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In order to deal with the difficulty of spectrum sensing in cognitive satellite wireless networks, a large-scale cognitive network spectrum sensing algorithm based on big data analysis theory is studied, and a new algorithm using mean exponential eigenvalue is proposed. This new approach fully uses all the eigenvalues in sample covariance matrix of the sensing results to make the decision, which can effectively improve the detection performance without obtaining the prior information from licensed users. Through simulation, the performance of various large scale cognitive radio spectrum sensing algorithms based on big data analysis theory is compared, and the influence of satellite to ground channel conditions and the number of sensing nodes on the performance of the algorithm is discussed. [ABSTRACT FROM AUTHOR]

The spectrum sensing function plays a significant role in the performance of cognitive radio (CR). Spectrum sensing specifies if free channels exist and identifies free channels for secondary users, actively helping in the improvement of spectrum usage and recognizing available channels in CR systems. Cyclostationary feature detection (CFD) is a spectrum sensing method that detects signals depending on different characteristics such as carrier frequency, modulation types, cyclic frequency, and symbol rates with an extremely low signal-to-noise ratio. At low SNR, CFD achieves a detection process with a high computation complexity. This paper designs Enhanced Cyclostationary Detector complexity with improved detection speed performances. For the sake of minimizing system complexity, utilizing the advantages of the Haar wavelet transform and signed correlator method for estimating the cyclic spectra of a detected signal. The proposed method performance was evaluated over Rayleigh flat fading and AWGN channel that had low SNR values. The acquired simulation results indicated the efficiency of the proposed method in terms of reduction 70% in complexity, 60% in time, and 7% in memory storage, with improved detection performance that is about 8% compared to conventional method at low SNR values reach to -30dB. [ABSTRACT FROM AUTHOR]

The internet of everything (IoEs) has become a well-known tool for transforming the concept of connected environments into reality in near future. The cognitive radio network is a potential candidate that addresses the issues of efficient spectrum utilization for next generation connected environments. Further, the spectrum mobility plays a significant role in cognitive connected environments during communication, for switching the channel on the appearance of primary user (PU) throughout the cognitive users' (CUs') data transmission. The performance of spectrum mobility relies on the ability of the system: 1) to detect the appearance of PU as soon as possible and 2) to stop the data transmission as-well-as switch to another channel. The potential approaches to detect the appearance of PU during CUs' data transmission are the "spectrum prediction (SP)" and "spectrum monitoring" (SM). The SP relies on the pre-available information about the channel and PUs' activities which is well explored technique, however, the SM is a real-time approach and is in its infancy. In this paper, several SM techniques with their effects on the spectrum mobility are illustrated. Moreover, the concept of imperfect SM is introduced and its effects on various performance metrics are investigated. Further, a potential approach of cooperative SM is proposed to diminish the effects of imperfections. In addition to this, the potential issues as well as research challenges regarding these techniques are presented. [ABSTRACT FROM AUTHOR]

In order to address the issue of spectrum scarcity, cognitive radio (CR) offers an effective usage of radio spectrum resources by offering dynamic spectrum access. One kind of wireless sensor networks having CR capabilities are wireless cognitive radio sensor networks (WCRSNs). The present work investigates throughput and energy-efficiency of WCRSN. More precisely, scenarios involving both noise and α - μ fading that affect the sensing (S) channels are taken into consideration. Each cognitive radio sensor (CRS) receives an unknown licensed signal information from the primary user (PU). The CRS uses an energy detection sensing technique and makes a local decision in one-bit binary form. The decisions from all the CRSs are forwarded to control center via reporting (R) channels and are combined based on the hard-decision fusion technique for making the final decision about active and inactive status of the PU. Throughput and energy-efficiency performances of WCRSN are assessed while taking into account the effects of pertinent network factors. To that end, first a brand-new mathematical probability of detection that is subject to noise plus generalized α - μ fading is established. Additionally, a computer based simulation and experimental setup are performed to verify the resulting expression. The analytical frameworks for assessing throughput and energy-efficiency performances under a variety of network and channel situations are then developed. Further, the effect of inaccurate S and R channels with channel error probability (q) on the general effectiveness of WCRSN is also examined. Finally, the effects of the α - μ fading parameters, the signal-to-noise ratio, the number of CRSs, and the detection threshold on the WCRSN are examined. For a number of network characteristics, throughput is maximized and energy-efficiency is up to 75% has been achieved. [ABSTRACT FROM AUTHOR]

