In this paper, we investigate the intelligent reflecting surface (IRS)/reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system based on sensing mutual information (MI). Specifically, the base station (BS) perceives the sensing target via the reflected sensing signal by the IRS, while communicating with the users simultaneously. Our aim is to maximize the sensing MI, subject to the quality of service (QoS) constraints for all communication users, the transmit power constraint at the BS, and the unit-modulus constraint on the IRS’s passive reflection. We solve this problem under two cases: one simplified case assuming a line-of-sight (LoS) channel between the BS and IRS and no clutter interference to sensing, and the other generalized case considering the Rician fading channel of the BS-IRS link and the presence of clutter interference to sensing. For the first case, we prove that the dedicated sensing beamformer is unnecessary for improving sensing MI and develop a low-complexity iterative algorithm to jointly optimize the BS and IRS active/passive beamformers. Then, for the second case, we propose an alternative iterative algorithm, which can also be applied to the first case, to solve the beamforming design problem under the general setup. Numerical results are provided to validate the performance of the proposed algorithms, as compared to various benchmark schemes.

This paper proposes a promising framework of multi-functional service incorporating sensing targets (STs), information receivers (IRs), and energy receivers (ERs) in an active reconfigurable intelligent surface (RIS)-aided integrated sensing, communication, and power transfer (ISCPT) system. In the proposed system, we aim to maximize the weighted sum of the received radar signal-to-interference-plus-noise-ratio (SINR) by jointly optimizing the transmit beamforming at the multi-functional base station (MFBS), the coefficients of active RIS, and the radar receive filter coefficients. Meanwhile, the constraints of the SINR of IRs, energy harvesting (EH) requirements of ERs, the power budget for the MFBS and active RIS, and the amplification gain should be satisfied. To guarantee the generality of formulated problems, we further incorporate the self-interference effects of echo signals, multi-target echo interference, simultaneous detection of multiple STs, and a nonlinear EH model into the generalized system model. Due to the presence of echo interference and multi-target echo interference, the MFBS transmits the dedicated sensing signal with the communication to enhance the sensing performance. The formulated problem is tackled by developing an efficient alternating optimization (AO) algorithm combined with fractional programming (FP) and majorization-minimization (MM) techniques. Finally, the numerical results reveal the impact of system parameters on the sensing performance, the trade-off relationship between multiple functionalities, and the deployment strategy of RIS. The main findings are as follows: 1) Active RIS is remarkably superior to passive RIS for ISCPT systems, especially for closer to the receivers with a 40 dB performance gain. 2) Comparatively, the radar sensing SINR is more sensitive to the number of active RIS units, while the SINR of IRs is more sensitive to the number of antennas at the base station. These results demonstrate that the proposed system holds the potential for practical deployment.

This work investigates a practical reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system, where a subset of RIS elements fail to function properly and reflect incident signals randomly towards unintended directions with attenuation, thereby degrading system performance. To date, no study has addressed such impairments caused by faulty RIS elements in ISAC systems. This work aims to fill the gap. First, to quantify the impact of faulty elements on ISAC performance, we derive the misspecified Cramér-Rao bound (MCRB) for sensing parameter estimation and signal-to-interference-and-noise ratio (SINR) for communication quality. Then, to mitigate the performance loss caused by faulty elements, we jointly design the remaining functional RIS phase shifts and transmit beamforming to minimize the MCRB, subject to the communication SINR and transmit power constraints. The resulting optimization problem is highly non-convex due to the intricate structure of the MCRB expression and constant-modulus constraint imposed on RIS. To address this, we reformulate it into a more tractable form and propose a block coordinate descent (BCD) algorithm that incorporates majorization-minimization (MM), successive convex approximation (SCA), and penalization techniques. Simulation results demonstrate that our proposed approach reduces the performance loss by 21.25% on average compared to the baseline where the presence of faulty elements is ignored. Furthermore, the performance gain becomes more evident as the number of faulty elements increases.

