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COGNITIVE RADIO PDF

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CHAPTER 11 Information theoretical limits on cognitive radio networks. corresponding continuous probability density function (pdf) superimposed on it. Cognitive radio technology / edited by Bruce A. Fette.—1st ed. p. cm. able at ronaldweinland.info [10] US General Accounting . A comprehensive treatment of cognitive radio networks and the specialized techniques used to improve wireless communications. The human.


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PDF | Cognitive radio (CR) is the enabling technology for supporting dynamic spectrum access: the policy that addresses the spectrum scarcity problem that is . PDF | On May 1, , Rajib Biswas and others published Basics of In book: Introduction to Cognitive Radio Networks and Applications. PDF | Cognitive Radio is a paradigm for wireless communication in which a wireless node (or a network) can change its transmission and reception parameters.

Resources and Help Spectrum sensing in cognitive radio networks: requirements, challenges and design trade-offs Abstract: Opportunistic unlicensed access to the temporarily unused frequency bands across the licensed radio spectrum is currently being investigated as a means to increase the efficiency of spectrum usage. Such opportunistic access calls for implementation of safeguards so that ongoing licensed operations are not compromised. Among different candidates, sensing-based access, where the unlicensed users transmit if they sense the licensed band to be free, is particularly appealing due to its low deployment cost and its compatibility with the legacy licensed systems. The ability to reliably and autonomously identify unused frequency bands is envisaged as one of the main functionalities of cognitive radios. In this article we provide an overview of the regulatory requirements and major challenges associated with the practical implementation of spectrum sensing functionality in cognitive radio systems.

H 1 Where y k is the sample to be analyzed at each instant k and n k is the noise of variance 2. However ED is always accompanied by a number of disadvantages i sensing time taken to achieve a given probability of detection may be high. As a result CR users need to be tightly synchronized and refrained from the transmissions during an interval called Quiet Period in cooperative sensing.

Block diagram of matched filter [1] A matched filter MF is a linear filter designed to maximize the output signal to noise ratio for a given input signal. When secondary user has a priori knowledge of primary user signal, matched filter detection is applied.

Matched filter operation is equivalent to correlation in which the unknown signal is convolved with the filter whose impulse response is the mirror and time shifted version of a reference signal. The operation of matched filter detection is expressed as: Detection by using matched filter is useful only in cases where the information from the primary users is known to the cognitive users.

When the information of the primary user signal is known to the cognitive radio user, matched filter detection is optimal detection in stationary gaussian noise [9]. Matched filter detection requires a prior knowledge of every primary signal.

If the information is not accurate, MF performs poorly. Also the most significant disadvantage of MF is that a CR would need a dedicated receiver for every type of primary user. Cyclostationary feature detector block diagram [1] It exploits the periodicity in the received primary signal to identify the presence of primary users PU. The periodicity is commonly embedded in sinusoidal carriers, pulse trains, spreading code, hopping sequences or cyclic prefixes of the primary signals.

Due to the periodicity, these cyclostationary signals exhibit the features of periodic statistics and spectral correlation, which is not found in stationary noise and interference [19]. Thus, cyclostationary feature detection is robust to noise uncertainties and performs better than energy detection in low SNR regions.

Although it requires a priori knowledge of the signal characteristics, cyclostationary feature detection is capable of distinguishing the CR transmissions from various types of PU signals.

Pdf cognitive radio

This eliminates the synchronization requirement of energy detection in cooperative sensing. Moreover, CR users may not be required to keep silent during cooperative sensing and thus improving the overall CR throughput.

This method has its own shortcomings owing to its high computational complexity and long sensing time. Due to these issues, this detection method is less common than energy detection in cooperative sensing [21]. The comparison of different transmitter detection techniques for spectrum sensing and the spectrum opportunities is shown in figure 8.

As it is evident from the figure, that matched filter based detection is complex to implement in CRs, but has highest accuracy. Similarly, the energy based detection is least complex to implement in CR system and least accurate compared to other approaches. And other approaches are in the middle of these two. Sensing accuracy and complexity of various sensing methods 5.

Various topologies are currently used and are broadly classifiable into three regimes according to their level of cooperation [9], [25]-[26], [28]. Figure 9: Cooperative sensing techniques: Uncoordinated techniques are fallible in comparison with coordinated techniques. Therefore, CR users that experience bad channel realizations detect the channel incorrectly thereby causing interference at the primary receiver.

In such networks, an infrastructure deployment is assumed for the CR users. One CR that detects the presence of a primary transmitter or receiver, informs a CR controller which can be a wired immobile device or another CR user. The CR controller notifies all the CR users in its range by means of a broadcast control message. Centralized schemes can be further classified according to their level of cooperation as: Partially cooperative where network nodes cooperate only in sensing the channel.

