To ensure future high-quality and reliable massive communication during 6G uplink transmissions, we propose a conditional diffusion model-driven massive MIMO detector, which can iteratively estimate channels and detect data for the uplink multiuser access. This approach utilizes a generative diffusion model to learn the score function of the joint posterior by integrating the prior distribution with the likelihood derived from the transmission model. The prior distribution is obtained either by learning from channel statistics or through analytical derivation from the symbol constellation. By employing noise matching initialization and an asynchronous annealed Langevin dynamics (ALD) sampling scheme, the receiver alternates efficiently between score-based channel estimation and data detection, thus avoiding traps of local minima. Simulation results demonstrate that this iterative diffusion process outperforms Bayesian-based and existing synchronous ALD channel estimation and data detection schemes in multiuser uplink scenarios.

Covert wireless communication ensures both information confidentiality and transmission untraceability, which is increasingly vital for mission-critical extended reality (XR) services. While unmanned aerial vehicles (UAVs) provide mobility and flexible coverage, and intelligent reflecting surfaces (IRSs) enable energy-efficient signal manipulation, their joint use for covert communications has not yet been sufficiently explored. This paper proposes a novel UAV-mounted IRS system for covert communications that passively reflects source signals toward a legitimate receiver while minimizing detection by an adversary warden. In contrast to previous work that treats trajectory design, beamforming, and power control in isolation, the proposed work develops a unified framework based on double deep Q-networks (DDQN) to jointly optimize the UAV trajectory, power allocation, and IRS phase shifts under covert constraints. We analytically derive the optimal detection threshold and the minimum detection error probability, which are dynamically integrated into the learning framework. The optimization problem is formulated as a constrained Markov decision process, which allows the agent to adaptively learn optimal policies in dynamic environments without relying on perfect channel knowledge. Simulation results demonstrate that the proposed framework significantly improves covert rate and energy efficiency compared with the iterative and random benchmark schemes, while also providing insights into the impact of system parameters on performance.

In this paper, we propose a multi-SNR adaptive Semantic Communication (SemCom) System based on Recon figurable Intelligent Surface (RIS) to solve the problem of insufficient adaptability of traditional SemCom in dynamic chan nel environments. We firstly design a RIS-enhanced Semantic Communication (RISemCom) System that innovatively combines a programmable wireless environment with Deep Learning (DL) to achieve joint optimization of channel environment and se mantic feature extraction. Next, two training algorithms are proposed: Dynamic Random Environment Adaptive Multi-SNR (DREAMS) algorithm and Two-Stage Training (TST) algorithm. The DREAMS dynamically adjusts SNR values during training, allowing a single model to adapt to a wide range of SNR conditions while significantly reducing deployment complexity. The TST serves as a comparison baseline, providing a dedicated optimized model for each specific SNR environment. Numerical results are demonstrated to confirm that the DREAMS algorithm maintains excellent performance across a wide range of SNRs with a single model, and significantly improves the PSNR and SSIM metrics compared to traditional methods under low SNR conditions. The performance gain is particularly notable in challenging low SNR environments, proving the system's robustness in adverse channel conditions. This work not only expands the applicability of SemCom but also provides new insights for reliable communication in variable channel environments in future 6G networks.

Researchers from both academia and industry have started exploring the potential of sixth generation cellular networks, envisioning novel concepts and futuristic capabilities. An empirical analysis of real-world fifth generation (5G) deployments serves a compass to direct the next stage of evolution and provides insights on the additional improvements required for the future services and applications. However, acquiring real-world measurement data at a city or county scale poses substantial challenges in terms of time and cost. To address this issue, this paper presents a practical and cost-effective data collection testbed and a methodology that harnesses the existing services provided by municipal council authorities, including curbside waste collections to generate large-scale realtime network coverage maps. Rich datasets of measurement data collected from multiple fourth generation (4G) and 5G cells over seven months in Nottingham, United Kingdom (UK) for all four major UK network operators, namely EE, Vodafone, O2, and Three Mobile, are provided. These large datasets can be utilized for analyzing network deployment options, coverage, and future service provisioning as well as designing and training artificial intelligence and machine learning algorithms to further optimize the mobile networks. In addition, the paper reviews the latest empirical 5G network analysis tools and techniques, which were not seen in previous generations.

Mobile network performance plays a crucial role in ensuring seamless connectivity for users, yet significant regional disparities persist across urban and rural areas. This study evaluates the coverage quality of four major mobile network operators (MNOs) across multiple counties around the River Severn Catchment Area, including performance metrics based on Acceptable Voice, Essential Data, Good Data, and Excellent Data etc. However, one limitation is that only the best available network connection is measured, meaning that areas where a handset holds onto a 4G connection despite reverting to 2G for voice calls (due to VoLTE limitations) may not be fully accounted for. Furthermore, call setup times and failure rates, key indicators of real-world voice service reliability, are not explicitly captured in this study, although they remain essential factors for future research. The study uses coverage measurements and correlation matrices to highlight network uniformity, infrastructure sharing patterns, and independent deployment strategies. The findings indicate that urban centres exhibit strong inter-provider correlations, suggesting some degree of infrastructure sharing. Many operators argue that network density remains insufficient due to high contention levels and regulatory constraints, such as planning departments restricting new infrastructure deployment. Particularly with EE, which operates with a more independent deployment model. Vodafone consistently provides superior data coverage, whereas EE leads in essential connectivity, ensuring basic service availability. These findings underscore the need for targeted rural investments, infrastructure-sharing policies, and AI-driven network optimisation to enhance service equality. The study provides valuable insights for policymakers, telecom providers, and researchers to bridge the digital divide and improve nationwide network reliability.

