Wireless Relay Network Topology Reconstruction via Traffic Feedback
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Solution Overview
Problem
Current solutions fail to effectively implement self-organization mechanisms, particularly self-optimization, in wireless relay communication networks, leading to inefficient load balance and network capacity issues due to dependence on overlapping areas and mobile station numbers.
Innovation Solution
Reconstructing network topology based on traffic-related information, including time-frequency resource usage and wireless channel quality, to achieve balanced load distribution through relay and mobile station handovers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If network topology reconstruction is performed based on traffic related information, then network capacity and service quality are enhanced, but system complexity increases due to the need for continuous monitoring and dynamic adjustment
Solution Approach 1:
The network performs self-optimization through automatic topology reconstruction based on monitored traffic information. The system autonomously detects load imbalances and executes handover operations without external intervention, enabling the network to self-adjust and maintain optimal performance states
Solution Approach 2:
The system continuously monitors traffic related information from multiple cells and uses this feedback to dynamically adjust network topology. The monitoring results trigger topology reconstruction when predefined conditions are met, creating a closed-loop control mechanism that adapts to changing network conditions
2Ease of operation
If network topology reconstruction is performed based on traffic related information, then load balance is improved, but deployment and maintenance costs increase
Solution Approach 1:
The network automatically performs topology reconstruction and load balancing operations based on monitored traffic conditions. This self-service capability eliminates the need for manual network planning and adjustment, reducing deployment complexity and maintenance costs while maintaining optimal load distribution
Solution Approach 2:
The system dynamically adjusts network topology in response to changing traffic conditions rather than relying on static pre-planned configurations. This dynamic adaptation allows the network to optimize performance for varying load patterns without requiring multiple pre-configured deployment scenarios
3Adaptability or versatility
If dependence on overlapping area and mobile station number is reduced, then self-organization capability is improved, but measurement precision requirements increase
Solution Approach 1:
The system uses continuous feedback from traffic related information monitoring to make informed topology reconstruction decisions. By relying on actual measured traffic data rather than theoretical models, the system achieves accurate load assessment and effective self-organization without depending on overlapping area calculations or mobile station counts
Data Source
AI summary
A solution of reconstructing the network topology according to the traffic related information of each cell is proposed in the present invention, so as to achieve self-optimization of network. The traffic related information of a cell includes the traffic related information applicable to network topology reconstruction, or load related information as is named, including but not limited to time-frequency resource related amount that is used by the traffic data in the cell, traffic throughout of each cell or the wireless channel quality of each cell for transmitting traffic data, etc. The solutions in the present invention realize the network topology reconstruction according to traffic related information of multiple cells, therefore the network capacity and service quality could be effectively improved and the wireless relay communication network is applicable to those areas with unpredictable traffic distribution. And the network topology reconstruction function in the present invention can simplify network planning and network management, so that network deployment cost and maintenance and management expenses could be saved.