Low-Complexity Linear Precoding for Secure Spatial Modulation

TL;DR

This paper develops low-complexity linear precoding methods for secure spatial modulation by deriving an analytical approximation of the secrecy rate, enabling efficient optimization and improved secrecy performance.

Contribution

It introduces two novel low-complexity precoding algorithms based on gradient descent and successive convex approximation, with the latter achieving near-optimal secrecy rates.

Findings

SCA-based precoding outperforms existing methods in secrecy rate.

Derived an accurate analytical approximation of the secrecy rate.

Proposed methods significantly reduce computational complexity.

Abstract

In this work, we investigate linear precoding for secure spatial modulation. With secure spatial modulation, the achievable secrecy rate does not have an easy-to-compute mathematical expression, and hence, has to be evaluated numerically, which leads to high complexity in the optimal precoder design. To address this issue, an accurate and analytical approximation of the secrecy rate is derived in this work. Using this approximation as the objective function, two low-complexity linear precoding methods based on gradient descend (GD) and successive convex approximation (SCA) are proposed. The GD-based method has much lower complexity but usually converges to a local optimum. On the other hand, the SCA-based method uses semi-definite relaxation to deal with the non-convexity in the precoder optimization problem and achieves near-optimal solution. Compared with the existing GD-based precoder design in the literature that directly uses the exact and numerically evaluated secrecy capacity as the objective function, the two proposed designs have significantly lower complexity. Our SCA-based design even achieves a higher secrecy rate than the existing GD-based design.