Traffic Scenario Clustering for Base Station Load Balancing
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Solution Overview
Problem
Existing wireless communication systems face challenges in managing load balancing policies across base stations due to varying traffic scenarios and increased base station density, leading to inefficiencies and redundancies in resource utilization.
Innovation Solution
Implementing reinforcement learning agents to generate load balancing policies for specific traffic scenarios and iteratively clustering these policies until a desired number is reached, reducing redundancy by deploying optimized policies across base stations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple learning agents are trained for different traffic scenarios, then load balancing performance improves, but system complexity and overhead increase
Solution Approach 1:
The patent merges multiple traffic scenario policies into a single clustered policy by identifying similar traffic scenarios and combining their respective load balancing policies. This reduces the number of policies from N (one per traffic scenario) to M (where M < N), thereby reducing system complexity while maintaining load balancing performance across diverse traffic conditions.
Solution Approach 2:
The clustered load balancing policy serves multiple traffic scenarios simultaneously, making a single policy universal across different traffic conditions. This multi-functional policy reduces overhead by eliminating the need for separate policies for each traffic scenario, while still adapting to varying traffic patterns through the clustering mechanism.
2Area of stationary object
If base station density increases, then network coverage improves, but policy management difficulty increases
Solution Approach 1:
The patent reduces the number of policies that need to be managed across base stations by merging policies for similar traffic scenarios. Instead of managing N separate policies across M base stations, the system manages M clustered policies, significantly reducing policy management overhead in dense networks with high base station density.
Solution Approach 2:
The patent segments traffic scenarios into clusters based on similarity, creating groups of scenarios that can share common policies. This segmentation approach organizes the policy management task into manageable clusters rather than handling each scenario individually, making policy deployment more tractable in dense networks.
3Quantity of substance
If traffic volume increases, then network capacity demands improve, but resource utilization efficiency decreases
Solution Approach 1:
The patent implements dynamic load balancing by training learning agents that adapt to different traffic scenarios and continuously updating clustered policies based on traffic conditions. This dynamic approach allows the system to efficiently allocate resources across base stations as traffic volume changes, improving resource utilization efficiency even as network capacity demands increase.
Data Source
AI summary
The present disclosure provides for methods, apparatuses, and non-transitory computer-readable storage media for load balancing traffic scenarios by a network device. In an embodiment, a method includes training a plurality of learning agents to load balance a respective plurality of traffic scenarios to obtain a plurality of control policies. The method further includes performing at least one clustering iteration. Each clustering iteration includes selecting a pair of control policies and merging the pair of control policies into a clustered control policy that replaces the pair of control policies. The method further includes determining to stop the performing of the at least one clustering iteration when a quantity of control policies remaining in the plurality of control policies meets a predetermined value. The method further includes deploying to each base station of a plurality of base stations a corresponding control policy from the plurality of control policies.