Top PDF Interference Mapping for Spectrum Sensing in Cognitive Radio

Interference Mapping for Spectrum Sensing in Cognitive Radio

Interference Mapping for Spectrum Sensing in Cognitive Radio

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RF-Spectrum Opportunities for Cognitive Radio Networks Operating Over GSM Channels

RF-Spectrum Opportunities for Cognitive Radio Networks Operating Over GSM Channels

Abstract—In this paper, we characterize the radio frequency spectrum opportunities available in a common global system for mobile communications (GSM) channel to support the opera- tion of a cognitive radio network (CRN). In a first step, we describe the technical details involved to sample the channel using a software defined radio device. Adopting a simple energy-based detector, we identify the two energy regions where the GSM system is active or inactive and evaluate the spectrum sensing accuracy. Based on the output of the detector, we show that the distribution of the durations of busy and idle periods are approximated by geometric distributions. Finally, we validate a theoretical model for the distribution of the service time. The validation results indicate that the service time can be success- fully represented by a discrete generalized Pareto distribution, which is confirmed by the Kolmogorov–Smirnov test. Because the throughput of the CRN is represented by the inverse of the service time, the proposed analysis provides an upper bound for the networks’ throughput, indicating the maximum throughput that can be attained when a single secondary user transmits over a GSM cellular channel. The results presented in this paper are validated with real data, confirming the accuracy of the proposed service time model.
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Routing in Cognitive Radio Networks - A Survey

Routing in Cognitive Radio Networks - A Survey

distance vector are not well suited to be applied to CRNs. The main reason is that there are frequent dynamic changes in the CRN that may trigger a large number of updates and lead to rapidly changing routing tables. One of the main tasks of the cognitive radio is to sense and determine whether a channel is available or not. Each node senses spectrum, divide the available band into channels, saves the list of available channels and then chooses the channel according to the priority depend upon the suitability according to the link metrics and The dynamic behavior of CRN inherits many characteristics of wireless mobile networks like: nodes mobility, nodes limited power and network life time along with some of the constraints like interference, signal fading, channel scarcity, conflict with licensed users. So a number of routing metrics are proposed in different routing protocols.
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Intelligent Wireless Communication System Using Cognitive Radio

Intelligent Wireless Communication System Using Cognitive Radio

The increasing demand for wireless communication introduces efficient spectrum utilization challenge. To address this challenge, cognitive radio (CR) is emerged as the key technology; which enables opportunistic access to the spectrum. CR is a form of wireless communication in which a transceiver can intelligently detect which communication channels are in use and which are not, and instantly move into vacant channels while avoiding occupied ones. This optimizes the use of available radio-frequency (RF) spectrum while minimizing interference to other users. In this paper, we present a state of the art on the use of Multi Agent Systems (MAS) for spectrum access using cooperation and competition to solve the problem of spectrum allocation and ensure better management. Then we propose a new approach which uses the CR for improving wireless communication for a single cognitive radio mobile terminal (CRMT).
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Analytical Model Analysis Of Distributed Cooperative Spectrum Sensing Method

Analytical Model Analysis Of Distributed Cooperative Spectrum Sensing Method

This section relates to spectrum sensing control, awareness networking and cooperative spectrum sensing in Figure 1. Cooperative spectrum sensing is a powerful concept to leverage the spatial separation of multiple spectrum sensing nodes in a wireless network. The optimal fusion of sensing results, acquired by distributed network nodes, allows to alleviate the hidden node problem and/or to share the sensing load between network nodes. The optimal fusion of decentralized observations has been studied since a long time, see e.g., [20] and the references therein. It has been shown already in [8], [9] that the optimal fusion rule is to compute the joint likelihood ratio of the distributed observations. Cooperative spectrum sensing requires a networking solution to communicate sensing results (sensing messages) between nodes. Using spectrum sensing individual network nodes, as well as the whole network by virtue of collaboration, becomes aware of the local radio spectrum situation. Consequently the distribution of spectrum sensing results can be understood as Awareness Signaling. Within E3 (End-to-End Efficiency project) an awareness signaling solution, namely Cognitive Control Radio (CCR) has been developed [10], [11].
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A Novel Reservation-based MAC Scheme for Distributed Cognitive Radio Networks

