CN105024954B - The fill method and system of time-domain training sequence based on compressed sensing channel estimation - Google Patents
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Abstract
The invention discloses the fill methods and system of a kind of time-domain training sequence based on compressed sensing channel estimation, which comprises S1. generates n according to required maximum channel length L at randomdThe constant amplitude sequence of a L long forms constant amplitude arrangement set；S2. the constant amplitude arrangement set is calculated using genetic algorithm calculating, obtains the optimal constant amplitude sequence of fitness；S3. the optimal constant amplitude sequence is subjected to leaf inverse transformation in L point discrete Fourier, the length M of cyclic extension to required training sequence, the time-domain training sequence as acquisition；S4. the timing training sequence is filled into block transmission system, emits the timing training sequence together with data field in transmitting terminal；In receiving end, compression reconstruction is carried out to channel without inter-block-interference region using training sequence.The present invention has the advantage that realizing the reconstruction of channel time domain impulse response, training sequence structure is simple, spectrum efficiency is high, precision is high.
Description
Technical field
The invention belongs to field of communication technology, in particular to a kind of time-domain training sequence based on compressed sensing channel estimation
Fill method and device.
Background technique
With the continuous emergence of the communication technology in recent years, block transmission system due to its low complex degree, spectral efficient and
The ability of anti-multipath channel, has been greatly developed.Two inter-block-interferences for continuously transmitting block in order to prevent, it usually needs
GI (Guard Interval, protection interval) is added between two blocks.GI can be divided by its type: CP (Cyclic
Prefix, cyclic prefix), ZP (zero padding, zero padding) and TDS (time domain synchronous, time domain
It is synchronous).The above two not can be carried out channel estimation, it usually needs the additional pilot tone that increases causes very big loss of spectral efficiency.It is another
Aspect, TDS system are realized between anti-piece by the way that GI and TS (training sequence, training sequence) to be combined
Interference, receiver synchronization and channel estimation, embody modularized design, the spectral efficient, Fast synchronization of TDS-TS system
Performance.Just because of its superior function, TDS-OFDM (Orthogonal Frequency Division Multiplexing,
Orthogonal frequency division multiplexing) technology have become China's independent intellectual property right research and development international forth generation ground digital television broadcast standard
One of core technology.
However, carrying out channel estimation with TDS-TS has very big challenge.Due to the multipath effect of channel, before TS will receive
The interference of one frame data block, the usual method is that the method with iteration eliminates interference, but its complexity is very high, precision
It is bad；Other methods are there are also using continuous two identical TS, in this way can be with but only using second TS progress channel estimation
Inter-block-interference is effectively eliminated, but will be greatly reduced spectrum efficiency.It is therefore proposed that low complex degree, spectral efficient, a high-precision
Channel estimation methods it is imperative, key technology is then that design one has excellent performance time-domain training sequence building method and right
Answer high-precision channel estimation methods.
Summary of the invention
The present invention is directed at least solve one of above-mentioned technical problem.
For this purpose, an object of the present invention is to provide a kind of time-domain training sequences based on compressed sensing channel estimation
Fill method, this method can effectively fill the training sequence in block transmission system, realize low complex degree, spectral efficient, height
The channel estimation of precision.
To achieve the goals above, the embodiment of the first aspect of the present invention is disclosed one kind and is estimated based on compressed sensing channel
The fill method of the time-domain training sequence of meter, comprising the following steps: S1. generates n according to required maximum channel length L at randomd
The constant amplitude sequence of a L long forms constant amplitude arrangement set, wherein L and ndIt is natural number；S2. using genetic algorithm to the constant amplitude
Arrangement set is calculated, and the optimal constant amplitude sequence of fitness is obtained；S3. it is discrete the optimal constant amplitude sequence to be subjected to L point
Inverse Fourier transform, the length M of cyclic extension to required training sequence, the time-domain training sequence as acquisition；It S4. will be described
Timing training sequence is filled into block transmission system, in transmitting terminal by the timing training sequence together with one starting of data field
It penetrates；In receiving end, compression reconstruction, the no inter-block-interference region are carried out to channel without inter-block-interference region using training sequence
For in training sequence not by the part of previous frame data field inter-block-interference.
The fill method of time-domain training sequence according to an embodiment of the present invention based on compressed sensing channel estimation realizes letter
The reconstruction of road time domain impulse response.Has the characteristics that low complex degree, spectral efficient, high-precision.
