Cognitive Radio Systems Research Articles

Performance Analysis of Underlay Cognitive Radio System with Self-Sustainable Relay and Statistical CSI

The relentlessly increasing number of small-sized devices with limited powering and computational capabilities requires the adoption of new approaches to spectrum access. In this paper, we analyze an underlay cooperative cognitive wireless system based on available statistical channel state information (CSI) that is applicable to the cognitive system with limited computational resources due to its low complexity. We considered the scenario where the primary and the cognitive network coexist in the same spectrum band, under the constraints of interference threshold and maximal tolerable outage permitted by the primary user. The communication in the secondary decode-and-forward (DF) relaying system is established via a self-sustainable relay, which harvests energy from both cognitive and primary transmitters. The closed-form expressions for the outage probability of the cognitive network are derived, which are valid for both time-switching relaying (TSR) and power-splitting relaying (PSR) protocols. We analyze the influence of both cognitive and primary systems as well as the impact of channel parameters on the cognitive system outage performance. The derived analytical results are corroborated by an independent simulation method.

Federated Multi-Armed Bandits

Federated multi-armed bandits (FMAB) is a new bandit paradigm that parallels the federated learning (FL) framework in supervised learning. It is inspired by practical applications in cognitive radio and recommender systems, and enjoys features that are analogous to FL. This paper proposes a general framework of FMAB and then studies two specific federated bandit models. We first study the approximate model where the heterogeneous local models are random realizations of the global model from an unknown distribution. This model introduces a new uncertainty of client sampling, as the global model may not be reliably learned even if the finite local models are perfectly known. Furthermore, this uncertainty cannot be quantified a priori without knowledge of the suboptimality gap. We solve the approximate model by proposing Federated Double UCB (Fed2-UCB), which constructs a novel “double UCB” principle accounting for uncertainties from both arm and client sampling. We show that gradually admitting new clients is critical in achieving an O(log(T)) regret while explicitly considering the communication loss. The exact model, where the global bandit model is the exact average of heterogeneous local models, is then studied as a special case. We show that, somewhat surprisingly, the order-optimal regret can be achieved independent of the number of clients with a careful choice of the update periodicity. Experiments using both synthetic and real-world datasets corroborate the theoretical analysis and demonstrate the effectiveness and efficiency of the proposed algorithms.

Retracted : Comparative analysis of distributive linear and nonlinear optimized spectrum sensing clustering techniques in cognitive radio network systems

In this study, a study has been conducted to compare the performance of different heuristic optimisation algorithms, such as distributed swarm optimised clustering (DSOC), distributed firefly optimised clustering (DFOC) and distributed jumper firefly optimised clustering (DJFOC) techniques used for the dynamic clustering. In DSOC, every group of clustering nodes moves towards its best swarm particle having the best neighbour location with random velocity to form an organised cluster. DFOC and DJFOC are non-linear optimisation tools based on the random attractiveness of firefly intensity behaviour with the least computation time. DJFOC is support to change the new appropriate situation by the status table. The DJFOC aims to save transmit power with shortest distances and less control overhead when secondary users (SUs) or primary users change its position. The convergence rate of DJFOC is better than the DSOC and DFOC. The results show that the proposed DJFOC has a better efficiency of 10.137% when compared to the DSOC and 2.801% with DFOC in SUs average node power. For a small signal-to-noise ratio <2 dB, the probability of detection is high. In primary detection, the proposed DJFOC is yielding a low false alarm rate compared to DSOC and DFOC.

A Systematic Literature Review: Is Military Cognitive Radio System on the Brink of the “Valley of Death”?

Cognitive radio (CR) was coined to address evolving user needs two decades ago. Since then, it also has been considered a way to mitigate perceived spectrum scarcity in various application areas. In this article, the status of military CR research is assessed through a systematic literature review of 193 articles, from 2013 to 2020, using IEEE thematic topics, technology readiness levels, as well as comprehensive capability meta-model as analytical frameworks. The annual distribution of military CR research indicates continuing interest. The military CR research seems to be prolific on topics like waveform design and security, not forgetting a steady interest in CR networking topics. Significantly low numbers of papers address applications, services, and standardization. In general, military CR research seemed sporadic and scattered. Our data suggest that a transition from individual techniques, component-level subsystem research in the direction of systems engineering approach, and system-level studies has not yet happened, and the overall TRLs remain low. The absence of publicly articulated comprehensive treatment of the notion of military cognitive radio system is graphic and has led to disjointed, scattered research at the subsystem level.

