CN112668352A - Environmental backscattering communication signal processing method - Google Patents
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Abstract
The invention discloses an environment backscatter communication signal processing method which comprises the steps of obtaining a mixed signal, obtaining a plurality of data points of the mixed signal corresponding to an IQ domain, clustering each data point by using a maximum expectation clustering algorithm based on a Gaussian mixture model, determining a target point cluster, and returning a decoding result of the mixed signal by using the possible state of a communication signal corresponding to each data point in the target point cluster. The invention can obtain the decoding result with higher accuracy under the condition that the communication signals sent by a plurality of electronic tags generate signal collision, thereby recovering the communication signals sent by each electronic tag, providing support for realizing parallel communication in environment backscattering communication, and improving the data throughput of the environment backscattering communication.
Description
Technical Field
The invention relates to the technical field of communication, in particular to an environment backscattering communication signal processing method.
Background
The principle of environment backscatter communication is shown in fig. 1, where a reader communicates with an electronic tag, the electronic tag collects energy from environment radio frequency sources such as WiFi transmitters and bluetooth transmitters of other communication systems to work, and data transmission with the reader is achieved by reflecting collected environment radio frequency signals to the reader to represent binary data bit 0 and not reflecting collected environment radio frequency signals to the reader to represent binary data bit 1. Because the energy source of the electronic tag is the environment radio frequency signal, the electronic tag has the characteristic of low energy consumption, does not need to be provided with a power supply battery, and is suitable for use occasions where the battery is difficult to replace. However, due to the low power consumption characteristics of electronic tags, the throughput of ambient backscatter communications is generally low.
Referring to fig. 1, the use of multiple tags for connection communication with a reader can improve the throughput of environmental backscatter communication, but at the same time, the problem of signal collision when multiple tags communicate with the reader at the same time is caused, and it is difficult for the reader to distinguish communication signals sent by different tags. The prior art deals with the problem of signal collision of a plurality of tags by means of time division multiplexing, IQ domain signal information decoding, time and IQ domain signal information decoding, signal state conversion rule-based decoding and the like. However, time division multiplexing based techniques suffer from significant channel waste; the technology of decoding based on time and signal information in IQ domain requires the reader to continuously evaluate and update the channel parameters, resulting in too high system overhead; the technology of decoding based on time and signal information in an IQ domain can be realized on the premise that an electronic tag has an accurate clock, but the clock drift rate of the electronic tag is very high in practical use, so that the decoding success rate is low; the technology of decoding based on the signal state transition rule faces the problem of difficult decoding caused by the overlap between signal clusters in the IQ domain.
Disclosure of Invention
In view of at least one of the above technical problems, the present invention provides an environmental backscatter communication signal processing method, including:
acquiring a mixed signal; the mixed signal comprises communication signals sent by a plurality of electronic tags respectively, and the communication signals are formed by the electronic tags through reflecting or not reflecting environmental signals;
acquiring a plurality of data points corresponding to the mixed signal on an IQ domain; wherein one of said data points corresponds to one possible state of one of said communication signals;
clustering each data point by using a maximum expected clustering algorithm based on a Gaussian mixture model to determine a target point cluster; the target point cluster comprises a plurality of the data points;
and returning the possible states of the communication signals corresponding to the data points in the target point cluster as decoding results of the mixed signals.
Further, the acquiring a plurality of data points corresponding to the mixed signal in an IQ domain includes:
carrying out IQ decomposition on the mixed signal to acquire amplitude information and phase information of each communication signal; wherein the amplitude information of the same communication signal comprises a plurality of possible states;
and determining the data point by taking the amplitude information and the phase information as coordinates on an IQ domain.
