An innovative algorithm has been developed by a joint research team that revolutionizes communication channel estimation. In today’s highly interconnected world, accurate estimation of communication channels is crucial for high-quality communication. The algorithm, which combines deep learning and federated learning, offers exceptional accuracy, privacy protection, and low computational and communication costs.
Published in Intelligent Computing, a Science Partner Journal, the research introduces a specially designed deep learning model for precise estimation and a federated learning framework to train the model while ensuring user data security and minimal overhead. The algorithm also incorporates a user motivation scheme to maximize computing resources.
The team conducted tests on a wireless communication network using both local user datasets and realistic environment datasets. The results demonstrated that the algorithm outperformed traditional and deep learning algorithms in estimating channel state information under various conditions. Furthermore, the algorithm’s effectiveness was proven in realistic environments, where it surpassed three state-of-the-art models in both sparse and dense scenarios.
Federated learning is a key component of this algorithm, leveraging the resources of local devices to exchange parameters with a central server instead of raw data. This approach reduces computational and communication costs, safeguards user data privacy, and suits large complex communication networks. The team also utilized a generative adversarial network, with a dual-U-shaped network design and added regularization functions, to ensure information consistency and stability during sampling.
While acknowledging certain limitations of the algorithm, such as numerous model parameters and reliance on labeled data, the research team plans to address these issues in future work. They aim to compress the model and explore unsupervised approaches for training. Additionally, the team intends to examine the application of federated learning in dynamic diverse networks, where each device possesses different resources for onboard verification and client selection.
The algorithm’s advancements in communication channel estimation offer significant potential for improving the quality and security of communication in our interconnected world. By combining deep learning and federated learning, this innovative approach provides high-level accuracy, privacy protection, and cost-efficiency. As the research team continues to refine and expand their algorithm, it is anticipated that these advancements will contribute to further advancements in communication technology.
