Channel estimation and pilot reduction for mmWave massive MIMO systems using deep neural networks
Biniam Tamiru, Jeongju Jee, Hyuncheol Park
Abstract
In this paper, we propose deep learning-based channel estimation and pilot reduction for mmWave point-to-point multi-input multi-output systems. The proposed scheme consists of a two-step approach where the first step is applying a denoising autoencoder for channel estimation. With the denoising characteristic of autoencoder, sparse channel estimation can be conducted although the orthogonality of pilot sequences is not guaranteed due to shorter pilots. The second step is exploiting the temporal correlation of the channel, using the previous estimate to extract information for the current estimate. Through simulation, the proposed scheme shows superior performance with reduced pilots.
Topics & Concepts
AutoencoderOrthogonalityChannel (broadcasting)Reduction (mathematics)Noise reductionMIMOPrecodingComputer scienceAlgorithmArtificial neural networkScheme (mathematics)Point (geometry)Deep learningArtificial intelligenceControl theory (sociology)MathematicsTelecommunicationsControl (management)Mathematical analysisGeometryWireless Signal Modulation ClassificationMillimeter-Wave Propagation and ModelingRadio Frequency Integrated Circuit Design