Latency Minimization Oriented Radio and Computation Resource Allocations for 6G V2X Networks with ISCC

TL;DR

This paper proposes a joint radio and computation resource allocation framework for 6G V2X networks with ISCC, aiming to minimize sensing latency while satisfying detection constraints, using an alternating optimization algorithm.

Contribution

It introduces a novel joint resource allocation method for ISCC-enabled V2X systems, optimizing sub-band, power, beamforming, and computation resources to reduce latency.

Findings

Significant reduction in maximum task completion latency.

Effective trade-offs between system performance and resource utilization.

Validated performance improvements through simulations.

Abstract

Incorporating mobile edge computing (MEC) and integrated sensing and communication (ISAC) has emerged as a promising technology to enable integrated sensing, communication, and computing (ISCC) in the sixth generation (6G) networks. ISCC is particularly attractive for vehicle-to-everything (V2X) applications, where vehicles perform ISAC to sense the environment and simultaneously offload the sensing data to roadside base stations (BSs) for remote processing. In this paper, we investigate a particular ISCC-enabled V2X system consisting of multiple multi-antenna BSs serving a set of single-antenna vehicles, in which the vehicles perform their respective ISAC operations (for simultaneous sensing and offloading to the associated BS) over orthogonal sub-bands. With the focus on fairly minimizing the sensing completion latency for vehicles while ensuring the detection probability constraints, we jointly optimize the allocations of radio resources (i.e., the sub-band allocation, transmit power control at vehicles, and receive beamforming at BSs) as well as computation resources at BS MEC servers. To solve the formulated complex mixed-integer nonlinear programming (MINLP) problem, we propose an alternating optimization algorithm. In this algorithm, we determine the sub-band allocation via the branch-and-bound method, optimize the transmit power control via successive convex approximation (SCA), and derive the receive beamforming and computation resource allocation at BSs in closed form based on generalized Rayleigh entropy and fairness criteria, respectively. Simulation results demonstrate that the proposed joint resource allocation design significantly reduces the maximum task completion latency among all vehicles. Furthermore, we also demonstrate several interesting trade-offs between the system performance and resource utilizations.