Two nascent technologies, rate-splitting multiple access (RSMA) and reconfigurable intelligent surfaces (RIS), present promising avenues to enhance spectral and energy efficiencies within multi-antenna frameworks. However, passive RIS may encounter challenges in delivering substantial capacity gains due to the cumulative path loss effect. Active RIS (ARIS) equipped with low-cost amplifiers in the reflective elements emerges as a solution to mitigate the limitation. This paper investigates a multi-user multiple-input single-output (MISO) simultaneous wireless information and power transfer (SWIPT) framework, augmented by an ARIS and leveraging RSMA. The primary objective is to maximize the system's SE, subject to constraints imposed by the design of BS beamforming vectors, PS ratios, and RIS phase shifts. To address the inherent nonconvexity of this optimization problem, we propose an innovative approach that combines alternating optimization (AO) and semidefinite relaxation (SDR) algorithms. Simulations results demonstrate the significant advantages of our proposed design over established benchmarks.

The increasing demand for emerging vehicular services, such as immersive entertainment, safety applications, and enhanced infotainment, has driven the development of Vehicle-to-Everything communication. However, vehicular networks face significant challenges due to stringent Quality of Service (QoS) requirements and the highly dynamic nature of wireless environments. Traditional Radio Access Network (RAN) architectures struggle to adapt to these conditions, necessitating more flexible and intelligent solutions. Open RAN, with its virtualised and intelligent architecture, offers a promising approach by incorporating Artificial Intelligence and Machine Learning for real-time network optimisation. This paper proposes a Deep Reinforcement Learning (DRL)-based Modulation and Coding Scheme (MCS) selection algorithm within an Open RAN-enabled vehicular network to optimise resource usage while ensuring QoS compliance. The proposed algorithm leverages the flexibility of Open RAN and the adaptability of DRL to dynamically configure MCS parameters, enhancing the Quality of Experience for users in challenging vehicular scenarios. Simulation results demonstrate that the DRL-based approach reduces network resource usage by 33% compared to conventional SNR-based MCS selection while improving QoS satisfaction by approximately 5%.

The escalating demands for secure wireless communications in the Internet of Everything envisioned for the 6G era emphasize the urgency of advanced security solutions beyond traditional methods, especially in extended reality applications where secure wireless communications are essential for functionality and user experience. This paper explores the integration of covert communication techniques with deep reinforcement learning to bolster security in wireless networks. Covert communication, which prevents adversaries from detecting transmissions, is a critical factor in protecting data transmission over vulnerable wireless channels. This paper considers a two-hop wireless system model that is optimized using a double-deep Q-network algorithm. The problem is formulated as a constrained Markov decision process, jointly optimizing relay selection, transmission, and jamming powers to maximize covert communication rates while minimizing detection by adversarial wardens. Comprehensive numerical analysis demonstrates the effectiveness of the proposed method under various system conditions, including different configurations of relay and jamming powers. The results confirm that our model aligns well with theoretical expectations and substantially enhances covert communication by intelligently adapting to environmental dynamics.

The Society of Automotive Engineers (SAE) J2954 Differential Inductive Positioning System (DIPS) is a ground breaking technology introduced to enable positioning technique for electric vehicle (EV) wireless power transfer (WPT). This technology and its related standard have great potential to bring EV wireless charging to mass production and opens the doors for commercializing autonomous vehicles. Although the DIPS standard has defined the hardware requirements [1], the positioning algorithm design has not been addressed in the literature. In this paper, we introduce the industry's first algorithm for DIPS. We mathematically derive the signal model and parameters estimation algorithm, then evaluate the estimation accuracy of the proposed algorithm using Monte Carlo simulations. The evaluation results have shown the algorithm can achieve centimeter-level accuracy and approach the Cramér-Rao bound.

Machine Learning (ML) models have proven effective in optimizing wireless and private networks. However, recent research highlights the threat of data poisoning attacks on ML models. To analyze such a threat on an industrial 5G private network, this work investigates the effectiveness of data poisoning against it. We primarily focus on poisoning four ML models: Support Vector Machines (SVM), Random Forest (RF), Decision Tree (DT), and Artificial Neural Networks (ANN), at three poisoning levels: 10%, 15%, and 20%. Our research shows that all models introduce instability in the network, rather than optimization, whereas neural networks are less affected by data poisoning compared to other models. At the 20 % data poisoning level, model performance degrades by about 6-7% for SVM, RF, and DT, while ANN shows a minimal disruption of 2%.

