Performance and Optimization of Coding Techniques for Deep Space Communication Channels

In today's world, space exploration and communication advancements are critical to ensuring effective connections for scientific missions, planetary exploration, and future interstellar travel. Deep space communication plays a pivotal role in supporting these endeavors, as it enables the transmission of vital data across vast distances and harsh environments. This paper presents a comprehensive study on the performance and optimization of coding techniques for deep-space communication channels. Based on the characteristics of the deep-space environment, the channels are modeled into three stages: near-Earth link, interstellar link, and near-planet link. The modeling for each channel type is summarized, and simulation results are provided. To evaluate coding techniques, M2M4, MMSE, and DNN are employed to approximate the signal-to-noise ratio (SNR), which serves as a critical parameter in assessing various encoding schemes. Three coding techniques-Hamming, convolutional, and turbo codes-are tested in the context of these models, with a particular focus on the turbo coding method. Detailed exploration is carried out on the SOVA and Log-MAP algorithms, as well as the impact of transmission parameters such as code rate. Overall, the results aim to provide a foundation for reliable signal transmission and error correction in deep-space, addressing challenges inherent to the unique environment of deep-space communication.