| Autor: |
Niloofar Bahadori, Mahmoud Nabil, Abdollah Homaifar |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 9, Pp 110601-110613 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2021.3102230 |
| Popis: |
Exploiting the millimeter wave (mmWave) band is an attractive solution to accommodate the bandwidth-intensive applications in device-to-device (D2D) communications. The directional nature of communications at mmWave frequencies and mobility of devices require beam alignment at both transmitter and receiver ends. The beam alignment signaling overhead leads to a loss in the network’s throughput. There exists a trade-off between antenna beamwidth and the achievable throughput. Although a narrower antenna beam increases the directivity gain, it leads to a higher signaling overhead and less stable D2D links which reduce the network’s throughput. Therefore, optimizing the antenna beamwidth is crucial to maintain the D2D users’ quality-of-experience (QoE). In this paper, we propose a novel distributed antenna beamwidth optimization algorithm based on multi-agent deep reinforcement learning. We model D2D links as agents that interact with the communication environment concurrently and learn to refine their antenna beamwidth policies. Agents aim to maximize the network sum-throughput and maintain reliable communication links while taking into account the application-specific quality-of-service (QoS) requirements and the cost associated with beam alignment. Online deployment of the proposed algorithm is distributed and does not require any coordination among users. The performance of the proposed antenna beamwidth optimization algorithm is compared with other widely used baseline algorithms. Numerical results show that our proposed algorithm improves the network performance significantly and outperforms existing approaches. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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