In addition, a kind of time-domain training sequence based on compressed sensing channel estimation according to the above embodiment of the present invention is filled out
Filling method can also have the following additional technical features:
Further, in step sl, the element of constant amplitude sequence value from set { 1, -1 }.
Further, in step s 2, maximum evolutionary generation n is setg, interaction Probability pc, mutation probability pmAnd discrete probabilistic
Be distributed P, the genetic algorithm the following steps are included:
S2A. by ndThe individual collections of the first generation of a L long constant amplitude sequence as algorithm；
S2B. if the contemporary evolutionary generation of the constant amplitude sequence is equal to maximum evolutionary generation ngOr the whole phases of present age individual
Together, then S2N is entered step, S2C is otherwise entered step；
S2C. the individual adaptation degree of Current generation is calculated；
It S2D. is individual distribution select probability according to fitness size；
S2E. interaction times are initialized as 0；
S2F. if interaction times are greater than nd/ 2, then S2L is entered step, S2G is otherwise entered；
S2G. two individuals are selected according to the select probability of each individual；
S2H. according to the interactive Probability pcAll elements in two individuals are successively judged whether to interact,
After the element to the individual all judges, count is incremented for interaction times；
S2I. to each individual, according to the mutation probability pmDecide whether to make a variation；
S2J. the new individual after interacting is as filial generation；
S2K. return step S2F；
S2L. the constant amplitude sequence evolution algebra adds 1；
S2M. return step S2B；
S2N. the smallest present age individual of fitness is obtained；
S2O. the optimal constant amplitude sequence of the fitness is generated according to the smallest present age individual of the fitness；
In step s3, the optimal constant amplitude sequence of the fitness is subjected to cyclic extension, obtains the time domain training sequence
Column.
Further, the fitness is that obtained Toeplitz square after M long is extended to by L long constant amplitude sequence loops
The matrix correlation value of battle array.
Further, the sequence after the constant amplitude sequence progress L point IDFT that length is L is PL=[P1,P2,...,PL], circulation
The sequence for being extended for length M is PM=[P1,P2,...,PL,P1,P2,...,PM-L], obtained Toeplitz matrix Ψ are as follows:
Further, in step s 2, the optimal constant amplitude sequence of fitness is to be observed by the constant amplitude sequence L according to optimization
The minimum value of matrix correlation value obtains, and the matrix correlation value is relevant maximum value between any two column of matrix；It is described suitable
Response is optimal for fitness numerical value minimum.
Further, in step s 4, the no inter-block-interference zone length is M-L+1.
Further, in step s 4, the model of compressed sensing reconstruction is
R=Ψ h+n
Wherein, observation vector r, length M-L+1 are the training sequence that receives without inter-block-interference region；Ψ is institute
State Toeplitz matrix；H is the time domain impulse response of channel, and length L, non-zero entry number S are much smaller than L；N length is M-L+
1, it is independent identically distributed Gaussian noise.
Further, the method for reconstructing includes compression sampling match tracing or orthogonal matching pursuit.
In addition, the embodiment of another aspect of the present invention also proposed a kind of time domain instruction based on compressed sensing channel estimation
Practice the fill system of sequence, comprising:
Constant amplitude sequence generating module: for generating n at randomdA L long constant amplitude sequence forms constant amplitude arrangement set, wherein L and nd
It is natural number；
Constant amplitude sequence optimisation module: for according to constant amplitude sequence generating module, using the irrelevant criterion of compressed sensing,
Obtain the optimal constant amplitude sequence of fitness；
Training sequence generation module: the constant amplitude sequence for being optimized according to constant amplitude sequence optimisation module successively carries out L point
IDFT and cyclic extension obtain M long training sequence；
Compressed sensing based channel impulse response estimation module, for using training sequence without inter-block-interference region,
Compression reconstruction is carried out to channel using the greedy algorithm that compressed sensing is rebuild.
Additional aspect and advantage of the invention will be set forth in part in the description, and will partially become from the following description
Obviously, or practice through the invention is recognized.
Detailed description of the invention
Above-mentioned and/or additional aspect of the invention and advantage will become from the description of the embodiment in conjunction with the following figures
Obviously and it is readily appreciated that, in which:
Fig. 1 is a kind of fill method flow chart of time-domain training sequence based on compressed sensing channel estimation；
Fig. 2 is OFDM frame structure and its inter-block-interference schematic diagram under multipath channel；
Fig. 3 is a kind of fill system structure chart of time-domain training sequence based on compressed sensing channel estimation.
