A modified discrete recurrent neural network as vector detector
Mohamad Mostafa, Werner G. Teich, Jürgen Lindner · 2010
A vector-valued transmission model is useful in those cases, where multiuser, multisubchannel, or multiantenna systems or combinations thereof are considered. To cope with interblock interference (IBI), interuser (IUI) and/or intersubchannel interference (ISCI), different interference cancellation techniques have been proposed. Recurrent neural networks (RNNs) are known for their capability in minimization of suitable cost functions. However, they are susceptible to get stuck in local minima of the cost function. To avoid this, different methods have been presented in the past. In this paper we investigate the application of a modified RNN to the problem of vector detection and we compare the results with a zero-forcing block linear equalizer ZF-BLE, a minimum mean square error block linear equalizer MMSE-BLE, and with a RNN with linearly increased steepness parameter of the activation function. The advantage of the proposed modified RNN is, that it does not need an adjustable activation function and can be interpreted as a discretised analog RNN. Analog RNNs improve the power/speed ratio and minimize the area consumption in the very large scale integration (VLSI) chip.