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.

We consider a routing overlay in which the delay of a path can be obtained at some fixed cost by sending probe packets, and investigate the joint minimization of the probing cost and the routing delay. Assuming that link delays are modelled by Markov chains, this problem can be cast as a Markov Decision Process (MDP). Unfortunately, computing the exact solution of this MDP is prohibitively expensive due to the well-known "curse of dimensionality". In this work we propose two scalable approaches that are fast enough to provide efficient solutions on practical time scales. We analyze the complexity of both approaches, and evaluate their accuracy in small synthetic scenarios for which the optimal monitoring policy can be computed. Finally, the robustness and the scalability of the proposed solutions are analyzed using real delay data collected over the Internet.

Several countries in the world are undertaking an immensely challenging task: successfully perform the so-called "analogical blackout" of television. In Latin America in particular, the chosen transmission scheme is, in most cases, ISDB-T (the Japanese standard, later adapted by Brazil). Key to the success of this blackout is a thorough understanding of the technology of choice. The present paper intends to be a contribution to this understanding: an open, free and software-based ISDB-T 1-segment receiver. Such receiver may for instance be used to evaluate improvements to the standard (or different algorithms for the receivers), or even be used as a measurement tool (since one has access to the whole receiving chain). In addition to presenting our implementation, we discuss the technology that enables the software-based receiver: Software Defined Radio, and in particular the software framework GNU Radio.

Cognitive Radio Networks have emerged in the last years as a solution of two problems: spectrum underutilization and spectrum scarcity. The main idea is to dynamically re-allocate unused licensed frequency bands to secondary users. The focus of this work is on the analysis and characterization of a dynamic spectrum sharing mechanism where primary users have strict priority over secondary ones. We present some tools and criteria that can be used in order to improve the mean spectrum utilization with the commitment of providing to secondary users a satisfactory grade of service and a small interruption probability. Our proposal is based on the application of the fluid limit technique to analyze a stochastic complex system. We support our analysis with representative simulated examples.

This paper studies the problem of allocating network capacity through periodic auctions. We impose the following conditions: fully distributed solutions over an arbitrary network topology, and the requirement that resources allocated in a given auction are reserved for the entire duration of the connection, not subject to future contention. Under these conditions, we study the problem of selling capacity to optimize revenue for the operator. We first study optimal revenue for a single distributed auction in a general network. Next, the periodic auctions case is considered for a single link, modelling the optimal revenue problem as a Markov decision process (MDP); we develop a sequence of receding horizon approximations to its solution. Combining the two approaches we formulate a receding horizon optimization of revenue over a general network topology, that yields a distributed implementation. The proposal is demonstrated through simulations.

This paper describes an implementation, analysis and improvements to MATE (MPLS adaptive traffic engineering) algorithm. MATE is an on-line load balancing algorithm. As MATE was originally thought for constant average incoming traffic, the first improvement is the usage of an adaptive update step size for time varying traffic. The second one modifies the time interval between updates to adequate itself to the traffic characteristics and calculates the amount of measurements in each interval to have a reliable statistic. The original algorithm and the new version, which would be called MATE-TV (MATE for time-varying traffic), were implemented in a LINUX-MPLS network, using the 'mpls-linux' packet distributed by Source Forge.