Liangming Pan, Alon Albalak, Xinyi Wang, William Wang. Findings of the Association for Computational Linguistics: EMNLP 2023. 2023.

Integrated sensing and communication (ISAC) technique has been viewed as a promising component in future network. A major challenge for ISAC systems is that the constraint introduced by sensing functionality will constrain the degrees of freedom in waveform design and results in large multi-user interference (MUI), thus degrading the communication performance. In this paper, we study the employment of RIS in mitigating MUI in ISAC systems. For practical consideration, we investigate joint constant-modulus waveform and discrete RIS phase shift design, with the aim of minimizing MUI under the Cramer-Rao bound (CRB) constraint for direction of arrival (DOA) estimation. An alternating optimization algorithm is proposed to solve the formulated problem, and two schemes are proposed to deal with the discrete RIS phase shifts. Simulation results show that the proposed algorithm can dramatically improve the sum rate, and the performance under moderate phase shift quantization level is close to that under continuous phase shifts.

Dual-functional radar-communication (DFRC) technique has been viewed as a promising component in the emerging platforms. When synthesizing the desired transmit beampattern, the degrees of freedom of waveform design is limited, which introduces high multi-user interference (MUI), thus degrading the communication performance. Inspired by the applications of the Reconfigurable Intelligent Surface (RIS) in mitigating MUI, in this paper, we investigate joint waveform design and passive beamforming in RIS-assisted DFRC system. We first study the minimization of MUI under the strict beampattern constraint by jointly optimizing DFRC waveform and RIS phase shift matrix. To deal with the coupled variables, we propose an alternating algorithm based on manifold optimization. Subsequently, the trade-off between radar and communication performances is investigated. Simulation results show that for both cases of strict beampattern and trade-off design, with the help of RIS, the system throughput can be significantly improved. Moreover, compared with the scenario where no RIS is employed, the obtained beampattern matches with the target transmit beampattern better.

Beamforming design is an important technique for enhancing the performance of integrated sensing and communication (ISAC) systems. However, related research based on the hybrid analog-digital (HAD) architecture is still limited. In this paper, we investigate the partially-connected hybrid beamforming design for multi-user ISAC systems. Instead of the commonly used beampattern related metric, the Cramér-Rao bound (CRB) is employed as the sensing performance metric for direction of arrival (DOA) estimation. We aim to minimize the CRB while satisfying the signal-to-interference-plus-noise ratio (SINR) constraints for individual communication users by jointly optimizing the digital and analog beamformers. Subsequently, we propose an alternating optimization based framework, which is significantly different from the conventional methods based on the approximation of the optimal fully-digital beamformer with a hybrid one. We also consider an alternative formulation of optimizing the SINR of radar echo signals. Based on optimal receive beamformer design, we transform the SINR based joint transmitter and receiver optimization problem to a series of problems sharing a similar form with the CRB based transmitter optimization problem, which can be efficiently solved via the proposed algorithm. Simulation results show that the proposed designs provide significant performance gains in DOA estimation over the existing beampattern approximation based design.

Reconfigurable intelligent surface (RIS) has been considered in wireless communication systems to improve spectral efficiency. This letter addresses the spectrum sharing problem between multiple-input-multiple-output (MIMO) radar and multi-user MISO communication systems, where RIS is exploited to deal with the interference imposed by the base station (BS). We aim to maximize the radar detection probability by jointly optimizing the BS transmit beamformer and RIS phase shift matrix, subject to the users’ signal-to-interference-plus-noise-ratio and power consumption constraints. Due to the coupled variables, the formulated problem is challenging to solve; hence, we convert it into a better tractable form and propose an alternating algorithm to handle the resulting problem. The simulation results validate the effectiveness of RIS in controlling interference and improving radar detection probability.

To facilitate the development of Internet of Things (IoT) services, future networks are expected to simultaneously provide sensing functionality and support low-power communications. In this paper, we investigate the system sum rate maximization problem in an integrated sensing and RIS backscatter communication system, where the base station (BS) simultaneously detects backscattered signals from multiple IoT devices and senses targets based on the echo signals. We formulate a joint transmit beamforming, RIS phase shifts and receive beamforming design problem under the Cramér-Rao bound (CRB) constraint for target angle estimation. To solve the non-convex problem, we then propose a fractional programming based alternating optimization algorithm. In particular, the fractional programming technique is firstly employed to transform the formulated problem into a more tractable form, and the exact penalty method and manifold optimization are then utilized to address the CRB constraint and constant-modulus constraint, respectively. Numerical results have shown that the proposed design significantly improves the system sum rate and illustrate the trade-off between the communication and sensing performance.