Exploring Smart Pilot for Wireless Rate Adaptation
Exploring Smart Pilot for Wireless Rate Adaptation
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DOI：
10.1109/twc.2016.2542807
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发表时间：
2016-03
影响因子：
10.4
通讯作者：
Lu Wang;Xiaoke Qi;Jiang Xiao;Kaishun Wu;M. Hamdi;Qian Zhang
中科院分区：
文献类型：
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作者：
Lu Wang;Xiaoke Qi;Jiang Xiao;Kaishun Wu;M. Hamdi;Qian Zhang
速率自适应是当今无线标准中的重要组成部分，它有助于接近信道容量并最大化吞吐量。然而，如何在波动的信道中估计最佳数据速率仍然是一个很大的关注。先前的WISTOM利用PHY层信息进行速率估计，包括像SoftPHY提示的置信度信息和信道状态信息（CSI）测量。然而，当经历快速时变和频率选择性衰落信道时，上述度量可能不准确。原因源于没有足够的成本有效的导频的事实，导频是插入在分组中用于信道估计的预先已知的符号。在本文中，我们观察到，通过挖掘PHY层解码器和上层协议报头，可以利用具有高置信度的更可靠的数据比特。这些数据比特，称为智能导频，可以用于成本有效地校准信道估计测量。基于校准的估计，我们进一步提出了一种新的贪婪速率选择算法来跟踪最佳数据速率，成功地避免了传统802.11a/g和802.11n MIMO系统中的深衰落子载波的影响。在GNU无线电测试平台上的实验表明，SmartPilot能够快速跟踪链路方差，信道估计精度提高了87%。此外，跟踪驱动的仿真表明，贪婪速率选择算法预测的数据速率一样好的802.11标准的最佳速率自适应算法。
Rate adaptation is an essential component in today's wireless standards, which help approach the channel capacity and maximize the throughput. However, how to estimate the optimal data rate in a fluctuated channel remains of great concern. Previous wisdoms leverage PHY layer information for rate estimation, including confidence information like SoftPHY hints, and channel state information (CSI) measurements. However, when experiencing rapid time varying and frequency selective fading channel, the above metrics can be inaccurate. The reason roots from the fact that there are not enough cost-efficient pilots, which are pre-known symbols inserted in a packet for channel estimation. In this paper, we observe that by digging into both PHY layer decoder and upper layer protocol headers, more reliable data bits with high confidence level can be exploited. These data bits, termed smart pilot, can be used to calibrate the channel estimation measurements cost-efficiently. Based on the calibrated estimation, we further propose a novel greedy rate selection algorithm to track the optimal data rate, which successfully avoids the impact of deep fading subcarriers in both legacy 802.11a/g and 802.11n MIMO systems. Our experiments on GNU radio testbed show that SmartPilot quickly tracks the link variance, and improve the channel estimation accuracy by 87%. Furthermore, the trace driven simulation reveals that greedy rate selection algorithm predicts the data rate as good as the optimal rate adaptation algorithms for 802.11 standards.