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.

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.

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 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.

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.

In this paper, we propose a hybrid beamforming design for multiple target detection in an orthogonal time frequency space (OTFS)-based integrated sensing and communication (ISAC) multiple-input multiple-output (MIMO) system. The proposed hybrid beamformer allows spatial separation of the beams for communication and sensing, thereby eliminating inter-beam interference (IBI), while reducing the number of required radio frequency (RF) chains. More specifically, in addition to the beams allocated for communication users, multiple beams are assigned for target scanning and detection. By applying a combiner to the received echo signals, information about the target's existence, along with its angular, range, and Doppler characteristics, can be directly obtained across different RF chains. To shed light on the system performance, we analyze the signal-to-interference-plusnoise ratio (SINR) and discuss the effect of the beamformer in the on-grid and off-grid cases, respectively. Our simulation results indicate that accurate sensing can be achieved with integer delay and Doppler indices; however, in the cases of fractional delay and Doppler, the sensing accuracy depends on the resolution of these parameters.

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.

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 localization of near-field sources is crucial in wireless communications. Practical arrays have mutual coupling between the elements of the array that can significantly affect the accuracy of localization algorithms. In this paper, we utilize a practical array to evaluate the performance of an iterative method based on oblique projection (IMOP) in estimating the location of near-field sources. The conventional two-dimensional (2D) search method for near-field source localization (TSMNSL) employs a 2D search to estimate the direction of arrival (DOA) and range of the source, resulting in a high computational load. In contrast, the IMOP method uses one-dimensional searches, which significantly reduces computational complexity. The results of numerical and full-wave electromagnetic simulations in this paper show that the performance of DOA and range estimations in the IMOP method is comparable to that in the TSMNSL method. The simulation results further indicate that the computational complexity of the IMOP method is at least 49 times lower than that of the TSMNSL method.

Securing Internet of Things (IoT) devices presents increasing challenges due to their limited computational and energy resources. Radio Frequency Fingerprint Identification (RFFI) emerges as a promising authentication technique to identify wireless devices through hardware impairments. RFFI performance under low signal-to-noise ratio (SNR) scenarios is significantly degraded because the minute hardware features can be easily swamped in noise. In this paper, we leveraged the diffusion model to effectively restore the RFF under low SNR scenarios. Specifically, we trained a powerful noise predictor and tailored a noise removal algorithm to effectively reduce the noise level in the received signal and restore the device fingerprints. We used Wi-Fi as a case study and created a testbed involving 6 commercial off-the-shelf Wi-Fi dongles and a USRP N210 software-defined radio (SDR) platform. We conducted experimental evaluations on various SNR scenarios. The experimental results show that the proposed algorithm can improve the classification accuracy by up to 34.9%.

Radio frequency fingerprint identification (RFFI) is a promising technique for authenticating Internet of Things (IoT) devices by leveraging unique RF hardware impairments. However, RFFI is vulnerable to channel variations and low signal- to- noise ratio (SNR) conditions. In this paper, we proposed a robust RFFI system specifically designed to tackle these issues for Bluetooth Low Energy (BLE), which is a popular IoT technology. Our system integrated a denoising autoencoder (DAE) to enhance feature robustness under low SNR conditions and employed data augmentation to mitigate the impact of channel and noise effects. We created a testbed consisting of 18 commercial off-the-shelf (COTS) BLE devices and a USRP N210 software-defined radio (SDR) platform and then carried out extensive experimental evaluation under various channel conditions. The experiments involved line-of-sight (LOS) and non-line-of-sight (NLOS) propagation as well as dynamic and static channels. The results demonstrated that our approach consistently achieved over 95 % accuracy in high SNR environments and maintained strong performance with over 75% accuracy at low SNR levels (10 dB).

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.

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.

This paper presents a general framework for deriving the statistics of RIS-based communication systems under diverse fading conditions. Specifically, the probability density function (PDF) and higher-order moments are derived considering direct and RIS-assisted links from the source to the destination. Recognizing the analytical challenges of these derivations in the context of RIS-assisted systems, we employ a more practical yet tractable and notably accurate α-μ approximation, which offers a simple and closed-form solution, producing results virtually indistinguishable from those obtained through simulations for various realistic communication scenarios of interest. This approach is a valuable tool for evaluating system performance metrics such as outage probability and average symbol error rate since such an approximation significantly outperforms the one derived from the widely employed central limit theorem, even when dealing with a reasonably large number of elements. We subsequently apply this general framework to investigate the achievable RIS performance in different realistic fading environments, including α-μ, κ-μ, and Extended η-μ, both individually and combined. Specifically, we derive reasonably simple and functional closed-form asymptotic expressions, in terms of well-known special functions, for each case study, contributing to a deeper understanding of how each fading parameter affects the system’s performance regarding coding gain and diversity gain. In particular, we demonstrate that the diversity order is directly determined by the number of reflecting elements, the density of multipath clusters, and the degree of non-linearity in the propagation medium, with higher values of these parameters leading to clear improvements in performance. The offered results enable the development of valuable insights on RIS-based communications, which will be useful in future system designs and deployments.

We consider a downlink (DL) massive multiple-input multiple-output (MIMO) system, where different users have different mobility profiles. To support this system, we categorize the users into two disjoint groups according to their mobility profile and implement a hybrid orthogonal time frequency space (OTFS)/orthogonal frequency division multiplexing (OFDM) modulation scheme. Building upon this framework, two precoding designs, namely full-pilot zero-forcing (FZF) precoding and partial zero-forcing (PZF) precoding are considered. To shed light on the system performance, the spectral efficiency (SE) with a minimum-mean-square-error (MMSE)-successive interference cancellation (SIC) detector is investigated. Closed-form expressions for the SE are obtained using some tight mathematical approximations. To improve fairness among different users, we consider max-min power control for both precoding schemes based on the closed-form SE expression. However, by noting the large performance gap for different groups of users with PZF precoding, the per-user SE will be compromised when pursuing overall fairness. Therefore, we propose a weighted max-min power control scheme. By introducing a weighting coefficient, the trade-off between the per-user performance and fairness can be enhanced. Our numerical results confirm the theoretical analysis and reveal that with mobility-based grouping, the proposed hybrid OTFS/OFDM modulation significantly outperforms the conventional OFDM modulation for high-mobility users.

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.

