Parametric MIMO-OFDM Channel Estimation: A Quasi Neural Network Approach

This paper presents a quasi-neural network (Quasi-NN) approach for parametric channel estimation in multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. A unified Quasi-NN framework is proposed for both the single-user (SU) and the multi-user (MU) scenarios, enabling the joint estimation of the direction of arrival, direction of departure, time delay, complex gain, and the number of multipaths. The artificial neural network-like structure of the Quasi-NN allows the application of the backpropagation algorithm while requiring only real-time pilot signals for online network training. Furthermore, considering the issue of high pilot overhead in MU MIMO-OFDM systems, we develop a location assisted Quasi-NN (LA-QNN) that utilizes a location-parameter database to reduce the pilot overhead while maintaining accurate channel estimation. Simulation results show that the proposed Quasi-NN approaches the Cramér Rao bound, and the proposed LA-QNN scheme provides a better balance between channel estimation performance and pilot overhead in the MU scenario.