A Comparative Performance Analysis of Source and Channel Coding Techniques for Digital Communication Systems

Anis Zebiane

doi:doi:10.11648/j.ajcst.20260903.13

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A Comparative Performance Analysis of Source and Channel Coding Techniques for Digital Communication Systems

Anis Zebiane^*

Published in American Journal of Computer Science and Technology (Volume 9, Issue 3)

Received: 12 October 2025 Accepted: 12 October 2025 Published: 23 September 2026

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Abstract

This research investigates and compares the performance of five major coding techniques - Huffman, Run-Length Encoding, Arithmetic, Convolutional, and Bose-Chaudhuri-Hocquenghem (BCH) coding - within a complete digital communication system comprising source coding, channel coding, Binary Phase Shift Keying (BPSK) modulation, and transmission over an Additive White Gaussian Noise (AWGN) channel. The study evaluates these methods in terms of compression efficiency, error correction capability, and overall system reliability under different signal-to-noise ratio (SNR) conditions. MATLAB simulations were conducted for both text and image data to quantify compression ratios and bit error rate (BER) performance across SNR values ranging from 0 dB to 24 dB. Results demonstrate that Run-Length Encoding achieves superior compression performance for highly repetitive text, whereas Arithmetic coding provides near-optimal compression efficiency for general, non-repetitive data distributions such as natural images. Among channel coding schemes, Convolutional codes exhibit better resilience to noise at low-to-moderate SNR levels compared to BCH codes, particularly when decoded using the Viterbi algorithm, while BCH codes retain an advantage where guaranteed multiple-error correction within fixed-length blocks is required. Moreover, integrating Arithmetic coding with Convolutional coding into a single hybrid pipeline enhances end-to-end robustness by balancing data reduction and error resilience, achieving a bit error rate of zero at SNR levels of 4 dB and above while sustaining channel capacities exceeding 1.8 Mbps. These findings provide valuable insights into the optimal combination of source and channel coding strategies for modern digital communication systems, emphasizing the trade-offs between computational complexity, compression efficiency, and transmission reliability, and offering practical guidance for system designers working on bandwidth-constrained or noise-limited communication links.

Published in	American Journal of Computer Science and Technology (Volume 9, Issue 3)
DOI	10.11648/j.ajcst.20260903.13
Page(s)	120-137
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Source Coding, Channel Coding, Huffman Coding, Arithmetic Coding, Run-Length Encoding, Convolutional Codes, BCH Codes, BPSK Modulation

References

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[2]	Huffman, D. (1952). "A Method for the Construction of Minimum-Redundancy Codes". Proceedings of the IRE 40 (9):1098-1101. https://doi.org/10.1109/JRPROC.1952.273898
[3]	Tuominen, Johanna and Plosila, Juha. Asynchronous Viterbi Decoder in Action Systems.
[4]	Revisiting Huffman Coding: Toward Extreme Performance on Modern GPU Architectures. Available at: https://arxiv.org/abs/2010.10039
[5]	Evaluation of Huffman and Arithmetic Algorithms for Multimedia Compression Standards. Available at: https://arxiv.org/abs/1109.0216
[6]	Jancy, S. and Jayakumar, C.. (2019). Sequence Statistical Code Based Data Compression Algorithm for Wireless Sensor Network. Wireless Personal Communications. 106. https://doi.org/10.1007/s11277-019-06171-x
[7]	Comparison of Huffman and Arithmetic Coding. Available at: https://codingtube.tech
[8]	Proakis, J. G., Salehi, M., & Bauch, G. (2001). Digital communications. McGraw-Hill.
[9]	Lin, S., & Costello, D. J. (2004). Error control coding. Pearson Education India.
[10]	Ko, C. C., & Chen, J. (2018). A new BCH coded modulation scheme based on combined block coding and RS codes. Electronics, 7(6), 89. https://doi.org/10.3390/electronics7060089
[11]	Costello, D. J., Jr., Dolinar, S. J., & Kunz, T. G. (1983). Performance of trellis-coded modulation with Viterbi decoding for satellite and space communication. IEEE Journal on Selected Areas in Communications, 1(6), 954-975. https://doi.org/10.1109/JSAC.1983.1145922
[12]	C. Shakti, K. Asvir, M. Himanshu,(2016). Comparative Performance Analysis of Block and Convolution Codes. Indian Journal of Science and Technology. 9. https://doi.org/10.17485/ijst/2015/v8i1/106868
[13]	Zhu, Y., Liu, Y., Zhu, Y., Huang, M., Jiang, J., & Zhang, Y. (2025). Lossy Infrared Image Compression Based on Wavelet Coefficient Probability Modeling and Run-Length-Enhanced Huffman Coding. Sensors, 25(8), 2491. https://doi.org/10.3390/s25082491
[14]	Lei, X. (2024). The Comparison of RS Codes, LDPC Codes, and Turbo Codes. Highlights in Science, Engineering and Technology, 81, 496–504. https://doi.org/10.54097/8tn0ya36
[15]	Patel, D., et al. (2023). Performance Evaluation of Conventional and Neural Network-Based Decoder for an Audio of Low-Girth LDPC Code. Journal of Electrical and Computer Engineering, 2023, Article 1071142. https://doi.org/10.1155/2023/1071142

