CN110365376A - The channel statistical information acquisition methods of non stationary channel in a kind of multiaerial system - Google Patents
The channel statistical information acquisition methods of non stationary channel in a kind of multiaerial system Download PDFInfo
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- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
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Abstract
The invention discloses a kind of channel statistical information acquisition methods of non stationary channel in multiaerial system to form observation sequence including obtaining from time t-L+1 to the reception signal of the receiving end of the multiaerial system of time t；It establishes statistical channel state and receives the relational model between signal, according to Xiang Bianliang after observation sequence and model parameter calculation scaling forward variable and scaling；Model parameter is estimated to variable to obtain optimal model parameters according to scaling forward variable and after scaling；Statistical channel state is estimated using observation sequence and optimal model parameters, completes the acquisition of channel statistical information.The present invention compares traditional channel statistical information acquisition methods, it can more accurately solve the problems, such as that the channel statistical information of non stationary channel is obtained with lower complexity and overhead, and it can be applied in the system parameter estimation of any non-stationary continuous time signal, convenient for manipulation.
Description
Technical field
The invention belongs to signal processing technology fields, more particularly, to non stationary channel in a kind of multiaerial system
Channel statistical information acquisition methods.
Background technique
In order to realize more high power system data flow and break through power system capacity bottleneck, improve the covering performance of system,
5th third-generation mobile communication (Fifth Generation, 5G) has been studied.In order to meet the requirement of 5G, for example, higher system
Capacity, higher message transmission rate support a large amount of connections, reduce time delay, reduce expense, higher energy efficiency and robust
Property, an important technology is exactly extensive multiple-input and multiple-output (Multiple-Input Multiple-Output, MIMO)
Technology.Extensive MIMO technology places hundreds of thousands of antennas, further excavated space at the end base station (Base Station, BS)
The radio resource of dimension obtains bigger spatial degrees of freedom, promotes data rate and link reliability, serves more use
Family.
For the advantage for giving full play to extensive MIMO technology, obtain in communication system accurately channel statistical information be must not
It can lack, because the Some Key Technologies of communication system rely on channel statistical information, for example, nonopiate access-in resource distributes, is wide
Broadcast channel user scheduling, three-dimensional beam forming etc..Effective channel statistical information acquisition is sufficiently to realize extensive MIMO advantage
One of precondition.
Problem is obtained in order to handle channel statistical information, has had solution to be suggested, two kinds of thought classes can be divided into
Not.The first classification is based on time statistical average thought, and main foundation channel statistical information is kept not in multiple time slots
What is become is common it is assumed that using common rule of channel estimation, based on pilot tone or the method for data auxiliary pilot come when obtaining multiple
The instantaneous channel state information at quarter.Then, statistics calculating is carried out by the information obtained to these to obtain the channel of the system
Statistical information.This thought needs that overhead can be improved using a large amount of pilot tone.Second of classification is based on expectation maximization
Thought learns channel statistical information in a manner of a kind of iteration.That is, giving a channel system that is random or setting
Information is counted as initial value, the expectation of probability is calculated using the signal observed value received.Then, by maximizing this expectation
Obtain a new channel statistical information.Then the channel statistical information that this is newly obtained is continued more as the initial value of algorithm
New channel statistical information stops iteration until the constraint condition of setting reaches, which is also required to pilot tone.
The method that above-mentioned two kinds of classifications are proposed assumes that channel has wide-sense stationarity.But since antenna size becomes
Greatly, under the influence of the raw factors such as phenomenon, the use of high band and transmitting terminal high-speed mobile of going out of scatterer, so that in general multiple antennas
The far-field effect assumed in system is no longer applicable in instead near-field effect, the hypothesis of plane wave also will be by spherical wave institutes
Replace, channel shows non-stationary property exclusive, different from conventional channel feature.This is non-stationary to will lead to channel statistical
Information and instantaneous channel state information change over time, so that being believed based on two quasi-tradition channel statisticals under extended stationary assumed condition
Acquisition methods are ceased no longer to be applicable in.
Summary of the invention
In view of the drawbacks of the prior art, the purpose of the present invention is to provide a kind of letters of non stationary channel in multiaerial system
Road counts information acquisition method, it is intended to which the technology for solving the channel statistical information difficulty acquisition of non stationary channel in multiaerial system is asked
Topic.
