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An upper bound on prototype set size for condensed nearest neighbor
The condensed nearest neighbor (CNN) algorithm is a heuristic for reduci...
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A Regression Approach to Certain Information Transmission Problems
A general information transmission model, under independent and identica...
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Nearest neighbor decoding for Tardos fingerprinting codes
Over the past decade, various improvements have been made to Tardos' col...
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Short Packets over Block-Memoryless Fading Channels: Pilot-Assisted or Noncoherent Transmission?
We present nonasymptotic upper and lower bounds on the maximum coding ra...
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Estimation of Rényi Entropy and Mutual Information Based on Generalized Nearest-Neighbor Graphs
We present simple and computationally efficient nonparametric estimators...
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Statistical and Machine Learning-based Decision Techniques for Physical Layer Authentication
In this paper we assess the security performance of key-less physical la...
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Combining Feature and Prototype Pruning by Uncertainty Minimization
We focus in this paper on dataset reduction techniques for use in k-near...
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Generalized Nearest Neighbor Decoding
It is well known that for linear Gaussian channels, a nearest neighbor decoding rule, which seeks the minimum Euclidean distance between a codeword and the received channel output vector, is the maximum likelihood solution and hence capacity-achieving. Nearest neighbor decoding remains a convenient and yet mismatched solution for general channels, and the key message of this paper is that the performance of the nearest neighbor decoding can be improved by generalizing its decoding metric to incorporate channel state dependent output processing and codeword scaling. Using generalized mutual information, which is a lower bound to the mismatched capacity under independent and identically distributed codebook ensemble, as the performance measure, this paper establishes the optimal generalized nearest neighbor decoding rule, under Gaussian channel input. Several suboptimal but reduced-complexity generalized nearest neighbor decoding rules are also derived and compared with existing solutions. The results are illustrated through several case studies for channels with nonlinear effects, and fading channels with receiver channel state information or with pilot-assisted training.
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