Fuzzy Sugeno Model for SNR-Based Adaptive Modulation in Underwater Acoustic Communication - Sholihah Ayu Wulandari; Ahmad Haris Hasanuddin Slamet (Jurnal Teknologi Informasi dan Terapan, 2025)
Underwater communication faces significant challenges due to the dynamic characteristics of the channel and is strongly influenced by the physicochemical parameters of the water. This study proposes channel quality modeling using the Sugeno Fuzzy Inference System (FIS) with input variables of temperature, salinity, dissolved oxygen (DO), and turbidity. The system produces a Signal-to-Noise Ratio (SNR) output that is used as a basis for channel quality mapping, Bit Error Rate (BER) estimation, and the selection of adaptive modulation techniques (BPSK, QPSK, or 16QAM). Simulation results show that the Sugeno fuzzy model is able to follow the theoretical pattern well, where increasing temperature, salinity, and turbidity decrease the SNR value, while DO plays a role in maintaining channel stability. Based on the test results, at high SNR (≥ 15 dB) the system recommends 16QAM, at medium SNR (11–15 dB) QPSK, and at low SNR (≤ 10 dB) BPSK. This approach has proven effective in suppressing BER and increasing the reliability of underwater acoustic communications in fluctuating mangrove water environments.