CN111917441B - Channel estimation method based on large-scale multi-input multi-output millimeter wave system - Google Patents

Channel estimation method based on large-scale multi-input multi-output millimeter wave system Download PDF

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CN111917441B
CN111917441B CN201910379541.2A CN201910379541A CN111917441B CN 111917441 B CN111917441 B CN 111917441B CN 201910379541 A CN201910379541 A CN 201910379541A CN 111917441 B CN111917441 B CN 111917441B
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angle
sparsity
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CN111917441A (en
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程翔
高诗简
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Peking University
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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
    • 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
    • H04L25/0204—Channel estimation of multiple channels
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H04L25/0242—Channel estimation channel estimation algorithms using matrix methods

Abstract

The invention discloses a channel estimation method of a large-scale multi-input multi-output millimeter wave system based on a mixed structure, which comprises the following steps: the sparsity of the beam domain is continuously utilized to discover the sparsity of the channel in the delay domain; establishing a brand new pilot frequency sequence and combining an energy detector to carry out multi-path detection, thereby greatly reducing the number of parameters to be estimated of a channel in a delay time domain; and the beam domain sparsity is utilized by establishing a new block orthogonal matching algorithm. The technical scheme provided by the invention can be applied to time-frequency double channel selection, can also obviously reduce the calculation complexity, the storage cost and the pilot frequency cost of channel estimation, and effectively improves the channel estimation performance.

Description

Channel estimation method based on large-scale multi-input multi-output millimeter wave system
Technical Field
The invention belongs to the technical field of wireless communication, relates to a millimeter wave system channel estimation technology, and particularly relates to a channel estimation scheme suitable for a large-scale multiple-input multiple-output millimeter wave communication system, which can effectively improve the channel estimation performance under the conditions of low computation complexity, low storage overhead and low pilot frequency overhead.
Background
It is now difficult for overcrowded radio bands to meet the explosive growth of data demand, and communication using higher radio bands has become a necessary tool. The millimeter wave band is located at 30 GHz-100 GHz, which is much higher than the existing communication frequency band, and has a potential of supporting Gb/s data transmission due to its extremely rich spectrum resources, and is recognized as one of the physical layer technologies of the fifth generation wireless communication core. Unlike current low frequency communication systems of all-digital radio frequency chains, which are limited by the high cost and high power consumption of millimeter wave radio frequency chains, millimeter wave systems often employ analog-to-digital mixing rather than all-digital radio frequency chain structures. However, the number of the radio frequency chains of the millimeter wave system is much smaller than that of the antennas, so that the channel estimation scheme has great difference compared with the conventional scheme. Meanwhile, the broadband characteristic of the millimeter wave causes the channel to have serious frequency selectivity; if the system is applied in a mobile environment, the time selectivity brought by the doppler effect further increases the difficulty of channel estimation. In order to enable the millimeter wave system to serve in future broadband mobile scenarios, it is of great significance to design an accurate channel estimation scheme.
The current channel estimation based on the millimeter wave mixed structure system considers at most one selection characteristic of the channel: frequency-selective or time-selective, and accordingly channels may be categorized into static frequency-selective channels and time-varying narrowband channels. Aiming at the two types of channel estimation algorithms, the sparse characteristic of the millimeter wave channel in the beam domain is fully utilized, and the (block) orthogonal matching algorithm is adopted for estimation from the beam domain, so that huge pilot frequency overhead and estimation delay caused by the traditional least square mode are avoided. The broadband characteristic of the millimeter wave system means that the work for the narrow-band channel is difficult to meet the requirements of practical application, however, under the broadband channel, the scheme only depending on the sparsity of the beam domain has great complexity, and the convolution effect and the time-varying effect of the channel make the existing broadband estimation scheme not directly applicable to the double-selection channel.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a channel estimation scheme applied to a large-scale multi-input multi-output millimeter wave system with a mixed structure, which can be applied to time-frequency double-selection channels, can also obviously reduce the calculation complexity, the storage cost and the pilot frequency cost of channel estimation, and effectively improve the channel estimation performance.
