OR Fusion Research Articles

Optimized Cluster-Based Cooperative Spectrum Sensing Over Weibull, Nakagami, Rician, and Rayleigh Fading Channels

This paper reports cluster-based cooperative spectrum sensing (CB-CSS) over Weibull, Nakagami, Rician, and Rayleigh fading channels in cognitive radio networks (CRNs). The performance assessment of centralized cooperative spectrum sensing (CCSS) and CB-CSS with different fusion rules over the Weibull fading channel is presented. The goal is to improve detection performance in a practical and efficient manner for CB-CSS. For this purpose, an expression for the optimal inter-fusion rule threshold (fusion rule between cluster head (CH) and fusion center (FC)) is proposed to minimize total error probability (TEP) over Weibull for CB-CSS. Simulations are conducted using analytical equations for missed detection and false alarm probability. The obtained expression for optimal inter-fusion rule threshold which minimizes TEP is verified by the results. The proposed model is validated with other fading channels such as Nakagami, Rician, and Rayleigh to show the superiority of the approach. It is observed that for a given range of detection threshold and for 4 × 6 cluster size (4 clusters and 6 CR users inside each cluster), the lowest total error probability is achieved over the Weibull fading channel. Minimum TEP over the Weibull fading channel for given network parameters is TEP = 1.656 × 10 − 6 when intra- and inter-fusion rules are l = 1 (OR fusion) and c = 3 . The least number of clusters and users essential to achieving target error bound over different fading channels are also determined.

Performance Evaluation of DCSS using Two Level 1-Bit Hard Decision Strategies over TWDP Fading Channel

The spectrum sensing method's dependability is greatly influenced by two of the most crucial factors, including various fading channels and nearby wireless users. Multipath fading, buried terminals, and shadowing are just a few of the challenges encountered by users of non-cooperative spectrum sensing systems. Cooperative spectrum sensing approach gives a remedy for this issue. With the use of the common receiver, CSS permits the user to detect the spectrum. Additionally, it has been separated into distributed CSS (D-CSS) and centralized CSS (C-CSS). By using particular rules to identify the presence of the licensed user, both concepts are compared to one another in this article. The effectiveness of cluster-based distributed cooperative spectrum sensing over two-wave diffuse power fading channels (TWDP Channel) is also examined in the article. Wei-bull and Hoyt fading channels are two examples of fading channels that have previously exploited this idea. In this paper, simulation findings for the less well-known two-wave with diffuse power channels are reviewed. This work mainly focused on CSS over TWDP fading channels along with several proposed approach for two stage hard decision strategies using AND fusion and OR fusion. The simulation performance findings for TWDP fading situation enhance the OR_AND fusion strategy detection performance at various SNR levels. The presented D-CSS approach helps users to get beyond the difficulty they have when using non-cooperative spectrum sensing and lists the relationship among detection efficiency and power consumption for Cognitive radio technology used in constrained wireless environments.

Effect of Clusters in Energy-Efficient Cooperative White Space Detection in a Cognitive Radio System

White Space Detection (WSD) is a core operation in a Cognitive Radio System (CRS) to identify idle spectrum for maximum utilization. However, WSD is often affected by multipath effects resulting in poor detection rate. Cooperative WSD (CWSD) which is one of the existing techniques used to address the problem is characterized by long sensing time, energy and bandwidth inefficiency. Energy-Efficient CWSD (EECWSD) was proposed in previous work to solve the problem associated with CWSD. Hence, in this paper, the effect of clusters in EECWSD is carried out with Radiometry Detector (RD). The investigation is carried out using multiple clusters and each cluster contained multiple Secondary Users (SUs). The SUs are used to perform local sensing and the sensing results are combined at individual cluster using majority fusion rule. The sensing results from individual cluster are combined to obtain global sensing result using OR fusion rule. The system is simulated using MATLAB software. The system is evaluated using Probability of Detection (PD), Total Error Probability (TEP), Spectral Efficiency (SE) and Sensing Time (ST). At SNR of 20 dB, PD values of 0.7890, 0.8376 and 0.8787 are obtained for clusters 3, 4 and 5, respectively, while the corresponding TEP values are 0.2210, 0.1724 and 0.1313 for clusters 3, 4 and 5, respectively. At SNR of 16 dB, 13.2594 and 16.4341 are the SE values obtained for clusters 3 and 5, respectively, while the corresponding ST values obtained are 4.2487 and 2.6177 s for clusters 3 and 5, respectively. The results obtained revealed that, PD and SE increase as number of cluster increases, while ST and TEP reduce as cluster increases. Keywords— Cognitive Radio, White Space, Spectrum Sensing, Probability of Detection, Spectral Efficiency.

