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Dynamic Code Selection Method for Content Transfer in Deep Space Network University of Mississippi eGrove Electronic Theses and Dissertations Graduate School 2019 Dynamic Code Selection Method for Content Transfer in Deep Space Network Rojina Adhikary University of Mississippi Follow this and additional works at: https://egrove.olemiss.edu/etd Part of the Electrical and Computer Engineering Commons Recommended Citation Adhikary, Rojina, "Dynamic Code Selection Method for Content Transfer in Deep Space Network" (2019). Electronic Theses and Dissertations. 1528. https://egrove.olemiss.edu/etd/1528 This Dissertation is brought to you for free and open access by the Graduate School at eGrove. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of eGrove. For more information, please contact [email protected]. DYNAMIC CODE SELECTION METHOD FOR CONTENT TRANSFER IN DEEP SPACE NETWORK A Dissertation presented in partial fulfillment of requirements for the degree of Doctor of Philosophy in the Department of Electrical Engineering The University of Mississippi by Rojina Adhikary May 2019 Copyright Rojina Adhikary 2019 ALL RIGHTS RESERVED ABSTRACT Space communications feature large round-trip time delays (for example, be- tween 6.5 and 44 minutes for Mars to Earth and return, depending on the actual distance between the two planets) and highly variable data error rates, for example, bit error rate (BER) of 10−5 is very common and even higher BERs on the order of 10−1 is observed in the deep-space environment. We develop a new content transfer protocol based on RaptorQ codes and turbo codes together with a real-time channel prediction model to maximize file transfer from space vehicles to the Earth stations. While turbo codes are used to correct channel errors, RaptorQ codes are applied to eliminate the need for negative-acknowledgment of the loss of any specific packet. To reduce the effect of channel variation, we develop a practical signal-to-noise ra- tio (SNR) prediction model that is used to periodically adjust the turbo encoder in distant source spacecraft. This new protocol, termed as dynamic code selection method (DCSM), is compared with two other methods: turbo based genie method (upper bound of DCSM performance) assuming that the channel condition is per- fectly known in advance and a static method in which a fixed turbo encoder is used throughout a communication pass. Simulation results demonstrate that the genie can increase telemetry channel throughput expressed in terms of the total number of successfully delivered files during a communication pass by about 20.3 % and DCSM achieves more than 99 % of genie, compared to the static approach being used currently. Logistic regression and neural networks are also used to develop two different SNR condition prediction models. Comparative performance analysis of these channel prediction models are conducted. ii ACKNOWLEDGEMENTS I would like to express my sincere gratitude to my adviser Prof. Daigle for the continuous support, guidance, and encouragement during the course of my masters and PhD studies. My heartfelt thank goes to him for the invaluable opportunity to pursue my graduate studies in the United States. I am especially grateful to Prof. Cao for his insightful suggestions and inputs over numerous discussions. I am grateful to Prof. Vish and Dr. Wang for all the valuable discussions we had during our departmental presentation sessions. I am thankful to Prof. Alidaee for consenting to be a member of my dissertation defense committee. This work was supported in part by NASA EPSCoR program under grants NNX13AB31A and NNX14AN38A. My sincere gratitude goes to Mr. Kamal Oudrhiri of Jet Propulsion Laboratory (JPL) Communications Architectures and Research Section, Radio Science Group and Mr. Philip Tsao of JPL Communications Archi- tectures and Research Section, Signal Processing and Networks Group for helping with the advanced water vapor radiometer data used in this work. My sincere ac- knowledgments to Mr. Philip Tsao for his helpful suggestions and comments. Lastly, I would like to thank my family for their continuous support. I am thankful to my loving husband Dr. Amrit Kharel for all the support and encourage- ment. Without him, I would not be able to come to this beautiful point in my life. iii TABLE OF CONTENTS ABSTRACT :::::::::::::::::::::::::::::::::::: ii ACKNOWLEDGEMENTS :::::::::::::::::::::::::::: iii LIST OF FIGURES :::::::::::::::::::::::::::::::: vi LIST OF TABLES ::::::::::::::::::::::::::::::::: x LIST OF ABBREVIATIONS ::::::::::::::::::::::::::: xii INTRODUCTION ::::::::::::::::::::::::::::::::: 1 DEEP SPACE NETWORK :::::::::::::::::::::::::::: 5 DCSM :::::::::::::::::::::::::::::::::::::::: 26 LOGISTIC REGRESSION BASED PREDICTION MODEL :::::::::: 87 NEURAL NETWORKS BASED PREDICTION MODEL ::::::::::: 110 CONCLUSION ::::::::::::::::::::::::::::::::::: 128 BIBLIOGRAPHY ::::::::::::::::::::::::::::::::: 130 APPENDICES ::::::::::::::::::::::::::::::::::: 136 PARTIAL AUTOCORRELATION FUNCTION ::::::::::::::::: 137 SAMPLING THEORY AND SPLINE INTERPOLATION ::::::::::: 140 iv VITA :::::::::::::::::::::::::::::::::::::::: 142 v LIST OF FIGURES 2.1 A functional view of the DSN in the context of mission operations. .6 2.2 A typical deep space telemetry system. 