coverage and interference analysis

Accurate and computationally tractable SINR coverage analysis in Poisson cellular networks remains challenging due to inherent trade-offs between precision and analytical feasibility. Method: This paper proposes a hybrid approximation framework: Monte Carlo sampling for dominant near-field interferers, and Laplace functional modeling for the residual far-field interference. Contribution/Results: The approach eliminates reliance on nested integrals and special functions in classical stochastic geometry models, while avoiding failure modes of probabilistic interference models under missing interference moments or restrictive parameter assumptions. Its modular design ensures numerical stability and path-loss independence, and—uniquely—provides a theoretically derived error bound that converges as the number of dominant interferers increases. Experiments demonstrate high accuracy and low computational overhead under both noise-limited and interference-limited regimes, with strong robustness and consistency across diverse channel conditions and network deployment parameters.

Accurately modeling coverage boundaries in large-scale wireless networks remains challenging due to the complex, stochastic geometry of signal propagation and interference. Method: This paper introduces *Q-cells*—geometric regions formed by intersections of a small number of disks—to analytically characterize the outer boundary of the coverage region; the union of Q-cells represents the full coverage manifold. Integrating computational geometry, stochastic geometry, and asymptotic scaling analysis, the framework leverages the meta-distribution of the signal-to-interference ratio (SIR) to enable theoretically scalable analysis for infinite-size networks. Contribution/Results: This is the first work to formalize Q-cells, establishing an explicit geometric outer boundary representation and a scalable estimation framework for coverage. Compared to conventional Voronoi-based approaches, it achieves significantly higher accuracy in coverage probability prediction—with markedly reduced estimation error—while retaining compact analytical tractability. The proposed paradigm thus provides a rigorous yet practically deployable foundation for performance evaluation of massive wireless networks.

This study addresses interference propagation induced by user mobility in large-scale multi-RIS wireless communication systems. For the first time, the epidemic SIS model is introduced into wireless interference analysis, combined with stochastic geometry to characterize spatial deployments: base stations are modeled via a Matérn hard-core point process, while reconfigurable intelligent surfaces (RISs) follow a Poisson point process. By constructing a dynamic interference propagation model, the work introduces the notion of “interference propagation intensity” and derives closed-form expressions for both received signal and interference power, along with a novel coverage probability formula. Numerical evaluations validate the theoretical analysis and uncover key factors governing interference propagation, thereby offering foundational insights and design guidelines for interference management and deployment optimization in multi-RIS networks.

This work addresses channel modeling and performance evaluation of distributed MIMO (D-MIMO) in industrial environments. We systematically compare, for the first time, deterministic ray-tracing models against stochastic Rayleigh fading models in predicting downlink/uplink single-user capacity. Leveraging a real-world 3D factory map, we construct multiple deployment scenarios to quantify how network densification affects user equipment (UE) multi-access point (AP) connectivity and coverage gain. Results show that densification significantly enhances D-MIMO capacity. Ray tracing more accurately captures spatial correlation and realistic propagation characteristics, whereas the Rayleigh model offers superior computational efficiency and maintains acceptable prediction error (<15%) in typical factory settings. The study establishes fundamental trade-offs among modeling accuracy, spatial correlation fidelity, and computational overhead, providing both theoretical guidance and empirical evidence for selecting appropriate D-MIMO channel models in industrial wireless systems.

Modeling performance in low Earth orbit (LEO) satellite networks is challenging due to the inherent heterogeneity in satellite altitudes, which complicates analytical tractability. Method: This paper proposes the first analytically tractable unified stochastic geometric model for integrated satellite-terrestrial networks: satellites and terrestrial base stations are jointly modeled as a Poisson point process (PPP) on concentric spherical surfaces, each node marked by a random height, with rigorous incorporation of line-of-sight visibility constraints. Contribution/Results: The derived closed-form expression for coverage probability explicitly captures its joint dependence on the path-loss exponent, altitude distribution, node density, and bias factor. Quantitative analysis reveals dual gains—enhanced wide-area coverage in remote rural regions and effective traffic offloading in dense urban areas—thereby providing a theoretical foundation and design guidelines for coordinated LEO network deployment.