Hybrid Detection for Cooperative Cognitive Radio Using AWT and HDWT

A lot of research shows that spectrum is not effectively used in wireless communications. There are several gaps in spectrum that are unused. Cognitive radio (CR) is a smart technique of addressing inefficient usage of spectrum resources. Spectrum sensing is a vital task in cognitive radio systems. The objective of the spectrum sensing process is to investigate the presence and absence of primary user signals in different spectrum holes. To increase the feasibility of the spectrum sensing process, it can be implemented in a cooperative mode. The cooperative spectrum sensing depends on a decision making task at a group of secondary users. One of the most popular spectrum sensing techniques is the energy detection. Unfortunately, this technique fails in low SNR scenarios to give high detection results. So, there is a need for cooperative spectrum sensing in addition to noise reduction techniques to enhance the performance of the CR system. This paper presents noise reduction techniques based on additive wavelet transform (AWT) with homomorphic decomposition and Haar discrete wavelet transform (HDWT) with homomorphic decomposition to reduce the noise prior to the decision making process. This scenario is investigated with several fusion rules that are used for the cooperative spectrum sensing with fixed-time sensing. Simulation results prove that with OR fusion rule, the proposed scenario increases the probability of detection in the range of SNR from − 20 to − 15 dB using the AWT with homomorphic decomposition. The achievable throughput increases using the HDWT with homomorphic decomposition. The AND fusion rule is not recommended with the HDWT, but the probability of detection is high with the AWT. The majority fusion rule achieves high throughput with the HDWT and a high probability of detection with the AWT.

Energy-Efficient Cluster-Based Cooperative Spectrum Sensing in a Multiple Antenna Cognitive Radio Network

Spectrum Hole Detection (SHD) is a major operation in a Cognitive Radio (CR) network to identify empty spectrum for maximum utilization. However, SHD is often affected by multipath effects resulting in interference. The existing techniques used to address these problems are faced by poor detection rate, long sensing time and bandwidth inefficiency. Hence, this paper proposes a cluster-based Energy-Efficient Multiple Antenna Cooperative Spectrum Sensing (EEMACSS) for SHD in CR networks using Energy Detector (ED) with a modified combiner. Multiple secondary users are used to carry out local sensing using ED in multiple antenna configurations. The local sensing results are combined at the cluster head using majority fusion rule to determine the sensing results at each cluster. The sensing results from individual cluster are combined to determine the global sensing result using OR fusion rule. The proposed EEMACSS is evaluated using Probability of Detection (PD), Sensing Time (ST) and Spectral Efficiency (SE) by comparing with existing techniques. The results reveal that the proposed technique shows better performance.

Automatic segmentation of nasopharyngeal carcinoma from CT images

The nasopharyngeal carcinoma (NPC) called also Cavum cancer becomes a public health problem for the Maghreb countries and Southeast Asia. The detection of this cancer could be carried out from computed tomography (CT) scans. In this context, we proposed two approaches based on image clustering to locate affected tissues by the Cavum cancer. These approaches are based respectively on E-M and Otsu segmentation. Compared to the physician manual contouring, our automatic detection proves that the detection of the cancer while using the Otsu clustering in terms of precision, recall and F-measure is more efficient than E-M. Then, we merged the results of these two methods by using the AND and the OR logical operators. The AND fusion yields to an increase of the precision while the OR fusion raises the recall. However, the detection of the NPC using Otsu remain the best solution in terms of F-Measure. Compared to previous studies that provide a surface analysis of the NPC, our approach provides a 3D estimation of this tumour ensuring a better analysis of the patient folder.

