CN113541749B - Method, system, device and storage medium for estimating transmitted signal - Google Patents

Method, system, device and storage medium for estimating transmitted signal Download PDF

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CN113541749B
CN113541749B CN202110816265.9A CN202110816265A CN113541749B CN 113541749 B CN113541749 B CN 113541749B CN 202110816265 A CN202110816265 A CN 202110816265A CN 113541749 B CN113541749 B CN 113541749B
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许耀华
丁梦琴
王翊
蒋芳
王惠平
朱成龙
刘瑜
柏娜
胡艳军
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Abstract

The invention provides a method, a system, equipment and a storage medium for estimating a transmitted signal, which are used for an MIMO system, and the estimation method comprises the following steps: receiving an external receiving signal; establishing a quantum population; extracting an optimal probability amplitude from the initial probability amplitude of the quantum chromosome; updating the initial probability amplitude: obtaining a variation probability amplitude, a mixed probability amplitude and a rotation probability amplitude according to the initial probability amplitude; updating the initial probability amplitude according to the mixed probability amplitude and the rotating probability amplitude, extracting the latest optimal probability amplitude, and adding one to the iteration times; judging whether the iteration times reach an iteration threshold value: if yes, obtaining an estimated signal of the transmitting signal according to the latest optimal probability amplitude; if not, the updating of the initial probability amplitude is continued. The invention adopts a maximum likelihood detection function algorithm to update the probability amplitude of the quantum chromosome, so that the probability amplitude approaches to a transmitting signal and converges; the problem of high signal estimation complexity is solved, the performance is guaranteed, meanwhile, the number of searching times is reduced, and the signal estimation complexity is reduced.

Description

Method, system, equipment and storage medium for estimating transmitted signal
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a method, a system, a device, and a storage medium for estimating a transmission signal.
Background
Generally, a transmitting end and a receiving end in a Multiple Input Multiple Output (MIMO) system are both configured with Multiple antennas, and the use of Multiple antennas provides higher channel capacity and also brings challenges to estimation of a transmission signal of the MIMO system; generally, Zero Forcing (ZF) and Minimum Mean Square Error (MMSE) are often used to estimate signals, however, when the ratio of the number of transmit antennas to the number of receive antennas is small, the performance of the two algorithms is poor; the maximum likelihood algorithm (ML) has the best performance, but is high in complexity and not easy to process.
Quantum computing is used as an emerging technical means, is widely applied to multiple fields, has great potential in practical use, and Quantum algorithms such as grover Quantum search algorithm, Quantum ant colony algorithm and Quantum heuristic algorithm can be well applied to signal estimation of the MIMO system, while Quantum Differential Evolution (QDE) can combine Quantum state with traditional Differential Evolution, so that the Quantum computing has better search function.
In summary, the estimation method of the transmitted signal in the prior art has the problems of high complexity, poor detection performance and the like.
Disclosure of Invention
In view of the above drawbacks of the prior art, an object of the present invention is to provide a method, a system, a device and a storage medium for estimating a transmission signal, so as to improve the problems of high complexity, poor detection performance and the like of the method for estimating a transmission signal in the prior art.
To achieve the above and other related objects, the present invention provides a method for estimating a transmitted signal, which is used in a MIMO system, the method comprising:
receiving a receiving signal from the outside;
establishing a quantum population; wherein the quantum population comprises a plurality of quantum chromosomes:
extracting an optimal probability amplitude from the initial probability amplitude of the quantum chromosome by adopting a maximum likelihood detection function algorithm;
updating the initial probability amplitude:
carrying out mutation, mixing and rotation transformation processing on the initial probability amplitude to respectively obtain a mutation probability amplitude, a mixed probability amplitude and a rotation probability amplitude;
updating all the initial probability amplitudes according to the mixed probability amplitude and the rotation probability amplitude by adopting a maximum likelihood detection function algorithm, extracting to obtain a latest optimal probability amplitude, and adding one to the iteration times;
judging whether the iteration times reach a preset iteration threshold value:
if so, processing to obtain an estimation signal of the transmitting signal according to the latest optimal probability amplitude;
if not, continuing to update the initial probability amplitude.
In an embodiment of the present invention, the step of extracting an optimal probability amplitude from the initial probability amplitude of the quantum chromosome by using a maximum likelihood detection function algorithm includes:
quantizing the initial probability magnitudes to binary numbers:
Figure BDA0003168070880000021
wherein, in the process,
Figure BDA0003168070880000022
representing an initial probability amplitude of 0 qubit at length j for an ith quantum chromosome in the quantum population at the time of the tth iteration; g represents the initial probability amplitude after quantization; i is an e [1, n ]]N represents the total number of quantum chromosomes; j is an element of [1, m ]]M represents the total length of the quantum chromosome;
modulating the quantized initial probability amplitude, and performing left multiplication on the modulated initial probability amplitude and a pre-stored channel matrix;
calculating the Euclidean distance between the pre-multiplied initial probability amplitude and the received signal:
Figure BDA0003168070880000023
wherein f represents the ohmA distance in degrees;
Figure BDA0003168070880000024
represents the initial probability amplitude of the ith quantum chromosome, quantum bit 0, in the quantum population at the t-th iteration
Figure BDA0003168070880000031
y represents the received signal; h represents the channel matrix; m represents the initial probability amplitude after modulation;
and taking the initial probability amplitude corresponding to the minimum Euclidean distance as the optimal probability amplitude.
