Learning-Based Resource Allocation for Backscatter-Aided Vehicular Networks

Heterogeneous backscatter networks are emerging as a promising solution to address the proliferating coverage and capacity demands of next-generation vehicular networks. However, despite its rapid evolution significance, optimization aspect such has been overlooked due their complexity scale. Motivated by this discrepancy in literature, work sheds light on novel learning-based framework for heterogeneous More specifically, article presents resource allocation user association scheme large-scale considering collaboration centric spectrum sharing mechanism. In considered network setup, multiple service providers (NSPs) own resources serve several legacy users network. For each NSP, vehicle operates under macro cell, whereas, small private cells using leased resources. A joint power allocation, association, problem formulated with an objective maximize utility NSPs. order overcome challenges high dimensionality non-convexity, is divided into two subproblems. Subsequently, reinforcement learning supervised deep approach have used solve both subproblems efficient effective manner. To evaluate benefits proposed scheme, extensive simulation studies conducted comparison provided benchmark techniques. The performance evaluation demonstrates presented system architecture framework.