Effective autocorrelation-based spectrum sensing technique for cognitive radio network applications

Spectrum sensing based on detection techniques enables cognitive radio networks to detect vacant frequency bands. The spectrum sensing gives the opportunity to increase the radio spectrum channels re-utilization. However, the main challenge in spectrum sensing is the simplicity of the considered detection approach and the amount of prior information needed to make an accurate decision. This paper proposes a novel sensing technique based on the autocorrelation function. This novel approach is based on the speed of convergence to zero of all autocorrelation coefficients. This technique shows the highest probability of detection for the same probability of false alarm target at low signal-to-noise ratio (SNR) compared with many standard detection techniques. The proposed method has been implemented using GNU Radio software and SDR (software-defined radio) platforms. The experimental results show the effectiveness of the proposed method under real scenarios.     

Trusted cognitive radio networking

Networking cognitive radios and nodes from primary system (PS) results in a heterogeneous coexisting multi‐radio wireless network, so that significant network throughput gain can be achieved. However, by investigating cognitive radio network (CRN) architecture, the links in CRNs are unlikely to support complete security check due to link dynamics, opportunistic availability, and uni‐directional in available time window. We therefore introduce trusted cognitive radio networking (TCRN) concept to facilitate network functions such as association in dynamic spectrum access and routing. First of all, we explore the mathematical framework for trust in CRNs. We then show successful association of node to CRN based on the mathematical structure of trust from statistical decision theory. Furthermore, we modify the machine‐learning algorithm to update the trust measure for each node, and develop rules of thumbs to facilitate TCRN with learning capability, based on numerical simulations. Trusted CRN can greatly alleviate heterogeneous challenge for CRN operation. Copyright © 2009 John Wiley & Sons, Ltd.     

Interference analysis of cognitive radio networks

In spite of spectrum sensing, aggregate interference from cognitive radios (CRs) remains as a deterring factor to the realization of spectrum sharing. We provide a systematic approach of evaluating the aggregate interference (I _aggr) experienced at a victim primary receiver. In our approach, we model the received power versus propagation distance relations between a primary transmitter, primary receiver, and CRs. Our analytical framework differs from the previous works in that we have formulated the relationship between I _aggr and the sensing inaccuracy of CRs. Energy detector is assumed for the purpose of spectrum sensing. I _aggr is expressed explicitly as a function of the number of energy samples collected (N ) and the threshold signal‐to‐noise ratio level used for comparison (SNR_ε ). The theoretical analysis is then applied to a practical scenario of spectrum sharing between digital TV broadcast and the IEEE 802.22 wireless regional area network systems. The impact on digital TV reception is evaluated in terms of signal‐to‐interference ratio. The proposed method allows us to determine the appropriate wireless regional area network operating conditions that fulfill the signal‐to‐interference ratio requirement imposed by regulator. Copyright © 2016 John Wiley & Sons, Ltd.     

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。     

Smart antenna‐assisted spectrum sensing for robust detection in cognitive radio networks

In this paper, we consider the problem of multiband spectrum sensing by employing smart antenna arrays at the cognitive receiver. Although energy detection is widely used for spectrum sensing in cognitive radio networks because of its simplicity and accuracy, it is severely deteriorated by the noise uncertainty. This paper introduces robust spectrum sensing techniques to circumvent this difficulty, which operate simultaneously over the total frequency channels rather than a single channel each time. To enhance the detection performance, the proposed schemes jointly utilize the information of eigenvalues and eigenvectors, signal and noise subspace components in conjunction with the likelihood functions and Gerschgorin radii. Neither subjective decision threshold setting nor the estimation of noise power is required in our schemes, making them robust to noise uncertainty. Simulations are presented to validate the performance of the proposed schemes, and the results show that our schemes can outperform other existing spectrum sensing methods. Copyright © 2014 John Wiley & Sons, Ltd.     

