Resource allocation for ultra-reliable low-latency vehicular networks in finite blocklength regime

Abstract

Ensuring ultra-reliable low-latency communication (URLLC) is crucial in the timely delivery of safety-critical messages in vehicle-to-vehicle (V2V) communications. The stringent latency requirement in URLLC requires the usage of finite block length information theory. Previously proposed resource allocation schemes for V2V communication rely on Shannon rate and do not incorporate spectrum allocation into the blocklength and power optimization while relying solely on slow-varying large-scale channel statistics. This paper investigates the combined spectrum, blocklength, and power allocation to minimize the worst-case decoding-error probability in the finite blocklength (FBL) regime for a URLLC-based V2V communication scenario. We first formulate the problem as a non-convex mixed-integer nonlinear programming problem (MINLP). To solve this challenging problem, we decompose the original problem into two interrelated subproblems. First, the spectrum allocation is performed by clustering vehicles into distinct zones. Second, an iterative block coordinate descent (BCD) based algorithm is developed for the blocklength and transmit power optimization. Via extensive simulations, we demonstrate that the proposed scheme outperforms the benchmark scheme based on a path-following iterative strategy and yields substantially higher network reliability for different network parameters.