Dual-functional QoS guaranteed resource allocation for ISAC-assisted V2X network

Integrated Sensing and Communication (ISAC) technology is promising to enable the functional integration of both communication and sensing (C&S) by sharing bandwidth efficiently in the advanced intelligent application fields of 6G Vehicle to Everything (V2X). However, the dynamic ISAC-assisted V2X system still faces challenges in the low-latency and high-sensing radio resource allocation when dealing with the heterogeneous requirements of dual-functional Quality of Service (QoS). In this paper, Generalized Likelihood Ratio Test Based Conditional Mutual Information (GMI) is introduced as the sensing metric to estimate the Conditional Mutual Information (CMI) in the echo channel. Then, by taking the sensing GMI as well as transmission latency as penalties, a dual-functional optimization objective of maximizing the communication rate is established. Furthermore, to solve the above Mixed-Integer Nonlinear Programming (MINLP) problem, the Bilinear Spatial Branch and Bound Algorithm (BSBBA) has been developed, resulting in the joint optimization solution on the mixed numerology, subcarrier number, and transmission power, adaptive to the dynamic ISAC-assisted V2X environment. In addition, for the sake of implementation, the Dynamic Programming Integerization Algorithm (DPIA) has been developed to obtain a suboptimal solution with significantly reduced computational complexity and good convergence. Finally, the proposed algorithms are evaluated in a dynamic simulated ISAC-assisted V2X system, with results of the C&S performance, numerology distribution, computational complexity, etc. In the end, the flexibility, effectiveness, and feasibility of the new method can be validated with the advantages of high sensing GMI, low-latency, and high data rate performances, which would bring attractive prospects in the ISAC-assisted V2X applications in the future.