CN104980202B - Uplink detection method based on MCMC technologies in a kind of extensive mimo system - Google Patents
Uplink detection method based on MCMC technologies in a kind of extensive mimo system Download PDFInfo
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- H—ELECTRICITY
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- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/0413—MIMO systems
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Abstract
The invention belongs to Communication Anti-Jamming Techniques fields, provide the uplink detection method based on MCMC technologies in a kind of extensive mimo system, for solving the problems, such as that the existing detection method computation complexity based on MCMC technologies is high.The present invention is by setting thresholding judgement that can effectively reduce blindness iterations, reduce computation complexity, and it is sampled using comprising the true distribution and a mixed distribution containing the distribution for sending signal prior information that send signal, effectively improves the performance of BER of system.
Description
Technical Field
The invention belongs to the technical field of communication anti-interference, relates to a large-scale MIMO (Multiple Input Multiple Output) technology, an M-H (Metropolis-Hastings) technology, a simulated annealing technology and a Gibbs sampling technology, and particularly relates to an uplink detection method based on an MCMC technology in a large-scale MIMO system.
Background
The large-scale MIMO technology is a high-speed transmission technology in a wireless environment, more antenna units are configured at a transmitting end and a receiving end, an advanced space-time coding modulation scheme is combined, and extra diversity, multiplexing and beam forming gains can be brought by fully utilizing the space freedom degree.
In a large-scale MIMO system, a detection method based on a markov monte carlo (MCMC) technique should be one of the detection methods with the best overall performance among the existing detection methods, and have a small computational complexity, and the bit error rate performance is very close to that of the maximum likelihood detection method.
The most representative is an improved Gibbs sampling detection method, which samples from the mixed distribution of the real distribution and the uniform distribution of a sending signal and well solves the problem of stall traps in the traditional Gibbs sampling detection method. Because the sampling from the uniform distribution is completely random, and no prior information about the transmitted signal is utilized, the correctness of signal detection cannot be increased, and the problem of the stall trap in the traditional Gibbs can be solved. Moreover, the detection method utilizes iteration updating to detect signals, and cannot determine that the next iteration can generate better detection signals in the detection process, namely, the iteration is blind. The invention aims at the two problems, improves the bit error rate performance of the method by sampling from the mixed distribution of the real distribution of the transmitted signal and the distribution containing the prior information of the transmitted signal, and reduces the number of blind iterations to the maximum extent by setting threshold judgment, thereby improving the calculation complexity of the detection method.
Disclosure of Invention
The invention aims to provide a detection method based on an MCMC technology, which has low computational complexity and performance similar to that of a maximum likelihood detection method, aiming at the problem that the existing detection method based on the MCMC technology has high computational complexity in the current large-scale MIMO system.
The technical scheme of the invention is as follows: an uplink detection method based on MCMC technology in a large-scale MIMO system is characterized by comprising the following steps:
A. initialization processing
In a massive MIMO system, the received signal vector is A transmission signal vector is The channel matrix ish mn (m＝1,2,...,N r ,n＝1,2,...,N t ) Represents a channel gain coefficient between the nth transmit antenna and the mth receive antenna, and h mn Is subject toA distributed random variable; an additive white Gaussian noise vector of n c ∈C Nr ，n i (i＝1，2，...，N r ) Is subject toA distributed random variable; wherein N is t For the number of transmitting antennas, N r For the number of receiving antennas, Y is satisfied c ＝H c X c +n c (ii) a The initialization comprises the following specific steps:
A1. firstly, real number processing is carried out on the system to obtainSatisfies the following conditions: y = HX + n; adopting QPSK modulation, obtaining the vector after the vector of the sending signal is real-valuedx i (i＝1，2，...，2N t ) Take a set of values as
A2. Presetting a threshold value V th And presetting an upper limit MAX _ ITER =16N of the iteration number t (ii) a (it should be noted that: because of the threshold V th The larger the value, the lower the computational complexity, but the worse the performance; threshold V th The smaller the value, the higher the computational complexity, but the better the performance; therefore the threshold V th Is determined according to the specific application requirements)
A3. Randomly generating a start vector The detection output vector of the linear detection method such as zero-crossing detection method (ZF) or minimum mean square error detection method (MMSE) may be used as the initial vector X 0 ；X opt Indicating the best performing vector among the currently detected vectors,at this time X opt ＝X 0 ；
B. Detection process
B1. Firstly, when | | Y-HX opt || 2 ＜V th Exit detection, X opt As the final output vector of the detection; otherwise, entering step B2;
B2. finding an approximately optimal estimated vector through multiple iterations:
each iteration process needs to update 2Nt symbols, and the symbol in the t-th iterationIs generated by the sampling process as follows:
the conditional probability distribution of the transmit vector X is known as:if the estimated vectors generated by two continuous iterations are the same, a stall trap is called to occur; therefore, in order to effectively avoid the occurrence of stall traps, the conditional probability distribution of the transmission signal vector X of the present invention adopts the following mixed distribution:
wherein,a is a temperature convergence coefficient, q =1/2N t ，a 1 ＝1，a 2 =10; symbol in the process of t-th iterationThe specific updating steps are as follows:
B21. randomly generating a random number k which follows uniform distribution from 0 to 1; when k is>, q, then a = a 1 When k is less than or equal to q, a = a 2 ；
B22.
