Model-Driven Approach to Fading-Aware Wireless Network Planning Leveraging Multiobjective Optimization and Deep Learning

Abstract

Efficient resource planning is recognized as one of the key enablers making the large-scale deployment of next-generation wireless networks available for mass usage. Modelling, planning, and software simulation tools reduce both the time needed and costs of their tuning and realization. In this paper, we propose a model-driven framework for proactive network planning relying on synergy of deep learning and multiobjective optimization. The predictions about service demand and energy consumption are taken into account. Also, the impact of degradations resulting from fading and cochannel interference (CCI) effects is also considered. The optimization task is treated as a component allocation problem (CAP) aiming to find the best possible base station allocation for the considered smart city locations with respect to performance and service demand constraints. The goal is to maximize Quality of Service (QoS) while keeping the costs and energy consumption as low as possible. The adoption of a model-driven approach in combination with model-to-model transformations and automated code generation does not only reduce the complexity, making experimentation more rapid and convenient at the same time, but also increase the overall reusability and expandability of the planning tool. According to the obtained results, the proposed solution seems to be promising not only due to achieved benefits but also regarding the execution time, which is shorter than that achieved in our previous works, especially for larger distances. Further, we adopt model-based representation of handover strategies within the planning tool, enabling examination of the dynamic behavior of user-created plan, which is not exploited in other similar works. The main contributions of the paper are (1) wireless network planning (WNP) metamodel, a modelling notation for network plans; (2) model-to-model transformation for conversion of WNP to generalized CAP metamodel; (3) prediction problem (PP) metamodel, high-level abstraction for representation of prediction-related regression and classification problems; (4) code generator that creates PyTorch neural network from PP representation; (5) service demand and energy consumption prediction modules performing regression; (6) multiobjective optimization model for base station allocation; (7) Handover Strategy (HS) metamodel used for description of dynamic aspects and adaptability relevant to network planning.

MDE and LLM Synergy for Network Experimentation: Case Analysis of Wireless System Performance in Beaulieu-Xie Fading and κ-µ Co-Channel Interference Environment with Diversity Combining

“…Taking into account the experiments carried out by curious researchers and enthusiasts around the world, it can be summarized that LLMs are able to cover various relevant aspects within the generation of computer applications and the software development process itself, as well. Among these adoptions, some of them, besides LLMs, rely on their synergy with model-driven engineering (MDE) [36], making many innovative usage scenarios possible [36][37][38]: (1) domain conceptualization-metamodel construction based on free-form textual information; (2) instance creation-metamodel and natural language text are used as inputs, while the target output is instance of a model with respect to that metamodel; (3) modeling constraint extraction-identification of rules that must hold within model instances, where inputs are these constraints in textual form, along with the given metamodel, while the outputs are formal logic rules, such as Object Constraints Language (OCL); (4) generation of code-code templates together with model instances are taken as inputs and used for the purpose of generating executable program code, targeting some specific platform or programming language.…”

“…Considering the previously mentioned LLM and MDE synergy use cases, in this paper, we adopt these techniques with the goal of reducing the overall cognitive load and effort needed for wireless network planning and experimentation. Due to the increasing complexity of infrastructure, besides the growing number of the involved devices and their heterogeneous nature, the process of next-generation network-related prototyping and experimentation is highly challenging task [36,38]. For that reason, in this paper, we used an approach leveraging MDE tools (Eclipse Ecore (https://eclipse.dev/modeling/ emf/, accessed on 8 April 2024) and Object Constraints Language-OCL (https://www.…”

“…Additionally, in order to speed up such calculations, a GPU-enabled approach is adopted introducing loop-based computation parallelization, building upon our past works presented in [36,38]. With the aim to achieve this, the following prompt for parametrizing experiment script leveraging model instance is executed: First, the user provides text describing the desired configuration of network planning experiment using natural language text, covering also constraints, such as design limitations and aspects related to performance.…”

“…Additionally, in order to speed up such calculations, a GPU-enabled approach is adopted introducing loop-based computation parallelization, building upon our past works presented in [36,38]. With the aim to achieve this, the following prompt for parametrizing experiment script leveraging model instance is executed:…”

Combining Receiver Damaged by Beaulieu-Xie Fading and Rician Co-Channel Interference with a Purpose of Machine Learning QoS Level Prediction

“…Predictive models that leverage machine learning techniques are among the enablers when it comes to innovative use cases and novel functionalities within stateof-the-art wireless and mobile networks. Notable scenarios include network load prediction, anomaly detection, adaptive QoS adjustment [21]. This paper proposes an approach to determination of QoS level based on supervised machine learning classification methods, taking into account the previously derived LCR value as one of the inputs among the other factors: 1) number of service users within area of interest; 2) identifier of base station responsible for providing service in that area; 3) identifier of area under consideration 4) day of week.…”