Related papers: Machine Learning Techniques in Cognitive Radio Net…
The emergence and continued reliance on the Internet and related technologies has resulted in the generation of large amounts of data that can be made available for analyses. However, humans do not possess the cognitive capabilities to…
In order to increase productivity, capability, and data exploitation, numerous defense applications are experiencing an integration of state-of-the-art machine learning and AI into their architectures. Especially for defense applications,…
In this contribution, we investigate the idea of using cognitive radio to reuse locally unused spectrum to increase the total system capacity. We consider a multiband/wideband system in which the primary and cognitive users wish to…
How useful can machine learning be in a quantum laboratory? Here we raise the question of the potential of intelligent machines in the context of scientific research. A major motivation for the present work is the unknown reachability of…
Machine learning can help us in solving problems in the context big data analysis and classification, as well as in playing complex games such as Go. But can it also be used to find novel protocols and algorithms for applications such as…
Machine learning methods adapt the parameters of a model, constrained to lie in a given model class, by using a fixed learning procedure based on data or active observations. Adaptation is done on a per-task basis, and retraining is needed…
In this work, we examine cognitive radio networks, where secondary users may act as relays for messages sent by the primary user, hence offering performance improvement of primary transmissions, while at the same time obtaining more…
Machine learning (ML) applications for wireless communications have gained momentum on the standardization discussions for 5G advanced and beyond. One of the biggest challenges for real world ML deployment is the need for labeled signals…
Underlay cognitive radios (UCRs) allow a secondary user to enter a primary user's spectrum through intelligent utilization of multiuser channel quality information (CQI) and sharing of codebook. The aim of this work is to study two-user…
The concept of CogRF, a novel tunable radio frequency (RF) frontend that uses artificial intelligence (AI) to meet mission requirements for beyond 5G and 6G systems, is introduced. CogRF utilizes AI as the core to control and operate RF…
The idea of end-to-end learning of communication systems through neural network-based autoencoders has the shortcoming that it requires a differentiable channel model. We present in this paper a novel learning algorithm which alleviates…
Communication protocols are the languages used by network nodes. Before a user equipment (UE) can exchange data with a base station (BS), it must first negotiate the conditions and parameters for that transmission. This negotiation is…
It is widely perceived that leveraging the success of modern machine learning techniques to mobile devices and wireless networks has the potential of enabling important new services. This, however, poses significant challenges, essentially…
Subcarrier assignment is of crucial importance in wideband cognitive radio (CR) networks. In order to tackle the challenge that the traditional optimization-based methods are inappropriate in the dynamic spectrum access environment, an…
With the advancement of communication, the spectrum shortage problem becomes a serious problem for future generations. The cognitive radio technology is proposed to address this concern. In cognitive radio networks, the secondary users can…
Quantum Machine Learning is where nowadays machine learning meets quantum information science. In order to implement this new paradigm for novel quantum technologies, we still need a much deeper understanding of its underlying mechanisms,…
The Internet of Things (IoT) is expected to require more effective and efficient wireless communications than ever before. For this reason, techniques such as spectrum sharing, dynamic spectrum access, extraction of signal intelligence and…
Spectrum prediction is considered to be a promising technology that enhances spectrum efficiency by assisting dynamic spectrum access (DSA) in cognitive radio networks (CRN). Nonetheless, the highly nonlinear nature of spectrum data across…
As the scarce spectrum resource is becoming over-crowded, cognitive radios (CRs) indicate great flexibility to improve the spectrum efficiency by opportunistically accessing the authorized frequency bands. One of the critical challenges for…
Active learning methods are rapidly becoming the integral component of automated experiment workflows in imaging, materials synthesis, and computation. The distinctive aspect of many experimental scenarios is the presence of multiple…