解决相位模糊问题，让盲信道估计更稳定可靠。
Phase-Aware Code-Aided EM Algorithm for Blind Channel Estimation in PSK-Modulated OFDM
- 利用译码器反馈信息识别相位候选模型，突破传统盲估计困局。
- 在频率选择性信道中，本地收敛率从80%降至接近0%。
- 仅初始化时运行一次，对后续迭代几乎无额外计算负担。
本文提出一种完全盲的相位感知期望最大化（EM）算法，用于相移键控（PSK）调制的正交频分复用（OFDM）系统。针对传统盲EM算法在信道估计中因相位模糊导致的局部最优问题，提出利用译码器输出的外信息作为模型证据，基于PSK调制的固有对称性生成有限候选模型集，由译码器选出最可能的模型。仿真表明，结合简单卷积码后，该算法可在初始化阶段可靠消除相位模糊，在具有恒定相位模糊的频率选择性信道中，将本地收敛率从80%降至近乎0%。算法仅在初始化阶段调用一次，后续湍流迭代中增加的复杂度可忽略不计。
原文摘要 · Abstract (English)
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.
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