Spectrum measurement and utilization in an outdoor 5‐GHz Wi‐Fi network using cooperative cognitive radio system

SummaryNowadays, the popular 2.4‐GHz band is used in different systems, such as Wi‐Fi, Bluetooth, wireless sensor systems, and wireless cameras. Instead of the over‐crowded 2.4‐GHz Wi‐Fi band, this research offers the experience of using the 5‐GHz Wi‐Fi band, which provides more spectrum availability, more channels, larger bandwidth, faster data transmission, higher data rates, higher speed, and better quality of service compared to those of the 2.4‐GHz band. In this paper, practical implementation and testing of a cooperative spectrum sensing system are presented. The spectrum utilization in the 5‐GHz Wi‐Fi licensed band at six different locations is investigated to allow the transition of secondary users (SUs) to free bands. The spectrum measurement is performed on a centralized cooperative spectrum sensing system, which consists of a master cognitive radio node and five cognitive radio stations. The measurement and simulation results for the practical system are compared with the previous related measurements obtained in Singapore, Barcelona, North Dakota (USA), and Germany. They all agree that the spectrum is underutilized, and it needs to be better utilized for increasing the spectrum efficiency. The practical results show that the newly implemented system in the 5‐GHz range fulfills the requirements of users with high efficiency and high quality of service compared to those of the 2.4‐GHz system.

Energy efficiency of full-duplex cognitive radio in low-power regimes under imperfect spectrum sensing

This paper investigates the energy efficiency of a full duplex (FD) cognitive radio (CR) system in the low-power regime under a practical self-interference cancellation performance and imperfect spectrum sensing. We consider an opportunistic spectrum access network in which the secondary user (SU) is capable of self-interference suppression (SIS). The SIS ability enables the SU to work in simultaneous transmit and sense (TS) mode in order to increase the quality of channel sensing. Towards this goal, we first study the sensing performance, i.e., false-alarm and miss-detection probabilities, of the FD CR networks using TS, and compare the results with traditional half duplex (HD) CR systems using transmit only mode (TO). In the next step, we show that due to imperfect spectrum sensing, the secondary network channel is best characterized as a Gaussian-Mixture (GM) channel, which is widely used to capture the asynchronism in heterogeneous cellular networks. We then analytically characterize the low-signal-to-noise-ratio (low-SNR) metrics of the minimum energy per bit and wideband slope of the spectral-efficiency curve, and obtain these fundamental limits in closed-form. Furthermore, the characterization of these fundamental metrics allows us to identify practical signaling strategies that are optimally efficient in the low-SNR regime for the considered FD CR system. The benefits in terms of energy efficiency offered by FD CR over HD CR are also clearly demonstrated.

Hybrid Spectrum Sensing Techniques in 5G Cognitive Radio Networks in Soft Computing: A Review

This paper describes an updated and efficient method for Hybrid spectrum sensing in cognitive radio (CR) system utilizing soft computing paradigms. The suggested soft computing approach utilizes an artificial neural network and for learning and decision making as a solution to the problems when a new product is subjected to the CR framework, developed the ability for unlicensed cognitive users to access radio frequencies through a spectrum hole and understand its implications through mechanisms spectrum sensing. The suggested soft computing approach could then be referred to as the Neuro technique. The need for higher bandwidth is important with the rise in the number of communication devices. Usage of cognitive radio for the fifth generation 5G communication network of the next generation Consider the fact that CR technology will efficiently optimize the use of much of the unused communication spectrum bands for the future 5G of wireless network and beyond. Keyword : Hybrid spectrum sensing, 5G Cognitive Radio (CR), ANN technique.

