Adaptive Load Management in Femtocell Networks
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Solution Overview
Problem
Femtocell networks face challenges in efficiently managing load distribution among base stations, leading to potential service disruptions and reduced quality due to uneven load states, which existing technologies have not adequately addressed.
Innovation Solution
Implementing adaptive load management by determining the load state of each femtocell base station and dynamically adjusting its reserved and available service capacities, as well as handover trigger conditions, to balance load across the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If femtocell base stations are installed to expand service coverage and distribute network load, then service capacity and coverage are improved, but load imbalance among base stations occurs leading to service disruptions
Solution Approach 1:
The patent implements dynamic load management by continuously monitoring load states of femtocell base stations and adjusting service capacities in real-time. The system transitions from static capacity allocation to dynamic adjustment based on current network conditions, enabling base stations to adapt their service capacities according to actual load demands and prevent service disruptions.
Solution Approach 2:
The patent changes the parameter of service capacity allocation from fixed to variable. By introducing load state-dependent capacity adjustment mechanisms, the system modifies service capacity parameters dynamically based on monitored load conditions, enabling optimal resource distribution across the network while maintaining service reliability.
2Productivity
If service capacity is increased to handle high load states, then productivity is improved, but system complexity increases due to need for load management
Solution Approach 1:
The patent implements self-service load management where femtocell base stations autonomously monitor their own load states and adjust their service capacities without requiring complex centralized control. Each base station independently makes decisions based on pre-defined load thresholds and adjustment rules, simplifying the overall system architecture while maintaining effective load management.
Solution Approach 2:
The patent establishes a feedback mechanism where load states are continuously monitored and used to trigger capacity adjustments. The system implements closed-loop control by feeding back load information to the capacity management function, enabling automatic adaptation to changing network conditions without requiring complex external intervention.
3Reliability
If handover trigger conditions are relaxed to enable easier handover, then service continuity is improved, but load balancing efficiency deteriorates
Solution Approach 1:
The patent applies local quality by implementing differentiated handover trigger conditions for different neighbor base stations based on their individual load states. Instead of using uniform handover parameters, the system adjusts handover thresholds locally for each target base station, enabling seamless handover to appropriate stations while maintaining effective load balancing across the network.
Data Source
AI summary
The disclosure is related to adaptive load management in a femtocell network. A load state of a femtocell base station may be determined as one of a low load state, a middle load state, and a high load state. According to the determined load state, a reserved service capacity and an available service capacity of the femtocell base station may be adaptive controlled. Furthermore, at least one handover trigger condition associated with the femtocell base station may be controlled according to the determined load state of the femtocell base station.