Angle Aware User Cooperation for Secure Massive MIMO in Rician Fading Channel

TL;DR

This paper introduces an angle aware user cooperation scheme for secure Massive MIMO systems in Rician fading channels, enhancing security against eavesdroppers with similar channels by using cooperative relaying based on angle information.

Contribution

It proposes a novel AAUC scheme that leverages eavesdropper angle information and develops optimization algorithms for secure communication in Massive MIMO systems.

Findings

The AAUC scheme improves secrecy rates in Rician fading environments.

The proposed algorithms converge efficiently and are suitable for large-scale systems.

Simulation results confirm the effectiveness and low complexity of the methods.

Abstract

Massive multiple-input multiple-output communications can achieve high-level security by concentrating radio frequency signals towards the legitimate users. However, this system is vulnerable in a Rician fading environment if the eavesdropper positions itself such that its channel is highly "similar" to the channel of a legitimate user. To address this problem, this paper proposes an angle aware user cooperation (AAUC) scheme, which avoids direct transmission to the attacked user and relies on other users for cooperative relaying. The proposed scheme only requires the eavesdropper's angle information, and adopts an angular secrecy model to represent the average secrecy rate of the attacked system. With this angular model, the AAUC problem turns out to be nonconvex, and a successive convex optimization algorithm, which converges to a Karush-Kuhn-Tucker solution, is proposed. Furthermore, a closed-form solution and a Bregman first-order method are derived for the cases of large-scale antennas and large-scale users, respectively. Extension to the intelligent reflecting surfaces based scheme is also discussed. Simulation results demonstrate the effectiveness of the proposed successive convex optimization based AAUC scheme, and also validate the low-complexity nature of the proposed large-scale optimization algorithms.