In an embodiment of the present invention, the step of performing mutation, mixing, and rotation transformation on the initial probability amplitude to obtain a mutation probability amplitude, a mixed probability amplitude, and a rotation probability amplitude respectively includes:
calculating the variation probability amplitude according to the initial probability amplitude:
Figure BDA0003168070880000032
wherein K represents the variation probability amplitude; f represents a mutation factor; rand is represented by [0,1 ]]A random number within a range; r is a radical of hydrogen1Denotes the r-th in a quantum population1A chromosome of a quantum; r is2Denotes the r-th in a quantum population2A chromosome of a quantum;
calculating the mixed probability amplitude according to the variation probability amplitude:
Figure BDA0003168070880000033
wherein h represents the mixing probability amplitude; randjIs represented by [0,1 ]]A random number within a range; CR represents a crossover factor; j is a function ofrandIs represented by [0, m]A random number within a range;
calculating the rotation probability amplitude according to the initial probability amplitude:
Figure BDA0003168070880000034
wherein μ represents a rotation probability amplitude of 0 qubit; λ represents the rotation probability amplitude with qubit 1; r (θ) represents the quantum rotating gate: beta is atRepresenting the initial probability amplitude of quantum bit 1 in the t iteration;
Figure BDA0003168070880000035
where θ represents a rotation angle.
In an embodiment of the present invention, the rotation angle is obtained by the following formula:
Figure BDA0003168070880000036
wherein the content of the first and second substances,
Figure BDA0003168070880000037
representing the optimal probability amplitude of the ith quantum chromosome in the quantum population at the length j with the quantum bit of 0 when the t iteration is performed; iteration represents a preset iteration threshold.
In an embodiment of the present invention, the step of updating all the initial probability amplitudes according to the mixed probability amplitude and the rotated probability amplitude by using a maximum likelihood detection function algorithm, and extracting to obtain a latest optimal probability amplitude, and adding one to the iteration number includes:
calculating to obtain an updated initial probability amplitude according to a maximum likelihood detection function algorithm:
Figure BDA0003168070880000041
wherein the content of the first and second substances,
Figure BDA0003168070880000042
represents the t +1 th iterationThen, the initial probability amplitude of the ith quantum chromosome in the quantum population, with the quantum bit being 0;
according to a maximum likelihood detection function algorithm, extracting the latest optimal probability amplitude from the updated initial probability amplitude, and adding one to the iteration times;
judging whether the current iteration times reach a preset iteration threshold value or not;
if so, processing to obtain an estimated signal of the transmitting signal according to the latest optimal probability amplitude;
if not, continuing to update the initial probability amplitude.
In an embodiment of the present invention, the step of extracting a latest optimal probability amplitude from the updated initial probability amplitudes according to a maximum likelihood detection function algorithm includes:
quantizing the updated initial probability amplitude into binary number, and after modulation, performing left multiplication on the binary number and the channel matrix, and calculating the Euclidean distance between the modulated binary number and the received signal:
and taking the corresponding updated initial probability amplitude when the Euclidean distance is minimum as the latest optimal probability amplitude.
In an embodiment of the present invention, the step of processing the estimation signal of the transmission signal according to the latest optimal probability amplitude includes:
and carrying out quantization processing on the latest optimal probability amplitude to obtain the estimation signal.
The invention also discloses a system for estimating the transmitted signal, which comprises:
the receiving signal receiving module is used for receiving signals from the outside;
the quantum population storage module is used for storing a quantum population comprising a plurality of quantum chromosomes;
the optimal probability amplitude acquisition module is used for extracting an optimal probability amplitude from the initial probability amplitude of the quantum chromosome by adopting a maximum likelihood detection function algorithm;
a probability amplitude updating module for updating the initial probability amplitude, comprising:
a probability amplitude processing unit, configured to perform mutation, mixing, and rotation transformation processing on the initial probability amplitude to obtain a mutation probability amplitude, a mixed probability amplitude, and a rotation probability amplitude, respectively;
a probability amplitude updating unit for updating all the initial probability amplitudes according to the mixed probability amplitude and the rotation probability amplitude by adopting a maximum likelihood detection function algorithm, extracting to obtain the latest optimal probability amplitude, and adding one to the iteration times;
the iteration frequency judging module is used for judging whether the iteration frequency reaches a preset iteration threshold value:
if so, processing to obtain an estimated signal of the transmitting signal according to the latest optimal probability amplitude;
if not, the initial probability amplitude is continuously updated until the estimation signal is obtained.
The invention also discloses a device for estimating the transmitted signal, which is characterized by comprising a processor, wherein the processor is coupled with a memory, the memory stores program instructions, and the program instructions stored in the memory realize the method for estimating the transmitted signal when being executed by the processor.
The present invention also discloses a computer-readable storage medium containing a program which, when run on a computer, causes the computer to execute the method of estimating the transmitted signal.
In summary, the estimation method, system, device and storage medium for the transmitted signal provided by the present invention adopt QDE algorithm, simulate the transmitted signal by constructing a series of probability amplitudes of the quantum chromosomes, and change the probability amplitudes of the quantum chromosomes through quantum mutation and cross-mixing operations to increase the diversity of the quantum population; the search range of the probability range of the quantum chromosomes is expanded by using the quantum revolving gate, so that the probability range is prevented from falling into a local optimal solution; updating the probability amplitude of the quantum chromosome by adopting a maximum likelihood detection function algorithm so that the probability amplitude approaches to a transmitting signal and converges; the problem of high complexity of signal estimation is solved, the estimation performance is ensured, the searching times are reduced, and the complexity of signal estimation is reduced.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a system flow diagram illustrating a method for estimating a transmitted signal according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of a system for estimating a transmitted signal according to an embodiment of the present invention.
