CN106059730A - Adaptive pilot frequency structure optimization design method based on sparse channel estimation - Google Patents
Adaptive pilot frequency structure optimization design method based on sparse channel estimation Download PDFInfo
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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
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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
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L27/00—Modulated-carrier systems
- H04L27/26—Systems using multi-frequency codes
- H04L27/2601—Multicarrier modulation systems
- H04L27/2602—Signal structure
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Abstract
The invention provides an adaptive pilot frequency structure optimization design method based on sparse channel estimation with the purpose of addressing the problem of real-time selecting optimal pilot frequency structure under a specific wireless channel. The method is different from traditional determinant pilot frequency structure design method in that the method considers variability of the wireless channel in actual transmission, combines the design of channel structure and channel estimation, such that the optimized pilot frequency structure has higher estimation precision and higher suitability. A simulation experiment result shows that the adaptive pilot frequency structure optimization design method, with proper increasing of system operation complexity as a cost, effectively increases the precision of sparse channel estimation, enables the optimized pilot frequency structure to be more representative and has better engineering application potential.
Description
Technical field
The invention belongs to the communications field, excellent particularly to a kind of adaptive pilot structure in condition of sparse channel estimation procedure
Change method for designing.
Background technology
In OFDM (OFDM) mobile radio system, wireless channel typically exhibits openness.Due to
Traditional channel estimation methods such as least square (LS) method, least mean-square error (MMSE) method etc. are all only applicable to condensed channel
Estimating, when channel presents sparse characteristic, traditional channel estimation methods performance is the most not ideal enough.Therefore, under condition of sparse channel
Channel estimation method has become a new study hotspot in the communications field.
Compressed sensing (CS) is the theory of emerging and great application prospect in applied mathematics field in recent years, the most by
It is extensively studied and is applied to numerous areas.According to compressive sensing theory, when a signal can in a certain particular space
During with rarefaction representation, just can utilize the method that it is sampled and passes through to optimize by the speed far below nyquist sampling rate
High probability ground reconstructs this signal.As can be seen here, when wireless channel shows openness, in ofdm system, channel estimation process is just
It is similar to the process of reconstruction of original sparse signal in compressed sensing.Therefore, CS theory is applied to the channel estimation of ofdm system
In algorithm, traditional channel estimation method will be different from, be greatly improved the channel estimating performance under condition of sparse channel.
Although there is now a lot of Sparse Channel Estimation Algorithm research based on compressive sensing theory, but major part research being concentrated
In the improvement and innovation of restructing algorithm.Research shows, in the recovery process of condition of sparse channel, the structure of pilot tone is to final dilute
Thin channel estimating performance also plays highly important effect equally, and therefore the present invention will mainly solve to estimate in condition of sparse channel
The optimization design problem of pilot configuration in journey.In existing pilot configuration method for designing, overwhelming majority method is at orthogonal frequency
The pilot configuration used under multiplexing (orthogonal frequency division multiplexing, OFDM) system sets
Meter standard is all based on the definitiveness pilot configuration design of contract measurement matrix cross correlation, and this standard can also be referred to as MIP
(mutual incoherence property).Such as, with patent of invention " pilot frequency optimization method of condition of sparse channel, device and letter
Channel estimation method " it is a series of definitiveness pilot design methods of representative, all complete on the basis of MIP standard.Permissible
Seeing, in the invention, the design of whole pilot configuration completed before signal transmits, and estimated with channel subsequently
Process is independent mutually.Although this kind of deterministic pilot design method reduces its complexity in Project Realization, but due to
In view of the uncertainty of wireless channel in actual channel transmitting procedure, therefore will could not tie through the pilot tone that such method optimized
Structure transmission process in the case of any is the most rigorous.
Summary of the invention
For not enough present in nowadays definitiveness pilot configuration method for designing, the present invention proposes a kind of based on sparse letter
The adaptive pilot structure Optimization Design that road is estimated.The method is different from common definitiveness pilot design method, by pin
Specific wireless channel is carried out the optimization design of pilot configuration, with suitably increase system complexity as cost, is effectively improved
Mean square error (Mean Square Error, the MSE) performance that condition of sparse channel is estimated, and it is applicable that this pilot configuration is had more
Property.