To achieve the above object, the present invention provides a kind of channel statistical informations of non stationary channel in multiaerial system to obtain
Take method, comprising the following steps:
(1) it obtains from time t-L+1 to the reception signal of the receiving end of the multiaerial system of time t, forms observation sequence,
Wherein, L indicates sequence length；
(2) it establishes statistical channel state and receives the relational model between signal, joined according to the observation sequence and model
Number calculates Xiang Bianliang after scaling forward variable and scaling；
(3) model parameter is estimated to variable to obtain optimal model parameters according to scaling forward variable and after scaling；
(4) statistical channel state is estimated using observation sequence and optimal model parameters, complete the acquisition of channel statistical information.
Preferably, the receiving end signal y (t) of multiaerial system model are as follows:
Y (t)=H (t) x (t)+z (t)
Wherein, x (t) is transmitting end signal, and H (t) is channel matrix, and z (t) is noise matrix.
Preferably, observation sequenceAre as follows:
Wherein,The reception signal of reception signal and preceding L-1 historical time comprising present time t.
Preferably, statistical channel state is to reflect channel matrix from channel statistical information expectation and variance mapping
Statistical property is shifted at any time.
Preferably, step (2) specifically includes:
(2.1) it establishes statistical channel state and receives the relational model between signal, model parameter are as follows:
λ (t)=(η (t), G (t), μ (t), ∑ (t))
Wherein, the finite aggregate of statistical channel state isSi(i=1,2 ..., K) is indicated i-th
Statistical channel state, K indicate set element quantity；
The hidden state sequence of the corresponding statistical channel state composition of signal is received in model are as follows:
Wherein,Indicate hidden state, l=L-1, L-2, L-3 ..., 0；η (t) is statistical channel state
Probability vector, G (t) are statistical channel state transition probability matrix,WithTwo set can be used in respectively expected vector set and variance matrix set
Middle element is according to polynary multiple Gauss Density functional calculationsThis is general
Rate density is with hidden state q (t-l)=SiFor the conditional probability of conditional access signals y (t-l), p () indicates probability density
Function；
(2.2) according to observation sequenceWith model parameter λ (t), forward variable is successively calculatedWith backward variableWherein, l
=L-1, L-2, L-3 ..., 0,1≤i≤K, forward variable αt-l(Si) be expressed as under the conditions of setting models parameter lambda (t) from when
Between t-L+1 to time t-l observation composition sequenceWith hidden state q (t-l)=SiConditional probability density, backward
Variable βt-l(Si) be expressed as in given hidden state q (t-l)=SiWith under the conditions of model parameter λ (t) from time t-l+1 to time
The sequence of the observation composition of tProbability density, then respectively multiplied by zoom factor ct-lObtain scaling forward variableWith Xiang Bianliang after scalingWherein, zoom factor is
Preferably, model parameter passes through maximization likelihood function acquisition optimal model parameters in step (3):
Wherein,It is represented to observation sequence under rational method λ (t)Conditional probability density.
Preferably, statistical channel state is obtained according to maximum a posteriori decision process in step (4):
Wherein, q (t) indicates current hidden state, andIndicate given observation sequence
ColumnAnd optimal model parametersThe conditional probability density of lower hidden state q (t).
Contemplated above technical scheme through the invention can obtain following compared with prior art
The utility model has the advantages that
1, the channel statistical information acquisition methods in multiaerial system proposed by the present invention are directed to non stationary channel, do not need
Pilot tone is sent, is only used only the observation sequence of suitable length, and traditional time statistical average and is based on expectation maximization method
It requires constantly to emit pilot tone and is suitable for stationary channel, thus compare traditional channel statistical information acquisition methods, the present invention
The channel statistical information acquisition that design method can more accurately solve non stationary channel with lower complexity and overhead is asked
Topic；
2, channel statistical information acquisition methods proposed by the present invention, convenient for manipulation, with certain exploitativeness and practical
Promotional value, so that the method for the present invention can be applied in the system parameter estimation of any non-stationary continuous time signal.