Wi-Fi-based device-free localization (DFL) will be an integral part of many emerging applications, such as smart healthcare and smart homes. One popular approach to DFL in Wi-Fi makes use of fingerprinting based on channel state information (CSI). Unfortunately, high-quality fingerprints cannot easily be obtained in many real-world environments due to the complicated, time-varying and multipath conditions which exist. Additionally, existing methods struggle to update the DFL models in a real-time manner to track changes of environment. To address these issues, an online data-driven modelling DFL framework is designed for robustness enhancement. Specifically, the raw CSI data is first augmented with the hidden layer parameters of an online deep neural network to strengthen the pair-to-pair mappings between signal variations and a target’s location. The radio map created with the augmented fingerprints can be updated with new sequential data collected from other domains, such as different times and layouts of the same environment. Subsequently, a novel online DFL model is established using these augmented fingerprints, which itself can be updated with new sequential data from other domains without the need for retraining. A forgetting mechanism is considered to mitigate the effects of outdated data on the localization performance. To validate our new framework, a comprehensive set of experiments have been performed in various environments for different scenarios. The experimental results verify the robustness and responsive tracking ability of the proposed online data-driven modelling DFL framework.

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.

Reconfigurable Intelligent Surfaces (RISs) are considered a key candidate technology for next-generation 6G wireless systems, promising to extend network coverage, enhance spectral efficiency, and effectively mitigate interference. RISs will achieve this by controlling the propagation environment, enabled by carefully altering the reflective properties of their elements. In this context, this work presents a general framework for deriving the exact probability density function (PDF) and higher-order moments of the signal-to-ratio (SNR) in a RIS system, considering the direct transmission and RIS-assisted links from source to destination. Recognizing the analytical challenges associated with obtaining exact statistics for RIS-assisted systems, which persist in both common and more generalized fading models, the proposed contribution is realized with the aid of the suitable α-µ fading model. To that end, we derive accurate approximations for the κ-µ and the extended η-µ distributions, which constitute effective and versatile multipath fading models. Based on this, the achieved results are virtually indistinguishable from those obtained through simulations for various environmental settings. This renders the proposed framework a valuable tool for evaluating the performance of RIS systems, particularly in terms of the corresponding average symbol error rate (ASER). Tractable closed-form asymptotic expressions are also derived and utilized to provide a deeper understanding of the system’s behavior.

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.

Localizing near-field sources considering practical arrays is important in wireless communications. Array-based apertures exhibit mutual coupling between the array elements, which can significantly degrade the performance of the localization method. In this paper, we propose two methods to localize near-field sources by direction of arrival (DOA) and range estimations in the presence of mutual coupling. The first method utilizes a two-dimensional search to estimate DOA and the range of the source. Therefore, it suffers from a high computational load. The second method reduces the two-dimensional search to one-dimensional, thus decreasing the computational complexity while offering similar DOA and range estimation performance. Besides, our second method reduces computational time by over 50% compared to the multiple signal classification (MUSIC) algorithm.

Inspired by the theory of natural evolution, Differential Evolution (DE) functions constitute an optimization tool in the field of evolutionary algorithms. Here, we propose employing DE to estimate physically acceptable fading parameters in device-to-device (D2D) communication channels. We examine four real-world D2D propagation channel measurements obtained under various conditions: indoor, outdoor, line-of-sight (LOS), and non-LOS. Four popular fading models,$\kappa-\mu, \eta-\mu, \kappa-\mu$/inverse gamma, and$\eta-\mu$/inverse gamma are used to characterize these links. Two fitness functions, Kullback-Leibler divergence (KLD) and mean squared error (MSE), are utilized for evaluation. We also compare the DE with another evolutionary algorithm, namely the genetic algorithm (GA). Notably, our results demonstrate that while both algorithms deliver excellent estimation performances, DE emerges as significantly faster and more robust compared to GA. Regarding fitness performances, the algorithm, when paired with KLD, outperforms the pairing with MSE, as assessed through the minimization of the Akaike information criterion.

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.

We present a simulation framework for evaluating the performance of cooperative reconfigurable intelligent surface (RIS) based systems, which may ultimately deploy an arbitrary number of RISs to overcome adverse propagation-related effects, such as cascaded fading. The physical model underlying the proposed framework considers the (optional) presence of a dominant signal path between the source and RIS, and then between each subsequent stage of the communication link to the destination. Accompanying the dominant signal component is a non-isotropic scattered signal contribution, which accounts for angular selectivity within the cascaded RIS stages between the source and destination. The simulation of the time-correlated scattered signal, reflected by the illuminated reflective elements, is achieved using autoregressive modelling. As a by-product of our analysis, significant insights are drawn which enable us to characterize the amplitude and phase properties of the received signal, and the associated complex autocorrelation functions (ACFs) for the product of multiple Rician channels. For both single and cooperative RIS systems, the outage probability (OP), and important second-order statistics, such as the level crossing rate (LCR) and average outage duration (AOD), are analyzed for a variety of system configurations, accounting for practical limitations, such as phase errors. It is shown that by using multiple RISs cooperatively, the AOD is reduced at a lower signal-to-noise-ratio (SNR) compared to single RIS-assisted transmission under the same operating conditions. Lastly, increased channel variations (i.e., higher maximum Doppler frequencies) are shown to decrease the AOD in the case of absent phase errors; yet, this improvement is not observed when phase errors are present.

In this paper, we consider a downlink (DL) massive multiple-input multiple-output (MIMO) system, where different users have different mobility profiles. To support this system, we propose to use a hybrid orthogonal time frequency space (OTFS)/orthogonal frequency division multiplexing (OFDM) modulation scheme, where OTFS is applied for high-mobility users and OFDM is used for low-mobility users. Two precoding designs, namely full zero-forcing (FZF) precoding and partial zero-forcing (PZF) precoding, are considered and analyzed in terms of per-user spectral efficiency (SE). With FZF, interference among users is totally eliminated at the cost of high computational complexity, while PZF can be used to provide a trade-off between complexity and performance. To apply PZF precoding, users are grouped into two disjoint groups according to their mobility profile or channel gain. Then, zero-forcing (ZF) is utilized for high-mobility or strong channel gain users to completely cancel the inter-group interference, while maximum ratio transmission (MRT) is applied for low-mobility users or users with weak channel gain. To shed light on the system performance, the SE for high-mobility and low-mobility users with a minimum-mean-square-error (MMSE)-successive interference cancellation (SIC) detector is investigated. Our numerical results reveal that the PZF precoding with channel gain grouping can guarantee a similar quality of service for all users. In addition, with mobility-based grouping, the hybrid OTFS/OFDM modulation outperforms the conventional OFDM modulation for high-mobility users.

