Optimum Full Information, Unlimited Complexity, Invariant, and Minimax Clock Skew and Offset Estimators for IEEE 1588

• Published in: IEEE transactions on communications 2019-05, Vol.67 (5), p.3624-3637
• Main Authors: Karthik, Anantha K., Blum, Rick S.
• Format: Article
• Language: English
• Subjects: Algorithms; Cellular networks; Clocks; Complexity; Complexity theory; Computation; Delays; ENGINEERING; Estimating techniques; Estimation; Estimators; Exchange schemes; IEEE 1588 Precision; IEEE 1588 precision time protocol; Invariants; Minimax Estimation; Minimax technique; Optimum Invariant Estimation; Parameter estimation; Probability density function; Protocols; Queues; Switching theory; Synchronism; Synchronization; Time Protocol; Time synchronization
• ISSN: 0090-6778; 1558-0857
• DOI: 10.1109/TCOMM.2019.2900317

This paper addresses the problem of clock skew and offset estimation (CSOE) for the IEEE 1588 precision time protocol. Built on the classical two-way message exchange scheme, IEEE 1588 is a prominent synchronization protocol for packet switched networks. Due to the presence of random queuing delays in a packet switched network, the joint recovery of clock skew and offset from the received packet timestamps can be viewed as a statistical estimation problem. Recently, assuming perfect clock skew information, minimax optimum clock offset estimators were developed for IEEE 1588. Building on this work, we first develop joint optimum invariant clock skew and offset estimators for IEEE 1588 for known queuing delay statistics and unlimited computational complexity. We then show that the developed estimators are minimax optimum, i.e., these estimators minimize the maximum skew normalized mean square estimation error over all possible values of the unknown parameters. Minimax optimum estimators that utilize information from past timestamps to improve accuracy are also introduced. The developed optimum estimators provide useful fundamental limits for evaluating the performance of CSOE schemes. These performance limits can aid system designers to develop algorithms with the desired computational complexity that achieve performance close to the performance of the optimum estimators. If a designer finds an approach with a complexity they find acceptable and which provides performance close to the optimum performance, they can use it and know they have near optimum performance. This is precisely the approach used in communications when comparing to capacity.