SpiderRadio: A Cognitive Radio Implementation Using IEEE 802.11 Components

Hong, Kai; Sengupta, Shamik; Chandramouli, R.

doi:10.1109/tmc.2012.192

Cited by 12 publications

(6 citation statements)

References 35 publications

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“…Several attempts have been made to use commodity IEEE 802.11 hardware to implement CR terminals [22]- [25]. While such an implementation approach provides a low-cost solution, it has a limited room for reconfigurability and customization since such an IEEE 802.11 hardware is restricted in providing accessability to the underlying physical layer parameters.…”

Section: B Commodity Hardware-based Osa Implementationsmentioning

confidence: 99%

Design, Implementation and Characterization of Practical Distributed Cognitive Radio Networks

Khattab

Perkins

Bayoumi

2013

IEEE Trans. Commun.

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Opportunistic Spectrum Access (OSA) in distributed cognitive radio networks (CRNs) has been well studied in the literature from a theoretical perspective. However, such theoretically-optimized distributed OSA approaches are challenged by several practical implementation issues. In this paper, we design a custom cross-layer framework that enables the: (i) clean-slate implementation of a wide variety of OSA mechanisms; (ii) experimental evaluation of the individual practical OSA components of the Rate-Adaptive Probabilistic (RAP) framework; and (iii) detailed comparison of the performance of such a practical OSA approach against theoretical OSA approaches developed for fully-capable CRNs. Our evaluation reveals the multi-fold goodput improvement and remarkable fairness characteristics of the practical RAP OSA approach compared to the OSA approaches that overlook the OSA and CR practical limitations. However, the superior performance of practical OSA comes at the expense of more outages to the primary licensed networks but within the permissible bounds. Another key finding is that the wide family of existing theoretically-optimized OSA protocols can benefit from the gains available to the individual components of the practical RAP approach, namely, the random spectrum sensing and the probabilistic non-greedy access.Index Terms-Cognitive radio; radio spectrum management; ad hoc networks; design for experiments; performance evaluation.

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Section: B Commodity Hardware-based Osa Implementationsmentioning

confidence: 99%

Design, Implementation and Characterization of Practical Distributed Cognitive Radio Networks

Khattab

Perkins

Bayoumi

2013

IEEE Trans. Commun.

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“…In this paper, we propose a solution that optimally decides which components of an application to offload and which to execute locally, while simultaneously optimizing the percentage of data (associated with this offloading) to be sent via each radio interface. Given recent advances in technologies that enable bandwidth aggregation in wireless devices [18], [19] our solution is implementable in practice. To the best of our knowledge, this is the first such solution that approaches cloud offloading for multi-radio enabled devices.…”

Section: Introductionmentioning

confidence: 99%

Cloud offloading for multi-radio enabled mobile devices

Mahmoodi

Subbalakshmi

Sagar

2015

2015 IEEE International Conference on Communications (ICC)

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Abstract-The advent of 5G networking technologies has increased the expectations from mobile devices, in that, more sophisticated, computationally intense applications are expected to be delivered on the mobile device which are themselves getting smaller and sleeker. This predicates a need for offloading computationally intense parts of the applications to a resource strong cloud. Parallely, in the wireless networking world, the trend has shifted to multi-radio (as opposed to multi-channel) enabled communications. In this paper, we provide a comprehensive computation offloading solution that uses the multiple radio links available for associated data transfer, optimally. Our contributions include: a comprehensive model for the energy consumption from the perspective of the mobile device; the formulation of the joint optimization problem to minimize the energy consumed as well as allocating the associated data transfer optimally through the available radio links and an iterative algorithm that converges to a locally optimal solution. Simulations on an HTC phone, running a 14-component application and using the Amazon EC2 as the cloud, show that the solution obtained through the iterative algorithm consumes only 3% more energy than the optimal solution (obtained via exhaustive search).

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“…Consequently, the secondary network searches for a new channel to use [3]. There exist several ongoing efforts that incorporate opportunistic channel access into IEEE 802.11 devices [4], [5], [6], [7], [8], [9], [10]. Those studies mainly focus on sensing the spectrum holes (also referred to as white spaces) and the channel selection mechanism.…”

Section: Introductionmentioning

confidence: 99%

“…Those studies mainly focus on sensing the spectrum holes (also referred to as white spaces) and the channel selection mechanism. Examples include [7] where OSA is implemented using the IEEE 802.11 standard and OSA is based on observing the PHY errors and received signal strength indicator (RSSI). Alternatively, [8] has OSA implemented through a modified MAC protocol with dynamic spectrum allocation capability.…”

Section: Introductionmentioning

confidence: 99%

Experimental evaluation of opportunistic access in shared contention-based channels

Elgaml

Raafat

Halim

et al. 2015

2015 IEEE International Conference on Electronics, Circuits, and Systems (ICECS)

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In this paper, we experimentally evaluate the gains of applying the concept of opportunistic channel access to the traditional contention-based access widely used in unlicensed channels. We consider the scenario in which multiple IEEE 802.11 networks coexist in a given channel and pose the following question: should all networks fairly compete for channel access or should they be prioritized in terms of channel access rights, and use opportunistic access to regulate their transmission? We implement a simple modification to the IEEE 802.11 medium access module of the wireless interface driver to allow opportunistic access of the shared channel. Then, we configure a secondary IEEE 802.11 network to opportunistically access the channel only when a primary network is not using it. Our empirical results show that such opportunistic channel access achieves high channel utilization (up to 188 % utilization gain) while respecting the prioritized access rights of the primary network with primary network outages below 7 %. In contrast, legacy contention-based access achieves higher channel utilization. However, it does not provide prioritized access and causes half the primary network packets to be denied transmission, which is not suitable for the targeted application scenarios.

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SpiderRadio: A Cognitive Radio Implementation Using IEEE 802.11 Components

Cited by 12 publications

References 35 publications

Design, Implementation and Characterization of Practical Distributed Cognitive Radio Networks

Design, Implementation and Characterization of Practical Distributed Cognitive Radio Networks

Cloud offloading for multi-radio enabled mobile devices

Experimental evaluation of opportunistic access in shared contention-based channels

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