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Quantized Identification With Dependent Noise and Fisher Information Ratio of Communication Channels

机译：通信信道相关噪声和Fisher信息比率的量化识别

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System identification is studied in which the system output is quantized, transmitted through a digital communication channel, and observed afterwards. This paper explores strong convergence, efficiency, and complexity of identification algorithms under colored noise and dependent communication channels. It first presents algorithms for certain core identification problems using quantized observations on the basis of empirical measures and nonlinear mappings. Strong consistency (with-probability-one convergence) is established under ¿-mixing noises. Furthermore, with pre-quantization signal processing, it is shown that certain modified algorithms can achieve asymptotic efficiency under correlated noises. To improve convergence speeds, quantization threshold adaptation algorithms are introduced. These results are then used to study the impact of communication channels on system identification under dependent channels. The concept of fisher information ratio is introduced to characterize such impact. It is shown that the fisher information ratio can be calculated from certain channel characteristic matrices. The relationship between the fisher information ratio and Shannon's channel capacity is discussed from the angle of time and space information. The methods of identification input designs that link general system parameters to core identification problems are reviewed.
机译：研究了系统识别，其中对系统输出进行量化，通过数字通信通道进行传输，然后进行观察。本文探讨了有色噪声和相关通信信道下识别算法的强收敛性，效率和复杂性。首先基于经验测度和非线性映射，提出了利用量化观测值解决某些岩心识别问题的算法。在ƒâ€混合噪声下建立强一致性（具有概率一收敛）。此外，通过预量化信号处理，表明某些改进的算法可以在相关噪声下实现渐近效率。为了提高收敛速度，引入了量化阈值自适应算法。然后将这些结果用于研究通信通道对依赖通道下系统标识的影响。引入渔民信息比率的概念来表征这种影响。结果表明，可以从某些信道特征矩阵中计算出费舍尔信息比率。从时空信息的角度探讨了费舍尔信息比率与香农河道通行能力之间的关系。审查了将通用系统参数链接到核心识别问题的识别输入设计方法。

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