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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, 2024

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. https://ieeexplore.ieee.org/document/10478322

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

DITTO: DIgital Twins for Testing and Optimizing Wireless Decisions

Published in IEEE Metacom 2024, 2024

Digital Twins (DTs) are powerful tools for decision making that mirror real-world systems and continuous interactions between them. We study on the application andcapabilities of DTs in the realm of wireless communications, using two leading wireless communication tools: Wireless InSite and Sionna. Specifically, we compare the fidelity of the two wireless communication tools by measuring several metrics with a real-life dataset. Our comprehensive analysis aims to determine the capabilities of Digital Twins in the context of wireless communications, offering valuable insights for future researchers in the field.

Recommended citation: R. Kumahia, U. Demir, S. Pradhan, B. Salehihikouei, K. Chowdhury and S. Ioannidis, "DITTO: DIgital Twins for Testing and Optimizing Wireless Decisions," 2024 IEEE International Conference on Metaverse Computing, Networking, and Applications (MetaCom), Hong Kong, China, 2024, pp. 121-128, doi: 10.1109/MetaCom62920.2024.00031. https://ieeexplore.ieee.org/abstract/document/10740116

Experimental Demonstration of Over the Air Federated Learning for Cellular Networks

Published in IEEE ICMLCN 2025 - Barcelona, 2025

Over-the-air federated learning (OTA-FL) offers an exciting new direction over classical FL by averaging model weights using the physics of analog signal propagation. Since each participant broadcasts its model weights concurrently in time and frequency, this paradigm conserves communication bandwidth and model upload latency. Despite its potential, there is no prior large-scale demonstration on a real-world experimental platform. This paper proves for the first time that OTA-FL can be deployed in a cellular network setting within the constraints of a 5G-compliant waveform. To achieve this, we identify challenges caused by multi-path fading effects, thermal noise at the radio devices, and maintaining highly precise synchronization across multiple clients to perform coherent OTA combining. To address these challenges, we propose a unified framework for real-time channel estimation, model weight to OFDM symbol mapping and dual-layer synchronization interface to perform OTA model training. We experimentally validate OTA-FL using two relevant applications - Channel Estimation and Object Classification, at a large-scale on ORBIT Testbed and a portable setup respectively, along with analyzing the benefits from an operator’s perspective. Under specific experimental conditions, OTA-FL achieves equivalent model performance, supplemented with 43× improvement in spectrum utilization and 7× improvement in energy efficiency over classical FL when considering 5 nodes.

Recommended citation: S. Pradhan et al., "Experimental Demonstration of Over the Air Federated Learning for Cellular Networks," 2025 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN), Barcelona, Spain, 2025, pp. 1-7, doi: 10.1109/ICMLCN64995.2025.11140494., https://ieeexplore.ieee.org/document/11140494

SMART: Sim2Real Meta-Learning-Based Training for mmWave Beam Selection in V2X Networks

Published in IEEE Transactions on Mobile Computing (TMC) 2025, 2025

Digital twins (DT) offer a low-overhead evaluation platform and the ability to generate rich datasets for training machine learning (ML) models before actual deployment. Specifically, for the scenario of ML-aided millimeter wave (mmWave) links between moving vehicles to roadside units, we show how DT can create an accurate replica of the real world for model training and testing. The contributions of this paper are twofold: First, we propose a framework to create a multimodal Digital Twin (DT), where synthetic images and LiDAR data for the deployment location are generated along with RF propagation measurements obtained via ray-tracing. Second, to ensure effective domain adaptation, we leverage meta-learning, specifically Model-Agnostic Meta-Learning (MAML), with transfer learning (TL) serving as a baseline validation approach. The proposed framework is validated using a comprehensive dataset containing both real and synthetic LiDAR and image data for mmWave V2X beam selection. It also enables the investigation of how each sensor modality impacts domain adaptation, taking into account the unique requirements of mmWave beam selection. Experimental results show that models trained on synthetic data using transfer learning and meta-learning, followed by minimal fine-tuning with real-world data, achieve up to 4.09× and 14.04× improvements in accuracy, respectively. These findings highlight the potential of synthetic data and meta-learning to bridge the domain gap and adapt rapidly to real-world beamforming challenges.

Recommended citation: D. Muruganandham, S. Pradhan, J. Gu, T. Braun, D. Roy and K. Chowdhury, "SMART: Sim2Real Meta-Learning-Based Training for mmWave Beam Selection in V2X Networks," in IEEE Transactions on Mobile Computing, vol. 24, no. 10, pp. 11076-11091, Oct. 2025, doi: 10.1109/TMC.2025.3576203. https://ieeexplore.ieee.org/abstract/document/11023025

ATLAS: AI-Native Receiver Test-and-Measurement by Leveraging AI-Guided Search

Published in IEEE PIMRC 2025, 2025

Industry adoption of Artificial Intelligence (AI)-native wireless receivers, or even modular, Machine Learning (ML)-aided wireless signal processing blocks, has been slow. The main concern is the lack of explainability of these trained ML models and the significant risks posed to network functionalities in case of failures, especially since (i) testing on every exhaustive case is infeasible and (ii) the data used for model training may not be available. This paper proposes ATLAS, an AI-guided approach that generates a battery of tests for pre-trained AI-native receiver models and benchmarks the performance against a classical receiver architecture. Using gradient-based optimization, it avoids spanning the exhaustive set of all environment and channel conditions; instead, it generates the next test in an online manner to further probe specific configurations that offer the highest risk of failure. We implement and validate our approach by adopting the well-known DeepRx AI-native receiver model as well as a classical receiver using differentiable tensors in NVIDIA Sionna environment. ATLAS uncovers specific combinations of mobility, channel delay spread, and noise, where fully and partially trained variants of AI-native DeepRx perform suboptimally compared to the classical receivers. Our proposed method reduces the number of tests required per failure found by 19% compared to grid search for a 3-parameters input optimization problem, demonstrating greater efficiency. In contrast, the computational cost of the grid-based approach scales exponentially with the number of variables, making it increasingly impractical for high-dimensional problems.

Recommended citation: Belgiovine, M., Pradhan, S., Lange, J., Löhning, M. and Chowdhury, K., 2025. ATLAS: AI-Native Receiver Test-and-Measurement by Leveraging AI-Guided Search. arXiv preprint arXiv:2508.12204. https://arxiv.org/pdf/2508.12204?