In order to differentiate with definitiveness pilot configuration method for designing, the main feature of the present invention and step include following
Aspect:
(1) present invention is from the beginning of a series of random pilot configurations, utilizes the condition of sparse channel after actual signal transmission to estimate
Result reacts in the reconstruct of pilot configuration.The method is different from common definitiveness pilot configuration design so that pilot tone is tied
Structure constantly adjusts in transmitting procedure continuously, until convergence.
(2) present invention is during pilot configuration constantly adjusts, and uses the minimum l in convex optimized algorithm1Norm Model
Theoretical with genetic algorithm (genetic algorithm, GA) pilot tone screened and rebuilds.Accurate with MIP compared to single
Then the pilot design method as pilot configuration design standard is more direct and comprehensive.
(3) in order to improve speed and the estimation performance of pilot configuration of pilot configuration convergence, the present invention can be by changing
The number and the gene intersection that become initial population in GA iterative process realize with the probability made a variation.
(4) after completing reconstruct to pilot configuration each time, receiving terminal needs to transmitting terminal feedback a series of new
Pilot configuration information, as the beginning of loop iteration next time, is repeated several times execution above-mentioned steps, until pilot configuration convergence.,
In sum, implement above-mentioned adaptive pilot Optimization Design, there is following beneficial effect:
(1) present invention is according to the structure design of pilot tone in actual transmission result constantly modulation transmissions signal so that
The pilot configuration designed eventually more conforms to the feature of actual channel, i.e. has more representativeness.
(2) present invention combines greedy algorithm and the respective feature of convex optimized algorithm, both guarantees during channel reconstruction
The implementation complexity that system is relatively low, in turn ensure that the degree of accuracy in channel reinstatement process, has accomplished that estimation performance is multiple with computing
Preferably compromise between miscellaneous degree.
(3) present invention utilizes that GA is theoretical to be reconstructed pilot frequency information and update, it is ensured that pilot configuration each time
Reconstruct all changes towards the target direction specified so that in the present invention, blood circulation is finally restrained.
(4) pilot configuration that adaptive pilot Optimization Design optimized in the present invention, can promote whole OFDM
The estimation degree of accuracy of condition of sparse channel estimating system.
Accompanying drawing explanation
In order to be illustrated more clearly that the technical method of the present invention, required use during embodiment being described below
Accompanying drawing be briefly described.
Fig. 1 is the adaptive pilot Optimization Design schematic flow sheet of the present invention；
Fig. 2 is the specific embodiment schematic flow sheet of the present invention；
Fig. 3 be in the present invention adaptive pilot Optimization Design in the case of different initial population number of individuals convergence
Situation compares.
Fig. 4 is the ratio that one embodiment of the present of invention and existing several representational pilot configuration methods for designing estimate performance
Relatively result figure.
Fig. 5 is the ratio that one embodiment of the present of invention and existing several representational pilot configuration methods for designing run the time
Relatively result.
Detailed description of the invention
In order to more clearly and completely describe purpose, technical method and the method characteristic in the present invention, below in conjunction with tool
Accompanying drawing in body embodiment and embodiment, is described in further detail the present invention.
Before the embodiment in the present invention is introduced, need first to set up a basic ofdm signal input defeated
Go out system model.The total number of subcarrier assuming ofdm signal is N, and wherein the number of pilot tone is p, lays respectively at subcarrier k1,
k2,…,kp(1≤k1,k2,…,kp≤ N) on.We define vector p=[k1,k2,…,kp]TFor pilot frequency locations vector (pilot
position vector,PPV).Additionally, define each ofdm signal biography on transmitting terminal and receiving terminal i-th subcarrier
Transmission of data be respectively x (i) (i=1,2 ..., N) and y (i) (i=1,2 ..., N).Then, we can be received signal and exist
The form of expression on frequency domain:
Y=XH+N* (1)
Wherein, Y=[y (1), y (2) ..., y (N)]T, X=diag [x (1), x (2) ..., x (N)], H=FN×Lh.Wherein
FN×LFor some discrete Fourier's matrix (Discrete Fourier Transformation, DFT), it is by N-dimensional DFT transform square
The front L row of battle array are constituted:
Wherein,N*Be a variance be σ2N-dimensional additivity white complex gaussian noise vector.
