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.

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.

This paper investigates the resource allocation problem for multi-user wideband cooperative integrated sensing and communication (ISAC) networks based on orthogonal frequency-division multiplexing (OFDM) waveforms. In order to balance sensing and communication performance with limited spectrum resources in this wideband cell-free system, we aim to maximize the sum rate, encompassing both communication and radar rates, while adhering to constraints related to access point (AP) power and spectrum resources. We utilize alternate optimization (AO) methods to optimize power and spectrum resources separately. For power optimization, we employ the fractional programming (FP) algorithm to convert the problem into a convex one, which can be quickly solved by the primal-dual subgradient (PDS) method. As for subcarrier allocation optimization, we derive its closed-form solution. Simulation results indicate that the communication and sensing performance of the cell-free ISAC system outperforms that of the conventional centralized ISAC system.

This paper studies a comprehensive framework for reconfigurable intelligent surface (RIS)-assisted integrated communication, sensing, and computation (ICSC) systems. To satisfy the critical need for low-latency sensing, we formulate a weighted latency minimization problem encompassing both multi-user equipment (UE) and simplified single-UE scenarios. To address the formulated non-convex problem in the multi-UE scenario, we decouple the original problem into two subproblems, where the computational and beamforming settings are optimized alternately. Specifically, for the computational settings, we derive a closed-form solution for the offloading volume and propose a low-complexity algorithm based on the bisection search method to optimize the edge computing resource allocation. Additionally, we employ two equivalent transformations to address the challenge posed by the non-convex sum-of-ratios form in the objective function (OF) of the subproblem related to active and passive beamforming. Several techniques are then combined to address these subproblems. To bridge the gap between theoretical assumptions and practical deployments, a robust design extension accounting for imperfect channel state information (CSI) is developed using statistical error modeling. Furthermore, a low-complexity algorithm that offers closed-form solutions is developed for the simplified single UE scenario. Finally, simulation results substantiate the effectiveness of the proposed framework.

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 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. Weighted minimum mean-square error (WMMSE) method is used to reformulate the original problem into an equivalent one. Then, we utilize an alternating optimization (AO) approach to separate the optimization variables and decompose this challenging problem into two subproblems. Given reflecting coefficients, a penalty-based algorithm is utilized to deal with the transmit power and 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.

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.

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.