Beamforming Design for Double-Active-RIS-aided Communication Systems with Inter-Excitation

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, the mutual influence between active RISs, which is termed as the "inter-excitation" effect, cannot be ignored. Then, we develop a feedback-type model to characterize the signal containing the inter-excitation effect. 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 low-complexity penalty dual decomposition (PDD) algorithm. Finally, simulation results indicate that it is crucial to consider the inter-excitation effect between active RISs in beamforming design for double-active-RIS-aided communication systems. Additionally, it prevails over other benchmark schemes with single active RIS and double passive RISs in terms of achievable rate.

Radar Rainbow Beams For Wideband mmWave Communication: Beam Training And Tracking

We propose a novel integrated sensing and communication (ISAC) system that leverages sensing to assist communication, 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' angles, 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 are typically necessary in existing tracking technologies, thereby reducing the demands on the control circurity. Simulation results reveal the effectiveness of the proposed rainbow beam-based training and tracking methods for mobile users. Notably, the scheme employing a multi-antenna radar receiver can accurately estimate the channel parameters and can support communication rates comparable to those achieved with perfect channel information.

User Tracking and Direction Estimation Codebook Design for IRS-Assisted mmWave Communication

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 likelihood-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 numerical results reveal a lower direction estimation error of the proposed UT algorithm when employing our designed codebook compared to codebooks from the literature. 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 two benchmark schemes based on direct phase shift optimization and hierarchical codebook search, respectively, via computer simulations.

Tensor Decomposition-based Time Varying Channel Estimation for mmWave MIMO-OFDM Systems

In this paper, we consider the time-varying channel estimation in millimeter wave (mmWave) multiple-input multiple-output MIMO systems with hybrid beamforming architectures. Different from the existing contributions that considered single-carrier mmWave systems with high mobility, the wideband orthogonal frequency division multiplexing (OFDM) system is considered in this work. To solve the channel estimation problem under channel double selectivity, we propose a pilot transmission scheme based on 5G OFDM, and the received signals are formed as a fourth-order tensor, which fits the low-rank CANDECOMP/PARAFAC (CP) model. By further exploring the Vandermonde structure of factor matrix, a tensor-subspace decomposition based channel estimation method is proposed to solve the CP decomposition, where the uniqueness condition is analyzed. Based on the decomposed factor matrices, the channel parameters, including angles of arrival/departure, delays, channel gains and Doppler shifts are estimated, and the Cram\'{e}r-Rao bound (CRB) results are derived as performance metrics. Simulation results demonstrate the superior performance of the proposed method over other benchmarks. Furthermore, the channel estimation methods are tested based on the channel parameters generated by Wireless InSites, and simulation results show the effectiveness of the proposed method in practical scenarios.

Reconfigurable Intelligent Surface-Aided Dual-Function Radar and Communication Systems With MU-MIMO Communication

In this paper, we investigate an reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system. Our objective is to maximize the achievable sum rate of the multi-antenna communication users through the joint active and passive beamforming. {Specifically}, the weighted minimum mean-square error (WMMSE) method is { first} used to reformulate the original problem into an equivalent one. Then, we utilize an alternating optimization (AO) { algorithm} to decouple the optimization variables and decompose this challenging problem into two subproblems. Given reflecting coefficients, a penalty-based algorithm is utilized to deal with the non-convex radar signal-to-noise ratio (SNR) constraints. For the given beamforming matrix of the BS, we apply majorization-minimization (MM) to transform the problem into a quadratic constraint quadratic programming (QCQP) problem, which is ultimately solved using a semidefinite relaxation (SDR)-based algorithm. Simulation results illustrate the advantage of deploying RIS in the considered multi-user MIMO (MU-MIMO) ISAC systems.

