Cooperative Sensing-Assisted Predictive Beam Tracking for MIMO-OFDM Networked ISAC Systems

TL;DR

This paper introduces a cooperative sensing-assisted predictive beam tracking method for MIMO-OFDM ISAC systems, enhancing communication and sensing by predicting device movement using EKF and optimizing beamforming.

Contribution

It proposes a novel cooperative sensing and predictive beam tracking framework combining 2D-DFT, EKF, and optimization algorithms for improved ISAC system performance.

Findings

Enhanced beam tracking accuracy through cooperative sensing.

Improved communication rates with optimized beamforming.

Effective joint sensing and communication performance.

Abstract

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 Cram\'er-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.