CN109981151A - Improved Gauss tree approximation message transmission detection algorithm in extensive mimo system - Google Patents
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- 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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- 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
- H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
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- 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
- H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
- H04B7/0837—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using pre-detection combining
- H04B7/0842—Weighted combining
- H04B7/0848—Joint weighting
- H04B7/0854—Joint weighting using error minimizing algorithms, e.g. minimum mean squared error [MMSE], "cross-correlation" or matrix inversion
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Abstract
The invention proposes Gauss tree approximation message transmission detection algorithms improved in extensive mimo system, the algorithm proposes multistage linear receiving algorithm approximate solution covariance matrix and Minimum Mean Squared Error estimation based on low complex degree, the two innovative points of the optimal tree of Gaussian Profile are solved based on Kruskal algorithm, when the receiving end of extensive mimo system carries out signal detection, first in view of existing Gauss tree approximation message transmission detection algorithm calculates covariance matrix during approximate message transmission and Minimum Mean Squared Error estimation is related to complicated Matrix for Inverse Problem, utilize multistage linear receiving algorithm approximate solution covariance matrix and Minimum Mean Squared Error estimation, to reduce computation complexity；Further by Kruskal algorithm first to the weight sequencing of entire connected graph, then it carries out searching from big to small and generates weight limit spanning tree, this process only needs to search for the weighted value of an adjacent side, the optimal tree of a Gaussian Profile is found by this efficient algorithm, the complexity of algorithm significantly is reduced, and improves the computational efficiency of algorithm.
Description
Technical field
The invention belongs to wireless communication technology field, mainly for application scenarios be extensive mimo system, mainly answer
With being carries out signal detection for the receiving end in extensive mimo system, and in particular to improved height in mimo system on a large scale
This tree message transmission detection algorithm.
Background technique
Technology is for multiple-input and multiple-output (Multiple Input Multiple Output, MIMO) in wireless communication system
Through increasingly mature, and have become the key technology in LTE/LTE-Advanced.MIMO technology is primarily referred to as respectively in transmitting-receiving two
End configures the transmission that more antennas carry out signal, in the case where not increasing transmission power and system bandwidth, more times of raising system
The channel capacity and the availability of frequency spectrum of system, while also can be improved channel reliability.However, LTE standard is supported most at present
Big number of antennas is 8, and actual spectrum efficiency is only 5bps/HZ, with the universal and mobile Internet business of smart terminal product
Rapid development, have higher requirement to the transmission rate of the 5th third-generation mobile communication technology.Extensive multiple-input and multiple-output
(Massive Multiple-Input Multiple-Output, Massive MIMO) also comes into being.
The basic characteristics of extensive MIMO technology are exactly: configured in the overlay area of base station tens of even hundreds of with
On antenna, extensive mimo system extends the scale of aerial array, takes full advantage of spatial degrees of freedom, has spectrum utilization
The technical advantages such as rate high, channel capacity is big, energy efficiency is high, strong antijamming capability, reliability height.Due in mobile telecommunication channel
There are factors such as various declines, multi-path jamming, multi-user interference, thermal noise and power limits, signal can be by transmission process
To various interference.The work of extensive MIMO detector is exactly to restore the letter that transmitting terminal is sent by multiple transmission antennas in receiving end
Number, accurately getting transmission signal can be improved the communication quality of entire communication system, therefore design low complex degree, high-performance
Signal detection algorithm have become one of the key technology in extensive MIMO communication.
While the rapid development of extensive MIMO technology, also the signal detection technique in extensive MIMO is proposed more
High requirement.Based on maximum likelihood (Maximum Likelihood, ML) or maximum a posteriori probability (Maximum a
Posteriori, MAP) criterion optimum receiver, complexity is exponentially increased with transmitting antenna increase, then advising greatly
They are impossible to be used in mould mimo system.Detection algorithm is usually all not only to have wanted to have to obtain good performance
Very low complexity, this causes the implementation of extensive mimo system to become unrealistic.For example, linear detection algorithm, force zero
(Zero Forcing, ZF) detection algorithm, least mean-square error (Minimum Mean Square Error, MMSE) detection are calculated
Method etc., this kind of algorithm has lower complexity, but its performance is too big compared to optimal detector loss.On the other hand, ball
Decoding (Sphere Decoding, SD) detection algorithm can reach the performance of approximate ML detection, but complexity is bad.In order to protect
Card also improves detection efficiency while meeting detection performance, the confidence level approximation message transmission (Belief based on test pattern
Propagation, BP) algorithm is applied in the detection of mimo system, since the graph model of extensive mimo system is complete
Scheme, there are a large amount of becates in figure, and message is caused to be difficult to restrain in transmittance process, using Gauss tree approximation, complete graph is close
Seemingly at arborescence, to improve convergence in the case where reaching no loop, hereby based on Gauss tree approximation (Gaussian
Tree Approach, GTA) Message Passing Algorithm be suggested.Gauss tree approximation Message Passing Algorithm is calculating lowest mean square mistake
When difference and covariance matrix, it is related to the Matrix for Inverse Problem of higher-dimension, the complexity of algorithm is excessively high, and looks by Prim algorithm
Need to be repeated several times the weight limit value for finding adjacent side during the Gauss tree for looking for weight limit, so that algorithm is in efficiency and again
It is all insufficient in terms of miscellaneous degree.