Further, the clustering each data point by using a maximum expected clustering algorithm based on a gaussian mixture model to determine a target point cluster includes:
randomly determining a plurality of initial point clusters;
adjusting the distribution of the initial point clusters by using a maximum expectation clustering algorithm based on a Gaussian mixture model, and respectively determining the probability that each data point belongs to each initial point cluster;
uniquely attributing each of the data points to the initial point cluster to which the maximum probability belongs;
when it is detected that each data point included in one initial point cluster can correspond to all the electronic tags, the initial point cluster is determined as the target point cluster.
Further, the randomly determining a plurality of initial point clusters includes:
set mNThe initial point cluster; wherein m is the number of possible states of one of said communication signals and N is said electronic tagThe number of tags;
randomly setting mNInitial mean value and mNAn initial variance;
assigning one of the initial means and one of the initial variances to each of the initial clusters;
setting each of the initial point clusters to a gaussian distribution determined by the initial mean and the initial variance assigned thereto.
Further, m is 2.
Further, the adjusting the distribution of the initial point clusters by using a maximum expected clustering algorithm based on a gaussian mixture model to determine the probability that each data point belongs to each initial point cluster respectively includes:
A. determining that the kth cluster of initiation points satisfies a Gaussian distribution
Wherein, mukMean, σ, of the Gaussian distribution satisfied by the kth cluster of initial pointsk 2A variance of a gaussian distribution satisfied by a kth of the initial point clusters;
B. determining the data point x according to the Gaussian distribution satisfied by the initial point clusternThe probability of belonging to the kth initial point cluster is pik；
C. Determining the data point x according to the Gaussian distribution satisfied by the initial point clusternA posterior probability of belonging to the kth initial point cluster of
D. Determining a maximum likelihood estimate of the mean as
The maximum likelihood of said variance is estimated as
The data point xnThe maximum likelihood estimation of the probability of belonging to the kth initial point cluster is
Wherein N iskThe number of data points included in the kth initial point cluster;
E. performing an iteration with steps C and D until the data point x is obtainednThe maximum likelihood estimates of the probabilities belonging to the kth of the initial point cluster converge.
Further, the maximum likelihood estimation of the mean is to make μkMaximum likelihood function pair mukA solution with a partial derivative of zero;
the maximum likelihood estimation of the variance is to make sigmak 2Maximum likelihood function pair ofk 2A solution with a partial derivative of zero;
the data point xnThe maximum likelihood estimate of the probability of belonging to the kth of said initial point cluster is such thatkMaximum likelihood function pair ofkThe partial derivative of (c) is a solution of zero.
Further, the environmental backscatter communication signal processing method is performed by a reader, and the reader is connected with each electronic tag.
The invention has the beneficial effects that: according to the environmental backscatter communication signal processing method in the embodiment, data points formed by communication signals simultaneously sent by all electronic tags are clustered by using a maximum expected clustering algorithm based on a Gaussian mixture model, and a decoding result of a mixed signal is determined according to a clustering result, so that the decoding result can be obtained at a high accuracy rate under the condition that the communication signals sent by a plurality of electronic tags generate signal collision, thereby recovering the communication signals sent by all the electronic tags, providing support for realizing parallel communication in the environmental backscatter communication, and improving the data throughput of the environmental backscatter communication through the parallel communication; the environmental backscattering communication signal processing method in the embodiment is a data processing method at the reader side, and the circuit structure or the control algorithm of the electronic tag does not need to be modified, so that the modification cost is low.
Drawings
Fig. 1 is a schematic structural diagram of a communication system to which an environmental backscatter communication signal processing method is applied in an embodiment;
fig. 2 is a flowchart of an environmental backscatter communication signal processing method in an embodiment.
Detailed Description
In this embodiment, referring to fig. 1, one reader is connected to and communicates with N electronic tags such as tag 1 and tag 2 … …, the electronic tags collect energy from environmental radio frequency sources such as WiFi transmitters and bluetooth transmitters of other communication systems to operate, and data transmission with the reader is implemented by reflecting collected environmental radio frequency signals to the reader to represent binary data bit 0 in communication signals and not reflecting collected environmental radio frequency signals to the reader to represent binary data bit 1 in the communication signals.
