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

Abstract

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