A Least Mean-Square Approach to Nonlinear Equalization of Digital Receivers

Abstract

With an increased demand for fully-digital arrays for radar and communications systems by making use of low-cost components with relaxed linearity requirements, nonlinear equalization (NLEQ) is needed to increase the linearity and dynamic range. An iterative solution is proposed in the least mean-square algorithm (LMS) and is shown to be very effective at mitigating intermodulation distortion (IMD) in digital array channels. Temperature and frequency changes in the system also cause the nonlinear characteristics of the system to change, requiring an adaptive NLEQ solution such as LMS. Odd-order IMD spurs correlate to predictable directions at the array level, but, with the use of NLEQ at the channel level, can be decorrelated. Decorrelation is made more difficult to achieve when the array channels are not identical, due to the temperature and process variations that make up all electronics, requiring the use of some type of coefficient averaging.