一种5G RedCap的强化学习辅助下行信道估计方法
By employing a reinforcement learning-assisted channel estimation method in the 5G RedCap system, combined with WOA and Q-learning algorithms, the problems of high bit error rate and high computational complexity in channel estimation are solved, achieving high-accuracy channel estimation in low-cost terminal devices.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- NANJING UNIV OF POSTS & TELECOMM
- Filing Date
- 2023-12-27
- Publication Date
- 2026-07-17
AI Technical Summary
Existing channel estimation methods suffer from high bit error rates and high computational complexity in 5G RedCap systems, especially in low-cost and low-complexity terminal devices, where traditional LS and MMSE algorithms are not effectively applicable.
A reinforcement learning-assisted channel estimation method is adopted, which combines the WOA algorithm to search for the optimal channel statistical characteristics in single-subframe channel estimation, and uses the Q-learning algorithm to optimize the weights of multiple subframes for joint estimation, thereby reducing the channel estimation error.
Without significantly increasing computational load and memory usage, it significantly improves the accuracy of channel estimation and reduces the system detection bit error rate, making it suitable for terminal devices in low-mobility environments.
Smart Images
Figure CN117749577B_ABST