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APA Style

Zebiane, A. (2026). A Comparative Performance Analysis of Source and Channel Coding Techniques for Digital Communication Systems. American Journal of Computer Science and Technology, 9(3), 120-137. https://doi.org/10.11648/j.ajcst.20260903.13

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Zebiane, A. A Comparative Performance Analysis of Source and Channel Coding Techniques for Digital Communication Systems. Am. J. Comput. Sci. Technol. 2026, 9(3), 120-137. doi: 10.11648/j.ajcst.20260903.13

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AMA Style

Zebiane A. A Comparative Performance Analysis of Source and Channel Coding Techniques for Digital Communication Systems. Am J Comput Sci Technol. 2026;9(3):120-137. doi: 10.11648/j.ajcst.20260903.13

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@article{10.11648/j.ajcst.20260903.13,
  author = {Anis Zebiane},
  title = {A Comparative Performance Analysis of Source and Channel Coding Techniques for Digital Communication Systems},
  journal = {American Journal of Computer Science and Technology},
  volume = {9},
  number = {3},
  pages = {120-137},
  doi = {10.11648/j.ajcst.20260903.13},
  url = {https://doi.org/10.11648/j.ajcst.20260903.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20260903.13},
  abstract = {This research investigates and compares the performance of five major coding techniques - Huffman, Run-Length Encoding, Arithmetic, Convolutional, and Bose-Chaudhuri-Hocquenghem (BCH) coding - within a complete digital communication system comprising source coding, channel coding, Binary Phase Shift Keying (BPSK) modulation, and transmission over an Additive White Gaussian Noise (AWGN) channel. The study evaluates these methods in terms of compression efficiency, error correction capability, and overall system reliability under different signal-to-noise ratio (SNR) conditions. MATLAB simulations were conducted for both text and image data to quantify compression ratios and bit error rate (BER) performance across SNR values ranging from 0 dB to 24 dB. Results demonstrate that Run-Length Encoding achieves superior compression performance for highly repetitive text, whereas Arithmetic coding provides near-optimal compression efficiency for general, non-repetitive data distributions such as natural images. Among channel coding schemes, Convolutional codes exhibit better resilience to noise at low-to-moderate SNR levels compared to BCH codes, particularly when decoded using the Viterbi algorithm, while BCH codes retain an advantage where guaranteed multiple-error correction within fixed-length blocks is required. Moreover, integrating Arithmetic coding with Convolutional coding into a single hybrid pipeline enhances end-to-end robustness by balancing data reduction and error resilience, achieving a bit error rate of zero at SNR levels of 4 dB and above while sustaining channel capacities exceeding 1.8 Mbps. These findings provide valuable insights into the optimal combination of source and channel coding strategies for modern digital communication systems, emphasizing the trade-offs between computational complexity, compression efficiency, and transmission reliability, and offering practical guidance for system designers working on bandwidth-constrained or noise-limited communication links.},
 year = {2026}
}

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TY  - JOUR
T1  - A Comparative Performance Analysis of Source and Channel Coding Techniques for Digital Communication Systems
AU  - Anis Zebiane
Y1  - 2026/09/23
PY  - 2026
N1  - https://doi.org/10.11648/j.ajcst.20260903.13
DO  - 10.11648/j.ajcst.20260903.13
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 120
EP  - 137
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20260903.13
AB  - This research investigates and compares the performance of five major coding techniques - Huffman, Run-Length Encoding, Arithmetic, Convolutional, and Bose-Chaudhuri-Hocquenghem (BCH) coding - within a complete digital communication system comprising source coding, channel coding, Binary Phase Shift Keying (BPSK) modulation, and transmission over an Additive White Gaussian Noise (AWGN) channel. The study evaluates these methods in terms of compression efficiency, error correction capability, and overall system reliability under different signal-to-noise ratio (SNR) conditions. MATLAB simulations were conducted for both text and image data to quantify compression ratios and bit error rate (BER) performance across SNR values ranging from 0 dB to 24 dB. Results demonstrate that Run-Length Encoding achieves superior compression performance for highly repetitive text, whereas Arithmetic coding provides near-optimal compression efficiency for general, non-repetitive data distributions such as natural images. Among channel coding schemes, Convolutional codes exhibit better resilience to noise at low-to-moderate SNR levels compared to BCH codes, particularly when decoded using the Viterbi algorithm, while BCH codes retain an advantage where guaranteed multiple-error correction within fixed-length blocks is required. Moreover, integrating Arithmetic coding with Convolutional coding into a single hybrid pipeline enhances end-to-end robustness by balancing data reduction and error resilience, achieving a bit error rate of zero at SNR levels of 4 dB and above while sustaining channel capacities exceeding 1.8 Mbps. These findings provide valuable insights into the optimal combination of source and channel coding strategies for modern digital communication systems, emphasizing the trade-offs between computational complexity, compression efficiency, and transmission reliability, and offering practical guidance for system designers working on bandwidth-constrained or noise-limited communication links.
VL  - 9
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Author Information

Anis Zebiane

Department of Electrical, Computer, and Communication Engineering (ECCE), Notre Dame University - Louaize, Deir El Kamar, Lebanon

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http://orcid.org/0009-0001-6435-708X

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APA Style

Zebiane, A. (2026). A Comparative Performance Analysis of Source and Channel Coding Techniques for Digital Communication Systems. American Journal of Computer Science and Technology, 9(3), 120-137. https://doi.org/10.11648/j.ajcst.20260903.13

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ACS Style

Zebiane, A. A Comparative Performance Analysis of Source and Channel Coding Techniques for Digital Communication Systems. Am. J. Comput. Sci. Technol. 2026, 9(3), 120-137. doi: 10.11648/j.ajcst.20260903.13

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AMA Style

Zebiane A. A Comparative Performance Analysis of Source and Channel Coding Techniques for Digital Communication Systems. Am J Comput Sci Technol. 2026;9(3):120-137. doi: 10.11648/j.ajcst.20260903.13

