LSTM-Based Predictive mmWave Beam Tracking via Sub-6 GHz Channels for V2I Communications

In this paper, we investigate the mmWave beam tracking for vehicle-to-infrastructure (V2I) communications to find the optimal beam via sub-6 GHz channel state information (CSI). We consider two scenarios: 1) sub-6 GHz and mmWave transceivers are co-located on the same base station (BS), and 2) sub-6 GHz and mmWave BSs are separated in different places constituting heterogeneous networks (HetNets) where one sub- 6 GHz BS controls multiple mmWave BSs. Considering the mobility of the vehicle and time-varying channels, we propose a predictive beam tracking method based on long short-term memory (LSTM) to construct the maps from historical sequential sub-6 GHz CSI to the future optimal mmWave beam. A single LSTM model can handle the beam tracking in the co-located scenario, since there is a one-to-one correspondence between the sub-6 GHz and mmWave transceivers, and the propagation of sub-6 GHz and mmWave signals is similar. However, in the HetNet scenario, it is difficult to select the best one among the beams of multiple mmWave BSs only via the CSI of one sub-6 GHz BS. To address this challenge, we design an LSTM fusion model, which exploits not only the historical sequential sub-6 GHz CSI but also a number of mmWave wide beam measurements, to obtain the optimal mmWave BS and beam in the HetNet. In this case, the collected sub-6 GHz CSI and mmWave wide beam measurements are analyzed by the LSTM and fully connected network (FCN) modules, respectively, providing two beam prediction results. Then the results are fused by an attention-based FCN module to accomplish the final prediction. Simulation results verify the effectiveness and superiority of our LSTM-based beam tracking models compared with other state-of-the-art deep learning beam tracking models that also leverage sub-6 GHz channels. Besides, the robustness and generalization of our proposed LSTM models are illustrated through simulations.