When in useThe time estimate vector is:
when in useThe time estimate vector is:
calculating outThe probability of (c) is:
B23. randomly generating a random number u which follows uniform distribution from 0 to 1; when u is less than or equal to p,when u > p, the ratio of the total of the two,
C. judging an estimated vector X generated by the t-th iteration t Whether the detection exit condition is met or not is specifically as follows:
C1. when | | | Y-HX t || 2 ＜||Y-HX opt || 2 When it is, X opt ＝X t (ii) a At this time, further judgment is made as | | Y-HX opt || 2 ＜V th When it is, the detection is exited, X opt As the final output vector of the detection; when | | | Y-HX opt || 2 ≥V th If so, carrying out the (t + 1) th iteration detection;
when | | | Y-HX t || 2 ≥||Y-HX opt || 2 Entering step C2;
C2. calculating vector X opt The maximum number of iterations allowed at stall is:
when the measured value is from t-theta s (X opt ) X in +1 iteration to tth iteration opt If the detection time is not changed, the detection is quittedMeasuring, X opt As the final output vector of the detection; otherwise, entering the (t + 1) th iteration detection;
all the steps are established when the iteration times t do not reach the preset iteration time upper limit value, namely t&MAX _ ITER; when t = MAX _ ITER, a further judgment is made when | | | Y-HX t || 2 ＜||Y-HX opt || 2 When it is, then X opt ＝X t Output X opt And exiting the detection; otherwise, X opt Remains unchanged and outputs X opt And exiting the detection.
Further, the specific implementation steps of the real quantization processing in the step A1 are as follows:
wherein,satisfies the following conditions: y = HX + n.
Further, the threshold value V in the step A2 th Is taken asOr N r σ 2 。
The invention has the beneficial effects that the invention provides a new detection method based on MCMC technology in a large-scale MIMO system, the detection method can effectively reduce blind iteration times and computational complexity by setting threshold judgment, and the bit error rate performance of the system is effectively improved by adopting mixed distribution comprising real distribution of a sending signal and distribution containing prior information of the sending signal for sampling.
Drawings
Fig. 1 is a block diagram of a conventional massive MIMO system.
FIG. 2 is a block diagram of a massive MIMO system for the detection method of the present invention.
Detailed Description
The invention is described in detail below with reference to the attached drawing
Specific embodiments of the present invention are described below in conjunction with the accompanying drawings so that those skilled in the art can better understand the present invention. It is to be expressly noted that in the following description, a detailed description of known functions and designs may be omitted when it may obscure the subject matter of the present invention.
For better explanation of the present invention, terms used in the present embodiment and a receiver structure of a massive MIMO system will be described. Large-scale MIMO systems: as shown in fig. 1, b is the bit data to be transmitted, and can be regarded as an L × T matrix, where L = log2 (4) is the number of bits carried by a Quadrature Phase Shift Keying (QPSK) symbol. Fig. 2 shows an embodiment of a method for signal detection using nTx transmit antennas and nRx receive antennas according to the present invention; a system block diagram of an embodiment of the present invention is shown in fig. 2.
The receiver structure is roughly divided into the following steps:
step 1: the parameters of the system to be selected are determined, i.e. the number of transmit antennas nTx, the number of receive antennas nRx, the order of modulation M.
Step 2: then, the vector of the received signal and the channel matrix are real-valued, a starting vector is randomly generated, if the Euclidean distance of the starting vector is smaller than a threshold value, the detection is finished, and the starting vector is used as a detection result. Otherwise, entering an iterative detection process until the condition of finishing detection is met, and finally keeping the optimal vector as a detection result.