Dynamic spectrum access has been promoted as a key technology in cognitive radio to achieve better spectrum utilization. It allows unauthorized secondary users to utilize the authorized primary user's spectrum opportunistically, when primary user is absent. Therefore, it is an important task for secondary users to observe primary user activity in the channel. Implementation of such spectrum access scheme, cognitive radio requires a fast and reliable spectrum sensing technique to monitor primary user activity. Among all the available spectrum sensing schemes, Energy detection is most widely used because of its low complexity. However, the conventional energy detection method produces poor performance in a lower signal regime, resulting in longer sensing duration and low detection probability. To overcome these challenges, we have proposed an adaptive decision threshold approach instead of a fixed decision threshold in a modified Three Event Energy Detection framework. Additionally, a new objective function is formulated prioritizing the PU over SU using a weight factor along with spectrum utilization factor which achieves a better trade-off between the miss detection and false alarm probability. Simulation results illustrate that the proposed approach has improved efficacy in decision error probability and detection performance compared to the existing methods. [ABSTRACT FROM AUTHOR]

Accurate threshold setting for energy detector is important for example in dynamic spectrum access. This requires accurate statistical distribution models of the observed energy. In this paper, we consider energy detection (ED) for $M$ -ary quadrature amplitude modulation (QAM) signals. The derivation of the exact solution of the distribution model (ES) requires all combinations of QAM signals in the observed signals based on the brute-force search and it leads to a significant computational cost. For this issue, this paper proposes three statistical distribution models which assume $M=\infty $ to avoid the brute-force search. Due to the assumption of $M$ , the proposed models are independent of $M$ and can handle adaptive modulation where $M$ can be changed dynamically. In the numerical evaluations, we compare the three proposed models with the other typical approximation models under additive white Gaussian noise (AWGN) channel and Rayleigh fading channel. In addition, the proposed models are extended for more realistic scenario where imperfect synchronization is considered. The comprehensive numerical evaluations show that the first proposed model is most accurate among all considered models except ES but requires relatively high computational cost. The second proposed model where the observed energy is assumed to follow Gaussian distribution is the least complexity but can have reduced accuracy. The third proposed model based on skew-normal distribution can achieve comparable accuracy and less complexity compared to the first model.

Recently, the Gini index detector (GID) has been proposed as an alternative for data-fusion cooperative spectrum sensing, being mostly suitable for channels with line-of-sight or dominant multi-path components. The GID is quite robust against time-varying noise and signal powers, has the constant false-alarm rate property, can outperform many the state-of-the-art robust detectors, and is one of the simplest detectors developed so far. The modified GID (mGID) is devised in this article. It inherits the attractive attributes of the GID, yet with a computational cost far below the GID. Specifically, the time complexity of the mGID obeys approximately the same run-time growth rate of the GID, but has a constant factor approximately 23.4 times smaller. Equivalently, the mGID takes approximately 4 % of the computation time spent to calculate the GID test statistic, which brings a huge reduction in the latency of the spectrum sensing process. Moreover, this latency reduction comes with no performance loss with respect to the GID. [ABSTRACT FROM AUTHOR]

A cognitive radio network (CRN) is an intelligent network that can detect unoccupied spectrum space without interfering with the primary user (PU). Spectrum scarcity arises due to the stable channel allocation, which the CRN handles. Spectrum handoff management is a critical problem that must be addressed in the CRN to ensure indefinite connection and profitable use of unallocated spectrum space for secondary users (SUs). Spectrum handoff (SHO) has some disadvantages, i.e., communication delay and power consumption. To overcome these drawbacks, a reduction in handoff should be a priority. This study proposes the use of dynamic spectrum access (DSA) to check for available channels for SU during handoff using a metaheuristic algorithm depending on machine learning. The simulation results show that the proposed "support vector machine-based red deer algorithm" (SVM-RDA) is resilient and has low complexity. The suggested algorithm's experimental setup offers several handoffs, unsuccessful handoffs, handoff delay, throughput, signal-to-noise ratio (SNR), SU bandwidth, and total spectrum bandwidth. This study provides an improved system performance during SHO. The inferred technique anticipates handoff delay and minimizes the handoff numbers. The results show that the recommended method is better at making predictions with fewer handoffs compared to the other three. [ABSTRACT FROM AUTHOR]

