In optical fiber communication, due to the random variation of the environment, the state of polarization (SOP) fluctuates randomly with time leading to distortion and performance degradation. The memory-less SOP fluctuations can be regarded as a two-by-two random unitary matrix. In this paper, for what we believe to be the first time, the capacity of the polarization drift channel under an average power constraint with imperfect channel knowledge is characterized. An achievable information rate (AIR) is derived when imperfect channel knowledge is available and is shown to be highly dependent on the channel estimation technique. It is also shown that a tighter lower bound can be achieved when a unitary estimation of the channel is available. However, the conventional estimation algorithms do not guarantee a unitary channel estimation. Therefore, by considering the unitary constraint of the channel, a data-aided channel estimator based on the Kabsch algorithm is proposed, and its performance is numerically evaluated in terms of AIR. Monte Carlo simulations show that Kabsch outperforms the least-square error algorithm. In particular, with complex, Gaussian inputs and eight pilot symbols per block, Kabsch improves the AIR by 0:2 to 0:35 bits/symbol throughout the range of studied signal-to-noise ratios.
翻译:在光纤通信中,由于环境的随机变化,极化状态随时间导致扭曲和性能退化而随机波动。不留记忆的SOP波动可被视为一个2x2的随机单一矩阵。在本文件中,我们认为这是第一次,在平均功率限制和频道知识不完善的情况下,对极化流流渠道的能力进行了定性;当有不完善的频道知识时,可以得出信息率(AIR),显示它高度依赖频道估测技术;还表明,在对频道进行统一估计时,可以实现更紧的更低约束。然而,传统的估算算法并不能保证对频道进行统一的估计。因此,通过考虑频道的统一限制,提议以卡布希算法为基础的数据辅助频道估计仪,其性能用AIR.M.Monte Carlo模拟显示,卡布希的测算法比最差的差的差值要高。特别是复杂、高调和8个试测符号,卡布希的测算法不能保证对频道进行统一估计。因此,通过考虑频道的统一限制,建议以卡布什至AIR2的信号范围:从0/BM2改进了A/CAR的信号范围。