WIRELESS CHANNEL ESTIMATION FOR MIMO-OFDM SYSTEMS USING HYBRID MESSAGE

Electronics and Communication Engineering

Nowadays, as wireless communication techniques develop, there is a lot more interest in improved data rates, system capacity, as well as the QoS. For the communication system's architecture to be successful in addressing these problems, accurate channel modeling, and exact channel state information evaluation is essential. Similarly, issues like channel fading and Doppler shifts brought on by high mobility make the channel parameter estimate situation more challenging. To represent the organized sparsity with temporal dependency characteristics of Multiple Inputs and Multiple Outputs-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) channels in the angle-delay domain, a hidden Markov model is suggested. The probabilistic model displays considerable flexibility to varied realistic propagating situations. The channel estimation challenge is then solved using a unique optimization framework called Bethe free energy (BFE) minimization, which is valid for a generic statistical model. A Hierarchical Hybrid Message Passing (HHMP) technique is provided to track dynamic spectrum variables iteratively. The suggested method may learn the sparse structure and temporal correlation of multiuser channels adaptively without knowing the hidden Markov channel parameters. Numerical simulations demonstrate how the proposed HHMP algorithm could predict angle-delay domain channels effectively while reducing iteration times or pilot overhead.In comparison to existing method, the suggested channel estimator performs better, demonstrating its effectiveness.

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