This paper introduces PyWiSim, a Python-based simulation framework designed for wireless systems that falls somewhere between a link simulator and a system simulator. Link simulators model all communication layers in detail, making large-scale simulations computationally expensive. On the other hand, system simulators typically perform throughput calculations for a given simulation scenario, allowing simulations with many devices but providing little detailed information. With this compromise between these two classes of simulators, PyWiSim seeks a simulator that enables simulations with a large number of devices but modeling the most relevant aspects of the system with a certain level of detail. This framework is well-documented and allows for the easy addition of new wireless channel models, traffic generators, scheduling algorithms, etc. Being built in Python—a language widely used in artificial intelligence (AI) applications—PyWiSim facilitates the natural integration of AI-based algorithms into wireless simulations. To demonstrate this versatility, we present an example of a scheduler developed using deep reinforcement learning, specifically the Deep Q-Network (DQN) algorithm. It natively supports multislice, a fundamental feature of modern networks like 5G, and provides a flexible architecture that allows extensions to various wireless technologies, as demonstrated in this paper. Finally, we also present some graphical results obtained from PyWiSim to illustrate its capabilities.

This article describes the integration of two software tools: Py5cheSim and DeepMIMO. The former is a Pythonbased 5G mobile network simulator specifically designed for resource allocation algorithm evaluation. It stands out as one of the few open-source simulators capable of emulating network slicing at the radio resource level, allowing the assessment of resource allocation algorithms at both slice and user levels within them. On the other hand, DeepMIMO is a data generator for mmWave/massive MIMO channels. The main contribution of this work is the integration of both systems, allowing the simulation of realistic scenarios in 5G networks. As a result of this integration, a new version of Py5cheSim was obtained. Leveraging the advantages of Py5cheSim's implementation for new resource allocation algorithms, a new MIMO-based algorithm was incorporated into the tool (functionality that was only rudimentarily supported in Py5cheSim v1.0). It is worth noting that this new development is fully compatible with the previous version of the base network simulator, ensuring easy adoption of the new features by the community. The obtained results demonstrate a significant improvement in simulation accuracy and a greater capacity to represent the challenges of 5G networks in real scenarios.

The fifth generation of mobile communications (5G) is the new 3GPP technology designed to solve a wide range of requirements. On the one hand, it must be able to support high bit rates and ultra-low latency services, and on the other hand, it should be able to connect a massive amount of devices with loose bandwidth and delay requirements. In this context, as scheduling is always a delicate vendor topic and there are not so many free and complete simulation tools to support all 5G features, in this paper we present Py5cheSim. Py5cheSim is a flexible and open-source simulator based on Python and specially oriented to simulate cell capacity in 3GPP 5G networks and beyond. To the best of our knowledge, Py5cheSim is the first simulator that supports Network Slicing at the Radio Access Network (RAN), one of the main innovations of 5G. The present work describes its design and implementation choices and the principal validation results. Finally, as another contribution, we present an exhaustive analysis of the existing available simulation tools highlighting the novelty of Py5cheSim comparing with the others existing simulation software for 5G.