Claims (3)
1. An uplink detection method based on MCMC technology in a large-scale MIMO system is characterized by comprising the following steps:
A. initialization processing
In a massive MIMO system, the received signal vector isThe transmission signal vector isThe channel matrix isDenotes a channel gain coefficient between the nth transmitting antenna and the mth receiving antenna, and h mn Is subject toA distributed random variable; an additive white Gaussian noise vector ofIs subject toA distributed random variable; wherein N is t For the number of transmitting antennas, N r For the number of receiving antennas, Y is satisfied c ＝H c X c +n c (ii) a The initialization comprises the following specific steps:
A1. firstly, real number processing is carried out on the system to obtain a receiving signal vector ofA transmission signal vector isAn additive white Gaussian noise vector ofThe channel matrix isSatisfies the following conditions: y = HX + n; adopting QPSK modulation, carrying out real-valued transformation on the vector of the transmitted signal to obtain a vectorx i Take a set of values asWherein i =1, 2., 2N t ；
A2. Presetting threshold value V th And presetting an upper limit MAX _ ITER =16N of the iteration number t ；
A3. Randomly generating a start vectorXo pt Representing the vector with the best performance among the currently detected vectors,at this time X opt ＝X 0 ；
B. Detection process
B1. First, when | | Y-HX opt || 2 ＜V th Exit detection, X opt As the final output vector of the detection; otherwise, entering step B2;
B2. finding an approximately optimal estimated vector through multiple iterations:
each iteration process needs to update 2Nt symbols, and the symbol in the t-th iterationIs generated by the following sampling process:
the conditional probability distribution of the transmission signal vector X adopts the following mixed distribution:
wherein,a is a temperature convergence coefficient, q =1/2N t ，a 1 ＝1，a 2 =10; symbol in the t-th iteration processThe specific updating steps are as follows:
B21. randomly generating a random number k obeying uniform distribution from 0 to 1; when k is&Q, then a = a 1 When k is less than or equal to q, a = a 2 ；
B22.
When the temperature is higher than the set temperatureThe estimated vector is then:
when the temperature is higher than the set temperatureThe estimated vector is then:
calculating outThe probability of (c) is:
B23. randomly generating a random number u obeying uniform distribution from 0 to 1; when u is less than or equal to p,when u > p, the sum of the values of,
C. judging the estimated vector X generated by the t-th iteration t Whether the detection exit condition is met or not is specifically as follows:
C1. when | | | Y-HX t || 2 ＜||Y-HX opt || 2 Then Xo pt ＝X t (ii) a At this time, further judgment is made as | | Y-HX opt || 2 ＜V th When it is, the detection is exited, X opt As the final output vector of the detection; when | | | Y-HX opt || 2 ≥V th If so, carrying out the (t + 1) th iteration detection;
when | | | Y-HX t || 2 ≥||Y-HX opt || 2 Step C2 is entered;
C2. calculating vector X opt The maximum number of iterations allowed at stall is:
when the measured value is from t-theta s (X opt ) X in +1 iteration to tth iteration opt If the signal is not changed, the detection is exited, X opt As the final output vector of the detection; otherwise, entering the (t + 1) th iteration detection;
all the steps do not reach the preset iteration times when the iteration times t are not reachedWhen the upper limit value is satisfied, i.e. t&MAX _ ITER; when t = MAX _ ITER, a further judgment is made when | | | Y-HX t || 2 ＜||Y-HX opt || 2 When it is, then X opt ＝X t Output X opt And exiting the detection; otherwise, X opt Remains unchanged and outputs X opt And exiting the detection.
2. The detection method according to claim 1, wherein the step A1 of performing the real quantization processing specifically comprises the steps of:
wherein,satisfies the following conditions: y = HX + n.
3. The detection method according to claim 1, wherein the threshold value V in step A2 th Is taken asOr N r σ 2 。
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| CN106788654B (en) * | 2016-11-29 | 2020-06-30 | 北京邮电大学 | Method and device for classifying multiple-input multiple-output signal detection results |
| CN108347267B (en) * | 2018-01-04 | 2020-04-24 | 东南大学 | Adaptive hybrid detection receiving method for large-scale MIMO |
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| CN104539312A (en) * | 2014-12-29 | 2015-04-22 | 中国人民解放军海军航空工程学院 | Spreading code and information sequence joint estimation method based on sequential monte-carlo |
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