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A cognitive approach to spectrum sensing in virtual unlicensed wireless network

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A Cognitive approach to spectrum sensing in VirtualUnlicensed wireless networkSunita S. BarveLecturer,Department of ComputerEngineering,Lokmanya Tilak College of Engineering,Navi Mumbai.Tel. No. +919224220849 barve_sunita@S. B. DeosarkarProfessor,Department of Electronics andTelecommunicationEngineering,Dr. Babasaheb AmbedkarTechnological University,Lonere.sbdeosarkar@Sonal A. BhopleLecturer,Department of ComputerEngineering,Lokmanya Tilak College ofEngineering, Navi Mumbai.Tel. No. +919820075468sonal.bhople@ABSTRACTToday’s wireless networks are characterized by a fixed spectrum assignment policy. However, a large portion of the assigned spectrum is used sporadically and geographical variations in the utilization of assigned spectrum ranges from 15% to 85% with a high variance in time. The limited available spectrum and the inefficiency in the spectrum usage necessitate a new communication paradigm to exploit the existing wireless spectrum opportunistically. In contrast to current systems where the spectrum allocation is static, future cognitive radio devices will be able to seek and use in a dynamic way the frequencies for network access; this will be done by autonomous detection of vacant bands in the radio spectrum. This new networking paradigm is referred to as NeXt Generation Networks or Dynamic Spectrum Access (DSA) or cognitive radio networks. In this paper the novel functionalities and current research challenges of the cognitive networks are explained in detail. More specifically, a brief overview of the cognitive radio technology is provided and the cognitive network architecture is introduced. Moreover, the cognitive network functions such as spectrum management, spectrum mobility and spectrum sharing are explained in detail. Open research issues in these areas are also outlined.Categories and Subject DescriptorsC.2[computer-communication networks] C.2.0General General TermsAlgorithms, PerformanceKeywordsCognitive radio networks; Dynamic spectrum access networks; Spectrum sensing; spectrum sharing; spectrum management.1.INTRODUCTIONCognitive radio is a paradigm for wireless communication in which either a network or a wireless node changes its transmission or reception parameters to communicate efficiently avoiding interference with licensed or unlicensed users. The idea of cognitive radio was first presented officially in an article by Joseph Mitola III and Gerald Q. Maguire, Jr in 1999.It was thought of as an ideal goal towards which a software-defined radio platform should evolve: a fully reconfigurable wireless black-box that automatically changes its communication variables in response to network and user demands.Regulatory bodies in various countries (including the Federal Communications Commission in the United States, and Ofcom in the United Kingdom) found that most of the radio frequency spectrum was inefficiently utilized[1]. For example, cellular network bands are overloaded in most parts of the world, but amateur radio and paging frequencies are not. Independent studies performed in some countries confirmed that observation and concluded that spectrum utilization depends strongly on time and place. Moreover, fixed spectrum allocation prevents rarely used frequencies (those assigned to specific services) from being used by unlicensed users, even when their transmissions would not interfere at all with the assigned service. This was the reason for allowing unlicensed users to utilize licensed bands whenever it would not cause any interference to legitimate user.Fig. 1 illustrate the signal strength distribution over a large portion of the wireless spectrum is shown. The spectrum usages concentrated on certain portions of the spectrum while a significant amount of the spectrum remains unutilized. According to Federal Communications Commission (FCC) ,Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.ICAC3’09, January 23–24, 2009, Mumbai, Maharashtra, India. Copyright 2009 ACM 978-1-60558-351-8…$5.00.Fig. 1. Spectrum utilizationtemporal and geographical variations in the utilization of the assigned spectrum range from 15% to 85%. Although the fixed spectrum assignment policy generally served well in the past, there is a dramatic increase in the access to the limited spectrum for mobile services in the recent years. This increase is straining the effectiveness of the traditional spectrum policies.The limited available spectrum and the inefficiency in the spectrum usage necessitate a new communication paradigm to exploit the existing wireless spectrum opportunistically. Dynamic spectrum access is proposed to solve these current spectrum inefficiency problems.Cognitive networks, also known as Dynamic Spectrum Access Networks (DSANs) as well as Next Generation networks will provide high bandwidth to mobile users via heterogeneous wireless architectures and dynamic spectrum access techniques. The inefficient usage of the existing spectrum can be improved through opportunistic access to the licensed bands without interfering with the existing users. Cognitive networks, however,impose several research challenges due to the broad range of available spectrum as well as diverse Quality-of-Service (QoS) requirements of applications. These heterogeneities must be captured and handled dynamically as mobile terminals roam between wireless architectures and along the available spectrum pool.Once a cognitive radio supports the capability to select the best available channel, the next challenge is to make the network protocols adaptive to the available spectrum. Hence, new functionalities are required in cognitive network to support this adaptivity. Cognitive radio techniques provide the capability to use or share the spectrum in an opportunistic manner. Dynamic spectrum access techniques allow the cognitive radio to operate in the best available channel. In summary, the main functions for cognitive radios networks can be summarized as follows: •Spectrum sensing: Detecting unused spectrum and sharing the spectrum without harmful interference with other users.It determine which portions of the spectrum is available and detect the presence of licensed users when a user operates in a licensed band•Spectrum management: Capturing the best available spectrum to meet user communication requirements. •Spectrum mobility: Maintaining seamless communication requirements during the transition to better spectrum. And vacate the channel when a licensed user is detected •Spectrum sharing: Providing the fair spectrum scheduling method among coexisting cognitive users. and coordinate access to this channel with other users.These functionalities of cognitive networks enable spectrum-aware communication protocols. However, the dynamic use of the spectrum causes adverse effects on the performance of conventional communication protocols, which were developed considering a fixed frequency band for communication. So far, networking in cognitive networks is an unexplored topic. In this paper, I will also capture the intrinsic challenges for networking in cognitive networks and lay out areas for further research in this area.More specifically, it is overview of the recent proposals for spectrum sharing and routing in cognitive networks as well as the challenges for transport protocols. Moreover, the effect of cross-layer design is addressed for communication in cognitive networks. 2.COGNITIVE RADIO NETWORKIn [5], a typical cognitive radio operation is presented as a simplification to the ‘‘cognition cycle” can be divided into three, tightly interconnected phases (see Fig. 2). The first is radio-scene analysis, during which different configurations are probed, and the respective environment conditions, e.g. interference related, are sensed. The second is channel estimation and predictive modeling, during which the capabilities of configurations are discovered (discovery process) and accordingly assessed, based on the measurements of the previous phase; moreover, past experience and knowledge can be exploited in this phase. The third is configuration selection, during which the transmitter sends the desired signal by means of the ‘‘best” radio configuration. The approach that is adopted here is that a cognitive radio results from the enhancement of a software radio with cognitive capabilities. Those capabilities are often provided by an intelligent software package called a cognitive engine, depicted in Fig. 3, ‘neither having software nor being programmable are requirements of a cognitive radio’. Within the cognition cycle, the cognitive engine derives and enforces decisions to the software-based radio by continuously adjusting its parameters, observing and measuring the outcomes and taking actions to move the radio toward some desired operational state. Meanwhile, cognitive radios are capable of learning lessons and storing them into a knowledge base, from where they may be retrieved, when needed, to