Centralized Controller Radio Resource Optimization
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Solution Overview
Problem
Traditional radio resource management in cellular networks faces challenges in coordinating shared resources across cells, leading to suboptimal performance, especially at cell edges and in distributed coordination due to limitations in information exchange and backhaul bandwidth.
Innovation Solution
A method that determines a work status series and policy series for each cell, adjusting resources based on active or passive states to maximize transmission rates and meet minimum requirements, with centralized coordination to minimize interference and optimize resource utilization across the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If centralized cell coordination is used to optimize radio resources from an entire network perspective, then network-wide performance is improved, but backhaul bandwidth requirements and system complexity increase
Solution Approach 1:
The patent segments the network into coordination sets of cells, where each set is managed by a designated coordination cell. This divides the centralized coordination task into smaller, manageable units, reducing the complexity burden on any single controller while maintaining network-wide optimization benefits.
Solution Approach 2:
The patent introduces a hierarchical dimension to coordination, with coordination cells managing subsets of cells rather than a single flat centralized controller managing all cells. This dimensional change distributes the coordination load across multiple levels, reducing backhaul bandwidth requirements while preserving optimization capabilities.
2Adaptability or versatility
If distributed cell coordination is used with information exchange between cells, then adaptability to fast network environment changes is improved, but optimal network-wide performance is difficult to achieve
Solution Approach 1:
The patent implements dynamic coordination states that can transition between active and passive modes based on network conditions. Cells can switch between distributed and centralized coordination modes, allowing the system to adapt to changing network environments while maintaining optimization capabilities through state transitions.
Solution Approach 2:
Coordination cells autonomously manage their designated sets of cells using local information and predefined coordination algorithms. This self-service capability enables fast adaptation to local changes without requiring constant centralized control, while still achieving network-wide optimization through coordinated actions across multiple cells.
3Ease of operation
If per-cell radio resource management is used, then cell autonomy is maintained, but resource coordination and cell edge performance are degraded
Solution Approach 1:
The patent merges coordination functions across multiple cells by designating coordination cells that represent groups of cells. These coordination cells aggregate resource management decisions, maintaining individual cell autonomy for local operations while achieving coordinated resource allocation at the group level, thus improving overall resource utilization without sacrificing cell-level independence.
Data Source
AI summary
The present invention provides a radio resource optimization and management method, a centralized controller, and a base station. In the method, the centralized controller determines a work status series and a work policy series of each cell during a first time period; and sends the work status series and the work policy series of each cell to each cell, so that each cell adjusts resources of this cell according to the work status series and the work policy series, thereby enhancing a network KPI at a large time granularity by means of distributed cell coordination while ensuring local KPI performance of each cell. In contrast with distributed coordination optimization in a single coordination state, the present invention provides a higher degree of freedom in terms of a time dimension, and therefore provides better performance in terms of a network KPI at a large time granularity.