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Intelligent Spectrum Management for UAV Swarm with Multiple Combat Missions: A DQN-Based Solution | IEEE Conference Publication | IEEE Xplore

Intelligent Spectrum Management for UAV Swarm with Multiple Combat Missions: A DQN-Based Solution


Abstract:

In unmanned aerial vehicles(UAV) swarm combat, efficient management of electromagnetic spectrum resources is crucial to ensure the operational performance of UAV swarm. H...Show More

Abstract:

In unmanned aerial vehicles(UAV) swarm combat, efficient management of electromagnetic spectrum resources is crucial to ensure the operational performance of UAV swarm. However, in the presence of external radiations, the spectrum interference and competition between UAV onboard communication, radar, reconnaissance, and jamming services greatly affect the combat effectiveness of UAV swarm. Besides, because of the heterogeneity and coupling of different services, traditional spectrum allocation methods lack flexibility and occupy many computing resources in the central nodes. In this paper, by setting the threshold and the utility function for each service, we model the multi-service spectrum scheduling as a joint utility maximization problem. Then, we propose an intelligent and lightweight spectrum management method based on deep Q-network for multi-service cooperative spectrum scheduling. By dynamically and collaboratively allocating spectrum resources, conflicts within and between services are greatly reduced. Extensive simulations demonstrate that the proposed approach provides robust and efficient multi-service spectrum management.
Date of Conference: 24-26 October 2024
Date Added to IEEE Xplore: 03 December 2024
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Conference Location: Guangzhou, China
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I. Introduction

The future unmanned aerial vehicles (UAV) combat is characterized by extreme dynamism and complexity, with an exceptionally intricate electromagnetic environment. It involves electronic reconnaissance, communication, radar, and jamming services, between which there may be interference and competition in the same frequency band [1]. Spectrum management needs to be flexible enough to cater to these diverse services which are usually heterogeneous [2]. Besides, adversaries may employ electromagnetic interference to disrupt these electromagnetic services, leading to intense competition for limited and undisturbed spectrum resources [3]. Therefore, managing the spectrum has become an incredibly complex task [4] and poses significant challenges to current spectrum management technologies [5].

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