Fig. 3 is a schematic structural diagram of a probability amplitude updating module according to an embodiment of the invention.
Fig. 4 is a schematic structural diagram of an apparatus for estimating a transmitted signal according to an embodiment of the present invention.
Fig. 5 is a graph showing the comparison of the error rate performance of the QDE algorithm, the ML algorithm, the MMSE algorithm, and the ZF algorithm in one embodiment in a MIMO system with an antenna size of 4 × 4.
Fig. 6 is a graph showing the comparison of the error rate performance of the QDE algorithm, the ML algorithm, the MMSE algorithm, and the ZF algorithm in one embodiment in a MIMO system with an antenna size of 8 × 16.
Fig. 7 is a graph showing a comparison of error rate performance of the QDE algorithm, ML algorithm, MMSE algorithm, and ZF algorithm in an embodiment in a MIMO system with an antenna size of 8 × 64.
Fig. 8 is a graph showing the error rate performance comparison of the QDE algorithm, MMSE algorithm, and ZF algorithm in one embodiment in a MIMO system with an antenna size of 16 × 64.
Description of the element reference numerals
100. An estimation system of the transmitted signal;
110. a received signal receiving module;
120. a quantum population storage module;
130. an optimal probability amplitude obtaining module;
140. a probability amplitude updating module;
141. a probability amplitude processing unit;
142. a probability amplitude updating unit;
150. an iteration number judging module:
200. an estimation device of the transmitted signal;
210. a processor;
220. a memory.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. It is also to be understood that the terminology used in the examples is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present invention. Test methods in which specific conditions are not noted in the following examples are generally performed under conventional conditions or conditions recommended by each manufacturer.
Please refer to fig. 1 to 8. It should be understood that the structures, ratios, sizes, and the like shown in the drawings are only used for matching the disclosure of the present disclosure, and are not used for limiting the conditions of the present disclosure, so that the present disclosure is not limited to the technical essence, and any modifications of the structures, changes of the ratios, or adjustments of the sizes, can still fall within the scope of the present disclosure without affecting the function and the achievable purpose of the present disclosure. In addition, the terms "upper", "lower", "left", "right", "middle" and "one" used in the present specification are for clarity of description, and are not intended to limit the scope of the present invention, and the relative relationship between the terms and the terms is not to be construed as a scope of the present invention.
When numerical ranges are given in the examples, it is understood that both endpoints of each of the numerical ranges and any value therebetween can be selected unless the invention otherwise indicated. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs and the description of the present invention, and any methods, apparatuses, and materials similar or equivalent to those described in the examples of the present invention may be used to practice the present invention.
The MIMO system uses a plurality of transmitting antennas and receiving antennas at a transmitting end and a receiving end, respectively, so that signals are transmitted and received through the plurality of antennas of the transmitting end and the receiving end, thereby improving communication quality. The multi-antenna multi-transmission multi-reception mobile communication system can fully utilize space resources, realizes multi-transmission and multi-reception through a plurality of antennas, can improve the system channel capacity by times under the condition of not increasing frequency spectrum resources and antenna transmitting power, shows obvious advantages, and is regarded as the core technology of next generation mobile communication.
The MIMO system comprises a base station provided with a plurality of receiving antennas, and when a user sends a transmitting signal to the base station, the transmitting signal is influenced by additive white Gaussian noise, so that the receiving signal received by the base station has a certain bit error rate.
The method for estimating the emission signal in the embodiment simulates the emission signal sent by the user by constructing a series of quantum chromosomes and adopting the probability amplitude of the quantum chromosomes, wherein the probability amplitude is also called as quantum amplitude in quantum mechanics and is a complex function for describing the quantum behavior of the particle. For example, the probability map may describe the position of the particle; when describing the position of a particle, the probability amplitude is a wave function, expressed as a function of position.
Qubits are the basic units of information given in quantum computing, each classical bit can only represent 0 or 1, qubits are the superposition states of these two states, and after superposition, a qubit can be either 1 or 0, or any linear combination of the two.
The probability amplitude of a quantum chromosome can be represented by the following formula:
Figure BDA0003168070880000081
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003168070880000082
indicates the probability amplitude of the quantum chromosome, α indicates the probability amplitude of 0 qubit, and β indicates the probability amplitude of 1 qubit.
Further, α and β satisfy the following formula:
|α|2+|β|2＝1
wherein | α |2Represents the probability that the qubit is 0, | β2Representing the probability of a qubit being 1.
In the present embodiment, the estimation of the transmission signal is performed based on the probability when the qubit is 0.
Referring to fig. 1, a system flow diagram of a method for estimating a transmitted signal in the present embodiment is shown, which is used in a MIMO system, and the method specifically includes:
s100, receiving a receiving signal from the outside;
the user sends a transmitting signal, and a base station in the MIMO system receives a receiving signal corresponding to the transmitting signal.