Fluid antenna (FA) is capable of achieving a significant degree of spatial diversity within the limited space of a wireless device by adjusting the radiating elements to optimal positions. In this article, we explore the potential of deploying FAs on the overall performance of wireless powered communication network (WPCN). Specifically, each Internet of Things (IoT) device in WPCN is equipped with a single FA comprising multiple ports. The IoT device (ID) selects the optimal receive port for energy harvesting from the power beacon (PB), followed by choosing the optimal transmit port to send its data to the access point (AP). Our objective is to maximize the sum throughput of IDs by jointly optimizing port activation and time scheduling, subject to constraints on the received signal-to-noise ratio (SNR) of each individual ID and the total transmission time. To tackle this nonconvex problem, we first apply the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions to find the optimal solutions for time slots. Then, we introduce an efficient algorithm based on the alternating optimization (AO) method to iteratively achieve a locally optimal solution for port activation. Additionally, a low-complexity scheme is proposed to minimize computational overhead. Simulation results reveal that incorporating FAs into a WPCN markedly improves the overall system performance, and highlights the benefits of port selection for the FA in comparison to baseline methods.

A novel integrated wireless powered sensing and communication (IWPSAC) framework is proposed. Specifically, a multi-antenna transmitter utilizes a radar signal for sensing targets while enabling multiple Internet of Things (IoT) devices to harvest energy from the signal, each of which employs the collected energy to upload information to an access point (AP). Our setup further considers a reconfigurable intelligent surface (RIS) to integrate sensing, wireless energy transfer (WET) and wireless information transfer (WIT) by optimizing the phase shifts. We formulate an optimization problem to maximize the weighted sum of the communication throughput and the beampattern gain by jointly designing the energy beamforming, transmission time scheduling and RIS phase shifts. The presence of multiple coupled variables in the formulated problem renders the optimization problem non-jointly convex. To address its non-convexity, we first derive a closed-form expression for the optimal RIS phase shifts in the WIT phase. Then, an alternating optimization (AO) algorithm is proposed to solve the tradeoff problem iteratively. Concretely, this involves alternating the design of the energy beamforming and the RIS phase shifts for sensing/WET by leveraging the semidefinite programming (SDP) relaxation method. To overcome the high complexity introduced by the SDP, we introduce a low complexity AO algorithm that derives the optimal solutions for energy beamforming, transmission time scheduling, and sensing/WET phase shift using successive convex approximation (SCA), Lagrangian duality methods, Karush-Kuhn-Tucker (KKT) conditions, and the element-wise block coordinate descent (EBCD) approach. Simulation results demonstrate the performance of the proposed algorithms and underscore the superior benefits of the RIS compared to baseline schemes.

Spatial modulation (SM) transmits additional information bits by the selection of antennas. Generalized spatial modulation (GSM), as an advanced type of SM, can be divided into diversity and multiplexing (MUX) schemes according to the symbols carried on the selected antennas are identical or different. Recently, reconfigurable intelligent surface (RIS) assisted SM exhibits better reception performance compared to conventional SM. To overcome the limitations of SM, this paper combines GSM with RIS and proposes the RIS-aided receive generalized spatial modulation (RIS-RGSM) scheme. The RIS-RGSM diversity scheme is realized via a simple improvement based on the state-of-the-art scheme. To further increase the transmission rate, a novel RIS-RGSM MUX scheme is proposed, where the reflection phase shifts and on/off states of RIS elements are configured to achieve bit mapping. The theoretical bit error rate (BER) of the proposed scheme is derived and agrees well with the simulation results. Numerical simulations show that the RIS-RGSM MUX scheme has better BER performance than the diversity scheme. The proposed scheme can significantly increase the transmission rate and maintain good performance compared to the existing scheme under a limited number of antennas.

With UAVs becoming increasingly prevalent across a diverse array of industries, their extensive adoption has encouraged a great increase in research attention. Combining multiple UAVs together to form UAV swarms provides increased mission performance over singular UAVs. Moreover, the use of multiple UAV’s brings the ability to distribute computation for enhanced efficiency, or widen perspectives for UAV applications with sensors. This paper proposes a novel leader-follower structured UAV swarm along with a suitable network for inter-UAV coordination and formation control. A simulation of the swarm’s kinematics will be completed using Simulink to gain insight into the accuracy of the proposed formation. Additionally a simulation of the formation control network using COOJA will be executed to investigate and analyse it’s effectiveness. Results suggest that the UAV’s have excellent tracking abilities of instructed trajectories and when incorporating the formation control network, transmission delays have a negligible effect on performance.