assist future decisions and actions. A reasoning engine determines which actions are executable in each environment state. Considering that this could result in a computationally intensive and time-consuming process, e.g. in case of a diversified radio environment with numerous state-action pairs, the need for a learning engine seems to be imperative. The integration of a learning engine can be important especially for the channel estimation and predictive modeling phase, for improving the stability and reliability of the discovery and evaluation of the configuration capabilities, without relying solely on the recent measurements. To this effect, many different learning techniques are available and can be used by a cognitive radio ranging from pure lookup tables to arbitrary combinations of machine learning techniques that include artificial neural networks, evolutionary/ genetic algorithms, reinforcement learning, hidden Markov models, etc.Fig. 2. Simplified representation of cognitive radio cycle.3. SOFTWARE DEFINED RADIOSoftware Defined radio technology is the key technology that enables an cognitive network to use spectrum in a dynamic manner. The term, software defined radio, can formally be defined as follows :A ‘‘Software defined Radio’’ is a radio that can change its transmitter parameters based on interaction with the environment in which it operates. From this definition, two main characteristics of the Software defined radio can be defined: •Cognitive capability: Cognitive capability refers to the ability of the radio technology to capture or sense the information from its radio environment. This capability cannot simply be realized by monitoring the power insome frequency band of interest but more sophisticatedtechniques are required in order to capture the temporal and spatial variations in the radio environment and avoid interference to other users. Through this capability, the portions of the spectrum that are unused at a specific time or location can be identified. Consequently, the best spectrum and appropriate operating parameters can be selected.•Reconfigurability: The cognitive capability provides spectrum awareness whereas reconfigurability enables the radio to be dynamically programmed according to the radio environment. More specifically, the cognitive radio can be programmed to transmit and receive on a variety of frequencies and to use different transmission access technologies supported by its hardware design.3.1 Cognitive capabilityThe cognitive capability of a cognitive radio enables real time interaction with its environment to determine appropriate communication parameters and adapt to the dynamic radio environment. The tasks required for adaptive operation in open spectrum are spectrum sensing, spectrum analysis, and spectrum decision.The steps of the cognitive cycle are as follows[4]:1. Spectrum sensing: A cognitive radio monitors theavailable spectrum bands, captures their information, and then detects the spectrum holes.2. Spectrum analysis: The characteristics of the spectrumholes that are detected through spectrum sensing are estimated.3. Spectrum decision: A cognitive radio determines the datarate, the transmission mode, and the bandwidth of the transmission. Then, the appropriate spectrum band ischosen according to the spectrum characteristics and user requirements. Once the operating spectrum band is determined, the communication can be performed over this spectrum band. However, since the radio environment changes over time and space, the cognitive radio should keep track of the changes of the radio environment.If the current spectrum band in use becomes unavailable, the spectrum mobility function is performed to provide a seamless transmission. Any environmental change during the transmission such as primary user appearance, user movement, or traffic variation can trigger this adjustment.3.2 ReconfigurabilityReconfigurability is the capability of adjusting operating parameters for the transmission on the fly without any modifications on the hardware components.This capability enables the cognitive radio to adapt easily to the dynamic radio environment. There are several reconfigurable parameters that can be incorporated into the cognitive radio as explained below:•Operating frequency: A cognitive radio is capable of changing the operating frequency. Based on the information about the radio environment, the most suitable operating frequency can be determined and the communication can be dynamically performed on this appropriate operating frequency.• Modulation: A cognitive radio should reconfigure themodulation scheme adaptive to the user requirements and channel conditions. For example, in the case of delay sensitive applications, the data rate is more important than the error rate.Thus, the modulation scheme that enables the higher spectral efficiency should be selected. Conversely, the loss-sensitive applications focus on the error rate, which necessitate modulation schemes with low bit error rate. • Transmission power: Transmission power can bereconfigured within the power constraints. Power control enables dynamic transmission power configuration within the permissible power limit. If higher power operation is not necessary, the cognitive radio reduces the transmitter power to a lower level to allow more users to share the spectrum and to decrease the interference.• Communication technology: A cognitive radio can alsobe used to provide interoperability among different communication systems.The transmission parameters of a cognitive radio can be reconfigured not only at the beginning of a transmission but also during the transmission. According to the spectrum characteristics, these parameters can be reconfigured such that the cognitive radio is switched to a different spectrum band, the transmitter and receiver parameters are reconfigured and the appropriate communicationProtocol parameters and modulation schemes are used. 4. SPECTRUM SENSINGAn important requirement of the cognitive network is to sense the spectrum holes. As explained in Section 2, a cognitive radio is designed to be aware of and sensitive to the changes in its surrounding. The spectrum sensing function enables the cognitive radio to adapt to its environment by detectingFig 3 Cognitive Enginespectrum holes. The most efficient way to detect spectrum holes is to detect the primary users that are receiving data within the communication range of an cognitive user. In reality, however, it is difficult for a cognitive radio to have a direct measurement of a channel between a primary receiver and a transmitter. Thus, the most recent work focuses on primary transmitter detection based on local observations of cognitive users. Generally, the spectrum sensing techniques can be classified as transmitter detection, cooperative detection, and interference-based detection, as shown in Fig. 5 [2]. In the following section, I describe these spectrum sensing methods for cognitive networks and discuss the open research topics in this area.4.1 Transmitter detection (non-cooperativedetection)The cognitive radio should distinguish between used and unused spectrum bands. Thus, the cognitive radio should have capability to determine if a signal from primary transmitter is locally present in a certain spectrum. Transmitter detection approach is based on the detection of the weak signal from a primary transmitter through the local observations of cognitive users. Basic hypothesis model for transmitter detection can be defined as follows :(1)where x(t) is the signal received by the cognitive user, s(t) is the transmitted signal of the primary user, n(t) is the AWGN and h is the amplitude gain of the channel. H0 is a null hypothesis, which states that there is no licensed user signal in a certain spectrum band. On the other hand, H1 is an alternativehypothesis, which indicates thatthere exist some licensed user signal.Three schemes are generally used for the transmitter detection according to the hypothesis model. In the following subsections, we investigate matched filter detection, energy detection and cyclostationary feature detection techniques proposed for transmitter detection in cognitive networks.4.1.1. Matched filter detectionWhen the information of the primary user signal is known to the cognitive user, the optimal detector in stationary Gaussian noise is the matched filter since it maximizes the received signal-to-noise ratio (SNR). While the main advantage of the matched filter is that it requires less time to achieve high processing gain due to coherency, it requires a priori knowledge of the primary user signal such as the modulation type and order, the pulseshape, and the packet format. Hence, if this information is not accurate, then the matched filter performs poorly. However, since most wireless network systems have pilot, preambles, synchronization word or spreading codes, these can be used for the coherent detection.4.1.2. Energy detectionIf the receiver cannot gather sufficient information about the primary user signal, for example, if the power of the