Optimal Sensing Time Strategy Under Primary Outage Constraint in Cognitive Radio Networks

With the underlay approach,Secondary users (SUs) can utilize the same frequency bands simultaneously with Primary users (PUs) in Cognitive radio networks (CRNs).How to choose the appropriate transmission power of SUs under the influence caused by other cells is a problem.To solve this problem,spectrum sensing is introduced to identify the existence of interference which using pilot signal to perform coherent processing.Consider the probability of detection of SUs,there exists a trade-off between the sensing time and the achievable throughput of CRNs.When the prior probability of other cells' activity is unknown to SUs,throughput of the CRNs can be viewed as a concave function.According to solving the optimization problem,the optimal sensing time is obtained.Simulation results show the feasibility and correctness.     

Optimal Mode Selection Policies in Cognitive Radio Networks with RF Energy Harvesting

Applying energy harvesting technology in cognitive radio networks (CRNs) leads to a tradeoff between the time allocated for spectrum sensing followed by spectrum accessing and that for energy harvesting. This tradeoff can be formulated as a mode selection problem for the secondary users. In this paper, we consider a CRN working in the time-slotted manner. The secondary users powered by radio frequency energy harvesting can perform overlay transmission or cooperate with the primary users. To maximize the long-term throughput of the secondary network, we propose two optimal mode selection policies by formulating this problem under a partially observable Markov decision process framework. Numerical simulations show that both of our proposed policies achieve more throughput than the overlay-only policy. Finally, we also evaluate the effect of the cooperative threshold and the energy harvesting process on the optimal policies.     

Prediction‐based MAC‐layer sensing in cognitive radio networks

We consider a cognitive radio network which coexists with multiple primary users (PUs) and secondary users (SUs) transmit over time‐varying channels. In this scenario, one problem of the existing work is the poor performances of throughput and fairness due to variances of SUs' channel conditions and PUs' traffic patterns. To solve this problem, we propose a novel prediction‐based MAC‐layer sensing algorithm. In the proposed algorithm, the SUs' channel quality information and the probability of the licensed channel being idle are predicted. Through the earlier predicted information, we schedule the SUs to sense and transmit on different licensed channels. Specifically, multiple significant factors, including network throughput and fairness, are jointly considered in the proposed algorithm. Then, we formulate the prediction‐based sensing scheduling problem as an optimization problem and solve it with the Hungarian algorithm in polynomial time. Simulation results show that the proposed prediction‐based sensing scheduling algorithm could achieve a good tradeoff between network throughput and fairness among SUs. Copyright © 2014 John Wiley & Sons, Ltd.     

MaxDiv: an optimal randomized spectrum access with maximum diversity scheme for cognitive radio networks

Cognitive radio network (CRN) is an emerging technology that can increase the utilization of spectrum underutilized by primary users (PUs). In the literature, most exiting investigations on CRNs have focused on how secondary users (SUs) can coexist harmlessly with the PUs. Despite the importance of such a coexistence issue, it is also crucial to investigate the coexistence of SUs because (i) the PUs usually rarely use the licensed spectrum and (ii) the advantages of CRN will significantly increase the number of SUs in the future. To address this challenging issue, we propose, in this paper, an optimal randomized spectrum access scheme, whose main ideas include the following: (i) an SU shares its sensing results with neighboring SUs and (ii) with the regional sensing results, an SU will access available channels with a non‐uniform probability distribution. We first formulate a multichannel optimal randomized multiple access (MC‐ORMA) problem that aims to maximize the throughput of the CRN; we then develop efficient distributed algorithms to solve the MC‐ORMA problem; we derive the closed‐form value of collision probability for each SU; and finally, we conduct extensive numerical experiments and compare our theoretical analysis with simulation results to demonstrate the advantages of our scheme. Copyright © 2011 John Wiley & Sons, Ltd.     