A scarcity of frequencies arises from the increased demand for the Industrial Internet of Things (IIoT) and networked systems in warehouse operations. This puts an additional burden on available bandwidth in cellular networks. This problem can be tackled by cognitive radio networks (CRNs), which use spectrum sensing to track and access unused frequencies, increasing spectrum efficiency. However CRNs have been extensively studied in several IoT projects, this study is the initial to examine the utilisation of CRN to intelligently manage the use of available radio frequencies. Two macro base stations are the central hubs of a network, communicating with IIoT devices in warehouse settings. This paper investigates the utilisation of CRN with machine-learning algorithms to intelligently manage the spectrum for connected IoT devices for intelligent Warehouse settings. A range of machine learning methods, including Support Vector Machine (SVM), k-nearest Neighbors (KNN), Decision Tree, Random Forest, and Naive Bayes, are provided to identify accessible bands for the best possible spectrum allocation and to recognize key users. Based on criteria like accuracy, precision, recall, and score for all ML techniques, the system’s performance is assessed. The numerical results demonstrate a noteworthy 20% reduction in false positives and a substantial improvement in cooperative spectrum sensing accuracy, which in turn improves the effectiveness of IIoT operations in warehouse environments.

Radio Access Network (RAN) deployments are evolving quickly owing to the innovative approaches of the Open Radio Access Network (O-RAN) Alliance. Specifically, they are moving away from closed, customized hardware implementations and toward virtualized instances operating on shared platforms. Future successful and affordable RAN deployments are made possible by this paradigm change, which is characterised by the separation of radio software components from hardware. Real-time network parameter configuration, sufficient computing resources for virtualized RAN (vRAN) deployment, and dependable processing unit sharing among numerous vRAN instances are some of the obstacles still standing in the way of successful O-RAN network implementations. Thus, this paper explored and compared the effectiveness of diverse optimization algorithms for minimising the number of resource blocks (nRBS), including machine learning (RandomForestRegressor), heuristic, and mathematical methods. Moreover, it investigates the lessons learned and the limitations of the proposed system. It demonstrates the practical success of a heuristic approach in O-RAN optimization, achieving significant reductions in resource blocks based on the Throughput-to-Bandwidth Ratio. It also provided insights into challenges with the RandomForestRegressor model, highlighted the importance of considering real-world network dynamics, and offered valuable lessons for future research, emphasizing the need for adaptive solutions and exploring hybrid optimization approaches, ultimately contributing to an enhanced understanding of O-RAN optimization.

Industry 4.0, driven by enhanced connectivity by wireless technologies such as 5G and Wi-Fi 6, fosters flexible industrial scenarios for high-yield production and services. Private 5G networks and 802.11ax networks in unlicensed spectrum offer very unique opportunities, however existing techniques limit the flexibility needed to serve diverse industrial use cases. In order to address a subset of these challenges, this paper offers a solution for time-sensitive application use cases. A new technique is proposed to enable data-driven operations through Machine Learning for technologies sharing unlicensed bands. This enables proportionate spectrum sharing informed by data to improve critical applications performance metrics. The results presented reveal improved performance to serve critical industrial operations, without degrading spectrum utilization.

With the rise of requirements for high-speed and low-latency connectivity, innovative approaches such as network slicing, Quality of Service (QoS) Provisioning, and reinforcement learning-based resource allocation, including the use of resource blocks (RBs) including radio resources, have to keep pace with these evolving requirements. By utilising network intelligence through machine learning and deep reinforcement learning, there is a potential to enhance QoS provisioning, expand the current network capacity, reduce congestion and latency, improve energy efficiency, and thus support new business models and revenue sources. The progress so far suggests that while considering RBs, network-slicing datasets, intertwined with QoS provisioning, have not been explored comprehensively, and packet drop probability or rate has not been taken into account in resource allocation. This paper proposes a network slicing method that uses seven machine learning algorithms and demonstrates its efficiency and accuracy compared to benchmarks in the literature with respect to QoS. Moreover, a priority algorithm is developed to ensure that packets with a high chance of being dropped (affecting QoS) are queued first. A resource allocation algorithm considering QoS provisioning and RBs is utilised to improve network performance based on a mathematical derivation of packet drop rate. Furthermore, a virtualisation of the processing between Cloud and Edge depends on the network slice. By intelligently distributing tasks between Cloud and Edge resources using deep reinforcement learning and genetic algorithms, an offloading script ensures uninterrupted service availability even when all network resources (i.e., RBs) are in use, thus maintaining the desired QoS.

This article studies an intelligent reflecting surface (IRS)-empowered wireless-powered communication network (WPCN) in Internet of Things (IoT) networks. In particular, a power station (PS) with multiple antennas uses energy beamforming to enable wireless charging to multiple IoT devices, in the downlink wireless energy transfer (WET) phase; then, during the uplink wireless information transfer (WIT) phase, these IoT devices utilize the harvested energy to concurrently transmit their individual information signal to a multiantenna access point (AP), which equips with multiuser decomposition (MUD) techniques to reconstruct the IoT devices’ signal. An IRS is deployed to improve the energy collection and information transmission capabilities in the WET and WIT phases, respectively. To examine the performance of the system under study, we maximize the sum throughput with the aim of jointly designing the optimal solutions for the active PS energy beamforming, AP receive beamforming, passive IRS beamforming, and time scheduling. Due to the multiple coupled variables, the resulting formulation is nonconvex, and a two-level scheme to solve the problem is proposed. At the outer level, a 1-D search method is applied to find the optimal time scheduling, while at the inner level, an iterative block coordinate descent (BCD) algorithm is proposed to design the optimal receive beamforming, energy beamforming, and IRS phase shifts. In particular, the receive beamforming part is designed by considering the equivalence between sum rate maximization and sum mean square error (MSE) minimization, thereby deriving a closed-form solution. Furthermore, we alternately optimize the energy beamforming and IRS phase shifts using Lagrange dual transformation (LDT), quadratic transformation (QT), and alternating direction method of multipliers (ADMMs) methods. Finally, numerical results are presented to showcase the performance of the proposed solution and highlight its advantages compared to some typical benchmark schemes.

