Secure Transceiver Design for Discrete RIS Enhanced Dual-Functional Radar-Communication: A Symbol-Level Precoding Approach

This letter investigates an active reconfigurable intelligent surface (RIS)-aided mobile edge computing (MEC) system. Compared with passive RIS, the active RIS is equipped with active reflective amplifier, which can effectively circumvent the “double path loss” attenuation. We propose a joint computing and communication design to minimize the maximum computational latency (MCL), subject to both the phase shift constraints and the edge computing capability constraints. Specifically, the original problem is decoupled into four subproblems, and then the block coordinate descent (BCD) method and the successive convex approximation (SCA) method are applied to alternately optimize the subproblems. The simulation results show that with the same power budget, the performance gain achieved by the active RIS is much larger than that by the passive RIS.

In this paper, energy efficiency (EE) is maximized for the reconfigurable intelligent surface (RIS) aided millimeter-Wave (mmWave) networks with non-orthogonal multiple access (NOMA) and multiple mobile devices. To this end, we first propose the EE optimization, under the constraints of maximum power, minimal rate of devices and constant modulus of beamforming (BF) vectors. Then, the joint resource allocation scheme of power allocation (PA) and BF is designed. Specifically, given PA, an effective iterative algorithm based on the majorizationminimization, concave-convex procedure and block coordinate descent (BCD) is presented to obtain closed-form solutions of suboptimal passive BF (PBF) and analog BF (ABF) for each iteration. Then, given PBF and ABF, an effective iterative algorithm based on the successive convex approximation, BCD and Dinkelbach methods is derived to achieve suboptimal closedform PA for each iteration. By incorporating these two algorithms into the BCD method, a joint optimization algorithm for EE maximization is presented. As a result, joint resource allocation of PA, PBF and ABF is attained. Besides, the convergence and complexity of the algorithms are analyzed. For comparison, the benchmark scheme based on the multidimensional search method and artificial bee colony algorithm is also presented. Simulation results show that the proposed joint scheme is effective and higher EE can be obtained with lower complexity.

Optimizing resource allocation for enhanced urban connectivity in LEO-UAV-RIS networks

Reconfigurable intelligent surface (RIS) has recently been regarded as a potential technique to enhance the performance of wireless communication systems by creating additional communication links. However, it is almost impossible to get the perfect channel state information (CSI) from the base station (BS) to the Internet of Things Devices (IoTDs) and the RIS-related channels. Furthermore, residual transceiver hardware impairments inevitably affect the performance of wireless communication systems. Hence, we study the robust design for an RIS-aided wireless communication system based on the imperfect CSI and hardware impairments. Minimizing the power consumption of BS is formulated by ensuring the minimum signal-to-interference-plus-noise ratio (SINR) demands of the IoTDs and the unit-modulus constraints of the RIS. Specifically, after approximating the nonconvex constraints by using the S-procedure and the successive convex approximation (SCA) methods, we adopt the block coordinate descent (BCD) technique to iteratively optimize one set of variables while keeping the other variables fixed in various channel uncertainty scenarios. Simulation results demonstrate that the influence of transceiver hardware impairments can be effectively decreased by deploying RIS even with channel uncertainty, which is more advantageous than increasing the number of BS’s antennas.

This article investigates the resource allocation of a reconfigurable intelligent surface (RIS)-aided joint communication and sensing (JCAS) system in a coal mine scenario. In the JCAS system, an RIS is implemented at the corner of the zigzag tunnels to improve the complicated wireless environment, where ground obstacles frequently block direct links. In addition, a wireless backhaul base station with a limited energy budget is deployed in the depth of the mine to sense the target area and provide Internet of Things (IoT) services and communication services for users. Furthermore, a data center is placed on the ground to analyze the obtained data and route the communication data. Under this deployment, a joint optimization problem of RIS phase-shift matrix, RIS element switches, and area sensing time is proposed. We aim to maximize the successful sensed bits under total completion time, and maximum transmit power constraints. In order to solve this problem, an iterative algorithm is proposed. The successive convex approximation (SCA)-based algorithm is used for the RIS phase-shift matrix optimization subproblem. For the sensing time optimization subproblem, the quadratic approximation method is proposed to optimize the number of area perceptions. The coordinate descent method is utilized to optimize the RIS element switches. Simulation results show that the energy efficiency is improved by up to 38%, and 7% increases the specific data size compared with the benchmark solutions.

