WO2009097755A1 - Procédé et appareil pour détecter localement un signal, procédé et appareil pour détecter un signal au niveau d'un centre, et système pour détecter un signal - Google Patents

Procédé et appareil pour détecter localement un signal, procédé et appareil pour détecter un signal au niveau d'un centre, et système pour détecter un signal Download PDF

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Publication number
WO2009097755A1
WO2009097755A1 PCT/CN2009/070075 CN2009070075W WO2009097755A1 WO 2009097755 A1 WO2009097755 A1 WO 2009097755A1 CN 2009070075 W CN2009070075 W CN 2009070075W WO 2009097755 A1 WO2009097755 A1 WO 2009097755A1
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quantization
local
signal
local node
result
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PCT/CN2009/070075
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English (en)
Chinese (zh)
Inventor
Lei Chen
Jun Wang
Shaoqian Li
Linjun Lu
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Huawei Technologies Co., Ltd.
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Publication of WO2009097755A1 publication Critical patent/WO2009097755A1/fr

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/06—Dc level restoring means; Bias distortion correction ; Decision circuits providing symbol by symbol detection

Definitions

  • the present invention relates to the field of detection technologies, and in particular, to a method for locally detecting a signal, a method for centrally detecting a signal, a local detecting device and a center detecting device, and a system for detecting a signal.
  • a distributed detection method when detecting an effective signal, a distributed detection method is often used, and at least one local node detects the detected signal, and obtains it locally. A detection statistic is then reported to the central node, and the central node obtains a global decision result of "whether the signal to be detected is a valid signal" according to each detection statistic.
  • cognitive wireless communication devices can operate in an authorized frequency band in an "Opportunistic Way" manner.
  • an authorized user can also be called a Primary User (PU). Therefore, the CR system needs to detect whether there is a PU signal on the target frequency band in order to provide the cognitive radio with a candidate channel that is not occupied by the PU and is accessible to the CR system.
  • PU Primary User
  • the system for implementing a distributed detection signal includes a local node and a central node, and the local node is configured to obtain a local decision result of whether a valid signal exists according to a local detection algorithm or obtain a detection statistic of a signal to be detected for determining, the center Node is used to fuse local decisions of various local nodes The result or the detected statistic is finally judged to determine whether there is a detection result of the valid signal.
  • the spectrum sensing technology for detecting PU signals can be divided into two categories: single-node sensing and cooperative sensing. In cooperative sensing, there are two types according to different transmission overheads: one is that each local detecting node makes a "PU". After the local decision of the signal exists, the local decision result is transmitted to the central node, and then the central node uses the "and" and "or” criteria to perform data fusion on each local node.
  • the second is that each local detecting node does not make a decision, and transmits some or all of the detected continuous data to the central node, such as the energy data of the signal to be detected. Then, the central node can use Bayesian criteria for data fusion, and The comprehensive judgment yields a global judgment result.
  • the detection performance of this method is good, but because the dynamic range of the transmission detection data is large, the transmission overhead is often large, which is difficult to implement in practice.
  • Embodiments of the present invention provide a method for locally detecting a signal, which can effectively improve signal detection performance.
  • Embodiments of the present invention also provide a method for detecting a center, which can effectively improve signal detection performance.
  • Embodiments of the present invention additionally provide a local detecting device that can effectively improve signal detection performance.
  • Embodiments of the present invention additionally provide a center detecting device that can effectively improve signal detection performance.
  • Embodiments of the present invention further provide a system for detecting a signal, which system can effectively improve signal detection performance.
  • An embodiment of the present invention provides a method for locally detecting a signal, where the method includes:
  • the embodiment of the invention provides a method for detecting a center, the method comprising:
  • An embodiment of the present invention provides a local detecting device, where the local detecting device includes:
  • a detector configured to detect a detection statistic corresponding to the signal to be detected in the local node
  • a quantizer configured to quantize the detection metric obtained by the detector according to a predetermined quantization threshold
  • An embodiment of the present invention provides a center detecting device, where the center detecting device includes:
  • An obtaining module configured to obtain a quantized result of the local detecting device, where the quantized result is a result obtained by quantifying a detection statistic corresponding to the signal to be detected;
  • the execution module obtains, according to the quantized result obtained by the obtaining module, a global decision result indicating whether the signal to be detected is a valid signal.
