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Full-Text Articles in Electrical and Computer Engineering
Detection For A Statistically-Known, Time-Varying Dispersive Channel, David W. Matolak, S. G. Wilson
Detection For A Statistically-Known, Time-Varying Dispersive Channel, David W. Matolak, S. G. Wilson
Faculty Publications
Detection for the statistically known channel (SKC) is aimed at obtaining good performance in situations where our statistical knowledge of a time-varying channel is good, and where other equalization/detection schemes are either too complex to implement, or their performance is limited due to the rapidity of channel fading, or where we are simply unable to perform channel estimation. By using a statistical characterization of the channel, we develop a new detector that performs maximum-likelihood sequence estimation (MLSE) (given the channel model) on blocks of N symbols. Both symbol-spaced and fractionally spaced samples are used, to obtain two different detectors, that …
Variable-Complexity Trellis Decoding Of Binary Convolutional Codes, David W. Matolak, S. G. Wilson
Variable-Complexity Trellis Decoding Of Binary Convolutional Codes, David W. Matolak, S. G. Wilson
Faculty Publications
Considers trellis decoding of convolutional codes with selectable effort, as measured by decoder complexity. Decoding is described for single parent codes with a variety of complexities, with performance "near" that of the optimal fixed receiver complexity coding system. Effective free distance is examined. Criteria are proposed for ranking parent codes, and some codes found to be best according to the criteria are tabulated, Several codes with effective free distance better than the best code of comparable complexity were found. Asymptotic (high SNR) performance analysis and error propagation are discussed. Simulation results are also provided.