Optimizing Computational and Communication Resources for MEC Network Empowered UAV-RIS Communication

Abstract

<p>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.</p> <p>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.</p>

Closed-Form Global Optimization of Beyond Diagonal Reconfigurable Intelligent Surfaces

“…More recently, beyond diagonal RIS (BD-RIS), i.e., the set of generalized RIS architectures with scattering matrices not restricted to being diagonal [3], has emerged to break through the limitation of diagonal scattering matrices, and shown notable gains in improving performance in wireless systems, such as rate splitting multiple access (RSMA) [4], dualfunction radar-communication (DFRC) [5], and mobile edge computing (MEC) systems [6]. In the framework of BD-RIS, the conventional RIS is categorized as single-connected RIS, and further extended to fully-connected RIS by connecting all the RIS elements through tunable impedance components [2].…”

Joint trajectory design and spectrum allocation for unmanned aerial vehicle task efficiency

“…Unmanned aerial vehicle (UAV) communications have emerged as a promising wireless technology that allows fast, flexible, and agile scalability at low deployment and configuration cost [1]. The current decade will witness an unprecedented growth in the number of UAVs employed for public and industrial purposes [2], such as UAV‐assisted wireless sensor networks (WSNs) [3, 4], UAV‐assisted delivery [5, 6], edge artificial intelligence [7, 8], and mobile edge cloud (MEC) [9, 10]. In UAV‐assisted WSNs, UAVs collect sensing data from each sensor node (SN) and bring the collected data back to the headquarters, thus avoiding complex wired‐network deployment between the headquarters and the SNs.…”

Speed-Gradient Adaptive Control for Parametrically Uncertain UAVs in Formation

“…In this paper, reinforcement learning was applied to develop an offloading strategy to alleviate the effect caused by attack suppression that was able to comply with latency and power consumption constraints. Mahmood et al [13] considered improving the quality of network service through the transfer of extensive computing to the mobile edge cloud and through the deployment of a UAV integrated with intelligent reflective surfaces. To achieve an effective solution to the formulated complex problem, the original optimization problem was divided into subtasks using the block coordinate method.…”