Deep Unfolding WMMSE Algorithm and Architecture Co-design for Relay-assisted Carrier Aggregation and MU-MIMO Systems

Low latency and high throughput are critical to enhance immersive Extended Reality (XR) experiences. To overcome the limitations of XR devices’ battery and antenna constraints, we investigate a relay-assisted carrier aggregation (RACA) system that transmits data across two frequency bands. However, the iterative weighted minimum mean square error (WMMSE) algorithm used for maximizing data rate incurs long latency. To address this, we propose a deep unfolding WMMSE (UWMMSE) with trainable step sizes in each layer to accelerate convergence via gradient descent. Furthermore, we reschedule serial updates into parallel updates, omit a less-sensitive precoder update for balanced computation, and develop hardware-friendly approximations to simplify matrix inversion and square root operations. To satisfy real-time communication, we implement the first dual-mode UWMMSE hardware engine with folding and pipeline techniques that reduces area by 75% compared to direct mapping and supports MU-MIMO precoder optimization. This engine achieves 1.3x higher hardware efficiency than prior generalized eigenvalue decomposition (GEVD)-based MU-MIMO processors, completing 4 iterations of UWMMSE within 0.4 µs.