CN106998307A - A kind of blind signal detection and channel estimation methods for extensive antenna system - Google Patents

A kind of blind signal detection and channel estimation methods for extensive antenna system Download PDF

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CN106998307A
CN106998307A CN201710406106.5A CN201710406106A CN106998307A CN 106998307 A CN106998307 A CN 106998307A CN 201710406106 A CN201710406106 A CN 201710406106A CN 106998307 A CN106998307 A CN 106998307A
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channel
data
user
base station
signal
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CN106998307B (en
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袁晓军
陈磊
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University of Electronic Science and Technology of China
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/0204—Channel estimation of multiple channels
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/0224—Channel estimation using sounding signals
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H04L1/0001—Systems modifying transmission characteristics according to link quality, e.g. power backoff
    • H04L1/0036—Systems modifying transmission characteristics according to link quality, e.g. power backoff arrangements specific to the receiver
    • H04L1/0038—Blind format detection

Abstract

The invention belongs to wireless communication technology field, and in particular to a kind of blind signal detection and channel estimation methods for extensive antenna system.The method of the present invention is used for large scale array antenna system, and base station end antenna alignment, into the regular array of geometry, the openness and correlation based on channel in angle domain carries out uplink and downlink transfer.It is openness in the correlation and time-domain in angle domain that the present invention considers signal, and increase correlation detection can effectively reduce signal restoration errors, while increasing the exploitativeness of signal transmission plan in systems in practice.

