Value-difference learning based mMTC devices access algorithm in multi-cell network
In the massive machine type communication scenario of 5G, the access congestion problem of massive machine type communication devices (mMTCD) in multi-cell network is very important.A double deep Q network with value-difference based exploration (VDBE-DDQN) algorithm was proposed.The algorithm focus...
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| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
Beijing Xintong Media Co., Ltd
2022-06-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022152/ |
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| Summary: | In the massive machine type communication scenario of 5G, the access congestion problem of massive machine type communication devices (mMTCD) in multi-cell network is very important.A double deep Q network with value-difference based exploration (VDBE-DDQN) algorithm was proposed.The algorithm focused on the solution that could reduce the collision when a number of mMTCDs accessed to eNB in multi-cell network.The state transition process of the deep reinforcement learning algorithm was modeled as Markov decision process.Furthermore, the algorithm used a double deep Q network to fit the target state-action value function, and it employed an exploration strategy based on value-difference to adapt the change of the environment, which could take advantage of both current conditions and expected future needs.Moreover, each mMTCD updated the probability of exploration according to the difference between the current value function and the next value function estimated by the network, rather than using the same standard to select the best base eNB for the mMTCD.Simulation results show that the proposed algorithm can effectively improve the access success rate of the system. |
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| ISSN: | 1000-0801 |