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Cognitive radio (CR) is a promising technology enabling efficient utilization of the spectrum resource for future wireless systems. As future CR networks are envisioned to operate over a wide frequency range, advanced wideband spectrum sensing (WBSS) capable of quickly and reliably detecting idle spectrum bands across a wide frequency span is essential. In this article, we provide an overview of recent advances on sub-Nyquist sampling-based WBSS techniques, including compressed sensing-based methods and compressive covariance sensing-based methods. An elaborate discussion of the pros and cons of each approach is presented, along with some challenging issues for future research. A comparative study suggests that the compressive covariance sensing-based approach offers a more competitive solution for reliable real-time WBSS., Comment: This paper has been accepted by IEEE Wireless Communications Magazine for future publication

Nowadays both satellite and terrestrial networks are expanding rapidly to meet the ever-increasing demands for higher throughput, lower latency, and wider coverage. However, spectrum scarcity places obstacles in the sustainable development. To accommodate the expanding network within a limited spectrum, spectrum sharing is deemed as a promising candidate. Particularly, cognitive radio (CR) has been proposed in the literature to allow satellite and terrestrial networks to share their spectrum dynamically. However, the existing CR-based schemes are found to be impractical and inefficient because they neglect the difficulty in obtaining the accurate and timely environment perception in satellite communications and only focus on link-level coexistence with limited interoperability. In this paper, we propose an intelligent spectrum management framework based on software defined network (SDN) and artificial intelligence (AI). Specifically, SDN transforms the heterogenous satellite and terrestrial networks into an integrated satellite and terrestrial network (ISTN) with reconfigurability and interoperability. AI is further used to make predictive environment perception and to configure the network for optimal resource allocation. Briefly, the proposed framework provides a new paradigm to integrate and exploit the spectrum of satellite and terrestrial networks.

The heterogenous wireless services and exponentially growing traffic call for novel spectrum- and energy-efficient wireless communication technologies. Recently, a new technique, called symbiotic radio (SR), is proposed to exploit the benefits and address the drawbacks of cognitive radio (CR) and ambient backscattering communications (AmBC), leading to mutualism spectrum sharing and highly reliable backscattering communications. In particular, the secondary transmitter (STx) in SR transmits messages to the secondary receiver (SRx) over the RF signals originating from the primary transmitter (PTx) based on cognitive backscattering communications, thus the secondary system shares not only the radio spectrum, but also the power, and infrastructure with the primary system. In return, the secondary transmission provides beneficial multipath diversity to the primary system, therefore the two systems form mutualism spectrum sharing. More importantly, joint decoding is exploited at SRx to achieve highly reliable backscattering communications. In this article, to exploit the full potential of SR, we provide a systematic view for SR and address three fundamental tasks in SR: (1) enhancing the backscattering link via active load; (2) achieving highly reliable communications through joint decoding; and (3) capturing PTx’s RF signals using reconfigurable intelligent surfaces. Emerging applications, design challenges and open research problems will also be discussed.

One of the key problems in spectrum sensing is to design the test statistic. Existing methods generally exploit the model-based features as the test statistic, such as energies and eigenvalues. However, these features could not accurately characterize the real environment. Motivated by this, in this paper, we use a deep neural network (DNN) to intelligently explore the data-driven test statistic. Firstly, we introduce a DNN-based detection framework, where a DNN-based likelihood ratio test (DNN-LRT) is derived to guarantee the optimality of the designed test statistic. As a realization of the developed DNN-based framework, we use the sample covariance matrix as the input of a convolutional neural network (CNN), and propose a covariance matrix-aware CNN (CM-CNN)-based spectrum sensing algorithm, which further improves the performance. In addition, we also provide the theoretical analysis of the proposed method. To the best of our knowledge, it’s the first time to analyze the theoretical performance of CNN-based methods. Finally, simulation results demonstrate that the performance of the proposed method is close to that of the optimal detector. Particularly, the proposed method could achieve a detection probability of 96.7% with a false alarm probability of 1.9% at SNR = −18dB, which significantly outperforms the conventional methods.

Massive wireless connections are emerging in Internet of Things (IoT) and will lead to a severe spectrum scarcity issue. To deal with this issue, we introduce the cognitive radio technology into the IoT, namely, cognitive IoT. Different from a conventional cognitive network, the cognitive IoT is dominated by short-packet transmissions, which suffer from a significant packet error rate even when the transmission rate is smaller than the Shannon capacity. In this paper, we jointly optimize the spectrum sensing time and packet error rate to maximize the cognitive effective-throughput, which is defined as the effective transmission rate by considering the packet error rate. First, we formulate an instantaneous effective-throughput maximization problem with the instantaneous channel state information (CSI) between cognitive transceivers, and develop a successive optimization algorithm. Second, we formulate an average effective-throughput maximization problem with the statistical CSI between cognitive transceivers. Due to the complicated expression of the average effective-throughput, we analyze its closed-form expression and adopt an exhaustive search method to obtain the optimal solution. Numerical and simulation results reveal that, the packet length has a significant impact on the optimal design. Meanwhile, the proposed algorithms can almost maximize the instantaneous/average effective-throughput.

The state-of-the-art goodness-of-fit (GoF) test algorithms for spectrum sensing directly make decisions based on temporal samples or energies of the observations, which perform well under the condition that the received primary user (PU) signals are independent. However, when the received PU signals are highly correlated, these methods cannot achieve satisfactory performance. In this case, the correlation feature is the main character of the signals and the maximum eigenvalue of the sample covariance matrix is a versatile statistic reflecting the correlation feature. Motivated by this, we make full use of the correlation feature involved in the eigenvalues to improve the GoF detection performance. Specifically, we first study the GoF test in eigenvalue domain and design a semi-blind maximum eigenvalue-based GoF detection scheme using the ratio of the maximum eigenvalue to the noise power. Considering that the accurate knowledge of the noise power is not always available in practice, we next design a totally blind maximum eigenvalue-based GoF detection method, which only uses the ratio of the maximum eigenvalue to the trace. Utilizing the recent results of random matrix theory, we provide a theoretical analysis of the proposed methods, including the probabilities of false alarm, detection thresholds, and probabilities of detection. Finally, simulation results show that the proposed algorithms outperform the related GoF detection methods in terms of the detection performance.

