Year: 2019
Type: preprint
Abstract: We propose two modified versions of the classical gradient ascent method to compute the capacity of finite-state channels with Markovian inputs. For the case that the channel mutual information is strongly concave in a parameter taking values in a compact convex subset of some Euclidean space, our first algorithm proves to achieve polynomial accuracy in polynomial time and, moreover, for some special families of finite-state channels our algorithm can achieve exponential accuracy in polynomial time under some technical conditions. For the case that the channel mutual information may not be strongly concave, our second algorithm proves to be at least locally convergent.
Source: arXiv (Cornell University)
Authors: Wu, Chengyu, Han, Guangyue, Anantharam, Venkat, Marcus, Brian
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G1513559549, NeTS: Small: PTERA: Prospect Theory Enhanced Resource Allocation, G2196882581, 高斯反馈容量, CIF: Small: Ensuring robustness of communication between networked energy harvesting nodes, FET: Medium: ROCS: Recurrent Oscillatory Computing Systems for Rapid Solution of NP-Complete and Deep Learning Problems, Emerging Frontiers of Science of Information
OpenAlex ID: W4310935008