Uplink integrated sensing and communication (ISAC) systems have recently emerged as a promising research direction, enabling simultaneous uplink signal detection and target sensing. In this paper, we propose the flexible projection (FP)-type receiver that unifies the projection-type receiver and the successive interference cancellation (SIC)-type receiver by using a flexible tradeoff factor to adapt to dynamically changing uplink ISAC scenarios. The FP-type receiver addresses the joint signal detection and target response estimation problem through two coordinated phases: 1) Communication signal detection using a reconstructed signal whose composition is controlled by the tradeoff factor, followed by 2) Target response estimation performed through subtraction of the detected communication signal from the received signal. With adjustable tradeoff factors, the FP-type receiver can balance the enhancement of the signal-to-interference-plus-noise ratio (SINR) with the reduction of correlation in the reconstructed signal for communication signal detection. The pairwise error probability (PEP) expressions are analyzed for both the maximum likelihood (ML) and the zero-forcing (ZF) detectors, revealing that the optimal tradeoff factor should be determined based on the adopted detection algorithm and the relative power of the sensing and communication (S&C) signals. A homotopy optimization framework is first applied for the FP-type receiver with a fixed tradeoff factor. This framework is then extended to develop the dynamic flexible projection (DFP)-type receiver, which iteratively adjusts the tradeoff factor for improved algorithm performance and environmental adaptability. Finally, we show that the length of the jointly processed signal should scale with the antenna size to fully unleash the potential of the uplink ISAC receiver.

In this paper, we investigate the channel estimation for mmWave multiple-input multiple-output-(MIMO) orthogonal frequency division multiplexing (OFDM) systems in high-mobility scenarios. By leveraging the low-rank nature of mmWave channels and the multidimensional characteristics of MIMO-OFDM signals across space, time, and frequency, the received signals are structured as a fourth-order tensor that fits a low-rank CANDECOMP/PARAFAC (CP) model. We propose an estimation of signal parameters via rotational invariance techniques (ESPRIT)type decomposition-based method to solve the CP decomposition, which exploits the Vandermonde structure of the factor matrix. The channel parameters are then estimated from the factor matrices. Simulation results show that our method outperforms existing benchmarks.

Projection-type receivers have recently emerged as a promising approach for uplink integrated sensing and communications (ISAC) systems, facilitating simultaneous uplink signal detection and target sensing. However, the signal detection problem within projection-type receivers faces challenges due to the high dimensionality and rank-deficiency of the equivalent channel matrix. To address this rank-deficiency issue, we introduce two novel variations to reconfigure the equivalent channel matrix: the Projection-Tikhonov (PT) receiver and the Projection-Orthogonal Multiple Access (P-OMA) transceiver. The PT receiver is specifically designed to mitigate the impact of rank-deficiency through regularization, while the P-OMA transceiver utilizes the independent columns of the equivalent channel matrix to transmit a reduced number of communication symbols. For signal detection with the reconfigured channel matrix, we demonstrate that while the linear decoding algorithm can be effectively computed for various projection-type receivers, it yields poor performance. Given the high dimensionality of the equivalent channel matrix, we propose an efficient iterative algorithm based on the extreme point pursuit (EXPP) framework, using a low-complexity linear detector as the initial point. Finally, simulation results validate the effectiveness of the proposed design.

The paper investigates MIMO integrated sensing and communication (ISAC) systems with extended radar clutter interference. Different from previous work considering a point target scatterer, we focus on a more general scenario with an extended target scatterer. We aim to maximize the sensing mutual information (MI) while satisfying constraints on communication quality of service (QoS) and base station (BS) transmit power. The existing algorithm exhibits high computational complexity in the general scenario proposed in this paper. Hence, we propose an efficient low-complexity algorithm that employs a parallel optimization strategy and closed-form solutions to significantly reduce the complexity of each iteration. Moreover, we derive a lower bound on sensing MI, which is a function of transmit beampattern, and provide a sufficient condition for its tightness. Numerical results demonstrate the low complexity and good performance of our proposed algorithm compared to the existing benchmarks and validate the relationship between the lower bound and sensing MI.

Symbiotic radio (SR) has emerged as a promising technology for enabling efficient spectrum and power sharing between active and backscattering transmissions. In this paper, we investigate the reconfigurable intelligent surface (RIS)-assisted SR system, where the primary transmission uses orthogonal frequency division multiplexing (OFDM) and the RIS transmits the secondary signal by backscattering the primary signal. The primary OFDM block and the secondary symbol have identical symbol periods but may not be perfectly synchronized, which can introduce inter-carrier interference (ICI) in the received OFDM blocks, thereby hindering joint signal detection. To address this issue, we propose a novel pilot structure and receiver design for SR. Specifically, the RIS sent a training sequence at the beginning of the secondary transmission, enabling the receiver to detect the presence of ICI and estimate essential parameters. If ICI is detected, two effective methods for synchronization offset estimation are proposed. Then, joint signal detection is improved by properly decoupling primary and secondary signals, mitigating the impact of synchronization offsets. On the other hand, if ICI is absent, the secondary signal arrival is identified using the training sequence, and joint signal detection is directly performed without suffering ICI. Simulation results validate the accuracy of the proposed estimation methods and show that the proposed detection methods ensure the reliable detection of both primary and secondary signals, even in the presence of ICI.

