# Identification of Terahertz Link Modulation in Atmospheric Weather Conditions
## 3 Simulation and Discussion
### Performance under Different Weather Conditions  
Comparing to microwaves, terahertz with large capacity is more sensitive to the impact of low SNR (such as 13 dB) under the massive amount of information. Therefore, this is an additional challenge for terahertz wireless communication. The simulation results in Figure 2 illustrate the performance of the identification method with CNN and LSTM networks employed, for a single-frequency (120 GHz) link propagating under different weather conditions and an SNR value of 13 dB. The value in each grid represents the number of one real label identified to be itself or other labels. If we take the first line of grids as an example, the first grid from the left side refers to the number of 16 QAM labels which are identified to be 16 QAM, and the second grid is that identified to be 64 QAM. We first explore the performance of this method by using the CNN network and observe the highest accuracy in identification of the BPSK modulation format under all the weather conditions (about 90%), which proves that this method is more efficient for lower-order modulation schemes. This is consistent with previous work results [26]. We  
Figure 2: Simulation results by CNN network and LSTM network with a 10 GHz baseband signal. (**a**–**d**) are for simulation by CNN in clear weather, rain, fog and haze and wind, respectively; (**e**–**h**) are for simulation by LSTM in clear weather, rain, fog and haze and wind, respectively. (The value of SNR is 13 dB for all the simulations; the value in each grid represents the number of real labels identified to be other labels, with 500 labels considered). also note that the accuracy is lowest in rainy weather, regardless of the modulation format. This is not surprising, as the terahertz link suffers a higher deterioration due to rain [5]. In the recognition process of all modulation formats, 16 QAM and 64 QAM are the most easily confused with each other, and QPSK is the most easily recognized as 8 PSK. We believe this is related to the distribution of points in the constellation diagram. The most surprising aspect of Figure 2 is the higher accuracy from using a LSTM network under all the weather conditions and for all the modulation formats, which is also confirmed by the performance comparison based on average accuracy in the last table for each situation. It can also be seen intuitively from the confusion matrix that the confusion degree of modulation formats in LSTM is weaker than that of CNN. In addition, the situation where QPSK was mistakenly identified as 8 PSK has been greatly corrected, and the degree to which M-QAM was mistakenly identified as M-PSK has also been weakened. This suggests that the LSTM network can be more a potent algorithm in assisting the method of terahertz modulation identification. This can be understood from the design of the networks, as the LSTM can discard invalid data automatically by using a bidirectional memory algorithm, which is unavailable in the CNN network algorithm. To improve the accuracy of the CNN network, there have been several methods using external algorithms (such as dynamic threshold updating) proposed and demonstrated [27]. They are based on signal preprocessing with a sacrifice of system complexity, which leads to additional computation. We use a voting algorithm to avoid such a drawback with its vote number determined by the amount of data [30; 31]. There are 25,000 data frames in our simulation, which means that at least a five-vote enhancement algorithm should be required to get enough voting efficiency. In this work, we choose a seven-vote algorithm in the simulation. Using the CNN network with the voting enhancement algorithm (CNN enhancement), an obvious improvement in the identification accuracy can be observed when comparing Figures 3 and 2a-d. From the last table, the average accuracy is increased to 64.32% in clear weather and to around 58% under the other three weather conditions. This means the voting algorithm is effective at improving the performance of the network.