An Intelligent Channel Prediction Method in Mobile Scenarios

‍ ‍Aiming at the channel aging problem in wireless communication channel estimation， a convolutional neural network （CNN）-based channel prediction method was proposed. It was implemented by joint trajectory prediction and channel reconstruction. The trajectory prediction was implemented by a CNN-based mobile direction prediction model， and the channel reconstruction was implemented by a CNN-based channel reconstruction model. Specifically， first， the prediction problem of the two elements of the direction vector， i.e.， the unit vector in the moving direction， was regard as a multi-task learning problem. Then， we adopted two CNNs to learn the mapping from the planned route and the current location of the mobile terminal， to the two elements of the moving direction， respectively. Next， based on the predicted direction vectors， the predicted position was calculated using kinematic principle. Then， the movement trajectory was predicted. Second， a CNN was adopted to learn the mapping from the channel characteristic matrices of the K closest position terms in the channel path map， to the channel characteristic matrix of target location. The channel estimation of the predicted trajectory was achieved， by predicting the channel characteristic matrix of each target location on the predicted trajectory. We utilized Wireless InSite software to generate sufficient samples for the training and testing of the mobile direction prediction and channel reconstruction models， and to construct the channel path maps. The collected data set included the planned routes， current locations and moving trajectories of the mobile terminal， as well as the multi-input multi-output （MIMO） channel characteristics （e.g.， the powers， arrival angles in vertical and horizontal directions of effective paths， etc.）， which was generated by the ray-tracing algorithm of Wireless InSite. Numerical simulation results showed that， the proposed channel reconstruction model could effectively estimate the MIMO channel characteristics， outperformed the fully connected neural network （FCN）-based channel reconstruction model， and was very close to the error performance of the K-nearest neighbor interpolation method， in terms of error. In addition， the proposed CNN-based channel prediction method had lower sum of relative error and better robustness in channel estimation than the K-nearest-neighbor interpolation method with smaller K values， and the FCN-based channel prediction method.