Channel Estimation and Passive Beamforming for Pixel-Based Reconfigurable Intelligent Surfaces With Non-Separable State Response

Reconfigurable intelligent surfaces (RISs) have recently emerged as an innovative technology for improving the energy and/or spectrum efficiency of future wireless communications. In this paper, we propose a transmission protocol for the wideband RIS-assisted single-input multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) system to execute channel estimation and passive beamforming simultaneously. A new channel estimation method is proposed to progressively resolve the channel state information (CSI) over the training symbols, based on which the passive beamforming at the RIS is optimized to improve the channel gains on the data tones in the remaining training symbols. Based on the incomplete CSI, we formulate an optimization problem to maximize the average achievable rate by designing the passive beamforming at the RIS, which needs to balance the received signal power over different subcarriers and different receive antennas. Moreover, we propose a low-complexity algorithm to find a high-quality solution for the formulated problem. Simulation results validate the effectiveness of the proposed channel estimation and beamforming optimization methods.

Recently, using a Reconfigurable Intelligent Surface (RIS) to aid wireless communication systems has received much attention from academia and industry. RIS is a uniform planar array composed of a large number of low-cost, passive, and tunable reflecting elements, in which the reflector elements steer the incident signal in fully customizable ways by passive beamforming. However, due to a large number of passive RIS elements without signal processing capability, which will result in a sharp increase of the pilot overhead for Channel Estimation (CE). To tackle this challenge, we propose a holographic version of RIS based on the holographic principle and Hall effect to accomplish communication without CE, which can simplify the complicated CE in RIS to the invisible CE containing holographic principle, avoiding the complicated CE. The scheme accomplished communication by interference patterns to restore the communication information required by the user. We loaded the Hall sensor on the RIS to fulfill the electromagnetic recording about the pattern. Finally, simulation results illustrate the effectiveness of the proposed scheme.

The reconfigurable intelligent surfaces (RIS) is a new technology that can be utilized to provide security to vehicle-to-vehicle (V2V) communications at the physical layer. In this paper, we achieve a higher key generation rate for V2V communications at lower cost and computational complexity. We investigate the use of a passive RIS as a relay, to introduce channel diversity and increase the key generation rate (KGR), accordingly. In this regard, we consider the subsets of consecutive reflecting elements instead of the RIS as a whole in a time slot, i.e., instead of a single reflector, the subsets of reflectors are utilized to redirect the signal to the receiver via passive beam forming. Simulations are conducted for different sizes of RISs and subsets of reflectors per RIS. From the results obtained, it can be seen that when we consider a subset of reflectors instead of the RIS as a single entity, it becomes increasingly difficult to intercept the signal at the eavesdropper. In the proposed scheme, the KGR reaches up to 6 bps per time slot.

Wireless-powered communication and reconfigurable intelligent surface (RIS) can complement each other for increasing energy utilization and spectrum efficiency by reconfiguring the surrounding radio environment, however, which has not been sufficiently studied by the existing works. In this paper, we propose a joint radio resource and passive beamforming optimization scheme for a downlink RIS-assisted wireless-powered communication network with a harvest-then-transmit protocol to improve system energy efficiency (EE). In the considered model, the single-antenna wireless devices (WDs) harvest wireless energy from a multi-antenna dedicated power station (PS) through the RIS in the downlink and transmit their independent information to a single-antenna receiver in the uplink by a time-division-multiple-access mode. Our goal is to maximize the total EE of all WDs. To make full use of the beamforming gain provided by both the PS and the RIS, we jointly optimize the active beamforming of the PS and the passive beamforming of the RIS. To deal with the challenging non-convex optimization problem with multiple coupled variables, we first consider fixing the passive beamforming, and converting the remaining radio resource allocation problem into an equivalent convex problem which is solved by using Lagrange dual theory. Then, we fix the optimized resource allocation parameters and optimize the passive beamforming of the RIS by using a semidefinite programming method. Simulation results demonstrate that the proposed algorithm achieves higher EE compared to the conventional schemes.

To support massive access for future wireless networks, we propose a novel reconfigurable intelligent surface (RIS)-enhanced downlink multi-user multi-input single-output (MU-MISO) symbiotic radio (SR) system. In the proposed system, each RIS not only enhances the primary transmission from the primary transmitter (PT) to the associated primary receiver (PR) nearby, but also acts as an Internet-of-Things (IoT) device to enable IoT transmissions to the same PR. Therefore, each PR needs to jointly decode the information from both the PT and its corresponding RIS. We are interested in maximizing the weighted sum-rate of both primary and IoT transmissions by jointly designing the active transmit beamforming at PT and the passive beamforming at each RIS under the maximum transmit power constraint at the PT and various constraints on the reflection coefficients (RCs), which include the ideal, continuous-phase and the discrete-phase cases. The formulated problem is non-convex, which cannot be solved directly. Thus, fractional programming (FP) method and alternating optimization (AO) technique are adopted to tackle the problem. In particular, three low-complexity algorithms are proposed to trade off between computational complexity and convergence rate. Compared to different benchmark schemes, simulation results demonstrate that with the aid of the RISs, the PRs can benefit from the enhanced primary transmission from the PT, and receive information from the associated RISs via IoT transmission.

Reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) communication systems relying on hybrid beamforming structures are capable of achieving high spectral efficiency at a low hardware complexity and low power consumption. In this article, we propose an RIS-assisted mmWave point-to-point system relying on dynamically configured subarray connected hybrid beamforming structures. More explicitly, an energy-efficient analog beamformer relying on the twin-resolution phase shifters is proposed. Then, we conceive a successive interference cancelation (SIC)-based method for jointly designing the hybrid beamforming matrix of the base station (BS) and the passive beamforming matrix of the RIS. Specifically, the associated bandwidth-efficiency maximization problem is transformed into a series of subproblems, where the subarray of phase shifters and RIS elements is jointly optimized for maximizing each subarray’s rate. Furthermore, a greedy method is proposed for determining the phase shifter configuration of each subarray. We then propose to update the RIS elements relying on a complex circle manifold (CCM)-based method. The proposed dynamic subconnected structure as well as the proposed joint hybrid and passive beamforming method strike an attractive tradeoff between the bandwidth efficiency and power consumption. Our simulation results demonstrate the superiority of the proposed method compared to its traditional counterparts.

This paper proposes a scheme for enhancing covert communication in cognitive radio networks (CRNs) using a reconfigurable intelligent surface (RIS), which ensures that transmissions by secondary users (SUs) remains statistically undetectable by adversaries (e.g., wardens like Willie). However, there exist stringent challenges in CRNs due to the dual constraints of avoiding detection and preventing harmful interference to primary users (PUs). Leveraging the RIS’s ability to dynamically reconfigure the wireless propagation environment, our scheme jointly optimizes the SU’s transmit power, communication block length, and RIS’s passive beamforming (phase shifts) to maximize the effective covert throughput (ECT) under rigorous covertness constraints quantified by detection error probability or relative entropy while strictly adhering to PU interference limits. Crucially, the RIS configuration is explicitly designed to simultaneously enhance signal quality at the legitimate SU receiver and degrade signal quality at the warden, thereby relaxing the inherent trade-off between covertness and throughput imposed by the fundamental square root law. Furthermore, we analyze the impact of unequal transmit prior probabilities (UTPPs), demonstrating their superiority over equal priors (ETPPs) in flexibly balancing throughput and covertness, and extend the framework to practical scenarios with Poisson packet arrivals typical of IoT networks. Extensive results confirm that RIS assistance significantly boosts ECT compared to non-RIS baselines and establishes the RIS as a key enabler for secure and spectrally efficient next-generation cognitive networks.

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.

Energy efficiency (EE) has become a key performance indicator in designing future wireless networks. In this regard, we propose a novel two reconfigurable intelligent surfaces (RISs) assisted coordinated multipoint (CoMP) network, where two RISs cooperate to reduce energy consumption by constructing an intelligent radio environment. Aiming to maximize this system’s energy efficiency (EE), the joint optimization problem on the cooperative beamforming among base stations (BSs) and passive beamforming among RISs, subject to each user’s data rate requirement, is formulated. An alternative optimization algorithm is proposed by decoupling it into a cooperative beamforming subproblem among BSs and a cooperative reflective coefficients design subproblem among RISs. Dinkelbach’s transform and complex fractional programming algorithms are also invoked to transform these two subproblems into convex optimization problems and obtain the optimal solution for each sub-problem. Numerical results validate the proposed algorithm’s feasibility, fast convergence, and flexibility. It also shows that the proposed scheme with cooperative beamforming design for both BSs and RISs outperforms that without RIS and that with randomly initialized RISs.

In this paper, we propose a location-aware channel estimation based on the atomic norm minimization (ANM) for the reconfigurable intelligent surface (RIS)-aided millimeter-wave multiple-input-multiple-output (MIMO) systems. The beam training overhead at the base station (BS) is reduced by the direct beam steering towards the RIS with the location of the BS and the RIS. The RIS beamwidth adaptation is proposed to reduce the beam training overhead at the RIS, and also it enables accurate channel estimation by ensuring the user equipment receives all the multipath components from the RIS. After the beam training, the cascaded effective channel of the RIS-aided MIMO systems is estimated by ANM. Depending on whether the beam training overhead at the BS or at the RIS is reduced or not, the channel is represented as a linear combination of either 1D atoms, 2D atoms, or 3D atoms, and the ANM is applied to estimate the channel. Simulation results show that the proposed location-aware channel estimation via 2D ANM and 3D ANM achieves superior estimation accuracy to benchmarks.

