# Identification of Terahertz Link Modulation in Atmospheric Weather Conditions
###### Abstract  
With the rapid increase of wireless connectivity, current spectrum resources are not enough for significant requirements for large data capacity. Research interests are moving towards the high-frequency band in the terahertz range for wider bandwidth. However, multipath scattering and induced time delay, suffered by terahertz links propagating in outdoor weather, lead inevitably to increasing expenses in baseband signal processing. This trades away the advantage of low time latency and high stability, which are commonly considered as important merits of terahertz wireless communication techniques. To reduce the burden in signal processing and explore the feasibility of modulation identification in the terahertz band, a method of wireless link modulation identification is considered as a potential solution. In this work, it is investigated theoretically by employing two kinds of neural networks: the convolutional neural network (CNN) and the long short-term memory network (LSTM). Link deterioration caused by different atmospheric weather is introduced into the theoretical model, and the performance of this method is evaluated. Results show that the identification accuracy of the constructed neural networks can be up to 99%, which means such a method is efficient for identification of the modulation format of terahertz wireless links under different weather conditions. This work demonstrates the feasibility of modulation identification in outdoor terahertz communication scenarios and provides specific references. terahertz communication; data modulation format identification; atmospheric weather; neural network +
Footnote †: journal: Medical  
recognize different link modulation formats based on the weight matrix of signal features and help to decrease the response time of the entire system finally. It has been considered as a potential solution to reduce the time consumed by the additional processing burden caused by the link deterioration. Recent years have witnessed rapid growth in the development of methods to enable link modulation identification. This technology can alleviate the impact of waveform changes caused by interference and address the inability in demodulation due to data interference. Examples of classifiers and neural networks have been discussed for many years [10; 11], with rapid improvements continuing to be reported. The research scenario in reference [11] is different from that of terrestrial communication, as the authors investigated signal modulation identification in underwater acoustic environments. [PERSON] et al. [12] presented a signal modulation identification technique using block repetition, which achieved an accuracy of around 99% in a filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) at a sampling frequency of 10 kHz. An accuracy of up to 100% has been achieved in a 150 kHz wireless link by using a decision tree classifier [12]. For wireless links operating at several hundred MHz, an accuracy of over 90% can also be achieved by exploring their autocorrelation or constellation features in a neural network [13; 14]. However, the works done by predecessors are mainly based on the low communication frequency band, and there is very little research on modulation identification in the terahertz frequency band. In addition, the modulation identification of terahertz signals, which takes the atmospheric environment into account as an important factor, has never been carried out. These research gaps are not conducive to the infrastructure construction of 6G or 7G networks. Considering the problems caused by resource congestion and the prospects for expanding communication frequency bands, it is necessary to study the modulation identification technique of terahertz signals in atmospheric environments. Therefore, this paper carries out this original work for the first time, focusing on the feasibility and effect of modulation identification in outdoor terahertz wireless scenarios.