Therefore problem above is considered, in order to there is a good compromise between detection performance and computation complexity, this
Invention will make improvement to Gauss tree approximation Message Passing Algorithm, propose that improved Gauss tree approximation disappears in extensive mimo system
Breath transmitting detection algorithm.
Summary of the invention
Improved Gauss tree approximation message transmission detection algorithm mainly has two in extensive mimo system proposed by the present invention
A innovative point: multistage linear receiving algorithm approximate solution covariance matrix and Minimum Mean Squared Error estimation based on low complex degree,
Maximum spanning tree is solved based on Kruskal algorithm, it is therefore intended that in the case where guaranteeing that performance loss is little in signal detection, drop
The computation complexity of low algorithm.Algorithm proposed by the present invention not only considers complicated Matrix Calculating before solving Gaussian density function
Inverse problem solves the problem for having the existing efficiency of algorithm of the maximum spanning tree of the figure of n node not high, also so as in signal detection
Performance and complexity between the good compromise of acquirement.
(1) basic ideas and operation of innovative point proposed by the present invention
" the multistage linear receiving algorithm approximate solution covariance based on low complex degree proposed by the present invention is introduced in detail below
Matrix and Minimum Mean Squared Error estimation ", the basic ideas of " optimal approximation tree is solved based on Kruskal algorithm " two algorithms and master
It operates.
1. the multistage linear receiving algorithm of low complex degree
Assuming that the antenna number of transmitting terminal and receiving end is respectively Nt、Nr, digital modulation order is M.Assuming thatIt is transmission signal vector, if H is Nr×NtThe channel gain matrix of dimension, then receiving end connects
The collection of letters number can be expressed as
Y=Hx+n (1)
Wherein, received vector It is mutually indepedent between element, and
Obey mean value be 0, variance σ2Multiple Gauss distribution additive white Gaussian noise (Additive White Gaussian
Noise, AWGN) vector.
In extensive mimo system, the detection of signal is carried out using GTA Message Passing Algorithm, which most starts to need
Calculating Minimum Mean Squared Error estimation z and covariance matrix C, and inverting there are matrix during calculating.Extensive MIMO system
The dimension of channel gain matrix H is big in system, causes its computation complexity high.The present invention is using multistage linear receiving algorithm come approximate
Minimum Mean Squared Error estimation z and covariance matrix C is solved, the complexity of detection algorithm is reduced with this.
Least mean-square error and covariance matrix in Gauss tree message process is as follows
Wherein e indicates the average energy of symbol, and I indicates unit matrix, enables G=HHH indicates Gram matrix, and filtering matrix
W=G+ σ2IK, using multistage linear receiving algorithm, invert filtering matrix W-1It is represented by
Wherein: ωsFor n-th grade of optimization weight, NsFor the expansion series of selection.
Define ω=[ω0,ω1,...,ωs,...,ωNs]T, then optimizing weight vector can be calculated as
ω=Φ-1c (5)
Wherein Φ is Ns×NsSquare matrix is tieed up, wherein element is
C is the column vector of (t+1) × 1, and element is
ci=tr [(HHH)i+1] (7)
So the least mean-square error and covariance matrix in calculating Gauss tree message process are as follows
2. solving optimal approximation tree based on Kruskal algorithm
For GTA Message Passing Algorithm, BP algorithm can reach optimum performance in acyclic factor graph model, if
Look for the near-optimization tree of accurate distribution, so that it may solve the problems, such as polycyclic.Therefore proposed by the present invention by Kruskal algorithm application
Into extensive mimo system, the optimal tree of a Gaussian Profile can be efficiently found, method is exactly to have n vertex
The spanning tree of user's weight limit is found in weighting complete graph.
