Mobile Network Proactive Scheduling Under Cell Load Thresholds
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Solution Overview
Problem
Existing mobile networks face challenges in determining optimal values for proactive scheduling parameters, leading to surplus interference and reduced capacity, which affects ping times, uplink quality, and throughput.
Innovation Solution
Adjust the proactive scheduling functionality in mobile networks based on real-time load conditions, disabling or adjusting the proactive scheduling parameter according to predefined thresholds and application types, and enabling or disabling it in mass event modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If proactive scheduling functionality is enabled with a fixed parameter value, then uplink connection stability is improved, but network capacity and throughput deteriorate due to surplus interference
Solution Approach 1:
The proactive scheduling parameter is changed from a fixed value to a dynamic value that adjusts based on current network load conditions. The system monitors load metrics and automatically modifies the proactive scheduling parameter to optimize the balance between connection stability and network throughput, preventing both excessive interference at high load and unnecessary releases at low load.
Solution Approach 2:
The invention changes the parameter value of proactive scheduling based on network conditions. When network load exceeds a threshold, the proactive scheduling parameter is reduced or disabled to decrease surplus interference and improve throughput. When load is below the threshold, the parameter is increased to maintain connection stability, thus dynamically optimizing system performance.
2Reliability
If proactive scheduling parameter is increased to maintain connections, then connection stability is improved, but interference and capacity loss increase
Solution Approach 1:
The system dynamically adjusts the proactive scheduling parameter based on real-time network load monitoring. When load increases, the parameter is reduced to minimize surplus interference generated by maintaining unnecessary connections. When load decreases, the parameter is increased to maintain stable connections, thus adaptively managing the trade-off between connection stability and interference generation.
Solution Approach 2:
The system implements a feedback mechanism that continuously monitors network load and adjusts the proactive scheduling parameter accordingly. The load measurement feedback loop enables the system to detect when increasing the parameter causes excessive interference and automatically reduce it, preventing capacity degradation while maintaining connection stability when beneficial.
3Productivity
If proactive scheduling is disabled to reduce interference, then network capacity is improved, but uplink quality and ping times deteriorate
Solution Approach 1:
Instead of completely disabling proactive scheduling, the system dynamically adjusts the parameter value based on network conditions. When load is low, the parameter is increased to maintain connections and preserve uplink quality. When load is high, the parameter is reduced or disabled to reduce interference and improve capacity, thus adaptively balancing uplink quality and network capacity.
Solution Approach 2:
The system transitions from a static binary state (proactive scheduling enabled or disabled) to a dynamic multi-state configuration where the parameter value adjusts continuously based on load conditions. This dynamic approach allows the system to maintain optimal uplink quality during low-load periods while preserving network capacity during high-load periods.
4Device complexity
If proactive scheduling parameter is set to default value, then system complexity is reduced, but performance optimization is lost
Solution Approach 1:
The system implements self-service by automatically monitoring network load and adjusting the proactive scheduling parameter without requiring manual configuration or complex optimization algorithms. The base station autonomously measures load metrics, compares them against thresholds, and modifies the parameter accordingly, simplifying operation while achieving performance optimization.
Solution Approach 2:
The system uses simple feedback-based control where the proactive scheduling parameter is adjusted based on direct measurement of network load. This feedback mechanism provides performance optimization through automatic adaptation without requiring complex predictive models or extensive configuration, thus achieving good performance with minimal system complexity.
Data Source
AI summary
Optimization of a mobile network, performed by adjusting a proactive scheduling functionality in a target cell of the mobile network based on load in the target cell, wherein the proactive scheduling functionality is configured by a proactive scheduling parameter that defines a period of time over which an uplink connection is kept alive after sending the last bit of uplink data; responsive to the load in the target cell exceeding a first threshold, disabling the proactive scheduling functionality; responsive to the load in the target cell being below a second threshold, configuring the proactive scheduling parameter to a default value or gradually increasing the proactive scheduling parameter value; and responsive to the load in the target cell being between the first threshold and the second threshold, keeping the current proactive scheduling parameter value.