CN106452534A - Pilot optimization method for large-scale MIMO channel estimation based on structural compressed sensing - Google Patents

Pilot optimization method for large-scale MIMO channel estimation based on structural compressed sensing Download PDF

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CN106452534A
CN106452534A CN201611042223.XA CN201611042223A CN106452534A CN 106452534 A CN106452534 A CN 106452534A CN 201611042223 A CN201611042223 A CN 201611042223A CN 106452534 A CN106452534 A CN 106452534A
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pilot
matrix
channel estimation
channel
compressed sensing
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何雪云
赵天
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Nanjing Post and Telecommunication University
Nanjing University of Posts and Telecommunications
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Nanjing Post and Telecommunication University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/003—Arrangements for allocating sub-channels of the transmission path
    • H04L5/0048—Allocation of pilot signals, i.e. of signals known to the receiver
    • 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
    • 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
    • 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
    • H04L25/024—Channel estimation channel estimation algorithms
    • H04L25/0256—Channel estimation using minimum mean square error criteria

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  • Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Power Engineering (AREA)
  • Radio Transmission System (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention discloses a pilot optimization method for large-scale MIMO channel estimation based on structural compressed sensing. The method comprises the steps of establishing a channel estimation model for a large-scale MIMO-OFDM (Multiple-Input-Multiple-Output-Orthogonal Frequency Division Multiplexing) system when pilots are placed in an overlapping mode; simplifying the channel estimation model for the large-scale MIMO-OFDM system, thereby enabling the channel estimation model to correspond to a structural compressed sensing model; and obtaining an optimum pilot matrix through utilization of a pilot optimization algorithm. Through adoption of the optimum pilot matrix, according to the channel estimation of the large-scale MIMO system based on structural compressed sensing, the mean square errors MSEs of the channel estimation are clearly reduced, and the channel estimation performance is improved.

Description

The pilot frequency optimization method estimated based on the extensive mimo channel of structuring compressed sensing
Technical field
The present invention relates to the channel estimation of communication system pilot aided and pilot design technical field, more particularly, to a kind of base The pilot frequency optimization method estimated in the extensive mimo channel of structuring compressed sensing.
Background technology
As the key technology of following 5G radio communication, extensive MIMO (Multiple-Input-Multiple- Output) system is to configure the tens of even antenna of more than hundreds of in base station coverage area, permissible by means of which The significantly lifting wireless communication system availability of frequency spectrum and power utilization.In addition, OFDM (Orthogonal Frequency Division Multiplexing) technology can be effective against frequency selective fading because of it, and strong antijamming capability has obtained extensively General application.Therefore both combinations can be greatly enhanced the validity and reliability of wireless communication system.Extensive In MIMO-OFDM system, obtain timely channel condition information (CSI, Channel State Information) and signal is examined Survey, precoding, all particularly significant the problems such as resource allocation, in actual MIMO-OFDM system, the acquisition of channel condition information Channel estimation technique is needed to complete, wherein applying most is channel estimation methods based on pilot tone.With regard to pilot tone, can be related to To selection and the modes of emplacement problem of frequency pilot sign, this two factors all can produce on the performance of channel estimation to be affected.
The inherence being had by typical wireless channels openness so that compressive sensing theory (CS, Compressed Sensing) with channel estimation be combined into possibility, be that channel time domain is estimated by pilot tone based on the channel estimation of compressed sensing Parameter, then reconstruct the frequency domain characteristic of channel, but this method assumes between each channel it is mutual when carrying out channel estimation Independent.And in extensive MIMO-OFDM system, be to concentrate to place between dual-mode antenna, between all dual-mode antennas Channel propagation delay is approximately uniform, exists common between this different channels making in extensive MIMO-OFDM system Sparse support.Due to this characteristic so that there is a kind of partitioned organization in multiple channel impulse response, therefore we can adopt A kind of theory more excellent than simple compressed sensing to carry out channel estimation, here it is the piecemeal in structuring compressed sensing is sparse Signal theory.
