it is corroborated that the asymptotic outage diversity gain is equal to the multipath gain of the frequency-selective channel in the secondary network. The asymptotic ergodic capacity also gives new insight into the additional power cost for different network parameters while maintaining a specified target ergodic capacity. Illustrative numerical examples are presented to validate the outage probability and ergodic capacity under different interference power profiles.

Distributed switch-and-stay combining (DSSC) has been envisioned as an effective transmission technique to achieve spatial diversity in a distributed fashion, with low implementation complexity. In this paper, we take a step further to incorporate DSSC into spectrum-limited environment, where the operating nodes have to share the frequency radio spectrum with licensed users. In particular, by deploying DSSC scheme in cognitive radio networks, we have shown that the low transmit power at unlicensed users, inflicted by the peak interference power constraint at licensed users, can be alleviated. We present closed-form expressions for outage probability and spectral efficiency, enabling us to evaluate and optimize the considered network performance. Numerical and simulation results show that when the switching threshold is below the outage threshold the full diversity order can be guaranteed at the secondary networks.

Cognitive relay technology has been envisioned as a promising transmission scheme to enhance the reliability and coverage of secondary networks. However, the performance of cognitive relay networks (CRNs) is limited by the lack of accurate channel state information (CSI). As such, this paper adequately addresses the impact of imperfect CSI on the performance of cognitive multihop networks by proposing a simple yet effective backoff control power method. In addition, novel exact and asymptotic expressions for outage probability and ergodic capacity over Rayleigh fading channels are also derived. These tractable analytical results reveal new insight into the design, e.g., the number of hops for secondary network, and optimization of cognitive multihop networks.

Previous works on cognitive relay networks (CRNs) considered only symmetric fading channels. However, in practical wireless propagation scenarios, it is likely that the channels of the secondary user (SU) and primary user (PU) may undergo different fading characteristics. In this paper, we assume that the channels of the secondary network (SU-source→SU-relay→SU-destination) are subject to Rician fading, whereas the channels of the link from the SU to the PU experience Rayleigh fading. Based on this framework, the end-to-end outage probability (OP) of CRNs is investigated for two different relaying schemes: i) in the absence of the direct link with decode-and-forward (DF) protocol and ii) in the presence of the direct link with incremental DF protocol. In particular, we derive both exact and asymptotic OP expressions for the considered CRNs. Our analysis reveals important insights into the impact of fading parameters on the CRN performance under distinct fading distributions.

This paper tackles the important question of how to compensate the inherent spectrum efficiency loss in cognitive relay networks. Particularly, by considering two-way cognitive relaying, we seek to enhance the performance of the secondary network in terms of the reliability due to limited transmit power, and the spectral efficiency of the half-duplex dual-hop relay transmission. We derive new closed-form expressions for the outage probability of a cognitive relay network with two-way communications in the presence of multiple primary users. Our expressions accurately take into account the impact of the maximum allowable interference constraint at the primary users on the secondary network.

We examine the impact of clusters of primary transceivers in cognitive multiple-input multiple-output (MIMO) relay networks with underlay spectrum sharing. In such a network, we propose antenna selection as an interference-aware design to satisfy the power constraints in the primary and secondary networks. To demonstrate this, we consider transmit antenna selection with maximal ratio combining (TAS/MRC) in the primary and secondary networks. With this in mind, we derive new closed-form asymptotic expressions for the outage probability and the symbol error rate (SER) over independent Nakagami-m fading channels. Our results lead to several new fundamental insights. In particular, we highlight that TAS/MRC achieves a full diversity gain when the maximum transmit power in the secondary network is proportional to the peak interference temperature in the primary network.

The performance of cognitive spectrum sharing systems with opportunistic relay selection over Nakagami-m fading is analyzed in the presence of multiple primary users (PUs). In particular, we derive an exact closed-form expression for the outage probability (OP) of the considered cognitive relay systems under the joint impact of maximal transmit power Ptat secondary transmitter and peak interference power Ipat the primary user. Our general formulas cover several specific practical scenarios, e.g., where the maximal transmit power can be neglected compared to the peak interference power. In addition, a tractable expression for the asymptotic OP is also derived and reveals important insights into the system performance. We show that the number of PUs only affects the coding gain but not the diversity gain.

