有关认知无线电频谱感知算法的入门教程,简单易懂,配合其中提到的论文....doc

有关认知无线电频谱感知算法的入门教程,简单易懂,配合其中提到的论文.本文由惩戒之疾风贡献 ppt文档可能在WAP端浏览体验不佳。建议您优先选择TXT，或下载源文件到本机查看。 CHAPTER 5. SPECTRUM SENSING IFA2007 COGNITIVE CLASS 1 What is Spectrum Sensing ? How to detect spectrum holes by the COGNITIVE RADIO so that it can adapt itself to its environment ! IFA2007 COGNITIVE CLASS 2 Spectrum Sensing Transmitted Signal Radio Environment RF Stimuli Spectrum Mobility Primary User Detection Decision Request Spectrum Sensing Spectrum Hole Spectrum Sharing Channel Capacity Spectrum Decision IFA2007 COGNITIVE CLASS 3 EFFICIENT WAY TO DETECT SPECTRUM HOLES A general CR based communication scenario CR User 2 No interaction between CR user and Primary Tx/Rx CR user must rely on locally sensed signals to infer primary user activity Licensed band 2 CR User 1 Licensed band 1 Primary Tx Primary Rx Channels found occupied by CR user (Licensed bands 1 and 2) are now avoided during communication between CRs IFA2007 COGNITIVE CLASS 4 EFFICIENT WAY TO DETECT SPECTRUM HOLES ! Detect primary users that are receiving data within the communication range of a CR user. In reality Difficult for a CR to detect primary user activity in the absence of interaction between primary users and itself. RECENT RESEARCH How to detect primary users based on local observation of CR users (from its environment) IFA2007 COGNITIVE CLASS 5 Classification of Spectrum Sensing Techniques Spectrum Sensing Transmitter Detection Receiver Detection Interference Temperature Management Matched Filter Detection Energy Detection Cyclostationary Feature Detection IFA2007 COGNITIVE CLASS 6 Transmitter Detection CR should distinguish between Used and Unused spectrum bands. CR should have the capability to determine if a signal from primary user (transmitter) is locally present in a certain spectrum. Transmitter Detection Approach Detection of the signal (weak signal as the worst case) from a primary user through local observations of CR users. COGNITIVE CLASS 7 IFA2007 Basic Hypothesis Model for Transmitter Detection The signal r(t) received (detected) by the CR (secondary) user is H0 n(t) r(t) = hs(t) + n(t) H1 where n(t) s(t) AWGN (Additive White Gaussian Noise) Transmitted signal of the primary user Amplitude gain of the channel Null hypothesis No licensed user signal in a certain spectrum band. Alternative hypothesis There exists some licensed user signal. COGNITIVE CLASS h H0 H1 IFA2007 8 Transmitter Detection Sensing for Cognitive Radios, in Proc. 38th Asilomar Conference on Signals, Systems and Computers, pp. 772-776, Nov. 2004. D. Cabric, S. M. Mishra, and R. W. Brodersen, Implementation Issues in Spectrum Three schemes are generally used for the transmitter detection according to the hypothesis model. Matched Filter Detection Energy Detection and Cyclostationary Feature Detection Techniques IFA2007 COGNITIVE CLASS 9 Matched Filter Detection Spectrum Sensing Transmitter Detection Receiver Detection Interference Temperature Management Matched Filter Energy Detection Detection Cyclostationary Feature Detection IFA2007 COGNITIVE CLASS 10 Matched Filter Detection Matched Filter Received Signal r(t) = s(t) + n(t) Sample at t = T Threshold Device t 0 r( )s(T t + )d Y Y Y | H1 = Qm( 2 , ) d (m, / 2) Pf = PY | H0 = (m) where is the SNR m = TW is the (observation/sensing) time bandwidth product () and (, ) are complete and incomplete gamma functions Qm( ) is the generalized Marcum Q-function. COGNITIVE CLASS 19 IFA2007 Energy Detection Fading Environment: The amplitude gain of the channel varies due to the shadowing/fading - variation of SNR Pf is the same as that of non-fading case (independent of SNR,) non(independent SNR, Pd gives the probability of the detection conditioned on instantaneous SNR as: P = Qm ( 2 , ) f (x)dx d x where f(x) is the probability distribution function of SNR under fading. COGNITIVE CLASS 20 IFA2007 Energy Detection A low Pd missing the presence of the primary user with high probability increases the interference to the primary user A high Pf low spectrum utilization (since false alarms increase the number of missed opportunities). Implementation is easy! COGNITIVE CLASS 21 IFA2007 Problems of Energy Detection Performance is susceptible to uncertainty in