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

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.