In cognitive radio, most spectrum sensing algorithms are model-based and their detection performance relies heavily on the accuracy of the assumed statistical model. In this letter, we propose a convolutional neural network-based deep learning algorithm for spectrum sensing. Compared with model-based spectrum sensing algorithms, our proposed deep learning approach is data-driven and requires neither signal-noise probability model nor primary user (PU) activity pattern model. The proposed algorithm simultaneously takes in the present sensing data and historical sensing data, with which the inherent PU activity pattern can be learned to benefit the detection of PU activity. With extensive numerical simulations, results show that the proposed algorithm outperforms the estimator-correlator detector and the hidden Markov model-based detector in terms of correct detection probability.

Prior works on performance analysis for cognitive radio networks often assume perfect channel state information (CSI), which may not be readily available. This paper investigates the effects of the outdated CSI on the performance of a dual-hop multi-antenna multi-relay spectrum sharing system. To exploit the benefits of available multiple antennas, collaborative zero-forcing beamforming is considered at the secondary user (SU) source node with the outdated CSI and maximum ratio combining diversity reception at the destination node. The $N$ th best relay selection strategy is adopted at the relays. Analytical outage probability and bit-error rate expressions are derived for the SU. Moreover, we examine the high signal-to-noise ratio regime and present closed-form expressions for the asymptotic outage probability and asymptotic bit-error rate with and without feedback delay. In addition, we derive the closed-form ergodic capacity expression for the special case of our considered system, and analyze the effects of delay on the capacity. Furthermore, the closed-form outage probability expression for the primary user is obtained and the impact of delay on the interference on the primary user is investigated. Monte Carlo simulations are carried out to verify our analysis.

Blind modulation classification is a fundamental step before signal detection in cognitive radio networks where the knowledge of modulation scheme is not completely known. In this paper, a modulation-constrained (MC) clustering classifier is proposed for recognizing the modulation scheme with unknown channel matrix and noise variance for MIMO systems. By recognizing the fact that the received signals within an observation interval form into clusters and exploiting the intrinsic relationships between different digital modulation schemes, the modulation classification is transformed into a number of clustering problems, one for each modulation scheme, with the final classification decision based on the maximum likelihood criterion. To improve the learning efficiency, centroid reconstruction is proposed to recover all cluster centroids through a limited number of parameters by exploiting the structural relationships in constellation diagrams. Furthermore, a method to initialize the cluster centroids is also proposed. The proposed MC classifier together with centroid reconstruction and initialization methods not only reduce the number of parameters to be estimated, but also help to initialize the clustering algorithm for the enhanced convergence performance. Simulation results show that our algorithm can perform excellently even at low SNR and with very short observation interval length.

Spectrum occupancy prediction allows cognitive radio secondary users to exploit temporal spectrum opportunities one step-ahead. Temporal correlations in spectrum sensing measurements can be utilized to predict primary user activity patterns. Where applicable, cooperative spectrum prediction has the potential to improve prediction accuracy compared to single user (local) spectrum prediction. This letter presents the concept and methods for soft fusion-based cooperative spectrum occupancy prediction. The proposed methods were simulated and the results show significant improvement in prediction error over local, and hard fusion-based spectrum prediction.

Cognitive radio (CR) is an effective solution to increase the spectral efficiency (SE) of wireless communications by allowing the secondary users (SUs) to share the spectrum with primary users (PUs). On the other hand, intelligent reflecting surface (IRS) is a promising approach to enhance the energy efficiency (EE) of wireless communication systems through passively reconfiguring the channel environments. In this paper, we propose an IRS enhanced downlink multiple-input single-output (MISO) CR systems to improve both SE and EE, where a single SU coexists with a primary network with multiple primary user receivers (PU-RXs). Specifically, for the MISO-CR system, we maximize the achievable rate of SU subject to a total power constraint on an SU transmitter (SU-TX) and an interference temperature (IT) constraint on PU-RXs, by jointly optimizing the beamforming vector at SU-TX and the phase shifts at the IRS. Furthermore, both perfect channel state information (CSI) and imperfect CSI are considered in the optimization. Numerical results demonstrate that the IRS can significantly improve the achievable rate of SU-RX under both the perfect and imperfect CSI conditions.

In this paper, we consider a spectrum-sharing cognitive radio network (CRN), in which the secondary user (SU) is an active-load assisted symbiotic radio (SR) system. Specifically, the SU transmitter (SU-Tx) exploits multiple antennas to simultaneously support the secondary transmission from the SU-Tx to the SU receiver (SU-Rx) and the backscatter transmission from the active-load assisted backscatter device (BD) to the SU-Rx. As a result, the PU receiver (PU-Rx) is interfered by not only the SU-Tx but also the BD. For such a scenario, the SR system needs to design the transmit beamforming at the SU-Tx and the amplification gain at the BD to balance two conflicting goals, namely, the rate maximization for SU and the interference control to PU-Rx. We formulate an SU rate maximization problem under its own transmit-power constraint, the interference-power constraint as well as some practical constraints introduced by the SR system. This non-convex problem is solved by an alternating optimization based method, which iteratively optimizes the beamforming vector and transmit power at the SU-Tx, and the amplification gain at the BD. Simulation results show that the proposed method outperforms the equal-gain allocation method.

