CN110380765A - The signal detecting method of adaptive convergence factor in mimo system - Google Patents

The signal detecting method of adaptive convergence factor in mimo system Download PDF

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Publication number
CN110380765A
CN110380765A CN201910843431.7A CN201910843431A CN110380765A CN 110380765 A CN110380765 A CN 110380765A CN 201910843431 A CN201910843431 A CN 201910843431A CN 110380765 A CN110380765 A CN 110380765A
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signal
algorithm
dimension
mimo system
convergence factor
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江晓林
渠苏苏
唐征宇
崔景岩
张广洲
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Heilongjiang University of Science and Technology
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Heilongjiang University of Science and Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/10—Monitoring; Testing of transmitters
    • H04B17/15—Performance testing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/20—Monitoring; Testing of receivers
    • H04B17/29—Performance testing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • 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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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Radio Transmission System (AREA)

Abstract

The invention discloses a kind of signal detecting methods of convergence factor adaptive in mimo system, and described method includes following steps: step 1: setting initial search radius as d, reception signal is y；Step 2: using y as the center of circle, d is that radius forms ball, obtains signal search range；Step 3: the size of search radius is constantly updated according to the sequence of ecto-entad, find out the restriction range per one-dimensional x from M dimension to the first dimension, effective lattice point is found out, and guarantees have and only one lattice point is nearest at a distance from y in circle, to carry out signal detection.In mimo systems, ACF algorithm of the invention enables antenna receiving end and receives using the smallest operand the smallest signal of distortion.That is: ACF algorithm detection performance of the invention is best, can guarantee that the detection performance of signal reaches maximization, and the complexity of algorithm is simple, complexity is excellent.

