This paper established a novel multi-input multi-output (MIMO) communication network, in the presence of full-duplex (FD) transmitters and receivers with the assistance of dual-side intelligent omni surface. Compared with the traditional IOS, the dual-side IOS allows signals from both sides to reflect and refract simultaneously, which further exploits the potential of metasurfaces to avoid frequency dependence, and size, weight, and power (SWaP) limitations. By considering both the downlink and uplink transmissions, we aim to maximize the weighted sum rate, subject to the transmit power constraints of the transmitter and the users and the dual-side reflecting and refracting phase shifts constraints. However, the formulated sum rate maximization problem is not convex, hence we exploit the weighted minimum mean square error (WMMSE) approach, and tackle the original problem iteratively by solving two sub-problems. For the beamforming matrices optimizations of the downlink and uplink, we resort to the Lagrangian dual method combined with a bisection search to obtain the results. Furthermore, we resort to the quadratically constrained quadratic programming (QCQP) method to optimize the reflecting and refracting phase shifts of both sides of the IOS. In addition, we introduce the case without a dual-side IOS for comparison. Simulation results validate the efficacy of the proposed algorithm and demonstrate the superiority of the dual-side IOS.

In the Internet of Things (IoT) based scenarios, the network may encounter significant issues related to energy and communication as a result of a progressively growing number of terminals. We introduce simultaneous wireless power transfer (SWIPT) technology assisted by a reconfigurable intelligent surface (RIS) to counteract this challenge. Thus, the network's flexibility and reliability will be further enhanced. According to this system architecture, the scattering-parameter-based communication model is introduced to disclose hardware features for an energy efficiency (EE) maximization problem. Specifically, the potential unauthorized demodulation is also considered in the problem formulation. To resolve the issue, an alternative strategy is utilized to optimize iteratively the coupled variables. In particular, the block coordinate descent (BCD) approach based on the Sherman-Morrison formulais proposed to solve the RIS subproblem. The numerical results prove the hardware effects cannot be dismissed lightly. Besides, the configuration of the RIS may impact the network performance directly.

As the Internet of things (IoT) becomes more widely promoted, the number of terminal nodes is expected to continue rising. This poses significant challenges for powering terminal devices and providing communication services. Fortunately, the simultaneous wireless information and power transfer (SWIPT) technique can support both information exchange and power supply at the same time. Unlike theoretical research summaries, this article enumerates the important roles of SWIPT in IoT-based scenarios, identifies practical challenges, and presents crucial techniques for constructing a SWIPT system from a deployment perspective. To demonstrate the cooperative relationships between the enabling techniques and the performance of the entire system, a prototype of the SWIPT system is constructed for a practical scenario. The results of the operation show that the constructed system can meet the differentiated requirements of nodes. Finally, the article summarizes the open research issues related to engineering-oriented SWIPT systems.