Spectrum sensing is a critical step in cognitive radio based DSM to learn the radio environment. Despite its long history, in the past decade, the study on spectrum sensing has attracted substantial interests from the wireless communications community. In this chapter, we first provide the fundamental theories on spectrum sensing from the optimal likelihood ratio test perspective, then we review the classical methods including Bayesian method, robust hypothesis test, energy detection, matched filtering detection, and cyclostationary detection. After that, we discuss the robustness of the classical methods and review techniques that can enhance the sensing reliability under hostile environment. These methods include eigenvalue based sensing method, covariance based detection method. Finally, we discuss the cooperative sensing that uses data fusion or decision fusion from multiple senors to enhance the sensing performance.

It has been 20 years since the concept of cognitive radio (CR) was proposed, which is an efficient approach to provide more access opportunities to connect massive wireless devices. To improve the spectrum efficiency, CR enables unlicensed usage of licensed spectrum resources. It has been regarded as the key enabler for intelligent communications. In this article, we will provide an overview on the intelligent communication in the past two decades to illustrate the revolution of its capability from cognition to artificial intelligence (AI). Particularly, this article starts from a comprehensive review of typical spectrum sensing and sharing, followed by the recent achievements on the AI-enabled intelligent radio. Moreover, research challenges in the future intelligent communications will be discussed to show a path to the real deployment of intelligent radio. After witnessing the glorious developments of CR in the past 20 years, we try to provide readers a clear picture on how intelligent radio could be further developed to smartly utilize the limited spectrum resources as well as to optimally configure wireless devices in the future communication systems., The paper has been accepted by IEEE Transactions on Cognitive Communications and Networking

Wideband spectrum sensing is a highly desirable feature in cognitive radio systems when the aim is to increase the probability of exploring spectral opportunities. Sub-Nyquist sampling has attracted significant interest for wideband spectrum sensing, while existing algorithms can only work with a sparse spectrum. In this paper, we propose a sub-Nyquist wideband spectrum sensing algorithm that achieves wideband sensing independent of signal sparsity without sampling at full bandwidth by using the low-speed analog-to-digital converters (ADCs) based on sparse fast Fourier transform. To lower signal spectrum sparsity while maintaining the channel state information, we preprocess the received signal through a proposed permutation and filtering algorithm. The proposed wideband spectrum sensing algorithm subsamples the time-domain signal and then directly estimates its frequency spectrum. We derive and verify the proposed algorithm by numerical analysis and test it on real-world TV white space signals. The results show that the proposed algorithm achieves high detection performance on sparse and nonsparse wideband signals with reduced runtime and implementation complexity in comparison with the conventional wideband spectrum sensing algorithms.

Spectrum efficiency is one of the key performance metrics in 5G communication networks. To enhance spectrum efficiency, advanced spectrum sharing techniques are normally used. In this article, we provide a survey of the recent development of advanced techniques for spectrum sharing. In particular, we elaborate cognitive radio, device-todevice communication, in-band full-duplex communication, non-orthogonal multiple access, and Long Term Evolution on unlicensed spectrum. For each technique, we present the basic principle and research methodology of the state of the art. By considering various promising evolutions in 5G networks, we emphasize challenges to deploy each technique. Finally, we discuss the integration issue of multiple spectrum sharing techniques and identify potential challenges.

In cognitive radio networks, a secondary user (SU) equipped with radio frequency (RF) energy-harvesting circuits can not only harvest the RF energy from the primary transmitter, but also recycle its self-power when transmitting. In this paper, we are concerned with the following design metrics: SU's harvested energy, SU's energy efficiency, and SU's harvesting efficiency, which is defined as the ratio of the average energy harvested by SU over its average energy consumption. We are interested in two tradeoff designs: one is the tradeoff between energy efficiency and harvested energy and the other is the tradeoff between energy efficiency and harvesting efficiency. Multiobjective optimization is used to solve the tradeoff problems. To simplify the original problems, we propose two schemes to obtain the lower bounds of the objective functions. The sensing threshold, sensing time, and transmit power of SU are jointly optimized to solve the tradeoff problems. Efficient algorithms are proposed to derive these design parameters. Simulation results are presented to validate the effectiveness of the proposed algorithms, to show the two tradeoff designs, and to validate the effects of system parameters on these tradeoffs.

In this paper, we consider the sequential detection problem in a new cognitive radio scenario when the primary user (PU) works with more than one transmit power level. Different from most existing literature where PU is assumed to operate with a constant transmit power only, this new consideration well matches the practical standards, e.g., IEEE 802.11 Series, LTE, LTE-A, and so on, as well as the adaptive powering concept that a user would vary its transmit power under different situations. The targets of the secondary user here are not only to detect the presence of PU but also to recognize PU’s transmit power levels. We first formulate a valid sequential detection approach via the modified Neyman-Pearson criterion and then derive the closed-form decision region for each PU’s transmit power level. Many interesting discussions are raised. Moreover, the average sample number, a key metric for any sequential detection method, is derived in closed form to facilitate the performance evaluation. The performance comparison of the sequential detection and the fixed sample detection for this multiple primary transmit power levels scenario is analyzed. Finally, the simulation results are presented to verify the correctness of the proposed studies.

This paper studies a green paradigm for the underlay coexistence of primary users (PUs) and secondary users (SUs) in energy harvesting cognitive radio networks (EH-CRNs), wherein battery-free SUs capture both the spectrum and the energy of PUs to enhance spectrum efficiency and green energy utilization. To lower the transmit powers of SUs, we employ multi-hop transmission with time division multiple access, by which SUs first harvest energy from the RF signals of PUs, and then, transmit data in the allocated time concurrently with PUs, all in the licensed spectrum. In this way, the available transmit energy of each SU mainly depends on the harvested energy before the turn to transmit, namely energy causality. Meanwhile, the transmit powers of SUs must be strictly controlled to protect PUs from harmful interference. Thus, subject to the energy causality constraint and the interference power constraint, we study the end-to-end throughput maximization problem for optimal time and power allocation. To solve this nonconvex problem, we first equivalently transform it into a convex optimization problem and then propose the joint optimal time and power allocation (JOTPA) algorithm that iteratively solves a series of feasibility problems until convergence. Extensive simulations evaluate the performance of EH-CRNs with JOTPA in three typical deployment scenarios and validate the superiority of JOTPA by making comparisons with two other resource allocation algorithms.