A Novel Reservation-based MAC Scheme for Distributed Cognitive Radio Networks

are used to synchronization and channel access (transmission). Initially, an SU assumes the synchronization task if it does not receive synchronization information during a predefined time interval. Such an SU is called synchronizer and is responsible for the transmission of a short synchronization packet during the ϕ interval containing the duration of the spectrum sens- ing and spectrum access periods. The synchronization (SYNC) packet is transmitted by the synchronizer whenever the chan- nel is sensed idle during the spectrum sensing period. By re- ceiving the SYNC packet, the neighbors (denoted as followers) know the duration of the spectrum sensing and spectrum ac- cess periods and may correct clock drifts. If the synchronizer SU does not transmit the SYNC packet within a given period of time denoted as SYNC_TIMEOUT (e.g., because that SU has finished its activity), any follower may then assume that role. To become a synchronizer, a follower is allowed to ran- domly transmit the SYNC packet after the SYNC_TIMEOUT has been elapsed. Randomness is used to avoid multiple nodes acting simultaneously as synchronizers. This is similar to the synchronization schemes already proposed for distributed MAC schemes of wireless sensor networks, where any node can act as a synchronizer [30]. For the sake of simplicity of the anal- ysis, in what follows, we consider that the SU Rx is always a synchronizer, being responsible for the synchronization of
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Current Trends and Research Challenges in Spectrum-Sensing for Cognitive Radios

Current Trends and Research Challenges in Spectrum-Sensing for Cognitive Radios

Cognition cycle involves the function of sensing the spectrum, making a decision about the hole and the licensed user, sharing the spectrum and mobility of secondary user in case a licensed user is detected. Accordingly, the four phases of cognition cycle can be described as: Spectrum Sensing is one of an important building block of cognitive radio. It involves the task of sensing the radio environment for the presence of spectrum holes and detection of PUs. Spectrum decision decides for the optimum selection of spectrum hole to transmit the data. As there are several CR users sharing the same spectrum, there is a need for a mechanism which coordinates the network access to all specified users. This can be defined under Spectrum Sharing. Under Spectrum Mobility if any primary licensed user is detected, then the CR should seamlessly switch over to some other suitable spectrum hole for further transmission [5]. Spectrum sensing encounters the issues like fading, shadowing and noise uncertainty. The scheme of cooperation has been suggested by researchers as an answer to these problems [6]. Here, CR users cooperate to share their sensing information for making a combined decision which is usually more accurate than individual decision. It reduces the probability of false alarm and mis- detection. Moreover, it solves the hidden primary user problem and reduces the sensing time [7].
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A Cooperative Spectrum Sensing Scheme Based on Trust and Fuzzy Logic for Cognitive Radio Sensor Networks

A Cooperative Spectrum Sensing Scheme Based on Trust and Fuzzy Logic for Cognitive Radio Sensor Networks

depicted in Fig. 4. It can be seen that the proposed scheme has the highest detection probability than the OR/AND fusion rules based scheme, the EGC based scheme, and the fuzzy logic scheme. It means that the method of node trust evaluation improves the FC decision accuracy.

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Mitigating Intrusion and Vulnerabilities in Cognitive Radio Networks

Mitigating Intrusion and Vulnerabilities in Cognitive Radio Networks

The security threats in cognitive radio entail majorly illegal information injection and forging of information transmission. The radio environment map (REM) fetches several characteristic data from a large spectrum sensing cognitive users. Attackers can maliciously falsify local spectrum sensing data to confuse the receiver in other to lunch attacks which can prompt the receiver to make wrong spectrum accessing decision [8]. A secure computer network is a trusted and reliable system that functions appropriately. Normally, information technology security is usually analyzed on the basics of confidentiality, integrity and availability. In the 1980s, computer systems had been equipped already with an audit capability. The operating system can be able to collect system-wide attributes using the audit trace capability. The analysis been done by humans became very tedious as collected events and activities increased. An automated method of collecting and analyzing data to produce vital information to check network intrusions became very necessary. However, the birth of this automated mechanism or tool became the foundation or root of intrusion detection.
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Overhead Reduction in Cooperative Spectrum Sensing Via Sequential Detection in Cognitive Radio Networks Under Bandwidth Constraint

Overhead Reduction in Cooperative Spectrum Sensing Via Sequential Detection in Cognitive Radio Networks Under Bandwidth Constraint

R eliable and efficient spectrum sensing plays an important role in cognitive radio networks. On the other hand, accurate and fast sensing of spectrum is the main function of a cognitive radio for cognitive users to avoid harmful interference in licensed users. However, detection performance issues are often shadowing, fading and receiver uncertainty. To mitigate the impact of these issues, cooperative spectrum sensing as an effective method is presented to improve the detection performance with help of spatial diversity. Cooperative spectrum sensing leads to CR detection performance improvement. In order to execute the cooperative spectrum sensing among cognitive radio users, data fusion schemes are superior to that of decision fusion ones in terms of the detection performance but suffer from the disadvantage of huge traffic overhead when bandwidth constraint of communication channels is taken into account. The overhead contains additional sensing time, delay, energy and sensing actions dedicated to cooperative sequential sensing any also contains any performance degradation that is caused by cooperative sensing. The purpose of this paper is reviewing of the cooperative sensing techniques with sequential methods. The simulation results show that with reducing the number of examined samples, the sensing time and the energy will be reduced and as the result, the overhead will be reduced. Index Terms: Cognitive radio, sequential spectrum sensing, cooperative sensing, overhead, hard decision.
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Channel Decision in Cognitive Radio Enabled Sensor Networks: A Reinforcement Learning Approach

