认知无线电网络的一种协作频谱感知方案

认知无线电技术能够让非授权用户利用已经分配给授权用户的频段.为了不对首要用户的工作造成干扰,认知用户需要对频谱进行不间断的监测来判断首要用户是否存在.因此,频谱的感知是认知无线电技术的关键.协作频谱感知能够充分的利用网络资源,提高网络中的认知用户的检测概率.文中笔者简单地介绍了一种协作频谱感知的方案.仿真结果表明,通过该方法能够提高网络中认知用户的检测概率,提高网络的检测灵敏度.     

Fault-Tolerant Cooperative Spectrum Sensing Scheme for Cognitive Radio Networks

In order to solve the uncertainty resulting from shadowing effect and resist the attack from malicious cognitive radio (CR) users, we propose a fault-tolerant cooperative spectrum sensing scheme for CR networks, where an energy detection-based local spectrum sensing is performed at each CR user, a coefficient is used to weight each CR user’s sensing result, a linear weighted fusion process is performed at the fusion center (FC) to combine received sensing results. For a fault-tolerant cooperative spectrum sensing scheme, the most important issue is to distinguish whether the CR user is reliable or not. In this paper, a reputation-based cooperative mechanism is presented to alleviate the influence of the unreliable sensing results from CR users suffering shadowing and the false sensing data from malicious CR users on the detection result at the FC. In proposed fault-tolerant cooperative scheme, each cooperative CR user has a reputation degree which is initialized and adjusted by the FC and used to weight the sensing result from the corresponding user in the fusion process at the FC. And then, two reputation degree adjusting methods are presented to manage the reputation degree of each CR user. Simulation results show that the proposed scheme can not only weaken the harmful influence caused by malicious CR users, but also alleviate the corrupted detection problem resulting from destructive channel condition between the primary transmitter and the CR user. Moreover, the detection performance of the fault-tolerant cooperative scheme, which has a feasible computational complexity and needs no instantaneous SNRs, is close to that of the optimal scheme.     

Cooperative Spectrum Prediction in Multi-PU Multi-SU Cognitive Radio Networks

Spectrum sensing is considered as the cornerstone of cognitive radio networks (CRNs). However, sensing the wide-band spectrum results in delays and resource wasting. Spectrum prediction, also known as channel status prediction, has been proposed as a promising approach to overcome these shortcomings. Prediction of the channel occupancy, when feasible, provides adequate means for an SU to determine, with a high probability, when to evacuate a channel it currently occupies in anticipation of the PU’s return. Spectrum prediction has great potential to reduce interference with PU activities and significantly enhance spectral efficiency. In this paper, we propose a novel, coalitional game theory based approach to investigate cooperative spectrum prediction in multi-PU multi-SU CRNs. In this approach, cooperative groups, also referred to as coalitions, are formed through a proposed coalition formation algorithm. The novelty of this work, in comparison to existing cooperative sensing approaches, stems from its focus on the more challenging case of multi-PU CRNs and the use of an efficient coalition formation algorithm, centered on the concept of core, to ensure stability. Theoretical analysis is conducted on the upper bound of the coalition size and the stability of the formed coalition structure. A through simulation study is performed to assess the effectiveness of the proposed approach. The simulation results indicate that cooperative spectrum prediction leads to more accurate prediction decisions, in comparison with local spectrum prediction individually performed by SUs. To the best of our knowledge, this work is the first to use coalitional game theory to study cooperative spectrum prediction in CRNs, involving multiple PUs.     

Soft Combination Schemes for Cooperative Spectrum Sensing in Cognitive Radio Networks

This paper investigates linear soft combination schemes for cooperative spectrum sensing in cognitive radio networks. We propose two weight-setting strategies under different basic optimality criteria to improve the overall sensing performance in the network. The corresponding optimal weights are derived, which are determined by the noise power levels and the received primary user signal energies of multiple cooperative secondary users in the network. However, to obtain the instantaneous measurement of these noise power levels and primary user signal energies with high accuracy is extremely challenging. It can even be infeasible in practical implementations under a low signal-to-noise ratio regime. We therefore propose reference data matrices to scavenge the indispensable information of primary user signal energies and noise power levels for setting the proposed combining weights adaptively by keeping records of the most recent spectrum observations. Analyses and simulation results demonstrate that the proposed linear soft combination schemes outperform the conventional maximal ratio combination and equal gain combination schemes and yield significant performance improvements in spectrum sensing.     

