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。     

利用压缩测量值的非重构宽带频谱感知

为了解决在认知无线电(CR)宽带系统中快速精确分析数据并实现频谱感知的技术难题,对非重构压缩频谱检测进行了研究。提出了一种利用压缩测量值的非重构宽带频谱感知算法,推导分析了虚警概率和检测概率的封闭表达式。该算法利用离散余弦变换（DCT）矩阵的能量压缩特性观测得到主用户能量压缩信息,并利用新型压缩检测器对压缩信息进行判决。仿真表明,所提算法降低了计算复杂度,提供了比传统的能量检测器和压缩检测器更好的工作特性。     

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.     

Jointly optimizing sensing time and resource allocation in multichannel cognitive radio networks

Spectrum sensing is a key technique for determining the spectrum available in cognitive radio (CR) networks. In this paper, we study how to jointly optimize sensing time and resource allocation to maximize the sum transmission rate of all CR users of a multichannel CR network. We take into consideration the transmission power and interference constraints to protect primary users from harmful interference, as well as constraints of detection probability and false alarm probability. Under these constraints, we propose an asymptotically optimal resource allocation algorithm. The optimal sensing time can be obtained using the traditional one‐dimensional exhaustive search. However, owing to the high complexity of searching for the sensing time, we propose a simplified method to get the optimal sensing time under the assumption that false alarm probability is small. Simulation results show that the simplified method can obtain the optimal sensing time efficiently under strict constraint of false alarm probability. Copyright © 2012 John Wiley & Sons, Ltd.     

基于功率谱分段对消频谱感知算法研究及性能分析

在实用的认知无线电系统中,频谱感知技术必须具备在噪声电平高动态变化和无线信道严重衰落电磁背景下,进行实时盲频谱感知的能力,这为经典的频谱感知算法带来巨大的挑战。该文提出的功率谱分段对消频谱感知算法,依据傅里叶变换的渐进正态性和相互独立性,计算出功率谱的统计特性,利用监测频带内部分谱线强度和与全部谱线强度和的比值作为检验统计量进行信号存在性的判断。该文推导了算法的虚警概率和不同信道模型下正确检测概率的数学表达式,并依据Neyman-Pearson准则得到判决门限的闭式表达式。理论分析和仿真结果均表明：功率谱分段对消频谱感知算法对噪声不确定度具有鲁棒性;固定信噪比,算法的频谱感知性能不受噪声电平改变的影响;应用于高斯白噪声和平坦慢衰落信道中,可在较宽的信噪比范围内获得较优越的频谱感知性能;算法计算复杂度低,可在微秒级时长内完成频谱感知。     

一种基于能量检测的协作式频谱感知算法

为提高认知无线电系统中频谱检测的可靠性,提出了一种基于能量检测的协作式频谱感知算法。利用授权用户的状态在相邻感知帧之间变化的概率小这一特性,通过将当前感知帧的能量值与相邻值相结合来判断授权用户状态,这样当授权用户使用授权频段时,所提算法能有效减小采样信号能量值骤减时发生误判的概率。另外给出了所提算法检测概率和虚警概率的闭式表达式。理论分析和仿真结果表明,所提算法比传统的协作式频谱检测算法检测性能好。     

Multi-channel energy detection under phase noise: analysis and mitigation

For the development of highly integrated, flexible and low-cost cognitive radio (CR) devices, simple transceiver architectures, like direct-conversion receiver, are expected to be deployed and provide viable radio frequency (RF) spectrum sensing solutions for practical implementation. Yet, this can be very challenging task especially if spectrum sensing and down-conversion are conducted over multiple RF channels simultaneously for improved efficiency in channel scans. Then, the so-called dirty RF problem that degrades link performance of traditional transmission systems starts to be influential from spectrum sensing perspective as well. The unavoidable RF impairments, e.g., oscillator phase noise in direct-conversion receiver, could generate crosstalk between multiple channels that are down-converted simultaneously, and thus considerably limit the spectrum sensing capabilities. Most of the existing spectrum sensing studies in literature assume an ideal RF receiver and have not considered such practical RF hardware problem. In this article, we study the impact of oscillator phase noise on energy detection (ED) based spectrum sensing in multi-channel direct-conversion receiver scenario. With complex Gaussian primary user (PU) signal models, we first derive the detection and false alarm probabilities in closed-form expression. The analytical results, verified through extensive simulations, show that the wideband multi-channel sensing receiver is very sensitive to the neighboring channel crosstalk induced by oscillator phase noise. More specifically, it is shown that the false alarm probability of multi-channel energy detection increases significantly, compared to the ideal RF receiver case. The exact performance degradation depends on the power of neighboring channels as well as statistical characteristics of the phase noise in the deployed receiver. In order to prevent such performance degradation in spectrum identification, an enhanced energy detection technique is proposed. The proposed technique calculates the leakage power from neighboring channels for each channel and improves the sample energy statistics by subtracting this leakage power from the raw values. An analytical expression is derived for the leakage power which is shown to be a function of power spectral levels of neighboring channels and 3-dB bandwidth of phase noise process. Practical schemes for estimating these two quantities are discussed. Extensive computer simulations show that the proposed enhanced detection yields false alarm rates that are very close to those of an ideal RF receiver and hence clearly outperforms classical energy detection.     