Chapter 13 Secrecy Rate Maximization for Intelligent Reflective Surface-Assisted MIMO Communication Radar Sisai Fang, Sisai Fang University of Surrey, 5GIC & 6GIC, Institute for Communication Systems, Department of Electrical and Electronic Engineering (ICS), Guildford, UKSearch for more papers by this authorGaojie Chen, Gaojie Chen University of Surrey, 5GIC & 6GIC, Institute for Communication Systems, Department of Electrical and Electronic Engineering (ICS), Guildford, UKSearch for more papers by this authorSangarapillai Lambotharan, Sangarapillai Lambotharan Loughborough University, School of Mechanical, Manufacturing and Electrical Engineering, Leicestershire, Loughborough, UKSearch for more papers by this authorCunhua Pan, Cunhua Pan Queen Mary University of London, School of Electronic Engineering and Computer Science, London, UKSearch for more papers by this authorJonathon A. Chambers, Jonathon A. Chambers University of Leicester, School of Engineering, Leicester, Leicestershire, UKSearch for more papers by this author Sisai Fang, Sisai Fang University of Surrey, 5GIC & 6GIC, Institute for Communication Systems, Department of Electrical and Electronic Engineering (ICS), Guildford, UKSearch for more papers by this authorGaojie Chen, Gaojie Chen University of Surrey, 5GIC & 6GIC, Institute for Communication Systems, Department of Electrical and Electronic Engineering (ICS), Guildford, UKSearch for more papers by this authorSangarapillai Lambotharan, Sangarapillai Lambotharan Loughborough University, School of Mechanical, Manufacturing and Electrical Engineering, Leicestershire, Loughborough, UKSearch for more papers by this authorCunhua Pan, Cunhua Pan Queen Mary University of London, School of Electronic Engineering and Computer Science, London, UKSearch for more papers by this authorJonathon A. Chambers, Jonathon A. Chambers University of Leicester, School of Engineering, Leicester, Leicestershire, UKSearch for more papers by this author Book Editor(s):Kumar Vijay Mishra, Kumar Vijay Mishra United States CCDC Army Research Laboratory, Adelphi, MD, United StatesSearch for more papers by this authorM. R. Bhavani Shankar, M. R. Bhavani Shankar University of Luxembourg, Interdisciplinary Centre for Security, Reliability and Trust (SnT), Luxembourg, LuxembourgSearch for more papers by this authorBjörn Ottersten, Björn Ottersten KTH Royal Institute of Technology, Stockholm, SwedenSearch for more papers by this authorA. Lee Swindlehurst, A. Lee Swindlehurst University of California, Irvine, CA United StatesSearch for more papers by this author First published: 06 March 2024 https://doi.org/10.1002/9781119795568.ch13 AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onEmailFacebookTwitterLinkedInRedditWechat Summary In this chapter, we investigate joint transmit beampattern and phase shift optimization techniques for an intelligent reflecting surface (IRS)-assisted multiple-input multiple-output (MIMO) radar in the presence of an eavesdropping target. We show that the IRS-assisted MIMO radar can enhance the secrecy rate performance compared to an ordinary MIMO radar. We propose two optimization techniques for the proposed scenario, namely, the secrecy rate maximization at the legitimate user and the transmit power minimization for the MIMO radar. However, these problems are non-convex due to the non-concavity of the secrecy rate function. We apply the block coordinate descent (BCD) algorithm to decompose the original problem into two subproblems to tackle this issue. The Lagrangian multiplier method and the majorization-minimization (MM) algorithm are leveraged to optimize the transmit power and the phase shifts of the IRS, respectively. In addition, thefirst-order Taylor series approximation is utilized for the signal-to-interference plus noise ratio (SINR) constraint convexification of both problems. Two transmit beamforming vectors are designed to detect the target and to convey information safely to the legitimate receiver. Simulation results validate the efficacy of the proposed optimizations. References M. Alageli , A. Ikhlef , F. Alsifiany , M. A. M. Abdullah , G. Chen , and J. Chambers . Optimal downlink transmission for cell-free SWIPT massive MIMO systems with active eavesdropping . 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The space-air-ground integrated network (SAGIN) has become a crucial research direction in future wireless communications due to its ubiquitous coverage, rapid and flexible deployment, and multi-layer cooperation capabilities. However, integrating hierarchical federated learning (HFL) with edge computing and SAGINs remains a complex open issue to be resolved. This paper proposes a novel framework for applying HFL in SAGINs, utilizing aerial platforms and low Earth orbit (LEO) satellites as edge servers and cloud servers, respectively, to provide multi-layer aggregation capabilities for HFL. The proposed system also considers the presence of inter-satellite links (ISLs), enabling satellites to exchange federated learning models with each other. Furthermore, we consider multiple different computational tasks that need to be completed within a limited satellite service time. To maximize the convergence performance of all tasks while ensuring fairness, we propose the use of the distributional soft-actor-critic (DSAC) algorithm to optimize resource allocation in the SAGIN and aggregation weights in HFL. Moreover, we address the efficiency issue of hybrid action spaces in deep reinforcement learning (DRL) through a decoupling and recoupling approach, and design a new dynamic adjusting reward function to ensure fairness among multiple tasks in federated learning. Simulation results demonstrate the superiority of our proposed algorithm, consistently outperforming baseline approaches and offering a promising solution for addressing highly complex optimization problems in SAGINs.

