Computer Explorations In Signals And Systems Using Matlab Solution Manuall __LINK__
the simulation and results of a multi-input multi-output (mimo) system for a variety of input signal types including non-zero frequency signals, anti-blockage signals with non-stationary power line interference signals, and types of interference from co-channel and adjacent-channel interference. a new performance analysis technique, a new complex arctangent demodulation function, and a new candidate for a model for the mimo demodulation functions
Computer Explorations In Signals And Systems Using Matlab Solution Manuall
an adaptive time-domain adaptive filter with a time-varying convergence rate using the error rate method is used to estimate the co-channel and adjacent-channel interference, frequency of a band-limited pulse train, and a data sequence from a continuous data stream. a recursive least-squares technique is used to estimate noise spectra for a number of different noise conditions. the results were obtained over a 50 mbit/s unmodulated data set with the adaptive filter and the recursive least-squares techniques. the error rates for each of the applications were determined by using the value of the signal-to-noise ratio that minimized the error rate. the results show the feasibility of the technique for use with real data. by filtering the data with a digital finite impulse response filter, the final results were obtained using the digitized data.
this work applies a bayesian methods as a method to recover subspace signals with a known covariance matrix. the basis generating signal is observed with a noise corrupted signal. the subspace signal is assumed to be generated by mixing the basis signal with an unknown time-variant mixing coefficient matrix. the covariance matrix of the basis signal is assumed to be known. the covariance matrix of the noise is assumed to be known.