In this embodiment, the reader may be provided with a plurality of antennas, and for the communication between each antenna and each electronic tag, the environmental backscatter communication signal processing method in this embodiment may be applied, and the reader explains the environmental backscatter communication signal processing method in this embodiment by using a communication process between 1 antenna and each electronic tag. The reader receives the communication signals transmitted by the electronic tags through the antenna. In order to improve the throughput, parallel transmission is carried out between the reader and the N electronic tags, namely the N electronic tags send communication signals to the reader at the same time, the possible state of the communication signal sent by each electronic tag is 0 or 1, and the plurality of communication signals are superposed with a signal directly sent to a reader antenna by an ambient radio frequency source and background noise existing in the environment to form a mixed signal or an aliasing signal.
In this embodiment, in a case that the computing performance of the reader is strong enough, the reader may execute each step in the environmental backscatter communication signal processing method, or connect the reader with a personal computer with strong enough computing performance, and the personal computer executes each step in the environmental backscatter communication signal processing method. In this embodiment, the "receiving end" is collectively referred to as a reader or a personal computer.
In this embodiment, referring to fig. 2, the method for processing the environmental backscatter communication signal includes the following steps:
s1, acquiring a mixed signal;
s2, acquiring a plurality of data points of the mixed signal corresponding to the IQ domain; wherein one of said data points corresponds to a possible state of a communication signal;
s3, clustering each data point by using a maximum expected clustering algorithm based on a Gaussian mixture model to determine a target point cluster; wherein the target point cluster comprises a plurality of data points;
and S4, returning the decoding result of the mixed signal by using the possible state of the communication signal corresponding to each data point in the target point cluster.
In step S1, after receiving the mixed signal, the reader may filter out a signal directly transmitted from the ambient radio frequency source to the reader antenna and a background noise existing in the environment by filtering, and then process a superimposed part of the remaining communication signals, where each communication signal is a component of the mixed signal.
In step S2, the reader or the personal computer specifically executes the following steps:
s201, IQ decomposition is carried out on the mixed signal, and amplitude information and phase information of each communication signal are obtained; wherein the amplitude information of the same communication signal comprises a plurality of possible states;
s202, determining data points by taking the amplitude information and the phase information as coordinates on an IQ domain.
In step S201, by performing IQ decomposition on the mixed signal, each component in the mixed signal, that is, an in-pass component (I component) and a quadrature component (Q component) of each communication signal can be obtained, where the in-pass component is amplitude information in the present embodiment, and the quadrature component is phase information in the present embodiment. In the environmental backscatter communication applied to this embodiment, the amplitude of a communication signal is used to represent binary information, and thus the amplitude of a communication signal has 2 possible states, i.e., high level 1 or low level 0, and in this embodiment, the I component and the Q component of a communication signal are respectively used as the abscissa and the ordinate, i.e., (I component, Q component) to determine a data point in the IQ domain. In step S202, the coordinates of the corresponding data points are determined for all the communication signals.
In step S3, the receiving end clusters each data point using a maximum expected clustering algorithm based on a gaussian mixture model, and determines a target point cluster. Specifically, the receiving end performs the following steps:
s301, randomly determining a plurality of initial point clusters;
s302, adjusting the distribution of the initial point clusters by using a maximum expectation clustering algorithm based on a Gaussian mixture model, and respectively determining the probability that each data point belongs to each initial point cluster;
s303, uniquely attributing each data point to an initial point cluster to which the maximum probability belongs;
s304, when detecting that each data point included in an initial point cluster can correspond to all electronic tags, determining the initial point cluster as a target point cluster.