Detailed description of the invention
Fig. 1 is the stream of the channel statistical information acquisition methods of non stationary channel in a kind of multiaerial system provided by the invention
Journey schematic diagram；
Fig. 2 is the relationship mould between a kind of statistical channel state provided in an embodiment of the present invention and reception signal observed value
Type；
Fig. 3 is the mean square error and system signal noise ratio of the embodiment of the present invention and the acquisition of existing channel statistical information acquisition methods
The graph of relation of SNR；
Fig. 4 is accuracy and the number of users under different Signal to Noise Ratio (SNR) that channel statistical information of the embodiment of the present invention obtains
Graph of relation.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.As long as in addition, technical characteristic involved in the various embodiments of the present invention described below
Not constituting conflict each other can be combined with each other.
The present invention provides a kind of channel statistical information acquisition methods of non stationary channel in multiaerial system, purpose exists
Observation sequence is formed in the reception signal for collecting receiving end；Based on observation sequence and model parameter, calculate scaling forward variable and
Xiang Bianliang after scaling；By Xiang Bianliang after observation sequence, scaling forward variable and scaling, optimal model parameters are estimated；According to sight
Sequencing column and optimal model parameters obtain the channel statistical information of current time using maximum a posteriori decision process.
As shown in Figure 1, comprising the following steps:
(1) it obtains from time t-L+1 to the reception signal of the receiving end of the multiaerial system of time t, forms observation sequence,
Wherein, L indicates sequence length；
(2) it establishes statistical channel state and receives the relational model between signal, joined according to the observation sequence and model
Number calculates Xiang Bianliang after scaling forward variable and scaling；
(3) estimated to obtain optimal models ginseng to model parameter to variable according to after the scaling forward variable and scaling
Number；
(4) statistical channel state is estimated using observation sequence and optimal model parameters, complete the acquisition of channel statistical information.
Specifically, the receiving end signal y (t) of multiaerial system model are as follows:
Y (t)=H (t) x (t)+z (t)
Wherein, x (t) is transmitting end signal, and H (t) is channel matrix, and z (t) is noise matrix.
Specifically, the observation sequence of receiving endAre as follows:
Wherein,The reception signal of reception signal and preceding L-1 historical time comprising present time t.
Specifically, statistical channel state is to reflect channel matrix from channel statistical information expectation and variance mapping
Statistical property is shifted at any time.
Specifically, step (2) specifically includes:
(2.1) it establishes statistical channel state and receives the relational model between signal, model parameter are as follows:
λ (t)=(η (t), G (t), μ (t), ∑ (t))
Wherein, the finite aggregate of statistical channel state isSi(i=1,2 ..., K) is indicated i-th
Statistical channel state, K indicate set element quantity；
The hidden state sequence of the corresponding statistical channel state composition of signal is received in model are as follows:
Wherein,Indicate hidden state, l=L-1, L-2, L-3 ..., 0；η (t) is statistical channel state
Probability vector, G (t) are statistical channel state transition probability matrix,WithTwo set can be used in respectively expected vector set and variance matrix set
Middle element is according to polynary multiple Gauss Density functional calculationsThis is general
Rate density is with hidden state q (t-l)=SiFor the conditional probability of conditional access signals y (t-l), p () indicates probability density
Function；
(2.2) according to observation sequenceWith model parameter λ (t), forward variable is successively calculatedWith backward variableWherein,
L=L-1, L-2, L-3 ..., 0,1≤i≤K, forward variable αt-l(Si) be expressed as under the conditions of setting models parameter lambda (t) from
The sequence of the observation composition of time t-L+1 to time t-lWith hidden state q (t-l)=SiConditional probability density, after
To variable βt-l(Si) be expressed as in given hidden state q (t-l)=SiWith under the conditions of model parameter λ (t) from time t-l+1 then
Between t observation composition sequenceProbability density, then respectively multiplied by zoom factor ct-lObtain scaling forward variableWith Xiang Bianliang after scalingWherein, zoom factor is
Specifically, model parameter passes through maximization likelihood function acquisition optimal model parameters in step (3):
Wherein,It is represented to observation sequence under rational method λ (t)Conditional probability density.
Specifically, statistical channel state is obtained according to maximum a posteriori decision process in step (4):
Wherein, q (t) indicates current hidden state, andIndicate given observation sequence
ColumnAnd optimal model parametersThe conditional probability density of lower hidden state q (t).