Multihop network has been considered as a breakthrough frontier to enhance the coverage of wireless network. Spectrum-sharing is an efficient technique to enhance the utilization of the limited radio frequency bandwidth. In this paper, we therefore consider the extension of multihop network to cognitive radio networks with the spectrum sharing approach. In particular, we investigate the cognitive multihop networks in the presence of multiple licensed transmitters and receivers. Under the stringent power constraint imposed by the licensed users, we derive the closed-form and asymptotic expressions for the outage probability over Nakagami-m fading channels. The tractable closed-form expressions reveal the impact of important network parameters such as the fading severity parameters of the unlicensed network, the number of licensed users, the peak interference power imposed by the licensed receivers, the interference power from licensed transmitters. A significant observation corroborated by our study shows that the cognitive multihop networks benefit both cognitive radio and multihop networks for improving the coverage extension and frequency spectrum utilization.

We consider a multi-pair relay channel where multiple sources simultaneously communicate with destinations using a relay. Each source or destination has only a single antenna, while the relay is equipped with a very large antenna array. We investigate the power efficiency of this system when maximum ratio combining/maximal ratio transmission (MRC/MRT) or zero-forcing (ZF) processing is used at the relay. Using a very large array, the transmit power of each source or relay (or both) can be made inversely proportional to the number of relay antennas while maintaining a given quality-of-service. At the same time, the achievable sum rate can be increased by a factor of the number of source-destination pairs. We show that when the number of antennas grows to infinity, the asymptotic achievable rates of MRC/MRT and ZF are the same if we scale the power at the sources. Depending on the large scale fading effect, MRC/MRT can outperform ZF or vice versa if we scale the power at the relay.

We examine the impact of multiple primary receivers on cognitive multiple-input multiple-output (MIMO) relay networks with underlay spectrum sharing. For such a network, we propose transmit antenna selection with receive maximal-ratio combining (TAS/MRC) as an interference-aware design to satisfy the power constraints in the primary and secondary networks. To demonstrate this, we derive new closed-form expressions for the exact and asymptotic outage probability with TAS/MRC and decode-and-forward (DF) relaying over independent Nakagami-m fading channels in the primary and secondary networks. Several important design insights are reached. We find that the TAS/MRC strategy achieves a full diversity gain when the transmit power in the secondary network is proportional to the peak interference power in the primary network. Furthermore, we highlight that the diversity-multiplexing tradeoff (DMT) of TAS/MRC is independent of the primary network and entirely dependent on the secondary network.

We propose transmit antenna selection (TAS) with decode-and-forward relaying as an effective approach to reduce interference in cognitive multiple-input multiple-output (MIMO) relay networks. To demonstrate this, we derive new closed-form expressions for the exact and asymptotic outage probability of TAS/MRC with multiple antennas at the primary and secondary users. We consider underlay spectrum sharing where the secondary users (SUs) transmit in the presence of multiple primary users (PUs). We consider independent Nakagami-m fading in both the primary and secondary networks. Several important design insights are revealed. We find that TAS/MRC achieves a full diversity when the transmit power at the SUs is proportional to the peak interference power at the PUs. Furthermore, we highlight that this diversity gain is completely independent of the number of antennas at the PUs.

In this study, the authors analyse the impact of channel estimation error (CEE), feedback delay (FD), and co‐channel interference (CCI) on the performance of multiple‐input multiple‐output (MIMO) systems deploying maximum ratio transmission (MRT). In particular, the authors derive closed‐form expressions for the ergodic capacity and the symbol error rate (SER) as well as the outage probability (OP). In addition, to reveal the effect of CEE, FD and CCI on the MIMO‐MRT system, the authors adopt more simplified and tractable formulas in terms of asymptotic expressions for the ergodic capacity, SER and OP. The analysis shows that the system performance is degraded considerably under imperfect transmission conditions such as CEE, FD and CCI. However, the authors can compensate part of this degradation by deploying MRT transmission with a large number of antennas at the transmitter and receiver. Finally, the selected examples exhibit consistency between analytical results and Monte Carlo simulations.

In this study, the authors consider distributed orthogonal space–time block coding for relay‐based channel state information‐assisted amplify‐and‐forward networks. Specifically, they show that opportunistic relaying (OR) is an optimal solution in terms of instantaneous signal‐to‐noise ratio (SNR), that is, it provides the maximum instantaneous SNR under the constraint of fixed transmit power for the relays. In particular, instead of allocating the given transmit power to all relays, letting the best relay transmit with this power is an optimal solution for maximising the received SNR. To exhibit this benefit, Monte Carlo simulations are presented showing superior performance of the OR scheme compared to equal power allocation policy for the considered relay networks. For the considered optimal scenario, the authors further derive analytical expressions for the outage probability and symbol error rate (SER) over quasi‐static independent, not necessarily identically distributed Nakagami‐ m fading channels. They further present asymptotically tight approximations for the outage probability and SER in the high SNR regime, rendering insights into the cooperative diversity behaviour. Finally, numerical results are provided to examine the effect of network parameters on the system performance of the considered network.

