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

Abstract

This paper studies an integrated sensing and communication (ISAC) system for single-target detection in a cloud radio access network architecture. The system considers downlink communication and multi-static sensing approach, where ISAC transmit access points (APs) jointly serve the user equipments (UEs) and optionally steer a beam toward the target. A centralized operation of cell-free massive MIMO (multiple-input multiple-output) is considered for communication and sensing purposes.A maximum a posteriori ratio test detector is developed to detect the target in the presence of clutter, socalled target-free signals. Moreover, a power allocation algorithm is proposed to maximize the sensing signal-to-interference-plus-noise ratio (SINR) while ensuring a minimum communication SINR value for each UE and meeting per-AP power constraints. Two ISAC setups are studied: i) using only existing communication beams for sensing and ii) using additional sensing beams. The proposed algorithm's efficiency is investigated in both realistic and idealistic scenarios, corresponding to the presence and absence of the target-free channels, respectively. Although detection probability degrades in the presence of target-free channels that act as interference, the proposed algorithm significantly outperforms the interference-unaware benchmark by exploiting the statistics of the clutter. It has also been shown that the proposed algorithm outperforms the fully communication-centric algorithm, both in the presence and absence of clutter. Moreover, using an additional sensing beam improves the detection performance for a target with lower radar cross-section variances compared to the case without sensing beams.

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

“…For the sensing channel model, we consider a singlepoint reflector, as commonly adopted in the literature [9]. Specifically, the transmit signal is scattered from the singlepoint reflector and received by the receiving APs in M r .…”

“…For distributed nodes, but assuming that each basestation serves only one user, i.e., not in a cell-free MIMO setup, [6]- [8] studied the power allocation and beamforming design problems. More relevantly, the optimization of the JSC power allocation has been investigated for distributed multi-antenna systems that consider a single user served by basestations or in a cell-free setup [9]. The authors in this work, however, adopted fixed beam designs, i.e., regularized zero beamforming for the communication with the sensing beamforming in the nullspace of the the communication channels without further optimization, and focused on optimizing the power allocated to these beams.…”

Resource Allocation for Large IRS-Assisted SWIPT Systems with Non-linear Energy Harvesting Model

“…As shown in (17), the uncertainty originates from the term λ pqq ij which includes the geometry information of APs and the EHR. However, λ pqq ij is nonlinear with respect to ∆p which makes the resource allocation design very challenging.…”

“…In this paper, we consider the sensing-aided SWIPT for mobile EHRs in networked ISAC systems. The network platform brings significant benefits, including networked sensing, macro-diversity, etc [15]- [17], but also causes big challenges. For example, coordinated beamforming design is required to manage multi-user interference.…”

Low-Complexity Transmit Beamforming Design for Massive MIMO-ISAC Systems

“…In [14], an architecture for MMIMO-ISAC was proposed, then a power allocation scheme to maximize the fairness across the users with guarantee of the radar signal to interference ratio was introduced in [15]. Designs for cellfree MMIMO-ISAC have also been reported in [16] and [17]. However, problem formulations in these works, e.g., convexconcave program and mixed-integer nonlinear program, are complicated for implementation.…”