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Register a new userEnergy Efficiency Maximization for Full-Duplex UAV Secrecy Communication
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Bin Duo
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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This letter proposes a new full-duplex (FD) secrecy communication scheme for the unmanned aerial vehicle (UAV) and investigates its optimal design to achieve the maximum energy efficiency (EE) of the UAV. Specifically, the UAV receives the confidential information from a ground source and meanwhile sends jamming signals to interfere with a potential ground eavesdropper. As the UAV has limited on-board energy in practice, we aim to maximize the EE for its secrecy communication, by jointly optimizing the UAV trajectory and the source/UAV transmit/jamming powers over a finite flight period with given initial and final locations. Although the problem is difficult to solve, we propose an efficient iterative algorithm to obtain its suboptimal solution. Simulation results show that the proposed joint design can significantly improve the EE of UAV secrecy communication, as compared to various benchmark schemes.
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Smart and reconfigurable wireless communication environments can be established by exploiting well-designed intelligent reflecting surfaces (IRSs) to shape the communication channels. In this paper, we investigate how multiple IRSs affect the performance of multi-user full-duplex communication systems under hardware impairment at each node, wherein the base station (BS) and the uplink users are subject to maximum transmission power constraints. Firstly, the uplink-downlink system weighted sum-rate (SWSR) is derived which serves as a system performance metric. Then, we formulate the resource allocation design for the maximization of SWSR as an optimization problem which jointly optimizes the beamforming and the combining vectors at the BS, the transmit powers of the uplink users, and the phase shifts of multiple IRSs. Since the SWSR optimization problem is non-convex, an efficient iterative alternating approach is proposed to obtain a suboptimal solution for the design problem considered and its complexity is also discussed. In particular, we firstly reformulate the main problem into an equivalent weighted minimum mean-square-error form and then transform it into several convex sub-problems which can be analytically solved for given phase shifts. Then, the IRSs phases are optimized via a gradient ascent-based algorithm. Finally, numerical results are presented to clarify how multiple IRSs enhance the performance metric under hardware impairment.
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