In a multipath fading scattering environment, the receiver typically sees several constantly changing, delayed versions of the transmitted signal. These time-dispersive channels cause intersymbol interference (ISI) which occurs when symbols received from multiple paths are delayed and overlap in time. ISI causes high error rates because the symbols from multiple received paths interfere with each other and become indistinguishable by the receiver. Equalizers attempt to mitigate ISI and improve the receiver performance. Equalizer structures are filters that attempt to match the propagation channel response. For time-varying propagation channels, adapting the equalization filter tap weights so that they maintain a match to the channel over time, improves the error rate performance.
The Communications Toolbox™ includes System objects and blocks, to recover transmitted data using linear, decision-feedback, or maximum-likelihood sequence estimation (MLSE) equalization structures. For background reference material, see the works listed in Selected Bibliography for Equalizers.
This figure shows the high-level configuration options for each equalization structure.
For each equalizer structure, you can configure structural settings (such as the number of taps and initial set of tap weights), algorithmic settings (such as the step size), and the signal constellation used by the modulator in your design. You also specify adaptability of the equalizer tap weights throughout the simulation.
Linear and decision-feedback filter equalizer structures adapt tap weights using the LMS, RLS, or CMA adaptive algorithm. When using linear and decision-feedback filter equalizer structures, the number of samples per symbols determines whether symbols are processed using whole or fractional symbol spacing.
When using LMS and RLS adaptive algorithms, the equalizer begins operating in tap weights training mode. Configure the equalizer to operate adaptively in decision-directed mode or without further adjustment of taps after training is completed.
When using the CMA adaptive algorithm, the equalizer has no training mode. You can configure the equalizer to operate adaptively in decision-directed mode or in nonadaptive mode.
To explore the linear and decision-feedback filter equalizer capabilities, see Adaptive Equalizers.
Maximum-Likelihood Sequence Estimation (MLSE) equalizers use the Viterbi algorithm. The MLSE equalization structure typically provides the optimal match but it requires an accurate channel estimate and is the most computationally complex structure. To explore MLSE equalizer capabilities, see MLSE Equalizers.
The computational complexity of each equalization structure grows with the length of the channel time dispersion. Considering the Doppler and frequency selectivity characteristics of the channel, use this table as a guide when selecting which equalization structure to use in your simulation.
|Equalizer Structure||Doppler Speed||Is Channel Frequency Selective?||Computational Complexity|
 Farhang-Boroujeny, B., Adaptive Filters: Theory and Applications, Chichester, England, John Wiley & Sons, 1998.
 Haykin, Simon, Adaptive Filter Theory, Third Ed., Upper Saddle River, NJ, Prentice-Hall, 1996.
 Kurzweil, Jack, An Introduction to Digital Communications, New York, John Wiley & Sons, 2000.
 Proakis, John G., Digital Communications, Fourth Ed., New York, McGraw-Hill, 2001.
 Steele, Raymond, Ed., Mobile Radio Communications, Chichester, England, John Wiley & Sons, 1996.