CN113922851B - Single-bit quantitative precoding method based on APGM method - Google Patents
Single-bit quantitative precoding method based on APGM method Download PDFInfo
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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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- 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
- H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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Abstract
The invention discloses a single-bit quantization precoding method based on an APGM method, which is based on a precoding algorithm of a 1-bit DAC with infinite norm square relaxation, and aims to solve the problem of overhigh computational complexity in the problem of infinite norm square relaxation with the advantage of high computational efficiency, and only needs simple matrix and vector operation and searching for a near-end operator of infinite norm square during each iteration. The pre-coding algorithm can realize rapid iteration and convergence, and can effectively reduce the error rate.
Description
Technical Field
The invention relates to the technical field of communication, in particular to a single-bit quantitative precoding method based on an APGM method.
Background
The Constant Envelope (CE) precoding design problem in large-scale multi-user multiple-input multiple-output (MU-MIMO) has become one of the core problems of the fifth generation (5G) communication technology, because CE precoding can avoid high hardware costs and high power consumption base stations equipped with a large number of antennas. In order to design CE precoding, the non-convex combining optimization problem needs to be solved by jointly optimizing the precoding vector and the precoding factor. However, the corresponding optimization problem is NP difficult and difficult to solve.
When Channel State Information (CSI) is non-causally known, some precoding methods have been proposed to approximate the performance of DPC. However, these precoding methods are still too complex and their complexity increases too fast with the number of antennas, which makes them difficult to be practically applied in massive MU-MIMO. Meanwhile, the APGM method is an efficient solution, but the traditional precoding method is difficult to solve because of the discrete combination optimization problem of NP. The iteration times of the method in the prior art are more, the convergence speed is lower, and the calculation efficiency is further reduced.
Disclosure of Invention
The invention provides a single-bit quantization precoding method based on an APGM method, which aims to solve the technical problems that in the prior art, the discrete combination optimization problem is difficult to solve and the convergence speed of iteration times is low.
The invention provides a single-bit quantitative precoding method based on an APGM method, which comprises the following steps:
step 1: acquiring a transmitting symbol vector of a base station;
step 2: the CE precoder is simplified, specifically as follows:
step 21: preliminary optimization of CE precoding formula:
wherein,is a transmit signal vector; s is the transmitted symbol vector, ">Is an estimated symbol vector; h= [ H ] u,b ]∈C U×B Is a downlink channel matrix, H u,b Is the channel gain between the transmit antenna and the user; u is the number of independent users of the base station service station;
step 22: optimizing the precoding factor by an approximate solving method:
step 23: rewriting a preliminarily optimized CE precoding formula according to the optimized coding factor, wherein let z=βx and
wherein,
step 24: the complexity of the formula obtained in the step 23 is reduced by an approximate solution method, and the reduced formula is as follows:
step 25: removing the non-convex constraint of the formula obtained in step 24 to obtain the following formula:
step 26: 1-bit quantization of the precoding vector is performed according to the step 25:
the complex precoding vector is:
completing CE precoding simplification;
step 3: and coding the simplified CE precoding to obtain a precoded transmitting signal vector X.
Further, the specific process of step 22 is as follows:
step 221: developing a preliminarily optimized CE precoding formula:
step 222: the gradient of β in the formula of step 221 is equal to zero, resulting in the following formula:
step 223: setting upObtaining optimized beta:
further, the specific process of step 25 is as follows:
by removing non-convex constraintsb=2, …,2B, the unconstrained problem based on infinite norm squared convex relaxation can be obtained:
the invention has the beneficial effects that:
the invention solves the problem of infinite norm square relaxation in a high-calculation efficiency mode, only needs simple matrix and vector operation in each iteration and evaluation of near-end operators of infinite norm square, can realize rapid iteration and convergence, and can effectively reduce Bit Error Rate (BER).
Drawings
The features and advantages of the present invention will be more clearly understood by reference to the accompanying drawings, which are illustrative and should not be construed as limiting the invention in any way, in which:
FIG. 1 is an objective function value for different precoding algorithms;
FIG. 2 is a graph of bit error rate performance of different algorithms in a massive MU-MIMO system;
fig. 3 is a graph showing bit error rate performance of different algorithms in a massive MU-MIMO system (after adjusting the number of antennas).