Adaptive Decode-and-Forward Protocol Based Cooperative Spectrum Sensing in Cognitive Radio with Interference at the Secondary Users

In this paper a novel multiple-user cooperative spectrum sensing scheme (MCSS) based on hybrid relay is proposed to achieve the spatial diversity gain in detection of the primary user (PU) in a cognitive radio (CR) network. A practically important case where co-channel interference signals are present at the network is considered for the analysis. Closed-form expressions of detection probability \((\hbox {P}_{\mathrm{d}})\) and false alarm probability \((\alpha )\) for the proposed adaptive decode-and-forward based multiple-user cooperative spectrum sensing scheme (ADF-MCSS) using energy detector over Rayleigh fading sensing channels is derived in presence of co-channel interference at the secondary user which is far away from the PU. Further we extend the concept of two user amplify-and-forward (AF) and decode-and-forward (DF) cooperative spectrum sensing schemes in multiple-user scenario (i.e. AF-MCSS and DF-MCSS) over Rayleigh fading channels when the secondary user (which is far away from PU) is affected by interference. Closed-form expressions of AF-MCSS and DF-MCSS schemes over a Rayleigh fading channels are also evaluated and compared with that of proposed ADF-MCSS in presence of interference signals at the secondary user. Further the performance analysis of AF-MCSS, DF-MCSS and ADF-MCSS schemes are compared with the existing non-cooperative spectrum sensing schemes in presence of interference at the secondary user. Our analysis is validated by numerical and simulation results for multiple-user CR network. The impact of number of cooperative relays, SNR in sensing channel, energy of interference signal, false alarm on detection probability in proposed ADF, AF and DF schemes is shown.     

Reputation-Based Hierarchically Cooperative Spectrum Sensing Scheme in Cognitive Radio Networks

Cooperative spectrum sensing in cog- nitive radio is investigated to improve the det- ection performance of Primary User （PU）. Meanwhile, cluster-based hierarchical coop- eration is introduced for reducing the overhead as well as maintaining a certain level of sens- ing performance. However, in existing hierar- chically cooperative spectrum sensing algo- rithms, the robustness problem of the system is seldom considered. In this paper, we pro- pose a reputation-based hierarchically coop- erative spectrum sensing scheme in Cognitive Radio Networks （CRNs）. Before spectrum sensing, clusters are grouped based on the location correlation coefficients of Secondary Users （SUs）. In the proposed scheme, there are two levels of cooperation, the first one is performed within a cluster and the second one is carried out among clusters. With the reputa- tion mechanism and modified MAJORITY rule in the second level cooperation, the pro- posed scheme can not only relieve the influ- ence of the shadowing, but also eliminate the impact of the PU emulation attack on a rela- tively large scale. Simulation results show that, in the scenarios with deep-shadowing or mul- tiple attacked SUs, our proposed scheme ach- ieves a better tradeoff between the system robustness and the energy saving compared with those conventionally cooperative sensing schemes.     

Review of Robust Cooperative Spectrum Sensing Techniques for Cognitive Radio Networks

Cognitive radio networks sense spectrum occupancy and manage themselves to operate in unused bands without disturbing licensed users. The detection capability of a radio system can be enhanced if the sensing process is performed jointly by a group of nodes so that the effects of wireless fading and shadowing can be minimized. However, taking a collaborative approach poses new security threats to the system as nodes can report false sensing data to reach a wrong decision. This paper makes a review of secure cooperative spectrum sensing in cognitive radio networks. The main objective of these protocols is to provide an accurate resolution about the availability of some spectrum channels, ensuring the contribution from incapable users as well as malicious ones is discarded. Issues, advantages and disadvantages of such protocols are investigated and summarized.     

Cooperative Spectrum Sensing with Adaptive Node Selection for Cognitive Radio Networks

In cooperative spectrum sensing, secondary users can cooperate based on their local sensing observations so as to detect primary signals in a more reliable manner. However, because of the low strength of received primary signals, observations from some secondary users may contribute little to global decisions at the fusion center. To reduce energy consumption and sensing overhead, cooperation is recommended only with a subset of nodes receiving high-strength primary signals. This paper proposes a selection probability based cooperative spectrum sensing scheme that exploits historical observations to exclude nodes receiving low-strength primary signals. The selection probability is maintained based on local sensing information and global decisions. Simulation results demonstrate that the proposed scheme can exploit location advantages and shows almost the same detection performance as cooperative spectrum sensing with accurate node selection.     

A Novel Evolutionary-Based Cooperative Spectrum Sensing Mechanism for Cognitive Radio Networks

Cognitive radio (CR) is considered as a feasible intelligent technology for 4G wireless networks or self-organization networks and envisioned as a promising paradigm of exploiting intelligence for enhancing efficiency of underutilized spectrum bands. In CR, one of the main concerns is to reliably sense the presence of primary users, to attain protection against harmful interference caused by the potential spectrum access of secondary users (SUs). In this paper, evolutionary algorithms, namely, genetic algorithm (GA) and particle swarm optimization (PSO) are investigated. An imperialistic competitive algorithm (ICA) is proposed to minimize error detection at the common soft data fusion (SDF) center for structurally centralized cognitive radio network (CRN). By using these techniques, evolutionary operations are invoked to optimize the weighting coefficients applied on the sensing measurement components received from multiple cooperative SUs. The proposed method is compared with other evolutionary algorithms, as well as other conventional deterministic, such as maximal ratio combining- (MRC-), modified deflection coefficient- (MDC-), normal deflection coefficient- (NDC-) based SDF schemes and OR-rule HDF based. MATLAB simulations confirm the superiority of the ICA-based scheme over the PSO-, GA-based and other conventional schemes in terms of detection performance. In addition, the ICA-based scheme also shows promising convergence and time running performance as compared to other iterative-based schemes. This makes ICA an adequate solution to meet real-time requirements.     

