# Identification of Terahertz Link Modulation in Atmospheric Weather Conditions
## 4 Conclusions  
Time consumed in baseband signal processing is usually considered as an important factor in evaluating the performance of wireless communication systems, especially when they operate in the terahertz frequency band to achieve a low time latency in the range of milli-seconds and micro-seconds. However, in outdoor scenarios, terahertz links are more vulnerable to the absorption and scattering effects caused by atmospheric weather than micro- or millimeter-wave setups. This degrades the efficiency of baseband signal processing and increases the time consumed, which can be alleviated by a prior identification of link modulation formats.  
In this work, a theoretical model, combining particle scattering theory and ITU models, is presented to investigate the efficiency of this method in different weather (rain, fog and haze, and wind). Two kinds of neural networks (CNN and LSTM) are employed, and the performance of this method is evaluated. We observe that introducing the LSTM network can offer a higher accuracy than the CNN network and can achieve an accuracy close to 100% in adverse weather. This is close to the situation in clear weather, which means that introducing LSTM can overcome the deterioration of outdoor weather. The voting enhancement algorithm is employed in the CNN network and can get an obvious improvement in accuracy, even though it is still lower than that of the single LSTM. We also find that this method still works when a terahertz link operates at critical frequencies (such as 183 GHz), corresponding to an absorption resonance by water vapor.  
These research results will help expand the practical application scenarios of neural networks, and they also provide a valuable reference for the construction of terahertz base stations under outdoor conditions. This work is the first to demonstrate the effectiveness of neural network algorithms in modulation format identification of terahertz links propagating in atmospheric weather, indicating that the proposed approach is feasible for assisting terahertz communication in outdoor environments. Neural networks can adjust their structure according to actual needs and even achieve multi-network fusion, so we believe that this approach has great potential to more effectively improve terahertz communication performance. We hope our findings can prompt more efforts in terahertz communication system designs, such as signal processing and physical-layer security.  
Methodology, calculation, investigation and writing, Z.W.; Modeling, Y.Q.; Conceptualization, writing, supervision and funding acquisition, J.M.; Validation and writing, D.L.; Writing and supervision, Y.Z. and H.Z. All authors have read and agreed to the published version of the manuscript.  
This work is partially supported by the National Natural Science Foundation of China (Research on Physical Layer Security of Terahertz Communication in Atmospheric Environment 62071046), the Graduate Innovative Practice Project of Tangshan Research Institute, BIT (TSDZX0202201), the Science and Technology Innovation Program of the Beijing Institute of Technology (2022 CX01023) and the Talent Support Program of the Beijing Institute of Technology \"Special Young Scholars\" (Terahertz Wireless Communication Technology and System 3050011182153).  
Data available on request due to restrictions eg privacy or ethical. The data are not publicly available due to the involvement of other subsequent studies.  
We acknowledge the support from [PERSON] of the Beijing Institute of Technology.  
The authors declare no conflict of interest.