Identification of Terahertz Link Modulation in Atmospheric Weather Conditions
Abstract
:1. Introduction
2. Channel Model
2.1. Atmospheric Model
2.1.1. Rainfall Weather
2.1.2. Fog and Haze
2.1.3. Wind
2.2. Neural Network Model
3. Simulation and Discussion
3.1. Performance under Different Weather Conditions
3.2. Performance at Water Vapor Absorption Peaks
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Weather | Clear | Rain | Fog and Haze | Wind | Remark | |
---|---|---|---|---|---|---|
Parameter | ||||||
Distance (km) | 1 | 1 | 1 | 1 | / | |
Atmospheric pressure (Pa) | 1.0037 × 105 | 1.0037 × 105 | 1.0037 × 105 | 1.0037 × 105 | ||
Temperature (K) | 287.63 | 287.63 | 287.63 | 287.63 | ||
Water vapor density (g/m3) | 7.7 | 12.3 | 12.3 | 7.7 | ||
Rainfall rate (mm/h) | / | 25 | / | / | heavy rain | |
Fog concentration (g/m3) | / | / | 0.5 | / | dense fog and heavy haze | |
Haze particle density (1/cm3) | / | / | 7000 | / | ||
Wind speed (m/s) | / | / | / | 15 | strong wind |
Parameter | CNN | LSTM |
---|---|---|
Optimizer | Adam | Adam |
Initial learning rate | 0.0001 | 0.0001 |
Regularization parameter | 0.0001 | 0.00001 |
Number of rounds | 65 | 30 |
Fading period | 40 | / |
Fading factor | 0.1 | / |
Training environment | GPU | GPU |
Multi-GPU parallel processing | No | No |
Variable | Model | Clear Weather | Rain | Fog and Haze | Wind |
---|---|---|---|---|---|
MSE | CNN (Voting) | 0.3568 | 0.4332 | 0.4104 | 0.4144 |
LSTM | 0.2084 | 0.248 | 0.28 | 0.2524 | |
RMSE | CNN (Voting) | 0.5973 | 0.6582 | 0.6406 | 0.6437 |
LSTM | 0.4565 | 0.4979 | 0.5291 | 0.5024 | |
MAPE | CNN (Voting) | 35.68% | 43.32% | 41.04% | 41.44% |
LSTM | 20.84% | 24.80% | 28.00% | 25.24% |
SNR | Network | Clear Weather | Rain | Fog and Haze | Wind |
---|---|---|---|---|---|
13 dB | CNN | 61.56% | 51.32% | 54.32% | 55.76% |
LSTM | 79.16% | 75.20% | 72.00% | 74.76% | |
CNN (Voting) | 64.32% | 56.68% | 58.96% | 58.56% | |
80 dB | CNN | 80.48% | 78.60% | 80.80% | 79.84% |
LSTM | 99.88% | 99.92% | 100% | 99.64% | |
CNN (Voting) | 82.04% | 80.96% | 80.84% | 80.36% |
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Wu, Z.; Qiao, Y.; Ma, J.; Zhang, Y.; Li, D.; Zhang, H. Identification of Terahertz Link Modulation in Atmospheric Weather Conditions. Appl. Sci. 2023, 13, 7831. https://doi.org/10.3390/app13137831
Wu Z, Qiao Y, Ma J, Zhang Y, Li D, Zhang H. Identification of Terahertz Link Modulation in Atmospheric Weather Conditions. Applied Sciences. 2023; 13(13):7831. https://doi.org/10.3390/app13137831
Chicago/Turabian StyleWu, Zhendong, Yige Qiao, Jianjun Ma, Yuping Zhang, Dehua Li, and Huiyun Zhang. 2023. "Identification of Terahertz Link Modulation in Atmospheric Weather Conditions" Applied Sciences 13, no. 13: 7831. https://doi.org/10.3390/app13137831