Location based spectrum sensing performance analysis over fading channels in cognitive radio networks

This paper analyzes the spectrum sensing performance over fading channel,in which a licensee and multiple unlicensed users coexist and operate in the licensed channel in a local area. The overall average probabilities of detection and false alarm by jointly taking the fading and the locations of all secondary users into account are derived,and a statistical model of cumulate interference is constructed. Based on the cumulate interference,a closed-form expression of outage probability at the primary user's receiver according to a specific distribution of the fading is obtained. Finally,the sensing parameters so as to minimize the total spectrum sensing error and maximize the average opportunistic throughput are obtained. It is noted that the overall average performance analysis and results here enable to benchmark the design of specific spectrum sensing algorithms.     

Genetic algorithm‐based scheduling in cognitive radio networks under interference temperature constraints

The proliferation of wireless technologies and services has intensified the demand for the radio spectrum. However, the currently existing fixed spectrum assignment policy leads to an inefficient and unevenly distributed spectrum utilization. Cognitive radio paradigm has been proposed to alleviate these drawbacks by employing dynamic spectrum access (DSA) methodology. Federal Communications Commission (FCC) has proposed the interference temperature model, which enables the unlicensed users to utilize the licensed frequencies simultaneously with the licensed users as long as they conform to the interference temperature constraints. Recently, throughput and delay optimal schedulers that meet the interference temperature constraints in cognitive radio networks have been formulated in the literature. However, these schedulers have high computational complexity. In this paper, we propose genetic algorithm (GA)‐based suboptimal methods addressing these throughput and delay optimal scheduling problems. The simulation results corroborate that our GA‐based approach yields very close performance to the optimal solutions and operates with much lower complexity. Copyright © 2010 John Wiley & Sons, Ltd.     

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.     

Securing cognitive radio networks

Cognitive radio is a promising technology aiming to improve the utilization of the radio electromagnetic spectrum. A cognitive radio device uses general purpose computer processors that run radio applications software to perform signal processing. The use of this software enables the device to sense and understand its environment and actively change its mode of operation based on its observations. Unfortunately, this solution entails new security challenges. Our objective in this paper is to analyze the security issues of the main recent developments and architectures of cognitive radio networks. We present vulnerabilities inherent to those systems, identify novel types of abuse, classify attacks, and analyze their impact on the operation of cognitive radio‐based systems. Moreover, we discuss and propose security solutions to mitigate such threats. Copyright © 2010 John Wiley & Sons, Ltd.     

认知无线电NC-OFDM系统中基于压缩感知的信道估计新方法

提出了一种基于压缩感知(CS,compressive sensing)理论的不连续子载波正交频分复用(NC-OFDM,non-contiguous orthogonal frequency division multiplexing)系统信道估计新方法,全面研究了认知无线电NC-OFDM系统CS信道估计的理论框架、导频图案的设计、信道估计算法,并依据CS测量矩阵设计理论提出了测量矩阵互相关最小化的导频图案优化准则。仿真结果表明,同已有的NC-OFDM系统信道估计方法相比,CS信道估计能够在多种禁用子载波场景下,使用较少导频获得很好的信道估计性能。     

Distributed power control for multiuser cognitive radio networks with quality of service and interference temperature constraints

One of the most challenging problems in dynamic resource allocation for cognitive radio networks is to adjust transmission power of secondary users (SUs) while quality of service needs of both SUs and primary users (PUs) are guaranteed. Most power control algorithms only consider interference temperature constraint in single user scenario while ignoring the interference from PUs to SUs and minimum signal to interference plus noise ratio (SINR) requirement of SUs. In this paper, a distributed power control algorithm without user cooperation is proposed for multiuser underlay CNRs. Specifically, we focus on maximizing total throughput of SUs subject to both maximum allowable transmission power constraint and SINR constraint, as well as interference temperature constraint. To reduce the burden of information exchange and computational complexity, an average interference constraint is proposed. Parameter range and convergence analysis are given for feasible solutions. The resource allocation is transformed into a convex optimization problem, which is solved by using Lagrange dual method. In computer simulations, the effectiveness of our proposed scheme is shown by comparing with distributed constrained power control algorithm and Nash bargaining power control game algorithm. Copyright © 2014 John Wiley & Sons, Ltd.     

