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

Abstract

This paper considers a cell-free integrated sensing and communication (ISAC) MIMO system, where distributed MIMO access points are jointly serving the communication users and sensing the targets. For this setup, we first develop two baseline approaches that separately design the sensing and communication beamforming vectors, namely communicationprioritized 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 JSC beamforming vectors based on a max-min fairness formulation. The results show that the developed JSC beamforming is capable of achieving nearly the same communication signal-to-interference-plus-noise ratio (SINR) that of the communication-prioritized sensing beamforming solutions with almost the same sensing SNR of the sensingprioritized communication beamforming approaches, yielding a promising strategy for cell-free ISAC MIMO systems. I. INTRODUCTIONThe integration of sensing functions into the communication systems is envisioned to be an integral part of the 6G and future communication systems [1], [2]. If the hardware and wireless resources are efficiently shared, this will enable the communication infrastructure to have sensing capabilities at minimal cost and open the sensing frequency bands for wireless communication operation. Achieving that, however, requires the careful design of the various aspects of the integrated sensing and communication (ISAC) systems, including the transmission waveform, the post-processing of the received signals, and the MIMO beamforming. While these problems have recently attracted increasing research interest, the prior work has mainly focused on the single ISAC basestation case. In practice, however, multiple ISAC basestations will operate in the same geographical region, frequency band, and time, causing interference on each other for both the sensing and communication functions. This motivates the coordination between these distributed nodes to improve both communication and sensing performance. This ultimately leads to cell-free ISAC MIMO systems, where distributed ISAC basestations jointly serve the same set of communication users and sense the same targets. With this motivation, this paper investigates the joint sensing and communication beamforming design of these cell-free ISAC MIMO systems.Prior work has mainly focused on the single-node case and investigated the design of the joint-sensing and communication (JSC) waveform [3], post-processing [4], and beamforming [5].

Power Allocation for Joint Communication and Sensing in Cell-Free Massive MIMO

“…The proposed BF design is based on a max-min fairness formulation and is compared with two baseline approaches: i) communication-prioritized sensing BF and ii) sensing-prioritized communication BF. The results in [28] show that the joint design can achieve the communication SINR close to the SINR of the communication-prioritized approach and almost the same sensing SNR achieved by the sensing-prioritized approach. In this work, the authors assume perfect CSI and do not consider the derivation of the target detector.…”

“…However, the study does not incorporate any power control mechanism. Joint sensing and communication beamforming (BF) design is proposed for a cell-free MIMO system in [28]. The proposed BF design is based on a max-min fairness formulation and is compared with two baseline approaches: i) communication-prioritized sensing BF and ii) sensing-prioritized communication BF.…”

“…In this work, the authors assume perfect CSI and do not consider the derivation of the target detector. Moreover, the works [27] and [28] neglect the clutter's influence. However, clutter is unavoidable in practical scenarios and acts as interference for sensing which reduces the SINR and degrades the accuracy of target detection.…”

“…APPENDIX B PROOF OF LEMMA 2 A. Optimization under H 0 Given that the hypothesis H 0 is true, we have y τ = h τ + n τ . In order to solve the optimization problem under H 0 , we first derive the conditional PDF of y τ given the target-free channel vector h and the hypothesis H 0 and PDF of h in (28) as…”

Next Generation Multiple Access with Cell-Free Massive MIMO

“…An integrated sensing and communication (ISAC) system empowered by CF-mMIMO could be an evolutionary framework for nextgeneration wireless networks. Distributed APs can be used to perform communication and target sensing through the power control [268], beamforming design [269], or joint power control and beamforming design [270]. As an alternative, NAFD CF-mMIMO networks can be deployed, where certain APs are designated for the target sensing, while the remaining APs are allocated for communication purposes.…”