The channel that structuring compressed sensing technology is applied in extensive MIMO-OFDM system our primary studies is estimated Meter, in structuring compressed sensing, we optimize recovery matrix by reducing correlation between complete block, and then improve restorative Can, in estimating in extensive MIMO-OFDM system channel, recover matrix and determine, we are permissible by pilot frequency locations and symbol By optimizing the criterion recovering matrix come optimizing pilot, thus improving the performance of channel estimation.Meanwhile, in traditional OFDM The mode that pilot orthogonal is placed, existing document proposes the pilot tone modes of emplacement of superposition, effectively can reduce pilot-frequency expense, but It is wherein not provide further pilot frequency optimization method, therefore we have studied specific pilot frequency optimization method, and transported Use in channel estimation, on the basis of improving channel estimating performance, further reduced pilot-frequency expense.
Content of the invention
The technical problem to be solved is for involved defect in background technology, provides a kind of being based on to tie The pilot frequency optimization method that the extensive mimo channel of structure compressed sensing is estimated, the MSE making channel estimation is significantly reduced, and improves The performance of channel estimation.
The present invention is to solve above-mentioned technical problem to employ the following technical solutions：
The pilot frequency optimization method estimated based on the extensive mimo channel of structuring compressed sensing, described extensive MIMO- In ofdm system, transmitting terminal has M root antenna, and receiving terminal has R root antenna；OFDM sub-carriers number is N, wherein leads for transmission The number of the subcarrier of frequency is P；
Described pilot frequency optimization method comprises the steps：
Step 1), set up the channel estimation model of extensive MIMO-OFDM system when pilot tone is overlapping to place：
Wherein, ykIt is that the receiving sequence of k-th OFDM symbol represents, k is the integer more than or equal to 1；Pm=diag { pmBe Vectorial p with P × 1mAs its cornerwise diagonal matrix, m is the sequence number of transmitting terminal transmission antenna, and m is to be less than more than or equal to 1 Integer equal to M；Vectorial pmIt is the pilot frequency sequence on m-th transmission antenna, it is the column vector of the Hadamard matrix of P × P；F It is the DFT matrix of N × N, FLIt is the matrix taking L row part before F, L is channel length, FL|ΩIt is according to pilot frequency sequence location sets Ω chooses FLThe matrix obtaining after corresponding row, hm,kIt is in k-th OFDM symbol between m root transmission antenna and user Channel impulse response, wkIt is additive white Gaussian noise；
Step 2), by formula (2), further abbreviation is formula (4), make the channel estimation model of extensive MIMO-OFDM system with Structuring compressed sensing model corresponds to：
Wherein, Φ is the matrix of P × ML, and its m-th sub-block is PmAnd FL|ΩProduct；
For size for ML × 1 equivalent channel impulse response vector, wherein T represent to Amount carries out transposition；
S=[S1,S2,…SL], Si=[δi,δL+i,…,δ(M-1)L×i], vectorial δiRepresent i-th element be 1, remaining be 0 Column vector, i be more than or equal to 1 be less than or equal to L integer；
E=Φ S=[E1,E2,…,EL]；
Wherein, pmnRepresent the frequency pilot sign in m root antenna, n-th pilot tone, w is the element in N rank DFT matrix,
Step 3), solve optimum pilot tone according to following steps：
Step 3.1), set the numerical value of Y, generate Y subset at randomEach subset unit The number of element is P, as the set of pilot frequency locations；
Step 3.2), choose Ω successivelye, calculate each ΩeUnder Φ and E, and then be calculated according to below equation Correlation between full block：
Wherein,H represents and carries out conjugate transpose to matrix,Represent and solve M's [l, r] Frobenius norm is with it is the quadratic sum of leading role's cosine between all sub-blocks in matrix E；L, r be more than or equal to 1 be less than etc. In the integer of L, and l, r are unequal；
Step 3.3), correlation between Y complete block is ranked up, selects correlation between wherein minimum complete block, and Obtain the pilot frequency locations index under correlation between here minimum complete block, be designated as Ωmin, ΩminI.e. optimal pilot location sets, Between minimum complete block, correlation is correspondingIt is corresponding optimum sequence of pilot symbols.
