Low-Complexity Resource Allocation for Dense Cellular Vehicle-to-Everything (C-V2X) Communications

TL;DR

This paper introduces a low-complexity, scalable resource allocation framework for dense C-V2X communications, improving sum-rate performance and practicality over existing methods.

Contribution

It proposes novel matching-based algorithms and a closed-form feasibility check for efficient resource allocation in dense vehicular networks.

Findings

Outperforms existing methods by up to 6% in sum-rate.

Algorithms are scalable and effective for dense scenarios.

Achieves near-optimal performance with low complexity.

Abstract

Vehicular communications are the key enabler of traffic reduction and road safety improvement referred to as cellular vehicle-to-everything (C-V2X) communications. Considering the numerous transmitting entities in next generation cellular networks, most existing resource allocation algorithms are impractical or non-effective to ensure reliable C-V2X communications which lead to safe intelligent transportation systems. We study a centralized framework to develop a low-complexity, scalable, and practical resource allocation scheme for dense C-V2X communications. The NP-hard sum-rate maximization resource allocation problem is formulated as a mixed-integer non-linear non-convex optimization problem considering both cellular vehicular links (CVLs) and non-cellular VLs (NCVLs) quality-of-service (QoS) constraints. By assuming that multiple NCVLs can simultaneously reuse a single cellular link (CL), we propose two low-complexity sub-optimal matching-based algorithms in four steps. The first two steps provide a channel gain-based CVL priority and CL assignment followed by an innovative scalable min-max channel-gain-based CVL-NCVL matching. We propose an analytically proven closed-form fast feasibility check theorem as the third step. The objective function is transformed into a difference of convex (DC) form and the power allocation problem is solved optimally using majorization-minimization (MaMi) method and interior point methods as the last step. Numerical results verify that our schemes are scalable and effective for dense C-V2X communications. The low-complexity and practicality of the proposed schemes for dense cellular networks is also shown. Furthermore, it is shown that the proposed schemes outperform other methods up to %6 in terms of overall sum-rate in dense scenarios and have a near optimal performance.