This contribution quantifies the achievable physical layer security of wireless transmission over realistic composite fading conditions in the presence of multiple eavesdroppers (Eves). To this end, exact closed-form analytic expressions are derived for the achievable secure outage probability (SOP) and the probability of strictly positive secrecy capacity (SPSC) for the case of$\mathcal{F}$composite fading channels. The derived expressions are tractable and their validity is justified through comparisons with respective computer simulation results. In addition, they allow for the development of useful theoretical and practical insights on the effect of the presence of numerous eavesdroppers under different multipath fading and shadowing conditions, as these are encountered in realistic wireless communication scenarios.

This contribution proposes a simulation framework for quantifying the performance of employed reconfigurable intelligent surface (RIS) based systems to overcome adverse propagation-related effects. The physical model underlying the proposed framework considers the presence of a dominant signal path between the source and RIS, and then between RIS and the destination. The simulation of the time-correlated scattered signal reflected by the illuminated reflective elements is achieved using autoregressive (AR) modeling. As a by-product of our analysis, significant insights are developed which allow for the characterization of the amplitude and phase properties of the received signal, and the associated complex autocorrelation function (ACF) for the product of two Rician channels. Capitalizing on this, we derive the corresponding first and second order statistics, which lead to the development of useful theoretical and practical insights.

In this contribution we evaluate the transmission of confidential information over ${\mathcal{F}}$ composite fading channels in the presence of an eavesdropper (Eve) who also experiences ${\mathcal{F}}$ composite fading. Upon obtaining tractable closed-form expressions for the secure outage probability and the probability of strictly positive secrecy capacity, we analyze extensively the achievable physical layer security performance in the context of mm-wave communications. This is realized with the aid of extensive measurement results from realistic communication scenarios, which show the behavior of composite ${\mathcal{F}}$ fading channels in device-to-device communication scenarios. The offered results provide meaningful insights of theoretical and practical importance that are expected to be useful in the design of mm-wave based communication systems.

The modeling of wireless communications channels is often broken down into two distinct states, defined according to the optical viewpoints of the transmitter (TX) and receiver (RX) antennas, namely line-of-sight (LoS) and non-LoS (NLoS). Movement by the TX, RX, both and/or objects in the surrounding environment means that channel conditions may transition between LoS and NLoS leading to a third state of signal propagation, namely quasi-LoS (QLoS). Unfortunately, this state is largely ignored in the analysis of signal propagation in wireless channels. We therefore propose a new statistical framework that unifies signal propagation for LoS, NLoS, and QLoS channel conditions, leading to the creation of the Three State Model (TSM). The TSM has a strong physical motivation, whereby the signal propagation mechanisms underlying each state are considered to be similar to those responsible for Rician fading. However, in the TSM, the dominant signal component, if present, can be subject to shadowing. To support the use of the TSM, we develop novel formulations for the probability density functions of the in-phase and quadrature components of the complex received signal as well of the received signal envelope. The offered results are corroborated with results from respective computer simulations, whilst it is shown that the proposed model is more versatile than existing conventional models.

The$\kappa $-$\mu $/ inverse gamma and$\eta $-$\mu $/ inverse gamma composite fading models are presented and extensively investigated in this paper. We derive closed-form expressions for the fundamental statistics of the$\kappa $-$\mu $/ inverse gamma composite fading model, such as the probability density function (PDF), cumulative distribution function (CDF). Additionally, we solve the associated integral that is commonly used to obtain the moment generating function (MGF) of statistical distributions to provide an MGF-type function which is valid for performance analysis over the specified parameter space. Analytic expressions for the PDF, higher order moments and AF are also derived for the$\eta $-$\mu $/ inverse gamma composite fading model, while infinite series expressions are obtained for the corresponding CDF and MGF-type function. The suitability of the new models for characterizing composite fading channels is demonstrated through a series of extensive field measurements for wearable, cellular, and vehicular communications. For all of the measurements, two propagation geometry problems with special relevance to the two new composite fading models, namely the line-of-sight (LOS) and non-LOS (NLOS) channel conditions, are considered. It is found that both the$\kappa $-$\mu $/ inverse gamma and$\eta $-$\mu $/ inverse gamma composite fading models provide an excellent fit to fading conditions encountered in the field. The goodness-of-fit of these two composite fading models is also evaluated and compared using the resistor-average distance. As a result, it is shown that the$\kappa $-$\mu $/ inverse gamma composite fading model provides a better fit compared to the$\eta $-$\mu $/ inverse gamma composite fading model when strong dominant signal components exist. On the contrary, the$\eta $-$\mu $/ inverse gamma composite fading model outperforms the$\kappa $-$\mu $/ inverse gamma composite fading model when there is no strong dominant signal component and/or the parameter$\eta $is not equal to unity, indicating that the scattered wave power of the in-phase and quadrature components of each cluster of multipath are not identical.

Indoor areas, such as offices and shopping malls, are a natural environment for initial millimeter-wave (mmWave) deployments. Although we already have the technology that enables us to realize indoor mmWave deployments, there are many remaining challenges associated with system-level design and planning for such. The objective of this article is to bring together multiple strands of research to provide a comprehensive and integrated framework for the design and performance evaluation of indoor mmWave systems. This article introduces the framework with a status update on mmWave technology, including ongoing fifth generation (5G) wireless standardization efforts and then moves on to experimentally validated channel models that inform performance evaluation and deployment planning. Together these yield insights on indoor mmWave deployment strategies and system configurations, from feasible deployment densities to beam management strategies and necessary capacity extensions.

This paper presents an analytical framework which lays the foundation for distributed massive multiple-input multiple-output (MIMO) supported wireless power transfer for wearable devices. In our approach, we consider multiple users, each wearing a number of wireless sensors along with one body worn hub which acts as a relay to forward information from the on-body sensors to nearby access points (APs). Each AP is equipped with a large number of antennas and is not only responsible for receiving data sent from the hubs, but also supplying wireless power to them. Interaction between hubs and APs is not exclusive to a single pairing, with APs assigned to supply energy to and receive data from more than one hub. More precisely, APs perform the downlink energy transmission and the uplink data transmission using maximum-ratio combining. Analytical approximations of the outage probability and spectral efficiency are derived. Based on these analytical results, two modes of operation are investigated. These are outage probability prioritized and spectral efficiency prioritized. For each mode, max-min power controls are proposed to ensure a uniformly good service throughout the area of coverage. By contrasting with the IEEE 802.15.6 standard, the numerical results illustrate that while using collocated massive MIMO may provide unsatisfactory performance, the distributed setting shows much promise for enabling wireless power transfer for wearable devices.

