Abstract
Recently, with the development of Internet of Things (IoT) technology, the devices with the various features of traffic and mobility are increasing exponentially, and now the existing traditional resource allocation algorithms are becoming more and more difficult to meet the ever-increasing demand for terminal transmission. Aiming at the problem of radio resource fragment for complex access users of existing traditional algorithms, this paper proposes a dynamic scheduling algorithm based on Double Deep Q-learning Network(DDQN). At the same time, we design and simulate the NPUSCH transmission environment of the NB-IoT as the interactive environment of the agent. After training iterations, the resource utilization rate of the dynamic scheduling algorithm based on DDQN can be stabilized above 81%, which is better than traditional scheduling algorithms.
L. Ning—Sponsored by the Project of Cooperation between SZTU and Enterprise (No. 2021010802015 and No. 20213108010030) and Experimental Equipment Development Foundation from SZTU (No. 20214027010032).
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References
Haridas, A., Rao, V.S., Prasad, R.V., Sarkar, C.: Opportunities and challenges in using energy-harvesting for NB-IoT. SIGBED Rev. 15(5), 7–13 (2018)
Feltrin, L., Tsoukaneri, G., Condoluci, M., Buratti, C., Mahmoodi, T., Dohler, M., Verdone, R.: Narrowband IoT: a survey on downlink and uplink perspectives. IEEE Wireless Commun. 26(1), 78–86 (2019)
Sinche, S., Raposo, D., Armando, N., Rodrigues, A., Boavida, F., Pereira, V., Silva, J.S.: A Survey of IoT management protocols and frameworks. IEEE Commun. Surv. Tutorials 22(2), 1168–1190 (2020)
Yang, N., Zhang, H., Long, K., Hsieh, H., Liu, J.: Deep neural network for resource management in NOMA networks. IEEE Trans. Veh. Technol. 69(1), 876–886 (2020)
Wu, M., Huang, W., Sun, K., Zhang, H.: A DQN-based handover management for SDN-enabled ultra-dense networks. In: 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall), pp. 1–6, November 2020. ISSN: 2577–2465
Yang, N., Zhang, H., Berry, R.: Partially observable multi-agent deep reinforcement learning for cognitive resource management. In: GLOBECOM 2020–2020 IEEE Global Communications Conference, pp. 1–6, December 2020. ISSN: 2576–6813
Zhang, Y., Wang, X., Xu, Y.: Energy-efficient resource allocation in uplink NOMA systems with deep reinforcement learning. In: 2019 11th International Conference on Wireless Communications and Signal Processing (WCSP), pp. 1–6, October 2019. ISSN: 2472–7628
Luong, N.C., et al.: Applications of deep reinforcement learning in communications and networking: a survey. IEEE Commun. Surv. Tutorials 21(4), 3133–3174 (2019)
Sultania, A.K., Delgado, C., Famaey, J.: Implementation of NB-IoT power saving schemes in ns-3. In: Proceedings of the 2019 Workshop on Next-Generation Wireless with ns-3, WNGW 2019, pp. 5–8, New York, June 2019. Association for Computing Machinery
Miao, Y., Li, W., Tian, D., Hossain, M.S., Alhamid, M.F.: Narrowband internet of things: simulation and modeling. IEEE Internet Things J. 5(4), 2304–2314 (2018)
Jiang, N., Deng, Y., Nallanathan, A., Chambers, J.A.: Reinforcement learning for real-time optimization in NB-IoT networks. IEEE J. Select. Areas Commun. 37(6), 1424–1440 (2019)
Zhang, R., Zhong, H., Zheng, T., Ning, L.: Trajectory mining-based city-level mobility model for 5G NB-IoT networks. Wireless Commun. Mobile Comput. 2021, 1–12 (2021)
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Zhong, H., Zhang, R., Jin, F., Ning, L. (2022). Optimization of NB-IoT Uplink Resource Allocation via Double Deep Q-Learning. In: Liang, Q., Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2021. Lecture Notes in Electrical Engineering, vol 878. Springer, Singapore. https://doi.org/10.1007/978-981-19-0390-8_96
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DOI: https://doi.org/10.1007/978-981-19-0390-8_96
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