In this work, a multiple waveguide pinching antenna system (PASS) assisted integrated sensing and communication (ISAC) system is proposed, where the base station (BS) is equipped with transmitting pinching antennas (PAs) and receiving uniform linear array (ULA) antennas. The PASS-transmitting- ULA-receiving (PTUR) BS transmits the communication and sensing signals through the PAs on waveguides and collects the echo sensing signals with the mounted ULA. Based on this configuration, a target sensing Cramèr–Rao Bound (CRB) minimization problem is formulated under communication quality-of-service (QoS) constraints, power budget constraint, and PA deployment constraints. To tackle the resulting non-convex problem, an alternating optimization (AO) framework is developed, which decomposes the problem into a digital beamforming sub-problem and a pinching beamforming sub-problem. The digital beamforming design is optimized via semidefinite relaxation (SDR), while the PA deployment is updated using penalty-based method. Simulation results demonstrate that: 1) the proposed PASS assisted ISAC framework achieves superior performance over benchmark schemes; and 2) the PASS assisted ISAC is less affected by stringent communication constraints compared to conventional MIMO-ISAC, and benefits from increasing the number of waveguides and PAs per waveguide.

A novel multiple-input multiple-output (MIMO) cognitive radio (CR) system is proposed in this work. Specifically, the underly secondary network in the proposed CR system reuses the same frequency resources occupied by the primary network with the help of the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). The secondary base station (SBS) beamformers and the STAR-RIS coefficients are jointly designed to maximize the sum rate of secondary users (SUs) considering the power limitation at the SBS, interference limitation at the primary users (PUs), and the coefficients constraint for the STAR-RIS. The block coordinate descent method is invoked to tackle the formulated optimization problem. In each iteration, the beamformers at the SBS are optimized by solving a quadratically constrained quadratic program problem, and the passive STAR beamforming problem is solved with the successive convex approximation-based algorithm. Simulation results show that the proposed STAR-RIS aided CR communication framework can significantly enhance the sum rate of the secondary system.

A downlink near-field integrated sensing and communication (ISAC) framework is proposed. A novel double-array structure at the BS is proposed, where an assisting receiver (AR) is attached to the main transmitter (MT) to enable the near-field communication (NFC) system with the ability of target positioning. The joint angle and distance Cramér-Rao bound (CRB) is derived and then minimized subject to the communication quality of ser-vice (QoS) requirement and the hybrid-analog-and-digital (HAD) structure constraint. A double-loop iterative algorithm utilizing the penalty dual decomposition (PDD) framework is proposed to tackle the non-convex problem. The numerical results show that: 1) The proposed ISAC system can locate the target in both angle and distance domains; 2) The performance of the HAD ISAC approaches the performance of fully digital (FD) ISAC when the communication QoS requirement is not stringent.

Reconfigurable intelligent surface (RIS) is a promising candidate technology for the upcoming sixth-generation (6G) communication system for its ability to manipulate the wireless communication environment by controlling the coefficients of reflection elements (REs). However, since the RIS usually consists of a large number of passive REs, the pilot overhead for channel estimation in the RIS-aided system is prohibitively high. In this article, the channel estimation problem for an RIS-aided multiuser multiple-input–single-output (MISO) communication system with clustered users is investigated. First, to describe the correlated feature for RIS–user channels, a beam-domain channel model is developed for RIS–user channels. Then, a pilot reuse strategy is put forward to reduce the pilot overhead and decompose the channel estimation problem into several subproblems. Finally, by leveraging the correlated nature of RIS–user channels, an eigenspace projection (EP) algorithm is proposed to solve each subproblem, respectively. Simulation results show that the proposed EP channel estimation scheme can achieve accurate channel estimation with lower pilot overhead than existing schemes.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided integrated sensing, computing, and communication (ISCC) Internet of Robotic Things (IoRT) framework is proposed. Specifically, the full-duplex (FD) base station (BS) simultaneously receives the offloading signals from decision robots (DRs) and carries out target robot (TR) sensing. A computation rate maximization problem is formulated to optimize the sensing and receive beamformers at the BS and the STAR-RIS coefficients under the BS power constraint, the sensing signal-to-noise ratio constraint, and STAR-RIS coefficients constraints. The alternating optimization (AO) method is adopted to solve the proposed optimization problem. With fixed STAR-RIS coefficients, the subproblem with respect to sensing and receiving beamformer at the BS is tackled with the weighted minimum mean-square error method. Given beamformers at the BS, the subproblem with respect to STAR-RIS coefficients is tacked with the penalty method and successive convex approximation method. The overall algorithm is guaranteed to converge to at least a stationary point of the computation rate maximization problem. Our simulation results validate that the proposed STAR-RIS aided ISCC IoRT system can enhance the sum computation rate compared with the benchmark schemes.

