Related papers: Machine Learning Techniques in Cognitive Radio Net…
Will AI/ML-defined radios become a reality in the near future? In this paper, we introduce the concept of an AI/ML-defined radio - a radio architecture specifically designed to support AI/ML-based optimization and decision-making in…
Federated learning becomes increasingly attractive in the areas of wireless communications and machine learning due to its powerful functions and potential applications. In contrast to other machine learning tools that require no…
We study the performance of cognitive radio networks (CRNs) when incorporating adaptive modulation at the physical layer. Three types of CRNs are considered, namely opportunistic spectrum access (OSA), spectrum sharing (SS) and…
As wireless networks evolve towards high mobility and providing better support for connected vehicles, a number of new challenges arise due to the resulting high dynamics in vehicular environments and thus motive rethinking of traditional…
In this paper, the performance of adaptive modulation in multi-user cognitive radio networks over fading channels is analyzed. Multi-user diversity is considered for opportunistic user selection among multiple secondary users. The analysis…
Predictable network performance is key in many low-power wireless sensor network applications. In this paper, we use machine learning as an effective technique for real-time characterization of the communication performance as observed by…
Machine learning provides automated means to capture complex dynamics of wireless spectrum and support better understanding of spectrum resources and their efficient utilization. As communication systems become smarter with cognitive radio…
Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks…
A learning machine, like all machines, is an open system driven far from thermal equilibrium by access to a low entropy source of free energy. We discuss the connection between machines that learn, with low probability of error, and the…
This work investigates online learning techniques for a cognitive radar network utilizing feedback from a central coordinator. The available spectrum is divided into channels, and each radar node must transmit in one channel per time step.…
Artificial neural networks are algorithms which have been developed to tackle a range of computational problems. These range from modelling brain function to making predictions of time-dependent phenomena to solving hard (NP-complete)…
This paper looks at various aspects of Machine Learning (ML) applications in wireless communication technologies, focusing mainly on fifth-generation (5G) and millimeter wave (mmWave) technologies. This paper includes the summaries of 3…
Network intelligence is a discipline that builds on the capabilities of network systems to act intelligently by the usage of network resources for delivering high-quality services in a changing environment. Wide area network intelligence is…
Multi-parameter cognition in a cognitive radio network (CRN) provides a more thorough understanding of the radio environments, and could potentially lead to far more intelligent and efficient spectrum usage for a secondary user. In this…
5G is designed to be an essential enabler and a leading infrastructure provider in the communication technology industry by supporting the demand for the growing data traffic and a variety of services with distinct requirements. The use of…
Artificial intelligence and machine learning paves the way to achieve greater technical feats. In this endeavor to hone these techniques, quantum machine learning is budding to serve as an important tool. Using the techniques of deep…
An important first step when deploying a wireless ad hoc network is neighbor discovery in which every node attempts to determine the set of nodes it can communicate with in one wireless hop. In the recent years, cognitive radio (CR)…
The era of Big Data is here now, which has brought both unprecedented opportunities and critical challenges. In this article, from a perspective of cognitive wireless networking, we start with a definition of Big Spectrum Data by analyzing…
We consider the problem of spectrum sharing in a cognitive radio system consisting of a primary user and a secondary user. The primary user and the secondary user work in a non-cooperative manner. Specifically, the primary user is assumed…
In the fifth generation (5G) of mobile broadband systems, Radio Resources Management (RRM) will reach unprecedented levels of complexity. To cope with the ever more sophisticated RRM functionalities and with the growing variety of…