Summary: Cognitive networks are stands out as intelligent technology which evolved to enhance spectrum utilization. Secondary users are allowed to utilize the primary user's frequency bands on idling times. Identifying the idle licensed spectrum is achieved through spectrum sensing. The spectrum holes should be explored such that a suitable spectrum can be selected and allocated to the secondary users. Existing spectrum sensing and selection schemes have limitations due to interferences. Thus, an optimization algorithm based on bio‐inspired improved weed optimization was presented in this research work for enhanced channel utilization. The optimization model explores the channel characteristics and reduces the primary network interferences through its optimal solution. Further, Markov greedy‐based auction scheme was presented for channel allocation. Considering the channel capacity, delay, and switching rates the allocation is performed to enhance the overall system performance. Simulation analysis demonstrates the superior performance of the proposed model over existing techniques like particle swarm optimization and genetic algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Spectrum occupancy modeling plays an important role in improving the performance of Cognitive Radio (CR) systems, because most of the methods to improve Dynamic Spectrum Access (DSA) performance need to make assumptions about the channel periods’ distribution. However, Secondary Users (SUs) cannot directly observe the accurate channel periods’ distribution due to the sensing errors caused by noise, which will lead to inaccurate analysis of transmission efficiency or throughput. In this paper, we analyze the influence of different types of sensing errors on the observed channel periods, and establish the relationship between the Probability Mass Function (PMF) of idle periods under Imperfect Spectrum Sensing (ISS) and the results under Perfect Spectrum Sensing (PSS). In addition, we derive a closed-form expression for estimating the PMF of channel periods from the sensing results under ISS. Simulation results show that the proposed estimation method is more accurate than the existing methods, and is not affected by the sensing period, channel mean period and sensing error probability.

Primary channel mean period plays an important role in improving the performance of Dynamic Spectrum Access (DSA), because many algorithms to improve the performance of Cognitive Radio (CR) need to use the channel mean period as a prior knowledge. Secondary Users (SUs) can obtain statistics of the primary channel by spectrum sensing. However, SUs’ estimation of the mean period is inaccurate due to errors in the spectrum sensing in the real world, which will lead to performance degradation of CR systems. In this paper, we use a two-state Markov chain to model channel states, and use state transition probability to analyze the influence of sensing errors on the mean period. At the same time, we derive the estimation formula of the mean period of the original channel. Simulation results confirm that the proposed estimation method is superior to the existing estimation methods, and can accurately estimate the original period even with high sensing error probability.

The rapid growth in wireless communications, coupled with insufficient utilization of the spectrum, led to the development of new wireless services and the promising technology of cognitive radio (CR) networks, which facilitate periodic access to the unoccupied spectrum bands and thus increases spectral efficiency. A fundamental task in CR networks is spectrum sensing, through which unauthorized secondary users (SUs) detect unoccupied bands in the spectrum. To achieve this, an accurate estimate of the power spectrum is necessary. From this perspective, and given that many other factors can affect individual detection, such as pathloss and receiver uncertainty, we aim to improve its estimate by exploiting the spatial diversity in the SUs' observations. Spectrum sensing is treated as a parameters estimation problem, assuming that the parameters' vector of each SU consists of some global and partially common parameters. To exploit this modeling, distributed and cooperative spectrum sensing is the subject of interest in this study. Diffusion techniques, and especially the Adapt-Then-Combine (ATC) method will be exploited, where each SU cooperates with a group of nodes in its neighborhood that share the same parameters of interest. We consider a network of three static PUs with overlapping power spectrums, and thus, frequency bands. The performance of the employed method will be evaluated under two scenarios: (i) when the PUs spectrum varies, since some frequency bands are not yet utilized, and (ii) when the frequency bands of the PUs are fixed, but there is a mobile SU in the network, changing regions and parameters of interest. Experimental results and performance analysis reveal the ATC algorithm robustness and efficiency. [ABSTRACT FROM AUTHOR]

Recently, the Gini index detector (GID) has been proposed as an alternative for data-fusion cooperative spectrum sensing, being mostly suitable for channels with line-of-sight or dominant multi-path components. The GID is quite robust against time-varying noise and signal powers, has the constant false-alarm rate property, can outperform many the state-of-the-art robust detectors, and is one of the simplest detectors developed so far. The modified GID (mGID) is devised in this article. It inherits the attractive attributes of the GID, yet with a computational cost far below the GID. Specifically, the time complexity of the mGID obeys approximately the same run-time growth rate of the GID, but has a constant factor approximately 23.4 times smaller. Equivalently, the mGID takes approximately 4% of the computation time spent to calculate the GID test statistic, which brings a huge reduction in the latency of the spectrum sensing process. Moreover, this latency reduction comes with no performance loss with respect to the GID.

In recent works, the statistical information of the channel traffic has been increasingly exploited to make effective decisions in spectrum sharing systems. However, these statistics cannot be obtained perfectly under (realistic) Imperfect Spectrum Sensing (ISS). Therefore, in this work we study comprehensively the approaches in the literature that correct the estimation of the channel traffic statistics under ISS, namely the closed-form expression approach and the algorithmic reconstruction approach. Then, we introduce a novel approach named Traffic Learning as a Deep Learning (DL) approach for providing accurate estimation of the channel traffic statistics under ISS. For this novel approach, deep neural networks using Multilayer Perceptron (MLP) models are found for the estimation of several statistical metrics. In addition, we show that utilising effective features from spectrum sensing observations can lead to a considerable improvement in statistics estimation for each, mean, variance, minimum and distribution of the channel traffic under ISS, outperforming the existing approaches in the literature, which are based on either closed-form expressions or reconstruction algorithms.

