Quasi Real-Time Intermodulation Interference Method: Analysis and Performance

Establishing interference-free wireless networks has become essential requirement with theadvent of 5G networks as wireless carriers becoming eager to start transmitting high volume of voice anddata to meet enterprise demands and consumers for dispersed information. Indeed, reduction of interferencelevel now emerges as one of the most viable solutions for the service provider networks, as 5G networksuse is increasingly on the rise. Therefore, a comprehensive interference solution is needed to enable serviceproviders (e.g., cellular, PCS, Wi-Fi and Broadband, and LTE wireless LAN/WAN services) to identify andresolve network interference quickly and reliably. However, currently existing methods and tools, used toconduct interference detection and analysis lack the necessary performance required in the RF engineeringand spectrum optimization fields, and hence, fast, and reliable networks. This paper presents a new highperformancemethod for detecting and mitigating intermodulation interference in various wireless networks.Clearly, reducing the level of interference in wireless communication contributes directly to having animproved and reliable signal to noise ratio (C/I). We will discuss the complexity of the intermodulationinterference problem. We will further compare the performance of the new approach with different existinginterference detection methods. We will show that the presented method intermodulation complexity isreduced to a near linear one, compared to the current non-linear methods

Simulation in the Cloud Using Handheld Devices

International audienceIn recent years, numerous applications have been deployed into mobile devices. However, until now, there have been no attempts to run simulations on handheld devices. We want investigate different architectures for running and managing simulations on handheld devices, and putting the simulation services in the Cloud. We propose a hybrid simulation and visualization approach, where a dedicated mobile application is running on the client side and the RISE simulation server is hosted in the Cloud. In particular, with our prototype, we explore the remote management of a simulation tool using a dedicated native application running on an Android Smartphone, and showing the evolution of a simulation model for a forest fire spread, mashing-up the generated graphics with online GIS services

Interfacing and Coordination for a DEVS Simulation Protocol Standard

We propose a flexible and scalable messageoriented mechanism allowing interoperability between different DEVS implementations towards DEVS standardization. The main objective of the proposed protocol is to enable different DEVS implementations to interface and coordinate among each other to simulate the same model structure across their diverse domains. To do so, the proposed simulation protocol uses SOAP-based Web-Services technology as the communication framework to exchange control and simulation messages. The objective is achievable with minimum design changes to each DEVS implementation, mainly by hiding the detailed implementation behind a wrapper and focusing only on the exchanged messages. 1

Fog and cloud collaboration to perform virtual simulation experiments

Fog Computing can enhance users’ quality of service, particularly when countless users spread around the globe to access the same Cloud resources. We present a set of collaboration mechanisms between Fog and Cloud computing resources to conduct simulation experiments. A user creates an experiment on a Fog server (with a model attached to it) without worrying where and how the simulation will be executed. Once a simulation starts, the experiment reach servers with simulation environments that can execute the model and then needs to select the best servers to perform the actual simulation. We introduce the concept of Virtual Experiments (VE) to decouple M&S environments specifics from the general experiment framework, providing interplay processing units between users and simulation servers. In addition, we present a Fog/Cloud scalable architecture, and discuss how the M&S capabilities are advertised, structured in pools, dynamically discovered, and selected to simulate. As a proof of concept, we built our concrete private Clouds and Fogs based on OpenStack to demonstrate the proposed ideas using various Fog and Cloud physical, deployment and computing capabilities