The incredible potentiality of reconfigurable intelligent surface (RIS) in addressing power supply and obstacle environment of Internet of Medical Things (IoMT) has been capturing our interest. Considering the nettlesome "double-fading" effect introduced by passive RIS, we investigate an active RIS-enhanced IoMT system in this article, where the wireless power transfer (WPT) from power station (PS) to IoMT devices and the wireless information transfer (WIT) from IoMT devices to the access point (AP) are both implemented with the assistance of active RIS. Aiming to maximize the sum throughput of the considered IoMT system, a joint design of time schedules and reflecting coefficient matrices of the active RIS is proposed. Trapped by the non-convex and obstinate optimization problem, we explore the semi-definite programming (SDP) relaxation and successive convex approximation (SCA) techniques based on alternating optimization (AO) algorithm. Simulation results verify our solution approach to the intractable optimization problem and showcase the boosted spectrum and energy efficiency of the active RIS-enhanced IoMT system.

Wireless sensing and wireless energy are enablers to pave the way for smart transportation and a greener future. In this paper, an intelligent reflecting surface (IRS) assisted integrated sensing and wireless power transfer (ISWPT) system is investigated, where the transmitter in transportation infrastructure networks sends signals to sense multiple targets and simultaneously to multiple energy harvesting devices (EHDs) to power them. Recognizing the inherent tradeoff between energy harvesting and sensing performance, we propose to jointly optimize the system performance via optimizing the beamforming and IRS phase shift. However, the coupling of optimization variables makes the formulated problem non-convex. Thus, an alternative optimization approach is introduced and based on which two algorithms are proposed to solve the problem. Specifically, the first algorithm involves the semi-positive definite programming techniques, and the second algorithm is based on the successive convex approximations and majorization minimization to design the closed form solutions of the optimization variables, which can effectively reduce the computational complexity. Our simulation results validate the proposed algorithms and demonstrate the advantages of using IRS to assist wireless power transfer in ISWPT systems. This research contributes to the integration of wireless sensing and wireless energy in intelligent transportation systems and underscores the optimization of system performance through the introduction of IRS.

This paper investigates a learning-based approach autonomously and jointly optimizing the trajectory of unmanned aerial vehicle (UAV), phase shifts of reconfigurable intelligent surfaces (RIS), and aggregation weights for federated learning (FL) in wireless communications, forming an autonomous RIS-assisted UAV-enabled network. The proposed network considers practical RIS reflection models and FL transmission errors in wireless communications. To optimize the RIS phase shifts, a double cascade correlation network (DCCN) is introduced. Additionally, the deep deterministic policy gradient (DDPG) algorithm is employed to address the optimization problem of UAV trajectory and FL aggregation weights based on the results obtained from DCCN. Simulation results demonstrate the substantial improvement in FL performance within the autonomous RIS-assisted UAV-enabled network setting achieved by the proposed algorithms compared to the benchmarks.

In this paper, we investigate an intelligent reflecting surface (IRS)-assisted wireless powered Internet of Things (WP-IoT) network that operates in multiple resource blocks (RBs). Particularly, the IRS helps in both downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT), in a way that it improves energy reflection in WET from a power station (PS) to various IoT devices and boosts information delivery in WIT from the IoT devices to an access point (AP). Those IoT devices are capable of utilizing the collected energy, and adopting the time-division multiple access (TDMA) or non-orthogonal multiple access (NOMA) scheme in the uplink WIT. Aiming to maximize the average throughput as the overall performance indicator of the considered network, we jointly optimize the transmit power allocation of the PS, the time scheduling, and the IRS phase shifts. These coupled variables lead to the non-convexity of this optimization problem, which cannot be solved directly. To address this problem, we first design the optimal PS’s transmit power allocation for each RB. For the TDMA-based scheme, we design the closed-form IRS beam pattern of the uplink WIT. Then, the closed-form downlink and uplink time allocations are derived by the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions. In addition, the quadratic transformation (QT)-based Alternating Direction Method of Multipliers (ADMM) approach is proposed to iteratively derive the sub-optimal IRS beam pattern of the downlink WET in an alternated fashion. For the NOMA-based scheme, we propose to apply an alternating optimization (AO) algorithm to iteratively optimize the IRS phase shifts, where the uplink IRS beam pattern is iteratively designed by the Riemannian Manifold Optimization (RMO) approach, and the QT-based ADMM method is adopted to alternately derive the sub-optimal downlink IRS phase shifts. Finally, numerical results demonstrate the improved performance of the proposed solution approaches compared to the benchmark schemes, also highlight advantages of the application of IRS in multiple RB scenarios.

