Primary User Spectrum Research Articles

Exploring and analyzing the role of hybrid spectrum sensing methods in 6G-based smart health care applications.

Researchers are focusing their emphasis on quick and real-time healthcare and monitoring systems because of the contemporary modern world's rapid technological improvements. One of the best options is smart healthcare, which uses a variety of on-body and off-body sensors and gadgets to monitor patients' health and exchange data with hospitals and healthcare professionals in real time. Utilizing the primary user (PU) spectrum, cognitive radio (CR) can be highly useful for efficient and intelligent healthcare systems to send and receive patient health data. In this work, we propose a method that combines energy detection (ED) and cyclostationary (CS) spectrum sensing (SS) algorithms. This method was used to test spectrum sensing in CR-based smart healthcare systems. The proposed ED-CS in cognitive radio systems improves the precision of the spectrum sensing. Owing to its straightforward implementation, ED is initially used to identify the idle spectrum. If the ED cannot find the idle spectrum, the signals are found using CS-SS, which uses the cyclic statistical properties of the signals to separate the main users from the interference. In the simulation analysis, the probability of detection (Pd), probability of a false alarm (Pfa), power spectral density (PSD), and bit error rate (BER) of the proposed ED-CS is compared to those of the traditional Matched Filter (MF), ED, and CS. The results indicate that the suggested strategy improves the performance of the framework, making it more appropriate for smart healthcare applications.

Sequential fusion‐based defense strategy against sophisticated Byzantine attacks in cognitive IoT networks

Sequential fusion‐based defense strategy against sophisticated Byzantine attacks in cognitive <scp>IoT</scp> networks

Exploring and analyzing the role of hybrid spectrum sensing methods in 6G-based smart health care applications

Background Researchers are focusing their emphasis on quick and real-time healthcare and monitoring systems because of the contemporary modern world’s rapid technological improvements. One of the best options is smart healthcare, which uses a variety of on-body and off-body sensors and gadgets to monitor patients’ health and exchange data with hospitals and healthcare professionals in real time. Utilizing the primary user (PU) spectrum, cognitive radio (CR) can be highly useful for efficient and intelligent healthcare systems to send and receive patient health data. Methods In this work, we propose a method that combines energy detection (ED) and cyclostationary (CS) spectrum sensing (SS) algorithms. This method was used to test spectrum sensing in CR-based smart healthcare systems. The proposed ED-CS in cognitive radio systems improves the precision of the spectrum sensing. Owing to its straightforward implementation, ED is initially used to identify the idle spectrum. If the ED cannot find the idle spectrum, the signals are found using CS-SS, which uses the cyclic statistical properties of the signals to separate the main users from the interference. Results In the simulation analysis, the probability of detection (Pd), probability of a false alarm (Pfa), power spectral density (PSD), and bit error rate (BER) of the proposed ED-CS is compared to those of the traditional Matched Filter (MF), ED, and CS. Conclusions The results indicate that the suggested strategy improves the performance of the framework, making it more appropriate for smart healthcare applications.

Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach.

The rapid technological advancements in the current modern world bring the attention of researchers to fast and real-time healthcare and monitoring systems. Smart healthcare is one of the best choices for this purpose, in which different on-body and off-body sensors and devices monitor and share patient data with healthcare personnel and hospitals for quick and real-time decisions about patients' health. Cognitive radio (CR) can be very useful for effective and smart healthcare systems to send and receive patient's health data by exploiting the primary user's (PU) spectrum. In this paper, tree-based algorithms (TBAs) of machine learning (ML) are investigated to evaluate spectrum sensing in CR-based smart healthcare systems. The required data sets for TBAs are created based on the probability of detection (Pd) and probability of false alarm (Pf). These data sets are used to train and test the system by using fine tree, coarse tree, ensemble boosted tree, medium tree, ensemble bagged tree, ensemble RUSBoosted tree, and optimizable tree. Training and testing accuracies of all TBAs are calculated for both simulated and theoretical data sets. The comparison of training and testing accuracies of all classifiers is presented for the different numbers of received signal samples. Results depict that optimizable tree gives the best accuracy results to evaluate the spectrum sensing with minimum classification error (MCE).