认知自组网分布式频谱感知最优协作算法

认知无线电技术使得自组织网络节点能够充分利用空闲频谱资源,提高了传输性能。通过协作频谱感知,可有效解决由于无线信道存在阴影、噪声和衰落等情况导致的单节点感知准确性偏低。为了解决梯度算法随着协作节点数量增大后计算复杂度变高,文中提出部分梯度算法ψ-GBCS,该模型通过基于SNR的动态阈值保证了感知准确性,同时通过最佳协作节点数提高了感知效率。仿真结果表明,该模型下,综合评估系统效率和性能的J函数值提高37%,能耗降低50%,有效保证大规模认知自组网频谱感知的鲁棒性,降低了对主用户的干扰及设备功耗。     

Routing for cognitive radio networks consisting of opportunistic links

Cognitive radio (CR) has been considered a key technology to enhance overall spectrum utilization by opportunistic transmissions in CR transmitter–receiver link(s). However, CRs must form a cognitive radio network (CRN) so that the messages can be forwarded from source to destination, on top of a number of opportunistic links from co‐existing multi‐radio systems. Unfortunately, appropriate routing in CRN of coexisting multi‐radio systems remains an open problem. We explore the fundamental behaviors of CR links to conclude three major challenges, and thus decompose general CRN into cognitive radio relay network (CRRN), CR uplink relay network, CR downlink relay network, and tunneling (or core) network. Due to extremely dynamic nature of CR links, traditional routing to maintain end‐to‐end routing table for ad hoc networks is not feasible. We locally build up one‐step forward table at each CR to proceed based on spectrum sensing to determine trend of paths from source to destination, while primary systems (PSs) follow original ways to forward packets like tunneling. From simulations over ad hoc with infrastructure network topology and random network topology, we demonstrate such simple routing concept known as CRN local on‐demand (CLOD) routing to be realistic at reasonable routing delay to route packets through. Copyright © 2009 John Wiley & Sons, Ltd.     

Sensing OFDM systems with timing and frequency offset for cognitive radio networks

In this paper, we consider the detection of orthogonal frequency division multiplexing with timing and frequency offset for cognitive radio over fast time‐varying multipath channels. By making different assumptions on the availability of the timing and frequency offset at the secondary user, we derive two algorithms based on likelihood ratio test and generalized likelihood ratio test, respectively and theoretically obtain the detection performances of them. The proposed algorithms jointly utilize energy of observations and correlation of the cyclic prefix (CP) data. The extensive simulations show that the two algorithms outperform the other CP‐based methods, perform well at very low signal to noise ratio (SNR) and are robust to the CP ratio. Copyright © 2012 John Wiley & Sons, Ltd.     

Modified Bayesian algorithm‐based compressive sampling for wideband spectrum sensing in cognitive radio network using wavelet transform

This paper presents the implementation of a modified version of Bayesian relevance vector machine (RVM)‐based compressive sensing method on cognitive radio network with wavelet transform for spectrum hole detection. Bayesian compressive sensing is used in this work to deal with the complexity and uncertainty of the process. The dependency of the Bayesian compressive sensing on the knowledge of noise levels in the measurement has been relaxed through the proposed Bayesian RVM‐based compressive sensing algorithm. This technique recovers the wideband signals even with fewer measurements maintaining considerably good accuracy and speed. Wavelet transform is used in this paper to enable the detection of primary user (PU) even in the low regulated transmission from unlicensed user. The advantage of this approach lies in the fact that it enables the evaluation of all possible hypotheses simultaneously in the global optimization framework. Simulation study is performed to evaluate the efficacy of the proposed technique over the cognitive radio environment. The performance of the proposed technique is compared with the conventional Bayesian approach on the basis of recovery error, recovery time and covariance to verify its superiority.     