Below in conjunction with specific embodiment, the present invention will be described in further detail, and Fig. 2 show the embodiment of the present invention
A kind of statistical channel state and receive the relational model between signal observed value.Statistical channel state migration procedure is defined as hidden
Hiding process, hiding process are a discrete, finite state and homogeneous Markov Chain, and will be relevant with statistical channel state
It receives signal acquisition process and is defined as observation process, observation process is to hide process as condition.Specifically includes the following steps:
(1) linear large-scale antenna array is used in embodiments of the present invention, collects connecing from time t-L+1 to time t
Receiving end receives signal and forms observation sequenceWherein, when t indicates current
Between, L indicates that sequence length, reception signal are y (t)=H (t) x (t)+z (t), and H (t) is channel matrix, and z (t) is noise matrix.
(2) according to limited scattering effect, statistical channel state set is finite aggregate, can be by the classification learning to training data
It obtains and is used as priori knowledge.The finite aggregate of default statistical channel state isWherein K indicates set member
Prime number amount, Si(i=1,2 ..., K) is i-th of statistical channel state of the expectation of channel distribution parameter and variance composition；It will collect
SequenceAs the observation sequence of model in Fig. 2, and presetMake
For the hiding sequence of model,Indicate hidden state；Model parameter are as follows: λ (t)=(η (t), G (t), μ (t), ∑
(t)), wherein η (t) is the probability vector of statistical channel state, and G (t) is statistical channel state transition probability matrix,WithRespectively expected vector set and
Variance matrix set, can be used two set in element according to polynary multiple Gauss Density functional calculationsThis probability density is with hidden state q (t-l)=SiFor item
Part receives the conditional probability of signal y (t-l), and p () indicates probability density function；According to observation sequenceWith model parameter λ
(t), forward variable is successively calculatedWith backward variableWherein, l=L-1, L-2, L-3 ..., 0,1≤i≤K, forward variable αt-l
(Si) it is expressed as the sequence formed from time t-L+1 to the observation of time t-l under the conditions of setting models parameter lambda (t)
With hidden state q (t-l)=SiConditional probability density, backward variable βt-l(Si) be expressed as given hidden state q (t-l)=
SiWith the sequence formed from time t-l+1 to the observation of time t under the conditions of model parameter λ (t)Probability density, then
Respectively multiplied by zoom factor ct-lObtain scaling forward variableWith Xiang Bianliang after scalingWherein, zoom factor
For
(3) likelihood function is maximizedOptimal model parameters are obtained, in embodiment
Optimized parameter is obtained with iterative algorithmBut it is not limited to the algorithm.Iterative algorithm specifically, first according to resulting scaling before
To variableXiang Bianliang after scalingAnd observation sequenceTo estimate model parameter λrThen, then root (t),
According to observation sequenceWith model parameter λr(t), new scaling forward variable is calculatedWith Xiang Bianliang after scalingNew model parameter λ is estimated againr+1(t) this need to be completed by maximizing following auxiliary function
Wherein, r indicates iteration index.Auxiliary function is
By maximizing auxiliary function, available incremental likelihood
Holding back standard setting isWherein, ε indicates the thresholding being set in advance, in embodiment
In be set as ε=10-6。
(4) statistical channel state is obtained with specific reference to maximum a posteriori decision process:
Afterwards channel statistical information is obtained, wherein q (t) indicates current hidden state to be obtained,Indicate given observation sequenceAnd optimal model parametersThe condition of lower hidden state q (t) is general
Rate density.
Fig. 3 be the embodiment of the present invention and it is traditional based on expectation maximization method be 16, BS antenna number, 128 He in number of users
The mean square error of accurate estimation performance and the graph of relation of system signal noise ratio SNR are realized in the case where observation sequence length 10,
As shown, design method of the present invention realizes the mean square error of S-CSI acquisition compared to based on expectation under three kinds of facilities
Maximization approach is all lower, wherein it is 3 that statistical channel status number is preset in setting 1 and setting 2, presets statistical channel in setting 3
Status number is 4.
Fig. 4 is the embodiment of the present invention in the feelings that statistical channel status number is 3, BS antenna number 128 and observation sequence length 30
Accuracy that accurate S-CSI is obtained and the graph of relation of number of users under different Signal to Noise Ratio (SNR) are realized under condition, such as Fig. 3 institute
Show, design method of the present invention can get a promotion in different Signal to Noise Ratio (SNR) with the increase of number of users.
As it will be easily appreciated by one skilled in the art that the foregoing is merely illustrative of the preferred embodiments of the present invention, not to
The limitation present invention, any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should all include
Within protection scope of the present invention.
