Q-Learning UE Association for 5G Network Throughput
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Solution Overview
Problem
In 5G networks, the traditional mechanism of user equipment (UE) association based on received signal strength indicator (RSSI) is inadequate in dense device-centric networks, leading to increased interference and suboptimal network throughput, as it prioritizes individual UE performance over overall network efficiency.
Innovation Solution
An online learning algorithm using Q-learning is employed to dynamically optimize UE association with base stations, considering current and past environmental data, such as location, beam patterns, and channel conditions, to maximize average network throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional RSSI-based UE association mechanism is used, then individual UE connection simplicity is maintained, but network throughput deteriorates in dense device-centric networks due to increased interference
Solution Approach 1:
The patent applies dynamics by transitioning from static RSSI-based association to dynamic Q-learning-based association. The system continuously adapts UE associations based on real-time network conditions, interference levels, and throughput metrics, allowing the network to respond dynamically to changing environmental factors in dense device-centric deployments
Solution Approach 2:
The patent implements feedback mechanisms where the network controller continuously monitors network throughput and interference metrics, then uses this feedback to adjust UE associations through Q-learning. The system learns from past associations and their outcomes, refining future association decisions to maximize throughput while minimizing interference
2Reliability
If UE associates with nearest or highest SINR BS, then individual UE performance is optimized, but overall network performance deteriorates due to interference caused to nearby UEs
Solution Approach 1:
The patent applies beforehand cushioning by using Q-learning to predict future interference conditions and prevent suboptimal associations before they occur. The system learns from historical data to anticipate scenarios where individual UE optimization would harm network throughput, and proactively selects associations that balance both individual and collective performance
Solution Approach 2:
The patent changes the association decision parameters from simple RSSI/SINR metrics to complex Q-learning state representations that include network throughput, interference levels, and environmental factors. This parameter transformation enables the system to consider network-wide implications rather than just individual link quality
3Productivity
If Q-learning algorithm is implemented, then network throughput is enhanced by up to 16%, but computational complexity and learning time are increased
Solution Approach 1:
The patent applies self-service by implementing Q-learning at the network controller rather than requiring complex algorithms at each UE. The centralized controller handles the computational burden of learning and decision-making, while UEs simply follow association instructions, distributing complexity appropriately across the network architecture
Data Source
AI summary
The present disclosure relates to a pre-5th-generation (5G) or 5G communication system to be provided for supporting higher data rates beyond 4th-generation (4G) communication system such as long term evolution (LTE). Disclosed is a method of managing a telecommunications network, comprising the steps of: obtaining data representing an operational parameter from the at least one of a plurality of network elements comprising a plurality of base stations and at least one terminal, determining mapping information for connection between the at least one terminal and one of the plurality of base stations based on the data representing the operational parameter, and transmitting, to the at least one terminal, the mapping information.