Description

The signal detecting method of adaptive convergence factor in mimo system
Technical field
The present invention relates to a kind of ACF (adaptive convergence factor) algorithms, and in particular to one kind is applied in mimo system, special It is not that can guarantee on the optimal basis of mimo system detection performance, the minimum ACF algorithm of the computation complexity of system.
Background technique
In recent years, what life style became is more and more convenient, and the torsion of people's daily life mode embodies communication skill invariably The great development of art.In wireless communications, being continuously increased with user needs the utilization scope of extending bandwidth, many researchs Scholar has focused on how to improve in the utilization rate of frequency spectrum.If transmitting-receiving both ends all use more days using mimo system Line carries out receiving signal and sends signal, channel can be widened in this way, increase capacity, improve the utilization rate of frequency spectrum.To MIMO For system, the quality of performance is affected by multiple factors, wherein signal detection is exactly to measure its a important indicator.Therefore, Performance is good, and complexity is small, and this signal detection algorithm is exceptionally important.
In multiple-input and multiple-output mimo system, biggish gain is obtained, detection algorithm is most important.Common detection Method have squeeze theorem (Zero forcing, ZF), minimum mean-squared error algorithm (Minimum mean square error, MMSE), maximum likelihood algorithm (Max likelihood, ML) etc..Three kinds of methods are compared it is found that the above two are simple, but its It requires in receiving end to matrix inversion operation, thus, complexity is big, and the performance of receiving end is simultaneously bad.ML overcomes to matrix The shortcomings that being inverted, therefore the diversity gain that receiving end obtains is maximum, the detection performance of ML is best.But the search point of ML with Selected set is related with the number of antenna, and searching times are excessively huge, therefore its computation complexity is very big.
Summary of the invention
In order to reduce the complexity of ML algorithm, the number of search is reduced, the present invention provides adaptive in a kind of mimo system Answer the signal detecting method of convergence factor.For this method when detecting signal, detection effect is consistent with ML, and complexity is but than ML algorithm It is small.
The purpose of the present invention is what is be achieved through the following technical solutions:
The signal detecting method of adaptive convergence factor, includes the following steps: in a kind of mimo system
Step 1: initial search radius is set as d, reception signal is y；
Step 2: using y as the center of circle, d is that radius forms ball, obtains signal search range；
Step 3: constantly updating the size of search radius according to the sequence of ecto-entad, finds out from M dimension to the first dimension Restriction range per one-dimensional x, that is, find out effective lattice point, and guarantees have and only one lattice point is nearest at a distance from y in circle, To carry out signal detection, wherein updated search radius meets following condition:
d”2=[d'2-(yM-rM,MxM)2]α；
In formula, d' is second of updated search radius, and x is to send signal, and r is the element in upper triangular matrix, and M is Dimension, yM、xM、rM,MThe element for receiving signal, sending signal, upper triangular matrix of M dimension is respectively indicated, α is convergence factor, α It is defined as follows:
In formula, k,It is compressibility factor with β, SNR is signal-to-noise ratio.
Detection method of the invention is ecto-entad detection, i.e., detects the first dimension from M dimension, it is every detect it is one-dimensional, partly Diameter will change once, and α is acted in radius of sphericity, for accelerating the contraction speed of radius, to be quickly detected effectively Lattice point completes the detection of signal.
Compared with the prior art, the present invention has the advantage that
In mimo systems, ACF algorithm of the invention is enable antenna receiving end and is received mistake using the smallest operand Very the smallest signal.That is: ACF algorithm detection performance of the invention is best, can guarantee that the detection performance of signal reaches maximization, And the complexity of algorithm is simple, complexity is excellent.
Detailed description of the invention
Fig. 1 is the detection performance analogous diagram of three kinds of detection algorithms；
Fig. 2 is the comparison diagram of three kinds of algorithms floating-point operation amount per second；
Fig. 3 is influence of the different β value to computation complexity in convergence factor, the ACF algorithm simulating under (a) difference β value Figure, (b) the ACF floating-point operation amount comparison diagram per second under different β value, β value is respectively 0.02,0.04,0.1,0.5.
Specific embodiment
Further description of the technical solution of the present invention with reference to the accompanying drawing, and however, it is not limited to this, all to this Inventive technique scheme is modified or replaced equivalently, and without departing from the spirit and scope of the technical solution of the present invention, should all be covered Within the protection scope of the present invention.
The main object of the present invention is to seek a more excellent algorithm in mimo systems, and performance is complicated close to ML algorithm Degree is small as far as possible, and VB detection algorithm performance is good, but complexity is high, is then studied VB algorithm, i.e. ACF algorithm. Convergence factor of the invention is used to accelerate the speed of constriction radius, in the case where detection performance is slightly lost, computation complexity Well below VB algorithm, in high s/n ratio, gradually CL algorithm.Particular content is as follows:
According to the main thought of ACF sphere decoding, there is following formula:
||Hx-y||≤d2(1)；
Wherein, H is channel, and x is to send signal, and y is to receive signal, and d is the search radius being previously set, and is divided H Solution, may be expressed as:
Wherein, Q0It is the orthogonal matrix of N row N column, R0It is a upper triangular matrix, Q1、Q2Respectively indicate M column matrix and (N-M) matrix arranged, R=[rij]M×MIt is the upper triangular matrix of M × M column, r is the element in upper triangular matrix, and i is member The row of element, j is the column of element.
Wherein, ()*For Hermite transformation.
It transplants, has to formula (3):
It is further simplified:
It enables Have:
D' is the radius of second of update, M-1 representation dimension.
From formula (5) as can be seen that formula becomes succinctly, to be then unfolded, the first item of right side of the equal sign after substitution of variable, Only and xMIt is related, now, if only considering first item, available following formula:
d'2≥(yM-rM,MxM)2 (6)。
Further abbreviation obtains:
Wherein,It indicates to be rounded the upper bound,Lower bound is rounded.
Then, xth is soughtM-1The x of dimensionM-1Value, update radius:
d”2=d'2-(yM-rM,MxM)2 (8)。
So there is following formula:
d”2≥(yM-1-rM-1,MxM-rM-1,M-1xM-1)2+…+(y1-r1,1x1-r1,2x2)2 (9)。
Equally, only consider the first item on the right of the sign of inequality, obtain:
d”2≥(yM-1-rM-1,MxM-rM-1,M-1xM-1)2 (10)。
Obtain xM-1Value range, it is as follows:
Enable yM-1|M=yM-1-rM-1,MxM, formula (11) abbreviation is obtained:
The value range of one-dimensional x is often found out, radius is updated once as above-mentioned formula (8) to (12), until Find out x1Value range.
If for every one-dimensional, having the value range of each dimension during value from outside to inside, that is, having had Whole { xM,…,x1, then, we will update radius, after updating radius, then find out the limit per one-dimensional x according to the above method Determine range, that is, finds out effective lattice point.If found according to the above method after updating radius, lattice point is not present, then, The result of output is just effective lattice point under a preceding radius.
It can be seen that, the selection and update of radius are most important to the lattice point found in hypersphere from the above analysis.Radius Search speed is fast, and effective lattice point in ball will lack, and operand is with regard to small, whereas if the value of radius is excessive, the speed of search Slack-off, the available point in ball will reduce the range of radius more, and operand is big, therefore, the selection of radius and update to seeking Look for the lattice point in hypersphere most important.
Now, we optimize in the update of the radius of a ball, do following variation to the radius of update, formula is as follows:
d”2=[d'2-(yM-rM,MxM)2]α (13)。
It is used to accelerate the speed of half path search multiplied by convergence factor a α, α on the radius updated every time before, quickly Effective lattice point is obtained, reduces the calculation amount of operation on the whole.
Meanwhile α being defined as follows:
Wherein, k,It is compressibility factor with β, SNR is signal-to-noise ratio, and the method is known as being based on compressibility factor ACF algorithm.
Then, the relationship between research spherical shape detection algorithm performance p loss and d:
d2=α n σ2(16)；
Wherein, Γ is gamma function, σ2Square error noise is represented, n is the quantity of big twice of transmitting antenna, and ε is general Rate finds the probability (generally 0.01) less than available point, and P is the performance of detection algorithm, σnIt is noise variance, NtIt is MIMO system T root transmitting antenna in system.
The value of radius d is different it can be seen from formula (15), and performance p is also different, and still, p is always at centainly In the range of.Therefore, the detection performance variation of algorithm is little.It is being calculated in the following, detecting ACF algorithm with computer artificial result Variation in complexity.
Firstly, when we first individually discuss that convergence factor is fixed value, the influence to computation complexity.The present invention is in 0 < k In the case where < 1, influence of the different k values to computation complexity is compared.Computer Simulation obtains table 1.It can from table 1 Out.Under Low SNR, when k=0.01, complexity is well below CL algorithm, and there is no apparent variations, so this Invention is k=0.01, referred to as K-ACF algorithm (being the special case of adaptive ACF algorithm) fixed compressibility factor value.
Relationship between 1 k value of table and signal-to-noise ratio
Secondly, we study the relationship of compressibility factor β Yu convergence factor α, enable_K value is 1, then formula is as follows:
α=1-0.98e-β×SNR (19)。
Compare influence of the different β value to computation complexity.Computer Simulation obtains table 2.As shown in Table 2, as β < 0.04 When, under Low SNR, computation complexity is low.
Relationship between 2 β value of table and signal-to-noise ratio
As can be seen that the bit error rate of ACF algorithm varies less from the analogous diagram of Fig. 1, almost with two kinds of traditional algorithms Unanimously.That is, ACF algorithm almost maintains the good detection performance of traditional algorithm in detection performance.
It is clear from figure 2 that the floating-point operation amount per second of ACF algorithm is significantly lower than biography when SNR is less than 16dB The VB algorithm and CL algorithm of system, that is, the complexity of ACF algorithm are better than traditional algorithm.
From figure 3, it can be seen that no matter what value β takes, detection performance is basically unchanged and the detection of traditional detection algorithm It can be consistent.It can also be seen that different β value, computation complexity are different from Fig. 3, in the range of 0~16dB, especially Prominent, in contrast, the complexity of inventive algorithm averagely has 10% decline.When signal-to-noise ratio is greater than 16dB, i.e., in high noise Than under conditions of, the computation complexity of ACF algorithm tends to traditional CL algorithm.Therefore, no matter under Low SNR or high Under the conditions of signal-to-noise ratio, algorithm of the invention, i.e. ACF algorithm, all well below VB algorithm on computation complexity.Simulation result It demonstrates as β < 0.04, ACF algorithm is good.

