Superallocation and Cluster‐Based Cooperative Spectrum Sensing in 5G Cognitive Radio Network

Consequently, the research and development for the 5G systems have already been started. This chapter presents an overview of potential system network architecture and highlights a superallocation technique that could be employed in the 5G cognitive radio network (CRN). A superallocation scheme is proposed to enhance the sensing detection performance by rescheduling the sensing and reporting time slots in the 5G cognitive radio network with a cluster‐based cooperative spectrum sensing (CCSS). In the 4G CCSS scheme, first, all secondary users (SUs) detect the primary user (PU) signal during a rigid sensing time slot to check the availability of the spectrum band. Second, during the SU reporting time slot, the sensing results from the SUs are reported to the corresponding cluster heads (CHs). Finally, during CH reporting time slots, the CHs forward their hard decision to a fusion center (FC) through the common control channels for the global decision. However, the reporting time slots for the SUs and CHs do not contribute to the detection performance. In this chapter, a superallocation scheme that merges the reporting time slots of SUs and CHs by rescheduling the reporting time slots as a nonfixed sensing time slot for SUs to detect the PU signal promptly and more accurately is proposed. In this regard, SUs in each cluster can obtain a nonfixed sensing time slot depending on their reporting time slot order. The effectiveness of the proposed chapter that can achieve better detection performance under –28 to –10 dB environments and thus reduce reporting overhead is shown through simulations

Performance comparison and analysis of mobile ad hoc routing

ABSTRACT A mobile ad hoc network (MANET) is a wireless network that uses multi-hop peer-to-peer routing instea

Far-Field DOA Estimation of Uncorrelated RADAR Signals through Coprime Arrays in Low SNR Regime by Implementing Cuckoo Search Algorithm

For the purpose of attaining a high degree of freedom (DOF) for the direction of arrival (DOA) estimations in radar technology, coprime sensor arrays (CSAs) are evaluated in this paper. In addition, the global and local minima of extremely non-linear functions are investigated, aiming to improve DOF. The optimization features of the cuckoo search (CS) algorithm are utilized for DOA estimation of far-field sources in a low signal-to-noise ratio (SNR) environment. The analytical approach of the proposed CSAs, CS and global and local minima in terms of cumulative distribution function (CDF), fitness function and SNR for DOA accuracy are presented. The parameters like root mean square error (RMSE) for frequency distribution, RMSE variability analysis, estimation accuracy, RMSE for CDF, robustness against snapshots and noise and RMSE for Monte Carlo simulation runs are explored for proposed model performance estimation. In conclusion, the proposed DOA estimation in radar technology through CS and CSA achievements are contrasted with existing tools such as particle swarm optimization (PSO).This project has received funding from Universidad Carlos III de Madrid and the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant 801538

Breast cancer detection based on simplified deep learning technique with histopathological image using BreaKHis database

Presented here are the results of an investigation conducted to determine the effectiveness of deep learning (DL)-based systems utilizing the power of transfer learning for detecting breast cancer in histopathological images. It is shown that DL models that are not specifically developed for breast cancer detection can be trained using transfer learning to effectively detect breast cancer in histopathological images. The outcome of the analysis enables the selection of the best DL architecture for detecting cancer with high accuracy. This should facilitate pathologists to achieve early diagnoses of breast cancer and administer appropriate treatment to the patient. The experimental work here used the BreaKHis database consisting of 7909 histopathological pictures from 82 clinical breast cancer patients. The strategy presented for DL training uses various image processing techniques for extracting various feature patterns. This is followed by applying transfer learning techniques in the deep convolutional networks like ResNet, ResNeXt, SENet, Dual Path Net, DenseNet, NASNet, and Wide ResNet. Comparison with recent literature shows that ResNext-50, ResNext-101, DPN131, DenseNet-169 and NASNet-A provide an accuracy of 99.8%, 99.5%, 99.675%, 99.725%, and 99.4%, respectively, and outperform previous studies

An energy efficient spectrum sensing scheme for the cognitive radio based internet of things