In recent years, the use of wireless communication systems and wireless network services has increased exponentially, causing spectrum scarcity. In addition, fixed spectrum assignment policies further contribute to spectrum scarcity. The application of dynamic spectrum access, which is the basis of Cognitive Radio systems rather than fixed spectrum assignment, plays an important role in solving the spectrum scarcity problem. Dynamic spectrum access means identifying and re-evaluating idle spectrum regions. Empty spectrum regions are determined with the help of suitable detectors, a process known as spectrum detection in the literature. In this study, we propose two new detectors under the assumption of normality with the help of Standard Condition Number and Demmel Condition Number statistics. Theoretical analyses were made, and thresholds obtained for the proposed detectors. To evaluate the performance of the proposed detectors, they were compared with several detectors in the literature (MME, DCN, SNE) in terms of detection performance and detection times. It is clear from the simulation studies that the detection performance of the proposed detectors was better. [ABSTRACT FROM AUTHOR]

For a future scenario where everything is connected, cognitive technology can be used for spectrum sensing and access, and emerging coding technologies can be used to address the erasure of packets caused by dynamic spectrum access and realize cognitive spectrum collaboration among users in mass connection scenarios. Machine learning technologies are being increasingly used in the implementation of smart networks. In this paper, after an overview of several key technologies in the cognitive spectrum collaboration, a joint optimization algorithm of dynamic spectrum access and coding is proposed and implemented using reinforcement learning, and the effectiveness of the algorithm is verified by simulations, thus providing a feasible research direction for the realization of cognitive spectrum collaboration.

Cognitive radio is a mechanism allowing dynamic access to spectrum channels. Since its beginnings, researchers have been working on using this inventive technology to control and manage the spectrum resources. Consequently, this research field has been progressing rapidly and important advances have been made. Spectrum sensing is a key function of cognitive radios that helps prevent the harmful interference with licensed users, as well as identifies the available spectrum to improve its utilization. Several spectrum sensing techniques are found in scientific literature. In this paper, we investigate the effect of the random sampling in spectrum sensing. We propose a spectrum sensing approach based on the energy detection and on the maximum eigenvalue detection (MED) combined with random sampling. The performance of the proposed approach is evaluated in terms of the receiver operating characteristics curves and in terms of the detection probability for different values of signal to noise ratio. The obtained results are compared to the uniform sampling case to show the added value of random sampling. [ABSTRACT FROM AUTHOR]

In this paper a dynamic spectrum access (DSA) concept is explored for mitigating the paucity of spectral band-width in cognitive radio (CR) for opportunistic, dynamic access of the spectrum without any interference. Dynamic spectrum access schemes are proposed for a distributed cognitive radio network consisting of one secondary user (SU) and many primary users (PUs). The SU has to make decisions for accessing PU channels within discrete time slots. The design of sensing and access strategies that govern channel choice in each slot for near-optimal throughput performance of the SU may be formulated as a partially observable Markov decision process (POMDP). Furthermore, it is considered that the SU incurs a cost whenever it switches to a different channel. The switching cost is expressed in terms of delay, packet loss and packet overhead. In this work, the SU access policy based on a myopic approach is proposed and evaluated. [ABSTRACT FROM AUTHOR]

Dynamic Spectrum Access/Cognitive Radio systems access the channel in an opportunistic, non-interfering manner with the primary network. These systems utilize spectrum sensing techniques to sense the occupancy of the primary user. In this paper, an artificial neural network based hybrid spectrum sensing technique is proposed, which considers sensing as a binary classification problem to detect whether the primary user is idle or busy. The proposed scheme utilizes energy detection and likelihood ratio test statistic as features to train the neural network. Moreover, we demonstrate the impact of hyperparameter tuning and carry out the detailed study of it, yielding a combination of best-suited hyperparameters. The performance of the proposed sensing scheme is validated on primary signals of various real world radio technologies acquired with an empirical testbed setup. We conclude that the best performing configuration results in an increase of approximately 63% in detection performance compared to classical energy detection and improved energy detection sensing schemes when averaged over all the radio technologies considered in this work. [ABSTRACT FROM AUTHOR]