Cyclostationarity-Based Decision Reporting Scheme for Cooperative Spectrum Sensing

Cooperative spectrum sensing has emerged as a promising solution to the hidden terminal problem in cognitive radio networks (CRNs). It could significantly promote the sensing capability of CRNs by exploiting space diversity gains in a fading environment. However, owing to the decision reporting overhead, there exists a tradeoff between the system throughput and performance of cooperative spectrum sensing. In this paper, we propose a cyclostationarity-based decision reporting scheme for cooperative spectrum sensing in CRNs with cyclic delay diversity orthogonal frequency division multiplexing (CDD-OFDM). Decision information would be embedded into the CDD-OFDM signals in terms of cyclostationary signatures and shared among cognitive radio (CR) users along with data transmissions. As a result, satisfied system throughput could be achieved without additional spectral or temporal resources consumption when the number of cooperative users increases. Numerical results are presented to show the system throughput enhancement.     

Joint routing and channel assignment using online learning in cognitive radio networks

Cognitive radio networks (CRNs) are the solution for the problem of underutilizing the licensed spectrum for which there are more requests in the last couple of decades. In CRNs, Secondary users (SUs) are permitted to access opportunistically the licensed spectrum owned by primary users (PUs). In this paper, we address the problem of joint routing and channel assignment for several flows generated by source SUs to a given destination. We consider a more realistic model based on Markov modulated Poisson process for modeling the PUs traffic at each channel and the SUs try to exploit short lived spectrum holes between the PUs packets at the selected channel. The SUs want to cooperatively minimize the end-to-end delay of source SUs flows meanwhile the quality of service requirements of the PUs would be met. To consider partial observation of SUs about PUs activity at all channels and quick adaptation of SUs decisions to environment changes and cooperative interaction of SUs, we use decentralized partially observable markov decision process for modeling the problem. Then, an online learning based scheme is proposed for solving the problem. Simulation results show that the performance of the proposed method and the optimal method is close to each other. Also, simulation results show that the proposed method greatly outperforms related works at control of interference to the PUs while maintains the end-to-end delay of SU flows in a low level.

     

Sensing-Throughput Tradeoff in Cluster-Based Cooperative Cognitive Radio Networks with A TDMA Reporting Frame Structure

This paper proposes clustering schemes to solve the sensing throughput tradeoff problem in cooperative cognitive radio networks (CCRNs). The throughput of CCRNs extremely depends on the spectrum sensing performance and data transmission time. In CCRNs, the more secondary users (SUs) for cooperation, the better performance of spectrum sensing. However, the overhead consumption increases as the quantity of cooperative SUs becomes huge, which will lead to less time for data transmission. In this paper, we propose a frame structure that takes the sensing results reporting time into consideration. In order to reduce the reporting time consumption, a centralized cluster-based cooperative cognitive radio system model is created based on the frame structure. The sensing-throughput tradeoff problem under both the perfect reporting channel and imperfect reporting channel scenarios are formulated. The proposed clustering schemes reduce the reporting time consumption and ensure the maximum transmission time of each SU. Numerical results show that the proposed clustering schemes achieve satisfying performance.     

Towards trustworthy collaboration in spectrum sensing for ad hoc cognitive radio networks

Cognitive radio networks (CRN) make use of dynamic spectrum access to communicate opportunistically in frequency bands otherwise licensed to incumbent primary users such as TV broadcast. To prevent interference to primary users it is vital for secondary users in CRNs to conduct accurate spectrum sensing, which is especially challenging when the transmission range of primary users is shorter compared to the size of the CRN. This task becomes even more challenging in the presence of malicious secondary users that launch spectrum sensing data falsification (SSDF) attacks by providing false spectrum reports. Existing solutions to detect such malicious behaviors cannot be utilized in scenarios where the transmission range of primary users is limited within a small sub-region of the CRN. In this paper, we present a framework for trustworthy collaboration in spectrum sensing for ad hoc CRNs. This framework incorporates a semi-supervised spatio-spectral anomaly/outlier detection system and a reputation system, both designed to detect byzantine attacks in the form of SSDF from malicious nodes within the CRN. The framework guarantees protection of incumbent primary users’ communication rights while at the same time making optimal use of the spectrum when it is not used by primary users. Simulation carried out under typical network conditions and attack scenarios shows that our proposed framework can achieve spectrum decision accuracy up to 99.3 % and detect malicious nodes up to 98 % of the time.     