random Gaussian noise is only known to the receiver, the optimal detector is an energy detector. In order to measure the energy of the received signal, the output signal of band pass filter with bandwidth W is squared and integrated over the observation interval T. Finally, the output of the integrator, Y, is compared with a threshold λ, to decide whether a licensed user is present or not.If the energy detection can be applied in a non fading environment where h is the amplitude gain of the channel as shown in equation (1), the probability of detection Pd and false alarm Pf are given as follows :where c is the SNR, u = TW is the time bandwidth product, Г(.) and Г(.,.) are complete and incomplete gamma functions and Qm( ) is the generalized Marcum Q-function. From the above functions, while a low Pd would result in missing the presence of the primary user with high probability which in turn increases the interference to the primary user, a high Pf would result in low spectrum utilization since false alarms increase the number of missed opportunities. Since it is easy to implement, the recent work on detection of the primary user has generally adopted the energy detector.In the shadowing and the multi-path fading factors are considered for the energy detector. In this case, while Pf is independent of C, when the amplitude gain of the channel, h, varies due to the shadowing/fading, Pd gives the probability of the detection conditioned on instantaneous SNR as follows:(4)where fc(x) is the probability distribution function of SNR under fading. However, the performance of energy detector is susceptible to uncertainty in noise power. Another shortcoming is that the energy detector cannot differentiate signal types but can only determine the presence of the signal. Thus, the energy detector is prone to the false detection triggered by the unintended signals.4.1.3. Cyclostationary feature detectionAn alternative detection method is the cyclostationary feature detection. Modulated signals are in general coupled with sine wave carriers, pulse trains, repeating spreading, hopping sequences, or cyclic prefixes, which result in built-in periodicity. These modulated signals are characterized as cyclostationarity since their mean and autocorrelation exhibit periodicity. These features are detected by analyzing a spectral correlation function. The main advantage of the spectral correlation function is that it differentiates the noise energy from modulated signal energy, which is a result of the fact that the noise is a wide-sense stationary signal with no correlation, whileFig. 4 Classification of Spectrum Sensing Techniquemodulated signals are cyclostationary with spectral correlation due to the embedded redundancy of signal periodicity. Therefore, a cyclostationary feature detector can perform better than the energy detector in discriminating against noise due to its robustness to the uncertainty in noise power. However, it is computationally complex and requires significantly long observation time.More efficient and reliable performance, the enhanced feature detection scheme combining cyclic spectral analysis can be achieved with the help of pattern recognition based on neural networks. Distinct features of the received signal are extracted using cyclic spectral analysis and represented by both spectral coherent function and spectral correlation density function. The neural network, then, classifies signals into different modulation types.4.2Cooperative detectionIn the case of non-cooperative detection explained in Section 4.1, the cognitive users detect the primary transmitter signal independently through their local observations. Cooperative detection refers to spectrum sensing methods where information from multiple cognitive users are incorporated for primary user detection. Cooperative detection can be implemented either in a centralized or in a distributed manner. In the centralized method, the cognitive base-station plays a role to gather all sensing information from the cognitive users and detect the spectrum holes. On the other hand, distributed solutions require exchange of observations among cognitive users. Cooperative detection among unlicensed users is theoretically more accurate since the uncertainty in a single user’s detection can be minimized. Moreover, the multi-path fading and shadowing effect are the main factors that degrade the performance of primary user detection methods. However, cooperative detection schemes allow to mitigate the multi-path fading and shadowing effects, which improves the detection probability in a heavily shadowed environment.4.3Interference-based detection Interference is typically regulated in a transmitter- centric way, which means interference can be controlled at the transmitter through the radiated power, the out-of-band emissions and location of individual transmitters[3]. However, interference actually takes place at the receivers. Therefore recently, a new model for measuring interference, referred to as interference temperature has been introduced by the FCC.The model shows the signal of a radio station designed to operate in a range at which the received power approaches the level of the noise floor. As additional interfering signals appear, the noise floor increases at various points within the service area, as indicated by the peaks above the original noise floor. Unlike the traditional transmitter-centric approach, the interference temperature model manages interference at the receiver through the interference temperature limit, which is represented by the amount of new interference that the receiver could tolerate. In other words, the interference temperature model accounts for the cumulative RF energy from multiple transmissions and sets a maximum cap on their aggregate level. As long as cognitive users do not exceed this limit by their transmissions, they can use this spectrum band. 5.SPECTRUM SENSING CHALLENGES There exist several open research challenges that need to be investigated for the development of the spectrum sensing function.•Interference temperature measurement:The difficulty of this receiver detection model lies in effectively measuring the interference temperature. An cognitive user is naturally aware of its transmit power level and its precise location with the help of a positioning system. With this ability, however, its transmission could cause significant interference at a neighboring receiver on the same frequency. However, currently, there exists no practical way for a cognitive radio to measure or estimate the interference temperature at nearby primary receivers. Since primary receivers are usually passive devices, an cognitive user cannot be aware of the precise locations of primary receivers. Furthermore, if cognitive users cannot measure the effect of their transmission on all possible receivers, a useful interference temperature measurement may not be feasible.•Spectrum sensing in multi-user networks:Usually, cognitive networks reside in a multi-user environment which consists of multiple cognitive users and primary users. Furthermore, the cognitive networks can also be co-located with other cognitive networks competing for the same spectrum band. However, current interference models do not consider the effect of multiple cognitive users.Multi-user environment makes it more difficult to sense the primary users and to estimate the actual interference.Hence, spectrum sensing functions should be developed considering the possibility of multiuser/ network environment. In order to solve the multi-user problem, the cooperative detection schemes can be considered, which exploit the spatial diversity inherent in a multi-user network.•Detection capability:One of the main requirements of cognitive networks is the detection of the primary users in a very short time. OFDM-based cognitive networks are known to be excellent fit for the physical architecture of cognitive networks. Since multi-carrier sensing can be exploited in OFDM-based Cognitive networks, the overall sensing time can be reduced. Once a primary user is detected in a single carrier, sensing in other carriers is not necessary. A power-based sensing algorithm in OFDM networks is there for detecting the presence of a primary user. It is shown that the overall detection time is reduced by collecting information from each carrier. However, this necessitates the use of a large number of carriers, which increases the design complexity. Hence, novel spectrum sensing algorithms need to be developed such that the number of samples needed to detect the primary user is minimized within a given detection error probability.6.CONCLUSIONCognitive networks are being developed to solve current wireless network problems resulting from the limited available spectrum and the inefficiency in the spectrum usage by exploiting the existing wireless spectrum opportunistically. Cognitive networks, equipped with the intrinsic capabilities of the cognitive radio, will provide an ultimate spectrum-aware communication paradigm in wireless communications. In this。

