CN107770105A - The channel estimation methods of extensive mimo system based on 1 bit A/D C - Google Patents
The channel estimation methods of extensive mimo system based on 1 bit A/D C Download PDFInfo
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- CN107770105A CN107770105A CN201711290287.6A CN201711290287A CN107770105A CN 107770105 A CN107770105 A CN 107770105A CN 201711290287 A CN201711290287 A CN 201711290287A CN 107770105 A CN107770105 A CN 107770105A
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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
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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
- H04L25/0224—Channel estimation using sounding signals
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- 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
- H04B7/0452—Multi-user MIMO systems
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Abstract
The invention belongs to wireless communication technology field, there is provided a kind of channel estimation methods of the extensive mimo system based on 1 bit A/D C, to improve channel estimating performance, reduce mean square error；The present invention gives the pilot frequency sequence A of one group of real number structuring first, and randomly generates N number of and channel condition information with distribution by channel condition information distribution prioriThus random quantization threshold is produced：N=1 ..., N；Then quantized signal is obtained by quantization threshold：bn=sgn (yn‑τn), n=1 ..., N；It is finally based on quantized signal and solves maximal possibility estimation：Obtain channel estimation valueThe present invention carries out Randomized Quantizing by channel statistic Randomized Quantizing strategy to 1 bit A/D C quantization thresholds, so as to be effectively improved the accuracy of channel estimation, significantly reduces channel estimation MSE, and Randomized Quantizing strategy is easily achieved；Pilot frequency sequence is longer, and the performance of channel estimation methods is also better.
Description
Technical field
It is more particularly to a kind of based on amount the invention belongs to radio communication (wireless communication) technical field
Change the extensive MU- of the 1 bit moduli converter (analog to digital converter, abbreviation ADC) of thresholding optimization
MIMO uplink channel estimation methods, to reduce mean square error (mean squared error, abbreviation MSE).
Background technology
It is extensive by increasing antenna for base station quantity structure to meet the performance requirements such as the message transmission rate of modern communicationses
Mimo system is exactly a kind of effective technological means, at the same its be also research contents most potential in the 5th third-generation mobile communication it
One；But in general mimo system, before follow-up Digital Signal Processing, each antenna element is required for connecting
One high-resolution ADC, but ADC power consumptions can produce exponential growth with the increase of resolution ratio, in addition ADC hardware
Cost can also greatly increase therewith.Just need to introduce low point in the antenna element of extensive mimo system to solve this problem
Resolution ADC, such as 1 bit A/D C；This is allowed in extensive mimo system, is designed a kind of effectively based on 1 bit A/D C quantizations
Under channel estimation methods be particularly important.However, at present for extensive MIMO uplink channel estimation problem, it is existing
The channel estimation of 1 bit A/D C mimo system is usually what is carried out under the setting based on 0 quantization threshold, its poor-performing, is estimated
Evaluation mean square error (mean squared error, abbreviation MSE) has much room for improvement.
The content of the invention
It is an object of the invention to provide a kind of channel estimation methods of the extensive mimo system based on 1 bit A/D C, use
To improve the uplink channel estimation performance of the extensive mimo system based on 1 bit A/D C；To realize the purpose, the present invention
The technical scheme used for：
The channel estimation methods of extensive mimo system based on 1 bit A/D C, it is characterised in that comprise the following steps：
Step 1, the pilot frequency sequence A for giving one group of real number structuring,A line n, n=1 ..., N is represented, wherein,M is number of transmission antennas, L is pilot sequence length；
Step 2, the noise power σ based on priori2, initial parameter σ is set；
Step 3, the same distribution of N number of and channel condition information is randomly generated by channel condition information distribution prioriThus
Produce random quantization threshold τ：
Wherein, τnRepresent n-th yuan of quantization threshold τ；
Step 4, quantized signal obtained by the quantization threshold set：
bn=sgn (yn-τn), n=1 ..., N
Wherein, ynRepresent n-th yuan of vectorization reception signal y；
Step 5, based on quantized signal solve maximal possibility estimation：
Wherein,
More than, that is, obtain channel estimation value
The beneficial effects of the present invention are：
A kind of channel estimation methods of the extensive mimo system based on 1 bit A/D C are provided, by channel statistic with
Machine quantization strategy carries out Randomized Quantizing to 1 bit A/D C quantization thresholds, while using orthogonal pilot design rule, so as to effectively change
The accuracy of kind channel estimation, makes channel estimation MSE be significantly reduced relative to the strategy of fixed quantisation thresholding, and Randomized Quantizing plan
Slightly it is easily achieved；Pilot frequency sequence is longer, and the performance of channel estimation methods is also better.
