Novel calibration algorithm of six-port receivers based on radial basis function network.

In this paper, the radial basis function neural network based on the orthogonal least squares (OLS-RBFNN) method is proposed for the first time for the calibration of the six-port receiver, which realises six-port accurate reception in the true sense. This algorithm overcomes the problems of an ageing circuit, narrow working frequency band and nonlinearity of the six-port receiver hardware. The RBFNN is a three-layer forward network, including an input layer, a hidden layer and an output layer. The transformation function of the hidden layer is the radial basis function. A six-port receiver test platform with an operating frequency band of 2.0–8.0 GHz is built to verify the performance of the six-port receiver based on OLS-RBFNN. The test signal is a 16-QAM signal with different bandwidths and different frequency bands. The test indicator uses error vector magnitude (EVM)， which represents the error between the received signal and the ideal signal sent. At last, the EVM of all tested data is less than 1.07%, and the best EVM is 0.57%. Therefore, the calibration accuracy of the OLS-RBFNN proposed in this paper is very high. [ABSTRACT FROM AUTHOR]