noise power. SNR problem! (Pilot tone from primary user helps to improve the accuracy of the energy detector) Energy detector cannot differentiate signal types but can only determine the presence of the signal. Energy detector is prone to the false detection triggered by the unintended signals. Energy detector needs longer sensing time Matched filter: T1/SNR when detecting weak signals: Energy Detector: T1/SNR2 SNR 1(-10dB to -40 dB) 1(dB) COGNITIVE CLASS 22 IFA2007 Cyclostationary Feature Detection Spectrum Sensing Transmitter Detection Receiver Detection Interference Temperature Management Matched Filter Detection Energy Detection Cyclostationary Feature Detection 23 IFA2007 COGNITIVE CLASS Cyclostationary Feature Detection Sensing for Cognitive Radios, in Proc. 38th Asilomar Conference on Signals, Systems and Computers, pp. 772-776, Nov. 2004. A. Fehske, J. D. Gaeddert, and J. H. Reed, A New Approach to Signal Classification Using Spectral Correlation and Neural Networks, in Proc. IEEE DySPAN, pp. 144-150, Nov. 2005. , H. Tang, Some Physical Layer Issues of Wideband Cognitive Radio System, in Proc. IEEE DySPAN, pp. 151-159, Nov. 2005. , D. Cabric, S. M. Mishra, and R. W. Brodersen, Implementation Issues in Spectrum Modulated signals are in general coupled with sine wave carriers, pulse trains, repeating spreading, hopping sequences, or cyclic prefixes, which result in built-in builtperiodicity. COGNITIVE CLASS 24 IFA2007 Cyclostationary Feature Detection These modulated signals are characterized as cyclostationary since their mean and autocorrelation exhibit periodicity. These features are detected by analyzing a spectral correlation function. Advantage of the spectral correlation function: differentiates the noise energy from modulated signal energy IFA2007 COGNITIVE CLASS 25 Cyclostationary Feature Detection Sine based Cyclostationary Detection Primary Tx frequency repeats over symbol durations at regular intervals T Problem: Can these cyclical regularities be detected at the CR user? IFA2007 COGNITIVE CLASS 26 Cyclostationary Feature Detection Sensing for Cognitive Radios, in Proc. 38th Asilomar Conference on Signals, Systems and Computers, pp. 772-776, Nov. 2004. r(t) Correlate R(f+)R*(f- ) Average over T Feature detect D. Cabric, S. M. Mishra, and R. W. Brodersen, Implementation Issues in Spectrum r(t): Received signal R(f): Fourier transform of r(t) : Cyclic frequency R*(f): Complex conjugate of R(f) If cyclostationary with period T then cycle autocorrelation has component at =1/T COGNITIVE CLASS 27 IFA2007 Cyclostationary Feature Detection If the correlation factor is high (greater than the threshold), there is a primary user IFA2007 COGNITIVE CLASS 28 Cyclostationary Feature Detection This scheme performs better than the energy detector in discriminating against noise due to its robustness to the uncertainty in noise power. Computationally complex and requires significantly long observation time. IFA2007 COGNITIVE CLASS 29 Limitations of the Transmitter Detection Receiver Uncertainty Problem Shadowing Problem IFA2007 COGNITIVE CLASS 30 Receiver Uncertainty Problem With the transmitter detection, the CR user cannot avoid the interference due to the lack of the primary receivers information (Fig.a). Moreover, the transmitter detection model cannot prevent the hidden terminal problem. IFA2007 COGNITIVE CLASS 31 Shadowing Problem A CR transmitter can have a good line-of-sight to a line-ofreceiver, but may not be able to detect the transmitter due to the shadowing (Fig. b). Consequently, the sensing information from other users is required for more accurate detection Cooperative Detection COGNITIVE CLASS 32 IFA2007 Limitations of the Transmitter Detection Interference Primary Transmitter Range Primary Base-station CR Transmitter Range CR User Hidden Terminal Problem due to Shadowing Primary User Cannot detect transmitter Interference CR User Primary Transmitter Range CR Transmitter Range Interference due to uncertainty of receiver location Primary Base-station Primary User Cannot detect transmitter IFA2007 COGNITIVE CLASS 33 Transmitter Detection NonNon-Cooperative vs Cooperative Detection Detection Method Transmitter Detection Detection Behavior Transmitter Detection Matched