In collaboration with Next Gen Research Lab at Quinnipiac University, We are working on Emerging applications with ever more stringent performance requirements and advanced systems with increasing complexity will continue driving the need for novel networking and security solutions. Our lab focuses on developing novel solutions for networking and security challenges in increasingly complex systems. Some of our ongoing projects:

Key Enabling Technologies for 5G, Beyond 5G and 6G Networks

We are exploring the key enabling technologies for B5G and 6G networks and its implementaion challenges. The goal is to improve data rate, latency, reliability, security, and energy efficiency.

• Explore AI, ML, and quantum communication for next-generation networks.


• Design signal detection, channel estimation, channel modeling, user scheduling algorithms, and frameworks for B5G and 6G networks.


• Explore Ultra-Massive MIMO, millimeter wave, and terahertz-wave bands.

AI and ML for Cybersecurity Offense and Defense

Our work is focused on exploring ML and AI techniques for phishing detection, automated attack and defense strategies, malware detection, intrusion detection, and secure authentication.

• Investigate effectiveness of ML algortihms and LLMs for phishing email and phishing URL detection.


• Explore ML and LLMs for phishing email and phishing URL detection.


• Explore AI and ML for malware, threat, intrusion detection, network traffic analysis, and secure authentication.

Massive MIMO and mmWave system for Next-Generation Networks

Our work on massive MIMO is focused on overcoming several implementation issues for the massive MIMO technology — improving signal detection, design of robust user scheduling algorithms, mitigation of pilot contamination, and hardware architecture designs.

• Develop low complex and efficient algorithms and their hardware designs to address current challenges on signal detection, precoding, channel estimation, user scheduling, and pilot contamination.


• Investigate the use of Massive MIMO for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. The goal is to allow efficient and faster exchange of data while mitigating interference.


• Explore the use of machine learning and deep learning algorithms for massive MIMO channel estimation to predict statistical channel characteristics for enhancing physical layer security.