Reconfigurable intelligent surfaces (RISs) have attracted extensive attention in millimeter wave (mmWave) systems because of the capability of configuring the wireless propagation environment. However, due to the existence of a RIS between the transmitter and receiver, a large number of channel coefficients need to be estimated, resulting in more pilot overhead. In this paper, we propose a joint sparse and low-rank based two-stage channel estimation scheme for RIS-assisted mmWave systems. Specifically, we first establish a low-rank approximation model against the noisy channel, fitting in with the precondition of the compressed sensing theory for perfect signal recovery. To overcome the difficulty of solving the low-rank problem, we propose a trace operator to replace the traditional nuclear norm operator, which can better approximate the rank of a matrix. Furthermore, by utilizing the sparse characteristics of the mmWave channel, sparse recovery is carried out to estimate the RIS-assisted channel in the second stage. Simulation results show that the proposed scheme achieves significant performance gain in terms of estimation accuracy compared to the benchmark schemes.

In recent years, the amalgamation of satellite communications and aerial platforms into space-air-ground integrated network (SAGINs) has emerged as an indispensable area of research for future communications due to the global coverage capacity of low Earth orbit (LEO) satellites and the flexible Deployment of aerial platforms. This paper presents a deep reinforcement learning (DRL)-based approach for the joint optimization of offloading and resource allocation in hybrid cloud and multi-access edge computing (MEC) scenarios within SAGINs. The proposed system considers the presence of multiple satellites, clouds and unmanned aerial vehicles (UAVs). The multiple tasks from ground users are modeled as directed acyclic graphs (DAGs). With the goal of reducing energy consumption and latency in MEC, we propose a novel multi-agent algorithm based on DRL that optimizes both the offloading strategy and the allocation of resources in the MEC infrastructure within SAGIN. A hybrid action algorithm is utilized to address the challenge of hybrid continuous and discrete action space in the proposed problems, and a decision-assisted DRL method is adopted to reduce the impact of unavailable actions in the training process of DRL. Through extensive simulations, the results demonstrate the efficacy of the proposed learning-based scheme, the proposed approach consistently outperforms benchmark schemes, highlighting its superior performance and potential for practical applications.

This paper investigates a reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV)-enabled wireless powered communication network (WPCN). In the system, a UAV acts as a hybrid access point (HAP) to charge users in the downlink (DL) and receive messages in the uplink (UL). In particular, the RIS is exploited to significantly enhance the efficiency of both the DL and UL transmission. Our objective is to enhance the minimum throughput among all ground users by jointly optimizing the horizontal location of UAVs, the transmit power of users, transmission time allocation, and passive beamforming vectors at the RIS. To address this problem, we present an alternating optimization-based algorithm with low complexity to decompose the problem into four subproblems and solve them sequentially. In particular, we derive a lower bound of the composite channel gain to tighten the constraints and employ successive convex approximation (SCA) to optimize the horizontal location of the UAV. The transmit power closed-form optimum solutions are then obtained, and the problem of time allocation is reformulated as a linear programming problem. Finally, we optimize the passive beamforming vectors by adopting semi-definite relaxation (SDR). The effectiveness of the algorithm is supported by numerical results, which also demonstrate that the RIS-assisted UAV-enabled WPCN outperforms the traditional WPCN in terms of the minimum throughput.

The problem of resilient optimal bipartite tracking control for heterogeneous multi-agent systems with multiple targets under denial-of-service (DoS) attacks is investigated in this paper. A bipartite tracking mechanism is devised in which the agents track the targets under bipartite consensus control, which becomes non-autonomous. This is owing to DoS attacks, as the agents cannot obtain real-time information on the tracked targets and neighboring agents. Consequently, A target observer with a storage module has been developed for the efficient estimation of agent states and the storage of observed information as historical data. By recalling the historical data of the observed state, a new type of distributed resilient optimal controller is formulated, which can achieve the control objective in the case of communication blockage, while minimizing the performance index function of the system. Numerical simulations are performed to verify the proposed secure control design.

