Intelligent Feedback Overhead Reduction (iFOR) in Wi-Fi 7 and Beyond
Intelligent Feedback Overhead Reduction (iFOR) in Wi-Fi 7 and Beyond
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DOI：
10.1109/vtc2022-spring54318.2022.9860553
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
M. Deshmukh;Zinan Lin;H. Lou;Mahmoud I. Kamel;Rui Yang;Ismail Güvenç
中科院分区：
文献类型：
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作者：
M. Deshmukh;Zinan Lin;H. Lou;Mahmoud I. Kamel;Rui Yang;Ismail Güvenç
基于IEEE 802.11标准的无线局域网（WLAN）或Wi-Fi网络对于在当今世界中提供互联网接入至关重要。Wi-Fi网络中对高数据速率的需求不断增长，导致802.11标准取得了一些进步。支持具有在更宽带宽上操作的更多数量的发射天线的MIMO传输是用于达到更高吞吐量的关键能力之一。然而，由于更高数量的发射天线而导致的探测反馈开销的增加可能显著抑制吞吐量增益。在本文中，我们开发了一种基于无监督学习的方法来减少Wi-Fi MIMO链路中的探测持续时间。仿真结果表明，我们的方法使用的比特数仅为现有反馈机制所需比特数的8%，系统吞吐量可提高52%。
The IEEE 802.11 standard based wireless local area networks (WLANs) or Wi-Fi networks are critical to provide internet access in today’s world. The increasing demand for high data rate in Wi-Fi networks has led to several advancements in the 802.11 standard. Supporting MIMO transmissions with higher number of transmit antennas operating on wider bandwidths is one of the key capabilities for reaching higher throughput. However, the increase in sounding feedback overhead due to higher number of transmit antennas may significantly curb the throughput gain. In this paper, we develop an unsupervised learning-based method to reduce the sounding duration in a Wi-Fi MIMO link. Simulation results show that our method uses approximately only 8% of the number of bits required by the existing feedback mechanism and it can boost the system throughput by up to 52%.