LAUSR

Intelligent vehicular communication is fundamental to manage vehicle-to-grid (V2G) interaction, where electric vehicles (EVs) provide energy to balance demand of critical loads (CLs). We propose resource efficiency (RE) to exploit the tradeoff between spectral efficiency (SE) and cost efficiency (CE) of EVs in a V2G communication network. The CE is the data rate of the V2G channel between EVs and base station (BS) over the operating cost of EVs to supply energy to CLs. We consider maximizing the RE in the downlink of a V2G communication network, where EVs are served by a BS and associated with CLs, while satisfying energy demand and charging station constraints. As the proposed RE problem is inherently non-convex and known to be NP-hard, we develop a suboptimal scheme based on a two-phase algorithm. Phase 1 derives optimum EV-CL association using a heuristic approach, while phase 2 finds optimum power allocation using geometric programming. We then derive upper and lower bounds to the optimal RE as a benchmark to study the performance gap of the suboptimal scheme. Simulation results demonstrate that the proposed suboptimal scheme is close to the optimal solution, while its complexity is relatively low, making it promising for V2G applications.