This paper exploits an intelligent reflecting surface (IRS) assisted wireless powered mobile edge computing and caching (WP-MECC) network. In particular, an IRS is utilized to reflect energy signals from a power station (PS) to various IoT devices for energy harvesting during uplink wireless energy transfer (WET). These devices collect energy to support their own partially local computing for computational tasks and their offloading capabilities to an access point (AP), with the help of IRS via time or frequency division multiple access (TDMA or FDMA). The AP is equipped with a local cache connected with a MEC server via a backhaul link, which prefetches the data to facilitate edge computing capabilities. The maximization of a utility function is formulated to evaluate the overall network performance, which is defined as the difference between the sum of computational bits (offloading bits and local computing bits) and total backhaul cost. Due to multiple coupled variables, we first design the optimal caching strategy. Then, an auxiliary vector is introduced to coordinate the energy consumption of local computing and offloading, where its optimal solution can be achieved by an exhaustive search. Moreover, we utilize the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions to derive the optimal time scheduling for the TDMA scheme or the optimal bandwidth allocation for the FDMA counterpart in closed form. The IRS phase shifts are iteratively designed by employing the quadratic transformation (QT) and the Riemannian Manifold Optimization (RMO). Finally, simulation results are demonstrated to validate the network utility performance and confirm the advantage of the employment of IRS, the optimal IRS phase shift design and caching strategy, in comparison to the benchmark schemes.

This paper proposes a multi-cluster wireless powered Internet of Things (WP-IoT) network assisted by multiple intelligent reflecting surfaces (multi-IRS). In this network, a power station (PS) first broadcasts wireless energy to the distributed IoT devices grouped into multiple clusters. The IoT devices then use the harvested energy to convey their information to an access point (AP), based on a hybrid time- and frequency-division multiple access (TDMA-FDMA) protocol. Furthermore, multiple IRSs are deployed to perform anomalous reflection for energy and information transfer, to improve energy harvesting and data transmission capabilities. Under the constraints of the unit-modulus phase shifts, the transmission time shared among clusters and the bandwidth shared by the devices in each cluster, the considered system is optimized by maximizing its sum throughput. The optimization problem is non-convex and with complicatedly coupled variables. To solve this problem, we propose to first apply the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions to derive closed-form solutions for transmission scheduling and bandwidth allocation, then the quadratic transformation (QT) and the alternating optimization (AO) algorithm are introduced to solve the downlink and uplink IRS phase shifts, whilst the Majorization-Minimization (MM) and Riemannian Manifold Optimization (RMO) methods are applied to iteratively derive their closed-form solutions. Additionally, we provide a benchmark scheme to facilitate the system design, where each IRS can control its “on/off” state to aid the downlink and uplink transmissions in the condition of at most one activated IRS during one certain time duration. Finally, simulation results are presented to verify the optimality of our proposed scheme and highlight the beneficial role of the IRS.

This article investigates a wireless-powered intelligent radio environment, where a fractional nonlinear energy harvesting (NLEH) is proposed to enable an intelligent reflecting surface (IRS)-assisted wireless-powered Internet of Things (WP IoT) network. The IRS engages in downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT). We aim to improve the overall performance of the considered network, and the approach is to maximize its sum throughput subject to constraints of two different types of IRS beam patterns and time durations. To solve the formulated problem, we first consider the Lagrange dual method and Karush–Kuhn–Tucker (KKT) conditions to optimally design the time durations in closed form. Then, a quadratic transformation (QT) is proposed to iteratively transform the fractional NLEH model into the subtractive form, where the IRS phase shifts are optimally derived by the complex circle manifold (CCM) method in each iteration. Finally, numerical results are demonstrated to promote the proposed scheme in comparison to the benchmark schemes, where the benefits are induced by the IRS compared with the benchmark schemes.

In this article, we propose an intelligent reflecting surface (IRS)-enabled wireless-powered caching system. In the proposed IRS model, a power station (PS) provides wireless energy to multiple Internet of Things (IoT) devices, delivering their information to an access point (AP) by utilizing the harvested power. The AP, equipped with a local cache, stores the IoT data to avoid waking up the IoT devices frequently. Meanwhile, we deploy the IRS involving in the wireless energy and information transfer process for performance enhancements. In this practical system, the PS and AP could belong to different service providers. Also, the AP requires to incentivize the PS to offer a provisional energy service. We model the interaction between the PS and AP as a Stackelberg game that jointly optimizes the transmit power of the PS, the energy price, the phase shifts of the wireless energy transfer (WET) and wireless information transfer (WIT) phases, as well as wireless caching strategies of the AP. In this way, we first derive the optimal solutions of the phase shifts and the transmit power of the PS in a closed form. We propose an alternating optimization (AO) algorithm to optimize the wireless caching strategies and the energy price iteratively. Finally, we present various numerical evaluations to validate the beneficial role of the IRS and the wireless caching strategies and the performance of the proposed scheme compared with the existing benchmark schemes.