Claims (7)
1. the channel statistical information acquisition methods of non stationary channel in a kind of multiaerial system, which is characterized in that including following step
It is rapid:
(1) it obtains from time t-L+1 to the reception signal of the receiving end of the multiaerial system of time t, forms observation sequence,
In, L indicates sequence length；
(2) it establishes statistical channel state and receives the relational model between signal, according to the observation sequence and model parameter meter
Xiang Bianliang after calculating scaling forward variable and scaling；
(3) model parameter is estimated to variable to obtain optimal model parameters according to the scaling forward variable and after scaling；
(4) statistical channel state is estimated using observation sequence and optimal model parameters, complete the acquisition of channel statistical information.
2. the method according to claim 1, wherein the reception signal y (t) of the multiaerial system receiving end
Are as follows:
Y (t)=H (t) x (t)+z (t)
Wherein, x (t) is transmitting end signal, and H (t) is channel matrix, and z (t) is noise matrix.
3. according to the method described in claim 2, it is characterized in that, the observation sequenceAre as follows:
Wherein,The reception signal of reception signal and preceding L-1 historical time comprising present time t.
4. the method according to claim 1, wherein the statistical channel state it is expected by channel statistical information
It is mapped with variance, reflects that the statistical property of channel matrix is shifted at any time.
5. the method according to claim 1, wherein the step (2) includes:
(2.1) it establishes statistical channel state and receives the relational model between signal, model parameter are as follows:
λ (t)=(η (t), G (t), μ (t), Σ (t))
Wherein, the finite aggregate of statistical channel state isSi(i=1,2 ..., K) indicate i-th of statistics
Channel status, K indicate set element quantity；
The hidden state sequence of the corresponding statistical channel state composition of signal is received in the model are as follows:
Wherein,Indicate hidden state, l=L-1, L-2, L-3 ..., 0；η (t) is the initial of statistical channel state
Probability vector, G (t) are statistical channel state transition probability matrix,WithTwo set can be used in respectively expected vector set and variance matrix set
In element according to polynary multiple Gauss Density functional calculationsThis
Probability density is with hidden state q (t-l)=SiFor the conditional probability of conditional access signals y (t-l), p () indicates that probability is close
Spend function；
(2.2) according to observation sequenceWith model parameter λ (t), forward variable is successively calculatedWith backward variableWherein,
L=L-1, L-2, L-3 ..., 0,1≤i≤K, forward variable αt-l(Si) be expressed as under the conditions of setting models parameter lambda (t) from
The sequence of the observation composition of time t-L+1 to time t-lWith hidden state q (t-l)=SiConditional probability density, after
To variable βt-l(Si) be expressed as in given hidden state q (t-l)=SiWith under the conditions of model parameter λ (t) from time t-l+1 then
Between t observation composition sequenceProbability density, then respectively multiplied by zoom factor ct-lObtain scaling forward variableWith Xiang Bianliang after scalingWherein, zoom factor is
6. the method according to claim 1, wherein model parameter is by maximizing likelihood in the step (3)
Function obtains optimal model parameters:
Wherein,It is represented to observation sequence under rational method λ (t)Conditional probability density.
7. the method according to claim 1, wherein after statistical channel state is by maximum in the step (4)
Test decision acquisition:
Wherein, q (t) indicates current hidden state, and Indicate given observation sequence
And optimal model parametersThe conditional probability density of lower hidden state q (t).
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111314030A (en) * | 2020-03-11 | 2020-06-19 | 重庆邮电大学 | SCMA (sparse code multiple access) multi-user detection method based on spherical decoding optimization |
| CN114629533A (en) * | 2022-02-17 | 2022-06-14 | 东南大学 | Information geometry method and system for large-scale MIMO channel estimation |
-
2019
- 2019-06-30 CN CN201910581648.5A patent/CN110365376A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111314030A (en) * | 2020-03-11 | 2020-06-19 | 重庆邮电大学 | SCMA (sparse code multiple access) multi-user detection method based on spherical decoding optimization |
| CN114629533A (en) * | 2022-02-17 | 2022-06-14 | 东南大学 | Information geometry method and system for large-scale MIMO channel estimation |
| CN114629533B (en) * | 2022-02-17 | 2023-04-18 | 东南大学 | Information geometry method and system for large-scale MIMO channel estimation |
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Application publication date: 20191022 |