At the network edges, the multi-tier computing framework provides mobile users with efficient cloud-like computing and signal processing capabilities.Deploying digital twins in the multi-tier computing system helps to realize ultrareliable and low-latency interactions between users and their virtual objects.Considering users in the system may roam between edge servers with limited coverage and increase the data synchronization latency to their digital twins, it is crucial to address the digital twin migration problem to enable real-time synchronization between digital twins and users.To this end, we formulate a joint digital twin migration, communication and computation resource management problem to minimize the data synchronization latency, where the time-varying network states and user mobility are considered.By decoupling edge servers under a deterministic migration strategy, we first derive the optimal communication and computation resource management policies at each server using convex optimization methods.For the digital twin migration problem between different servers, we transform it as a decentralized partially observable Markov decision process (Dec-POMDP).To solve this problem, we propose a novel agent-contribution-enabled multi-agent reinforcement learning (AC-MARL) algorithm to enable distributed digital twin migration for users, in which the counterfactual baseline method is adopted to characterize the contribution of each agent and facilitate cooperation among agents.In addition, we utilize embedding matrices to code agents' actions and states to release the scalability issue under the high dimensional state in AC-MARL.Simulation results based on two real-world taxi mobility trace datasets show that the proposed digital twin migration scheme is able to reduce 23%-30% data synchronization latency for users compared to the benchmark schemes.

In this paper, the problem of synchronization control of cyber-physical systems with time- varying dynamics under Denial-of-Service attacks is studied. The communication channels of the synchronization protocol between the subsystems in the cyber-physical systems may be injected into DoS attacks. To counteract the adverse effects of these attacks on synchronization, a target reference model is initially created for each subsystem to mimic its standard operational dynamics. Subsequently, utilizing this reference model, an adaptive compensator is proposed. The unified synchronization protocol guarantees that the synchronization error remains minimal through the adjustment of specific parameters, even under DoS attacks. To showcase the efficiency of this approach, an illustrative numerical simulation example is conducted.

This paper investigates a multiuser reconfigurable intelligent surface (RIS)-assisted simultaneous wireless information and power transfer (SWIPT) system, in which a RIS assists in establishing favorable wireless communication environment. Particularly, we study the max-min signal-to-interference-plus-noise ratio (SINR) problem of the system by joint optimizing the base station (BS) active beamforming vectors, the RIS passive beamforming vector, and the power splitting (PS) ratios while guaranteeing the BS maximum transmit power budget and the minimum harvested energy threshold. The considered max-min SINR problem is highly non-convex, which is arduous to directly solve. Hence, we present an efficient alternating optimization (AO) algorithm to decompose the max-min SINR problem into three tractable subproblems, which are solved in an alternating manner. Particularly, we apply semi-definite relaxation (SDR) technique and the bisection method to tackle the beamforming optimization subproblems, and then derive a closed-form solution to solve the PS ratios optimization subproblem. Numerical simulations verify the superiority of our proposed AO algorithm and demonstrate that deploying RIS can achieve significantly increased min-SINR value.

This study provides an overview of the advancements, applications, and challenges in the field of heterogeneous MASs with a focus on cooperative control. It summarizes the existing secure control methods under cyber-attacks and safe control approaches in physical threats. Additionally, we discuss the potential applications of heterogeneous MASs and future research directions to address remaining challenges. We hope that this work can inspire researchers studying cooperative control of heterogeneous MASs.

