# Identification of Terahertz Link Modulation in Atmospheric Weather Conditions
## 3 Simulation and Discussion  
In this work, a baseband signal of 10 GHz is simulated and upconverted to 120 GHz, which has been confirmed for data transmission over a long distance up to several km [25]. Five modulation schemes, namely binary-phase shift keying (BPSK), quad-phase shift keying (QPSK), eight-phase shift keying (8 PSK), 16-quadrature amplitude modulation (16 QAM), 64-quadrature amplitude modulation (64 QAM) are selected and have been reported to achieve large data capacity [26; 27]. In order to reflect the influence of signal-to-noise (SNR) level, different values are considered and changed by adjusting the transmitter output power, antenna gain and/or receiver sensitivity [28; 29]. Specific parameter settings for path losses due to atmospheric weather are given in Section 2. The simulation and signal processing of terahertz links are carried out on MATLAB R2021b. To demonstrate the impact caused by harsh weather, a clear weather condition is considered and used for comparison. Usually, the humidity in clear weather is much lower than that in rain, fog and haze, where it is measured to be around 60% (96% in rain and fog and haze weather, 60% in wind weather) [4]. The attenuation suffered by the 120 GHz link in clear weather is around 1.62 dB, which is much smaller than that due to other atmospheric environments. Attenuation caused by different weather conditions will be introduced into the THz simulation link as additive Gaussian white noise (AWGN). Other losses caused by internal hardware are not considered, as they are difficult to measure. These channel attenuations are calculated using models in Section 2.1 and incorporated into the experiment to directly attenuate the simulated terahertz signal, ultimately resulting in different path gains. We will use identification accuracy as an evaluation indicator for the proposed method. In terms of experiments, we assume that the overall process is as follows. The CNN and LSTM models used operate at the receiving end. The transmitted signal is attenuated through the atmospheric channel model and reaches the neural network at the receiving end. The neural network recognizes the modulation type of the signal and assigns type labels, which will ultimately assist in subsequent data processing. The specific object processed by these neural network models is the IQ constellation of the signal. The neural \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **CNN** & **LSTM** \\ \hline Optimizer & Adam & Adam \\ \hline Initial learning rate & 0.0001 & 0.0001 \\ \hline Regularization parameter & 0.0001 & 0.00001 \\ \hline Number of rounds & 65 & 30 \\ \hline Fading period & 40 & / \\ \hline Fading factor & 0.1 & / \\ \hline Training environment & GPU & GPU \\ \hline Multi-GPU parallel processing & No & No \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Training parameters of CNN and LSTM networks. Figure 1: Architectures of (**a**) CNN network and (**b**) LSTM network. network utilizes known constellation data for training to obtain constellation laws of various modulation formats, ultimately achieving the goal of using constellation information to complete modulation identification. Due to the lack of terahertz signal datasets in open-source datasets related to constellation maps, the relevant constellation data is generated by MATLAB software. The main modulation types used are M-PSK and M-QAM, mentioned above. In each modulation scheme, there are 5000 frames generated, each containing 1024 samples. Each transmission frame contains 128 symbols (i.e., 128 spf), and we set the symbol rate not to change with the modulation format. Finally, there should be a total number of 25,000 data frames and a four-dimensional data matrix of [2, 1024, 1, 25,000]. We choose 80% of them for network training, 10% for validation, and 10% for testing, which is a common setting in data training.