My research addresses emerging problems in 5G technology that supports high-speed data services. An unprecedented increase in data traffic and user demands for reliable connections anywhere make this area ripe for new research. This new scale, e.g., real-time VR/AR, two-way video conferencing, and driverless cars, motivates continued innovations for high-bandwidth, ubiquitous, and ultra-reliable communications.

My research questions whether the current architecture truly meets this vision and focuses on redesigning the lower layer architecture to realize this vision. Specifically, I build programmable smart surfaces to create a software-configurable wireless radio environment, which reconfigures itself to guarantee reliability and connectivity of cellular services.

RIS-assisted mmWave Networks

mmWall’s hardware implementation.
mmWall’s hardware implementation.
High frequency, mmWave spectrum has emerged in the 5G era as a key next-generation wireless network enabler, fulfilling user demands for extremely high bandwidth networks. However, this technology faces significant headwinds. First, 5G outdoor coverage is difficult to bring indoors, as exterior building walls block mmWave signal. Currently, operators are forced to offload mmWave traffic onto lower frequencies or off their networks entirely (Wi-Fi) when users move indoors, incurring handover delays and application disruptions. Secondly, people, buildings, and other clutter block mmWave links, forcing data to flow over a much less reliable path for outdoor users. Lastly, mmWave has a narrow beam and works only when the transmitter’s beam is perfectly aligned with the receiver’s beam. Scanning the entire space to find the best alignment incurs a huge overhead.

mmWall is the first reconfigurable intelligent surface that uses metamaterials to refract, reflect, and/or split incoming mmWave beams in desired directions. Its key technical contribution is ensuring reliability for mmWave networks. As a user enters the building, mmWall deployed on the window refracts outdoor signals and precisely steers them toward the user indoors, making outdoor-to-indoor transition seamless. When buildings occlude 5G signals towards an outdoor user, mmWall provides a strong alternative path by reflecting signals towards the user. Furthermore, it enables fast beam search by splitting the incoming beam and simultaneously sweeping multi-armed beams, dramatically improving reliability and spectral efficiency of networks as a whole.

This video summarizes mmWall’s key contributions in 3 minutes:

RIS-assisted Satellite Networks

Various use cases for a satellite smart surface.
Various use cases for a satellite smart surface.
Realizing cost-effective network densification remains another major challenge in 5G, especially in remote and rural areas where deploying fixed infrastructures is not economically viable. Recently, there has been much interest in complementing existing cellular networks with Low Earth Orbit (LEO) satellite networks for coverage extension. LEO networks, however, introduce new challenges: the process of aligning the wireless beam directions between user and satellite is very complex as both the satellite and user moves. Additionally, the constellation of LEO satellites collectively moves over the earth, necessitating a handoff from one satellite to another to serve each user. Such handovers result in increased round-trip-time and a significant drop in throughput.

Wall-E is an electronically tunable surface mounted on a window/skylight, either refracting the satellite link into the home directly or reflecting signals to support outdoor users. Owing to angular reciprocity, Wall-E can simultaneously steer the downlink and uplink beams at the same angle, speeding up the beam alignment process for the uplink via downlink, and vice-versa. Also, Wall-E supports soft handovers by allowing two (or multiple) satellites impinge on the surface at the same time, thus reducing network jitter during handovers. A key obstacle in designing Wall-E was the frequency duplex division (FDD) in LEO networks, which complicates operation due to the use of different frequency sub-bands in the uplink (upper Ku band) and downlink (lower Ku band) directions. Wall-E is the first of its kind to target dual channels in the Ku radio frequency band, allowing ultra-reliable downlink and uplink data transmissions across the globe.

AI-assisted massive IoT Networks

Tomorrow’s massive-scale IoT sensor networks are poised to drive uplink traffic demand, especially in areas of dense deployment. To meet this demand, however, network designers leverage tools that often require accurate estimates of Channel State Information (CSI), which incurs a high over- head and thus reduces network throughput. Furthermore, the overhead generally scales with the number of clients, and so is of special concern in such massive IoT sensor networks. While prior work has used transmissions over one frequency band to predict the channel of another frequency band on the same link, this paper takes the next step in the effort to reduce CSI overhead: predict the CSI of a nearby but distinct link. We propose Cross-Link Channel Prediction (CLCP), a technique that leverages multi-view representation learn- ing to predict the channel response of a large number of users, thereby reducing channel estimation overhead further than previously possible. CLCP’s design is highly practical, exploiting existing transmissions rather than dedicated chan- nel sounding or extra pilot signals. We have implemented CLCP for two different Wi-Fi versions, namely 802.11n and 802.11ax, the latter being the leading candidate for future IoT networks.