Specific embodiment
The embodiment of the present invention is described below in detail, examples of the embodiments are shown in the accompanying drawings, wherein from beginning to end
Same or similar label indicates same or similar element or element with the same or similar functions.Below with reference to attached
The embodiment of figure description is exemplary, and for explaining only the invention, and is not considered as limiting the invention.
In the description of the present invention, it is to be understood that, term " center ", " longitudinal direction ", " transverse direction ", "upper", "lower",
The orientation or positional relationship of the instructions such as "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outside" is
It is based on the orientation or positional relationship shown in the drawings, is merely for convenience of description of the present invention and simplification of the description, rather than instruction or dark
Show that signified device or element must have a particular orientation, be constructed and operated in a specific orientation, therefore should not be understood as pair
Limitation of the invention.In addition, term " first ", " second " are used for description purposes only, it is not understood to indicate or imply opposite
Importance.
In the description of the present invention, it should be noted that unless otherwise clearly defined and limited, term " installation ", " phase
Even ", " connection " shall be understood in a broad sense, for example, it may be being fixedly connected, may be a detachable connection, or be integrally connected；It can
To be mechanical connection, it is also possible to be electrically connected；It can be directly connected, can also can be indirectly connected through an intermediary
Connection inside two elements.For the ordinary skill in the art, above-mentioned term can be understood at this with concrete condition
Concrete meaning in invention.
Referring to following description and drawings, it will be clear that these and other aspects of the embodiment of the present invention.In these descriptions
In attached drawing, some particular implementations in the embodiment of the present invention are specifically disclosed, to indicate to implement implementation of the invention
Some modes of the principle of example, but it is to be understood that the scope of embodiments of the invention is not limited.On the contrary, of the invention
Embodiment includes all changes, modification and the equivalent fallen within the scope of the spirit and intension of attached claims.
Embodiment 1
Fig. 1 is a kind of fill method flow chart of time-domain training sequence based on compressed sensing channel estimation, Fig. 2 OFDM
Frame structure and its inter-block-interference schematic diagram under multipath channel.As shown in Figure 1, simultaneously referring to Fig. 2, the present invention proposes a kind of TDS-
In ofdm system, the fill method of the time-domain training sequence based on compressed sensing channel estimation of long channel time delay, this method packet
It includes:
S1. 100 192 long constant amplitude sequences are generated according to required maximum channel length 192 at random, form constant amplitude sequence sets
It closes；Specifically, each frame of ofdm system includes 255 long training sequences and 4096 long data fields.100 generated at random
192 long constant amplitude sequential element values are in set { 1, -1 }.Specific frame structure is shown in Fig. 2.
S2. the constant amplitude arrangement set generated is defeated as the genetic algorithm based on the optimization of compressed sensing training sequence proposed
Enter, according to the irrelevant criterion of compressed sensing, obtains the constant amplitude sequence for being suitable for channel estimation；
Specifically, the genetic algorithm used based on the optimization of compressed sensing training sequence are as follows:
Input:
1. training sequence length 255
2. maximum channel length 192
3.100 192 long constant amplitude sequences
4. maximum evolutionary generation 100
5. interaction Probability pc=0.5
6. mutation probability pm=0.005
7. a discrete probability distribution P
Start:
1. by ndThe individual collections of the first generation of a 192 long constant amplitude sequence as algorithm；
2.While (reaches maximum evolutionary generation ngOr certain generation individual is identical)；
3. calculating the individual adaptation degree of Current generation；
4. being individual distribution select probability according to fitness size；
5.For interaction times are from 1 to 50；
6. selecting two individuals according to the select probability of each individual；
7. pair two individual chromosomes pair, according to pcDecide whether to interact
8. each chromosome of a pair individual, according to pmDecide whether to make a variation
9. the new individual after interaction is as filial generation
10.End for
11.End while
12. obtaining the smallest present age individual of fitness.
The fitness is that obtained Toeplitz matrix after 256 length is extended to by 192 long constant amplitude sequence loops
Matrix correlation value, the matrix correlation value are relevant maximum value between any two column of matrix, and the fitness is bigger, i.e.,
The matrix correlation value of corresponding calculation matrix is bigger, and the select probability that corresponding individual is assigned to is then smaller, can leave filial generation
Probability with regard to smaller.