Description

A kind of blind signal detection and channel estimation methods for extensive antenna system
Technical field
The invention belongs to wireless communication technology field, and in particular to a kind of blind signal detection for extensive antenna system And channel estimation methods, present invention is particularly suitable for the 5G communication technologys.
Background technology
To ensure the quality of radio communication, channel estimation is essential link in communication process.Channel estimation refers to The process and method of channel condition information (channel state information) are obtained in receiving terminal, its accuracy can be right The receptivity and data transmission quality of system.Traditional channel estimation methods are included in addition pilot tone or training before transmission signal Sequence, receiving terminal estimates channel coefficients by pilot tone or training sequence, is ensured using these channel condition informations to rear supervention The accuracy for sending signal detection to estimate.In 5G radio communications, based on large-scale antenna array multiple-input, multiple-output (multiple- Input multiple-output, MIMO) use of system has become a trend.Led if every antenna configuration Frequency symbol or training sequence can largely consume system resource, reduce message transmission rate, it is impossible to meet the requirement of 5G standards.Cause How existing channel estimation methods are improved the research emphasis as 5G radio communications by this.One of improvement direction It is exactly to utilize the characteristic of extensive antenna system channel in itself.Increasing field data show is in extensive antenna system In, it is most of in angle domain that channel shows sparsity structure, i.e. channel matrix in angle domain (angle domain) in itself Element is 0.Propose to carry out channel estimation using compressed sensing (compressed sensing) method using this property, it is this Method can reduce pilot tone number to a certain extent, improve resource utilization.Another common methods independent of pilot tone and Training sequence, but directly to channel and send signal progress blind estimate.This method faces that computation complexity is higher, estimation is inaccurate Really the problem of.
At present, Chinese patent (the signal transmission of a kind of extensive antenna system of existing Application No. 2016110133047 Method) give transmitting terminal and in the case that receiving terminal is linear antenna arrays, solved using matrix decomposition algorithm extensive The scheme of blind signal detection and channel estimation in antenna system, but there is problems with the program：
1. half-wavelength is generally taken between excellent performance, antenna as interval to have system；If antenna alignment is into line Property array can occupy very big space, or even can not physically realize.
2. in base station end, the reception signal of adjacent antenna is to have correlation on transform domain (such as angle domain, time-domain) Property, but matrix decomposition algorithm does not account for the correlation of signal sampling in detection.
The content of the invention
It is an object of the present invention in view of the above-mentioned problems, propose to tie up at each using matrix disassembling method and antenna channel There is provided the blind signal detection in extensive antenna system and channel estimation methods for correlation on degree.
The technical scheme is that：
A kind of blind signal detection and channel estimation methods for extensive antenna system, this method is used for large scale array Antenna system, and base station end antenna alignment is into the regular array of geometry, the openness and phase based on channel in angle domain Closing property carries out uplink and downlink transfer, it is characterised in that：
Uplink process comprises the following steps：
S1, user send signal to base station, at least including Customs Assigned Number, pilot signal and user data；Wherein, Yong Hubian Number it is located at user data front end, for different user to be identified, each pilot signal takes a resource block；
S2, identification of base stations simultaneously delete the user in deep fade channel；
S3, base station send data using matrix decomposition and correlation detection methods estimation signal and user,
Base station is received after the signal Y that user sends, and progress sparse matrix decomposition obtains the estimate on H and XAndH is the up channel from user terminal to base station end, and X is the user data of uplink user；
During matrix decomposition, using correlation and for the openness detection method of orthogonal frequency division multiplexi, phase The detection of closing property is the correlation based on channel data in angle domain, using information transmission algorithm from noisy channel data The parameter of middle calculating probability distribution p (H), then reuses Minimum Mean Squared Error estimation device and channel data is estimatedIt is described it is openness detection be it is openness in time domain based on channel data, using inversefouriertransform by channel data from Frequency domain hfTransform to time domain ht, and denoising is carried out to time domain data, including：Using the Denoising Algorithm based on sparsity structure to data Denoising, zero-setting operation is carried out using pruning algorithm to partial data；Then, using Fourier transformation by the time domain data after denoising Frequency domain is converted into, and proceeds matrix decomposition；
Base station can determine permutation matrix Π and magnitude matrix Σ according to Customs Assigned Number and pilot signal, eliminate in estimate Amplitude ambiguities and displacement fuzziness, obtain the final estimate to H and XAnd
In such scheme, two kinds of detection methods can effectively lift the accuracy to channel and signal data estimation.
Downlink transmission process comprises the following steps：
If time division duplex, then up channel estimation is directly used in downlink transfer；
If FDD, a resource block transmission pilot signal is distributed in base station, and user is according to the pilot signal received Then the down channel yield value drawn estimates data, and base station is designed precoding strategy according to the direction of up channel and sent User data.
Beneficial effects of the present invention are, it is contemplated that signal is openness in the correlation and time-domain in angle domain, Increase correlation detection can effectively reduce signal restoration errors, at the same increase signal transmission plan in systems in practice can be real Shi Xing.
Brief description of the drawings
Fig. 1 is the system block diagram of downstream transmission scheme of the present invention；
Fig. 2 is that squaerial array signal propagates schematic diagram；
Fig. 3 is Antenna Array Channel HkThe amplitude schematic diagram in time-domain and angle domain；
Fig. 4 is Markov field structure detection effect contrast figure；
Fig. 5 is Antenna Array Channel H factor graph model.
Embodiment
With reference to the accompanying drawings and examples, technical scheme is described in detail：
Present invention primarily contemplates large scale array antenna system, i.e. number of users (K) and base station end antenna total (N) are all very big Situation.Meanwhile, base station end antenna alignment is into the regular array of certain geometry, including conventional rectangular array, circle Row etc..Transmission plan is made up of two parts：Uplink (from user terminal to base station end) and downlink transfer are (from base station end to user End).
First, we briefly introduce the channel model of large scale array antenna.Represent that user k is sent in T time with xk Data, then the reception signal of base station end can be write as：
In formula,Channel additive noise is represented,Represent that user is to the channel of base station, X=in angle domain [x1,…,xk]HThe data that uplink user is sent are represented, B represents the mapping matrix between channel space domain and angle domain, with day The geometry of line arrangement is different and changes.Use BHPremultiplicationN × T matrix Y can be obtained：
Y=HX+Z
Wherein,Represent additive noise of the channel in angle domain.Now, Y is that base station end receives signal at angle The expression spent on domain.