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sensitivity enhancement in surface plasmon resonance sensors

sensitivity enhancement in surface plasmon resonance sensors

Sensors and Actuators A 159 (2010) 24–32Contents lists available at ScienceDirectSensors and Actuators A:Physicalj o u r n a l h o m e p a g e :w w w.e l s e v i e r.c o m /l o c a t e /s naElectromagnetic fields distribution in multilayer thin film structures and the origin of sensitivity enhancement in surface plasmon resonance sensorsAtef Shalabney,Ibrahim Abdulhalim ∗Department of Electro-Optic Engineering,Ben Gurion University,Beer Sheva 84105,Israela r t i c l e i n f o Article history:Received 6October 2009Received in revised form 8January 2010Accepted 7February 2010Available online 13 February 2010Keywords:Surface plasmon resonance Optical sensors Surface wavesa b s t r a c tThe performance of surface plasmon resonance (SPR)sensors depends on the design parameters.An algorithm for calculating the electromagnetic fields distribution in multilayer structure is developed relying on Abeles matrices method for wave propagation in isotropic stratified media.The correlation between field enhancement and sensitivity enhancement is examined and found to agree with the overlap integral in the analyte region.This correlation was verified in the conventional SPR sensor based on Kretschmann configuration,and in the improved SPR sensor with high refractive index dielectric top layer for several cases,e.g.field enhancement due to resonance,the sensitivity dependence on the wavelength,the influence of prism refractive index on sensitivity,and the effect of the layers materials and thicknesses.© 2010 Elsevier B.V. All rights reserved.1.IntroductionSurface plasmon resonance (SPR)sensors are widely recognized as valuable tools for investigating surface interactions and sensing of gases and biomaterials [1].A surface plasmon wave is a longi-tudinal compressional charge density wave that can propagate at the interface between a metal and a dielectric media under certain conditions.One of the most common techniques for plasmon exci-tation is the Kretschmann configuration shown in Fig.1(a)in which the resonance realized by a dip in the reflectivity versus incidence angle or alternatively in the reflectivity versus wavelength.Since the dip location depends on the substrate (analyte)features,one can measure tiny fluctuations in the refractive index of the analyte by tracking either the shift in the resonance angle or the shift in the resonance wavelength [2–4].Due to the pioneering works of Kretschmann [5]and Otto [6],practical devices were proposed for chemical and biological sensors applications.Several parameters and features define the perfor-mance quality of SPR sensor:(1)reflectance profile shape (dip depth and width),(2)chemical stability of the metal layer,(3)sensor reso-lution,and (4)sensor sensitivity.The sensor’s sensitivity is defined as the ratio between the resonance angle or wavelength shift per analyte refractive index unit.Gent et al.[7]defined the sensitivity∗Corresponding author at:Ben Gurion University,Department of Electro-optic Engineering,P.O.Box 653,84105Beer Sheva,Israel.Tel.:+97286479803.E-mail addresses:shalaban@bgu.ac.il (A.Shalabney),abdulhlm@bgu.ac.il (I.Abdulhalim).as the ratio between the shift and width of the dip.This definition is somehow misleading and questionable because several algorithms can be applied to determine the dip position with high accuracy even with moderate resolution.The later definition was considered by Golosovsky et al.[8]recently.They demonstrated that the sensi-tivity of SPR technique in the infrared range using Fourier transform infrared (FTIR)spectrometry is not lower compared to the sensitiv-ity of the SPR technique in the visible range.In the present work we define the sensitivity as the dip shift per sample refractive index unit and will ignore dip width aspect in the sensitivity definition.In the last two decades few methods were proposed to improve the SPR sensor ing long range SPR (LRSPR)sensor [9,10]where the excitation of two SP waves on the interfaces of the metal layer placed between two dielectric layers increases the propagation distance on the surfaces and correspondingly increases the sensitivity.Although many works reported sensitiv-ity enhancement by measuring phase instead of intensity [11–14],recent study [15]showed that these reports are questionable and the accuracy in phase measurements is limited by the accuracy of intensity measurements.Another method to enhance SPR sensor sensitivity was using periodic metallic structures combined with TIR Kretschmann configuration [16,17].Also the use of bimetallic layers [18–20],and modification of prism refractive index [21,22]were proposed in order to enhance SPR sensor hav et al.presented for the first time the nearly guided wave SPR (NGWSPR)configuration that is similar to the conventional con-figuration with the addition of 10–15nm dielectric layer with a high refractive index between the metal layer and the cover mate-rial (the analyte to be sensed)[23,24]as shown in Fig.1(b).The0924-4247/$–see front matter © 2010 Elsevier B.V. All rights reserved.doi:10.1016/j.sna.2010.02.005A.Shalabney,I.Abdulhalim/Sensors and Actuators A159 (2010) 24–3225Fig.1.(a)Schematic of single layer Kretschmann configuration and(b)schematic of double layer Kretschmann configuration. authors choose silicon(Si)to accomplish the task because Si hashigh real part of refractive hav et al.configuration has twoprominent advantages:first the sensitivity was enhanced by fewtimes compared to conventional SPR sensor,and second the sta-bility of the metal layer was improved because the silicon servedas protection layer for the silver which suffers from a poor chem-ical stability.When the addition of a thin dielectric layer witha high refractive index on top of the metal layer is considered,one should emphasize that the added layer is very thin(typically10nm).One should distinguish this case from the case of coupledplasmon-waveguide resonance(CPWR).CPWR biosensors incorpo-rate a thick waveguide layer(typically500nm)beneath the surfaceof the conventional SPR biosensor[25,26].Unlike the conventionalSPR biosensors,whose reflectivity demonstrates a dip only in theTM mode,the interference in the waveguide layer causes a dip bothin the TE and TM modes in the CPWR device[27].Although CPWRsensors exhibit sharp dips and improve significantly the SNR of themeasurement,their sensitivity is less than that of conventional SPRdevices by an order of magnitude since the biosensing surface islocated at a considerable distance from the SPs which exist on theinterface between the metal and the waveguide layer[28,29].Thecontribution of a thin dielectric top layer to the sensitivity enhance-ment of SPR sensor was reported for thefirst time by Lahav et al.Because the nm-thick layer does not support guided modes,it wascalled nearly guided wave SPR(NGWSPR)sensor.In the present work we investigate the sensitivity enhancementmechanism of SPR sensors by electromagneticfield and energydistribution considerations.Since the SPR is accompanied by anenhanced evanescentfield in the metal–analyte interface region,the sensor sensitivity for a perturbation in the analyte is determinedby thefield distribution in this region.According to Abdulhalim[30],the shift in the wave vector is proportional to the overlap inte-gral which in turn is proportional to the interaction volume V in(seeAppendix A):ık≈k i2V inıε·៝E∗i· E f·drVε·៝E∗i· E i·dr(1)where E i,k i are the electricalfield and its wave vector before the variation in the analyte refractive index took place,while E f is the field after the index perturbation andık is the associated shift in the wave vector due to a change fromεtoε+ıεin the analyte dielectric constant.Sinceık expresses the change in the incidence angle or alternatively the change in the wavelength,thenık/ıεrep-resents the sensitivity of the sensor,which is proportional to the overlap integral in the numerator of Eq.(1)normalized to the total energy.Hence to maximize the sensitivity one needs to maximize this integral which can be accomplished by increasing the inter-action volume,that is the evanescence depth,the SP propagation length along the surface or by increasing thefield intensity in the analyte region.One of our purposes in this paper is to demonstrate that sensitivity enhancement mechanisms are accompanied with field enhancement and correlated with the overlap integral in the analyte region.Full description of the electromagneticfield in multilayer struc-tures within the SPR modes context was done by Davis[31]where the purpose was obtaining the eigenfunctions of the differential equation for the magneticfield distribution and simultaneously the eigenvalues were derived from the dispersion relation.Chin et al.[32]presented calculations for electromagneticfield distribution by matrices approach to extract the reflectivity for the multilayer structure.In both previous works[31,32],they used propagation matrices to calculate the two components of the forward and back-ward propagating magneticfield amplitudes in arbitrary layer from the boundary values of the aforementioned amplitudes.Ohta et al.[33]proposed a method to calculate the forward and backward propagating electricfield without using inverse matrices;however, he described the fullfields by exponential expressions which may be problematic due to some numerical instability.Ekgasit et al.