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Mobile Communications & Networking

We develop next generation wireless technologies for sensing the world, localizing critical assets, and improving the capacity, coverage and scalability of 5G communications networks.

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Department Description

Our 5G cellular network research encompasses development of technologies both on the radio access network (RAN) as well as the mobile packet core (EPC). Within the RAN, technologies such as massive MIMO/MU-MIMO, millimeter-wave access (e.g., transmission at 28 GHz to nomadic/mobile users), dual-connectivity LTE-A solutions and LTE transmission in the unlicensed band are being developed. At the mobile core, our focus is on virtualization, scalability and cloud deployment of EPC network elements, as well as provision of appropriate services at the mobile edge (MEC) and the cloud using service chaining.

Our department is also conducting extensive research on creating end-to-end solutions using multimodal sensing technologies in the retail, public safety and transportation domains.

Featured research project background

Featured Research Projects

End-to-End Solutions Using Wireless Sensing & Communications

In this area of research, we focus on developing solutions for different industry domains based on multimodal sensing and communication technologies, with wireless playing the major role. In the retail world, we have been developing a suite of technologies to be deployed in the storefront to both enable a better customer shopping experience and provide valuable data to the retailer. Our Virtual Shielding technology provides a seamless checkout solution using RFID, which is more cost effective for the retailer and less cumbersome to use for the customer compared to existing solutions. A second RFID-based technology provides a generalized, battery-free, touch-and-gesture-sensing user interface (UI) primitive applicable in retail environments.

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5G Cellular Networks

Our current research considers a range of topics in the domain of 5G Cellular Networks within the RAN and the EPC. In developing technologies for deploying massive MIMO systems, we are exploiting the systems' decorrelation property to avoid unnecessary pre-coder computations, thereby reducing scheduler overhead. In our work with millimeter wave technologies, we are developing solutions using beam tracking technology that do not require explicit channel measurement. We are also creating a novel design framework for MU-MIMO scheduling in millimeter-wave networks that considers hybrid beamforming (analog plus digital precoding) at the transmitter and receiver nodes. We use transmit and receive analog precoding to reduce the CSI feedback overhead. Dual connectivity (DC) is a feature that targets emerging practical HetNet deployments that will consist of non-ideal (higher latency) connections between transmission nodes. It has been recently introduced to the LTE-Advanced standard. DC allows for a user to be simultaneously served by a macro node as well as one other (typically micro or pico) node and requires coarser level coordination among serving nodes. For such a DC-enabled HetNet, we comprehensively analyze the problem of determining an optimal user association and then develop efficient solutions.

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NavigateIO

First responders, a critical lifeline of any society, often find themselves in precarious situations. The ability to track them real-time in unknown indoor environments, significantly contributes to the success of their mission as well as their safety. In this work, we present the design, implementation and evaluation of TrackIO – a novel system that is capable of accurately localizing and tracking mobile responders real-time in large indoor environments. NavigateIO leverages the ultra wide-band (UWB) technology, fusing it with inertial sensors to accomplish this objective directly from outside, without relying on access to any indoor infrastructure. Towards a practical system, NavigateIO incorporates four novel mechanisms in its design that address key challenges to enable tracking responders: (i) who are mobile with potentially non-uniform velocities (e.g. during turns), NavigateIO’s real-world performance reveals that it can track static nodes with a median accuracy of about 1–1.5 m and mobile nodes with a median accuracy of 1.5-2m.

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