Base Station Clustering via Delaunay Triangulation for Energy Optimization
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Solution Overview
Problem
Current wireless communication networks face challenges in energy efficiency due to high energy consumption by base stations, with 50% to 80% of power used for power supply, cooling, and monitoring, and dynamic traffic patterns leading to low utilization, necessitating a method to optimize base station on/off operations.
Innovation Solution
A distributed algorithm using Delaunay triangulation to form clusters of base stations, where each station compares utilization with neighbors and performs turn-off operations based on predetermined thresholds, broadcasting messages to manage power levels and handovers efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If base stations are turned off to reduce energy consumption, then energy efficiency is improved, but coverage holes and service interruption occur
Solution Approach 1:
The network is divided into multiple clusters using Delaunay triangulation, where each cluster independently manages its base stations. This segmentation allows localized on/off decisions without affecting the entire network, enabling energy savings while maintaining coverage through distributed cluster coordination.
Solution Approach 2:
The system performs preliminary actions by pre-configuring backup base stations and establishing handover protocols before base stations are turned off. When a base station is deactivated, user equipment is proactively transferred to neighboring base stations, preventing coverage holes and service interruption.
2Use of energy by moving object
If centralized control algorithms (GON/GOFF) are used to manage base station on/off operations, then energy efficiency is improved, but system complexity and signaling overhead increase
Solution Approach 1:
The patent implements local quality by enabling each base station to autonomously make on/off decisions based on local traffic conditions and cluster state, rather than relying on centralized control. This distributed approach reduces signaling overhead and system complexity while maintaining energy efficiency through localized optimization.
Solution Approach 2:
Base stations perform self-service by autonomously evaluating their own utilization metrics and making on/off decisions without external control. Each station monitors its traffic load, compares it with cluster thresholds, and independently adjusts its operational state, reducing the need for complex centralized management.
3Reliability
If base stations operate at high utilization to maintain coverage, then service reliability is improved, but energy consumption increases
Solution Approach 1:
The patent applies dynamics by enabling base stations to dynamically adjust their operational state between active and sleep modes based on real-time traffic conditions. This dynamic adaptation allows the network to maintain service reliability during high-demand periods while reducing power consumption during low-utilization periods through automated on/off transitions.
Data Source
AI summary
The present invention relates to a method for supporting a self-organization network and an apparatus for the same. Particularly, the method comprises the steps of: transmitting, by a first base station, a broadcasting message including a utilization of the first base station; comparing, by a second base station, the operation rate of the first base station and the operation rate of the second base station using the broadcasting message; and performing, by the second base station, turn-off if the operation rate of the second base station is lower than the operation rate of the first base station, wherein the first base station and the second base station form a cluster by means of Delaunay triangulation.