A symbiotic radio (SR) network is built to address the situations of limited spectrum and energy resources. Particularly, a hybrid SR network is proposed based on simultaneous wireless information and power transfer (SWIPT). Specifically, the backscatter devices (BDs) will have the capability to provide active communications for the primary receiver (PR) with the harvested energy from SWIPT. Within the network, the BDs and primary transmitter (PT) based transmissions will achieve symbiotic status. In detail, the whole network will operate in three phases, which are assisted by a reconfigurable intelligent surface (RIS). Further, an end-to-end S-parameter based model is introduced to reveal the hardware characteristics. Under these settings, a multi-objective problem with energy efficiency (EE) and sum rate maximization is proposed. Additionally, the constraints are related to the communication demands and physical restrictions. To resolve the problem, the $\epsilon $ -constraint approach is utilized to transform it into a single-objective problem. Then the coupled variables are separated into three parts to allow an iterative solution. Specifically, an element-wise approach based on the Sherman-Morrison transformation is adopted to optimize the subproblem for RIS with the double-inverse form. Simulation results confirm the significance of the hardware features and the effectiveness of the proposed scheme.

This paper proposes a new framework for reconfigurable intelligent surface (RIS)-equipped unmanned aerial vehicles (UAVs) in free-space optical (FSO) communication. To ensure practicality, we consider atmospheric loss caused by fog, which leads to an inhomogeneous medium for laser propagation. In addition, we incorporate the pointing error loss caused by the power fraction on the photodetector (PD) into the system and derive a closed-form expression for the elliptical beam footprint in the pointing error loss. We then propose a leading angle assisted particle swarm optimization (PSO) method to efficiently optimize the numerical results of pointing error loss. Furthermore, after obtaining these numerical results as a precondition, the UAV trajectory is optimized using the proximal policy optimization (PPO) method to achieve the maximum average capacity. Numerical simulations demonstrate that the proposed optimization method achieves greater efficiency and accuracy compared to the decode-and-forward (DF) relay and deep Q-learning (DQN) methods.

Combining simultaneous wireless information and power transfer (SWIPT) and an intelligent reflecting surface (IRS) is a feasible scheme to enhance energy efficiency (EE) performance. In this paper, we investigate a multiuser IRS-aided multiple-input single-output (MISO) system with SWIPT. For the purpose of maximizing the EE of the system, we jointly optimize the base station (BS) transmit beamforming vectors, the IRS reflective beamforming vector, and the power splitting (PS) ratios, while considering the maximum transmit power budget, the IRS reflection constraints, and the quality of service (QoS) requirements containing the minimum data rate and the minimum harvested energy of each user. The formulated EE maximization problem is non-convex and extremely complex. To tackle it, we develop an efficient alternating optimization (AO) algorithm by decoupling the original nonconvex problem into three subproblems, which are solved iteratively by using the Dinkelbach method. In particular, we apply the successive convex approximation (SCA) as well as the semi-definite relaxation (SDR) techniques to solve the non-convex transmit beamforming and reflective beamforming optimization subproblems. Simulation results verify the effectiveness of the AO algorithm as well as the benefit of deploying IRS for enhancing the EE performance compared with the benchmark schemes.

The simultaneous wireless information and power transfer (SWIPT) technology assisted by the reconfigurable intelligent surface (RIS) can bring flexibility and stability to the end nodes of the internet of things (IoT) during the deployment. In this paper, we propose a RIS-aided SWIPT system based on a hardware transfer model from the electromagnetic perspective. Particularly, an energy efficiency (EE) maximization problem subject to the quality of service (QoS) demands, power resource budget and circuit restrains is introduced. Furthermore, the active beamforming vectors of the BS and the circuit parameters at the RIS are optimized jointly. The problem can be decomposed into two sub-problems and solved iteratively until convergence. In particular, semi-definite relaxation (SDR), successive convex approximation (SCA), Dinkelbach's algorithm are applied to the solutions of the sub-problems. Numerical results reveal the influences of the various QoS requirements on EE performance. Moreover, the actual generated beams of the BS and the RIS are shown to demonstrate the effectiveness of the proposed optimization strategy.

