Part 2: Machine Learning improves Wi-Fi Quality of Experience
In the previous Machine Learning Primer article (published as a blog on ASSIA’s web site on May 30, 2018), we tried to give a short overview of basic AI/Machine learning definitions and the associated challenges. In this article, we aim to provide some real-world examples of how ASSIA applies these AI/machine learning concepts on its CloudCheck® product line.
CloudCheck uses AI and machine learning techniques to solve a wide range of Wi-Fi related operational problems including AP (Access Point) level diagnostics, optimization, and network-wide trends and predictions.
AI and Machine Learning Examples:
Here we outline a step-by step process that tracks two example applications – Client Steering and Temperature Detection – at the AP level:
- Measure Health, Environment and Performance: CloudCheck measures the AP’s health, its environment and the performance of connected clients periodically. The period of observation varies by the type of data being collected, ensuring sufficient coverage of the observed phenomenon. For example, the RSSI of connected clients, which can show high variation with time, is measured at a small time-scale, while the temperature of the CPU, which remains largely constant for long time periods, is collected at a larger time-scale.
- Diagnose: CloudCheck analyzes the data to determine trends and patterns at various levels. For example, for each client, a mapping of RSSI and throughput is established in both 2.4GHz and 5GHz frequency bands. In contrast, the temperature measurement is aggregated by the device model of the AP to determine the temperature distributions of different chipsets.
- Set Thresholds and Policies: CloudCheck uses the detected patterns to establish thresholds or policies for diagnostics and optimization. For example, for each client, CloudCheck individually determines the best RSSI to transition from the 5GHz to 2.4GHz band, and the best RSSI to transition from the 2.4GHz to 5GHz band, optimizing for both throughput and coverage. For CPU temperature distributions, CloudCheck determines thresholds for finding outlying or problem temperatures.
- Monitor Deviations: CloudCheck uses these policies to monitor deviations from the defined behavior. For Wi-Fi clients, any RSSI transition that crosses the established threshold for the particular band results in a real-time alert. For temperature distribution, daily temperature data collected for each AP is compared with the appropriate model-specific threshold.
- Recommend or Optimize: The last step of the process is either an optimization action, or a diagnostic output combined with a trouble-shooting recommendation. Just like the period of observation, the output/response time is in accordance with the need of the application. In the client RSSI example, clients requiring band-steering are steered in near real-time to the appropriate band. For temperature, alerts are raised on a daily basis for each AP if required.
As more data is collected, CloudCheck is able to learn from the incoming data, improving the performance of the diagnostics and optimization actions over time.
Another application of machine learning techniques within CloudCheck is the prediction of user Quality of Experience (QoE). A supervised learning approach is employed for this purpose, using network-wide statistics:
- Training data: Supervised learning for QoE requires a labeled data set for training. The features in this data come from CloudCheck’s daily AP diagnostics. The diagnostics are assessments of AP performance along individual dimensions/features including but not limited to client coverage and Wi-Fi throughput, interference in various bands, broadband disconnections and throughput, etc. The labels for the training data come from an assessment of user experience either based on customer surveys, or customer call/churn data provided by the service provider.
- QoE Model Training: Supervised machine learning techniques are used on this training data-set to build a model that maps CloudCheck diagnostics to user QoE. This diagnostic is applied for daily assessment of customer QoE.
- QoE Determined: Based on the needs of the Service Provider, this model can be used either for predictive purposes or for trouble-shooting. CloudCheck further employs feature selection techniques on the training data-set to identify the key drivers of customer dissatisfaction in the service provider’s network.
Over its 15 year history, ASSIA has pioneered development of AI/machine learning applications for last-mile and home network management challenges. From their inception, ASSIA’s DSL, GPON and Wi-Fi solutions have utilized AI and machine learning to enable many of the world’s most progressive carriers and service providers to manage well over a hundred million internet connections. Stay tuned for more innovation in AI at ASSIA in the future!