In HetNets (Heterogeneous Networks), each network is allocated with fixed spectrum resource and provides service to its assigned users using specific RAT (Radio Access Technology). Due to the high dynamics of load distribution among different networks, simply optimizing the performance of individual network can hardly meet the demands from the dramatically increasing access devices, the consequent upsurge of data traffic, and dynamic user QoE (Quality-of-Experience). The deployment of smart networks, which are supported by SRA (Smart Resource Allocation) among different networks and CUA (Cognitive User Access) among different users, is deemed a promising solution to these challenges. In this paper, we propose a frame-work to transform HetNets to smart networks by leveraging WBD (Wireless Big Data), CR (Cognitive Radio) and NFV (Network Function Virtualization) techniques. CR and NFV support resource slicing in spectrum, physical layers, and network layers, while WBD is used to design intelligent mechanisms for resource mapping and traffic prediction through powerful AI (Artificial Intelligence) methods. We analyze the characteristics of WBD and review possible AI methods to be utilized in smart networks. In particular, the potential of WBD is revealed through high level view on SRA, which intelligently maps radio and network resources to each network for meeting the dynamic traffic demand, as well as CUA, which allows mobile users to access the best available network with manageable cost, yet achieving target QoS (Quality-of-Service) or QoE.

The unknown noise variance and time-variant fading channels make the spectrum sensing design a challenging task for cognitive radios. Most existing sensing methods suffer from the information uncertainty and can hardly acquire promising performances in the adverse situations. To address this challenge, in this paper, we first formulate a dynamic state-space model for spectrum sensing, in which the unknown noise variance and time-variant flat fading channels are all taken into considerations. The dynamic behaviors of both primary user states and fading channels are characterized by two discrete state Markov chains. Based on this model, a novel spectrum sensing scheme is designed to recursively estimate the occupancy state of primary users, by estimating the time-variant fading channel gain and noise parameters jointly. The joint estimation is primarily premised on a maximum a posteriori probability criterion and the marginal particle filtering schemes. Simulation results are provided to demonstrate the advantages of our proposed method, which can significantly improve the sensing performance over time-variant flat fading channels, even with unknown noise variance.

In this paper, we investigate the robust power allocation issue in orthogonal frequency division multiplexing-based cognitive radio networks (CRNs) with unavoidable uncertainties (channel perturbations and variable environment). In this case, to control the performance degradation due to the uncertainties, we maximize the data rate of secondary users (SUs) by considering maximum allowable interference constraints and total power budget of SUs. To solve this problem, we design a controller for a switched affine system with state constraint. This system is based on a distributed projected dynamic system in accordance with the classical distributed convex optimization model for the power allocation and dynamic property of the Nash equilibrium. The robust controller design is on the basis of Lyapunov stability theory and linear matrix inequality to better realize the original power allocation from the control perspective. To the best of our knowledge, we are the first to solve the aforementioned problem by this kind of approach under the control frame, which is more practical for the realization in CRNs. Simulation results are provided to show the validation of the effectiveness of this approach in comparison with iterative water filling algorithm and the worst-case method.

Channel rendezvous is a vital step to form a cognitive radio network (CRN). It is intractable to guarantee rendezvous for secondary users (SUs) within a short finite time in asynchronous, heterogeneous, and anonymous CRNs. However, most previous heterogeneous algorithms rely on explicit SUs' identifiers (IDs) to guide rendezvous, which is not fully distributed. In this paper, we exploit the mathematical construction of sunflower sets to develop a single-radio sunflower set (SSS)-based pairwise rendezvous algorithm. We propose an approximation algorithm to construct disjoint sunflower sets. Then, the SSS leverages the variant permutations of elements in sunflower sets to adjust the order of accessing channels instead of SUs' IDs, which is more favorable for anonymous SUs in distributed environments. We also propose a multiradio-sunflower-set-based pairwise rendezvous algorithm to bring additional rendezvous diversity and accelerate the rendezvous process. Moreover, for the case with more than two SUs, we propose a multiuser collaborative scheme in which SUs cooperatively exchange and update their channel-hopping sequences until rendezvous. We derive the theoretical upper and lower bounds of rendezvous latency of the proposed algorithms. Extensive simulation comparisons with the state-of-the-art blind-rendezvous algorithms are conducted, incorporating the metrics of maximum and expected time-to-rendezvous. The simulation results show that our algorithms can achieve rendezvous faster than previous works.

In an underlay cognitive radio network, the cross-channel gain from a cognitive transmitter (CT) to a primary receiver (PR) is crucial for spectrum sharing. By exploiting the relaying capability of the CT, we propose a proactive estimation scheme for the cross-channel gain. Specifically, the CT proactively acts as a full-duplex amplify-and-forward (AF) relay for primary transceivers to trigger the power adaption of a primary transmitter (PT). By carefully designing the relay signal, the CT is able to obtain an estimation of the cross-channel gain by observing the power adaption. To demonstrate the accuracy of the estimation, we analytically characterize both an upper bound and a lower bound of the estimation performance. Furthermore, we study the impact of CT’s relaying on the primary transmission and observe that the impact is related to the CT’s location. By introducing a factor $\phi~(0\leq \phi \leq 1)$ to denote the probability that the CT’s relaying improves the primary transmission instead of causes interference, we design the CT location as a function of $\phi $ . Numerical results show that the estimation error of the proactive estimation scheme can be as small as 1.7% with success estimation probability around 91%. By comparing with the state of the art, we show the advantages of the proposed estimator.