The pilot frequency optimization method estimated based on the extensive mimo channel of structuring compressed sensing as the present invention is further Prioritization scheme, the setting value of described Y is 500000.
The present invention adopts above technical scheme compared with prior art, has following technique effect：
In extensive MIMO-OFDM system based in structuring compressed sensing channel estimation, and not using institute of the present invention The method mentioned is compared, and can make the mean square error (mean of channel estimation using the optimal pilot matrix that the present invention obtains Square error, MSE) reduction 3dB about, considerably improve the performance of channel estimation.
Brief description
Fig. 1 is the simulation result contrast schematic diagram that transmission antenna number is under different pilot tones when 4；
Fig. 2 is the contrast schematic diagram that pilot number is pilot tone optimization and simulation result when being not optimised when 128；
Fig. 3 is the contrast schematic diagram that pilot number is pilot tone optimization when 512 and simulation result when being not optimised；
Fig. 4 is the contrast schematic diagram of packet and simulation result when not being grouped.
Specific embodiment
Below in conjunction with the accompanying drawings technical scheme is described in further detail：
The present invention comprises two technical problem underlying, and one is channel estimation problems to be converted into structuring compressed sensing ask Topic, thus pilot frequency sequence optimization problem is modeled as calculation matrix optimization problem in structuring compressed sensing；Another is to propose Pilot tone optimized algorithm, thus obtain the pilot matrix of optimum.Introduce the embodiment of this two parts separately below, and by imitative Really illustrate this pilot distribution method to the beneficial effect improving based on structuring compressed sensing channel estimating performance.
(1) acquisition (channel estimation problems are converted to structuring compressed sensing problem) of pilot tone Optimality Criteria
It is assumed that transmitting terminal has M root antenna in extensive MIMO-OFDM system, receiving terminal has R antenna.We are by kth In individual OFDM symbol, the channel impulse response between m root transmission antenna and some user represents
hm,k=[hm,k(0),hm,k(1),…,hm,k(L-1)]T(1)
Here L is channel length, and k takes the integer more than or equal to 1, and m takes the integer being less than or equal to M more than or equal to 1；? hm,kThe number K ＜ ＜ L of middle nonzero element.
Consider such ofdm system scene it is assumed that the number of subcarrier is N, the son load that wherein pilot symbol transmission occupies Ripple number is P, and when traditional orthogonal guide frequency is placed, the pilot tone of different transmission antennas is nonoverlapping, if sending at one The a certain position of antenna placed pilot tone, then can not place pilot tone again in the same location of other transmission antenna, these The data element that pilot tone cannot be placed is referred to as sky pilot tone, and it is to be used to eliminate the reciprocal effect between different transmission antennas, So will result in great pilot-frequency expense, particularly when number of antennas is more, existing document proposes a kind of superposition Pilot tone modes of emplacement, that is, allow the pilot tone of different transmission antennas to occupy identical subcarrier in frequency domain, so will no longer Need empty pilot tone, so will be significantly reduced pilot-frequency expense.But do not study the optimization problem of pilot frequency sequence in document.We will The location sets distributing to the subcarrier sequence of pilot tone are expressed as Ω, and for all of transmission antenna, Ω is identical.With When, in order to distinguish the channel corresponding to different transmission antennas, the sequence of pilot symbols of placement on each transmission antennaShould This is different, herein we adopt frequency pilot sign be hadamard matrix (by -1 and+1 orthogonal square formation constituting).
In receiving terminal, remove Cyclic Prefix and carry out DFT (Discrete Fourier Transform, discrete fourier Conversion) demodulation after, receiving sequence y of k-th OFDM symbolkShould be the coefficient result of M root transmission antenna, by its table It is shown as
Wherein, ykIt is that the receiving sequence of k-th OFDM symbol represents；Pm=diag { pmIt is vectorial p with P × 1mAs Its cornerwise diagonal matrix, m is the sequence number of transmitting terminal transmission antenna, and it is taken from 1 integer arriving M；Vectorial pmIt is m-th Pilot frequency sequence on antennas, it is the column vector of the Hadamard matrix of P × P；F is the DFT matrix of N × N, FLIt is to take L row before F Partial matrix, L is channel length, FL|ΩIt is to choose F according to pilot frequency sequence location sets ΩLThe square obtaining after corresponding row Battle array, hm,kIt is the channel impulse response in k-th OFDM symbol between m root transmission antenna and some user, wkIt is that additivity is high This white noise.
