CN100547944C - Quantum nerve network is used for the method for Multiuser Detection - Google Patents
Quantum nerve network is used for the method for Multiuser Detection Download PDFInfo
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Abstract
The method of quantum nerve network Multiuser Detection relates to this method of The Realization of Simulation on classic computer, this method constitutes multi-user detector with quantum nerve network, network core adopts the feedback-type quantum neuron to simplify the structure of multi-user detector, network evolution utilizes quantum parallel computation characteristic to carry out quick optimizing, reduces the complexity of multi-user detector; Be specially: design a feedback-type quantum neuron, a kind ofly show the method for multiuser receiver received signal, a quantum nerve network multi-user detector, a parallel evolutionary operator F with the quantum register tables
1With parallel evolutionary operator F
1Act on the output quantum state of quantum nerve network multi-user detector, it is upgraded evolution; Repeat previous step suddenly the output quantum state after upgrading with upgrade before no change; Design the operator F that develops at random
2Substitute parallel evolutionary operator F
1Repeat previous step suddenly the output quantum state after upgrading with upgrade before no change.
Description
Technical Field
Quantum Neural Networks (QNN-Quantum Neural Networks) are a new intelligent computing paradigm combining conventional Artificial Neural Networks (ANNs) with Quantum computing theory. The invention relates to a method for applying a quantum neural network to multi-user detection and a method for realizing the method on a classical computer in a simulation way, and belongs to the technical field of communication signal processing.
Background
The concept of quantum neural computation was first proposed by professors Kak in the united states in 1995, after which some scholars proposed quantum neural network models such as quantum derived neural networks, quantum dot neural networks, quantum associative memory models, quantum entanglement neural networks, and the like. Research shows that Quantum Neural Networks (QNN) are superior to ANN in performance in the aspects of memory capacity, processing speed and the like due to quantum computing characteristics such as quantum parallelism and quantum entanglement, have stronger parallel processing capability than ANN, can process larger data sets, and can solve certain problems which cannot be solved by ANN, such as a catastrophe memory loss problem, a single-layer neural network cannot solve a linear indivisible problem and the like. In recent years, the research of quantum neural networks is active daily and has been primarily applied in the aspects of pattern recognition, entanglement calculation, function approximation and the like, but the research report of applying quantum neural networks to communication signal processing is rare.
The multi-user communication with large user amount is the basic communication form of future broadband high-speed multimedia mobile communication, is the main mode of 3G and updated generation mobile communication, and the real-time multi-user detection (MUD-MUTIUSER DETECTION) technology is one of the key technologies. Verdu proposed and analyzed an optimal multi-user detector in 1986, the detection performance of which can approach to the single-user reception performance, the near-far effect can be effectively overcome, and the system capacity is greatly improved, but the computational complexity of the optimal solution solving process is an exponential function of the number of users, and the optimal multi-user detector is an NP-hard problem along with the increase of the number of users. Many scholars have proposed linear and nonlinear suboptimal algorithms, including classical neural network detection methods, more typically multi-user detection methods using Hopfield neural networks.
The Hopfield neural network multi-user detection method has hardware complexity in proportion to the number of users, is easy to realize by a large-scale integrated circuit, but is not easy to converge to a global optimum point due to the adoption of a gradient descent algorithm in an iterative process. In addition, the complexity of the network increases as the square of the number of users, and when a plurality of users are simultaneously active in the channel, the network is very large and is not suitable for the channel with a plurality of users.
Disclosure of Invention
The technical problem is as follows: the invention aims to provide a method for realizing quantum neural network multi-user detection by simulation on a classical computer, thereby solving the problems that the classical neural network multi-user detector has high hardware complexity, is difficult to converge to a global optimum point and is not suitable for multi-user communication with large user quantity.
