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IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

Artificial-noise-assisted energy-efficient secure transmission in 5G with imperfect CSIT and antenna correlation

Alessio Zappone, Pin-Hsun Lin, Eduard Axel Jorswieck

Published Jul 1, 2016• 4 citations
Publisher

Abstract

The problem of energy-efficient resource allocation in multiple-antenna wiretap channels where transmit antennas are correlated is investigated. The use of artificial noise (AN) in combination with only statistical channel state information (CSI) at the legitimate transmitter is considered. The goal of this work is to do the resource allocation such that the amount of bits which can be reliably and confidentially transmitted per unit of consumed energy, namely, the secrecy energy efficiency (SEE), is maximized. The resulting non-convex maximization problems are tackled by means of fractional programming and sequential convex optimization tools, and first-order optimal solutions are obtained with polynomial complexity. Numerical results show the merits of the proposed algorithms.

DOI
10.1109/spawc.2016.7536827

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The problem of energy-efficient resource allocation in multiple-antenna wiretap channels where transmit antennas are correlated is investigated. The use of artificial noise (AN) in combination with only statistical channel state information (CSI) at the legitimate transmitter is considered. The goal of this work is to do the resource allocation such that the amount of bits which can be reliably and confidentially transmitted per unit of consumed energy, namely, the secrecy energy efficiency (SEE), is maximized. The resulting non-convex maximization problems are tackled by means of fractional programming and sequential convex optimization tools, and first-order optimal solutions are obtained with polynomial complexity. Numerical results show the merits of the proposed algorithms.
The problem of energy-efficient resource allocation in multiple-antenna wiretap channels is investigated, wherein a malicious user tries to eavesdrop the communication between two legitimate users. The use of artificial noise (AN) in combination with different statistical channel state information scenarios at the legitimate transmitter is considered. Unlike most previous related papers, the goal of the resource allocation is to maximize the amount of bits which can be reliably and confidentially transmitted per Joule of consumed energy. This leads to the maximization of the ratio between the system secrecy capacity and consumed power, a metric which we label secrecy energy efficiency (SEE). The resulting nonconvex maximization problems are tackled by means of the fractional programing and sequential convex optimization tools. The resulting algorithm monotonically increases the objective value, and upon convergence, yields a first-order optimal solution of the problem with a polynomial complexity. Moreover, the impact of using the AN technique on the energy consumption due to digital signal processing operations is explicitly accounted for, providing insight as to when AN is beneficial from an energy-efficient perspective, too. Numerical results show the merits of the proposed algorithms, also showing that AN does not always improve the system SEE, depending on the digital signal processor used to compute the resource allocation.
This work develops a radio resource allocation framework for energy-efficient and confidential communications in multiple-antenna wiretap channels. Statistical channel state information (CSI) is considered at the transmitter, and the artificial noise (AN) technique is used. The formulated problem is a challenging non-convex fractional program which is tackled by combining fractional programming and sequential optimization. The resulting sequential fractional programming algorithm exhibits polynomial complexity and is provably convergent to a first-order optimal resource allocation. Numerical results show the merits of the proposed algorithm with AN.
The problem of resource allocation in multiple-antenna wiretap channels is investigated, wherein a malicious user tries to eavesdrop the communication between two legitimate users. Both multiple input single output single-antenna eavesdropper (MISO-SE) and multiple input multiple output multiple-antenna eavesdropper (MIMO-ME) systems are considered. Unlike most papers dealing with physical layer security, the focus of the resource allocation process here is not to maximize the secrecy capacity, but rather to maximize the energy efficiency of the system. Two fractional energy-efficient metrics are introduced, namely the ratios between the system secrecy capacity and the consumed power, and between the system secret-key rate and the consumed power. Both performance metrics are measured in bit/Joule, and result in non-concave fractional optimization problems, which are tackled by fractional programming theory and sequential convex optimization. For both performance metrics, the energy-efficient resource allocation is carried out considering both perfect as well as statistical channel state information (CSI) as to the channel from the legitimate transmitter to the eavesdropper.
Consider a wireless scenario in which a multi-antenna transmitter wants to send a confidential message to a single-antenna information receiver (IR) while transferring wireless energy to a number of multi-antenna energy receivers (ERs). In order to keep the ERs from retrieving the confidential message, an artificial noise (AN)-aided physical-layer secrecy approach is employed at the transmitter. The AN has dual purpose: First, it can interfere with the ERs' information receptions and thus help improve security. Secondly, it provides wireless energy for the ERs to harvest. Assuming imperfect channel state information at the transmitter, we jointly optimize the co-variances of confidential information and AN such that the secrecy rate at the IR is maximized, while each ER receives a prescribed amount of wireless energy. Although this secrecy-rate maximization problem is non-convex, we show that it can be handled by solving a sequence of convex optimization problems. Numerical results are provided to demonstrate the efficacy of the proposed design.
This paper considers an energy-efficient transmit design in a general multiple-input single-output multiple-eavesdropper (MISOME) wiretap channel. Specifically, a transmitter sends one confidential message to a legitimate receiver, which must be kept perfectly secure from several external multi-antenna eavesdroppers. Assuming imperfect channel state information (CSI) at the transmitter, our goal is to design the input transmit covariance, such that the worst-case secrecy energy efficiency (WC-SEE) is maximized, subject to the quality of service (QoS) and total transmit power constraints. Due to the nonconvexity of this problem, an equivalent parametric reformulation, based on the fractional programming theory, is proposed to recast this problem as a sequence of semidefinite programs. By this means, the maximum WC-SEE can be found via a root search algorithm. Moreover, we also give an approach to constructing a rank-one optimal covariance matrix of the confidential message from our proposed algorithm, which implies the optimality of transmit beamforming to achieve the maximum WC-SEE. Numerical results are presented to verify the efficacy of our proposed method.
This paper studies the artificial noise (AN) assisted secure transmission for a distributed antenna systems (DAS). To avoid a significant overhead caused by full legitimate channel state information (CSI) acquisition, tracking and collection in the central processor, we propose a distributed AN scheme utilizing the large-scale CSI of the legitimate receiver and eavesdropper. Our objective is to maximize the ergodic secrecy rate (ESR) via optimizing the power allocation between the confidential signal and AN for each remote antenna (RA) under the per-antenna power constraint. Specifically, exploiting random matrix theory, we first establish an analytical expression of the achievable ESR, which leads to a non-convex optimization problem with multiple non-convex constraints in the form of high-order fixed-point equations. To handle the intractable constraints, we recast it into a max-min optimization problem, and propose an iterative block coordinate descent (BCD) algorithm to provide a stationary solution. The BCD algorithm is composed of three subproblems, where the first two subproblems are convex with closed-form solutions, and the last one is a convex-concave game whose saddle-point is located by a tailored barrier algorithm. Simulation results validate the effectiveness of the proposed iterative algorithm and show that our scheme not only reduces the system overhead greatly but also maintains a good secrecy performance.