Spectrum Allocation Based on Deep Reinforcement Learning in mmWave Integrated Access and Backhaul Network 


Vol. 48,  No. 9, pp. 1057-1063, Sep.  2023
10.7840/kics.2023.48.9.1057


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  Abstract

As mmWave has been utilized on fifth-generation mobile communication networks for a high data rate, network densification is in progress. Under the circumstances, interest in an integrated access and backhaul network is increasing. The integrated access and backhaul network replaces a traditional wired backhaul link with a wireless backhaul link reducing financial burden. However, inter-link interference gets harsher as access and backhaul link shares the same bandwidth. Consequently, spectrum allocation optimization is required for network efficiency. In this paper, we formulate a problem maximizing network capacity through backhaul spectrum allocation when the access spectrum is allocated uniformly to users. Then, propose deep reinforcement learning-based backhaul spectrum strategy which can solve the problem.

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[IEEE Style]

J. Park, H. Jin, S. Jeong, S. C. Kim, "Spectrum Allocation Based on Deep Reinforcement Learning in mmWave Integrated Access and Backhaul Network," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 9, pp. 1057-1063, 2023. DOI: 10.7840/kics.2023.48.9.1057.

[ACM Style]

Jeonghun Park, Heetae Jin, Sumin Jeong, and Suk Chan Kim. 2023. Spectrum Allocation Based on Deep Reinforcement Learning in mmWave Integrated Access and Backhaul Network. The Journal of Korean Institute of Communications and Information Sciences, 48, 9, (2023), 1057-1063. DOI: 10.7840/kics.2023.48.9.1057.

[KICS Style]

Jeonghun Park, Heetae Jin, Sumin Jeong, Suk Chan Kim, "Spectrum Allocation Based on Deep Reinforcement Learning in mmWave Integrated Access and Backhaul Network," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 9, pp. 1057-1063, 9. 2023. (https://doi.org/10.7840/kics.2023.48.9.1057)
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