This paper proposes and analyzes cognitive multihop decode-and-forward networks in the presence of interference due to channel estimation errors. To reduce interference on the primary network, a simple yet effective back-off control power method is applied for secondary multihop networks. For a given threshold of interference probability at the primary network, we derive the maximum back-off control power coefficient, which provides the best performance for secondary multihop networks. Moreover, it is shown that the number of hops for secondary network is upper-bounded under the fixed settings of the primary network. For secondary multihop networks, new exact and asymptotic expressions for outage probability (OP), bit error rate (BER) and ergodic capacity over Rayleigh fading channels are derived. Based on the asymptotic OP and BEP, a pivotal conclusion is reached that the secondary multihop network offers the same diversity order as compared with the network without back off. Finally, we verify the performance analysis through various numerical examples which confirm the correctness of our analysis for many channel and system settings and provide new insight into the design and optimization of cognitive multihop networks.

Due to the limited resources and dynamically varying nature of wireless links, guaranteeing high quality demands for a large number of heterogeneous users is very challenging in video streaming over wireless networks. In this paper, we introduce a layered multiple description coding with an embedded forward error correction scheme (LMDC-FEC). The combination of layered MDC and FEC aims at coping with not only the diverse bandwidth and unreliability of wireless links but also the heterogeneous user devices. We further propose a joint replication density and rate allocation (RD-RA) optimization problem in the context of video on-demand systems (VoDs) over wireless mesh networks (WMNs), and employ a genetic algorithms based approach to solve the optimization problem. Our objective is to elaborately distribute proper replication density for each video segment and allocate optimal bit rate to each layer of each segment in order to gain high user-perceived quality (UPQ) with small consumption of storage resource. By this method, both optimal replication densities and rate allocations are found in accordance with the access rate of segments, the diverse loss characteristics of descriptions in each segment, and the rate-distortion relationship of segments, so as to thoroughly optimize UPQ and storage resource consumption. Simulation results demonstrate that our proposed method significantly enhances the streaming performance of VoDs over WMNs.

In this paper, maximal-ratio combining (MRC) and selection combining (SC) are proposed in spectrum sharing single-carrier networks with multiple primary user receivers (PU-Rxs). Taking into account the peak interference power at the PU-Rx's and the maximum transmit power at the secondary user (SU), the impact of multiple PU-Rx's on the secondary network is characterized when the secondary user receiver (SU-Rx) is equipped with multiple antennas. In doing so, exact and asymptotic expressions are derived for the cumulative distribution function, taking into account two realistic scenarios: non-identical frequency selective fading between the secondary user transmitter (SU-Tx) and the PUs, and frequency selective fading between the SU-Tx and the SU-Rx. Based on these, exact and asymptotic expressions for the outage probability and average bit error rate are derived. Furthermore, an exact closed-form expression for the ergodic capacity is derived. It is shown that the asymptotic diversity gain depends only on the number of receive antennas and the number of multipath channels. It is further shown that the number of PU-Rx's and fading severities between the SU-Tx and the PU-Rx's have no impact on the asymptotic diversity gain.

A cyclic prefixed single-carrier (CP-SC) relaying system is considered for cognitive radio networks under spectrum sharing condition. The outage probability of secondary users employing a two-hop amplify-and-forward (AF) relay protocol is investigated under an interference constraint inflicted by the secondary user-source and the secondary user-relay on a primary user (PU). Assuming channel-state-information (CSI) is available, the end-to-end signal-to-noise-ratio (e2e-SNR) is first derived, and then an analytical expression for the cumulative distribution function (CDF) of this e2e-SNR is derived. Based on this derived CDF, analytical expressions for the exact outage probability, the approximate symbol error rate (SER), and approximate achievable rate can be obtained. In addition, to reveal further insights into the impact of channel lengths on the diversity and coding gains, the asymptotic outage probability and SER are provided. It is important to note that the performance of cognitive radio networks is degraded due to the limited power constraint inflicted on the primary network. As such, the optimal power allocation (OPA) is also derived to achieve a better asymptotic outage probability.

