Downlink Beamforming for Cell-Free ISAC: A Fast Complex Oblique Manifold Approach

TL;DR

This paper introduces a novel Riemannian manifold-based beamforming algorithm for cell-free ISAC systems, significantly improving sum rate, beamforming gains, and computational efficiency over existing methods.

Contribution

It develops the ALMCI algorithm utilizing complex oblique manifold optimization, outperforming traditional convex-concave and quadratic transform methods in CF-ISAC beamforming.

Findings

22.7% sum rate gain over CCPA

6.7% sum rate gain over MCQT-CSA

Superior beamforming gains and reduced complexity

Abstract

Cell-free integrated sensing and communication (CF-ISAC) systems are just emerging as an interesting technique for future communications. Such a system comprises several multiple-antenna access points (APs), serving multiple single-antenna communication users and sensing targets. However, efficient beamforming designs that achieve high precision and robust performance in densely populated networks are lacking. This paper proposes a new beamforming algorithm by exploiting the inherent Riemannian manifold structure. The aim is to maximize the communication sum rate while satisfying sensing beampattern gains and per AP transmit power constraints. To address this constrained optimization problem, a highly efficient augmented Lagrangian model-based iterative manifold optimization for CF-ISAC (ALMCI) algorithm is developed. This algorithm exploits the geometry of the proposed problem and uses a complex oblique manifold. Conventional convex-concave procedure (CCPA) and multidimensional complex quadratic transform (MCQT)-CSA algorithms are also developed as comparative benchmarks. The ALMCI algorithm significantly outperforms both of these. For example, with 16 APs having 12 antennas and 30 dBm transmit power each, our proposed ALMCI algorithm yields 22.7% and 6.7% sum rate gains over the CCPA and MCQT-CSA algorithms, respectively. In addition to improvement in communication capacity, the ALMCI algorithm achieves superior beamforming gains and reduced complexity.