20 (31:4) 3.1 Zenith sky brightness temperature Tsky (K) vs time (hour) plot of DSS-25 measured at 31.4 GHz during a communication pass on Septem- ber 26, 2005. 33 (32) 3.2 Zenith atmospheric noise temperature Latm (dB) vs time (hours) plot of DSS-25 at Ka-band (32 GHz) during a communication pass on Septem- ber 26, 2005. 34 3.3 System operating noise temperature Top (K) and sky brightness tem- perature Tsky (K) vs time (hours) plot of DSS-25 at 32 GHz during a communication pass on September 26, 2005. 36 3.4 Channel bit-SNR (dB) vs time (hours) plot of DSS-25 during a com- munication pass on September 26, 2005. 39 3.5 Summary of encoding and decoding processes occurring at a DSN sta- tion and the distant spacecraft. 39 3.6 Turbo-CRC encoder. 44 3.7 Turbo encoded frame with rate dependent attached synchronization marker, a RaptorQ packet contribution, and termination sequence. 44 3.8 A tree showing the genie, the DCSM and the static method and their sub-categories. 52 3.9 Autocorrelation of bit-SNR of a communication pass on September 26, 2005. 68 vi 3.10 Partial autocorrelation of bit-SNR of a communication pass on Septem- ber 26, 2005. 68 3.11 Predicted and actual temporal bit-SNR during a communication pass on September 26, 2005. 71 3.12 Percentage of correct channel bit-SNR estimations obtained with our channel bit-SNR prediction model for passes under consideration when RTT = 20 minutes. 71 3.13 Percentage of correct channel bit-SNR estimations obtained with our channel bit-SNR prediction model for passes under consideration with RTT = 6:5 minutes. 74 3.14 Percentage of correct channel bit-SNR estimations obtained with our channel bit-SNR prediction model for passes under consideration with RTT = 44 minutes. 75 3.15 An example output showing serial transmissions of files during a com- munication pass on March 13 2008 using DCSM adaptive y with av- erage FER approach. The color green indicates successful decoding of the file and the red indicates that the number of clean packets received were not enough to decode the file and additional transmissions are needed. 77 3.16 Bits transmitted (BTx), bits received (BRcvd), data transmitted (DTx), and data received (DRcvd) during all the communication passes with adaptive best approach. 78 vii 3.17 A plot showing the number of files successfully received at Earth station by time t = 100 minutes on a communication pass of December 9, 2001. 84 3.18 Total number of files of size 50 MBs successfully transmitted from MRO and successfully received by DSS-25. 85 4.1 Supervised learning process. 89 4.2 Sigmoid function. 90 4.3 Zenith wet-path delay vs bit-SNR curve of all the 202 communication passes. 96 4.4 Antenna elevation angle vs bit-SNR curve of all the 202 communication passes. 96 4.5 Antenna azimuth vs bit-SNR curve of all the 202 communication passes. 97 4.6 An example of under-fitting, proper-fit, and over-fitting. 101 4.7 Plots obtained using the logistic regression based channel bit-SNR range prediction model for our test dataset with RTT = 20 minutes. 106 4.8 Percentage of correct channel bit-SNR estimations obtained with the logistic regression based channel bit-SNR range prediction model for our test dataset with RTT = 6:5 minutes. 107 4.9 Percentage of correct channel bit-SNR estimations obtained with the logistic regression based channel bit-SNR range prediction model for our test dataset with RTT = 44 minutes. 108 5.1 A simple representation of the body of a single neuron represented by the orange circle, with three inputs x1; x2; x3 and an output hθ(x). 111 viii 5.2 A neural network with three inputs x1; x2; x3, a hidden layer, and three 3 output classes. The hΘ(x) 2 R ...................... 113 5.3 Plots obtained using a neural networks based channel bit-SNR range prediction model for our test dataset. 126 ix LIST OF TABLES 3.1 Table of symbols used in Section 3.1. 30 3.2 Probability of successful decoding of a RaptorQ encoded source block for different overhead values. 45 3.3 Dynamic turbo code assignment for different channel bit-SNR ranges and their names. 47 3.4 An illustration of an actual and predicted bit-SNR occurrence propor- tion of a communication pass. 58 3.5 Channel bit-SNR estimation accuracy (in %) achieved with the AR(1) based prediction model. 72 3.6 Parameters used in the simulation setup. 75 3.7 Data rates, Rd, for different channel modulation rates and turbo codes with K = 8920 bits combination. 76 3.8 Amount of data transferred (in Gb) and number of file transmissions on average during a communication pass with static approach. 81 3.9 Amount of data transferred (in Gb) and number of file transmissions on average during a communication pass with DCSM approach. 81 3.10 Amount of data transferred (in Gb) and number of file transmissions on average during a communication pass with genie approach.
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