Advancing Signal-to-Noise Ratio Estimation in Deep Space Communication

Advancing Signal-to-Noise Ratio Estimation in Deep Space Communication

연구 분야: Infrastructure

학회: ICDSP '24: Proceedings of the 2024 8th International Conference on Digital Signal Processing

In the context of deep-space wireless communication, the complexity of the cosmic environment often leads to the degradation of received signal quality. In such intricate environments, accurately assessing the signal-to-noise ratio (SNR) becomes paramount, as it directly impacts the reliability, performance, and success rate of data transmission within the communication system. However, traditional SNR estimation methods have limitations, such as accuracy issues and poor adaptability, especially in low SNR scenarios. Given the widespread adoption of deep learning methods in various domains, this paper introduces a novel SNR estimation technique. This approach leverages a network architecture that combines elements of Temporal Convolutional Networks (TCN), Long Short-Term Memory (LSTM) algorithms and attention mechanism, constituting a Non-Data Assisted (NDA) methodology. To evaluate the effectiveness of our technique, we construct a simulated QPSK dataset with varying SNR levels in deep space channels and conduct a series of simulation experiments. These experiments involve a detailed comparison of our proposed approach with existing algorithms, including the Second and Fourth Moments (M2M4) algorithm, the Signal-to-Noise Variance (SNV) algorithm, the Signal-to-Variation Ratio (SVR) algorithm, and the Singular Value Decomposition (SVD) algorithm. Additionally, we conducted an exhaustive comparative analysis with classical neural network methodologies. Empirical findings undeniably establish the superiority of our proposed approach, outperforming both traditional methods and classical deep learning techniques, showcasing its superior estimation capabilities.