The rising popularity of wireless services resulting in spectrum shortage has motivated dynamic spectrum sharing to facilitate efficient usage of the underutilized spectrum. Wideband spectrum sensing is a critical functionality to enable dynamic spectrum access by enhancing the opportunities of exploring spectral holes, but entails a major implementation challenge in compact commodity radios that only have limited energy and computation capabilities. In contrast to the traditional sub-Nyquist approaches where a wideband signal or its power spectrum is first reconstructed from compressed samples, this paper proposes a sub-Nyquist wideband spectrum sensing scheme that locates occupied channels blindly by recovering the signal support, based on the jointly sparse nature of multiband signals. Exploiting the common signal support shared among multiple secondary users (SUs), an efficient cooperative spectrum sensing scheme is developed, in which the energy consumption on wideband signal acquisition, processing, and transmission is reduced with detection performance guarantee. Based on subspace decomposition, the low-dimensional measurement matrix, computed at each SU from local sub-Nyquist samples, is deployed to reduce the transmission and computation overhead while improving noise robustness. The theoretical analysis of the proposed sub-Nyquist wideband sensing algorithm is derived and verified by numerical analysis and further tested on real-world TV white space signals. It shows that the proposed scheme can achieve good detection performance as well as reduce the computation and implementation complexity, in comparison with the conventional cooperative wideband spectrum sensing schemes.

In this paper, we propose a novel robust resource allocation algorithm for downlink non-orthogonal multiple access (NOMA) cognitive radio networks with the underlay spectrum sharing mode. To characterize the effect of channel uncertainty, a robust energy efficiency (EE) maximization optimization problem is formulated under the constraints of the maximum interference temperature threshold of primary user, the maximum transmit power of secondary base station, and the outage probability of secondary user’s rate. The non-linear optimization problem is converted into a convex form by using a new parameter transformation. Moreover, the analytical solution of power allocation is obtained by using the Lagrange dual theory. The provided results demonstrate the proposed robust resource allocation scheme can always achieve better EE and outage performance than that of the traditional orthogonal multiple access scheme and the non-robust NOMA scheme.

The existing spectrum sensing methods mostly make decisions using model-driven test statistics, such as energy and eigenvalues. A weakness of these model-driven methods is the difficulty in accurately modeling for practical environment. In contrast to the model-driven approach, in this paper, we use a deep neural network to automatically learn features from data itself, and develop a data-driven detection approach. Inspired by the powerful capability of convolutional neural network (CNN) in extracting features of matrix-shaped data, we use the sample covariance matrix as the input of CNN, proposing a novel covariance matrix-aware CNN-based detection scheme, which consists of offline training and online detection. Different from the existing deep learning-based detection methods which replace the whole detection system by an end-to-end neural network, in this work, we use CNN for offline test statistic design and develop a practical threshold-based online detection mechanism. Specially, according to the maximum a posteriori probability (MAP) criterion, we derive the cost function for offline training in the spectrum sensing model, which guarantees the optimality of the designed test statistic. Simulation results have shown that whether the PU signals are independent or correlated, the detection performance of the proposed method is close to the optimal bound of estimator-correlator detector. Particularly, when the PU signals are correlated with a correlation coefficient 0.7, the probability of detection of the proposed method outperforms the conventional maximum eigenvalue detection method by nearly 7.5 times at SNR = −14dB.

To ensure that the data aggregation, data storage, and data processing are all performed in a decentralized but trusted manner, we propose to use the blockchain with the mining pool to support IoT services based on cognitive radio networks. As such, the secondary user can send its sensing data, i.e., transactions, to the mining pools. After being verified by miners, the transactions are added to the blocks. However, under the dynamics of the primary channel and the uncertainty of the mempool state of the mining pool, it is challenging for the secondary user to determine an optimal transaction transmission policy. In this paper, we propose to use the deep reinforcement learning algorithm to derive an optimal transaction transmission policy for the secondary user. Specifically, we adopt a Double Deep-Q Network (DDQN) that allows the secondary user to learn the optimal policy. The simulation results clearly show that the proposed deep reinforcement learning algorithm outperforms the conventional Q-learning scheme in terms of reward and learning speed.

In an RF-powered backscatter cognitive radio network, multiple secondary users communicate with a secondary gateway by backscattering or harvesting energy and actively transmitting their data depending on the primary channel state. To coordinate the transmission of multiple secondary transmitters, the secondary gateway needs to schedule the backscattering time, energy harvesting time, and transmission time among them. However, under the dynamics of the primary channel and the uncertainty of the energy state of the secondary transmitters, it is challenging for the gateway to find a time scheduling mechanism which maximizes the total throughput. In this paper, we propose to use the deep reinforcement learning algorithm to derive an optimal time scheduling policy for the gateway. Specifically, to deal with the problem with large state and action spaces, we adopt a Double Deep-Q Network (DDQN) that enables the gateway to learn the optimal policy. The simulation results clearly show that the proposed deep reinforcement learning algorithm outperforms non-learning schemes in terms of network throughput.

We investigate the energy efficiency (EE) in cognitive radio networks, where cognitive users are allowed to access a licensed frequency band opportunistically, provided that the licensed band is vacant. In particular, we study the impact of imperfect spectrum sensing and formulate the average EE maximization problem in fading channels as a joint optimization problem of the spectrum sensing duration and the transmit power of cognitive users. Meanwhile, we consider the constraints of both the collision probability between the primary and cognitive transmissions and the outage probability of cognitive transmissions. However, the joint optimization problem subject to the constraints is complicated and it is computationally hard to obtain the optimal solution. Alternatively, we develop two algorithms, i.e., a linear search algorithm and an iterative-based algorithm, with considerable complexity to solve the problem. Numerical results verify the correctness of both algorithms and show that the proposed algorithms can achieve the performance close to that of the exhaustive search algorithm and outperform the state of arts.