Energy-Efficient Resource Allocation in Multiple UAVs-Assisted Energy Harvesting-Powered Two-Hop Cognitive Radio Network

The unmanned aerial vehicle (UAV)-assisted energy harvesting (EH)-based cognitive wireless networks can solve the problems of spectrum scarcity, energy shortage, and challenging layout of communication facilities faced by traditional wireless networks, envisioned as a vital frontier communication technology in the 5G/6G era. In this article, we consider a UAVs-assisted two-hop cognitive radio network (CRN) with EH (UAVs-EH-DHCRN), where multiple UAVs are employed to act as air access points (AAPs) and relay nodes (RNs). Since all EH-powered secondary users (SUs) can simultaneously associate with AAPs by sharing the licensed spectrum of primary users (PUs), intercell interference (ICI) occurs among AAPs. Our goal is to maximize the end-to-end energy efficiency (EE) of the UAVs-EH-DHCRN while considering constraints like ICI, energy, minimum data rate, and interference to PUs. However, the formulated end-to-end EE maximization problem is a typical nonconvex optimization problem with complex constraints that off-the-shelf optimization strategies cannot solve. Therefore, we propose a resource allocation algorithm called approximate convex policy concerning associations, power allocation, and placements (ACP-APP). Specifically, in ACP-APP, we introduce additional parameters to convert the fractional objective function into a polynomial function. Then, the optimal AAPs’ placements, optimal associations between SUs and AAPs, and power allocation are obtained alternately by leveraging the proposed circular-based recursive random search (CRRS), 1-D linear programming, and Frank–Wolfe (FW) policy. Simulation results show that our proposed strategy can converge quickly to obtain the approximate optimal solution. Moreover, the ACP-APP can obtain significant end-to-end EE gains compared with benchmark schemes.

Reconfigurable Sensing Time in Cooperative Cognitive Network Using Machine Learning

A cognitive radio network (CRN) intelligently utilizes the available spectral resources by sensing and learning from the radio environment to maximize spectrum utilization. In CRNs, the secondary users (SUs) opportunistically access the primary users (PUs) spectrum. Therefore, unambiguous detection of the PU channel occupancy is the most critical aspect of the operations of CRNs. Cooperative spectrum sensing (CSS) is rated as the best choice for making reliable sensing decisions. This paper employs machine-learning tools to sense the PU channels reliably in CSS. The sensing parameters are reconfigured to maximize the spectrum utilization while reducing sensing error and cost with improved channel throughput. The fine-k-nearest neighbor algorithm (FKNN), employed in this paper, estimates the number of samples based on the nature of the channel under-specific detection and false alarm probability demands. The simulation results reveal that the sensing cost is suppressed by reducing the sensing time and exploiting the traditional fusion rules, validating the effectiveness of the proposed scheme. Furthermore, the global decision made at the fusion center (FC) based on the modified sensing samples, results low energy consumption, higher throughput, and improved detection with low error probabilities.

Joint antenna selection and waveform design for coexistence of MIMO radar and communications

As the problem of spectral congestion is becoming severe, the coexistence between two primary spectrum users, radar and communications, has spurred extensive research interest. To reduce the mutual interferences between the two functions, MIMO radar waveform design needs to consider the compatibility in both spectral and spatial domains, where the former is achieved by null forming in the frequency domain and the latter is achieved by shaped beampattern synthesis. Additionally, high power efficiency and low system overhead are two desirable characteristics for MIMO radar system design. To this end, we first introduce a new realistic waveform constraint, peak-to-valley-power-ratio (PVPR) constraint per antenna to improve the power efficiency. Then, combined with PVPR constraint, we propose a switchable individual antenna power control scheme to jointly optimize waveforms and antenna locations. We adopt a max–min beampattern matching criterion and impose the ell _{2,1}-norm penalty on the waveform matrix to promote the sparsity of the array. To solve the resultant non-smooth and non-convex problem, we develop a modified alternating directions method of multipliers, where a surrogate subproblem over primal variables is solved instead of the original problem, and its local convergence is analyzed. Finally, numerical experiments demonstrate the effectiveness and superiority of the proposed method over counterparts, especially obtaining the lowest sidelobe level and deeper spectral nulls using much fewer antennas.

