基于改进的LMS算法的自干扰抵消方法、系统、设备和介质
By establishing a nonlinear relationship between the improved snipe line function and the error signal, and constructing a step size adjustment function, the contradiction between the convergence speed and steady-state error of the LMS algorithm in the full-duplex ISAC system is resolved, achieving fast convergence and low-complexity self-interference cancellation under low signal-to-noise ratio conditions.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SUN YAT SEN UNIV
- Filing Date
- 2023-03-23
- Publication Date
- 2026-07-17
AI Technical Summary
The existing LMS algorithm cannot effectively coordinate convergence speed and steady-state error in full-duplex ISAC systems. Fixed step size cannot maintain low computational complexity while maintaining fast convergence, and its performance is poor under low signal-to-noise ratio conditions.
By establishing a new nonlinear relationship between the improved snipe line function and the error signal, a step size adjustment function is constructed to dynamically adjust the step size factor of the weight coefficients. The LMS algorithm is then optimized to maintain good performance and low computational complexity in the digital domain self-interference elimination process of the full-duplex ISAC system.
Under low signal-to-noise ratio conditions, the improved LMS algorithm maintains fast convergence and low computational complexity, improves the interference cancellation ratio, and achieves faster convergence speed and higher steady-state performance.
Smart Images
Figure CN116405129B_ABST