We propose a novel integrated sensing and communication (ISAC) scheme that leverages sensing to assist communication in light-of-sight (LoS) environments, ensuring fast initial access, seamless user tracking, and uninterrupted communication for millimeter wave (mmWave) wideband systems. True-time-delayers (TTDs) are utilized to generate frequency-dependent radar rainbow beams by controlling the beam squint effect. These beams cover users across the entire angular space simultaneously for fast beam training using just one orthogonal frequency-division multiplexing (OFDM) symbol. Three detection and estimation schemes are proposed based on radar rainbow beams for estimation of the users’ directions, distances, and velocities, which are then exploited for communication beamformer design. The first proposed scheme utilizes a single-antenna radar receiver and one set of rainbow beams, but may cause a Doppler ambiguity. To tackle this limitation, two additional schemes are introduced, utilizing two sets of rainbow beams and a multi-antenna receiver, respectively. Furthermore, the proposed detection and estimation schemes are extended to realize user tracking by choosing different subsets of OFDM subcarriers. This approach eliminates the need to switch phase shifters and TTDs, which is typically required for existing tracking schemes. Simulation results reveal the effectiveness of the proposed rainbow beam-based training and tracking methods for mobile users.

Simultaneous transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS) technique has recently received considerable attention due to its omni-directional radiation capability. In this paper, motivated by the interference-mitigation-based (IMB) and signal-enhancement-based (SEB) designs, we introduce an innovative STAR-RIS assisted simultaneous-signal-enhancement-and-interference-mitigation (SSEIM) design in non-orthogonal multiple access (NOMA) multiple-input single-output cellular communication networks. Our objective is to maximize the system spectral efficiency (SE) by jointly optimizing the reflection and transmission phase shifts at the STAR-RIS, the precoding matrix of BSs, and the power allocation factors of NOMA users. We propose a low-complexity simultaneous enhancement and mitigation algorithm. Furthermore, by exploiting the manifold optimization technique, we introduce the Riemannian conjugate gradient algorithm to solve the non-convex subproblems with unit modulus constraint. Our analysis reveals that the proposed SSEIM design exceeds the traditional RIS-aided SEB and IMB designs.

Time-varying channels are significantly influenced by the Doppler effect, which leads to rapid changes in channel gain and requires Doppler frequency estimation to compensate for channel phase shifts and improve communication quality. In this paper, we propose a novel fifth-generation (5G) new radio (NR) orthogonal frequency division multiplexing (OFDM)-based transmission structure for time-varying channel estimation in high-mobility scenarios. By designing an appropriate subcarrier spacing and slot format, we ensure that the pilot signals remain nearly invariant within a single slot and exhibit rotational invariance between different slots. Leveraging this rotational invariance, we introduce a novel algorithm based on Vandermonde-structured tensor decomposition, which is non-iterative and has lower computational complexity and higher robustness than other tensor-based algorithms. Moreover, we provide a theoretical analysis of the uniqueness condition of tensor decomposition, proving that the proposed algorithm has strong feasibility and requires low pilot overhead. We also analyze the mean square errors (MSEs) of the parameter estimates and present a concise derivation of the Cramér-Rao Bound (CRB). The results demonstrate that the proposed algorithm significantly outperforms compressed sensing (CS)-based methods and other tensor-based methods in terms of parameter estimation performance at medium to high SNR. Furthermore, the proposed algorithm, based on the instantaneous channel model, offers higher channel estimation accuracy than the Kalman filtering-based algorithm, which relies on statistical channel models. Simulation results with channel data generated by Wireless InSite, which constructs a real-world scattering environment, demonstrate the high estimation accuracy of the proposed algorithm, validating its effectiveness in practical scenarios.