Spectrum sensing in a cognitive radio network involves detecting when a primary user vacates their licensed spectrum to enable secondary users to broadcast on the same band. Accurately sensing the absence of the primary user ensures maximum utilization of the licensed spectrum and is fundamental to building effective cognitive radio networks. Within that context, this thesis makes the following contributions: Firstly, for saving the cooperative bandwidth of the spectrum sensing process, we present an enhanced sum rate in the cluster based cognitive radio relay network (CCRRN) utilizing a reporting framework in the sequential approach. With such extended sensing intervals and amplified reporting, a better sensing performance can be obtained compared to a conventional non-sequential approach, therefore making it applicable for the future Internet of things (IoT). In addition, the sum rate of the primary network (PN) and CCRRN are also investigated for the utilization reporting framework in the sequential approach with a relay using the "n-out-of-k" rule. The simulation results show that the proposed sequential approach with a relay achieves a better sensing gain and an enhanced sum rate when compared with the conventional non-sequential approach with no relay under any condition. Secondly, state-of-the-art energy detection (ED) based spectrum sensing requires perfect knowledge of noise power and is vulnerable to noise uncertainty. An eigenvalue-based spectrum sensing approach performs well in such an uncertain environment, but does not mitigate the spectrum scarcity problem, which evolves with the future IoT rollout. For this reason, we propose a multi-user multiple-input and multiple-output (MU-MIMO) based cognitive radio scheme for the Internet of things (CR-IoT) with weighted-Eigenvalue detection (WEVD) for the analysis of sensing, system throughput, energy efficiency and expected lifetime. In this scheme, each CR-IoT user is being equipped with multiple-input and multiple-output (MIMO) antennas; we calculate the weighted Eigenvalue detection ratio, which is defined as the ratio between the difference of the maximum eigenvalue and minimum eigenvalue to the sum of the maximum eigenvalue and minimum eigenvalue. This mitigates against the spectrum scarcity problem, enhances system throughput, improves energy efficiency, prolongs expected lifetime and lowers error probability. Simulation results confirm the effectiveness of the proposed scheme; here the WEVD technique demonstrates a better detection gain, an enhanced system throughput, a lower energy consumption, prolonged expected lifetime and a lower error probability in comparison to the conventional scheme with Eigenvalue based detection (EVD) and ED techniques in a noise uncertainty environment. Thirdly, we address the issues of enhancing sensing gain, average throughput, energy consumption and network lifetime in a cognitive radio based Internet of things (CR-IoT) network under the non-sequential approach. As a solution, we propose a Dempster-Shafer theory based throughput analysis of an energy efficient spectrum sensing scheme for a heterogeneous CR-IoT network under the sequential approach, which utilizes both the signal-to-noise ratio (SNR) to evaluate the degree of reliability, and the reporting time slot to merge as a flexible sensing time slot in order to evaluate spectrum sensing more accurately. Before making a global decision based on both the Dempster-Shafer theory and the "n-out-of-k" rule at the fusion center, a flexible sensing time slot is applied to adapt its sensing data. Using the proposed Dempster-Shafer theory, evidence is aggregated during the reporting time slot and then a global decision is made at the fusion center. Simulation results show that the proposed approach improves sensing performance by 13% over previous approaches. In addition, it also improves overall throughput, reduces energy consumption, prolongs expected lifetime and reduces global error probability compared to the previous approaches under any condition. Finally, in a noise uncertain environment, the sensing performance of the conventional ED scheme is significantly degraded because of noise fluctuation, which is caused by the noise temperature, interference, and filtering. To mitigate this problem, we propose an analysis approach of the cooperative spectrum sensing and sum rate calculation for CR-IoT networks in noise uncertain environments using the Kullback–Leibler divergence (KLD) technique, excluding the deep fading CR-IoT users at a coordinator centre. The results obtained through simulations show that the proposed KLD scheme achieves a better sensing performance, an enhanced sum rate, a lower energy consumption, a longer network lifetime, a lower global error probability and a lower reporting overhead when compared to the conventional ED scheme in a noise uncertain environment

An energy efficient spectrum sensing scheme for the cognitive radio based internet of things