Brief description of the drawings
Fig. 1 is the channel estimation methods schematic flow sheet of the extensive mimo system of the invention based on 1 bit A/D C.
Fig. 2 is the extensive MU-MIMO system up-link block diagrams of 1 bit A/D C in the embodiment of the present invention.
Fig. 3 is channel estimation value MSE, CRB and pilot sequence length graph of relation in the embodiment of the present invention.
Fig. 4 is channel estimation value MSE, CRB and SNR graph of relation in the embodiment of the present invention.
Embodiment
The present invention will be described in further detail with reference to the accompanying drawings and examples.
In the present invention, by Cramér-Rao lower bound (Cram é r-Rao bound, the letter of analyzing channel maximal possibility estimation problem
Claim CRB) it can be found that the validity performance of estimating step is relevant with ADC threshold parameter, i.e. the threshold parameter of ADC is set will
The performance of channel estimation methods is influenceed, while the optimum pilot tone rule under optimal threshold scheme is orthogonal pilot design.
In the present invention, the channel estimation problems using the extensive MU-MIMO up-links of 1 bit A/D C are modeled as a maximum likelihood
Estimation problem, while quantization threshold parameter is optimized using the Randomized Quantizing method of channel statistic, and using orthogonal
Pilot tone, thus, it is possible to make it that the MSE of channel estimation results of the extensive MU-MIMO up-links of 1 bit moduli converter is notable
Decline.
The present embodiment provides a kind of channel estimation methods of the extensive mimo system based on 1 bit A/D C, for reducing base
In the MSE of 1 bit A/D C extensive MU-MIMO system uplink channel estimation, for convenience of description, following embodiments will be built
Following system model is found to illustrate：
Using the system block diagram of 1 bit A/D C MU-MIMO system up-link as shown in Fig. 2 wherein, base station is equipped with M
Root transmitting antenna, the single-antenna subscriber number of synchronization service are K, and K ＜ ＜ M, and the two-way received after each RF link is just
Signal is handed over to quantify respectively by 1 bit A/D C.In traffic model modeling, channel fading is constant within certain correlation time, ifFor reception signal,It is that the length that each behavior corresponds to user is the training matrix of L pilot frequency sequence,For channel matrix,For the σ of zero-mean variance 22Multiple Gauss distribution additive white noise, i.e. multiple Gauss is white
The power of noise is 2 σ2,For quantization threshold, B is the data obtained after reception signal quantifies；Thus quantify to receive
Journey is modeled as：
Y=HX+W
Wherein, Expression takes real part computing,Expression takes imaginary-part operation；
Have by the above-mentioned model of real numberization：
Wherein,
Further received signal vector is had：
Y=Ah+w
Wherein, Vec () representing matrix
Vector quantities operation, IMRepresent M rank unit matrix；Obviously have
Then 1 bit quantization process of vectorization is as follows
B=sgn (y- τ)
Wherein,Facilitate for writing and be specified below, Make bn、yn、τn、wnN-th yuan of b, y, τ, w is represented respectively, is madeA line n is represented, then is had：
The problem of by the extensive MU-MIMO system uplink channel estimation of the 1 bit A/D C based on maximal possibility estimation
It is modeled as：
Wherein, Fw() is zero-mean variances sigma2Gaussian Profile distribution function；
Based on above-mentioned Maximum-likelihood estimation problem, it is as follows to continue to analyze its CRB, can obtain the expression of Fisher matrixes first
It is as follows
Wherein, g (τn,an) be defined as follows,
Wherein, fw() and Fw() is zero-mean variances sigma respectively2The probability density function and distribution letter of Gaussian Profile
Number, i.e.,
The CRB matrixes of corresponding estimation problem are then Fisher inverse of a matrix matrixes, then the quantization door based on estimation problem CRB
Limit optimization problem then represents as follows：
Wherein, tr (XXH)≤P is the transimission power constraint of pilot signal；Fixed pilot frequency sequence matrix, can be with by analysis
The optimal rules for obtaining quantization threshold are as follows：
Optimum quantization thresholding is substituted into CRB optimization problem again, and to can obtain the minimum value of object function be π σ2MK2/ P, optimization
Target gets minimum value pilot matrix X condition, i.e. the generation rule of optimal pilot is：
XXH=(P/K) IK, IKTo represent K rank unit matrix
In emulation, if the base station of the mimo system there are M=64 root transmitting antennas, K=8 single-antenna subscriber is serviced.Channel
Each member of matrix H obeys separate zero-mean complex Gaussian distribution, if P constrains for signal transmission power, randomly generates pilot tone sequence
Column matrix X simultaneously is allowed to meet XXH=(P/K) IK。
Based on above-mentioned constructed model and definition, the invention provides the Randomized Quantizing based on channel statistical feature to optimize
The methods of 1 bit A/D C quantization thresholds realizes channel estimation, can significantly reduce the MSE of channel estimation results.
