DeepAir: A Multi-Agent Deep Reinforcement Learning Based Scheme for an Unknown User Location Problem

TL;DR

DeepAir employs multi-agent deep reinforcement learning with UAVs to localize users, allocate resources, and ensure QoS in unknown environments, reducing the number of detector UAVs needed for effective task offloading.

Contribution

This paper introduces DeepAir, a novel DRL-based scheme integrating sensing, localization, and resource management for UAV networks in unknown user location scenarios.

Findings

DeepAir achieves higher task success rates than benchmarks.

Fewer detector UAVs are needed for effective operation.

DeepAir adapts well to different user distributions.

Abstract

The deployment of unmanned aerial vehicles (UAVs) in many different settings has provided various solutions and strategies for networking paradigms. Therefore, it reduces the complexity of the developments for the existing problems, which otherwise require more sophisticated approaches. One of those existing problems is the unknown user locations in an infrastructure-less environment in which users cannot connect to any communication device or computation-providing server, which is essential to task offloading in order to achieve the required quality of service (QoS). Therefore, in this study, we investigate this problem thoroughly and propose a novel deep reinforcement learning (DRL) based scheme, DeepAir. DeepAir considers all of the necessary steps including sensing, localization, resource allocation, and multi-access edge computing (MEC) to achieve QoS requirements for the offloaded tasks without violating the maximum tolerable delay. To this end, we use two types of UAVs including detector UAVs, and serving UAVs. We utilize detector UAVs as DRL agents which ensure sensing, localization, and resource allocation. On the other hand, we utilize serving UAVs to provide MEC features. Our experiments show that DeepAir provides a high task success rate by deploying fewer detector UAVs in the environment, which includes different numbers of users and user attraction points, compared to benchmark methods.