Increasing spectrum-efficiency (SE) as well as energy-efficiency (EE) has attracted much attention recently due to the fact that the future wireless networks need to address the issues of high throughput and low power consumption. However, the objective for optimizing SE sometimes conflicts with the one for optimizing EE, and the methods for improving EE may result in a decrease in SE. In this paper, we consider the SE-EE tradeoff for cognitive radio (CR) networks with co-operative spectrum sensing (CSS). First, we formulate the general problem, and analyze two special cases: the SE maximization problem and the EE maximization problem. The SE and EE are optimized separately via joint optimization of sensing duration and final decision threshold in CSS. Based on the solutions of the two special cases, the general problem for SE-EE tradeoff is solved. Then, we consider the tradeoff of SE and EE from two perspectives: (1) maximizing EE while satisfying SE requirement; and (2) maximizing SE while satisfying EE requirement. Efficient algorithms for sensing strategy design are proposed for each scenario. Finally, we demonstrate the effectiveness of the proposed sensing strategies and illustrate the tradeoff between SE and EE via simulations.

Blind modulation classification is a fundamental step before signal detection for cognitive radio networks where the users may not have the complete knowledge of the modulation scheme due to the flexibility of operating dynamically in multiple frequency bands. In this paper, a modulation-constrained (MC) clustering classifier is proposed for recognizing the modulation scheme with unknown channel matrix and noise variance for MIMO systems. By recognizing the fact that the received signals within an observation interval form into clusters and exploiting the intrinsic relationships of different digital modulation schemes, the modulation classification problem is transformed into a clustering problem without direct channel estimation for each modulation scheme and the maximum likelihood criterion is applied for the final classification decision. A central component of the proposed classifier is a method called centroid reconstruction, which exploits the structural relationships in constellation diagrams to reconstruct cluster centroids with fewer number of parameters. Furthermore, a method to initialize the cluster centroids is also proposed. The proposed MC classifier together with centroid reconstruction and initialization methods not only reduce the number of parameters to be estimated, but also help to initialize the centroids for enhanced convergence of expectation- maximization (EM) algorithm. Simulation results show that our algorithm can perform excellently even at low SNR and with very short observation interval length.

ᅟ Spectrum scarcity due to inefficient utilisation has ignited a plethora of dynamic spectrum access solutions to accommodate the expanding demand for future wireless networks. Dynamic spectrum access systems allow secondary users to utilise spectrum bands owned by primary users if the resulting interference is kept below a pre-designated threshold. Primary and secondary user spectrum occupancy patterns determine if minimum interference and seamless communications can be guaranteed. Thus, spectrum occupancy prediction is a key component of an optimised dynamic spectrum access system. Spectrum occupancy prediction recently received significant attention in the wireless communications literature. Nevertheless, a single consolidated literature source on statistical spectrum occupancy prediction is not yet available in the open literature. Our main contribution in this paper is to provide a statistical prediction classification framework to categorise and assess current spectrum occupancy models. An overview of statistical sequential prediction is presented first. This statistical background is used to analyse current techniques for spectrum occupancy prediction. This review also extends spectrum occupancy prediction to include cooperative prediction. Finally, theoretical and implementation challenges are discussed.

The explosive growth of mobile devices and the rapid increase of wideband wireless services call for advanced communication techniques that can achieve high spectral efficiency and meet the massive connectivity requirement. Cognitive radio (CR) and non-orthogonal multiple access (NOMA) are envisioned to be important solutions for the fifth generation wireless networks. Integrating NOMA techniques into CR networks (CRNs) has the tremendous potential to improve spectral efficiency and increase the system capacity. However, there are many technical challenges due to the severe interference caused by using NOMA. Many efforts have been made to facilitate the application of NOMA into CRNs and to investigate the performance of CRNs with NOMA. This article aims to survey the latest research results along this direction. A taxonomy is devised to categorize the literature based on operation paradigms, enabling techniques, design objectives and optimization characteristics. Moreover, the key challenges are outlined to provide guidelines for the domain researchers and designers to realize CRNs with NOMA. Finally, the open issues are discussed., This paper has been accepted by IEEE Wireless Communications Magazine. Pages 16, Figures 6

Although heterogeneous cloud radio access networks (H-CRAN) have emerged for efficient network management, resource management, and throughput enhancement, new technical challenges remain to support the urgent need to achieve full automation for ultra-low-latency connections. To simultaneously accommodate such diverse technical requirements in the HCRAN, in this article, a new paradigm of HCRAN design to significantly reduce latency is presented. From the refined radio access in the air interface, new methodology of radio resource optimization in the system architecture, and intelligent routing/paging in the backhaul, the provided foundation suggests open-loop communications and other new directions to facilitate state-of-the-art practices of the H-CRAN.

Spectrum refarming (SR) refers to a radio resource management technique which allows different generations of cellular networks to operate in the same radio spectrum. In this paper, an underlay SR model is proposed, in which an Orthogonal Frequency Division Multiple Access (OFDMA) system refarms the spectrum of a Code Division Multiple Access (CDMA) system through intelligently exploiting the interference margin provided by the CDMA system. We investigate the mutual effect of the two systems by evaluating the asymptotic signal-to-interference-plus-noise ratio (SINR) of the users, based on which the interference margin tolerable by the CDMA system is determined. By using the interference margin together with the transmit power constraints, the uplink resource allocation problem of OFDMA system is formulated and solved through dual decomposition method. Simulation results have verified our theoretical analysis, and validated the effectiveness of the proposed resource allocation algorithm and its capability to protect the legacy CDMA users. The proposed SR system requires the least information flow from the CDMA system to the OFDMA system, and importantly, no upgrading of legacy CDMA system is needed; thus it can be deployed by telecom operators to maximize the spectral efficiency of their cellular networks., 29 pages, 10 figures, submitted to IEEE Trans. Wireless Comms., accepted conference version to GC'14

Traditional spectrum allocation policies may result in temporarily unused radio spectrum. Cognitive radio (CR) has emerged as a promising technology to exploit the radio spectrum in a more efficient manner by allowing spectrum sharing between secondary users (SUs) and primary users (PUs). Power control and beamforming are two key techniques in CR design used to maximize the benefits of SUs, yet to maintain the quality of service of PUs. In practice, the available system parameters (e.g., channel state information and interference power) to enable power control and beamforming could be uncertain due to various factors such as estimation error and/or measurement error, thus the robustness of the designed algorithms should be considered in order to overcome the effects of parametric uncertainties. The objective of this paper is to give an overview on robust design for power control and beamforming in cognitive radio networks (CRNs). We will analyze modeling methods for parametric uncertainties, introduce various design methodologies, and present robust algorithms that have appeared in the literatures. Finally, some potential issues and future research directions in this field will be presented.

