Transformer-based Predictive Channel Estimation for mmWave Massive MIMO Systems

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems have been considered a promising solution to provide high-quality data services. To achieve full beamforming gain, the acquisition of accurate channel information is crucial for the success of massive MIMO systems. In recent years, numerous approaches have been suggested to acquire the downlink channel state information (CSI). However, a significant mismatch between the estimated channel and the actual channel for data transmission causes outdated CSI, leading to a severe degradation in spectral efficiency. In this paper, we propose a channel estimation technique for mmWave massive MIMO systems that obtains multipath components of the downlink channel from the previous channel sequence. To be specific, the proposed technique learns the spatio-temporal correlation be-tween multipath components by exploiting a Transformer-based framework. From the numerical results, we demonstrate that the proposed technique outperforms the conventional channel acquisition techniques in terms of normalized mean square error (NMSE).