Adaptively Controlled Cooperative Spectrum Sensing Using OR Fusion Rule for Throughput Maximization in Cognitive Radio

Utilization of wireless spectrum resources should be improved with the great increase of using wireless services. Cognitive radio (CR) has been proposed to overcome the problem of inefficient use of spectrum holes. Spectrum sensing is the most important functional block, which is used to estimate the spectrum holes in the CR system. Cooperative spectrum sensing improves the detection performance and minimizes miss-detection of the primary user. The objective of this paper is to achieve the maximum throughput for all values signal-to-noise ratio (SNR). Cooperative spectrum sensing with fixed sensing time does not provide efficient throughput performance for all values of SNR. In this paper, we propose an adaptively controlled cooperative spectrum sensing scheme using OR fusion rule for throughput maximization in CR system. The proposed scheme estimates the required optimal sensing time for each value of the SNR to achieve the maximum throughput. Simulation results of the proposed scheme demonstrate that it maximizes the achieved throughput for all values of SNR and minimizes the probability of false alarm.

Energy efficient collaborative spectrum sensing with clustering of secondary users in cognitive radio networks

Collaborative sensing also known as cooperative spectrum sensing (CSS) is an efficient spectrum sensing technique to improve the sensing accuracy in cognitive radio (CR). However, it brings extra collaborative sensing overhead due to mutual exchange of large information among CR users. In CSS, the number of cooperative users, fusion rule, transmission power and sensing time affects the energy efficiency (EE) of the CSS. In this study, a cluster-based CSS is proposed with four fusion rules. Optimised fusion rule is determined that maximises the EE. For the proposed CSS, joint optimal sensing time and transmission power of energy efficient CSS is determined. The joint optimisation design problem is formulated as function of sensing time and transmission power subjected to primary user protection constraints. An iterative algorithm is proposed to determine joint optimal sensing time and transmission power that maximises the EE of CSS. Results show that EE is maximum for AND–OR fusion at sensing duration 2.3 ms and transmission power 1.11 W at SNR = −20 dB. Maximum EE at optimal point is = 3.735 Mbits/Hz/J.

Cluster‐based distributed cooperative spectrum sensing over Nakagami fading using diversity reception

Cooperative spectrum sensing (CSS) can solve the problem of hidden terminal in cognitive radio (CR). CSS can be employed in two ways distributed and centralised. In wireless networking, clustering can provide network scalability, better resource allocation, and energy efficiency. Here, cluster-based distributed CSS is investigated over Nakagami channel. In the proposed scheme, four fusion policies OR–OR, OR–AND, AND–OR, and AND–AND are investigated. An analytical framework has been presented to evaluate different parameters related to spectrum sensing, i.e. detection probability, false alarm probability, and missed detection probability for Nakagami fading channel. On the basis of the developed framework, the performance of cluster-based distributed CSS has been compared with the centralised CSS. Results show that OR–OR fusion rule of the cluster-based distributed CSS outperforms other fusion rules and centralised CSS. To achieve 80% probability of detection, SNR required is 10 dB for other fusion schemes. Further performance improvement is achieved by using square law selection and maximum ratio combining diversity schemes.

Spectrum and energy efficiency of cooperative spectrum prediction in cognitive radio networks

In this paper, the spectrum and energy efficiency of cooperative spectrum prediction (CSP) in cognitive radio networks are investigated. In addition, the performance of cooperative spectrum prediction is evaluated using a hidden Markov model (HMM) and a multilayer perceptron (MLP) neural network. The cooperation between secondary users in predicting the next channel status employs AND, OR and majority rule fusion schemes. These schemes are compared for HMM and MLP predictors as a function of channel occupancy in term of prediction error, spectrum efficiency and energy efficiency. The impact of busy and idle state prediction errors on the spectrum efficiency is also investigated. Simulation results are presented which show a significant improvement in the spectrum efficiency of the secondary users CSP with the majority rule at the cost of a small degradation in energy efficiency compared to single spectrum prediction and traditional spectrum sensing.

A critical comparison of techniques for MRI-targeted biopsy of the prostate.

