A Strategy on Channel Equalization for Digital Communication Based on Neural Network System

In present scenario when we talk about digital signal transmission inter symbol interference is the most exciting problem over frequency selective communication channel. To avoid such drawback and to get back initial transmitted information there is an equalization process at the receiver end which compensating corrupted data due to ISI. Hence the basic techniques as Channel equalizers are generally adapted to minimize consequences are Inter-Symbol Interference. An adaptive equalizer is highly suitable to handle the time varying and random nature of communication channel. This conversational equalizer by nature inverse to channel and channel’s influence can be compensating. Also for non inverse channel there is no existence of equalizer. Now we think about better performance comparing with traditional, and a neural equalizer can be proposed. This research paper also reflecting the process of minimizing of mean square error and also distortion due to ISI. Analysis outcome of this work satisfy us that neural equalizer operational behavior much better than all existing conversational system of equalizers. The outcome on the planned equalizer is deeply considered for every channel having its own bit-error rate with noisy data. Result after Simulation expressing the properly designed equalizer has lower Bit Error Rate (BER) with respect to performance.