The technical scheme is as follows: the quantum neural network is used for the multi-user detection, the quantum neural network forms a multi-user detector, the network core adopts a feedback type quantum neural element to simplify the structure of the multi-user detector, the network evolution utilizes the quantum parallel computing characteristic to carry out rapid optimization, and the complexity of the multi-user detector is reduced; the specific implementation method comprises the following steps:
1.) designing a feedback type quantum neuron: the input and output of the feedback type quantum neuron are all n-bit quantum bits, namely n-bit quantum registers, the output is simultaneously used as the feedback input of the feedback type quantum neuron, the threshold value of the feedback type quantum neuron is also n-bit quantum bits, the connection weight of the feedback type quantum neuron is a K multiplied by K matrix, and the evolution operator of the feedback type quantum neuron
The operator acts on the n-bit quantum register and controls the evolution of the state of the feedback type quantum neuron;
2.) design a method for representing the received signal of the multi-user receiver by using a quantum register: preparing a quantum register of n-bit quantum bits using y>Indicating, selecting the number of bits of the quantum register K is the number of users of the multi-user receiver, order | y>Y is a received signal information bit vector of the multi-user receiver;
3.) design a quantum neural network multi-user detector: the core of the method is a feedback quantum neuron, and a quantum register y>As input to the multi-user detectorb>Is the output thereof;
4.) design a parallel evolution operator <math>
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5.) will evolve operator in parallel
Acting on the output quantum state of the quantum neural network multi-user detector, and updating and evolving the output quantum state;
6.) repeating the step 5) until the updated output quantum state is unchanged from the state before updating, namely the network state is stable, and the sending information sequence corresponding to the output quantum state at the moment is the detection result of the quantum neural network multi-user detector;
7.) design a random evolution operator
Surrogate parallel evolution operator
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</math> Wherein the function f (·) is defined as f (x) sign (u- | x |)2) Where sign (·) is a sign function and u is a term belonging to [0, 1]The | x | is the probability amplitude of quantum bit output by the quantum neural network;
8.) will evolve operator randomly
Acting on the output quantum state of the quantum neural network multi-user detector, and updating and evolving the output quantum state;
9.) repeating the step 8) until the updated output quantum state is unchanged from the state before updating, namely the network state is stable, and the sending information sequence corresponding to the output quantum state at the moment is the optimal solution of the quantum neural network multi-user detector.
Has the advantages that: the invention adopts a single quantum neuron to replace the traditional Hopfield neural network to form the quantum neural network multi-user detector, and the multi-user detector has simple structure and low hardware complexity. In the evolution of the network state, the quantum neural network multi-user detector can improve the optimization speed exponentially by utilizing the quantum parallel computing characteristic. Computer simulation results show that the performance of the quantum neural network multi-user detector designed by the invention is superior to that of a classical neural network multi-user detector; the improved quantum neural network multi-user detector adopting the random operator has better detection performance than the quantum neural network multi-user detector adopting the parallel operator, is closer to the performance of the optimal detector, can accommodate more users in the same detector performance, and can better solve the multi-user communication problem of large user quantity.
Drawings
Figure 1 is a Quantum Neural Network (QNN) multi-user detector architecture,
figure 2 is a flow chart of an implementation method of a quantum neural network for multi-user detection,
figure 3 is a Feedback Quantum Neuron (FQN) model,
figure 4 is a plot of bit error rate as a function of signal to noise ratio for user 1 of the 8 user synchronous system,
figure 5 is a plot of the bit error rate for user 1 as a function of the number of active users for a synchronous system signal-to-noise ratio of 9dB,
figure 6 is a plot of bit error rate as a function of signal to noise ratio for user 1 of an 8-user asynchronous system,
figure 7 is a plot of the bit error rate for user 1 as a function of the number of active users for an asynchronous system signal-to-noise ratio of 9dB for each,
figure 8 is a plot of bit error rate as a function of signal to noise ratio for user 1 of the 8-user synchronous system,
figure 9 is a plot of the bit error rate for user 1 as a function of the number of active users for a synchronous system signal-to-noise ratio of 9dB,
figure 10 is a plot of bit error rate as a function of signal to noise ratio for user 1 of an 8-user asynchronous system,
fig. 11 is a graph of the bit error rate of user 1 as a function of the number of active users for an asynchronous system signal-to-noise ratio of 9 dB.
Detailed Description
The invention designs a method for multi-user detection by adopting a quantum neural network and realizes the method on a classical computer in a simulation way. A feedback quantum neuron model and a method for representing a multi-user receiver receiving signal by using a quantum register are designed, and a multi-user detector based on a quantum neural network is designed on the basis, and the structure of the multi-user detector is shown in figure 1. In the figure, FQN is a feedback quantum neuron, the output of a classical multi-user detector matched filter is prepared (preparation) as a quantum register | y > as the input of a QNN multi-user detector, and R is a cross-correlation matrix of user characteristic waveforms.
