Predictive precoding based on the Grassmannian manifold for UAV-enabled cache-assisted B5G communication systems

Abstract

Abstract The unmanned aerial vehicle (UAV) can extend the network coverage and improve the system throughput for 5th generation (5G) communication systems; hence, it receives a lot of attention recently. This paper considers the problem of channel predictive precoding for UAV-enabled cache-assisted B5G multi-input multi-output (MIMO) systems. A novel channel precoder predictor is proposed, in which the prediction is conducted on a non-linear vector space—Grassmannian manifold. The predictor at the receiver utilizes the current and previous channel matrices to solve the precoder at the next time and then feeds it back to the transmitter for precoding. More specifically, two sub-matrices are extracted from the channel right singular matrices and modeled as two points on the Grassmannian manifold. Then, the geodesic between the two points is conducted. Unlike the conventional method in which the tangent vector at the previous point is parallel transported along the geodesic, we predict the next point by use of the geodesic equation directly. We analyze the computational complexity of the proposed method and demonstrate the superiority of the proposed method by comparing with the conventional one. Besides, we adopt a general Ricean channel model in the UAV MIMO system, where both the Kronecker model and Jake’s model are incorporated. The effects of various channel model parameters on the system performance in terms of the chordal error of channel predictor and the optimum step are thoroughly investigated.

Keywords

• Grassmannia manifold
• Prediction
• UAV
• MIMO