Wherein fitness minimum is selected in the generation population of algorithm after circulation terminates, that is, corresponds to the matrix phase of calculation matrix
Pass is worth the smallest individual, obtains the constant amplitude sequence p for being suitable for channel estimationL=[p1,p2,...,p192]。
S3. the constant amplitude sequence of generation is carried out in 192 point discrete Fouriers after leaf inverse transformation (IDFT), cyclic extension is needed for
Training sequence length 255, the time-domain training sequence as acquisition；
Specifically, the constant amplitude sequence p suitable for channel estimation obtained to genetic algorithmL=[p1,p2,...,p192] carry out from
Inverse Fourier transform is dissipated, P is obtainedL=[P1,P2,...,P192], then cyclic extension is carried out, become the sequence P of length 255255=
[P1,P2,...,P192,P1,P2,...,P63], the time-domain training sequence as acquisition.
S4. training sequence is filled into block transmission system, is emitted in transmitting terminal training sequence together with data field；
In receiving end using training sequence without inter-block-interference region compressed sensing based algorithm can be used, channel is compressed
It rebuilds.Specifically, the inter-block-interference is since 192 long channel multipath effect bring data fields are to the 191 of training sequence
Influence caused by long hangover, referring to Fig. 2.The training sequence is 255 long training without inter-block-interference region
Not by the part of previous frame data field inter-block-interference in sequence, the length is 255-192+1=64.
The channel reconstructing of compressed sensing, model are carried out using this 64 points are as follows:
r64=Ψ64×192h192+n64
Wherein r64It is the training sequence that receives without inter-block-interference region；Ψ64×192For observing matrix, meet:
h192It is the time domain impulse response of channel, non-zero entry number S is much smaller than 192；n64It is that independent identically distributed Gauss makes an uproar
Sound.
Compression reconstruction is carried out to channel using CoSamp algorithm, obtains channel time domain impulse response h192。
Embodiment 2
Referring to Fig. 1, the present invention is proposed in a kind of TDS-OFDM system, and short channel time delay is believed based on compressed sensing
The fill method of the time-domain training sequence of road estimation, this method comprises:
S1. a 128 long constant amplitude sequences are generated according to required maximum channel length 128 at random, form constant amplitude sequence sets
It closes；
Specifically, each frame of ofdm system includes 255 long training sequences and 4096 long data fields.It generates at random
128 long constant amplitude sequential element values are in set { 1, -1 }.
S2. obtained after the cyclic extension of the discrete Fourier transform for the constant amplitude sequence for being 128 due to the length of generation
Toeplitz matrix is an orthogonal matrix, thus its each column be it is completely orthogonal, the correlation of matrix is 0, has been had reached most
It is small, it is advanced optimized without genetic algorithm is carried out.
S3. the constant amplitude sequence of generation is carried out in 128 point discrete Fouriers after leaf inverse transformation (IDFT), cyclic extension is needed for
Training sequence length 255, the time-domain training sequence as acquisition；
Specifically, the constant amplitude sequence p suitable for channel estimation obtained to genetic algorithmL=[p1,p2,...,p128] carry out from
Inverse Fourier transform is dissipated, P is obtainedL=[P1,P2,...,P128], then cyclic extension is carried out, become the sequence P of length 255255=
[P1,P2,...,P128,P1,P2,...,P127], the time-domain training sequence as acquisition.
S4. training sequence is filled into block transmission system, is emitted in transmitting terminal together with data field；It is receiving
End, using training sequence without inter-block-interference region, can be used compressed sensing based algorithm and carries out compression reconstruction to channel.
Specifically, the inter-block-interference is since 128 long channel multipath effect bring data fields are to training sequence
Influence caused by 127 long hangovers, is shown in Fig. 2.The training sequence is in 255 long training sequences without inter-block-interference region
Not by the part of previous frame data field inter-block-interference, the length is 255-128+1=128.
The channel reconstructing of compressed sensing, model are carried out using this 128 points are as follows:
R=Ψ h+n
Wherein r is the training sequence received without inter-block-interference region；Ψ is observing matrix, is met:
h192It is the time domain impulse response of channel, non-zero entry number S is much smaller than 192；n64It is that independent identically distributed Gauss makes an uproar
Sound.