The spanning tree of connection figure is the subgraph comprising all vertex and is one tree.Assuming that the side of figure is that have power
Weight.The weight of spanning tree is the simple summation of the weight on its side.The optimal approximation tree problem for finding Gaussian Profile is exactly to look for
The maximum spanning tree of n node diagram is weighted out, and weight is exactly x between side (i, j)iAnd xjMutual information.xiAnd xjBetween mutual trust
Breath is joint Gauss
I(xi；xj)=- log (1- ρij 2) (10)
Wherein ρijIt is xiAnd xjBetween related coefficient.
It is first to searching after the weight sequencing of entire connected graph, only that maximum spanning tree is found with Kruskal algorithm
Need to search for the weighted value of an adjacent side.Specific step is as follows for algorithm:
1. regarding the complete connected graph in signal detection as a forest, each variable is an independent tree.
2. set S, i.e., S=E at the beginning is added in all sides.
3. taking out a longest side (u, v) from S, if (u, v) connects u not in same one tree, v merges this
Two are set, while (u, v) being added to the side collection E of spanning tree.
4. repeating third step until all the points belong to same one tree, side collection E is exactly a maximum spanning tree.
(2) present invention has the advantage that
The invention proposes " the multistage linear receiving algorithm approximate matrix of low complex degree is inverted ", " it is based on Kruskal algorithm
Two innovative points of solution optimal approximation tree ", have the advantage that as follows:
1, the present invention most starts with multistage linear receiving algorithm after using Gauss tree approximation Message Passing Algorithm
It calculates Minimum Mean Squared Error estimation z and covariance estimates C, it is can to pass through the lower truncation of expansion based on polynomial expansion
Series reaches preferable performance.Compared to mean square error estimation z and covariance estimation C is directly calculated, do not need to carry out complicated square
Battle array inversion operation, effectively reduces the complexity of calculating.
2, the present invention is after using Gauss tree approximation Message Passing Algorithm, by BP algorithm be used in approximate tree it is upper it
Before, best Gaussian approximation tree is solved using the Kruskal algorithm in graph theory, that is, find out the maximum of weighting n node diagram and generate
Tree, first to being searched after the weight sequencing of entire connected graph, it is only necessary to search for the weighted value of an adjacent side.Compared to existing
Prim algorithm needs that the weight limit value for finding adjacent side is repeated several times, and improves the efficiency of algorithm, and reduce the complexity of algorithm
Degree.
Detailed description of the invention
To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention provides the following figures to illustrate:
Attached drawing 1 is extensive mimo system model
Attached drawing 2 is the flow diagram of algorithm after improvement of the present invention
In order to make those skilled in the art that the object, technical solutions and advantages of the present invention may be better understood, tie below
Specific example and attached drawing are closed completely to be described.
Attached drawing 1 is extensive mimo system model
The figure is general extensive mimo system model, as shown, in extensive MIMO up-link, transmitting terminal
Have K single-antenna subscriber, the originally transmitted bit stream of user terminal obtains complex signal collection after ovennodulation maps, then using
Transmission antenna is transmitted, wherein introducing mean value is 0, variance σ2=K*EsThe noise vector of/γ, EsIt sends and accords with for transmitting terminal
Number average energy, γ be receiving end every antenna average received symbol signal-to-noise ratio.Finally in receiving end using different
Signal detection algorithm estimates the signal of transmission.
Attached drawing 2 is the flow diagram of algorithm after improvement of the present invention, as shown, algorithm of the present invention specifically wraps
Include following steps:
Step 1: Minimum Mean Squared Error estimation z and covariance estimation C are calculated first with multistage linear receiving algorithm；
Step 2: the Distribution value of C being estimated by Minimum Mean Squared Error estimation z and covariance to solve the height of no prior information
This density f (xi；Z, C), formula is defined asAnd solve the Gauss for having prior information
Density f (xi|xj；Z, C), formula is defined as
Step 3: the maximum spanning tree for having the figure of n node, every i-node of figure are calculated using Kruskal algorithm in graph theory
Weight to the side of j node is the quadratic sum with their related coefficients, i.e. ρ2=Cij 2/(CiiCij).The root node of hypothesis tree is
xi, the father node of node i is by xp(i)It indicates；
Step 4: carrying out message transmission downwards, i.e., from variable node xiIt is transmitted to its father node xp(i)Information calculating be base
In its all xiThe information that receives of child node, formula is defined as
It is obtained after this simplification of a formulaWhat is represented is to work as xiMessage when being the leaf node in tree
The definition of transmitting.
Step 5: upward message transmission is carried out, from father node xp(i)It is transmitted to its child node xiInformation calculating be to be based on
Its father node x from itp(p(i))The information received and the slave x that it is receivediThe brotgher of node be transmitted through come downward information, it is as follows
Shown in formula:
If xp(i)Be tree root node then information can simplify for
Step 6: downwards after the completion of upward message process, " confidence level " in each variable can be calculated,
It is all products for being sent to its father node and child node variable information, the confidence level formula of root nodeIt indicates, the confidence level formula of other nodesIt indicates.