The F composite fading model was recently proposed as an accurate and tractable statistical model for the characterization of the composite fading conditions encountered in realistic wireless communication scenarios. In the present contribution we capitalize on the distinct properties of this composite model to evaluate the achievable effective rate over F composite fading channels. To this end, we derive an exact closed-form expression for the effective rate, which is subsequently used as a benchmark for the derivation of tight upper and lower bounds, as well as of an accurate approximation. The derived analytic expressions are provided in closed-form and benefit from being tractable both analytically and numerically. This enables the development of meaningful insights on the effect of fading conditions and/or latency on the overall system performance. Also, it allows the accurate quantification of the signal to noise ratio required in target quality of service requirements under different composite fading conditions.

In this paper, we introduce a new fading model which is capable of characterizing both the shadowing of the dominant component and composite shadowing which may exist in wireless channels. More precisely, this new model assumes a κ-μ envelope where the dominant component is fluctuated by a Nakagami-m random variable (RV) which is preceded (or succeeded) by a secondary round of shadowing brought about by an inverse Nakagami-m RV. We conveniently refer to this as the double shadowed κ-μ fading model. In this context, novel closed-form and analytical expressions are developed for a range of channel related statistics, such as the probability density function, cumulative distribution function, and moments. All of the derived expressions have been validated through Monte-Carlo simulations and reduction to a number of well-known special cases. It is worth highlighting that the proposed fading model offers remarkable flexibility as it includes the κ-μ, η-μ, Rician shadowed, double shadowed Rician, κ-μ shadowed, κ-μ/inverse gamma and η-μ/inverse gamma distributions as special cases.

In this paper, we investigate the performance of energy detection-based spectrum sensing over F composite fading channels. To this end, an analytical expression for the average detection probability is first derived. This expression is then extended to account for collaborative spectrum sensing, square-law selection diversity reception, and noise power uncertainty. The corresponding receiver operating characteristics (ROC) are analyzed for different conditions of the average signal-to-noise ratio (SNR), noise power uncertainty, time-bandwidth product, multipath fading, shadowing, number of diversity branches, and number of collaborating users. It is shown that the energy detection performance is sensitive to the severity of the multipath fading and the amount of shadowing, whereby even small variations in either of these physical phenomena can significantly impact the detection probability. As a figure of merit to evaluate the detection performance, the area under the ROC curve (AUC) is derived and evaluated for different multipath fading and shadowing conditions. Closed-form expressions for the differential entropy and cross entropy are also formulated and assessed for different average SNR, multipath fading, and shadowing conditions. Then, the relationship between the differential entropy of F composite fading channels and the corresponding ROC/AUC is examined where it is shown that the average number of bits required for encoding a signal becomes small (i.e., low differential entropy) when the detection probability is high or when the AUC is large. The difference between composite fading and traditional small-scale fading is emphasized by comparing the cross entropy for Rayleigh and Nakagami-m fading. A validation of the analytical results is provided through a careful comparison with the results of some simulations.

This paper investigates the performance of energy detection-based spectrum sensing over Fisher-Snedecor F fading channels. To this end, an analytical expression for the corre- sponding average detection probability is firstly derived and then this is extended to account for collaborative spectrum sensing. The complementary receiver operating characteristics (ROC) are analyzed for different conditions of the average signal-to- noise ratio (SNR), time-bandwidth product, multipath fading, shadowing and number of collaborating users. It is shown that the energy detection performance is strongly linked to the severity of the multipath fading and amount of shadowing, whereby even small variations in either of these physical phenomena significantly impact the detection probability. Also, the versatile modeling capability of the Fisher-Snedecor F distribution is verified in the context of energy detection based spectrum sensing as it provides considerably more accurate characterization than the conventional Rayleigh fading model. To confirm the validity of the analytical results presented in this paper, we compare them with the results of some simulations.

Unbounded growth in cellular traffic is continuing to increase network power consumption meaning that the need for energy efficient cellular network design is more critical than ever. To find the trade-off between the spectral and energy efficiency, stochastic geometry has been widely employed where the cellular nodes are considered as being distributed according to a Poisson point process (PPP). Most of the prior works using stochastic geometry commonly assumed Rayleigh fading as the de facto fading model due to its tractability and simplicity. However, the propagation environments in which emerging cellular networks will operate, are diverse in nature, constituted by many different propagation phenomena which can not be fully captured by the Rayleigh distribution. To incorporate these physical attributes into the calculation of the network performance metrics, we consider κ-μ shadowed fading, which contains the majority of the well-known fading models as special cases. Using stochastic geometry, we evaluate the spectral efficiency and energy efficiency of a K-tier HetNet with K classes of BSs, differing in terms of the transmit power, BS density, shadowing and fading. Through numerical evaluation, we observe a trade-off relationship between the shadowing and fading parameters, spectral efficiency and energy efficiency, which provides important new insights into energy efficient network design.

Novel composite fading models were recently proposed based on inverse gamma distributed shadowing conditions. These models were extensively shown to provide remarkable modeling of the simultaneous occurrence of multipath fading and shadowing phenomena in emerging wireless scenarios such as cellular, off-body and vehicle-to-vehicle communications. Furthermore, the algebraic representation of these models is rather tractable, which renders them convenient to handle both analytically and numerically. Based on this, the present contribution analyzes the ergodic capacity over the recently proposed $\kappa-\mu$ / inverse gamma composite fading channels, which were shown to characterize excellently multipath fading and shadowing in line-of-sight communication scenarios, including realistic vehicular communications. Novel analytic expressions are derived which are subsequently used in the analysis of the corresponding system performance. In this context, the offered results are compared with respective results from cases assuming conventional fading conditions, which leads to the development of numerous insights on the effect of the multipath fading and shadowing severity on the achieved capacity levels. It is expected that these results will be useful in the design of timely and demanding wireless technologies such as wearable, cellular and inter-vehicular communications.

The η-μ / inverse gamma and κ-μ / inverse gamma distributions were recently introduced as particularly flexible and tractable composite fading models that provide accurate characterization of multipath and shadowing effects, which are encountered simultaneously during wireless transmission in emerging communication scenarios such as off-body, cellular and vehicular-to-vehicular communications. The present contribution analyzes the symbol error rate performance of digital communications over these fading channels. To this end, we derive novel analytic expressions for the symbol error rate of multiple amplitude based modulated systems under these fading conditions, which are subsequently used in the analysis of the corresponding system performance. In this context, numerous insights are developed on the effect of different fading conditions on the corresponding error rate, which are expected to be useful in the design of timely and demanding wireless technologies such as wearable, cellular and vehicular communication systems.

