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Resource Allocation for Multi-service NOMA System Based on Deep Reinforcement Learning

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Human Centered Computing (HCC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13795))

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Abstract

In this paper, a resource allocation algorithm based on deep reinforcement learning (DRL) is proposed to solve the problem of co-existing enhanced mobile broadband (eMBB) slices and ultrareliable low latency communication (URLLC) slices based on Non-orthogonal Multiple Access (NOMA) in downlink network scenarios. Double Deep Q network (DDQN) is designed to output subcarrier and power allocation simultaneously. In addition, expert data is added in the training process to accelerate network convergence, and our goal is to optimize the spectral efficiency of the system. Simulation results show that compared with the baseline based on heuristic joint user pairing and power allocation algorithm [1] and Orthogonal Multiple Access (OMA), proposed algorithm can achieve higher spectral efficiency and ensure isolation between slices.

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References

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Acknowledgment

This work was supported by Headquarters Science and Technology Project of State Grid Corporation of China: Research and Application of Optimized Evolution Technology of Wireless Private Network for Multi-service Ubiquitous Access (Grant No. 5700-202019174A-0-0-00).

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Correspondence to Yong Zhang .

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Zhang, Z., Zheng, W., Shao, W., Zhang, Y., Guo, D. (2022). Resource Allocation for Multi-service NOMA System Based on Deep Reinforcement Learning. In: Zu, Q., Tang, Y., Mladenovic, V., Naseer, A., Wan, J. (eds) Human Centered Computing. HCC 2021. Lecture Notes in Computer Science, vol 13795. Springer, Cham. https://doi.org/10.1007/978-3-031-23741-6_20

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  • DOI: https://doi.org/10.1007/978-3-031-23741-6_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23740-9

  • Online ISBN: 978-3-031-23741-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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