6 papers
Machine Learning Decoder for 5G NR PUCCH Format 0
Anil Kumar Yerrapragada, Jeeva Keshav S, Ankit Gautam +1
5G cellular systems depend on the timely exchange of feedback control information between the user equipment and the base station. Proper decoding of this control information is ne…
UCINet0: A Machine Learning based Receiver for 5G NR PUCCH Format 0
Jeeva Keshav Sattianarayanin, Anil Kumar Yerrapragada, Radha Krishna Ganti
Accurate decoding of Uplink Control Information (UCI) on the Physical Uplink Control Channel (PUCCH) is essential for enabling 5G wireless links. This paper explores an AI/ML-based…
Study on Downlink CSI compression: Are Neural Networks the Only Solution?
K. Sai Praneeth, Anil Kumar Yerrapragada, Achyuth Sagireddi +2
Massive Multi Input Multi Output (MIMO) systems enable higher data rates in the downlink (DL) with spatial multiplexing achieved by forming narrow beams. The higher DL data rates a…
Physical Layer Design for Ambient IoT
Rohit Singh, Anil Kumar Yerrapragada, Radha Krishna Ganti
There is a growing demand for ultra low power and ultra low complexity devices for applications which require maintenance-free and battery-less operation. One way to serve such app…
A Machine Learning based Hybrid Receiver for 5G NR PRACH
Rohit Singh, Anil Kumar Yerrapragada, Radha Krishna Ganti
Random Access is a critical procedure using which a User Equipment (UE) identifies itself to a Base Station (BS). Random Access starts with the UE transmitting a random preamble on…
On the Application of Deep Learning for Precise Indoor Positioning in 6G
Sai Prasanth Kotturi, Anil Kumar Yerrapragada, Sai Prasad +1
Accurate localization in indoor environments is a challenge due to the Non Line of Sight (NLoS) nature of the signaling. In this paper, we explore the use of AI/ML techniques for p…