基于二维正交化WLCLMS的自干扰对消方法

广义线性结构广泛用于解决共平台系统中同相/正交相位(in-phase/quadrature-phase, IQ)不平衡引起的自干扰镜像信号残留问题。由于在共轭项维度上的拓展, 输入信号间的相关性使得用于实现权值向量自适应更新的广义线性复数最小均方(widely linear complex least mean square, WLCLMS)算法出现明显的收敛速度下降。针对这一问题, 本文提出了一种二维正交化方法, 通过特征值分解实现输入信号在样本延时以及共轭项两个维度上的去相关。同时建立了包括格形预测器、基于梯度的白化器, 以及数字对消器三级结构的自适应二维正交化WLCLMS模型, 实现了时变环境中自干扰信号的实时跟踪。仿真结果表明, 该方法在有效提升收敛速度的同时, 有着稳定的稳态误差性能, 并且针对系统采样率、滤波器阶数、IQ不平衡量的变化具有良好的鲁棒性。

Two-dimensional orthogonalization WLCLMS scheme for self-interference cancellation

Widely linear model has been adopted in co-site system to suppress the mirror self-interference due to in-phase/quadrature-phase (IQ) im-balance. Due to the extension in conjunction dimension, the widely linear complex least mean square (WLCLMS) will suffer from quickly-dropped convergence rate when the input signals are highly correlated. In this paper, a two-dimensional orthogonalization method based on eigen-decomposition is proposed to decouple the input signal vector on sample delay and conjunction dimension. Moreover, an adaptive two-dimensional orthogonalization scheme with a lattice predictor, a gradient-based whitener and a digital canceller is established, which enables to track self-interference signal in time-varying environment. The simulation results indicate that the proposed method is robust to the change of system sampling frequency, filter order and IQ imbalance quantity, and can effectively improve the convergence of WLCLMS while keeping an excellent steady-state performance.