For cyclic prefixed single-carrier (CP-SC) spectrum sharing relaying systems, a two-hop decode-and-forward (DF) relaying protocol with a direct link and selection combining are employed in the secondary user relay network. For this cooperative CP-SC spectrum sharing system, the end-to-end signal-to-noise ratio (e2e-SNR) is first derived, and then the outage probability performance of the secondary user relaying system is investigated. Having derived an asymptotic expression for the cumulative distribution function of the e2e-SNR, the asymptotic outage diversity is obtained under a limited maximum transmit power at the secondary source and relay while satisfying the maximum allowable interference at the primary user. Notably, when the maximum allowable interference is independent of a limited transmission power, an outage probability floor is observed. Moreover, under the assumption of an unlimited transmit power at the secondary nodes, the asymptotic outage diversity is derived as a function of the interference. It can be seen that the same outage diversity gain can be achieved as in the non-spectrum-sharing CP-SC relaying system. Analytically derived asymptotic outage diversity gains for limited and unlimited transmission power cases are verified by Monte Carlo simulations.

The distributed Alamouti space-time code in two-way fixed gain amplify-and-forward (AF) relay is proposed in this paper. In particular, closed-form expressions for approximated ergodic sum-rate and exact pairwise error probability (PWEP) are derived for Nakagami-m fading channels. To reveal further insights into array and diversity gains, an asymptotic PWEP is also obtained. Finally, numerical results are provided to corroborate the proposed theoretical analysis.

We analyze the outage performance of a two-way fixed gain amplify-and-forward (AF) relay system with beam-forming, arbitrary antenna correlation, and co-channel interference (CCI). Assuming CCI at the relay, we derive the exact individual user outage probability in closed-form. Additionally, while neglecting CCI, we also investigate the system outage probability of the considered network, which is declared if any of the two users is in transmission outage. Our results indicate that in this system, the position of the relay plays an important role in determining the user as well as the system outage probability via such parameters as signal-to-noise imbalance, antenna configuration, spatial correlation, and CCI power. To render further insights into the effect of antenna correlation and CCI on the diversity and array gains, an asymptotic expression which tightly converges to exact results is also derived.

Interference due to the sharing of common spectrum band among links and congestion due to the contention among flows sharing the same link have become obstacles to good performance in wireless networks, especially in multi-hop cognitive radio networks (MHCRNs). Consequently, the high end-to-end throughput for MHCRNs calls for a framework of cross-layer optimization design. In this paper, by taking into account the problem of joint optimal rate, power, and spectrum allocation (JORPS), we propose a new cross-layer optimization framework for MHCRNs under spectrum underlay manner using orthogonal frequency division multiple access (OFDMA). The formulation is shown to be a mix-integer non-linear non-convex optimization problem, which is μμ-hard in general. To tackle this cumbersome, we firstly applied a partially distributed solution. Then, we showed that this approach fast converges to the global optimum at a cost of computational complexity.

In this paper, we aim at maximizing quality playback of received video streaming perceived by users in video on-demand systems (VoDs) over wireless mesh networks (WMNs). To do so, we exploit the benefits of both multi-source multi-path (MSMP) delivery scheme and broadcast nature of wireless media in WMNs. In this way, a new user can efficiently receive the descriptions of a video streaming from different peers (i.e., the available users who have cached the video) via the best parallel selected paths between each peer-user pair. Based on MSMP delivery scheme, we propose an optimization problem and solve it for optimal encoding rate (i.e., optimal bit allocation) of the video streaming encoded by multiple description coding (MDC) in order to provide the user with maximum reconstructed quality playback. In this technique, the optimal bit allocation is found in connection with not only the skewed lossy characteristic of multi-path but also the skewed intra popularity of the video streaming modeled by Zipf-like distribution. Furthermore, we apply MDC embedded forward error correction (MDC-FEC) for more robust in video transmission. Simulation results demonstrate that the proposed method efficiently improves the quality playback of MSMP P2P video streaming in VoDs over WMNs.

Sleep scheduling algorithms for duty-cycled sensor networks have received great attention, since sensors should dynamically be awake and asleep to save energy consumption. Among all current sleep scheduling algorithms, only the recently proposed energy consumed uniformly-connected k-neighborhood (EC-CKN) algorithm focuses on not only the coverage of the resultant network, but also the network lifetime after sleep scheduling. However, all sleep scheduling processes ignore the fact that potential insider attacks actually can seriously affect or destroy proper sleep scheduling operations and properties. In this paper, we discuss these vulnerabilities and propose corresponding countermeasures for EC-CKN, i.e., secure neighborhood authentication.

In this paper, we examine the performance of multiple-input multiple-output (MIMO) cognitive amplify-and-forward (AF) relay systems with maximum ratio transmission (MRT). In particular, closed-form expressions in terms of a tight upper bound for outage probability (OP) and symbol error rate (SER) of the system are derived when considering channel estimation error (CEE) and feedback delay (FD) in our analysis. Through our works, one can see the impact of FD and CEE on the system as well as the benefits of deploying multiple antennas at the transceivers utilizing the spatial diversity of an MRT system. Finally, we also provide a comparison between analytical results and Monte Carlo simulations for some examples to verify our work.