The technical scheme provided by the invention is as follows:
a channel estimation method of a large-scale multiple-input multiple-output millimeter wave system based on a mixed structure is named as a double-selection channel estimation scheme based on double sparsity, and the complexity of channel estimation is reduced by optimizing the utilization of channel sparsity, and comprises the following steps: the utilization of the sparsity of the beam domain is continued, and the sparsity of the channel in the time delay domain is explored; by designing a brand new pilot frequency sequence and combining an energy detector to carry out multipath detection, the number of parameters to be estimated of a channel in a delay time domain is greatly reduced; by designing a new block orthogonal matching algorithm, the sparsity of a beam domain is utilized, and the robustness of channel estimation in a time-varying environment can be obviously improved.
In the large-scale multiple-input multiple-output millimeter wave systemThe originating terminal and the receiving terminal are respectively provided with NtAnd NrA dimensional antenna; the two transmitting and receiving ends carry out channel estimation by being provided with a set of radio frequency chains, and the radio frequency chains are connected with the antennas at the two ends through a phase-shifting network with the precision of b bits;
defining time n, sending symbol, receiving symbol of system, channel H with multi-path time delay dd(n)(0≤d＜Nc，NcRepresenting the maximum delay of the multipath channel) and the received noise are s (n), y (n), Hd(n) and xi (n) to CN (0, sigma)2) The precoding vectors of the transmitting end and the receiving end phase shifting networks are respectively pt(n) and pr(n), the system input-output relationship may be expressed as formula 1:
Figure GDA0003041646820000021
in formula 1, NcRepresenting the maximum delay of a multipath channel, multipath channel Hd(n) is represented by formula 2:
Figure GDA0003041646820000022
in the formula 2, P, αp，τpAnd ωpRespectively representing the number of paths, the gain of the path p, the time delay of the path p and the frequency offset of the path p; a ist(φP) And ar(θP) The wave beam response of the half-wavelength uniform array antenna at a transmitting end and a receiving end is realized; the h function is the response of the raised cosine filter. The double-selection channel estimation based on double sparsity can realize the channel estimation of a large-scale multiple-input multiple-output millimeter wave system, and comprises the following steps: time-delay domain multipath channel detection, beam domain angle estimation and combined estimation of beam amplitude and frequency offset; the method specifically comprises the following steps:
1) time-delay domain multipath channel detection: by utilizing the sparsity of a multipath domain, selecting an effective multipath channel by designing a novel pilot frequency sequence and combining an energy detector; the following operations are specifically executed:
11) transmitting pilots comprised of L subframesA sequence of frequency frames (each sub-frame is s as shown in equation 3)i(0 ≦ i < L)), in a rake channel:
Figure GDA0003041646820000023
12) setting si＝1(0≤i＜L)，
Figure GDA0003041646820000024
Wherein p ist(n) and prPhase angle theta of (n)nAnd ηnRandom equiprobable phase shifter phase set B with B bits precision {0,2 pi/2 }b,2×2π/2b,...,(2b-1)×2π/2bAnd keeping unchanged in each subframe.
13) Calculating a detection statistic YdAnd normalizing the detection statistics
Figure GDA0003041646820000031
Expressed as formula 4 and formula 5:
Figure GDA0003041646820000032
Figure GDA0003041646820000033
14) setting the energy detection threshold as mu, and roughly selecting to obtain an effective multipath channel set, which is expressed as formula 6:
Figure GDA0003041646820000034
in formula 6, P1Representing an effective multipath set obtained by rough selection based on energy detection; b is the precision of the phase shifter;
15) definition of lambdaAAnd
Figure GDA0003041646820000035
the detection statistics and the normalization detection statistics are respectively of the A-th size, the | S | represents the base number of the set S, and finally an effective multipath set is selected and represented as formula 7:
Figure GDA0003041646820000036
the above equations 4 to 7 represent energy detectors.
