Secrecy Sum-Rates for Multi-User MIMO Regularized Channel Inversion Precoding

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 $\alpha$ 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 $\alpha_{\mathrm{LS}}$ of the precoder using large-system analysis. We show that the RCI precoder with $\alpha_{\mathrm{LS}}$ 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 optimum RCI precoder without secrecy requirements. We propose a power allocation algorithm to maximize the secrecy sum-rate for fixed $\alpha$. We then extend our algorithm to maximize the secrecy sum-rate by jointly optimizing $\alpha$ and the power allocation vector. The jointly optimized precoder outperforms RCI with $\alpha_{\mathrm{LS}}$ and equal power allocation by up to 20 percent at practical values of the signal-to-noise ratio and for 4 users and 4 transmit antennas.Comment: IEEE Transactions on Communications, accepted for publicatio

Linear Precoding for Broadcast Channels with Confidential Messages under Transmit-Side Channel Correlation

In this paper, we analyze the performance of regularized channel inversion (RCI) precoding in multiple-input single-output (MISO) broadcast channels with confidential messages under transmit-side channel correlation. We derive a deterministic equivalent for the achievable per-user secrecy rate which is almost surely exact as the number of transmit antennas and the number of users grow to infinity in a fixed ratio, and we determine the optimal regularization parameter that maximizes the secrecy rate. Furthermore, we obtain deterministic equivalents for the secrecy rates achievable by: (i) zero forcing precoding and (ii) single user beamforming. The accuracy of our analysis is validated by simulations of finite-size systems.Comment: to appear IEEE Communications Letter

Knowledge Distillation-aided End-to-End Learning for Linear Precoding in Multiuser MIMO Downlink Systems with Finite-Rate Feedback

We propose a deep learning-based channel estimation, quantization, feedback, and precoding method for downlink multiuser multiple-input and multiple-output systems. In the proposed system, channel estimation and quantization for limited feedback are handled by a receiver deep neural network (DNN). Precoder selection is handled by a transmitter DNN. To emulate the traditional channel quantization, a binarization layer is adopted at each receiver DNN, and the binarization layer is also used to enable end-to-end learning. However, this can lead to inaccurate gradients, which can trap the receiver DNNs at a poor local minimum during training. To address this, we consider knowledge distillation, in which the existing DNNs are jointly trained with an auxiliary transmitter DNN. The use of an auxiliary DNN as a teacher network allows the receiver DNNs to additionally exploit lossless gradients, which is useful in avoiding a poor local minimum. For the same number of feedback bits, our DNN-based precoding scheme can achieve a higher downlink rate compared to conventional linear precoding with codebook-based limited feedback.Comment: 6 pages, 4 figures, submitted to IEEE Transactions on Vehicular Technolog