Open Access
Optimal Power Allocation and Capacity Analysis for D2D-Enabled Vehicular Communications
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d8741.118419
Subject(s) - computer science , wireless , computer network , throughput , resource allocation , capacity optimization , channel (broadcasting) , maximization , reliability (semiconductor) , vehicular ad hoc network , fading , wireless ad hoc network , power (physics) , telecommunications , mathematical optimization , physics , mathematics , quantum mechanics
Wireless Communication is important to recover transmitted information by accommodating reliable Information flow to allow safety, mobility and environmental applications. In cellular communication resources are shared with the users to improve spectral reuse and enhance channel capacity. Device-to-Device (D2D) communication has become a promising technology for wireless engineers to optimize the network performance. In vehicular environment, the design of resource allocation schemes for D2D-enabled networks need to be properly addressed because of the fast channel variations due to high mobility. In this work, Radio Resource Management (RRM) for D2D-based V2X (Vehicle to Everything) communications including both vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication are implemented. Power is allocated based on slowly varying large-scale fading information of wireless channels of LTE standard The objective is to maximize the ergodic capacity of V2I connections by ensuring reliability for each V2V link. Sum ergodic capacity of all V2I links is first taken as the optimization goal to maximize the general V2I link throughput. Minimum ergodic capacity maximization is then taken into consideration to offer a more uniform capacity performance throughout all V2I links. Various algorithms that gives optimal power allocation are proposed and compared. Here, the capacity maximization between highway areas and urban areas are compared and concluded that capacity maximization will be higher in urban areas then on highways.