An upper bound on the performance of a novel feedforward perceptron equalizer

The emulation of a nonadaptive, binary decision feedback equalizer, operating on a noiseless, finite impulse response channel by a feedforward multilayer processor is considered. This feedforward perceptron equalizer comprises a triangular array of hard-limiting processing elements. The functional similarity between the two systems is exploited to obtain a tight upper bound on the probability of error as a function of the number of layers, using the theory of finite state Markov processes.