COGNITIVE RADIOS FOR DYNAMIC SPECTRUM ACCESS - The Path to Market Success for Dynamic Spectrum Access Technology

Chapin, John; Lehr, William

doi:10.1109/mcom.2007.358855

Cited by 154 publications

(96 citation statements)

References 2 publications

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“…Major areas for innovation concern spectrum policy and ways of increasing spectrum sharing -but these are topics for another paper. 41 41 See, for example, Chapin and Lehr (2007) or Lehr and Chapin (2009b). Figure 1.…”

Section: Discussionmentioning

confidence: 99%

“…For example, in a series of studies conducted by Shared Spectrum Company for the US National Science Foundation in 2004 and 2005 (http://www.sharedspectrum.com/measurements/), data showed that the average occupancy over all locations tested was 5.2% and that the maximum occupancy was in New York City at 13.1%. 5 See Chapin and Lehr (2007).…”

Section: Wired Versus Wireless Networkingmentioning

confidence: 99%

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On the convergence of wired and wireless access network architectures

Lehr

Chapin

2010

Information Economics and Policy

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Wired and wireless access networks continue to evolve toward higher-capacity, multi-service systems. Recent wireless broadband networks such as 3G LTE and WiMax provide a general-purpose IP platform with over-the-top services at the application layer, which is similar to the design of wired IP platform networks. This paper examines whether wired and wireless access networks are likely to converge on a common architecture, or if not, whether wireless networks are likely to converge on a common wireless architecture. We conclude that the answer to both questions is No. We identify fundamental and persistent differences between wired and wireless networking that will propel wired and wireless access network architectures on divergent evolutionary paths. Whereas we expect wired broadband access networks to continue to evolve toward a common general-purpose platform architecture, we expect wireless networks to remain heterogeneous. The inherent scarcity of radio frequency spectrum emerges as the key reason for this prediction. We examine the implications of divergent evolutionary paths for market structure and regulatory policy.

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Section: Discussionmentioning

confidence: 99%

Section: Wired Versus Wireless Networkingmentioning

confidence: 99%

On the convergence of wired and wireless access network architectures

Lehr

Chapin

2010

Information Economics and Policy

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“…To ameliorate such negative effects, a DSA approach called spectrum leasing has been proposed [24]. This approach allows PUs to temporarily transfer and trade the licensed spectrum usage rights of the white spaces to SUs in exchange for monetary compensation [25][26][27] or packets relaying [28,29]. To the best of our knowledge, the security features in spectrum leasing in CRNs is lacking and further security enhancements would need to be developed in order to provide a better measure of security.…”

Section: Application Of Rl-based Trm Model In Cogntive Radiomentioning

confidence: 99%

Reinforcement Learning-Based Trust and Reputation Model for Spectrum Leasing in Cognitive Radio Networks

Ling

Yau

2013

2013 International Conference on IT Convergence and Security (ICITCS)

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Abstract-Cognitive Radio (CR), which is the next generation wireless communication system, enables unlicensed users or Secondary Users (SUs) to exploit underutilized spectrum (called white spaces) owned by the licensed users or Primary Users (PUs) so that bandwidth availability improves at the SUs, which helps to improve the overall spectrum utilization. Collaboration, which has been adopted in various schemes such distributed channel sensing and channel access, is an intrinsic characteristic of CR to improve network performance. However, the requirement to collaborate has inevitably open doors to various forms of attacks by malicious SUs, and this can be addressed using Trust and Reputation Management (TRM). Generally speaking, TRM detects malicious SUs including honest SUs that turn malicious. To achieve a more efficient detection, we advocate the use of Reinforcement Learning (RL), which is known to be flexible and adaptable to the changes in operating environment in order to achieve optimal network performance. Its ability to learn and re-learn throughout the duration of its existence provides intelligence to the proposed TRM model, and so the focus on RL-based TRM model in this paper. Our preliminary results show that the detection performance of RLbased TRM model has an improvement of 15% over the traditional TRM in a centralized cognitive radio network. The investigation in the paper serves as an important foundation for future work in this research field.

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“…To prevent potential attackers from exploiting such vulnerability, each single rule should be signed with its creator's unique signature [54]. Rules not signed by a trusted authority should be immediately filtered out, while their respective sender can be black-listed.…”

Section: Fake Rules Injectionmentioning

confidence: 99%

Misbehavior Scenarios in Cognitive Radio Networks

et al. 2010

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Recent advances in the fields of Cognitive Radio and the proliferation of open spectrum access promise that spectrum-agile wireless communication will be widespread in the near future, and will bring significant flexibility and potential utility improvements for end users. With spectrum efficiency being a key objective, most relevant research focuses on smart coexistence mechanisms. However, wireless nodes may behave selfishly and should be considered as rational autonomous entities. Selfishness, pure malice or even faulty equipment can lead to behavior that does not conform to sharing protocols and etiquette. Thus, there is a need to secure spectrum sharing mechanisms against attacks in the various phases of the sharing process. Identifying these attacks and possible countermeasures is the focus of this work.

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COGNITIVE RADIOS FOR DYNAMIC SPECTRUM ACCESS - The Path to Market Success for Dynamic Spectrum Access Technology

Cited by 154 publications

References 2 publications

On the convergence of wired and wireless access network architectures

On the convergence of wired and wireless access network architectures

Reinforcement Learning-Based Trust and Reputation Model for Spectrum Leasing in Cognitive Radio Networks

Misbehavior Scenarios in Cognitive Radio Networks

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