Resource Scheduling Based on Deep Reinforcement Learning in UAV Assisted Emergency Communication Networks

Abstract

The advent of fifth generation (5G) networks has opened new avenues for enhancing connectivity, particularly in challenging environments like remote areas or disaster-struck regions. Unmanned aerial vehicles (UAVs) have been identified as a versatile tool in this context, particularly for improving network performance through the Integrated access and backhaul (IAB) feature of 5G. However, existing approaches to UAV-assisted network enhancement face limitations in dynamically adapting to varying user locations and network demands. This paper introduces a novel approach leveraging deep reinforcement learning (DRL) to optimize UAV placement in real-time, dynamically adjusting to changing network conditions and user requirements. Our method focuses on the intricate balance between fronthaul and backhaul links, a critical aspect often overlooked in current solutions. The unique contribution of this work lies in its ability to autonomously position UAVs in a way that not only ensures robust connectivity to ground users but also maintains seamless integration with central network infrastructure. Through various simulated scenarios, we demonstrate how our approach effectively addresses these challenges, enhancing coverage and network performance in critical areas. This research fills a significant gap in UAV-assisted 5G networks, providing a scalable and adaptive solution for future mobile networks.

On the performance of uplink NOMA‐based satellite‐aerial‐terrestrial integrated networks

“…𝜃 a is the departure angle of signal path. In (6), gsv is the independent and identically distributed, complex Gaussian random variables, ∼ 𝚽 sv denotes the spatial correlation matrix, ∼ 𝚽 1∕2 sv gsv represents the scattering component.…”

“…4,5 Besides satellite and terrestrial cellular network, the application of cost-effectiveness and high flexible unmanned aerial vehicle (UAV) has a great potential to expand coverage range and improve system performance. [6][7][8][9][10] Then, a UAV acting as aerial relay is integrated into satellite network, which we refer to as satellite-aerial-terrestrial integrated network (SATIN). [11][12][13][14] In Reference 12, the authors studied the ergodic capacity performance in UAV-assisted SATIN.…”

The Current Trends of Deep Learning in Autonomous Vehicles: A Review

“…In [93], the authors propose a novel DRL method to optimize energy consumption. In [94], the authors approach this problem with a DRL based on Q-Learning and CNN to optimize macro base power allocation and UAV service selection. Recently, vehicular ad hoc networks have been used in autonomous vehicles for vehicles to improve safety and comfort [95].…”

Analysis of Coverage and Capacity for UAV-Aided Networks with Directional mmWave Communications

“…By utilizing UAVs as aerial base stations (ABSs) or relay nodes, UAV-aided networks can reduce the challenges of high flexibility, mobility, and stability in wireless communication in combination with ground base stations (GBSs), overcoming the signal coverage issues of traditional terrestrial networks [2]. Additionally, UAV-aided networks offer larger coverage areas and higher opportunities for line-of-sight (LOS) links [3], which are considered to have tremendous application potential in improving communication quality in both military and civilian domains [4].…”