Link Quality Estimation Using Adaptive Forgetting Factor
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Solution Overview
Problem
In wireless communication networks, especially ad-hoc networks, the dynamic nature of node mobility leads to fluctuations in communication link quality due to changes in signal strength, noise levels, and environmental factors, making it challenging to maintain reliable packet routing and optimal transmit power settings.
Innovation Solution
A system and method that employs an exponentially-weighted moving average filter to estimate the quality of communication links by adjusting a forgetting factor based on link mobility and traffic conditions, allowing for dynamic adaptation of link quality values and optimal routing decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If nodes move freely in wireless networks, then network adaptability and mobility are improved, but communication link quality and routing reliability deteriorate due to changing signal conditions
Solution Approach 1:
The patent applies dynamics by making the link quality estimation adaptive to node mobility. The system dynamically adjusts the forgetting factor based on whether nodes are stationary or mobile, and continuously updates link quality predictions to reflect changing conditions. This allows the network to maintain reliable routing decisions even as nodes move and communication conditions change.
Solution Approach 2:
The patent implements feedback through continuous monitoring of link quality metrics and using this information to adjust routing decisions and transmit power settings. The system feeds back link quality estimates to routing protocols, enabling them to adapt their behavior based on actual communication conditions, thus maintaining reliability despite node mobility.
2Measurement precision
If link quality estimation responds quickly to changes, then routing decisions become more accurate, but convergence speed and computational efficiency deteriorate
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the forgetting factor based on network conditions. For mobile nodes, a higher forgetting factor is used to quickly adapt to changing conditions, while stationary nodes use a lower forgetting factor for more stable estimation. This parameter adjustment balances between estimation accuracy and convergence speed according to actual network state.
3Use of energy by moving object
If transmit power is optimized for each link, then energy efficiency and signal quality are improved, but system complexity and control overhead increase
Solution Approach 1:
The patent applies self-service by enabling each node to autonomously estimate link quality and adjust its own transmit power settings based on local conditions. Each node uses the link quality estimation to independently optimize its transmission parameters without requiring centralized control, thus achieving energy efficiency while keeping control complexity manageable through distributed decision-making.
Data Source
AI summary
A system and method for management of communication links between nodes in a wireless communication network (100). The system and method perform the operations of estimating an expected rate of change in the characteristics pertaining to communication over a communication link in the network (100), and assigning a value associated with the link based on the expected rate of change. The system and method also adjust the rate of change of the value associated with the link based on the condition of the link.