Linear Precoding of Data and Artificial Noise in Secure Massive MIMO Systems

TL;DR

This paper investigates secure downlink transmission in massive MIMO systems using linear precoding of data and artificial noise, analyzing various precoding schemes and proposing polynomial-based precoders for improved performance.

Contribution

It introduces polynomial precoders for data and artificial noise that balance complexity and performance, with analytical optimization and extensive analysis of secrecy rates.

Findings

Polynomial precoders closely match the performance of more complex schemes.

Analysis includes effects of channel estimation, pilot contamination, and multi-cell interference.

Proposed methods improve secrecy rates in massive MIMO systems.

Abstract

In this paper, we consider secure downlink transmission in a multi-cell massive multiple-input multiple-output (MIMO) system where the numbers of base station (BS) antennas, mobile terminals, and eavesdropper antennas are asymptotically large. The channel state information of the eavesdropper is assumed to be unavailable at the BS and hence, linear precoding of data and artificial noise (AN) are employed for secrecy enhancement. Four different data precoders (i.e., selfish zero-forcing (ZF)/regularized channel inversion (RCI) and collaborative ZF/RCI precoders) and three different AN precoders (i.e., random, selfish/collaborative null-space based precoders) are investigated and the corresponding achievable ergodic secrecy rates are analyzed. Our analysis includes the effects of uplink channel estimation, pilot contamination, multi-cell interference, and path-loss. Furthermore, to strike a balance between complexity and performance, linear precoders that are based on matrix polynomials are proposed for both data and AN precoding. The polynomial coefficients of the data and AN precoders are optimized respectively for minimization of the sum mean squared error of and the AN leakage to the mobile terminals in the cell of interest using tools from free probability and random matrix theory. Our analytical and simulation results provide interesting insights for the design of secure multi-cell massive MIMO systems and reveal that the proposed polynomial data and AN precoders closely approach the performance of selfish RCI data and null-space based AN precoders, respectively.