Adaptive Cell Clustering for CoMP Network Optimization
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Solution Overview
Problem
Static cell clustering in wireless communication systems is suboptimal as it does not adapt to changing user traffic conditions, leading to inefficient spectral usage and performance at cell edges.
Innovation Solution
Adaptive clustering is implemented through a CoMP control unit that aggregates measurement information from cells to dynamically form and adjust cell clusters based on signal strength and traffic conditions, optimizing performance indicators such as signal strength, system complexity, and user priority.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static cell clustering is used, then device complexity is reduced and ease of operation is improved, but adaptability to changing traffic conditions deteriorates and spectral efficiency decreases
Solution Approach 1:
The patent implements dynamic cell clustering where clusters are formed and adjusted based on real-time measurement information from mobile devices. The clustering configuration is updated periodically or when traffic conditions change, allowing the system to adapt to varying load conditions while maintaining manageable complexity through automated algorithms.
Solution Approach 2:
The system collects measurement information from mobile devices about signal strength and traffic conditions, processes this feedback data, and uses it to dynamically adjust cluster configurations. This closed-loop feedback mechanism enables the system to automatically adapt to changing conditions without requiring manual intervention.
2Device complexity
If static cell clustering is used, then system complexity is reduced, but spectral efficiency and performance at cell edges deteriorate
Solution Approach 1:
The patent implements dynamic cell clustering where clusters are formed and adjusted based on real-time measurement information from mobile devices. The clustering configuration is updated periodically or when traffic conditions change, allowing the system to adapt to varying load conditions while maintaining manageable complexity through automated algorithms.
Solution Approach 2:
The system changes clustering parameters such as cluster size, member cells, and coordination configurations based on measured traffic conditions and signal quality metrics. This allows optimization of spectral efficiency by forming appropriate cluster sizes and compositions matched to current network conditions.
3Productivity
If adaptive clustering is implemented, then spectral efficiency and adaptability are improved, but measurement and control complexity increases
Solution Approach 1:
Mobile devices perform self-measurement of signal strength from multiple cells and automatically report this measurement information to the network. The system uses this self-collected data from devices to drive adaptive clustering decisions, reducing the need for complex network-side measurement and monitoring infrastructure.
Data Source
AI summary
Systems, methods, devices, and computer program products are described for adaptive clustering. Serving cells may receive measurement information from mobile devices, and may each form reporting sets including measurement information for various cells. A CoMP (coordinated multi-point) control unit may receive the measurement information from each of a number of serving cells. The received measurement information may be aggregated for a population of the mobile devices. Based on the aggregated measurement information, cell clusters may be formed to perform coordinated transmissions, each including a different subset of the cells. An indication of the determined cell clusters may be transmitted to respective cells.