Deep Learning–Aided Adaptive MIMO-OFDM Receiver with Real-Time Channel Estimation and Hardware-in-the-Loop Validation on Embedded Raspberry Pi Platforms

Abstract

Accurate channel estimation remains a fundamental bottleneck in the performance of any coherent Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) receiver, particularly when the system is required to operate over a wide range of signal-to-noise ratios (SNRs) and under multipath fading. In this paper, we present the design, implementation, and experimental validation of a complete MIMO-OFDM transceiver running on two Raspberry Pi 4 single-board computers connected over a Wi-Fi link, in which the conventional Least Squares (LS) channel estimator is enhanced with a four-layer feedforward Deep Neural Network (DNN). The transmitter supports adaptive Quadrature Amplitude Modulation (QAM) schemes ranging from 16-QAM to 256-QAM, which can be selected by the user through a browser-based Flask dashboard. At the receiver, the bit error rate (BER) is computed in real time, while the active processing stage is displayed on an onboard 16×2 LCD. The DNN was trained offline using 100,000 synthetic complex channel samples and reduces the channel estimation mean squared error (MSE) from 0.1810 (LS) to 0.1676, corresponding to an improvement of approximately 0.33 dB in MSE. This improvement translates into an equivalent signal-to-noise ratio (SNR) gain of approximately 1.0–1.5 dB over the LS baseline in the 22–30 dB region of the BER-versus-SNR curve for 256-QAM. The end-to-end system reliably transmits text, grayscale images, and parallel text-and-image streams across the configured channel models. To the best of our knowledge, this work represents one of the first hardware-validated demonstrations of DNN-assisted OFDM channel estimation on a low-cost embedded platform.