2) Beam domain angle estimation: utilizing sparsity of a wave beam domain, designing an adaptive block orthogonal matching algorithm to estimate angle information of the wave beam; the following operations are specifically executed:
21) defining the beam response function as equation 8:
Figure GDA0003041646820000037
constructing the number of columns as G based on the response functiontAnd GrDictionary matrix DtAnd DrExpressed as formula 9 and formula 10, respectively:
Figure GDA0003041646820000038
Figure GDA0003041646820000039
22) obtaining a beam domain representation using a dictionary matrix
Figure GDA00030416468200000310
Satisfy the requirement of
Figure GDA00030416468200000311
23) Note the book
Figure GDA00030416468200000312
Efficient reception of multipath channel d by equation 11Conversion of signal vector into yd：
Figure GDA0003041646820000041
Wherein, ydIs the effective received signal vector of the multipath channel d;
Figure GDA0003041646820000042
Figure GDA0003041646820000043
24) running adaptive block orthogonal matching algorithm to obtain radix cdIs estimated at a set of angles
Figure GDA0003041646820000044
Algorithm gate
The key steps are as follows
a) Selecting K frame pilot frequency frames to carry out channel estimation, setting the grouping length of orthogonal matching as S, the grouping number as G as KL/S, the initialization iteration number as C as 0, the iteration change rate as infinity, and roughly selecting an angle set Ad/DdAnd fine selection of angle sets
Figure GDA0003041646820000045
Is an empty set, residual error rd＝yd；
b) A sensing matrix according to equation 11)
Figure GDA0003041646820000046
Orthogonal matching is carried out on the G groups simultaneously, the angle coordinate corresponding to the sum of the maximum correlation values is obtained and recorded as the current rough selection angle, and if the angle does not belong to the current rough selection angle set Ad/DdPut it into the rough selection angle set Ad/Dd；
c) According to the rough selection angle set Ad/DdFor the original perception matrix
Figure GDA0003041646820000047
The up-sampling is carried out to obtain a sensing matrix with improved resolution, and the new sensing matrix is utilized to carry out orthogonal matching again to obtain an angle with improved resolution and is placed into the angle
Figure GDA0003041646820000048
d) Updating iteration times C, iteration change rate beta and G groups of residual errors r after orthogonal matchingd；
e) Repeating b), c), d) until the iteration change rate is lower than a specified epsilon or the iteration number reaches a specified KI。
25) Definition of
Figure GDA0003041646820000049
Is c to be estimateddA vector of x 1, the vector of x 1,
Figure GDA00030416468200000410
Figure GDA00030416468200000411
the multipath channel d can be approximately represented by equation 12:
Figure GDA00030416468200000412
by the above operation, the second stage, i.e., the angle estimation of the beam domain, is completed.
3) Joint estimation of beam amplitude and frequency offset: jointly estimating the wave beam amplitude and the Doppler frequency offset by using the angle information; the following operations are specifically executed:
31) finding angular coordinates in step 24)
Figure GDA0003041646820000051
And quantizing to obtain a set according to the precision of the phase shifter
Figure GDA0003041646820000052
Wherein
Figure GDA0003041646820000053
Each element of (quantized emergence angle, quantized incidence angle) is a two-dimensional coordinate. Further taking out
Figure GDA0003041646820000054
The coordinates of the medium and the incident angles are quantified, and the obtained sets are respectively recorded as
Figure GDA0003041646820000055
And
Figure GDA0003041646820000056
32) setting the start time of the joint estimation to 0, and expressing the precoding vector of the time n phase shifter network as equation 13 and equation 14 using the f function defined by equation 8):
Figure GDA0003041646820000057
Figure GDA0003041646820000058
wherein the content of the first and second substances,
Figure GDA0003041646820000059
pt(n) a transmit-side precoding vector; p is a radical ofrAnd (n) is a receiving end precoding vector.
33) Note the book
Figure GDA00030416468200000510
The received signal in the ith poll is represented approximately by equation 15:
Figure GDA00030416468200000511
wherein the least squares estimation
Figure GDA00030416468200000512
34) After R times of same polling, taking out
Figure GDA00030416468200000513
The jth element in (a) results in the following sequence, represented by formula 16:
Figure GDA00030416468200000514
the amplitude and doppler frequency offset of the jth path of the multipath channel d can be obtained by operating a Weighted Normalized Averaged Linear Predictor (WNALP estimator).
And repeatedly executing the steps 31) -34) to estimate the amplitude and the frequency offset of other paths, thereby completing the joint estimation of the beam amplitude and the frequency offset in the third stage.
