5G Neural Decoder Architecture for PUCCH Format 0
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Solution Overview
Problem
Existing PUCCH Format 0 decoding methods in 5G wireless communication face challenges in accurately decoding Uplink Control Information (UCI) due to the absence of pilot reference signals and varying channel conditions, leading to inefficiencies in multiplexed user detection and false signal detection.
Innovation Solution
A neural network-based receiver, termed UCINet0, is developed to predict phase rotations applied to base sequences for UCI decoding, utilizing a multi-label neural network classifier with input layers receiving sequence inputs and metadata, trained with simulated and real-time datasets to enhance decoding accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional DFT-based decoding methods are used for PUCCH Format 0, then the decoding process is simple and computationally efficient, but the decoding accuracy deteriorates due to absence of pilot reference signals and varying channel conditions
Solution Approach 1:
The patent replaces the conventional DFT-based mechanical decoding system with a neural network-based intelligent system. The UCINet0 model uses learned patterns from training data to decode PUCCH Format 0 signals, substituting the rigid mathematical transformations of DFT with adaptive neural network inference that can handle varying channel conditions and achieve higher decoding accuracy.
Solution Approach 2:
The patent applies preliminary action by training the neural network decoder extensively before deployment. The UCINet0 model is trained offline using simulated and real-time datasets to learn optimal decoding strategies for various channel conditions. This pre-learning phase enables the model to perform accurate decoding without requiring complex real-time processing during actual operation.
2Measurement precision
If neural network-based decoding is implemented to improve decoding accuracy, then the prediction accuracy improves to over 90-95%, but the computational complexity and processing time increase
Solution Approach 1:
The neural network model is trained offline using extensive simulated and real-time datasets to learn optimal decoding patterns. This pre-training phase transfers computational burden from real-time operation to offline preparation, enabling fast inference during actual PUCCH decoding while achieving high accuracy.
Solution Approach 2:
The patent employs a dynamic approach by using a multi-layer neural network architecture that can adapt its processing based on input characteristics. The UCINet0 model dynamically adjusts its inference process based on the received signal properties, optimizing the balance between processing speed and accuracy for different channel conditions.
3Adaptability or versatility
If phase rotation prediction is used to decode UCI content, then the decoding capability for multiplexed users improves, but the difficulty of detecting and measuring increases due to varying channel conditions
Solution Approach 1:
The neural network is pre-trained with extensive datasets that include various channel conditions, multiplexing scenarios, and phase rotation patterns. This preliminary training enables the model to recognize and decode phase-rotated signals from multiple multiplexed users even under challenging channel conditions, without requiring complex real-time detection algorithms.
Solution Approach 2:
The patent incorporates feedback mechanisms in the neural network training process, where the model learns from both simulated and real-time field data. This feedback loop enables the UCINet0 model to continuously improve its ability to detect and decode phase-rotated signals from multiplexed users, adapting to varying channel conditions through iterative learning.
Data Source
AI summary
The invention discloses systems and methods for decoding sequence based feedback signaling in wireless communication. The system comprises a plurality of User Equipment (UE) and the base station configured for establishing wireless communication links. The base station includes a multi-label neural network classifier configured to decode the signals. The method for decoding the wireless communication includes the steps of: providing the multi-label neural network classifier, generating training datasets and training the network using same equipment as used for communication or any other system. The method involves receiving the input PUCCH Format 0 signal, and predicting the NUE phase rotations a using the neural network. The α values are used to map back with the UCI-specific cyclic shift mcs for each UE. The system and method outperform conventional DFT-based decoders across all SNR ranges and dopplers and are robust enough to identify false transmissions, thus eliminating the need for thresholds.