Hierarchical Backhaul Network Aggregation for Expenditure Optimization
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Solution Overview
Problem
Wireless service providers face high recurring operational expenses for backhaul transport due to current methods requiring extensive and sensitive information for detailed analysis, making it tedious and costly to optimize backhaul network expenditures.
Innovation Solution
A hierarchical representation of the backhaul network is used, categorizing base stations into regions with similar traffic demands and subscriber densities, allowing for the aggregation of traffic at intermediate points, replacing low-speed links with a combination of low-speed and high-speed links to reduce costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed base station location and traffic information is collected and analyzed, then accurate backhaul expenditure analysis is achieved, but the process becomes tedious, time-consuming, and expensive
Solution Approach 1:
The network is divided into hierarchical regions (macro regions, micro regions, and cells) that can be analyzed independently. This segmentation allows expenditure analysis to be performed at multiple levels of granularity, enabling accurate results without requiring detailed analysis of every individual base station parameter simultaneously.
Solution Approach 2:
The patent applies partial action by analyzing only the necessary hierarchical levels required for accurate expenditure assessment. Instead of collecting and processing all possible detailed information, the method performs analysis at the appropriate level of abstraction (region-level aggregations combined with selective cell-level details), reducing time and resource requirements while maintaining sufficient accuracy.
2Measurement precision
If detailed base station information is collected for expense analysis, then accurate expenditure determination is achieved, but the process becomes costly and complex
Solution Approach 1:
The complex task of analyzing all base stations is segmented into manageable hierarchical components (macro regions, micro regions, cells). Each segment can be analyzed independently using standardized procedures, reducing the overall system complexity while maintaining comprehensive coverage and accuracy.
Solution Approach 2:
The patent merges analysis results from multiple hierarchical levels (region-level aggregated data combined with cell-level specific data) to determine final expenditures. This combining approach simplifies the processing system by working with aggregated data where appropriate, reducing complexity compared to analyzing every individual base station in isolation.
3Ease of operation
If hierarchical representation with region aggregation is used, then the need for detailed base station information is reduced, but measurement precision may be compromised
Solution Approach 1:
The hierarchical segmentation allows the system to collect less detailed information at higher levels (regions) while maintaining precision through selective detailed analysis at lower levels (cells). This multi-level approach balances ease of operation with measurement precision by matching data collection effort to the required analysis granularity.
Solution Approach 2:
The patent adds a hierarchical dimension to the analysis, moving from flat individual base station analysis to multi-level regional aggregation. This dimensional change enables easier information collection at aggregate levels while preserving precision through the ability to drill down to cell-level details when necessary, effectively resolving the trade-off between operational ease and measurement precision.
Data Source
AI summary
The invention includes a method for determining an expenditure associated with a network. A method includes obtaining network information associated with a portion of a network, categorizing each of a respective plurality of cells as one of an aggregated cell and a non-aggregated cell, determining an aggregating node configuration for at least one aggregating node associated with at least one base station associated with an aggregated cell, determining a deaggregating node configuration for at least one deaggregating node associated with at least one of the at least one aggregating node, and determining the expenditure according to at least one of the aggregating node configuration and the deaggregating node configuration. The categorization of cells is performed using the network information.