The exponential increase in the number of the Internet of Things (IoT) necessitates dynamic and shared spectrum access at the edge of network. In cognitive radio (CR)-based IoT communications, spectrum sensing (SS) plays a pivotal role, if designed carefully, to enable a coexistence between licensed users (LUs) and unlicensed IoT devices for efficient and dynamic spectrum utilization. Though several SS techniques have been proposed in the literature, energy detection (ED) is renowned for its time and resource efficiency. Despite its suitability for IoT devices owing to its low hardware complexity and absence of a priori LU information, the detection performance of ED is poor at very low signal-to-noise ratio (SNR) channel conditions. While cooperative sensing can alleviate the performance problem of ED sensing in IoT network, significant detection cannot be achieved under adverse channel environments using non-cooperative IoT applications. Recently, stochastic resonance (SR) has been employed in CRs to boost the performance of SS in weak signal detection. In this paper, we propose a metaheuristic firefly algorithm (FFA) to determine the SR parameters through an objective function defined by the output SNR of a dynamic IoT system. In particular, we use an optimization scheme to optimally compute a noise level to achieve the best SR effect. Hence, the proposed FFA-based optimization problem significantly improves the sensing time and utilization of IoT communication channels in the weak heterogeneous IoT application introductions into the market. Our proposed system achieves a better detection probability of 80% compared to the 50% obtained through previous SR-based ED research works taking into account of SNR value of − 20 dB and a 10% false alarm probability ( Q FA ). Moreover, for SNR value of − 20 dB, the sensing error probability of our proposed technique (20%) is 30% less than the previous SR-based ED considering Q FA = 5%. [ABSTRACT FROM AUTHOR]

Cognitive Radio based Wireless Sensor Network is a novel concept that integrates the dynamic spectrum access capability of cognitive radio into wireless sensor networks for the futuristic sensor networks and wireless communication technology. Spectrum sensing plays a quintessential role in a cognitive radio network but is a major constraint for a battery powered sensor with stringent energy limitations. The spectrum sensing algorithms are expected to yield acceptable detection probability at low SNR under noise uncertainty with minimum power consumption in a WSN. In this paper, a new spectrum sensing method has been proposed to overcome sensing failure under low SNR environment. The proposed technique is based on adaptive double threshold theory which improves the detection performance by 39.63 and 27.22% at SNR = −10dB as compared to the conventional energy detection and available double threshold-based method respectively. Furthermore, the proposed method of spectrum sensing is evaluated for its deployment into a CR-WSN using the evaluation metrics: Time and Sample Complexity. The comparative evaluation of the spectrum sensing method in a WSN through simulations shows that the proposed technique offers substantial reduction in sample and time complexity of the wireless sensor nodes. [ABSTRACT FROM AUTHOR]

Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Primary channel statistics have recently gained increasing attention due to its remarkable role in the performance improvement of Dynamic Spectrum Access (DSA)/Cognitive Radio (CR) systems. These statistics can be calculated from the outcomes of spectrum sensing, which is the well-known method used to identify the available instantaneous opportunities in the spectrum. Computing statistical information from spectrum sensing, however, may sometimes be unreliable due to the fact that spectrum sensing is imperfect in the real world and errors are likely to occur in the sensing decisions. In this context, this work provides a detailed analysis of a broad range of primary channel statistics under Imperfect Spectrum Sensing (ISS) and finds a set of closed-form expressions for the calculated statistics under ISS as a function of the original primary channel statistics, probability of error, and the employed sensing period. In addition, the obtained mathematical expressions are employed to find and propose novel estimators for the primary channel statistics, which outperform the existing estimators in the literature and can provide accurate estimations of the original statistics even under high probability of error of spectrum sensing. The correctness of the obtained analytical expressions and the accuracy of the proposed estimators are corroborated with both simulation and experimental results. [ABSTRACT FROM AUTHOR]

Dynamic Spectrum Access (DSA)/Cognitive Radio (CR) systems access the channel in an opportunistic, non-interfering manner with the primary network. DSA/CR systems utilize spectrum sensing techniques to sense the availability of Primary user (PU). CR users can benefit from the knowledge of PU activity statistics. In this work, comprehensive analysis of estimation of distribution of PU idle and busy periods is carried out using Generalized Pareto and Pareto distributions for long and short time scale models respectively and closed form expression is derived. Moreover, the impact of sensing periods on the accuracy of estimated PU idle/busy periods is studied. Furthermore, the error in proposed estimation of distribution of PU idle and busy periods is quantified using the Kolmogorov–Smirnov test. From this study we conclude that the proposed model is better fit for the real scenarios eliminating practical limitations. Mathematical analysis is substantiated with the simulation results. [ABSTRACT FROM AUTHOR]

The radio-frequency spectrum shortage, which is primarily caused by the fixed allocation policy, is one of the main bottlenecks to the deployment of existing wireless communication networks, and to the development of new ones. The dynamic spectrum access policy is foreseen as the solution to this problem, since it allows shared spectrum usage by primary licensed and secondary unlicensed networks. In order to turn this policy into reality, the secondary network must be capable of acquiring reliable, real-time information on available bands within the service area, which can be achieved by means of spectrum sensing, spectrum occupancy databases, or a combination of them. This Review presents guidelines related to the design of a framework that can be adopted to foster dynamic spectrum access policies. The framework applies special-purpose Internet of Things (IoT) devices that perform spectrum sensing, subsequently feeding a spectrum occupancy database, which in turn will be used by the secondary network to gather information on location-dependent spectrum availability. The guidelines address technological enablers capable of making the framework feasible, reliable and secure.

