Wireless Scene Identification via Classifier Fusion
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Solution Overview
Problem
The increasing demand for wireless communication has led to scarce spectrum resources, necessitating the efficient use of cognitive radio technology to identify and utilize idle spectrum resources, which requires intelligent cognition of complex wireless environments for effective channel modeling and resource allocation.
Innovation Solution
A machine learning-based wireless scene identification apparatus and method that classifies wireless channel conditions using multiple trained classifiers and decision fusion to identify scene categories, enabling accurate identification of wireless channel conditions and interference types, thereby optimizing spectrum resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple trained classifiers and decision fusion are used to classify wireless channel conditions, then the accuracy of scene identification is improved, but the device complexity and computational burden increase
Solution Approach 1:
The patent segments the wireless scene identification task into multiple independent classifier modules, each responsible for specific classification functions. The system divides complex channel condition classification into multiple binary classification tasks (e.g., LOS/NLOS, urban/suburban/rural scenarios), where each classifier handles a specific aspect. This segmentation allows the system to achieve high overall accuracy while keeping individual classifier complexity manageable.
Solution Approach 2:
The patent merges multiple classifier outputs through decision fusion to achieve superior identification accuracy. The system combines results from various trained classifiers (such as SVM, neural network, decision tree classifiers) using fusion strategies like voting or weighted combination. This merging approach leverages the strengths of different classification algorithms to improve overall scene identification precision while distributing the computational burden across multiple specialized components.
2Reliability
If multiple trained classifiers are employed to classify wireless channel conditions, then the reliability of channel condition identification is improved, but the processing time and loss of time increase
Solution Approach 1:
The patent implements preliminary action by pre-training multiple classifiers offline with extensive wireless channel data before actual scene identification operations. The classifiers are trained in advance to recognize various channel conditions (LOS, NLOS, different propagation scenarios), storing learned patterns and decision boundaries. During real-time operation, the pre-trained classifiers can quickly process incoming channel measurements without requiring extensive computation, thus maintaining high reliability while minimizing processing time.
3Measurement precision
If decision fusion is performed on classification results from multiple classifiers, then the accuracy of wireless channel condition classification is improved, but the computational complexity and use of energy increase
Solution Approach 1:
The patent applies partial action by implementing a staged decision fusion approach where not all classifiers are always activated. The system can selectively engage specific classifiers based on the detected signal characteristics, environmental conditions, or confidence levels from preliminary classifiers. For example, if a simple classifier already provides high-confidence results, more computationally intensive classifiers may be skipped. This partial activation strategy maintains classification accuracy for critical cases while reducing overall energy consumption during normal operation.
Data Source
AI summary
Provided in the present disclosure are a wireless scene identification apparatus and method for performing identification on the scene category of a predetermined wireless scene, and a wireless communication device and system. The wireless scene identification apparatus comprises: a processing circuit, configured to classify, on the basis of features extracted from environment parameters in a predetermined wireless scene, wireless channel conditions of wireless signals in the predetermined wireless scene by means of a plurality of trained classifiers, and perform decision fusion on the classification results of the plurality of classifiers, so as to classify the wireless channel conditions as wireless channel conditions in the predetermined plurality of wireless channel conditions.