| Autor: |
Zhe Wang, Hongxiang Li, Eric J. Knoblock, Rafael D. Apaza |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 12, Pp 132056-132069 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3430191 |
| Popis: |
As the airspace is experiencing an increasing number of low-altitude aircraft, the concept of spectrum sharing between aerial and terrestrial users emerges as a compelling solution to improve the spectrum utilization efficiency. In this paper, we consider a new Aerial and Terrestrial Hybrid Network (ATHN) comprising aerial vehicles (AVs), ground base stations (BSs), and terrestrial users (TUs). In this ATHN, AVs and BSs collaboratively form a multi-hop ad-hoc network with the objective of minimizing the average end-to-end (E2E) packet transmission delay. Meanwhile, the BSs and TUs form a terrestrial network aimed at maximizing the uplink and downlink sum capacity. Given the concept of spectrum sharing between aerial and terrestrial users in ATHN, we formulate a joint routing and capacity optimization (JRCO) problem, which is a multi-stage combinatorial problem subject to the curse of dimensionality. To address this problem, we propose a Deep Reinforcement Learning (DRL) based algorithm. Specifically, the Dueling Double Deep Q-Network (D3QN) structure is constructed to learn an optimal policy through trial and error. Extensive simulation results demonstrate the efficacy of our proposed solution. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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