RIS Reflecting Element Selection for Low-Latency URLLC
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Solution Overview
Problem
The challenge in RIS-aided URLLC systems is the high computational complexity and latency due to the need to estimate a large number of channel coefficients, which is not suitable for the low-latency requirements of ultra-reliable and low-latency communication.
Innovation Solution
The use of NWDAF to smartly select the number of active reflecting elements at RIS by applying machine learning algorithms, such as supervised ML, to determine the feasible number of reflecting elements based on delay outage rate (DOR) and other system parameters, reducing the computational time and overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large number of reflecting elements are used at RIS, then reliability is improved, but computational complexity and latency increase
Solution Approach 1:
The patent extracts and separates the channel estimation process into two parts: a codebook-based initial estimation that provides a coarse channel estimate, and a subsequent refinement stage. This extraction allows the system to achieve reliable communication without requiring all reflecting elements to be fully activated and processed, thereby reducing computational complexity while maintaining acceptable reliability levels.
Solution Approach 2:
The patent segments the channel estimation process into multiple stages: initial codebook-based estimation, followed by selective refinement. This segmentation allows the system to process only a subset of reflecting elements in detail, reducing the overall computational burden while maintaining communication reliability through the multi-stage approach.
2Reliability
If a large number of reflecting elements are used at RIS, then reliability is improved, but latency increases
Solution Approach 1:
The patent extracts the essential channel estimation function and implements it through a codebook-based approach that provides rapid initial estimation. This extraction eliminates the need to process all reflecting elements in full detail, significantly reducing processing latency while maintaining sufficient reliability for URLLC applications.
Solution Approach 2:
The patent performs preliminary channel estimation using a codebook before proceeding to more computationally intensive refinement steps. This preliminary action provides a sufficient initial estimate that can be used immediately, reducing latency, while allowing optional refinement only when necessary for maintaining reliability.
3Measurement precision
If traditional channel estimation is used, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent extracts the core channel estimation function and implements it through a codebook-based method that provides sufficient precision for URLLC applications without requiring the full traditional estimation process. This extraction achieves an optimal balance between precision and speed by providing a rapid initial estimate that can be used immediately.
Solution Approach 2:
The patent applies partial action by using only a codebook-based estimation rather than the full traditional channel estimation process. This partial approach provides sufficient measurement precision for URLLC requirements while dramatically reducing the time required, as the codebook method processes information more efficiently than traditional pilot-based estimation.
Data Source
AI summary
Methods and apparatuses for smart selecting reflecting elements with a network apparatus (e.g., network data analytics function (NWDAF)) in re-configurable intelligent surface (RIS)-aided URLLC (ultra-reliable and low-latency communication) system are disclosed. A method comprises transmitting, to a network apparatus, parameters necessary to determine an active number of reflecting elements at an RIS between the base station and a UE in an RIS-aided URLLC system; and receiving the active number of reflecting elements from the network apparatus.