Abstract

Estimation of Multi Input Multi Output (MIMO) channels can be performed by Artificial Neural Network (ANN)s such as Multi Layer Perceptron (MLP)s. However, the cost of training overload in case of time varying MIMO channels is the main bottleneck of such ANN architectures for which a viable alterative, namely, the Recursive Recurrent Network (RNN) is explored. Although for tightly coupled real and imaginary components of a transmitted signal RNN cannot provide a satisfactory solution, nevertheless, a split - complex activation RNN approach can be adopted to deal with such cases averaging the output obtained for a given time length. The results demonstrate better performance as well as computational simplicity compared to MLP architectures with temporal characteristics.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.