S200, establishing a quantum population; wherein the quantum population comprises a plurality of quantum chromosomes:
for example, in the present embodiment, there are N quantum populations, and i quantum chromosomes are present in each quantum population.
S300, extracting an optimal probability amplitude from the initial probability amplitude of the quantum chromosome by adopting a maximum likelihood detection function algorithm;
in this embodiment, the channel matrix of the MIMO system is stored in advance, and step S300 includes:
the initial probability magnitudes are quantized to discrete binary numbers using the following formula:
Figure BDA0003168070880000091
wherein the content of the first and second substances,
Figure BDA0003168070880000092
representing an initial probability amplitude of 0 qubit at length j for an ith quantum chromosome in the quantum population at the time of the tth iteration; g represents the initial probability amplitude after quantization; i belongs to [1, n ]]N represents the total number of quantum chromosomes; j is an element of [1, m ]]M represents the total length of the quantum chromosome;
modulating the quantized initial probability amplitude, and performing left multiplication on the modulated initial probability amplitude and a channel matrix;
and calculating the Euclidean distance between the left-multiplied initial probability amplitude and the received signal by adopting the following formula:
Figure BDA0003168070880000093
wherein f represents the euclidean distance;
Figure BDA0003168070880000096
represents the initial probability amplitude of the ith quantum chromosome in the quantum population with quantum bit of 0 at the t iteration, and
Figure BDA0003168070880000097
y represents the received signal; h represents the channel matrix; m represents the initial probability amplitude after modulation;
and taking the initial probability amplitude corresponding to the minimum Euclidean distance as the optimal probability amplitude.
In step S300, t is 0.
And S400, updating the initial probability amplitude.
The method specifically comprises the following steps:
s410, carrying out mutation, mixing and rotation transformation processing on the initial probability amplitude to respectively obtain a mutation probability amplitude, a mixed probability amplitude and a rotation probability amplitude;
the method specifically comprises the following steps:
and (3) according to the initial probability amplitude, obtaining a variation probability amplitude by adopting the following formula:
Figure BDA0003168070880000094
wherein K represents the variation probability amplitude; f represents a mutation factor; rand is represented by [0,1 ]]A random number within a range; r is1Denotes the r-th in a quantum population1A chromosome of a quantum; r is2Denotes the r-th in a quantum population2A chromosome of a quantum;
and according to the variation probability amplitude, processing by adopting the following formula to obtain a mixed probability amplitude:
Figure BDA0003168070880000095
wherein h represents a mixing probability amplitude; randjIs represented by [0,1 ]]A random number within a range; CR represents a crossover factor; j is a function ofrandIs represented by [0, m]Random number in the range, m represents the total length of the quantum chromosome;
and according to the initial probability amplitude, calculating by adopting the following formula to obtain a rotation probability amplitude:
Figure BDA0003168070880000101
wherein μ represents a rotation probability amplitude of 0 qubit; λ represents the rotation probability amplitude with qubit 1; beta is atRepresenting the initial probability amplitude of quantum bit 1 in the t iteration; r (theta) represents a quantum rotary gate.
Further, the quantum revolving door is expressed by the following formula:
Figure BDA0003168070880000102
where θ represents a rotation angle.
Further, the rotation angle θ is obtained by processing using the following equation:
Figure BDA0003168070880000103
wherein the content of the first and second substances,
Figure BDA0003168070880000104
representing the optimal probability amplitude of the ith quantum chromosome in the quantum population at the length j and the quantum bit of 0 at the t iteration; iteration denotes a preset iteration threshold.
The quantum population is random when just generated, in order to increase the diversity of the quantum population, the quantum population is subjected to mutation operation, and two different chromosomes r are randomly selected from the quantum population1And r2And taking the corresponding initial probability amplitude as a difference, and carrying out linear combination on the difference and the initial probability amplitude of the rest child chromosome to obtain a variation probability amplitude.
In order to improve the global search capability of the estimation method of the transmitted signal in this embodiment, the variation probability amplitude and the initial probability amplitude are mixed to obtain a mixed probability amplitude, so as to generate a new quantum chromosome, in the mixing process, the adopted cross factor is usually between 0 and 1, if the set value of the cross factor is too large, the convergence rate of the quantum chromosome is accelerated, and if the set value of the cross factor is smaller, the robustness of the finally obtained mixed probability amplitude is better.
In the calculation process of the quantum chromosome, the quantum state of the quantum chromosome can be converted through a quantum revolving gate, so that the quantum chromosome quickly approaches to an optimal solution.
S420, updating all initial probability amplitudes according to the mixed probability amplitude and the rotation probability amplitude by adopting a maximum likelihood detection function algorithm, extracting to obtain a latest optimal probability amplitude, and adding one to the iteration times;
the method specifically comprises the following steps:
according to the maximum likelihood detection function algorithm, the updated initial probability amplitude is calculated by adopting the following formula:
Figure BDA0003168070880000111
wherein the content of the first and second substances,
Figure BDA0003168070880000112
representing the initial probability amplitude of the ith quantum chromosome in the quantum population with the quantum bit of 0 at the time of the t +1 iteration;
and according to a maximum likelihood detection function algorithm, extracting the latest optimal probability amplitude from the updated initial probability amplitude, and adding one to the iteration times.