Congestion in the RF spectrum is rapidly increasing, which has motivated the need for efficient spectrum sharing techniques. A cognitive radar system has been developed to implement a perception action cycle, for spectrum sharing, in which the RF spectrum is sensed, other RF signals are identified, and the radar frequency band of operation is adapted to avoid interfering signals in the spectrum. The system operates in real time and is capable of coexisting with common communications signals. A system with this capability requires efficient programming that pushes the limits of the technology available. In order to properly test the performance of a radar system designed for this kind of reactive spectrum sharing, a rigorous set of synthetic interference signals is generated and several informative evaluation metrics are defined. Additionally, the system's performance is evaluated with common communications signals such as LTE and GSM. The performance of the system is found to be adequate for avoiding signals that are either varying in frequency or turning on and off at rates on the order of 10 ms. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Efficient spectrum sensing is essential for the successful application of the Dynamic Spectrum Assignment (DSA) technology in Cognitive Radio Networks (CRNs). In conventional spectrum sensing schemes, secondary users (SUs) have to intelligently schedule their sensing and accessing so that the spectrum opportunities are thoroughly exploited while the primary users are not harmed. In this article, we propose a new sensing servicemodel in which a Spectrum Sensor Network (SSN) is employed for spectrum sensing tasks.We will describe the general framework for this SSN-enabled CRN and present the major challenges in such an architecture. We will address one of these challenges and formulate it as a boundary detection problem with unknown erroneous inputs. A novel cooperative boundary detection algorithm is designed which explores recent advances in Support Vector Machines (SVM) and computational geometry.We prove that cooperative spectrum sensing can asymptotically approach the optimal solution. Real testbed as well as comprehensive simulation experiments are conducted, and the results show that, compared with the traditional schemes, cooperative boundary detection can dramatically reduce the spectrum sensing overhead and improve the effectiveness of DSA. [ABSTRACT FROM AUTHOR]

Primary channel activity statistics (such as the minimum idle/busy periods of the channel, the moments or the underlying distributions) can be exploited by cognitive radio (CR) systems to adapt their operation and improve their performance. Such statistics can be directly estimated from periodic observations of the instantaneous idle/busy state of the primary channel (i.e., periodic spectrum sensing). However, the periodicity of such observations (i.e., the sensing period) imposes a fundamental limit on the time resolution in which idle/busy periods can be observed, and consequently on the accuracy of any subsequent estimated statistics. In this context, this paper provides a comprehensive analysis on the estimation of the primary activity statistics based on periodic channel state observations performed with a finite sensing period. In particular, this paper provides a comprehensive set of closed-form expressions for the estimated statistics as a function of the true primary activity statistics and the employed sensing period. These expressions can find a wide range of applications in the analysis, design, and simulation of CR systems. Moreover, several methods to minimize the estimation errors and improve the accuracy are proposed and validated with both simulations and hardware experiments. [ABSTRACT FROM AUTHOR]

Implementation of dynamic spectrum access (DSA) in cognitive radio (CR) systems requires the unlicensed secondary users (SU) to implement spectrum sensing to monitor the activity of the licensed primary users (PU). Energy detection (ED) is one of the most widely used methods for spectrum sensing in CR systems, and in this paper we present a novel ED algorithm with an adaptive sensing threshold. The three-event ED (3EED) algorithm for spectrum sensing is considered for which an accurate approximation of the optimal decision threshold that minimizes the decision error probability (DEP) is found using Newton’s method with forced convergence in one iteration. The proposed algorithm is analyzed and illustrated with numerical results obtained from simulations that closely match the theoretical results and show that it outperforms the conventional ED (CED) algorithm for spectrum sensing.

Apart from the received signal energy, auxiliary information plays an important role in remarkably ameliorating conventional spectrum sensing. In this paper, a novel spectrum sensing scheme aided by geolocation information is proposed. In the cellular cognitive radio network (CCRN), secondary user equipments (SUEs) first acquire their wireless fingerprints via either received signal strength (RSS) or time of arrival (TOA) estimation over the reference signals received from their surrounding base-stations (BSs) and then pinpoint their geographical locations through a wireless fingerprint (WFP) matching process in the wireless fingerprint database (WFPD). Driven by the WFPD, the SUEs can easily ascertain for themselves the white licensed frequency band (LFB) for opportunistic access. In view of the fact that the locations of the primary user (PU) transmitters in the CCRN are either readily known or practically unavailable, the SUEs can either search the WFPD directly or rely on the support vector machine (SVM) algorithm to determine the availability of the LFB. Additionally, in order to alleviate the deficiency of single SUE-based sensing, a joint prediction mechanism is proposed on the basis of cooperation of multiple SUEs that are geographically nearby. Simulations verify that the proposed scheme achieves higher detection probability and demands less energy consumption than the conventional spectrum sensing algorithms.

