Unsupervised Learning Approach for Beamforming in Cell-Free Integrated Sensing and Communication

TL;DR

This paper introduces an unsupervised learning method for beamforming in cell-free ISAC systems, achieving near state-of-the-art performance with significantly reduced computational complexity and decentralized operation.

Contribution

It proposes a novel teacher-student unsupervised learning framework for joint beamforming design in cell-free ISAC, reducing computational load and fronthaul requirements.

Findings

Performance close to state-of-the-art methods

At least three orders of magnitude more computationally efficient

Decentralized scheme reduces fronthaul load

Abstract

Cell-free massive multiple input multiple output (MIMO) systems can provide reliable connectivity and increase user throughput and spectral efficiency of integrated sensing and communication (ISAC) systems. This can only be achieved through intelligent beamforming design. While many works have proposed optimization methods to design beamformers for cell-free systems, the underlying algorithms are computationally complex and potentially increase fronthaul link loads. To address this concern, we propose an unsupervised learning algorithm to jointly design the communication and sensing beamformers for cell-free ISAC system. Specifically, we adopt a teacher-student training model to guarantee a balanced maximization of sensing signal to noise ratio (SSNR) and signal to interference plus noise ratio (SINR), which represent the sensing and communication metrics, respectively. The proposed scheme is decentralized, which can reduce the load on the central processing unit (CPU) and the required fronthaul links. To avoid the tradeoff problem between sensing and communication counterparts of the cell-free system, we first train two identical models (teacher models) each biased towards one of the two tasks. A third identical model (a student model) is trained based on the maximum sensing and communication performance information obtained by the teacher models. While the results show that our proposed unsupervised DL approach yields a performance close to the state-of-the-art solution, the proposed approach is more computationally efficient than the state of the art by at least three orders of magnitude.