We examine transmit antenna selection with receiver maximal-ratio combining (TAS/MRC) and transmit antenna selection with receiver selection combining (TAS/SC) in multiple-input multiple-output (MIMO) relay networks. Amongst L two-hop relay links, a single relay offering the highest end-to-end signal-to-noise ratio (SNR) is activated. Assuming independent non-identically distributed Weibull fading between the hops, new closed-form asymptotic expressions for the outage probability and the symbol error rate are derived considering NS, NR, and NDantennas at the source, the relays, and the destination, respectively. Based on such expressions, the diversity order and the array gain for M-ary phase shift keying and M-ary quadrature amplitude modulation are analyzed. We highlight that the diversity order of TAS/MRC is the same as TAS/SC. As such, we explicitly characterize the SNR gap between TAS/MRC and TAS/SC as the ratio of their respective array gains. An interesting observation is reached that for equal per-hop SNRs, the SNR gap between the two protocols is independent of L.

In this paper, the performance of a dual-hop multiple-input multiple-output (MIMO) cooperative multiple-relay network using orthogonal space-time block coding (OS-TBC) transmission is examined. The fading environment is modeled as independent, not identical distributed (i.n.i.d.) Nakagami-m fading. Also, we consider the case that each terminal in the network has multiple-antennas, the relays operate in channel state information (CSI)-assisted amplify-and-forward (AF) relaying mode, and the direct transmission from the source to the destination is applicable. In particular, we derive a closed-form expression for the moment generating function (MGF) of the instantaneous signal-to-noise ratio (SNR). Utilizing this derivation, we can analyze the SER performance of the considered system for M-ary phase shift-keying (M-PSK) and M-ary quadrature amplitude modulation (M-QAM) scheme. Also, the outage performance of the considered relay networks is evaluated. We further investigate the cooperative diversity behavior by deriving asymptotic approximations for the outage probability and the SER. Finally, numerical results are provided showing very close agreement between the analytical results and the Monte-Carlo simulations.

When a wireless mesh network accommodates interactive applications with quality of service requirements, schedule-based protocols are more suitable than contention-based protocols. In this paper, the problem of determining an appropriate schedule assignment for multiple group transmissions within a spatial time division multiple access link scheduling network is referred to as an integrated multiple-group communication and link scheduling problem. A polynomial-time scheduling algorithm, designated as a source-parallel-aware assignment (SPAA), is proposed to increase the spatial utilisation within each time slot in order to enhance the network throughput. Furthermore, an advanced version of SPAA, designated as joint source-parallel-aware assignment with network coding (JSANC), is proposed to reduce the effects of bottleneck paths on the schedule frame length by flexibly applying conventional or opportunistic network coding approaches. Simulation results show that the proposed algorithms achieve a better network throughput than existing flow-based or particular order-based scheduling schemes.

We examine the impact of multiple primary transmitters and receivers (PU-TxRx) on the outage performance of cognitive decode-and-forward relay networks. In such a joint relaying/spectrum-sharing arrangement, we address fundamental questions concerning three key power constraints: 1) maximum transmit power at the secondary transmitter (SU-Tx), 2) peak interference power at the primary receivers (PU-Rx), and 3) interference power at SU-Rx caused by the primary transmitter (PU-Tx). Our answers to these are given in new analytical expressions for the exact and asymptotic outage probability of the secondary relay network. Based on our asymptotic expressions, important design insights into the impact of primary transceivers on the performance of cognitive relay networks is reached. We have shown that zero diversity order is attained when the peak interference power at the PU-Rx is independent of the maximum transmit power at the SU-Tx.

In this letter, we investigate the effect of feedback delay on the performance of a dual-hop amplify-and-forward (AF) relay network with beamforming in the presence of multiple interferers over Rayleigh fading channels. Specifically, we derive closed-form expressions for the outage probability (OP) and the symbol error rate (SER). Furthermore, to render insights into the effect of feedback delay and interference on the network performance, asymptotic OP and SER are also presented. These asymptotic expressions are very tight in the high signal-to-noise ratio regime, readily enabling us to obtain the diversity and coding gains of the considered network.