Considering a reconfigurable intelligent surface (RIS) aided wireless powered Internet of Things (WP IoT) network. To address the energy-limitation issue, IoT devices in such a network can be wirelessly powered by a power station (PS) first and then connect with an access point (AP) using their own harvested energy. The RIS helps enhance energy and information receptions in the downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT), respectively. This work unveils the impact of phase shift error (PSE) and transceiver hardware impairment (THI) on the considered network. Our investigation starts with a scenario where only the impact of the PSE on system under study is considered, then moves toward a scenario with the compound effect of both PSE and THI. A maximization problem of the system sum throughput is formulated to evaluate the overall performance for these two scenarios, subject to the constraints of the adjustable RIS phase shifts, the statistical PSE and the transmission time scheduling. To handle the non-convexity of the formulated problem due to those coupled variables, we first adopt the Lagrange dual method and Karush-Kuhn-Tucker (KKT) conditions to derive the optimal time scheduling in closed-form. Next, we recast the stochastic PSE into the deterministic counterpart for its tractability. Then, we adopt a successive convex approximation (SCA) to iteratively derive the optimal WIT’s phase shifts, and element-wise block coordinate decent (EBCD) and complex circle manifold (CCM) methods to iteratively derive the optimal WET’s phase shifts. Finally, we complete our solution approach for the scenario with both PSE and THI. Simulation results highlight the performance of the proposed scheme and the benefits induced by the RIS in comparison to benchmark schemes.

This paper investigates the impact of intelligent reflecting surface (IRS) enabled wireless secure transmission. Specifically, an IRS is deployed to assist multiple-input multiple-output (MIMO) secure system to enhance the secrecy performance, and artificial noise (AN) is employed to introduce interference to degrade the reception of the eavesdropper. To improve the secrecy performance, we aim to maximize the achievable secrecy rate, subject to the transmit power constraint, by jointly designing the precoding of the secure transmission, the AN jamming, and the reflecting phase shift of the IRS. We first propose an alternative optimization algorithm (i.e., block coordinate descent (BCD) algorithm) to tackle the non-convexity of the formulated problem. This is made by deriving the transmit precoding and AN matrices via the Lagrange dual method and the phase shifts by the Majorization-Minimization (MM) algorithm. Our analysis reveals that the proposed BCD algorithm converges in a monotonically non-decreasing manner which leads to guaranteed optimal solution. Finally, we provide numerical results to validate the secrecy performance enhancement of the proposed scheme in comparison to the benchmark schemes.

This work provides the first link level performance evaluation of the Rate-Splitting (RS) based precoding scheme in a downlink multi-user multiple input single output (MU-MISO) system. Contrary to the existing works on the RS precoding that mostly focused on the sum rate or minimum rate maximization, this work bridges the optimization results with the bit error rate (BER) performance, initiating the RS software implementation. We demonstrate that, in an overloaded scenario, the conventional precoding schemes suffer from the BER error floor that corresponds to their rate saturation, which can be overcome by the RS-based strategy that adding the message decodability of certain users with the interference-limited message rate.

In this paper, Generalized Space-Time Block Coded Spatial Modulation (GSTBC-SM) is proposed for Multiple-Input and Multiple-Output (MIMO) system, which can be extended into an arbitrary even number of Transmit Antennas (TAs). The proposed GSTBC-SM scheme employs the hybrid concepts of Generalized Space-Time Block Coding (GSTBC) and Spatial Modulation (SM) to further exploit the diversity benefits of GSTBC using sparse Radio Frequency (RF) chains. To be more specific, the information bits are divided into Nugroups and each group is modulated by SM scheme. Finally, the Nusymbols are invoked for GSTBC structure. In order to demonstrated the advantages of our proposed GSTBC-SM schemes, the theoretical Average Bit Error Probability (ABEP) of our proposed GSTBC-SM is derived. Both our analytical and simulation results demonstrated that the proposed GSTBC-SM scheme is capable of providing considerable performance gains over the corresponding GSTBC schemes at the same transmit rate associated with the same number of RF chains.

Orthogonal Frequency Division Multiplexing (OFDM) with Index Modulation (OFDM-IM), which conveyed information bits via the activated indices and constellation symbols is a promising technique in the next wireless communications. In the OFDM-IM scheme, only part of subcarriers are activated to transmit information, the inactive subcarriers transmit zero symbols, so that the conventional differential coding is not suitable for the adjacent subcarriers. In order to address this issue, in this paper, a novel Rectangular Differential OFDM-IM (RD-OFDM-IM) scheme is proposed to exploit the benefits of OFDM-IM dispensing with Channel State Information (CSI). In the proposed RD-OFDM-IM scheme, N subcarriers are partitioned into G subblocks and index modulation is employed in each subblock first. Then rectangular differential coding is invoked during two adjacent subblocks, so that non-coherent detection can be employed for the proposed RD-OFDM-IM scheme. Simulation results are shown that the proposed RD-OFDM-IM scheme is capable of providing considerable performance gain over conventional Differential OFDM (D-OFDM) scheme with lower Peak Average Power Ratio (PAPR).