This type of coordination implies building up a network of cognitive radios without having the need of a controller. Various algorithms have been proposed for the decentralized techniques among which are the gossiping algorithms or clustering schemes, where cognitive users gather to clusters, auto coordinating themselves [23].

The cooperative spectrum sensing raises the need for a control channel, which can be implemented as a dedicated frequency channel or as an underlay UWB channel. Cognitive users selflessly cooperating to sense the channel have lot of benefits among which the plummeting sensitivity requirements: Employing cooperation between nodes can drastically reduce the sensitivity requirements up to dBm, also reduction in sensitivity threshold can be obtained by using this scheme; agility improvement: The CR users need to perform sensing at periodic intervals as sensed information become obsolete fast due to factors like mobility, channel impairments etc.

This considerably increases the data overhead; large sensory data: Even though cooperatively sensing data poses lot of challenges, it could be carried out without incurring much overhead because only approximate sensing information is required, eliminating the need for complex signal processing schemes at the receiver and reducing the data load.

Also, even though a wide channel has to be scanned, only a portion of it changes at a time requiring updating only the changed information and not all the details of the entire scanned spectrum [].

Radio pdf cognitive

It has been suggested as a method to detect primary user by mounting a low cost sensor node close to a primary user's receiver in order to detect the local oscillator LO leakage power emitted by the RF front end of the primary user's receiver which are within the communication range of CR system users. We note that this method can also be used to identify the spectrum opportunities to operate CR users in spectrum overlay. Typically, CR user transmitters control their interference by regulating their transmission power their out of band emissions based on their locations with respect to primary users.

This method basically concentrates on measuring interference at the receiver [24]. The operating principle of this method is like an UWB technology where the CR users are allowed to coexist and transmit simultaneously with primary users using low transmit power that is restricted by the interference temperature level so as not to cause harmful interference to primary users.

Figure Interference temperature model [6] Here CR users do not perform spectrum sensing for spectrum opportunities and can transmit right way with specified preset power mask. However, the CR users can not transmit their data with higher power even if the licensed system is completely idle since they are not allowed to transmit with higher than the preset power to limit the interference at primary users.

It is noted that the CR users in this method are required to know the location and corresponding upper level of allowed transmit power levels. Otherwise they will interfere with the primary user transmissions. By exploiting this feature, CR user can easily identify the spectrum opportunities in given band. As MTSE uses multiple prototype filters and is better for small sample spaces since the computational complexity increases with large number of samples. Filter bank based spectrum estimation FBSE is regarded as the simplified version of MTSE which uses only one prototype filter for each band and has been proposed for multi-carrier modulation based CR systems by using a pair of matched root Nyquist filter.

Cognitive Radio

By exploiting this information, CR user identifies the spectrum occupancy and hence the spectrum opportunities. It is widely used technique in image processing for edge detection applications.

Tian and Giannakis have proposed this approach in spectrum sensing where wavelets are used for detecting edges in the power spectral density PSD of a wideband channel. The edges in power spectral density are the boundary between spectrum holes and occupied bands and hence it helps to find vacant bands.

Based on this information CR can identify the spectrum opportunities. It is also widely used for pattern such as lines, circles detection in image processing applications.

Challapali et al. Once the features are extracted from the received signal, CR users exploit those features and can select suitable transmission parameters for them.

Cognitive radio is a promising technology which enables spectrum sensing for opportunistic spectrum usage by providing a means for the use of white spaces. Considering the challenges raised by cognitive radios, the use of spectrum sensing method appears as a crucial need to achieve satisfactory results in terms of efficient use of available spectrum and limited interference with the licensed primary users.

Spectrum Mobility Issues The spectrum mobility functions in a cognitive radio network allow an unlicensed user to change its operating spectrum dynamically based on the spectrum conditions. This issue can be addressed in following ways. During transmission by an unlicensed user, the condition of the frequency band has to be observed. In a similar way to spectrum sensing, this would of course incur some overhead. The observation can be performed in a proactive manner or in an on demand basis.

In the proactive approach, the condition of the available channels is periodically observed and the knowledge about these channels is continuously updated. In an on demand approach, channel observation can be performed only when an unlicensed user needs to switch the channel.

For example, when an unlicensed user switches channel, the TCP timer at the transport layer can be frozen to avoid any miss interpretation of the delay incurred for the acknowledgement message. A cross layer optimized framework for protocol adaptation has to be developed to cope up with spectrum mobility.

First, the target channel must not currently be used by any other secondary user i. For synchronization, the MAC protocol must be designed with provision for spectrum handoff information exchange. Spectrum Sensing A major challenge in cognitive radio is that the secondary users need to detect the presence of primary users in a licensed spectrum and quit the frequency band as quickly as possible if the corresponding primary radio emerges in order to avoid interference to primary users.

This technique is called spectrum sensing. Spectrum sensing and estimation is the first step to implement Cognitive Radio system [5].