Formula (2) can be simplified shown as by further
Here Φ is the matrix of a P × ML, and m-th sub-block therein is PmAnd FL|ΩProduct； It is the equivalent channel impulse response vector that a size is ML × 1.
Displacement unit matrix IML×ML=[δ1,δ2,…,δML] row, obtain permutation matrix S=[S1,S2,…SL], wherein Si =[δi,δL+i,…,δ(M-1)L×i], vectorial δiRepresent i-th element be 1, remaining be 0 column vector, i is to be less than more than or equal to 1 Integer equal to L.Due to SST=I, S can be brought in formula (3) by we, and this up-to-date style (3) can be expressed as
Wherein E=Φ S=[E1,E2,…,EL],And
Here pmnRepresent m root antenna, the frequency pilot sign in n-th pilot tone, w is the element in N rank DFT matrix,
Due in extensive MIMO-OFDM system openness, therefore can be by channel impulse responseRegard connection as Close K sparse signal, i.e. vectorial hm,k, m=1 ..., the position all same that in M, nonzero element is located.Now, vectorial b is considered as It is that piecemeal K is sparse.So the recovery for the vectorial b in formula (4) can be using the recovery for x in structuring compressed sensing Algorithm obtains, then the channel estimation problems in extensive MIMO-OFDM system have translated in structuring compressed sensing Sparse signal Problems of Reconstruction.
By analysis above, we have drawn such conclusion：In channel estimation problems and structuring compressed sensing Sparse signal Problems of Reconstruction is corresponding, and that is, the E in formula (4) is corresponding with the recovery matrix presence in structuring compressed sensing model Relation, is previously mentioned, and is designed to work as recovering matrix, the restorability making algorithm for reconstructing is lifted.The like, right Matrix E carries out well-designed it is also possible to improve the reconstruction quality of vectorial b.Observation type (4) is it is found that E is to be accorded with by pilot tone simultaneously Number and position choose and determine, therefore recovery matrix has been translated in structuring compressed sensing for the optimization problem of pilot tone Design problem.
(2) random search algorithm (pilot tone optimized algorithm) of correlation between based on smallest blocks
Existing document indicates and can use the orthogonal tracking of divided-fit surface (Block Orthogonal Matching Pursuit, BOMP) algorithm recovering piecemeal sparse signal, and we have found that between the performance of BOMP algorithm and block correlation with And correlation is all relevant in block, observe EiIn element, due to we use frequency pilot sign be Kazakhstan orthogonal between the column and the column Reach agate matrix notation, correlation ν { E }=0 therefore in sub-block, then error performance circle now correlation μ only and between blockB{E} Relevant, and μB{ E } is less, and error performance circle is tighter, so that the reconstruction quality of BOMP algorithm is improved, separately has document to point out Correlation μ between blockBWhat { E } represented is the cosine value of minimum angles between any two sub-block in matrix in block form, and it can not Represent the average recovery capability of BOMP algorithm well, in order to improve average recovery capability it should make all in E as far as possible Between sub-block and orthogonal between all elements of each sub-block, introduce the concept of correlation between complete block for this, that is,HereH represents and carries out conjugate transpose to matrix,Expression is asked The Frobenius norm of solution M [l, r] is with it is the quadratic sum of leading role's cosine between all sub-blocks in matrix E.L, r represent and take from 1 To any two integer of L, and l, r is unequal.So we have to look for making the pilot tone that between complete block, correlation reduces Optimized algorithm, to improve channel estimating performance, we have proposed following random search algorithm to obtain optimal pilot tone for this.