MRI-targeted biopsy is a promising technique that offers an improved detection of clinically significant prostate cancer over standard non-targeted biopsy. It is established that prostate MRI is of use in both the primary and repeat biopsy setting for the detection of significant prostate cancer. There are three approaches to targeting biopsies to areas of interest seen on prostate MRI. They each rely on the acquisition and reporting of a diagnostic quality multi-parametric MRI scan used to identify areas of interest, and the subsequent use of those diagnostic quality images in combination with real-time images of the prostate during the biopsy procedure. The three techniques are: visual registration of the MRI images with a real-time ultrasound image; software-assisted fusion of the MRI images and the real-time ultrasound images, and in-bore biopsy, which requires registration of a diagnostic quality MRI scan with a real time interventional MRI image. In this paper we compare the three techniques and evaluate those studies where there is a direct comparison of more than one MRI-targeting technique. PubMed was searched from inception to November 2016 using the search terms (cognitive registration OR visual registration OR fusion biopsy OR in-bore biopsy OR targeted biopsy) AND (prostate cancer OR prostate adenocarcinoma OR prostate carcinoma OR prostatic carcinoma OR prostatic adenocarcinoma) AND (MRI OR NMR OR magnetic resonance imaging OR mpMRI OR multiparametric MRI). The initial search included 731 abstracts. Eleven full text papers directly compared two or more techniques of MRI-targeting, and were selected for inclusion. The detection of clinically significant prostate cancer varied from 0% to 93.3% for visual registration, 23.2% to 100% for software-assisted registration and 29% to 80% for in-bore biopsy. Detection rates for clinically significant cancer are dependent on the prevalence of cancer within the population biopsied, which in turn is determined by the selection criteria [biopsy naïve, previous negative biopsy, prostate specific antigen (PSA) selection criteria, presence of a lesion on MRI]. Cancer detection rates varied more between study populations than between biopsy approaches. Currently there is no consensus on which type of MRI-targeted biopsy performs better in a given setting. Although there have been studies supporting each of the three techniques, substantial differences in methodology and reporting the findings make it difficult to reliably compare their outcomes.

Spectrum mobility in cognitive radio network using spectrum prediction and monitoring techniques

The spectrum mobility during data transmission is an integral part of the cognitive radio network (CRN) which is conventionally two types for instance reactive and proactive. In the reactive approach, the cognitive user (CU) switches its communication after the emergence of the primary user (PU), where the detection of emergence of PU relies either on spectrum sensing and/or monitoring. Due to certain limitations of the reactive approach such as: (1) loss at least one packet on the emergence of PU and (2) resource (bandwidth) wastage if the periodic sensing is used for mobility, the researchers have introduced the concept of proactive spectrum mobility. In this approach, the emergence of PU is predicted on the bases of pre-available spectrum information, and switching is performed before true emergence of the PU, in order to avoid even the single packet loss. However, the imperfect spectrum prediction is a major milestone for the proactive spectrum mobility. Recently, due to introduction of the spectrum monitoring simultaneous to the data transmission, the reactive approach has come into lime-light again, however, it suffers from the ‘single packet loss’ and ‘imperfect spectrum monitoring’ issues. Therefore in this paper, we have exploited the spectrum monitoring and prediction techniques, simultaneously for the spectrum mobility, in order to enhance the performance of cognitive radio network (CRN). In the proposed strategy, the decision results of the spectrum prediction and monitoring techniques are fused using AND and OR fusion rules, for the detection of emergence of PU during the data transmission. Further, the closed-form expressions of the resource wastage, achieved throughput, interference power at PU and data-loss for the proposed approaches as well as for the prediction and monitoring approaches are derived. Moreover, the simulation results for the proposed approaches are presented and validation is performed by comparing the results with prediction and monitoring approach. In a special case, when the prediction error is zero, the graphs of all metric values overlies the spectrum monitoring approach, which further validates the proposed approach.

