Candidate Frequency Blanking for Dynamic PIM Interference Isolation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods assume predictable and static interference like PIM can be reliably compensated for, but in reality, PIM can be transient or caused by environmental unknowns, leading to ineffective mitigation strategies.
Innovation Solution
A method involving a binary search approach to divide candidate frequencies into subsets, blank one subset, and determine interference causation based on interference reduction, allowing for real-time identification and mitigation of PIM caused by known or unknown factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If simulation and/or ML models are used to predict and compensate for PIM, then interference mitigation is improved, but reliability deteriorates because PIM may be transient or changeable
Solution Approach 1:
The patent transitions from static simulation/ML models to a dynamic binary search approach that adapts to changing PIM conditions. The system continuously divides candidate frequencies into subsets and tests them in real-time, allowing the interference mitigation strategy to evolve as PIM sources change or disappear, thereby maintaining reliability in transient environments.
Solution Approach 2:
The system performs self-diagnosis by autonomously identifying PIM-causing frequencies through the binary search method. It automatically divides candidate frequencies, blanks subsets, measures interference reduction, and determines causation without external intervention, enabling the network to self-correct against transient PIM interference.
2Measurement precision
If all candidate frequencies are continuously monitored to identify PIM sources, then measurement precision is improved, but productivity deteriorates due to increased processing time
Solution Approach 1:
The patent segments the set of candidate frequencies into smaller subsets through the binary search process. Instead of monitoring all candidate frequencies simultaneously, the system divides them into first and second subsets, blanks one subset at a time, and identifies PIM sources by measuring interference reduction in each segment, thereby achieving precise identification with reduced processing time.
Solution Approach 2:
The binary search methodology enables continuous interference identification without exhaustive testing. By systematically dividing candidate frequencies and testing subsets in sequence, the system maintains continuous useful action of identifying PIM sources efficiently, avoiding the productivity loss associated with monitoring all frequencies simultaneously while preserving measurement precision.
3Object-affected harmful factors
If candidate frequencies are blanked to identify PIM sources, then harmful factors are reduced, but loss of information occurs about potential interference sources
Solution Approach 1:
The system implements feedback by measuring uplink noise levels before and after blanking candidate frequency subsets. This feedback mechanism allows the system to determine whether a blanked subset contained PIM-causing frequencies based on interference reduction, thereby reducing harmful PIM effects while preserving information about which frequencies are actual interference sources through the measurement results.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A computer implemented method (200) for reducing interference experienced at a first frequency due to transmissions made at one or more of a plurality of candidate frequencies. The method comprises i) dividing (202) the candidate frequencies into a first subset of candidate frequencies and a second subset of candidate frequencies; ii) blanking (204) the first subset of candidate frequencies; and iii) determining (206) whether the interference experienced at the first frequency is due to transmissions made on the first subset of candidate frequencies, dependent on whether said interference is reduced as a result of blanking the first subset of candidate frequencies.