Algorithm 1：Random search algorithm
(1) set Y=500000, generate Y subset at randomEach subset elements Number is P, as the set of pilot frequency locations.
(2) choose Ω successivelye, calculate each ΩeUnder Φ and E, and then be calculated correlation between complete block
(3) correlation between Y complete block is ranked up, selects correlation between wherein minimum complete block, and obtain Pilot frequency locations index under this minima, is designated as Ωmin, it is exactly our optimal pilot location sets to be looked for, this minimum complete Between full block, correlation is correspondingIt is corresponding optimum sequence of pilot symbols.
In lower section, we will be illustrated using algorithm 1 and the channel estimating performance phase not obtained using algorithm 1 by emulation Than, will can make to obtain less mean square error based on channel estimation in the extensive MIMO-OFDM system of BOMP, so that system Obtain higher channel estimating performance.
(3) simulation result
Using Monte-Carlo Simulation, set OFDM subcarrier number in emulation as 512, the channel model of use is length L For 50, non-zero tap number is the channel of 6 (i.e. degree of rarefication is 6).We to weigh channel restorability using normalization MSE Quality.MSE is defined as：
Wherein NMCRepresent is the number of times of Monte-Carlo Simulation,WithIt is estimating in n-th emulation respectively With real channel vector.
Next we will carry out simulation analysis in terms of three below：
(1) when transmission antenna number is fixing, how to choose suitable number of pilots
In transmission antenna number one timing, whether pilot number is chosen proper most important.Choose during pilot tone it should comprehensive examine Consider pilot-frequency expense and factor of both MSE, when pilot tone is larger, preferable MSE performance can be obtained, but pilot-frequency expense is excessive, And if save pilot-frequency expense, MSE degradation will certainly be made it is therefore desirable to these two aspects is carried out compromise consider.
As shown in figure 1, our fixing transmission antenna numbers are 4, make the MSE performance under different number of pilots respectively, In this case, we, according to obtaining preferable MSE performance, save the principle of pilot-frequency expense, the pilot number of selection again Mesh is 64.In practice, when MSE little to a certain extent after, little to the improvement of system performance of BER, so we do not have It is necessary that increasing pilot tone simply also exchanges very low MSE for.Likewise, we have also done transmission antenna number is 8,16,32 and 64 feelings Emulation under condition, result shows, under this several transmission antenna number, optimal pilot number is 128,128,256 and 512.
(2) when pilot number is fixing, pilot tone optimized algorithm performance gain presented herein
Under fixing pilot number, our emulation compare using pilot frequency optimization method mentioned in this article with do not take Channel estimation MSE performance under pilot frequency optimization method.The pilot tone being not optimised as comparison is that we generate at random.Optimize Pilot tone is under each pilot number, is obtained by the searching algorithm being given in literary composition.
Fig. 2 is given, when pilot number is 128, antenna number be 8 and 16 two kind in the case of employing optimizing pilot and unexcellent Change the contrast of pilot tone.
Likewise, Fig. 3 show pilot number when being 512, antenna number is to take pilot tone optimization in the case of 32 and 64 two kind With the contrast not taking pilot tone to optimize.
When pilot number reaches 512, the optimized algorithm putting forward is no longer position to be chosen be optimized, because all 512 subcarriers are used to the transmission of pilot tone, now in random search, due to each Ω generatingiBe 1 to 512 random Sequence, this is equivalent to frequency pilot sign column vector is Hadamard matrix column vector pmIn element enter the random exchange of every trade, that is, The frequency pilot sign placed on each subcarrier is optimized.
From this two width simulation curve as can be seen that under any parameter, will be than use using MSE during optimizing pilot The MSE being not optimised during pilot tone is little, and that is, the performance of channel estimation is more preferable.In the case of pilot number identical, estimated simultaneously When the antenna number of meter is more, the performance gain that pilot tone optimization brings is more obvious.As in Fig. 2 antenna number be 16 when, with employing It is not optimised pilot tone to compare, using optimizing pilot so that MSE declines 3～5dB.When tailing off with the antenna number estimated simultaneously, The gain meeting step-down that pilot tone optimizes, now, for this transmission antenna number, the pilot number of use is on the high side, so leading The advantage that frequency optimizes is inconspicuous, but system effectiveness now can be made to decline because pilot number is on the high side.
