Energy Efficiency-Oriented Joint Communication and Computation Resource Management in NOMA-Enabled V2X Underlaid Cellular Networks

The existing computation and communication (2C) optimization schemes for cellular-based vehicle-to-everything (C-V2X) networks are done without considering the influence of the social trust. Computational tasks may be offloaded to the untrusted vehicles, making it difficult to accurately execute computational tasks. This may lead to a re-offloading of the computational tasks, consuming additional power, and decreasing the EE for offloading. To address the issue, this work devotes itself to investigate social-mobility-aware underlaid C-V2X framework and proposes a novel EE-oriented 2C assignment scheme. In doing so, we assume that the task vehicular user (T-VU) can offload computation tasks to the service vehicular user (S-VU) and the road side unit (RSU) by non-orthogonal multiple access (NOMA). We formulate an EE maximization problem to assign the 2C resources simultaneously through joint optimization, which turns out to be a mixed integer nonlinear programming (MINLP) problem. To solve it, we transform it into separated computation and communication resource allocation subproblems. Dealing with the first subproblem, we fully integrate the characteristic of both social and mobility, and design a heuristic algorithm to achieve edge server selection and task splitting. Then, to address the complex co-channel interference in the second subproblem, the power allocation and spectrum assignment solutions are obtained utilizing a tightening lower bound method and a Kuhn-Munkres (KM) algorithm. Finally, we solve the original problem through an iterative method. Simulation results show that the proposed scheme can significantly enhance the system EE.