Channel Decision in Cognitive Radio Enabled Sensor Networks: A Reinforcement Learning Approach

cluster head adjusts the sensing frequency based on the amount of PU activity detected on the current channel. Channel access decision is based on the reward function in (8) and the value in (9) and (10). For the three approaches, the average interference decreases with the reduced PU activity. Intuitively, the random approach incurs the highest probability to channel handoff due to PU activities, while DCA and RL approach provides the highest PU protection because each CR cluster head immediately handoff to a new channel each time a PU is detected in the current channel. In the RL-based approach, the sensing frequency is dynamically adjusted based on the amount of PU activity through the action selection function in (12). The difference with the DCA approach can be said to be due the fact that in our RL approach, each cluster head use a probabilistic policy , over the set of actions. Though randomization allows choosing suboptimal actions, Figure 9 shows that the impact is quite limited. Thus, our RL-based approach provides a similar impact on PU receivers than the DCA approach, but with a consistent performance gains for CR users.
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Multiple Detectors Based Analytical Performance of Spectrum Sensing

Multiple Detectors Based Analytical Performance of Spectrum Sensing

During their communication, SUs do not sense the channel, so they must periodically suspend their transmission and enter a sensing period so as to determine whether the PU has emerged or not. The main task of spectrum sensing is to detect the presence of PU correctly, i.e. to show the presence when PU is actually present and to show the vacant band when PU is actually not operating. This is called probability of detection. Previous research has shown that Probability of detection increases with increase in Signal-to-Noise ratio[5]. There are 3 major techniques for spectrum sensing, namely Energy Detection, Matched-Filter Detection and Cyclostationary detection. Energy detection is of particular interest because it does not require any prior knowledge about the signal [6]. A major challenge here is that when a single energy detector is used in cognitive radio, it raises a question over the reliability of cognitive radio and its performance. The motivation of this paper is to provide an unfailing system with improved spectrum sensing performance.
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A Novel Scheme to Improve the Spectrum Sensing Performance

A Novel Scheme to Improve the Spectrum Sensing Performance

Due to limited availability of spectrum for licensed users only, the need for secondary access by unlicensed users is increasing. Cognitive radio turns out to be helping this situation because all that is needed is a technique that could efficiently detect the empty spaces and provide them to the secondary devices without causing any interference to the primary (licensed) users. Spectrum sensing is the foremost function of the cognitive radio which senses the environment for white spaces. Energy detection is one of the various spectrum sensing techniques that are under research. Earlier it was shown that energy detection works better under AWGN channel as compared to Rayleigh channel, however the conventional spectrum sensing techniques have a high probability of false alarm and also show a better probability of detection for higher values of SNR. There is a need for a new technique that shows a reduced probability of false alarm as well as an increase in the probability of detection for lower values of SNR. In the present work the conventional energy detection technique has been enhanced to get better results.
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Power Allocation with Random Removal Scheme in Cognitive Radio System

Power Allocation with Random Removal Scheme in Cognitive Radio System

The SUs can access the radio frequency spectrum simultaneously. This system model is making the scenario in which all PUs are staying in receiving mode, and SUs are trying to transmit data in the uplink to the BS. Hence PUs will receive the interference from SUs. An assumption is made that the channel usage pattern of the primary users is fairly static over time so that the cognitive radio network can carry out primary user detection easily and thereby avoiding interfering with primary user operation. A free-space path loss model with the path-loss exponent of n is assumed. To facilitate further discussion; let us introduce the following notations:
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SPECTRUM SENSING IN COGNITIVE RADIO NETWORKS: QOS CONSIDERATIONS

