Cellular Load Balancing via Radio Resource Occupation Metrics
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Solution Overview
Problem
In 4th generation mobile communication systems like WiMAX/LTE, load balancing is challenging due to differences in costs and user distribution, with existing methods struggling to accurately express load metrics and implement network-initiated handovers efficiently, leading to issues like network entry failures and QoS deterioration.
Innovation Solution
A method and apparatus for load balancing that define a load metric considering QoS requirements and wireless channel states, allowing for network-initiated handovers between cells, with a signaling overhead optimization to minimize costs and side effects, using a radio resource occupation rate to trigger load balancing algorithms and select target user equipment and cells.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If load balancing is implemented through network-initiated handover, then network performance and QoS are improved, but signaling overhead and operational costs increase
Solution Approach 1:
The base station proactively identifies overloaded cells and initiates handover decisions before severe congestion occurs. By calculating load metrics in advance and pre-selecting target cells, the system performs load balancing proactively rather than reactively, improving network performance while minimizing last-minute signaling overhead.
Solution Approach 2:
The system continuously monitors load metrics including radio resource occupation rates and QoS parameters, using this feedback to dynamically adjust handover decisions. The base station receives load information from neighboring cells and uses this feedback loop to optimize handover timing and target selection, balancing performance improvement with overhead reduction.
2Measurement precision
If handover is triggered by radio resource occupation rate, then load balancing accuracy is improved, but handover frequency and signaling overhead increase
Solution Approach 1:
The system uses radio resource occupation rate as a key load metric but applies it selectively rather than triggering handovers for every threshold crossing. By setting appropriate thresholds and hysteresis margins, the system performs partial load balancing actions only when truly necessary, maintaining accuracy while avoiding excessive handovers that would waste time and resources.
Solution Approach 2:
The handover triggering mechanism is dynamic rather than static. The system adjusts load thresholds and triggering conditions based on current network state, traffic patterns, and QoS requirements. This dynamic approach allows the system to be sensitive to real load changes while being tolerant of normal fluctuations, reducing unnecessary handovers.
3Ease of operation
If terminal performs handover selection, then device autonomy is improved, but load balancing effectiveness deteriorates in centralized scheduling systems
Solution Approach 1:
Instead of the terminal autonomously selecting handover targets based on local information, the invention inverts the control direction: the base station (network side) calculates load metrics, identifies suitable target cells, and initiates handover decisions. This network-initiated approach leverages centralized scheduling authority to make globally optimal handover decisions that effectively balance load across the network.
Solution Approach 2:
The base station acts as an intermediary between terminals and the core network for handover decisions. It collects load information from neighboring base stations, evaluates handover candidates, and makes informed decisions that balance terminal service continuity with overall network load distribution. This intermediary role resolves the conflict between terminal autonomy and network-wide load balancing effectiveness.
Data Source
AI summary
A method and an apparatus for load balancing a serving subcell providing a data service to one or more user equipments in a cellular communication system are provided. The method includes calculating a load metric by using a radio resource occupation rate of data traffic in one or more scheduling types except for a Best Effort (BE) scheduling type, determining whether the serving subcell is in an overload state by using the load metric, and triggering a load balancing algorithm when it is determined that the serving subcell is in the overload state. Accordingly, the load metric used for load balancing may be defined to have a value closer to an actual free load, and thus the load balancing may be efficiently performed.