This paper investigates a multiuser millimeter-wave (mmWave) uplink system in which each user is equipped with a multi-antenna fluid antenna system (FAS) while the base station (BS) has multiple fixed-position antennas. Our primary objective is to maximize the system capacity by optimizing the transmit covariance matrices and the antenna position vectors of the users jointly. To gain insights, we start by deriving upper bounds and approximations for the capacity. Then we delve into the capacity maximization problem. Beginning with the simple scenario of a single user equipped with a single-antenna FAS, we demonstrate that a closed-form optimal solution exists when there are only two propagation paths between the user and the BS. In the case where multiple propagation paths are present, a near-optimal solution can also be obtained through a one-dimensional search method. Expanding our focus to multiuser cases, in which users are equipped with either single- or multi-antenna FAS, we show that the original capacity maximization problems can be reformulated into distinct rank-one programmings. Then, we propose alternating optimization algorithms to deal with the transformed problems. Simulation results indicate that FAS can improve the capacity of the multiple access channel (MAC) greatly, and the proposed algorithms outperform all the benchmarks.

In this paper, we investigate a double-active-reconfigurable intelligent surface (RIS)-aided downlink wireless communication system, where a multi-antenna base station (BS) serves multiple single-antenna users with both double reflection and single reflection links. Due to the signal amplification capability of active RISs, they can effectively mitigate the multiplicative fading effect. However, this also induces signal bouncing between the two active RISs that cannot be ignored. This phenomenon is termed as the “inter-excitation” effect and is characterized in the received signal by proposing a feedback-type model. Based on the signal model, we formulate a weighted sum rate (WSR) maximization problem by jointly optimizing the beamforming matrix at the BS and the reflecting coefficient matrices at the two active RISs, subject to power constraints at the BS and active RISs, as well as the maximum amplification gain constraints of the active RISs. To solve this non-convex problem, we first transform the problem into a more tractable form using the fractional programming (FP) method. Then, by introducing auxiliary variables, the problem can be converted into an equivalent form that can be solved by using a penalty dual decomposition (PDD) algorithm. Furthermore, the power scaling order of the signal-to-noise ratio (SNR) in double-active-RIS-aided system considering inter-excitation effect is derived. Finally, simulation results indicate that the proposed scheme outperforms benchmark schemes with single active RIS and double passive RISs in terms of achievable rate. Furthermore, the results demonstrate that the proposed scheme can enhance the WSR by 30% compared to scenarios that do not take this effect into account when the maximum amplification gain is 40 dB. Additionally, the proposed scheme is capable of achieving high WSR performance at most locations where double active RISs are deployed between the BS and the users, thereby providing greater flexibility in their deployment.

In this paper, we investigate reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) systems based on sensing mutual information (MI). With the deployment of the RIS, the base station (BS) can perceive the sensing target via the reflected sensing link created by the RIS and communicate with users simultaneously, and the channel between the BS and the RIS is considered as a light-of-sight (LoS) channel. Our aim is to maximize the sensing MI, subject to the quality of service (QoS) constraints of different users, power constraint of the BS, and the unit-modulus constraint of the RIS. Although the formulated nonconvex optimization problem with quartic objective function is difficult to solve, we propose an iterative algorithm based on rank-1 projection, majorization-minimization (MM), eigenvalue decomposition (EVD), and convex-concave procedure (CCP) methods. We also prove that the dedicated sensing beamformer can improve the sensing MI unless the BS-users link is blocked by some obstacles. Numerical results demonstrate that our proposed algorithm outperforms the other benchmarks on the performance of sensing MI and beampattern.

Reconfigurable intelligent surfaces (RISs) have emerged as a disruptive technology that can reconfigure wireless communication environments cost-effectively. In order to fully unveil the potential of RIS-aided wireless communications, some existing contributions considered the double cooperative passive RISs to achieve a higher capacity scaling orders. However, due to the multiplicative fading effect, the double cooperative passive RISs performs poorly when deployed far from the BS and user respectively. To address this issue, we investigate double cooperative active RISs which are equipped with amplifiers and can overcome the severe path loss caused by the multiplicative fading. Specifically, we aim to maximize the downlink achievable rate subject to the transmit power constraints of the base station (BS) and the double active RISs. The formulated problem is solved by using an alternating optimization (AO) algorithm based on the majorization-minimization (MM) algorithm and the fractional programming (FP) method. Simulation results demonstrate that much better rate performance can be achieved by adopting active RIS compared to passive RIS in the double RIS-aided systems. Meanwhile, deploying them appropriately far away from the BS and user and more elements assigned to the active RIS near the user will achieve better performance.

