Joint Resource Allocation and Computation Offloading using Transformer-Based Learned Surrogate
Ultra-reliable low-latency communications (URLLC) in vehicular edge computing environments demands stringent end-to-end delay guarantees with reliability bounds exceeding conventional Shannon-theoretic limits. This research addresses the joint optimization of resource block allocation, transmit power control, and computation offloading decisions for vehicular URLLC users under finite blocklength information theory constraints. We consider a comprehensive vehicle-to-everything (V2X) architecture comprising both cellular user equipment (CUE) vehicles communicating with road-side units and vehicle-to-vehicle (V2V) pairs utilizing direct device-to-device links.
Approximation of optimal policies with sub-millisecond inference time, enabling real-time vehicular deployments.
Optimization under short-packet information theory constraints for URLLC scenarios.
Formal proof through polynomial-time reduction from 0-1 Knapsack problem characterizing computational complexity.
Integrated M/D/1 models with dimensionally consistent service rate definitions for deterministic packet sizes.
Authors: AlHussein A. AlSahati, Dr. Houda Chihi
Status: Submitted to IEEE • Research Lab: InnovCOM Lab, Sup'COM Tunisia