Filter Detection Energy Detection Cyclostationary Feature Detection Non-cooperative Detection Cooperative Detection IFA2007 COGNITIVE CLASS 34 NonNon-Cooperative vs Cooperative Detection NonNon-Cooperative Detection CR users detect the primary transmitter signal independently through their local observations. Cooperative Detection Refers to spectrum sensing methods where information from multiple CR users are incorporated for primary user detection. Allow to mitigate the multi-path fading and shadowing effects, which multiimproves the detection probability in a heavily shadowed environment IFA2007 COGNITIVE CLASS 35 Cooperative Detection G. Ganesan and Y.G. Li, Cooperative Spectrum Sensing in Cognitive Radio Networks, in Proc. IEEE DySPAN 2005 S. M. Mishra, A. Sahai and R. W. Brodersen, Cooperative sensing among cognitive radios , in Proc. IEEE ICC 2005. Cooperative Detection can be implemented either in a centralized or in a distributed manner. Centralized Method CR base-station plays a role to gather all sensing information from basethe CR users and detect the spectrum holes. Distributed Method require exchange of observations among CR users. COGNITIVE CLASS 36 IFA2007 Cooperative Detection Cooperative Methods provide more accurate sensing performance, they cause adverse effects on resource-constrained resourcenetworks due to the additional operations and overhead traffic. PROBLEM The primary receiver uncertainty problem caused by the lack of the primary receiver location knowledge is still unsolved! COGNITIVE CLASS 37 IFA2007 Primary Receiver Detection Spectrum Sensing Transmitter Detection Receiver Detection Cyclostationary Feature Detection Interference Temperature Management Matched Filter Detection Energy Detection IFA2007 COGNITIVE CLASS 38 Primary Receiver Detection B. Wild and K. Ramchandran, Detecting Primary Receivers for Cognitive Radio Applications in Proc. IEEE DySPAN, pp. 124-130, Nov. 2005. A direct receiver detection method is developed for detection of primary receivers where the local oscillator (LO) leakage power emitted by the RF front-end of the primary receiver front- IFA2007 COGNITIVE CLASS 39 Primary Receiver Detection However, since LO leakage signal is typically weak, implementation of a reliable detector is not trivial. Currently this method is only feasible in the detection of the TV receivers. IFA2007 COGNITIVE CLASS 40 Interference Temperature Management Spectrum Sensing Transmitter Detection Receiver Detection Interference Temperature Management Matched Filter Detection Energy Detection Cyclostationary Feature Detection IFA2007 COGNITIVE CLASS 41 Interference Temperature Management Interference is typically regulated in a transmittertransmittercentric way Interference can be controlled at the transmitter through * radiated power, * out-of-band emissions and out-of* location of individual transmitters. COGNITIVE CLASS 42 IFA2007 Interference Temperature Management FCC, ET Docket No 03-289 Notice of Inquiry and Notice of Proposed Rulemaking Nov. 2003. However, interference actually takes place at the receivers Therefore a new model for measuring interference, referred to as interference temperature introduced by the FCC. IFA2007 COGNITIVE CLASS 43 Interference Temperature Model Licensed Signal New Opportunities for Spectrum Access Minimum Service Range with Interference Cap Power at Receiver Interference Temperature Limit Service Range at Original Noise Floor Original Noise Floor Distance from Licensed Transmitting Antenna IFA2007 COGNITIVE CLASS 44 Interference Temperature Model The model shows the signal of a radio 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. COGNITIVE CLASS 45 IFA2007 Interference Temperature Model 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. IFA2007 COGNITIVE CLASS 46 Interference Temperature Model I.o.w., 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 CR users do not exceed this limit by their transmissions, they can use this spectrum band. COGNITIVE CLASS 47 IFA2007 Spectrum Sensing Challenges: Interference Temperature Measurement No practical way for a CR to measure or estimate the interference temperature. Only the noise and signals due to other CR users are considere as interference, and CR users