Soft Cooperative Spectrum Sensing using Quantization Method in the Presence of Smart PUE Attack

To overcome the frequency spectrum scarcity, cognitive radio (CR) network is proposed where secondary users opportunistically use vacant frequency band. CR intrinsic properties impose threats to these wireless networks. Smart primary user emulation attack (PUEA) is one of these threats where a malicious user performs spectrum sensing and after being aware of radio environment sends fake signals identical to the primary user signals with the desired signal occurrences in both vacant and occupied licensed frequency bands. In this paper, while considering smart PUEA, we propose a soft cooperative spectrum sensing method. In this method, the sensing information values of N secondary users are transmitted to the fusion center (FC) and combined with some coefficients with the aim of minimizing the error probability of primary user detection, under the given constant false alarm probability. We also propose a method for quantization of this sensing information that should be sent to FC. Furthermore, we have worked on the investigation of channel estimation error impact on detection performance. Simulation results show that our method significantly improves the spectrum sensing probability of the error, in comparison with hard combination scheme, AND fusion and OR fusion rules, either considering quantization or in the presence of channel estimation error.

Wideband spectrum compressive sensing for frequency availability in LEO‐based mobile satellite systems

SummaryAvailability of L/S frequency band is a critical requirement for global communication provided by a constellation of low Earth orbit satellites. This paper investigates the issue of low Earth orbit mobile satellite system frequency availability, where wideband compressed signal detection approach is utilized to obtain active user subbands and their locations which should be avoided during frequency allocation. Compressed sensing can be achieved by exploiting the sparsity of wideband signal spectrum, whose useful frequency support occupies only a small portion of the entirely wide spectrum. An estimate of the signal spectrum can be obtained by using reconstruction algorithms. We define two novel wideband spectrum compressed sensing methods based on discrete cosine transform and Walsh–Hadamard transform, briefly named DCT‐WSCS and WHT‐WSCS, which significantly improve the performance of spectrum recovery and signal detection compared with conventional discrete Fourier transform‐based compressed spectrum sensing method. Furthermore, with the help of inter‐satellite links, the scheme of multiple satellites cooperatively sensing with a final decision according to OR and MAJ fusion rules is proposed, which can bring diversity gains. Finally, in‐depth numerical simulations under a particular scenario demonstrate the performance of the proposed scheme in the aspect of signal reconstruction precision, detection probability, and processing time. Copyright © 2017 John Wiley & Sons, Ltd.

Automatic segmentation of Nasopharyngeal carcinoma from CT images

The nasopharyngeal carcinoma (NPC) called also Cavum cancer becomes a public health problem for the Maghreb countries and Southeast Asia. The detection of this cancer could be carried out from computed tomography (CT) scans. In this context, we proposed two approaches based on image clustering to locate affected tissues by the Cavum cancer. These approaches are based respectively on E-M and Otsu segmentation. Compared to the physician manual contouring, our automatic detection proves that the detection of the cancer while using the Otsu clustering in terms of precision, recall and F-measure is more efficient than E-M. Then, we merged the results of these two methods by using the AND and the OR logical operators. The AND fusion yields to an increase of the precision while the OR fusion raises the recall. However, the detection of the NPC using Otsu remain the best solution in terms of F-Measure. Compared to previous studies that provide a surface analysis of the NPC, our approach provides a 3D estimation of this tumour ensuring a better analysis of the patient folder.

Throughput Maximization of CSMA in Cognitive Radio Networks with Cooperative Spectrum Sensing

This study aims the throughput maximization of carrier sense multiple access (CSMA) in random access cognitive radio networks (CRNs) by using cooperative spectrum sensing. In the cooperative spectrum sensing, multiple cognitive radio (CR) users individually monitor the spectrum for the primary users (PUs)' activities and send their local decisions to the CR access point. Upon receiving the individual decisions, the CR access point gives the final decision about the PU's presence at the spectrum and sends it to all CR users in the network. At decision stage, CR access point utilizes the k-out-of-N decision fusion rule with optimal k value in order to obtain maximum CRN throughput. In this paper, we model a CRN utilizing CSMA medium access control scheme and focus on analyzing the throughput performance of it. Throughput performances of our CRN model which separately utilizes AND, OR, MAJORITY fusion rules and k-out-of-N decision fusion rule with the optimum k value are obtained and compared under the same network level probability of detection constraint. The throughput results obtained show that our CRN model utilizing k-out-of-N decision fusion rule with the optimum k value achieves higher throughput performance than AND, OR and MAJORITY fusion rules when they all have the same CRN load.

