Wi-Fi Channel Selection via Reinforcement Learning
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Solution Overview
Problem
Wi-Fi performance is degraded due to interference from LTE users operating on unlicensed spectrum, as existing technologies lack effective methods to mitigate interference and improve network performance in co-existing communication networks.
Innovation Solution
A system and method that senses inputs related to wireless signals in co-existing communication networks, detects interfering channels, classifies them based on energy and carrier sensitivity thresholds, assigns channel states, and uses a frequency hopping technique to switch to an optimal channel for interference-free transmission, employing reinforcement learning for optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If LTE users operate on unlicensed spectrum alongside Wi-Fi, then spectrum utilization is improved, but Wi-Fi performance is degraded due to interference
Solution Approach 1:
The system dynamically changes transmission parameters including channel selection, transmission power, and modulation schemes based on detected interference levels from LTE users. By adjusting these parameters in real-time, the Wi-Fi system adapts to coexistence conditions while maintaining performance
Solution Approach 2:
The invention implements dynamic channel selection and power adjustment mechanisms that continuously monitor the radio environment and adapt transmission strategies. This dynamic behavior allows Wi-Fi to respond to changing LTE interference patterns and maintain reliable communication
2Area of stationary object
If Wi-Fi operates in high interference environments from LTE, then network coverage is extended, but throughput and reliability are substantially degraded
Solution Approach 1:
The system employs feedback mechanisms where the receiver detects interference levels and channel conditions, then communicates this information back to the transmitter. This feedback enables the transmitter to adjust channel selection, power levels, and modulation schemes to maintain throughput while extending coverage into challenging environments
Solution Approach 2:
Before transmitting data, the system performs preliminary channel assessment and interference detection. By identifying suitable channels and adjusting parameters in advance, the system prevents throughput degradation before it occurs, allowing coverage extension without sacrificing performance
Data Source
AI summary
This disclosure relates to method and system for improving Wi-Fi performance in co-existing communication networks using learning methodologies. In recent times, most of telecom operators have expressed interest in deploying LTE (Long-Term Evolution) over the unlicensed spectrum. However, simultaneous use of unlicensed band (by operators using LTE and other Wi-Fi) presents coexistence challenges in terms of network performance especially for the Wi-Fi. The disclosed techniques enable improving the Wi-Fi performance in the co-existing communication networks based on learning methodologies. The disclosed techniques improve Wi-Fi performance based on several steps that includes detecting an interfering channel, and further identifying an optimal channel to mitigate the interference caused by the detected interfering channel. The optimal channel is identified based on an optimization technique, wherein the optimization technique is a reinforcement learning technique based on a Q-learning.