According to the maximum likelihood detection function algorithm, referring to step S300, the quantized mixed probability amplitude is modulated, and after being multiplied with the channel matrix, the Euclidean distance between the quantized mixed probability amplitude and the received signal is calculated to obtain the maximum likelihood detection function algorithm
Figure BDA0003168070880000113
Modulating the initial probability amplitude, and calculating the Euclidean distance between the initial probability amplitude and the received signal after the initial probability amplitude is multiplied by the channel matrix to obtain
Figure BDA0003168070880000114
If it is
Figure BDA0003168070880000115
Is less than
Figure BDA0003168070880000116
And taking the mixed probability amplitude as the updated initial probability amplitude, and taking the rotation probability amplitude as the updated initial probability amplitude under other conditions.
Further, referring to step S300, the updated initial probability amplitude is modulated, and is multiplied by the channel matrix, and then the euclidean distance between the updated initial probability amplitude and the received signal is calculated, and the probability amplitude with the minimum euclidean distance is extracted from the updated initial probability amplitude as the latest optimal probability amplitude, and meanwhile, the iteration number is increased by one, for example, when the initial probability amplitude is updated for the first time, the iteration number is 1, and when the initial probability amplitude is updated for the second time, the iteration number is 2.
S50, judging whether the iteration number reaches a preset iteration threshold value:
if so, processing to obtain an estimated signal of the transmitting signal according to the latest optimal probability amplitude;
if not, the initial probability amplitude is continuously updated until the estimation signal is obtained.
Judging whether the iteration times t +1 meet a preset iteration threshold, if so, quantizing the latest optimal probability amplitude into a binary system to obtain an estimation signal, and processing the estimation signal by adopting the following formula to obtain the estimation signal:
Figure BDA0003168070880000117
where S denotes the estimation signal.
Referring to fig. 5-8, shown are error rate performance comparison graphs when the estimation method of the transmission signal (i.e. QDE algorithm) in the present embodiment is respectively adopted based on MATLAB platform and the common ML algorithm, MMSE algorithm, ZF algorithm.
For example, in the MATLAB platform, the transmission channel is a rayleigh fading channel, the baseband modulation scheme is 4QAM, the variance factor F is set to 0.4, the cross factor CR is set to 0.3, and the quantum population N is set to 50.
Referring to fig. 5, in the MIMO system with an antenna scale of 4 × 4 (the number of users is 4, the number of antennas of the base station is 4), the number of iterations is preset to 30, since the modulation scheme is 4QAM, the number of antennas is 4, and each symbol contains two information bits during the modulation process, the length m of each quantum chromosome is 8; and taking the error rate as a y axis and the signal-to-noise ratio as an x axis as a line graph, wherein the minimum value of the signal-to-noise ratio is 0dB, the maximum value of the signal-to-noise ratio is 18dB, and the interval of the values is 2dB each time. As can be seen from FIG. 5, when the SNR is less than or equal to 14dB, the error rate performance curves of the QDE algorithm and the ML algorithm almost completely coincide, the detection performance is good, and until the SNR is 18dB, the error rate of the QDE algorithm is 1.4 multiplied by 10-4The error rate of ML algorithm is 1.2 x 10-4The two are very close.
Referring to fig. 6, in the case of an antenna size of 8 × 16 (the number of users is 8,base station antenna number 16), the number of iterations is preset to be 50, and the length m of each quantum chromosome is 16; and taking the error rate as a y axis and the signal-to-noise ratio as an x axis as a line graph, wherein the minimum value of the signal-to-noise ratio is 0dB, the maximum value of the signal-to-noise ratio is 10dB, and the interval of the values is 1dB each time. As can be seen from FIG. 6, when the SNR is between 0dB and 6dB, the error rate performance curves of the QDE algorithm and the ML algorithm almost completely coincide, when the SNR is between 7 dB and 10dB, the error rate performance of the QDE algorithm is slightly better than that of the ML algorithm, and when the SNR is 10dB, the error rate of the ML algorithm is 1.125 × 10-4The error rate of QDE algorithm is 2 × 10-4The detection difference from the ML algorithm is small, and the error rate is far better than that of the error rate of 3 multiplied by 10-3MMSE algorithm and bit error rate of 4.6 x 10-3ZF algorithm of (1).
Referring to fig. 7, in the MIMO system with an antenna size of 8 × 64 (the number of users is 8, and the number of base station antennas is 64), the number of iterations is set to 50 in advance, and the length m of each quantum chromosome is 16; and taking the error rate as a y axis and the signal-to-noise ratio as an x axis as a line graph, wherein the minimum value of the signal-to-noise ratio is-3 dB, the maximum value of the signal-to-noise ratio is 4dB, and the interval of the values is 1dB each time. As can be seen from FIG. 7, in the MIMO system, the error rate performance curves of the QDE algorithm and the ML algorithm almost completely coincide, and when the signal-to-noise ratio is 4dB, the error rates obtained by the QDE algorithm and the ML algorithm are both 1.75 multiplied by 10-5And the error rate of the MMSE algorithm is 4.5 multiplied by 10 when the signal-to-noise ratio is 4dB-5And the detection performance of the QDE algorithm is optimal at the moment.
Referring to fig. 8, in the MIMO system with an antenna size of 16 × 64 (the number of users is 16, and the number of base station antennas is 64), the number of iterations is set to 80 in advance, and the length m of each quantum chromosome is 32; because the number of the transmitting antennas is large, it is difficult to obtain the error rate curve of the ML algorithm, so the error rate performance curves of the QDE algorithm, the MMSE algorithm and the ZF algorithm are only compared: and taking the bit error rate as a y axis and the signal-to-noise ratio as an x axis as a line graph, wherein the minimum value of the signal-to-noise ratio is 0dB, the maximum value of the signal-to-noise ratio is 8dB, and the interval of the values of each time is 1 dB. As can be seen from fig. 8, in the MIMO system, the error rate performance of the QDE algorithm is far better than that of the other two algorithms.
