This paper proposes a hardware-software co-design approach to efficiently optimize beamforming and port selection in fluid antenna systems (FASs). To begin with, a fluid-antenna (FA)-enabled downlink multi-cell multiple-input multiple-output (MIMO) network is modeled, and a weighted sum-rate (WSR) maximization problem is formulated. Second, a method that integrates graph neural networks (GNNs) with random port selection (RPS) is proposed to jointly optimize beamforming and port selection, while also assessing the benefits and limitations of random selection. Third, an instruction-driven deep learning accelerator based on a field-programmable gate array (FPGA) is developed to minimize inference latency. To further enhance efficiency, a scheduling algorithm is introduced to reduce redundant computations and minimize the idle time of computing cores. Simulation results demonstrate that the proposed GNN-RPS approach achieves competitive communication performance. Furthermore, experimental evaluations indicate that the FPGA-based accelerator maintains low latency while simultaneously executing beamforming inference for multiple port selections.

This paper proposes to leverage intelligent reflecting surface (IRS) to realize radio-frequency-chain-free uplink-transmissions (RFCF-UT) for an integrated sensing, communication and computation system. In this communication paradigm, IRS works as an information carrier, whose elements are capable of adjusting their amplitudes and phases to collaboratively portray an electromagnetic image like a dynamic quick response (QR) code, rather than a familiar reflection device. Meanwhile, a dual-functional radar-communication base station (BS) is used as a scanner to collect and recognize the information on each IRS carrying the data of its associated user equipment, while detecting the radar target. Based on the established system model, partial and binary data offloading strategies are respectively considered. By defining a performance metric named weighted throughput capacity (WTC), two maximization problems of WTC are formulated. According to the coupling degree of optimization variables in the objective function and the constraints, each optimization problem is firstly decomposed into two subproblems. Then, the methods of linear programming, fractional programming, integer programming and alternative optimization are developed to solve the subproblems. The simulation results demonstrate the achievable WTC of the considered system, thereby validating RFCF-UT.

This paper proposes to leverage intelligent reflecting surface (IRS) backscatter to implement downlink communications for a multi-cell multiple-input multiple-output (MIMO) network, which represents a brand-new communication framework. In such a network, one active macro cell base station (MBS) is deployed for radiating energy signal, while each IRS acts as a small cell base station to realize its own information transmission by modulating and reflecting the signal from the MBS. Under this paradigm, we investigate two optimization problems, namely weighted sum rate maximization problem and max-min fairness problem, aiming at satisfying different communication requirements. To seek their optimal solutions, Lagrangian dual transform and alternate methods are employed to optimize the active beamforming vector at the MBS and the passive beamforming vectors at all the IRSs. Additionally, an element clustering scheme is developed to reduce computation and control complexity, in which each element cluster works like a unit and all clusters can collaborate to communicate with users. Extensive simulations are conducted to evaluate the achievable communication performance and to verify the feasibility of the proposed IRS backscatter enabled downlink multi-cell MIMO network.

This paper investigates a novel framework of intelligent reflecting surface (IRS) backscatter enabled multi-tier computing system. In such a hierarchical network, the data bits of computational task requested by each user equipment (UE) are broken up into three parts, which are respectively computed at tier-1 UEs, tier-2 access points (APs) and a tier-3 central server. Distinguished from conventional active antennas, IRS backscatter at the UEs is leveraged to offload data bits to the APs. Based on the established network framework, an optimization problem is formulated, which aims at maximizing the sum computational bits of system during the considered time block by jointly optimizing the active beamforming at the power beacon, the passive beamforming at the UEs, the active beamforming at the APs, the bandwidth and power allocation among all the UEs, as well as the time of local computing. To seek the optimal solution, the optimization problem is decomposed into two, namely the maximization of stage-1 sum computational bits and the minimization of stage-2 delay. By the objective function conversion and alternative optimization methods, the two problems are addressed. Extensive simulations are performed to confirm the feasibility of the proposed system and show the achievable performance in processing computational bits.

This paper proposes an intelligent reflecting surface (IRS) backscatter based uplink coordinated transmission strategy for a radio cellular network, where IRS serves as a transmitter enabling uplink transmission from each user to the associated base station (BS). To be specific, the considered network is made up of multiple cells, each of which consists of one multi-antenna BS and its served users. While one multi-antenna power beacon (PB) is deployed to radiate energy-bearing electromagnetic wave, the radio signal received by each IRS is modulated to send its connective user’s information to the associated BS. Based on such a network framework, this paper aims to maximize the weighted sum rate (WSR) under the constraints of total transmit power and reflecting coefficient by joint optimization of active beamforming at the PB, passive beamforming at the IRSs and uplink user scheduling. To address this challenging problem, fractional programming (FP), alternative optimization and weighted bipartite matching are employed to convert the logarithm objective function into a more tractable form and to handle the optimization variables. The simulation results demonstrate the achievable WSR of the considered network.

