The statistical characteristics of the propagation environment and traffic arrival process are known to affect the user performance in 5G/6G millimeter wave (mmWave) and sub-terahertz (sub-THz) systems. While the former topic has received considerable attention recently, little is known about the impact of traffic statistics. In this study, we characterize the effects of correlation and variability in the session arrival process on the performance of 5G/6G mmWave/sub-THz systems. To this end, we use the tools of stochastic geometry and queuing theory to model the service process at base stations (BS) and specifics of the mmWave/sub-THz radio part. The metrics considered include the system resource utilization and session loss probability. Our results show that the normalized autocorrelation function (NACF), coefficient of variation (CoV), and variance of the resource request distribution have a significant impact on the considered parameters. For the same arrival rate, high values of lag-1 NACF and CoV may lead the system out of the operational regime, affecting the loss probability and resource utilization by up to an order of magnitude. Even a slight deviation from the uncorrelated Poisson process decreases the utilization by 10-20% and increases the session loss probability multiple times. Radio and environmental characteristics may further increase the variability in resource request distribution and decrease resource utilization. In general, the use of the commonly accepted Poisson assumption leads to a severe underestimation of the actual performance of 5G/6G mmWave/sub-THz systems. Therefore, both traffic arrival and propagation statistics are equally important for accurate performance assessment of such systems.

Channel state information in millimeter wave (mmWave) and terahertz (THz) communications systems is vital for various tasks ranging from planning the optimal locations of BSs to efficient beam tracking mechanisms to handover design. Due to the use of large-scale phased antenna arrays and high sensitivity to environmental geometry and materials, precise propagation models for these bands are obtained via ray-tracing modeling. However, the propagation conditions in mmWave/THz systems may theoretically change at very small distances, that is, 1 mm–1 $\mathsf{\mu }$m, which requires extreme computational effort for modeling. In this paper, we first will assess the effective correlation distances in mmWave/THz systems for different outdoor scenarios, user mobility patterns, and line-of-sight (LoS) and non-LoS (nLoS) conditions. As the metrics of interest, we utilize the angle of arrival/departure (AoA/AoD) and path loss of the first few strongest rays. Then, to reduce the computational efforts required for the ray-tracing procedure, we propose a methodology for the extrapolation and interpolation of these metrics based on the convolutional long short-term memory (ConvLSTM) model. The proposed methodology is based on a special representation of the channel state information in a form suitable for state-of-the-art video enhancement machine learning (ML) techniques, which allows for the use of their powerful prediction capabilities. To assess the prediction performance of the ConvLSTM model, we utilize precision and recall as the main metrics of interest. Our numerical results demonstrate that the channel state correlation in AoA/AoD parameters is preserved up until approximately 0.3–0.6 m, which is 300–600 times larger than the wavelength at 300 GHz. The use of a ConvLSTM model allows us to accurately predict AoA and AoD angles up to the 0.6 m distance with AoA being characterized by a higher mean squared error (MSE). Our results can be utilized to speed up ray-tracing simulations by selecting the grid step size, resulting in the desired trade-off between modeling accuracy and computational time. Additionally, it can also be utilized to improve beam tracking in mmWave/THz systems via a selection of the time step between beam realignment procedures.

Blockage of propagation paths between the base station (BS) and user equipment (UE), as well as the micromobility of the UE, are known to be critical phenomena affecting the performance of 6G subterahertz/terahertz (sub-THz/THz, 0.1–0.3/0.3–3 THz) cellular systems. The development of functions that target the performance improvement of such systems requires understanding of the dynamics of the received signal. However, measurements of the signal received power (SRP) reported to date are limited to the blockage and micromobility phenomena in isolation. In this study, by utilizing individual measurements of blockage and micromobility processes, we propose a procedure for generating synthetic time series of the received signal strength simultaneously capturing blockage, micromobility, and beam-tracking procedures. Our results reveal that out of all the considered applications, only the most dynamic ones, racing game and VR, are characterized by significant differences between on-demand and regular beam tracking with 4-6.5 bits/Hz/s spectral efficiency degradation in the case of on-demand beam tracking as compared to the regular one. For applications with high-speed micromobility (VR watching and race gaming), the minimal beam tracking interval is 80 ms, whereas for low-speed applications, a period of 320 ms and even sometimes 1000 ms is sufficient. Our results show that the availability of information regarding the type of application allows one to decrease the overhead required for beam tracking by up to 20-30 times. The traces produced can be further utilized for various tasks, including the development of statistical tests for discriminating blockage and micromobility events, designing blockage detection algorithms, and improving beam tracking procedures. We made the data produced available to the research community.