Scalable and Robust ANN Based Cooperative Spectrum Sensing for Cognitive Radio Networks

Cognitive radio network (CRN) supports dynamic spectrum access addressing spectrum scarcity issue experienced by today’s wireless communication network. Sensing is an important task and cooperative spectrum sensing is used for improving detection performance of spectrum. The sensing information from individual secondary users is sent to fusion center to infer a common global decision regarding primary user’s presence. Various fusion schemes for decision making are proposed in the literature but they lack scalability and robustness. We have introduced artificial neural network (ANN) at fusion center thereby achieving significant improvement in detection performance and reduction in false alarm rate as compared to conventional schemes. The proposed ANN scheme is found capable to deal with scalability of CRN with consistent performance. Further, SNR of individual Secondary user is taken into consideration in decision making at fusion center. Moreover the proposed scheme is tested against security attack (malicious users) and inadvertent errors occurring at SUs are found to be robust.     

Cooperative Communications for Cognitive Radio Networks

Cognitive radio is an exciting emerging technology that has the potential of dealing with the stringent requirement and scarcity of the radio spectrum. Such revolutionary and transforming technology represents a paradigm shift in the design of wireless systems, as it will allow the agile and efficient utilization of the radio spectrum by offering distributed terminals or radio cells the ability of radio sensing, self-adaptation, and dynamic spectrum sharing. Cooperative communications and networking is another new communication technology paradigm that allows distributed terminals in a wireless network to collaborate through some distributed transmission or signal processing so as to realize a new form of space diversity to combat the detrimental effects of fading channels. In this paper, we consider the application of these technologies to spectrum sensing and spectrum sharing. One of the most important challenges for cognitive radio systems is to identify the presence of primary (licensed) users over a wide range of spectrum at a particular time and specific geographic location. We consider the use of cooperative spectrum sensing in cognitive radio systems to enhance the reliability of detecting primary users. We shall describe spectrum sensing for cognitive radios and propose robust cooperative spectrum sensing techniques for a practical framework employing cognitive radios. We also investigate cooperative communications for spectrum sharing in a cognitive wireless relay network. To exploit the maximum spectrum opportunities, we present a cognitive space–time–frequency coding technique that can opportunistically adjust its coding structure by adapting itself to the dynamic spectrum environment.      

Cooperative Diversity in Secondary Systems: Efficient Relaying Protocol for Cognitive Radio

Wireless Personal Communications - This paper considers half-duplex cooperative spectrum sharing scheme where both primary and secondary systems mutually cooperate with each other to exploit...     

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.     

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.     

Energy Harvesting for Cooperative Cognitive Radio Networks

Wireless Personal Communications - In this paper, we analyze the performance of cooperative cognitive radio networks where the secondary nodes harvest energy from radio frequency signals. Our...     

Optimized Reliable Data Combining Cooperative Spectrum Sensing Method in Cognitive Radio Networks

Spectrum sensing is one of the most important tasks of each cognitive radio network. Cooperation among secondary users, by increasing the sensing accuracy can improve the network throughput, but also increases the energy consumption of cognitive radio network. In this paper, we propose the reliable data combining method for cooperative spectrum sensing, according to which the fusion center by using two threshold values, makes the final decision only if it is confident enough in validity of received local data. Otherwise, an additional sensing will be performed. Throughput maximization problems under interference constraints are formulated for both soft and hard fusion schemes and the optimal sensing time and threshold values are obtained. Simulations show that for all SNRs, higher absolute throughput and also higher throughput per energy consumption are accessible, rather than conventional cooperative sensing. Moreover, for a large range of SNRs the less energy is consumed.     

Spectrum Sensing Framework for Cognitive Radio Networks

Spectrum sensing feature of cognitive radio devices represents a cornerstone characteristic facilitating real-time and accurate spectrum occupancy measurements in cognitive radio networks. It practically enables the cognitive radio devices to detect vacant spectrum holes and use them for their communication purposes. There are numerous spectrum sensing methods proposed in the literature ranging from local based ones to cooperative strategies among several devices increasing the confidence level of the detected spectrum. This paper gives a general spectrum sensing framework for cognitive radio networks, classifies and explores different spectrum sensing techniques and approaches and shows practical examples, from authors’ own experience, of realized spectrum sensing engines and strategies along with some obtained results.     

认知无线电网络中基于最大似然准则的协同频谱检测算法研究

提出一种集中式频谱协同检测算法。各认知节点采用能量检测算法,然后使用最大似然准则进行本地判决,且把似然比作为本地判决可靠性的度量;中心节点基于可信度对接收到的认知节点本地检测数据进行数据融合。仿真结果显示,文中提出的认知节点协同频谱检测方案能够减少误检概率。特别是当信道处于深衰落时,少量节点参与协同就能获得较好的检测性能。