Cooperation and energy harvesting based spectrum sensing schemes for green cognitive radio networks

AbstractCognitive radio network (CRNs) with energy harvesting approach to identify unused spectrum bands and utilize them opportunistically is observed as an evolving technology to handle spectrum scarcity problems. In CRNs, the secondary users (SUs) continuously sense the primary user's (PUs) spectrum to secure the data transmission of PUs and to access the vacant PU's spectrum for their data transmission. For designing future green networks, it is essential that the spectrum sensing process must be accurate, rapid, and energy‐efficient. Therefore, in this work, three sensing schemes are proposed. The first is a cooperation‐based joint cooperation spectrum sensing (JCSS) scheme in which the cooperative CRNs permit the SUs, working in an identical band, to cooperate, and this cooperation approach decreases sensing time and improves sensing probability for vacant spectrum. The result shows that the sensing probability for a vacant spectrum is increased by 34%. The proposed JCSS scheme considers the inherent asymmetry property of the network; therefore, it helps to improve the overall rapidity gain of the networks. The other two proposed sensing schemes, preset spectrum sensing (PSS) and viscous spectrum sensing (VSS), are based on energy harvesting that analyzes the percentage of energy‐saving for various states. The suggested schemes are better in terms of scalability because, in these schemes, it is not essential to sense all spectrums, which leads to a reduction in sensing time.

Hybrid Cooperative Spectrum Sharing Protocols Based on OMA and NOMA in Cognitive Radio Networks

In cognitive radio networks (CRNs), the performance of the primary users (PUs) may suffer adverse effects from the secondary users (SUs) if the spectrum of PUs is haphazardly shared with SUs. In this paper, we propose a hybrid cognitive cooperative protocol based on orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) that improves spectrum utilization by allowing the SUs opportunistic access to the spectrum of the Pus, while guaranteeing the performance of the PU. Specifically, the system can switch between non-cognitive transmission mode, underlay OMA mode, and overlay OMA/NOMA mode, according to the automatic repeat request (ARQ) feedback of PU. The SU has the opportunity to acquire the spectrum to activate the underlay OMA and overlay OMA/NOMA modes only if it listens to the acknowledge (ACK) or negative acknowledge (NACK) feedback from the PU. In order to describe the switching between these three switching modes, a Markov model is developed to analyze the corresponding steady-state probabilities and end-to-end outage probabilities. So, we derive closed-form expressions for the throughput of PU and SU to investigate the spectrum utilization. Numerical and simulation results show that the proposed hybrid cooperative cognitive protocol outperforms the pure OMA hybrid cooperative cognitive protocol.

Supervised Dictionary Learning for Block Threshold Feature in Compressive Spectrum Sensing

With the development of communication systems towards the high-frequency band, the demand for spectrum resources is ever-increasing, where the research interest has changed from narrowband spectrum sensing to wideband spectrum sensing. The Nyquist-rate-based wideband spectrum sensing with high-rate sampling is being questioned whether it is suitable for real-time applications. On the contrary, the well-known compressive spectrum sensing (CSS) is more appealing due to the compressive sensing (CS) technology, resulting in lower signal acquisition costs. However, the CS algorithms for recovering sparse spectrum generally require multiple iterations, which presents a challenge to the low complexity implementation of spectrum sensing. To address this issue, this paper proposes a novel method for solving the block CSS (BCSS) problem, where the spectrum of primary users is modeled as a block structure signal. Specifically, the block threshold feature (BTF) is utilized to reconstruct the spectrum while bypassing any iterative operations. Furthermore, to improve the performance of the BTF-based BCSS, we develop a novel supervised dictionary learning (SDL) model, based on the theoretical analysis of mutual incoherence and restricted isometry properties. Simulation results not only verify the compatibility of BTF and the SDL model, but also demonstrate the effectiveness and robustness of SDL-BTF-based BCSS for practical implementation.

A Novel Prediction Model for Malicious Users Detection and Spectrum Sensing Based on Stacking and Deep Learning.

