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In multi-cell massive MIMO systems, channel estimation is deteriorated bypilot contamination and the effects of pilot contamination become more severedue to hardware impairments. In this paper, we propose a joint pilot design andchannel estimation based on deep residual learning in order to mitigate theeffects of pilot contamination under the consideration of hardware impairments.We first investigate a conventional linear minimum mean square error (LMMSE)based channel estimator to suppress the interference caused by pilotcontamination. After that, a deep learning based pilot design is proposed tominimize the mean square error (MSE) of LMMSE channel estimation, which isutilized to the joint pilot design and channel estimator for transfer learningapproach. For the channel estimator, we use a deep residual learning whichextracts the features of interference caused by pilot contamination andeliminates them to estimate the channel information. Simulation resultsdemonstrate that the proposed joint pilot design and channel estimatoroutperforms the conventional approach in multi-cell massive MIMO scenarios.Furthermore, the joint pilot design and channel estimator using transferlearning enhances the estimation performance by reducing the effects of pilotcontamination when the prior knowledge of pilot contamination cannot beexploited.

Joint Pilot Design and Channel Estimation using Deep Residual Learning for Multi-Cell Massive MIMO under Hardware Impairments