The operational complexity of the QDE algorithm in this embodiment mainly comes from the operation of the maximum likelihood detection function, and the maximum likelihood detection function needs to be calculated twice on the quantum chromosome in each iteration process. Whereas the search of the ML algorithm is much more complex than the QDE algorithm.
Preferably, assuming that in the MIMO system, the number of transmit antennas is 8, the number of receive antennas is 64, the snr is 2dB, and the number of iterations of the QDE algorithm is 50, the operating time ratios required for transmitting two symbol vectors by the QDE algorithm, the ML algorithm, the MMSE algorithm, and the ZF algorithm are compared as shown in table 1:
table 1: run time comparison
Algorithm QDE algorithm ML algorithm MMSE algorithm ZF algorithm
Time/s 2.1549 0.5589 0.0094 0.0071
As can be seen from table 1, the QDE algorithm operates less frequently than the ML algorithm and more frequently than the MMSE algorithm and ZF algorithm.
Further, compared with the ML algorithm, the QDE algorithm has a smaller number of searches than the ML algorithm.
Therefore, in summary, the QDE algorithm reduces the error rate to some extent, and reduces the computational complexity.
Referring to fig. 2-3, the present embodiment further provides a system 100 for estimating a transmitted signal, including:
a received signal receiving module 110, configured to receive a received signal from the outside;
a quantum population storage module 120 for storing a quantum population comprising a plurality of quantum chromosomes;
a probability amplitude updating module 130, configured to update the initial probability amplitude, including:
an optimal probability amplitude obtaining unit 131, configured to extract an optimal probability amplitude from the initial probability amplitude of the quantum chromosome by using a maximum likelihood detection function algorithm;
a probability amplitude processing unit 132, configured to perform mutation, mixing, and rotation transformation on the initial probability amplitude to obtain a mutation probability amplitude, a mixed probability amplitude, and a rotation probability amplitude, respectively;
a probability amplitude updating unit 133, configured to update all initial probability amplitudes according to the mixed probability amplitude and the rotation probability amplitude by using a maximum likelihood detection function algorithm, extract a latest optimal probability amplitude, and add one to the iteration number;
an iteration number judging module 140, configured to judge whether the iteration number reaches a preset iteration threshold:
if so, processing to obtain an estimated signal of the transmitting signal according to the latest optimal probability amplitude;
if not, the updating of the initial probability amplitude is continued until an estimation signal is obtained.
Referring to fig. 4, the present embodiment further provides an estimation apparatus 200 for a transmitted signal, where the estimation apparatus 200 includes a processor 210 and a memory 220, the processor 210 is coupled to the memory 220, the memory 220 stores program instructions, and when the program instructions stored in the memory 220 are executed by the processor 210, the estimation method for the transmitted signal is implemented. The Processor 210 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the system can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component; the Memory 220 may include a Random Access Memory (RAM), and may also include a Non-Volatile Memory (Non-Volatile Memory), such as at least one disk Memory. The Memory 220 may also be an internal Memory of Random Access Memory (RAM) type, and the processor 210 and the Memory 220 may be integrated into one or more independent circuits or hardware, such as: application Specific Integrated Circuit (ASIC). It should be noted that the computer program in the memory 220 can be implemented in the form of software functional units and stored in a computer readable storage medium when the computer program is sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, an electronic device, or a network device) to perform all or part of the steps of the method according to the embodiments of the present invention.
The present embodiment also proposes a computer-readable storage medium storing computer instructions for causing a computer to execute the above-mentioned estimation method of a transmission signal. The storage medium may be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system or a propagation medium. The storage medium may also include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a Random Access Memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk. Optical disks may include compact disk-read only memory (CD-ROM), compact disk-read/write (CD-RW), and DVD.
In summary, the method, the system, the device and the storage medium for estimating the transmitted signal provided by the invention adopt the QDE algorithm, simulate the transmitted signal by constructing a series of probability amplitudes of the quantum chromosomes, and change the probability amplitudes of the quantum chromosomes through quantum variation and cross mixing operation to increase the diversity of the quantum population; the search range of the probability range of the quantum chromosomes is expanded by using quantum rotation, so that the probability range is prevented from falling into a local optimal solution; updating the probability amplitude of the quantum chromosome by adopting a maximum likelihood detection function algorithm to enable the probability amplitude to approach to a transmission signal and converge; the problem of high complexity of signal estimation is solved, the estimation performance is ensured, the searching times are reduced, and the complexity of signal estimation is reduced. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.