[34] and Hansen[35]used the characteristic matrices approach and the total transmission coefficients for explaining their SPR spec-troscopy experiments;however,they only emphasized absorbance aspects in multilayer system.The uniqueness of our work is in that it involves a detailed simplified algorithm for electromagneticfield distribution and full comprehensive investigation for the evanes-centfield role in sensitivity enhancement which was never done before to the best of our knowledge.2.The algorithm forfield distributionWe consider the general case of plane wave radiation interact-ing with a stack of N−1layers as shown in Fig.2so that we have N interfaces.The layers are homogenous and isotropic with plane boundaries,the optical properties of each layer are characterized completely by two constants which are functions of wavelength, e.g.the dielectric constantεj=n2j where n j the refractive index for layer-j and the magnetic permeability is j.The constantsεj, n j, j are complex in the general case.Under the SPR conditions, the electricfield must have a component which is perpendicular to the surface;hence the incident light should be TM polarized.We take the plane of incidence to be the XZ-plane and the Z-axis is the direction of stratification.For TM wave,H x=H z=0and E y=0.The non-vanishing components of thefield vectors into each layer-j are26 A.Shalabney,I.Abdulhalim /Sensors and Actuators A 159 (2010) 24–32of the form:H yj (x,z,t )=H yj (z )exp i (k 0˛j x −ωt ),E xj (x,z,t )=E xj (z )exp i (k 0˛j x −ωt ),E zj (x,z,t )=E zj (z )exp i (k 0˛j x −ωt ),(2)here H yj (z ),E xj (z ),E zj (z )are the amplitudes of the appropriate fields in layer-j that are in general complex functions of z ;˛j =n j sin Âj when Âj the propagation angle and k 0=2 / 0the wave number in vacuum.According to Snell’s law one can consider the following:˛j =n j sin Âj =const =˛=n 0sin Â0(3)When the general structure shown in Fig.2constitutes an SPR sensor,the substrate and the ambient will represent the ana-lyte and the prism,respectively,to be consistent with Fig.1.As was mentioned in the introduction,methods for calculating field’s distribution in N layered structure were presented by several inves-tigators [33–35],however,as algorithms we found them not clear enough and difficult to use particularly for researchers from the bio-sciences and bio-technology.In the present section we introduce a detailed and simplified algorithm for field distribution calculation.Our description completely detailed to allow convenience numer-ical implementation in three steps.Step 1:Calculating total characteristic matrix,reflection and trans-mission coefficients for the whole structure .The magnetic and electric fields amplitudes in the entrance of each layer are connected to the corresponding amplitudes at a distance z inside the layer by the well known Abeles [36]matrices:H 0yj −E 0xj=M j ·H yj (z )−E xj (z )=⎡⎣cos ˇj −iq j sin ˇj −iq j sin ˇjcos ˇj⎤⎦·H yj (z )−E xj (z )(4)H 0yj ,E 0xjare the amplitudes of H yj ,E xj ,respectively,at the appropriate boundary z =Z j of layer-j ,M j is called the characteristic matrix for the layer-j and it is determined by the optical properties and the layer thickness (d j )where:ˇj =k 0n j d j cos Âj ;q j = ( j /εj )cos Âj.Fig.2.Interaction of plane wave with a multilayer system,the stack composed of(N −1)homogenous and isotropic media with N interfaces that bounded by two semi-infinite homogenous,isotropic,and dielectric media.For a multilayered structure the field amplitudes at the first boundary are connected to those at the last boundary by the total characteristic matrix:H 0y 1−E 0x 1=M TOT ·H 0yN −E 0xN,M TOT =j =N −1j =1M j(5)The complex reflection and transmission coefficients r and t can beexpressed in terms of the elements of the total characteristic matrix of the whole system M ij :r ≡H refyH inc y =(M 11+M 12·q N )·q 0−(M 21+M 22·q N )(M 11+M 12·q N )·q 0+(M 21+M 22·q N )t ≡H 0yN H inc y=2·q 0(M 11+M 12·q N )·q 0+(M 21+M 22·q N )(6)H inc y ,H refy are the incident and reflected magnetic field amplitudes,H 0yN is the transmitted magnetic field amplitude and simultane-ously the amplitude at the last interface (z =Z N ).Step 2:Calculating the field within the 1st layer:H y 1(z )−E x 1(z )=P 1(z )·(1+r )q 0(1−r )H inc y ,Z 1≤z ≤Z 2(7)where P 1(z )is given by:P 1(z )=⎡⎣cos(k 0n 1z cos Â1)iq 1sin(k 0n 1z cos Â1)iq 1sin(k 0n 1z cos Â1)cos(k 0n 1z cos Â1)⎤⎦(8)And the incident magnetic field amplitude maybe taken asunity:H inc y=1for normalized magnetic field,while if one needs to normalize the electric field to the incident electric field ampli-tude,the following substitution H inc y =E inc x/q 0should be used in equations (7)and (9).Step 3:Calculating the field distribution within layer-j ≥2from the following expression:H yj (z )−E xj (z )=P j (z )·⎛⎝1l =j −1P l (z =Z l +d l )⎞⎠·(1+r )q 0(1−r )H inc y ,Z j ≤z ≤Z j +1(9)where P j (z )is the propagation matrix for the layer-j (inverse of thecharacteristic matrix M j (z ))and it is given by:P j (z )=cos(k 0n j (z −Z j )cos Âj )iq jsin(k 0n j (z −Z j )cos Âj )iq j sin(k 0n j (z −Z j )cos Âj )cos(k 0n j (z −Z j )cos Âj )(10)One should be careful when distinguishing between P j (z )and P l (z =Z l +d l )that appear in Eq.(9).While the first is z -dependent as was defined through Eq.(10),the second is the propagation matrix for the layer-l with thickness d l ,which is constant for layer-l .Normalizing to the incidence magnetic field amplitude could beachieved by setting H inc yto unity.Since in many cases the SPR sensor structure is composed of a single metal layer which is embedded between two semi-infinite dielectric media,namely,the prism and the analyte,the distribution expression in this case was separated and represented by step 2.For TE polarization all the expressions for the characteristic and propagation matrices and reflection or transmission coefficients are valid simply by replacing the expression for q j = ( j /εj )cos Âjwith p j =(εj / j )cos Âj and the field column for TE isE yjH xj.A.Shalabney,I.Abdulhalim /Sensors and Actuators A 159 (2010) 24–32273.ApplicationsOur 2nd purpose in this work is to investigate the origin of sensitivity enhancement in SPR sensors based on Kretschmann con-figuration.From Eq.(1)the sensitivity proportional to the ratio between the energy flow in the analyte region and the total energy.In this sense we intend to examine the correlation between sensi-tivity enhancement and the field’s distribution for several cases.In part of the cases,we deal with the conventional configuration as shown in Fig.1(a),and afterwards with the improved configuration as shown in Fig.1(b).In the simulations the resonance angle was calculated with accuracy of Â=0.001◦and the variation in the analyte refractive index was n a =0.01RIU.Hence the sensitivity accuracy obtained to be S =0.01◦/RIU.For field calculations,the amplitudes were computed in steps of z =0.5nm.3.1.Evanescent field treatment for the standard Kretschmann configurationFirst we consider the basic configuration shown in Fig.1(a).The surface plasmon wave at the metal/dielectric interface is excited if the wave vector in X -direction of the incident wave matches that of the surface plasmon.When the surface plasmon is excited,a sub-stantial decrease in reflectance is observed at the resonance angle,Âr as shown in Fig.3(a).The resonance angle,Âr depends on theanalyte refractive index,n a =√εa so that a change in the refrac-tive index causes an appropriate shift in the resonance angle,see Fig.3(b).The ratio between the angle shift and the refractive index change is defined as the angular sensitivity S Â(S Â=d Âr /dn a ).At the resonance,the reflectivity R reaches its minimum value,the intensity of the electromagnetic field reaches its maximum at the surface,see Fig.4.Near the resonance angle,an extremely strong evanescent field at the metal/dielectric interface is gener-ated by the surface plasmon wave.The unique characteristic of generating evanescent filed,where the field amplitude is greatest at the interface and exponentially decaying as a function of dis-tance from the metal/dielectric interface,makes the SPR signal very sensitive to changes at the vicinity of the metal surface.The X -component of the electric field is continuous;however,the Z -component is discontinuous.Due to the small dielectric con-stant of the analyte (εa )compared to that of the metal (|εm |),|E z |has a strong enhancement at the metal/analyte interface.As shown in Fig.4(a)and (b)the enhancement of the electric field is largest at the resonance compared to the cases near the resonance,where the resonance angle in this case is Âres =54.619◦.Since there is a need sometimes of detecting at various wave-lengths,sensitivity versus wavelength investigation is essential.As shown in Fig.5(a),the sensitivity of the configuration describedin Fig.1(a)decreases when the wavelength increases.This result was presented by Homola [37]for the Kretschmann configuration without physical interpretation.Now in terms of the evanescent field,we can attribute the high sensitivity for lower wavelength to the larger interaction of the electric field in the analyte region.Although for larger wavelengths there is a larger penetration depth into the analyte region,the amplitude becomes smaller,which indi-cates that sensitivity is not governed only by the penetration depth,see Fig.5(b),rather by both the interaction region and the energy distribution as expressed by the overlap integral of Eq.