The specific steps of the implementation method of the quantum neural network for multi-user detection are shown in fig. 2.
The QNN form of the optimal multi-user detection criterion is
The output quantum state update criterion of the QNN multi-user detector is
|b′(t+1)>＝W|b(t)>-|θ>＝|y>-R|b(t)>(2)
Subsequently to quantum state-b′(t+1)>＝[b′1(t+1)，b′2(t+1)，L，b′K(t+1)]TPerforming evolution with the evolution result state as
Wherein,
is an evolution operator.
The invention designs a series of simulation experiments to test the performance of the proposed multi-user detector based on the quantum neural network. A Bit Error rate (BER-Bit Error Ratio) change curve of an expected user (user 1 without loss of generality) of the quantum neural network multi-user detector at different signal-to-noise ratios and different user numbers is made through simulation, and the multiple access interference resistance of the QNN and other detection algorithms and the performance of the QNN when the QNN increases along with the user numbers are compared. Fig. 4-11 are simulation results for different situations, respectively.
As can be seen from simulation results in the figure, the QNN-MUD has better bit error rate detection performance no matter the synchronous condition and the asynchronous condition, and is superior to a traditional detector and the HNN-MUD, and the performance of the QNN-MUD is still superior to the HNN-MUD along with the increase of the number of the activated users. The improved QNN-MUD of the random operator designed by the invention has better bit error rate detection performance than the QNN-MUD, and can accommodate more users under the same detection performance.
The method forms a multi-user detector by a quantum neural network, the network core adopts a feedback type quantum neuron to simplify the structure of the multi-user detector, the network evolution utilizes the quantum parallel computing characteristic to carry out rapid optimization, and the complexity of the multi-user detector is reduced, and the method comprises the following steps:
1.) designing a feedback type quantum neuron: the input and output of the feedback type quantum neuron are all n-bit quantum bits, namely n-bit quantum registers, the output is simultaneously used as the feedback input of the feedback type quantum neuron, the threshold value of the feedback type quantum neuron is also n-bit quantum bits, the connection weight of the feedback type quantum neuron is a K multiplied by K matrix, and the evolution operator of the feedback type quantum neuron
The operator acts on the n-bit quantum register and controls the evolution of the state of the feedback type quantum neuron;
2.) design a method for representing the received signal of the multi-user receiver by using a quantum register: preparing a quantum register of n-bit quantum bits using y>Indicating, selecting the number of bits of the quantum register K is the number of users of the multi-user receiver, order | y>Y is a received signal information bit vector of the multi-user receiver;
3.) design a quantum neural network multi-user detector: the core of the method is a feedback quantum neuron, and a quantum register y>As input to the multi-user detectorb>Is the output thereof;
4.) design a parallel evolution operator
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</math> Where sign (·) is a sign function;
5.) will evolve operator in parallel
Acting on the output quantum state of the quantum neural network multi-user detector, and updating and evolving the output quantum state;
6.) repeating the step 5) until the updated output quantum state is unchanged from the state before updating, namely the network state is stable, and the sending information sequence corresponding to the output quantum state at the moment is the detection result of the quantum neural network multi-user detector;
7.) design a followMachine evolution operator
Surrogate parallel evolution operator
<math>
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</math> Wherein the function f (·) is defined as f (x) sign (u- | x |)2) Where sign (·) is a sign function and u is a term belonging to [0, 1]The | x | is the probability amplitude of quantum bit output by the quantum neural network;
8.) will evolve operator randomly
Acting on the output quantum state of the quantum neural network multi-user detector, and updating and evolving the output quantum state;
9.) repeating the step 8) until the updated output quantum state is unchanged from the state before updating, namely the network state is stable, and the sending information sequence corresponding to the output quantum state at the moment is the optimal solution of the quantum neural network multi-user detector.