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

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

Internet enabled body area networks (BANs) will form a core part of future remote health monitoring and ambient assisted living technology. In BAN applications, due to the dynamic nature of human activity, the off-body BAN channel can be prone to deep fading caused by body shadowing and multipath fading. Using this knowledge, we present some novel practical adaptive power control protocols based on the channel deviation to simultaneously prolong the lifetime of wearable devices and reduce outage probability. The proposed schemes are both flexible and relatively simple to implement on hardware platforms with constrained resources making them inherently suitable for BAN applications. We present the key algorithm parameters used to dynamically respond to the channel variation. This allows the algorithms to achieve a better energy efficiency and signal reliability in everyday usage scenarios such as those in which a person undertakes many different activities (e.g., sitting, walking, standing, etc.). We also profile their performance against traditional, optimal, and other existing schemes for which it is demonstrated that not only does the outage probability reduce significantly, but the proposed algorithms also save up to average transmit power compared to the competing schemes.

In this paper, we perform a systematic investigation of the statistics associated with the product of two independent and non-identically distributed κ-μ random variables. More specifically, we develop novel analytical formulations for many of the fundamental statistics of interest, namely, the probability density function, cumulative distribution function, and moment-generating function. Using these new results, closedform expressions are obtained for the higher order moments, amount of fading and channel quality estimation index, while analytical formulations are obtained for the outage probability, average channel capacity, average symbol error probability, and average bit error probability. These general expressions can be reduced to a number of fading scenarios, such as the double Rayleigh, double Rice, double Nakagami-m, κ-μ/Nakagami-m, and Rice/Nakagami-m, which all occur as special cases. Additionally, as a byproduct of the work performed here, formulations for the κ-μ/κ-μ composite fading model can also be deduced. To illustrate the efficacy of the novel expressions proposed here, we provide useful insights into the outage probability of a dualhop system used in body area networks, and demonstrate the suitability of the κ-μ/κ-μ composite fading for characterizing shadowed fading in device-to-device channels.

Applications are emerging that feature multiple implanted devices as part of an intra-body network. Establishing high bandwidth communications between such devices is challenging and there is a need to understand the principles of the intra-body channel. This paper presents a numerical analysis of the wave propagation between identical antennas in the MedRadio operating band (2.36-2.40 GHz) within cylindrical three layered tissue equivalent phantoms. The results presented show the effect of dielectric boundaries and different tissue properties on dominant wave propagation paths and link gain which provides essential information for efficient system design.

In this paper, we initially consider the product of two independent and non-identically distributed κ-μ variates, and obtain its probability density function (PDF) in novel analytical form. Following from this, the PDF for the κ-μ/κ-μ composite fading model is then deduced. Monte-Carlo simulations are performed to verify the derived results. It is worth highlighting that both sets of expressions can be reduced to a number of double and composite fading scenarios such as the double Rice, double Nakagami-m, κ-μ/Nakagami-m, and Rice/Nakagami-m. Finally, we illustrate the usefulness of the obtained formulations by characterizing the shadowed fading encountered in body area networks, device-to-device and vehicle-to-vehicle communication channels. To this end, it is shown that the κ-μ/κ-μ composite fading model provides an excellent fit to the measurement data.

In this paper, we obtain novel analytical formulations for the outage probability (OP) in scenarios with α-μ or κ-μ faded signals of interest (SoI), and κ-μ or α-μ faded co-channel interference (CCI). Both single and multiple interfering scenarios have been considered, and exact formulations are provided when the SoI experiences α-μ fading and the CCI experiences κ-μ fading. When the SoI is subject to κ-μ fading and the CCI is subject to α-μ fading, exact and highly accurate approximate OP expressions have been derived for the single and multiple interfering cases, respectively. We also analyze the asymptotic behavior of the OP when the average signal-to-noise-ratio (SNR) of the SoI is significantly larger or smaller than that of the interferers. Monte-Carlo simulations are performed to verify the derived results. It is worth highlighting that these general expressions are important as they unify the CCI for a number of previously disjoint fading scenarios.

We provide some comments and subsequent corrections to a paper recently published in this Journal (see ibid., vol. 33, no. 1, pp. 111-119, Jan. 2015). More precisely, we show that the pdf for the κ-μ shadowed fading model given by S.L. Cotton cannot be obtained from the underlying statistical model proposed therein. To support this observation, we present a detailed mathematical analysis as well as some Monte Carlo simulations. We also demonstrate that by simply adopting the underlying statistical model for the κ-μ shadowed fading distribution proposed in an earlier and independent work by J.F. Paris enables the κ-μ shadowed pdf later presented by S.L. Cotton to be obtained.

Device-to-device (D2D) communications are recognized as a key enabler of future cellular networks, which will help to drive improvements in spectral efficiency and assist with the offload of network traffic. Relay-assisted D2D communications will be essential when there is an extended distance between the source and the destination or when the transmit power is constrained below a certain level. Although a number of works on relay-assisted D2D communications have been presented in the literature, most of those assume that relay nodes cooperate unequivocally. In reality, this cannot be assumed, since there is little incentive to cooperate without a guarantee of future reciprocal behavior. To incorporate the social behavior of D2D nodes, we consider the decision to relay using the donation game based on social comparison, characterize the probability of cooperation in an evolutionary context and then evaluate the network performance of relay-assisted D2D communications. Through numerical evaluations, we investigate the performance gap between the ideal case of 100% cooperation and practical scenarios with a lower cooperation probability. It shows that practical scenarios achieve lower transmission capacity and higher outage probability than idealistic network views, which assume full cooperation. After a sufficient number of generations, however, the cooperation probability follows the natural rules of evolution and the transmission performance of practical scenarios approach that of the full cooperation case, indicating that all D2D relay nodes adapt the same dominant cooperative strategy based on social comparison, without the need for external enforcement.

