Adaptive Channel Prediction, Beamforming and Scheduling Design for 5G V2I Network: Analytical and Machine Learning Approaches

Abstract

The fifth-generation wireless network (5G) is expected to support vehicle to infrastructure (V2I) communication which requires an accurate understanding of its highly mobile and dynamic propagation environment for efficient physical layer design. Towards this end, this paper proposes a joint design of adaptive channel prediction, beamforming and scheduling for 5G V2I communications. The channel prediction algorithm is designed without using pilot signals and assuming the channel impulse response (CIR) model. In this regard, we first propose an adaptive recursive least squares (RLS) CIR prediction approach that uses few of the past estimated CIRs to predict one or more future orthogonal frequency division multiplexing (OFDM) block CIR coefficients. Then, we jointly design beamforming and vehicle equipment (VE) scheduling for each sub-carrier to maximize the uplink channel average sum spectrum efficiency (SE) by employing the predicted CIR. The beamforming problem is formulated as a Rayleigh quotient optimization where its global optimal solution can be obtained. Moreover, the VE scheduling design is formulated as an integer programming problem solved using a greedy search. To enhance the prediction performance, we propose a model-free Q-learning technique to learn a strategy for selecting the best CIR predictor at the current OFDM block. In addition, as the CIR coefficients might contain undesired taps with zero (negligible) average powers, we further examine a dominant CIR tap index identification problem (formulated as convex) from the past observed signals. We carry out extensive simulations to validate analytical expressions and examine the effects of different parameters including the VE speed on the achievable sum SE. Numerical simulations corroborate the superiority of the proposed channel prediction and scheduling algorithms over the existing ones and the relevance of identifying dominant CIR taps.