In this paper, the effect of keyhole on the performance of multiple-input multiple-output (MIMO) amplify-and-forward (AF) relay networks with orthogonal space-time block codes (OSTBCs) transmission is investigated. In particular, we analyze the asymptotic symbol error probability (SEP) performance of a downlink communication system where the amplifying processing at the relay can be implemented by either the linear or squaring approach. Our tractable asymptotic SEP expressions enable us to obtain both diversity and array gains. Our finding reveals that with condition n_S > min(n_R,n_D), the linear approach can provide the full achievable diversity gain of min(n_R,n_D) when only the second hop suffers from the keyhole effect, i.e., single keyhole effect (SKE), where n_S, n_R, and n_D are the number of antennas at source, relay, and destination, respectively. However, for the case that both the source-relay and relay-destination links experience the keyhole effect, i.e., double keyhole effect (DKE), the achievable diversity order is only one regardless of the number of antennas. In contrast, utilizing the squaring approach, the overall diversity gain can be achieved as min(n_R,n_D) for both SKE and DKE. An important observation corroborated by our studies is that for satisfying the tradeoff between performance and complexity, we should use the linear approach for SKE and the squaring approach for DKE.

Multi-keyhole bridges the gap between single keyhole and full-scattering multiple-input multiple-output (MIMO) channels. In this paper, we therefore investigate the multikeyhole effect on the MIMO amplify-and-forward (AF) relay downlink transmission with orthogonal space-time block codes. In particular, we derive the analytical symbol error rate (SER) expression for the considered system with arbitrary number of keyholes. Moreover, SER approximations in the high SNR regime for several important special scenarios of multi-keyhole channels are further derived. These asymptotic results provide important insights into the impact of system parameters on the SER performance. Our analysis is confirmed by comparing with Monte-Carlo simulations.

Cognitive relaying is a promising technique to improve spectrum utilization in mobile networks. In this paper, we consider a scenario in which a secondary transmitter communicates with a secondary receiver by the help of a secondary relay. The secondary user should adjust its transmission power in order to not cause any harmful interference to the multiple primary users operating in the licensed spectrum. In this context, we investigate the end-to-end performance of cognitive relay networks in terms of outage probability and ergodic capacity over Nakagami-m fading channels. A closed-form expression for outage probability and an approximation for ergodic capacity are derived. Furthermore, the presented numerical examples illustrate the impact of the number of primary users on the performance of cognitive relay networks.

In this paper, we investigate the degenerative effects of antenna correlation and keyhole on the performance of multiple-input multiple-output (MIMO) amplify-and-forward (AF) relay networks. In particular, we considered a downlink MIMO AF relay system consisting of an nS-antenna base station S, an nR-antenna relay station R, and an nD-antenna mobile station D, in which the signal propagation originated from the mobile station suffers from keyhole and spatial correlation effects. We have derived an exact expression for the moment generation function of the instantaneous signal-to-noise ratio which enables us to analyze the symbol error probability and outage probability of the considered system. We have shown that although the mobile station is in a poor scattering environment, i.e., keyhole, the relay channel (S → R → D link) still achieves a cooperative diversity order of min(nR, nD) provided that the channel from the source (S → R link) is keyhole-free. This result is important to radio system designers since under such a severe scenario it is unnecessary to deploy a large number of antennas on the relay station (nR) but only requires nR= nDto obtain the maximum achievable diversity gain.

In this paper, we propose a cross-layer optimization approach for replication of video streaming over multihop wireless network by jointly taking into account application, network, MAC, and physical layers. Specifically, we design a middleware replication strategy layer with a stack profile, in which parameters across the hops and all considered layers are exchanged for optimal video replication. The simulation results demonstrate that our method improves the replication strategy performance in terms of user-perceived video quality. In turn, it satisfies the heterogeneous receive bandwidth constraint of users for efficiently using the network bandwidth compared to other optimal replication strategies without cross-layer approach.

In this paper, we consider the replication strategy for the applications of video streaming in wireless mesh networks (WMNs). In particular, we propose a closed-form of optimal replication densities for a set of frames of a video streaming based on exploiting not only the skewed access probability of each frame but also the skewed loss probability and skewed encoding rate-distortion information. The simulation results demonstrate that our method improves the replication performance in terms of user-perceived quality (UPQ) including: 1) minimum average maximum reconstructed distortion for high peak signal-to-noise ratio (PSNR), 2) small reconstructed distortion fluctuation among frames for smooth playback, and 3) reasonable average maximum transmission distance for continuous playback. Furthermore, the proposed strategy consumes small storage capacity requirement for saving the resource of network compared to other existing optimal replication strategies.

