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Joint Pushing and Caching for Bandwidth Utilization Maximization in Wireless Networks

机译：无线网络中带宽利用率最大化的联合推送和缓存

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摘要

Joint pushing and caching is recognized as an efficient remedy to the problem of spectrum scarcity incurred by tremendous mobile data traffic. In this paper, we design the optimal joint pushing and caching policy to maximize bandwidth utilization, which is of fundamental importance to mobile telecom carriers. In particular, we consider a multiuser wireless network with multicast opportunities where each user is equipped with a cache of limited size. First, we formulate the stochastic optimization problem as an infinite horizon average cost Markov decision process. By the structural analysis, we show that how the optimal policy achieves a balance between the current transmission cost and the future average transmission cost. We also show that the optimal average transmission cost decreases with the cache sizes, revealing a tradeoff between storage and bandwidth. Then, due to the fact that obtaining a numerical optimal solution suffers the curse of dimensionality and implementing it requires a centralized controller and global system information, we develop a low-complexity decentralized policy (LDP) by using a linear approximation of the value function and transforming challenging discrete optimization problems into difference of convex (DC) problems, which can be efficiently solved by using DC algorithms. We also obtain an upper bound on the performance gap between the average cost of LDP and the minimum average cost, which can be easily evaluated. Next, we propose an online decentralized algorithm to implement the proposed LDP, when priori knowledge of user demand processes is not available. Finally, using numerical results, we demonstrate the advantage of the proposed solutions over some existing designs. The results in this paper offer useful guidelines for designing practical cache-enabled multiuser wireless networks.
机译：联合推送和缓存被认为是对巨大的移动数据流量引起的频谱稀缺问题的有效补救方法。在本文中，我们设计了最佳的联合推送和缓存策略，以最大程度地利用带宽，这对于移动电信运营商至关重要。特别地，我们考虑具有多播机会的多用户无线网络，其中每个用户都配备了有限大小的缓存。首先，我们将随机优化问题公式化为无限远景平均成本马尔可夫决策过程。通过结构分析，我们表明最优策略如何在当前传输成本和未来平均传输成本之间取得平衡。我们还表明，最佳平均传输成本随缓存大小的增加而降低，从而揭示了存储和带宽之间的权衡。然后，由于要获得数值最优解会遭受维数的困扰，并且要实现它需要集中的控制器和全局系统信息，因此，我们使用值函数的线性逼近来开发低复杂度的分散策略（LDP），并且将具有挑战性的离散优化问题转换为凸（DC）问题的差异，可以使用DC算法有效解决。我们还获得了LDP的平均成本与最低平均成本之间的性能差距的上限，可以轻松地对其进行评估。接下来，当用户需求过程的先验知识不可用时，我们提出一种在线分散算法来实现所提出的LDP。最后，使用数值结果，我们证明了所提出的解决方案相对于某些现有设计的优势。本文的结果为设计实用的启用缓存的多用户无线网络提供了有用的指导。

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  • 来源
    《IEEE Transactions on Communications》 |2019年第1期|391-404|共14页
  • 作者

    Sun Yaping; Cui Ying; Liu Hui;

  • 作者单位

    Shanghai Jiao Tong Univ, Dept Elect Engn, Shanghai Inst Adv Commun & Data Sci, Inst Wireless Commun Technol, Shanghai 200240, Peoples R China;

    Shanghai Jiao Tong Univ, Dept Elect Engn, Shanghai Inst Adv Commun & Data Sci, Inst Wireless Commun Technol, Shanghai 200240, Peoples R China;

    Shanghai Jiao Tong Univ, Dept Elect Engn, Shanghai 200240, Peoples R China|Univ Washington, Dept Elect Engn, Seattle, WA 98195 USA|Silkwave Holdings, Hong Kong, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Pushing; caching; multicast; Markov decision process; DC problem; Q-learning; bandwidth utilization;

    机译：推送;缓存;组播;Markov决策过程;DC问题;Q学习;带宽利用率;

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