Dynamic Queue Weight Adjustment for Multi-AP WLAN Fairness

The patent implements dynamic queue weight adjustment using reinforcement learning algorithms that continuously adapt queue weights based on observed network traffic patterns and fairness metrics. The system transitions from static traditional queueing to dynamic adaptive queueing, where queue weights are automatically adjusted in real-time to optimize Quality of Service while accounting for individual client demands and varying traffic conditions.

The system changes the parameter of queue weights from fixed values to dynamically adjustable values based on observed network conditions. By using reinforcement learning, the system modifies queue weight parameters in response to changing traffic patterns, client demands, and fairness metrics, enabling flexible QoS optimization without increasing fundamental system complexity.

The patent changes the parameter of queue weights to enable flexible bandwidth allocation within a limited number of queues. By dynamically adjusting weights assigned to each queue based on client demands and fairness metrics, the system achieves efficient throughput distribution without needing to create additional queues, thus maintaining low device complexity while optimizing productivity.

The limited number of queues are made multi-functional through dynamic weight adjustment, allowing each queue to serve different clients with varying demands at different times. The reinforcement learning system enables queues to adapt their priority levels and resource allocation dynamically, making the limited queue infrastructure universally applicable to diverse traffic patterns and client requirements.

The patent implements self-service queue management through reinforcement learning algorithms that automatically observe network traffic, analyze fairness metrics, and adjust queue weights without human intervention. The system serves itself by continuously learning from observed patterns and adapting to changing conditions, maintaining ease of operation while achieving high adaptability to dynamic traffic patterns and individual client demands.

The system incorporates feedback mechanisms where fairness metrics and observed throughput are continuously monitored and fed back to the reinforcement learning algorithm. This feedback loop enables automatic adaptation to changing traffic patterns and client demands, with the system using observed performance data to dynamically adjust queue weights and optimize allocation, thereby achieving versatility without compromising operational simplicity.