In this paper we have analyzed macro-diversity (MD) system with one macro SC diversity (MD SC) receiver and two micro MRC (mD MRC) receivers over correlated Gamma-shadowed k-? fading channel. The average bit error probability (ABEP) is calculated using the moment generating function (MGF) approach for BDPSK and BPSK modulations. Graphical representation of the results illustrates the effects of different parameters of the system on its performance as well as the improvements due to the benefits of a combined micro and macro diversity. The obtained analytical expressions are used for the GPU-enabled mobile network modeling, planning and simulation environment to determine the value of Quality of Service (QoS) parameter. Finally, linear optimization is proposed as an approach to improve the QoS parameter of the fading-affected system observed in this paper.

Channel modeling is a first step towards the successful projecting of any wireless communication system. Hence, in this paper, we analyze the performance at the output of a multi-branch selection combining (SC) diversity receiver in a wireless environment that has been distracted by fading and co-channel interference (CCI), whereby the fading is modelled by newer Beaulieu-Xie (BX) distribution, and the CCI is modelled by the κ-µ distribution. The BX distribution provides the ability to include in consideration any number of line-of-sight (LOS) useful signal components and non-LOS (NLOS) useful signal components. This distribution contains characteristics of some other fading models thanks to its flexible fading parameters, which also applies to the κ-µ distribution. We derived here the expressions for the probability density function (PDF) and cumulative distribution function (CDF) for the output signal-to-co-channel interference ratio (SIR). After that, other performances are obtained, namely: outage probability (Pout), channel capacity (CC), moment-generating function (MGF), average bit error probability (ABEP), level crossing rate (LCR), and average fade duration (AFD). Numerical results are presented in several graphs versus the SIR for different values of fading and CCI parameters, as well as the number of input branches in the SC receiver. Then, the impact of parameters on all performance is checked. From our numerical results, it is possible to directly obtain the performance for all derived and displayed quantities for cases of previously known distributions of fading and CCI by inserting the appropriate parameter values. In the second part of the paper, a workflow for automated network experimentation relying on the synergy of Large Language Models (LLMs) and model-driven engineering (MDE) is presented, while the previously derived expressions are used for evaluation. Due to the aforementioned, the biggest value of the obtained results is the applicability to the cases of a large number of other distributions for fading and CCI by replacing the corresponding parameters in the formulas for the respective performances.

Since the beginning of this year, the novel large language model (LLM) based ChatGPT conversational agent has been in spotlight, due to its comprehensiveness across many fields – from novel writing to playing board games. In this paper, it is explored how it can be leveraged within IoT systems, taking into account both the novel scenarios enabled relying on ChatGPT’s power of question answering and software development as well. As example, two case studies related to Arduino platform are considered: 1) ChatGPT-based predictions on sensor data collected by Arduino 2) model-driven automated Arduino code generation.

Artificial intelligence has been identified as one of the main driving forces of innovation in state-of-the-art mobile and wireless networks. It has enabled many novel usage scenarios, relying on predictive models for increasing network management efficiency. However, its adoption requires additional efforts, such as mastering the terminology, tools, and newly required steps of data importing and preparation, all of which increase the time required for experimentation. Therefore, we aimed to automate the manual steps as much as possible while reducing the overall cognitive load. In this paper, we explore the potential use of a novel Chat Generative Pre-trained Transformer (ChatGPT) conversational agent together with a model-driven approach relying on the Neo4j graph database in order to aid experimentation and analytics in the case of wireless network planning. As a case study, we present a derivation of the expression for the channel capacity (CC) metric in the case of η-µ multipath fading and η-µ co-channel interference. Moreover, the derived expression is leveraged for quality of service (QoS) estimation within the software simulation environment. ChatGPT, in synergy with a model-driven approach, is used to automate several steps: data importing, generation of graph construction, and machine learning-related Neo4j queries. According to the achieved outcomes, the proposed QoS estimation method, based on the derived CC expression (with precision up to the fifth significant digit), demonstrates satisfactory accuracy (up to 98%) and faster training than the deep neural network-based solution. On the other hand, compared to the manual approach based on our previous work, ChatGPT-based code generation reduces the time required for experimentation by more than 4 times.