The specific method of the invention is as follows:
designing a feedback type quantum neuron model in the first step
The Feedback Quantum Neuron (FQN-Feedback Quantum Neuron) model designed by the invention is shown in figure 3. The output | v > of FQN in the figure is n-bit qubit, i.e. n-bit quantum register (Qregister), then
The output | v > is simultaneously used as the feedback input of FQN, | theta > is a threshold value and is also an n-bit qubit, and the column vector form expressed as Hilbert space is
|θ>＝[θ1θ2LθK]T，K＝2n (5)
W is a K connection weight matrix,
is an operator acting on an n-bit quantum register.
Second step of designing a method for representing multi-user receiver received signal by using quantum register
An n-bit quantum register | y > of qubit is prepared,
|y>is 2nColumn vector of the Vi Hilbert space, order 2nK is the number of users, then the number of bits of the quantum register Further can let | y>Y, quantum register | y>As input to a quantum multi-user detector. Notably, an n-bit quantum register | y>Can store from 0 to 2 simultaneouslynAll K of-1-2nThe number of the cells, each of which is present at the same time with a certain probability. According to the quantum parallel computing characteristic, any transformation acting on the quantum register operates on all K numbers simultaneously, so that one operation of the quantum computer can generate 2nThe result of the operation is equivalent to a classical computer 2nAnd (5) performing secondary operation.
Thirdly, designing a quantum neural network multi-user detector
The multi-user detector structure based on the quantum neural network designed by the invention is shown in figure 1.
In the figure, FQN is a feedback quantum neuron, the output of a classical multi-user detector matched filter is prepared (preparation) as a quantum register | y > as the input of a QNN multi-user detector, and R is a cross-correlation matrix of user characteristic waveforms. The QNN form of the optimal multi-user detection criterion is
The output quantum state update criterion of the QNN multi-user detector is
|b′(t+1)>＝W|b(t)>-|θ>＝|y>-R|b(t)>(8)
Subsequently to quantum state-b′(t+1)>＝[b′1(t+1)，b′2(t+1)，L，b′K(t+1)]TPerforming evolution with the evolution result state as
Wherein,
is an evolution operator.
The invention designs two evolution operators-a parallel operator
And random operator
Operator <math>
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</math> Where sign (·) is a sign function. Parallel operatorsK-2 forming quantum neuron superposition state in evolution process of quantum statenThe transformation of the ground states is performed synchronously.
Operator <math>
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</math> Wherein the function f (·) is defined as f (x) sign (u- | x |)2) Where sign (·) is a sign function and u is a term belonging to [0, 1]The random number of (2). Operator
Acting on the quantum state represented by formula (5) & ltb′(t+1)>In the above, if the random number u is greater than the square of the qubit probability widthb′i(t+1)|2Then result state utissinob(t+1)>The ith element inb i(t +1) takes the value 1; otherwise, take the value-1. Therefore, the result state of the quantum neuron in the evolution process is obtained according to the probability of the quantum bit, and the principle that the quantum superposition state collapses to a certain ground state with a certain probability in the measurement process is utilized. The invention defines operators
Is a random operator.
Fourth step, quantum neural network multi-user detection method realized by simulation on classical computer
The invention designs a series of simulation experiments to test the performance of the proposed multi-user detector based on the quantum neural network. A DS-CDMA system using BPSK modulation in a gaussian channel was simulated, the spreading sequence used 31-bit Gold sequence, and the maximum normalized cross-correlation coefficient was 9/31. By way of comparison, we simulated the following multi-user detectors in the synchronous and asynchronous cases, respectively:
conventional detectors
Hopfield neural network multi-user detector (HNN-MUD)
Using parallel operators
Quantum neural network multi-user detector (QNN-MUD)
Using random operatorsImproved quantum neural network multi-user detector (improved QNN-MUD)
A Bit Error rate (BER-Bit Error Ratio) change curve of an expected user (user 1 without loss of generality) of the multi-user detector under different signal-to-noise ratios and different user numbers is made through simulation, and the multiple access interference resistance of the QNN and other detection algorithms and the performance of the QNN when the QNN increases along with the user numbers are compared. Fig. 4-11 are simulation results for different situations, respectively.