Optimal Transmission Strategies for Dynamic Spectrum Access in Cognitive Radio Networks

Cognitive radio offers a promising technology to mitigate spectrum shortage in wireless communications. It enables secondary users (SUs) to opportunistically access low-occupancy primary spectral bands as long as their negative effect on the primary user (PU) access is constrained. This PU protection requirement is particularly challenging for multiple SUs over a wide geographical area. In this paper, we study the fundamental performance limit on the throughput of cognitive radio networks under the PU packet collision constraint. With perfect sensing, we develop an optimum spectrum access strategy under generic PU traffic patterns. Without perfect sensing, we quantify the impact of missed detection and false alarm, and propose a modified threshold-based spectrum access strategy that achieves close-to-optimal performance. Moreover, we develop and evaluate a distributed access scheme that enables multiple SUs to collectively protect the PU while adapting to behavioral changes in PU usage patterns. Our results provide useful insight on the trade-off between the protection of the primary user and the throughput performance of cognitive radios.     

An artificial intelligence-based spectrum sensing methodology for LoRa and cognitive radio networks

The artificial intelligence-based spectrum sensing approach is extremely important in terms of effective bandwidth utilization for low power wide area networks (LPWANs) based on cognitive radio networks (CRNs). Most studies perform spectrum detection with CRNs using optimization or deep neural network methods. However, optimization-based spectrum detection approaches based on current LPWANs are scarce. For this purpose, in this study, a hybrid optimization methodology integrated with CRNs is proposed for LoRa, which is one of the most compatible LPWAN technologies in the Internet of Things (IoTs) recently. In the particle swarm optimization (PSO) part of this hybrid methodology, agent users are created so that secondary users (SUs) could use the licensed band of primary users (PUs) in cognitive radio. On the genetic algorithm side, LoRa error rates are minimized in order to further improve the performance of the proposed method. In this way, effective spectrum sensing is performed in the LoRa network. Various LoRa-CRN experiments have been carried out in the simulation environment, and the probability of detection and false alarm performances have been compared with both theoretical and proposed approaches in terms of quality estimation parameters. It is clear from the results that the proposed methods give successful results for the LoRa-CRNs.     

A novel spectrum sensing scheme with sensing time optimization for energy-efficient CRSNs

Wireless Networks - The cognitive radio technology enables secondary users (SUs) to occupy licensed bands when primary users (PUs) are not occupy them. Spectrum sensing is a key technology for SUs...     

CogNS: A Simulation Framework for Cognitive Radio Networks

Cognitive radio technology has been used to efficiently utilize the spectrum in wireless networks. Although many research studies have been done recently in the area of cognitive radio networks (CRNs), little effort has been made to propose a simulation framework for CRNs. In this paper, a simulation framework based on NS2 (CogNS) for cognitive radio networks is proposed. This framework can be used to investigate and evaluate the impact of lower layers, i.e., MAC and physical layer, on the transport and network layers protocols. Due to the importance of packet drop probability, end-to-end delay and throughput as QoS requirements in real-time reliable applications, these metrics are evaluated over CRNs through CogNS framework. Our simulations demonstrate that the design of new network and transport layer protocols over CRNs should be considered based on CR-related parameters such as activity model of primary users, sensing time and frequency.     

Resource allocation based on dynamic hybrid overlay/underlay for heterogeneous services of cognitive radio networks

In cognitive radio networks (CRNs), hybrid overlay and underlay sharing transmission mode is an effective technique to improve the efficiency of radio spectrum. Unlike existing works in literatures where only one secondary user (SU) uses both overlay and underlay mode, the different transmission modes should dynamically be allocated to different SUs according to their different quality of services (QoS) to achieve the maximal efficiency of radio spectrum. However, dynamic sharing mode allocation for heterogeneous services is still a great challenge in CNRs. In this paper, we propose a new resource allocation method based on dynamic allocation hybrid sharing transmission mode of overlay and underlay (Dy-HySOU) to obtain extra spectrum resource for SUs without interfering with the primary users. We formulate the Dy-HySOU resource allocation problem as a mixed-integer programming to optimize the total system throughput with simultaneous heterogeneous QoS guarantee. To decrease the algorithm complexity, we divide the problem into two sub-problems: subchannel allocation and power allocation. Cutset is used to achieve the optimal subchannel allocation, and the optimal power allocation is obtained by Lagrangian dual function decomposition and subgradient algorithm. Simulation results show that the proposed algorithm further improves spectrum utilization with simultaneous fairness guarantee, and the achieved Dy-HySOU diversity gain is satisfying.     