In this paper, we study the symbol error rate (SER) performance of dual-hop multiple-input multiple-output (MIMO) cooperative relay networks with orthogonal space-time block coding (OSTBC) transmission over independent, not necessarily identical Nakagami-m fading channels. Our analysis considers channel state information (CSI)-assisted amplify-and-forward (AF) relaying with the availability of the direct communication from the source to the destination. In particular, we derive a closed-form expression for moment generating function (MGF) of the instantaneous signal-to-noise ratio (SNR). Utilizing this result, we can analyze the SER performance of the considered systems for M-ary phase shift-keying (M-PSK) and M-ary quadrature amplitude (M-QAM) modulations. We further investigate the cooperative diversity behavior of the considered relay systems by deriving asymptotic approximations of SER in the high SNR regime. Finally, representative numerical results are presented showing an excellent agreement with the Monte-Carlo simulations which validates our analysis.

In this letter, we investigate the performance of dual-hop channel state information-assisted amplify-and-forward relaying systems over Nakagami-mfading channels in the presence of multiple interferers at the relay. Assuming integer fading parameterm, we derive closed-form expressions for the exact outage probability and accurate approximation for symbol error rate of the system. Furthermore, we look into the asymptotical high signal to noise ratio regime, and characterize the diversity order achieved by the system. All the analytical results are validated via Monte Carlo simulations.

The performance of two-way amplify-and-forward (AF) relaying networks over independently but not necessarily identically distributed (i.n.i.d.) Nakagami-m fading channels, with integer and integer plus one-half values of fading parameter m, is studied. Closed-form expressions for the cumulative distribution function (CDF), probability density function (PDF), and moment generating function (MGF) of the end-to-end signal-to-noise ratio (SNR) are presented. Utilizing these results, we analyze the performance of two-way AF relaying system in terms of outage probability, average symbol error rate (SER), and average sum-rate. Simulations are performed to verify the correctness of our theoretical analysis.

In this paper, we analyze the performance of dual-hop channel state information (CSI)-assisted amplify-and-forward (AF) cooperative networks using orthogonal space-time block codes (OSTBCs) over independent but not identically distributed Nakagami-$m$ fading channels. Specifically, we present closed-form expressions of the outage probability (OP) and the symbol error probability (SEP). The analytical results are given in tractable forms which readily allow us to assess the performance of AF relay systems using OSTBCs. For sufficiently large signal-to-noise ratio, we obtain the asymptotic results for OP and SEP which reveal insights into the effect of fading factors on the diversity and coding gains. It has been shown that between the two hops the more severe link solely determines the diversity and coding gains. In particular, these two gains strictly depend on the fading severity parameters and the channel mean powers of the more rigorous hop, respectively. Numerical results are provided showing an excellent agreement between our analytical results and those of Monte-Carlo simulations for selected scenarios.

The exact expressions for symbol error probability and outage probability of randomized distributed space-time codes (RDSTC) under Rayleigh fading are derived. The diversity gain derived in the literature uses the Chernoff bound which may not be achievable in general. Utilizing the exact expression, the achievable diversity gain of RDSTCs is validated. The analytical expressions are verified by Monte-Carlo simulations.

We analyze the performance of amplify-and-forward (AF) multiple-input multiple-output (MIMO) cooperative networks over independent but not identically distributed Nakagami-m fading channels. Specifically, we consider an AF MIMO relay network where the source deploys the transmit antenna selection scheme to communicate with the destination through the help of a relay. We derive analytical expressions for outage probability (OP) and symbol error probability (SEP). Moreover, asymptotically tight approximations for OP and SEP have been obtained to facilitate insights on how fading parameters affect the performance. Our analytical results are verified by Monte-Carlo simulations for respective scenarios.

In this paper, we investigate the average symbol error probability (SEP) of fixed decode-and-forward relay networks over independent but not identically distributed Nakagami-m fading channels. We have derived closed form expression of SEP for general rectangular quadrature amplitude modulation (QAM) under relay selection scheme where only the best relay forwards message from the source to the destination. The expressions are in terms of hypergeometric series which can be efficiently numerically evaluated. The numerical results are validated with Monte-Carlo simulations.

In mobile multimedia networks, different traffic classes require various levels of quality of service (QoS). In this paper, we propose a cross-layer design to provide diverse QoS provisions for integrated realtime/non-realtime traffic by jointly taking into account the physical layer, data link layer, and application layer. Specifically, at the physical layer, adaptive modulation and coding (AMC) is employed to cope with the time-varying nature of fading channels whereas impairments due to queuing effects are considered at the data link layer. At the application layer, realtime and non-realtime traffic are classified into high and low priority classes, respectively, with strict priority levels. The cross-layer scheme is characterized as a discrete Markov modulated Poisson process. Solving the steady state probability of this Markov model readily enables us to investigate several important performance metrics such as packet drop rate due to buffer overflow and packet error rate due to transmission errors and network impairments. Our proposed cross-layer design is verified by comparing the analytical results with Monte-Carlo simulations. Furthermore, it has been shown that the proposed cross-layer design can flexibly guarantee QoS requirements for each traffic class.