Spectrum sensing in a cognitive radio network involves detecting when a primary user vacates their licensed spectrum to enable secondary users to broadcast on the same band. Accurately sensing the absence of the primary user ensures maximum utilization of the licensed spectrum and is fundamental to building effective cognitive radio networks. Within that context, this thesis makes the following contributions: Firstly, for saving the cooperative bandwidth of the spectrum sensing process, we present an enhanced sum rate in the cluster based cognitive radio relay network (CCRRN) utilizing a reporting framework in the sequential approach. With such extended sensing intervals and amplified reporting, a better sensing performance can be obtained compared to a conventional non-sequential approach, therefore making it applicable for the future Internet of things (IoT). In addition, the sum rate of the primary network (PN) and CCRRN are also investigated for the utilization reporting framework in the sequential approach with a relay using the "n-out-of-k" rule. The simulation results show that the proposed sequential approach with a relay achieves a better sensing gain and an enhanced sum rate when compared with the conventional non-sequential approach with no relay under any condition. Secondly, state-of-the-art energy detection (ED) based spectrum sensing requires perfect knowledge of noise power and is vulnerable to noise uncertainty. An eigenvalue-based spectrum sensing approach performs well in such an uncertain environment, but does not mitigate the spectrum scarcity problem, which evolves with the future IoT rollout. For this reason, we propose a multi-user multiple-input and multiple-output (MU-MIMO) based cognitive radio scheme for the Internet of things (CR-IoT) with weighted-Eigenvalue detection (WEVD) for the analysis of sensing, system throughput, energy efficiency and expected lifetime. In this scheme, each CR-IoT user is being equipped with multiple-input and multiple-output (MIMO) antennas; we calculate the weighted Eigenvalue detection ratio, which is defined as the ratio between the difference of the maximum eigenvalue and minimum eigenvalue to the sum of the maximum eigenvalue and minimum eigenvalue. This mitigates against the spectrum scarcity problem, enhances system throughput, improves energy efficiency, prolongs expected lifetime and lowers error probability. Simulation results confirm the effectiveness of the proposed scheme; here the WEVD technique demonstrates a better detection gain, an enhanced system throughput, a lower energy consumption, prolonged expected lifetime and a lower error probability in comparison to the conventional scheme with Eigenvalue based detection (EVD) and ED techniques in a noise uncertainty environment. Thirdly, we address the issues of enhancing sensing gain, average throughput, energy consumption and network lifetime in a cognitive radio based Internet of things (CR-IoT) network under the non-sequential approach. As a solution, we propose a Dempster-Shafer theory based throughput analysis of an energy efficient spectrum sensing scheme for a heterogeneous CR-IoT network under the sequential approach, which utilizes both the signal-to-noise ratio (SNR) to evaluate the degree of reliability, and the reporting time slot to merge as a flexible sensing time slot in order to evaluate spectrum sensing more accurately. Before making a global decision based on both the Dempster-Shafer theory and the "n-out-of-k" rule at the fusion center, a flexible sensing time slot is applied to adapt its sensing data. Using the proposed Dempster-Shafer theory, evidence is aggregated during the reporting time slot and then a global decision is made at the fusion center. Simulation results show that the proposed approach improves sensing performance by 13% over previous approaches. In addition, it also improves overall throughput, reduces energy consumption, prolongs expected lifetime and reduces global error probability compared to the previous approaches under any condition. Finally, in a noise uncertain environment, the sensing performance of the conventional ED scheme is significantly degraded because of noise fluctuation, which is caused by the noise temperature, interference, and filtering. To mitigate this problem, we propose an analysis approach of the cooperative spectrum sensing and sum rate calculation for CR-IoT networks in noise uncertain environments using the Kullback–Leibler divergence (KLD) technique, excluding the deep fading CR-IoT users at a coordinator centre. The results obtained through simulations show that the proposed KLD scheme achieves a better sensing performance, an enhanced sum rate, a lower energy consumption, a longer network lifetime, a lower global error probability and a lower reporting overhead when compared to the conventional ED scheme in a noise uncertain environment

Machine learning-based malicious user detection for reliable cooperative radio spectrum sensing in Cognitive Radio-Internet of Things

The Cognitive Radio based Internet of Things (CR-IoT) is a promising technology that provides IoT endpoints, i.e., CR-IoT users the capability to share the radio spectrum otherwise allocated to licensed Primary Users (PUs). Cooperative Spectrum Sensing (CSS) improves spectrum sensing accuracy in a CR-IoT network. However, its performance may be degraded by potential attacks of the malicious CR-IoT users that send their incorrect sensing information to the corresponding Fusion Center (FC). This study presents a promising Machine Learning (ML)-based malicious user detection scheme for a CR-IoT network that uses a Support Vector Machine (SVM) algorithm to identify and classify malicious CR-IoT users. The classification allows the FC to make a more robust global decision based on the sensing results (i.e., energy vectors) which are reported only by the normal CR-IoT users. The effectiveness of the proposed SVM algorithm based ML in a CR-IoT network with the malicious CR-IoT users is verified via simulations

An enhanced sum rate in the cluster based cognitive radio relay network using the sequential approach for the future internet of things

The cognitive radio relay plays a vital role in cognitive radio networking (CRN), as it can improve the cognitive sum rate, extend the coverage, and improve the spectral efficiency. However, cognitive relay aided CRNs cannot obtain a maximal sum rate, when the existing sensing approach is applied to a CRN. In this paper, we present an enhanced sum rate in the cluster based cognitive radio relay network utilizing a reporting framework in the sequential approach. In this approach a secondary user (SU) extends its sensing time until right before the beginning of its reporting time slot by utilizing the reporting framework. Secondly all the individual measurement results from each relay aided SU are passed on to the corresponding cluster head (CH) through a noisy reporting channel, while the CH with a soft-fusion report is forwarded to the fusion center that provides the final decision using the n-out-of-k-rule. With such extended sensing intervals and amplified reporting, a better sensing performance can be obtained than with a conventional non-sequential approach, therefore making it applicable for the future Internet of Things. In addition, the sum rate of the primary network and CCRRN are also investigated for the utilization reporting framework in the sequential approach with a relay using the n-out-of-k rule. By simulation, we show that the proposed sequential approach with a relay (Lemma 2) provides a significant sum rate gain compared to the conventional non-sequential approach with no relay (Lemma 1) under any condition