Wireless Network Resource Balancing via Dynamic Parameter Adjustment
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Solution Overview
Problem
Wireless communication networks face challenges in efficiently managing resources such as voice and data capacity due to exponential growth in demand, leading to dropped calls and poor communication quality, especially during high usage periods.
Innovation Solution
Implementing a system that balances network traffic across radios and sectors by analyzing and redistributing resources using a resource manager and balancer, which adjusts parameters like Qoffset2sn to optimize spectral efficiency and capacity utilization, allowing for cost-effective use of available bandwidth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network resources are increased to meet exponential demand growth, then user satisfaction and communication quality improve, but network cost and resource scarcity worsen
Solution Approach 1:
The system dynamically changes network parameters including Qoffset2sn values, handover thresholds, and resource allocation policies to optimize spectral efficiency and balance traffic loads across cells, thereby improving communication quality without requiring proportional increases in physical network resources
Solution Approach 2:
The network implements self-organizing features where base stations autonomously monitor traffic patterns, detect congestion conditions, and adjust resource allocation and handover parameters without centralized control, enabling the network to self-optimize resource utilization and maintain service quality during demand fluctuations
2Productivity
If traffic load is concentrated on fewer radios during high usage, then spectral efficiency on active radios improves, but dropped call rates increase due to overload
Solution Approach 1:
The system dynamically adjusts Qoffset2sn parameters and handover thresholds in real-time based on traffic conditions, enabling flexible redistribution of calls between radios. When congestion is detected on a radio, the system automatically modifies parameters to steer new calls to less loaded radios, maintaining both spectral efficiency and call reliability
Solution Approach 2:
The network continuously monitors traffic load, call success rates, and spectral efficiency metrics on each radio, using this feedback to dynamically adjust resource allocation and handover parameters. This closed-loop control enables the system to respond to changing conditions and maintain optimal performance across varying traffic patterns
3Reliability
If network parameters are adjusted to balance traffic across radios, then dropped call rates decrease, but system complexity increases
Solution Approach 1:
Base stations are equipped with autonomous resource management capabilities that enable them to independently monitor local traffic conditions, evaluate parameter adjustment needs, and implement optimization decisions without centralized coordination. This distributed self-service approach reduces the complexity burden on the core network while achieving traffic balancing
Solution Approach 2:
The system pre-configures multiple Qoffset2sn parameter sets and handover threshold combinations that have been optimized for different traffic scenarios. When specific congestion patterns are detected, the system rapidly switches to pre-computed parameter sets, avoiding the need for complex real-time calculations and reducing system complexity
Data Source
AI summary
Detection of an unbalanced network load and redistribution of network traffic to balance the network load is provided herein. Load balancing across different radios in the same sector of a cell site can be facilitated through detection of the unbalance network load and changes to one or more parameters can be made to rebalance the network load. After radios within a sector are more evenly balanced, network load balancing across sectors can be facilitated. The balancing can be performed to improve system performance, reduce a dropped call rate, as well as to achieve other benefits that can provide an improved user experience as compared to systems that do not attempt to balance the network load.