Title
Learning the Optimal LTP Segment Size for Minimal Turnaround Times
Date Issued
01 January 2022
Access level
metadata only access
Resource Type
conference paper
Author(s)
University of Houston
Publisher(s)
Institute of Electrical and Electronics Engineers Inc.
Abstract
Delay-tolerant networking (DTN) provides the mechanisms needed for both single and multihop space communications when cyclical bandwidth is available between the nodes. Because node contacts can be infrequent, achieving high bandwidth utilization during contacts or extending their duration as possible are topics of special relevance. In this work, an online approach for the Licklider Transmission Protocol (LTP) that is suitable for small size, weight, and power (SWaP) devices is suggested. The method dynamically searches for the optimal segmentation, i.e., the maximum payload size to be used to transmit data blocks. It is contrasted with a Q-learning-based method, which achieves similar performance with larger space complexity. Either option attains reliable bundle transmissions with acceptable delay and throughput levels well beyond the range achievable with static payload lengths and adverse conditions of high loss rates. The approach has minimal information requirements and does not require knowledge of the current bit-error-rate, transmission rate, modulation type, power, gains, nor the one-way light times. The method also requires minimal changes to the sending engine and is fully compatible with standard LTP receive engines. The technique can help to improve the effective DTN capacity by extending the usable contact times and achieving transmissions with improved performance when signal fluctuations occur.
Start page
4703
End page
4708
Volume
2022-May
Language
English
OCDE Knowledge area
Ingeniería de sistemas y comunicaciones
Scopus EID
2-s2.0-85137273343
ISSN of the container
15503607
ISBN of the container
9781538683477
Conference
IEEE International Conference on Communications: 2022 IEEE International Conference on Communications, ICC 2022
Sponsor(s)
This work was supported by grants #80NSSC21P2S44 and #80NSSC22K0259 from the National Aeronautics and Space Administration (NASA).
Sources of information: Directorio de Producción Científica Scopus