Secondly, we introduce openness and correlation of the channel in each dimension.In large scale array antenna, including mesh Preceding conventional squaerial array and circular antenna array, extensive antenna system channel square is understood according to current measured data Battle array most elements in angle domain level off to 0, influence will not be produced on systematic function, it can be considered that matrix H is sparse Matrix.Meanwhile, antenna for base station is with having certain correlation on the channel angle domain of user.Such as Fig. 3, in angle domain, base station Acceptance angle is related between antenna, i.e., channel matrix nonzero element has aggregation (group sparsity) in position.This Invention using information transmission algorithm calculated from noisy channel data probability distribution p (Hij) parameter (i=1 ..., N, J=1 ..., T), least mean-square error (Minimum Mean Square Error) estimator is then reused to channel data EstimatedIts factor graph model as shown in figure 5, wherein, f represents channel relevancy structure,Inputted for estimator,For priori average and variance, then the estimation of channel data is public Formula is
The movement of multipath effect and antenna relative position when in systems in practice, due to Electromagnetic Wave Propagation, in addition it is also necessary to examine Consider the delay spread (delay spread) and Doppler shift (Doppler shift) of channel.Channel gain is in delay spread It can concentrate and appear in there is also correlation, i.e. channel gain in the corresponding frequency dimension of corresponding time dimension and Doppler shift On some moment and frequency, remaining moment and frequency channels gain are close to 0.
When solving inter-symbol interference problem caused by multipath effect, orthogonal frequency division multiplexi (Orthogonal can be used Frequency Division Multiplexing).It is described below possible dilute based on data in the technology channel estimation process Dredge property and correlation.Orthogonal frequency division multiplexi is to break a channel into some orthogonal sub-channels, and high-speed data signal is changed Into parallel low rate data streams, data flow is modulated onto in the subchannel of each different frequency and propagated afterwards, and intersymbol is solved with this Cross-interference issue.Now, channel model can be write as
Wherein, fkThe carrier frequency of k-th of subchannel is represented, P represents subchannel number.Wherein,It is f to represent carrier frequency respectivelykReception data, channel sends data and additive noise.Due to Electromagnetic Wave Propagation number of path is limited, therefore channel only has a small number of main gains in time domain, meets sparse property, in frequency domain On there is corresponding correlation.WithRepresent that carrier frequency is f on (i, j) individual position in aerial arraykData；hf Represent the corresponding data of each sub-carrier frequencies on (i, j) individual position；htCorrespondence time domain data is represented, meets openness；Then hf With htRelation can be expressed as
Wherein, F represents Discrete Fourier transform.For the openness of channel time domain, the present invention is considered frequency domain number Denoising Algorithm is reused in time domain recovered according to transforming to.
Channel estimation errors can be greatly reduced using the property of channel in each dimension in itself in detection process, carry Rise systematic function.The conventional modeling pattern of channel relevancy has but is not limited to Markov Chain, Markov field, based on sparse shellfish Modeling that Ye Si learns and variance is coupled etc..
In uplink, base station should be screened to all users first, deleted and be in deep fading (deep Fading) the user of channel, to ensure the detection performance of whole system.Base station carries out sparse after the signal Y of user is received Matrix decomposition (sparse matrix factorization) obtains the estimation on H and XAndIn the process of matrix decomposition In, algorithm consider antenna between in angle domain data correlation, the restoration errors of signal and channel matrix can be reduced.For Eliminate the amplitude ambiguities (scalar ambiguity) in blind symbol estimation problem and replace fuzziness (permutation Ambiguity), i.e.,WithIdentical reception signal can be obtained, and (Σ is one to angular moment Battle array, Π is a permutation matrix), user increases log when sending data in front end2(K) bits is used for the unique identification user, And need with a resource block pilot signal transmitted.Base station can uniquely determine permutation matrix according to front end data and pilot signal Π and magnitude matrix Σ, and finally give the estimation to H and XAnd
In downlink transfer, for a tdd systems (time division duplex, TDD), it is possible to use The estimation of up channel directly designs corresponding downlink transfer scheme.Therefore, FDD (frequency is mainly considered Division duplex, FDD) downlink transfer conceptual design in system.Because up-downgoing channel separates path in angle domain It is upper that there is separable angle of arrival (the resolvable angle of of symmetry, i.e. up-downgoing channel in angle domain Arrival) it is identical, and difference is the yield value in these paths.Therefore it may only be necessary to distribute a resource block transmission Pilot signal, user is just estimated that corresponding channel yield value, and base station end is then directly designed according to these channel directions Precoding strategy simultaneously sends user data.Compared with conventional transmission scheme, this transmission plan has larger in resource utilization Advantage, complete FB(flow block) is as shown in Figure 1.
Embodiment
This example considers a simplified ascending communication system, as shown in Fig. 2 the antenna alignment of base station end into 64 × 64 it is flat Surface antenna array, user's number is 32, and user terminal configuration single antenna, channel coherency time is set to 32 time slots.Channel is in angle Bernoulli Jacob's Gaussian Profile with Markov field structure is obeyed on domain, degree of rarefication is set to 0.25, i.e. angle domain upper signal channel is non- Neutral element ratio is 25%.Planar antenna array channel model can be written as：
Wherein, NpRepresent number of path, βk,nRepresent path gain, Nv× 1 vectorial avAnd Nh× 1 vectorial ahRepresent respectively The steering vector (steering vector) of aerial array horizontally and vertically,
Andλ is carrier wavelength, dv、dhBe antenna in the horizontal direction Spacing between vertical direction, θ, φ are horizontal angle and the angle of pitch.According to virtual representation method widely used at present (virtual representation), channel model can be transformed into angle domain from spatial domain and be expressed as vector form：
Wherein,Represent Kronecker product, Bv, BhBoth horizontally and vertically change of the upper spatial domain to angle domain is represented respectively Change matrix.
Multiuser channel model is explained below, x is usedkThe data that user k is sent in T time are represented, each user matches somebody with somebody Single antenna is put, then the reception signal of base station end can be write as：
In formula,Channel additive noise is represented,Represent angle domain on user to the channel of base station be one The individual sparse matrix with Markov field structure, X=[x1,…,xk]HRepresent the data that uplink user is sent.Use BHPremultiplication And N × T matrix Y can be obtained：
Y=HX+Z
In formula
According to the transmission plan provided in the content of the invention, with reference to the system block diagram in Fig. 1, each user is needed before data IncreaseCoding, while using a time slot pilot signal transmitted.Base station end is sharp after signal Y is received Signal is decomposed with sparse matrix decomposition algorithm, user is obtained and sends signal matrix X and channel matrix H.Conventional is sparse Matrix decomposition algorithm has K-SVD, SPAMS, ER-SpUD etc..In this example, the best BiGAMP of current performance is used to calculate Method.After sparse matrix decomposition, amplitude ambiguities matrix Σ is estimated using MMSE schemes, and displacement fuzziness matrix Π can root Unique consequence is obtained according to subscriber-coded.
Fig. 4 gives base station when docking collection of letters Y progress matrix decompositions, uses Markov field structure detection user to send out The standard mean square error for the number of delivering letters.Red solid line represented in the case of known to channel matrix nonzero element position, subscriber signal Evaluated error, therefore can as matrix decomposition algorithm signal evaluated error theory lower bound；Blue solid lines and blue dotted line When coherent detection structure is not used in representing matrix decomposition algorithm respectively and coherent detection structure is used, the estimation of subscriber signal is missed Difference.As can be seen that in the case of low signal-to-noise ratio, nearly 15 points of signal errors reduction can be made using Markov field detection structure Shellfish, structure is detected compared to unused Markov field, and the present invention program systematic function is substantially improved.Using detection structure Afterwards, the signal restoration errors in the present invention program are under the conditions of each signal to noise ratio, closer to theory lower bound.

