CSI acquisition in RIS-assisted mobile communication systems.

Reconfigurable intelligent surfaces (RISs) have emerged as a promising solution that can provide dynamic control over the propagation of electromagnetic waves. The RIS technology is envisioned as a key enabler of sixth-generation networks by offering the ability to adaptively manipulate signal propagation through the smart configuration of its phase shift coefficients, thereby optimizing signal strength, coverage, and capacity. However, the realization of this technology's full potential hinges on the accurate acquisition of channel state information (CSI). In this paper, we propose an efficient CSI prediction framework for a RIS-assisted communication system based on the machine learning (ML) transformer architecture. Architectural modifications are introduced to the vanilla transformer for multivariate time series forecasting to achieve high prediction accuracy. The predicted channel coefficients are then used to optimize the RIS phase shifts. Simulation results present a comprehensive analysis of key performance metrics, including data rate and outage probability. Our results confirm the effectiveness of the proposed ML approach and demonstrate its superiority over other baseline ML-based CSI prediction schemes such as conventional deep neural networks and long short-term memory architectures, albeit at the cost of slightly increased complexity.

The reconfigurable intelligent surfaces (RISs) have garnered a lot of interest in the development of smart radios. The acquisition of channel state information in RIS aided systems are extremely challenging as pilot overhead is considerably large. Previous works have shown an improvement in reducing pilot over head by exploiting the structured sparsity. In this contribution, we consider a sparse RIS under the environment of multiuser MIMO communication system by randomly selecting the elements of RIS in uniform planar arrays to eliminate unnecessary elements and to reduce the large pilot overhead. The random distribution models i.e Gaussian, Poisson and Geometric are adopted and analyzed for selection of sparse RIS. The numerical results show a significant improvement in NMSE performance with reduced pilot overhead.

In reconfigurable intelligent surface (RIS)-assisted systems the acquisition of channel state information and the optimization of reflecting coefficients constitute major design challenges. In this paper, a novel channel training-based protocol is proposed, which is capable of striking a flexible trade-off between performance, pilot overhead and complexity. More specifically, first of all , we conceive a holistic protocol that intrinsically amalgamates the existing channel estimation and passive beamforming optimization for creating a new unified scheme. Secondly , we propose a new channel training framework. In contrast to the conventional channel estimation arrangements, our new framework divides the training phase into several periods and has the compelling benefit of directly estimating the superimposed end-to-end channel instead of separately estimating the direct BS-user and reflected RIS links, which would not lend itself to near-instantaneous reconfiguration in the face of high-Doppler mobility. As a result, the RIS reflecting coefficients are optimized by comparing the objective function values over multiple training periods, which leads to optimal performance, despite its reduced complexity as well as reduced signaling and pilot overhead. Thirdly , we analyze the theoretical performance of both the channel estimation-based protocol and the channel training-based protocol in the presence of channel estimation errors. Finally , our theoretical analysis is confirmed by numerical simulations. In particular, the simulation results demonstrate that our channel training-based protocol is more competitive than the channel estimation-based protocol in the presence of channel estimation errors.

In this paper, we explore the use of reconfigurable intelligent surface (RIS) in unmanned aerial vehicle (UAV) based multiuser downlink communications, where a flying UAV serves multiple single antenna users through multiple RISs mounted on various buildings. More specifically, we consider the selection of RISs based on the outdated and imperfect channel state information (CSI) of the composite UAV-RIS-User channels at the UAV. After selection process, the UAV communicates to the user via the selected RISs and also with the direct link. Particularly, we derive an infinite series based expression for selection probability of RISs under both the outdated and imperfect CSI of composite channels based selection scheme. We also derive the statistical distribution of instantaneously received signal-to-noise ratio (SNR) under outdated and imperfect CSI conditions of both the direct and composite links at the user. Next, using the derived statistics, we analyze the network’s performance in terms of the average coverage probability (ACP) and average bit error rate (ABER) over the complete UAV flight time. Moreover, we discuss the behavior of ACP and ABER for very small and very large values of UAV transmit power, respectively. It is depicted through numerical results that selecting more RISs from a group of small-sized RISs may not be as advantageous as selecting fewer RISs from a group of large-sized RISs. Moreover, we also demonstrate the effect of several system parameters such as number of RIS reflecting elements, number of selected RISs, the severity of UAV-RIS and RIS-User links, and the severity of imperfect and outdated CSI on the network’s performance. The analytical results are corroborated with Monte-Carlo simulations.