In step S301, since there are N tags, the communication signal sent by each tag has two possible states, i.e. 0 and 1, and coexists at 2NA possible combination of communication signal states, so that the receiving end sets 2NAn initial point cluster; in this embodiment, each initial point cluster is subject to gaussian distribution, which involves two parameters, i.e., mean and variance, and the receiving end randomly sets 2NInitial mean value and 2NThe initial variance is used for respectively distributing an initial mean value and an initial variance for each initial point cluster;
in step S302, the reader or the personal computer specifically executes the following steps a to E:
A. determining that the kth initial point cluster satisfies the Gaussian distribution
Wherein, mukMean, σ, of the Gaussian distribution satisfied by the kth initial point clusterk 2A variance of the gaussian distribution satisfied by the kth initial point cluster;
B. determining a data point x based on the Gaussian distribution satisfied by the initial point clusternProbability of belonging to the kth initial point clusterIs pik；
C. Determining a data point x based on the Gaussian distribution satisfied by the initial point clusternThe posterior probability of belonging to the kth initial point cluster is
D. Determining a maximum likelihood estimate of the mean
Maximum likelihood estimation of variance as
Data point xnThe maximum likelihood estimate of the probability of belonging to the kth initial point cluster is
Wherein N iskThe number of data points included in the kth initial point cluster; wherein the content of the first and second substances,
in fact, make mukMaximum likelihood function pair mukThe partial derivative of (a) is a solution of zero,
in fact, make σk 2Maximum likelihood function pair ofk 2The partial derivative of (a) is a solution of zero,
is actually to make pikMaximum likelihood function pair ofkA solution with a partial derivative of zero;
E. an iteration is performed with steps C and D until the data point x is obtainednThe maximum likelihood estimates of the probabilities belonging to the kth initial point cluster converge.
Performing iterations in steps C and D in step E includes: after the first execution of step D, the obtained μk、
And pikThen substituting into the formula in step C
Calculating gamma (z)k) Then, step D is performed a second time, the calculated gamma (z)k) Substituting into the formula in step D
And
calculate a new μk、
And pikD, calculating pi by executing step D for the second timekPi calculated by the first execution of step DkMaking a comparison, i.e. determining the pi calculated in the second execution of step DkPi calculated by the first execution of step DkWhen the absolute value of the difference is less than a preset threshold value, the difference is considered as pikConverge and otherwise consider pikIf not, continuing to execute the mu calculated in the step D for the second timek、
And pikSubstituting into step C, repeating the above process until pikAnd (6) converging.
By performing steps A-E once, which may include multiple iterations resulting from performing steps C-D, data point x may be determinednProbability pi of belonging to the kth initial point clusterkAnd a Gaussian distribution parameter μ of the kth initial point clusterk、
Is also adjusted. By performing steps A-E (which are performed multiple times)Each execution of steps a-E may include multiple iterations of steps C-D), the probability that any one data point belongs to any one initial cluster of points may be determined.
In step S303, according to the probability that a data point belongs to each initial point cluster, determining the initial point cluster corresponding to the maximum probability, and attributing the data point to the initial point cluster with the maximum probability, where the attribution relationship is unique, that is, after the data point is attributed to the initial point cluster with the maximum probability, the data point is not attributed to other initial point clusters.
After step S303 is performed, each data point is assigned to be attributed to a corresponding initial point cluster, wherein at least a portion of the initial point clusters are attributed a plurality of data points, which may be represented in the form of (I component, Q component).
In step S304, when it is detected that each data point included in an initial point cluster can correspond to all electronic tags, for example, an initial point cluster includes data points of (I component)1Component Q1) (I component)2Component Q2) … … (I component)NComponent QN) Wherein (I component)1Component Q1) Corresponding tag 1, (I component)2Component Q2) Corresponding label 2 … … (I component)NComponent QN) Corresponding to label N, then the initial point cluster is determined as the target point cluster. The data points included in the cluster of target points may be represented as an I-component after ignoring the phase information in each data point coordinate and observing only the possible states of the amplitude information in each data point coordinate1Component I2… … I componentN。
In step S4, binary sequences in the form of 0, 1, and 1 … … 0 represented by possible states of the communication signal corresponding to each data point in the target point cluster are combined and returned as a result of decoding the mixed signal.