(1).The sensitivity versus wavelength was calculated after per-forming optimization of the metal layer thickness.The silver layer thickness was chosen such that the resonance condition is preserved for each examined wavelength and the reflectivity at the dip is less than 0.01.As seen from the caption of Fig.5,the silver thickness is decreasing when increasing wavelength.The losses in the metal increases with wavelength,and in order to overcome these losses and preserve resonance condition,the thickness should be reduced.In this case the sensitivity is approx-imately constant for large wavelengths,because in the IR range the sensitivity is mainly governed by the difference between the prism refractive index and the analyte refractive index S Â=(dÂ/dn a )−→ →∞(1/ n 2p −n 2a )(rad/RIU).3.2.Evanescent field treatment for SPR sensor with nm-thick topdielectric layerAs pointed our before,the addition of a thin dielectric layer with a high real part of the refractive index causes substantial enhancement in the sensor sensitivity.Silicon was chosen for its high real part of the refractive index because the sensitivity enhancement was found [23,24]to increase with the real part of the dielectric constant of the top layer.As an optimization process for the silicon layer thickness,d Si =10.5nm is the opti-mum thickness for maximum sensitivity as shown in Fig.6(a).The evanescent field distribution demonstrates that for this silicon layer thickness,maximum enhancement ratio for the amplitude E x is obtained.The addition of the silicon layer increases the sensitiv-ity by threefold,e.g.67.5◦/RIU sensitivity without the silicon layer,compared with 200◦/RIU sensitivity with d Si =10.5nm silicon top layer.The optimum silicon thickness of d Si =10.5nm was chosen for 43nm silver layer thickness because it gave the highest sensitiv-ity enhancement.As done before with the standard Kretschmann configuration,one can perform an optimization for the improved sensor with the silicon dielectric layer.The sensitivity dependence on the wavelength has been examined for the improved sensor,and the same proportion was obtained.As shown in Fig.7(a)and (b),a similar behavior for the sensitivity versus wavelength isobtained,Fig.3.(a)Reflectivity as a function of incidence angle for the configuration shown in Fig.1(a).(b)Reflectivity versus analyte refractive index for the configuration shown in Fig.1(b). =633nm,d m =43nm,n p =1.732,Âr =54.61◦,n a =1.325(blue curve),n a =1.335(red curve),silicon refractive index at 633nm is n s =3.8354+0.0245i and the silver refractive index is n s =0.1325+4.0203i .(For interpretation of the references to color in this figure legend,the reader is referred to the web version of the article.)28 A.Shalabney,I.Abdulhalim /Sensors and Actuators A159 (2010) 24–32Fig.4.(a)The density distribution of the X -component of the electric field.(b)The density distribution of the Z -component of the electric field through the layers at different incidence angles at and near the resonance.Both graphs correspond to the following parameters: =633nm,d m =43nm,n a =1.33,n p =1.732,Âr =54.61◦.Fig.5.(a)Sensitivity versus wavelength corresponding to the configuration in Fig.1(a).(b)The density distribution of the electric field X -component along the metal and the analyte regions for different wavelengths at the resonance.d m =43nm,n a =1.33,n p =1.732.The thicknesses for obtaining resonance in each wavelength are:47,43.25,and 29.75nm for 632,850,and 1550nm,respectively.except that in the case with the silicon layer the sensitivity starts from a higher value compared to the case without the silicon layer.The origin of the sensitivity enhancement with decreasing the operation wavelength is basically related to the metal behavior.Both the real part and the imaginary part of the metal refractive index are responsible for the transparency features of the metal layer and they become larger when increasing the wavelength.Increasing the real part makes the metal more reflective from the initial prism/metal interface,whereas increasing the imagi-nary part makes it more absorptive and as a result of the double effect the evanescent field experiences more attenuation when it propagates through the whole system.When the refractive index of the metal changes from 0.12+3.75i at 600nm wavelength up to 0.53+10.43i at 1500nm,the amplitude of the fields at the analyteinterface are significantly attenuated and consequently the sensi-tivity of the structure becomes moderate.Although the penetration depth was found to be 94and 697nm at 600and 1500nm wave-lengths,respectively,in the conventional Kretschmann SPR sensor configuration,the sensitivity is still larger for smaller wavelength.The values were calculated from the following expression of the penetration depth:ıd =4εa +εmr −ε2a(13)Here ıd is the penetration depth inside the analyte layer, ,εa ,εmrare the wavelength,analyte dielectric constant,and the metal real part dielectric constant,respectively.This last fact indicatesthatFig.6.(a)Sensitivity versus silicon layer thickness corresponding to the configuration in Fig.1(b).(b)Density distribution of the electric field X -component for different Si layer thicknesses.A.Shalabney,I.Abdulhalim/Sensors and Actuators A159 (2010) 24–3229Fig.7.(a)Sensitivity versus wavelength in the Kretschmann configuration with the silicon layer shown in Fig.1(b).(b)The density distribution of the electricfield X-component through the layers at different resonance wavelengths.The silicon refractive indices are:3.64+0.0085i;3.59+0.0057i;3.4+0.0002i;3.22+0.002i at740,780, 1000and1600nm,respectively,and(n a=1.33,n p=1.732).The silver and silicon thicknesses in this case were chosen under the resonance condition R min<0.01for each wavelength.Table1shows the corresponding thicknesses and sensitivities for(b).Table1Silver and silicon layers thicknesses that were chosen for each wavelength that appear in Fig.7(b)in order to preserve R min<0.01as a condition for resonance.The sensitivity and the E x intensity at the analyte interface were calculated for each wavelength.(nm)wavelength d m(nm)metal thickness d s(nm)silicon thickness Sensitivity(◦/RIU)Field intensity at the analyte interface74045.51720410378044.5202101411000402510536160029548225sensitivity as it was defined in the present study is not governed only by the penetration depth.Absorption considerations will be further discussed in Eqs.(14)and(15)in Section3.3.Sensitivity versus wavelength with the silicon top layer was calculated when the structure was optimized to fulfill the resonance condition at each wavelength.Table1shows the combinations(d m,d s)of the silver and the silicon thicknesses that maintain the structure at resonance,with reflectivity dip level of R min<0.01.3.3.Sensitivity enhancement due to prism refractive indexmodificationThe prism refractive index has an important role in the sensi-tivity determination of the SPR sensor based on TIR configuration. Two works[21,22]were recently published on this issue,and both showed that the sensitivity increases with decreasing the prism refractive index.Thefirst work was on the sensitivity enhancement with the angular interrogation mode[21],in which the authors demonstrated the prism influence without giving any physical explanation for the phenomenon.In the second work which was done by Yulk et al.[22],the spectral interrogation mode was con-sidered and the sensitivity enhancement was attributed to the large penetration depth obtained in the case of small prism refractive index.Under the last hypothesis,the sensitivity for a large wave-length should be larger than the sensitivity for smaller wavelength for all the cases,while we showed the opposite in the former discussion when we examined the wavelength influence on the sensitivity.Hence the explanation that was given by Yulk et al.is inadequate in our opinion.Our approach is based on the correla-tion between sensitivity and overlap integral in the analyte region. Larger enhancement for the electricfield at the metal/dielectric interface for smaller prism refractive index was observed as shown in Fig.8.Fig.8(a)and(b)clearly demonstrates the correlation between the sensitivity enhancement and thefield enhancement.The case demonstrated by Fig.8(a)and(b)relates tofixed opera-tion wavelength( =632nm)while the variation is in the prism refractive index which in turn varies the incidence angle.The sensitivity versus prism refractive index was calculated when the metal layer thickness is optimized under the resonance con-dition(R min<0.01),where R min is the dip level at resonance. Considering the Kretschmann configuration,the resonance condi-tion obtained by equating the SP wave vector and the emerging light wave vector which can explicitly be written as k0n p sinÂ= k0(εmr n2a/(εmr+n2a))where n a and n p are the analyte and the prism refractive indices,respectively,andεmr is the real part of the metal refractive index.By analyzing the last condition one can con-clude that the coupling condition is fulfilled if|εmr|is higher than the quantityÁ=(n2a·n2p/(n2p−n2a))which corresponds to having the resonance anglesÂ≤90◦.For afixed wavelength,the last con-dition creates a singularity in the sensitivity for a prism refractive index which allows the quantityÁto approach|εmr|at the given wavelength.The last interpretation properly explains the dramatic increase in the sensitivity for n p≈1.41in Fig.8(a)for a wavelength of633nm.In a similar manner the influence of the prism refrac-tive index was investigated for the NGWSPR,and similar behavior of the sensitivity versus prism refractive index was found as the conventional SPR sensor.Results are shown in Fig.9(a)and(b).In the case of NGWSPR,there is a shift in the cut-off wavelength which can be clearly seen in the resonance angle positive shift due to the addition of the silicon layer.Basically,the incident light feels a higher refractive index beyond the metal layer and therefore,an appropriate modification in the dispersion relation of the SP.Since the wavelength is keptfixed,the resonance condition is satisfied at larger angle.The sensitivity in the NGWSPR case was calculated when the silver–silicon structure is optimized to maintain the res-onance condition(R min<0.01);the values correspond to thefield intensity distribution in Fig.9(b)are given in Table2.Enhancing thefields at the metal interface increases the absorp-tion as well as increasing the sensitivity of the structure.For conventional SPR sensor without dielectric layer,the only absorb-ing medium in the system is the metalfilm.When adding the silicon layer,the absorption will be both in the metal and the silicon layer. The absorption in the whole system can be expressed by the fol-。

