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IEEE Transactions on Information Forensics and Security

Secrecy Energy Efficiency for MIMO Single- and Multi-Cell Downlink Transmission With Confidential Messages

Alessio Zappone, Pin-Hsun Lin, Eduard Axel Jorswieck

Published Jan 9, 2019• 15 citations
Publisher

Abstract

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.

DOI
10.1109/tifs.2019.2891231

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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.
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.
Consider a multiple-input multiple-output (MIMO) beamforming system, where a multi-antenna base station transmits information and energy simultaneously to a multi-antenna information receiver (IR) and a number of multi-antenna energy receivers (ERs). The ERs are assumed to possess dual functionality such that they can also decode information. This gives rise to the physical layer security issue of providing confidential information to the IR while ensuring efficient energy harvesting for the ERs. Hence, we employ an artificial noise (AN)-aided secrecy approach at the transmitter, where the AN improves the security by interfering with the information decoding at the ERs while, at the same time, providing them with wireless energy to harvest. In this paper, we address the problem of jointly optimizing the beamforming matrix for the IR and the covariance matrix of the AN such that the secrecy rate is maximized subject to energy harvesting constraints and a total power constraint. The corresponding optimization problem is a difference of convex functions (DC) programming problem, which is generally non-convex. Nevertheless, we propose an alternating optimization (AO) strategy to tackle this problem for the cases of a square and a non-square IR channel. The performance of the proposed algorithms is demonstrated by simulation results.
We study the achievable rate regions of the multiple-input multiple-output (MIMO) interference channel with confidential messages sent to two receivers. Under this model, each receiver is an eavesdropper for the other link and tries to decode both its own message and the message intended for the other receiver. We describe several transmission schemes for Gaussian interference channels and derive their achievable secrecy rate regions under the assumption that the transmitters use low complexity beamforming algorithms. We also consider the trade-offs associated with their need for channel state information at the transmitter (CSIT).We approach the above topics from a game-theoretic perspective.
In this paper, we consider secure communications in a multi-cell downlink multi-antenna system, where each multi-antenna base station (BS) sends confidential messages to one intended legitimate user (LU) with a passive multi-antenna eavesdropper trying to intercept the messages in this cell. To enhance the secure communications of the cell-edge users, the multiple BSs employ coordinated beamforming with the aid of artificial-noise (AN) beamforming. We conduct a mathematically rigorous secrecy performance analysis of the considered system and obtain new accurate closed-form expressions of a lower bound on the ergodic rate of each LU and an upper bound of the ergodic rate of each eavesdropper without assuming asymptotes for any system parameter (except for the “worst-case” scenario with zero noise power). Then, a lower bound on the ergodic secrecy rate (ESR) of each LU follows. Based on the derived analytical results, we propose a low-complexity algorithm to optimize the channel sate information (CSI) feedback bits allocation for the channels of target signal link and inter-cell interference links. Furthermore, we develop explicit sufficient conditions on the set of system parameters under which there are at least two BSs to coordinated secure transmission. Through both theoretical analysis and numerical results, we can verify that the obtained analytical lower bound on ESR of each LU as a function of the set of power allocation coefficients is generallyneither convex nor concave. Thus, we propose to employ some numerical method to find a sub-optimal power allocation solution. Finally, numerical results are also provided to validate our theoretical results and the proposed feedback bits and power allocation methods.
In this paper, we consider a multiuser multiple-input multiple-output (MIMO) downlink communication system with simultaneous wireless information and power transfer (SWIPT). In particular, we focus on a realistic and efficient multi-receiver multi-eavesdropper MIMO SWIPT system, in which the channel state information (CSI) of each legitimate receiver and energy receiver (i.e., potential eavesdropper) is partially known to the transmitter. Based on this, we propose a robust artificial noise (AN)-aided secure transmission scheme for the system, where the channel uncertainties are modeled by the worst-case model. In the proposed scheme, we aim to maximize the worst-case achievable secrecy rate under the transmit power constraint and the energy harvesting (EH) constraint, by jointly optimizing the transmit precoding matrix and the AN covariance matrix. We utilize the S-Procedure and Taylor series approximation to transform the non-convex problem. Then, we apply the interior point method to tackle the transformed convex problem, obtaining the approximate optimal matrices and the corresponding maximum worst-case secrecy rate. Simulation results show that our proposed scheme achieves significant performance improvements in terms of convergence and the worst-case achievable secrecy rate.
This paper studies the secure transmission issue for simultaneous wireless information and power transfer in a multiuser multiple-input-multiple-output system with multiple external eavesdroppers, where the transmitter broadcasts independent confidential messages to different legitimate receivers. Each receiver can be seen as an internal eavesdropper intended by other receivers. Our objective is to achieve the robust beamforming design under imperfect channel state information, in which the total transmission power is minimized with constraints on the achievable secrecy rate and the energy harvesting. Since the problem is nonconvex, we propose a two-level optimization scheme. For the inner problem, we investigate two conservative relaxation approaches, large-deviation inequality and Bernstein-type inequality (BTI), to reformulate the outage secrecy rate constraints into some convex ones, yielding a convex optimization by semidefinite programming (SDP) relaxation. For the outer problem, it is a K-variable optimization problem, which can be solved via the novel line-dimensional-like search method. Moreover, we characterize the rank profile of SDP relaxed solution for these two approaches. Specifically, the optimal solution is proved to be rank-one. Numerical results are provided to verify the performance of the proposed algorithms, where the LDI-based scheme outperforms the BTI-based scheme in terms of energy efficiency.