Service-Optimized App-Communication Interface for Low-Latency Wireless Data
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Solution Overview
Problem
Existing wireless communication systems face challenges in meeting strict latency requirements for applications like extended Reality (XR), co-presence, and cloud gaming under dynamic radio conditions and congested resources, particularly with the advent of semantic communication.
Innovation Solution
Implementing a service-optimized representation structure and application-optimized transport block (TB) to manage data flow, using semantic communications that transmit semantic representations or metadata rather than actual data, and incorporating a hierarchical protocol data unit (PDU) structure to prioritize and encode data blocks based on their relevance and dependencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional data transmission methods are used, then data完整性 is maintained, but transmission latency increases and throughput decreases under congested conditions
Solution Approach 1:
The patent segments data into hierarchical PDU structures with different priority levels (first PDU set with first priority, second PDU set with second priority). This segmentation allows critical data to be transmitted and processed separately from less critical data, reducing overall transmission latency while maintaining data completeness through structured reassembly at the receiver.
Solution Approach 2:
The patent applies local quality by assigning different priority levels and handling characteristics to different portions of the data stream. High-priority PDUs receive preferential treatment in scheduling and resource allocation, while lower-priority PDUs are handled differently, optimizing overall system performance without compromising the integrity of critical information.
2Productivity
If all data is transmitted with equal priority, then data completeness is ensured, but throughput efficiency decreases under heavy traffic
Solution Approach 1:
The patent divides data into multiple PDU sets with different priority levels (first PDU set, second PDU set, etc.). This segmentation enables the system to prioritize transmission of critical data segments while efficiently managing less critical segments, thereby improving overall throughput efficiency without losing any information, as all segments are eventually transmitted and reassembled.
Solution Approach 2:
The patent implements dynamic priority-based scheduling where the handling of data packets varies based on their assigned priority level. High-priority PDUs are scheduled for transmission before low-priority PDUs, and resource allocation is dynamically adjusted based on priority, enabling efficient throughput under heavy traffic while ensuring complete data delivery.
3Loss of time
If data is prioritized by relevance, then latency is reduced for critical data, but system complexity increases
Solution Approach 1:
The patent segments data into hierarchical PDU sets with assigned priority levels, creating a structured framework for priority management. This segmentation provides clear rules for scheduling and resource allocation based on priority, reducing the complexity of making individual decisions about each data packet while still achieving low latency for critical data through systematic preferential handling of high-priority PDUs.
Data Source
AI summary
An apparatus configured to map a data unit to one or more representations, map each of the representations to one or more data blocks, wherein each of the data blocks comprises a weight and encode the one or more data blocks of each of the representations into one or more packets.