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A dynamic programming approximation for downlink channel allocation in cognitive femtocell networks

机译：认知毫微微小区网络中下行链路信道分配的动态规划近似

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摘要

Both femtocells and cognitive radio (CR) are envisioned as promising technologies for the NeXt Generation (xG) cellular networks. Cognitive femtocell networks (CogFem) incorporate CR technology into femtocell deployment to reduce its demand for more spectrum bands, thereby improving the spectrum utilization. In this paper, we focus on the channel allocation problem in CogFem, and formulate it as a stochastic dynamic programming (SDP) problem aiming at optimizing the long-term cumulative system throughput of individual femtocells. However, the multi-dimensional state variables resulted from complex exogenous stochastic information make the SDP problem computationally intractable using standard value iteration algorithms. To address this issue, we propose an approximate dynamic programming (ADP) algorithm in pursuit of an approximate solution to the SDP problem. The proposed ADP algorithm relies on an efficient value function approximation (VFA) architecture that we design and a stochastic gradient learning strategy to function, enabling each femtocell to learn and improve its own channel allocation policy. The algorithm is computationally attractive for large-scale downlink channel allocation problems in CogFem since its time complexity does not grow exponentially with the number of femtocells. Simulation results have shown that the proposed ADP algorithm exhibits great advantages: (1) it is feasible for online implementation with a fair rate of convergence and adaptability to both long-term and short-term network dynamics; and (2) it produces high-quality solutions fast, reaching approximately 80% of the upper bounds provided by optimal backward dynamic programming (DP) solutions to a set of deterministic counterparts of the formulated SDP problem.
机译：毫微微小区和认知无线电（CR）都被视为NeXt一代（xG）蜂窝网络的有前途的技术。认知毫微微小区网络（CogFem）将CR技术纳入毫微微小区部署中，以减少其对更多频段的需求，从而提高频谱利用率。在本文中，我们将重点放在CogFem中的信道分配问题上，并将其表述为旨在优化单个毫微微小区的长期累积系统吞吐量的随机动态规划（SDP）问题。然而，由复杂的外部随机信息产生的多维状态变量使SDP问题在使用标准值迭代算法时在计算上难以解决。为了解决此问题，我们提出了一种近似动态规划（ADP）算法，以寻求SDP问题的近似解决方案。拟议的ADP算法依赖于我们设计的高效值函数逼近（VFA）体系结构和随机梯度学习策略来起作用，从而使每个毫微微小区能够学习和改进自己的信道分配策略。该算法对于CogFem中的大规模下行链路信道分配问题在计算上具有吸引力，因为其时间复杂度不会随毫微微小区的数量呈指数增长。仿真结果表明，所提出的ADP算法具有很大的优势：（1）在线实施是可行的，并且具有合理的收敛速度和对长期和短期网络动态的适应性； （2）它快速产生高质量的解决方案，达到最佳反向动态规划（DP）解决方案所提供的上限的80％，该解决方案与公式化SDP问题的一组确定性对等物相对应。

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  • 来源
    《Computer networks》 |2013年第15期|2976-2991|共16页
  • 作者

    Xudong Xiang; Jianxiong Wan; Chuang Lin; Xin Chen;

  • 作者单位

    Department of Computer Science and Technology, University of Science and Technology Beijing (USTB), Xueyuan Road #30, Haidian District, Beijing 100083, China,Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;

    College of Information Engineering, Inner Mongolia University of Technology, Hohhot, Inner Mongolia 010080, China;

    Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;

    Computer School, Beijing Information Science and Technology University, Beijing 100101, China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Cognitive radio; Approximate dynamic programming; Femtocell; Channel allocation;

    机译：认知广播;近似动态编程;Femtocell;渠道分配;

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