A Comparative Study of Channel Coding Schemes in Wireless Communication: Turbo, LDPC, and Polar Codes
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A Comparative Study of Channel Coding Schemes in Wireless Communication: Turbo, LDPC, and Polar Codes

Zhengxu Li 1*
1 International School, Beijing University of Posts and Telecommunications, Beijing, 100876, China
*Corresponding author: lizhengxu@bupt.edu.cn
Published on 3 December 2025
Volume Cover
ACE Vol.211
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-579-0
ISBN (Online): 978-1-80590-580-6
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Abstract

The fast growth of wireless communication means we need to send data reliably, even with noise and interference, which therefore makes channel coding indispensable. This paper looks at three landmark schemes that shaped modern wireless communications: Turbo codes, Low-Density Parity-Check (LDPC) codes, and Polar codes. Specifically, it explains the basic ideas of channel coding, and it discusses methods to approach the Shannon limit. After that, this paper describes the structure and decoding steps for each code. Turbo codes use parallel concatenation and iterative decoding. LDPC codes need sparse parity-check matrices and belief propagation. Also, Polar codes get reliability using channel polarization. Moreover, the codes are also compared based on their error performance, how complex they are to decode, the delay they introduce, and whether they are used in standards. The results reveal that Turbo codes work best for medium to long blocks but have problems with delay and error floors. LDPC codes perform well with long blocks and high throughput. Polar codes are useful for short blocks, even though they are harder to decode. This comparison shows that the codes complement each other and suggests that adaptive, AI-assisted coding could be a promising approach for 6G.

Keywords:

Channel Coding, Turbo Codes, Low-Density Parity-Check (LDPC) Codes, Polar Codes, Wireless Communication

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Li,Z. (2025). A Comparative Study of Channel Coding Schemes in Wireless Communication: Turbo, LDPC, and Polar Codes. Applied and Computational Engineering,211,42-49.

References

[1]. Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379-423.

[2]. Berrou, C., Glavieux, A., & Thitimajshima, P. (1993). Near Shannon limit error-correcting coding and decoding: Turbo codes. In Proceedings of IEEE ICC.

[3]. Gallager, R. G. (1962). Low-density parity-check codes. IRE Transactions on Information Theory, 8(1), 21-28.

[4]. Arıkan, E. (2009). Channel polarization: A method for constructing capacity-achieving codes for symmetric binary-input memoryless channels. IEEE Transactions on Information Theory, 55(7), 3051-3073.

[5]. Berrou, C., & Glavieux, A. (1996). Near optimum error correcting coding and decoding: Turbo codes. IEEE Transactions on Communications, 44(10), 1261-1271.

[6]. Bahl, L. R., Cocke, J., Jelinek, F., & Raviv, J. (1974). Optimal decoding of linear codes for minimizing symbol error rate. IEEE Transactions on Information Theory, 20(2), 284-287.

[7]. 3GPP TS 25.212. (2007). Multiplexing and channel coding (FDD) (Release 7).

[8]. 3GPP TS 36.212. (2011). Multiplexing and channel coding (E-UTRA) (Release 10).

[9]. MacKay, D. J. C. (1999). Good error-correcting codes based on very sparse matrices. IEEE Transactions on Information Theory, 45(2), 399–431.

[10]. Kschischang, F. R., Frey, B. J., & Loeliger, H.-A. (2001). Factor graphs and the sum-product algorithm. IEEE Transactions on Information Theory, 47(2), 498–519.

[11]. ETSI EN 302 307-1 V1.4.1. (2014). Digital Video Broadcasting (DVB); Second generation framing structure, channel coding and modulation systems for broadcasting, interactive services, news gathering and other broadband satellite applications (DVB-S2).

[12]. 3GPP TS 38.212. (2018). NR; Multiplexing and channel coding (Release 15).

[13]. Tal, I., & Vardy, A. (2015). List decoding of polar codes. IEEE Transactions on Information Theory, 61(5), 2213–2226.

[14]. Niu, K., & Chen, K. (2012). CRC-aided decoding of polar codes. IEEE Communications Letters, 16(10), 1668–1671.

[15]. Richardson, T., & Urbanke, R. (2008). Modern coding theory. Cambridge University Press.

[16]. IEEE Std 802.11-2016. IEEE standard for information technology-Telecommunications and information exchange between systems-Local and metropolitan area networks-Specific requirements.

[17]. ETSI EN 302 307-2. (2015). Digital Video Broadcasting (DVB); Second generation framing structure, channel coding and modulation systems for broadcasting, interactive services, news gathering and other broadband satellite applications; Part 2: DVB-S2 Extensions (S2X).

[18]. Balatsoukas-Stimming, A., Parizi, M. B., & Burg, A. (2015). LLR-based successive cancellation list decoding of polar codes. IEEE Transactions on Signal Processing, 63(19), 5165–5179.

[19]. 3GPP TR 38.802. (2017). NR; Study on new radio access technology; Physical layer aspects (v14.2.0).

[20]. Han, Y., & Ryan, W. E. (2009). Low-floor decoders for LDPC codes. IEEE Transactions on Communications, 57(6), 1663–1673.

[21]. Zhang, Z., Zhang, L., & Chen, D. (2018). Design of hybrid concatenated coding schemes for next-generation wireless systems. IEEE Communications Magazine, 56(3), 124–130.

[22]. Nachmani, E., Be’ery, Y., & Burshtein, D. (2016). Learning to decode linear codes using deep learning. In 54th Annual Allerton Conference on Communication, Control, and Computing (pp. 341-346).

[23]. Gruber, T., Cammerer, S., Hoydis, J., & ten Brink, S. (2017). On deep learning-based channel decoding. In 51st Annual Conference on Information Sciences and Systems (CISS) (pp. 1–6).

[24]. Xie, H., Qin, Z., Li, G. Y., & Zhao, B. (2021). A lite distributed semantic communication system for Internet of Things. IEEE Journal on Selected Areas in Communications, 39(1), 142–153.

[25]. Qin, Z., Tao, X., Lu, J., & Li, G. Y. (2021). Semantic communications: Principles and challenges. IEEE Communications Surveys & Tutorials, 23(3), 1575–1600.

Cite this article

Li,Z. (2025). A Comparative Study of Channel Coding Schemes in Wireless Communication: Turbo, LDPC, and Polar Codes. Applied and Computational Engineering,211,42-49.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-SPML 2026 Symposium: The 2nd Neural Computing and Applications Workshop 2025

ISBN: 978-1-80590-579-0(Print) / 978-1-80590-580-6(Online)
Editor: Marwan Omar, Guozheng Rao
Conference date: 21 December 2025
Series: Applied and Computational Engineering
Volume number: Vol.211
ISSN: 2755-2721(Print) / 2755-273X(Online)