Device-to-device (D2D) communications are now considered an integral part of future 5G networks, which will enable direct communication between user equipments and achieve higher throughputs than conventional cellular networks, but with the increased potential for co-channel interference. The physical channels, which constitute D2D communications, can be expected to be complex in nature, experiencing both line-ofsight (LOS) and non-LOS conditions across closely located D2D pairs. In addition to this, given the diverse range of operating environments, they may also be subject to clustering of the scattered multipath contribution, i.e., propagation characteristics which are quite dissimilar to conventional Rayleigh fading environments. To address these challenges, we consider two recently proposed generalized fading models, namely κ-μ and η-μ, to characterize the fading behavior in D2D communications. Together, these models encompass many of the most widely utilized fading models in the literature such as Rayleigh, Rice (Nakagami-n), Nakagami-m, Hoyt (Nakagami-q), and One-sided Gaussian. Using stochastic geometry, we evaluate the spectral efficiency and outage probability of D2D networks under generalized fading conditions and present new insights into the tradeoffs between the reliability, rate, and mode selection. Through numerical evaluations, we also investigate the performance gains of D2D networks and demonstrate their superiority over traditional cellular networks.

Emerging cellular technologies such as those proposed for use in 5G communications will accommodate a wide range of usage scenarios with diverse link requirements. This will necessitate operation over a versatile set of wireless channels ranging from indoor to outdoor, from line-of-sight (LOS) to non-LOS, and from circularly symmetric scattering to environments which promote the clustering of scattered multipath waves. Unfortunately, many of the conventional fading models lack the flexibility to account for such disparate signal propagation mechanisms. To bridge the gap between theory and practical channels, we consider κ-μ shadowed fading, which contains as special cases the majority of the linear fading models proposed in the open literature. In particular, we propose an analytic framework to evaluate the average of an arbitrary function of the signal-to-noise-plus-interference ratio (SINR) over κ-μ shadowed fading channels by using an orthogonal expansion with tools from stochastic geometry. Using the proposed method, we evaluate the spectral efficiency, moments of the SINR, and outage probability of a K-tier heterogeneous cellular network with K classes of base stations (BSs), differing in terms of the transmit power, BS density, shadowing, and fading characteristics. Building upon these results, we provide important new insights into the network performance of these emerging wireless applications while considering a diverse range of fading conditions and link qualities.

This paper investigates the potential improvement in signal reliability for outdoor wearable communications channels operating at 868 MHz using switched combining based macro-diversity. In this paper, a number of different macrodiversity configurations consisting of two and four base stations were considered to help mitigate the impact of body shadowing upon a wearable node, which was located on the central chest region of an adult male. During the field measurements, five different walking movements were performed, and then analyzed to investigate the efficacy of using macro-diversity. It was found that all of the considered switched combining schemes, including switch-and-stay combining, switch-and-examine combining (SEC) and SEC with post-examining selection (SECps) provided a worthwhile signal improvement when an appropriate switching threshold was adopted. The maximum diversity gain obtained in this paper was found to be 19.5 dB when using fourbase station SECps. The diversity gain, the number of path examinations, and the number of path switches between base stations for the switched combiner output varied according to the determined switching threshold, highlighting the importance of the selection of an appropriate threshold level. Furthermore, the performance/complexity tradeoff is demonstrated. Finally, the fading behavior at the output of the switched diversity combiners was then characterized using the diversity specific equations developed under the assumption of independent and non-identically distributed Nakagami-m fading channels. Over all of the measurement scenarios considered in this paper, the theoretical models provided an adequate fit to the fading observed at the output of the virtual switched combiner.

In this paper, we consider the transmission of confidential information over an α-μ and a κ-μ fading channel in the presence of an eavesdropper who experiences κ-μ and α-μ fading, respectively. In particular, we derive novel analytical expressions for the probability of strictly positive secrecy capacity (SPSC) and a lower bound on secrecy outage probability (SOPL) for independent and non-identically distributed (i.n.i.d.) channel coefficients. These results have been validated through simulations and reduction to known special cases. These new, very general formulations, are important as they bring together previously non-unified secrecy capacity and secrecy outage probabilities (SOP) such as those involving α-μ, Nakagami-m, Rayleigh, OneSided Gaussian, Weibull, Negative Exponential with κ-μ and Rice. Additionally, due to the duality of analysis of secrecy capacity and co-channel interference (CCI), the results presented here will also have immediate applicability in the analysis of outage probability in wireless systems affected by CCI and background noise for the aforementioned fading scenarios.

In this paper, we characterize the simultaneous channel response of narrowband on-body and body-to-body channels at 2.48 GHz using the κ-μ fading model. The measurements considered three nodes, two of these were located on the front-chest and front-central-waist of an adult male, and the third node was located on the front-central-waist of an adult female. Multiple human mobility scenarios were considered for three different environments namely an anechoic chamber, reverberation chamber and laboratory environment. For all of the measured channels, the κ-μ probability density function (pdf) provided an excellent fit to the empirical distribution of the measured short-term fading. In addition, the channel measurements also demonstrated the channel reciprocity property for each of the on-body and body-to-body links, and scenarios considered here. Finally, we also show that the sample period of the measurement system developed for this study was well within the coherence time of the channel.

Device-to-device (D2D) communications are recognized as a key component of future wireless networks which will help to improve spectral efficiency and network densification simultaneously. In order to guarantee a quality of service (QoS) to the cellular links, the transmit power of the D2D nodes needs to be restricted, which has lead to a poor link quality over D2D transmission. One viable option to improve the D2D link quality is incorporating cooperative relays into D2D networks. However most of the existing published work in relay assisted D2D networks has assumed that relay nodes cooperate spontaneously. This cannot always be guaranteed and we take this into account by considering a fundamental model on which donation-based cooperation depends. In particular we model relay cooperation as a donation game based on social comparison and characterize cooperation probability in an evolutionary context. When applying this model we evaluate the outage and capacity of relay assisted D2D network using a stochastic geometric framework.

Future wireless networks are expected to achieve higher data rates and ubiquitous coverage by seamless cooperation among diverse network technologies. However, it also increases the risk of co-channel interference and introduces the possibility of correlation in the aggregated interference. To address this problem, we adopt a stochastic geometry based approach by assuming that the interfering nodes are randomly distributed according to a Poisson point process (PPP). Using this approach, we derive closed-form expressions for the successful transmission probability and local delay in relay networks with correlated interference. Additionally, we find the optimal transmission probability p that jointly maximizes the successful transmission probability and minimizes the local delay. Numerical results are provided to confirm that the proposed joint optimization strategy achieves significant performance gains compared to conventional schemes.