Cooperative subcarrier sensing using antenna diversity based weighted virtual sub clustering

The idea of cooperation and the clustering amongst cognitive radios (CRs) has recently been focus of attention of research community, owing to its potential to improve performance of spectrum sensing (SS) schemes. This focus has led to the paradigm of cluster based cooperative spectrum sensing (CBCSS). In perspective of high date rate 4th generation wireless systems, which are characterized by orthogonal frequency division multiplexing (OFDM) and spatial diversity, there is a need to devise effective SS strategies. A novel CBCSS scheme is proposed for OFDM subcarrier detection in order to enable the non-contiguous OFDM (NC-OFDM) at the physical layer of CRs for efficient utilization of spectrum holes. Proposed scheme is based on the energy detection in MIMO CR network, using equal gain combiner as diversity combining technique, hard combining (AND, OR and Majority) rule as data fusion technique and antenna diversity based weighted clustering as virtual sub clustering algorithm. Results of proposed CBCSS are compared with conventional CBCSS scheme for AND, OR and Majority data fusion rules. Moreover the effects of antenna diversity, cooperation and cooperating clusters are also discussed.

A Novel Approach to Wideband Spectrum Compressive Sensing Based on DST for Frequency Availability in LEO Mobile Satellite Systems

In LEO mobile satellite network, the L/S frequency availability is an essential task for global communication but entails several major technical challenges: high sampling rate required for wideband sensing, limited power and computing resources for processing load, and frequency-selective wireless fading. This paper investigates the issue of frequency availability in LEO mobile satellite system, and a novel wideband spectrum compressed signal detection approach is proposed to obtain active primary users (PUs) subbands and their locations that should be avoided during frequency allocation. We define the novel wideband spectrum compressed sensing method based on discrete sine transform (DST-WSCS), which significantly improves the performance of spectrum detection and recovery accuracy compared with conventional discrete Fourier transform based wideband spectrum compressed sensing scheme (DFT-WSCS). Additionally, with the help of intersatellite links (ISL), the scheme of multiple satellites cooperative sensing according to OR and MAJ decision fusion rules is presented to achieve spatial diversity against wireless fading. Finally, in-depth numerical simulations are performed to demonstrate the performance of the proposed scheme in aspect of signal detection probability, reconstruction precision, processing time, and so forth.

Distributed fuzzy maximum‐censored mean level detector‐constant false alarm rate detector based on voting fuzzy fusion rule

In this study, a new distributed fuzzy maximum-censored mean level detector (MX-CMLD) constant false alarm rate (CFAR) detection based on fuzzy space and voting fuzzy fusion rule is presented. In the distributed fuzzy MX-CMLD CFAR detector, each sensor computes the value of the membership function to the false alarm space from the samples of the reference cells and transmits it to the fusion centre. The values are combined according to the voting fuzzy fusion rule and the credibility measure of each sensor to produce a global membership function to the false alarm space in the fusion centre. The simulation results show that the detection performance of the distributed fuzzy MX-CMLD CFAR detector is better than the other fuzzy distributed detectors in homogeneous and non-homogeneous background. Furthermore, the simulation results indicate that the fuzzy algebraic product operator rule gives better performance than the binary AND and the binary OR fusion rules.

Performance Analysis of Cooperative Spectrum Sensing Technique for Low SNR Regime over Fading Channels for Cognitive Radio Networks

Cognitive Radio is a technology for Next Generation Wireless Networks to support Dynamic Spectrum Access which addresses the spectrum scarcity and under utilization problems experienced by today’s wireless communication networks. Spectrum Sensing is an important function of cognitive radio that identifies the unused licensed frequency bands of Primary Users for opportunistic access by Secondary Users. In this paper, we have proposed a sensing technique incorporating wavelet denoising to enhance the sensing performance at low SNR regime. The combination of wavelet denoising preceeding energy detector, hard decision for local sensing and OR fusion rule at the fusion center for cooperative sensing has improved the sensing performance. We have quantified the performance improvement of the proposed technique in fading environments and studied the effect of wavelet denoising. Probability of missed detection has been reduced by 38% over Rayleigh Fading channel and 52% over Nakagami Fading channel compared to conventional sensing technique for ten users cooperation. Sensing time has been reduced by 1 msec and throughput improved by 50 Mbits/Sec/Hz compared to conventional energy detection based sensing method.