Hybrid Beamforming for Multi-User Joint Communication and Sensing with URA under Partially Connected Architectures

Joint Communication and Sensing (JCAS) is a key enabler for future 6G networks, allowing data transmission and environmental sensing through shared spectral and hardware resources. To address challenges such as the sensing-communication trade-off, hardware limitations, and multi-user interference, we propose a scalable Multiple Input Multiple Output (MIMO) framework based on Uniform Rectangular Arrays (URA) with partially connected hybrid beamforming (PC-HBF). On the transmitter side, a fairnessaware beamforming optimization maximizes sensing gain while ensuring communication quality, solved via an Interior-Point Method (IPM) with adaptive initialization for improved convergence. At the receiver, a two-stage Block Orthogonal Matching Pursuit (Block-OMP) with Adaptive Dictionary Selection and Refinement (ADSR) enhances combining accuracy under hardware constraints while maintaining low complexity. For radar sensing, we introduce a hybrid direction-of-arrival (DoA) estimation method combining Beamscan and MUSIC algorithms. This design enables accurate angle estimation despite PC-HBF limitations, providing a practical sensing solution for JCAS scenarios. Simulation results demonstrate the practical effectiveness of our design: IPM-based beamforming converges within 15 iterations, ADSR-enhanced combining improves spectral efficiency by up to 1 dB over baseline OMP, approaching the performance of fully digital beamforming while supporting joint sensing, and the DoA method resolves targets spaced as closely as 5°. These gains underline the framework’s potential in real-world applications such as smart mobility and autonomous driving, enabling scalable and high-performance JCAS systems.