Cellular QoS Latency Monitoring With Packet Redirection and Prediction
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Solution Overview
Problem
Existing technologies fail to effectively monitor and predict latency in cellular networks, particularly in low-latency applications like telemedicine and autonomous transportation, which can be negatively impacted by latency variations.
Innovation Solution
A system and method for monitoring quality of service (QoS) in cellular networks using a monitoring server that generates test data, redirects packets for latency measurement, and utilizes a QoS neural network trained with past and real-time latency measurements to predict current and future latency states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used, then device complexity is reduced, but measurement precision of latency is insufficient
Solution Approach 1:
The patent introduces test packets as intermediary objects to measure latency. These packets travel through the network path and carry timing information, enabling precise measurement without directly modifying network infrastructure. The monitoring server uses these intermediary packets to indirectly measure latency by comparing send and receive timestamps.
Solution Approach 2:
The monitoring server pre-configures test packets with timestamp information before transmission. By embedding timing data in advance within the test packets, the system enables precise latency measurement without requiring complex real-time synchronization mechanisms at the measurement point.
2Reliability
If continuous real-time monitoring is implemented, then reliability of latency prediction is improved, but use of energy increases
Solution Approach 1:
The system implements continuous latency monitoring by repeatedly sending test packets at regular intervals. This continuous measurement approach ensures reliable latency prediction for time-sensitive applications while maintaining a sustainable measurement rhythm that balances accuracy requirements with resource consumption.
Solution Approach 2:
The monitoring server sends test packets continuously at configured intervals, which may be more frequent than strictly necessary for all network conditions. This excessive action ensures reliable prediction coverage for worst-case scenarios while the interval configuration allows tuning to reduce energy consumption when lower precision is acceptable.
3Measurement precision
If packet redirection is implemented at target device, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts the complex latency measurement and packet redirection functionality from the target device and concentrates it in the monitoring server. The target device only needs to perform simple packet reception and forwarding based on predefined rules, while the monitoring server handles the complex timestamp comparison and latency calculation.
Solution Approach 2:
The monitoring server creates duplicate test packets and sends them through the network path to the target device, which redirects them back. This copying approach allows the server to measure round-trip latency without requiring the target device to generate or modify packet content, simplifying the target device's role.
Data Source
AI summary
A method and a system for monitoring a quality of service (QoS) of a cellular network are provided. The method includes generating, using a monitoring module, test data based on latency parameters, the test data comprising initial test packets, and transmitting the initial test packets of the test data to a target module. The method includes transmitting, using the target module, redirected test packets back to the monitoring module, the redirected test packets associated with the initial test packets. The method further includes processing, using the monitoring module, the redirected test packets to calculate latency measurements between transmission of the initial test packets and reception of the redirected test packets. The method also includes transmitting the latency measurements to a processing module comprising a QoS neural network; and producing at least one of a current latency state and a future latency state using the QoS neural network.