Adaptive Modulation and Coding Scheme Based on Multi-source Indicator Fusion

Underwater acoustic communication systems play a crucial role in ocean development and management. However, the complexity and variability of ocean communication channels pose challenges for ensuring reliable and stable communication quality using single-modulation coding systems. Consequently, the effectiveness of underwater acoustic communication technology is significantly impacted. To enhance the throughput of ocean communication systems, researchers have explored and implemented adaptive modulation and coding (AMC) techniques in underwater acoustic communication systems. However, traditional AMC methods that rely on fixed thresholds suffer from issues such as inaccurate channel state assessment and imprecise Modulation and Coding Scheme (MCS) selection. In this paper, we propose the integration of neural network algorithms into adaptive modulation and coding for ocean channels. Specifically, we present a novel approach called the multi-source index fusion underwater adaptive modulation and coding method, which leverages neural networks. We also devise a neural network-based solution to address the Canonical Correlation Analysis (CCA) feature matrix, thereby enhancing system classification accuracy and throughput. Experimental results demonstrate that our proposed adaptive modulation and coding method, based on multi-source index fusion, outperforms the traditional method that relies on fixed threshold MCS judgment. It achieves a significant increase in system throughput.