Mesh Client Steering Using Wi-Fi RTT and RSSI for Throughput
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Wireless networks experience reduced throughput and reliability when client devices are far from routers, especially with multiple devices in use, leading to degraded performance and bandwidth issues.
Innovation Solution
Implementing a machine learning model in mesh networks to steer client devices to optimal access points based on received signal strength indication (RSSI), distance, and time/day of week, using Wi-Fi round trip time (RTT) for location determination, and training on cloud servers to optimize network throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If client devices connect to distant routers in mesh networks, then coverage area is extended, but throughput and connection reliability deteriorate
Solution Approach 1:
The network is segmented into multiple mesh access points distributed throughout the coverage area. Each access point serves as an independent routing node, creating smaller localized network segments that maintain high reliability while collectively providing extensive coverage. Client devices connect to the nearest access point rather than a distant central router.
Solution Approach 2:
Mesh access points act as intermediary nodes between client devices and the central router. These intermediaries relay data traffic, extending coverage area while maintaining connection reliability through multiple hops. The intermediaries buffer and forward packets, ensuring reliable delivery even when clients are far from the central router.
2Adaptability or versatility
If multiple client devices use the network simultaneously, then network versatility increases, but available bandwidth per device decreases
Solution Approach 1:
The network traffic is segmented and distributed across multiple access points. Each access point handles a subset of client devices, dividing the total bandwidth load. This segmentation allows multiple devices to simultaneously access the network with adequate throughput while maintaining overall network versatility.
Solution Approach 2:
The mesh network dynamically assigns clients to access points based on current load conditions, signal strength, and bandwidth availability. This dynamic load balancing ensures that throughput per device is optimized while accommodating multiple simultaneous users, adapting to changing network conditions in real-time.
3Productivity
If machine learning models are implemented for client steering, then network optimization improves, but system complexity increases
Solution Approach 1:
The mesh access points autonomously implement machine learning-based client steering without requiring complex external control systems. Each access point independently collects RSSI data, determines client distances using Wi-Fi RTT, and makes steering decisions locally. This self-service approach optimizes throughput while minimizing additional system complexity.
Solution Approach 2:
The system uses simple measurable parameters like RSSI and Wi-Fi RTT to feed the machine learning model, avoiding complex input requirements. The model processes these basic parameters to generate steering decisions, achieving network optimization through changes in simple, easily captured parameters rather than complex system modifications.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves wireless connectivity for streaming and gaming, reduces downlink and uplink times, enhances steering performance, and preserves client battery life by avoiding IEEE Std. 802.11k functionality, while enabling decentralized or centrally managed networks with reliable and resilient data transmission.
Implementation Method 1
A received signal strength indication (RSSI) of the client device is captured using a quality of service (QoS) data packet
Implementation Method 2
A distance between the wireless access point and the client device is determined using a Wi-Fi round trip time (Wi-Fi RTT)
Data Source
AI summary
Apparatuses, methods, and systems for client steering in mesh networks are disclosed. A first access point (AP) of a network sends a quality of service (QoS) data packet to a client device. A received signal strength indication (RSSI) of the client device is captured using the QoS data packet. A distance between the AP and client device is determined using a Wi-Fi round trip time. A time and day of week is determined. Using machine learning, a second AP is identified for steering the client device to, based on the RSSI, the distance, and the time and day of week. A machine learning model is trained to steer each client device to a respective AP for increasing a throughput of the network based on features extracted from client behavior of the client devices. The second AP is connected to the client device.