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.     

Efficient error detection in soft data fusion for cooperative spectrum sensing

The primary objective of cooperative spectrum sensing (CSS) is to determine whether a particular spectrum is occupied by a licensed user or not, so that unlicensed users called secondary users (SUs) can utilize that spectrum, if it is not occupied. For CSS, all SUs report their sensing information through reporting channel to the central base station called fusion center (FC). During transmission, some of the SUs are subjected to fading and shadowing, due to which the overall performance of CSS is degraded. We have proposed an algorithm which uses error detection technique on sensing measurement of all SUs. Each SU is required to re-transmit the sensing data to the FC, if error is detected on it. Our proposed algorithm combines the sensing measurement of limited number of SUs. Using Proposed algorithm, we have achieved the improved probability of detection (PD) and throughput. The simulation results compare the proposed algorithm with conventional scheme.     

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.     

Cluster based power efficient cooperative spectrum sensing under reduced bandwidth using location information

Frequency spectrum sharing between licensed primary users (PUs) and unlicensed secondary users (SUs) requires the SUs to reliably detect the spectrum occupancy. Due to multipath fading, single terminal detection is unreliable and results in a high probability of missed detection. This is solved by applying cooperative detection. However, when the number of SUs is large, the bandwidth for reporting their sensing results to the common receiver will be very huge. In this paper, we propose an improved frequency divisional cluster based cooperative spectrum sensing based on the location information which employs a censoring method to reduce the average number of sensing bits sent to the common receiver. The reduction in the number of sensing bits using proposed method in turn reduces the total transmission power of the secondary users in the cognitive network thus improving its battery life.     

Joint power allocation and beamforming with users selection for cognitive radio networks via discrete stochastic optimization

Cognitive radio is a promising technique to dynamic utilize the spectrum resource and improve spectrum efficiency. In this paper, we study the problem of mutual interference cancellation among secondary users (SUs) and interference control to primary users (PUs) in spectrum sharing underlay cognitive radio networks. Multiple antennas are used at the secondary base station to form multiple beams towards individual SUs, and a set of SUs are selected to adapt to the beams. For the interference control to PUs, we study power allocation among SUs to guarantee the interference to PUs below a tolerable level while maximizing SUs?? QoS. Based on these conditions, the problem of joint power allocation and beamforming with SUs selection is studied. Specifically, we emphasize on the condition of imperfect channel sensing due to hardware limitation, short sensing time and network connectivity issues, which means that only the noisy estimate of channel information for SUs can be obtained. We formulate the optimization problem to maximize the sum rate as a discrete stochastic optimization problem, then an efficient algorithm based on a discrete stochastic optimization method is proposed to solve the joint power allocation and beamforming with SUs selection problem. We verify that the proposed algorithm has fast convergence rate, low computation complexity and good tracking capability in time-varying radio environment. Finally, extensive simulation results are presented to demonstrate the performance of the proposed scheme.     

Spatial Spectrum Reuse for Opportunistic Spectrum Access in Infrastructure-Based Systems

Opportunistic spectrum access (OSA) receives a constantly growing interest due to its potential to mitigate spectrum scarcity and meet the increasing communication needs of mobile users. OSA refers to identifying and exploiting spatiotemporal unused portions of licensed spectrum to allow communication among unlicensed–secondary users (SUs) without adverse impact to the licensees (primary users—PUs). Key parameters in OSA are the spectrum opportunities detection method used by the SUs, and the interference level perceived by the PUs. A spatial spectrum reuse framework is proposed, where broadcast messages of an infrastructure-based primary system are exploited and combined with location-aware methods to detect spectrum opportunities and establish interference-free secondary links. The study of secondary link establishment probabilities revealed a spectrum reuse of up to 25% for omni-directional and up to 90% for directional antennas. Moreover, increased throughput is achieved in both cases, with directional antennas attaining significantly better performance.     

Robust Probabilistic Distributed Power Control Algorithm for Underlay Cognitive Radio Networks under Channel Uncertainties

Due to limited cooperation among users and erratic nature of wireless channel, it is difficult for secondary users (SUs) to obtain exact values of system parameters, which may lead to severe interference to primary users (PUs) and cause communication interruption for SUs. In this paper, we study robust power control problem for spectrum underlay cognitive radio networks with multiple SUs and PUs under channel uncertainties. Precisely, our objective is to minimize total transmit power of SUs under the constraints that the satisfaction probabilities of both interference temperature of PUs and signal-to-interference-plus-noise ratio of SUs exceed some thresholds. With knowledge of statistical distribution of fading channel, probabilistic constraints are transformed into closed forms. Under a weighted interference temperature constraint, a globally distributed power control iterative algorithm with forgetting factor to increase convergence speed is obtained by dual decomposition methods. Numerical results show that our proposed algorithm outperforms worst case method and non-robust method.     