To have a good understanding of attackers’ behaviour and seek effective defensive measures, an optimal strategy of false data injection (FDI) and denial-of-service (DoS) attacks is proposed to intrude sensors and transmission channels of cyber-physical systems (CPSs) from the perspective of an attacker. Specifically, all sensors of CPSs make measurements of the state and send them to a fusion center for state estimation. Intruders attempt to modify the measurements and block innovations’ transmission by respectively launching FDI and DoS attacks to maliciously degrade the system estimation performance. A cost function is introduced to quantify the estimation performance. In order to maximize the cost function, it is important for the attacker to decide which sensors and transmission channels to intrude due to energy constraint. A convex optimization problem is formed, by solving which the optimal attack strategy is obtained. Simulations demonstrate the optimality of our proposed attack strategy.

This paper investigates intelligent reflecting surface based backscatter communication (IRS-BackCom), in order to realize computational task offloading of energy-constrained mobile edge computing network in a self-sustainable manner. Specifically, the system operation is divided into two phases. In the first one, the ambient signal energy from a power beacon (PB) either provides the energy supply of local computing and energy harvesting circuits, or flows into the energy storage, when reaching the IRS. In the second one, the stored energy is used to enable IRS-BackCom for partial computational data offloading and energize local computing circuit. Based on this, the maximization problem of sum computational bits is formulated. By jointly optimizing the beamforming vector at the PB, the backscatter matrix at the IRS, the time scheduling of two-phase process, as well as the time of local computing, sum computational bits are maximized. In addition, this paper proposes element clustering to realize BackCom, so as to reduce the control and computation complexity of IRS. According to different operating mechanisms, two cluster operation modes are considered, namely independent cluster operation mode and joint cluster operation mode. Simulation results demonstrate the achievable sum computational bits by the proposed IRS-BackCom schemes.

This paper investigates the state estimation error covariances of cyber-physical systems (CPSs) when its physical system and transmission channels are attacked by false data injection (FDI). Specifically, sensors in CPSs measure the physical systems' state and then send these measurement information to a fusion center through transmission channels. Due to easy deployment and maintenance of sensors as well as unprotected wireless transmission medium, CPSs in sensing and communication are vulnerable to malicious attacks. To maliciously degrade the system estimation performance, an intruder attempts to modify the state and measurements by launching FDI attacks. To seek an optimal attack strategy from the perspective of an attacker, the corresponding estimate error covariances of CPSs are calculated. Based on this, we compare the estimation error covariances of different attacks on the physical system and transmission channels. Simulations demonstrate the effectiveness of the theoretical results.

Abstract This paper proposes a novel secure diffusion Kalman filter (dKF) algorithm to improve the estimation performance crippled by false data injection attacks on sensors in wireless sensor networks (WSNs). Different from the conventional dKF, each adjacent node in the WSNs is detected to ascertain its trustworthiness before local estimate fusion, so as to form a new secure network topology. Then the combination step is performed to fuse the information collected from the secure topology. The proposed secure dKF algorithm, having a better estimation performance, is robust to false data injection attacks on multiple sensors and partial elements of measurements. For the proposed secure dKF algorithm, its mean and mean‐square performance are derived, based on which its convergence is analysed. Additionally, the estimating and tracking problem of projectile position is investigated to confirm the effectiveness of the proposed secure dKF algorithm. It is shown by simulations that the proposed secure dKF algorithm achieves a significant estimation performance gain.

This paper proposes to employ multiple cooperative jammers to reduce the secrecy loss due to the correlation between main and wiretap channels. We consider slow-fading multiple-input single-output (MISO) wiretap channels with a passive single-antenna eavesdropper, in which an independent transmitter side and correlated receiver side are assumed. Considering that signal processing techniques as well as the blind growth of power at the source play a limited role in reducing the secrecy loss due to the receiver-side correlation, multiple cooperative jammers equipped with multiple antennas are introduced into the system. Owing to spatial diversity, the channel correlation from the different jammers to the destination and the eavesdropper varies. To interfere with the reception at the eavesdropper efficiently and consequently enhance security, some jammers in favorable channel conditions are selected to emit artificial noise (AN) with power optimization. Based on this, the secrecy outage probability is analyzed. The simulation results verify that the proposed scheme of multiple cooperative jammers provides substantial gains in terms of secrecy.

In PHY-security, high correlation between main and wiretap channels, which are frequently observed, can cause a significant loss of secrecy. Unfortunately, signal processing techniques at the transmitter (Alice), such as precoding and artificial noise (AN) techniques, are ineffective. Under this circumstance, this paper focuses on a slowly fading and reciprocal channel scenario wherein Alice sends a confidential message to an authorised receiver (Bob) with the transmission overheard by a passive unauthorised receiver (Eve), and all of them are equipped with multiple antennas. To prevent interception and ensure secrecy, we redesign a novel scheme of discriminatory channel estimation (DCE), in which training procedures are developed to limit the channel estimation performance at Eve while producing little effect on Alice and Bob. As a result, Eve's ability to obtain the channel information would deteriorate, thereby effectively increase the difference in decoding the message between Bob and Eve. Simulation results demonstrate the proposed scheme could provide substantial gains with respect to secrecy.