Non-linear channel equalization using adaptive MPNN

In this paper the Modified Probabilistic Neural Network (MPNN) is used for dealing with the problem of channel equalization. Some improvements are suggested for the MPNN so that it is more suitable for the current problem. Firstly, the MPNN is extended to process complex signals. Secondly, a stochastic gradient adaptation technique is proposed, such that when the network is being employed to equalize a slowly varying channel, it can self-adapt to the changing environment. Simulations have shown that the MPNN is able to effectively equalize 4-QAM symbol sequences transmitted through a non-linear, slowly time-varying channel. Finally, methods that further reduce the size of the network are proposed. Simulations show that the proposed method is able to reduce the size of the network considerably.