The Variational Inference Approach to Joint Data Detection and Phase Noise Estimation in OFDM
The Variational Inference Approach to Joint Data Detection and Phase Noise Estimation in OFDM
复制标题
DOI：
10.1109/tsp.2006.890915
复制
发表时间：
2007-05
影响因子：
5.4
通讯作者：
D. Lin;Teng Joon Lim
中科院分区：
文献类型：
--
作者：
D. Lin;Teng Joon Lim
研究了正交频分复用（OFDM）数据检测中相位噪声的抑制问题。我们提出了一个系统的概率框架，导致最佳和接近最佳的OFDM检测方案中存在的未知PHN。与取消公共（平均）PHN的传统方法相比，我们的目标是联合估计完整的PHN序列和数据符号序列。为此，我们推导出一个家庭的低复杂度的OFDM检测器。这些探测器所基于的理论基础被称为变分推理，一种与变分自由能最小化相关的近似概率推理技术。在推导所提出的方案，我们还指出，期望最大化算法是一个特殊的情况下，变分推理为基础的联合估计。使用共轭梯度（CG）方法进一步降低了复杂度，并且只需要少量的CG迭代就可以接近理想的联合估计器输出
This paper studies the mitigation of phase noise (PHN) in orthogonal frequency-division multiplexing (OFDM) data detection. We present a systematic probabilistic framework that leads to both optimal and near-optimal OFDM detection schemes in the presence of unknown PHN. In contrast to the conventional approach that cancels the common (average) PHN, our aim is to jointly estimate the complete PHN sequence and the data symbol sequence. We derive a family of low-complexity OFDM detectors for this purpose. The theoretical foundation on which these detectors are based is called variational inference, an approximate probabilistic inference technique associated with the minimization of variational free energy. In deriving the proposed schemes, we also point out that the expectation-maximization algorithm is a special case of the variational-inference-based joint estimator. Further complexity reduction is obtained using the conjugate gradient (CG) method, and only a few CG iterations are needed to closely approach the ideal joint estimator output