A novel approach for energy‐efficient resource allocation in double threshold‐based cognitive radio network

This paper mainly focuses on solving the energy efficiency (EE) maximization problem in double threshold‐based soft decision fusion (SDF) cooperative spectrum sensing (CSS) in the cognitive radio network (CRN). The solution to this objective problem starts with the selection of suitable secondary users (SUs) both for the spectrum sensing and data transmission. Here, energy efficiency is maximized under the constraints of interference to the primary user (PU), an acceptable outage of SUs, the transmission power of the SUs and the probability of false alarm. We propose a novel algorithm called iterative Dinkelbach method (IDM) which jointly optimizes the sensing time and transmission power allocation to the SUs. Further, Lagrangian duality theorem is employed to find the exact power assigned to the SUs. Finally, simulation results are carried out to validate the effectiveness of our proposed scheme by comparing with the other existing schemes. The performance is also analyzed for different system parameters.     

Cooperative spectrum sharing of multiple primary users and multiple secondary users

This paper proposes a multiple-input multiple-output (MIMO) based cooperative dynamic spectrum access (DSA) framework that enables multiple primary users (PUs) and multiple secondary users (SUs) to cooperate in spectrum sharing. By exploiting MIMO in cooperative DSA, SUs can relay the primary traffic and send their own data at the same time, which greatly improves the performance of both PUs and SUs when compared to the non-MIMO time-division spectrum sharing schemes. Especially, we focus on the relay selection optimization problem among multiple PUs and multiple SUs. The network-wide cooperation and competition are formulated as a bargaining game, and an algorithm is developed to derive the optimal PU-SU relay assignment and resource allocation. Evaluation results show that both primary and secondary users achieve significant utility gains with the proposed framework, which gives all of them incentive for cooperation.     

Joint Spectrum Sensing and Spectrum Access for Defending Massive SSDF Attacks:A Novel Defense Framework

Multiple secondary users(SUs)perform collaborative spectrum sensing(CSS)in cognitive radio networks to improve the sensing performance.However,this system sever...     

A modeling framework for supporting and evaluating connectivity in cognitive radio ad hoc networks with beamforming

In this paper, we present an analytical modeling framework for supporting and evaluating the impact of shadowing and beamforming on the topological connectivity of cognitive radio ad-hoc networks (CRAHNs) where primary users (PUs) are equipped with omnidirectional antennas while secondary users (SUs) are equipped with directional antennas such as uniform linear array (ULA) antenna and uniform circular array (UCA) antenna. The main features and contributions in this paper are as follows. First, we derive a formula for calculating effective coverage area of a node in secondary network by taking the effect of path loss, antenna model, and beamforming scheme into consideration. Second, we mathematically analyze the expected number of neighbors and communication probability of a SU based on the effective coverage area of SU and the spatial–temporal existence of PU’s operation. We also derive the expression of the upper bound of path connectivity between two arbitrary SUs in the networks. Third, we point out that UCA antenna is the most suitable antenna for CRAHNs. We find the optimal number of elements corresponding to each type of directional antenna at which the highest connectivity can be achieved. The validity of our analysis is verified by comparing with simulations. The results in this paper provide efficient guidelines for system designers to characterize and optimize the connectivity of CRAHNs with beamforming.     

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.

     

A fair MAC protocol for resource sharing in ad-hoc cognitive networks

Cognitive Radio (CR) technology allows an opportunistic use of the licensed spectrum by the CR users, avoiding harmful interference to Primary Users (PUs). To perform their activity, secondary users (SUs) have particular features compared to the PUs; sensing is one of the principal. Specifically the CR users must detect the spectrum holes left by PUs by performing sensing. All the protocol stack need to be redesigned taking into account the specificity of the cognitive environment. In this paper a novel MAC protocol for cognitive radio wireless ad-hoc networks is presented and analysed. The introduced protocol is distributed, collision-free and guarantees a fair channel assignment, also taking into account the requests of Quality of Service (QoS) of the CR nodes. The performance of the protocol has been studied through statistical computations. In particular, a Markov chain has been introduced to study the global performance of the protocol. In addition a simulation tool has been developed to validate the mathematical model and to prove the fairness of the resource sharing mechanism. The results prove the protocol efficiency and the high fairness, measured with the help of the Jain’s fairness index.     

