ZAP: a distributed channel assignment algorithm for cognitive radio networks

Walenga, Paulo Roberto; Fonseca, Mauro; Munaretto, Anelise; Viana, Aline Carneiro; Ziviani, Artur

doi:10.1186/1687-1499-2011-27

Cited by 9 publications

(14 citation statements)

References 20 publications

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“…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 assignment of orthogonal channels on different interfaces of a node enables the parallel transmission from the node. 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.…”

Section: B General Comparison Of Channel Assignment Algorithmsmentioning

confidence: 99%

“…(g) ZAP: The ZAP is a simple, adaptive, and distributed channel assignment algorithm that incurs low communication overhead [147]. The ZAP requires a common control channel to eliminate the contention for the channel access between the control and data messages.…”

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%

“…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%

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

152

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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: ) Cwlanmentioning

confidence: 99%

Section: B General Comparison Of Channel Assignment Algorithmsmentioning

confidence: 99%

Section: ) Cwlanmentioning

confidence: 99%

Section: ) Cwlanmentioning

confidence: 99%

Section: ) Cwlanmentioning

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

152

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On energy‐efficient performance‐guaranteed channel assignment in cognitive radio‐based wireless mesh networks

Hasan

2015

Int J Communication

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In recent years, in order to make efficient use of spectrum resources, much attention has been given to solving the problem of channel assignment in cognitive radio-based wireless mesh networks (CR-WMNs). Current approaches focus mainly on avoiding interference in order to enhance performance in terms of throughput. WMNs are intended to provide low-cost multimedia communication. Therefore, in order to provide lowcost real-time communication, channel assignment in CR-WMNs should take into consideration not only the issue of throughput, but also energy consumption and delays. In this paper, we first define an optimization problem to maximize the end-to-end throughput per unit of energy consumption while minimizing, as well as guaranteeing, the delay constraint specified for a data stream. Based on this, we then propose a novel distributive heuristic channel assignment approach to solve the optimization problem in a self-organized manner. Finally, we present the simulation results to evaluate the performance of the proposed solution in terms of end-to-end throughput per unit of energy consumption and delays.require that network performance should be optimized not only in terms of throughput, but also in terms of energy consumption (given the fact that the cost of energy continues to rise) and delay, because several broadband services such as video streaming, video conferencing and online gaming are delay sensitive. Moreover, in recent years, a dramatic growth has been witnessed in Internet of Things (IoT). Because it is anticipated that IoT will be carried over multi-hop mesh-type networks with a large number of energy-constrained devices and delay-sensitive applications [6], an energy and delay efficient WMN will be the most suitable choice for IoT applications. Hence, a channel assignment mechanism optimizing performance in terms of throughput and energy consumption while considering the delay constraints for CR-WMNs is highly desired.This paper focuses on the channel assignment problem in multi-hop CR-WMNs with the aim of maximizing the end-to-end throughput per unit of energy consumption while keeping the end-toend delay within the specified limit for each flow. Therefore, we first establish link/hop throughput, energy consumption and delay models based on PU activities. Then, an optimization problem is formulated for channel assignment in CR-WMN with the objective function of maximizing the end-to-end throughput per unit of energy consumption while satisfying the spectrum-sharing constraints among the nodes and the end-to-end constraints for each flow. To solve the problem, a novel distributive heuristic channel assignment (DHCA) is presented, which works in a distributive and self-organized manner, while keeping in view the scalability concerns of CR-WMNs and the simplicity of the solution to be implemented in real time. DHCA works in two steps. The first step is to introduce the concept of delay gradient. The delay gradient is defined as the difference in the accumulative delay incurred by the packets of a fl...

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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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ZAP: a distributed channel assignment algorithm for cognitive radio networks

Cited by 9 publications

References 20 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

On energy‐efficient performance‐guaranteed channel assignment in cognitive radio‐based wireless mesh networks

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

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