We present a novel fast beam training method for fast moving targets in millimeter wave (mmWave) wideband radar systems. True-time-delayers (TTDs) are utilized to generate frequency-dependent radar rainbow beams using one orthogonal frequency-division multiplexing (OFDM) symbol, simultaneously covering targets located in the entire angular space for fast beam training. We first propose a scheme based on a single-antenna radar receiver. It can effectively detect and estimate different parameters of interest of targets, including their angles, distance related delays, and velocity related Doppler frequencies, but faces a Doppler ambiguity challenge. To tackle this limitation, we further introduce a scheme based on a multi-antenna receiver, which provides high-precision estimation performance. Simulation results reveal the effectiveness of the proposed rainbow beam-based training method for detecting and estimating mobile targets.

This paper considers an intelligent reflecting surface (IRS) aided secure communication with direct communication links where a legitimate receiver (Bob) served by a base station (BS) is overheard by multiple eavesdroppers (Eves), meanwhile the artificial noise (AN) is incorporated to confuse Eves. Since Eves are not legitimate users, their channels cannot be estimated perfectly. We investigate two scenarios with partial channel state information (CSI) error of only cascaded BS-IRS-Eve channel and full CSI errors of both cascaded BS-IRS-Eve channel and direct BS-Eve channel under the statistical CSI error model. To ensure the security performance under CSI errors, the transmit beamforming, AN spatial distribution at the BS, and phase shifts at IRS are jointly optimized to minimize the transmit power constrained by the minimum data rate requirement of Bob and the outage probability of maximum data rate limitation of Eves. In contrast to existing works, the direct link considered in our work makes the optimization of phase shifts at IRS much more challenging, thus we propose a series of novel and artful mathematical manipulations to tackle this issue. Moreover, the proposed algorithm can be applied for both uncorrelated and correlated CSI errors. Simulations confirm the superiority of our proposed algorithm.

This paper considers an active reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system. We aim to maximize radar signal-to-interference-plus-noise-ratio (SINR) by jointly optimizing the beamforming matrix at a dual-function radar-communication (DFRC) base station (BS) and the reflecting coefficients at an active RIS subject to the quality of service (QoS) constraints of communication user equipments (UEs) and the transmit power constraints of active RIS and DFRC BS. To tackle the optimization problem, the majorization-minimization (MM) algorithm is applied to address the nonconvex radar SINR objective function, and the resulting quartic problem is solved by developing an semidefinite relaxation (SDR)-based approach. Moreover, we derive the scaling order of the radar SINR with a large number of reflecting elements. Next, the transmit power allocation problem and the deployment strategy of the active RIS are studied with a moderate number of reflecting elements. Finally, we validate the potential of the active RIS in ISAC systems compared to passive RIS. Additionally, we deliberate on several open problems that remain for future research.

Future communication systems are envisioned to employ intelligent reflecting surfaces (IRSs) and the millimeter wave (mmWave) frequency band to provide reliable high-rate services. For mobile users, the time-varying channel state information (CSI) requires adequate adjustment of the reflection pattern of the IRS. We propose a novel codebook-based user tracking (UT) algorithm for IRS-assisted mmWave communication, allowing suitable reconfiguration of the IRS unit cell phase shifts, resulting in a high reflection gain. The presented algorithm acquires the direction information of the user based on a peak maximum likelihood (ML)-based direction estimation. Using the direction information, the user’s trajectory is extrapolated to proactively update the adopted codeword and adjust the IRS phase shift configuration accordingly. Furthermore, we conduct a theoretical analysis of the direction estimation error and utilize the obtained insights to design a codebook specifically optimized for direction estimation. Our results show that the proposed ML-based direction estimation algorithm outperforms a multiple signal classification (MUSIC)-based reference scheme. The proposed direction estimation codebook improves the direction estimation error for both these schemes as compared to when a reference codebook is used. Also, the accuracy of the proposed UT algorithm is shown to be competitive with Kalman filter-based UT, while the proposed scheme requires fewer a priori assumptions on the user movement. Furthermore, the average achieved signal-to-noise ratio (SNR) as well as the average effective rate of the proposed UT algorithm are analyzed. The proposed UT algorithm requires only a low overhead for direction and channel estimation and avoids outdated IRS phase shifts. Furthermore, it is shown to outperform three benchmark schemes based on direct phase shift optimization, optimal codeword selection, and hierarchical codebook search, respectively, via computer simulations.

