Modeling of RTS noise in MOSFETs under steady-state and large-signal excitation

The behavior of RTS noise in MOSFETs under large-signal excitation is experimentally studied. Our measurements show a significant transient effect, in line with earlier reports. We present a new physical model to describe this transient behavior and to predict RTS noise in MOSFETs under large-signal excitation. With only three model parameters the behavior is well described, contrary to existing models

Spectrum occupancy measurements and lessons learned in the context of cognitive radio

Various measurement campaigns have shown that numerous spectrum bands are vacant even though licenses have been issued by the regulatory agencies. Dynamic spectrum access (DSA) based on Cognitive Radio (CR) has been regarded as a prospective solution to improve spectrum utilization for wireless communications. Empirical measurement of the radio environment to promote understanding of the current spectrum usage of the different wireless services is the first step towards deployment of future CR networks. In this paper we present our spectrum measurement setup and discuss lessons learned during our measurement activities. The main contribution of the paper is to introduce global spectrum occupancy measurements and address the major drawbacks of previous spectrum occupancy studies by providing a unifying methodological framework for future spectrum measurement campaigns

Broadband Spectrum Survey Measurements for Cognitive Radio Applications

It is well known that the existing spectrum licensing system results in a gross under-utilization of the frequency spectrum. Spectrum background measurements – spectrum surveys – provide useful data for spectrum regulation, planning or finding frequency niches for spectrum sharing. Dynamic spectrum sharing as a main goal of cognitive radio (CR) is the modern option on how to optimize usage of the frequency spectrum. A spectrum survey measurement system is introduced with results obtained from a variety of markedly different scenarios allowing us, unlike other studies, to focus on wideband and fast spectrum scans. The sensitivity of the receiver is no worse than -113 dBm in the whole band. The utilization of the frequency spectrum is analyzed to prove its under-utilization and to show spectrum sharing opportunities. This was shown to be true in the frequency band higher than 2.5 GHz. A comparison with other spectrum survey campaigns is provided

Deep Learning Meets Cognitive Radio: Predicting Future Steps

Learning the channel occupancy patterns to reuse the underutilised spectrum frequencies without interfering with the incumbent is a promising approach to overcome the spectrum limitations. In this work we proposed a Deep Learning (DL) approach to learn the channel occupancy model and predict its availability in the next time slots. Our results show that the proposed DL approach outperforms existing works by 5%. We also show that our proposed DL approach predicts the availability of channels accurately for more than one time slot

A survey of measurement-based spectrum occupancy modeling for cognitive radios

Spectrum occupancy models are very useful in cognitive radio designs. They can be used to increase spectrum sensing accuracy for more reliable operation, to remove spectrum sensing for higher resource usage efficiency, or to select channels for better opportunistic access, among other applications. In this survey, various spectrum occupancy models from measurement campaigns taken around the world are investigated. These models extract different statistical properties of the spectrum occupancy from the measured data. In addition to these models, spectrum occupancy prediction is also discussed, where autoregressive and/or moving-average models are used to predict the channel status at future time instants. After comparing these different methods and models, several challenges are also summarized based on this survey

Low-Frequency Noise Phenomena in Switched MOSFETs

In small-area MOSFETs widely used in analog and RF circuit design, low-frequency (LF) noise behavior is increasingly dominated by single-electron effects. In this paper, the authors review the limitations of current compact noise models which do not model such single-electron effects. The authors present measurement results that illustrate typical LF noise behavior in small-area MOSFETs, and a model based on Shockley-Read-Hall statistics to explain the behavior. Finally, the authors treat practical examples that illustrate the relevance of these effects to analog circuit design. To the analog circuit designer, awareness of these single-electron noise phenomena is crucial if optimal circuits are to be designed, especially since the effects can aid in low-noise circuit design if used properly, while they may be detrimental to performance if inadvertently applie

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs