Iterative Reception Feedback for 5G Latency Optimization
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Solution Overview
Problem
Current digital communication systems face challenges in reducing processing time for iterative receivers, particularly in 5G networks, where complex interference cancellation processes require significant computational resources, leading to increased latency and difficulties in determining the minimum processing time for decoding data packets, which is crucial for low-latency services like URLLC.
Innovation Solution
A telecommunication method and system that allows terminals to feed back a minimum processing time required for N detection and decoding iterations to access points, enabling efficient scheduling and optimization of data transmission by determining the best compromise between latency and data rate, while supporting terminals with varying capabilities and interference cancellation types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If iterative interference cancellation processing is performed to improve data transmission reliability, then the reliability is improved, but the processing time increases
Solution Approach 1:
The terminal performs channel estimation using reference signals before the actual data reception, preparing the channel state information in advance. This preliminary action reduces the processing time required during the actual iterative interference cancellation by having the channel characteristics already available.
Solution Approach 2:
The iterative reception process is divided into distinct segments: reference signal reception and channel estimation, followed by data signal reception and iterative interference cancellation. This segmentation allows the system to optimize each segment independently and manage processing time more effectively.
2Manufacturing precision
If the number of iterations is increased to improve decoding accuracy, then the manufacturing precision is improved, but the productivity decreases
Solution Approach 1:
The system performs a predetermined number of iterations that is sufficient to achieve the required decoding accuracy for most cases, rather than performing excessive iterations. This partial action approach achieves adequate precision while maintaining productivity, avoiding the diminishing returns of excessive iterations.
Solution Approach 2:
The terminal feeds back its specific iteration capability and processing time requirements to the base station. The base station then dynamically adjusts the number of iterations and scheduling parameters based on the terminal's capabilities, optimizing the balance between decoding accuracy and transmission rate for each specific terminal.
3Reliability
If complex interference cancellation algorithms are used to improve reception performance, then the reliability is improved, but the device complexity increases
Solution Approach 1:
The terminal uses different processing approaches for different signal components: simple reference signal processing for channel estimation, and more complex iterative interference cancellation only for the data signal portion. This local quality approach applies computational complexity only where necessary, improving reception performance without uniformly increasing device complexity across all processing stages.
4Loss of time
If the processing time is reduced to improve latency performance, then the loss of time is reduced, but the reliability may deteriorate
Solution Approach 1:
Channel estimation is performed in advance using reference signals before data reception begins. This preliminary action reduces the processing time required during actual data decoding, thereby reducing latency while maintaining decoding reliability since the channel state information is already prepared.
Solution Approach 2:
The system dynamically adjusts the number of iterations based on terminal capabilities and channel conditions. For terminals with higher processing capabilities or better channel conditions, fewer iterations are performed, reducing latency while maintaining adequate reliability. This dynamic adjustment optimizes the trade-off between latency and reliability.
Data Source
AI summary
A telecommunication method with feedback, from a terminal to an access point, of a minimum processing time required for the terminal to perform N iterations of iterative decoding with interference cancellation on at least one data packet transmitted by the access point and carried by a single physical channel.