Optimizing Computational and Communication Resources for MEC Network
Empowered UAV-RIS Communication
Abstract
With the technological evolution and new applications, user equipment
(UEs) has become a vital part of our lives. However, limited
computational capabilities and finite battery life bottleneck the
performance of computationally demanding applications. A practical
solution to enhance the quality of experience (QoE) is to offload the
extensive computation to the mobile edge cloud (MEC). Moreover, the
network’s performance can be further improved by deploying an unmanned
aerial vehicle (UAV) integrated with intelligent reflective surfaces
(IRS): an effective alternative to massive antenna systems to enhance
the signal quality and suppress interference. In this work, the MEC
network architecture is assisted by UAV-IRS to provide computational
services to the UEs. To do so, a cost minimization problem in terms of
computing time and hovering energy consumption is formulated.
Furthermore, to achieve an efficient solution to a formulated
challenging problem, the original optimization problem is decoupled into
sub-problems using the block-coordinate decent method. Moreover,
numerical results are compared to baseline schemes to determine the
effectiveness of the proposed scheme. Simulation results demonstrate
that the optimal allocation of local computational resources results in
minimizing tasks’ computational time and hovering energy consumption.