Subhramoy Mohanti

InterDigital (United States)

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Kaushik Chowdhury

The University of Texas at Austin

Debashri Roy

The University of Texas at Arlington

Gökhan Seçinti

Istanbul Technical University

M. Yousof Naderi

Northeastern University

Carlos Bocanegra

Northeastern University

Kubra Alemdar

Northeastern University

Marco Di Felice

University of Bologna

Shweta Shrivastava

Aryabhatta Knowledge University

Kurt Yoder

Chinenye Tassie

Northeastern University

Cooperative Institutions

Northeastern University

Universidad del Noreste

Boston University

University of Bologna

Sapienza University of Rome

Centre National de la Recherche Scientifique

Genesys (United States)

Istanbul Technical University

Intel (United States)

Intel (United Kingdom)

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

AirID: Injecting a Custom RF Fingerprint for Enhanced UAV Identification using Deep Learning

GLOBECOM 2022 - 2022 IEEE Global Communications Conference (2020)

Subhramoy Mohanti Nasim Soltani Kunal Sankhe Dheryta Jaisinghani Marco Di Felice

We propose a framework called AirID that identifies friendly/authorized UAVs using RF signals emitted by radios mounted on them through a technique called as RF fingerprinting. Our main contribution is a method of intentionally inserting `signatures' in the transmitted I/Q samples from each UAV, which are detected through a deep convolutional neural network (CNN) at the physical layer, without affecting the ongoing UAV data communication process. Specifically, AirID addresses the challenge of how to overcome the channel-induced perturbations in the transmitted signal that lowers identification accuracy. AirID is implemented using Ettus B200mini Software Defined Radios (SDRs) that serve as both static ground UAV identifiers, as well as mounted on DJI Matrice M100 UAVs to perform the identification collaboratively as an aerial swarm. AirID tackles the well-known problem of low RF fingerprinting accuracy in `train on one day test on another day' conditions as the aerial environment is constantly changing. Results reveal 98% identification accuracy for authorized UAVs, while maintaining a stable communication BER of 10 ^-4 for the evaluated cases.

Identification

Signature (topology)

SIGNAL (programming language)

10.1109/globecom42002.2020.9322561

Cite

Citations (28)

L-NORM: Learning and Network Orchestration at the Edge for Robot Connectivity and Mobility in Factory Floor Environments

IEEE Transactions on Mobile Computing (2023)

Subhramoy Mohanti Debashri Roy Mark Eisen Dave Cavalcanti Kaushik Chowdhury

Robotic factory floors will revolutionize the future of manufacturing and the service industry by automating tasks. However, to fully supplement human effort, these robots will need low-latency, reliable connectivity throughout the work zone through links established by wireless access points (APs). This will allow the robot to assuredly respond to programming directives that rely on the real-time relaying of robot-generated sensor data to the Mobile Edge Computing (MEC) server. In this paper, we propose L-NORM, a multi-AP and multi-robot coordination framework, as a multi-tiered solution for such autonomous edge networks. First, multi-robot motion planning through reinforcement learning occurs at the MEC, using as input multi-modal robot sensor data. Second, multi-AP resource orchestration is performed using another reinforcement learning-based method that maps a subset of available APs to each robot toward meeting their sensor data delivery requirements. Furthermore, we suggest diversity combination of uplink channels with the 802.11ax scheduled access mode that will (i) support high reliability of multi-robot uplink sensor packets and (ii) enable multi-AP coordination, for optimized resource utilization. Through extensive simulation studies, we show that the probability of robot deviation to remain within 0.5 m from its optimal path, is 19% more in L-NORM compared to classical 802.11ax based edge network solution, considering

$\sim$

1 MB of sensor data per robot.

10.1109/tmc.2023.3266643

Cite

Citations (0)

PROMPT: Prediction of Channel Metrics for Proactive Optimization in Cellular Networks

Subhramoy Mohanti Akshay Malhotra Muhammad Umar Bin Farooq Jaideep Chandrashekar

10.1109/wowmom60985.2024.00065

Cite

Citations (0)

Going Beyond RF: How AI-enabled Multimodal Beamforming will Shape the NextG Standard

arXiv (Cornell University) (2022)