It is well known that broadband wireless communications (BWC) are necessary for high speed vehicles since passengers need many broadband wireless multimedia services. However, one design challenge is to identify sufficient spectrum resource to support BWC in high speed vehicles. Recently, cognitive radios (CRs) are being considered as a promising technology to solve scarcity problem of spectrum resource. Therefore, we investigate the spectrum resource allocation problem in the CR based high speed vehicle network (CR-HSVN) in this paper. Specifically, we propose a spectrum resource allocation framework, where high speed vehicles could effectively utilize the TV white spaces. Subsequently, we formulate the allocation of spectrum resource as an optimization problem to maximize the available spectrum resources (i.e., TV white spaces) utilized by the CR-HSVN, and investigate the property of the CR-HSVN to reduce the complexity by separation computing method without loss of optimality. Furthermore, we analyze the optimal solution based on branch and bound method, the suboptimal solutions based on single channel and linear programming, respectively. Simulation results show that the proposed framework could offer excellent performances of the spectrum utilization and fairness for the CR-HSVN. Meanwhile, the aggregated interference from vehicles to primary users is constrained.

Dynamic spectrum access or cognitive radio is widely recognized as a major technology in the coming 5G communications to greatly increase spectral efficiency. In cognitive radio, different networks#x002F;systems may dynamically share some spectrum to achieve the maximum overall capacity. Each network#x002F;system may just use a few subcarriers or a few non-contiguous frequencies at a given time. It is very challenging to design the waveform to effectively use the fragmented spectrum with high flexibility for dynamical change. Filter bank multicarrier (FBMC) and generalized frequency division multiplexing (GFDM) are regarded as good candidates for the waveform at such situations. In this paper, we propose fast algorithms to implement the FBMC and GFDM when a number of cognitive radio users sharing the same spectrum. The major contributions are as follows. (1) We use the "sparse" property of the user's spectrum to reduce the sampling rate and complexity in the discrete time implementation; (2) We propose fast algorithms for computing the special Fourier transform, which reduce the complexity by nearly half.

The heterogeneous network (HetNet) architecture, device-to-device (D2D) communications, and coexistence with existing wireless systems have been regarded as new communication paradigms introduced in LTE-A/LTE-B cellular networks. To facilitate these paradigms, considerable research has shown promise of the cognitive radio (CR) technology, particularly the cognitive radio resource management (CRRM) on the top of resource allocation to control Layer-1 and Layer-2 radio operations, thus eliminating the concerns of potential system impacts and operation unreliability to bridge the gap between cellular and CR technologies. To support diverse communication paradigms with different challenges, a variety of CRRM schemes have been recently proposed, which however significantly perplexes the system implementation. To provide a general reconfigurable framework, in this article, we reveal a software-defined design of the CRRM. Through proper configurations, this software- defined design is able to adapt to diverse communication paradigms in LTE-A/LTE-B, and provides transmission reliability in terms of quality- of-service guarantees via the optimum control of the design. Supporting diverse CRRM schemes, this design substantially simplifies the system realization to bring the development of the CRRM to the next stage of practice for the fifth generation (5G) cellular network.

Wideband spectrum sensing is a critical functionality in cognitive radio networks to enable dynamic spectrum sharing, but entails a major implementation challenge in compact commodity radios with restricted energy and computation capabilities. Exploiting jointly sparse nature of multiband signals, this paper proposes an efficient blind sub-Nyquist cooperative wideband spectrum sensing scheme, which reduces energy consumption in wideband signal acquisition, processing and transmission, with performance guarantee. In contrast to traditional sub-Nyquist approaches where a wideband signal or its power spectrum is first reconstructed from compressed samples, the proposed scheme locates occupied channels by recovering the signal support jointly from multiple secondary user (SU) measurements. Based on subspace decomposition, the low-dimensional measurement matrix computed at each SU from local sub-Nyquist samples can reduce transmission overhead while improving noise robustness. Numerical analysis and simulation results show that the proposed scheme can achieve good detection performance as well as reduce computation and implementation complexity in comparison with conventional cooperative wideband spectrum sensing schemes.

In this paper, we consider the medium access control (MAC) protocol design for random access cognitive radio network (CRN). Based on asynchronous spectrum sensing technique and RTS/CTS mechanism, a new MAC protocol, namely, cognitive-radio-based carrier sense medium access with collision avoidance (CR-CSMA/CA) is proposed to coordinate the channel access of secondary network as well as protect the operation of primary network, which applies to both single and multiple channel models. Using the G/G/1 queuing model with consideration of unsaturated and saturated network condition, we develop a framework to analyze the proposed MAC protocol and also derive closed-form expressions of specific performance metrics such as normalized throughput, average packet service time, etc. Performance evaluations illustrate and validate that the performance of CR-CSMA/CA varies with the offered traffic load of secondary network and the spectrum utilization rate of primary network, respectively, and also show that CR-CSMA/CA outperforms other relevant MAC protocols.

