This research group focusses primarily on PHY and MAC layer issues of wireless networks. Current focus of his research group is on Millimeter wave communications for 5G wireless, waveforms for 5G and anti-jamming techniques for LTE-A cellular wireless networks. "Learning for Wireless Networks" is another exciting research topic that the research group started working on in the recent past. Regression and/or classification framework developed in the machine learning community might help designing more efficient wireless receivers. With regard to MAC layer, learning the dynamics of a multi-user network (either through reinforcement learning or supervised learning) can help wireless engineers in achieving optimal radio resource management. Srinivas's research group is highly enthusiastic and aspires to learn the state-of-the-art learning concepts in order to learn how to communicate better.

• Wireless Communication
• Nano-Communication Networks
• Optical communication: Non-traditional optical fibers, FBG based optical filters
• Deep Learning/Machine Learning-based Wireless Communication Systems Design
• Intelligent Signal Processing, Machine Learning, Image Processing, Internet-of-Things, Edge Computing, and Biomedical Instrumentation
• Development of the framework for joint sensing and communication technology for the industrial scenarios,
• Physical layer design for the Ultra-Reliable Low-Latency Communication (URLLC),
• Control communication co-design for tactile and Ambient internets for 6G and 7G applications,
• Interference Modeling in LoRa and low power wide area network (LPWA) systems,
• Small World Models for Development of Wireless Sensor Network Services 
• Cyber Physical Systems (CPS), Internet of Things (IoT)