Abstract : Delivering low-latency, high-throughput wireless communication in the Terahertz (THZ) frequency range presents challenges for 6G implementation, including signal distortion and destructive interference. To overcome these limitations, we propose a novel approach using multi-antenna beamforming with Massive MIMO technology at Base-stations (BS) and User Equipment (UE). Our centralized optimization mechanism improves spectral efficiency by clustering UEs within a User-centric Cell-free Massive MIMO framework. We propose an optimization problem to jointly determine user clustering and BS assignment to maximize total throughput. In this architecture, multi-antenna beamforming at the BSs enables spatially directed transmission and reception, while Non-Orthogonal Multiple Access (NOMA) is employed within each cluster to allow multiple UEs to share subcarriers. Uplink signals are resolved using Successive Interference Cancellation (SIC), and downlink precoding leverages estimated uplink channels. This combination ensures efficient resource reuse and robust connectivity even under high user density. Experimental results demonstrate its superiority over existing methods in diverse 6G scenarios, effectively addressing the challenges and requirements of future wireless networks while maintaining computational efficiency.
Index terms : 6G Wireless Networks, Spectral Efficiency, Deep-learning, UE Clustering, Multi-user Massive MIMO