RIS-Assisted UAV Communications for IoT With Wireless Power Transfer Using Deep Reinforcement Learning

Abstract

Many of the devices used in Internet-of-Things (IoT) applications are energy-limited, and thus supplying energy while maintaining seamless connectivity for IoT devices is of considerable importance. In this context, we propose a simultaneous wireless power transfer and information transmission scheme for IoT devices with support from the reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicle (UAV) communications. In particular, in the first phase, IoT devices harvest energy from the UAV through wireless power transfer; and then in the second phase, the UAV collects data from the IoT devices through information transmission. To characterise the agility of the UAV, we consider two scenarios: a hovering UAV and a mobile UAV. Aiming at maximising the total network sum-rate, we jointly optimise the trajectory and the power allocation of the UAV, the energy harvesting scheduling of IoT devices, and the phase-shift matrix of the RIS. We formulate a Markov decision process and propose two deep reinforcement learning algorithms to solve the optimisation problem of maximising the total network sum-rate. Numerical results illustrate the effectiveness of the UAV's flying path optimisation and the network's throughput of our proposed techniques compared with other benchmark schemes. Given the strict requirements of the RIS and UAV, the significant improvement in processing time and throughput performance demonstrates that our proposed scheme is well applicable for practical IoT applications.

Trajectory Design for Multi-UAV-Aided Wireless Power Transfer toward Future Wireless Systems

“…Nowadays, UAV-enabled WPT has been investigated in [11,[33][34][35][36][37][38], in which UAVs are applied as mobile ETs to supply low-power ERs distributed in the given area. Specifically, considering the UAV-enabled WPT systems where the UAV is flying at a fixed height, the authors maximized the energy transfer performance by optimizing the one-dimensional (1D) or two-dimensional (2D) UAV trajectory under the constraints of maximum UAV speed [11,33].…”

UAV-Aided Dual-User Wireless Power Transfer: 3D Trajectory Design and Energy Optimization

“…Specifically, UAVs have been explored to improve the security of wireless information delivery [ 4 ], to enhance the wireless connection as a reliable relay [ 5 , 6 ], to break through the energy supply bottleneck of wireless networks [ 7 , 8 ], and to provide a stable and reliable communication framework [ 9 , 10 ]. Meanwhile, UAVs can be well integrated with current pioneering wireless transmission techniques such as reconfigurable intelligent surface (RIS) [ 11 ], millimeter-wave (mmWave) [ 12 ], and terahertz [ 13 ]. Therefore, they are also considered as a flexible new type of base station or relay toward the realization of the sixth-generation networks [ 14 , 15 ].…”

Symbol error probability of mmWave communications with transmit antenna selection and RISs under the impacts of hardware impairments

“…1,2 Specifically, millimeter-wave (mmWave) communications and reconfigurable intelligent surfaces (RISs) are two promising methods emerging in recent years. 3,4 In particular, mmWave communications utilize high carrier frequencies (f c ½30,300 GHz). Consequently, the wavelength ranges from 1 to 10 mm.…”

“…12 In the literature, mmWave communications and RISs have been combined in the wireless networks. 2,4,13 Since the RISs are often deployed on the top/wall of factories/buildings, they can support mmWave communications to significantly reduce the effects of blocking objects. [14][15][16] In particular, the design of beamforming and reflecting coefficients was proposed to enhance the performance of RISs-assisted mmWave communications.…”