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In Vehicle-to-Everything (V2X) communication, the high mobility of vehiclesgenerates the Doppler shift which leads to channel uncertainties. Moreover, thereasons for channel uncertainties also include the finite channel feedback,channels state information (CSI) loss and latency. With this concern, weformulate a joint spectrum and power allocation problem for V2X communicationwith imperfect CSI. Specifically, the sum capacity of cellular user equipments(CUEs) is maximized subject to the minimum Signal-to-Interference-and-NoiseRatio (SINR) requirements of CUEs and the outage probability constraints ofvehicular user equipments (VUEs). Then, two different robust resourceallocation approaches are designed to solve the problem. One is BernsteinApproximation-based Robust Resource Allocation approach. More specifically,Bernstein approximations are employed to convert the chance constraint into acalculable constraint, and Bisection search method is proposed to obtain theoptimal allocation solution with low complexity. Then, for further reducing thecomputational complexity, Self-learning Robust Resource Allocation approach,which includes a learning method and an analytical mapping method, is proposedas the second approach. The learning method is devised to learn the uncertaintyset which transforms the chance constraint into calculable constraints, and theanalytical mapping method is proposed to obtain closed-form solutions of theresource allocation problem. Finally, the simulation results prove that theproposed approaches can improve the capacity of all CUEs effectively whilstensuring the reliability of the channel.

Joint Spectrum and Power Allocation for V2X Communications with Imperfect CSI