2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | N.1.3 | ||
| Paper Title | Upper Bound Scalability on Achievable Rates of Batched Codes for Line Networks | ||
| Authors | Shenghao Yang, Jie Wang, The Chinese University of Hong Kong, Shenzhen, China | ||
| Session | N.1: Network Coding I | ||
| Presentation | Lecture | ||
| Track | Networking and Network Coding | ||
| Manuscript | Click here to download the manuscript | ||
| Virtual Presentation | Click here to watch in the Virtual Symposium | ||
| Abstract | The capacity of line networks with buffer size constraints is an open, but practically important problem. In this paper, the upper bound on the achievable rate of a class of codes, called batched codes, is studied for line networks where the channels have 0 zero-error capacity. Batched codes enable a range of buffer size constraints, and are general enough to include special coding schemes studied in the literature for line networks. Existing works have characterized the achievable rates of batched codes for several classes of parameter sets, but leave the cut-set bound as the best existing general upper bound. In this paper, we provide upper bounds on the achievable rates of batched codes as functions of line network length for these parameter sets. Our upper bounds in order of the network length match with the existing achievability results. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
