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Batool Salehihikouei PhD Dissertation Defense

April 3, 2024 @ 11:00 am - 12:30 pm

Announcing:
PhD Dissertation Defense

Name:
Batool Salehihikouei

Title:
Leveraging Deep Learning on Multimodal Sensor Data for Wireless Communication: From mmWave Beamforming to Digital Twins

Date:
4/3/2024

Time:
11:00:00 AM

Location: EXP-601A

Committee Members:
Prof. Kaushik Chowdhury (Advisor)
Prof. Hanumant Singh
Prof. Josep Jornet
Dr. Mark Eisen

Abstract:
With the widespread Internet of Things (IoT) devices, a wide variety of sensors are now present in different environments. For example, self-driving vehicles and automated warehouses depend on sensor information for navigation and management of the robots, respectively. In this dissertation, we present methods, where these sensors are re-purposed to assist network management in wireless communication, especially when classic approaches fall short to provide the required quality of service (QoS). This thesis presents data-driven and AI-based methods, where the multimodal sensor information is used for two applications: (i) beamforming at the mmWave band and (ii) joint optimization of the navigation and network management in warehouse environments. In the first part, we study multimodal beamforming methods for mmWave vehicular networks. First, we present deep learning fusion algorithms, where the inputs from a multitude of sensor modalities such as GPS (Global Positioning System), camera, and LiDAR (Light Detection and Ranging) are combined towards predicting the optimum beam at the mmWave band. We prove that fusing the multimodal sensor data improves the prediction accuracy, compared to using single modalities. Second, we study the trade-off between the accuracy and cost of different learning strategies and demonstrate that federated learning is the most successful learning strategy, with respect to the communication overhead. Third, we propose algorithms to further optimize the communication overhead by incorporating a pruning strategy tailored to the disturbed nature of the federated learning systems. Fourth, we propose a modality-agnostic deep learning paradigm that operates on any possible combination of sensor modalities. In part two, we propose using digital twins to overcome the challenges of scarcity of data and close-world assumption in deep learning algorithms. A digital twin is a replica of a real world entity, which is typically used for studying the impact of any configuration settings in a safe, digital environment. In this dissertation, we propose a framework that operates by harmonic usage of the DL models and running emulations in the twin. Moreover, we use digital twins to generate training labels and fine-tune the models for unseen scenarios. Finally, we study a robotic industrial setting, where the path planning policy is continuously updated by monitoring the dynamics of the real world, constructing the digital twin, and updating the policy. The constructed twin captures the features of both physical and RF environments in the digital world and includes a reinforcement learning algorithm that jointly optimizes navigation and network resource management.

Other

Department
Electrical and Computer Engineering
Topics
MS/PhD Thesis Defense
Audience
MS, PhD, Faculty