Femtocell网络中基于双门限的两层协作频谱感知技术

在Femtocell和Macrocell构成的两层异构网络中,前人提出运用认知无线电的技术来解决网络中存在的干扰问题,但大多数研究主要集中在频谱资源的管理上,没有提出高效的频谱检测方法。本文结合认知无线电中频谱感知技术提出了基于双门限的两层协作频谱感知,检测出空洞的频谱资源分配给Femtocell用户使用,既能提高频谱资源的利用率,又能有效的抑制Femtocell与Macrocell之间的干扰。文中推导出了Femtocell用户基于双门限的两层频谱感知的检测概率和虚警概率,给出了Femtocell网络中感知信息两比特编码的融合准则和基于双门限的两层协作频谱感知的具体实施方法。仿真结果表明,所提算法能够有效提高Femtocell用户的频谱检测概率,通过对感知信息进行两比特编码,再将编码后的信息发送至融合中心进行融合,能够有效的提高整个系统的检测性能,抗噪声能力强。      

D-S证据理论在认知无线电中的应用

针对认知无线电（CR）中协作频谱感知时信息不确定性带来严重的性能影响,在协作开销允许的情况下,人们渴望得到感知增益很高的协作感知方案。由于D-S证据理论在决策系统中处理不确定性信息时能获得令人满意的性能,使得其在认知无线电中的应用会起很重要作用。一种基于D-S证据理论的协作频谱感知新方案被提出和发展。该方案能有效地提高...     

Novel distributed algorithm for coalition formation in cognitive radio networks for throughput enhancement using matching theory

In this paper, a novel algorithm is proposed for increasing the throughput in cognitive radio networks by forming coalitions among cognitive radio users in AWGN and fading channel environment. Although there have been numerous studies exploring the benefits of coalition formation in cognitive radio networks from the game theory perspective, there are several limitations in their application. To overcome the limitations of game theory, concepts from matching theory are used. Specifically, the stable marriage problem is used to formulate the interactions among the cognitive radio users as a matching game for collaborative distributed spectrum sensing under target detection probability constraint. The utility function is defined as the average probability of false alarm per cognitive radio user. The advantage of stable marriage is that it always converges to a stable matching and is Pareto optimal when the preferences of cognitive radios are strict. In the proposed model, the stable matching problem is extended to propose a novel algorithm to form coalitions of varying sizes for improving the utility of CR (false alarm and throughput). The coalitions formed using the algorithm are stable and do not deviate from the final matching. Using simulations and mathematical analysis, it is shown that the proposed algorithm leads to stable coalitions and returns significant improvement in terms of reduced probability of false alarm and improved throughput per cognitive radio user as compared to the noncooperative scenario.     

Capacity maximization in spectrum sensing for Cognitive Radio Networks thru Outage Probability

With today's increase in the usage of wireless devices and the consequent spectrum allocation, radio spectrum is becoming scarce. In practice most of the allotted spectrum is not used for large periods of time. Cognitive radio has been proposed to exploit the presence of these unused spectrum band (called as spectrum hole). Cognitive radios perform radio environment analysis, identify the spectrum holes and operate in those holes. Several factors like fading and shadowing affects the ability of the cognitive radio to detect the primary user. The current research shows that cooperation among the cognitive users can increase the detection probability for a given probability of false alarm. We proposed the system that to have maximized the capacity in spectrum sensing for Cognitive Radio Networks thru Outage Probability for Rayleigh fading channel.     

CR网络中采用平均检测概率约束的空时二维感知方法

针对认知无线电网络的空闲频谱检测,提出一种采用平均检测概率约束的空时二维感知方法。以检测概率在整个检测区域内的统计平均值,作为从用户（认知用户）接入主用户（授权用户）频谱的约束,并由此设置检测门限,在理论上得到时域、空域及空时二维空闲频谱接入概率。分析和仿真结果表明,与采用检测概率做约束的传统方法相比,以平均检测概率做约束所得到的时域虚警概率和空域虚警概率均降低,认知无线电网络空时二维空闲频谱的接入概率显著提高。      

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.     