  • An embodiment of the present invention provides a system for detecting a signal, where the system for detecting a signal includes: a local node, configured to determine a quantization threshold corresponding to the local node, and quantize the detection statistic corresponding to the detection signal according to the quantization threshold, Obtaining a quantized result, the local node is at least one; the central node is configured to obtain a quantized result obtained by the local node to quantize the detection statistic, And according to the quantized result, a global decision result indicating whether the signal to be detected is a valid signal is obtained.
  • Embodiments of the present invention have the following beneficial effects:
  • FIG. 1 is a schematic structural diagram of a system for detecting a signal according to an embodiment of the present invention
  • Embodiment 1 of the present invention is a schematic flow chart of a method for detecting a signal in Embodiment 1 of the present invention
  • FIG. 3 is a schematic flowchart of a method for detecting a signal according to Embodiment 2 of the present invention.
  • 3-1 is a schematic flowchart of step 303 in the second embodiment of the present invention.
  • FIG. 4 is a schematic structural diagram of a local detecting apparatus according to an embodiment of the present invention.
  • FIG. 5 is a schematic structural diagram of a center detecting apparatus according to an embodiment of the present invention.
  • Figure 6 is a graph showing the relationship between the error probability and the number of locally detected nodes
  • FIG. 1 is a schematic structural diagram of a system for detecting a signal according to an embodiment of the present invention.
  • the system includes: at least one local node and one central node, which are connected by a channel between the local node and the central node.
  • the local node may be a physical entity that implements detection locally
  • the central node is a physical entity that aggregates detection results of at least one local node.
  • the local node includes: a detector that locally detects the detected signal and a quantizer that quantizes the detected statistic obtained by the detector; and the central node includes: an estimator for estimating the likelihood ratio of each local node based on the quantized result from each local node And an execution module that fuses the likelihood ratios obtained by the respective estimators and the respective quantized results.
  • the quantizer in the local node further compresses the locally determined decision data, and uses ⁇ ( ⁇ ) to represent the quantization process of the quantizer, expressed as equation (1):
  • JC denotes the quantized data
  • ⁇ denotes the quantizer output
  • q denotes the number of quantization levels
  • ⁇ , [ ⁇ 3 ⁇ 4, ⁇ +1 ;) , where Represents the quantization threshold.
  • the quantizer used in this embodiment may be a local optimum quantizer or a uniform quantizer.
  • the first quantizer For the first quantizer, it is designed to be optimal at the lowest signal-to-noise ratio required by the system, because in signal detection, it is more challenging to detect weak signals, but higher in signal-to-noise. Signals, even with some non-optimal techniques, can easily meet system requirements.
  • SNR Signal to Noise Ratio
  • the optimal quantizer is actually the local optimal quantizer (Locally Optimal).
  • Quantizer, LOQ Quantizer
  • the local optimum quantizer can be designed using the deflection criteria, and the deviation is used to indicate that the detection statistic is at H. And the statistical distance between the two cases, the greater the deviation, indicating the better the detection performance, Q represents the quantized detection statistic, then the deviation D ( ⁇ ) can be expressed as the formula ( 2 ):
  • V is a vector consisting of 1 ⁇ 4 , for a given quantization interval ⁇ , the maximum V of equation (7) is satisfied:
  • Equation (8) can be converted to:
  • the second quantizer it is a dynamic range based uniform quantizer (Dynamic Range based Uniform Quantizer, DRUQ):
  • the local node may further include:
  • a quantization threshold determiner is configured to quantify the number of levels according to a transmission overhead requirement of the local node to the central node. And determining, according to a statistical rule of the detection statistics obtained by the detector, a quantization threshold corresponding to the local node.
  • the quantization threshold determiner includes:
  • a statistical acquisition unit configured to acquire a distribution function, a mean value, or a variance of the detection statistic
  • a first quantization threshold determining unit configured to determine, according to a distribution function, a mean value, or a variance of the detection statistic obtained by the statistical acquisition unit, a quantization interval that maximizes a deviation between the detection statistic and a maximum deviation, according to the quantization Interval, determining a quantization threshold corresponding to the local node, where ⁇ .
  • the signal to be detected is a valid signal, indicating that the signal to be detected is not a valid signal.