Cooperative network is a promising concept for achieving a high-accuracy decision of spectrum sensing in cognitive radio networks. It enables a collaborative exchange of the sensing measurements among the network users to monitor the primary spectrum occupancy. However, the presence of malicious users leads to harmful interferences in the system by transmitting incorrect local sensing observations.To overcome this security related problem and to improve the accuracy decision of spectrum sensing in cooperative cognitive radio networks, we proposed a new approach based on two machine learning solutions. For the first solution, a new stacking model-based malicious users detection is proposed, using two innovative techniques, including chaotic compressive sensing technique-based authentication for feature extraction with a minimum of measurements and an ensemble machine learning technique for users classification. For the second solution, a novel deep learning technique is proposed, using scalogram images as inputs for the primary user spectrum’s classification. The simulation results show the high efficiency of both proposed solutions, where the accuracy of the new stacking model reaches 97% in the presence of 50% of malicious users, while the new scalogram technique-based spectrum sensing is fast and achieves a high probability of detection with a lower number of epochs and a low probability of false alarm.

Optimization on information freshness for multi‐access users with energy harvesting cognitive radio networks

AbstractFor the multi‐access channel wireless sensor network scenario with limited channel resources, the transmission of real‐time information needs to consider the channel environment, spectrum utilization and the freshness of information. First, we present a cognitive network model where secondary user (SU) shares the primary user (PU) spectrum through the underlay scheme. The sensor nodes with energy harvesting (EH) capabilities dynamically monitor changes in the surrounding environment and transmit status updates to the common secondary destination node. Considering the stability of the data queue in the SU as well as the working state and stable service interference constraints of PU, an optimization problem of minimizing the average AoI and average peak AoI (PAoI) of the EH node is proposed. Second, two policies are used to solve the above problems, including the decentralized probabilistic‐random‐access (PRA) policy and the drift plus penalty (DPP) policyin. In PRA policy, two SU nodes make independent transmission decisions based on corresponding probability distributions, and in DPP policy, the Lyapunov optimization framework is used to optimize scheduling decisions in the timeslots for optimizing the age of data packets. The simulation results show that the optimization of AoI under the DPP policy is more significant than the PRA policy, which can effectively improve the timeliness and freshness of data packets.

QoE-Aware Cross-Layer Adaptation for D2D Video Communication in Cooperative Cognitive Radio Networks

In this article, a cross-layer parameter optimization is considered for H.264 scalable video transmission in cognitive radio enabled device-to-device networks. Adaptive modulation and coding is considered at the physical layer along with video layer aware automatic repeat request (VLA-ARQ) at the link layer. The VLA-ARQ scheme is aware of video characteristics. Scalable video coding (SVC) based video streaming is carried out at the application layer. A novel cognitive radio network frame structure is proposed accommodating cooperative spectrum sensing (CSS), relaying primary user’s (PU) transmission, and secondary users’ (SU) opportunistic transmission to maximize quality of experience (QoE) and to minimize total power consumption. In SVC-based video streaming, successful reception of a particular video layer is dependent upon the successful reception of its higher layers, which influences the eventual QoE at the user end. A set of optimal values of CSS time duration, sensing decision threshold, cooperation, and transmission power control are obtained under the constraints of detection reliability, PU spectrum efficiency, and SU power budget. Extensive simulation results with respect to the system parameters show the efficacy of the proposed system. The numerical results demonstrate that the proposed technique offers more than 14% gain in overall utility with the proposed CSS and cross-layer parameter optimization strategy.

CNN-aided multiple PU spectrum sensing in uncalibrated massive antennas SU system

