This paper presents a fully blind phase-aware expectation-maximization (EM) algorithm for OFDM systems with the phase-shift keying (PSK) modulation. We address the well-known local maximum problem of the EM algorithm for blind channel estimation. This is primarily caused by the unknown phase ambiguity in the channel estimates, which conventional blind EM estimators cannot resolve. To overcome this limitation, we propose to exploit the extrinsic information from the decoder as model evidence metrics. A finite set of candidate models is generated based on the inherent symmetries of PSK modulation, and the decoder selects the most likely candidate model. Simulation results demonstrate that, when combined with a simple convolutional code, the phase-aware EM algorithm reliably resolves phase ambiguity during the initialization stage and reduces the local convergence rate from 80% to nearly 0% in frequency-selective channels with a constant phase ambiguity. The algorithm is invoked only once after the EM initialization stage, resulting in negligible additional complexity during subsequent turbo iterations.

翻译：本文针对采用相移键控（PSK）调制的正交频分复用系统，提出了一种完全盲的相位感知期望最大化算法。我们解决了盲信道估计中EM算法众所周知的局部极大值问题，该问题主要由信道估计中未知的相位模糊引起，而传统盲EM估计器无法解决此问题。为克服这一局限，我们提出利用译码器输出的外信息作为模型证据度量。基于PSK调制固有的对称性生成一组有限候选模型，并由译码器选择最可能的候选模型。仿真结果表明，当与简单卷积码结合时，相位感知EM算法在初始化阶段能可靠地消除相位模糊，在具有恒定相位模糊的频率选择性信道中，将局部收敛率从80%降低至接近0%。该算法仅在EM初始化阶段后调用一次，在后续Turbo迭代中增加的复杂度可忽略不计。