Programmable switched-capacitor neural network for MVDR beamforming

Abstract

In this paper, a real-time adaptive antenna array based on a neural network approach is presented. Since an array operating in a nonstationary environment requires a programmable synaptic weight matrix for the neural network, the switched-capacitor (SC) circuits with the capability of programmability and reconfigurability is conducted to implement the neural-based adaptive array. Moreover, the SC techniques can directly implement the neural network with less chip area and provide the ratio of SC-equivalent resistors with accuracy of 0.1 percent. Programming of the switched-capacitor values could be made by allocating each synaptic weight to a set of parallel capacitors with values in a digitally programmable capacitor array (PCA). A relatively wide range of values (5 to 10 binary bits resolution) can be realized for each synaptic weight. A simulation tool called SWITCAP is used to verify the validity and performance of the proposed implementation. Experimental results show that the computation time of solving a linear array of 5 elements is about 0.1 ns for 1 ns time constant and is independent of signal power levels.