Accurately evaluating SINR coverage performance in low-Earth-orbit (LEO) satellite networks remains challenging due to their spherical geometry and spatial correlations. Method: This paper proposes the first analytical framework for SINR coverage analysis based on spherical stochastic network calculus. It introduces a novel strongly constrained spherical point process to model the inherent spatial repulsion among satellites and extends stochastic network calculus—originally developed for Euclidean spaces—to the spherical domain. The framework jointly incorporates Nakagami-m and Rayleigh fading channel models. Results: It yields a computationally efficient, closed-form lower bound on the coverage probability with high accuracy. Validated against real Starlink constellation data, the bound exhibits negligible computational complexity and aligns closely with Monte Carlo simulations (SINR deviation ≈ 1 dB), significantly outperforming existing approximations. This work provides a scalable, tractable, and analytically rigorous tool for performance prediction of large-scale LEO constellations.

In finite three-dimensional wireless networks, the coverage probability lacks accurate closed-form analytical solutions due to enhanced spatial dependence among nodes within bounded domains and strong coupling between link distances and interference. Method: This paper proposes the first rigorous analytical framework based on a cylindrical-domain binomial point process (BPP), innovatively decoupling the inter-node distance distribution from the interference term—overcoming inherent limitations of Poisson point process (PPP) modeling in bounded spaces. Leveraging stochastic geometry, along with convolution and derivative properties of Laplace transforms, we derive a computationally efficient and mathematically rigorous closed-form expression for the coverage probability. Results: Monte Carlo simulations validate that the proposed model achieves significantly higher accuracy than conventional PPP-based approaches in constrained 3D scenarios—including UAV, underwater, and robotic networks—with error reductions of 30%–50%. The framework thus bridges theoretical rigor and practical engineering applicability.

This work addresses interference management in multi-operator secondary spectrum sharing within millimeter-wave (mmWave) networks by proposing a spatially aware licensed shared access mechanism. For the first time, the directional and blockage-prone nature of mmWave propagation is explicitly incorporated into the sharing policy, which dynamically grants secondary access based on the distance, orientation, and line-of-sight conditions between primary and secondary links. Leveraging stochastic geometry, the authors develop an analytical framework that jointly models beam directionality, obstacle distribution, user density, and channel characteristics to quantify secondary transmission opportunities as well as the coverage probabilities of both primary and secondary links. The results demonstrate that spatial isolation—exploiting directionality and blockage—significantly enhances the feasibility and performance of spectrum sharing in mmWave networks.

This study addresses the sustainable deployment of 5G networks by evaluating and mitigating electromagnetic field (EMF) exposure while maintaining performance. Leveraging a stochastic geometry framework, it models base station spatial distributions using both Poisson point processes (PPP) and the more realistic beta-Ginibre point process (beta-GPP), within an EN-DC multi-connectivity scenario. The analysis combines theoretical derivations, Monte Carlo simulations, and empirical validation using real-world measurements from Paris. The work introduces a novel metric—Radiation Energy per Bit in the Downlink (REBT-DL)—and, for the first time, incorporates beta-GPP into EMF exposure modeling. Results reveal that network configuration significantly influences both EMF exposure and energy efficiency, offering a practical assessment tool to support the design of environmentally sustainable 5G networks.

This work addresses a critical limitation in conventional performance analyses of non-orthogonal multiple access (NOMA) systems, which typically neglect the statistical dependence between successive interference cancellation (SIC) residual noise and channel fading, leading to inaccurate outage probability and ergodic capacity evaluations. Focusing on a two-user downlink NOMA scenario, the study derives for the first time the joint probability density function of SIC-induced noise and Rayleigh fading channels. By leveraging random variable transformation and closed-form integration techniques, it obtains an exact closed-form expression for the near user’s outage probability and a single-integral representation for its ergodic capacity. The proposed parameter-free model exposes the fundamental inadequacy of treating the residual interference factor as statistically independent. Simulations confirm that conventional models—assuming Gaussian or fixed residual interference—exhibit significant deviations from actual system performance, particularly at medium to low signal-to-noise ratios.