Distributed Robust Channel Assignment for Multi-Radio Cognitive Radio Networks

Ahmadi, Maryam; Zhuang, Yanyan; Pan, Jianping

doi:10.1109/vtcfall.2012.6399203

Cited by 12 publications

(15 citation statements)

References 18 publications

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“…(j) Robust interference minimizing channel assignment (RIMCA): The RIMCA [125] is proposed to utilize the multiple available channels and to minimize the interference among the SUs and the interference to the PUs. The SUs perform the channel selection considering the available local information obtained from either sensing or from the neighboring nodes.…”

Section: ) Crahnmentioning

confidence: 99%

“…Channel Selection Characteristics and Requirement-based Comparison Table III shows comparison of the channel assignment algorithms based on the channel selection characteristics and requirements. The parameters Segment-based CA [122] n/a n/a ACO-based CA [126] n/a n/a Centralized and distributed CA [123] n/a n/a MRF-based CA [124] RMCA [118] Path-centric CA [132] Cross-layer design [117] Risk-based CA [44] Dynamic source routing [128] Joint power CA [129] Maximum flow segment-based CA [131] Throughput-oriented CA [42] Effective CA [127] Genetic algorithm-based CA [130] Distance-and traffic-aware CA [34] Spectrum-aware CA [35] RIMCA [125] Probabilistic CA [133] Receiver-centric CA [33] Access contention resolution [116] CCAR [134] Joint link & CA routing optimization [135] Cross-layer optimization framework [136] Transmission opportunity-based CA [137] n/a n/a n/a n/a CA for CSS [43] Downlink CA [138] QoS-aware CA [57] n/a n/a Residual energy-aware CA [139] Dynamic CA [140] Cognitive dynamic fair CA [142] n/a n/a Q-learning-based CA [56] n/a n/a Uplink CA [146] n/a n/a ZAP [147] CoSAP [143] Unified CA [148] CA and routing [145] n/a n/a Route tree-based CA [144] n/a n/a CARD …”

Section: ) Cwlanmentioning

confidence: 99%

“…The algorithms presented in [56], [57], [122], [123], [126], [136], [138], [142], [144]- [146] use the inband signaling where data and control information travel on a data channel. The channel assignment algorithms proposed in [31]- [35], [42]- [44], [116]- [118], [124], [125], [127]- [135], [139], [140], [143], [147], [148] implement out-of-band signaling.…”

Section: ) Cwlanmentioning

confidence: 99%

“…The scope of the control channel can be local and global. The research works presented in [118], [124], [125], [132], [133], [138], [139] use the locally common control channel to exchange the control information within the one-hop neighborhood; whereas, the channel assignment algorithms proposed in [31]- [35], [42]- [44], [116], [117], [125], [127]- [131], [134]- [136], [140], [143], [147], [148] use the globally common control channel to exchange coordination information among all of the CRN nodes via multi-hops. Nevertheless, the channel assignment algorithms proposed in [56], [57], [122], [123], [126], [137], [142], [144]- [146] do not employ a control channel.…”

Section: ) Cwlanmentioning

confidence: 99%

“…The channel assignment solutions proposed in [33], [42]- [44], [56], [57], [116], [122], [123], [126]- [133], [135], [137]- [140], [142], [144]- [146] are designed for singleradio CRNs; whereas, the algorithms presented in [124], [147] are designed for dual-radio CRNs. The channel assignment algorithms proposed in [31], [32], [34], [35], [117], [118], [125], [134], [136], [143], [148] are designed to leverage the multiradio support of CRNs. The single-radio channel assignment algorithms are simple to implement because the interference handling is easy; whereas, the multi-radio increase network capacity by isolating the collision domains into multiple non-overlapping channels within the unlicensed bands.…”

Section: B General Comparison Of Channel Assignment Algorithmsmentioning

confidence: 99%

See 4 more Smart Citations

Channel Assignment Algorithms in Cognitive Radio Networks: Taxonomy, Open Issues, and Challenges

Ahmed

Gani

Abolfazli

et al. 2016

IEEE Commun. Surv. Tutorials

153

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Abstract-The cognitive radio is an emerging technology that enables dynamic spectrum access in wireless networks. The cognitive radio is capable of opportunistically using the available portions of a licensed spectrum to improve the application performance for unlicensed users. The opportunistic use of the available channels in the wireless environment requires dynamic channel assignment to efficiently utilize the available resources while minimizing the interference in the network. A challenging aspect of such algorithms is the incorporation of the channels' diverse characteristics, highly dynamic network conditions with respect to primary users' activity, and different fragmented sizes of the available channels. This paper presents a comprehensive survey on the state-of-the-art channel assignment algorithms in cognitive radio networks. We also classify the algorithms by presenting a thematic taxonomy of the current channel assignment algorithms in cognitive radio networks. Moreover, the critical aspects of the current channel assignment algorithms in cognitive radio networks are analyzed to determine the strengths and weaknesses of such algorithms. The similarities and differences of the algorithms based on the important parameters, such as routing dependencies, channel models, assignment methods, execution model, and optimization objectives, are also investigated. We also discuss open research issues and challenges of channel assignment in the cognitive radio networks.

