CCI mitigation in UWB framework using DFE-RNN hybrid approach

Ultra Wide Band (UWB) schemes have become a viable option to meet the demand of high data rate wireless communication. Appealing features such as flexibility and robustness, as well as high precision ranging capability, have polarized attention and made UWB an excellent candidate for variety of applications. There are many communications scenarios where multiple wideband transmissions in the same radio channel may exist. The resulting co-channel interference (CCI) from multiple user devices is taken into consideration. CCI is a degrading phenomenon and it deteriorates the quality of desired communication link. Artificial Neural Network (ANN) as non parametric pattern mapping tool can tackle time varying nature of UWB setup while carrying out channel modeling and estimation. The Recurrent Neural Network (RNN) being dynamic ANN have better time tracking capability. The temporal characteristics of RNN along with Decision Feedback Equalization (DFE) are used in the proposed work. A hybrid approach using RNN and DFE is formulated which is able to satisfactorily mitigate the CCI effects, lower the bit error rate (BER) and better tracking capability in UWB framework.