This paper investigates a secure wireless-powered integrated service system with full-duplex self-energy recycling. Specifically, an energy-constrained information transmitter (IT), powered by a power station (PS) in a wireless fashion, broadcasts two types of services to all users: a multicast service intended for all users and a confidential unicast service subscribed to by only one user while protecting it from any other unsubscribed users and an eavesdropper. Our goal is to jointly design the optimal input covariance matrices for the energy beamforming, the multicast service, the confidential unicast service, and the artificial noises from the PS and the IT, such that the secrecy-multicast rate region (SMRR) is maximized subject to the transmit power constraints. Due to the non-convexity of the SMRR maximization (SMRRM) problem, we employ a semidefinite programming-based two-level approach to solve this problem and find all of its Pareto optimal points. In addition, we extend the SMRRM problem to the imperfect channel-state information case, where a worst-case SMRRM formulation is investigated. Moreover, we exploit the optimized transmission strategies for the confidential service and energy transfer by analyzing their own rank-one profile. Finally, numerical results are provided to validate our proposed schemes.

This paper investigates a two-tier heterogeneous networks (HetNets), where millimeter wave (mmWave) frequency is employed at the macro base station (MBS), and the small cell BSs (SBSs) consider orthogonal frequency division multiple access (OFDMA). Subarray structure based hybrid analog/digital precoding scheme is studied to reduce the hardware cost and energy consumption. Our goal is to maximize the energy efficiency (EE) of the HetNets with limited wireless backhaul capacity and all users' quality of service (QoS) constraints. Due to nonconvexity of the mixed integer nonlinear fraction programming (MINLFP), the formulated problem cannot be solved directly. In order to circumvent this issue, we propose a two-loop iterative resource allocation algorithm. Specifically, we reformulate the outer-loop problem into a difference of convex programming (DCP) by employing integer relaxation and Dinkelback method. In addition, the first-order approximation is adopted to linearize this inner-loop DCP problem into a convex optimization framework. Lagrange dual method is adapted to achieve the optimal power allocation. Furthermore, the convergence of the proposed iterative algorithm is analyzed. Numerical results are presented to demonstrate our proposed algorithms.

In time-division-duplexing (TDD) massive multiple-input multiple-output (MIMO) systems, channel reciprocity is exploited to overcome the overwhelming pilot training and the feedback overhead. However, in practical scenarios, the imperfections in channel reciprocity, mainly caused by radio-frequency mismatches among the antennas at the base station side, can significantly degrade the system performance and might become a performance limiting factor. In order to compensate for these imperfections, we present and investigate two new calibration schemes for TDD-based massive multi-user MIMO systems, namely, relative calibration and inverse calibration. In particular, the design of the proposed inverse calibration takes into account a compound effect of channel reciprocity error and channel estimation error. We further derive closed-form expressions for the ergodic sum rate, assuming maximum ratio transmissions with the compound effect of both errors. We demonstrate that the inverse calibration scheme outperforms the traditional relative calibration scheme. The proposed analytical results are also verified by simulated illustrations.

Channel reciprocity in time-division duplexing (TDD) massive multiple-input multiple-output (MIMO) systems can be exploited to reduce the overhead required for the acquisition of channel state information (CSI). However, perfect reciprocity is unrealistic in practical systems due to random radio-frequency (RF) circuit mismatches in uplink and downlink channels. This can result in a significant degradation in the performance of linear precoding schemes, which are sensitive to the accuracy of the CSI. In this paper, we model and analyse the impact of RF mismatches on the performance of linear precoding in a TDD multi-user massive MIMO system, by taking the channel estimation error into considerations. We use the truncated Gaussian distribution to model the RF mismatch, and derive closed-form expressions of the output signal-to-interference-plus-noise ratio for maximum ratio transmission and zero forcing precoders. We further investigate the asymptotic performance of the derived expressions, to provide valuable insights into the practical system designs, including useful guidelines for the selection of the effective precoding schemes. Simulation results are presented to demonstrate the validity and accuracy of the proposed analytical results.

We propose a new antenna selection scheme for a massive MIMO system with a single user terminal and a base station with a large number of antennas. We consider a practical scenario where there is a realistic correlation among the antennas and imperfect channel estimation at the receiver side. The proposed scheme exploits the sparsity of the channel matrix for the effective selection of a limited number of antennas. To this end, we compute a sparse channel matrix by minimising the mean squared error. This optimisation problem is then solved by the well-known orthogonal matching pursuit algorithm. Widely used models for spatial correlation among the antennas and channel estimation errors are considered in this work. Simulation results demonstrate that when the impacts of spatial correlation and imperfect channel estimation introduced, the proposed scheme in the paper can significantly reduce complexity of the receiver, without degrading the system performance compared to the maximum ratio combining.