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 surfaces (RIS) have emerged as a promising approach to enhance the secrecy performance of wireless networks from a physical layer security perspective. However, its effectiveness critically depends on the availability of accurate instantaneous channel state information (CSI) at the RIS controller. This work evaluates the impact of outdated CSI on the secrecy performance of a multiple RIS-aided wireless network. To this end, a closed-form expression for the secrecy outage probability (SOP) is derived assuming the availability of an outdated CSI at the RIS controller. The obtained results demonstrate the network’s secrecy degradation level due to the CSI outdatedness, while the increase in the number of RISs can compensate for such a secrecy loss. For instance, incorporating three RISs improves the SOP by two orders of magnitude with respect to a single RIS for 25% of CSI outdatedness. Furthermore, the results reveal that, under specific scenarios of several channel outdatedness, a baseline single RIS with a perfect CSI can outperform multiple RISs with outdated CSI. These findings highlight a critical limitation in incorporating multiple RISs in a wireless network with severe channel aging, requiring the increase of the RISs size or the number of RISs to combat its effect.

Reconfigurable intelligent surface (RIS) partitioning offers a strategic solution for serving multiple users equipment (UEs) simultaneously in blockage-prone wireless environments, leveraging multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) technologies within the millimeter-wave (mmWave) spectrum. However, deploying RIS partitioning (RISP) in MIMONOMA is hindered by the exacerbated computational complexity involved in acquiring accurate channel state information (CSI). This paper proposes a novel learning framework based on multi-agent deep reinforcement learning (MA-DRL) that maximizes the sum rate of the RISP-aided MIMO-NOMA system without requiring UEs’ CSI. The proposed framework jointly optimizes RIS phase shifts, number of RIS partitions, UEs beamformers, and NOMA power allocation (PA), transforming the challenge into a high-dimensional combinatorial optimization problem. The MA-DRL algorithm integrates double deep Q networks (DDQN) and deep deterministic policy gradient (DDPG) agents, where each UE acts as a DDQN agent optimizing its beamformer, while the RIS serves as a DDPG agent handling partitioning and power control. An experimental testbed is developed to gather real-world data for training and evaluation. Results show that the MA-DRL algorithm closely approaches optimal performance, trailing the exhaustive search by only 8%, while reducing complexity by 95% and improving the sum rate by an average of 18% compared to traditional full RIS setups.

Reconfigurable intelligent surfaces (RISs) have been introduced as a remedy for mitigating blockages in millimeter wave (mmWave) and terahertz (THz) communications networks. However, perfect or nearly perfect channel state information (CSI) is fundamental in order to achieve their full potential. Tra-ditionally, an RIS is fully passive without any baseband processing capabilities, which poses great challenges for CSI acquisition. Thus, we focus on the hybrid RIS architecture, where a small portion of RIS elements are active and able to processing the received pilot signals for estimating the corresponding channel. The channel estimation (CE) is done by resorting to off-the-grid compressive sensing technique, i.e., atomic norm minimization, for extracting channel parameters through two stages. Simulation results show that the proposed scheme outperforms the passive RIS CE under the same training overhead.

The reconfigurable intelligent surface (RIS) is promising in fulfilling the requirement of green communication in the fifth- and sixth-generation (5G/6G) mobile communication networks. Considering the limits of the conventional channel state information (CSI) acquisition techniques for the RIS-aided communication system, this paper proposes a location-aware beamforming design for the RIS-aided millimeter-wave (mmWave) communication system without the channel estimation process. First, according to the system geometry, the relationship between the channel model and the user location is established, which is further used to derive the CSI error bound using the location error bound. Then, a worst-case robust beamforming optimization problem is formulated and solved to combat the effect of location error on the beamforming design. Simulation results demonstrate that the proposed approach outperforms the conventional non-robust approach in terms of the worst-case signal-to-noise ratio (SNR) at the receiver, and becomes more advantageous in the presence of higher level of the user location uncertainty.

Reconfigurable intelligent surface (RIS) has the potential to significantly enhance the network secure transmission performance by reconfiguring the wireless propagation environment. However, due to the passive nature of eavesdroppers and the cascaded channel brought by the RIS, the eavesdroppers’ channel state information is imperfect at the base station. Under channel uncertainty, the optimal phase-shift, power allocation, and transmission rate design for massive antennas and reflecting elements secure transmission are challenging to solve due to the outage probabilistic constraint with coupled variables. To fill this gap, this paper formulates a problem of energy-efficient secure transmission design with the probabilistic outage constraint. By leveraging the exponential distribution property of the received signal power, the stochastic resource allocation is equivalently transformed into a deterministic one, and the secure energy efficiency maximization problem can be iteratively solved via low complexity first-order algorithms under the alternating maximization (AM) framework. However, due to the nonsmooth problem, the convergence of the objective function value and nature of the converged solution under AM iteration are uncertain. Therefore, the convergence properties with respect to the objective function value and sequence of solutions are further established. Simulation results corroborate the convergence results of the first-order algorithms and show that the proposed algorithm achieves identical performance to the conventional method but saves at least two orders of magnitude in computation time. Moreover, the resultant RIS aided secure transmission significantly improves the energy efficiency compared to baseline schemes of random phase-shift, fixed phase-shift, and RIS ignoring CSI uncertainty.

