TV white‐space sensing prototype

Oh, Ser Wah; Le, Thanh-Dat; Zhang, W.; Ahmed, Syed Naveen Altaf; Zeng, Yonghong; Kua, K. J. M.

doi:10.1002/wcm.708

Cited by 9 publications

(4 citation statements)

References 8 publications

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“…Although there have been quite a few methods proposed for spectrum sensing, their realization and performance in practical cognitive radio applications need to be tested [50][51][52]. To build a practical sensing device, many factors should be considered.…”

Section: Practical Considerations and Future Developmentsmentioning

confidence: 99%

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A Review on Spectrum Sensing for Cognitive Radio: Challenges and Solutions

Zeng

Liang

Hoang

et al. 2010

EURASIP J. Adv. Signal Process.

599

388

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Cognitive radio is widely expected to be the next Big Bang in wireless communications. Spectrum sensing, that is, detecting the presence of the primary users in a licensed spectrum, is a fundamental problem for cognitive radio. As a result, spectrum sensing has reborn as a very active research area in recent years despite its long history. In this paper, spectrum sensing techniques from the optimal likelihood ratio test to energy detection, matched filtering detection, cyclostationary detection, eigenvalue-based sensing, joint space-time sensing, and robust sensing methods are reviewed. Cooperative spectrum sensing with multiple receivers is also discussed. Special attention is paid to sensing methods that need little prior information on the source signal and the propagation channel. Practical challenges such as noise power uncertainty are discussed and possible solutions are provided. Theoretical analysis on the test statistic distribution and threshold setting is also investigated.

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Section: Practical Considerations and Future Developmentsmentioning

confidence: 99%

“…We will pay special attention to sensing methods with practical application potentials. The focus of this paper is on practical sensing algorithm designs; for other aspects of spectrum sensing in cognitive radio, the interested readers may refer to other resources like [45][46][47][48][49][50][51][52].…”

Section: Introductionmentioning

confidence: 99%

A Review on Spectrum Sensing for Cognitive Radio: Challenges and Solutions

Zeng

Liang

Hoang

et al. 2010

EURASIP J. Adv. Signal Process.

599

388

View full text Add to dashboard Cite

show abstract

“…To avoid interference and performance degradation of primary users in the TV band, the 802.22 working group has set −116 dBm as the sensitivity level for detecting whether a particular channel is free or not. While this might prevent interference to television receivers from unfortunately faded cognitive radios, the −116 dBm rule leaves little spectrum in the TV‐band open to detection and utilization by cognitive radios 109. Various studies have shown that the spectrum utilization in time and space is far lower than what would actually be possible because of the −116 dBm restriction.…”

Section: Channel Allocation In Cognitive Radio Based Networkmentioning

confidence: 99%

A survey on the channel assignment problem in wireless networks

Audhya¹,

Sinha

Ghosh

et al. 2011

Wireless Communications

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Efficient allocation of channels for wireless communication in different network scenarios has become an extremely important topic of recent research. The main challenge lies in the fact that the channel allocation problem is NP-complete. Because of a maximum allowable time limit imposed in practical situations for allocation of channels, sometimes we may need to be satisfied with a near-optimal solution. In this correspondence, we present a discussion on the various challenges and approaches that have been used by different researchers to solve the problem of channel allocation taking into account different interference issues and efficient utilization of available communication channels for cellular mobile (including multimedia communication) environment and cognitive radio based networks. important classes of wireless networks -cellular mobile networks and cognitive radio networks.Mobile computing, particularly over cellular networks, has emerged as an important topic of research because of the need for computing abilities even when people are on the move. Over the last decade, the number of such cellular network dependent mobile users has been increasing nearly exponentially. With the advances in technology, there has been an increasing demand from the mobile users for providing multimedia services like voice, text, still image, and video over wireless networks. On the other hand, the available bandwidth required for such multimedia data communication for a large number of mobile users is very much limited. Such a limited availability of the radio spectrum, signal distortion, and the interference caused by the environment and other mobile users impose an inherent bound on the capacity of such wireless networks. Therefore, developing methods to utilize the scarce radio spectrum efficiently is more critical than ever before.Based on the application scenario, the common ways to design wireless networks are: (i) infrastructure-based network design such as the cellular structure approach, (ii) infrastructure-less networks such as ad hoc networks, and (iii) hybrid networks which uses a mix of infrastructure with ad hoc network architecture and can typically be found in many sensor network applications. In an infrastructurebased network design-such as the cellular networks, the geographical region under consideration is spatially divided into a number of cells-which can be either overlapping or non-overlapping. A base station is established in each cell, and each mobile station in the cell communicates through this base station via a channel assigned by the base station. Several techniques such as frequency division multiplexing (FDM), time division multiplexing (TDM), or

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“…In addition to tremendous efforts on theoretical investigation, work on hardware implementation of spectrum sensing has been reported as well in [12], [13], [14], [15]. Experience gained in developing software defined radio (SDR) can benefit CR work, and existing SDR hardware platforms can be extended for developing CR transceivers.…”

Section: Introductionmentioning

confidence: 99%

Experimental Validation of Channel State Prediction Considering Delays in Practical Cognitive Radio

Chen

Guo

et al. 2011

IEEE Trans. Veh. Technol.

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Abstract-As a part of the effort toward building a cognitive radio network testbed, we have demonstrated real-time spectrum sensing. Spectrum sensing is the cornerstone of cognitive radio. However, current hardware platforms for cognitive radio introduce time delays that undermine the accuracy of spectrum sensing. The time delay, named response delay, incurred by hardware and software can be measured at two antennas colocated at a secondary user (SU), the receiving antenna and the transmitting antenna. In this paper, minimum response delays are experimentally quantified and reported based on two hardware platforms, the universal software radio peripheral 2 (USRP2) and the small form factor software defined radio development platform (SFF SDR DP). The response delay has negative impact on the accuracy of spectrum sensing.

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TV white‐space sensing prototype

Cited by 9 publications

References 8 publications

A Review on Spectrum Sensing for Cognitive Radio: Challenges and Solutions

A Review on Spectrum Sensing for Cognitive Radio: Challenges and Solutions

A survey on the channel assignment problem in wireless networks

Experimental Validation of Channel State Prediction Considering Delays in Practical Cognitive Radio

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