In this paper, we consider the reconfigurable intelligent surface (RIS)‐aided MIMO systems to realize a high quality link between the base station (BS) and the users via a RIS. To achieve this goal, the active beamforming at the BS and the passive beamforming at the RIS should be jointly optimized. However, most of the existing joint optimization schemes that maximize the sum‐rate have high computational complexity due to the complex derivative calculations and matrix inversion. To this end, a biologically inspired particle swarm optimization (PSO) algorithm is exploited to solve the non‐convex sum‐rate optimization problem with low‐complexity. Simulation results show that the proposed scheme provides a better trade‐off between performance and complexity than existing solutions for RIS‐aided MIMO systems.

Reconfigurable intelligent surfaces (RISs) are viewed as key enablers for next-generation wireless communications. This paper investigates a multiuser downlink multiple-input single-output (MISO) system in which a multiantenna base station (BS) transmits information to multiple single-antenna users with the aid of both a half-duplex decode-and-forward (DF) relay and a full-duplex RIS. Active beamforming at the BS and the DF relay, as well as passive beamforming at the RIS, are jointly designed for system sum-rate maximization. The design problem is challenging to solve due to coupled beamforming variables. An alternating optimization (AO) based algorithm is proposed to tackle this complex co-design problem. Numerical results demonstrate the superior performance of the proposed hybrid relay–RIS system with a judicious joint beamforming design. Convergence and complexity analysis shows that the convergence rate of the proposed algorithm is dominated by the numbers of users and RIS elements, and the proposed scheme can converge in a few iterations even in the configuration of large numbers of users and RIS elements. Interesting tradeoffs posed in the joint design are discussed. An extension of the proposed design method to a related energy efficiency (EE) optimization problem is also outlined and implemented.

As a promising imaging technique, computational imaging is able to reconstruct the target by transmitting temporal-spatial incoherent signals. With the reconstructed image, a deterministic model of the scene can be established. However, in previous computational imaging systems, the transceiver is an antenna array and each antenna element needs to be modulated respectively, which requires many radio-frequency (RF) chains and complicated signal processing techniques. In addition, the traditional method uses colocated transmitting and receiving antenna which is not conducive to the flexibility of the system. Reconfigurable intelligent surface (RIS) is a revolutionary technology for achieving spectrum and energy efficient in future wireless communication systems. In this paper, we propose distributed computational imaging with reconfigurable intelligent surface. The transmitting antenna, receiving antenna and RIS are distributed in different spatial positions. By deploying reconfigurable intelligent surface, the phase and amplitude of the detecting signals are stochastically modulated by reconfigurable passive elements of RIS, which achieves high spectrum and energy efficiency with low hardware cost. Furthermore, we analyze four models of the detecting signal in detail and subdivide region of interest (ROI) into a collection of grids to formulate the reconstruction process as a sparse recovery problem. Simulation results demonstrate that the proposed distributed computational imaging with RIS can achieve outstanding imaging performance and reconstruct the target effectively.

A novel framework is proposed for the deployment and passive beamforming design of a reconfigurable intelligent surface (RIS) with the aid of non-orthogonal multiple access (NOMA) technology. The problem of joint deployment, phase shift design, as well as power allocation in the multiple-input-single-output (MISO) NOMA network is formulated for maximizing the energy efficiency with considering users particular data requirements. To tackle this pertinent problem, machine learning approaches are adopted in two steps. Firstly, a novel long short-term memory (LSTM) based echo state network (ESN) algorithm is proposed to predict users' tele-traffic demand by leveraging a real dataset. Secondly, a decaying double deep Q-network (D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> QN) based position-acquisition and phase-control algorithm is proposed to solve the joint problem of deployment and design of the RIS. In the proposed algorithm, the base station, which controls the RIS by a controller, acts as an agent. The agent periodically observes the state of the RIS-enhanced system for attaining the optimal deployment and design policies of the RIS by learning from its mistakes and the feedback of users. Additionally, it is proved that the proposed D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> QN based deployment and design algorithm is capable of converging within mild conditions. Simulation results are provided for illustrating that the proposed LSTM-based ESN algorithm is capable of striking a tradeoff between the prediction accuracy and computational complexity. Finally, it is demonstrated that the proposed D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> QN based algorithm outperforms the benchmarks, while the NOMA-enhanced RIS system is capable of achieving higher energy efficiency than orthogonal multiple access (OMA) enabled RIS system.

In this paper, we investigate a reconfigurable in-telligent surface (RIS)-based uplink massive multi-input multi-output symbiotic radio system, where each RIS, as an IoT device, enhances the primary transmission from a nearby user to the base station (BS) and simultaneously transmits its own infor-mation to the BS by backscattering modulation. By embedding environmental sensors on the RISs, the proposed system enables the IoT transmission of locally collected environmental data to the BS while assisting the primary transmission. Assuming imperfect channel state information (CSI), we jointly design the active beamforming at the BS and the passive beamforming at the RISs to maximize the weighted sum-rate of both the primary and IoT transmissions. An algorithm based on the block coordinate descent method is proposed to solve it. Simulation results show that the proposed system achieves significant performance gain compared with different baseline schemes. Besides, when the channel estimation error is small, the performance loss due to imperfect CSI is insignificant.