IQ不平衡OFDM系统两种逼近最大似然性能的亚最大似然补偿算法

Two quasi-ML compensation methods for OFDM systems with IQ imbalances

针对在同相正交IQ不平衡的正交频分复用(OFDM)系统中用传统的最大似然(ML)补偿算法需要二维复向量的搜索,从而导致计算复杂度较高的问题,提出了两种逼近ML算法性能的补偿算法,即一维实向量迭代搜索算法和二维实向量迭代搜索算法,它们通过在空间中迭代搜索一维或二维实向量来降低计算复杂度。仿真结果表明,上述改进的亚ML补偿算法的性能优于现有的频域和时域最小二乘补偿方法,逼近ML算法,其计算复杂度明显低于传统的ML算法,误比特性能与ML算法相当。

Based on the study of the problem that for orthogonal frequency division multiplexing （OFDM） systems with inphase and quadrature （IQ） imbalances, the conventional maximum likelihood （ML） compensation method has the higher computational complexity since it adopts exhaustive searching in space of two-dimensional complex vectors, two quasi-ML compensation methods, the 1D vector searching and the 2D vector searching, were proposed based on the conventional ML compensation method, which reduce the complexity by iterative searching in space of one or two-dimensional real vectors. The simulation results show that the performance of the proposed quasi-ML compensation methods outperform the existing compensation methods such as the time-domain least square （LS） and the frequency-domain LS, and are close to the conventional ML but the computational complexity is obviously lower than it.