Flow-Based Wi-Fi Client Steering for Better QoE
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Solution Overview
Problem
Conventional Wi-Fi systems rely solely on Received Signal Strength Indicator (RSSI) for client-radio matching, leading to suboptimal associations that result in increased jitter, packet loss, and low throughput due to neglecting active traffic flow characteristics, client mobility, and overlapping basic service set interference.
Innovation Solution
A cloud-based QoE service that computes client QoE scores and radio availability scores using telemetry data, recommending optimal radio associations by considering traffic flow priorities, client health, and neighboring radio conditions to dynamically steer clients to better APs, thereby enhancing Quality of Experience (QoE).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional Wi-Fi systems rely solely on RSSI for client-radio matching, then the system complexity is low, but the Quality of Experience (QoE) deteriorates due to suboptimal associations
Solution Approach 1:
A cloud-based QoE service is introduced as an intermediary between access points and clients. This service receives telemetry data from access points, computes QoE scores and radio availability scores, and generates steering recommendations. The intermediary handles the complex computations centrally, allowing access points to maintain relatively simple operations while achieving improved QoE through cloud-based intelligence.
Solution Approach 2:
The system implements a feedback mechanism where the QoE service continuously receives telemetry data from access points, computes QoE metrics, and sends steering recommendations back to access points. This closed-loop feedback enables dynamic optimization of client-radio associations based on real-time network conditions, traffic flow characteristics, and client mobility patterns.
2Reliability
If cloud-based QoE service computes and steers clients dynamically, then QoE is improved, but the device complexity and computational requirements increase
Solution Approach 1:
The QoE service operates autonomously by automatically receiving telemetry data, computing QoE scores, generating steering recommendations, and sending them to access points without human intervention. The system self-manages the complex computations and decision-making processes, reducing the need for manual configuration and management while maintaining high levels of automation.
3Adaptability or versatility
If multiple transmission protocols (OFDMA, MU-MIMO) are supported, then adaptability is improved, but managing client associations across different protocols increases complexity
Solution Approach 1:
The QoE service is designed as a universal platform that handles multiple transmission protocols (OFDMA, MU-MIMO) through a single centralized system. Rather than requiring separate management mechanisms for each protocol, the service provides multi-functional capability to compute QoE scores and generate steering recommendations applicable across all supported protocols, simplifying the overall management complexity.
Data Source
AI summary
Systems and methods are provided for receiving telemetry data and computing a client quality of experience (QoE) score for each traffic flow associated with a client from the received telemetry data. Performance data for each traffic flow is aggregated to arrive at an overall radio availability score, which can then be used to identify a list of recommended target radios that are more likely to service the client with better QoE than the radio to which the client is currently associated. Depending on certain considerations, e.g., whether a client has recently been steered to a new radio(s), or if steering is imminent, the recommended list of target radios are filtered to arrive at a subset of the recommended list identifying the most preferred target radios to which clients can be steered. Steering recommendations can be generated based on the recommended list.