Multi-Interface Network Aggregation for Low-Latency Data
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Solution Overview
Problem
Current communication systems face challenges in achieving low-latency data transmission required for applications like kinesthetic communication, self-driving, and online gaming due to limitations in radio resource management, network architecture, and latency constraints, especially in achieving the goals of the Tactile Internet which demands near-zero latency.
Innovation Solution
An AI-based object-aware fast data transmission and prediction framework that aggregates multiple network interfaces, including cellular and wireless networks, using convolutional coding and multi-threading processors to optimize data transmission and prediction, enabling zero-latency performance through network slicing and advanced signaling mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If multiple network interfaces are aggregated to reduce latency, then the data transmission speed and reliability improve, but the system complexity and resource management difficulty increase
Solution Approach 1:
The patent segments data streams into multiple parallel streams that can be transmitted through different network interfaces simultaneously. Each stream is processed independently through dedicated threads, allowing the system to leverage multiple networks (cellular, Wi-Fi, etc.) without requiring complex centralized coordination, thus reducing latency while managing complexity through modular processing
Solution Approach 2:
The patent combines multiple network interfaces (cellular, Wi-Fi, Bluetooth, etc.) into a unified communication system that aggregates their capabilities. By merging these interfaces and their associated resources under a common management framework, the system achieves improved reliability and reduced latency through redundant paths and load distribution
2Reliability
If radio resources are allocated prioritarily for haptic communications to meet latency requirements, then the tracking performance and reliability improve, but the availability for other applications decreases
Solution Approach 1:
The patent implements dynamic radio resource allocation that adapts to real-time network conditions and application requirements. The system can dynamically adjust the priority and resource allocation for haptic communications versus other applications based on current latency requirements, network availability, and QoS parameters, ensuring high reliability when needed while maintaining versatility for other uses
Solution Approach 2:
The system changes key parameters such as transmission power, modulation schemes, and resource block allocation dynamically based on the specific needs of haptic communications versus other applications. By adjusting these parameters in real-time, the system can optimize tracking performance for time-sensitive applications while preserving adequate resources for other services
3Reliability
If joint resource allocation in uplink and downlink is implemented to improve tracking performance, then the communication reliability improves, but the complexity of resource management increases
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors the performance of uplink and downlink transmissions and uses this information to adjust resource allocation jointly. By establishing feedback loops that track communication quality, latency, and resource utilization, the system can maintain high reliability through coordinated resource management without requiring overly complex centralized control
Data Source
AI summary
The invention generally relates to a method and system for enabling low-latency data communication by aggregating a plurality of network interfaces, each network interface associated with a different network. The method and system measures in real-time, network performance capabilities associated with the networks via the respective network interfaces. The method and system then assigns two or more multi-threading processors in a multi-processor architecture configured to execute a plurality of threads for processing one or more data streams. The threading in each processor is interlinked with two or more network interfaces based on the measured network performance capabilities and network performance capability requirements of the one or more data streams, thereby enabling threading-based cooperation among multi-core processors in the multi-processor architecture and the plurality of network interfaces. The one or more data streams are then transmitted to the two or more network interfaces and thereon to the associated networks for transport.