Adaptive Load Balancing in Wireless Networks
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Solution Overview
Problem
Current wireless network load balancing systems often prioritize signal strength over cell loading conditions, leading to imbalances and poor performance, particularly in scenarios like 850 MHz and 1900 MHz co-deployment, resulting in suboptimal spectral efficiency and throughput.
Innovation Solution
The implementation of adaptive load balancing algorithms, such as Equal Utilization (EqUtil), Maximum Average Throughput (MaxAvgTpt), and Maximum Minimum Throughput (MaxMinTpt), which consider both cell loading conditions and signal quality to optimize cell selection and resource allocation in wireless networks, ensuring balanced load distribution and improved spectral efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cell selection is based purely on signal strength, then connection quality is improved, but load balancing deteriorates
Solution Approach 1:
The system changes the selection criteria from purely signal strength to a composite metric that incorporates both signal quality and cell loading conditions. This parameter change enables the system to simultaneously consider connection quality and load distribution, resolving the contradiction between these two objectives.
Solution Approach 2:
The system implements feedback mechanisms that monitor cell loading conditions and use this information to dynamically adjust cell selection decisions. By continuously feeding back load status information, the system can balance loads across cells while maintaining quality connections, preventing the imbalance that would occur with signal-strength-only selection.
2Ease of operation
If cell state is ignored in cell selection, then selection simplicity is improved, but performance deteriorates
Solution Approach 1:
The system enhances the cell selection process by incorporating cell state parameters (loading conditions) into the selection criteria. This parameter enrichment transforms the simple signal-strength-based selection into a more sophisticated multi-parameter evaluation, improving spectral efficiency while maintaining operational feasibility through automated algorithms.
3Productivity
If load balancing considers cell loading conditions, then load distribution is improved, but system complexity increases
Solution Approach 1:
The system uses feedback loops to automatically monitor and adjust load distribution based on cell loading conditions. This automated feedback mechanism handles the complexity of multi-parameter optimization without requiring manual intervention, improving load distribution while keeping operational complexity manageable through algorithmic automation.
Solution Approach 2:
The load balancing system operates autonomously by automatically collecting cell state information, evaluating multiple parameters, and making selection decisions without external control. This self-service approach manages system complexity internally while delivering improved load distribution performance.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, a non-transitory machine-readable storage medium comprising executable instructions that, when executed by a processing system including a processor, perform operations comprising: identifying a first plurality of cells as a controlled group of cells; determining, for each cell of the controlled group of cells, an average number of allocated physical resource blocks; determining, for each cell of the controlled group of cells, a total number of physical resource blocks available to carry payload traffic; determining, for each cell of the controlled group of cells, a metric equal to: (a) the average number of allocated physical resource blocks of the cell divided by (b) the total number of physical resource blocks of the cell available to carry payload traffic; and performing a load balancing of the controlled group of cells based upon the metric. Other embodiments are disclosed.