SPECTRUM SENSING IN COGNITIVE RADIO NETWORKS: QOS CONSIDERATIONS

Detecting the presence of a PU, or more precisely finding out whether the PU is using its allocated spectrum or not, is an essential task for a CR device. On one hand, this fundamental task requires improving sensing accuracy by avoiding false positive results while detecting the presence of a PU. On the other, the employed sensing technique should achieve a high detecting probability of the available spectrum holes. The nature of the electromagnetic signals makes accurate sensing a complicated process. More specifically, the Signal to Noise Ratio (SNR), the multipath fading of the PU signals, and the changing levels of noise can significantly affect the sensing accuracy [5, 6]. Moreover, imperfect spectrum sensing can result in increased transmission error rates, for both the PU and the SUs [7]. Such errors may contribute to the degradation of the quality of the services provided by a PU and SUs. Noticeably, any QoS degradation that can be attributed to the CR technology can potentially harm the progress of the CR-based solutions. In this paper, the main features and limitations of the prevalent spectrum sensing methods are examined. Furthermore, the key aspects that should be involved in selecting the appropriate sensing method are highlighted and discussed.
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User Characterization through Dynamic Bayesian Networks in Cognitive Radio Wireless Networks

User Characterization through Dynamic Bayesian Networks in Cognitive Radio Wireless Networks

Cognitive Radio is a technology that allows to implement new and innovative skills to wireless systems such as dynamic access to spectrum; a concept comprising the autonomous control of multiple variables such as sensing, decision making, sharing and spectral mobility within the system. To include these skills in CR, researchers have proposed the use of some artificial intelligence techniques in each of the stages comprised in the concept of CR. One of the methodologies that has had less acceptance and could be an important reference for application in the dynamic spectrum access is known as Bayesian networks, the focus of our proposal to improve the percentage of modeling and characterization of PUs in the spectral decision making stage from spectrum sensing.
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Two stage spectrum sensing for cognitive radio using cyclostationarity detection and energy detection

Two stage spectrum sensing for cognitive radio using cyclostationarity detection and energy detection

The two-stage spectrum sensing that we propose is shown in figure 1. We assume that there are L channels to be sensed and that channels are sensed serially. In the coarse sensing stage, the channel is sensed using energy detection. If the decision metric is greater than a threshold λ, the channel is declared to be occupied. Else, the received signal is analyzed by fine sensing consisting of cyclostationary detection. If the constituent detection metric is greater than a threshold , the channel is declared occupied, else it is declared to be empty. In the following we shall discuss the two stages of energy detection [5], [6] and cyclostationary detection [8], [9] in the context of two- stage sensing.
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ENERGY EFFICIENT COOPERATIVE SPECTRUM SENSING IN COGNITIVE RADIO

ENERGY EFFICIENT COOPERATIVE SPECTRUM SENSING IN COGNITIVE RADIO

Average throughput versus sensing time is presented in Figure 5. Note that increasing the sensing time for each CR leads to increase in the number of samples which improves the accuracy (smaller false alarm and miss-detection probabilities) of sensing results. Smaller false alarm probability increases the chances of transmission and hence increases the throughput. Additionally, smaller miss-detection probability reduces un-useful transmission cases in which the CR node interferes with the PU transmission. On the other hand, more sensing time leads to decrease in transmission time, which decreases throughput. Therefore, curves have peaks where they start increasing (due to more accurate sensing) then decreasing again (due to less transmission time).
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WAVELET AND S-TRANSFORM BASED SPECTRUM SENSING IN COGNITIVE RADIO

WAVELET AND S-TRANSFORM BASED SPECTRUM SENSING IN COGNITIVE RADIO

In this paper Continuous Wavelet technique is used to sense vacant spectrum and sub band edges with low computational complexity. To find the frequency resolution exactly at given frequency the S-Transform is used here since it is more fast in sensing time and flexibility and can effectively detect frequency boundaries accurately even at low Signal to Noise Ratio. Wavelets have two aspects: scale and time. To sense the spectrum at different frequency bands with less bandwidth this reduces hardware complexity and sensing time by using the wavelet properties. To overcome the loss of frequency resolution occur at certain frequency in wavelet based method here S-Transform is used to found the small variation and spike signal to differentiate the real and imaginary signal. The main advantage of above method is ability to detect the availability of primary user even at high frequency and high noise. Here average power spectrum density (PSD) within each sub-band is identified to determine the unoccupied band and to identify the frequency locations of the non-overlapping spectrum.
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ENERGY DETECTION BASED SPECTRUM SENSING FOR COGNITIVE RADIO

ENERGY DETECTION BASED SPECTRUM SENSING FOR COGNITIVE RADIO

Abstract — Cognitive Radio is an emerging technology which avoids the congestion in wireless communication by exploiting unused radio spectrum . The Spectrum sensing plays a fundamental requirement of CR which finds an unused free spectrum and detects the licensed user transmissions.energy detection constitutes a preferred approach for cognitive rdio spectrum sensing due to its simple applicability. In this paper Wiener khinchin theorem ,QAM techniques are used for the energy detection

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