Secrecy Wireless Information and Power Transfer With MISO Beamforming

Liu, Liang; Zhang, Rui; Chua, Kee-Chaing

doi:10.1109/tsp.2014.2303422

Cited by 432 publications

(392 citation statements)

References 36 publications

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“…Several practical receiver architectures for simultaneous information and power transfer were investigated in [8], [21]. Exploiting multiple antenna technologies in WPT has been widely studied: multiple-input-multiple-output broadcast channels [8], beamforming designs for multiuser multiple-input-single-output (MISO) [23], physical-layer security problems for multiuser MISO [24], and multiple-antenna interference channels [25]. On the other hand, there are a relatively limited number of studies on WPT for WSNs; different WPT technologies for addressing energy/lifetime problems in WSNs were reviewed in [26], [27]; in [28], the authors studied a distributed estimation system in which some of the multiple-antenna sensors, named super sensors, are capable of WPT to its neighboring sensors via beamforming; and in [29], several multiple-antenna RF-based chargers were used to replenish the wireless sensors and then to switch to the information transmission phase, where each sensor sent a quantized version of its measurement to the FC for estimation.…”

Section: A Previous Workmentioning

confidence: 99%

Wireless Power Transfer for Distributed Estimation in Sensor Networks

Vien¹,

Shin²,

Ishibashi³

2017

IEEE J. Sel. Top. Signal Process.

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This paper studies power allocation for distributed estimation of an unknown scalar random source in sensor networks with a multiple-antenna fusion center (FC), where wireless sensors are equipped with radio-frequency based energy harvesting technology. The sensors' observation is locally processed by using an uncoded amplifyand-forward scheme. The processed signals are then sent to the FC, and are coherently combined at the FC, at which the best linear unbiased estimator (BLUE) is adopted for reliable estimation. We aim to solve the following two power allocation problems: 1) minimizing distortion under various power constraints; and 2) minimizing total transmit power under distortion constraints, where the distortion is measured in terms of mean-squared error of the BLUE. Two iterative algorithms are developed to solve the non-convex problems, which converge at least to a local optimum. In particular, the above algorithms are designed to jointly optimize the amplification coefficients, energy beamforming, and receive filtering. For each problem, a suboptimal design, a single-antenna FC scenario, and a common harvester deployment for colocated sensors, are also studied. Using the powerful semidefinite relaxation framework, our result is shown to be valid for any number of sensors, each with different noise power, and for an arbitrarily number of antennas at the FC. Index TermsAmplify-and-forwarding, best linear unbiased estimator (BLUE), distributed estimation, mean-squared error (MSE), wireless power transfer (WPT).

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Section: A Previous Workmentioning

confidence: 99%

Wireless Power Transfer for Distributed Estimation in Sensor Networks

Vien¹,

Shin²,

Ishibashi³

2017

IEEE J. Sel. Top. Signal Process.

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“…Since problem P2-A-SDP is a convex problem, it can be solved via existing software (e.g., Matlab CVX developed by MathWorks, Natick, MA, USA [22,23]). …”

Section: A Feasibility Analysis Of Problem P2mentioning

confidence: 99%

“…Therefore, P2-SDR-Equ can be efficiently solved by existing solvers, such as Matlab CVX developed by MathWorks Natick, MA, USA [23]. Since there are two design variables, based on the result in [2], an optimal solution (S * , Q * ) exists for P2-SDR-Equ such that rank 2 (S * ) + rank 2 (Q * ) ≤ 2K + 2.…”

Section: The Optimization Algorithm For Problem P2mentioning

confidence: 99%

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Precoding Design and Power Allocation in Two-User MU-MIMO Wireless Ad Hoc Networks

et al. 2017

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Abstract:In this paper, we consider the precoding design and power allocation problem for multi-user multiple-input multiple-output (MU-MIMO) wireless ad hoc networks. In the first timeslot, the source node (SN) transmits energy and information to a relay node (RN) simultaneously within the simultaneous wireless information and power transfer (SWIPT) framework. Then, in the second timeslot, based on the decoder and the forwarding (DF) protocol, after reassembling the received signal and its own signal, the RN forwards the information to the main user (U1) and simultaneously sends its own information to the secondary user (U2). In this paper, when the transmission rate of the U1 is restricted, the precoding, beamforming, and power splitting (PS) transmission ratio are jointly considered to maximize the transmission rate of U2. To maximize the system rate, we design an optimal beamforming matrix and solve the optimization problem by semi-definite relaxation (SDR), considering the high complexity of implementing the optimal solution. Two sub-optimal precoding programs are also discussed: singular value decomposition and block diagonalization. Finally, the performance of the optimization and sub-optimization schemes are compared using a simulation.

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“…In [12], [13], the authors presented the optimal beamforming design and power allocation scheme for multiple-input single-output (MISO) systems in the presence of passive eavesdroppers, later in [14], the issue of uncertain eavesdroppers was tackled, where joint optimization of information and energy beamforming and power allocation were studied. Latest works have considered the security issue in more sophisticated SWIPT systems, such as relay [15], multicast [16], cognitive radio [17] and OFDMA systems [18].…”

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confidence: 99%