Heterogeneous Self-Organizing Network for Access and Backhaul Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current wireless communication networks face high operational costs due to the complexity and skill requirements for establishing, managing, and maintaining them, with existing self-organizing network (SON) techniques primarily focused on the access portion of eNodeBs, limiting their ability to optimize network performance across the entire network and not effectively utilizing frequency bands with restrictions.
Innovation Solution
Implementing self-organizing network modules in heterogeneous mesh networks that can dynamically switch roles, operate on white-space frequencies, and integrate disparate technologies, allowing for real-time optimization of operational parameters such as power, frequency allocation, and network configuration across both access and backhaul sides, leveraging a computing cloud and software-defined networking for centralized decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If SON techniques are implemented only at the eNodeB level, then individual eNodeB optimization is achieved, but the network-wide view and coordination capability is lost
Solution Approach 1:
The SON functionality is segmented into two distinct components: a distributed SON module at each eNodeB for local autonomous optimization, and a centralized SON module at the network management level for global coordination. This segmentation allows each component to perform its specialized function while maintaining overall system intelligence.
Solution Approach 2:
The distributed SON modules are nested within the broader centralized SON framework. Each eNodeB contains its own SON capabilities, which are themselves nested within the overall network-wide SON management system, creating a hierarchical structure where local and global optimization work together.
2Ease of manufacture
If network resources are configured with static roles (access or backhaul), then configuration simplicity is maintained, but network flexibility and dynamic adaptation capability is reduced
Solution Approach 1:
The network resource configuration transitions from static role assignment to dynamic role switching. The SON module continuously monitors network conditions and automatically reconfigures resource roles (access or backhaul) based on real-time environmental factors, traffic patterns, and network performance requirements.
Solution Approach 2:
The system changes the operational parameters of network resources dynamically. Instead of fixed role assignments, the SON module adjusts configuration parameters such as frequency allocation, power levels, and functional roles based on measured environmental conditions and network state.
3Object-affected harmful factors
If frequency bands with restrictions are avoided, then interference mitigation is achieved, but spectrum utilization efficiency is reduced
Solution Approach 1:
The SON module transforms restricted frequency bands from harmful sources of potential interference into beneficial additional spectrum resources. By implementing dynamic power control and coordination, the system converts previously unusable restricted bands into productive capacity, turning a limitation into an opportunity for increased spectrum utilization.
Solution Approach 2:
The system changes power parameters dynamically for restricted frequency bands. Instead of avoiding these bands entirely, the SON module adjusts transmit power levels and operational parameters to comply with restrictions while still utilizing the spectrum effectively, thereby increasing overall capacity without causing harmful interference.
4Manufacturing precision
If manual network provisioning and maintenance is performed, then precise control over network parameters is achieved, but operational costs and time consumption increase
Solution Approach 1:
The SON module implements self-service capabilities that allow the network to automatically provision, configure, optimize, and heal itself without human intervention. The system autonomously monitors its own state, detects issues, and performs corrective actions, eliminating the need for manual operations while maintaining precise parameter control.
Solution Approach 2:
The SON module incorporates continuous feedback mechanisms that monitor network performance and environmental conditions. This feedback loop enables the system to automatically adjust parameters and respond to changing conditions in real-time, replacing manual control with intelligent automated decision-making that maintains precision while reducing time and cost.
Data Source
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.