CN107707493A - A kind of channel estimation methods based on compressed sensing - Google Patents
A kind of channel estimation methods based on compressed sensing Download PDFInfo
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- CN107707493A CN107707493A CN201610633190.XA CN201610633190A CN107707493A CN 107707493 A CN107707493 A CN 107707493A CN 201610633190 A CN201610633190 A CN 201610633190A CN 107707493 A CN107707493 A CN 107707493A
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- 239000013598 vector Substances 0.000 claims description 18
- 239000011159 matrix material Substances 0.000 claims description 16
- 238000012549 training Methods 0.000 description 7
- 238000004891 communication Methods 0.000 description 6
- 238000005070 sampling Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 238000005562 fading Methods 0.000 description 2
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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
- 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
- H04B7/0426—Power distribution
- H04B7/043—Power distribution using best eigenmode, e.g. beam forming or beam steering
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- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Computer Networks & Wireless Communication (AREA)
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Abstract
The present invention proposes a kind of channel estimation methods based on compressed sensing, including：Receiving terminal estimates optimal beam pair, and feeds back to transmitting terminal；Transmitting terminal sends pilot frequency sequence using the launching beam of the optimal beam centering；Receiving terminal receives pilot frequency sequence using the reception wave beam of the optimal beam centering, then carries out channel estimation to receiving pilot frequency sequence using compressed sensing algorithm, obtains channel condition information.The quantity of frequency pilot sign can be reduced using the present invention, reduces the expense of channel estimation.
Description
Technical Field
The invention relates to the field of wireless communication, in particular to a channel estimation method based on compressed sensing.
Background
In a wireless communication system, a receiving end needs to first obtain channel state information in order to detect data from a transmitting end. In actual environment, the wireless signal is affected by large-scale fading and small-scale fading, which causes distortion of the communication signal, thereby affecting the communication quality. In order to compensate for signal distortion caused by a wireless channel, a current scheme is to perform channel estimation at a receiving end, that is, to estimate channel state information of a signal by using information such as a received signal and channel characteristics, so as to compensate for distortion caused by the channel to the signal. The channel estimation technology provides accurate channel state information for a wireless communication system, and creates conditions for relieving interference among users and reducing the complexity of the system.
In the conventional channel estimation method, a pilot symbol is generally transmitted on subcarriers at equal intervals in a frequency domain, channel information at a pilot position is estimated by using a frequency domain channel estimation algorithm such as a least square method, a least mean square error method, and the like, and channel information of all subcarriers is recovered by using an interpolation method. These conventional methods all require a large number of pilot symbols to be transmitted, and the channel estimation overhead is large.
Disclosure of Invention
In order to solve the problem of high channel estimation overhead in the background art, the invention provides a channel estimation method combined with a beam forming technology, which comprises the following steps:
the receiving end estimates the optimal beam pair and feeds back the optimal beam pair to the transmitting end;
the transmitting end transmits a pilot sequence by using the transmitting beam in the optimal beam pair;
and the receiving end receives the pilot frequency sequence by using the receiving wave beam in the optimal wave beam pair, and then performs channel estimation on the receiving pilot frequency sequence by adopting a compressed sensing algorithm to obtain channel state information.
Preferably, the estimating, by the receiving end, the optimal beam pair specifically includes:
the transmitting end sequentially transmits the same pilot frequency sequence to the receiving end by using k groups of random transmitting wave beams, wherein k is more than or equal to 2;
the receiving end receives the pilot frequency series by using k groups of random receiving wave beams, performs channel estimation (preferably, an LS channel estimation algorithm) on a receiving matrix formed by k receiving pilot frequency sequence column vectors to obtain an estimated channel matrix, determines a channel vector corresponding to a strongest path in the estimated channel matrix, and estimates an optimal wave beam pair according to the channel vector by using a compressed sensing algorithm.
Preferably, k is α log (N)tNr) Wherein α is a positive integer, NtAnd NrThe number of random beams at the transmitting end and the receiving end, respectively, wherein α ═ 3 is preferred.
The invention provides a channel estimation method based on compressed sensing, which adopts an optimal wave beam pair to send pilot signals, then utilizes the compressed sensing principle to carry out channel estimation to obtain accurate channel state information, can reduce the number of pilot symbols and reduce the cost of channel estimation. In addition, the invention also provides that a small number of random wave beam pairs can be adopted to send training pilot frequency in the wave beam training stage, and the optimal wave beam pair is estimated by utilizing the compressed sensing principle, so that a great deal of expenditure caused by the wave beam training by using an exhaustion method can be avoided.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments; it should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The basic principle of the present invention is that when the optimal beam pair is used to transmit the pilot signal, the receiving end can obtain the pilot signal with high signal-to-noise ratio by using the optimal beam pair, and the received pilot signal is represented as:
y-diag (x) Fh + z- Θ h + z, where x is the pilot symbol, F is the unit DFT transform matrix of the subcarrier, and h is the subcarrier equivalent channel vector.
