Learning-based Resource Allocation for Backscatter-aided Vehicular Networks
preprintposted on 18.10.2021, 05:51 by Wali Ullah KhanWali Ullah Khan, Tu N. Nguyen, Furqan Jameel, Muhammad Ali Jamshed, Haris bin PervaizHaris bin Pervaiz, Muhammad Awais Javed, Riku Jantti
This work sheds light on a novel learning-based optimization framework for heterogeneous backscatter vehicular networks. More specifically, the article presents a resource allocation and user association scheme for large-scale heterogeneous backscatter vehicular networks by considering a collaboration centric spectrum sharing mechanism. In the considered network setup, multiple network service providers (NSPs) own the resources to serve several legacy and backscatter vehicular users in the network. For each NSP, the legacy vehicle user operates under the macro cell, whereas, the backscatter vehicle user operates under small private cells using leased spectrum resources. A joint power allocation, user association, and spectrum sharing problem has been formulated with an objective to maximize the utility of NSPs. In order to overcome challenges of high dimensionality and non-convexity, the problem is divided into two subproblems. Subsequently, a reinforcement learning and a supervised deep learning approach have been used to solve both subproblems in an efficient and effective manner.
Email Address of Submitting Authorwaliullah.firstname.lastname@example.org
Submitting Author's InstitutionUniversity of Luxembourg
Submitting Author's CountryLuxembourg
Read the peer-reviewed publication
in IEEE Transactions on Intelligent Transportation Systems