Compression reconstruction is carried out to channel using OMP algorithm, obtains channel time domain impulse response h.
Embodiment 3
The present invention discloses a kind of fill system of time-domain training sequence based on compressed sensing channel estimation, referring to
3, comprising:
Constant amplitude sequence generating module: for generating n at randomdA L long constant amplitude sequence forms constant amplitude arrangement set, wherein L and nd
It is natural number；
Constant amplitude sequence optimisation module: for according to constant amplitude sequence generating module, using the irrelevant criterion of compressed sensing,
Obtain the optimal constant amplitude sequence of fitness；
Training sequence generation module: the constant amplitude sequence for being optimized according to constant amplitude sequence optimisation module successively carries out L point
IDFT and cyclic extension obtain M long training sequence；
Compressed sensing based channel impulse response estimation module, for using training sequence without inter-block-interference region,
Compression reconstruction is carried out to channel using the greedy algorithm that compressed sensing is rebuild.
In addition, the time-domain training sequence of the compressed sensing channel estimation of the embodiment of the present invention and other compositions of system and
Acting on all is known for a person skilled in the art, in order to reduce redundancy, is not repeated them here.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or spy described in conjunction with this embodiment or example
Point is included at least one embodiment or example of the invention.In the present specification, schematic expression of the above terms are not
Centainly refer to identical embodiment or example.Moreover, particular features, structures, materials, or characteristics described can be any
One or more embodiment or examples in can be combined in any suitable manner.
Although an embodiment of the present invention has been shown and described, it will be understood by those skilled in the art that: not
A variety of change, modification, replacement and modification can be carried out to these embodiments in the case where being detached from the principle of the present invention and objective, this
The range of invention is by claim and its equivalent limits.
Claims (9)
1. a kind of fill method of the time-domain training sequence based on compressed sensing channel estimation, which is characterized in that including following step
It is rapid:
S1. n is generated according to required maximum channel length L at randomdThe constant amplitude sequence of a L long forms constant amplitude arrangement set, wherein L
And ndIt is natural number；
S2. the constant amplitude arrangement set is calculated using genetic algorithm, obtains the optimal constant amplitude sequence of fitness, wherein
Set maximum evolutionary generation ng, interaction Probability pc, mutation probability pmWith discrete probability distribution P, the genetic algorithm includes following step
Rapid: S2A. is by ndThe individual collections of the first generation of a L long constant amplitude sequence as algorithm；S2B. if the constant amplitude sequence
Contemporary evolutionary generation is equal to maximum evolutionary generation ngOr present age individual is all identical, then enters step S2N, otherwise enter step
S2C；S2C. the individual adaptation degree of Current generation is calculated；It S2D. is individual distribution select probability according to fitness size；S2E. it hands over
Mutual number is initialized as 0；S2F. if interaction times are greater than nd/ 2, then S2L is entered step, S2G is otherwise entered；S2G. according to every
The select probability of an individual selects two individuals；S2H. according to the interactive Probability pcTo all members in two individuals
Element successively judges whether to interact, and after the element to the individual all judges, count is incremented for interaction times；S2I. right
Each individual, according to the mutation probability pmDecide whether to make a variation；S2J. the new individual after interacting is as filial generation；S2K. step is returned
Rapid S2F；S2L. the constant amplitude sequence evolution algebra adds 1；S2M. return step S2B；S2N. the fitness the smallest present age is obtained
Body；S2O. the optimal constant amplitude sequence of the fitness is generated according to the smallest present age individual of the fitness；
S3. the optimal constant amplitude sequence of the fitness is subjected to leaf inverse transformation in L point discrete Fourier, cyclic extension to required training
The length M of sequence, the time-domain training sequence as acquisition；And
S4. time-domain training sequence is filled into block transmission system, in transmitting terminal by the time-domain training sequence together with data
Domain emits together；In receiving end, compression reconstruction carried out to channel without inter-block-interference region using training sequence, between the no block
Interference region is in training sequence not by the part of previous frame data field inter-block-interference.
2. the fill method of the time-domain training sequence according to claim 1 based on compressed sensing channel estimation, feature
It is, in step sl, the element of constant amplitude sequence value from set { 1, -1 }.
3. the fill method of the time-domain training sequence according to claim 1 based on compressed sensing channel estimation, feature
It is, the fitness is the matrix correlation that obtained Toeplitz matrix after M long is extended to by L long constant amplitude sequence loops
Value.
