Search

Operators of modern mobile networks are faced with significant challenges in providing the requested level of service to an ever increasing number of user entities. Advanced machine learning techniques based on deep architectures and appropriate learning methods are recognized as promising ways of tackling the said challenges in many aspects of mobile networks, such as mobile data and mobility analysis, network control, network security and signal processing. Having firstly presented the background of deep learning and related technologies, the paper goes on to present the architectures used for deployment of deep learning in mobile networks. The paper continues with an overview of applications and services related to the new generation of mobile networks that employ deep learning methods. Finally, the paper presents practical use case of modulation classification as implementation of deep learning in an application essential for modern spectrum management. We complete this work by pinpointing future directions for research.

The concept of Smart City started its development path around two to three decades ago; it has been mainly influenced and driven by radical changes in technological, social and business environments. Big Data, Internet of Things and Networked Cyber-Physical Systems, together with the concepts of Cloud, Fog and Edge Computing, have tremendous impact on the development of Smart City, reforming its frame and tasks and redefining its requirements and challenges. We consider feasible architectures of the IT infrastructure and signal processing, taking into account aspects of Big Data, followed by summary of benefits and main challenges, like security of infrastructure and private data. As a practical example we present a public safety application of multi-sensor imaging system: a smart device with target detection subsystem based on artificial intelligence used for activation of target tracking. The experiments have been performed in the cities of Abu Dhabi and Belgrade, which have very different environment. The experiments have shown the effects of videostreaming compression on thermal imagers and the importance of distributed processing power that optimizes requirements for amount of transmitted data and delay.

This paper is described different strategies for traffic control and simulation, namely - Vehicle Actuated (VA) compared to Fixed Time (FT) traffic control. We are comparing and evaluating the impact of FT and adaptive traffic signal control strategies for one of the biggest and busiest intersections in Tashkent, the capital of Uzbekistan. The parameters used for comparing these strategies are the average vehicle travel times and average vehicle queue lengths. TRL TRANSYT 15 software was used to determine the fixed signal timing plan based on intersection geometry, inter-green matrix, and traffic flow. PTV VISSIM software is used for VA algorithm development and simulation to get the maximum and average vehicle travel times as well as queue lengths. It is shown that the parameter optimization can reduce the queue lengths down to 65% and the travel time down to 51%. [ABSTRACT FROM AUTHOR]

Copyright of InfoM is the property of Belgrade University, Faculty of Organizational Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of InfoM is the property of Belgrade University, Faculty of Organizational Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of InfoM is the property of Belgrade University, Faculty of Organizational Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)