KUAR: A Flexible Software-Defined Radio Development Platform

Minden, Gary J.; Evans, Joseph; Searl, Leon S.; DePardo, Daniel; Petty, V.R.; Rajbanshi, Rakesh; Newman, T. J.; Chen, Qianqiao; Weidling, F.; Guffey, Jordan; Datla, Dinesh; Barker, Brett A.; Peck, M.; Cordill, Brian D.; Wyglinski, Alexander M.; Agah, Arvin

doi:10.1109/dyspan.2007.62

Cited by 62 publications

(32 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Finally, many recent works have designed prototypes and testbeds for frequency-agile radios [5], [7], [9], [29], [30], [31], [32]. These efforts recognize that increasing the frequency agility requires sophisticated hardware and software designs.…”

Section: Related Workmentioning

confidence: 99%

The Impact of Frequency-Agility on Dynamic Spectrum Sharing

Cao

Yang

Zheng

2010

2010 IEEE Symposium on New Frontiers in Dynamic Spectrum (DySPAN)

View full text Add to dashboard Cite

Abstract-Designed to adapt spectrum usage on-the-fly, frequency-agile radios can drastically improve performance of wireless networks. Such flexibility, however, comes with a cost of increased hardware complexity. This motivates us to understand when and why having higher degree of frequency-agility helps and how much improvement it can lead to. In this paper, we approach this question by comparing two types of agile radios in the context of dynamic spectrum sharing in any given spectrum chunk. We consider 1-agile radios that use a single frequency channel but can adjust the channel's width and central frequency, and k-agile radios that can combine up to k non-contiguously aligned frequency segments into one transmission. We show that, due to inherent demand dynamics and conflict heterogeneity, networks using 1-agile radios often face the problem of spectrum fragmentation. But k-agile radios can effectively suppress this problem directly at the physical layer. Using theoretical analysis and simulation experiments, we quantify the advantage of kagile radios over 1-agile radios in their network spectrum usage. For a fair comparison, we abstract the impact of demand and topology configurations by evaluating the worst case and average case performance. Our results show that in worst cases, the improvement of using fully-agile radios is arbitrarily large, although the improvement of using k-agile radios is upper bounded by k. In average cases, the improvement reduces to 10-40% under typical network configurations. Interestingly, in the context of dynamic spectrum sharing, 2-agile radios realize the majority of the improvement brought by fully-agile radios.

show abstract

Section: Related Workmentioning

confidence: 99%

The Impact of Frequency-Agility on Dynamic Spectrum Sharing

Cao

Yang

Zheng

2010

2010 IEEE Symposium on New Frontiers in Dynamic Spectrum (DySPAN)

View full text Add to dashboard Cite

show abstract

“…The KUAR consists of five major sub-systems on three printed circuit boards: a power supply, a control processor (Intel Pentium-M: 1.4 GHz), a digital board with a programmable signal processor (Xilinx Vertex II Pro V30), A/D (LTC2284), and D/A (AD9777) converters, an RF transceiver, and antennas. It is compatible with GNU radio software [28][29].…”

Section: Kansas University Agile Radio (Kuar) Platformmentioning

confidence: 99%

Cognitive Radio New Dimension in Wireless Communication State of Art

Singh¹,

Singh²,

Kang³

2013

IJCA

View full text Add to dashboard Cite

In today's wireless communication environment, the Radio Frequency spectrum is occupied for different purposes like cellular, Television, military, emergency or satellite communication. The frequency spectrum used for cellular communication is getting over crowded with increasing number of subscribers and demand for high data rate text or video transmission, but the frequency spectrum in other wireless broadcast and communication is not utilized efficiently, example in Television broadcast band, some of frequency spectrum is vacant at some instant or for particular time. The vacant spectrum could be used in cellular communication, means the spectrum is borrowed from Television Broadcast band to be used for cellular communication. A borrow/use of spectrum from other licensed frequency bands will improve the efficiency of spectrum use. In this paper, aspects of Cognitive Radio and comparison of different sensing techniques and methods are discussed. Secondly, the layer architecture of Cognitive Radio is presented. Thirdly, hardware and software platforms are discussed for Cognitive Radio testing.

show abstract

“…This information is later fed to the simulated annealing algorithm to give its search a "direction". Basically, we assign probabilities to parameters to be chosen for alteration, as well as probabilities of parameters to take certain values 2 . The probabilities are altered during the work of the optimizer depending on the explored search space.…”

Section: B Approximate Probabilistic Graphical Modelsmentioning

confidence: 99%

“…Good example of the latter trend is the proliferation of different TCP variants over the past years, with highly different performance characteristics over wireless and fixed network types. Cognitive Wireless Networks (CWNs) [1], [2] have emerged as a promising paradigm for managing this increasing complexity and choosing for each application or device the most appropriate protocol and parameter settings. However, most of the work on CWNs has focused on optimization applied at a single protocol or stack level.…”

Section: Introductionmentioning

confidence: 99%

Exploring Simulated Annealing and Graphical Models for Optimization in Cognitive Wireless Networks

Meshkova

Riihijärvi

Achtzehn

et al. 2009

GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference

View full text Add to dashboard Cite

Abstract-In this paper we discuss the design of optimization algorithms for cognitive wireless networks (CWNs). Maximizing the perceived network performance towards applications by selecting appropriate protocols and carrying out cross-layer optimization on the resulting stack is a key functionality of any CWN. We take a "black box" approach to the problem and study the use of simulated annealing for solving it. To improve the convergence rate of the basic algorithm we apply machine learning techniques to construct graphical models on the perceived relations between network stack parameters and application-specific network utilities. We test our optimizer design both in a simulation environment as well as on a network testbed with low-power radios. Our results show that even basic simulated annealing works well, but simple graphical models can further increase the convergence rate. However, use of sophisticated models such as Bayesian networks does not always lead to substantially better performance. The results indicate that enhanced simulated annealing indeed appears to be a promising foundation for future cognitive engine designs.

show abstract

KUAR: A Flexible Software-Defined Radio Development Platform

Cited by 62 publications

References 6 publications

The Impact of Frequency-Agility on Dynamic Spectrum Sharing

The Impact of Frequency-Agility on Dynamic Spectrum Sharing

Cognitive Radio New Dimension in Wireless Communication State of Art

Exploring Simulated Annealing and Graphical Models for Optimization in Cognitive Wireless Networks

Contact Info

Product

Resources

About