research-article

Data Compression Scheme Based on Discrete Sine Transform and Lloyd-Max Quantization

Authors:

Zewang ZhangAuthors Info & Claims

ICIIP '18: Proceedings of the 3rd International Conference on Intelligent Information Processing

Pages 46 - 51

https://doi.org/10.1145/3232116.3232124

Published: 19 May 2018 Publication History

Editorial Notes

A corrigendum was issued for this article on January 21, 2019. This can be found under the Source Materials tab.

Abstract

With the increase of mobile equipment and transmission data, Common Public Radio Interface (CPRI) between Building Base band Unit (BBU) and Remote Radio Unit (RRU) suffers amounts of increasing transmission data. It is essential to compress the data in CPRI if more data should be transferred without congestion under the premise of restriction of fiber consumption. A data compression scheme based on Discrete Sine Transform (DST) and Lloyd-Max quantization is proposed in distributed Base Station (BS) architecture. The time-domain samples are transformed by DST according to the characteristics of Orthogonal Frequency Division Multiplexing (OFDM) baseband signals, and then the coefficients after transformation are quantified by the Lloyd-Max quantizer. The simulation results show that the proposed scheme can work at various Compression Ratios (CRs) while the values of Error Vector Magnitude (EVM) are better than the limits in 3GPP.

Supplementary Material

pldi17-main189-corrigendum.pdf (p46-feng-corrigendum.pdf)

Corrigendum to "Data Compression Scheme Based on Discrete Sine Transform and Lloyd-Max Quantization", by Feng et al., ICIIP '18 Proceedings of the 3rd International Conference on Intelligent Information Processing

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References

[1]

Pfeiffer T. Next Generation Mobile Fronthaul Architectures {C}// Optical Fiber Communications Conference and Exhibition. IEEE, 2015:1--3.

[2]

Yao H J, Yuan F S. Exponential Stability Control for Networked Systems with Interval Distribution Time Delays {J}. Acta Informatica Malaysia, 2018, 2(1):7--9.

[3]

IM Ali, YY Jusoh, R Abdullah, et al. A Conceptual Framework For Measuring The Performance of Big Data Analytics Process {J}. Acta Informatica Malaysia, 2017, 1(2):13--14.

[4]

Guo B, Cao W, Tao A, et al. CPRI Compression Transport for LTE and LTE-A Signal in C-RAN {C}// International ICST Conference on Communications and Networking in China. 2012:843--849.

[5]

Yu Z C. An Improved Infrared and Visible Image Fusion Algorithm Based On Curvelet Transform {J}. Acta Informatica Malaysia, 2017, 1(1):36--38.

[6]

Li B Q, Li Z. The Design of Wireless Responder System Based on Radio Frequency Technology {J}. Acta Electronica Malaysia, 2018, 1(2):11--24.

[7]

Zhou J. Research on Lossless Compression Algorithms for Time Domain Sequences {D}. Xidian University, 2011.

[8]

Liu Y. Research on Time Sequence Compression Algorithms Based on Time Domain Processing {D}. Xidian University, 2011.

[9]

Feng C. Compression and Coding Algorithms for OFDM Baseband Modulated Signals {D}. Xidian University, 2012.

[10]

Wu H X. Research on Compression Algorithms for LTE Baseband Signals {D}. Xidian University, 2013.

[11]

Ramalho L, Fonseca M N, Klautau A, et al. An LPC-Based Fronthaul Compression Scheme {J}. IEEE Communications Letters, 2016, PP(99):1--1.

[12]

Chang W, Xie T, Zhou F, et al. A Prefiltering C-RAN Architecture with Compressed Link Data Rate in Massive MIMO {C}// IEEE Vehicular Technology Conference. IEEE, 2016:1--6.

[13]

IRA Hamid, NHA Sukor, CFM Foozy, et al. Network Monitoring System to Detect Unauthorized Connection {J}. Acta Electronica Malaysia, 2017, 1(2):13--16.

[14]

Adrian C, Abdullah R, Atan R, Jusoh Y. Theoretical Aspect In Formulating Assessment Model Of Big Data Analytics Environment {J}. Acta Mechanica Malaysia, 2018, 1(1):16--17.

[15]

Yao Q D. Fundamentals of Image Coding {M}. Tsinghua University Press, 2006.

[16]

Cao X H, Liu H W, Wu S J. The Application of Discrete Consine (Sine)Transform in Radar Target Recognition {J}. Journal of Circuits and Systems, 2007, 12(5):72--75.

[17]

Sayood K. Introduction to Data Compression {M}. Morgan Kaufmann, 2017.

[18]

Li X J. Discussion of Data Compression Performance for DST, DST_1 and Other Transform {J}. Telemetry Technology, 1986(1):16--21.

[19]

Max J. Quantizing for Minimum Distortion {J}. IRE Transactions on Information Theory, 1960, 6(1):7--12.

[20]

Nunna S, Kousaridas A, Ibrahim M, et al. Enabling Real-Time Context-Aware Collaboration through 5G and Mobile Edge Computing {C}// International Conference on Information Technology - New Generations. IEEE, 2015:601--605.

Digital Library

[21]

ETSI LTE. Evolved Universal Terrestrial Radio Access (E-UTRA); User Equipment (UE) Radio Transmission and Reception (3GPP TS 36.101 version 10.10.0 Release 10), March 2013 {J}. ETSI TS, 136(101): V10.

[22]

ETSI LTE. Evolved Universal Terrestrial Radio Access (E-UTRA); Base Station (BS) Radio Transmission and Reception (3GPP TS 36.104 version10.10.0 Release 10), March 2013 {J}. ETSI TS, 136(104): V10.

Cited By

Feng CLin MXie XZhang M(2019)Data Compression Scheme for Fronthaul Based on Vector QuantizationProceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence10.1145/3366194.3366238(252-257)Online publication date: 20-Sep-2019
https://dl.acm.org/doi/10.1145/3366194.3366238

Index Terms

Data Compression Scheme Based on Discrete Sine Transform and Lloyd-Max Quantization
1. Networks
  1. Network performance evaluation
    1. Network performance analysis

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cover image ACM Other conferences

ICIIP '18: Proceedings of the 3rd International Conference on Intelligent Information Processing

May 2018

249 pages

ISBN:9781450364966

DOI:10.1145/3232116

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Guilin: Guilin University of Technology, Guilin, China
International Engineering and Technology Institute, Hong Kong: International Engineering and Technology Institute, Hong Kong

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Association for Computing Machinery

New York, NY, United States

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Published: 19 May 2018

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ICIIP '18

ICIIP '18: 2018 3rd International Conference on Intelligent Information Processing

May 19 - 20, 2018

Guilin, China

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Cited By

Feng CLin MXie XZhang M(2019)Data Compression Scheme for Fronthaul Based on Vector QuantizationProceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence10.1145/3366194.3366238(252-257)Online publication date: 20-Sep-2019
https://dl.acm.org/doi/10.1145/3366194.3366238

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