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Section: ) Crahnmentioning

confidence: 99%

Section: ) Cwlanmentioning

confidence: 99%

Section: ) Cwlanmentioning

confidence: 99%

Section: ) Cwlanmentioning

confidence: 99%

Section: B General Comparison Of Channel Assignment Algorithmsmentioning

confidence: 99%

See 3 more Smart Citations

Channel Assignment Algorithms in Cognitive Radio Networks: Taxonomy, Open Issues, and Challenges

Ahmed

Gani

Abolfazli

et al. 2016

IEEE Commun. Surv. Tutorials

153

View full text Add to dashboard Cite

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Enhancing throughput in multi-radio cognitive radio networks

Khan

Islam

2019

Wireless Netw

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Capacity Analysis in Multi-Radio Multi-Channel Cognitive Radio Networks: A Small World Perspective

Zhong

Qin

2014

Wireless Pers Commun

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Cognitive radio (CR) has emerged as a promising technology to improve spectrum utilization. Capacity analysis is very useful in investigating the ultimate performance limits for wireless networks. Meanwhile, with increasing potential future applications for the CR systems, it is necessary to explore the limitations on their capacity in a dynamic spectrum access environment. However, due to spectrum sharing in cognitive radio networks (CRNs), the capacity of the secondary network (SRN) is much more difficult to analyze than that of traditional wireless networks. To overcome this difficulty, in this paper we introduce a novel solution based on small world model to analyze the capacity of SRN. First, we propose a new method of shortcut creation for CRNs, which is based on connectivity ratio. Also, a new channel assignment algorithm is proposed, which jointly considers the available time and transmission time of the channels. And then, we derive the capacity of SRN based on the small world model over multi-radio multi-channel (MRMC) environment. The simulation results show that our proposed scheme can obtain a higher capacity and smaller latency compared with traditional schemes in MRMC CRNs. InstroductionThe CR principle has introduced the idea to exploit spectrum holes (i.e., bands) which result from the proven underutilization of the electromagnetic spectrum by modern wireless communication and broadcasting technologies [1]. The exploitation of these holes can be accomplished by the notion of cognitive radio networks (CRNs). CRNs have emerged as a prominent solution to improve the efficiency of spectrum utilization and network capacity. In CRNs, secondary users (SUs) can exploit frequency bands when the primary users (PUs) do not occupy the bands. The objective of CRNs is to optimize the performance, e.g., the capacity of the SRN, without causing harmful effects on PUs. Existing research works on capacity of CRNs have mainly focused on improving the performance of the physical layer and media access control (MAC) [2]. These approaches can provide high capacity in single-hop topology, which are ineffective in multi-hop scenarios. For example, an optimized sensing threshold method may provide a higher capacity for a particular link. However, such a method may be inefficient when considering the average path length of a given multi-hop CRN.Capacity analysis is very useful in investigating the ultimate performance limits for CR systems. Some efforts have been taken to improve the CR channel capacity through optimizing the lower-layer parameters [2][3]. In a CRN, the power control and the spectrum sensing are properly incorporated for the capacity optimization providing the PU's protection [4]. In [5], Jararian et al. studied a symmetric multiuser cognitive radio system and presented a lattice coding scheme which all the L transmit-receive pairs can simultaneously communicate as if all cross channels were absent from the system. In [6], assuming a path loss shadow-fading model with multiple PUs and SUs, the system-leve...

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Distributed Robust Channel Assignment for Multi-Radio Cognitive Radio Networks

Cited by 12 publications

References 18 publications

Channel Assignment Algorithms in Cognitive Radio Networks: Taxonomy, Open Issues, and Challenges

Channel Assignment Algorithms in Cognitive Radio Networks: Taxonomy, Open Issues, and Challenges

Enhancing throughput in multi-radio cognitive radio networks

Capacity Analysis in Multi-Radio Multi-Channel Cognitive Radio Networks: A Small World Perspective

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