Claims (8)

1. A method for estimating a transmitted signal, for use in a MIMO system, the method comprising:
receiving a receiving signal from the outside;
establishing a quantum population; wherein the quantum population comprises a plurality of quantum chromosomes:
extracting an optimal probability amplitude from the initial probability amplitude of the quantum chromosome by adopting a maximum likelihood detection function algorithm;
updating the initial probability amplitude:
carrying out mutation, mixing and rotation transformation processing on the initial probability amplitude to respectively obtain a mutation probability amplitude, a mixed probability amplitude and a rotation probability amplitude;
updating all the initial probability amplitudes according to the mixed probability amplitude and the rotation probability amplitude by adopting a maximum likelihood detection function algorithm, extracting to obtain a latest optimal probability amplitude, and simultaneously adding one to the iteration times;
judging whether the iteration times reach a preset iteration threshold value:
if so, processing to obtain an estimated signal of the transmitting signal according to the latest optimal probability amplitude;
if not, continuing to update the initial probability amplitude;
wherein, the step of extracting the optimal probability amplitude from the initial probability amplitude of the quantum chromosome by adopting a maximum likelihood detection function algorithm comprises the following steps:
quantizing the initial probability magnitudes to binary numbers:
Figure FDA0003644279650000011
wherein the content of the first and second substances,
Figure FDA0003644279650000012
representing an initial probability amplitude of 0 qubit at length j for an ith quantum chromosome in the quantum population at the time of the tth iteration; g represents the initial probability amplitude after quantization; i is an e [1, n ]]N represents the total number of quantum chromosomes; j is an element of [1, m ]]M represents the total length of the quantum chromosome;
modulating the quantized initial probability amplitude, and performing left multiplication on the modulated initial probability amplitude and a pre-stored channel matrix;
calculating the Euclidean distance between the pre-multiplied initial probability amplitude and the received signal:
Figure FDA0003644279650000021
wherein f represents the euclidean distance;
Figure FDA0003644279650000022
to representAt the t iteration, the ith quantum chromosome in the quantum population has an initial probability amplitude of 0 qubit, and
Figure FDA0003644279650000023
y represents the received signal; h represents the channel matrix; m represents the initial probability amplitude after modulation;
taking the initial probability amplitude corresponding to the minimum Euclidean distance as the optimal probability amplitude;
wherein, the step of performing mutation, mixing and rotation transformation processing on the initial probability amplitude to respectively obtain a mutation probability amplitude, a mixed probability amplitude and a rotation probability amplitude comprises:
calculating the mutation probability amplitude according to the initial probability amplitude:
Figure FDA0003644279650000024
wherein the content of the first and second substances,
Figure FDA0003644279650000025
representing the variation probability amplitude of the ith quantum chromosome in the quantum population at the length j and the quantum bit of 0 at the t +1 iteration; f represents a mutation factor; rand is represented by [0,1 ]]A random number within a range; r is1Denotes the r-th in a quantum population1A chromosome of a quantum; r is2Denotes the r-th in a quantum population2A chromosome of a quantum;
calculating the mixed probability amplitude according to the variation probability amplitude:
Figure FDA0003644279650000026
wherein the content of the first and second substances,
Figure FDA0003644279650000027
representing the ith quantum chromosome in the quantum population at length j with qubit 0 at iteration t +1Mixing probability amplitude; rand is represented by [0,1 ]]A random number within a range; CR represents a crossover factor; j is a function ofrandIs represented by [0, m]A random number within a range;
calculating the rotation probability amplitude according to the initial probability amplitude:
Figure FDA0003644279650000028
wherein, mut+1Representing the rotation probability amplitude of quantum bit 0 when the (t + 1) th iteration is performed; lambda [ alpha ]t+1Representing the rotation probability amplitude of quantum bit 1 when the (t + 1) th iteration is performed; alpha (alpha) ("alpha")tShowing the initial probability amplitude of 0 quantum bit during the t iteration; beta is atRepresenting the initial probability amplitude of quantum bit 1 in the t iteration; r (θ) represents a quantum rotating gate:
Figure FDA0003644279650000029
where θ represents a rotation angle.
2. The estimation method according to claim 1, wherein the rotation angle of the ith quantum chromosome in the quantum population at length j with qubit of 0 is obtained at the t-th iteration by processing the following formula
Figure FDA0003644279650000038
Figure FDA0003644279650000032
Wherein the content of the first and second substances,
Figure FDA0003644279650000033
representing the optimal probability amplitude of the ith quantum chromosome in the quantum population at the length j and the quantum bit of 0 at the t iteration; iteration means presettingThe iteration threshold of (2).
3. The estimation method according to claim 1, wherein the step of updating all the initial probability magnitudes and extracting the latest optimal probability magnitude according to the mixed probability magnitude and the rotated probability magnitude by using a maximum likelihood detection function algorithm, and the step of adding one to the iteration number comprises:
calculating to obtain an updated initial probability amplitude according to a maximum likelihood detection function algorithm:
Figure FDA0003644279650000034
wherein the content of the first and second substances,
Figure FDA0003644279650000035
represents the initial probability amplitude of the ith quantum chromosome, quantum bit 0 in the quantum population at the time of the t +1 iteration,
Figure FDA0003644279650000036
represents the mixed probability amplitude of the ith quantum chromosome and the quantum bit of 0 in the quantum population at the time of the t +1 iteration,
Figure FDA0003644279650000037
representing the rotation probability amplitude of the ith quantum chromosome in the quantum population with the quantum bit of 0 at the time of the t +1 iteration;
according to a maximum likelihood detection function algorithm, extracting the latest optimal probability amplitude from the updated initial probability amplitude, and adding one to the iteration times;
judging whether the current iteration times reach a preset iteration threshold value or not;
if yes, processing to obtain the estimation signal according to the latest optimal probability amplitude;
if not, continuing to update the initial probability amplitude.
