Analytical modeling of modern wireless networks

Modern telecommunication networks, including wireless systems, are growing increasingly complex. They are shaped by ever more demanding service requirements across diverse scenarios such as industrial automation and ultra-reliable communications, sophisticated protocols and applications like extended reality, and new user behavior patterns driven by the rise of AI. Evaluating the performance of such systems through real-world testbeds or field experiments is expensive, time-consuming, and difficult to scale. Simulation offers more flexibility, but under extreme reliability constraints and in highly dynamic environments, it can still be computationally prohibitive.

This is where analytical modeling comes in. By abstracting system behavior into mathematical frameworks, it delivers performance estimates for any parameters through closed-form or numerical solutions with far greater efficiency. Yet flexibility and speed are not the only benefits. Analytical models provide deep insight into the nature of the technologies they describe, helping researchers understand and predict how systems actually behave in practice. They also serve as an essential first step in designing and validating new mechanisms. Before anything is built, deployed, or simulated, a solid analytical foundation guides the way. In this sense, analytical modeling connects fundamental science and practical engineering, translating theoretical understanding into tangible benefits for the networks that connect people every day.

Our laboratory has extensive and long-established experience in mathematical modeling of modern wireless networks. We leverage a broad range of analytical approaches, including discrete and continuous Markov chains, probabilistic modeling, queueing theory, and stochastic processes. These methods allow us not only to capture the unique complexities of each specific technology but also to reveal more general and fundamental dependencies that shape wireless systems as a whole.

In the area of Wi-Fi, WNL has developed numerous analytical frameworks that have evolved from fundamental models of core Wi-Fi behavior to advanced tools addressing state-of-the-art mechanisms such as RAW, R-TWT, MLO, SR, preemption, and more. These frameworks enable not only performance evaluation but also complex optimization of modern Wi-Fi systems.

In cellular networks, the laboratory has a strong background in evaluating 5G and beyond systems, with a particular focus on challenging scenarios such as URLLC and V2X.

In the IoT domain, mathematically grounded tools developed by WNL members shed light on aspects such as scalability, performance, and power efficiency, all of which become increasingly important as the number of connected sensors continues to grow under popular IoT protocols.

Looking ahead, the laboratory is extending its toolkit toward future technologies. For instance, WNL explores optimization techniques such as MCMC for the design and configuration of RIS, a promising tool expected to play a key role in next-generation networks.

Together, these efforts create a continually evolving body of analytical knowledge within WNL. This spans from well-established wireless technologies to emerging paradigms. By merging solid math with real-world use, WNL aims to make future networks more efficient, reliable, and accessible.