Secrecy Sum-Rates for Multi-User MIMO Regularized Channel Inversion Precoding
Giovanni Geraci, Malcolm Egan, Jinhong Yuan, Adeel Razi, and Iain B., Collings

TL;DR
This paper introduces a regularized channel inversion precoder optimized for maximizing secrecy sum-rate in multi-user MIMO systems with potential eavesdroppers, providing closed-form solutions and joint optimization strategies.
Contribution
It derives the optimal regularization parameter for RCI precoding under secrecy constraints and proposes a joint optimization algorithm for power and regularization, outperforming existing schemes.
Findings
RCI precoder with optimal regularization outperforms other linear schemes.
The proposed joint optimization improves secrecy sum-rate by up to 20%.
The scheme achieves the same scaling as the optimal non-secrecy RCI sum-rate.
Abstract
In this paper, we propose a linear precoder for the downlink of a multi-user MIMO system with multiple users that potentially act as eavesdroppers. The proposed precoder is based on regularized channel inversion (RCI) with a regularization parameter and power allocation vector chosen in such a way that the achievable secrecy sum-rate is maximized. We consider the worst-case scenario for the multi-user MIMO system, where the transmitter assumes users cooperate to eavesdrop on other users. We derive the achievable secrecy sum-rate and obtain the closed-form expression for the optimal regularization parameter of the precoder using large-system analysis. We show that the RCI precoder with outperforms several other linear precoding schemes, and it achieves a secrecy sum-rate that has same scaling factor as the sum-rate achieved by the…
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