Compared with the prior art, the invention has the following technical advantages:
the channel estimation method of the large-scale multi-input multi-output millimeter wave system reduces the complexity of channel estimation by continuously utilizing the sparsity of a beam domain and further exploring the sparsity of a channel in a time delay domain; by designing a brand new pilot frequency sequence and combining an energy detector to carry out multipath detection, the number of parameters to be estimated of a channel in a delay time domain is greatly reduced; by designing a new block orthogonal matching algorithm, the robustness of channel estimation in a time-varying environment is obviously improved, and the utilization of the sparsity of a beam domain is realized. Compared with the prior art, the technical scheme of the invention innovatively utilizes dual sparsity of a beam domain and a delay domain to estimate the millimeter wave dual-selection channel, and meanwhile, the performance of channel estimation can be effectively improved under the conditions of lower calculation complexity, less storage overhead and pilot frequency overhead; the method can be applied to time-frequency double channel selection, can obviously reduce the calculation complexity, the storage cost and the pilot frequency cost, and effectively improves the channel estimation performance.
Drawings
Fig. 1 is a schematic diagram of a large-scale multiple-input multiple-output millimeter wave system embodying the method of the present invention.
Fig. 2 is a comparison of the performance of the channel estimation scheme in a static frequency selective channel.
Fig. 3 is a comparison of the performance of the channel estimation scheme in a narrow-band time-varying channel.
Fig. 4 is a performance display of the proposed scheme under the dual channel selection.
Fig. 5 is a block flow diagram of the method of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a schematic view of an application scenario in which the present invention is embodied. Fig. 5 is a block flow diagram of the method of the present invention. In the downlink shown in fig. 1, the left side represents a base station side (origination) and the right side represents a mobile station (reception), and the base station performs channel estimation at the mobile station by transmitting a pilot signal to the mobile station. The transmitting end and the receiving end are respectively provided with NtAnd NrDimensional antenna, which in practical applications usually satisfies Nt,NrNot less than 8. Due to power consumption and cost limitations, the number of radio frequency chains actually provided by the system is much smaller than the dimension of the antenna. Without loss of generality, it is assumed that the transceiver end is only equipped with one set of radio frequency chains for channel estimation, and the radio frequency chains are connected with the antenna through a phase shifter network with b bit precision (b is usually satisfied to be greater than or equal to 4). Defining the sending symbol, receiving symbol and multi-path channel d (d is more than or equal to 0 and less than N) of the system at the time Nc) And the received noise is respectively marked as s (n), y (n), Hd(n) and xi (n) to CN (0, sigma)2) Precoding vectors of the transmitting and receiving phase shift networks are respectively marked as pt(n) and pr(n) of (a). The system input output relationship may be expressed as:
Figure GDA0003041646820000061
in the above formula, NcRepresents the maximum delay of the multipath channel;
Figure GDA0003041646820000062
P，αp，τpand ωpRespectively representing the number of paths, the gain of the path p, the delay of the path p and the frequency offset of the path p. The purpose of our invention is to utilize the sparsity of both the time domain and the beam domain to assist channel estimation. To achieve the utilization of the former (sparsity in time domain), the following pilot frame is first designed to separate multipath channels
Figure GDA0003041646820000071
Where L is the number of subframes, siIs the starting transmission symbol of subframe i. In this scenario, s is seti1 (i is 0. ltoreq. L). In the first phase of the process,
Figure GDA0003041646820000072
θnand ηnRandom equipotent phase set B from phase shifters {0,2 pi/2 }b,2×2π/2b,...,(2b-1)×2π/2bAnd keeping unchanged in each subframe. For the multipath channel d, the following detection statistics and normalized detection statistics are defined
Figure GDA0003041646820000073
Figure GDA0003041646820000074
Although the high sampling frequency of the millimeter wave system improves the resolution of a multipath channel, the number of effective paths of the millimeter wave channel is small, so that the channel has sparsity in a delay domain. To obtain efficient multipath, we use an energy detector for multipath selection. Defining the detection threshold of the energy detector as mu, and preliminarily selecting an effective multipath channel set through energy detection
Figure GDA0003041646820000075
To avoid the selected multipath aggregation base number being 0 or too large, the energy detection needs to be further refined. Definition of lambdaAAnd
Figure GDA0003041646820000076
the detection statistic and the normalized detection statistic are respectively of the A-th size, | S | represents the cardinality of the set S, and the optimized energy detector is represented as follows:
Figure GDA0003041646820000077
by this we complete the first stage of channel estimation, i.e. the selection of the effective multipath channel.
