Cooperative Relay for Cognitive Radio Networks

Jia, Juncheng; Zhang, J.; Zhang, Q.

doi:10.1109/infcom.2009.5062156

Cited by 201 publications

(116 citation statements)

References 14 publications

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“…The other research focuses on the use of the relay to improve CRN performance such as capacity enhancement, enlarging coverage, and reducing interference [94]. In these two research areas, key challenges such as spectrum allocation for cognitive relaying, relay-selection, and multi-hop routing have been widely investigated in [95][96][97].…”

Section: Optimized Decision In a Crnmentioning

confidence: 99%

Intelligent and efficient development of wireless networks: A review of cognitive radio networks

et al. 2012

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Facing the challenges on how to improve spectrum efficiency and how to realize heterogeneous network convergence in future wireless networks, a cognitive radio network (CRN) is proposed as one of the solutions. This has become a major research topic in recent years and it is timely to give an overview of the development of CRN and to summarize key issues and technologies. The fundamental concepts of CRN, including the cognitive cycle model, the network architecture, and the cognitive ability and intelligent decision functions, are introduced in detail based on recent advances. Key issues for each topic, followed with recent research on theory and method, are then classified and the industrialization developments of CRN testbeds based on TD-LTE cellular system and standards are briefly presented. Finally, conclusions are reached on the perspectives and directions of future development.cognitive radio networks, network architecture, cognitive ability, spectrum sensing, intelligent decision and dynamic spectrum allocation, end-to-end performance Driven by the requirements of ubiquitous wireless access and personalized wireless applications [1], worldwide, numerous wireless technologies are invading our lives, such as evolutionary telecommunication technologies, wideband wireless access and short distance wireless technologies. Furthermore, the concept of the internet of things (IoT) has become widespread based on the development of wireless sensor networks. With the explosive development of wireless technologies, several challenges must be addressed. The electromagnetic spectrum, whose use is licensed and managed by government, has been scarce for allocation to ubiquitous wireless applications. However, contrary to the physical scarcity of the available spectrum, the report published by the federal communication commission (FCC) shows that over 60 % of the licensed spectrum below 6 GHz remains underused [2,3]. The measurement results in Figure 1 taken in Beijing over a one month period, also indicate low spectrum use consistent with the results released by the FCC. Therefore, improvement of spectrum efficiency is the first challenge to be examined. On the other hand, the end-to-end performance of wireless networks is considered to be the performance indicator for ubiquitous and personalized wireless applications [4], such as end-to-end throughput, end-to-end delay and routing scheme, which take into account all network elements in a data transmission flow across different layers for the global optimization from the network aspect. Because the property of heterogeneity causes low radio resource use and mutual interference among heterogeneous networks, the other big challenge is that heterogeneous network convergence is needed to improve the end-to-end performance of wireless networks.Cognitive radio (CR), proposed by Mitola in 1999 [5,6], is considered a feasible solution for improving spectrum efficiency. Furthermore, a cognitive radio network (CRN), which is defined as a wireless network with the capabilities

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Section: Optimized Decision In a Crnmentioning

confidence: 99%

Intelligent and efficient development of wireless networks: A review of cognitive radio networks

et al. 2012

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“…al [26] propose approximate approaches to find the spectral efficient path in random networks. Experiment results in [27] show that cooperative relay could greatly improve the spectrum efficiency and network throughput by exploiting the diversity of spectrum resources. Joint routing and resource management is proposed to achieve optimal spectrum efficiency in the network with frequency-agile radios [28].…”

Section: Related Workmentioning

confidence: 99%

Coolest Path: Spectrum Mobility Aware Routing Metrics in Cognitive Ad Hoc Networks

Huang

et al. 2011

2011 31st International Conference on Distributed Computing Systems

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Abstract-Cognitive Radio (CR) emerges as a promising solution to current unbalanced spectrum utilization. The cognitive ad hoc network can take advantage of dynamic spectrum access and spectrum diversity over wide spectrum. It could achieve higher network capacity compared to traditional ad hoc networks, thus supporting bandwidth-demanding applications. A cognitive radio operates over wide spectrum with unpredictable channel availability. Moreover, the transmission opportunity of a cognitive node is not guaranteed due to the presence of primary users (PUs). These two unique features define new routing problems in cognitive ad hoc networks. To better characterize the unique features of cognitive radio networks, we propose new routing metrics, including accumulated spectrum temperature, highest spectrum temperature, and mixed spectrum temperature to account for the timevarying spectrum availability. The proposed metrics favor the "coolest" path, or the path with the most balanced and/or the lowest spectrum utilization by the primary users. We also study the computational complexity of the routing algorithm in cognitive ad hoc networks. Experiment results on our USRP-2 testbed show that the proposed metrics are capable of capturing the fluctuation of spectrum availability and suitable for cognitive ad hoc networks.

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“…Furthermore, we assume there is only one SU, who can access these available channels simultaneously, and its transmission on one channel will not interfere with other channels. To achieve this, we can simply adopt D-OFDM as the physical layer technique with a single radio equipment [13,14]. The SU can be regarded as one node of an ad hoc network, which communicates with another one in multiple channels, or a CR base station, who can serve multiple SUs at the same time.…”

Section: System Modelmentioning

confidence: 99%

Selective sensing and transmission for multi-channel cognitive radio networks

Zhao

et al. 2011

J Wireless Com Network

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In this article, we consider a continuous time Markov chain (CTMC) modeled multi-channel CR network, where there are multiple independent primary users and one slotted secondary user (SU) who can access multiple channels simultaneously. To maximize SU's temporal channel utilization while limiting its interference to PUs, a selective sensing and selective access (SS-SA) strategy is proposed. With SS strategy, each channel is sensed almost periodically with different periods according to parameter T c , which reflects the maximal period that each channel should be probed. The effect of sensing period is also considered. When the sensing period is suitable, the SA strategy can be regarded as greedy access strategy. Numerical simulations illustrate that T c is a valid measurement to indicate how often each channel should be sensed, and with SS-SA strategy, SU can effectively utilize the channels and consume less energy and time for sensing than adopting reference strategies.

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Cooperative Relay for Cognitive Radio Networks

Cited by 201 publications

References 14 publications

Intelligent and efficient development of wireless networks: A review of cognitive radio networks

Intelligent and efficient development of wireless networks: A review of cognitive radio networks

Coolest Path: Spectrum Mobility Aware Routing Metrics in Cognitive Ad Hoc Networks

Selective sensing and transmission for multi-channel cognitive radio networks

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