Priority-Tagged Data Transmission for uRLLC and eMBB Balance
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Solution Overview
Problem
Existing 5G wireless communication technologies face challenges in simultaneously meeting the low latency requirements of Ultra-reliable and Low Latency Communications (uRLLC) and the high throughput requirements of Enhanced Mobile Broadband (eMBB) due to conflicting data transmission needs.
Innovation Solution
A data transmitting and receiving method that involves encoding, modulation, and demodulation processes tailored to different resource priority tags, allowing for independent processing of data with distinct requirements, such as uRLLC and eMBB, by using a network device with a memory and processor to execute computer programs that manage these methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If unified encoding and modulation processing is used for all data, then device complexity is reduced, but different data types cannot meet their respective transmission requirements
Solution Approach 1:
The patent segments the data processing system by introducing resource priority tags to classify different data types (e.g., uRLLC, eMBB) and applies separate encoding and modulation processing to each segment based on its specific requirements. This allows tailored processing for different data types while maintaining manageable system complexity through structured classification.
Solution Approach 2:
The patent implements local quality by applying different encoding rates and modulation schemes to different data segments based on their resource priority tags. Each data type receives customized processing parameters (e.g., higher encoding rate for eMBB, more robust modulation for uRLLC) rather than uniform processing, enabling optimized performance for each specific requirement.
2Productivity
If high encoding rate is used to improve throughput, then data transmission speed increases, but transmission reliability decreases
Solution Approach 1:
The patent changes the encoding rate parameter dynamically based on the resource priority tag of each data type. For eMBB data requiring high throughput, higher encoding rates are applied; for uRLLC data requiring high reliability, lower encoding rates with more robust error correction are used. This parameter adaptation resolves the contradiction by optimizing for different goals in different contexts.
Solution Approach 2:
The system dynamically adjusts encoding and modulation parameters based on the identified data type and its requirements. The processing parameters are not fixed but adapt in real-time based on the resource priority tags, allowing the system to optimize throughput for eMBB and reliability for uRLLC as needed.
3Reliability
If robust encoding is used to improve reliability, then data transmission reliability increases, but data transmission speed decreases
Solution Approach 1:
The patent applies parameter changes by selecting different encoding rates based on data type requirements. Robust encoding with lower rates is applied to uRLLC data where reliability is paramount, while higher rates are applied to eMBB data where speed is the priority. This resolves the contradiction by matching the encoding strategy to the specific operational requirements.
Solution Approach 2:
The encoding strategy dynamically adapts based on the resource priority tag identified in each data segment. The system switches between robust and high-speed encoding modes depending on the data type, allowing optimal performance for each scenario without being constrained by a single fixed approach.
4Loss of time
If low latency processing is applied to meet uRLLC requirements, then transmission delay is reduced, but throughput for other data types decreases
Solution Approach 1:
The patent segments data processing by resource priority tags, allowing uRLLC data to receive fast-track processing with reduced latency while other data types (eMBB) receive standard processing. This segmentation enables different quality levels of service for different data types simultaneously, resolving the latency-throughput contradiction.
Solution Approach 2:
The processing parameters dynamically adjust based on data type requirements. uRLLC data triggers fast processing paths with lower latency, while eMBB data utilizes throughput-optimized paths. This dynamic adaptation allows the system to meet diverse requirements without compromising either latency or throughput for their respective data types.
Data Source
AI summary
Disclosed are a data transmitting method, a data receiving method, a network device and a non-transitory computer-readable storage medium. The data transmitting method may include: acquiring first data information and second data information; performing encoding processing on the first data information according to a first resource priority tag to obtain first encoded information, and performing encoding processing on the second data information according to a second resource priority tag to obtain second encoded information; performing signal modulation processing on the first encoded information to obtain first service information, and performing signal modulation processing on the second encoded information to obtain second service information, wherein the first service information comprises the first resource priority tag, and the second service information comprises the second resource priority tag; and transmitting the first service information and the second service information.