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On the Effects of Channel Sparsity on Joint Estimators in Aeronautical Telemetry

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Minimum mean-squared error (MMSE) equalizers are a viable solution to mitigate the frequency selectivity of the aeronautical telemetry channel. Because the MMSE equalizer filter coefficients are a function of the… Click to show full abstract

Minimum mean-squared error (MMSE) equalizers are a viable solution to mitigate the frequency selectivity of the aeronautical telemetry channel. Because the MMSE equalizer filter coefficients are a function of the carrier frequency offset (CFO), the equivalent discrete-time channel, and the noise variance, reliable estimates of those parameters are required. The CFO is due primarily to the high velocity of the airborne transmitter. Because the equivalent discrete-time channel in aeronautical telemetry is sparse, the performance of the joint estimator based on a sparse channel estimator is superior to the performance of a traditional nonsparse maximum-likelihood-inspired joint estimator. The improvement is seen in lower estimator error variances for the parameters, particularly for the CFO and channel estimate, and in the postequalizer bit-error rate. Simulation results demonstrate that the postequalizer bit-error rate using the sparse estimator is almost as good as that using ideal estimators.

Keywords: effects channel; joint; estimator; channel sparsity; aeronautical telemetry; telemetry

Journal Title: IEEE Transactions on Aerospace and Electronic Systems
Year Published: 2020

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