Design of a Naive Bayes–Based Adaptive Modulation Model in a Time-Varying Channel Environment
Abstract
Orthogonal Frequency Division Multiplexing (OFDM) systems require an effective modulation adaptation mechanism to maintain transmission reliability over dynamic and noise-affected channels. This study proposes a machine learning–based adaptive modulation method using Naive Bayes classification to select the most appropriate modulation scheme—BPSK, QPSK, or 16-QAM—based on Signal-to-Noise Ratio (SNR) values. The Naive Bayes model is trained using the probabilistic performance distributions of each modulation scheme, enabling optimal modulation mode prediction under various channel conditions. Simulation results demonstrate that the proposed adaptive method achieves a lower Bit Error Rate (BER) compared to fixed modulation schemes, particularly under low to medium SNR conditions. Furthermore, the Naive Bayes–based approach exhibits more stable performance, especially in recovering transmitted messages. BER curves and demodulated message results indicate that the artificial intelligence–based adaptive scheme using Naive Bayes improves the reliability of transmitting the text message “HELLO WORLD” across an SNR range of –5 dB to 15 dB. These findings confirm that integrating intelligent methods into adaptive OFDM modulation provides an effective solution for wireless communication in fluctuating channel environments.
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