Modeling MIMO channels using a class of complex recurrent neural network architectures

Artificial Neural Network (ANN) as non-parametric pattern mapping tool with suitable modification can tackle time varying nature of Multiple Input Multiple Output (MIMO) wireless set-up while carrying out channel modeling and estimation. Modified ANNs with temporal characteristics, however, suffer from configuration complexities. The Recurrent Neural Network (RNN), having better time tracking capability, provides a viable alternative with certain challenges. The RNN as Complex Time Delay Fully Recurrent Neural Network (CTDFRNN) block can be combined at the output using time averaging and Self Organization Map (SOM)-based optimization, yielding a new architectural framework. The CTDFRNN based designs are explored here and several such blocks are coupled together to form a cluster which generates certain diversity aspects that improves overall performance. A Modular Network SOM (MNSOM) architecture which is regarded to have certain resemblance with biological computation with an inherent reinforced modular learning, is also proposed and formulated using CTDFRNN blocks for application in MIMO channel estimation. It is found that such architectures offer considerable amount of processing time saving than the conventional stochastic estimation.