Heterogeneous Self-Organizing Network for Dynamic Mesh Optimization
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Solution Overview
Problem
Current self-organizing network (SON) techniques are limited in their ability to dynamically optimize and manage wireless communication networks, particularly in mesh networks, due to their focus on specific protocols and frequency ranges, and lack integration with disparate technologies and protocols, which restricts network flexibility and efficiency.
Innovation Solution
The implementation of self-organizing network modules in heterogeneous mesh networks that can dynamically adjust operational parameters based on environmental conditions, using multi-RAT nodes and a computing cloud to leverage different radio technologies and protocols, enabling real-time optimization and self-healing across access and backhaul sides of the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If SON techniques are implemented only at the eNodeB level, then each eNodeB can independently optimize its operations, but the eNodeB has a limited view of the entire network and cannot make dynamic operational changes based on network-wide conditions
Solution Approach 1:
The patent combines distributed SON capabilities at each eNodeB with centralized SON coordination through a network management entity. Each eNodeB maintains local optimization autonomy while the central entity aggregates network-wide information and coordinates dynamic parameter adjustments across multiple eNodeBs, resolving the contradiction between independent operation and network-wide adaptability
Solution Approach 2:
The SON functionality is segmented into multiple hierarchical levels: local SON modules at each eNodeB handling immediate operational optimizations, and a central SON coordination entity managing network-wide parameters. This segmentation allows simultaneous independent local optimization and coordinated network-wide adaptation
2Reliability
If a central management node implements SON techniques by querying all eNodeBs for interference conditions, then network-wide interference mitigation can be achieved, but the process is time-consuming and reduces network responsiveness
Solution Approach 1:
The patent implements preliminary action by having eNodeBs continuously monitor and report key operational parameters and environmental conditions to the central management node in real-time, rather than waiting for queries. This proactive information availability enables faster centralized decision-making and reduces the time required for network-wide interference mitigation
Solution Approach 2:
A continuous feedback mechanism is established where eNodeBs automatically report operational status and environmental conditions to the central management node, which then provides real-time coordination instructions. This closed-loop feedback system eliminates time-consuming manual queries and enables dynamic, responsive network-wide optimization
3Ease of manufacture
If network resources are configured with static roles (access or backhaul) for the life of the product, then configuration simplicity is maintained, but network flexibility and ability to provision on-the-fly are reduced
Solution Approach 1:
The patent transforms static resource configuration into a dynamic system where the central management node continuously evaluates network conditions and automatically reassigns roles of network resources (access or backhaul) based on real-time demands. This dynamic reconfiguration maintains operational simplicity while enabling flexible, on-the-fly network provisioning and adaptation
4Object-affected harmful factors
If frequency bands have transmit power restrictions placed on certain portions, then interference is avoided, but network capacity is reduced as those portions cannot be utilized
Solution Approach 1:
The patent applies local quality by enabling different transmit power levels for different portions of frequency bands at different locations. The central management node coordinates spatial and spectral resource allocation, allowing certain frequency portions to be used with restricted power in specific geographic areas while maintaining full power elsewhere, thereby increasing overall network capacity while avoiding interference
Data Source
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AI summary
This application discloses methods for creating self-organizing networks implemented on heterogeneous mesh networks. The self-organizing networks can include a computing cloud component coupled to the heterogeneous mesh network. In the methods and computer-readable mediums disclosed herein, a processor receives an environmental condition for a mesh network. The processor may have measured the environmental condition, or it could have received it from elsewhere, e.g., internally stored information, a neighboring node, a server located in a computing cloud, a network element, user equipment ("UE"), and the like. After receiving the environmental condition, the processor evaluates it and determines whether an operational parameter within the mesh network should change to better optimize network performance.