This paper studies a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) networked integrated sensing and communication (ISAC) system, in which multiple base stations (BSs) perform beam tracking to communicate with a mobile device. In particular, we focus on the beam tracking over a number of tracking time slots (TTSs) and suppose that these BSs operate at non-overlapping frequency bands to avoid the severe inter-cell interference. Under this setup, we propose a new cooperative sensing-assisted predictive beam tracking design. In each TTS, the BSs use echo signals to cooperatively track the mobile device as a sensing target, and continuously adjust the beam directions to follow the device for enhancing the performance for both communication and sensing. First, we propose a cooperative sensing design to track the device, in which the BSs first employ the two-dimensional discrete Fourier transform (2D-DFT) technique to perform local target estimation, and then use the extended Kalman filter (EKF) method to fuse their individual measurement results for predicting the target parameters. Next, based on the predicted results, we obtain the achievable rate for communication and the predicted conditional Cramér-Rao lower bound (PC-CRLB) for target parameters estimation in the next TTS, as a function of the beamforming vectors. Accordingly, we formulate the predictive beamforming design problem, with the objective of maximizing the achievable communication rate in the following TTS, while satisfying the PC-CRLB requirement for sensing. To address the resulting non-convex problem, we first propose a semi-definite relaxation (SDR)-based algorithm to obtain the optimal solution, and then develop an alternative penalty-based algorithm to get a high-quality low-complexity solution. Simulation results indicate that the proposed cooperative sensing design achieves higher target tracking accuracy than other benchmark schemes. The results also validate the benefits of multi-BS cooperative sensing in improving tracking performance compared with the conventional single-BS sensing.

This paper investigates a reconfigurable intelligent surface (RIS) assisted cooperative multicell integrated sensing and communication (ISAC) system with multiple users and targets. In particular, the RIS is leveraged to assist the joint transmission of the multiple base stations (BSs) to multiple users, while assisting cooperative sensing by multiple BSs to perform multiple targets sensing. We formulate a problem for the purpose of minimizing the transmit power via jointly designing the transmit beamforming of the BSs and phase shifts of the RIS, while guaranteeing the achievable communication rate requirements and the sensing mutual information requirements. To address this non-convex problem, a high-quality alternating optimization algorithm is developed to split the intractable problem into two sub-problems. Specifically, with the given phase shifts of the RIS, the transmit beamforming sub-problem is addressed by semidefinite relaxation-based algorithm. A successive convex approximation (SCA) method-based and penalty function-based convex-concave procedure algorithm is proposed to tackle the RIS phase-shift optimization sub-problem. To reduce the computational complexity, an efficient low-complexity alternating optimization algorithm is developed. For the transmit beamforming design, an SCA method-based second-order cone programming algorithm is proposed, while for the RIS phase-shift design, a circle manifold optimization-based algorithm is introduced by utilizing penalty function. Simulation results validate the advancement of deploying RIS in enhancing the performance of cooperative multicell ISAC systems in terms of transmit power. Furthermore, our results illustrate the significant superiority of the proposed algorithms over the benchmark schemes.

This paper studies a networked integrated sensing and communication (ISAC) system, where distributed base stations (BSs) implement coordinated transmit beamforming to communicate with their respective user and cooperatively perform multi-static target sensing. To fully reap the performance gains provided by the networked ISAC system, accurate channel state information (CSI) and time synchronization (TS) among distributed BSs are crucial. However, CSI errors and TS errors are inevitable in practice due to the imperfect channel training and the inaccurate synchronization. To reveal the effect of CSI errors on communication, a Gaussian distributed CSI error model is formulated based on the channel estimation process, and accordingly, the users’ achievable rates with CSI errors are derived. To characterize the effect of TS errors on multi-static sensing, the Cramér-Rao lower bound (CRLB) for estimating target position in the presence of TS errors is derived. It is shown that due to the existence of CSI errors and TS errors, additional terms are introduced in the achievable rate and CRLB formulas, degrading the communication and sensing performance, respectively. Based on the above derivations, we aim at maximizing the sum-rate of users by designing the coordinated transmit beamforming at the BSs, while guaranteeing the CRLB requirements for target sensing. In particular, we consider two cases with and without TS errors, for which the corresponding non-convex optimization problems are solved via a penalty-based algorithm and an alternating optimization algorithm, respectively. Simulation results show that the proposed algorithms significantly outperform benchmark schemes for both cases with and without CSI/TS errors, thus validating the robustness in ISAC performance optimization.

As the uplink sensing has the advantage of easy implementation, it attracts great attention in integrated sensing and communication (ISAC) system. This paper presents an uplink ISAC system based on multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) technology. The mutual information (MI) is introduced as a unified metric to evaluate the performance of communication and sensing. In this paper, firstly, the upper and lower bounds of communication and sensing MI are derived in details based on the interaction between communication and sensing. And the ISAC waveform is optimized by maximizing the weighted sum of sensing and communication MI. The Monte Carlo simulation results show that, compared with other waveform optimization schemes, the proposed ISAC scheme has the best overall performance.