In the second stage, angle estimation is performed for the selected effective multipath channel. Without loss of generality, the multipath channel d is taken as an example for the correlation explanation. Defining a beam response function
Figure GDA0003041646820000078
Constructing the number of columns as G based on the response functiontAnd GrDictionary matrix DtAnd DrIs shown as
Figure GDA0003041646820000079
Figure GDA0003041646820000081
Decomposing the dictionary matrix to obtain
Figure GDA0003041646820000082
Figure GDA0003041646820000083
Can be viewed as a beam domain representation of the spatial channels. By using
Figure GDA0003041646820000084
The sparsity of (a) can greatly simplify the complexity of angle estimation.
Specifically, the effective received signals of the multipath channel d are arranged in order
yd＝[y(d),y(Nc+d),...,y((L-1)Nc+d)]T
Note the book
Figure GDA0003041646820000085
To simplify the representation, the noise term, y, is ignored for the momentdCan be further converted into
Figure GDA0003041646820000086
Wherein the content of the first and second substances,
Figure GDA0003041646820000087
the pseudo code of the self-adaptive block orthogonal matching algorithm provided by the invention is as follows:
Figure GDA0003041646820000091
running the self-adaptive block orthogonal matching algorithm to obtain a radix cdSet of estimated angles of
Figure GDA0003041646820000092
With the beam correspondence function, the multipath channel d can be expressed approximately as:
Figure GDA0003041646820000093
wherein
Figure GDA0003041646820000094
Figure GDA0003041646820000095
Is c to be estimateddVector of x 1.
And in the third stage, the amplitude of the wave beam and the Doppler frequency offset are jointly estimated by utilizing the angle information obtained in the second stage. Firstly, a union set of all estimation angle (emergence angle and arrival angle) sets is solved, and the sets are quantized according to the precision of the phase shifter
Figure GDA0003041646820000101
Then separated out
Figure GDA0003041646820000102
The information of the outgoing angle and the arrival angle is obtained, and the obtained set is recorded as
Figure GDA0003041646820000103
And
Figure GDA0003041646820000104
without loss of generality, the phase start time is set to 0. At time n, the precoding vector of the phase shifter network is designed to be
Figure GDA0003041646820000105
Wherein
Figure GDA0003041646820000106
It can thus be seen that a poll contains
Figure GDA0003041646820000107
And a sub-frame. Take the ith polling as an example
Figure GDA0003041646820000108
The received signal within this poll may be approximated as
Figure GDA0003041646820000109
Derived by least squares estimation
Figure GDA00030416468200001010
After R times of same polling, taking out
Figure GDA00030416468200001011
Get the sequence for the jth element in (1)
Figure GDA00030416468200001012
Repeat polling so that
Figure GDA00030416468200001013
The sampled signal is approximated to be a single frequency plus noise, and the amplitude and doppler frequency offset of the jth path of the multipath channel d can be estimated by the classical WNALP estimator. Similarly, the amplitudes and Doppler shifts of other paths may be estimated. So far, we introduce how to estimate the channel by using the dual sparsity of the beam domain and the delay domain in three stages.
Referring to fig. 2, shown is under a static frequency selective channel (N)c＝16，L＝5，P＝3，Nt＝Nr32), the performance of the beam selection scheme we designed is compared to that based on least squares and using only beam domain sparsity. It can be seen that the scheme we have designed has the smallest normalized mean square error over the entire range of signal-to-noise ratios; meanwhile, due to the fact that sparsity of a multipath domain is utilized, storage cost required by the scheme is far smaller than that of the scheme only utilizing sparsity of a beam domain.
Referring to fig. 3, a comparison of the performance of the present scheme with the existing block orthogonal matching scheme under a narrow-band time-varying channel (ω -4 e-4, other parameters as above) is shown. As can be seen from the figure, under the strong time-varying effect, the scheme has obvious advantages; meanwhile, the matching combination size can be adaptively adjusted by the scheme, and the complexity of characteristic value decomposition of the existing scheme is avoided.
Referring to fig. 4, the estimated performance of the scheme at different path numbers under the double-channel selection is shown. As can be seen from the figure, the scheme has stronger robustness to the number of paths; meanwhile, due to accurate angular domain estimation, the channel after frequency offset compensation has a good tracking effect in a time-varying environment, and even after 20 frames, the error is still maintained at a low level.
Although specific embodiments of the invention have been disclosed for illustrative purposes and the accompanying drawings, which are included to provide a further understanding of the invention and are incorporated by reference, those skilled in the art will appreciate that: various substitutions, changes and modifications are possible without departing from the spirit and scope of the present invention and the appended claims. Therefore, the present invention should not be limited to the disclosure of the preferred embodiments and the drawings, but the scope of the invention is defined by the appended claims.