A near-field integrated sensing, positioning, and communication (ISPAC) framework is proposed, where a base station (BS) simultaneously serves multiple communication users and carries out target sensing and positioning. A novel double-array structure is proposed to enable the near-field ISPAC at the BS. Specifically, a small-scale assisting transceiver (AT) is attached to the large-scale main transceiver (MT) to empower the communication system with the ability of sensing and positioning. Based on the proposed framework, the joint angle and distance Cramér-Rao bound (CRB) is first derived. Then, the CRB is minimized subject to the minimum communication rate requirement in both downlink and uplink ISPAC scenarios: 1) For downlink ISPAC, a downlink target positioning algorithm is proposed and a penalty dual decomposition (PDD)-based double-loop algorithm is developed to tackle the non-convex optimization problem. 2) For uplink ISPAC, an uplink target positioning algorithm is proposed and an efficient alternating optimization algorithm is conceived to solve the non-convex CRB minimization problem with coupled user communication and target probing design. Both proposed optimization algorithms can converge to a stationary point of the CRB minimization problem. Numerical results show that: 1) The proposed ISPAC system can locate the target in both angle and distance domains merely relying on single BS and limited bandwidths; and 2) the positioning performance achieved by the hybrid-analog-and-digital ISPAC approaches that achieved by fully digital ISPAC when the communication rate requirement is not stringent.

The development of sixth-generation (6G) communication technologies is confronted with the significant challenge of spectrum resource shortage. To alleviate this issue, we propose a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided multiple-input multiple-output (MIMO) cognitive radio (CR) system. Specifically, the underlying secondary network in the proposed CR system reuses the same frequency resources occupied by the primary network with the help of the STAR-RIS. The secondary network sum rate maximization problem is first formulated for the STAR-RIS aided MIMO CR system. The adoption of STAR-RIS necessitates an intricate beamforming design for the considered system due to its large number of coupled coefficients. The block coordinate descent method is employed to address the formulated optimization problem. In each iteration, the beamformers at the secondary base station (SBS) are optimized by solving a quadratically constrained quadratic program (QCQP) problem. Concurrently, the STAR-RIS passive beamforming problem is resolved using tailored algorithms designed for the two phase-shift models: 1) For theindependent phase-shift model, a successive convex approximation-based algorithm is proposed; 2) For thecoupled phase-shift model, a penalty dual decomposition-based algorithm is conceived, in which the phase shifts and amplitudes of the STAR-RIS elements are optimized using closed-form solutions. Simulation results show that: 1) The proposed STAR-RIS aided CR communication framework can significantly enhance the sum rate of the secondary system; 2) The coupled phase-shift model results in limited performance degradation compared to the independent phase-shift model.

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided near-field multiple-input multiple-output (MIMO) communication framework is proposed. A weighted sum rate maximization problem for the joint optimization of the active beamforming at the base station (BS) and the transmission/reflection-coefficients (TRCs) at the STAR-RIS is formulated. The resulting non-convex problem is solved by the developed block coordinate descent (BCD)-based algorithm. Numerical results illustrate that the near-field beamforming for the STAR-RIS aided MIMO communications significantly improve the achieved weighted sum rate.