We propose new pilot transmission protocols for acquiring channel state information (CSI) of individual reconfigurable intelligent surface (RIS) assisted channels. Our approach addresses the challenge of individual CSI acquisition when the RIS lacks sensing and signal processing capabilities. We use monostatic and bistatic full-duplex base stations (BSs) and exploit the reciprocity of the uplink and downlink channels to design channel estimation algorithms based on both unstructured and geometric channel models. Specifically, for unstructured channel models, we develop two different channel estimation algorithms that provide high accuracy and low pilot overhead, respectively, depending on the type of full-duplex BS used. Moreover, a unified estimation framework is proposed to determine the CSI based on geometric channel models for both types of full-duplex BSs. For the angle estimation required as part of the proposed framework, we further develop a high-precision algorithm based on atomic norm minimization (ANM) and a low-complexity algorithm based on orthogonal matching pursuit (OMP). Simulation results reveal that the proposed algorithms are superior to existing methods in terms of estimation accuracy, complexity, and pilot overhead.

Active reconfigurable intelligent surfaces (RISs) have recently been proposed to compensate for the severe multiplicative fading effect of conventional passive RIS-aided systems. Each reflecting element of active RISs is assisted by an amplifier such that the incident signal can be reflected and amplified instead of only being reflected as in passive RIS-aided systems. This work addresses the practical challenge that, on the one hand, in active RIS-aided systems the perfect individual channel state information (CSI) of the RIS-aided channels cannot be acquired due to the lack of signal processing power at the active RISs, but, on the other hand, this CSI is required to calculate the expected system data rate and RIS transmit power needed for transceiver design. To address this issue, we first derive closed-form expressions for the average achievable rate and the average RIS transmit power based on partial CSI of the RIS-aided channels. Then, we formulate an average achievable rate maximization problem for jointly optimizing the active beamforming at both the base station (BS) and the RIS. This problem is then tackled using the majorization–minimization (MM) algorithm framework, and, in each iteration low-complexity solutions for the BS and RIS beamforming are found based on the Karush-Kuhn-Tucker (KKT) conditions. To ensure the quality of service (QoS) of each user, we further formulate a rate outage constrained beamforming problem, which is solved using the Bernstein-Type inequality (BTI) and semidefinite relaxation (SDR) techniques. Numerical results show that the proposed algorithms can efficiently overcome the challenges imposed by imperfect CSI in active RIS-aided wireless systems.

We propose new pilot transmission protocols for acquiring channel state information (CSI) of individual reconfig-urable intelligent surface (RIS) assisted channels. Our approach addresses the challenge of individual CSI acquisition when the RIS lacks sensing and signal processing capabilities. We use monostatic and bistatic full-duplex (FD) base stations (BSs) and exploit the reciprocity of the uplink and downlink channels to design channel estimation algorithms. Specifically, a unified estimation framework is proposed to estimate the CSI based on geometric channel models for both types of FD BSs. To handle the angle estimation required as part of the proposed framework, we further investigate a high-accuracy algorithm based on atomic norm minimization (ANM) and a low-complexity algorithm based on orthogonal matching pursuit (OMP). Simulation results reveal that the proposed ANM based estimation scheme for bistatic BSs outperforms that for monostatic BSs, since the former requires a identical pilot overhead and achieves a similar estimation accuracy while having a much simpler hardware implementation.

In this paper, an efficient two-phase channel estimation scheme with reduced pilot overhead and error propagation is proposed for a uniform planar array (UPA)-type reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter wave (mmWave) system. In Phase I, based on the carefully designed RIS phase shift matrix, all users jointly transmit the pilot signals to estimate the correlation factors between different propagation paths of the common RIS-base station (BS) channel, which facilitates a significant MU diversity gain. Then, in Phase II, with the constructed ambiguous RIS-BS channel composed of the correlation factors obtained in the previous phase, each user independently sends a few pilots to estimate their own ambiguous user-RIS channel so as to obtain the entire cascaded channel. Simulation results validate that the proposed algorithm outperforms the existing algorithms in terms of both pilot overhead and estimation accuracy, and that its estimation performance improves as the number of users increases.

