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A Survey of Spectrum Sensing Techniques and Issues in Cognitive Radio Ad hoc Networks
Shilpa Jain, Nidhi Taneja

Since 1999, cognitive radio (CR) technology is envisaged to solve the problems in wireless networks resulting from the limited available spectrum and the inefficiency in the spectrum usage by exploiting the existing wireless spectrum opportunistically. CR networks, however, impose unique challenges due to the high fluctuation in the available spectrum as well as diverse quality-of-service (QoS) requirements. In this paper, a survey of spectrum sensing methodologies for cognitive radio ad hoc networks (CRAHNs) is presented. Spectrum management functionalities such as spectrum sensing, spectrum decision, spectrum sharing, and spectrum mobility are introduced. Various functionalities of spectrum sensing are studied from a cognitive radio perspective and multi-dimensional spectrum sensing concept is introduced. A particular emphasis is given to cooperative sensing concept and its various forms. Finally, the Challenges associated with spectrum sensing are discussed.
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Evolution from SDR to Cognitive Radio
Shilpa Jain, Nidhi Taneja

Software Defined Radio (SDR) is a flexible radio architecture which can be configured to adapt various waveforms, frequency bands, bandwidths, modes of operations and wireless standards simply by altering the physical layer behavior through changes in its software. This paper presents a detailed survey of the existing hardware and software platform for SDRs. However, an SDR can switch functions and operations only on demand; it is not capable of reconfiguring itself into the most effective form without its user even knowing it. Therefore, Cognitive radio (CR) came into existence which extends the software radio with radio-domain model-based reasoning and would be trainable in a broad sense, instead of just programmable. In this paper a survey of spectrum sensing methodologies for cognitive radio is presented. These cognitive technologies may be considered as an application on top of a basic SDR platform.
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A Review on Software Cost Estimation Models and Techniques
Kulvinder Singh

This paper summarizes several classes of software cost estimation models and techniques: parametric models, expertise based techniques, learning oriented techniques, dynamics based models, regression based models, and composite Bayesian techniques for integrating expertise based and regression based models. Experience to date indicates that neural net and dynamics based techniques are less mature than the other classes of techniques, but that all classes of techniques are challenged by the rapid pace of change in software technology. The primary conclusion is that no single technique is best for all situations, and that a careful comparison of the results of several approaches is most likely to produce realistic estimates.
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Volume 5, Issue 1 
(January 2018)

Submission: 15 December 2017
Publication: January 2018


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This work is licensed under a Creative Commons Attribution 3.0 Unported License.