一种窄带感知实现高效宽带频谱预测的机制

一种窄带感知实现高效宽带频谱预测的机制

•工程麁用•航天电子对抗2017年第5期一种窄带感知实现高效宽带频谱预测的机制罗明鉴S吴媛媛S韩寒2,董杰1(1.中国人民解放军31003部队，北京100191; 2.中国人民解放军理工大学通信工程学院，江苏南京210094)摘要：针对只有单一用户的频谱探测场景，提出了以窄带感知实现高效宽带频谱预测的 机制，以能够全面有效掌握本地频谱态势，实现自由通信。

基本思想是依据历史统计信息动态调整感知信道，在状态变化较快的信道上增加感知次数，在状态变化缓慢的信道上减少感知次数。

经实验仿真，该机制相比随机感知和顺序感知有较大的性能提升。

关键词：频谱态势；频谱感知；宽带感知；窄带检测中图分类号：TN97 文献标识码：AA wideband spectrum prediction scheme using narrowband spectrum sensingLuo Mingjian1,Wu Yuanyuan1,Han Han2,Dong Jie1(1.Unit 31003 of PLA,Beijing 100191, China;2.Institute of communication engineering of PLAUST,Nanjing 210094, Jiangsu,China)Abstract：Aiming for the spectrum detection circumstance within only one user, a new scheme which realized the wideband spectrum prediction effectively using the narrowband spectrum sensing is proposed, soas to obtain the whole spectrum situation comprehensively and unrestrained communication. The basic idea isto dynamically adjust the sensing channel according to the historical information, i. e. increase the sensingtimes in the channel that changes rapidly and decrease the sensing times in the channel that changes slowly. Bythe experimental simulation, the performance of the proposed schemeis shown to be better than the randomand the sequence ones.Key words：spectrum situation；spectrum sensing；wideband sensing；narrowband detection〇引言电磁频谱已经成为信息时代不可或缺的国家战略 资源，面对越来越复杂的电磁频谱环境所带来的重大 挑战，加强电磁频谱空间管理就有重要的现实意义[1]。

基于循环平稳检测的协作频谱感知

基于循环平稳检测的协作频谱感知

0 引言近年来，随着电子技术和通信技术的飞速发展，有限的频谱资源同通信业务需求的不断增大形成了鲜明的矛盾。

而另一方面，大量宝贵的频谱资源处于闲置状态，根据美国联邦通信委员会的研究表明，通信频谱资源的利用率仅为15%-85%[1]，造成了严重的资源浪费。

因此，在这一背景需求下，通过对频谱资源的感知和再分配来提高频谱资源使用率的认知无线电技术应运而生。

简单来说，认知无线电技术是一种智能无线电通信技术，它可以通过连续不断地对外部电磁环境的感知[2]，并对这些感知信息进行分析、处理、判断、融合，进而形成对频段使用情况的判断。

通过有效搜索和利用空闲频段资源而不影响其他用户的使用，从而实现对不可再生资源的再次利用[2-5]。

电磁频谱感知技术是认知无线电技术的根本和前提，是保障高效分配和利用频谱资源的先决条件。

频谱感知技术是指对指定的空域、时域、频域内的频谱使用情况进行分析和感知，必须具有良好的检测性能才能避免用频冲突。

在单节点频谱感知技术中，在获得待检信号的先验信息情况下，匹配滤波检测能达到最佳的检测性能。

但在现实情况下，要获取检测信号的全部先验信息几乎是不可能的。

同匹配滤波检测相比，能量检测可以通过检测信号的能量同预先设定门限的比较来判断频谱使用情况，其计算复杂度最低。

但是检测精度受到噪声影响很大，在低信噪比情况下噪声能量难以估计，其判决性能极易受到影响。

因此，能量检测往往不适用于噪声不明情况下的频谱检测。

且当噪声能量不确定度较大时，无论能量检测的时间多长，都不能达到要求的检测精度。

与匹配滤波检测和能量检测相比，循环平稳检测是利用信号的循环平稳特性来检测指定频段上是否存在信号，其循环平稳特性来源于信号的码元速率、载波频率以及采样频率等调制参数。