(3), when number of antennas is more, adopt the emulation that " packet " thought is carried out
We can see that by above simulation result, with increasing of transmission antenna number, relatively low in order to ensure MSE it is necessary to increase pilot tone quantity.The total number of sub-carriers of ofdm system in practice is certain, if the quantity of pilot tone The total sub-carrier number exceeding, an OFDM symbol insertion frequency pilot sign is not just much of that.And need multiple symbols.Now, multiple Channel estimation on antenna needs packet to carry out.When transmission antenna number is larger, we have two class packet schemes, and one is by it Be divided into a lot of groups, every group of antenna number is less；Another kind is to be divided into less group, and every group of antenna number is more.Both feelings Under condition, which is higher for the quality of channel estimation and system effectiveness.We have done following emulation.In order to complete to send sky Line is channel estimation task when 64, and we take following four channel estimation and pilot allocation scheme：
(scheme one) allows 64 transmission antennas take all subcarrier N=512 of symbol to carry out pilot transmission, connect simultaneously Receiving end adopts a BOMP algorithm, completes the corresponding channel estimation of 64 transmission antennas.
64 transmission antennas are divided into two groups by (scheme two), and every 32 transmission antennas take 256 subcarriers and carry out pilot tone Transmission；To every 32 transmission antennas, corresponding channel is respectively adopted a BOMP algorithm and estimates receiving terminal.
64 transmission antennas are divided into four groups by (scheme three), and every 16 transmission antennas take 128 subcarriers and carry out pilot tone Transmission；To every 16 transmission antennas, corresponding channel is respectively adopted a BOMP algorithm and estimates receiving terminal.
64 transmission antennas are divided into eight groups by (scheme four), and every 8 transmission antennas take 64 subcarriers and carry out pilot tone biography Defeated, to every 8 transmission antennas, corresponding channel is respectively adopted a BOMP algorithm and estimates receiving terminal.
In above four kinds of channel estimations and pilot distribution method, the pilot number of use is identical, so system effectiveness Identical；Computational complexity [] further according to BOMP algorithm is known that the computational complexity of above four kinds of channel estimation scheme It is consistent.The channel estimation MSE performance of this four schemes how？
Fig. 4 shows the MSE performance of this 4 kinds of schemes, and during for there being packet, we are averaging to multigroup MSE, Then it is shown in figure.
From simulation result as can be seen that the performance difference of scheme one and scheme two is little, the performance of scheme one is slightly good, scheme Four performance is the worst, and scheme three performance is placed in the middle.
From general trend, packet count is more, and every group of antenna number is fewer, and the performance of MSE is poorer.This is due to extensive Multiple joint sparse signal hm,kWhen number is more, the sparse algorithm for reconstructing of piecemeal is more accurate.So increasing with antenna number, such as Carrying out channel estimation, then the quantity of every group of antenna can not be too small for fruit packet to be carried out；It is it desired to receive an OFDM symbol Number just carry out channel estimation, then optimal pilot number is just the antenna amount of N is exactly every group of optimal antenna amount.Such as, Total sub-carrier number N=512 in our emulation, according to emulation estimation, when antenna number is 64, its optimal pilot number is just 512, so after total transmission antenna is more than 64, such as transmission antenna is 128, and we can be classified into two groups, often Group antenna number is 64, that is, need to take two complete OFDM symbol and complete the channel estimation in the coherence time.Now, lead to Cross the optimization of pilot tone, we can also reduce the MSE of channel estimation further.
It is understood that unless otherwise defined, all terms used herein (include skill to those skilled in the art of the present technique Art term and scientific terminology) there is general understanding identical meaning with the those of ordinary skill in art of the present invention.Also It should be understood that those terms defined in such as general dictionary should be understood that have with the context of prior art in The consistent meaning of meaning, and unless defined as here, will not be explained with idealization or excessively formal implication.
