Adaptive channel estimation method based on multi-model weighting soft handoff

Abstract

The invention belongs to the field of radio communication and discloses an adaptive channel estimation method based on multi-model weighting soft handoff. The method comprises the steps of establishing a channel system model, selecting a time-frequency double-selection channel and determining channel parameters; establishing a channel estimation submodel and a multi-model channel estimation bank based on the channel model and the estimation method, and analyzing and calculating the model error and estimation error of the channel estimation model; finally, carrying out model switching according to a switching index with the model error combined with the estimation error by means of the LUMV weighting multi-model adaptive estimation algorithm. According to the adaptive channel estimation method based on multi-model weighting soft handoff, under the condition of uncertainty of a transmission channel model, the multi-model idea in the multi-model adaptive control theory is introduced to channel estimation by means of the channel model and estimation method, switching is achieved by means of the LUMV weighting multi-model adaptive estimation method, and therefore the channel estimation method is high in robustness and accuracy within the complicated channel range.

Description

technical field [0001] The invention belongs to the field of radio communication, and relates to a channel self-adaptive estimation method based on multi-model weighted soft handover. Background technique [0002] Channel Estimation Algorithm According to the established channel model, the estimation algorithm is designed according to different criteria to obtain model parameter values. Commonly used channel estimation algorithms include maximum likelihood ML estimation, EM estimation (EM algorithm is an iterative algorithm for progressive ML estimation when the observation data is incomplete), LS estimation, RLS estimation, LMMSE estimation, Kalman filter, etc. ML / EM estimation is based on the maximum likelihood criterion. It uses several known observations (complete or incomplete) to estimate the parameter without any prior knowledge of the estimated parameter. It is more effective and complex. The degree is also high; LS aims to minimize the square error between the esti...

AI Technical Summary

Problems solved by technology

The current research analyzes the errors under different models and estimation methods and continuously improves the methods to reduce the errors. However, the performance of different models and estimation processes in different channel environments has great differences in the performance of channel estimation errors, and the performance is relatively low. A good estimation method is closely related to the acquisition of channel statistics. Therefore, it is urgent to study a robust channel estimation scheme and apply it to the actual unknown complex environment

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