Copy | Download

@article{10.11648/j.ajcst.20260903.13,
  author = {Anis Zebiane},
  title = {A Comparative Performance Analysis of Source and Channel Coding Techniques for Digital Communication Systems},
  journal = {American Journal of Computer Science and Technology},
  volume = {9},
  number = {3},
  pages = {120-137},
  doi = {10.11648/j.ajcst.20260903.13},
  url = {https://doi.org/10.11648/j.ajcst.20260903.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20260903.13},
  abstract = {This research investigates and compares the performance of five major coding techniques - Huffman, Run-Length Encoding, Arithmetic, Convolutional, and Bose-Chaudhuri-Hocquenghem (BCH) coding - within a complete digital communication system comprising source coding, channel coding, Binary Phase Shift Keying (BPSK) modulation, and transmission over an Additive White Gaussian Noise (AWGN) channel. The study evaluates these methods in terms of compression efficiency, error correction capability, and overall system reliability under different signal-to-noise ratio (SNR) conditions. MATLAB simulations were conducted for both text and image data to quantify compression ratios and bit error rate (BER) performance across SNR values ranging from 0 dB to 24 dB. Results demonstrate that Run-Length Encoding achieves superior compression performance for highly repetitive text, whereas Arithmetic coding provides near-optimal compression efficiency for general, non-repetitive data distributions such as natural images. Among channel coding schemes, Convolutional codes exhibit better resilience to noise at low-to-moderate SNR levels compared to BCH codes, particularly when decoded using the Viterbi algorithm, while BCH codes retain an advantage where guaranteed multiple-error correction within fixed-length blocks is required. Moreover, integrating Arithmetic coding with Convolutional coding into a single hybrid pipeline enhances end-to-end robustness by balancing data reduction and error resilience, achieving a bit error rate of zero at SNR levels of 4 dB and above while sustaining channel capacities exceeding 1.8 Mbps. These findings provide valuable insights into the optimal combination of source and channel coding strategies for modern digital communication systems, emphasizing the trade-offs between computational complexity, compression efficiency, and transmission reliability, and offering practical guidance for system designers working on bandwidth-constrained or noise-limited communication links.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - A Comparative Performance Analysis of Source and Channel Coding Techniques for Digital Communication Systems
AU  - Anis Zebiane
Y1  - 2026/09/23
PY  - 2026
N1  - https://doi.org/10.11648/j.ajcst.20260903.13
DO  - 10.11648/j.ajcst.20260903.13
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 120
EP  - 137
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20260903.13
AB  - This research investigates and compares the performance of five major coding techniques - Huffman, Run-Length Encoding, Arithmetic, Convolutional, and Bose-Chaudhuri-Hocquenghem (BCH) coding - within a complete digital communication system comprising source coding, channel coding, Binary Phase Shift Keying (BPSK) modulation, and transmission over an Additive White Gaussian Noise (AWGN) channel. The study evaluates these methods in terms of compression efficiency, error correction capability, and overall system reliability under different signal-to-noise ratio (SNR) conditions. MATLAB simulations were conducted for both text and image data to quantify compression ratios and bit error rate (BER) performance across SNR values ranging from 0 dB to 24 dB. Results demonstrate that Run-Length Encoding achieves superior compression performance for highly repetitive text, whereas Arithmetic coding provides near-optimal compression efficiency for general, non-repetitive data distributions such as natural images. Among channel coding schemes, Convolutional codes exhibit better resilience to noise at low-to-moderate SNR levels compared to BCH codes, particularly when decoded using the Viterbi algorithm, while BCH codes retain an advantage where guaranteed multiple-error correction within fixed-length blocks is required. Moreover, integrating Arithmetic coding with Convolutional coding into a single hybrid pipeline enhances end-to-end robustness by balancing data reduction and error resilience, achieving a bit error rate of zero at SNR levels of 4 dB and above while sustaining channel capacities exceeding 1.8 Mbps. These findings provide valuable insights into the optimal combination of source and channel coding strategies for modern digital communication systems, emphasizing the trade-offs between computational complexity, compression efficiency, and transmission reliability, and offering practical guidance for system designers working on bandwidth-constrained or noise-limited communication links.
VL  - 9
IS  - 3
ER  -

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