A MULTIPLEXED TRANSMISSION SYSTEM FOR ENVIRONMENTAL MONITORING WITH CONVOLUTION ENCODING AND VITERBI DECODING

Reliable transmission of environmental sensor data over noisy wireless channels remains a critical challenge in large scale environment monitoring systems. Ensuring data integrity while maintaining low latency and bandwidth efficiency is essential for real-time applications. The proposed work presents an efficient simulation framework for multiplexed transmission of data using Convolutional Encoding and Viterbi Decoding. The proposed system combines multiple sensor streams via Time Division Multiplexing and applies a Convolutional encoder with a code rate 1/2, constraint length 7, generator polynomials (171, 133) to enhance error resilience. Transmission is modeled over an Additive White Gaussian Noise (AWGN) channel, and both hard and soft decision Viterbi decoding algorithms are evaluated. Simulation results show that at 6 dB SNR, the system achieves a zero Bit Error Rate (BER) for all four sensors using both hard and soft decision decoding. At lower SNRs (e.g., 2 dB), soft decision decoding reduces BER to as low as 0.0100, significantly outperforming hard decision decoding.