Joint spectrum sensing, power and bandwidth allocation for multiband cognitive radio systems

Multiband cognitive radio (CR) signaling allows CR networks to efficiently take advantage of the unused frequency bands. Joint spectrum sensing and power allocation in CR systems has been shown to provide higher opportunities for CR transmission. In this work, joint spectrum sensing and resource allocation (power and bandwidth) are performed in a multiband CR system. This is achieved by defining an optimization problem which is formulated with the aim of minimizing CR network outage probability. It is shown that minimizing the outage probability of CR network is equivalent to maximizing the CR network achievable data rate. Hence, the related optimization problem converts to maximizing the average opportunistic CR data rate under constraints on interference, power budget and total available bandwidth leading to a non-convex optimization problem. As the original formulated optimization problem is inherently non-convex, it is converted into a convex problem where the optimal solution is obtained using the Lagrange multipliers method and linear programming. For performance comparison, the classical method based on equal bandwidth allocation for each subchannel is considered. Several numerical results are provided to contrast the performance of the proposed method compared to the classical method, in terms of the average achievable CR data rate.

Spectrum Sensing Based On Deep Learning To Increase Spectrum Utilization

This paper proposes a new spectrum sensing technique for cognitive radio systems. To determine vacancy of the spectrum, the proposed method employs the recurrent neural network (RNN), one of the popular deep learning techniques. The proposed technique determines the spectrum occupancy of the primary user (PU) by observing the received signal’s energy and any information on the PU signal characteristic is not used. To this end, the received signal’s spectrum is obtained by fast Fourier transform (FFT). This process is performed on consecutive received signals and the resulting spectrums are stacked. Finally, a 2-dimensional spectrum (or spectrogram) is made. This 2-D spectrum is cut into sensing channel bandwidths and inputted to the deep learning model to decide the channel’s occupancy. While the recently published spectrum sensing technique based on convolutional neural network (CNN) relies on an empty channel, the proposed technique does not require any empty channel. Only the channel signal of interest to sense is needed. Since spectrum sensing results is two (busy or idle), binary classification deep learning model is developed. According to the computer simulation results, the proposed method has similar performance with the conventional CNN-based method while the spectral efficiency of the proposed method is much higher than that of the existing scheme. In addition, the overall learnable parameters of the proposed deep learning model is only 2/3 of the existing method

Analysis of a Network Stability-Aware Clustering Protocol for Cognitive Radio Sensor Networks

Cognitive radio is becoming an increasingly important component of Internet of Things technologies, because it improves spectrum efficiency and provides a way to overcome the challenge of excessive demand for wireless communications. Research and development of cognitive radio communication systems are timely and helpful. Recently, Zheng et al. proposed a network stability-aware clustering protocol for cognitive radio sensor networks. In this letter, we discuss its fatal flaws, as well as ways to overcome them.

Method for optimization of frequency resource allocation with frequency reuse for cognitive radio system

The concept of cognitive radio can be described as a radio with the study of capabilities, i.e. as a radio that is able to gain knowledge about the radio environment and adjust its operating parameters and protocols accordingly.&#x0D; The task of minimizing the frequency band is relevant at the stage of the cognitive radio network functioning when distributing the frequency resource between subscriber stations. With the ever-growing demand for frequency bands, this challenge is driven by the need to improve the efficient use of the radio frequency spectrum through frequency reuse methods.&#x0D; This paper proposes a method for ensuring the reuse of frequencies based on obtaining estimates of mutual distances between subscriber stations in real time. An algorithm is proposed for solving the problem of frequency resource allocation optimization for a cognitive radio network with frequency reuse. The algorithm is based on the method of local optimization, one of the approximate methods of discrete programming. In this case, the condition of local optimality is that the operating frequency assigned to the next subscriber station must be the closest to the frequency assigned in the previous step.&#x0D; The efficiency of the frequency resource optimization algorithm for the LTE network was analyzed using simulation modeling. The dependences of the bandwidth on the number of subscriber stations served are obtained. The analysis showed that the use of this algorithm allows to reduce the frequency band by 2 -3 times. The analysis also showed that the efficiency of the algorithm increases with the growth of the number of subscriber stations served simultaneously.

