PIM Cancellation Architecture Using Neural Network Compensation
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Solution Overview
Problem
Passive intermodulation (PIM) interference poses a significant challenge in cellular network operations, particularly when existing equipment ages, new carriers are co-located, or new equipment is installed, leading to interference that reduces receive sensitivity and can block calls, affecting both the cell generating the interference and nearby receivers.
Innovation Solution
An integrated circuit comprising an equalizer to mitigate RX channel memory effects, a TX modeling circuit to model TX channel effects, and a PIM compensation circuit to generate a compensation value for mitigating PIM interference from RX data, thereby simplifying PIM cancellation complexity and reducing hardware costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If traditional frequency planning is used to avoid PIM, then PIM interference is reduced, but spectrum utilization becomes inefficient and flexible carrier deployment is limited
Solution Approach 1:
The patent converts the harmful PIM interference into a useful signal by using the interfered RX data to train a neural network model that learns to generate PIM compensation values. The mitigation circuit then uses this trained model to cancel PIM interference in real-time, transforming the previously harmful interference into a beneficial training resource for improving receiver performance.
Solution Approach 2:
The patent replaces traditional mechanical frequency planning approaches with a digital signal processing system using neural networks. Instead of avoiding PIM through frequency allocation constraints, the system uses machine learning models to identify and compensate for PIM effects, enabling flexible carrier deployment while maintaining signal quality.
2Object-affected harmful factors
If complex PIM cancellation systems are implemented, then PIM interference is effectively mitigated, but device complexity and hardware cost increase
Solution Approach 1:
The patent uses a neural network model to create a digital copy of the PIM interference characteristics. Instead of physically separating or filtering PIM signals through complex analog circuits, the system creates a computational model that replicates PIM behavior, allowing for simpler hardware implementation while achieving effective cancellation through digital signal processing.
Solution Approach 2:
The patent changes the approach from physical PIM cancellation to parameter-based compensation. By training neural networks to predict and compensate for PIM effects through parameter adjustment in the digital domain, the system achieves effective PIM mitigation without requiring complex hardware modifications, thereby reducing device complexity and cost.
3Ease of manufacture
If existing equipment is reused in antenna sharing schemes, then hardware cost is reduced, but PIM interference increases due to aging and co-location
Solution Approach 1:
The patent implements a feedback mechanism where RX data that has been interfered with by PIM is fed back into the neural network training process. This feedback loop allows the system to continuously learn from actual PIM interference patterns and improve its compensation accuracy, enabling the reuse of existing equipment while effectively managing the increased PIM interference through adaptive learning.
Data Source
AI summary
Embodiments herein describe a PIM correction circuit. In a base station, TX and RX RF changes, band pass filters, duplexers, and diplexers can have severe memory effects due to their sharp transition bandwidth from pass band to stop band. PIM interference, generated by the TX signals and reflected onto the RX RF chain will include these memory effects. These memory effects make PIM cancellation complex, requiring complicated computations and circuits. However, the embodiments herein use a PIM correction circuit that separates the memory effects of the TX and RX paths from the memory effects of PIM, thereby reducing PIM cancellation complexity and hardware implementation cost.