Debashri Roy Batool Salehi Stella Banou Subhramoy Mohanti Guillem Reus-Muns

Incorporating artificial intelligence and machine learning (AI/ML) methods within the 5G wireless standard promises autonomous network behavior and ultra-low-latency reconfiguration. However, the effort so far has purely focused on learning from radio frequency (RF) signals. Future standards and next-generation (nextG) networks beyond 5G will have two significant evolutions over the state-of-the-art 5G implementations: (i) massive number of antenna elements, scaling up to hundreds-to-thousands in number, and (ii) inclusion of AI/ML in the critical path of the network reconfiguration process that can access sensor feeds from a variety of RF and non-RF sources. While the former allows unprecedented flexibility in 'beamforming', where signals combine constructively at a target receiver, the latter enables the network with enhanced situation awareness not captured by a single and isolated data modality. This survey presents a thorough analysis of the different approaches used for beamforming today, focusing on mmWave bands, and then proceeds to make a compelling case for considering non-RF sensor data from multiple modalities, such as LiDAR, Radar, GPS for increasing beamforming directional accuracy and reducing processing time. This so called idea of multimodal beamforming will require deep learning based fusion techniques, which will serve to augment the current RF-only and classical signal processing methods that do not scale well for massive antenna arrays. The survey describes relevant deep learning architectures for multimodal beamforming, identifies computational challenges and the role of edge computing in this process, dataset generation tools, and finally, lists open challenges that the community should tackle to realize this transformative vision of the future of beamforming.

Control reconfiguration

10.48550/arxiv.2203.16706

Cite

Citations (2)

Colosseum: Large-Scale Wireless Experimentation Through Hardware-in-the-Loop Network Emulation

Leonardo Bonati Pedram Johari Michele Polese Salvatore D’Oro Subhramoy Mohanti

Colosseum is an open-access and publicly-available large-scale wireless testbed for experimental research via virtualized and softwarized waveforms and protocol stacks on a fully programmable, "white-box" platform. Through 256 state-of-the-art software-defined radios and a massive channel emulator core, Colosseum can model virtually any scenario, enabling the design, development and testing of solutions at scale in a variety of deployments and channel conditions. These Colosseum radio-frequency scenarios are reproduced through high-fidelity FPGAbased emulation with finite-impulse response filters. Filters model the taps of desired wireless channels and apply them to the signals generated by the radio nodes, faithfully mimicking the conditions of real-world wireless environments. In this paper, we introduce Colosseum as a testbed that is for the first time open to the research community. We describe the architecture of Colosseum and its experimentation and emulation capabilities. We then demonstrate the effectiveness of Colosseum for experimental research at scale through exemplary use cases including prevailing wireless technologies (e.g., cellular and Wi-Fi) in spectrum sharing and unmanned aerial vehicle scenarios. A roadmap for Colosseum future updates concludes the paper.

Testbed

Firmware

10.1109/dyspan53946.2021.9677430

Cite

Citations (73)

FOCUS: Fog Computing in UAS Software-Defined Mesh Networks

IEEE Transactions on Intelligent Transportation Systems (2019)

Gökhan Seçinti Angelo Trotta Subhramoy Mohanti Marco Di Felice Kaushik Chowdhury

Unmanned aerial systems (UASs) allow easy deployment, three-dimensional maneuverability and high reconfigurability, as they sustain communication network in the absence of pre-installed infrastructure. The proposed FOg Computing in UAS Software-defined mesh network (FOCUS) paradigm aims to realize an implementable network design that considers practical issues of aerial connectivity and computation. It allocates UASs to the tasks of data forwarding and in-network fog computing while maximizing number of ground-users in UAS coverage. FOCUS improves efficient utilization of network resources by introducing on-board computation and innovates on top of software-defined networking stack by integrating the capabilities of network and ground controllers to enable simultaneous orchestration of both UASs and communication flows. There are three main contributions of the paper: First, a SDN-based architecture is designed enabling autonomous configuration of computation and communication as well as managing multi-hop aerial links. Second, a global optimization problem to achieve optimal forwarding and computational allocation is formulated using Open Jackson Network model and solved via a heuristic approach with well defined complexity. Third, FOCUS framework is implemented on a small-scale testbed of Intel ^® Aero UASs performing image analysis with a full software stack. Experiments reveal at least 32% latency improvement in computation service time compared to traditional centralized computation at the end-server or greedy task allocation schemes within the network.

