Spectrum Resource Management for Multi-Access Edge Computing in Autonomous Vehicular Networks

TL;DR

This paper introduces a dynamic spectrum management framework for autonomous vehicular networks that optimizes spectrum slicing, allocation, and power control to enhance resource utilization and network utility.

Contribution

It presents a novel joint optimization approach for spectrum slicing, allocation, and power control in MEC-enabled AVNETs using an ACS algorithm, improving upon existing schemes.

Findings

Achieves higher network utility compared to existing schemes.

Effectively optimizes spectrum slicing and power control.

Demonstrates improved resource utilization in simulations.

Abstract

In this paper, a dynamic spectrum management framework is proposed to improve spectrum resource utilization in a multi-access edge computing (MEC) in autonomous vehicular network (AVNET). To support the increasing data traffic and guarantee quality-of-service (QoS), spectrum slicing, spectrum allocating, and transmit power controlling are jointly considered. Accordingly, three non-convex network utility maximization problems are formulated to slice spectrum among BSs, allocate spectrum among autonomous vehicles (AVs) associated with a BS, and control transmit powers of BSs, respectively. Via linear programming relaxation and first-order Taylor series approximation, these problems are transformed into tractable forms and then are jointly solved through an alternate concave search (ACS) algorithm. As a result, optimal spectrum slicing ratios among BSs, optimal BS-vehicle association patterns, optimal fractions of spectrum resources allocated to AVs, and optimal transmit powers of BSs are obtained. Based on our simulation, a high aggregate network utility is achieved by the proposed spectrum management scheme compared with two existing schemes.