Data traffic‐based analysis of delay and energy consumption in cognitive radio networks with and without resource reservation

A new opportunistic cross‐layer MAC protocol involving channel allocation and packet scheduling for cognitive radio networks is proposed. Cognitive radio allows secondary users (SUs) to exploit the available portions of the licensed spectrum bands without interfering with primary users. In particular, we consider a cognitive radio system, where SUs are equipped with two transceivers: a control transceiver and a software‐defined radio transceiver. Data traffic characteristics of SUs are considered to ameliorate system performance. So, we propose a mechanism of resource reservation to improve QoS requirements that favors successful SUs to transmit data during x time slots without interfering with primary users. The key novelty of this paper is giving priority for SUs with important data traffic and which frequently solicits data channels to transmit for the remaining time of the ongoing time slot and for the next time slots directly after checking the channel availability. We develop a new analytical model to evaluate delay parameter for two scenarios with and without resource reservation and we then investigate the impact of those scenarios on the energy consumption. We show through simulations that cognitive radio performances increase noticeably with the proposed scheme. Copyright © 2014 John Wiley & Sons, Ltd.     

Efficient Spectrum Sensing with Minimum Transmission Delay in Cognitive Radio Networks

In cognitive radio (CR) networks, the secondary users (SUs) need to find idle channels via spectrum sensing for their transmission. In this paper, we study the problem of designing the sensing time to minimize the SU transmission delay under the condition of sufficient protection to primary users (PUs). Energy detection sensing scheme is used to prove that the formulated problem indeed has one optimal sensing time which yields the minimum SU transmission delay. Then, we propose a novel cooperative spectrum sensing (CSS) framework, in which one SU’s reporting time can be used for other SUs’ sensing. The analysis focuses on two fusion strategies: soft information fusion and hard information fusion. Under soft information fusion, it is proved that there exists one optimal sensing time that minimizes the SU transmission delay. Under hard information fusion, for time varying channels, the novel multi-slot CSS is derived. The performance of SU transmission delay is studied in both perfect and imperfect reporting channels. Some simple algorithms are derived to calculate the optimal sensing settings that minimize the SU transmission delay. Computer simulations show that fundamental improvement of delay performance can be obtained by the optimal sensing settings. In addition, the novel multi-slot CSS scheme shows a much lower transmission delay than CSS based on general frame structure.     

Overhead-Throughput Tradeoff Under a Novel Frame Structure in Centralized Cooperative Cognitive Networks

Cooperative spectrum sensing plays an important role in cognitive radio networks since it improves the detection performance by exploiting spatial diversity. However, the cooperation among terminals also brings additional communication overhead. In this paper, overhead-throughput tradeoff issues are investigated in four scenarios namely (1) identical sensing channel and perfect reporting channel, (2) identical sensing channel and imperfect reporting channel, (3) different sensing channel and perfect reporting channel, (4) different sensing channel and imperfect reporting channel of each secondary user (SU). Taking the reporting overhead into consideration, a novel frame structure consisting of an initial subframe and M consecutive subframes, is proposed to maximize the achievable throughput of the secondary network. And for each scenario, the overhead-throughput tradeoff is formulated as an optimization problem with respect to the number of reporting SUs. A brute-force approach is then used to resolve such optimization problem. Given the optimal number of reporting SUs, a set of candidate SUs is then selected according to the probability of detection, the probability of false alarm and the probability of reporting error. Numerical results show that an optimal overhead-throughput tradeoff is achieved given the optimal number of reporting SUs. In addition, the probability of false alarm is shown to be the most important factor affecting the performance of achievable throughput within the secondary network because the lower probability of false alarm corresponds to the case that the secondary network can use the channel with a higher chance.     