  • the quantization threshold determiner includes:
  • the dynamic range determining unit is configured to determine a dynamic range of the detected statistic according to the maximum value and the minimum value of the detected statistic.
  • a second quantization threshold determining unit wherein the dynamic range obtained by the dynamic range determining unit is divided by the number of quantization levels, and the quantization interval of the local node is obtained, and the local node quantization interval and the minimum value of the detection statistic are determined.
  • the quantization threshold corresponding to the local node.
  • the local check detected by the local node by adding a quantizer to the local node The transmission data of the measured statistic is adjusted to provide more detection statistic information to the central node, and has better synergistic gain, and can obtain better performance under the condition of less local node and lower local detection performance. . Therefore, the design difficulty of the system topology and the design difficulty of the local sensing algorithm can be alleviated, so that the detection performance can be effectively improved.
  • FIG. 2 is a schematic flow chart of a method for detecting a signal according to Embodiment 1 of the present invention.
  • the local optimal statistic is used to quantize the local detection statistic.
  • the method includes the following steps:
  • Step 201 Determine the number of quantization levels of the local node according to the requirement of the transmission overhead.
  • the local nodes are ( ⁇ 1).
  • the number of quantization levels can be determined according to the transmission overhead requirement of the local node to the central node, that is, when the system can tolerate a large transmission overhead, the quantization level of the quantizer can be set to a larger number of bits to increase the accuracy of the decision. When the system cannot tolerate a large transmission overhead, the quantization level of the quantizer is set to a small number of bits to reduce the transmission overhead.
  • Step 202 Determine a quantization threshold of the local node according to the number of quantization levels of the local node.
  • the local node uses the theoretical analysis method to find the distribution function and F of the detection statistic.
  • (x) and F are determined by theoretical analysis by detecting the analytical expression of the statistic and the information of the noise and the signal. (x) expression. Assuming that the local node uses the energy detection to obtain the detection statistic, then ( ) and F Q (x) are determined by three factors: noise, signal power and detection time. Usually, thermal noise is used to estimate or measure on the no-load channel. So that can be based on F. For the definition of (x), find F. ⁇ ).
  • the present embodiment can also be obtained by a simulation method. Since the detection statistic at different moments can be regarded as independent and identical distribution, according to the central limit theorem, the detection statistic can be regarded as obeying the normal distribution. Therefore, it is only necessary to directly obtain the mean and variance of the detected statistics by running the simulation based on the easily available information such as the signal type and noise power. Then, based on the mean and variance of the detected statistics, it is determined that the detection statistic is at H.
  • the quantization interval and the quantization threshold ⁇ 3 ⁇ 4 (/ 1, 2, ⁇ ⁇ ⁇ , ) with the largest deviation from the two cases.
  • Step 203 The local node performs local detection to obtain local detection statistics before quantization.
  • the local detection statistic such as signal energy data
  • the local detection method can be obtained by using the local detection method in the prior art.
  • Step 204 Quantify the local detection statistic according to the determined quantization threshold.
  • the local detection statistic is quantized using equation (1) based on the determined quantization threshold.
  • Step 205 The central node acquires the quantized result after quantizing each local detection statistic.
  • Step 206 The central node performs data fusion on each quantized result to obtain a global decision balance.
  • the method of minimum error probability criterion is adopted to perform data fusion on each quantization result.
  • the minimum error probability criterion can be expressed as:
  • the corpse ( ) indicates a prior probability that the signal to be detected is a valid signal
  • ⁇ ( ⁇ .;) indicates a prior probability that the signal to be detected is not a valid signal.
  • the left side of the formula indicates the likelihood ratio of the test result
  • the right side indicates the decision threshold.
  • ⁇ . ⁇ , is the /th detection node
  • the probability of the detection statistic of 11 is the likelihood ratio.
  • the central node gets the global decision result is The a priori rate and likelihood ratio of the local node are determined.
  • the quantized local detection statistic is one bit
  • the "and" or “or” criteria used in the prior art can also be used for data fusion.
  • the quantized discrete local detection statistic can be data fusion through the minimum error criterion, thereby estimating the correct one. Global judgment result.
  • FIG. 3 is a schematic diagram of a method for detecting a signal in the first embodiment of the present invention by using a uniform quantizer to quantize the local detection statistic.
