Link Quality Clustering for Wireless Backhaul Networks

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Solution Overview

Problem

Conventional geographic location-based clustering methods are inefficient for non-line-of-sight (NLOS) wireless backhaul networks, as they fail to account for signal-to-interference and noise ratio (SINR) information and environmental clutter, leading to suboptimal network performance.

Innovation Solution

A method for determining network clusters in wireless backhaul networks based on link quality metrics, specifically using link quality values (LQV) such as SINR, weighted data rate, or average channel gain to rank and cluster hub-RBM links, ensuring each RBM is clustered only once and adhering to a maximum number of RBMs per hub constraint.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If geographic location-based clustering is used, then network deployment is simplified, but network performance deteriorates due to failure to account for SINR information and environmental clutter

Engineering Contradiction:

Improveclustering implementation simplicityVSAvoidnetwork performance

Core Design Contradiction:

Ease of manufactureVSReliability

Solution Approach 1:

The patent changes the clustering parameter from geographic location to link quality metrics (SINR, data rate, channel gain). This allows the system to account for environmental clutter and interference conditions dynamically, improving network performance while maintaining implementation feasibility through standardized measurement procedures

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent replaces the static geographic-based clustering mechanism with a dynamic link-quality-based clustering mechanism. This substitution enables the system to adapt to changing environmental conditions and interference patterns, resolving the contradiction between simplicity and performance

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Device complexity

If conventional geographic clustering is used, then deployment complexity is reduced, but data rate deteriorates due to suboptimal cluster formation

Engineering Contradiction:

Improveclustering deployment complexityVSAvoidnetwork data rate

Core Design Contradiction:

Device complexityVSProductivity

Solution Approach 1:

The patent changes the clustering criterion from geographic parameters to performance parameters (link quality, SINR, data rate). This enables optimal cluster formation that maximizes network data rate while keeping deployment complexity manageable through automated measurements and calculations

Inventive Principle:

Principle #35Parameter changes

3Reliability

If link quality-based clustering is implemented, then network performance improves, but measurement and calculation complexity increases

Engineering Contradiction:

Improvenetwork performanceVSAvoidlink quality measurement complexity

Core Design Contradiction:

ReliabilityVSDifficulty of detecting and measuring

Solution Approach 1:

The patent implements self-service by having each hub and RBM autonomously measure their own link quality metrics (SINR, data rate, channel gain) and perform local calculations. This distributed approach improves network performance through accurate link-quality-based clustering while avoiding centralized measurement complexity

Inventive Principle:

Principle #25Self-service

4Productivity

If directional antennas with fixed beam patterns are used, then spectrum efficiency improves, but adaptability to changing environments deteriorates

Engineering Contradiction:

Improvespectrum efficiencyVSAvoidenvironmental adaptability

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The patent introduces dynamics by periodically re-evaluating link quality metrics and re-clustering networks based on current conditions. This allows fixed beam directional antennas to maintain high spectrum efficiency while adapting to environmental changes through dynamic cluster reconfiguration

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS9345032B2Method and apparatus for determining network clusters for wireless backhaul networks

Publication Date: 2016.05.17 BLINQ NETWORKS

AI summary

Practical methods and apparatuses are provided for determining network clusters in wireless backhaul networks comprising a plurality of hubs (102) and Remote Backhaul Modules (RBM) (104) based on link quality value (LQV) metrics. From an input LQV table of LQV values for each hub-RBM link (110), the link quality values are first ranked. Clusters are then identified from all the possible links based on the order of the highest link quality value to the lowest link quality value, any constraints on the number of RBMs per cluster, and clustering each RBM only once. Links with strong link quality values are chosen to optimize the LQV metric. LQV based clustering achieves a higher average LQV, e.g., average spectrum efficiency or weighted sum spectrum efficiency, for the entire backhaul network compared to the geographic location based clustering. The method is straightforward to implement and has low computational complexity.