This paper deploys the Convolutional Neural Network (CNN) to learn and set the statistical test in Spectrum Sensing (SS) task of multiple primary user (PU) sources in massive uncalibrated antennas of secondary users (SU) sharing the same spectrum resources. The proposed deep learning-based SS method (DL-SS) is based on the CNN architecture that has the capability of extracting features of the sample covariance matrices (SCMs) that are given as the network input, improving the overall performance and robustness. The proposed CNN-SS method is compared with nine recent multiple-antennas SS methods, namely the arithmetic–geometric detector (AGM), John’s detector (JD), sphericity detector (SD), generalized likelihood test (GLRT), locally most powerful invariant test (LMPIT), maximum–minimum eigenvalue detector (MME), covariance detector (CAV), Hadamard detector (HD) and volume detector (VD) methods; besides, the proposed method is also compared with five recent state-of-art CNN-based SS methodologies. Performance-complexity trade-off of the proposed and reference SS methods are corroborated via Monte Carlo Simulations (MCS). The proposed CNN-SS method under uncalibrated massive antennas reveals substantial benefits w.r.t. the reference methods and is competitive with others CNN-SS methodologies, both in terms of complexity and performance, achieving detection probability of Pd=0.9(@SNR=−20dB) under very low false alarm probability Pf=0.1. Under different figures of merit, the performance of the CNN-based SS detector has revealed to be indubitably superior regarding the state-of-art SS detectors. However, the proposed CNN-based SS detector presents relative computational complexity increases. Hence, to be effective, such a superior operational performance requires a very efficient processing structure in the SU base stations.

Sidelobe Reduction in NC-OFDM-Based CRNs Using Differential Evolution-Assisted Generalized Sidelobe Canceller

Noncontiguous orthogonal frequency division multiplexing (NC-OFDM) is considered a suitable candidate for the cognitive radio network (CRN) to accomplish efficient data transmission. The NC-OFDM allows secondary users (SUs) to access the primary user (PU) spectrum while being detected idle. However, interference may occur in the adjacent frequency bands of the PU due to sidelobes of the SU transmission. The use of cancellation carriers (CCs) and generalized sidelobe canceller (GSC) is a widely adopted technique to tackle the sidelobes. To this end, this paper presents a differential evolution- (DE-) based GSC (DE-GSC) scheme to suppress unwanted sidelobes. At first, in the DE-GSC1 scheme, the adaptive weight vector is calculated using the DE algorithm while considering the complete samples of the sidelobes for optimization. The optimized weights are then added with the original weights to reduce the sidelobe issue. Next, in the DE-GSC2 scheme, selected elements for the adaptive weight vector near the main NC-OFDM signal are computed using the DE to reduce the search space. The performance of the proposed methods in terms of power spectral density (PSD) is compared with some of the recent techniques employing five different scenarios. Simulation results in the presence of single and multiple spectral hole scenarios validate that the proposed DE-GSC1 and DE-GSC2 methods result in enhanced suppression performance compared with the: original signal, simple CC, simple GSC, DE-based CC (DE-CC), and genetic algorithm- (GA-) based CC (GA-CC) schemes.

Optimal Cooperative Spectrum Sensing Based on Butterfly Optimization Algorithm

Since the introduction of the Internet of Things (IoT), several researchers have been exploring its productivity to utilize and organize the spectrum assets. Cognitive radio (CR) technology is characterized as the best aspirant for wireless communications to augment IoT competencies. In the CR networks, secondary users (SUs) opportunistically get access to the primary users (PUs) spectrum through spectrum sensing. The multipath issues in the wireless channel can fluster the sensing ability of the individual SUs. Therefore, several cooperative SUs are engaged in cooperative spectrum sensing (CSS) to ensure reliable sensing results. In CSS, security is still a major concern for the researchers to safeguard the fusion center (FC) against abnormal sensing reports initiated by the malicious users (MUs). In this paper, butterfly optimization algorithm (BOA)-based soft decision method is proposed to find an optimized weighting coefficient vector correlated to the SUs sensing notifications. The coefficient vector is utilized in the soft decision rule at the FC before making any global decision. The effectiveness of the proposed scheme is compared for a variety of parameters with existing schemes through simulation results. The results confirmed the supremacy of the proposed BOA scheme in both the normal SUs’ environment and when lower and higher SNRs information is carried by the different categories of MUs.

