Related papers: Simulating Dense Small Cell Networks
Today by growing network systems, security is a key feature of each network infrastructure. Network Intrusion Detection Systems (IDS) provide defense model for all security threats which are harmful to any network. The IDS could detect and…
How would a cellular network designed for maximal energy efficiency look like? To answer this fundamental question, tools from stochastic geometry are used in this paper to model future cellular networks and obtain a new lower bound on the…
Deep neural networks (DNNs) have a wide range of applications, and software employing them must be thoroughly tested, especially in safety-critical domains. However, traditional software test coverage metrics cannot be applied directly to…
Software Defined Networks offer flexible and intelligent network operations by splitting a traditional network into a centralized control plane and a programmable data plane. The intelligent control plane is responsible for providing flow…
Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning (DL) is already present in many applications ranging from computer vision for medicine to autonomous driving of modern cars as well as other sectors in…
Recently, Deep Neural Networks (DNNs) have emerged as the dominant model across various AI applications. In the era of IoT and mobile systems, the efficient deployment of DNNs on embedded platforms is vital to enable the development of…
Graph Neural Networks (GNNs) have recently been explored as surrogate models for numerical simulations. While their applications in computational fluid dynamics have been investigated, little attention has been given to structural problems,…
Spiking neural networks (SNNs) have closer dynamics to the brain than current deep neural networks. Their low power consumption and sample efficiency make these networks interesting. Recently, several deep convolutional spiking neural…
Nowadays, Software Defined Network (SDN) architectures and applications are revolutionizing the way wired networks are built and operate. However, little is known about the potential of this disruptive technology in wireless mobile…
We develop a framework for downlink heterogeneous cellular networks with line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions. Using stochastic geometry, we derive tight approximation of achievable downlink rate that enables us to…
The ability to construct a realistic simulator of financial exchanges, including reproducing the dynamics of the limit order book, can give insight into many counterfactual scenarios, such as a flash crash, a margin call, or changes in…
Wireless Sensor Networks (WSNs) are highly distributed networks consisting of a large number of tiny, low-cost, light-weight wireless nodes deployed to monitor an environment or a system. Each node in a WSN consists of three subsystems: the…
In this work we present a prototype for simulating computer network attacks. Our objective is to simulate large networks (thousands of hosts, with applications and vulnerabilities) while remaining realistic from the attacker's point of…
High-performance scientific simulations, important for comprehension of complex systems, encounter computational challenges especially when exploring extensive parameter spaces. There has been an increasing interest in developing deep…
The emerging Software Defined Networking (SDN) paradigm separates the data plane from the control plane and centralizes network control in an SDN controller. Applications interact with controllers to implement network services, such as…
Discrete choice models (DCMs) have been widely utilized in various scientific fields, especially economics, for many years. These models consider a stochastic environment influencing each decision maker's choices. Extensive research has…
Cellular traffic forecasting is a critical task that enables network operators to efficiently allocate resources and address anomalies in rapidly evolving environments. The exponential growth of data collected from base stations poses…
Development of fast methods to conduct in silico experiments using computational models of cellular signaling is a promising approach toward advances in personalized medicine. However, software-based cellular network simulation has…
This short report describes the scaling, up to 1024 software processes and hardware cores, of a distributed simulator of plastic spiking neural networks. A previous report demonstrated good scalability of the simulator up to 128 processes.…
In this paper we present new developments in the expressiveness and in the theory of a Calculus for Sensor Networks (CSN). We combine a network layer of sensor devices with a local object model to describe sensor devices with state. The…