Bit Error Rate and Decoding Energy Consumption Analysis of Classical and Modern Error Control Codes for Wireless Sensor Networks

Chekwube Chukwunwendu Onyeonwu; Chimaihe Barnabas Mbachu; Chidiebere Nnaedozie Muoghalu; Chibuogwu Happy Ajieh

doi:10.61424/ijans.v4i3.1009

Authors

Chekwube Chukwunwendu Onyeonwu Department of Electrical/ Electronic Engineering, Faculty of Engineering, Chukwuemeka Odumegwu Ojukwu University
Chimaihe Barnabas Mbachu Department of Electrical/ Electronic Engineering, Faculty of Engineering, Chukwuemeka Odumegwu Ojukwu University
Chidiebere Nnaedozie Muoghalu Department of Electrical/ Electronic Engineering, Faculty of Engineering, Chukwuemeka Odumegwu Ojukwu University
Chibuogwu Happy Ajieh Department of Electrical/ Electronic Engineering, Faculty of Engineering, Chukwuemeka Odumegwu Ojukwu University

DOI:

https://doi.org/10.61424/ijans.v4i3.1009

Keywords:

Wireless Sensor Networks, Forward Error Correction, Bit Error Rate, Decoding Energy per Bit, MATLAB Simulation

Abstract

Wireless Sensor Networks (WSNs) require reliable and energy-efficient communication because sensor nodes operate under limited energy resources and are frequently deployed in hostile environments where transmission errors are inevitable. Forward Error Correction (FEC) techniques improve communication reliability by correcting transmission errors without retransmission; however, different coding schemes exhibit varying trade-offs between error correction capability and decoding energy consumption. This study presents a comparative analysis of the Bit Error Rate (BER) and Decoding Energy per Bit (DEB) performances of four widely used error control codes, namely Bose–Chaudhuri–Hocquenghem (BCH), Reed–Solomon (RS), Convolutional, and Low-Density Parity-Check (LDPC) codes, for Wireless Sensor Networks. MATLAB simulations were conducted using identical communication conditions comprising Binary Phase Shift Keying (BPSK) modulation, an Additive White Gaussian Noise (AWGN) channel, and Signal-to-Noise Ratio (Eb/No) values ranging from 1 to 9 dB. The LDPC scheme employed the Min-Sum decoding algorithm, while BCH, Reed–Solomon, and Convolutional codes employed their conventional decoding algorithms. The results showed that BER decreased with increasing Eb/No for all coding schemes, with LDPC consistently achieving the best error correction performance across the evaluated SNR range. In contrast, BCH recorded the lowest mean decoding energy consumption (0.400 nJ/bit), followed by Convolutional (0.440 nJ/bit), Reed–Solomon (0.480 nJ/bit), and LDPC (1.766 nJ/bit). The findings reveal a clear trade-off between communication reliability and decoding energy efficiency, demonstrating that although LDPC provides superior BER performance, it requires significantly higher decoding energy than the classical coding schemes. This study provides useful insights for selecting suitable error control codes based on the reliability and energy requirements of Wireless Sensor Network applications.

Downloads

Download data is not yet available.

References

Abdulwahid, A., & Salih, M. (2022). Wireless Sensor Networks Applications, Challenges, and Security Requirements. Proceedings of 2nd International Multi-Disciplinary Conference Theme: Integrated Sciences and Technologies, IMDC-IST 2021, 7-9 September 2021, Sakarya, Turkey. https://doi.org/10.4108/eai.7-9-2021.2314823

Ali, M. M., Hashim, S. J., Chaudhary, M. A., Ferré, G., Rokhani, F. Z., & Ahmad, Z. (2023). A Reviewing Approach to Analyze the Advancements of Error Detection and Correction Codes in Channel Coding with Emphasis on LPWAN and IoT Systems. IEEE Access, 11, 127077–127097. https://doi.org/10.1109/ACCESS.2023.3331417

Alrajeh, N. A., Marwat, U., Shams, B., & Shah, S. S. H. (2015). Error-Correcting Codes in Wireless Sensor Networks: An Energy Perspective. 2.

Chowdhury, S. M., & Hossain, A. (2020). Different Energy Saving Schemes in Wireless Sensor Networks: A Survey. Wireless Personal Communications, 114(3), 2043–2062. https://doi.org/10.1007/s11277-020-07461-5

Gupta, S. K., & Sinha, P. (2014). Overview of Wireless Sensor Network: A Survey. 3(1).

Hamdan, M., & Abdullah, A. (2016). Analysis and Performance Evaluation of Convolutional Codes over Binary Symmetric Channel Using MATLAB.

Kadel, R., Paudel, K., Guruge, D. B., & Halder, S. J. (2020). Opportunities and Challenges for Error Control Schemes for Wireless Sensor Networks: A Review. Electronics, 9(3), 504. https://doi.org/10.3390/electronics9030504

Kundaeli, H. N. (2020). The Analysis of Truncated ARQ and HARQ Schemes Using Transition Diagrams. International Journal of Engineering, Science and Technology, 12(2), 35–51. https://doi.org/10.4314/ijest.v12i2.5

Pandey, M., & Pandey, V. K. (2015). Comparative Performance Analysis of Block and Convolution Codes. International Journal of Computer Applications, 119.

Roca, V., Teibi, B., Burdinat, C., Tran-Thai, T., & Thienot, C. (2017). Block or Convolutional AL-FEC Codes? A Performance Comparison for Robust Low-Latency Communications.

Sai, K., Pavankumar, K., Jangam, S., Goud Kumar, & Kosgi. (2017). Wireless Sensor Networks and Applications. https://doi.org/10.13140/RG.2.2.23192.19207

Tzimpragos, G., Kachris, C., Djordjevic, I. B., Cvijetic, M., Soudris, D., & Tomkos, I. (2016). A Survey on FEC Codes for 100 G and Beyond Optical Networks. IEEE Communications Surveys & Tutorials, 18(1), 209–221. https://doi.org/10.1109/COMST.2014.2361754

Venkatesan, D., Sunil, B., Reddy, B. A., & Naidu, K. J. (2016). Low Power ASIC Implementation of LDPC Decoder.

Yang, K., & Du, W. (2024). A Low-Density Parity-Check Coding Scheme for LoRa Networking. ACM Transactions on Sensor Networks, 20(4), 98:1–98:29. https://doi.org/10.1145/3665928