outage probability analysis

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.

本文研究了相关复合广义伽马衰落信道下的物理层安全性，利用Mellin变换和Fox-H函数推导了单链路概率密度函数、联合分布等表达式，并分析了保密中断概率。

This study addresses the asymptotic distribution of the $k$-th largest signal-to-noise ratio (SNR) order statistic over non-identically distributed $\kappa$-$\mu$ fading channels. By systematically applying extreme value theory for the first time in this context, the authors derive a general asymptotic distribution that subsumes classical models—including Rice, Rayleigh, and Nakagami-$m$—as special cases. Integrating order statistics with $\kappa$-$\mu$ channel modeling, the proposed framework yields closed-form expressions for key performance metrics such as outage probability and average throughput. Monte Carlo simulations confirm the high accuracy of the theoretical results. These findings provide a robust analytical foundation for performance evaluation and optimization in emerging 6G scenarios, including MIMO antenna selection, reconfigurable intelligent surfaces, backscatter communications, and unmanned aerial vehicle relaying.

Non-convex optimization problems involving product or fractional terms arise frequently in communication networks, posing significant challenges for efficient and reliable solution methods. Method: This paper proposes a generic decoupling framework and closed-form bounding technique grounded in the HM–GM–AM–QM inequality hierarchy, centered on an alternating minimization (AM)-based upper-bounding successive convex approximation (SCA) algorithm with provable convergence guarantees. Contribution/Results: We establish, for the first time, a unified decoupling mechanism applicable to arbitrary numbers of coupled multiplicative/divisive terms; rigorously prove the convexity of the AM-based upper bound and the global convergence of the SCA iterations; and enable joint modeling of AM upper bounds in both objective and constraints. Numerical evaluations on energy-efficiency optimization and quantum source localization demonstrate substantial improvements in convergence speed and solution quality over conventional quadratic approximations and parametric convexification approaches.

This paper addresses the calibration of outage probability predictions for machine learning–driven resource allocation in next-generation wireless networks, focusing on the single-user, multi-resource scenario. We derive the theoretical lower bound on outage probability under perfect calibration and prove—novelly—that post-hoc calibration cannot reduce the minimum achievable outage probability. We further establish monotonicity conditions on the accuracy–confidence function to ensure calibration validity. Methodologically, we combine Platt scaling with isotonic regression for post-hoc calibration and jointly optimize the predictor using an outage-aware loss function tailored to system reliability requirements. Evaluation under Rayleigh fading and Clarke’s two-dimensional channel model demonstrates that, as the number of resources increases, the outage probability of perfectly calibrated models converges to the target threshold, thereby significantly enhancing the predictability and reliability of wireless systems.

This study addresses the impact of deterministic line-of-sight (LoS) components on communication and sensing performance in downlink MIMO integrated sensing and communication systems operating over Rician fading channels. The authors propose and analyze two beamforming strategies—subspace joint beamforming (SJB) and linear beamforming (LB)—and, for the first time, derive outage probabilities under arbitrary and potentially correlated angular distributions of users and targets by leveraging the Cramér–Rao bound and random matrix theory to establish fundamental performance limits. Theoretical and simulation results reveal that the Rician K-factor affects communication reliability more significantly than sensing performance, with overall system performance exhibiting a non-monotonic dependence on K. LB combined with dirty-paper coding achieves optimal overall performance under strong LoS conditions and is the only scheme capable of supporting ultra-high communication reliability in Rayleigh fading, whereas SJB offers a low-complexity and robust alternative.

该研究通过使用Logistic-Lerch近似方法解决了在Nakagami-m衰落信道下计算有限码长传输平均包错误率的问题。

Conventional Gaussian coupling models—based on channel coefficient correlation matrices—fail to accurately capture the true dependence structure of fading envelopes in fluid antenna systems (FAS) under Nakagami-𝑚 fading, particularly due to phase sensitivity and distribution mismatch. Method: This paper proposes, for the first time, an envelope-level modeling approach within the Gaussian copula framework: multivariate normal variables are generated using the envelope correlation matrix, enabling more accurate characterization of inter-port dependencies—especially in sparse-port configurations. Contribution/Results: Monte Carlo simulations demonstrate that the proposed envelope-based method significantly improves outage probability prediction accuracy under low-outage regimes (<10⁻³) and sparse deployments, reducing estimation error by over 40% compared to coefficient-level approaches. This establishes a more reliable and physically grounded paradigm for modeling spatial correlation in FAS performance 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.

This work addresses the long-standing challenge of establishing an exact closed-form mapping between physical-layer Gaussian fading and the two-state Gilbert–Elliott (GE) Markov model at the link layer, a task typically reliant on simulation or approximation. By thresholding a stationary Gaussian process into binary states over discrete time slots, the authors derive closed-form expressions for the GE state transition probabilities using Owen’s T function, requiring only the first-order correlation coefficient ρ. The study makes three key contributions: it establishes, for the first time, an exact analytical bridge from Gaussian fading to the GE model; reveals how the smoothness of the covariance kernel governs the scaling law of link persistence times; and unifies two distinct Markov approximation diagnostic criteria. Monte Carlo simulations validate the theoretical results, which reduce to the classical arcsine identity when the threshold equals the process mean.