STOCHASTIC MODEL OF LOAD MANAGEMENT IN HETEROGENEOUS 6G NETWORKS WITH RADIO CHANNEL QUALITY CONSIDERATION

Abstract

The study examines the load management process in heterogeneous sixth-generation (6G) wireless networks, accounting for the impact of radio channel quality on traffic service characteristics. The aim is to develop a stochastic load management model for 6G networks that establishes the relationship between physical radio channel parameters, particularly transmitter – receiver distance and signal-to-interference-plus-noise ratio (SINR), and quality of service (QoS) indicators. Objectives: to develop a radio channel model based on a generalized signal attenuation model with a power-law path loss and molecular absorption; to determine the dependence of SINR on distance; to establish the relationship between SINR and channel capacity; to build a stochastic queueing model with variable service rate; and to study the influence of radio channel parameters on QoS metrics. Methods include wireless

communication theory, information theory, queueing theory, and numerical simulation. A generalized signal attenuation model, the Shannon capacity formula, and analytical queueing expressions are applied. Numerical simulations are performed using scenario-based modelling under different propagation conditions. Results show analytical dependencies between distance, radio channel quality, and traffic service performance. It is demonstrated that increasing distance leads to a nonlinear decrease in SINR, resulting in reduced channel capacity and service rate. Consequently, system utilization, average delay, and queue length increase. The results confirm a strong dependence of QoS on propagation conditions and highlight differences between line-of-sight and boundary operating scenarios. Conclusions indicate that the proposed model provides a comprehensive assessment of how physical radio channel characteristics affect network QoS. The integration of physical-layer modelling with queueing theory enables a more realistic representation of 6G heterogeneous networks. The results can be used for load analysis, prediction, optimization, and the design of resource management algorithms for next-generation wireless systems.

Keywords: heterogeneous 6g networks, load management, radio channel quality, signal-noise, channel capacity, queueing theory.