Energy-Aware Multi-Cell Load Balancing and Traffic Steering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Advanced communication networks face challenges in balancing network efficiency with high power consumption, particularly in 5G and beyond networks, due to increased traffic and complex task processing needs.
Innovation Solution
A multi-cell framework that employs AI-based models for dynamic load balancing, admission control, and traffic steering, utilizing reinforcement learning to optimize network operations and mitigate conflicts between policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If advanced communication networks process more traffic and complex tasks to serve more users, then network productivity and service quality improve, but power consumption increases
Solution Approach 1:
The patent implements dynamic load balancing that continuously adjusts traffic distribution across cells based on real-time power consumption metrics and network conditions. The system dynamically modifies routing decisions, admission control parameters, and resource allocation to optimize the trade-off between network throughput and power consumption, rather than using static configurations
Solution Approach 2:
The system changes operational parameters such as cell transmission power levels, resource block allocation, and admission control thresholds based on network conditions and power consumption targets. By adjusting these parameters dynamically, the network can maintain service quality while reducing energy usage during low-demand periods
2Productivity
If the network admits more incoming user equipment to increase service coverage, then network productivity improves, but power consumption and QoS violations increase
Solution Approach 1:
The system performs preliminary admission control assessments before admitting new user equipment. The machine learning models predict the impact of admitting additional users on QoS and power consumption, allowing the network to pre-evaluate and reject admissions that would cause QoS violations or excessive power usage before they occur
3Productivity
If the network implements multiple concurrent policies for load balancing, admission control, and traffic steering, then network efficiency improves, but policy conflicts arise
Solution Approach 1:
The patent merges multiple separate policy decision-making processes into a unified machine learning framework. The load balancing, admission control, and traffic steering policies are coordinated through a single neural network model that jointly optimizes all three functions, eliminating conflicts that arise from separate policy implementations
Data Source
AI summary
Facilitating concurrent energy aware admission control, dynamic load balancing, and traffic steering in advanced communication networks is provided. A method includes facilitating energy efficiency aware load balancing of already served user equipment to distribute the already served user equipment among a group of cells of a communication network. The method also includes facilitating controlling of admission of incoming user equipment to the communication network and facilitating traffic steering of the incoming user equipment among the group of cells. Further, the method includes facilitating conflict mitigation among respective policies associated with facilitating the energy efficiency aware load balancing, facilitating of the controlling of the admission, and facilitating of the traffic steering. Facilitating of the energy efficiency aware load balancing, facilitating of the controlling of the admission, facilitating of the traffic steering, and facilitating of the conflict mitigation are performed concurrently and are based on a utility function.