Dynamic spectrum access and management [Dynamic Spectrum Management

Jabbari, Bijan; Pickholtz, R.L.; Norton, Mark

doi:10.1109/mwc.2010.5547916

Cited by 48 publications

(19 citation statements)

References 48 publications

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“…Spectrum sharing or dynamic spectrum access techniques have been proposed to address the spectrum inefficiency problem inherent in current static channel allocation policies. Radio networks that employ such techniques are generally referred to as cognitive radio networks [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Long-Term Spectrum Monitoring with Big Data Analysis and Machine Learning for Cloud-Based Radio Access Networks

Baltiiski

Iliev

Kehaiov

et al. 2015

Wireless Pers Commun

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Spectrum monitoring is important for efficient spectrum sharing and resource management in cloud-based radio access networks (C-RAN). In this paper we show how data obtained from long-term spectrum monitoring together with machine learning (ML) operating on big data (BD) can be used in a C-RAN scenario for spectrum management purposes. We propose an approach for spectrum occupancy forecasting which can be used to reduce the delay in making dynamic spectrum allocation decisions and improve the cognitive and management functionalities of cloud-based architectures such as C-RAN. The spectrum occupancy and usage activity in a predefined frequency band is based on the statistical processing of a large amount of collected data and the introduction of a frequency-time resources indicator as a measure of spectrum usage. Furthermore, we apply ML algorithms to predict spectrum usage and compare the predicted with actual measured data. Taking into consideration that the accuracy of the prediction depends on the volume of collected data and the time of prediction on the BD and ML approach, we propose the development of a cloud-based generic processing architecture to solve the ''accuracy versus latency'' trade-off problem. The proposed architecture is appropriate for deployment in cognitive C-RAN.

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Section: Introductionmentioning

confidence: 99%

Long-Term Spectrum Monitoring with Big Data Analysis and Machine Learning for Cloud-Based Radio Access Networks

Baltiiski

Iliev

Kehaiov

et al. 2015

Wireless Pers Commun

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“…Spectrum renting is a technique to relieve the temporary capacity shortage of a specific service area in wireless cellular networks, which have been intensively researched by the telecommunication industry in recent years (Buddhikot, 2007;Gandhi et al, 2008;Peha, 2009;Jabbari et al, 2010). Some queueing models for spectrum renting were proposed.…”

Section: Introductionmentioning

confidence: 99%

A New Finite-Source Queueing Model for Mobile Cellular Networks Applying Spectrum Renting

Wüchner

Bérczes

et al. 2014

Asia Pac. J. Oper. Res.

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This paper proposes a retrial queueing model with the finite number of sources to evaluate the performance of spectrum renting in mobile cellular networks. The model incorporates necessary ingredients such as the finite number of subscribers, their impatience and a queue for the outbound service. To consider the specific feature of spectrum renting and the current mobile cellular technology, a variable number of servers that are switched on and off in groups is introduced. We present a novel way to take into account the renting * Corresponding author. 1440004-1 2nd ReadingJanuary 21, 2014 9:50 WSPC/S0217-5959 APJOR 1440004.tex T. V. Do et al.fee, which can be used to fine-tune the operation of the spectrum renting procedure. Numerical results show that it is still profitable to initiate a spectrum renting request at high loads, even if no discount is offered by the frequency bands' owners.

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“…In hierarchical cognitive radio networks, the design of an efficient distributed medium access scheme for secondary users (SUs) is challenging due to the conflicts with licensed primary users (PUs), and hence, has received considerable attention [1]- [3]. When studying medium access design in a cognitive radio network, a key concern is how to manage spectrum sharing among SUs in a distributed manner.…”

Section: Introductionmentioning

confidence: 99%

Distributed Learning-Based Spectrum Allocation with Noisy Observations in Cognitive Radio Networks

Derakhshani

Le‐Ngoc

2014

IEEE Trans. Veh. Technol.

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Abstract-This paper studies the medium access design for secondary users (SUs) from a game-theoretic learning perspective. In consideration of the random return of primary users, a distributed SU access approach is presented based on an adaptive CSMA scheme, in which each SU accesses multiple idle frequency slots of a licensed frequency band with adaptive activity factors. The problem of finding optimal activity factors of SUs is formulated as a potential game, and the existence, feasibility and optimality of Nash Equilibrium (NE) are analyzed. Furthermore, to achieve NEs of the formulated game, learning-based algorithms are developed in which each SU independently adjusts its activity factors. Convergence properties of best-response dynamics and log-linear dynamics are studied. Subsequently, by learning other SUs' behavior from locally available information, the convergence with probability 1 to an arbitrarily small neighborhood of the globally optimal solution is investigated by both analysis and simulation.

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Dynamic spectrum access and management [Dynamic Spectrum Management

Cited by 48 publications

References 48 publications

Long-Term Spectrum Monitoring with Big Data Analysis and Machine Learning for Cloud-Based Radio Access Networks

Long-Term Spectrum Monitoring with Big Data Analysis and Machine Learning for Cloud-Based Radio Access Networks

A New Finite-Source Queueing Model for Mobile Cellular Networks Applying Spectrum Renting

Distributed Learning-Based Spectrum Allocation with Noisy Observations in Cognitive Radio Networks

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