In this letter, we investigate the optimal power allocation for interference-limited cognitive multiple access channels. The objective is to minimize the outage probability of cognitive users (CUs) while satisfying the interference constraint on the primary user (PU). The formulated optimization problem is non-convex, but can be efficiently solved by the aid of logarithmic transformation. For comparison, we also analyze two suboptimal power allocation schemes by assigning equal interference (EI) or equal transmit power (ETP) to each CU. We derive closed-form expressions for both schemes, and asymptotically analyze their performance. Simulations results confirm the correctness of our analysis and effectiveness of the proposed schemes.

With the prior knowledge that the primary user is highly likely idle and the primary signals are digitally modulated, we propose an optimal Bayesian detector for spectrum sensing to achieve higher spectrum utilization in cognitive radio networks. We derive the optimal detector structure for MPSK modulated primary signals with known order over AWGN channels and give its corresponding suboptimal detectors in both low and high SNR (Signal-to-Noise Ratio) regimes. Through approximations, it is found that, in low SNR regime, for MPSK (M>2) signals, the suboptimal detector is the energy detector, while for BPSK signals the suboptimal detector is the energy detection on the real part. In high SNR regime, it is shown that, for BPSK signals, the test statistic is the sum of signal magnitudes, but uses the real part of the phase-shifted signals as the input. We provide the performance analysis of the suboptimal detectors in terms of probabilities of detection and false alarm, and selection of detection threshold and number of samples. The simulations have shown that Bayesian detector has a performance similar to the energy detector in low SNR regime, but has better performance in high SNR regime in terms of spectrum utilization and secondary users' throughput.

Orthogonal frequency division multiplex (OFDM) has been widely used in various wireless communications systems. Thus the detection of OFDM signals is of significant importance in cognitive radio and other spectrum sharing systems. A common feature of OFDM in many popular standards is that some pilot subcarriers repeat periodically after certain OFDM blocks. In this paper, sensing methods for OFDM signals are proposed by using such repetition structure of the pilots. Firstly, special properties for the auto-correlation (AC) of the received signals are identified, from which the optimal likelihood ratio test (LRT) is derived. However, this method requires the knowledge of channel information, carrier frequency offset (CFO) and noise power. To make the LRT method practical, we then propose an approximated LRT (ALRT) method that does not rely on the channel information and noise power, thus the CFO is the only remaining obstacle to the ALRT. To handle the problem, we propose a method to estimate the composite CFO and compensate its effect in the AC using multiple taps of ACs of the received signals. Computer simulations have shown that the proposed sensing methods are robust to frequency offset, noise power uncertainty, time delay uncertainty, and frequency selectiveness of the channel.

We investigate the energy efficiency (EE) in cognitive radio networks, where cognitive users are allowed to access the licensed frequency band opportunistically, provided that the licensed band is vacant. In particular, we study the impact of imperfect spectrum sensing and formulate the EE maximization as a joint optimization problem of the spectrum sensing duration and the transmit power of the cognitive transmitter. Specially, we consider the constraints of both the collision probability between the primary and cognitive transmission and the outage probability of the cognitive transmission. Since the joint optimization problem of the sensing duration and the transmit power is hard to be solved directly, we decompose it into two sub-problems with the spectrum sensing duration and the transmit power as variables, respectively. Based on the analytical solvers of the two sub-problems, we propose an iterative-based algorithm to solve the joint optimization problem. Finally, numerical results are provided to validate the analysis and performance of our proposed algorithms.

Spectrum occupancy prediction provides cognitive radio secondary users with proactive ability to exploit spectrum opportunities. When feasible, spectrum sensing outcome can be further analysed to assist secondary users to pre-emptively avoid interfering with primary users. Where applicable, cooperative spectrum prediction in multi-user environment has the potential to overcome limitations of local spectrum prediction accuracy. In this paper, we provide a thorough analysis on hard fusion based spectrum occupancy prediction. We present accuracy enhancement in terms of prediction error. The analysis and simulation results showed the improvement in prediction accuracy, and the required threshold settings to gain such improvement.

In this paper, we propose a FBMC (filter bank multicarrier) duplexing scheme, where downlink and uplink use FBMC for their transmission. Downlink and uplink transmit at the same time and in the same frequency band but with different subcarriers. We then analyze the advantages and problems of the scheme. We show that it is impossible to achieve full synchronization in time and frequency with both downlink and uplink signals. Thus the synchronization errors will destroy the orthogonality between the downlink and uplink. It is shown that the synchronization errors cause the OFDM (orthogonal frequency division multiplexing) duplexing to be unreliable, while FBMC duplexing with proper prototype filter is very robust to such errors. An additional advantage of FBMC duplexing is the reduced number of null subcarriers required to control the out-of-band emission. The distinguished advantages make FBMC duplexing a better choice in applications like small cell, spectrum sharing, spectrum refarming and cognitive radio. Some problems are pointed out, which include high peak to average power ratio (PAPR), large dynamic range, and requirement for high resolution analog to digital converter (ADC). Simulations at various situations are shown to verify the results.

In an opportunistic spectrum access (OSA) based cognitive radio system, a secondary user (SU) is allowed to access the licensed spectrum of the primary user (PU) when it is inactive. Conventional power allocation strategies, which do not consider spectrum sensing information (SSI), may not be optimal in OSA based cognitive radio system because when the SU mis-detects the PU's presence, the interference from the PU will cause a lower data rate or a higher outage probability to the SU. In this paper, power allocation strategies for each frame are designed based on the SSI gathered by the SU during the sensing period of the frame. We consider both cooperative and non-cooperative spectrum sensing scenarios. In non-cooperative spectrum sensing, the SU transmitter (SU-Tx) has its SSI while in cooperative spectrum sensing, the SU-Tx has both its SSI and the SU receiver's (SU-Rx's) SSI. Using the SSIs, power allocation strategies are designed to either maximize the average data rate or minimize the outage probability of the SU. The proposed power allocation strategies have to ensure that the PU is sufficiently protected and the SU satisfies its long-term transmit power budget. Optimization of the sensing time is also considered to further enhance the performances of the system.