MDP and LLM Synergy for Network Experimentation: Case Analysis of Wireless System Performance in Beaulieu-Xie Fading and \(\kappa\)-\(\mu\) Co-Channel Interference Environment with Diversity Combining +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))). measurements should be carried out in the environment. After these measurements, it is essential to create as adequate models as possible. For that purpose, [PERSON] and [PERSON] defined a new distribution model, called the Beaulieu-Xie (BX), which is suitable to successfully describe fading that contains an arbitrary number of both, the line-of-sight (LOS) and the non-LOS (NLOS) components of the useful signal [2]. This mathematical model contains the features of other fading distributions due to three parameters that characterize BX distribution (\(m\), \(\lambda\), and \(\Omega\)). Because of the above, the BX distribution is used for modeling fading in densely packed small cells called femtocells, for millimeter (mmWave) and terahertz (THz) communication systems, as well as for short-range 6G random-access channels, when there are multiple reflected signals. In any case, for now, BX distribution has its greatest application in signal propagation in small buildings and fast-moving trains. The BX fading model is a general model. Other models of known fading distributions are included in the BX model: \(\kappa\)-\(\mu\), non-central chi, generalized Rician, and others which can be obtained from it: Rician distribution, one-sided Gaussian distribution, Rayleigh and Nakagami-\(m\) distribution. By approximation, a log-normal distribution can also be derived from the \(\kappa\)-\(\mu\) distribution. It was shown that \(\kappa\)-\(\mu\) distribution is in quite good agreement with the experimentally obtained data when it was revealed [3]. It was established by measurements at that time that the Nakagami-\(m\) fading model is almost ideally suited only to model wireless channels with scattered fading components, but it became clear that the Nakagami-\(m\) model cannot describe wireless channels with LOS components. The Ricean fading model is a usual single model for a presentation signal composed of several scattered components and only one LOS component. It was obvious that none of the abovementioned distributions are suitable for modeling the fading channel where multiple dominant specular components are present in addition to the diffuse scatter power. Therefore, it was necessary to construct a new model that would overcome this deficiency by including several direct components [2]. Thus, a BX model is defined to include not only one LOS component but more LOS and NLOS components. Recently, some groups of authors have paid attention to the performance over the BX fading channel and analyzed it under different transmission conditions. [PERSON] and [PERSON] analyzed effective capacity of the BX fading channel for SISO systems in [4]; the capacity of this channel with a maximal ratio combining (MRC) receiver in [5]; the average bit error rate (ABER) of binary phase-shift keying (BPSK) and differential phase-shift keying (DPSK) in [6]; the ABER and the outage probability (Pout) of a selection combining (SC) receiver in [7]; the average symbol error probability (ASEP) of M-ary differential phase-shift keying (MDPSK), non-coherent M-ary frequency shift keying (MFSK), and coherent M-ary phase-shift keying (MPSK) in [8]; and the ASEP of generalized M-ary quadrature amplitude modulation (M-QAM) in [9]. Performance analysis of a dual-branch switch-and-stay combining (SSC) diversity receiver for a BX fading model is presented in [10]. The expressions of probability density function (PDF), cumulative distribution function (CDF), and moment-generating function (MGF) are derived.