Power Minimizer Symbol-Level Precoding: A Closed-Form Sub-Optimal Solution

TL;DR

This paper proposes a closed-form sub-optimal symbol-level precoding solution that minimizes transmit power under SINR constraints, offering near-optimal performance with significantly reduced computational complexity.

Contribution

It introduces a novel closed-form sub-optimal SLP solution derived from the problem's structure, improving efficiency over existing optimal methods.

Findings

CF-SLP achieves significant power savings over ZFBF.

CF-SLP performs close to optimal SLP in small-user scenarios.

Computational time is reduced by approximately three orders of magnitude.

Abstract

In this letter, we study the optimal solution of the multiuser symbol-level precoding (SLP) for minimization of the total transmit power under given signal-to-interference-plus-noise ratio (SINR) constraints. Adopting the distance preserving constructive interference regions (DPCIR), we first derive a simplified reformulation of the problem. Then, we analyze the structure of the optimal solution using the Karush-Kuhn-Tucker (KKT) optimality conditions, thereby we obtain the necessary and sufficient condition under which the power minimizer SLP is equivalent to the conventional zero-forcing beamforming (ZFBF). This further leads us to a closed-form sub-optimal SLP solution (CF-SLP) for the original problem. Simulation results show that CF-SLP provides significant gains over ZFBF, while performing quite close to the optimal SLP in scenarios with rather small number of users. The results further indicate that the CF-SLP method has a reduction of order $10^3$ in computational time compared to the optimal solution.