Analytical modeling and resource planning for cognitive radio systems

Cognitive radio heralds the next step in the evolution of wireless communications. In this paper, an analytical model for infrastructure based cognitive radio systems is proposed, and its performance is evaluated under bursty traffic scenarios in a multiple cell environment. Performance metrics like probabilities of dropping and blocking for primary and secondary users as well as forced termination and forced frequency handoff for secondary users are investigated, and the analytical model is verified with simulations. In addition to the analytical model, a new resource planning method is proposed to compensate for uneven traffic load distribution. The proposed method considers offered traffic, hop count to the heavily loaded cell, and velocity of mobile users during resource planning and performs better in terms of probability of blocking, dropping, and forced termination. The proposed method is promising in balancing the system performance measures, yet there is room for further improvement for finding a closed formula for the proposed analytical model. Copyright © 2010 John Wiley & Sons, Ltd.     

Sensor node-assisted asynchronous cooperative spectrum sensing for cognitive radio network

In order to take advantage of the asynchronous sensing information, alleviate the sensing overhead of secondary users （SUs） and improve the detection performance, a sensor node-assisted asynchronous cooperative spectrum sensing （SN-ACSS） scheme for cognitive radio （CR） network （CRN） was proposed. In SN-ACSS, each SU is surrounded by sensor nodes （SNs）, which asynchronously make hard decisions and soft decisions based on the Bayesian fusion rule instead of the SU. The SU combines these soft decisions and makes the local soft decision. Finally, the fusion center （FC） fuses the local soft decisions transmitted from SUs with different weight coefficients to attain the final soft decision. Besides, the impact of the statistics of licensed band occupancy on detection performance and the fact that different SNs have different sensing contributions are also considered in SN-ACSS scheme. Numerical results show that compared with the conventional synchronous cooperative spectrum sensing （SCSS） and the existing ACSS schemes, SN-ACSS algorithm achieves a better detection performance and lower cost with the same number of SNs.     

Detection efficiency of cooperative spectrum sensing in cognitive radio network

Sensing the spectrum in a reliable and efficient manner is crucial to cognitive radio. To combat the channel fading suffered by the single radio, cooperative spectrum sensing is employed, to associate the detection of multiple radios. In this article, the optimization problem of detection efficiency under the constraint of detection probability is investigated, and an algorithm to evaluate the required radio number and sensing time for maximal detection efficiency is presented. To show the effect of cooperation on the detection efficiency, the proposed algorithm is applied to cooperative sensing using the spectral correlation detector under the Rayleigh flat fading channel.     

面向认知无线电网络能量高效协作感知的在线节点选择算法

面向认知无线电网络中能量高效协作感知任务需求,提出了面向单次协作感知过程的能量最小化节点选择问题EMNS和面向在线协作感知的能量高效节点选择问题OENS。证明了两问题均为NP-hard难题。针对EMNS问题,提出采用分枝定界算法BAB求最优解和贪婪节点选择算法GS求近似解。针对OENS问题,提出为每个节点引入考虑能量消耗负载均衡的动态权重系数,基于BAB和GS算法设计了启发式的在线节点选择算法OBAB、OGS1。仿真实验结果表明,提出的算法可显著增加网络完成的协作感知过程次数,可有效延长网络"生存期"。     

认知无线电中协作压缩频谱感知算法

针对宽带频谱认知无线电环境中,传统能量检测法在信噪比较低时,容易出现误检而使系统的检测性能下降的问题,文中提出了一种新型高性能的协作频谱感知算法,它是基于压缩理论的多节点频谱感知方法,各节点之间采用基于双判决门限的协作方式。仿真结果显示,双门限协作压缩频谱感知算法在低信噪比的情况下,检测性能明显优于传统能量检测法。