Neural RF Transceiver Control Using Sensor Fusion for mmWave Links
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Solution Overview
Problem
Conventional wireless communication systems face challenges in effectively managing RF signaling at extremely high frequencies due to susceptibility to transmission errors from multipath fading, atmospheric absorption, bodily absorption, and interference, which are influenced by the line-of-sight or non-line-of-sight propagation paths between base stations and user equipment.
Innovation Solution
Implementing jointly-trained neural networks at both base stations and user equipment to fuse sensor data with RF transceiver operations, allowing adaptive configuration of RF front ends based on environmental conditions detected by sensors, such as object detection and positioning data, to enhance RF signaling performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If extremely high frequency carrier bands are used for RF signaling, then data transmission capacity is improved, but transmission reliability deteriorates due to susceptibility to multipath fading, atmospheric absorption, and interference
Solution Approach 1:
The system employs sensor feedback mechanisms where sensors detect environmental conditions (obstacles, propagation path characteristics) and feed this information back to the RF transceiver. The neural network processes this feedback to dynamically adjust transmission parameters, thereby maintaining reliable communication despite the inherent susceptibility of extremely high frequency bands to environmental factors.
Solution Approach 2:
The patent implements dynamic adaptation of RF signaling parameters based on real-time environmental conditions. The neural network continuously adjusts transmission characteristics such as beam direction, power levels, and modulation schemes in response to changing propagation conditions, transforming the static RF system into a dynamic one that can overcome the limitations of extremely high frequency bands.
2Reliability
If line-of-sight propagation is maintained for extremely high frequency signaling, then transmission reliability is improved, but system adaptability to non-line-of-sight conditions deteriorates
Solution Approach 1:
The system performs self-diagnosis and self-adjustment through integrated sensors and neural network processing. When propagation conditions change from line-of-sight to non-line-of-sight, the sensors detect the change, the neural network analyzes the new conditions, and the system automatically adjusts its transmission strategy without external intervention, thereby maintaining reliability across diverse propagation scenarios.
Solution Approach 2:
The patent utilizes parameter changes in the RF signaling based on detected propagation conditions. The neural network modifies transmission parameters such as frequency selection, beamforming patterns, and power allocation when transitioning between line-of-sight and non-line-of-sight conditions, enabling the system to adapt its behavior to maintain reliable communication regardless of propagation path characteristics.
3Reliability
If sensor data processing is added to RF transceiver operations, then communication reliability is improved through environmental adaptation, but device complexity increases
Solution Approach 1:
The patent merges the sensor processing functions with the existing RF transceiver operations through a unified neural network architecture. Instead of adding separate independent processing systems, the sensor data is integrated into the existing signal processing pipeline, allowing the same neural network to handle both RF signal processing and environmental sensor interpretation, thereby reducing overall system complexity while maintaining improved reliability.
Data Source
AI summary
A method includes receiving an information block as an input to a transmitter neural network, receiving, as an input to the transmitter neural network, sensor data from one or more sensors, processing the information block and sensor data at the transmitter neural network to generate an output, and controlling an RF transceiver based on the output to generate an RF signal (134) for wireless transmission. Another method includes receiving a first output from an RF transceiver as a first input to a receiver neural network, receiving, as a second input to the receiver neural network, a set of sensor data from one or more sensors, processing the first input and the second input at the receiver neural network to generate an output, and processing the output to generate an information block representative of information communicated by a data sending device.