Clutter-Aware Target Detection for ISAC in a Millimeter-Wave Cell-Free Massive MIMO System

TL;DR

The paper tackles clutter-aware target detection in a RIS-enabled, cell-free mmWave ISAC network, where LoS sensing paths are assumed known. It develops an alternating-optimization approach to jointly optimize transmit power and RIS phase shifts to maximize the target SCNR $\overline{\textrm{SCNR}}$ while guaranteeing UE SINR, and pairs this with a GLRT detector that accounts for clutter subspace via a low-rank model. Numerical results show substantial gains from clutter awareness and from including dedicated sensing streams, with larger advantages at higher clutter-to-noise ratios and for configurations with many transmit clusters. The work demonstrates that RIS-aided, multi-static CF-MIMO ISAC systems can achieve reliable sensing performance without sacrificing downlink QoS in dense mmWave networks.

Abstract

In this paper, we investigate the performance of an integrated sensing and communication (ISAC) system within a cell-free massive multiple-input multiple-output (MIMO) system. Each access point (AP) operates in the millimeter-wave (mmWave) frequency band. The APs jointly serve the user equipments (UEs) in the downlink while simultaneously detecting a target through dedicated sensing beams, which are directed toward a reconfigurable intelligent surface (RIS). Although the AP-RIS, RIS-target, and AP-target channels have both line-of-sight (LoS) and non-line-of-sight (NLoS) parts, it is assumed only knowledge of the LoS paths is available. A key contribution of this study is the consideration of clutter, which degrades the target detection if not handled. We propose an algorithm to alternatively optimize the transmit power allocation and the RIS phase-shift matrix, maximizing the target signal-to-clutter-plus-noise ratio (SCNR) while ensuring a minimum signal-to-interference-plus-noise ratio (SINR) for the UEs. Numerical results demonstrate that exploiting clutter subspace significantly enhances detection probability, particularly at high clutter-to-noise ratios, and reveal that an increased number of transmit side clusters impair detection performance. Finally, we highlight the performance gains achieved using a dedicated sensing stream.

Figures (5)

• Figure 1: Depiction of a cell-free massive MIMO ISAC network performing multi-static sensing.
• Figure 2: 2D locations in the simulated scenario.
• Figure 3: Probability of detection vs RCS power with CNR = $20$ dB.
• Figure 4: Probability of detectionvs the RCS power with a CNR=$40$ dB.
• Figure 5: Impact of a dedicated sensing stream onto the probability of detection with a CNR=$20$ dB.