Hybrid CNN-LSTM Channel Estimation for 5G Massive MIMO Systems using Sparse Pilot Reconstruction and Time-Varying 3GPP Channels
TL;DR
A two-stage hybrid deep learning estimator is proposed in which least-squares estimates at pilots placed at every twelfth subcarrier are expanded by two-dimensional bilinear interpolation and refined by a time-distributed convolutional neural network coupled with a long short-term memory (LSTM) recurrent stage.
Abstract
Massive multiple-input multiple-output (MIMO) technology underpins the spectral efficiency targets of fifth-generation (5G) New Radio (NR) networks, and its performance depends critically on accurate channel state information at the receiver. Classical pilot-based channel estimators face three fundamental constraints: pilot overhead that scales with the antenna count, matrix inversion complexity that grows cubically with the array size, and severe noise sensitivity at low signal-to-noise ratio (SNR) or under high-mobility conditions. The objective of this study is to develop a channel estimator that accurately reconstructs the full time-frequency channel response from a sparse pilot grid while remaining computationally feasible for real-time operation. A two-stage hybrid deep learning estimator is proposed in which least-squares estimates at pilots placed at every twelfth subcarrier are first expanded by two-dimensional bilinear interpolation and then refined by a time-distributed convolutional neural network (CNN) coupled with a long short-term memory (LSTM) recurrent stage; the model is trained and evaluated on time-varying 3GPP TR 38.901 tapped delay line channels with Jakes Doppler fading at speeds of up to 120 km/h. Evaluated on a 4×4 MIMO-OFDM link with 624 subcarriers over an SNR range of 0-20 dB, the proposed estimator reduces the normalized mean-squared error by up to 88%, approximately halves the bit-error rate at mid-range SNRs, and raises the spectral efficiency from approximately 0.13 to 4.3-5.0 bits/s/Hz relative to a conventional two-dimensional interpolation baseline, while consuming only half the pilot overhead of a dense-pilot configuration; inference latency on a graphics processing unit is below 1 ms per frame. These results indicate that hybrid CNN-LSTM processing offers a practical route to accurate, low-overhead, and latency-compliant channel estimation for 5G massive MIMO deployments.