Today, the GHz-scale unlicensed spectrum coupled with directional antennas can support Gbps data rates in point-to-point links.
Next-generation devices aim to scale to dense user populations via the concurrent transmission of multiple independent data streams, i.e., downlink and uplink multi-user MIMO.
However, while the utilization of high frequencies and wide spectrum represents a significant advancement in achieving higher data rates, the challenge of ensuring robustness in the presence of human blockage and client mobility remains a significant obstacle for highly directional links.
To address these challenges, we are actively engaged in the theoretical and experimental development of novel cross-layer techniques.
These techniques aim to enable adaptable and resilient mm-Wave networks that can effectively scale to accommodate dense user populations.
Scaling spectrum access towards terahertz will provide a key ingredient to realize the sixth generation and beyond of wireless networks.
This work develops the foundations for scaling wireless networks to Terabits per second (Tb/sec) via coupling new advances in Terahertz-scale (THz) communication and sensing.
Indeed, different propagation characteristics, wide bandwidth, directionality, and lack of real-time adaptation prevent today’s THz wireless technologies from efficiently scaling up and supporting mobile users.
This work will tackle the fundamental barriers of mobile THz communication and sensing by designing and building practical, scalable, mobile THz wireless technologies for next-generation communications systems.
Our research focuses on developing cutting-edge technologies that harness the power of intelligent surfaces to revolutionize wireless networks operating in the millimeter-wave (mmWave) and terahertz (THz) frequency ranges.
These frequencies hold immense potential for ultra-high-speed connectivity and support for next-generation applications, including industrial automation, augmented and virtual reality, and cyber-physical systems.
Intelligent surfaces serve as intelligent reflectors, dynamically modifying wireless channel properties to enhance coverage, reliability, and security. Through cross-layer PHY/MAC protocol design and rigorous experimental evaluations, we dynamically reprogram channel properties, ensuring optimal transmission conditions and mitigating challenges posed by human blockage and client mobility.
AI and ML are pivotal in managing the complex demands of 5G and beyond, ensuring network performance aligns with the evolving needs of data-intensive applications.
By harnessing these technologies, network operators attain unparalleled levels of automation, scalability, and intelligence in network management.
Our research is dedicated to developing AI and ML frameworks for autonomously analyzing extensive network data and creating intelligent systems that optimize performance, adapt to dynamic environments, and enhance user experiences.