Multilayer Perceptron Relay Selection for 5G mmWave Signal Reliability
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Solution Overview
Problem
Current methods for selecting optimal relay signals in 5G-NR wireless communication face challenges with high rates of false positives and low rates of true positives, particularly in mmWave frequencies, due to signal path obstacles and the need for reliable signal strength predictions.
Innovation Solution
A method utilizing a multilayer perceptron (MLP) based artificial neural network (ANN) to analyze and classify propagation signals, measuring path loss and selecting optimal signal paths based on threshold energy strength, thereby improving signal selection accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional relay selection methods are used in mmWave frequencies, then signal transmission can be established, but high rates of false positives and low rates of true positives occur in signal strength predictions
Solution Approach 1:
The patent segments the signal path analysis into multiple classification stages using a multi-layer perceptron neural network. The network divides the classification task into hidden layers that progressively analyze different features of propagation signals, allowing for more precise discrimination between true and false signal paths by breaking down the complex classification problem into manageable sequential steps.
Solution Approach 2:
The patent introduces an artificial neural network as an intermediary between raw signal reception and final relay selection. This intermediary processes propagation signals through multiple hidden layers, extracting meaningful patterns and classifications that directly improve the accuracy of signal strength predictions and reduce false positives in relay selection.
2Reliability
If detailed analysis of potential signal paths is performed to determine optimal relay selection, then signal consistency is improved, but system complexity increases
Solution Approach 1:
The patent replaces traditional mechanical or algorithmic signal path analysis methods with an artificial neural network system. The MLP-based ANN automatically performs detailed analysis of propagation signals through learned patterns and relationships, achieving high signal consistency without requiring manual configuration or complex rule-based systems.
Solution Approach 2:
The neural network performs self-service by automatically learning optimal classification patterns from training data and independently analyzing propagation signals without external intervention. The system self-adjusts its internal weights and biases to improve signal consistency, eliminating the need for continuous manual optimization or complex external control mechanisms.
Data Source
AI summary
A system and method of propagating signal links by using artificial neural networks and a relay link selection protocol to predict an optimal signal path. The artificial neural networks used in the method classify training and testing datasets into sufficient signal strengths and insufficient signal strengths, such that paths are evaluated for predicted propagation links, such that the strongest propagation link can be selected. Specifically, a multilayer perceptron method is used to identify and characterize new link candidates using the path loss parameter or the received signal strength, such that optimal links can be selected and updated.