4. the fill method of the time-domain training sequence according to claim 3 based on compressed sensing channel estimation, feature
It is, the sequence after the constant amplitude sequence progress L point IDFT that length is L is PL=[P1,P2,...,PL], cyclic extension is length M
Sequence be PM=[P1,P2,...,PL,P1,P2,...,PM-L], obtained Toeplitz matrix Ψ are as follows:
5. the fill method of the time-domain training sequence according to claim 3 or 4 based on compressed sensing channel estimation, special
Sign is that in step s 2, the optimal constant amplitude sequence of fitness is by the constant amplitude sequence L according to optimization observing matrix correlation
Minimum value obtain, the matrix correlation value be matrix it is any two column between relevant maximum value；The fitness is optimal to be
Fitness numerical value is minimum.
6. the fill method of the time-domain training sequence according to claim 1 based on compressed sensing channel estimation, feature
It is, in step s 4, the no inter-block-interference zone length is M-L+1,.
7. the fill method of the time-domain training sequence according to claim 3 based on compressed sensing channel estimation, feature
It is, in step s 4, the model that the compressed sensing is rebuild is
R=Ψ h+n
Wherein, observation vector r, length M-L+1 are the training sequence that receives without inter-block-interference region；Ψ is described
Toeplitz matrix；H is the time domain impulse response of channel, and length L, non-zero entry number S are much smaller than L；N length is M-L+1,
It is independent identically distributed Gaussian noise.
8. the fill method of the time-domain training sequence according to claim 1 or 6 based on compressed sensing channel estimation, special
Sign is that the method for reconstructing includes compression sampling match tracing or orthogonal matching pursuit.
9. a kind of fill system of the time-domain training sequence based on compressed sensing channel estimation characterized by comprising
Constant amplitude sequence generating module: for generating n at randomdA L long constant amplitude sequence forms constant amplitude arrangement set, wherein L and ndIt is
Natural number；
Constant amplitude sequence optimisation module: for being obtained according to constant amplitude sequence generating module using the irrelevant criterion of compressed sensing
The optimal constant amplitude sequence of fitness calculates the constant amplitude arrangement set using genetic algorithm, wherein setting is maximum to evolve
Algebra ng, interaction Probability pc, mutation probability pmWith discrete probability distribution P, genetic algorithm the following steps are included: S2A. by ndA L long
The individual collections of the first generation of the constant amplitude sequence as algorithm；S2B. if the contemporary evolutionary generation of the constant amplitude sequence is equal to
Maximum evolutionary generation ngOr present age individual is all identical, then enters step S2N, otherwise enter step S2C；S2C. it calculates when previous
The individual adaptation degree in generation；It S2D. is individual distribution select probability according to fitness size；S2E. interaction times are initialized as 0；
S2F. if interaction times are greater than nd/ 2, then S2L is entered step, S2G is otherwise entered；S2G. the selection according to each individual is general
Rate selects two individuals；S2H. according to the interactive Probability pcTo two it is described individual in all elements successively judge whether into
Row interaction, after the element to the individual all judges, count is incremented for interaction times；S2I. to each individual, according to described
Mutation probability pmDecide whether to make a variation；S2J. the new individual after interacting is as filial generation；S2K. return step S2F；S2L. described etc.
Width sequence evolution algebra adds 1；S2M. return step S2B；S2N. the smallest present age individual of fitness is obtained；S2O. according to described suitable
The smallest present age individual of response generates the optimal constant amplitude sequence of the fitness；
Training sequence generation module: the constant amplitude sequence for being optimized according to constant amplitude sequence optimisation module, successively carry out L point IDFT and
Cyclic extension obtains M long time-domain training sequence；
Compressed sensing based channel impulse response estimation module is used in receiving end, using training sequence without inter-block-interference
Region, the greedy algorithm rebuild using compressed sensing carry out compression reconstruction to channel, and the time-domain training sequence is filled into block biography
In defeated system, the time-domain training sequence is emitted together with data field in transmitting terminal.
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| CN105530209B (en) * | 2015-12-25 | 2019-04-02 | 清华大学 | Multiple antennas time-domain training sequence fill method and device |
| CN106059732B (en) * | 2016-05-26 | 2019-01-15 | 重庆邮电大学 | A kind of pilot frequency optimization method in compressed sensing based OFDM condition of sparse channel estimation |
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