Cell-Free ISAC MIMO Systems: Joint Sensing and Communication Beamforming

TL;DR

The developed JSC beamforming is capable of achieving nearly the same communication signal-to-interference-plus-noise ratio (SINR) of the communication-prioritized sensing beamforming solution with almost the same sensing SNR of the sensing-prioritized communication beamforming approach.

Abstract

This paper considers a cell-free integrated sensing and communication (ISAC) MIMO system, where distributed MIMO access points (APs) jointly serve the communication users and sense the target. For this setup, we derive a sensing SNR for multi-static sensing where both joint communication and sensing signals transmitted by different APs are utilized. With this sensing objective, we develop two baseline approaches that separately design the sensing and communication beamforming vectors, namely communication-prioritized sensing beamforming and sensing-prioritized communication beamforming. Then, we consider the joint sensing and communication (JSC) beamforming design and derive the optimal structure of these beamforming vectors based on a max-min fairness formulation. In addition, considering any pre-determined JSC beam design, we devise a power allocation approach. The results show that the developed JSC beamforming is capable of achieving nearly the same communication signal-to-interference-plus-noise ratio (SINR) of the communication-prioritized sensing beamforming solution with almost the same sensing SNR of the sensing-prioritized communication beamforming approach. The proposed JSC beamforming optimization also provides a noticeable gain over the power allocation with regularized zero-forcing beamforming, yielding a promising strategy for cell-free ISAC MIMO systems.

Figures (6)

• Figure 1: The system model with the joint sensing and communication transmissions is illustrated. The APs serve multiple users while aiming to sense the target.
• Figure 2: The simulation placement is illustrated. For different realizations, the AP positions are fixed. In (a), the UEs and target are randomly placed over the $y$-axis, while in (b), the UEs and target are randomly placed over the square area of $100$m$\times100$m.
• Figure 3: Performance of the solutions for different power allocation ratios for the communications and sensing. The proposed JSC optimization provides a significant gain for sensing while satisfying the best communication SINR.
• Figure 4: Performance of the solutions versus the distance between the target and closest AP. The proposed JSC optimization provides almost a constant sensing SNR for different distances, with a significant gain over the NS solutions.
• Figure 5: Performance of the mean SINR versus the distance between the target and closest AP. The optimizations are carried out to satisfy individual rates achieved by RZF. A similar pattern to the previous case is observed.