Gaussian Channels with Feedback: Optimality, Fundamental Limitations, and Connections of Communication, Estimation, and Control
<|reference_start|>Gaussian Channels with Feedback: Optimality, Fundamental Limitations, and Connections of Communication, Estimation, and Control: Gaussian channels with memory and with noiseless feedback have been widely studied in the information theory literature. However, a coding scheme to achieve the feedback capacity is not available. In this paper, a coding scheme is proposed to achieve the feedback capacity for Gaussian channels. The coding scheme essentially implements the celebrated Kalman filter algorithm, and is equivalent to an estimation system over the same channel without feedback. It reveals that the achievable information rate of the feedback communication system can be alternatively given by the decay rate of the Cramer-Rao bound of the associated estimation system. Thus, combined with the control theoretic characterizations of feedback communication (proposed by Elia), this implies that the fundamental limitations in feedback communication, estimation, and control coincide. This leads to a unifying perspective that integrates information, estimation, and control. We also establish the optimality of the Kalman filtering in the sense of information transmission, a supplement to the optimality of Kalman filtering in the sense of information processing proposed by Mitter and Newton. In addition, the proposed coding scheme generalizes the Schalkwijk-Kailath codes and reduces the coding complexity and coding delay. The construction of the coding scheme amounts to solving a finite-dimensional optimization problem. A simplification to the optimal stationary input distribution developed by Yang, Kavcic, and Tatikonda is also obtained. The results are verified in a numerical example.<|reference_end|>
@article{liu2005gaussian,
    title={Gaussian Channels with Feedback: Optimality, Fundamental Limitations,
  and Connections of Communication, Estimation, and Control},
    author={Jialing Liu and Nicola Elia},
    journal={arXiv preprint arXiv:cs/0512097},
    year={2005},
    archivePrefix={arXiv},
    eprint={cs/0512097},
    primaryClass={cs.IT math.IT}
}