Multihop Network Topology Learning Optimization
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Solution Overview
Problem
In multihop communication networks, frequent topology changes due to mobile relay stations generate significant control traffic, overwhelming the base station and reducing network capacity.
Innovation Solution
Implementing techniques that allow nodes to transmit node identifiers and status information to the base station, reserving channel resources for mobile relay stations to exchange topology information, thereby reducing control traffic and optimizing topology learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If relay stations are deployed to extend coverage and improve capacity in IEEE 802.16 networks, then network coverage and capacity are improved, but control traffic increases significantly due to frequent topology changes
Solution Approach 1:
The patent segments the network into hierarchical levels (base stations and relay stations) with different topology learning responsibilities. Base stations perform comprehensive topology learning while relay stations perform simplified topology learning, dividing the control traffic management task to reduce overall control traffic volume while maintaining coverage extension benefits
Solution Approach 2:
The patent changes the topology learning parameter configuration based on node type. Relay stations use a simplified topology learning parameter set compared to base stations, adapting the learning frequency and scope to their specific roles. This parameter differentiation reduces control traffic from relay stations while preserving their coverage extension function
2Measurement precision
If relay stations frequently update topology information to the base station, then topology accuracy is improved, but network capacity is reduced due to increased control traffic
Solution Approach 1:
Relay stations perform partial topology learning focused on their immediate neighborhood rather than comprehensive network-wide topology learning. This partial action maintains sufficient topology accuracy for relay operations while generating significantly less control traffic, preserving network capacity for subscriber data
Solution Approach 2:
The patent implements periodic topology information updates from relay stations to base stations at optimized intervals. Rather than continuous or event-driven updates for every topology change, periodic updates maintain topology accuracy while reducing the frequency of control traffic transmissions, thereby preserving network capacity
Data Source
AI summary
In a multihop network having a first type of node, a second type of node and a third type of node, techniques are provided for optimizing topology learning in the multihop network which can reduce the amount of control traffic that occurs due to frequent topology changes. For example, each of the nodes can transmit a node identifier and status information to the first type of node. The status information associated with each node can include a node type and a mobility state of the node. The first type of node can store the node identifier and the status information from each of the nodes. The first type of node can reserve or allocate a channel resource to each of the second type of nodes having a mobile state. The channel resource is used by the second type of node for exchanging topology information with the first type of node.