认知中继协同无线通信系统功率分配和混合频谱接入方案

文章首先对Underlay 认知-中继协作（cognitive radio relay cooperation, CR-RC）系统信源和中继的功率分配问题进行了研究,获得了Underlay CR-RC系统信源、中继独立功率分配(independent power allocation, IPA）和联合功率分配（joint power allocation, JPA）方案,并给出了基于IPA和JPA的CR-RC系统的中断概率和各态历经容量的封闭解析解;其次通过对Underlay CR-RC系统中断性能的比较分析,提出了高频谱效率、高能量效率的混合Interweave-Underlay CR-RC方案。在该方案中,定义了主、从用户中断概率约束,充分考虑了主、从系统的服务质量(quality of service, QoS),当即使从用户的发射功率为零,主系统的QoS仍不能满足时,从用户以最大功率发送信号,无需考虑其对主用户的影响;当由于很强的主用户干扰,使得从用户的QoS不能得到满足时,从用户不再发射信号,发信功率为零;当主、从系统的QoS能够同时满足时,以Underlay模式工作。      

Reinforcement Learning Enhanced Iterative Power Allocation in Stochastic Cognitive Wireless Mesh Networks

As the scarce spectrum resource is becoming over-crowded, cognitive wireless mesh networks have great flexibility to improve the spectrum utilization by opportunistically accessing the licensed frequency bands. One of the critical challenges for realizing such network is how to adaptively allocate transmit powers and frequency resources among secondary users (SUs) of the licensed frequency bands while maintaining the quality-of-service (QoS) requirement of the primary users (PUs). In this paper, we consider the power control problem in the context of cognitive wireless mesh networks formed by a number of clusters under the total transmit power constraint by each SU as well as the mean-squared error (MSE) constraint by PUs. The problem is modeled as a non-cooperative game. A distributed iterative power allocation algorithm is designed to reach the Nash equilibrium (NE) between the coexisting interfered links. It offers an opportunity for SUs to negotiate the best use of power and frequency with each other. Furthermore, how to adaptively negotiate the transmission power level and spectrum usage among the SUs according to the changing networking environment is discussed. We present an intelligent policy based on reinforcement learning to acquire the stochastic behavior of PUs. Based on the learning approach, the SUs can adapt to the dynamics of the interference environment state and reach new NEs quickly through partially cooperative information sharing via a common control channel. Theoretical analysis and numerical results both show effectiveness of the intelligent policy.     

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.     

Quality of service‐aware coordinated dynamic spectrum access: prioritized Markov model and call admission control

In this paper, we propose a heterogeneous‐prioritized spectrum sharing policy for coordinated dynamic spectrum access networks, where a centralized spectrum manager coordinates the access of primary users (PUs) and secondary users (SUs) to the spectrum. Through modeling the access of PUs and multiple classes of SUs as continuous‐time Markov chains, we analyze the overall system performance with consideration of a grade‐of‐service guarantee for both the PUs and the SUs. In addition, two new call admission control (CAC) strategies are devised in our models to enhance the maximum admitted traffic of SUs for the system. Numerical results show that the proposed heterogeneous‐prioritized policy achieves higher maximum admitted traffic for SUs. The trade‐off between the system's serving capability and the fairness among multiple classes of SUs is also studied. Moreover, the proposed CAC strategies can achieve better performance under max‐sum, proportional, and max‐min fairness criteria than the conventional CAC strategies. Copyright © 2011 John Wiley & Sons, Ltd.     

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.     

Cooperative primary–secondary dynamic spectrum leasing game via decentralized bargaining

Dynamic spectrum leasing (DSL) has been proposed as a solution for better spectrum utilization. Most of the work focused on non-cooperative game to model primary/secondary users interactions in DSL approach. Some others introduced cooperative game just for secondary users (SUs). In this paper, both primary users (PUs) and SUs incentives and level of satisfactions are considered. Nash bargaining is developed with both PUs and SUs as bargainers. A simple pricing approach is introduced which makes the proposed method practically feasible. On one hand, SUs adjust their power regarding to price and tolerable interference which are announced by PU. On the other hand, PU adjusts its tolerable interference to maximize its profit. Simulation results verify the viability of proposed method.     

基于DOA估计的认知无线网络频谱感知

认知无线电技术中频谱感知性能的优劣直接影响认知通信系统的性能。针对该特点提出了认知无线电网络中基于波达方向（ DOA)估计的主用户频谱感知模型,即单主用户多次用户和多主用户多次用户的系统模型,选取基于特征分解的多重信号分类（ MUSIC)算法分析两种模型的感知性能,包括虚警概率、漏检概率、最小总错误概率、算法复杂度等,获得了闭值表达式,最后在两种模型下对算法进行了仿真。仿真结果表明：各参数主要影响虚警概率,而漏检概率几乎不受影响,验证了方法的有效性。