Symbol-Level Precoding Design Based on Distance Preserving Constructive Interference Regions

TL;DR

This paper proposes a novel symbol-level precoding design using distance preserving constructive interference regions for multiuser MISO channels, optimizing power and fairness with new formulations and algorithms.

Contribution

It introduces the use of distance preserving CIRs in SLP design, deriving properties, and developing computationally efficient optimization methods for power minimization and SINR balancing.

Findings

Power minimization problem formulated as a quadratic program.

Proposed alternative optimization methods for non-convex problems.

Extensive simulations demonstrate effectiveness of the approaches.

Abstract

In this paper, we investigate the symbol-level precoding (SLP) design problem in the downlink of a multiuser multiple-input single-output (MISO) channel. We consider generic constellations with any arbitrary shape and size, and confine ourselves to one of the main categories of constructive interference regions (CIR), namely, distance preserving CIR (DPCIR). We provide a comprehensive study of DPCIRs and derive some properties for these regions. Using these properties, we first show that any signal in a given DPCIR has a norm greater than or equal to the norm of the corresponding constellation point if and only if the convex hull of the constellation contains the origin. It is followed by proving that the power of the noiseless received signal lying on a DPCIR is a monotonic strictly increasing function of two parameters relating to the infinite Voronoi edges. Using the convex description of DPCIRs and their properties, we formulate two design problems, namely, the SLP power minimization with signal-to-interference-plus-noise ratio (SINR) constraints, and the SLP SINR balancing problem under max-min fairness criterion. The SLP power minimization based on DPCIRs can straightforwardly be written as a quadratic program (QP). We provide a simplified reformulation of this problem which is less computationally complex. The SLP max-min SINR, however, is non-convex in its original form, and hence difficult to tackle. We propose several alternative optimization approaches, including semidefinite program (SDP) formulation and block coordinate descent (BCD) optimization. We discuss and evaluate the loss due to the proposed alternative methods through extensive simulation results.