An adaptive transmission scheme for cooperative communications is proposed in this paper. Communication protocols with amplify-and-forward relays based on the distributed-Alamouti space-time code (achieving spatial diversity gain) and cooperative spatial multiplexing (C-SM) (pertaining spatial multiplexing gain) are considered. Specifically, under a fix transmission rate, we use adaptively the cooperative diversity (C-DIV) and C-SM according to channel conditions. With this strategy, either C-DIV with higher order modulation or C-SM with lower order modulation can be properly selected to enhance the error rate performance. The proposed adaptive protocol is shown to provide superior performance compared to C-DIV and C-SM.

Cooperative spatial multiplexing (CSM) systems can achieve high spatial multiplexing gain in wireless relay networks since each single-antenna relay forwards simultaneously a different subset of source messages to the destination. In this paper, the performance of CSM with amplify-and-forward (AF) relays are presented. Specifically, we derive exact closed-form expressions for symbol error probability (SEP) and achievable spectral efficiency of CSM with linear receiver in dissimilar Rayleigh fading channels. Assessing the SEP performance in the high signal-to-noise regime, we further show that CSM with AF relays obtains the first order of diversity gain. Furthermore, it has been observed that CSM outperforms two well-known cooperative diversity systems, e.g., selection combining and maximal ratio combining, in term of spectral efficiency. We also perform Monte-Carlo simulations to validate our analysis.

In this paper, we investigate the performance gain of hybrid decode-amplify-forward (HDAF) relay protocol over the two conventional ones: the adaptive decode-forward (ADF) and amplify-forward (AF) in dual-hop multiple-relay networks. We analyze the performance gain in terms of the symbol error probability (SEP) in the high signal-to-noise ratio (SNR) regime. Similarly as in the single relay case [1], under a specific modulation scheme, these gains only depend on the ratio of channel mean power between the first-hop and second-hop transmission. In particular, we show that the HDAF protocol obtains a large gain, medium gain, and small gain over ADF, AF as the relays are placed close to the destination, half-way between the source and destination (symmetric case), and nearby the source, respectively. In contrast to the single-relay case in which the HDAF scheme has no benefit compared to ADF and AF as the relay is located close to the source [1], HDAF still achieves a small gain with multiple relays. Interestingly, we further show that for all cases the performance gains are saturated as the number of relays is large.

In this paper, we analyze the maximum likelihood decoding performance of non-regenerative cooperation employing Alamouti scheme. Specifically, we derive two closed-form expressions for average symbol error probability (SEP) when the relays are located near by the source or destination. The analytical results are obtained as a single integral with finite limits and an integrand composed solely of trigonometric functions. Assessing the asymptotic (high signal-to-noise ratio) behavior of SEP formulas, we show that the distributed-Alamouti codes achieves a full diversity order. We also perform Monte-Carlo simulations to validate the analysis.

In this paper, we analyze the effect of the line-of-sight (LOS) on the symbol error probability (SEP) of nonregenerative cooperation in multiple-input multiple-output (MIMO) dual-hop relay channels with nSsource antennas, nRrelay antennas, and no destination antennas-referred to as a (nS,nR,nD)-MIMO dual-hop nonregenerative relay channel. In particular, we consider the channels of the source-to-relay (S-to-R) and the relay-to-destination (R-to-D) links are Rayleigh and Rician fading distribution, respectively. To be specific, we derive the exact SEP for maximum likelihood (ML) decoding of orthogonal space-time block codes (OSTBCs) over such channels and validate the analytical results by comparing with Monte-Carlo simulation. It is shown that for a fixed channel gain a strong LOS component degrades the error performance, e.g. SEP, of MIMO cooperative communications due to the lack of scattering.

As an important space-time code, V-BLAST (vertical-Bell lab layered space-time) code has been studied recently. Zero-forcing (ZF) and minimum mean square error (MMSE) are two nulling criteria that are commonly used in the V-BLAST system. In this paper, we propose the new detection algorithm for MMSE criterion that exterminates mutual interferences; hence the BER performance is improved. It can be done by modifying the channel matrix after each detection step. We also perform simulation to validate the analysis.