OPAST子空间跟踪数据压缩半盲多用户检测

Data Compression Group-blind Multiuser Detection Based on OPAST Subspace Tracking Algorithm

基于子空间方法的最小均方误差半盲多用户检测的计算核心是对信号子空间的特征值与特征向量的同时跟踪.仅跟踪计算信号子空间特征向量的子空间跟踪算法不能直接应用于这种检测方法.利用数据压缩技术,提出一种只需跟踪计算信号子空间正交规范基的自适应数据压缩半盲多用户检测.将著名的正交投影逼近子空间跟踪(OPAST)算法应用于这种数据压缩半盲多用户检测,发现OPAST算法具有自然的数据压缩结构,在几乎不增加运算量的情况下即可实现数据压缩半盲多用户检测.仿真实验表明:基于OPAST算法的数据压缩半盲多用户检测具有良好的检测性能.

The principle of minimum mean square error group-blind multiuser detection （GBMUD） based on subspace techniques was expounded. A key computational step of the detector is the subspace tracking algorithm that tracks both the eigenvectors and eigenvalues of the autocorrelation matrix. The subspace tracking algorithms tracks only the signal subspace basis （eigenvectors, not both eigenvectors and eigenvalues） were not directly used by the detector. A data compression group-blind multiuser detector （DC-GBMUD）, which only employs the signal subspace basis, is proposed utilizing the data compression method. When the OPAST subspace tracking algorithm was used for DC-GBMUD, it was discovered that the OPAST subspace tracking algorithm had the nature structure of date compression and was suitable for DC-GBMUD and could achieve blind multiuser detection under the circumstance of almost no additional computation. The results of DC-GBMUD＇s simulations based on the OPAST subspace tracking algorithm show that the performance of the proposed detection method behaves better.