Claims (1)
1. A quantum neural network is used for the implement method that the multiuser detects, characterized by that the method forms the quantum neural network into the multiuser detector, the network core adopts the feedback type quantum neuron to simplify the structure of the multiuser detector, the network evolves and adopts the parallel evolution operator, utilize the quantum to calculate the characteristic to seek the optimization fast, reduce the complexity of the multiuser detector, the concrete implement method is:
1.) designing a feedback type quantum neuron: the input and output of the quantum register are n-bit quantum bits, namely n-bit quantum registers, and the output is simultaneously used as a feedback type quantumFeedback input of the neuron, the threshold value of the feedback type quantum neuron is also n-bit quantum bit, the connection weight of the feedback type quantum neuron is a K multiplied by K matrix, and the evolution operator of the feedback type quantum neuron
The operator acts on the n-bit quantum register and controls the evolution of the state of the feedback type quantum neuron;
2.) design a method for representing the received signal of the multi-user receiver by using a quantum register: preparing a quantum register of n-bit quantum bits using y>Indicating, selecting the number of bits of the quantum register K is the number of users of the multi-user receiver, order | y>Y is a received signal information bit vector of the multi-user receiver;
3.) design a quantum neural network multi-user detector: the core of the method is a feedback quantum neuron, and a quantum register y>As input to the multi-user detectorb>Is the output thereof;
4.) design a parallel evolution operator
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5.) will evolve operator in parallel
Acting on output quantum state of quantum neural network multi-user detector, performing update evolution on output quantum state, and performing parallel evolution operator
In the evolution process of the output quantum state, K-2 for forming the superposition state of the quantum neuronnSynchronously transforming the ground states;
6.) repeating the step 5) until the updated output quantum state is unchanged from the state before updating, namely the network state is stable, and the sending information sequence corresponding to the output quantum state at the moment is the detection result of the quantum neural network multi-user detector.
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| CN101464965B (en) * | 2009-01-16 | 2011-08-17 | 北京航空航天大学 | Multi-nuclear parallel ant group design method based on TBB |
| PL406171A1 (en) * | 2013-11-21 | 2015-05-25 | Wojciech Burkot | Method and the device data processing |
| CN103795436B (en) * | 2014-02-24 | 2016-01-27 | 哈尔滨工程大学 | Based on the robust multi-user test method of Quantum Hopfield Neural Network and quantum fish-swarm algorithm |
| CN108713206B (en) * | 2016-02-18 | 2022-12-27 | 微软技术许可有限责任公司 | Randomized gap and amplitude estimation |
| CN106127300A (en) * | 2016-07-04 | 2016-11-16 | 哈尔滨理工大学 | A kind of rotating machinery health status Forecasting Methodology |
| CN107231214B (en) * | 2017-06-12 | 2020-07-28 | 哈尔滨工程大学 | Optimal multi-user detection method based on evolutionary chaotic quantum neural network |
| CN108194143B (en) * | 2017-12-25 | 2019-10-11 | 陕西煤业化工技术研究院有限责任公司 | Masonry beam DEFORMATION MONITORING SYSTEM and monitoring and pre-alarming method based on Hopfield neural network |
| CN110727462B (en) * | 2018-07-16 | 2021-10-19 | 上海寒武纪信息科技有限公司 | Data processor and data processing method |
| CN109063359A (en) * | 2018-08-16 | 2018-12-21 | 燕山大学 | A kind of dynamic modelling method of Process of Circulating Fluidized Bed Boiler |
| CN113360747B (en) * | 2020-03-04 | 2024-01-12 | 阿里巴巴集团控股有限公司 | Data processing method and device based on neural network model |
| CN116458872B (en) * | 2023-06-13 | 2023-09-05 | 汶上县人民医院 | Method and system for analyzing respiratory data |
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| 量子神经网络及其在CDMA多用户检测中的应用. 李飞，赵生妹，郑宝玉.信号处理，第21卷第6期. 2005 |
| 量子神经网络及其在CDMA多用户检测中的应用. 李飞，赵生妹，郑宝玉.信息处理，第21卷第6期. 2005 |
| 量子神经网络及其在CDMA多用户检测中的应用. 李飞，赵生妹，郑宝玉.信号处理，第21卷第6期. 2005 * |
| 量子神经网络及其在CDMA多用户检测中的应用. 李飞，赵生妹，郑宝玉.信息处理，第21卷第6期. 2005 * |
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