LLM-Driven Proactive Scheduling for Fairer Wi-Fi Bandwidth Allocation
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Solution Overview
Problem
Conventional Wi-Fi bandwidth allocation in MU-MIMO systems primarily relies on device feedback, failing to consider user intentions and priorities, leading to inefficiencies and high overhead in optimizing bandwidth distribution.
Innovation Solution
A Large Language Model (LLM) driven proactive scheduling system that integrates a Proactive Feedback Module, Instructive Interpreter Module, and User-Reinforced Scheduling Optimization Module to gather user requests and device feedback, generate instructive prompts, and continuously enhance bandwidth scheduling based on user satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional Wi-Fi bandwidth allocation relies on device feedback, then implementation is simple, but user intentions and priorities are not considered leading to optimization inefficiency
Solution Approach 1:
The patent introduces an LLM-based intermediary layer between device feedback and bandwidth allocation decisions. This intermediary interprets user intentions from feedback signals and translates them into scheduling priorities, resolving the contradiction by adding intelligence without requiring direct complex user input processing
Solution Approach 2:
The system implements enhanced feedback mechanisms where device feedback is not only used for channel quality assessment but also for inferring user intentions. This multi-purpose feedback approach improves optimization efficiency by extracting additional value from existing feedback signals
2Reliability
If proactive scheduling considers user intentions, then bandwidth distribution fairness improves, but system overhead increases
Solution Approach 1:
The LLM-based scheduler operates autonomously by interpreting device feedback and user intentions without requiring explicit user commands or additional signaling overhead. The system serves itself by making intelligent inferences from available data, achieving fairness without proportional increases in overhead
Solution Approach 2:
The system performs preliminary interpretation of user intentions from feedback signals before bandwidth allocation decisions are made. This advance processing of intent information allows the scheduler to proactively adjust allocations in favor of high-priority users before resource exhaustion occurs
3Productivity
If device feedback is the primary basis for bandwidth allocation, then implementation is straightforward, but user priorities are ignored leading to suboptimal resource utilization
Solution Approach 1:
The patent transforms the interpretation of feedback parameters from purely technical metrics to include inferred user priority indicators. By changing how feedback data is processed and weighted, the system improves resource utilization while maintaining the same basic feedback collection mechanism
Solution Approach 2:
The patent replaces simple rule-based bandwidth allocation mechanics with an LLM-based intelligent decision system. This substitution enables nuanced consideration of user priorities through natural language interpretation of feedback, dramatically improving resource utilization
Data Source
AI summary
Large Language Model (LLM) driven proactive scheduling may be provided. First, a proactive feedback module may be used that gathers user requests and device feedback. Next, an instructive interpreter module may be used that receives the user requests and the device feedback and produces instructive prompts based on the user requests and the device feedback. Then a user-reinforced scheduling optimization module may be used that receives responses to the instructive prompts and continuously enhances bandwidth scheduling based on the receives responses.