LLR Scaling Reference Signals for Pre-equalized XR Decoding
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently decoding pre-equalized data transmissions, particularly in scenarios where extended reality (XR) devices have limited processing capacity and power consumption constraints.
Innovation Solution
The implementation of dedicated reference signals for log-likelihood ratio (LLR) scaling estimation, configured based on the waveform type and operational conditions, allows XR devices to perform LLR scaling measurements and decode pre-equalized transmissions with reduced processing complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If XR devices perform decoding operations for pre-equalized data transmissions, then decoding capability is improved, but power consumption and processing complexity increase
Solution Approach 1:
The system performs LLR scaling estimation and applies scaling factors to reference signals before transmission. This preliminary processing prepares the data in advance, reducing the computational burden on the XR device during decoding operations and thereby lowering power consumption while maintaining decoding capability.
Solution Approach 2:
The patent introduces LLR scaling reference signals as an intermediary element between the transmitted data and the decoding process. These reference signals carry scaling information that mediates the decoding operation, enabling the XR device to decode with reduced complexity and power consumption while preserving reliability.
2Reliability
If XR devices perform full decoding operations, then decoding accuracy is improved, but processing complexity increases
Solution Approach 1:
LLR scaling estimation is performed in advance using reference signals, and the scaling factors are applied before the main decoding operation. This preliminary action simplifies the subsequent decoding process, reducing processing complexity while maintaining decoding accuracy through pre-computed scaling adjustments.
Solution Approach 2:
The decoding process is segmented into distinct stages: receiving reference signals, performing LLR scaling estimation, applying scaling factors, and then executing the main decoding operation. This segmentation allows complex processing to be distributed and managed in manageable parts, reducing overall processing complexity while preserving accuracy.
3Productivity
If dedicated reference signals for LLR scaling are implemented, then decoding efficiency is improved, but signal overhead increases
Solution Approach 1:
The LLR scaling reference signals are designed to serve multiple functions: they enable LLR scaling estimation for decoding efficiency while also maintaining compatibility with existing reference signal structures in the communication system. This multi-functionality reduces the need for entirely separate dedicated signals, thereby limiting the increase in signal overhead.
Data Source
AI summary
Methods, systems, and devices for wireless communication are described. A wireless device may receive, from another device (e.g., a user equipment (UE)), a message indicating a configuration of a set of log-likelihood ratio (LLR) scaling reference signals. The configuration may indicate a resource allocation for the set of LLR scaling reference signals, where the resource allocation may be based on a type of waveform used for communications between the wireless device and the UE. The wireless device may receive one or more pre-equalized data transmissions having the waveform type, and the pre-equalized data transmission may include the LLR scaling reference signals in accordance with the resource allocation. The wireless device may perform measurements of the set of LLR scaling reference signals for performing LLR scaling, and the wireless device may decode the one or more pre-equalized transmissions based on the LLR scaling using the set of LLR scaling reference signals.