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.

A typical unmanned aerial vehicle (UAV) aided communication system with multiple user equipments (UEs) is investigated in this paper. The UE's information and energy maximization problem is proposed by optimizing the UAV's trajectory, transmit power and hovering time. The problem is decomposed into four sub-problems: dividing UEs, solving hovering positions of the UAV, designing UAV's trajectory and optimizing problem. Then, a K-clustering algorithm combined with Fermat Problem is considered to solve some sub-problems. In addition, UAV's trajectory is obtained by using dynamic programming (DP) to solve the TSP problem. The numerical results verify that our proposed algorithms effectively reduce the number of hovering positions of the UAV. Besides, the proposed optimization problems also improve the performance of information and energy transmission compared with the energy optimization only and information optimization only schema.

Due to its long-distance propagation, radio-frequency (RF) signals have been relied upon for remotely charging low-power communication devices, which significantly extends the lifetime of battery-powered devices. Delivering both wireless information provision (WIP) and wireless energy provision (WEP) services in the RF band requires a holistic design of wireless information and energy provision (WIEP) for achieving energy self-sustainability in the next generation of wireless networks, such as 6G. However, most of the existing works studied the integrated WIEP performance by exploiting Gaussian distributed signals with the infinite alphabet, which cannot be realised in a practical communication system. Therefore, we investigate the WIEP performance by considering practical modulation schemes having finite alphabet in a well-known Nakagami-m wireless fading channel. Furthermore, we jointly optimise the transmit power allocation and the transmission switching threshold of a WIEP transmitter and the power splitting ratio of a WIEP receiver in order to maximise the attainable spectrum efficiency for WIP, while satisfying both the WEP requirement and the WIP reliability constraint. Numerical results are provided for characterising the rate-energy-reliability trade-off of different modulation schemes in various wireless fading conditions.

A typical data and energy integrated communication network (DEIN) conceives a conventional base station, which is capable of simultaneously transmitting the data and energy to user equipments (UEs) during the downlink (DL) transmissions by invoking the time-division-multiple-access (TDMA) protocol in the medium access control (MAC) layer. Several UEs operating in this DEIN are capable of harvesting the energy from the DL transmissions by adopting the power splitting (PS) technique and they are also capable of exploiting the harvested energy for powering their uplink (UL) data transmissions by invoking the TDMA protocol in the MAC layer. Both of the UL sum-throughput and the UL fair-throughput of the DEIN is maximized by deciding the duration of each time-slot during the DL/UL transmissions and by determining the optimal PS factor for each UE. Both of these optimization problems are finally solved by the classic method of Lagrange multipliers in close-form. An interesting observation shows that supporting lowthroughput data services during the DL transmissions does not degrade the wireless energy transfer and hence does not reduce the throughput of the UL transmissions.

RF signals can be relied upon for transferring energy to those power-thirsty wireless devices. Thanks to the broadcast nature of wireless channels, dedicated RF signals for delivering information to specific devices can also be received by others for energy harvesting. Coordinating the conventional wireless information transfer and the wireless energy transfer (WET) requires a systematic design in all OSI layers, which yields data and energy integrated communication networks. Based on the classic carrier-sense-multiple-access with collision avoidance protocol, we originally propose a distributed multiple access protocol in the medium access control (MAC) layer for the indoor wireless-local-area-network (WLAN) powered by RF signal-based WET. The operation of our proposed protocol in the WET powered indoor WLAN is modeled by a multi-dimensional Markov chain, which is exploited for deriving the closed-form throughput of the WLAN studied. The simulation results demonstrate the accuracy of our theoretical analysis, which pave the way for the future optimization design in the MAC layer of WET powered indoor WLAN.

In order to address the energy shortage in communication networks, RF signals are exploited for transferring energy to miniature devices, which yields wireless powered communication networks (WPCNs). A full-duplex aided hybrid-base-station (H-BS) is conceived in a WPCN for simultaneously transferring energy during downlink transmissions and receiving data during uplink transmissions. UEs may deplete all the energy received from the H-BS for supporting their own uplink transmissions. In this full-duplex WPCN, A joint time allocation and UE scheduling algorithm is proposed for the sake of maximising the sum-uplink-throughput of multiple UEs by further considering UEs' actual data uploading requirements. The numerical results demonstrate that the suboptimal solution is capable of achieving almost the same performance with its optimal counterparts, while our scheme outperforms other existing peers in terms of the sum-uplink-throughput.

With the conversion capability from radio frequency into electricity, Radio Frequency based Energy Harvesting (RF-EH) has appeared as a promising means to overcome the battery exhaustion problem of mobile devices. However, despite the great efforts in RF-EH communication techniques made recently, a big drawback that limits the application of RF-EH is the large propagation loss of radio signal energy. This paper attempts to address this problem through deploying multiple energy sources in the network based on cognitive techniques that can perceive and analyze user behaviors. Under a Mobile Social Network (MSN) scenario, we conduct energy source deployment in combination with the throwbox deployment problem, which aims at improving data forwarding efficiency. Our focus is on both data forwarding (DF) and energy harvesting (EH) efficiency. We propose three deployment schemes, i.e., D-deployment, E-deployment and T-deployment. With a continuous-time Markov chain based mobility model, we formulate these deployment schemes as three optimization problems, respectively. Simulation results indicate that the proposed schemes outperform an existing deployment scheme in terms of both DF efficiency and EH efficiency.

This paper studies the problem of optimal power allocation towards maximizing the throughput of point-to-point wireless communication systems with energy harvesting. A novel energy-transferring approach is proposed to analyze the throughput maximization problem with causality constraints, in which we study the transfer energy rather than the water level widely used in the existing literature. The proposed approach simplifies the power allocation as a linear function with respect to only two transfer energy variables, i.e., the energy transferred from the previous epoch and the energy transferred to the next epoch. Moreover, we prove that all the positive transfer energy variables can be determined by solving a set of linear equations with a special coefficient matrix derived from the KKT conditions for the dual problem. Based on the energy-transferring approach, we propose an iterative algorithm to obtain the optimal solution with a much lower complexity compared to those existing algorithms based on the directional water-filling structure results. Numerical studies verify the analytical results as well as the effectiveness of the proposed algorithm.