Attack Detection Scheme Against Cooperative Spectrum Sensing Data Falsification on Common Control Channel in Cognitive Radio Networks

Cognitive radio is an intelligent technology designed to help secondary users (SUs) increase their opportunity to access unused spectrum channels while avoiding interference with the primary users. In cognitive radio networks (CRNs), to find the available channels, SUs execute cooperative spectrum sensing and exchange channels-related control information, namely an available channels list (ACL), on a common control channel (CCC) before determining which channels they may transmit. However, some SUs, defined as attackers, could create a security issue by sharing false ACL information with other SUs to increase their own utilization of the available channels, which significantly decreases the performance of CRNs. In this paper, we propose an efficient detection scheme for CCC security to identify any attacker among the cooperating SUs. In the proposed scheme, all SUs share their ACL information on the CCC, with an associated reputation, which is updated according to its own behavior in each cooperation round, to cooperatively identify attackers. An attacker will be excluded from cooperating group with the result that its updated reputation value exceeds a certain threshold. Simulation results show how to further improve the performance of the proposed scheme by choosing optimized thresholds. In addition, we also illustrate that the proposed scheme can achieve considerable performance improvement compared with a attack detection technique COOPON for secure ACL information exchange.     

A new sensing‐throughput tradeoff scheme in cooperative multiband cognitive radio network

A conventional cognitive radio network (CRN) uses the spectrum of the licensed primary network (PN) on the premise of detecting the absence of the PN by the spectrum sensing of the sensor node (SN). In this paper, a cooperative multiband CRN is considered, wherein the SNs are allowed to use some time of the transmission slot to relay PN data by cooperative communication, while using the remaining time of the transmission slot to forward its own data, over multiple sub‐bands during each frame, if the presence of PN is detected by cooperative spectrum sensing of the SNs in the sensing slot. A new sensing–throughput tradeoff scheme is formulated as a multi‐variable optimization problem, which maximizes the average aggregate throughput of the CRN over all the sub‐bands by jointly optimizing spectrum sensing time and sub‐band transmission power, subject to the constraints on the average aggregate throughput of the PN, the maximal aggregate power of each SN, and the false alarm and detection probabilities of each sub‐band. The bi‐level optimization method is adopted to obtain the optimal solution by dividing the multi‐variable optimization problem into two convex single‐variable sub‐optimization problems. The simulations show that there exists the optimal sensing time and sub‐band transmission power that maximize the average aggregate throughput of the CRN and, compared with the conventional scheme, the throughput obtained by the proposed scheme is outstanding. Copyright © 2014 John Wiley & Sons, Ltd.     

Coalitional game‐based behavior analysis for spectrum access in cognitive radios

The core of cognitive radio paradigm is to introduce cognitive devices able to opportunistically access the licensed radio bands. The coexistence of licensed and unlicensed users prescribes an effective spectrum hole‐detection and a non‐interfering sharing of those frequencies. Collaborative resource allocation and spectrum information exchange are required but often costly in terms of energy and delay. In this paper, each secondary user (SU) can achieve spectrum sensing and data transmission through a coalitional game‐based mechanism. SUs are called upon to report their sensing results to the elected coalition head, which properly decides on the channel state and the transmitter in each time slot according to a proposed algorithm. The goal of this paper is to provide a more holistic view on the spectrum and enhance the cognitive system performance through SUs behavior analysis. We formulate the problem as a coalitional game in partition form with non‐transferable utility, and we investigate on the impact of both coalition formation and the combining reports costs. We discuss the Nash Equilibrium solution for our coalitional game and propose a distributed strategic learning algorithm to illustrate a concrete case of coalition formation and the SUs competitive and cooperative behaviors inter‐coalitions and intra‐coalitions. We show through simulations that cognitive network performances, the energy consumption and transmission delay, improve evidently with the proposed scheme. Copyright © 2016 John Wiley & Sons, Ltd.     

Optimization of Time-Domain Combining Cooperative Spectrum Sensing in Cognitive Radio Networks

In cognitive radio networks, the secondary users take chances to access the spectrum without causing interference to the primary users so that the spectrum access is dynamic and somewhat opportunistic. Therefore, spectrum sensing is of significant importance. In this paper, we propose a novel time-domain combining cooperative spectrum sensing framework, in which the time consumed by reporting for one secondary user is also utilized for other secondary users’ sensing. We focus on the optimal sensing settings of the proposed sensing scheme to maximize the secondary users’ throughput and minimize the average sensing error probability under the constraint that the primary users are sufficiently protected. Some simple algorithms are also derived to calculate the optimal solutions. Simulation results show that fundamental improvement of the achievable throughput and sensing performance can be obtained by optimal sensing settings. In addition, our proposed scheme outperforms the general frame structure on either achievable throughput or the performance of average sensing error probability.