Claims (4)

1. A double-selection channel estimation method based on double sparsity comprises the following steps: the sparsity of the beam domain is continuously utilized to discover the sparsity of the channel in the delay domain; establishing a brand new pilot frequency sequence and combining an energy detector to carry out multi-path detection, thereby greatly reducing the number of parameters to be estimated of a channel in a delay time domain; utilizing the sparsity of a wave beam domain by establishing a new block orthogonal matching algorithm; channel estimation of a large-scale multi-input multi-output millimeter wave system based on a mixed structure is realized;
in the large-scale multi-input multi-output millimeter wave system, a transmitting end and a receiving end are respectively provided with NtAnd NrA dimensional antenna; the transmitting end and the receiving end carry out channel estimation by being provided with radio frequency chains, and the radio frequency chains are connected with the antennas at the two ends through a phase shifting network with the precision of b bits; defining time n, sending symbol, receiving symbol of system, channel H with multi-path time delay dd(n) and the reception noise is s (n), y (n), Hd(n) and xi (n) to CN (0, sigma)2)；0≤d＜Nc，NcRepresents the maximum delay of the multipath channel; precoding vectors of the transmitting end phase shifting network and the receiving end phase shifting network are respectively pt(n) and pr(n)；
The system input-output relationship is expressed as equation 1:
Figure FDA0003041646810000011
in formula 1, NcRepresents the maximum delay of the multipath channel; multipath channel Hd(n) is represented by formula 2:
Figure FDA0003041646810000012
in the formula 2, P, αp，τpAnd ωpRespectively representing the number of paths, the gain of the path p, the time delay of the path p and the frequency offset of the path p; a ist(φP) And ar(θP) Respectively the wave beam response of the half-wavelength uniform array antenna at the transmitting end and the receiving end; the h function is the response of the raised cosine filter;
the dual sparsity-based dual channel estimation specifically comprises the following steps:
1) the time-delay domain multipath channel detection specifically executes the following operations:
11) designing a pilot sequence by utilizing the sparsity of a multipath domain, and expressing the pilot sequence consisting of L subframes as formula 3:
Figure FDA0003041646810000013
in equation 3, each subframe is si(0≤i＜L)；
Transmitting a pilot frame sequence composed of L subframes to separate a multipath channel;
12) setting si＝1(0≤i＜L)，
Figure FDA0003041646810000014
Wherein, thetanAnd ηnRandomly and equally derived from a phase set B of phase shifters with B bits precision {0,2 pi/2 }b,2×2π/2b,...,(2b-1)×2π/2bAnd keeping the same in each subframe;
13) calculating detection statistic Y by equations 4 and 5dAnd normalized detectionStatistics
Figure FDA0003041646810000021
Figure FDA0003041646810000022
Figure FDA0003041646810000023
14) Setting the energy detection threshold as mu, and roughly selecting by a formula 6 to obtain an effective multipath channel set:
Figure FDA0003041646810000024
15) definition of lambdaAAnd
Figure FDA0003041646810000025
respectively representing the detection statistic quantity A and the normalization detection statistic quantity A, wherein | S | represents the base number of the set S, and finally selecting an effective multipath set by the following formula 7:
Figure FDA0003041646810000026
2) beam domain angle estimation: utilizing sparsity of a wave beam domain, designing an adaptive block orthogonal matching algorithm to estimate angle information of the wave beam; the following operations are specifically executed:
21) defining the beam response function as equation 8:
Figure FDA0003041646810000027
constructing the number of columns as G based on the beam response functiontAnd GrDictionary matrix DtAnd DrExpressed as formula 9 and formula 10, respectively:
Figure FDA0003041646810000028
Figure FDA0003041646810000029
22) obtaining a beam domain representation using a dictionary matrix
Figure FDA00030416468100000210
Satisfy the requirement of
Figure FDA00030416468100000211
23) Note the book
Figure FDA00030416468100000212
Converting the effective received signal vector of multipath channel d into y by equation 11d：
Figure FDA0003041646810000031
Wherein, ydIs the effective received signal vector of the multipath channel d;
Figure FDA0003041646810000032
Figure FDA0003041646810000033
24) calculating to obtain a radix number c by an adaptive block orthogonal matching algorithmdIs estimated at a set of angles
Figure FDA0003041646810000034