Internet enabled body area networks (BANs) will form a core part of future Ambient Assisted Living (AAL) technology. In BAN based AAL applications, due to the dynamic nature of human behavior, the off-body BAN channel can be prone to deep fading phenomenon caused by body shadowing and multipath fading. This emphasizes the necessity of a power control mechanism, especially in AAL applications where battery replacement is difficult or undesirable. To that end, we present a novel practical adaptive power control protocol based on the knowledge of the channel deviation to simultaneously prolong the lifetime of wearable devices and reduce outage probability. The proposed scheme is both flexible and relatively simple to implement on hardware platforms with constrained resources making it inherently suitable for AAL applications. We also profile its performance against traditional, optimal and other existing power control schemes. It is demonstrated that not only does the outage probability reduce significantly, but the proposed algorithm also saves between 20-60% average energy consumption compared to the competing schemes.

In this paper, we consider the transmission of confidential information over a κ-μ fading channel in the presence of an eavesdropper who also experiences κ-μ fading. In particular, we obtain novel analytical solutions for the probability of strictly positive secrecy capacity (SPSC) and a lower bound of secure outage probability (SOPL) for independent and non-identically distributed channel coefficients without parameter constraints. We also provide a closed-form expression for the probability of SPSC when the μ parameter is assumed to take positive integer values. Monte-Carlo simulations are performed to verify the derived results. The versatility of the κ-μ fading model means that the results presented in this paper can be used to determine the probability of SPSC and SOPL for a large number of other fading scenarios, such as Rayleigh, Rice (Nakagamin), Nakagami-m, One-Sided Gaussian, and mixtures of these common fading models. In addition, due to the duality of the analysis of secrecy capacity and co-channel interference (CCI), the results presented here will have immediate applicability in the analysis of outage probability in wireless systems affected by CCI and background noise (BN). To demonstrate the efficacy of the novel formulations proposed here, we use the derived equations to provide a useful insight into the probability of SPSC and SOPL for a range of emerging wireless applications, such as cellular device-to-device, peer-to-peer, vehicle-to-vehicle, and body centric communications using data obtained from real channel measurements.

This paper investigates the characteristics of the shadowed fading observed in off-body communications channels at 5.8 GHz. This is realized with the aid of the κ-μ/gamma composite fading model, which assumes that the transmitted signal undergoes κ-μ fading, which is subject to multiplicative shadowing. Based on this, the total power of the multipath components, including both the dominant and scattered components, is subject to non-negligible variations that follow the gamma distribution. For this model, we present an integral form of the probability density function (PDF) as well as important analytic expressions for the PDF, cumulative distribution function, moments, and moment generating function. In the case of indoor off-body communications, the corresponding measurements were carried out in the context of four explicit individual scenarios, namely: line of sight (LOS), non-LOS walking, rotational, and random movements. The measurements were repeated within three different indoor environments and considered three different hypothetical body worn node locations. With the aid of these results, the parameters for the κ-μ/gamma composite fading model were estimated and analyzed extensively. Interestingly, for the majority of the indoor environments and movement scenarios, the parameter estimates suggested that dominant signal components existed even when the direct signal path was obscured by the test subject's body. In addition, it is shown that the κ-μ/gamma composite fading model provides an adequate fit to the fading effects involved in off-body communications channels. Using the Kullback-Leibler divergence, we have also compared our results with another recently proposed shadowed fading model, namely, the κ-μ/lognormal LOS shadowed fading model. It was found that the κ-μ/gamma composite fading model provided a better fit for the majority of the scenarios considered in this paper.

Statistical distributions have been extensively used in modeling fading effects in conventional and modern wireless communications. In the present work, we propose a novel κ - μ composite shadowed fading model, which is based on the valid assumption that the mean signal power follows the inverse gamma distribution instead of the lognormal or commonly used gamma distributions. This distribution has a simple relationship with the gamma distribution, but most importantly, its semi heavy-tailed characteristics constitute it suitable for applications relating to modeling of shadowed fading. Furthermore, the derived probability density function of the κ - μ / inverse gamma composite distribution admits a rather simple algebraic representation that renders it convenient to handle both analytically and numerically. The validity and utility of this fading model are demonstrated by means of modeling the fading effects encountered in body centric communications channels, which have been known to be susceptible to the shadowing effect. To this end, extensive comparisons are provided between theoretical and respective real-time measurement results. It is shown that these comparisons exhibit accurate fitting of the new model for various measurement set ups that correspond to realistic communication scenarios.

In this paper we propose a new composite fading model which assumes that the mean signal power of an η — μ signal envelope follows an inverse gamma distribution. The inverse gamma distribution has a simple relationship with the gamma distribution and can be used to model shadowed fading due to its semi heavy-tailed characteristics. To demonstrate the utility of the new η — μ / inverse gamma composite fading model, we investigate the characteristics of the shadowed fading behavior observed in body centric communications channels which are known to be susceptible to shadowing effects, particularly generated by the human body. It is shown that the η — μ / inverse gamma composite fading model provided an excellent fit to the measurement data. Moreover, using Kullback-Leibler divergence, the η — μ / inverse gamma composite fading model was found to provide a better fit to the measured data than the k — μ / inverse gamma composite fading model, for the communication scenarios considered here.

We describe a novel technique that can be used to allow two parties to exchange a secret key over an orthogonal frequency division multiplexing (OFDM) channel with perfect secrecy, provided subcarriers are subject to independent fading and a malicious attacker is passively eavesdropping. Our approach is shown to be robust against the eavesdropper's channel being correlated with that of the legitimate users, and also active attacks in which the eavesdropper injects fraudulent messages into the system. For the active attack, we show that when the eavesdropper injects fraudulent messages into the system, they will also inadvertently allow the legitimate users to calculate a lower bound on the secrecy capacity of the channel. This allows them to establish whether secrecy has been achieved. A consequence of our approach is that the degrees of freedom within the channel are halved.

Using device-to-device communications as an underlay for cellular communications will provide an exciting opportunity to increase network capacity as well as improving spectral efficiency. The unique geometry of device-to-device links, where user equipment is often held or carried at low elevation and in close proximity to the human body, will mean that they are particularly susceptible to shadowing events caused not only by the local environment but also by the user's body. In this paper, the shadowed κ - μ fading model is proposed, which is capable of characterizing shadowed fading in wireless communication channels. In this model, the statistics of the received signal are manifested by the clustering of multipath components. Within each of these clusters, a dominant signal component with arbitrary power may exist. The resultant dominant signal component, which is formed by the phasor addition of these leading contributions, is assumed to follow a Nakagami- m distribution. The probability density function, moments, and the moment-generating function are also derived. The new model is then applied to device-to-device links operating at 868 MHz in an outdoor urban environment. It was found that shadowing of the resultant dominant component can vary significantly depending upon the position of the user equipment relative to the body and the link geometry. Overall, the shadowed κ - μ fading model is shown to provide a good fit to the field data as well as providing a useful insight into the characteristics of the received signal.