Step 7: in order to obtain Hard decision decoding information, selecting the maximum symbol of posterior probability, i.e.,Required value is exactly the best transmission signal that detected.
Claims (3)
1. improved Gauss tree approximation message transmission detection algorithm in extensive mimo system, which is characterized in that Gauss tree approximation
Message transmission detection algorithm is as new signal detection algorithm a kind of in extensive mimo system, and the algorithm is based on gaussian density
The thought of optimal approximation tree, the polycyclic complete connection being able to solve in the extensive MIMO communication system of high-order QAM constellation modulation
The problem of figure is not restrained.Since complexity is higher during message transmission for Gauss tree approximation Message Passing Algorithm, in order to
Good compromise is obtained between the detection performance and computation complexity of extensive mimo system, proposes that a kind of improved Gauss tree is close
Like message transmission detection algorithm, the multistage linear receiving algorithm of low complex degree is applied to the beginning in message process, is led to
Optimization weight vector is crossed to solve complicated Matrix for Inverse Problem, is estimated with lower truncation order approximate solution least mean-square error
Z and covariance matrix C is counted, the computational complexity for solving Minimum Mean Squared Error estimation z and covariance matrix C is reduced；To Gauss tree
For approximate Message Passing Algorithm, optimum performance can be reached in acyclic factor graph model, need to find the close of accurate distribution
Like optimal tree, existing algorithm is to find near-optimization tree using prim algorithm, and the weight limit value for finding adjacent side is repeated several times, and is increased
Add algorithm complexity, proposes a kind of weight limit spanning tree based on Kruscal algorithm, it is only necessary to search for the power of an adjacent side
Weight values, then sorting to carry out searching from big to small obtains optimal approximation tree, improves efficiency of algorithm, and reduce the complexity of algorithm
Degree.
2. improved Gauss tree approximation message transmission detection algorithm in extensive mimo system according to claim 1,
It is characterized in that, the work solves covariance matrix and least mean-square error in the multistage linear receiving algorithm based on low complex degree
The basic ideas of estimation are: for Gauss tree message transmission detection algorithm, due to most starting to need minimum in algorithm
Square estimation error z and covariance matrix C, with the increase of number of antennas, matrix dimensionality increases, Minimum Mean Squared Error estimation z and
Covariance matrix C is related to the Matrix for Inverse Problem of high complexity, and algorithm complexity will dramatically increase, therefore the present invention proposes benefit
With the multistage linear receiving algorithm of low complex degree come approximate solution Minimum Mean Squared Error estimation z and covariance matrix, standard is most
Small mean square error can be written asCovariance matrix isThe MMSE of standard
Filtering matrix can be written as W=HHH+σ2IK, N is solved by channel gain matrix firsts×NsSquare matrix Φ is tieed up, formula is passed throughIt is calculated, then calculates the column vector c of (t+1) × 1, wherein in vector c
Element be ci=tr [(HHH)i+1], further inverted using the optimization weight vector in multistage linear receiving algorithm to solve complexity
Problem can be written asNear optimal lowest mean square is realized with low complex degree
Estimation error z and covariance matrix C.
3. improved Gauss tree approximation message transmission detection algorithm in extensive mimo system according to claim 1,
It is characterized in that, the work is in the basic ideas for solving the weight limit spanning tree based on Kruscal algorithm: based on low multiple
After the multistage linear receiving algorithm of miscellaneous degree solves covariance matrix and Minimum Mean Squared Error estimation, in order to guarantee in the acyclic factor
Reach optimal performance in graph model, find the near-optimization tree of accurate distribution, polycyclic, present invention proposition is solved the problems, such as with this
The thought of weight limit spanning tree is calculated using the Kruscal algorithm in graph theory, and the weight of entire connected graph is carried out first
Sequence from big to small, and regard complete connected graph as a forest, each variable regards an independent tree as, further will
Set S is all added in all sides, at the beginning S=E.A longest side (u, v) is further taken out from S, if (u, v) is no
In same one tree, then u is connected, v merges this two trees, while (u, v) being added to the side collection E of spanning tree, finally repeats always
The step of upper searching longest edge, belongs to same one tree until all the points, and side collection E is exactly a maximum spanning tree, by this
The algorithm for searching for maximum spanning tree, efficiently finds the optimal tree of a Gaussian Profile, and reduce answering for search process
Miscellaneous degree.
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