Small Cell Handover Parameters Based on Radio Node Density
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Solution Overview
Problem
Determining optimal handover parameters in small cells is challenging, leading to degraded communication performance and service disruptions.
Innovation Solution
A radio node in a small cell receives predefined handover parameters based on the density of radio nodes, using communication-performance metrics to recommend handovers, thereby reducing the need for optimization and inaccuracies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If handover parameters are determined dynamically in small cells, then communication performance can be optimized for specific conditions, but the complexity of parameter management increases and service disruptions may occur due to inaccurate or sub-optimal parameters
Solution Approach 1:
The patent applies preliminary action by pre-determining handover parameters based on network density categories (sparse, moderate, dense) before actual handover operations. The system classifies the small cell environment into density categories and assigns predetermined handover parameters accordingly, eliminating the need for dynamic parameter determination during operation. This resolves the contradiction by providing reliable communication performance through pre-optimized parameters while reducing parameter management complexity through automated classification and assignment.
2Reliability
If handover parameters are optimized for specific network conditions, then communication performance improves, but the difficulty of determining correct parameters increases
Solution Approach 1:
The patent applies parameter changes by transforming the continuous variable of network density into discrete categories (sparse, moderate, dense). The system changes the parameter representation from requiring precise continuous optimization to selecting from predefined categories, each associated with optimized handover parameters. This resolves the contradiction by improving communication performance through condition-specific optimization while reducing parameter determination difficulty through categorical classification rather than continuous optimization.
3Measurement precision
If manual optimization of handover parameters is performed, then accuracy can be improved, but the time and resources required for optimization increase
Solution Approach 1:
The patent applies self-service by enabling the small cell system to automatically determine its own network density category and select appropriate handover parameters without external manual optimization. The system autonomously classifies its operating environment and applies the corresponding parameter set, achieving accurate handover parameters instantaneously. This resolves the contradiction by maintaining high accuracy through automated intelligent selection while eliminating the time loss associated with manual optimization processes.
Data Source
AI summary
A radio node in a network (such as a small cell) that receives predefined handover parameters is described. During operation, the radio node may receive, from a computer (such as a controller), information specifying the predefined handover parameters, where the predefined handover parameters correspond to a density of radio nodes in the network. For example, the radio node may provide, to the computer, information specifying the density of radio nodes in the network. Moreover, the radio node may receive, from the electronic device, information specifying a communication-performance metric associated with the radio node. Next, the radio node may provide, to the electronic device, a recommendation for a handover from the radio node to another radio node in the network based at least in part on the predefined handover parameters, the communication-performance metric and a second communication-performance metric associated with the other radio node.