  • the specific implementation method of the minimum error probability criterion is also specifically given in this embodiment.
  • Step 301 Each local node quantizes the local detection statistic according to a predetermined quantization threshold.
  • each local node detects the target frequency band by itself and quantizes according to the quantization threshold determined by the formula (10) to obtain the quantized detection statistic.
  • Step 303 Estimate the prior probability and likelihood ratio of the local node at the current moment according to the global decision result of the previous moment and the quantization result of the local node. The following is specifically to estimate the prior probability k at time k. ( ) and the initial likelihood ratio are taken as an example for explanation. Step 303 can be specifically divided into the following steps:
  • the embodiment replaces the actual signal information of whether the signal to be detected is a valid signal by the global judgment result, and an estimated value equal to or close to the real situation can be obtained.
  • D Indicates H.
  • j' denotes a global decision result
  • Equation (19) can be rewritten as:
  • a limited memory method can be used to avoid the influence of historical data, specifically: It can be determined according to a preset fixed window length. The cumulative value of the decision status value before the update is superimposed on the judgment status value of the previous time to the accumulated value of the decision status before the update. That is, when statistically determining the state value, a fixed-length window is used, and each time a decision state value of a new time is counted, a decision state value at the earliest time is removed. Let the previous moment be k, N is the preset fixed window length, and & (t) is the judgment state value of the i-th local node at the kth time, then the formula (20) can be adjusted to obtain the formula (21). ):
  • Equation (20) can be adjusted to: Wherein, the forgetting factor is a number greater than 0.9 and less than 1.
  • ⁇ is judged.
  • Step 304 According to the estimated first risk rate ⁇ . (k) and the initial likelihood ratio A a and the local detection statistics quantized at the current time, a relatively accurate global judgment result is calculated.
  • the local detection statistic is quantized by using the homo-quantizer, and the quantized discrete local detection statistic can be data fusion through the minimum error criterion, the correct global is estimated.
  • the finite memory method is also used to eliminate the influence of historical data, which can increase the adaptability of the estimation algorithm to the time-varying detection environment.
  • FIG. 4 is a schematic structural diagram of a local detecting apparatus according to an embodiment of the present invention.
  • the local detecting device includes: a detector 410, which detects a detection statistic corresponding to a signal to be detected in the local node.
  • the quantizer 420 is configured to quantize the detection statistic obtained by the detector according to a quantization threshold corresponding to the local detecting device.
  • the local detecting device may further include:
  • the quantization threshold determiner 430 determines the quantization level according to the transmission overhead requirement between the local detection device and the central node; and determines the quantization threshold corresponding to the local detection device according to the statistical rule of the detection statistics obtained by the detector 410.
  • the quantization threshold determination module 430 includes:
  • the statistics obtaining unit 431 acquires a distribution function, a mean value, or a variance of the detection statistic.
  • the first quantization threshold determination unit 432 determines that the detection statistic is at H based on the distribution function, the mean value, or the variance of the detection statistic obtained by the statistical acquisition unit 431. And the quantization interval with the largest deviation between the two, and the quantization threshold corresponding to the local detecting device is determined according to the quantization interval, where H is.
  • the signal to be detected is a valid signal, indicating that the signal to be detected is not a valid signal.
  • the quantization threshold determination module includes:
  • the dynamic range determining unit is configured to determine a dynamic range of the detected statistic according to the maximum value and the minimum value of the detected statistic.
  • a second quantization threshold determining unit wherein a dynamic range of the detection statistic obtained by the dynamic range determining unit is divided by the number of the quantized levels, and a quantization interval of the local detecting device is obtained, according to the quantization interval and detection of the local detecting device The minimum value of the statistic determines the quantization threshold corresponding to the local detecting device.
  • FIG. 5 is a schematic structural diagram of a center detecting device according to an embodiment of the present invention. As shown in Figure 5, the center detecting device includes:
  • the obtaining module 510 is configured to obtain a quantization result of at least one local detecting device, where the quantization result is a quantized result obtained by quantizing the detection statistic corresponding to the detection signal.
  • the executing module 520 obtains, according to the quantization result obtained by the obtaining module 510, a global decision result indicating whether the signal to be detected is a valid signal.
