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Published November 2015 | public
Journal Article

Delay Reduction for Instantly Decodable Network Coding in Persistent Channels With Feedback Imperfections

Abstract

This paper considers the multicast decoding delay reduction problem for generalized instantly decodable network coding (G-IDNC) over persistent erasure channels with feedback imperfections. The feedback scenario discussed is the most general situation in which the sender does not always receive acknowledgments from the receivers after each transmission and the feedback communications are subject to loss. The decoding delay increment expressions are derived and employed to express the decoding delay reduction problem as a maximum weight clique problem in the G-IDNC graph. This paper provides a theoretical analysis of the expected decoding delay increase at each time instant. Problem formulations in simpler channel and feedback models are shown to be special cases of the proposed generalized formulation. Since finding the optimal solution to the problem is known to be NP-hard, a suboptimal greedy algorithm is designed and compared with blind approaches proposed in the literature. Through extensive simulations, the proposed algorithm is shown to outperform the blind methods in all situations and to achieve significant improvement, particularly for high time-correlated channels.

Additional Information

© 2015 IEEE. Manuscript received July 9, 2014; revised November 19, 2014 and March 23, 2015; accepted June 1, 2015. Date of publication July 17, 2015; date of current version November 9, 2015. The work of S. Sorour and T. Y. Al-Naffouri was supported by KAUST Project EE002355 at the Research Institute, King Fahd University of Petroleum and Minerals. A part of this paper [1] appeared in Proceedings of the IEEE International Conference on Wireless and Mobile Computing, Networking, and Communications (WiMob'13), Lyon, France. The associate editor coordinating the review of this paper and approving it for publication was Z. Wang.

Additional details

Created:
August 20, 2023
Modified:
October 25, 2023