2022/03/09 by Mrugen Deshmukh, Deshmukh, Mrugen, Zinan Lin +11 · 1 citation
Computer Science · Engineering · #Advanced MIMO Systems Optimization #Advanced Wireless Network Optimization #FOS: Computer and information sciences #FOS: Electrical engineering #Networking and Internet Architecture (cs.NI) #Signal Processing (eess.SP) #Wireless Networks and Protocols #cs.NI #eess.SP #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2203.04493
arxiv created 2022/03/09 · openalex publication_date 2022/03/09 · arxiv updated 2022/03/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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%.