Finite-Length Linear Schemes for Joint Source-Channel Coding Over Gaussian Broadcast Channels With Feedback

被引:1
作者
Murin, Yonathan [1 ]
Kaspi, Yonatan [2 ,3 ]
Dabora, Ron [4 ]
Gunduz, Deniz [5 ]
机构
[1] Stanford Univ, Dept Elect Engn, Stanford, CA 94305 USA
[2] Univ Calif San Diego, Informat Theory & Applicat Ctr, La Jolla, CA 92093 USA
[3] Goldman Sachs, New York, NY 10282 USA
[4] Ben Gurion Univ Negev, Dept Elect & Comp Engn, IL-8410501 Beer Sheva, Israel
[5] Imperial Coll London, Dept Elect & Elect Engn, London SW7 2AZ, England
基金
以色列科学基金会; 欧洲研究理事会;
关键词
Broadcasting; channel coding; feedback communications; Gaussian channels; source coding; MULTIPLE-ACCESS; CAPACITY; COMMUNICATION; STRATEGIES; REGION;
D O I
10.1109/TIT.2017.2678988
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, we study linear encoding for a pair of correlated Gaussian sources transmitted over a two-user Gaussian broadcast channel in the presence of unit-delay noiseless feedback, abbreviated as the GBCF. Each pair of source samples is transmitted using a linear transmission scheme in a finite number of channel uses. We investigate three linear transmission schemes: A scheme based on the Ozarow-Leung (OL) code, a scheme based on the linear quadratic Gaussian (LQG) code of Ardestanizadeh et al., and a novel scheme derived in this paper using a dynamic programming (DP) approach. For the OL and LQG schemes we present lower and upper bounds on the minimal number of channel uses needed to achieve a target mean-square error (MSE) pair. For the LQG scheme in the symmetric setting, we identify the optimal scaling of the sources, which results in a significant improvement of its finite horizon performance, and, in addition, characterize the (exact) minimal number of channel uses required to achieve a target MSE. Finally, for the symmetric setting, we show that for any fixed and finite number of channel uses, the DP scheme achieves an MSE lower than the MSE achieved by either the LQG or the OL schemes.
引用
收藏
页码:2737 / 2772
页数:36
相关论文
empty
未找到相关数据