Estimation of the parameters of sinusoidal signals in non-Gaussian noise
IEEE Transactions on Signal Processing
Adaptive Lp-norm diversity combining in non-Gaussian noise and interference
IEEE Transactions on Wireless Communications
Adaptive Lp-norm metric for secondary BICM-OFDM systems
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Lp-norm spectrum sensing for cognitive radio networks impaired by non-Gaussian noise
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Fusion of decisions modeled as weak signals in wireless sensor networks
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Adaptive coherent Lp-norm combining
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
IEEE Transactions on Communications
Robust Lp-norm decoding for BICM-based secondary user systems
IEEE Transactions on Communications
Impulsive noise cancelation with simplified Cauchy-based p-norm filter
Signal Processing
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In practical communication environments, it is frequently observed that the underlying noise distribution is not Gaussian and may vary in a wide range from short-tailed to heavy-tailed forms. To describe partially known noise distribution densities, a distribution class characterized by the upper-bounds upon a noise variance and a density dispersion in the central part is used. The results on the minimax variance estimation in the Huber sense are applied to the problem of asymptotically minimax detection of a weak signal. The least favorable density minimizing Fisher information over this class is called the Weber-Hermite density and it has the Gaussian and Laplace densities as limiting cases. The subsequent minimax detector has the following form: i) with relatively small variances, it is the minimum L2-norm distance rule; ii) with relatively large variances, it is the L1 -norm distance rule; iii) it is a compromise between these extremes with relatively moderate variances. It is shown that the proposed minimax detector is robust and close to Huber's for heavy-tailed distributions and more efficient than Huber's for short-tailed ones both in asymptotics and on finite samples