In order to select p pilot tone from N number of subcarrier, we define the pilot tone selection matrix of a p × N-dimensional here
(pilot selection matrix, PSM) chooses for pilot tone:Wherein,It is the unit column vector of an a length of N, the most only kiOn element be 1, remaining element is all 0.With
After, by PSM premultiplication in the two ends of (1) formula, following equation can be obtained,
Make respectively
Then above formula can be further simplified as YP=XpFph
+Np.Here, we make T=XpFp, and T is exactly the calculation matrix in CS theory.So far, pilot signal under whole ofdm system
Input/output model has built complete, and its model may finally be expressed as Yp=Th+Np。
After completing the structure to ofdm system input/output model, Fig. 1 gives proposed by the invention based on sparse
The substantially flow process of the adaptive pilot structure Optimization Design that channel is estimated.The step of whole method can be largely classified into as follows
Five parts: the generation of initial population, preliminary condition of sparse channel estimation, the screening of pilot configuration, the reconstruct of pilot configuration, circulation
Iteration.
Fig. 2 is then of the adaptive pilot structure Optimization Design estimated based on condition of sparse channel that the present invention proposes
Embodiment schematic flow sheet, concretely comprising the following steps of the method:
Pilot configuration one initial population Z of composition that S100: this method is random by generating z group(0), every individual in population
Can represent with one group of PPV.
S200: successively by population Z(j)(j=0,1 ..., C) in z group individuality carry out ofdm signal as pilot configuration
Transmission, and directly utilize the OMP algorithm ofdm signal to receiving at receiving terminal and carry out preliminary channel and estimate.Due to transmitting terminal
Have sent the ofdm signal that z group is made up of different pilot configurations, therefore receiving terminal obtains and can arrive the channel estimation value that z group is different
hguess(i), (i=1,2 ..., z).
S300: then, excellent pilot configuration is screened and reconstructs by recycling GA principle.Here, this method utilizes
Minimum l in convex optimized algorithm1Norm Model:s.t.||Y-Th||2≤ση, give the adaptation in GA iteration
Degree criterion: V (i)=| | Yp-Tp·hguess(i)||2, i=1,2 ..., z.Wherein, | | x | |2Two norms of representation vector x,
It is individual that i represents the i-th in population.
S400: after providing fitness criterion, by z group hguessI () value is brought in fitness criterion, and choose
Go out wherein hguessI the PPV corresponding to front z/2 group of () value minimum replaces remaining z/2 group as defect individual individual.
S500: the gene in simulation GA theory intersects and mutation process, thus forms the individuality that z group is new, is also formed simultaneously
One new population Z(j+1)。
S600: last, feeds back to transmitting terminal by new species information, repeats the step of S200 to S600, until circulation time
Till number reaches C.
S700: after cycle-index reaches C, selects now population Z(C)Corresponding to middle minimum fitness value V (i)
Pilot configuration, and this pilot configuration is exactly taking-up optimal pilot selected by the adaptive pilot Optimization Design in the present invention
Structure.
From GA theory, although genetic iteration process can be continuously updated and filter out the pilot tone estimating that performance is more excellent
Structure, but limit all situations is the most unpractical.It means that lead selected by this adaptive pilot Optimization Design
Frequently the most theoretic optimum of structure, is only the optimum on local.However, we still can be by suitably increasing system
System complexity improves the estimation performance of whole pilot configuration, is allowed to the satisfactory solution meeting in engineering.
In emulation experiment, embodiments of the invention have employed the ofdm signal that subcarrier number is 512, and uses 16-QAM
It is modulated.Additionally, the number of Cyclic Prefix is 128, the number of pilot tone is 30, and channel length is 60, and degree of rarefication is 6.And it is right
In GA iterative process, the gene crossover probability in the present embodiment is 0.2, and genovariation probability is 0.02.
Fig. 3 be in the present invention adaptive pilot Optimization Design in the case of different initial population number of individuals convergence
Situation schematic diagram.Which give the determination method of cycle-index C in step S200 and S600.It can be seen in figure 3 that not
In the case of initial population number of individuals, it is different for making population minimum fitness reach cycle-index C required during convergence.This
Outward, after iterations reaches C, the minimum fitness V of populationmin (j)=min{V (i) } will no longer change.This also means that
In population, the assortment of genes of optimum individual has tended towards stability, even if now continuing to be iterated updating, in population to population
Genes of individuals the most no longer changes, and this is to be determined by the character of GA theory self.Therefore, after species information is stable, now
Optimum individual in population is i.e. the local optimum pilot configuration under given channel.