Claims (1)

1. a kind of blind signal detection and channel estimation methods for extensive antenna system, this method is used for large scale array day Linear system is united, and base station end antenna alignment is into the regular array of geometry, the openness and correlation based on channel in angle domain Property carry out uplink and downlink transfer, it is characterised in that：
Uplink process comprises the following steps：
S1, user send signal to base station, at least including Customs Assigned Number, pilot signal and user data；Wherein, Customs Assigned Number position In user data front end, for different user to be identified, each pilot signal takes a resource block；
S2, identification of base stations simultaneously delete the user in deep fade channel；
S3, base station send data using matrix decomposition and correlation detection methods estimation signal and user,
Base station is received after the signal Y that user sends, and progress sparse matrix decomposition obtains the estimate on H and XAndH is Up channel from user terminal to base station end, X is the user data of uplink user；
During matrix decomposition, using correlation and for the openness detection method of orthogonal frequency division multiplexi, correlation Detection is the correlation in angle domain based on channel data, is fallen into a trap using information transmission algorithm from noisy channel data Probability distribution p (H) parameter is calculated, Minimum Mean Squared Error estimation device is then reused and channel data is estimatedInstitute It is openness in time domain based on channel data to state openness detection, using inversefouriertransform by channel data from frequency domain hf Transform to time domain ht, and denoising is carried out to time domain data, including：Using the Denoising Algorithm based on sparsity structure to data de-noising, Zero-setting operation is carried out to partial data using pruning algorithm；Then, the time domain data after denoising is converted using Fourier transformation For frequency domain, and proceed matrix decomposition；
Base station can determine permutation matrix Π and magnitude matrix Σ according to Customs Assigned Number and pilot signal, eliminate the width in estimate Fuzziness and displacement fuzziness are spent, the final estimate to H and X is obtainedAnd
Downlink transmission process comprises the following steps：
If time division duplex, then up channel estimation is directly used in downlink transfer；
If FDD, a resource block transmission pilot signal is distributed in base station, and user draws according to the pilot signal received Down channel yield value then estimate data, base station designs precoding strategy according to the direction of up channel and sends user Data.
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