Deep Learning Neural Network for Real-Time Spectrum Analysis
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Solution Overview
Problem
Current wireless communication systems face challenges in real-time spectrum analysis and decision-making due to high latency in CPU-based machine learning and the need for computationally intensive feature extraction, especially in crowded RF environments like those expected with 5G networks and IoT, where traditional methods struggle to process vast amounts of unprocessed I/Q samples effectively.
Innovation Solution
The integration of deep learning algorithms directly into hardware components of wireless devices, specifically a programmable logic system with a front-end configuration core, a learning core, and a learning actuation core, enabling real-time extraction of RF, optical, or acoustic spectrum information through a deep learning neural network, which configures communication parameters without CPU involvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If CPU-based machine learning is used for spectrum analysis, then machine learning functionality is achieved, but latency is high and processing speed is slow
Solution Approach 1:
The patent replaces the mechanical CPU-based processing system with a dedicated neural network hardware accelerator. This substitution moves the machine learning workload from general-purpose CPU execution to specialized hardware that processes spectral data directly at wire speed, eliminating software overhead and achieving deterministic low-latency performance.
Solution Approach 2:
The patent introduces an intermediary neural network processing layer between the spectral sensing hardware and the control system. This intermediary component pre-processes and analyzes spectral data in real-time, filtering and preparing information before it reaches higher-level systems, thereby reducing overall processing latency and improving response time.
2Use of energy by moving object
If traditional feature extraction algorithms are used, then spectrum analysis is performed, but computational complexity and resource consumption are high
Solution Approach 1:
The patent extracts and isolates the essential spectral feature detection functionality into a dedicated neural network hardware module. By separating this critical function from the main processor, the system performs complex spectral analysis with minimal computational resources, reducing overall device complexity and power consumption while maintaining analysis effectiveness.
Solution Approach 2:
The patent changes the operational parameters of the processing system by implementing fixed-point arithmetic instead of floating-point operations in the neural network hardware. This parameter change reduces computational complexity and resource requirements while maintaining sufficient precision for spectral analysis applications, thereby lowering power consumption and device complexity.
3Productivity
If deep learning is implemented in software, then spectrum analysis capability is achieved, but real-time processing is not possible
Solution Approach 1:
The patent replaces software-based deep learning with hardware-based neural network processing. This substitution enables real-time spectral analysis by executing neural network operations directly in hardware at wire speed, eliminating software interpretation overhead and achieving deterministic processing times suitable for real-time wireless communication systems.
Data Source
AI summary
Apparatuses and methods for real-time spectrum-driven embedded wireless networking through deep learning are provided. Radio frequency, optical, or acoustic communication apparatus include a programmable logic system having a front-end configuration core, a learning core, and a learning actuation core. The learning core includes a deep learning neural network that receives and processes input in-phase/quadrature (I/Q) input samples through the neural network layers to extract RF, optical, or acoustic spectrum information. A processing system having a learning controller module controls operations of the learning core and the learning actuation core. The processing system and the programmable logic system are operable to configure one or more communication and networking parameters for transmission via the transceiver in response to extracted spectrum information.