Neural Network Spectrum Management for Aggregate Interference
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Solution Overview
Problem
Conventional methods for calculating aggregate interference in cognitive radio systems are inaccurate due to complex and volatile environmental conditions, making it difficult to manage spectrum effectively.
Innovation Solution
A spectrum management apparatus that uses a neural network model to accurately calculate aggregate interference by training on sample data from multiple secondary users and their interference with primary users, based on actual environmental conditions and user distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional statistical calculation method is used for aggregate interference, then calculation process is simple, but calculation accuracy is low due to complex and volatile environmental conditions
Solution Approach 1:
The patent replaces the conventional statistical calculation method (mechanical/computational approach) with a neural network model (biological inspiration approach). The neural network learns complex interference patterns from training data and generalizes to new scenarios, achieving high accuracy without requiring explicit statistical formulas or assumptions about environmental conditions.
Solution Approach 2:
The patent transforms the interference calculation problem from using fixed statistical parameters to using learned neural network parameters (weights and biases). The neural network adapts its parameters through training on labeled data, enabling it to capture complex relationships between user distributions, environmental conditions, and aggregate interference that traditional statistical methods cannot represent.
2Measurement precision
If neural network model is used to calculate aggregate interference, then calculation accuracy is improved based on actual environmental conditions, but model training complexity increases
Solution Approach 1:
The patent performs preliminary training of the neural network model offline using historical labeled data before actual interference calculation. This preliminary action creates a pre-trained model that can be deployed for real-time predictions without requiring complex training procedures during operation. The heavy computational burden of training is shifted to an offline phase, simplifying the online calculation process.
Solution Approach 2:
The patent uses labeled historical data as copies of actual interference scenarios to train the neural network. Instead of requiring complex real-time measurements and annotations, the system leverages existing historical data that has been labeled with ground truth interference values, creating training samples that replicate real-world conditions without the complexity of real-time data collection and labeling.
3Adaptability or versatility
If conventional statistical method is used, then system implementation is easy, but adaptability to dynamic user distribution is poor
Solution Approach 1:
The patent implements a dynamic system where the neural network model adapts to changing user distributions and environmental conditions. Unlike static statistical methods that assume fixed distributions, the neural network learns from historical data representing various dynamic scenarios and can generalize to new dynamic conditions, making the system adaptable without requiring explicit modeling of distribution changes.
Data Source
AI summary
A frequency spectrum management device includes a processing circuit configured to acquire, with regard to a position of a primary user sample, a plurality pieces of sample information, each of the plurality pieces of sample information comprising information of a plurality of secondary user samples, and an aggregate interference generated by the plurality of secondary users on the primary user sample; and training a neural network model by taking the information of the plurality of secondary user samples as an input of the neural network model and taking the aggregate interference generated by the plurality of secondary user samples on the primary user sample as an output of the neural network model, so as to determine a parameter set of the neural network model corresponding to the position of the primary user sample.