  • Execution module 520 includes:
  • the estimating unit 521 estimates the prior probability and the likelihood ratio of the local node at the current moment according to the global decision result of the previous moment and the quantization result obtained by the obtaining module 510.
  • the data fusion unit 522 obtains the global decision result based on the prior probability and the likelihood ratio of the local node at the current time estimated by the estimation unit 521.
  • the estimating unit 521 includes:
  • the accumulation sub-unit 523 accumulates the decision status values of the previous time, where the decision status value is generated by comparing the global decision result according to the previous time with the quantization result obtained by the acquisition module.
  • the selecting sub-unit 524 selects the weighting value of each decision state value from the accumulated result of the accumulating sub-unit 523, and calculates the prior probability and the likelihood ratio estimation of the local node by using the weighting values of the respective decision state values. value.
  • the CR system In order to better reflect the performance of the algorithm, it is assumed that the CR system is in a difficult detection environment. If the algorithm can achieve good performance under extremely difficult conditions, it will naturally achieve satisfactory results in a better test environment.
  • the difficulty of detecting the environment is reflected in two aspects: First, the performance of the local detection algorithm is limited, assuming that the local node uses energy detection, and the detection time is only 10,000 samples of duration, when the primary user is a DVB-T digital television signal, The corresponding time is about lms. Second, the signals at all local nodes are in deep fading, and their SNR is distributed between -21dB and -19dB. This SNR range is about the minimum signal-to-noise ratio required by the system.
  • the performance of the algorithm is measured by the error probability P e of the global decision, which can be expressed as: where / ⁇ ! ⁇ and / ⁇ !. ) indicates that the primary user's pending detection signal is a valid signal and the prior probability in the absence condition, we make them both 0.5 in the simulation.
  • the ⁇ ⁇ and P GF distributions represent the global missed detection probability and the global false alarm probability.
  • Fig. 6 and Fig. 7 The simulation is run under the above assumptions, and the relationship between the error probability and the number of local detection nodes is shown in Fig. 6 and Fig. 7.
  • the "LOQ-x Bit” in Fig. 6 indicates the case where the local optimum quantizer is used, and the "LOQ-x Bit” based on the dynamic range in Fig. 7 quantizes the local detected data into X bits.
  • "Raw Data” means that all the detected data is transmitted to the central node, and the central node uses the minimum error criterion for data fusion. Use “or” and “and” for 1-bit detection statistics The criterion is compared with the data fusion method used in the second embodiment. By comparing the simulations in FIG. 6 and FIG.
  • the cooperative sensing method used in the second embodiment of the present invention is adopted regardless of the quantization method. It is 4 times better than the "or" and "and” criteria. Even if only the local detection result is quantized to 1 bit, only less than 50 local detection nodes are needed, and the global error probability can be controlled to a lower level, which is about 0.1.
  • the cooperative sensing method adopting the "or" and "and” fusion criteria although the number of nodes increases, its error probability decreases, but the change is very slow, even when the number of nodes increases to 1000, the error probability is still At a high level, about 0.15.
  • steps does not mean that the process flow of the embodiment has a sequence, and the serial number of the embodiment of the present invention is merely for the description, and does not represent the advantages and disadvantages of the embodiment.

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Abstract

L'invention porte sur un procédé et un appareil pour détecter localement un signal, sur un procédé et un appareil pour détecter un signal au niveau d'un centre, et sur un système pour détecter un signal. Le procédé pour détecter localement un signal comprend les opérations suivantes : selon le seuil de quantification déterminé correspondant au nœud local, des statistiques de détection correspondant au signal devant être détecté sont quantifiées et le résultat de quantification est obtenu. Le résultat de quantification est utilisé pour déterminer si le signal devant être détecté est un signal valide ou non. Le procédé pour détecter un signal au niveau d'un centre comprend : l'obtention du résultat de quantification qui est acquis par quantification de statistiques de détection par un nœud local. Selon le résultat de quantification, un résultat de décision globale indiquant si le signal devant être détecté est un signal valide ou non est obtenu. L'application des solutions décrites permet d'améliorer de façon avantageuse et simple les performances de détection.
PCT/CN2009/070075 2008-02-01 2009-01-08 Procédé et appareil pour détecter localement un signal, procédé et appareil pour détecter un signal au niveau d'un centre, et système pour détecter un signal WO2009097755A1 (fr)

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