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IEEE Transactions on Vehicular Technology

Sum Secrecy Spectral Efficiency Maximization in Downlink MU-MIMO: Colluding Eavesdroppers

Jinseok Choi, Jeonghun Park

Published Jan 1, 2021• 29 citations
Publisher

Abstract

In this paper, we consider a single-cell downlink multi-user multi-input multi-output (MU-MIMO) system in which multiple eavesdroppers coexist. The eavesdroppers overhear confidential information sent from a base station (BS). In particular, we assume a scenario of colluding eavesdroppers in which they cooperate with each other to decode the information. We develop a secure linear precoding method that maximizes the sum secrecy spectral efficiency. To this end, we formulate an optimization problem and propose a computationally-efficient algorithm that achieves a sub-optimal stationary point of the problem. Simulation results validate the proposed algorithm. The proposed method possesses several benefits: i) it is applicable in general systems, for example, an arbitrary number of eavesdroppers and confidential information set, ii) the associated complexity is relatively low, and iii) it is also applicable when only channel covariance of the eavesdroppers is available.

DOI
10.1109/tvt.2020.3048736

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In this paper, we consider a single-cell downlink multi-user multi-input multi-output (MU-MIMO) system in which multiple eavesdroppers coexist. The eavesdroppers overhear confidential information sent from a base station (BS). In particular, we assume a scenario of colluding eavesdroppers in which they cooperate with each other to decode the information. We develop a secure linear precoding method that maximizes the sum secrecy spectral efficiency. To this end, we formulate an optimization problem and propose a computationally-efficient algorithm that achieves a sub-optimal stationary point of the problem. Simulation results validate the proposed algorithm. The proposed method possesses several benefits: i) it is applicable in general systems, for example, an arbitrary number of eavesdroppers and confidential information set, ii) the associated complexity is relatively low, and iii) it is also applicable when only channel covariance of the eavesdroppers is available.
Secure precoding superimposed with artificial noise (AN) is a promising transmission technique to improve security by harnessing the superposition nature of the wireless medium. However, finding a jointly optimal precoding and AN structure is very challenging in downlink multi-user multiple-input multiple-output wiretap channels with multiple eavesdroppers. The major challenge in maximizing the secrecy rate arises from the non-convexity and non-smoothness of the rate function. Traditionally, an alternating optimization framework that identifies beamforming vectors and AN covariance matrix has been adopted; yet this alternating approach has limitations in maximizing the secrecy rate. In this paper, we put forth a novel secure precoding algorithm that jointly and simultaneously optimizes the beams and AN covariance matrix for maximizing the secrecy rate when a transmitter has either perfect or partial channel knowledge of eavesdroppers. To this end, we first establish an approximate secrecy rate in a smooth function. Then, we derive the first-order optimality condition in the form of the nonlinear eigenvalue problem (NEP). We present a computationally efficient algorithm to identify the principal eigenvector of the NEP as a suboptimal solution for secure precoding. Simulations demonstrate that the proposed methods improve secrecy rate significantly compared to the existing methods.
In this paper, we propose a robust and adaptable secure precoding framework designed to encapsulate a intricate scenario where legitimate users have different information security: secure private or normal public information. Leveraging rate-splitting multiple access (RSMA), we formulate the sum secrecy spectral efficiency (SE) maximization problem in downlink multi-user multiple-input multiple-output (MIMO) systems with multi-eavesdropper. To resolve the challenges including the heterogeneity of security, non-convexity, and non-smoothness of the problem, we initially approximate the problem using a LogSumExp technique. Subsequently, we derive the first-order optimality condition in the form of a generalized eigenvalue problem. We utilize a power iteration-based method to solve the condition, thereby achieving a superior local optimal solution. The proposed algorithm is further extended to a more realistic scenario involving limited channel state information at the transmitter (CSIT). To effectively utilize the limited channel information, we employ a conditional average rate approach. Handling the conditional average by deriving useful bounds, we establish a lower bound for the objective function under the conditional average. Then we apply the similar optimization method as for the perfect CSIT case. In simulations, we validate the proposed algorithm in terms of the sum secrecy SE.
The joint design of secure precoding and artificial noise (AN) transmission scheme is promising to improve secrecy performance. However, in downlink multi-user multiple-input multiple-output (MU-MIMO) systems with multiple eavesdroppers, joint design of secure precoding and AN structure involves several challenges: an objective function is non-convex and non-smooth, and a precoding matrix and AN matrix have different design principles. Classically, to jointly design precoding and AN covariance matrix, an alternating optimization approach is used which has limitations in terms of the secrecy rate performance since it does not offer joint and simultaneous optimization of the precoding and AN covariance matrices. In this paper, we propose a novel optimization framework that optimizes the precoder and the AN covariance matrix jointly and simultaneously to maximize the secrecy rate. First, we approximate the objective function to a tractable non-convex form. Next, we derive the first-order optimality condition by leveraging the nonlinear eigenvalue problem (NEP) form. Finally, we utilize an efficient technique with low computational complexity for identifying the major eigenvector of the NEP which corresponds to the best stationary point. Simulations illustrate that the proposed methods enhance the secrecy rate performance compared to the existing secure precoding methods.
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 α 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 αLSof the precoder using large-system analysis. We show that the RCI precoder with αLSoutperforms 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 α. We then extend our algorithm to maximize the secrecy sum-rate by jointly optimizing α and the power allocation vector. The jointly optimized precoder outperforms RCI with αLSand 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.
In this paper, we consider secure transmission in the downlink of a multicell multiple-input multiple-output (MIMO) system with M cells; each cell consists of an N -antenna base station (BS) and K single-antenna users. Coordinated beamforming (CBf) is employed for secrecy enhancement. For CBf, the BSs can jointly design their respective beamformers to control intercell interference and information leakage. Based on regularized channel inversion (RCI) precoding structures, we derive the concise formulas for the signal-to-interference-plus-noise ratio (SINR) for the legitimate users and the colluding malicious users and the achievable secrecy sum rate for the CBf. Furthermore, we obtain a novel closed-form expression of the secrecy sum in the large-system regime where N and K tend to infinity with a fixed ratio β=K/N . Based on the large-system regime result, the regularization parameter is optimized to maximize the secrecy sum rate. It shows that the optimal regularization parameter decreases with the signal-to-noise ratio (SNR) and stays constant at high SNR if β>1/M . Compared with single-cell processing (SCP) without BS cooperation, simulation results show that the secrecy performance can be significantly enhanced by exploiting CBf. The analytical results are validated with the finite-size system by simulation.
This work develops a beamforming framework for energy efficiency optimization in MIMO multi-user systems with confidentiality constraints. Two channel models are considered, namely, a broadcast channel with confidential messages (corresponding to single-cell downlink) and an interference channel with confidential messages (corresponding to multi-cell downlink), in which multiple messages are transmitted, and it must be ensured that only the intended receiver is able to perform data decoding, thus treating non-intended receivers as potential eavesdroppers. In this multi-user scenario, the new metric global secrecy energy efficiency is introduced and optimized. Moreover, the coupling among the secrecy energy efficiencies of different users is analyzed by providing an efficient way of computing the system secrecy energy efficiency Pareto boundary. Both contributions are achieved by developing an optimization framework which suitably combines fractional programming theory and sequential optimization theory. The proposed framework is provably convergent, enjoys affordable complexity, and fulfills first-order optimality properties. Finally, the closed form conditions are provided to support smart user selection algorithms for downlink transmission.