A Cognitive Cross-Layer Architecture for Next-Generation Tactical Networks

Ci, Song; Sonnenberg, Jerome

doi:10.1109/milcom.2007.4455117

Cited by 9 publications

(3 citation statements)

References 31 publications

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“…Also, it can protect the exchanged control information over the channel from predictable control channel hopping sequences, thereby preventing itself from being saturated [13,92]. Since the layers within CRNs have their own characteristics and parameters [74,93], they are vulnerable and allow an attacker to make a decision to launch a specific attack for the purpose of degrading the whole network performance. In MAC layer frames, an adversary has a variety of aims for misbehaving and launching such an attack.…”

Section: C) Dynamic Local CCCmentioning

confidence: 99%

Spectrum Sharing Security and Attacks in CRNs: a Review

Alhakami¹,

Mansour²,

Safdar³

2014

IJACSA

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Abstract-Cognitive Radio plays a major part in communication technology by resolving the shortage of the spectrum through usage of dynamic spectrum access and artificial intelligence characteristics. The element of spectrum sharing in cognitive radio is a fundamental approach in utilising free channels. Cooperatively communicating cognitive radio devices use the common control channel of the cognitive radio medium access control to achieve spectrum sharing. Thus, the common control channel and consequently spectrum sharing security are vital to ensuring security in the subsequent data communication among cognitive radio nodes. In addition to well known security problems in wireless networks, cognitive radio networks introduce new classes of security threats and challenges, such as licensed user emulation attacks in spectrum sensing and misbehaviours in the common control channel transactions, which degrade the overall network operation and performance. This review paper briefly presents the known threats and attacks in wireless networks before it looks into the concept of cognitive radio and its main functionality. The paper then mainly focuses on spectrum sharing security and its related challenges. Since spectrum sharing is enabled through usage of the common control channel, more attention is paid to the security of the common control channel by looking into its security threats as well as protection and detection mechanisms. Finally, the pros and cons as well as the comparisons of different CR-specific security mechanisms are presented with some open research issues and challenges.

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Section: C) Dynamic Local CCCmentioning

confidence: 99%

Spectrum Sharing Security and Attacks in CRNs: a Review

Alhakami¹,

Mansour²,

Safdar³

2014

IJACSA

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“…Therefore, system performance at the upper layers is usually ignored. For example, some cross-layer ideas about cognitive wireless networks were mentioned in [3], [4]. The authors have proposed a cross-layer based framework where each node in the network can sense and learn from the wireless environment and then respond the environment changes by adapting system parameters and network services based on the learned results.…”

Section: Introductionmentioning

confidence: 99%

Cross-Layer Design for Real-Time Video Transmission in Cognitive Wireless Networks

Luo

et al. 2010

2010 INFOCOM IEEE Conference on Computer Communications Workshops

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Cognitive Radio (CR) technologies have been proposed as a promising solution to improve connectivity, selfadaptability, and efficiency of spectrum usage by using dynamic spectrum resource management. When used in multimedia applications in wireless networks, the end-to-end user-perceived video quality experienced by secondary users is a very important performance metric to evaluate the effectiveness of CR technologies. However, most of the current research activities consider only spectrum utilization and effectiveness, ignoring the system performance of upper layers. In this paper, we propose an end-to-end system to optimize the user-perceived video quality at the receiver end under the constraint of packet delay bound in cognitive wireless networks. By mathematically formulating the design objective and the design constraint, the impact of inextricably interrelated system parameters residing in different network layers are jointly considered and optimized in a holistic way to achieve the best-possible user-perceived video quality for secondary users. Further, we formulate the problem into a MIN-MAX problem, which is then solved by using dynamic programming. The performance enhancement by using the proposed system is demonstrated through extensive experiments based on H.264/AVC.

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“…For instance, the spectrum management decision to switch to a better channel needs to be balanced against the brief communications interruption it will cause, termed spectrum handoff. For this reason researchers are considering a cross-layer approach to DSAN architecture design, particularly to supply a certain Quality of Service (QoS) [23] [94].…”

Section: Dynamic Spectrum Access Networkmentioning

confidence: 99%

Analysis and Implementation of Reinforcement Learning on a GNU Radio Cognitive Radio Platform

Yu¹

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<p>Spectrum today is regulated based on fixed licensees. In the past radio operators have been allocated a frequency band for exclusive use. This has become problem for new users and the modern explosion in wireless services that, having arrived late find there is a scarcity in the remaining available spectrum. Cognitive radio (CR) presents a solution. CRs combine intelligence, spectrum sensing and software reconfigurable radio capabilities. This allows them to opportunistically transmit among several licensed bands for seamless communications, switching to another channel when a licensee is sensed in the original band without causing interference. Enabling this is an intelligent dynamic channel selection strategy capable of finding the best quality channel to transmit on that suffers from the least licensee interruption. This thesis evaluates a Q-learning channel selection scheme using an experimental approach. A cognitive radio deploying the scheme is implemented on GNU Radio and its performance is measured among channels with different utilizations in terms of its packet transmission success rate, goodput and interference caused. We derive similar analytical expressions in the general case of large-scale networks. Our results show that using the Q-learning scheme for channel selection significantly improves the goodput and packet transmission success rate of the system.</p>

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A Cognitive Cross-Layer Architecture for Next-Generation Tactical Networks

Cited by 9 publications

References 31 publications

Spectrum Sharing Security and Attacks in CRNs: a Review

Spectrum Sharing Security and Attacks in CRNs: a Review

Cross-Layer Design for Real-Time Video Transmission in Cognitive Wireless Networks

Analysis and Implementation of Reinforcement Learning on a GNU Radio Cognitive Radio Platform

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