Online Spur Detection in Wireless UE Using High Pass Filter
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Solution Overview
Problem
Wireless communication systems face performance degradation due to spurs, which are localized spikes in channel noise that can lead to inaccurate channel estimation and decoding failures, especially in high modulation and coding scheme allocations, as existing characterization-based spur identification and mitigation methods lack real-time responsiveness and flexibility.
Innovation Solution
User equipment (UE) performs online spur detection and mitigation by applying a high pass filter to descrambled reference signals in the frequency domain, comparing channel noise power to an average noise level, and performing noise equalization to remove spurs, thereby improving channel estimation and decoding accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If characterization-based spur identification methods are used, then spur mitigation can be performed, but real-time responsiveness and flexibility are insufficient
Solution Approach 1:
The system performs preliminary spur detection by monitoring reference signal tones before they are used for channel estimation. By identifying spurs in advance during the reference signal processing stage, the system can prepare mitigation parameters ahead of time, enabling real-time responsiveness without compromising decoding accuracy.
Solution Approach 2:
The system implements continuous feedback by monitoring channel noise power and comparing it against threshold values. When spurs are detected, the system immediately adjusts channel estimation parameters and noise equalization settings, creating a closed-loop control system that responds in real-time to changing channel conditions.
2Measurement precision
If high pass filter is applied to reference signals, then noise reduction is achieved, but channel estimation complexity increases
Solution Approach 1:
The system segments the frequency domain processing by applying the high pass filter only to specific reference signal tones rather than the entire bandwidth. This selective filtering approach reduces the computational burden while maintaining channel estimation accuracy in the affected frequency regions where spurs are detected.
Solution Approach 2:
The system applies different processing qualities to different frequency regions. In frequency regions where spurs are detected, aggressive high pass filtering and noise equalization are applied. In regions without spurs, minimal processing is applied, optimizing the balance between estimation accuracy and computational complexity.
3Reliability
If noise equalization is performed on adjacent tones, then decoding accuracy improves, but processing time increases
Solution Approach 1:
The system performs noise equalization selectively on only those adjacent tones that are determined to be affected by detected spurs, rather than applying equalization across the entire bandwidth. This partial action approach maintains decoding accuracy for affected tones while minimizing unnecessary processing time on clean tones.
Solution Approach 2:
The system performs preliminary identification of affected tone regions using the high pass filter detection results before applying noise equalization. By pre-identifying which tones require equalization, the system avoids processing time waste on tones that don't need mitigation while ensuring accurate processing of affected tones.
Data Source
AI summary
Methods, systems, and devices for wireless communications are described. A user equipment (UE) may perform online spur detection and mitigation scheme. The UE may identify spurs during operation, in real time, and apply cancelation and noise equalization to address identified spurs. The UE may apply a high pass filter to reference signals. During a symbol, the UE may apply the high pass filter by estimating the channel on one or more neighbor tones (e.g., tones of higher frequency and tones of lower frequency that also carry reference symbols). Because the UE may assume that a channel will generally be smooth, and that noise may vary slowly or steadily across frequency resources, the UE may compare the channel noise of a particular tone to an average or normalized channel noise of the one or more neighbor tones.