Intelligent Feedback Overhead Reduction (iFOR) in Wi-Fi 7 and Beyond

被引:4
作者
Deshmukh, Mrugen [1 ]
Lin, Zinan [1 ]
Lou, Hanqing [1 ]
Kamel, Mahmoud [1 ]
Yang, Rui
Guvenc, Ismail [2 ]
机构
[1] InterDigital Inc, New York, NY USA
[2] North Carolina State Univ, Dept Elect & Comp Eng, Raleigh, NC USA
来源
2022 IEEE 95TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2022-SPRING) | 2022年
关键词
Beamforming; CSI Overhead Reduction; K-means; MIMO; Unsupervised Learning; WLAN; IEEE; 802.11BE;
D O I
10.1109/VTC2022-Spring54318.2022.9860553
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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%.
引用
收藏
页数:5
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