Nakagami衰落信道下噪声跟踪干扰检测性能分析

针对慢跳频通信中噪声跟踪干扰的检测问题,提出了一种新的噪声跟踪干扰检测算法.该检测算法应用认知无线电中协作频谱感知方法,分析了接收信号的条件概率密度函数,研究了加性高斯白噪声信道下单跳信号存在干扰的检测性能.在此基础上,推导了Nakagami衰落信道下单跳信号存在干扰的检测概率和虚警概率,通过对检测概率和虚警概率中的多重积分进行化简,得到了检测概率和虚警概率的级数表达式.单跳信号检测后,把检测结果上报到融合中心,应用协作频谱感知中的“k out of n”准则分析了噪声跟踪干扰的检测性能.仿真结果验证了理论分析的正确性.     

基于循环平稳特征的频谱检测

频谱检测是实现认知无线电的前提。从认知无线电的核心功能出发,分析了循环平稳特征检测抗噪方面的性能以及影响其检测性能的主要因素,通过理论推导和仿真验证了噪声对检测性能的影响,提出了一种基于小波变换的频谱检测方案。该方案通过小波变换将接收到的信号进行主信号与噪声分离,并对信号进行重构,提高了频谱检测的性能。仿真结果表明,在低信噪比情况下,采用基于小波变换的频谱检测方法,无论是系统的频谱检测虚警率还是频谱检测漏检率都有了显著的降低。     

基于中继协作的认知无线电性能研究

频谱感知是认知无线电技术的关键。协作频谱感知能够充分利用网络资源,提高网络中认知用户的检测概率,降低认知用户的虚警概率。基于两个认知用户之间的中继协作,研究了协作频谱感知的感知性能与吞吐量的折衷。仿真结果表明：中继协作的频谱感知方法能够使得在充分保证对授权用户不造成干扰即检测概率一定时,能够有效地降低认知用户感知授权用户的虚警概率,缩短＂弱＂认知用户获得最大吞吐量的最优感知时间,提高其最大吞吐量,并提高认知系统的最大吞吐量。     

多天线认知无线电网络多信道联合频谱感知策略

当前,以单天线认知用户组成的认知无线电网络同时协作检测多个信道的分配策略得到了广泛研究,而在认知用户可以灵活选取自身天线进行空间分集接收的条件下,当联合感知多信道时,多天线认知无线电网络如何获取最优的天线分配策略仍有待进一步研究。为解决这一问题,在限制各信道最大虚警概率的前提下,以最小化所有信道漏检概率之和为目标,建立了优化模型,并提出了基于分支定界的算法和基于贪婪思想的启发式算法。前者可以获得最优策略,但复杂度较高,后者以牺牲较小检测性能为代价,明显降低了复杂度,有效实现了检测性能与复杂度的平衡,并且在保护各个信道上主用户免受认知用户干扰层面,一定程度上兼顾了公平性。      

Joint sensing and transmission for cognitive amplify‐and‐forward two‐way relay networks

In this paper, we propose a cognitive transmission scheme for Amplify‐and‐Forward (AF) two‐way relay networks (TWRNs) and investigate its joint sensing and transmission performance. Specifically, we derive the overall false alarm probability, the overall detection probability, the outage probability of the cognitive TWRN over Rayleigh fading channels. Furthermore, based on these probabilities, the spectrum hole utilization efficiency of the cognitive TWRN is defined and evaluated. It is shown that smaller individual or overall false alarm probability can result in less outage probability and thus larger spectrum hole utilization efficiency for cognitive TWRN, and however produce more interference to the primary users. Interestingly, it is found that given data rate, more transmission power for the cognitive TWRN does not necessarily obtain higher spectrum hole utilization efficiency. Moreover, our results show that a maximum spectrum hole utilization efficiency can be achieved through an optimal allocation of the time slots between the spectrum sensing and data transmission phases. Finally, simulation results are provided to corroborate our proposed studies. Copyright © 2016 John Wiley & Sons, Ltd.     

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.     

Quickest Detection of Multi-channel Based on STFT and Compressed Sensing

This paper proposes a multi-channel quickest detection method based on compressed sensing and short-time Fourier transform. Quickest detection performs a statistical test to obtain the minimal detection delay subject to given false alarm constrains. Short-time Fourier transform, which reflects the time–frequency information, implements the multi-channel quickest detection. Compressed sensing reduces the sampling rate at first. Compared with single-channel spectrum sensing, this method substantially improves the spectrum access opportunity in time and frequency domain. The relationship between the detection delay and other parameters, such as the probability of false alarm, SNR, sparsity, and sampling rate, verifies the validity of the method. While simulation results show that this method can perform spectrum sensing in high detection probability and low probability of false alarm.