3GPP-Like THz Channel Modeling for Indoor Office and Urban Microcellular Scenarios

Terahertz (THz) communication is envisioned as the possible technology for the sixth-generation (6G) communication system. THz channel propagation characteristics are the basis of designing and evaluating for THz communication system. In this paper, THz channel measurements at 100 GHz and 132 GHz are conducted in an indoor office scenario and an urban microcellular (UMi) scenario, respectively. Based on the measurement, the 3GPP-like channel parameters are extracted and analyzed. Moreover, the parameters models are available for the simulation of the channel impulse response by the geometry-based stochastic model (GBSM). Then, the comparisons between measurement-based parameter models and 3rd Generation Partnership Project (3GPP) channel models are investigated. It is observed that the case with path loss approaching free space exists in the NLoS scenario. Besides, the cluster number are 4 at LoS and 5 at NLoS in the indoor office and 4 at LoS and 3 at NLoS in the UMi, which are much less than 3GPP. The multipath component (MPC) in the THz channel distributes more simpler and more sparsely than the 3GPP millimeter wave (mm-wave) channel models. Furthermore, the ergodic capacity of mm-wave and THz are evaluated by the proposed THz GBSM implementation framework. The THz measurement model predicts the smallest capacity, indicating that high carrier frequency is limited to the single transmission mechanism of reflection and results in the reduction of cluster numbers and ergodic capacity. Generally, these results are helpful to understand and model the THz channel and apply the THz communication technique for 6G.

Using evolutionary machine learning to characterize and optimize co-pyrolysis of biomass feedstocks and polymeric wastes

Co-pyrolysis of biomass feedstocks with polymeric wastes is a promising strategy for improving the quantity and quality parameters of the resulting liquid fuel. Numerous experimental measurements are typically conducted to find the optimal operating conditions. However, performing co-pyrolysis experiments is highly challenging due to the need for costly and lengthy procedures. Machine learning (ML) provides capabilities to cope with such issues by leveraging on existing data. This work aims to introduce an evolutionary ML approach to quantify the (by)products of the biomass-polymer co-pyrolysis process. A comprehensive dataset covering various biomass-polymer mixtures under a broad range of process conditions is compiled from the qualified literature. The database was subjected to statistical analysis and mechanistic discussion. The input features are constructed using an innovative approach to reflect the physics of the process. The constructed features are subjected to principal component analysis to reduce their dimensionality. The obtained scores are introduced into six ML models. Gaussian process regression model tuned by particle swarm optimization algorithm presents better prediction performance (R2 > 0.9, MAE < 0.03, and RMSE < 0.06) than other developed models. The multi-objective particle swarm optimization algorithm successfully finds optimal independent parameters.