Because the number of the channel multipaths is limited, and the beam forming enables the equivalent channel vector h to have strong directivity in the angle space, the channel multipaths outside the angle range of the beam are inhibited, and the equivalent channel can be similar to a sparse multipath channel, the channel estimation can be performed on the received pilot signal y by utilizing the compressed sensing principle, and therefore more accurate channel state information can be obtained. Here, compressed sensing is a well-known sampling theory, which obtains discrete samples of a signal by random sampling through the sparseness of the signal under the condition of being much smaller than the Nyquist sampling rate, and then reconstructs the signal through a nonlinear reconstruction algorithm.
The invention needs to determine the optimal beam pair at the transmitting end and the receiving end in advance, namely needs to carry out beam training. When the number of the receiving and transmitting beam pairs is small, the searching can be carried out by adopting an exhaustion method, and when the number of the receiving and transmitting beam pairs is large, the exhaustion method brings large expense, so the invention also provides a beam training method which only needs to use a small number of the receiving and transmitting beam pairs. The principle is as follows:
if beam training is performed by using k sets of transmit-receive beam pairs, and each set transmits the same pilot sequence, a receive matrix formed by sequence column vectors finally received can be represented as:
performing channel estimation to obtain equivalent channel matrixIs estimated value of
Wherein each column is an equivalent channel formed by a group of beam pairs, and each row represents the equivalent gain of a multipath under different beam pairs.
Determining the corresponding row value of the strongest path direction according to the following criteria, and recording the row value as lmax：
Wherein,is an equivalent channel matrixLine i.
For the ith path tap of the equivalent channel vector formed by the ith random beam at the transmitting end and the receiving end, the following can be expressed:
andi th random wave beam g at the receiving and transmitting ends respectivelyl、plThe receiving direction vector and the transmitting direction vector of the ith multipath respectively,
therefore, the equivalent channel vector formed by different transmit-receive random beam pairs is represented as:
wherein the ith row of the matrix T is composed ofThe structure of the utility model is that the material,representing equivalent channels formed by different transmit-receive beam pairs.
For each channel multipath, different beams point to different directions usually, and the side lobe energy of the beam is far lower than the main lobe energy, because only one group of beam pairs can make the equivalent channel gain maximum, and the equivalent channel gain brought by adopting other beam pairs is very small, the vector formed by the equivalent channel gains brought by different beam pairs is a sparse vector, namely q is qlCan be regarded as having sparsity, then can utilize the compressed sensing principle to pass throughCorresponding to the l-th of the strongest pathmaxLine vectorAnd obtaining the optimal beam pair of the strongest path direction.
The following examples will illustrate in detail how the invention can be carried out.
In the wireless communication system, the number of transmitting antennas of a base station is M, the number of receiving antennas of a user is N, and the numbers of random wave beams of a transmitting end and a receiving end are respectivelyIs NtAnd Nr. To better satisfy randomness, the elements defining the random beam vectors at both the transmit and receive ends are each subject to a ± 1Bernoulli distribution. The method comprises the following implementation steps:
step 1, the transmitting end and the receiving end construct k groups of random beam pairs according to the number of the transmitting and receiving beam pairs, where k may be α log (N)tNr) Where α is a smaller integer, an empirical value of α — 3 may be taken.
And step 2, starting a beam training process, and sequentially transmitting pilot sequences to the receiving end by the transmitting end by using k groups of random transmitting beams, wherein the pilot sequences transmitted each time are the same.
And 3, the receiving end receives the pilot frequency sequence by using k groups of random receiving wave beams.
And 4, detecting the optimal beam pair by the receiving end: firstly, an LS estimation algorithm is used for estimating a current channel matrix, then a channel vector corresponding to a strongest path is determined, and finally a compressed sensing algorithm is used for estimating an optimal beam pair in the path direction.
And 5, the receiving end feeds back the estimated optimal beam pair to the transmitting end.
And 6, the transmitting end transmits the pilot sequence by using the transmitting beam in the optimal beam pair.
Step 7, the receiving end obtains a receiving pilot frequency with high signal-to-noise ratio by using the receiving beam in the optimal beam pair, and performs channel estimation by using a compressed sensing algorithm to obtain an estimated channel matrix;
and 8, the receiving end utilizes the estimated channel matrix to demodulate data or form channel state information to be fed back to the sending end.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (6)
1. A channel estimation method based on compressed sensing, the method comprising:
the receiving end estimates the optimal beam pair and feeds back the optimal beam pair to the transmitting end;
the transmitting end transmits a pilot sequence by using the transmitting beam in the optimal beam pair;
and the receiving end receives the pilot frequency sequence by using the receiving wave beam in the optimal wave beam pair, and then performs channel estimation on the receiving pilot frequency sequence by adopting a compressed sensing algorithm to obtain channel state information.
