A trace-based approach for modeling wireless channel behavior
WSC '96 Proceedings of the 28th conference on Winter simulation
A Markov-based channel model algorithm for wireless networks
MSWIM '01 Proceedings of the 4th ACM international workshop on Modeling, analysis and simulation of wireless and mobile systems
Mobile Radio Networks: Networking and Protocols
Mobile Radio Networks: Networking and Protocols
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Low complexity channel models for approximating flat rayleigh fading in network simulations
Low complexity channel models for approximating flat rayleigh fading in network simulations
Building a Better Wireless Mousetrap: Need for More Realism in Simulations
WONS '05 Proceedings of the Second Annual Conference on Wireless On-demand Network Systems and Services
DIMSUMNet: New Directions in Wireless Networking Using Coordinated Dynamic Spectrum Access
WOWMOM '05 Proceedings of the Sixth IEEE International Symposium on World of Wireless Mobile and Multimedia Networks
NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Dynamic spectrum access in WLAN channels: empirical model and its stochastic analysis
TAPAS '06 Proceedings of the first international workshop on Technology and policy for accessing spectrum
ICN '07 Proceedings of the Sixth International Conference on Networking
Efficient Discovery of Spectrum Opportunities with MAC-Layer Sensing in Cognitive Radio Networks
IEEE Transactions on Mobile Computing
Cognitive Wireless Networks: Concepts, Methodologies and Visions Inspiring the Age of Enlightenment of Wireless Communications
Spatial statistics of spectrum usage: from measurements to spectrum models
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: A POMDP framework
IEEE Journal on Selected Areas in Communications
MILCOM'09 Proceedings of the 28th IEEE conference on Military communications
Channel selection in spectrum agile and cognitive MAC protocols for wireless sensor networks
Proceedings of the 8th ACM international workshop on Mobility management and wireless access
Mobile Networks and Applications
On the feasibility of effective opportunistic spectrum access
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Cross-layer optimal spectrum sensing duration and scheduling in cognitive networks
CoRoNet '11 Proceedings of the 3rd ACM workshop on Cognitive radio networks
Discrete time analysis of cognitive radio networks with saturated source of secondary users
NETWORKING'11 Proceedings of the IFIP TC 6th international conference on Networking
An overview of spectrum occupancy models for cognitive radio networks
NETWORKING'11 Proceedings of the IFIP TC 6th international conference on Networking
Cognitive engine: design aspects for mobile clouds
Proceedings of the 4th International Conference on Cognitive Radio and Advanced Spectrum Management
Analytic performance evaluation of opportunistic spectrum access with detection errors
Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools
Measuring channel occupancy for 802.11 wireless LAN in the 2.4 GHz ISM band
Proceedings of the 15th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
The effectiveness of opportunistic spectrum access: a measurement study
IEEE/ACM Transactions on Networking (TON)
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Dynamic spectrum access (DSA) has been proposed as a solution to the spectrum scarcity problem. However, the models for spectrum use, that are commonly used in DSA research, are either limited in scope or have not been validated against real-life measurement data. In this paper we introduce a flexible spectrum use model based on extensive measurement results that can be configured to represent various wireless systems. We show that spectrum use is clustered in the frequency domain and should be modelled in the time domain using geometric or lognormal distributions. In the latter case the probability of missed detection is significantly higher due to the heavy-tailed behaviour of the lognormal distribution. The listed model parameters enable accurate modelling of primary user spectrum use in time and frequency domain for future DSA studies. Additionally, they also provide a more empirical basis to develop regulatory or business models.