423 research outputs found

    Secure Beamforming For MIMO Broadcasting With Wireless Information And Power Transfer

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    This paper considers a basic MIMO information-energy (I-E) broadcast system, where a multi-antenna transmitter transmits information and energy simultaneously to a multi-antenna information receiver and a dual-functional multi-antenna energy receiver which is also capable of decoding information. Due to the open nature of wireless medium and the dual purpose of information and energy transmission, secure information transmission while ensuring efficient energy harvesting is a critical issue for such a broadcast system. Assuming that physical layer security techniques are applied to the system to ensure secure transmission from the transmitter to the information receiver, we study beamforming design to maximize the achievable secrecy rate subject to a total power constraint and an energy harvesting constraint. First, based on semidefinite relaxation, we propose global optimal solutions to the secrecy rate maximization (SRM) problem in the single-stream case and a specific full-stream case where the difference of Gram matrices of the channel matrices is positive semidefinite. Then, we propose a simple iterative algorithm named inexact block coordinate descent (IBCD) algorithm to tackle the SRM problem of general case with arbitrary number of streams. We proves that the IBCD algorithm can monotonically converge to a Karush-Kuhn-Tucker (KKT) solution to the SRM problem. Furthermore, we extend the IBCD algorithm to the joint beamforming and artificial noise design problem. Finally, simulations are performed to validate the performance of the proposed beamforming algorithms.Comment: Submitted to journal for possible publication. First submission to arXiv Mar. 14 201

    Joint Unitary Triangularization for MIMO Networks

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    This work considers communication networks where individual links can be described as MIMO channels. Unlike orthogonal modulation methods (such as the singular-value decomposition), we allow interference between sub-channels, which can be removed by the receivers via successive cancellation. The degrees of freedom earned by this relaxation are used for obtaining a basis which is simultaneously good for more than one link. Specifically, we derive necessary and sufficient conditions for shaping the ratio vector of sub-channel gains of two broadcast-channel receivers. We then apply this to two scenarios: First, in digital multicasting we present a practical capacity-achieving scheme which only uses scalar codes and linear processing. Then, we consider the joint source-channel problem of transmitting a Gaussian source over a two-user MIMO channel, where we show the existence of non-trivial cases, where the optimal distortion pair (which for high signal-to-noise ratios equals the optimal point-to-point distortions of the individual users) may be achieved by employing a hybrid digital-analog scheme over the induced equivalent channel. These scenarios demonstrate the advantage of choosing a modulation basis based upon multiple links in the network, thus we coin the approach "network modulation".Comment: Submitted to IEEE Tran. Signal Processing. Revised versio

    Sufficient Dimension Reduction and Modeling Responses Conditioned on Covariates: An Integrated Approach via Convex Optimization

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    Given observations of a collection of covariates and responses (Y,X)∈Rp×Rq(Y, X) \in \mathbb{R}^p \times \mathbb{R}^q, sufficient dimension reduction (SDR) techniques aim to identify a mapping f:Rq→Rkf: \mathbb{R}^q \rightarrow \mathbb{R}^k with k≪qk \ll q such that Y∣f(X)Y|f(X) is independent of XX. The image f(X)f(X) summarizes the relevant information in a potentially large number of covariates XX that influence the responses YY. In many contemporary settings, the number of responses pp is also quite large, in addition to a large number qq of covariates. This leads to the challenge of fitting a succinctly parameterized statistical model to Y∣f(X)Y|f(X), which is a problem that is usually not addressed in a traditional SDR framework. In this paper, we present a computationally tractable convex relaxation based estimator for simultaneously (a) identifying a linear dimension reduction f(X)f(X) of the covariates that is sufficient with respect to the responses, and (b) fitting several types of structured low-dimensional models -- factor models, graphical models, latent-variable graphical models -- to the conditional distribution of Y∣f(X)Y|f(X). We analyze the consistency properties of our estimator in a high-dimensional scaling regime. We also illustrate the performance of our approach on a newsgroup dataset and on a dataset consisting of financial asset prices.Comment: 34 pages, 1 figur

    Throughput Analysis and Optimization of Wireless-Powered Multiple Antenna Full-Duplex Relay Systems

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    We consider a full-duplex (FD) decode-and-forward system in which the time-switching protocol is employed by the multi-antenna relay to receive energy from the source and transmit information to the destination. The instantaneous throughput is maximized by optimizing receive and transmit beamformers at the relay and the time-split parameter. We study both optimum and suboptimum schemes. The reformulated problem in the optimum scheme achieves closed-form solutions in terms of transmit beamformer for some scenarios. In other scenarios, the optimization problem is formulated as a semi-definite relaxation problem and a rank-one optimum solution is always guaranteed. In the suboptimum schemes, the beamformers are obtained using maximum ratio combining, zero-forcing, and maximum ratio transmission. When beamformers have closed-form solutions, the achievable instantaneous and delay-constrained throughput are analytically characterized. Our results reveal that, beamforming increases both the energy harvesting and loop interference suppression capabilities at the FD relay. Moreover, simulation results demonstrate that the choice of the linear processing scheme as well as the time-split plays a critical role in determining the FD gains.Comment: Accepted for publication in IEEE Transactions on Communication

    Secrecy Wireless Information and Power Transfer with MISO Beamforming

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    The dual use of radio signals for simultaneous wireless information and power transfer (SWIPT) has recently drawn significant attention. To meet the practical requirement that energy receivers (ERs) operate with significantly higher received power as compared to information receivers (IRs), ERs need to be deployed in more proximity to the transmitter than IRs. However, due to the broadcast nature of wireless channels, one critical issue arises that the messages sent to IRs can be eavesdropped by ERs, which possess better channels from the transmitter. In this paper, we address this new secrecy communication problem in a multiuser multiple-input single-output (MISO) SWIPT system where one multi-antenna transmitter sends information and energy simultaneously to an IR and multiple ERs, each with one single antenna. To optimally design transmit beamforming vectors and their power allocation, two problems are investigated with different aims: the first problem maximizes the secrecy rate for IR subject to individual harvested energy constraints of ERs, while the second problem maximizes the weighted sum-energy transferred to ERs subject to a secrecy rate constraint for IR. We solve these two non-convex problems optimally by reformulating each of them into a two-stage problem. First, by fixing the signal-to-interference-plus-noise ratio (SINR) target for ERs (for the first problem) or IR (for the second problem), we obtain the optimal beamforming and power allocation solution by applying the technique of semidefinite relaxation (SDR). Then, the original problems are solved by a one-dimension search over the optimal SINR target for ERs or IR. Furthermore, for each of the two studied problems, suboptimal solutions of lower complexity are also proposed in which the information and energy beamforming vectors are separately designed with their power allocation.Comment: accepted by IEEE Transactions on Signal Processing. Longer version of arXiv:1306.096

    Beamforming Optimization for Full-Duplex Wireless-powered MIMO Systems

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    We propose techniques for optimizing transmit beamforming in a full-duplex multiple-input-multiple-output (MIMO) wireless-powered communication system, which consists of two phases. In the first phase, the wireless-powered mobile station (MS) harvests energy using signals from the base station (BS), whereas in the second phase, both MS and BS communicate to each other in a full-duplex mode. When complete instantaneous channel state information (CSI) is available, the BS beamformer and the time-splitting (TS) parameter of energy harvesting are jointly optimized in order to obtain the BS-MS rate region. The joint optimization problem is non-convex, however, a computationally efficient optimum technique, based upon semidefinite relaxation and line-search, is proposed to solve the problem. A sub-optimum zero-forcing approach is also proposed, in which a closed-form solution of TS parameter is obtained. When only second-order statistics of transmit CSI is available, we propose to maximize the ergodic information rate at the MS, while maintaining the outage probability at the BS below a certain threshold. An upper bound for the outage probability is also derived and an approximate convex optimization framework is proposed for efficiently solving the underlying non-convex problem. Simulations demonstrate the advantages of the proposed methods over the sub-optimum and half-duplex ones.Comment: 14 pages, accepte

    Joint Secure Beamforming for Cognitive Radio Networks with Untrusted Secondary Users

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    In this paper, we consider simultaneous wireless information and power transfer (SWIPT) in orthogonal frequency division multiple access (OFDMA) systems with the coexistence of information receivers (IRs) and energy receivers (ERs). The IRs are served with best-effort secrecy data and the ERs harvest energy with minimum required harvested power. To enhance physical-layer security and yet satisfy energy harvesting requirements, we introduce a new frequency-domain artificial noise based approach. We study the optimal resource allocation for the weighted sum secrecy rate maximization via transmit power and subcarrier allocation. The considered problem is non-convex, while we propose an efficient algorithm for solving it based on Lagrange duality method. Simulation results illustrate the effectiveness of the proposed algorithm as compared against other heuristic schemes.Comment: To appear in Globecom 201
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