Low power machine Learning FPGA/ASIC for Edge Computing
Number of Students: 2
Guides : Chetan Singh Thakur, Mahesh Mehendale
The nodes in WLAN adapt their transmission rates via selecting appropriate modulation and coding schemes (MCS) in response to observed channels conditions and interference. MCS selection has gained even more importance in view of Spatial Reuse (SR) opportunities facilitated by newer WLANs standards (WiFi6 and WiFi7). WLAN radios have largely been using a MCS selection algorithm, Minstrel-HT, which is implemented in the Linux kernel. However, Minstrel-HT’s performance has been found to be unsatisfactory in dense and/or mobile environments. We in this project aim to
- Understand Minstrel-HT and identify the issues in dense and/or mobile environments
- Modify Minstrel-HT to achieve better throughput
The project will entail considerable programming, analytics and working with WiFi nodes.