利用信号的循环平稳特性，不仅可以完成对目标存在性的检测，还能够完成对载波速率、码元速率以及调制方式的估计。

循环平稳检测的稳健性高，能有效应对噪声不确定等问题。

本文提出以单节点循环平稳检测为基础，通过各频谱感知节点等增益合并完成对频谱占用情况感知的策略，兼顾了系统的高可靠性和低复杂度，具有较强的应用前景。

ZEISS 光谱仪模块产品详细介绍说明书

ZEISS 光谱仪模块产品详细介绍说明书

Explanatory product video on Page navigation Previous page | Next pageZEISS Spectrometer Modules CompendiumIntroductionWavelength rangesMMS FamilyCGS FamilyMCS FLEX FamilyPGS FamilySoftwareAreas of application Definitions and explanations The moment you discover that your expectations have been exceeded.This is the moment we work for.ZEISS Spectrometer ModulesCompendiumIntroduction Wavelength rangesMMS FamilyCGS FamilyMCS FLEX FamilyPGS FamilySoftwareAreas of application Definitions and explanations Your application is our motivationA traditional spectrometer and/or a traditional monochromatorconsists of a dispersive medium, an entrance and an exit slit andimaging elements which generate a parallel beam path. Tocapture a spectrum, a detector behind the exit slit must capturethe light sequentially while the dispersive element or the exit slitis moved. This sort of mechanical movement requires time andis prone to interference. However, short measuring times andinsensitivity to external influences are quite advantageous formany applications – especially in industry. That is why ZEISSbegan developing the diode array spectrometers at the end ofthe 1970s. In place of the exit slit, these spectrometers have adiode array and, through this replacement, capture a completespectrum simultaneously in a fraction of a second, makingmoving components unnecessary. The design of the spectrom-eter module family from ZEISS is based on reducing theoptical-mechanical design and the number of components tothe physical minimum while using the greatest possible numberof identical components for different versions.In the last few years, ZEISS has developed a large number ofdiverse spectrometer modules for very different applicationsand requirements. All of these modules offer a key benefit: allspectrometer parts are permanently affixed to each other. ThisAt ZEISS, the complete solution is consis-tently aligned with the customer's applica-tion. Not only is the corresponding modulefamily available for every measuring job, butthe electronics, interface and processingsoftware are always optimally configured.Furthermore, this approach ensures that thecustomer enjoys a consistently high level ofperformance and quality for all systemArea of applicationDetermining the measuring methodCriteria for the spectrometer system+Interface moduleDriver / softwareSpectrometer modules2500Watch the video on YoutubeWatch the video on YoutubeIntegration time (dependent on on-site electronics)Resolution (FWHM) with 50 µm slitIntegration time (dependent on on-site electronics)Wavelength rangeCGS UV-NIR PDASMA connector, 600 µm fiber SMA connector, 600 µm fiberWavelength rangeWatch the video on YoutubeWatch the video on YoutubeZEISS Spectrometer Modules CompendiumIntroductionWavelength rangesMMS FamilyCGS FamilyMCS FLEX FamilyPGS FamilySoftwareAreas of application Definitions and explanationsThe architecture of the software products for capturing and processing spectral data is based on a modular structure. This ensures that the software meets diverse, customer-specific specifications and enables different hardware configurations to be adjusted flexibly. For the various operating electronics units, device drivers are available for Windows 2000, XP and Vista. The universal Aspect Plus program package featuring compre-hensive functions is available along with the drivers for the PC bus interface. A programming interface for the SDACQ 32 MP function library is also offered to ensure easy integration into customer-specific applications. This interface directly supports C/C++/Visual Basic and Delphi, and a LabVIEW™ driver for programming in a LabVIEW™ environment. It is possible to program with finished menu structures for data capture by using the SDPROC32 function library for data capture, configu-ration and entering parameters.The SDACQ32MP function library directly addresses these device drivers and supplies a hardware-independent collection of functions, enabling the configuration of the on-site electron-Software SolutionsDirectly in the processAspect Plus is the complex and flexible modular spectral analyti-cal software for MS Windows with special options available asadd-ons. Spectral measurements with the spectrometers fromZEISS can be performed and evaluated using Aspect Plus.Comprehensive functions – from the measurement all the wayto the formated printout – simplify analytical evaluation.Modular software package for spectral analysisAspect Plus driver for PCs and USBsThe areas of application for these spectrometers are diverse because of their flexible design. They can be classified in ac-cordance with measurement principles, areas of application or the materials to be analyzed. Compactness and insensitivity to external influences are crucial so that modules can be installed in close proximity to production. An on-line control option is provided in most of the applications mentioned below.A part of the light is injected into the spectrometer to deter-mine the spectral emission of a light source. In many cases, the coupling fiber bundle only needs to be brought close to the light source because of the high light sensitivity. An achromatic converging lens can be used for optimization.Determining the wavelength of LEDs or (tunable) lasers Monitoring the solar spectrum, burns, discharges orDetermining the temperature T as per Wien's> 966 nmx T = 2.8978 x 10³ m x KAreas of application nti-reflective coatings of surfaces using metals oretermining the fat content in meat and sausages etermining the moisture content in humidity in grains,dentifying plastics for recycling and disposalMany reflection spectra do not exhibit particularly clear struc-tures. Thus absolute wavelength accuracy is often significantly more important than a good spectral resolving power.Interferences are the result of radiating white light on opticallytransparent interfaces because, for certain wavelengths, the optical path difference is exactly the multiple of the optical layer thick-nesses n x d (λ₁, λ₂: position of the extrema; distance: one period). If the refractive index n is known, then the geometric layer thick-ness d can be determined. The fiber interface ensures easy coupling to microscopes or flanging onto coating systems. Inversely: if layer thickness d is known, then the dispersion n (λ) can be determined.erforming layer thickness measurement on photo resists2 n x d = λ₁ x λ₂ / (λ₁ - λ₂)Measurements of minimal changes and stability depend directlyon each other and are essentially determined by the noise with-in the electronics because most spectrometers ensure a stable 'light path.' As with all sizes, it is important how a value – in the truest sense of the word – is determined. For the spectrometer module specifications, e.g. a 10 ms integration time is selectedis calculated above 20 captures. This supplies a measure for the accuracy Δl which can be used The dynamic is understood as the relationship between theand corresponds to the signal-noise raio S/N. (The usable range is still reduced by the dark current.) The S/N depends not only on the detector but also the digitization which provides the small step width into Of course the weakest link in the chain determines the signal-to-noise ratio to be achieved. With a 14 bit converter e.g. – this corresponds to 16384 steps or increments – and a noise of Δ= 1 count, a signal (fully controlled) can actually be divided into 16384 steps. The slightest measurable change is thus 1/16384 of the saturation signal. There is an uncertainty of four counts with a noise of four counts, i.e. only 4/16384 of the saturation signal can be measured as a definitive change and/or the signal At this point it should be noted that a higher dynamic range is only useful if the detector is adjusted so that it is equally high: you should always try to reach a high level of light so that the high sensitivity of the ZEISS spectrometers is beneficial.These statements only apply to an ideal detector linearity and the connected electronics, i.e. if the measured charge is linearly dependent on the irradiated intensity. The admissible deviation must be specified for quantitative information to be obtained. Fortunately, modern semi-conductor detectors exhibit almost perfect linear behavior over many ranges. Before reaching satu-ration (the extreme case of non-linearity), however, the increase in the electricity supplied (information carrier for intensity) is no longer linear to the number of photons hitting the photosensi-Specifying the stray light value only makes sense in conjunc-tion with the measuring instructions. Stray light values for the spectrometer modules are determined with three different light sources to determine the different spectral components in stray light and/or false light: a deuterium lamp for the UV range and a halogen lamp for the VIS-NIR range.The stray light level is the ratio between the respective measure-ment with a GG495 or KG3 filter and the maximum useful sig-nal. Thus the stray light given is given for the shortwave range, showing that, on the spectrometer modules, the essential stray light proportion comes from the NIR. This is beneficial because these spectrally 'remote' components can be easily filtered out. For the PGS NIR, the stray light value is reduced to 0.1% (mea-sured with a halogen lamp at 1450 nm, RG 850 filter and 10 mmStray light affects the dynamic range because the full dynamic range is no longer available due to false light.Changes in the causative radiation only break through in rela-tion to the stray light proportion: e.g. if the stray light propor-tion is at 1 per mill, a 10% change in the effective radiation means a change of 10⁻⁴. If the causative radiation is not used, then the proportion can be further reduced via filtering. In the example described, a blockage of 10³ leads to a total changeof 10⁻⁷. There are only small limitations to measuring minuscule changes because the noise is usually much stronger. The stray light proportion can be 'calculated out' if the cause of the signal Interfaces must be defined mechanically and optically. The SMA plug-in connection – such as that used on all models – is a use-ful mechanical interface in the optic, resulting in a clear inter-face along with the well-defined etendue of a fiber bundle.The light etendue G is the product of the light entrance surface F and the opening or spatial angle of the light bundel where-by the calculation index n must still be observed. The first factor corresponds to the fiber (bundle) cross-section. The second fac-tor is the result of the numeric aperture (NA).In the MMS family, the etendue is calculated at G = 0.157 mm²sr.G = F x x n2= 2 x (1-cos= arcsin NAZEISS Spectrometer Modules CompendiumIntroduction Wavelength ranges MMS Family CGS FamilyMCS FLEX Family PGS Family SoftwareAreas of applicationDefinitions and explanationsEmail// SPECTROMETER MODULES MADE BY ZEISSThe moment you achieve absolute confidence.This is the moment we work for.72-1-0022/e P r i n t e d i n G e r m a n y C Z -04/203S u b j e c t t o c h a n g e i n d e s i g n a n d s c o p e o f d e l i v e r y a n d a s a r e s u l t o f o n g o i n g t e c h n i c a l d e v e l o p m e n t .。