Energy efficiency (EE) is one of the most important metrics for the beyond fifth generation (B5G) and the future sixth generation (6G) wireless networks. Reconfigurable intelligent surface (RIS) has been widely focused on EE enhancement for wireless networks because it is power-saving, programmable, and easy to be deployed. However, RIS is generally passive and thus difficult to obtain corresponding full channel state information (CSI), which severely impacts the EE enhancement of RIS-assisted wireless communications. To solve this problem, in this paper we propose the new single-active-antenna combined RIS transmitter structure, which can replace traditional multiple antennas to reduce hardware cost and power consumption. Based on the single-active-antenna combined RIS structure, we develop the Dumb RIS-Assisted Random Beamforming (Darb)-based Joint RIS-Elements and Transmit-power optimizAtion (Jeta) scheme, where dumb RIS randomly changes its phase shift according to isotropic distribution only depending on the CSI feedback from users to RIS-assisted transmitter. Then, we jointly design the number of RIS elements and optimize the transmit power to maximize the EE of RIS-assisted wireless communications. Simulation results show that compared with the traditional multi-antenna system, our developed Darb-based-Jeta scheme can significantly increase the EE without the full CSI.

Active reconfigurable intelligent surface (RIS) emerges as an effective technique to resist the double-fading attenuation of passive RIS. By embedding with power harvesting function, it further evolves to zero-power active RIS, which can effectively enhance the flexibility of RIS deployment without external power demand. Nevertheless, existing works neglected the inherent difficulty of channel estimation (CE) for RIS-assisted systems, and the discrete phase shift constraint in practical deployment. In this paper we design a new element-wise RIS architecture and propose a distributed location-aided transmission scheme with low complexity to enhance the reflected gain for channel state information (CSI)-limited RIS-assisted near-field communications. Specifically, the new element-wise RIS provides dynamic element selection capability with low hardware resources. Based on Fresnel diffraction theory, we construct the mapping from locations in space-domain to phase distributions of waves in phase-domain and reveal the priority of elements for harvesting and reflecting. Then, the distributed beamforming design with the phase of determine-then-align is proposed, where the estimation overhead reduction stems from exempted requirements of RIS-associated CE at base station (BS). The asymptotic analysis indicates that the proposed scheme can achieve the optimal gain with a fixed proportion of reflective elements when RIS is large, followed by simulations to verify its superiority to other protocols.

In this paper, we study the uplink transmission scheme and channel estimation design for reconfigurable intelligent surfaces (RIS)-aided orthogonal time frequency space (OTFS) systems in high-mobility scenarios. To this end, we first propose an efficient and reliable transmission scheme which utilizes the delay-Doppler information in OTFS to facilitate the configuration of RIS. Specifically, the proposed scheme exploits the estimated delay and Doppler shifts of the cascaded channel to sense the channel parameters, and the sensing parameters are then used for RIS passive beamforming. It is noteworthy that we estimate the channel state information (CSI) by employing only one OTFS frame and configure the RIS based on the predicted channel parameters, leading to substantially reduced channel training overhead and more real-time RIS configuration. To obtain the essential information for channel information sensing, we then propose a low-complexity algorithm which determines the Doppler and delay shifts of the channel between the user and RIS based on linear systems and the mapping relationship of the delay-Doppler pairs, respectively. With the delay-Doppler information in hand, a user localization algorithm constructed by the least square (LS) and a channel tracking method relying on extended Kalman filter (EKF) are then presented to obtain the spatial angle information. By making use of the channel parameters acquired at the base station (BS), the RIS reflection vector is designed to maximize the achievable rate. The results obtained from the simulation experiments affirm the efficacy of the proposed scheme, thereby confirming its capability to attain efficient communications under high Doppler channels.

A High-Efficiency and Versatile Reconfigurable Intelligent Surface Design Paradigm with Novel Topological Representation

Reconfigurable Intelligent Surfaces (RIS) hold the promise of significantly improving coverage, as well as spectral and energy efficiency in wireless communication systems. Techniques based on RIS form a key technology for 6G systems, attracting a significant research interest. An important issue in RIS technology is Channel State Information (CSI), which is much more difficult to acquire in such systems. This work considers a Differential Space-Time Modulation (DSTM) technique integrated with Differential Reflecting Modulation (DRM) termed DRM-DSTM. Such a scheme does not require CSI, while providing error rate gains. The DSTM scheme is based on unitary group codes. We first consider uncoded DRM for RIS to serve as a reference point. Next we provide an overview of DSTM and outline the procedures for its integration with DRM. We consider the issue of reflecting patter selection in such systems, and explore the extension of both the original DRM and the coded DRM-DSTM scheme to a larger number of RIS reflecting patterns <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i>. Furthermore we provide tables of cyclic and dicyclic codes for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i>' = 2, 3, 4 suitable for DRM-DSTM. Encoding and decoding complexities are studied as well. Extensives Monte-Carlo simulation results over quasi-static Rayleigh fading channels confirm the effectiveness of the DRM-DSTM coded scheme, illustrating its advantages over uncoded DRM with proper system parameters.