Characterizing fading channel under abrupt temporal variations

The statistics of the fading channel are changing due to the temporal and spatial inhomogeneity. To characterize the temporal variation of the channel, short-term statistics need to be estimated. Instead of estimating the statistics over a fixed short period, we applied the Bayesian change point detection (CPD) on the Nakagami-m channel model to capture the abrupt changes in time. The detected change points partition the channel into segments that are characterized by different parameters. We also derive the MAP estimators for the model parameters of each segment based on the hyperparameters generated by CPD. Test results on the channel measurement show the effectiveness of the CPD and the proposed estimators.

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