Spectrum Sensing in Cognitive Radio Networks. These new technologies are becoming evermore bandwidth de- manding due to their higher rate requirements. Cognitive radio networks and spectrum sensing techniques are a natural way to allow these new technologies to be deployed. In this chapter we will discuss spectrum sensing for cognitive radio networks.
We will begin by introducing the subject in Sec. Then we will move on to the spectrum sensing problem itself in Sec. Finally we will close this chapter with some conclusions and open issues.
This dynamic spectrum access is one of the fundamental requirements for transmitters to adapt to varying channel quality, network congestion, interference and service requirements. Cognitive radio networks from now on called secondary networks will also need to coexist with legacy ones hereafter called primary networkswhich have the right to their spectrum slice and thus can not accept interference.
Based on these facts, under-utilization of the current spectrum and the need to increase the network capacity is pushing research towards new means of exploiting the wireless medium. In this direction, the Federal Communications Commission FCC Spectrum Pol- icy Task Force has published a report  inin which it thoroughly investigates the under utilization of the radio spectrum.
While the FCC is in charge of determining the spectrum usage and its policies, the Whitespace Coalition, formed by companies such as Microsoft, Google, Dell, HP and Intel is studying ways to exploit the spectrum vacancies in the television band.
Spectrum sensing is the technique that will enable cognitive radio networks to achieve this goal. These policies are based on the central idea where there are primary systems which have the right to the spectrum and secondary systems which are allowed to use the spectrum so long as they do not disturb the communications of the primary systems.
Roughly speaking, these policies deal with controlling the amount of interference that the secondary systems can incur to primary ones. Thus, the problem is one of interference management [3, 4].
We can address this problem 1. Receiver Centric Interference Management In the receiver centric approach [3, 4], an interference limit at the receiver is calculated and used to determine the restriction on the power of the transmitters around it. This interference limit, called the interference temperature, is chosen to be the worst interference level that can be accepted without disturbing the receiver operation beyond its operating point.
Although very interesting, this approach requires knowledge of the interference limits of all receivers in a primary system. Such knowledge depends on a many variables, including individual locations, fading situations, modulations, coding schemes and services.
Receiver centric interference management techniques will not be addressed in this chapter as they have been recently ruled out by the IEEE SCC41 cognitive radio network standard. Transmitter Centric Interference Management In the transmitter centric approach, the focus is shifted to the source of interference [3, 4].
The transmitter does not know the interference temperature, but by means fo sensing, it tries to detect free bandwidth. The sensing procedure allows the transmitter to classify the chan- nel status to decide whether it can transmit and with how much power.
In actual systems, however, since the transmitter does not know the location of the receivers or their channel conditions, it is not able to infer how much interference these receivers can tolerate.
Thus, spectrum sensing solves the problem for worst case scenario, assuming strong interference channels, so the secondary system transmits only when it senses an empty medium.
Since this is not a question of de- tection determining whether a given frequency band is being usedthese types of signal identification issues such as  are not addressed in this chapter.
Rather we focus on their detection. Ultimately, a spectrum sensing device must be able to give a general picture of the medium over the entire radio spectrum.
This allows the cognitive radio network to analyze all degrees of freedom time, frequency and space in order to predict the spectrum usage. Feasible spectrum sensing device can quickly sweep the radio spectrum, analyzing one narrowband segment at a time.
|Thesis List – Hamdard University||Dissertation Mohamed Abdelgader Ahmed Yassin. Mustafa Hariri ChairmanDr.|
|A survey of placement strategies, buffering capacity, and mobility model s, Journal of Network and Computer Applications, vol. Models, measurement methods, applications, and future research directionsJournal of Network and Computer Applications, vol.|
|Mustafa Hariri ChairmanDr.|
This chapter will focus on narrowband sensing techniques. In this section, we have emphasized the importance of the spectrum sensing technique for cognitive radio networks. In the next section, we aim at understanding the underlying char- acteristics of the spectrum sensing problem, which will enable us to develop the approaches presented further in this chapter.
In a nutshell, signal detection can be described as a method for identifying the presence of a signal in a noisy environment. Signal detection has been throughly studied for radar purposes since the fifties . Analytically, signal detection can be reduced to a simple identification problem, formalized as a hypothesis test [14, 15, 16]: Hypothesis test and possible outcomes with their corresponding probabilities.SPECTRUM SENSING IN COGNITIVE RADIO NETWORKS By Waleed Ejaz NUST-MS PhD-ComE Thesis Advisor Dr.
Shoab A. Khan A thesis submitted to the faculty of Computer Engineering Department College of Electrical & Mechanical Engineering, National University of Sciences and Technology, Pakistan in Waleed Ejaz June 26, iv.
It is referred as the Field hereafter.1 The Main Goals of the Thesis The producing oil and gas field in the Sultanate of Oman presented in this thesis has been one of the largest oil-producing fields in the country since its discovery in MS Thesis Title Integration of Electrofacies and Geomechanical Characteristics of Sarah Formation (Potential Tight Gas Reservoir), Rub’ Al-Khali Basin, Saudi Arabia MS Thesis .
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[J40] Muhammad Khalil Afzal, Mubashir Husain Rehmani, Antonio Pescape, Sung Won Kim, and Waleed Ejaz, Special Issue on The New Era of Smart Cities: Sensors, Communication Technologies and Applications, IEEE Access, [J40] Muhammad Khalil Afzal, Mubashir Husain Rehmani, Antonio Pescape, Sung Won Kim, and Waleed Ejaz, Special Issue on The New Era of Smart Cities: Sensors, Communication Technologies and Applications, IEEE Access,