Suyash Pradhan

Suyash Pradhan

Ph.D. student in Computer Engineering at Northeastern University

Publications

COPILOT: Cooperative Perception using Lidar for Handoffs between Road Side Units

Published in IEEE INFOCOM 2024 - Vancouver, 2024

This paper presents COPILOT, a ML-based approach that allows vehicles requiring ubiquitous high bandwidth connectivity to identify the most suitable road side units (RSUs) through proactive handoffs. By cooperatively exchanging the data obtained from local 3D Lidar point clouds within adjacent vehicles and with coarse knowledge of their relative positions, COPILOT identifies transient blockages to all candidate RSUs along the path under study. Such cooperative perception is critical for choosing RSUs with highly directional links required for mmWave bands, which majorly degrade in the absence of LOS. COPILOT proposes three modules that operate in an inter-connected manner: (i) As an alternative to sending raw Lidar point clouds, it extracts and transmits low-dimensional intermediate features to lower the overhead of inter-vehicle messaging; (ii) It utilizes an attention-mechanism to place greater emphasis on data collected from specific vehicles, as opposed to nearest neighbor and distance-based selection schemes, and (iii) it experimentally validates the outcomes using an outdoor testbed composed of an autonomous car and Talon AD7200 60GHz routers emulating the RSUs, accompanied by the public release of the datasets. Results reveal COPILOT yields upto 69.8% and 20.42% improvement in latency and throughput compared to traditional reactive handoffs for mmWave networks, respectively.

Recommended citation: S. Pradhan, D. Roy, B. Salehi, and K. R. Chowdhury, “COPILOT: Cooperative Perception using Lidar for Handoffs between Road Side Units,” IEEE Conference on Computer Communications (INFOCOM), May. 2024 https://ieeexplore.ieee.org/document/10621174

Digital Twins for Maintaining QoS in Programmable Vehicular Networks

Published in IEEE Network Magazine, 2023

This paper is a vision for future V2X networks equipped with programmable base stations and Reconfigurble Intelligent Surfaces (RIS) to mitigate the frequent blockages in dynamic vehicular networks. In our method, a LiDAR sensor mounted autonomous car detects obstacles between itself and the base station (BS) it is connected to during its voyage in order to predict NLOS situations, which are then reported to the BS, where the digital twin is deployed.

Recommended citation: U. Demir, S. Pradhan, R. Kumahia, D. Roy, S. Ioannidis, and K. Chowdhury, "Digital Twins for Maintaining QoS in Programmable Vehicular Networks," in IEEE Network Magazine, May 2023. https://genesys-lab.org/papers/Digital_Twins_2023-.pdf

Multiverse at the Edge: Interacting Real World and Digital Twins for Wireless Beamforming

Published in IEEE Transactions on Networking, 2010

Creating a digital world that closely mimics the real world with its many complex interactions and outcomes is possible today through advanced emulation software and ubiquitous computing power. Such a software-based emulation of an entity that exists in the real world is called a ‘digital twin’. In this paper, we consider a twin of a wireless millimeter-wave band radio that is mounted on a vehicle and show how it speeds up directional beam selection in mobile environments. To achieve this, we go beyond instantiating a single twin and propose the ‘Multiverse’ paradigm, with several possible digital twins attempting to capture the real world at different levels of fidelity. Towards this goal, this paper describes (i) a decision strategy at the vehicle that determines which twin must be used given the latency limitation, and (ii) a self-learning scheme that uses the Multiverse-guided beam outcomes to enhance DL-based decisionmaking in the real world over time. Our work is distinguished from prior works as follows: First, we use a publicly available RF dataset collected from an autonomous car for creating different twins. Second, we present a framework with continuous interaction between the real world and Multiverse of twins at the edge, as opposed to a one-time emulation that is completed prior to actual deployment. Results reveal that Multiverse offers up to 79.43% and 85.22% top-10 beam selection accuracy for LOS and NLOS scenarios, respectively. Moreover, we observe 67.70−90.79% improvement in beam selection time compared to 802.11ad standard and 5G-NR standards

Recommended citation: B. Salehi, U. Demir, D. Roy, S. Pradhan, J. Dy, S. Ioannidis, and K. R. Chowdhury, "Multiverse at the Edge: Interacting Real World and Digital Twins for Wireless Beamforming", IEEE Transactions on Networking, accepted, March 2024. http://academicpages.github.io/files/paper2.pdf