The proliferation of Internet-of-Things (IoT) technology and its reliance on the license-free Industrial, Scientific, and Medical (ISM) bands have rendered radio spectrum scarce. The IoT can nevertheless obtain great advantage from Cognitive Radio (CR) technology for efficient use of a spectrum, to be implemented in IEEE 802.11af-based primary networks. However, such networks require a geolocation database and a centralized architecture to communicate white space information on channels. On the other hand, in spectrum sensing, CR presents various challenges such as the Hidden Primary Terminal (HPT) problem. To this end, we focus on the most recently released standard, i.e., IEEE 802.11ah, in which IoT stations can first be classified into multiple groups to reduce collisions and then they can periodically access the channel. Therein, both services are similarly supported by a centralized server that requires signaling overhead to control the groups of stations. In addition, more regroupings are required over time due to the frequent variations in the number of participating stations, which leads to more overhead. In this paper, we propose a new Multiple Access Control (MAC) protocol for CR-based IEEE 802.11ah systems, called Restricted Access with Collision and Interference Resolution (RACIR).We introduce a decentralized group split algorithm that distributes the participating stations into multiple groups based on a probabilistic estimation in order to resolve collisions. Furthermore, we propose a decentralized channel access procedure that avoids the HPT problem and resolves interference with the incumbent receiver. We analyze the performance of our proposed MAC protocol in terms of normalized throughput, packet delay and energy consumption with the Markov model and analytic expressions. The results are quite promising, which makes the RACIR protocol a strong candidate for the CR-based IoT environment. [ABSTRACT FROM AUTHOR]

Dynamic spectrum sharing is considered as one of the key features in the next-generation communications. In this correspondence, we investigate the dynamic tradeoff between the sensing performance and the achievable throughput, in the presence of time-varying fading (TVF) channels. We first establish a unified dynamic state-space model to characterize the involved dynamic behaviors. On this basis, a promising dynamic sensing schedule framework is proposed, whereby the sensing duration is adaptively adjusted based on the estimated real-time TVF channel. We formulate the sensing-throughput tradeoff problem mathematically. We then show that there exists the optimal sensing duration maximizing the throughput for the secondary user (SU), which will change dynamically with channel gains. Relying on our designed recursive sensing paradigm that is able to blindly acquire varying channel gains as well as the PU states, the sensing duration can be then adjusted in line with the evolving channel gains. Numerical simulations are provided to validate our dynamic sensing schedule algorithm, which can significantly improve the SU's throughput by reconfiguring the sensing duration according to dynamic channel conditions. [ABSTRACT FROM AUTHOR]

Spectrum reuse has attracted much concern of researchers and scientists, however, the dynamic spectrum access is challenging, since an individual secondary user usually just has limited sensing abilities. One key insight is that spectrum usage forecasting among secondary users, this inspiration enables users to obtain more informed spectrum opportunities. Therefore, spectrum usage forecasting is vital to cognitive radio networks (CRNs). With this insight, a spectrum usage forecasting model for the occurrence of primary users prediction is derived in this paper. The proposed model is based on auto regressive enhanced primary user emergence reasoning (AR-PUER), which combines linear prediction and primary user emergence reasoning. Historical samples are selected to train the spectrum usage forecasting model in order to capture the current distinction pattern of primary users. The proposed scheme does not require the knowledge of signal or of noise power. To verify the performance of proposed spectrum usage forecasting model, we apply it to the data during the past two months, and then compare it with some other sensing techniques. The simulation results demonstrate that the spectrum usage forecasting model is effective and generates the most accurate prediction of primary users occasion in several cases. [ABSTRACT FROM AUTHOR]

Access to the electromagnetic spectrum is an ever-growing challenge for radar. Future radar will be required to mitigate RF interference from other RF sources, relocate to new frequency bands while maintaining quality of service, and share frequency bands with other RF systems. The spectrum sensing, multioptimization (SS-MO) technique was recently investigated as a possible solution to these challenges. Prior results have indicated significant improvement in the signal-to-interference plus noise ratio at the cost of a high computational complexity. However, the optimization computational cost must be manageable in real time to address the dynamically changing spectral environment. In this paper, a bioinspired filtering technique is investigated to reduce the computational complexity of SS-MO. The proposed technique is analogous to the processing of the thalamus in the human brain in that the number of samples input to SS-MO is significantly decreased, thus, resulting in a reduction in computational complexity. The performance and computational complexity of SS-MO and the proposed technique are investigated. Both techniques are used to process a variety of measured spectral data. The results indicate a significant decrease in computational complexity for the proposed approach while maintaining performance of the SS-MO technique. [ABSTRACT FROM AUTHOR]