Rate Maximization of Wireless-Powered Cognitive Massive MIMO Systems

In order to combat the challenges of scarce spectrum bandwidth and energy supply in the Internet-of-Things networks, this article investigates a wireless-powered massive multiple-input-multiple-output (MIMO) underlay cognitive radio (CR) system, where the secondary user (SU) harvests the energy from the primary network through a time-switching (TS) strategy. Moreover, the detection technique of maximum-ratio combining (MRC) is applied at base stations (BSs) due to its low complexity. Thereon, the achievable rate of the secondary network is thoroughly analyzed by accounting for spatial correlations at both users and BSs, which significantly differentiates the analysis from the prior literature. With the analytical results, the impacts of the number of antennas and the spatial correlations are quantified. In particular, the negative impact of the spatial correlation on the achievable rate is theoretically proved based on the majorization theory. Furthermore, the TS factor and/or power allocation coefficients are optimally designed based on the statistical channel state information (CSI) only to maximize the achievable rate while ensuring the maximum endurable interference constraint. It is found that the optimal power allocation matrix is a variant of water-filling solution with two water levels associated with sum power constraint and sum weighted power constraint, respectively. The weighting matrix is aligned with the transmit spatial correlation in the SU. The joint design of TS factor and power allocation coefficients is also shown to reach a superior performance over the algorithms-based solely on TS factor or power allocation coefficients optimizations. Finally, the analytical results are validated by conducting simulations.

Fuzzy Hypothesis Test for Cognitive Radios

This article presents a statistical approach called hypothesis testing for Cognitive Radio systems. Hyposthesis tests have a wide range of applications from detection theory to radar systems. Hypothesis testing is frequently used in Cognitive Radio systems, which are the solution to the spectrum shortage problem today. Hypothesis testing in Cognitive Radio systems is the basis of spectrum detection. By means of hypothesis testing, spectrum gaps are determined so that spectrum gaps are opened to different users' access. In this study, a fuzzy hypothesis test based spectrum detection method is proposed with the signal detected by Cognitive Radio users. Theoretical bases and simulation results of the proposed spectrum detection model are also given. Simulation studies prove the computational cost advantage and detection performance success of the proposed detection method.

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.

Performance of Massive MIMO systems in cognitive spectrum sensing

Cognitive radio is a revolutionary technology that guarantees a viable solution to the spectrum scarcity problem. This prominent new technology has the power to change future technological developments forever if intelligently applied. In Cognitive Radio Networks, secondary (unlicensed) user uses the licensed spectrum band opportunistically, without creating any interference with primary user (licensed) transmission. Spectrum sensing is a vital challenge for spectrum usage and to prevent harmful interference with licensed users. Energy detection has been for a long, constituting the most widely known sensing technique in cognitive radio systems. In this paper, Neyman - Pearson criterion-based detection rules are used to incorporate the local probabilities of detection and false alarm to analyse an energy detector sensing behaviour over additive white Gaussian (AWGN) channel, fading channel and massive multiple input multiple output(MIMO) cases. Closed-form solutions for the probabilities of false alarm and detection were derived. The analytical results were verified by numerical computations using the Monte Carlo method with MATLAB.

Robust Spectrum Sensing Detector Based on MIMO Cognitive Radios with Non-Perfect Channel Gain

The spectrum has increasingly become occupied by various wireless technologies. For this reason, the spectrum has become a scarce resource. In prior work, the authors have addressed the spectrum sensing problem by using multi-input and multi-output (MIMO) in cognitive radio systems. We considered the detection and estimation framework for MIMO cognitive network where the noise covariance matrix is unknown with perfect channel state information. In this study, we propose a generalized likelihood ratio test (GLRT) for the spectrum sensing problem in cognitive radio where the noise covariance matrix is unknown with non-perfect channel state information. Two scenarios are examined in this study: (i) in the first scenario, the sub-optimal solution of the worst case of the system’s performance is considered; (ii) in the second scenario, we present a robust detector for the MIMO spectrum sensing problem. For both scenarios, the Bayesian approach with a generalized likelihood ratio test based on the binary hypothesis problem is used. From the results, it can be seen that our approach provides the best performance in the spectrum sensing problem under specified assumptions. The simulation results also demonstrate that our approach significantly outperforms other state-of-the-art spectrum sensing detectors when the channel uncertainty is addressed.