Secure Transmission in Wireless Sensor Networks via Reconfigurable Intelligent Surface

International audience

In this article, a novel multistream downlink communication system based on the transmissive reconfigurable intelligent surface (RIS) transceiver is proposed. Specifically, a transmissive RIS transceiver architecture is first elaborated, where the downlink communication mechanism and the difference from the conventional multiantenna transceivers are introduced, respectively. More importantly, the generation of RIS element control signals based on time-modulated array (TMA) is illustrated in detail, which can jointly take into account multistream modulation signals and beamforming design. Correspondingly, the harmonic signal extraction scheme at the user is also given. Then, since the design of beamforming has an impact on the system performance, we propose a beamforming optimization algorithm based on matrix lifting, successive convex approximation (SCA) and difference-convex (DC) programming under the constraints of user signal-to-interference-plus-noise ratio (SINR) and available useful power of RIS elements. Furthermore, we analyze the bit error rate (BER) performance of the proposed architecture from two perspectives of transmit multiplexing and transmit diversity. Finally, the convergence behavior of the beamforming algorithm, the impact of different system parameter configurations on system performance and the BER performance of different schemes under the system are verified by numerical simulations.

This paper focuses on the problem of reconfigurable intelligent surface (RIS) aided co-design for multiple-input-multiple-output (MIMO) radar and MIMO communication systems with shared spectrum resources. The purpose is to maximize the radar output signal-to-interference-plus-noise-ratio (SINR) while ensuring the communication functionalities, where there is no line-of-sight (NLOS) propagation between the radar and the target. To achieve the goal, we develop a cyclic framework based on semi-definite programming (SDP), semi-definite relaxation (SDR), and alternating direction method of multiplier (ADMM) to jointly optimize the radar transmit waveforms, the receive filters, the communication codebook, and the RIS coefficients. Numerical experiments are conducted to evaluate the effectiveness of the proposed algorithm.

In this article, a cooperative non-orthogonal multiple access (NOMA) mobile edge computing (MEC) network aided by reconfigurable intelligent surfaces (RIS) and simultaneous wireless information and power transfer (SWIPT) is considered, where the transmitters offload parts of their computing tasks to the MEC server via a relay and an RIS for enhancing the transmission environment. The goal is to minimize the task delay and energy consumption for the considered networks by jointly designing the RIS's reflection beamforming vector, the power splitting (PS) ratio, the power allocation factors of the relay, as well as the offloading transmit power and the offloading task ratio. To solve the proposed problems, an iterative optimization algorithm based on the successive convex approximation (SCA) method and the penalty method is proposed to decompose the formulated problems into two sub-problems. In particular, we jointly optimize the PS ratio, the power allocation coefficients and the edge computing resource based on the SCA technology, and then the penalty method is used to optimize the RIS's reflecting beamforming vector. Simulation results illustrate that the proposed NOMA-MEC networks aided by RIS and SWIPT are superior to the conventional orthogonal multiple access MEC networks with SWIPT aided by RIS in terms of task delay and energy consumption.

This paper optimizes the energy consumption of the downlink of a multiple-antenna base station (BS) transmitting to several single-antenna users. The BS utilizes simultaneous wireless information and power transfer (SWIPT) while receivers apply power-splitting (PS) with a nonlinear energy harvesting model leading to PS-SWIPT. We use an intelligent reflecting surface (IRS) and propose a joint design to optimize the active data and the BS's energy beamformers, IRS's passive beamformers, and the receivers' PS ratios under perfect and imperfect CSI availability. In particular, the total BS transmit power is minimized while guaranteeing a minimum rate and harvested energy for each receiver. We apply the block coordinate descent (BCD) method to optimize active and passive beamformers iteratively. We enforce the rank-one constraint and solve the corresponding optimization problem via successive convex approximation (SCA) for accurate semidefinite relaxations. Furthermore, we propose a worst-case robust design for the imperfect CSI case and reformulate this problem with infinitely many constraints. With the BCD method, the problem is iteratively solved via semidefinite programming (SDP) and a second sub-problem with a linear objective and quadratic matrix inequalities, which is also solved via SCA. Numerical results show significant improvements (e.g., 30% decrease in transmit power) than those of no-IRS and IRS with random phase shifts.

