Baseband-Efficient WMMSE Precoding: From a Signal Weighting Cost Perspective
Shuai Gao, Fan Xu, Mian Li, Xinzhi Ning, Lei Qiu, Ye Yang, Qingjiang Shi

TL;DR
This paper introduces a novel sparse precoding framework for massive MU-MIMO systems that reduces computational complexity and signal weighting cost without sacrificing sum-rate performance.
Contribution
It proposes two new sparse precoding architectures, proves their optimality in a low-dimensional subspace, and develops an efficient alternating optimization algorithm within the WMMSE framework.
Findings
Achieves near-optimal sum-rate performance.
Reduces precoding computation complexity significantly.
Substantially lowers overall signal weighting cost.
Abstract
For downlink transmission in massive multi-user multiple-input multiple-output (MU-MIMO) systems, conventional precoding research heavily focuses on reducing the computational complexity of precoding matrix design, while largely overlooking another critical bottleneck: the substantial signal weighting cost incurred by repeatedly applying the precoder to high-speed data streams. To address both challenges simultaneously, this paper proposes a novel sparse precoding framework tailored for fully-digital architectures. Within this framework, from the sum-rate maximization perspective, we design two sparse precoding architectures: a common-support row-sparse architecture and a user-specific row-sparse architecture, so as to reduce the number of multiplication operations required in baseband signal weighting without sacrificing system capacity. For the formulated mixed-integer non-linear…
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