This paper investigates reconfigurable intelligent surface (RIS)-assisted secure multiuser communication systems in the presence of hardware impairments (HIs) at the RIS and the transceivers. We jointly optimize the beamforming vectors at the base station (BS) and the phase shifts of the reflecting elements at the RIS so as to maximize the weighted minimum approximate ergodic secrecy rate (WMAESR), subject to the transmission power constraints at the BS and unit-modulus constraints at the RIS. To solve the formulated optimization problem, we first decouple it into two tractable subproblems and then use the block coordinate descent (BCD) method to alternately optimize the subproblems. Two different methods are proposed to solve the two obtained subproblems. The first method transforms each subproblem into a second order cone programming (SOCP) problem by invoking the penalty convex–concave procedure (CCP) method and the closed-form fractional programming (FP) criterion, and then directly solves them by using CVX. The second method leverages the minorization-maximization (MM) algorithm. Specifically, we first derive a concave approximation function, which is a lower bound of the original objective function, and then the two subproblems are transformed into two simple surrogate problems that admit closed-form solutions. Simulation results verify the performance gains of the proposed robust transmission methods over existing non-robust designs. In addition, the MM algorithm is shown to have much lower complexity than the SOCP-based algorithm.

In this paper, we develop a two-phase based uplink channel estimation strategy with reduced pilot overhead for an reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) multiple-input multiple-output (MIMO) communication system. Specifically, in Phase I, an OMP-based method is adopted to estimate the AoDs at the users. The remaining parameters including the common AoAs at the BS, the cascaded AoDs at the RIS, and the cascaded channel gains are estimated in Phase II. In particular, the estimation of cascaded AoDs and channel gains can be formulated as 1-sparse recovery problems by decomposing the estimation of a multi-antenna channel with$J$scatterers into estimating$J$single-scatterer channels for virtual single-antenna users. Finally, the theoretical number of pilots required for the proposed method are analyzed and the simulation results are presented to demonstrate the high channel estimation accuracy.

This paper analyzes and optimizes the reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) systems with zero-forcing (ZF) detectors under imperfect channel state information (CSI). We first propose a low-overhead minimum mean square error (MMSE) channel estimator, and then derive and analyze closed-form expressions for the uplink achievable rate. Our analytical results prove that: 1) regardless of the RIS phase shift design, the rate of all users scales at least on the order of $\mathcal{O}\left( {{{\log }_2}(MN)} \right)$, where M and N are the numbers of antennas and reflecting elements, respectively; 2) by aligning the RIS phase shifts to one user, the rate of this user can at most scale on the order of $\mathcal{O}\left( {{{\log }_2}(MN)} \right)$. Furthermore, we propose a low-complexity majorization-minimization (MM)-based algorithm to optimize the sum user rate, where closed-form solutions are obtained in each iteration. Finally, simulation results validate all derived analytical results. Our simulation results also show that the maximum sum rate can be closely approached by simply aligning the RIS phase shifts to an arbitrary user.

This paper provides a theoretical framework for understanding the performance of reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) with zero-forcing (ZF) detectors under imperfect channel state information (CSI). We first introduce a low-overhead minimum mean square error (MMSE) channel estimator, and then derive and analyze closed-form expressions for the uplink achievable rate. Our analytical results demonstrate that: 1) regardless of the RIS phase shift design, the rate of all users scales at least on the order of$\mathcal {O}\left ({\log _{2}\left ({MN}\right)}\right)$, where$M$and$N$are the numbers of antennas and reflecting elements, respectively; 2) by aligning the RIS phase shifts to one user, the rate of this user can at most scale on the order of$\mathcal {O}\left ({\log _{2}\left ({MN^{2}}\right)}\right)$; 3) either$M$or the transmit power can be reduced inversely proportional to$N$, while maintaining a given rate. Furthermore, we propose two low-complexity majorization-minimization (MM)-based algorithms to optimize the sum user rate and the minimum user rate, respectively, where closed-form solutions are obtained in each iteration. Finally, simulation results validate the accuracy of all derived analytical results. Our simulation results also show that the maximum sum rate can be closely approached by simply aligning the RIS phase shifts to an arbitrary user.

Optimally extracting the advantages available from reconfigurable intelligent surfaces (RISs) in wireless communications systems requires estimation of the channels to and from the RIS. The process of determining these channels is complicated when the RIS is composed of passive elements without any sensing or data processing capabilities, and thus, the channels must be estimated indirectly by a noncolocated device, typically a controlling base station (BS). In this article, we examine channel estimation for passive RIS-based systems from a fundamental viewpoint. We study various possible channel models and the identifiability of the models as a function of the available pilot data and behavior of the RIS during training. In particular, we will consider situations with and without line-of-sight propagation, single-antenna and multi-antenna configurations for the users and BS, correlated and sparse channel models, single-carrier and wideband orthogonal frequency-division multiplexing (OFDM) scenarios, availability of direct links between the users and BS, exploitation of prior information, as well as a number of other special cases. We further conduct simulations of representative algorithms and comparisons of their performance for various channel models using the relevant Cramér-Rao bounds.

In this paper, we adopt a three-stage based uplink channel estimation protocol with reduced pilot overhead for an reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter wave (mmWave) communication system, in which both the base station (BS) and the RIS are equipped with a uniform planar array (UPA). Specifically, in Stage I, the channel state information (CSI) of a typical user is estimated. To address the power leakage issue for the common angles-of-arrival (AoAs) estimation in this stage, we develop a low-complexity one-dimensional search method. In Stage II, a re-parameterized common BS-RIS channel is constructed with the estimated information from Stage I to estimate other users’ CSI. In Stage III, only the rapidly varying channel gains need to re-estimated. Furthermore, the proposed method can be extended to multi-antenna UPA-type users, by decomposing the estimation of a multi-antenna channel with$J$scatterers into estimating$J$single-scatterer channels for a virtual single-antenna user. An orthogonal matching pursuit (OMP)-based method is proposed to estimate the angles-of-departure (AoDs) at the users. Simulation results demonstrate that the proposed algorithm significantly achieves high channel estimation accuracy, which approaches the genie-aided upper bound in the high signal-to-noise ratio (SNR) regime.

Rate-splitting (RS) has recently been recognized as a promising physical-layer technique for multi-antenna broadcast channels (BC). Due to its ability to partially decode the interference and partially treat the remaining interference as noise, RS is an enabler for a powerful multiple access, namely rate-splitting multiple access (RSMA), that has been shown to achieve higher spectral efficiency (SE) and energy efficiency (EE) than both space division multiple access (SDMA) and non-orthogonal multiple access (NOMA) in a wide range of user deployments and network loads. As SE maximization and EE maximization are two conflicting objectives in the moderate and high signal-to-noise ratio (SNR) regimes, the study of the tradeoff between the two criteria is of particular interest. In this work, we address the SE-EE tradeoff by studying the joint SE and EE maximization problem of RSMA in multiple input single output (MISO) BC with rate-dependent circuit power consumption at the transmitter. To tackle the challenges coming from multiple objective functions and rate-dependent circuit power consumption, we first propose two models to transform the original problem into two single-objective problems, namely, weighted-sum method and weighted-power method. A low-complexity algorithm with closed-form solution is proposed to solve each single-objective problem in the two-user system. For the generalized$K$-user system, a successive convex approximation (SCA)-based algorithm is then proposed to optimize the precoders of each transformed problem. Numerical results show that our algorithm converges much faster than existing algorithms. In addition, the performance of RSMA is superior to or equal to SDMA and NOMA in terms of SE, EE and their tradeoff.

A fundamental challenge for millimeter wave (mmWave) communications lies in its sensitivity to the presence of blockages, which impact the connectivity of the communication links and ultimately the reliability of the entire network. In this paper, we are exploited to deal with the link outage issue caused by a reconfigurable intelligent surface (RIS)-aided mmWave communication system for enhancing the network reliability and connectivity in the presence of random blockages. To enhance the robustness of the beamforming in the presence of random blockages, we formulate a stochastic optimization problem with the aim of minimizing the outage probability. To tackle the proposed optimization problem, we introduce a low-complexity algorithm based on the stochastic majorization-minimization method, which learns sensible blockage patterns without searching for all combinations of potentially blocked links. Numerical results confirm the performance benefits of the proposed algorithm in terms of outage probability and effective data rate.

Intelligent reflection surface (IRS) has recently been recognized as a promising technique to enhance the performance of wireless systems due to its ability of reconfiguring the signal propagation environment. However, the perfect channel state information (CSI) is challenging to obtain at the base station (BS) due to the lack of radio frequency (RF) chains at the IRS. Since most of the existing channel estimation methods were developed to acquire the cascaded BS-IRS-user channels, this paper is the first work to study the robust beamforming based on the imperfect cascaded BS-IRS-user channels at the transmitter (CBIUT). Specifically, the transmit power minimization problems are formulated subject to the rate outage probability constraints under the statistical CSI error model, respectively. After approximating the rate outage probability constraints by using the Bernstein-type inequality, the reformulated problems can be efficiently solved. Numerical results show that the negative impact of the CBIUT error on the system performance is greater than that of the direct CSI error.