In this embodiment, the principle of steps S1-S4 is: according to the central limit theorem, when the data volume is large, the statistical characteristics of the same kind of data tend to be in Gaussian distribution, the statistical characteristics of the same kind of signals conform to the Gaussian distribution, when a plurality of signals are mixed together, the method is equivalent to that a plurality of Gaussian models are mixed together, and communication signals sent by a plurality of electronic tags mixed together can be distinguished by using a maximum expectation clustering algorithm based on the Gaussian mixture model. Moreover, the maximum expected clustering algorithm based on the Gaussian mixture model can efficiently cluster a plurality of data points which are distributed in an IQ domain to present an ellipse, and the IQ domain distribution of the data points formed by communication signals sent by a plurality of electronic tags presents an ellipse in actual use, so that the maximum expected clustering algorithm based on the Gaussian mixture model has better pertinence to a scene of communication between the plurality of electronic tags and one reader.
In this embodiment, a maximum expected clustering algorithm based on a gaussian mixture model is used to cluster data points formed by communication signals simultaneously transmitted by each electronic tag, and a decoding result of a mixed signal is determined according to a clustering result, so that the decoding result can be obtained at a high accuracy rate under the condition that the communication signals transmitted by a plurality of electronic tags generate signal collision, thereby recovering the communication signals transmitted by each electronic tag, providing support for realizing parallel communication in environment backscatter communication, and improving data throughput of the environment backscatter communication through the parallel communication. The environmental backscatter communication signal processing method in this embodiment is a data processing method on the reader side, and does not need to modify a circuit structure or a control algorithm of an electronic tag, so that the modification cost is low.
It should be noted that, unless otherwise specified, when a feature is referred to as being "fixed" or "connected" to another feature, it may be directly fixed or connected to the other feature or indirectly fixed or connected to the other feature. Furthermore, the descriptions of upper, lower, left, right, etc. used in the present disclosure are only relative to the mutual positional relationship of the constituent parts of the present disclosure in the drawings. As used in this disclosure, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. In addition, unless defined otherwise, all technical and scientific terms used in this example have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in the description of the embodiments herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this embodiment, the term "and/or" includes any combination of one or more of the associated listed items.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element of the same type from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of the present disclosure. The use of any and all examples, or exemplary language ("e.g.," such as "or the like") provided with this embodiment is intended merely to better illuminate embodiments of the invention and does not pose a limitation on the scope of the invention unless otherwise claimed.
It should be recognized that embodiments of the present invention can be realized and implemented by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with the computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, according to the methods and figures described in the detailed description. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Further, operations of processes described in this embodiment can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described in this embodiment (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or combinations thereof. The computer program includes a plurality of instructions executable by one or more processors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable interface, including but not limited to a personal computer, mini computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and the like. Aspects of the invention may be embodied in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optically read and/or write storage medium, RAM, ROM, or the like, such that it may be read by a programmable computer, which when read by the storage medium or device, is operative to configure and operate the computer to perform the procedures described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described in this embodiment includes these and other different types of non-transitory computer-readable storage media when such media include instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention also includes the computer itself when programmed according to the methods and techniques described herein.
A computer program can be applied to input data to perform the functions described in the present embodiment to convert the input data to generate output data that is stored to a non-volatile memory. The output information may also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including particular visual depictions of physical and tangible objects produced on a display.
The above description is only a preferred embodiment of the present invention, and the present invention is not limited to the above embodiment, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention as long as the technical effects of the present invention are achieved by the same means. The invention is capable of other modifications and variations in its technical solution and/or its implementation, within the scope of protection of the invention.
