In this paper, we modify the slotted ALOHA protocol to quickly identify a swarm of drones in unmanned aerial vehicle (UAV) communication environments. Considering the unreliable UAV channel and the fundamental tradeoff between collision and idle slots in slotted ALOHA, we propose an idle-less slotted ALOHA (ILS-ALOHA) protocol that detects and utilizes idle slots to transmit control messages to drones. In ILS-ALOHA, the ground control station recognizes the idle slot promptly at the beginning of each slot and then transmits acknowledgment (ACK) and beacon messages during the remaining period of the idle slot. Therefore, it can control the access of the drone swarm more adaptively and feed back the ACK to the drones more frequently in an error-prone UAV environment without allocating additional slots. The analysis and simulation results show that the proposed ILS-ALOHA protocol outperforms the conventional slotted ALOHA-based protocols in terms of identification time, channel throughput, and probability of successful identification.

Atmospheric elements affect signal propagation and cause significant signal loss called atmospheric attenuation, especially in high-frequency range including sub-THz frequencies. Atmospheric attenuation mainly consists of gaseous, fog, and rain attenuation and the traditional approach considers them separately to develop good prediction models. However, the traditional models are not accurate and even unavailable for the high-frequency range such as sub-THz frequencies. In this letter, we propose a new attenuation model to predict atmospheric attenuation for terrestrial line-of-sight (LoS) propagation at high frequencies like sub-THz. The proposed model is based on Gaussian Process Regression (GPR). To train our model with a big measurement dataset, we use a scalable variant of GPR called Blockbox Matrix-Matrix Gaussian Process (BBMM-GP). We validate our model with a dataset obtained from a long-term measurement campaign at sub-THz frequencies. Our experiments show that our model significantly outperforms the existing model. We also show that our model provides reliable prediction intervals of atmospheric attenuation.

Rain attenuation based on the drop size distribution (DSD) with different rainfall rates (R) at low THz frequencies is investigated in this paper. The rain attenuation is calculated using the DSD measured for one year and the extinction cross-section (ECS) by the Mie scattering theory. Moreover, the obtained specific rain attenuation is verified by the empirical model using the measurement system consisting of a transmitter, a receiver, and weather measurement units. We measured the received power against the uniform transmitted power at 240, 270, and 300 GHz on the rooftop of the National Radio Research Agency (RRA) in Korea during the same period as the DSD measurement period. After curve fitting by regression analysis, we compared both rain attenuations obtained in two methods with the recommendation International Telecommunication Union Radiocommunication Sector (ITU-R) P.838-3. The root mean square errors (RMSEs) of the DSD model are 2.8977, 2.8646, and 2.8331 at 240, 270, and 300 GHz, respectively. The calculated result using the Mie scattering and the measured DSD methods shows the best fit to the data of the ITU-R recommendation for a rainfall rate of up to 5 mm/h. On the other hand, the empirical results using the T/Rx antenna system are slightly higher compared to the data of the ITU-R recommendation. As the rainfall rate increases, the difference between our results and ITU-R recommendation increases. This study will be useful for predicting rain attenuation for terrestrial wireless links operating at low THz frequencies.

In this paper, we have derived R.M.S. delay spread characteristics and models according to the influence of antenna beam width in the mobile kiosk data download environment, the inter-rack communication environment in the data center, the intra-device communication environment, and the experimental laboratory measurement environment scenario in the 275 GHz to 295 GHz bands. The measurement system used in this paper used a vector network analyzer and a frequency expander to derive the statistical characteristics of terahertz frequency band signals, and used antennas with different beamwidths for measurement. It is confirmed that the distribution of delay spread varies depending on the beam width of the antenna used for measurement and the type of measurement scenario.