Spectrum Sharing between Cellular and Wi-Fi Networks based on Deep Reinforcement Learning
Spectrum Sharing between Cellular and Wi-Fi Networks based on Deep Reinforcement Learning
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DOI：
10.5121/ijcnc.2023.15108
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发表时间：
2023-01
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影响因子：
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通讯作者：
Bayarmaa Ragchaa;K. Kinoshita
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文献类型：
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作者：
Bayarmaa Ragchaa;K. Kinoshita
近来，移动的业务快速增长，并且无线网络中的频谱资源变得稀缺。因此，无线网络容量将无法满足业务需求。为了解决这个问题，在未经许可的频谱中使用蜂窝系统成为一种有效的解决方案。在这种情况下，蜂窝系统需要与Wi-Fi和其他系统共存。为此，我们提出了一种基于DRL方法的Wi-Fi AP和蜂窝NB的高效信道分配方法。为了训练DDQN模型，我们实现了一个仿真器作为在密集部署的NB和AP在无线异构网络中的频谱共享的环境。我们提出的DDQN算法提高了平均吞吐量从25.5%到48.7%，在不同的用户到达率相比，传统的方法。我们评估的泛化性能的训练代理，以确认在不同的用户到达率下的平均吞吐量的信道分配效率。
Recently, mobile traffic is growing rapidly and spectrum resources are becoming scarce in wireless networks. Due to this, the wireless network capacity will not meet the traffic demand. To address this problem, using cellular systems in an unlicensed spectrum emerged as an effective solution. In this case, cellular systems need to coexist with Wi-Fi and other systems. For that, we propose an efficient channel assignment method for Wi-Fi AP and cellular NB, based on the DRL method. To train the DDQN model, we implement an emulator as an environment for spectrum sharing in densely deployed NB and APs in wireless heterogeneous networks. Our proposed DDQN algorithm improves the average throughput from 25.5% to 48.7% in different user arrival rates compared to the conventional method. We evaluated the generalization performance of the trained agent, to confirm channel allocation efficiency in terms of average throughput under the different user arrival rates.