Deep Learning-Based Channel Estimation to Mitigate Channel Aging in Massive MIMO With Pilot Contamination

In time division duplex (TDD)-based massive multiple-input multiple-output (MIMO) systems, accurate channel state information (CSI) between the base station (BS) and user terminal (UT) is crucial for efficient signal processing, including received signal separation and transmission precoding. However, due to the time-varying nature of wireless channels and the limited coherence time, the pilot signals must be short, and the number of orthogonal pilot sequences is finite. Consequently, pilot signal reuse across neighboring cells leads to pilot contamination, significantly degrading channel estimation accuracy. Traditional methods like minimum mean square error (MMSE) estimation require prior knowledge of the channel covariance matrix, which is often unavailable. This paper proposes a novel deep learning-based channel estimation approach that effectively mitigates the dual challenges of channel aging and pilot contamination. The method leverages two distinct convolutional neural networks (CNNs): one for processing pilot signals to suppress inter-cell interference and another for data signals to address both inter-cell and intra-cell interference. To further enhance estimation accuracy, a smoothing filter is employed to minimize local distortions caused by incorrect symbol detection in the time domain. Simulation results demonstrate the effectiveness of the proposed method, particularly at normalized Doppler frequencies above 0.01, where it significantly outperforms conventional techniques and interpolation-based approaches. The results highlight the proposed method's robustness in maintaining high channel estimation accuracy in dynamic and interference-prone environments.