Deep Learning Based User Association in Heterogeneous Wireless Networks

Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks

This paper investigates the use of deep reinforcement learning (DRL) in the design of a “universal” MAC protocol referred to as Deep-reinforcement Learning Multiple Access (DLMA). The design framework is partially inspired by the vision of DARPA SC2, a 3-year competition whereby competitors are to come up with a clean-slate design that “best share spectrum with any network(s), in any environmen...

Detecting Overlapping Communities in Social Networks using Deep Learning

In network analysis, a community is typically considered of as a group of nodes with a great density of edges among themselves and a low density of edges relative to other network parts. Detecting a community structure is important in any network analysis task, especially for revealing patterns between specified nodes. There is a variety of approaches presented in the literature for overlapping...

Fingerprinting based localization in heterogeneous wireless networks

Transparent End-User Authentication Across Heterogeneous Wireless Networks

With the increasing number of mobile and wireless networks that are deployed, the handovers between these systems take place more often. In order to provide end-users with uninterrupted access to services, support for fast handover is essential. One of the prerequisites of a fast-handover is transparent end-user authentication. In this paper we address end-user authentication at network level. ...

Target Tracking Based on Virtual Grid in Wireless Sensor Networks

One of the most important and typical application of wireless sensor networks (WSNs) is target tracking. Although target tracking, can provide benefits for large-scale WSNs and organize them into clusters but tracking a moving target in cluster-based WSNs suffers a boundary problem. The main goal of this paper was to introduce an efficient and novel mobility management protocol namely Target Tr...