Reconfigurability

Orchestration

Testbed

10.1109/tits.2019.2960305

Cite

Citations (14)

SABRE: Swarm-Based Aerial Beamforming Radios: Experimentation and Emulation

IEEE Transactions on Wireless Communications (2022)

Subhramoy Mohanti Carlos Bocanegra Sara García Sánchez Kubra Alemdar Kaushik Chowdhury

We propose a novel distributed beamforming framework for UAVs, called SABRE, wherein airborne transmitters synchronize their operations for data communication with target receivers. SABRE chooses the best-suited subset of transmitters that maximizes user-defined QoS, considering relative distances from receivers, traffic characteristics, cumulative SNR desired at the receiver, and individual SNR estimated for each link. This paper makes three main contributions: (i) It shows how to achieve distributed beamforming in challenging, aerial hovering conditions by accurately synchronizing start-times and eliminating relative clock offsets. (ii) It proposes an algorithm with polynomial complexity that groups transmitters and chooses the receiver, maximizing the number of satisfied receivers in each round. (iii) It experimentally validates the concept of aerial beamforming in a testbed composed of four DJI-M100 UAVs in realistic outdoor environments. We follow this up with at-scale emulation involving beamforming with multiple candidate UAV transmitters in Colosseum, the world's largest RF emulator. SABRE keeps the overall network frame error rate below 10% with a probability of 0.95 and manifests a 40% improvement in meeting user QoS thresholds over classical resource allocation methods. From a community viewpoint, the beamforming code, UAV interfacing designs, and the Colosseum container will be released publicly, allowing further independent investigations.

Testbed

10.1109/twc.2022.3158866

Cite

Citations (7)

Going beyond RF: A survey on how AI-enabled multimodal beamforming will shape the NextG standard

Computer Networks (2023)

Debashri Roy Batool Salehi Stella Banou Subhramoy Mohanti Guillem Reus-Muns

10.1016/j.comnet.2023.109729

Cite

Citations (8)

RFClock

Proceedings of the 28th Annual International Conference on Mobile Computing And Networking (2021)

Kubra Alemdar Divashree Varshney Subhramoy Mohanti Ufuk Muncuk Kaushik Chowdhury

Emerging applications like distributed coordinated beamforming (DCB), intelligent reflector arrays, and networked robotic devices will transform wireless applications. However, for systems-centric work on these topics, the research community must first overcome the hurdle of implementing fine-grained, over-the-air timing synchronization, which is critical for any coordinated operation. To address this gap, this paper presents an open-source design and implementation of 'RFClock' that provides timing, frequency and phase synchronization for software defined radios (SDRs). It shows how RFClock can be used for a practical, 5-node DCB application without modifying existing physical/link layer protocols. By utilizing a leader-follower architecture, RFClock-leader allows follower clocks to synchronize with mean offset under 0.107Hz, and then corrects the time/phase alignment to be within a 5ns deviation. RFClock is designed to operate in generalized environments: as standalone unit, it generates a 10MHz/1PPS signal reference suitable for most commercial-off-the-shelf (COTS) SDRs today; it does not require custom protocol-specific headers or messaging; and it is robust to interference through a frequency-agile operation. Using RFClock for DCB, we verify significant increase in channel gain and low BER in a range of [0 -- 10--3] for different modulation schemes. We also demonstrate performance that is similar to a popular wired solution and significant improvement over a GPS-based solution, while delivering this functionality at a fractional price/power point.

Carrier frequency offset

10.1145/3447993.3448623

Cite

Citations (18)

WiFED Mobile: WiFi Friendly Energy Delivery With Mobile Distributed Beamforming

IEEE/ACM Transactions on Networking (2021)

Subhramoy Mohanti Elif Bozkaya M. Yousof Naderi Berk Canberk Gökhan Seçinti

Wireless RF energy transfer for indoor sensors is an emerging paradigm ensuring continuous operation without battery limitations. However, high power radiation within ISM band interferes with packet reception for existing WiFi devices. The paper proposes the first effort in merging RF energy transfer within a standards compliant 802.11 protocol, realizing practical and WiFi-friendly Energy Delivery with Mobile Transmitters (WiFED Mobile). WiFED Mobile architecture is composed of a centralized controller coordinating the actions of multiple energy transmitters (ETs), and deployed sensors that periodically requires charging. The paper first describes 802.11 supported protocol features that can be exploited by sensors to request energy and for ETs to participate in energy transfer. Second, it devises a controller-driven bipartite matching algorithm, assigning appropriate number of ETs to sensors for efficient energy delivery. Thirdly, it detects outlier sensors (OS), which have limited power reception from static ETs and utilizes mobile ETs (METs) to satisfy their charging cycles. The proposed in-band and protocol supported coexistence in WiFED Mobile is validated via simulations and partly in a software defined radio testbed, showing that METs reduce latency by 42% and improve throughput by 83% in scenarios where using only static ETs fails to satisfy charging cycles of OS.

Testbed

10.1109/tnet.2021.3061082

Cite

Citations (13)