This paper investigates the characteristics of the complex received signal in body area networks for two environments at the opposite ends of the multipath spectrum at 2.45 GHz. Important attributes of the complex channel such as the Gaussianity of the quadrature components and power imbalance, which form the basis of many popular fading models, are investigated. It is found that in anechoic environments the assumption of Gaussian distributed quadrature components will not always yield a satisfactory fit. Using a complex received signal model which considers a non-isotropic scattered signal contribution along with the presence of an optional dominant signal component, we use an autocorrelation function originally derived for mobile-to-mobile communications to model the temporal behavior of a range of dynamic body area network channels with considerable success. In reverberant environments, it was observed that the real part of the complex autocorrelation function for body area network channels decayed slightly quicker than that expected in traditional land mobile channels.

Research into localization has produced a wealth of algorithms and techniques to estimate the location of wireless network nodes, however the majority of these schemes do not explicitly account for non-line of sight conditions. Disregarding this common situation reduces their accuracy and their potential for exploitation in real world applications. This is a particular problem for personnel tracking where the user's body itself will inherently cause time-varying blocking according to their movements. Using empirical data, this paper demonstrates that, by accounting for non-line of sight conditions and using received signal strength based Monte Carlo localization, meter scale accuracy can be achieved for a wrist-worn personnel tracking tag in a 120 m2indoor office environment.

Indoor personnel localization research has generated a range of potential techniques and algorithms. However, these typically do not account for the influence of the user's body upon the radio channel. In this paper an active RFID based patient tracking system is demonstrated and three localization algorithms are used to estimate the location of a user within a modern office building. It is shown that disregarding body effects reduces the accuracy of the algorithms' location estimates and that body shadowing effects create a systematic position error that estimates the user's location as closer to the RFID reader that the active tag has line of sight to.

A combined antennas and propagation study has been undertaken with a view to directly improving link conditions for wireless body area networks. Using tissue-equivalent numerical and experimental phantoms representative of muscle tissue at 2.45 GHz, we show that the node to node |S21| path gain performance of a new wearable integrated antenna (WIA) is up to 9 dB better than a conventional compact Printed-F antenna, both of which are suitable for integration with wireless node circuitry. Overall, the WIA performed extremely well with a measured radiation efficiency of 38% and an impedance bandwidth of 24%. Further benefits were also obtained using spatial diversity, with the WIA providing up to 7.7 dB of diversity gain for maximal ratio combining. The results also show that correlation was lower for a multipath environment leading to higher diversity gain. Furthermore, a diversity implementation with the new antenna gave up to 18 dB better performance in terms of mean power level and there was a significant improvement in level crossing rates and average fade durations when moving from a single-branch to a two-branch diversity system.

Using seven strategically placed, time-synchronized body worn receivers covering the head, upper front and back torso, and the limbs, we have investigated the effect of user state:stationaryormobile and local environment: anechoic chamber, open office area and hallway upon first and second order statistics for on-body fading channels. Three candidate models were considered: Nakagami, Rice and lognormal. Using maximum likelihood estimation and the Akaike information criterion it was established that the Nakagami-m distribution best described small-scale fading for the majority of on-body channels over all the measurement scenarios. When the user was stationary, Nakagami-m parameters were found to be much greater than 1, irrespective of local surroundings. For mobile channels, Nakagami-m parameters significantly decreased, with channels in the open office area and hallway experiencing the worst fading conditions.

For the first time in the open literature we present a full characterization of the performance of receiver diversity for the on-body channels found in body area networks. The study involved three commonly encountered diversity combining schemes: selection combination (SC), maximal ratio combining (MRC) and equal gain combining (EGC). Measurements were conducted for both stationary and mobile user scenarios in an anechoic chamber and open office area environment. Achievable diversity gain for various on-body dual branch diversity receivers, consisting of horizontal and vertical spatially separated antennas, was found to be dependent upon transmitter-receive array separation, user state and level of multipath contribution from the local environment. The maximum diversity gain (6.4 dB) was observed for a horizontal two branch MRC combiner while the transmitter and receiver were on opposite sides of the body, and the user was mobile in the open office area. A novel statistical characterization of the fading experienced in on-body diversity channels is also performed using purposely derived first and second order diversity statistics for combiners operating in Nakagami fading.

For the first time, cross-correlation values for the fading experienced for antennas mounted at different locations on the upper human torso are reported. Time-synchronized narrowband measurements at 868 MHz have demonstrated that when the human body is mobile, signal branches on the upper limbs, anterior chest, abdomen and back shoulders are adequately de-correlated with cross-correlation coefficients no greater than 0.6. Selection, maximal ratio and equal gain diversity combining techniques were used to combine spatially separated branches, with up to 9.6 dB diversity gain for a system operating with left chest and back right positioned antennas. When six bodyworn diversity branches are available, maximal ratio and equal gain combining eradicate all fades beyond 3 dB below the local mean signal level.

A thorough statistical analysis of multipath effects for on-body propagation channels in wireless body area networks (WBANs) is presented. Experiments were conducted at 868 MHz for both stationary and mobile scenarios in an anechoic chamber and two typical indoor environments. When the WBAN is stationary, fading in bodyworn channels is determined by body-centric processes with Nakagami fading (m Gt 1) shown to provide the optimum fit. Equivalent Rician KdB-factors for these channels are also shown to be high, peaking at 36.1 dB for channels which cross the anterior chest region. However, mobile fading channels were predominantly Rice distributed in multipath environments. Movement in a multipath environment also caused a reduction in m and K values beyond that observed in anechoic conditions

Correlated channel measurements were made for an array of 8 bodyworn antennas moving in the indoor environment at 868 MHz. First and second-order statistical methods were used to investigate the effect of antenna positioning and human body interaction on the fading experienced by each antenna element. Average received signal strength was reduced for antenna placements lower on the body. The maximum variation in received signal power was observed for receiver positions on the upper torso. The results also show that reductions in crossing rates are experienced for wrist-worn antennas. A selection combination diversity configuration was shown to significantly reduce fade depths by as much as 6 dB for four on-body elements when compared to a single branch