Novel Deep-Learning Modulation Recognition Algorithm Using 2D Histograms over Wireless Communications Channels
Abstract
:1. Introduction
- We loosen the prior limitation by proposing a novel MR applicable to flat-fading wireless channels.
- We show that each modulation choice has a distinct two-dimensional in-phase quadrature histogram (2-D IQH), which is beneficially utilized to design a CNN-based MR algorithm.
- In addition to operating over practical wireless environments, the proposed algorithm provides much less complexity when compared to [23,24]. It only requires two CNN deep layers, which greatly shortens the training and recognition times. The conceptual diagram of the proposed receiver is shown in Figure 1.
2. Related Works
3. Preliminary Studies
- The transmitter broadcasts uncorrelated information symbols with
- The channel gain is modelled as a zero-mean complex Gaussian random variable, with .
- Noise samples are considered to be Additive White zero-mean Gaussian Noise (AWGN) samples that are symmetric, independent, and identically distributed, with variance .
4. Data Generation and Feature Extraction
- We split the incoming signal into its in-phase and quadrature components, and , respectively.
- We determine the maximum and minimum values for each of and .
- We create 80 bins across the entire scale of and .
- We calculate the number of samples that fit inside each grid. This matrix represents the histogram of the received signal. The prior information is visualized in Figure 3.
5. Proposed MR Algorithm
5.1. CNNs Architecture
5.2. Modulation Recognition CNN Structure
6. Simulation Work
6.1. Experimental Environment
6.2. Results and Discussion
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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(SNR = −6 dB, BPSK) | (SNR = −6 dB, QPSK) | … | (SNR = 15 dB, 64QAM) | |
---|---|---|---|---|
8192 Samples | . | |||
. | ||||
. | ||||
. | ||||
. |
CNN Parameter | Type/Value |
---|---|
Optimizer | Adam |
Initial Learning Rate | 0.001 |
Decay Rate of Squared Gradient Moving Average | 0.99 |
Max Number of Epochs | 10 |
Mini Batch Size | 64 |
Number of Deep Layers | 2 |
Number of Filters in Each Layer | 128 |
Processor | Intel(R) Core(TM) i7-4790K CPU @ 4.00 GHz |
---|---|
RAM | 16 GB |
Graphics Card | AMD Radeon (TM) R9 390 Series |
SSD | Samsung SSD 870 EVO 500 GB |
Operating System | Ubuntu 22.04.1 LTS |
Software | Matlab-R2022b |
BPSK | QPSK | 8PSK | 8QAM | 32QAM | 64QAM | |
---|---|---|---|---|---|---|
BPSK | 100 | 0 | 0 | 0 | 0 | 0 |
QPSK | 0 | 100 | 0 | 0 | 0 | 0 |
8PSK | 0 | 0 | 100 | 0 | 0 | 0 |
8QAM | 0 | 0 | 0 | 100 | 0 | 0 |
32QAM | 0 | 0 | 0 | 0 | 100 | 0 |
64QAM | 0 | 0 | 0 | 0 | 0 | 100 |
BPSK | QPSK | 8PSK | 8QAM | 32QAM | 64QAM | |
---|---|---|---|---|---|---|
BPSK | 95 | 1 | 1 | 1 | 1 | 1 |
QPSK | 1 | 96 | 1 | 1 | 1 | 0 |
8PSK | 1 | 2 | 94 | 1 | 1 | 1 |
8QAM | 1 | 1 | 1 | 95 | 1 | 1 |
32QAM | 1 | 1 | 1 | 2 | 94 | 1 |
64QAM | 1 | 1 | 1 | 1 | 2 | 94 |
BPSK | QPSK | 8PSK | 8QAM | 32QAM | 64QAM | |
---|---|---|---|---|---|---|
BPSK | 38 | 15 | 14 | 11 | 9 | 13 |
QPSK | 10 | 37 | 16 | 15 | 12 | 10 |
8PSK | 13 | 19 | 38 | 12 | 10 | 8 |
8QAM | 7 | 8 | 20 | 36 | 15 | 14 |
32QAM | 5 | 10 | 7 | 21 | 34 | 23 |
64QAM | 7 | 8 | 14 | 16 | 22 | 33 |
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Marey, A.; Marey, M.; Mostafa, H. Novel Deep-Learning Modulation Recognition Algorithm Using 2D Histograms over Wireless Communications Channels. Micromachines 2022, 13, 1533. https://doi.org/10.3390/mi13091533
Marey A, Marey M, Mostafa H. Novel Deep-Learning Modulation Recognition Algorithm Using 2D Histograms over Wireless Communications Channels. Micromachines. 2022; 13(9):1533. https://doi.org/10.3390/mi13091533
Chicago/Turabian StyleMarey, Amr, Mohamed Marey, and Hala Mostafa. 2022. "Novel Deep-Learning Modulation Recognition Algorithm Using 2D Histograms over Wireless Communications Channels" Micromachines 13, no. 9: 1533. https://doi.org/10.3390/mi13091533