All existing physiological tremor filtering algorithms, developed for robotic microsurgery, use nonlinear phase prefilters to isolate the tremor signal. Such filters cause phase distortion to the filtered tremor signal and limit the filtering accuracy. We revisited this long-standing problem to enable filtering of the physiological tremor without any phase distortion. We developed a combined estimation–prediction paradigm that offers zero-phase type filtering. The estimation is achieved with the mathematically modified recursive singular spectrum analysis algorithm, and the prediction is delivered with the standard extreme learning machine. In addition, to limit the computational cost, we developed two moving window versions of this structure, which are appropriate for real-time implementation. The proposed paradigm preserved the natural phase of the filtered tremor. It achieved the key performance index of error limitation below $10\mu \text{m}$ , yielding the estimation accuracy larger than 70%, at a time delay of 36 ms only. Both moving window versions of the proposed approach restricted the computational cost considerably while offering the same performance. It is the first time that the effective estimation of the physiological tremor is achieved, without any prefiltering and phase distortion. This proposed method is feasible for real-time implantation. Clinical translation of the proposed paradigm can significantly enhance the outcome in hand-held surgical robotics. Note to Practitioners —The imprecision caused by physiological hand tremor in microsurgeries has motivated researchers to innovate an efficient tremor compensating technique that can improve surgical performance. Yet, all the existing tremor filtering algorithms, implemented in hand-held surgical instruments, use nonlinear phase prefilters to separate the tremor signal. The inherent phase distortion caused by such prefilters restricts the filtering performance significantly and renders the existing methods inadequate for hand-held robotic surgery. Motivated by this, we proposed a novel estimator-predictor-based framework, by adopting the modified recursive singular spectrum analysis estimator and the extreme learning machine predictor. The proposed framework filters the tremor signal accurately, without distorting it, but at a small fixed lag. In a set of rigorous testing performed by emulating real-time processing, the proposed algorithm showed higher performance compared with the state-of-the-art algorithms. This validates not only its suitability for real-time implantation but also its potential to improve surgical performance, which has been limited by the distorted filtering. Nonetheless, we have presented a proof-of-principle framework for distortion-free filtering, but its full implementation in a real surgical instrument, such as Micron or ITrem, requires a substantial amount of experimental testing and verification. It can be also applicable in a wide range of areas, including health-care, digital manufacturing, smart automation and control, and various other robotic technologies where efficient filtering of advanced sensor data is highly desirable. In the future, we will develop the multidimensional model of the proposed framework to enable filtering of tremor in the xyz -axes simultaneously.

Fall detection and classification become an imper- ative problem for healthcare applications particularity with the increasingly ageing population. Currently, most of the fall clas- sification algorithms provide binary fall or no-fall classification. For better healthcare, it is thus not enough to do binary fall classification but to extend it to multiple fall events classification. In this work, we utilize the privacy mitigating human skeleton data for multiple fall events classification. The skeleton features are extracted from the original RGB images to not only mitigate the personal privacy, but also to reduce the impact of the dynamic illuminations. The proposed fall events classification method is divided into two stages. In the first stage, the model is trained to achieve the binary classification to filter out the no-fall events. Then, in the second stage, the deep neural network (DNN) model is trained to further classify the five types of fall events. In order to confirm the efficiency of the proposed method, the experiments on the UP-Fall dataset outperform the state-of-the-art.

Abstract An accurate tumour segmentation in brain images is a complicated task due to the complext structure and irregular shape of the tumour. In this letter, our contribution is twofold: (1) a lightweight brain tumour segmentation network (LBTS‐Net) is proposed for a fast yet accurate brain tumour segmentation; (2) transfer learning is integrated within the LBTS‐Net to fine‐tune the network and achieve a robust tumour segmentation. To the best of knowledge, this work is amongst the first in the literature which proposes a lightweight and tailored convolution neural network for brain tumour segmentation. The proposed model is based on the VGG architecture in which the number of convolution filters is cut to half in the first layer and the depth‐wise convolution is employed to lighten the VGG‐16 and VGG‐19 networks. Also, the original pixel‐labels in the LBTS‐Net are replaced by the new tumour labels in order to form the classification layer. Experimental results on the BRATS2015 database and comparisons with the state‐of‐the‐art methods confirmed the robustness of the proposed method achieving a global accuracy and a Dice score of 98.11% and 91%, respectively, while being much more computationally efficient due to containing almost half the number of parameters as in the standard VGG network.