Claims (5)

1. the signal detecting method of adaptive convergence factor in a kind of mimo system, it is characterised in that the method includes walking as follows It is rapid:
Step 1: initial search radius is set as d, reception signal is y；
Step 2: using y as the center of circle, d is that radius forms ball, obtains signal search range；
Step 3: constantly updating the size of search radius according to the sequence of ecto-entad, finds out from M dimension to each of the first dimension The restriction range of the x of dimension, that is, find out effective lattice point, and guarantees have and only one lattice point is nearest at a distance from y in circle, thus Carry out signal detection.
2. the signal detecting method of adaptive convergence factor in mimo system according to claim 1, it is characterised in that institute It states in step 3, updated search radius meets following condition:
d”2=[d'2-(yM-rM,MxM)2]α；
In formula, d' is second of updated search radius, and x is to send signal, and r is the element in upper triangular matrix, and M is dimension Degree, yM、xM、rM,MThe element for receiving signal, sending signal, upper triangular matrix of M dimension is respectively indicated, α is convergence factor.
3. the signal detecting method of adaptive convergence factor in mimo system according to claim 2, it is characterised in that institute α is stated to be defined as follows:
In formula, k,It is compressibility factor with β, SNR is signal-to-noise ratio.
4. the signal detecting method of adaptive convergence factor in mimo system according to claim 2, it is characterised in that institute State k=0.01.
5. the signal detecting method of adaptive convergence factor in mimo system according to claim 2, it is characterised in that institute State β < 0.04.
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