The purpose of the present invention is achieved by the steps of：
S1, the pilot frequency sequence A for giving one group of real number structuring, based on the priori being distributed to channel condition information, according to excellent
Change regular random and produce quantization threshold, and maximal possibility estimation is made to the signal after the threshold quantization, it becomes possible to reduce channel
The MSE of estimation；
S2, the noise power σ based on priori2, initial parameter σ is set；
S3, the same distribution of N number of and channel condition information is randomly generated by channel condition information distribution prioriThus produce
Raw random quantization threshold τ is as follows：
S4, quantized signal obtained by the quantization threshold set
bn=sgn (yn-τn), n=1 ..., N
S5, based on quantized signal with base searching method solve maximal possibility estimation
Wherein,
S6, final output channel estimation
By aforesaid operations, the estimation to H is just completed.
The channel maximum- likelihood estimation without 1 bit A/D C of optimization quantization threshold will be make use of below with this hair
The algorithm performance comparative analysis of bright method, further to verify the performance of the present invention.
Carry out the performance of metric algorithm as measurement index using mean square error (mean squared error, abbreviation MSE),
The MSE of channel estimation in emulation experiment is defined as：
CRB-FQ and CRB-RQ refers to fixed quantisation thresholding as the CRB under 0 and Randomized Quantizing strategy respectively in Fig. 3 and Fig. 4；
MLE-FQ and MLE-RQ refers to fixed quantisation thresholding as the 0 and MSE of Randomized Quantizing strategy channel estimation results respectively；Fig. 3 is described
Under two quantization strategies, the relation of channel Maximum-likelihood estimation MSE, CRB and pilot sequence length can from figure
Go out, the MSE under quantization strategy each first approaches CRB with the increase of pilot sequence length, and CRB is reduced by optimizing thresholding
The MSE of channel estimation can effectively be improved, further for identical MSE precision, the pilot frequency sequence needed for Randomized Quantizing strategy
Length is less than fixed quantisation strategy；Complex chart 3 and Fig. 4 simulation result, Randomized Quantizing strategy is compared to fixed quantisation thresholding
It can significantly improve the MSE performances of channel estimation for 0 quantization strategy.
In summary, the present invention is in the extensive MU-MIMO system using 1 bit A/D C, utilizes channel statistic
Randomized Quantizing strategy is to optimize the channel estimation methods of the quantization threshold of up-link and orthogonal pilot design rule, to improve letter
The accuracy of road estimation；Quantization threshold is optimized by Randomized Quantizing, enables to channel estimation MSE relative to fixed quantisation thresholding
Strategy have obvious reduction, and Randomized Quantizing strategy is easily achieved；Pilot frequency sequence is longer, and the performance of channel estimation methods is also got over
It is good.
The foregoing is only a specific embodiment of the invention, any feature disclosed in this specification, except non-specifically
Narration, can alternative features equivalent by other or with similar purpose replaced；Disclosed all features or all sides
Method or during the step of, in addition to mutually exclusive feature and/or step, can be combined in any way.
Claims (1)
1. the channel estimation methods of the extensive mimo system based on 1 bit A/D C, it is characterised in that comprise the following steps：
Step 1, the pilot frequency sequence A for giving one group of real number structuring,A line n, n=1 ..., N is represented, wherein,M is number of transmission antennas, L is pilot sequence length；
Step 2, the noise power σ based on priori2, initial parameter σ is set；
Step 3, the same distribution of N number of and channel condition information is randomly generated by channel condition information distribution prioriThus produce
Random quantization threshold τ：
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Wherein, τnRepresent n-th yuan of quantization threshold τ；
Step 4, quantized signal obtained by the quantization threshold set：
bn=sgn (yn-τn), n=1 ..., N
Wherein, ynRepresent n-th yuan of vectorization reception signal y；
Step 5, based on quantized signal solve maximal possibility estimation：
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More than, that is, obtain channel estimation value
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112104579A (en) * | 2020-08-25 | 2020-12-18 | 西安交通大学 | Channel estimation method, system, device and storage medium based on model constraint |
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Cited By (2)
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| CN112104579A (en) * | 2020-08-25 | 2020-12-18 | 西安交通大学 | Channel estimation method, system, device and storage medium based on model constraint |
| CN112104579B (en) * | 2020-08-25 | 2021-11-19 | 西安交通大学 | Channel estimation method, system, device and storage medium based on model constraint |
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