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Mobility-Driven Association Policies for Dense Wireless Networks
Journal of the Indian Institute of Science ( IF 2.3 ) Pub Date : 2020-04-01 , DOI: 10.1007/s41745-020-00171-8
Pranav Madadi , François Baccelli , Gustavo de Veciana

The primary approach to increase coverage and capacity in infrastructure-based wireless networks is densification. Densification, however, presents a major challenge when serving mobile users, which is the overhead associated with the increased rate of base station handovers. Assuming a prior knowledge of a mobile’s trajectory and base stations’ locations, we formulate the problem of determining the sequence of handovers that optimize the trade-off between the mobile user’s perceived throughput and handover overheads in noise-limited environments. Under appropriate conditions, we show that the problem reduces to determining a maximum weight path in a directed acyclic graph induced by the mobile user’s trajectory. In practice, knowledge of a mobiles’ trajectory may be limited and one may also want to limit the handover complexity, whence we propose a new class of mobility-driven greedy association policies. The greedy policies are based on defining a handover support set, which constrains both the possible handovers and the complexity/information requirements. In a setting where base station locations follow a Poisson point process, we show that the performance of such handover processes follows a continuous-time Markov process which can be analyzed using complex variable techniques. This enables one to explore the optimization size/shape of the handover support set for mobility-driven greedy handover strategies and their relative performance compared to traditional association policies.

中文翻译:

用于密集无线网络的移动驱动关联策略

在基于基础设施的无线网络中增加覆盖范围和容量的主要方法是密集化。然而,在为移动用户提供服务时,密集化提出了一个重大挑战,这是与基站切换速率增加相关的开销。假设有移动轨迹和基站位置的先验知识,我们制定了确定切换顺序的问题,以优化移动用户感知吞吐量和噪声受限环境中的切换开销之间的权衡。在适当的条件下,我们表明问题简化为确定移动用户轨迹引起的有向无环图中的最大权重路径。在实践中,对移动设备轨迹的了解可能是有限的,人们可能还想限制切换的复杂性,因此我们提出了一类新的移动驱动的贪婪关联策略。贪婪策略基于定义切换支持集,它限制可能的切换和复杂性/信息要求。在基站位置遵循泊松点过程的设置中,我们表明此类切换过程的性能遵循连续时间马尔可夫过程,可以使用复变量技术进行分析。这使人们能够探索移动驱动的贪婪切换策略的切换支持集的优化大小/形状及其与传统关联策略相比的相对性能。这限制了可能的切换和复杂性/信息要求。在基站位置遵循泊松点过程的设置中,我们表明此类切换过程的性能遵循连续时间马尔可夫过程,可以使用复变量技术进行分析。这使人们能够探索移动驱动的贪婪切换策略的切换支持集的优化大小/形状及其与传统关联策略相比的相对性能。这限制了可能的切换和复杂性/信息要求。在基站位置遵循泊松点过程的设置中,我们表明此类切换过程的性能遵循连续时间马尔可夫过程,可以使用复变量技术进行分析。这使人们能够探索移动驱动的贪婪切换策略的切换支持集的优化大小/形状及其与传统关联策略相比的相对性能。
更新日期:2020-04-01
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