Channel Estimation for FAS-assisted Multiuser mmWave Systems

This letter investigates the challenge of channel estimation in a multiuser millimeter-wave (mmWave) time-division duplexing (TDD) system. In this system, the base station (BS) employs a multi-antenna uniform linear array (ULA), while each mobile user is equipped with a fluid antenna system (FAS). Accurate channel state information (CSI) plays a crucial role in the precise placement of antennas in FAS. Traditional channel estimation methods designed for fixed-antenna systems are inadequate due to the high dimensionality of FAS. To address this issue, we propose a low-sample-size sparse channel reconstruction (L3SCR) method, capitalizing on the sparse propagation paths characteristic of mmWave channels. In this approach, each fluid antenna only needs to switch and measure the channel at a few specific locations. By observing this reduced-dimensional data, we can effectively extract angular and gain information related to the sparse channel, enabling us to reconstruct the full CSI. Simulation results demonstrate that our proposed method allows us to obtain precise CSI with minimal hardware switching and pilot overhead. As a result, the system sum-rate approaches the upper bound achievable with perfect CSI.

A Framework for Transmission Design for Active RIS-Aided Communication with Partial CSI

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 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, for each iteration, semi-closed-form solutions for the BS and RIS beamforming are derived 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.

Active RIS Aided Integrated Sensing and Communication Systems

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 the dual-function Radar-communication (DFRC) base station (BS) and the reflecting coefficient matrix at the active RIS subject to the quality of service (QoS) constraint of communication users (UE) and the transmit power constraints of active RIS and DFRC BS. In the proposed scenario, we mainly focus on the four-hop BS-RIS-target-RIS-BS sensing link, and the direct BS-target-BS link is assumed to be blocked. Due to the coupling of the beamforming matrix and the reflecting coefficient matrix, we use the alternating optimization (AO) method to solve the problem. Given reflecting coefficients, we apply majorization-minimization (MM) and semidefinite programming (SDP) methods to deal with the nonconvex QoS constraints and Radar SINR objective functions. An initialization method is proposed to obtain a high-quality converged solution, and a sufficient condition of the feasibility of the original problem is provided. Since the signal for sensing is reflected twice at the same active RIS panel, the Radar SINR and active RIS transmit power are quartic functions of RIS coefficients after using the MM algorithm. We then transform the problem into a sum of square (SOS) form, and a semidefinite relaxation (SDR)-based algorithm is developed to solve the problem. Finally, simulation results validate the potential of active RIS in enhancing the performance of the ISAC system compared to the passive RIS, and indicate that the transmit power and physical location of the active RIS should be carefully chosen.

A Framework for Mutual Information-based MIMO Integrated Sensing and Communication Beamforming Design

Integrated sensing and communication (ISAC) unifies sensing and communication, and improves the efficiency of the spectrum, energy, and hardware. In this work, we investigate the ISAC beamforming design to maximize the mutual information between the target response matrix of a point radar target and the echo signals, while ensuring the data rate requirements of communication users. In order to study the impact of the echo interference caused by communication users on sensing performance, we consider three types of echo interference problems caused by a single communication user, including no interference, a point interference, and an extended interference, as well as the problem of an extended interference caused by multiple communication users. To address these problems, we provide a closed-form solution with low complexiy, a semidefinite relaxation (SDR) method, a low-complexity algorithm based on the Majorization-Minimization (MM) method and the successive convex approximation (SCA) method, and an algorithm based on MM method and SCA method, respectively. Numerical results demonstrate that, compared to the ISAC beamforming schemes based on the Cram\'er-Rao bound (CRB) metric and the beampattern metric, the proposed maximizing mutual information metric can bring better beampattern and root mean square error (RMSE) of angle estimation. Apart from this, our proposed schemes based on the mutual information can suppress the echo interference from the communication users effectively.

Error Propagation and Overhead Reduced Channel Estimation for RIS-Aided Multi-User mmWave Systems

In this paper, we propose a novel two-stage based uplink channel estimation strategy with reduced pilot overhead and error propagation for a reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter wave (mmWave) system. Specifically, in Stage I, with the carefully designed RIS phase shift matrix and introduced matching matrices, all users jointly estimate the correlation factors between different paths of the common RIS-base station (BS) channel, which achieves significant multi-user diversity gain. Then, the inherent scaling ambiguity and angle ambiguity of the mmWave cascaded channel are utilized to construct an ambiguous common RIS-BS channel composed of the estimated correlation factors. In Stage II, with the constructed ambiguous common RIS-BS channel, each user uses reduced pilots to estimate their specific user-RIS channel independently so as to obtain the entire cascaded channel. The theoretical number of pilots required for the proposed method is analyzed and the simulation results are presented to validate the effectiveness of this strategy.