Spectral Vacancy Prediction Using Time Series Forecasting for Cognitive Radio Applications

An identification of unfilled primary user spectrum using a novel method is presented in this paper. Cooperation among users with the utilization of machine learning methods is analyzed. Learning methods are applied to construct the classifier, which selects the suitable fusion algorithm for the considered environment so that the out of band sensing is performed efficiently. Sensing performance is looked into with the existence of fading and it is observed that sensing performance degrades with fading which coincides with earlier findings. From the simulation, it can be inferred that Weibull fading outperforms all the other fading models considered. To accomplish missed detection probability of 1% in the Rayleigh channel, the false alarm probability obtained is almost 0.8 however to obtain the same missed detection probability in the Weibull channel, false alarm probability is less than 0.1 which is very favorable for both indoor and outdoor scenarios. Numerical analyses are carried out here to predict Primary User (PU) channel condition using Hidden Markov Model with the help of Time series forecasting learning method. It is evident that the prediction performance has reached 100% as the result of using the Weibull Fading Model for a period of 200 ms when compared to the Rayleigh model which is achieving only 84.5% accuracy in prediction.

An Optimized Algorithm for CR-MIMO Wireless Networks

With the rapid development of wireless communication technology, the spectrum resources are increasingly strained which needs optimal solutions. Cognitive radio (CR) is one of the key technologies to solve this problem. Spectrum sensing not only includes the precise detection of the communication signal of the primary user (PU), but also the precise identification of its modulation type, which can then determine the a priori information such as the PU’ service category, so as to use this information to make the cognitive user (CU) aware to discover and use the idle spectrum more effectively, and improve the spectrum utilization. Spectrum sensing is the primary feature and core part of CR. Classical sensing algorithms includes energy detection, cyclostationary feature detection, matched filter detection, and so on. The energy detection algorithm has a simple structure and does not require prior knowledge of the PU transmitter signal, but it is easily affected by noise and the threshold is not easy to determine. The combination of multiple-input multiple-output (MIMO) with CR improves the spectral efficiency and multi-path fading utilization. To best utilize the PU spectrum while minimizing the overall transmit power, an iterative technique based on semidefinite programming (SDP) and minimum mean squared error (MMSE) is proposed. Also, this article proposed a new method for max-min fairness beamforming. When compared to existing algorithms, the simulation results show that the proposed algorithms perform better in terms of total transmitted power and signal-to-interference plus noise ratio (SINR). Furthermore, the proposed algorithm effectively improved the system performance in terms of number of iterations, interference temperature threshold and balance SINR level which makes it superior over the conventional schemes.

Use Hierarchical Codebook to Improve the Primary User Detection in the Cognitive Radio’s Cooperative Spectrum Sensing

Currently, the cognitive network is receiving much attention due to the advantages it brings to users. An important method in cognitive radio networks is spectrum sensing, as it allows secondary users (SUs) to detect the existence of a primary user (PU). Information of probability of false detection or warning about the PU is sent to a fusion center (FC) by the SUs, from which the FC will decide whether or not to allow the SUs to use the PU spectrum to obtain information. The transmission of information with a high signal to noise ratio (SNR) will increase the FC's ability to detect the existence of the PU. However, researchers are currently focusing on probabilistic formulas assuming that the channel is known ideally or there is nominal channel information at the FC; moreover, one model where the FC only knows the channel correlation matrix. Furthermore, studies are still assuming this is a simple multiple input – multiple output (MIMO) channel model but do not pay much attention to the signal processing at the transmitting and receiving antennas between the SUs and the FCs. A new method introduced in this paper when combining beamforming and hierarchical codebook makes the ability to detect the existence of the PU at the FC significantly increased compared to traditional methods.

Identifying Malicious Secondary User Presence Within Primary User Range in Cognitive Radio Networks

Cognitive Radio (CR) Network is a wireless communication technology, in which a detection device smartly detects occupied and unoccupied channels. During traffic, unused primary user spectrum space is allotted to a secondary user, without causing any intervention with the primary user. Though this has reduced network traffic to a great extent, many issues related to security has became an alarming problem, in which the primary user’s space is misused by some malicious secondary users without the knowledge of primary user. To address this issue, in this paper we have proposed a Boundary detection method that uses the estimated location of each SU, which is obtained using the Recurrent Neural Network algorithm, to determine the boundary of PU coverage. Then Malicious User Detection by Ordering (MUDO) methodology is proposed, in which all secondary users are weighed using Basic Probability Analysis (BPA), and based on the orders the SUs are paired with corresponding PUs. The SUs with the least orders are discarded as they might be malicious users. The proposed methodology possesses higher detection speed and precise detection thereby enhancing the performance of CR.