Intelligent Secure Networking in In-band Full-duplex Dynamic Access Networks: Spectrum Management and Routing Protocol

Recent developments in self-interference cancellation (SIC) techniques have made in-band full-duplex (IBFD) transmission systems possible. IBFD technology can enhance, or theoretically speaking double, spectrum utilization by allowing simultaneous transmission and reception using the same radio-frequency channel. Integrating the IBFD technology in cognitive radio (CR) systems can further enhance spectrum utilization and provide huge spectrum opportunities for efficient deployment of next-generation multi-hop wireless systems. Routing protocols for typical CR networks (CRNs) assume that CR users are equipped with half-duplex receivers. Limited number of protocols have been designed for FD-based CRNs. However, these protocols do not account for security threats in wireless environment, which could significantly affect system performance. In this paper, we develop a security-aware intelligent routing scheme that aims at mitigating the effects of jamming attacks on IBFD CRNs. This scheme considers the unique characteristics of the CRN environment while being IBFD-aware. Specifically, it considers the primary user’s channel-availability time, channel quality and jamming strategy. For a source-destination pair, our protocol collects a set of loop-free paths. Then, it attempts to assign channels for the hops along each path with the goal of achieving the highest Probability of Success (PoS) of each path. The per-hop channel selection for each path is derived as an optimization problem, for which sub-optimal solutions are found using a polynomial-time approximate method. Based on the computed channel assignment and PoS over each path, the path with the highest PoS between the source and destination is selected. Simulation results show that our secure-aware IBFD-based routing can significantly mitigate the jamming effects on CRN performance by improving the packet-delivery ratio, and consequently network throughput compared with existing FD-based routing protocols.

Design of Frequency Reconfigurable Patch Antenna for Sensing and Tracking Communications

This paper presents a front-end structure of a reconfigurable patch antenna for cognitive radio systems. The antenna structure consists of an Ultrawideband (UWB) sensing antenna and an array of frequency reconfigurable antennas incorporated on the same substrate. The UWB and reconfigurable antennas are fed by co-planar waveguides (CPW). The reconfigurability is achieved by rotating the series of patch antennas through a certain angle and the rotation is controlled by mechanical means using an Arduino microcontroller. The rotational reconfigurability has been preferred over MEMS switches, PIN diodes, and other lumped elements because the latter requires the need for bias lines. The entire structure is designed using High Frequency Structure Simulator (HFSS) software and the prototype is fabricated over FR-4 substrate having a thickness of 1.6mm and measurements are carried out. This antenna achieves a wideband frequency from 2 GHz to 12 GHz and distinct narrow band of frequencies by reconfigurability using single antenna consisting of different shapes spaced accurately to ensure isolation between adjacent frequency bands and each antenna element working for a bandwidth of 2 GHz for frequency from 2 GHz to 12 GHz upon a single substrate and the reconfigurable elements are controlled using a low cost Arduino microcontroller connected directly to the antenna which ensures accurate controlling of the rotation and fast switching between the antenna elements. The measured results agree with the simulated results and have less than 10 dB impedance bandwidth.

Cooperative beamforming in cognitive radio systems without feedback of receiver beamforming vectors to transmitter

In this paper, we investigate the problem of transceiver beamforming and power allocation in the downlink of a multiple input multiple output Cognitive radio system. In this system, a base station (BS) services multiple primary users (PU) and multiple secondary users (SU). In order to perform transceiver beamforming and power allocation, three new algorithms are proposed. The first one is an iterative single-objective constrained algorithm, in which the signal to interference plus noise (SINR) of SUs are improved at each iteration subject to constraints on quality of services of PUs (PUs SINR should be above a predefined threshold) and total transmit power of BS. The second and third algorithms, are based on a multi-objective constraint optimization problem, in which the goal is to improve both PUs and SUs SINR at each iteration subject to previous constraints of the first algorithm. The difference between the second and third algorithms is that in the joint maximization of the second algorithm, the cross-influence of each user on the others is ignored, but in the third algorithm, the cross effect of each users SINR on the others is considered. The novelty of these algorithms is the independency of the transmit precoding vectors from the receive beamforming vectors. Therefore, the feedback of receive beamforming to the BS is not needed. The performances of three proposed algorithms are evaluated in simulations vis bit error rate of PUs and SUs, SINR of PUs and SUs and the percent of BS power allocation to SUs.