2. The method of claim 1, wherein the estimating, by the receiving end, the optimal beam pair specifically comprises:
the transmitting end sequentially transmits the same pilot frequency sequence to the receiving end by using k groups of random transmitting wave beams, wherein k is more than or equal to 2;
the receiving end receives the pilot frequency series by using k groups of random receiving wave beams, carries out channel estimation on a receiving matrix formed by k receiving pilot frequency sequence column vectors to obtain an estimated channel matrix, determines a channel vector corresponding to a strongest path in the estimated channel matrix, and estimates an optimal wave beam pair according to the channel vector by adopting a compressed sensing algorithm.
3. The method of claim 2, wherein:
k＝αlog(NtNr) Wherein α is a positive integer, NtAnd NrThe number of random beams at the receiving end and the transmitting end respectively.
4. The method of claim 3, wherein:
α＝3。
5. the method according to claim 3 or 4, characterized in that:
the random beams at the transmitting end and the receiving end follow a +/-1 Bernoulli distribution.
6. The method of claim 5, wherein:
and the receiving end carries out channel estimation on the receiving matrix by adopting an LS channel estimation algorithm to obtain the estimated channel matrix.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019206157A1 (en) * | 2018-04-25 | 2019-10-31 | 华为技术有限公司 | Method for training downlink beam, network device and terminal device |
| CN110808926A (en) * | 2019-10-12 | 2020-02-18 | 三维通信股份有限公司 | Interference signal estimation method, apparatus, device and computer readable storage medium |
| CN115361045A (en) * | 2022-07-28 | 2022-11-18 | 鹏城实验室 | Communication method, device, terminal and storage medium based on side lobe energy perception |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101227217A (en) * | 2008-02-04 | 2008-07-23 | 浙江大学 | Method and system for random wave packet forming based on multi-aerial receiver |
| CN101494627A (en) * | 2009-03-11 | 2009-07-29 | 北京邮电大学 | Channel estimation method for reducing pilot number by using compression perception in wideband mobile communication |
| WO2011136985A1 (en) * | 2010-04-27 | 2011-11-03 | Qualcomm Incorporated | Compressed sensing channel estimation in ofdm communication systems |
| CN102271014A (en) * | 2011-06-09 | 2011-12-07 | 华为技术有限公司 | Method and device for pairing wave beams among devices |
| CN103688474A (en) * | 2013-09-27 | 2014-03-26 | 华为技术有限公司 | Communication method, base station, and user equipment |
-
2016
- 2016-08-04 CN CN201610633190.XA patent/CN107707493A/en not_active Withdrawn
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101227217A (en) * | 2008-02-04 | 2008-07-23 | 浙江大学 | Method and system for random wave packet forming based on multi-aerial receiver |
| CN101494627A (en) * | 2009-03-11 | 2009-07-29 | 北京邮电大学 | Channel estimation method for reducing pilot number by using compression perception in wideband mobile communication |
| WO2011136985A1 (en) * | 2010-04-27 | 2011-11-03 | Qualcomm Incorporated | Compressed sensing channel estimation in ofdm communication systems |
| CN102271014A (en) * | 2011-06-09 | 2011-12-07 | 华为技术有限公司 | Method and device for pairing wave beams among devices |
| CN103688474A (en) * | 2013-09-27 | 2014-03-26 | 华为技术有限公司 | Communication method, base station, and user equipment |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019206157A1 (en) * | 2018-04-25 | 2019-10-31 | 华为技术有限公司 | Method for training downlink beam, network device and terminal device |
| CN110401475A (en) * | 2018-04-25 | 2019-11-01 | 华为技术有限公司 | Downlink wave beam training method, the network equipment and terminal device |
| CN110401475B (en) * | 2018-04-25 | 2021-10-15 | 华为技术有限公司 | Downlink beam training method, network equipment and terminal equipment |
| CN110808926A (en) * | 2019-10-12 | 2020-02-18 | 三维通信股份有限公司 | Interference signal estimation method, apparatus, device and computer readable storage medium |
| CN110808926B (en) * | 2019-10-12 | 2022-04-01 | 三维通信股份有限公司 | Interference signal estimation method, apparatus, device and computer readable storage medium |
| CN115361045A (en) * | 2022-07-28 | 2022-11-18 | 鹏城实验室 | Communication method, device, terminal and storage medium based on side lobe energy perception |
| CN115361045B (en) * | 2022-07-28 | 2024-05-14 | 鹏城实验室 | Communication method, device, terminal and storage medium based on sidelobe energy perception |
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