Interference Data Reliability Classification in Heterogeneous Wireless Networks
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Solution Overview
Problem
In heterogeneous wireless communication networks, the decentralized nature of information exchange among femto cells/HeNBs leads to unreliable interference data, as there is no centralized control over communication interfaces, resulting in varying data quality and reliability, which affects decision-making in interference management.
Innovation Solution
An apparatus and method that determine and classify the reliability of wireless channel interference information, using metrics like the Fano factor, to assess and report the trustworthiness of interference data shared among network access nodes, enabling a quantitative measure of data reliability and facilitating peer review for improved interference management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If decentralized information exchange is used among femto cells/HeNBs, then network autonomy and flexibility are improved, but reliability of interference data deteriorates due to lack of centralized control
Solution Approach 1:
The patent implements a feedback mechanism where receiving nodes send reliability feedback information back to transmitting nodes. This feedback contains assessments of the reliability of received interference data, allowing transmitting nodes to adjust their data collection and sharing strategies. The feedback loop enables continuous improvement of data reliability while preserving decentralized autonomy, as each node learns from its peers' assessments and optimizes its contribution quality.
Solution Approach 2:
The patent introduces reliability assessment metrics and classification mechanisms as intermediaries between the decentralized nodes. These intermediaries (reliability metrics, classification systems) mediate the information exchange by providing structured ways to evaluate and communicate data quality. This allows nodes to share interference data with associated reliability indicators, enabling informed decision-making without requiring centralized control.
2Productivity
If interference information is shared among all neighbor nodes, then interference management capability is improved, but information security and trustworthiness deteriorate due to varying node reliability
Solution Approach 1:
The patent applies local quality by allowing each node to assess and classify the reliability of interference data based on local conditions and node characteristics. Different nodes can have different reliability assessments for the same data depending on their local environment, measurement capabilities, and historical performance. This enables differentiated trust levels rather than uniform treatment of all shared information.
Solution Approach 2:
The patent changes the parameter of data quality by introducing reliability metrics and classification schemes. Instead of treating all interference data as equally reliable, the system transforms raw interference measurements into classified data with associated reliability parameters. This allows nodes to adjust their interference management decisions based on the reliability parameters, sharing information more widely while maintaining security through parameter-based filtering.
3Measurement precision
If reliability classification is added to interference data, then decision-making accuracy is improved, but system complexity increases due to additional assessment and reporting mechanisms
Solution Approach 1:
The patent segments the reliability assessment into distinct components: measurement phase, classification phase, and reporting phase. By dividing the complexity into manageable segments, each node can implement reliability classification without overwhelming system complexity. The segmentation allows for modular implementation where each component can be optimized independently.
Solution Approach 2:
The patent implements partial action by allowing nodes to perform reliability assessment and classification to the extent needed for their specific operations. Nodes can choose to implement basic or advanced reliability metrics depending on their requirements. This partial implementation approach reduces overall system complexity while still providing sufficient decision-making accuracy for interference management.
Data Source
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AI summary
Wireless channel interference information is determined (e.g., a background interference matrix BIM constructed from multiple user equipment measurements). Reliability of the interference information is classified and sent with the interference information to a neighbor network access node such as a HeNB. In various embodiments there are three layers of reliability, the above being the first. The second layer utilizes variability of a plurality of such reliability indications received from the same neighbor HeNB, which is updated as new interference information and reliability indications are received from that same neighbor HeNB. The third layer utilizes a peer review/update process on the neighbor HeNBs themselves, where each HeNB's performance is shared among all and updated by one another as different HeNBs utilize one another's BIM and assesses how reliably it reflected actual interference conditions. This addresses concerns for data/source reliability since communication interfaces among HeNB groups may not be controlled by a single network operator.