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IEEE 802.11ac Link Adaptation Under Mobility

机译:移动性下的IEEE 802.11ac链路自适应

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High fluctuation of signal strength is evident in wireless channel under mobile environment. IEEE 802.11n and IEEE 802.11ac based wireless technologies experience a challenge for selecting link configuration parameters, like number of spatial streams, channel bonding, advanced modulation and coding schemes, frame aggregation etc., dynamically under mobility. Selection of the best possible data rate by tuning link parameters is a challenging issue due to the channel asymmetry in mobile environment. In this paper, we propose an adaptive learning mechanism, HT-MobiRate, for high throughput dynamic link adaptation under mobile scenario. HT-MobiRate is based on Thompson sampling and inspired from multi-armed bandit approach. To the best of our knowledge, this invention is first in the direction of link adaptation for IEEE 802.11ac under mobile environment. We analyze the performance of HT-MobiRate with a practical high throughput wireless testbed built over 6 IEEE 802.11ac supported access points and 20 IEEE 802.11ac clients (both client boards as well as smart-phones). We recognize that it performs considerably better than other competing schemes proposed in the literature for link adaptation in static environment.
机译:在移动环境下,无线信道中信号强度的波动很大。基于IEEE 802.11n和IEEE 802.11ac的无线技术面临着在移动性下动态选择链路配置参数(如空间流数量,信道绑定,高级调制和编码方案,帧聚合等)的挑战。由于移动环境中的信道不对称,通过调整链路参数来选择最佳可能的数据速率是一个具有挑战性的问题。在本文中,我们提出了一种自适应学习机制HT-MobiRate,用于移动场景下的高吞吐量动态链接自适应。 HT-MobiRate基于Thompson采样,并受到多臂匪徒方法的启发。据我们所知,本发明首先是在移动环境下针对IEEE 802.11ac的链路自适应的方向。我们使用一个实用的高吞吐量无线测试平台来分析HT-MobiRate的性能,该测试平台构建在6个支持IEEE 802.11ac的接入点和20个IEEE 802.11ac客户端(客户端板以及智能电话)上。我们认识到,与静态环境中的链路自适应相比,它的性能要比文献中提出的其他竞争方案好得多。

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