基于深度无监督学习的多小区蜂窝网资源分配方法
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作者单位:

1.齐齐哈尔大学;2.哈尔滨商业大学

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中图分类号:

TP273

基金项目:

国家自然科学基金项目(61872204,71803095);全国统计科学研究项目(2020LY074);黑龙江省自然科学基金联合引导项目(LH2019F038);黑龙江省省属高校青年创新人才基本科研业务专项(135309340)


Deep Unsupervised Learning based Resource Allocation Method for Multi-cell Cellular Networks
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Affiliation:

Qiqihar University

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    摘要:

    针对多小区蜂窝网络资源分配所要求的低能耗、高速率和低延时问题,提出了一种基于深度无监督学习的多小区蜂窝网络资源分配方法.首先构建基于无监督学习的深度功率控制神经网络,通过约束处理输出优化的信道功率控制方案以最大化能量效率的期望;然后构建基于无监督学习的深度信道分配神经网络,通过约束处理输出优化的信道分配方案,并联合前期训练好的深度功率控制神经网络拟合输出优化的信道功率,进一步优化能量效率的期望.仿真结果表明,所提出的方法在保证低计算时延的同时可获得优于其它算法的能量效率和传输速率.

    Abstract:

    Aiming at the problem of low energy consumption, high speed, and low latency required for resource allocation for multi-cell cellular networks, a deep unsupervised learning based resource allocation method is proposed. Firstly, an unsupervised learning based deep power control neural network is constructed to output optimized channel power control scheme by constraint handling to maximize the expectation of energy efficiency. Then, an unsupervised learning based deep channel allocation neural network is constructed to output optimized channel allocation scheme by constraint handling, and the unsupervised learning based deep power control neural network trained well previously is combined to fit and output the optimized channel power control scheme to further optimize the expectation of energy efficiency. The simulation results show that the proposed method can obtain better transmission rate and energy efficiency than other algorithms while ensuring low computational latency.

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历史
  • 收稿日期:2020-11-16
  • 最后修改日期:2021-07-06
  • 录用日期:2021-07-06
  • 在线发布日期: 2021-08-01
  • 出版日期: