Access Point Self-Tuning Data Unit Aggregation
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Solution Overview
Problem
Existing data unit aggregation approaches at access points often suffer from poor performance and channel utilization due to fixed aggregation settings that do not adapt to varying operating conditions, leading to degraded service quality and channel inefficiency.
Innovation Solution
An access point dynamically determines weights for metrics such as link rate and service load to set optimal aggregation parameters like maximum and minimum data unit sizes and counts for each mobile communication device, enabling adaptive aggregation settings based on real-time conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data units are aggregated as many as possible during a time period, then channel utilization improves, but transmission latency increases and service quality degrades
Solution Approach 1:
The patent implements dynamic aggregation parameters that adjust in real-time based on network conditions, station characteristics, and traffic patterns. The system transitions from fixed aggregation settings to adaptive ones, where the access point continuously monitors performance metrics and modifies aggregation depth, time period, and threshold values to optimize both channel utilization and latency trade-offs for different operating conditions
Solution Approach 2:
The system changes multiple aggregation parameters simultaneously including aggregation depth (number of data units), aggregation time period duration, and aggregation threshold values. By dynamically adjusting these parameters based on real-time network state, the system resolves the contradiction between maximizing channel utilization through deeper aggregation and minimizing latency through shallower aggregation
2Device complexity
If fixed aggregation criteria are used, then device complexity is reduced, but adaptability to different operating conditions deteriorates
Solution Approach 1:
The access point autonomously monitors network conditions, evaluates performance metrics, and self-adjusts aggregation parameters without external intervention. The system implements self-service by automatically detecting changes in traffic patterns, station capabilities, and network load, then dynamically modifying aggregation settings to adapt to different operating conditions while maintaining optimal performance
Solution Approach 2:
The system establishes feedback loops where the access point continuously monitors transmission performance, channel utilization, and latency metrics, then uses this feedback to adjust aggregation parameters. The feedback mechanism enables the system to learn from past performance and adapt aggregation criteria to different operating conditions, resolving the contradiction between simple fixed control and complex adaptive control
3Loss of energy
If minimum number of data units is required for aggregation, then transmission overhead is reduced, but service quality for low-traffic stations deteriorates
Solution Approach 1:
The patent implements different aggregation minimum thresholds and parameters for different stations based on their individual characteristics, traffic patterns, and service requirements. High-traffic stations receive more aggressive aggregation settings with higher minimum thresholds to reduce overhead, while low-traffic or latency-sensitive stations receive more conservative settings with lower thresholds to maintain service quality. This localized customization resolves the contradiction between overhead reduction and service quality maintenance
Data Source
AI summary
In some embodiments, an apparatus includes an access point (AP) wirelessly coupled to a set of mobile communication devices. The AP is configured to determine a set of weights for a set of metrics based on a performance goal collectively associated with the AP and the set of mobile communication devices. The AP is configured to define, for each mobile communication device from the set of mobile communication devices, a maximum aggregation data unit size, a minimum aggregation data unit count, and a maximum aggregation data unit count based on the set of weights and a set of values for the set of metrics. The AP is configured to send, to each mobile communication device from the set of mobile communication devices, a signal based on the maximum aggregation data unit size, the minimum aggregation data unit count and the maximum aggregation data unit count.