25) Definition of
Figure FDA0003041646810000035
Is c to be estimateddA vector of x 1, the vector of x 1,
Figure FDA0003041646810000036
Figure FDA0003041646810000037
the multipath channel d is represented by equation 12:
Figure FDA0003041646810000038
finishing the angle estimation of the beam domain through the operation;
3) the joint estimation of the beam amplitude and the frequency offset specifically executes the following operations:
31) determining angular coordinates
Figure FDA0003041646810000039
A union of (1); quantizing to obtain sets according to the precision of the phase shifters
Figure FDA00030416468100000310
Wherein
Figure FDA00030416468100000311
Is a two-dimensional coordinate: (quantized exit angle, quantized incident angle); further taking out
Figure FDA00030416468100000312
And (4) quantizing the coordinates of the emergent angle and the quantized incident angle to obtain a coordinate set of the quantized emergent angle and a coordinate set of the quantized incident angle, which are respectively recorded as
Figure FDA00030416468100000313
And
Figure FDA00030416468100000314
32) setting the start time of the joint estimation to 0, and setting the precoding vector of the phase shifter network at the time n to be formula 13 and formula 14:
Figure FDA00030416468100000315
Figure FDA00030416468100000316
wherein the content of the first and second substances,
Figure FDA00030416468100000317
pt(n) a transmit-side precoding vector; p is a radical ofr(n) is a receiving-end precoding vector;
33) note the book
Figure FDA00030416468100000318
The received signal in the ith poll is represented approximately by equation 15:
Figure FDA0003041646810000041
wherein the least squares estimation
Figure FDA0003041646810000042
34) After R times of same polling, taking out
Figure FDA0003041646810000043
The jth element in (a) results in a sequence, represented by equation 16:
Figure FDA0003041646810000044
running the weighted mean normalized linear predictor to obtain the amplitude and Doppler frequency offset of the jth path of the multipath channel d;
repeatedly executing operations 31) -34), namely estimating the amplitudes and frequency offsets of all paths of the multipath channel d, thereby completing the joint estimation of the beam amplitudes and frequency offsets;
through the steps, the channel estimation of the large-scale multi-input multi-output millimeter wave system is achieved by adopting a double-selection channel estimation method based on double sparsity.
2. The dual sparsity based dual channel estimation method as claimed in claim 1, wherein step 24) obtains the radix c by calculation through adaptive block orthogonal matching algorithmdSet of estimated angles of
Figure FDA0003041646810000045
The method specifically comprises the following steps:
a) selecting K frame pilot frequency frames for channel estimation:
setting the length of orthogonal matching as S and the number of groups as G-KL/S;
and (3) initializing: initializing the iteration times C to 0, the iteration change rate beta to infinity, and roughly selecting an angle set Ad/DdAnd fine selection of angle sets
Figure FDA0003041646810000046
Is an empty set, residual error rd＝yd；
b) A sensing matrix according to equation 11)
Figure FDA0003041646810000047
Orthogonal matching is carried out on the G groups simultaneously, the angle coordinate corresponding to the sum of the maximum correlation values is obtained and recorded as the current rough selection angle, and if the angle does not belong to the current rough selection angle set Ad/DdPut it into the rough selection angle set Ad/Dd；
c) According to the rough selection angle set Ad/DdTo, forPrimitive perceptual matrix
Figure FDA0003041646810000048
The up-sampling is carried out to obtain a sensing matrix with improved resolution, and the new sensing matrix is utilized to carry out orthogonal matching again to obtain an angle with improved resolution and is placed into the angle
Figure FDA0003041646810000051
d) Updating iteration times C, iteration change rate beta and G groups of residual errors r after orthogonal matchingd；
And b), c) and d) are repeatedly executed until the iteration change rate is lower than a set threshold value epsilon or the iteration number reaches a set K times.
3. The dual sparsity based dual channel estimation method as claimed in claim 1, wherein the transmitting side and the receiving side are respectively provided with NtAnd NrDimensional antenna, NtAnd NrThe value of (A) is not less than 8.
4. The dual sparsity based dual channel estimation method as claimed in claim 1, wherein the precision of the phase shifter network is b bits, b ≧ 4.
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