Claims (8)
1. An ambient backscatter communications signal processing method, comprising:
acquiring a mixed signal; the mixed signal comprises communication signals sent by a plurality of electronic tags respectively, and the communication signals are formed by the electronic tags through reflecting or not reflecting environmental signals;
acquiring a plurality of data points corresponding to the mixed signal on an IQ domain; wherein one of said data points corresponds to one possible state of one of said communication signals;
clustering each data point by using a maximum expected clustering algorithm based on a Gaussian mixture model to determine a target point cluster; the target point cluster comprises a plurality of the data points;
and returning the possible states of the communication signals corresponding to the data points in the target point cluster as decoding results of the mixed signals.
2. The method according to claim 1, wherein said obtaining a plurality of data points corresponding to said hybrid signal in an IQ domain comprises:
carrying out IQ decomposition on the mixed signal to acquire amplitude information and phase information of each communication signal; wherein the amplitude information of the same communication signal comprises a plurality of possible states;
and determining the data point by taking the amplitude information and the phase information as coordinates on an IQ domain.
3. The method of claim 1, wherein the clustering each of the data points using a maximum expected clustering algorithm based on a gaussian mixture model to determine a cluster of target points comprises:
randomly determining a plurality of initial point clusters;
adjusting the distribution of the initial point clusters by using a maximum expectation clustering algorithm based on a Gaussian mixture model, and respectively determining the probability that each data point belongs to each initial point cluster;
uniquely attributing each of the data points to the initial point cluster to which the maximum probability belongs;
when it is detected that each data point included in one initial point cluster can correspond to all the electronic tags, the initial point cluster is determined as the target point cluster.
4. The method of claim 3, wherein randomly determining a plurality of initial clusters of points comprises:
set mNThe initial point cluster; wherein m is the number of possible states of one of the communication signals, and N is the number of the electronic tags;
randomly setting mNInitial mean value and mNAn initial variance;
assigning one of the initial means and one of the initial variances to each of the initial clusters;
setting each of the initial point clusters to a gaussian distribution determined by the initial mean and the initial variance assigned thereto.
5. The method of ambient backscatter communications signal processing according to claim 4 wherein m is 2.
6. The method of claim 5, wherein the adjusting the distribution of the initial point clusters using a maximum expected clustering algorithm based on a Gaussian mixture model to determine the probability of each data point belonging to each initial point cluster comprises:
A. determining that the kth cluster of initiation points satisfies a Gaussian distribution
Wherein, mukMean, σ, of the Gaussian distribution satisfied by the kth cluster of initial pointsk 2A variance of a gaussian distribution satisfied by a kth of the initial point clusters;
B. determining the data point x according to the Gaussian distribution satisfied by the initial point clusternThe probability of belonging to the kth initial point cluster is pik；
C. Determining the data point x according to the Gaussian distribution satisfied by the initial point clusternA posterior probability of belonging to the kth initial point cluster of
D. Determining a maximum likelihood estimate of the mean as
The maximum likelihood of said variance is estimated as
The data point xnThe maximum likelihood estimation of the probability of belonging to the kth initial point cluster is
Wherein N iskThe number of data points included in the kth initial point cluster;
E. performing an iteration with steps C and D until the data point x is obtainednThe maximum likelihood estimates of the probabilities belonging to the kth of the initial point cluster converge.
7. The method of ambient backscatter communications signal processing according to claim 6, wherein:
the maximum likelihood estimate of the mean is such thatkMaximum likelihood function pair mukA solution with a partial derivative of zero;
the maximum likelihood estimation of the variance is to make sigmak 2Maximum likelihood function pair ofk 2A solution with a partial derivative of zero;
the data point xnThe maximum likelihood estimate of the probability of belonging to the kth of said initial point cluster is such thatkMaximum likelihood function pair ofkThe partial derivative of (c) is a solution of zero.
8. The method of any of claims 1-7, wherein the method is performed by a reader, the reader being coupled to each of the electronic tags.
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