High-Capacity Complex Convolutional Neural Networks For I/Q Modulation Classification

10/21/2020
by   Jakob Krzyston, et al.
0

I/Q modulation classification is a unique pattern recognition problem as the data for each class varies in quality, quantified by signal to noise ratio (SNR), and has structure in the complex-plane. Previous work shows treating these samples as complex-valued signals and computing complex-valued convolutions within deep learning frameworks significantly increases the performance over comparable shallow CNN architectures. In this work, we claim state of the art performance by enabling high-capacity architectures containing residual and/or dense connections to compute complex-valued convolutions, with peak classification accuracy of 92.4 the RadioML 2016.10a dataset. We show statistically significant improvements in all networks with complex convolutions for I/Q modulation classification. Complexity and inference speed analyses show models with complex convolutions substantially outperform architectures with a comparable number of parameters and comparable speed by over 10

READ FULL TEXT
research
12/14/2022

Fully complex-valued deep learning model for visual perception

Deep learning models operating in the complex domain are used due to the...
research
04/03/2020

Complex-Valued Convolutional Neural Networks for MRI Reconstruction

Many real-world signal sources are complex-valued, having real and imagi...
research
01/27/2023

Automatic Modulation Classification with Deep Neural Networks

Automatic modulation classification is a desired feature in many modern ...
research
08/22/2023

Using Early Exits for Fast Inference in Automatic Modulation Classification

Automatic modulation classification (AMC) plays a critical role in wirel...
research
09/09/2020

Generalizing Complex/Hyper-complex Convolutions to Vector Map Convolutions

We show that the core reasons that complex and hypercomplex valued neura...
research
02/16/2022

On Measuring Excess Capacity in Neural Networks

We study the excess capacity of deep networks in the context of supervis...
research
11/22/2022

RiSi: Spectro-temporal RAN-agnostic Modulation Identification for OFDMA Signals

Blind modulation identification is essential for 6G's RAN-agnostic commu...

Please sign up or login with your details

Forgot password? Click here to reset