Fig. 4 sets forth random pilot structure (Random OMP), equidistant pilot configuration (Equalinterval
OMP) and definitiveness pilot configuration (MIP-OMP) based on MIP criterion OMP restructing algorithm lower channel estimate performance, give
The definitiveness pilot configuration after designing based on MIP criterion estimation performance under the convex optimal reconfiguration algorithm (MIP-Cov).This
Outward, in order to preferably embody the spy of the adaptive pilot structure Optimization Design estimated in the present invention based on condition of sparse channel
Point, it is channel estimating performance when 200,600,1000 that Fig. 4 sets forth the method at initial population individual amount.
It will be seen that when the number of individuals of initial population is 200, the estimation performance of the method is with MIP-OMP method almost
Identical.And when quantity individual in initial population increases to 1000, its estimated accuracy alreadys more than MIP-OMP side and sends out and ten taps
It is bordering on MIP-Cov method.It should be noted that owing to convex optimized algorithm has higher complexity, engineering in Project Realization
Upper is generally all to be replaced with OMP algorithm.This also means that, the high accuracy letter that MIP-Cov method shows in emulation experiment
Road estimates that performance is often difficult on Practical Project.
Fig. 5 is the comparative result that the present invention and existing several representational pilot configuration methods for designing run the time.Consider
It is that pilot configuration design is combined with channel estimation process to adaptive pilot optimization method presented here, therefore for justice
For the sake of, herein operation timing definition is designed and channel estimation process total run time sum for pilot configuration.
According to the operation result of simulation software, the data in Fig. 5 can be obtained.Although from fig. 5, it is seen that MIP-
Cov method has best channel estimating performance, but its total run time is the longest, and this is also that it is difficult on Practical Project
One of reason.Although and the total run time of MIP-OMP method is the shortest, but its cost is reduction of the accurate of channel estimation
Property.For the adaptive pilot Optimization Design proposed in the present invention, although total operation time is along with initial population
The increase of body number and increase, and be above the operation time of MIP-OMP method, but almost identical compared to precision of channel estimation
For MIP-Cov method, the having been obtained in terms of computational complexity of this method effectively controls.
The above, only the present invention detailed description of the invention and preferably under a series of simulation results,
The interest field of the present invention can not be limited with this.Therefore, if any being made same with any feature disclosed in this specification
Deng or similar change, still fall within the context that the present invention is contained.
Claims (5)
1. the adaptive pilot structure Optimization Design estimated based on condition of sparse channel, it is characterised in that include following master
Want step:
Step 1: generate many group random pilot structure vector, merge into an initial population.
Step 2: the pilot configuration in initial population is applied and is transmitted successively from different ofdm signals.
Step 3: use the greedy algorithm signal to receiving to carry out the estimation of condition of sparse channel, utilizes the condition of sparse channel estimated to estimate
Pilot configuration is further screened by evaluation.
Step 4: combine GA theory and the pilot configuration after screening is reconstructed.
Step 5: the pilot configuration information after reconstruct is fed back to transmitting terminal and is circulated iteration until pilot configuration is restrained.
2. the method for claim 1, it is characterised in that described whole adaptive pilot structure Optimization Design will
The design of pilot configuration combines with channel estimation process, is different from traditional definitiveness pilot configuration method for designing, will be for
Different wireless channels selects and has more representational pilot configuration.
3. method as claimed in claim 1 or 2, it is characterised in that in described step 3, used in convex optimized algorithm
Minimum l1The pilot configuration that estimation performance is more excellent is screened by Norm Model as fitness criterion.Compensate for coveting
The problem that greedy algorithm recovery precision on condition of sparse channel reconstructs is not enough.
4. method as claimed in claim 1 or 2, it is characterised in that in described step 4, theoretical to leading of selecting in conjunction with GA
Frequently structure is reconstructed, and produces the pilot configuration information that many groups are new.On the basis of ensure that estimated accuracy, it is to avoid directly make
The huge complexity brought is solved with convex optimized algorithm.
5. method as claimed in claim 1 or 2, it is characterised in that in described step 5, by the pilot configuration information after reconstruct
Feed back to transmitting terminal, repeat step 2-5 in claim 1, until pilot configuration convergence.
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| CN111245752A (en) * | 2020-01-13 | 2020-06-05 | 重庆邮电大学 | Low-complexity 5G NR channel estimation method based on compressed sensing |
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Application publication date: 20161026 |