We can categorize spectrum sensing techniques into direct method, which is considered as frequency domain approach, where the estimation is carried out directly from signal and indirect method, which is known as time domain approach, where the estimation is performed using autocorrelation of the signal.

Another way of categorizing the spectrum sensing and estimation methods is by making group into model based parametric method and periodogram based non- parametric method. Another way of classification depends on the need of spectrum sensing as stated below [13]: 4.

Fundamentals of Cognitive Radio | Wiley Online Books

Primary transmitter detection: In this case, the detection of primary users is performed based on the received signal at CR users. This approach includes matched filter MF based detection, energy based detection, covariance based detection, waveform based detection, cyclostationary based detection, radio identification based detection and random Hough Transform based detection.

Cooperative and collaborative detection: In this approach, the primary signals for spectrum opportunities are detected reliably by interacting or cooperating with other users, and the method can be implemented as either centralized access to spectrum coordinated by a spectrum server or distributed approach implied by the spectrum load smoothing algorithm or external detection.

Interference temperature detection: In this approach, CR system works as in the ultra wide band UWB technology where the secondary users coexist with primary users and are allowed to transmit with low power and are restricted by the interference temperature level so as not to cause harmful interference to primary users.

Classification of Spectrum Sensing Techniques Figure 4: Classification of spectrum sensing techniques [6] Figure 4 shows the detailed classification of spectrum Sensing techniques.

Introduction to Cognitive Radio Networks: Communication Protocols and Security Issues

They are broadly classified into three main types, transmitter detection or non cooperative sensing, cooperative sensing and interference based sensing. Transmitter detection technique is further classified into energy detection, matched filter detection and cyclostationary feature detection [14]. Due to its simplicity and no requirement on a priori knowledge of primary user signal, energy detection ED is the most popular sensing technique in cooperative sensing [15]-[17].

Figure 5: Energy detector block diagram [1] The block diagram for the energy detection technique is shown in the Figure 5. In this method, signal is passed through band pass filter of the bandwidth W and is integrated over time interval. The output from the integrator block is then compared to a predefined threshold. This comparison is used to discover the existence of absence of the primary user. The threshold value can set to be fixed or variable based on the channel conditions.

The ED is said to be the Blind signal detector because it ignores the structure of the signal. It estimates the presence of the signal by comparing the energy received with a known threshold derived from the statistics of the noise. H 1 Where y k is the sample to be analyzed at each instant k and n k is the noise of variance 2.

However ED is always accompanied by a number of disadvantages i sensing time taken to achieve a given probability of detection may be high. As a result CR users need to be tightly synchronized and refrained from the transmissions during an interval called Quiet Period in cooperative sensing.

When secondary user has a priori knowledge of primary user signal, matched filter detection is applied. Matched filter operation is equivalent to correlation in which the unknown signal is convolved with the filter whose impulse response is the mirror and time shifted version of a reference signal.

Detection by using matched filter is useful only in cases where the information from the primary users is known to the cognitive users. When the information of the primary user signal is known to the cognitive radio user, matched filter detection is optimal detection in stationary gaussian noise [9].

If the information is not accurate, MF performs poorly. Also the most significant disadvantage of MF is that a CR would need a dedicated receiver for every type of primary user.

The periodicity is commonly embedded in sinusoidal carriers, pulse trains, spreading code, hopping sequences or cyclic prefixes of the primary signals. Due to the periodicity, these cyclostationary signals exhibit the features of periodic statistics and spectral correlation, which is not found in stationary noise and interference [19]. Thus, cyclostationary feature detection is robust to noise uncertainties and performs better than energy detection in low SNR regions.

Although it requires a priori knowledge of the signal characteristics, cyclostationary feature detection is capable of distinguishing the CR transmissions from various types of PU signals.

This eliminates the synchronization requirement of energy detection in cooperative sensing. Moreover, CR users may not be required to keep silent during cooperative sensing and thus improving the overall CR throughput.

Pdf cognitive radio

This method has its own shortcomings owing to its high computational complexity and long sensing time. Due to these issues, this detection method is less common than energy detection in cooperative sensing [21]. The comparison of different transmitter detection techniques for spectrum sensing and the spectrum opportunities is shown in figure 8. As it is evident from the figure, that matched filter based detection is complex to implement in CRs, but has highest accuracy. Similarly, the energy based detection is least complex to implement in CR system and least accurate compared to other approaches.

And other approaches are in the middle of these two. Various topologies are currently used and are broadly classifiable into three regimes according to their level of cooperation [9], [25]-[26], [28].

Figure 9: Cooperative sensing techniques: a-Centralised Coordinated, b- Decentralised Coordinated, and c-Decentralised Uncoordinated [9], [29]. Uncoordinated techniques are fallible in comparison with coordinated techniques. Free Access. Summary PDF Request permissions. Tools Get online access For authors. Email or Customer ID. Forgot password? Old Password. New Password. Returning user. Request Username Can't sign in?

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