Secure Information Transmission in the Presence of Energy-Harvesting Eavesdroppers in Multi-Cell Networks

Abstract

In this paper, we study simultaneous data transmission and power transfer for multiple information receivers (IRs) and energy-harvesting receivers (ERs) in cellular networks. We propose an optimization problem to maximize a linear combination of the useful signal power at each IR and total received power at each ER subject to the following three sets of constraints: i) data reliability by maintaining the required level of signal to interference plus noise ratio (SINR) for all IRs; ii) information security by keeping leakage SINR levels of all IRs at every ER below a predefined value, which helps prevent possible eavesdroppers, i.e., ERs, from detecting information aimed for the IRs; and iii) power consumption by guaranteeing that the transmit power at each base station is within its available budget. Using semidefinite relaxation (SDR) technique, we then transform the proposed problem into a convex form. We further prove that the transformed problem always yields rank-one optimal solution. Hence, our approach does not require any computationally expensive randomization procedure, as usually utilized in a typical SDR method, in obtaining optimal solutions. Simulation results indicate that the proposed approach prevails conventional scheme in terms of providing higher IRs' total throughput, higher received power level at each ER and more secure for the transmission of information.

A Rank-One Optimization Framework and Its Applications to Transmit Beamforming

“…Let us define a sub-class optimization problem of the general framework as an optimization problem including an objective function as in (12), constraint C6 and at least one constraint selected from {C1, C2, C3, C4(a), C5}. As the result of Theorem 1, we have the following corollary.…”

“…1 Proof: Please refer to Appendix C. With a suitable selection of A i , e.g., A i = −I M , the minimization can be turned into a maximization. For instance, the proposed framework can capture the weighted sum-power transfer maximization in [9], [17], the wireless-information transfer efficiency maximization in [10], the intended signal power maximization in [12], etc.…”

“…Note that none of the SDR counterparts of the transmit beamforming approaches in the literature considers all the constraints presented in the optimization framework. Constraints C1, C2, C4/C4(a), and C6 usually appear on beamforming problems with perfect CSI, see e.g., [2], [4]- [8], [11], [12]. As for imperfect CSI scenarios, constraints C3 and C6 are adopted in robust conservative beamforming approaches, such as [18]- [20], whereas constraints C4/C4(a), C5, and C6 are exploited in probabilistic beamforming approaches, e.g., [13], [21]- [25].…”

“…Thanks to its capable of providing accurate or even near optimal approximations [1], the SDR technique has been exploited for QCQPs representing beamforming problems for simultaneous wireless information and power transfer (SWIPT), e.g., [9], [10], physical-layer security for SWIPT, e.g., [11], [12], intelligent reflecting surface (IRS), e.g., [13]- [15], and IRS-Aided SWIPT, e.g., [16], [17]. With respect to SWIPT scenarios, adopting an assumption that all users' channel vectors are mutually and statically independent, reference [9] considered the maximization of a weighted sumpower transferred to all the energy receivers while reference [10] studied a wireless-information-transfer maximization with both linear and non-linear energy-harvesting models.…”

“…It was shown in [9] and [10] when the considered channels are statistically independent with each other, with probability one, the beamforming matrices for the information receivers are rank one while those for the energy receivers have rank less or equal one. Considering physical-layer security for SWIPT scenarios, it was proved that the SDR counterpart of a QCQP proposed in [11], i.e., a total-transmit-power minimization problem, and the SDR counterpart of a QCQP introduced in [12], i.e., the maximization problem of the intended signal power at every information receiver and the total received power at each energy harvesting receiver, yield rank-one optimal solutions if they are feasible. Investigating an IRS scenario in [15], the authors showed that the optimal beamforming rank-one matrix for their IRS SDR counterpart can always be obtained for any positive transmit power budget at BS.…”