Neural Network Receiver Adaptation for Telecommunication Channels
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Solution Overview
Problem
Existing neural-network-based receivers in telecommunication networks struggle to adapt to varying channel conditions after deployment, leading to inefficiencies in signal detection and limited scalability due to hardware and bandwidth constraints.
Innovation Solution
A node in the telecommunication network is configured with a neural-network-based receiver that can generate modified receiver frameworks to calculate variance in signal detection, determining whether to select signals for training based on this variance and a threshold value, allowing for adaptive training and calibration post-deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a receiver uses preconfigured algorithms for channel estimation and equalization, then the receiver can demodulate signals based on fixed procedures, but the receiver fails to adapt to varying channel conditions and cannot demodulate all signals
Solution Approach 1:
The patent applies dynamics by transitioning from static preconfigured algorithms to dynamic neural network models that can adapt to varying channel conditions. The neural network receives input signals and dynamically adjusts its internal parameters and processing steps based on the characteristics of the received signal, enabling the receiver to adapt to different channel conditions without requiring multiple fixed algorithms.
Solution Approach 2:
The patent utilizes parameter changes by modifying the neural network's internal parameters (weights, biases, activation functions) based on the input signal characteristics. The network can change its operational parameters dynamically to optimize signal processing for different channel conditions, such as changing the degree of non-linearity or adjusting filter characteristics based on signal quality metrics.
2Reliability
If neural network models are trained with diverse data to handle various channel conditions, then the model can improve generalization capability, but the training process requires significant computational resources and time
Solution Approach 1:
The patent applies preliminary action by pretraining neural network models on diverse channel conditions before deployment. The model is trained in advance on a comprehensive dataset representing various channel scenarios, allowing it to generalize well to unseen conditions during actual operation without requiring extensive real-time training.
Solution Approach 2:
The patent uses copying by creating multiple neural network models that replicate different aspects of channel processing. Instead of training a single complex model from scratch, the system can use copies or variants of pretrained models that have been optimized for specific channel conditions, then combine or select from these copies based on current channel state information.
3Measurement precision
If the receiver collects and processes more data for training the neural network, then the model can improve accuracy, but the system requires additional hardware resources and bandwidth
Solution Approach 1:
The patent applies self-service by enabling the receiver to collect and process data locally using its existing hardware resources. The receiver utilizes its own received signals and processing capabilities to gather training data without requiring external hardware assistance or additional dedicated data collection equipment, thereby maintaining accuracy improvement while avoiding increased hardware complexity.
Solution Approach 2:
The patent uses partial action by selectively processing only the most informative portions of received signals for training purposes. Instead of processing all available data equally, the system identifies and processes only those signal segments that provide maximum information gain for improving detection accuracy, thereby achieving measurement precision without proportionally increasing hardware requirements.
Data Source
AI summary
In some examples, a node for a telecommunication network includes a neural-network-based receiver for uplink communications. The node is configured to modify the neural-network-based receiver to generate a set of modified receiver frameworks defining respective different versions for the receiver, using each of the modified receiver frameworks, generate respective measures representing bits encoded by a signal received at the node, calculate a value representing a variance of the measures, and on the basis of the value, determine whether to select the signal received at the node for use as part of a training set of data for the neural-network-based receiver.