Above-described specific embodiment, has been carried out to the purpose of the present invention, technical scheme and beneficial effect further Describe in detail, be should be understood that the specific embodiment that the foregoing is only the present invention, be not limited to this Bright, all any modification, equivalent substitution and improvement within the spirit and principles in the present invention, done etc., should be included in the present invention Protection domain within.

Claims (2)

1. the pilot frequency optimization method estimated based on the extensive mimo channel of structuring compressed sensing, described extensive MIMO- In ofdm system, transmitting terminal has M root antenna, and receiving terminal has R root antenna；OFDM sub-carriers number is N, wherein leads for transmission The number of the subcarrier of frequency is P it is characterised in that described pilot frequency optimization method comprises the steps：
Step 1), set up the channel estimation model of extensive MIMO-OFDM system when pilot tone is overlapping to place：
y k = Σ m = 1 M P m F L | Ω h m , k + w k - - - ( 2 )
Wherein, ykIt is that the receiving sequence of k-th OFDM symbol represents, k is the integer more than or equal to 1；Pm=diag { pmIt is with P × 1 vectorial pmAs its cornerwise diagonal matrix, m is the sequence number of transmitting terminal transmission antenna, m be more than or equal to 1 be less than etc. Integer in M；Vectorial pmIt is the pilot frequency sequence on m-th transmission antenna, it is the column vector of the Hadamard matrix of P × P；F is N The DFT matrix of × N, FLIt is the matrix taking L row part before F, L is channel length, FL|ΩIt is to be selected according to pilot frequency sequence location sets Ω Take FLThe matrix obtaining after corresponding row, hm,kIt is the channel in k-th OFDM symbol between m root transmission antenna and user Impulse response, wkIt is additive white Gaussian noise；
Step 2), by formula (2), abbreviation is formula (4) further, makes channel estimation model and the structure of extensive MIMO-OFDM system Change compressed sensing model to correspond to：
y k = ΦSS T h ^ k + w k = E b + w k - - - ( 4 )
Wherein, Φ is the matrix of P × ML, and its m-th sub-block is PmAnd FL|ΩProduct；
For size for ML × 1 equivalent channel impulse response vector, wherein T represents vector is entered Row transposition；
S=[S1,S2,…SL], Si=[δi,δL+i,…,δ(M-1)L×i], vectorial δiRepresent i-th element be 1, remaining be 0 row to Amount, i is the integer being less than or equal to L more than or equal to 1；
E=Φ S=[E1,E2,…,EL]；
Wherein, pmnRepresent the frequency pilot sign in m root antenna, n-th pilot tone, w is the element in N rank DFT matrix,
Step 3), solve optimum pilot tone according to following steps：
Step 3.1), set the numerical value of Y, generate Y subset at randomEach subset elements Number is P, as the set of pilot frequency locations；
Step 3.2), choose Ω successivelye, calculate each ΩeUnder Φ and E, and then complete block is calculated according to below equation Between correlation：
μ t B { E } = Σ l = 1 L Σ l ≠ r | | M [ l , r ] | | F 2
Wherein,H represents and carries out conjugate transpose to matrix,Represent and solve M's [l, r] Frobenius norm is with it is the quadratic sum of leading role's cosine between all sub-blocks in matrix E；L, r be more than or equal to 1 be less than etc. In the integer of L, and l, r are unequal；
Step 3.3), correlation between Y complete block is ranked up, selects correlation between wherein minimum complete block, and obtain Pilot frequency locations index under correlation between here minimum complete block, is designated as Ωmin, ΩminI.e. optimal pilot location sets, minimum Complete block between correlation correspondingIt is corresponding optimum sequence of pilot symbols.
2. the pilot tone optimization side being estimated based on the extensive mimo channel of structuring compressed sensing according to claim 1 Method is it is characterised in that the setting value of described Y is 500000.
CN201611042223.XA 2016-11-23 2016-11-23 Pilot optimization method for large-scale MIMO channel estimation based on structural compressed sensing Pending CN106452534A (en)

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