Internet-of-Things (IoT) enabling technologies such as ZigBee, WiFi, 6LowPAN, RFID, Machine-to-Machine, LTE-Advanced, etc. depend on the license-free Industrial Scientific and Medical (ISM) bands for the Internet. The proliferation of IoT devices is not only anticipated to create a huge amount of congestion in the near future, but even now the unlicensed spectrum is not enough in the ISM bands. Towards this end, Cognitive Radio (CR) technology can resolve the spectrum shortage issue since CR users can opportunistically exploit white spaces in licensed channels of the adjacent wireless systems. In CR networks, it is critical to coordinate spectrum access among Secondary Users (SUs) while protecting priority rights of Primary Users (PUs). Therein, SUs need to take good care of hidden PUs in order to avoid harmful interference. Further, a densely deployed CR network can compromise spectrum sensing quality and certainty of the results when a large number of SUs contends to access the same channel. Therefore, based on the vulnerable sensing results, SUs can cause interference to the PUs. In this paper, we first investigate the leading issues and then propose a novel Handshake Sense Multiple Access with Collision Avoidance (HSMA/CA) protocol for CR-based IoT networks. Our proposed HSMA/CA scheme resolves hidden primary terminal problem and maintains sufficient priority rights to PUs in a densely distributed network. In addition, we optimize the spectrum sensing period to maximize the system performance by maintaining peculiarities in the sensing operation like false alarm and misdetection. To evaluate the performance of HSMA/CA, we have analyzed the protocol through the Markov chain model in terms of throughput and verify its accuracy by simulations. Simulation results show that our scheme is suitable for non-collaborative densely deployed CR-based IoT networks.

Spectrum sensing is a prominent functionality to enable dynamic spectrum access (DSA) in cognitive radio (CR) networks. It provides protection to primary users (PUs) from interference and creates opportunities of spectrum access for secondary users (SUs). It should be performed efficiently to reduce the number of false alarms and missed detections. Continuous sensing for a long time incurs cost in terms of increased energy consumption; thus, spectrum sensing ought to be energy efficient to ensure the prolonged existence of CR devices. This paper focuses on using of history to help achieve energy-efficient spectrum sensing in infrastructure-based CR networks. The scheme employs an iteratively developed history processing database that is used by CRs to make decisions about spectrum sensing, subsequently resulting in reduced spectrum scanning and improved energy efficiency. Two conventional spectrum sensing schemes, i.e., energy detection (ED) and cyclostationary feature detection (CFD), are enriched by history to demonstrate the effectiveness of the proposed scheme. System-level simulations are performed to investigate the sensitivity of the proposed history-based scheme by performing detailed energy consumption analysis for the aforementioned schemes. Results demonstrate that the employment of history ensued in improved energy efficiency due to reduced spectrum scanning. This paper also suggests which spectrum sensing scheme can be the best candidate in a particular scenario by looking into computational complexity before comparative analysis is presented with other states of the art. [ABSTRACT FROM PUBLISHER]

This manuscript presents a survey on new challenges in wireless communication systems and discusses recent approaches to address some recently raised problems by the wireless community. At first a historical background is briefly introduced. Challenges based on modern and real life applications are then described. Up to date research fields to solve limitations of existing systems and emerging new technologies are discussed. Theoretical and experimental results based on several research projects or studies are briefly provided. Essential, basic and many self references are cited. Future researcher axes are briefly introduced. [ABSTRACT FROM AUTHOR]

As the complexities of wireless technologies increase, novel multidisciplinary approaches for the spectrum sharing/management are required with inputs from the technology, economics and regulations. Recently, the cognitive radio technology comes into action to handle the spectrum scarcity problem. To identify the available spectrum resource, decision on the optimal sensing and transmission time with proper coordination among the users for spectrum access are the important characteristics of spectrum sharing methods. In this paper, we have technically overviewed the state-of-the-art of the various spectrum sharing techniques and discussed their potential issues with emerging applications of the communication system, especially to enhance the spectral efficiency. The potential advantages, limiting factors, and characteristic features of the existing cognitive radio spectrum sharing domains are thoroughly discussed and an overview of the spectrum sharing is provided as it ensures the channel access without the interference/collision to the licensed users in the spectrum. [ABSTRACT FROM AUTHOR]