Secure and Energy-Efficient Beamforming for Simultaneous Information and Energy Transfer

Abstract

Next-generation communication networks will likely involve the energy-efficient transfer of information and energy over the same wireless channel, for which the physical layer will become more vulnerable to cyber attacks by potential multi-antenna eavesdroppers. To address this issue, this paper considers transmit time-switching (TS) mode, in which energy and information signals are transmitted separately in time by the BS. This protocol is not only easy to implement but also delivers the opportunity of multi-purpose beamforming, in which energy beamformers during wireless power transfer are useful in jamming the eavesdropper. In the presence of imperfect channel estimation and multi-antenna eavesdroppers, the energy and information beamformers and the transmit TS ratio are jointly optimized to maximize the worst-case user secrecy rate subject to UEs harvested energy thresholds and a BS transmit power budget. New robust path-following algorithms, which involve one simple convex quadratic program at each iteration are proposed for computational solutions of this difficult optimization problem and also the problem of secure energy efficiency maximization. The latter is further complex due to additional optimization variables appearing in the denominator of the secrecy rate function. Numerical results confirm that the performance of the proposed computational solutions is robust against the channel uncertainties.

NOMA Throughput and Energy Efficiency in Energy Harvesting Enabled Networks

“…Though the PS approach has been shown to mostly outperform the receive-TS approach, however, the PS is complicated and inefficient for practical implementation. Recent findings in [4], [6] and [7] demonstrate the advantages of new "transmit-TS" approach over PS approach, where information and energy is transferred separately and energy-constrained users' receivers do not need any sophisticated device.…”

“…Let w I [w I i,j ] i∈I,j∈J define the information beamforming vectors and suppose that R i,p(j) is the achievable rate by decoding s i,p(j) . Recalling the definitions (6), (7), (12) and (8), we have…”

“…, L e i,p(j) (w I,(κ) )), and (x (κ) , y (κ) ) = (h i,i,j w I,(κ) i,j , L c i,j (w I,(κ) )), respectively. The EH constraint (42b) is innerly approximated by [7] (s, )∈Sc…”

Resource Allocation for Energy Efficiency in 5G Wireless Networks

“…In fact, wireless cellular communications may face a significant challenge to satisfy the demand of higher data-rate communications at reduced greenhouse emission [16]. Hence, the energy efficiency (EE) performance is one of the major topics and will be attached to green communications in the deployment of 5G technology [16,32,51]. Nevertheless, the EE maximization problem usually results in a nonconvex program [5].…”

“…Nevertheless, the EE maximization problem usually results in a nonconvex program [5]. Specifically, the network energy-efficiency merit is usually defined as the ratio of the total information throughput and the total energy consumption, and thus, the EE maximization problem is introduced under the class of fractional programs [32,49,55] in the field of optimization. To enhance the performance of communication systems, the combination of interference alignment [59], spectrum allocation and energy-efficient management should be necessarily considered and implemented simultaneously.…”