4. The estimation method according to claim 3, wherein the step of extracting the latest optimal probability amplitude from the updated initial probability amplitudes according to the maximum likelihood detection function algorithm comprises:
quantizing the updated initial probability amplitude into binary number, and after modulation, performing left multiplication on the binary number and the channel matrix, and calculating Euclidean distance between the modulated binary number and the received signal:
and taking the corresponding updated initial probability amplitude when the Euclidean distance is minimum as the latest optimal probability amplitude.
5. The estimation method according to claim 4, wherein the step of processing the estimated signal of the transmitted signal according to the latest optimal probability amplitude comprises:
and carrying out quantization processing on the latest optimal probability amplitude to obtain the estimation signal.
6. An estimation system for a transmitted signal, for use in a MIMO system, comprising:
the receiving signal receiving module is used for receiving signals from the outside;
the quantum population storage module is used for storing a quantum population comprising a plurality of quantum chromosomes;
the optimal probability amplitude acquisition module is used for extracting an optimal probability amplitude from the initial probability amplitude of the quantum chromosome by adopting a maximum likelihood detection function algorithm;
a probability amplitude updating module for updating the initial probability amplitude, comprising:
a probability amplitude processing unit, configured to perform mutation, mixing, and rotation transformation processing on the initial probability amplitude to obtain a mutation probability amplitude, a mixed probability amplitude, and a rotation probability amplitude, respectively;
a probability amplitude updating unit for updating all the initial probability amplitudes according to the mixed probability amplitude and the rotation probability amplitude by adopting a maximum likelihood detection function algorithm, extracting to obtain the latest optimal probability amplitude, and adding one to the iteration times;
the iteration frequency judging module is used for judging whether the iteration frequency reaches a preset iteration threshold value:
if so, processing to obtain an estimated signal of the transmitting signal according to the latest optimal probability amplitude;
if not, continuing to update the initial probability amplitude;
wherein, the step of extracting the optimal probability amplitude from the initial probability amplitude of the quantum chromosome by adopting a maximum likelihood detection function algorithm comprises the following steps:
quantizing the initial probability magnitudes to binary numbers:
Figure FDA0003644279650000041
wherein the content of the first and second substances,
Figure FDA0003644279650000042
representing an initial probability amplitude of 0 qubit at length j for an ith quantum chromosome in the quantum population at the time of the tth iteration; g represents the initial probability amplitude after quantization; i is an e [1, n ]]N represents the total number of quantum chromosomes; j is an element of [1, m ]]M represents the total length of the quantum chromosome;
modulating the quantized initial probability amplitude, and performing a left multiplication on the modulated initial probability amplitude and a pre-stored channel matrix;
calculating the Euclidean distance between the pre-multiplied initial probability amplitude and the received signal:
Figure FDA0003644279650000051
wherein f represents the euclidean distance;
Figure FDA0003644279650000052
represents the initial probability amplitude of the ith quantum chromosome, quantum bit 0, in the quantum population at the t-th iteration
Figure FDA0003644279650000053
y represents the received signal; h represents the channel matrix; m represents the initial probability amplitude after modulation;
taking the initial probability amplitude corresponding to the minimum Euclidean distance as an optimal probability amplitude;
wherein, the step of performing mutation, mixing and rotation transformation processing on the initial probability amplitude to respectively obtain a mutation probability amplitude, a mixed probability amplitude and a rotation probability amplitude comprises:
calculating the variation probability amplitude according to the initial probability amplitude:
Figure FDA0003644279650000054
wherein the content of the first and second substances,
Figure FDA0003644279650000055
representing the variation probability amplitude of the ith quantum chromosome in the quantum population at the length j and the quantum bit of 0 at the t +1 iteration; f represents a mutation factor; rand is represented by [0,1 ]]A random number within a range; r is1Denotes the r-th in a quantum population1A chromosome of a quantum; r is a radical of hydrogen2Representing the r-th in a quantum population2A chromosome of a quantum;
calculating the mixed probability amplitude according to the variation probability amplitude:
Figure FDA0003644279650000056
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003644279650000057
representing the mixed probability amplitude of the ith quantum chromosome in the quantum population at the length j and the quantum bit of 0 in the t +1 iteration; rand is represented by [0,1 ]]A random number within a range; CR represents a crossover factor; j is a function ofrandIs represented by [0, m]Within range ofThe number of machines;
calculating the rotation probability amplitude according to the initial probability amplitude:
Figure FDA0003644279650000058
wherein, mut+1Representing the rotation probability amplitude of quantum bit 0 when the (t + 1) th iteration is performed; lambda [ alpha ]t+1Representing the rotation probability amplitude of the quantum bit of 1 when the iteration is performed for the (t + 1) th time; alpha is alphatShowing the initial probability amplitude of 0 quantum bit during the t iteration; beta is atRepresenting the initial probability amplitude of 1 quantum bit when the t iteration is carried out; r (θ) represents a quantum rotating gate:
Figure FDA0003644279650000061
where θ represents a rotation angle.
7. An estimation device of a transmitted signal, comprising a processor coupled to a memory, the memory storing program instructions which, when executed by the processor, implement the estimation method of a transmitted signal according to any one of claims 1 to 5.
8. A computer-readable storage medium, characterized by comprising a program which, when run on a computer, causes the computer to execute the method of estimation of a transmitted signal according to any one of claims 1 to 5.
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