Reconfigurable Intelligent Surface (RIS) is an emerging technology that can improve the spectrum and energy efficiency of next-generation wireless networks. However, attaining accurate channel state information (CSI) for the cascaded RIS channel is particularly challenging. Imperfect CSI is a major bottleneck to achieving the spectral efficiency benefit of RIS-assisted networks. Rate splitting (RS), a promising multiple access technology, has been shown to be able to achieve an improved spectrum efficiency and be robust to channel uncertainties. This paper investigates the interplay between RIS and RS by considering a RIS-assisted RS beamforming problem. Active beamforming at the base station (BS) and passive beamforming at the RIS are jointly considered to maximize the minimum user rate under both the perfect CSI case and the imperfect CSI case. A block coordinate descent (BCD) algorithm is developed to solve this non-convex problem. Compared with the conventional semi-definite relaxation (SDR) approach, the proposed method does not require that the covariance matrix of the common beamforming vector be rank-one. Moreover, we present theoretical results to help reveal the impact of the system parameters and explain the performance gain resulting from the integration of RIS and RS. Extensive simulation results are also provided to show that RIS-assisted RS can improve the max-min rate performance significantly compared with conventional multiple access technologies, such as non-orthogonal multiple access (NOMA) and space division multiple access (SDMA) with/without RIS, especially in overloaded systems. With the proposed method, RS shows great potential in combating realistic CSI errors in RIS-assisted networks.

Reconfigurable Intelligent Surface (RIS) has been considered as a promising technology for 6G networks, which is capable of directionally reflecting the incident signal by adjusting phase shift of its reflecting elements, and thus to achieve beamforming. In this paper, we investigate the potential of employing RIS in dual-functional radar-communication (DFRC) vehicular networks. Considering a vehicle-to-infrastructure (V2I) communication in non-line-of-sight(N-LoS) scenario, where RIS is deployed to establish sensing and communication links, we propose a scheme to optimize the beamforming based on the detected vehicle position for the sake of avoiding the relatively large channel estimation overhead. More specifically, we build a codebook to maintain the optimal phase shift of RIS corresponding to each candidate angle offline, which can be used for detecting the direction of vehicle in N-LoS scenario. In addition, the position-based channel state information (CSI) is introduced and used for maximize the downlink rate. Simulation results show that by deploying RIS in DFRC vehicular networks, the base station can detect the vehicle position in N-LoS scenario and provide it with reliable communication rates without channel estimation.

This paper proposes a partition-based reconfigurable intelligent surface (RIS)-assisted multi-user multiple-input single-output (MU-MISO) symbiotic radio (SR) system. Unlike conventional RIS designs in SR systems that utilize all elements for secondary transmissions, which restricts its ability to support the primary transmission, the RIS is divided into two subsurfaces: one enhances primary transmissions while the other transmits IoT information via spectrum sharing. We formulate a joint optimization problem to minimize transmit power under rate constraints for both primary and IoT transmissions through active beamforming and passive RIS phase-shift design. To tackle the non-convex constraints and variable coupling in the formulated problem, we propose efficient optimization techniques, including alternating optimization and difference-of-convex methods. Furthermore, we propose a low-complexity interference-free scheme leveraging partitioned RIS to eliminate inter-user interference, which is unachievable with conventional RIS design. Simulation results reveal that compared to the conventional scheme, which can be viewed as a special case of partition-based RIS where all the reflecting elements are used to transmit IoT information, partition-based RIS demonstrates superior performance, thereby validating the advantages of RIS partitioning in SR systems.

In reconfigurable intelligent surfaces (RISs) aided millimeter wave communication systems, accurate channel state information (CSI) is particularly crucial to achieve high passive beamforming gain. However, the complexity of channel estimation increases significantly due to the high-dimensional channel matrix and the additional RISs' assisted channels. To tackle this challenge, an alternating direction method of multipliers (ADMM) based channel estimation method is proposed to improve the CSI estimation accuracy and reduce the training overhead. By converting the cascaded channel matrix into a sparse matrix recovery problem, both low rank and sparsity properties are exploited by the ADMM algorithm to solve the sparse matrix recovery problem. The simulation results verify that the proposed ADMM algorithm can significantly increase the accuracy of channel estimation and reduce the training overhead, compared with the well-known competing channel estimation algorithms.