Joint Optimization of Resource Allocation, Phase Shift, and UAV Trajectory for Energy-Efficient RIS-Assisted UAV-Enabled MEC Systems

Abstract

The unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) has been deemed a promising paradigm to provide ubiquitous communication and computing services for the Internet of Things (IoT). Besides, by intelligently reflecting the received signals, the reconfigurable intelligent surface (RIS) can significantly improve the propagation environment and further enhance the service quality of the UAV-enabled MEC. Motivated by this vision, in this paper, we consider both the amount of completed task bits and the energy consumption to maximize the energy efficiency of the RIS-assisted UAV-enabled MEC systems with non-orthogonal multiple access (NOMA) protocol, where the bit allocation, transmit power, phase shift, and UAV trajectory are jointly optimized by an iterative algorithm with a double-loop structure based on the Dinkelbach's method and block coordinate decent (BCD) technique. Simulation results demonstrate that: 1) our proposed algorithm can achieve higher energy efficiency than baseline schemes while satisfying the task tolerance latency; 2) the energy efficiency first increases and then decreases with the increase of the mission period and the total amount of taskinput bits of IoT devices; 3) the energy efficiencies of schemes with imperfect channel state information (CSI) are lower than corresponding schemes with perfect CSI, and the performance gain of NOMA over OMA diminishes under the imperfect CSI.

STAR-RIS-aided UAV NOMA Mobile Edge Computing Network with RF Energy Harvesting

“…where m 0 is the fading severity factor, λ 0 = E(|h 0 | 2 ). By substituting (7) and (A-2) into (A-1) and setting z = e −x and applying the Gaussian-Chebyshev quadrature method according to variable z calculating the integral, we obtain the final result as (17). This ends our proof.…”

“…However, this study did not consider UAV users and the NOMA scheme. The authors in [17] investigated the RIS-assisted UAV-enabled NOMA MEC systems. In this setup, a UAV serves as an access point and is equipped with an MEC server that offers computing services to users.…”

Multi-IRS-Assisted mmWave UAV-BS Network for Coverage Extension

“…Kefeng et al [ 33 ] showed that the heavy channel fading, fewer signals, larger interferences, and larger impairment levels would bring worse system performance. Some researchers have aimed to minimize power consumption and maximize system energy efficiency [ 34 , 35 , 36 ]. But, in these research endeavors, one RIS was used, and the position of the UAV or IRS was fixed.…”

Joint Phase Shift Design and Resource Management for a Non-Orthogonal Multiple Access-Enhanced Internet of Vehicle Assisted by an Intelligent Reflecting Surface-Equipped Unmanned Aerial Vehicle

“…Finally, on rural roads where communication is obstructed, the stable communication service provided by UAVs can effectively support the operation of an ITS, such as automatic vehicle queuing, traffic flow management, and emergency response, improving road utilization efficiency, reducing traffic congestion, and enhancing driving safety [28,29]. To maximize the energy efficiency of IRS-equipped UAVs, a scheme for the joint optimization of resource allocation, phase shift, and trajectory was proposed in [30]. Additionally, the non-orthogonal multiple access (NOMA) technique has been emerging as a potential solution in UAV-assisted networks, which can achieve timely, reliable, and seamless data exchange [31].…”

“…However, it is challenging to integrate the IRS-equipped UAV and NOMA technique into IoV while optimizing the resource management and phase shift design. First, the works in [21,22,30] make an implicit assumption that spectrum-efficient resource management can be achieved. Actually, in dynamic IoV, the subcarrier allocation and power control policies need to be delicately designed to improve the communication performance.…”