Intelligent Reflecting Surface Aided Wireless Power Transfer With a DC-Combining Based Energy Receiver and Practical Waveforms

This paper studies intelligent reflecting surface (IRS) aided wireless power transfer (WPT) to batteryless Internet of Everything (IoE) devices. A practical energy receiver (ER) with multiple antennas is investigated. Multiple RF energy flows gleaned by all the receive antennas are input multiple energy harvesters, which are further rectified to direct-current (DC) energy. The resultant multiple DC energy flows are then combined in the DC domain for energy storage. Three classic waveforms, namely deterministic waveform, M-QAM waveform, and Gaussian waveform, are considered for WPT. We maximize the output DC power by jointly designing the active transmit beamformer of the transmitter and the passive reflecting beamformer of the IRS with the above-mentioned waveforms, respectively, subject to the transmit power constraint at the transmitter and to the limited resolution constraints on the phase-shifters of the IRS. A low complexity alternating optimization (AO) algorithm is proposed, which converges to a Karush-Kuhn-Tucker (KKT) point and thus results in a locally optimal solution. The numerical results demonstrate that the Gaussian waveform has the best energy performance with a low input RF power to the energy harvesters. By contrast, the deterministic waveform becomes superior with a high input RF power to the energy harvesters

Joint Transceiving and Reflecting Design for Intelligent Reflecting Surface Aided Wireless Power Transfer

In an intelligent reflecting surface (IRS) aided wireless power transfer (WPT) system, a practical architecture of an energy receiver (ER) is proposed, which includes multiple receive antennas, an analog energy combiner, a power splitter and multiple energy harvesters. In order to maximise the output direct-current (DC) power, the transmit beamformer of the transmitter, the passive beamformer of the IRS, the energy combiner, and the power splitter of the ER are jointly optimised. The optimisation problem is equivalently divided into two sub-problems, which independently maximises the input RF power and the output DC power of the energy harvesters, respectively. A successive linear approximation (SLA) based algorithm with a low complexity is proposed to maximise the input RF power to the energy harvesters, which converges to a Karush-Kuhn-Tucker (KKT) point. We also propose an improved greedy randomized adaptive search procedure (I-GRASP) based algorithm having better performance to maximise the input RF power. Furthermore, the optimal power splitter for maximising the output DC power of the energy harvesters is derived in closed-form. The numerical results are provided to verify the performance advantage of the IRS-aided WPT and to demonstrate that conceiving the optimised energy combiner achieves better WPT performance than the deterministic counterpart

Robust Sum-Rate Maximization in Transmissive RMS Transceiver-Enabled SWIPT Networks

In this paper, we propose a state-of-the-art downlink communication transceiver design for transmissive reconfigurable metasurface (RMS)-enabled simultaneous wireless information and power transfer (SWIPT) networks. Specifically, a feed antenna is deployed in the transmissive RMS-based transceiver, which can be used to implement beamforming. According to the relationship between wavelength and propagation distance, the spatial propagation models of plane and spherical waves are built. Then, in the case of imperfect channel state information (CSI), we formulate a robust system sum-rate maximization problem that jointly optimizes RMS transmissive coefficient, transmit power allocation, and power splitting ratio design while taking account of the non-linear energy harvesting model and outage probability criterion. Since the coupling of optimization variables, the whole optimization problem is non-convex and cannot be solved directly. Therefore, the alternating optimization (AO) framework is implemented to decompose the non-convex original problem. In detail, the whole problem is divided into three sub-problems to solve. For the non-convexity of the objective function, successive convex approximation (SCA) is used to transform it, and penalty function method and difference-of-convex (DC) programming are applied to deal with the non-convex constraints. Finally, we alternately solve the three sub-problems until the entire optimization problem converges. Numerical results show that our proposed algorithm has convergence and better performance than other benchmark algorithms

Energy-Efficient Hybrid Beamforming for Multi-Layer RIS-Assisted Secure Integrated Terrestrial-Aerial Networks

The integration of aerial platforms to provide ubiquitous coverage and connectivity for densely deployed terrestrial networks is expected to be a reality in the emerging sixth-generation networks. Energy-effificient and secure transmission designs are two important components for integrated terrestrial-aerial networks (ITAN). Inlight of the potential of reconfigurable intelligent surface (RIS) for significantly reducing the system power consumption and boosting information security, this paper proposes a multi-layer RIS-assisted secure ITAN architecture to defend against simultaneous jamming and eavesdropping attacks, and investigates energy-efficient hybrid beamforming for it. Specifically, with the availability of imperfect angular channel state information (CSI), we propose a block coordinate descent (BCD) framework for the joint optimization of the user’s received decoder, the terrestrial and aerial digital precoder, and the multi-layer RIS analog precoder to maximize the system energy efficiency (EE) performance. For the design of the received decoder, a heuristic beamforming scheme is proposed to convert the worst-case design problem into a min-max one and facilitate the developing a closed-form solution. For the design of the digital precoder, we propose an iterative sequential convex approximation approach via capitalizing the auxiliary variables and first-order Taylor series expansion. Finally, a monotonic vertex-update algorithm with a penalty convex-concave procedure (P-CCP) is proposed to obtain the analog precoder with satisfactory performance. Numerical results show the superiority and effectiveness of the proposed optimization framework and architecture over various benchmark schemes

1 Energy-Efficient Hybrid Beamforming for Multi-Layer RIS-Assisted Secure Integrated Terrestrial-Aerial Network

The integration of aerial platforms to provide ubiq- uitous coverage and connectivity for densely deployed terrestrial networks is expected to be a reality in emerging sixth-generation networks. Energy-effificient design and secure transmission are two crucial issues for integrated terrestrial-aerial networks. With this focus, due to the potential of RIS in substantially saving power consumption and boosting the security of private information by enabling a smart radio environment, this paper investigates the energy-efficient hybrid beamforming for multi- layer reconfigurable intelligent surface (RIS)-assisted secure in- tegrated terrestrial-aerial network for defending against simul- taneous jamming and eavesdropping attacks. Specifically, with the available of angular information based imperfect channel state information (CSI), we propose a framework for the joint optimization of user’s received precoder, terrestrial BS’s and HAP’s digital precoder, and multi-layer RIS analog precoder to maximize the system energy efficiency (EE) performance. For the design of received precoder, a heuristic beamforming scheme is proposed to convert the worst-case problem into a min-max one such that a closed-form solution is derived. For the design of digital precoder, we propose an iterative sequential convex approximation approach via capitalizing the auxiliary variables and first-order Taylor series expansion. Finally, a monotonic vertex-update algorithm with penalty convex concave procedure is proposed to obtain analog precoder with low computational complexity. Numerical results show the superiority and effective- ness of proposed optimization framework and architectur