Neural Line-of-Sight Detection for 5G Positioning Accuracy
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Solution Overview
Problem
Existing positioning methods in wireless communication systems, particularly in 5G networks, face challenges in accurately estimating line-of-sight delays and angles of arrival due to weak signals and dense multipath conditions, which affect the precision of location estimation.
Innovation Solution
Utilizing deep neural networks to process channel impulse responses and determine line-of-sight delays and angles of arrival by integrating receiver configuration information and channel state information, enabling improved positioning accuracy even in challenging scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional signal processing algorithms are used for positioning in 5G networks, then device compatibility and implementation simplicity are maintained, but positioning accuracy deteriorates in weak signal and dense multipath environments
Solution Approach 1:
The patent introduces neural networks as an intermediary component between the received signal and the positioning calculation. The neural network processes channel impulse responses and extracts line-of-sight delay and angle of arrival information, serving as a mediator that transforms complex signal processing into more manageable tasks for traditional positioning algorithms.
Solution Approach 2:
The patent segments the positioning problem into distinct processing stages: first using neural networks to extract specific parameters (line-of-sight delay and angle of arrival) from channel impulse responses, then feeding these extracted parameters into traditional positioning algorithms. This segmentation allows each component to specialize in what it does best.
2Measurement precision
If neural networks are introduced to improve positioning accuracy in challenging environments, then measurement precision improves, but device complexity and computational requirements increase
Solution Approach 1:
The patent designs the neural network to perform multiple functions simultaneously: it processes channel impulse responses, identifies line-of-sight paths, estimates delay values, and determines angle of arrival information. This multi-functionality reduces the need for separate processing modules and simplifies the overall system architecture.
Solution Approach 2:
The neural network performs preliminary processing of channel impulse responses before the main positioning calculation. By pre-extracting line-of-sight delay and angle of arrival information, the neural network prepares the data in a form that is more suitable for subsequent positioning algorithms, reducing their computational burden.
3Measurement precision
If more sophisticated processing of channel impulse responses is applied, then positioning accuracy in multipath environments improves, but processing time and computational energy increase
Solution Approach 1:
The patent replaces traditional mechanical signal processing methods (such as peak detection and correlation techniques) with neural network-based processing. This substitution allows for more sophisticated analysis of channel impulse responses without proportionally increasing processing time, as neural networks can parallelize computations efficiently.
Data Source
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AI summary
Techniques are provide for neural network based positioning of a mobile device. An example method for determining a line of sight delay, an angle of arrival, or an angle of departure value, according to the disclosure includes receiving reference signal information, determining a channel frequency response or a channel impulse response based on the reference signal information, processing the channel frequency response or the channel impulse response with a neural network, and determining the line of sight delay, the angle of arrival, or the angle of departure value based on an output of the neural network.