A Joint Sensing, Learning, and Communication Framework for Vehicular Metaverse

Date of Award


Document Type


Degree Name

Master of Science in Machine Learning


Machine Learning

First Advisor

Dr. Mohsen Guizani

Second Advisor

Dr. Martin Takac


Recently, metaverse-empowered wireless systems have gained significant interest in the research community because of the appealing features of proactive learning and self-sustainability. Proactive learning allows for the development of machine learning models prior to user requests, whereas self-sustainability allows a system to run with the least amount of assistance from network administrators. As a result, the idea of the metaverse in vehicular networks can be utilized to enable a wide range of applications (e.g., infotainment and collision avoidance) for a massive number of autonomous vehicles. However, metaverse-empowered vehicular networks are challenging to implement due to computing (i.e., at autonomous cars and network edges) and communication resource constraints. We present a novel framework for joint sensing, communication, and task offloading for vehicular networks empowered by the metaverse in order to address these issues. We formulate a problem to minimize a cost function that takes into account transmission latency, sensing, and transmission energy. Sensing interval, resource distribution, and task offloading are all optimized for minimizing the cost. We use convex optimization for the sensing problem while a decomposition-relaxation-based approach is used for joint resource allocation and task offloading. Finally, numerical results are presented to validate the proposed scheme.


Thesis submitted to the Deanship of Graduate and Postdoctoral Studies

In partial fulfilment of the requirements for the M.Sc degree in Machine Learning

Advisors: Mohsen Guizani, Dr. Martin Takac

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