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to fall within the scope of the invention.
The embodiment is a process for simulating the method in MATLAB, and comprises the following steps:
step 1: is arranged in a single-cell massive MU-MIMO downlink system with N antenna Base Stations (BS) which simultaneously serve M single-antenna users, and in which vectors are inputWherein θ is the constellation point set;
step 2: CE precoding with limited resolution Phase Shifter (PS) is designed to minimize the input vector s and the estimated signal by jointly optimizing the precoding transmission vector X and the precoding factor betaMean Square Error (MSE) between, expressed as:E n indicating the desire for n.
Step 3: the CE precoder is simplified, specifically as follows:
step 21: setting the system to work in a high signal-to-noise ratio (SNR) range, namely epsilon-0, and preliminarily optimizing a CE precoding formula:
wherein, the input and output relationship of the downlink can be expressed as: y=hx+n, y= [ y ] 1 ,...,y U ] T ∈C U Including the received signal vectors of all users,is the transmitted signal vector from the base station, s is the transmitted symbol vector,is an estimated symbol vector, h= [ H ] u,b ]∈C U×B Is a downlink channel matrix, its element H u,b Representing the channel gain between the transmit antenna and the user. Assuming that the base station knows the relevant information of H completely, n= [ n ] 1 ,...,n U ] T ∈C U Is a noise vector whose element obeys a mean of 0 and variance of ε 2 Is a complex circularly symmetric gaussian distribution. U is the number of independent users of the base station service premises.
Step 22: the precoding factors are optimized through an approximate solving method, and the method comprises the following specific steps:
step 221: developing a preliminarily optimized CE precoding formula:
step 222: the gradient of β in the formula of step 221 is equal to zero, resulting in the following formula:
step 223: setting upObtaining optimized beta:
step 23: rewriting a preliminarily optimized CE precoding formula according to the optimized coding factor, wherein let z=βx and
wherein,
step 24: the complexity of the formula obtained in the step 23 is reduced by an approximate solution method, and the reduced formula is as follows:
wherein, will beUse->And (5) replacing. B is the number of base station transmitting antennas.
Step 25: removing the non-convex constraint of the formula obtained in said step 24, in particular by removing the non-convex constraintb=2, …,2B, can be obtained on an infinite scaleUnconstrained problem formulation of digital square convex relaxation:
step 26: 1-bit quantization of the precoding vector is performed according to the step 25:
the complex precoding vector is:
completing CE precoding simplification;
step 3: and (3) encoding the CE precoder with the most simplified transmission signal vector to obtain a precoded transmission vector.
In fig. 1, the APGM can converge to a good objective function value after about 20 iterations, and the number of iterations is small. And the situation that the objective function can shake up and down does not exist, and the iteration process is stable.
In fig. 2, the APGM algorithm only needs to iterate 30 times and converges faster, and 30 times can fully converge when simulating BER. The complexity of computation when the APGM algorithm iterates can be greatly reduced. The APGM precoder provides a certain performance gain when the BER is 10-4.
It can be seen from fig. 3 that the APGM algorithm still exhibits its stable performance. The number of iterations of the APGM algorithm is still small for the same BER effect. The APGM algorithm can still provide a certain performance gain when the BER is 10-4. Therefore, when the number of antennas is adjusted, the APGM algorithm is still better than other algorithms, and the performance of the APGM algorithm can be improved along with the continuous increase of the number of antennas.
Although embodiments of the present invention have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the invention, and such modifications and variations are within the scope of the invention as defined by the appended claims.
Claims (3)
1. The single-bit quantitative precoding method based on the APGM method is characterized by comprising the following steps of:
step 1: acquiring a transmitting symbol vector of a base station;
step 2: the CE precoder is simplified, specifically as follows:
step 21: preliminary optimization of CE precoding formula:
wherein,is a transmit signal vector; s is the transmitted symbol vector, ">Is an estimated symbol vector; h= [ H ] u,b ]∈C U×B Is a downlink channel matrix, H u,b Is the channel gain between the transmit antenna and the user; u is the number of independent users of the base station service station;
step 22: optimizing the precoding factor by an approximate solving method:
step 23: rewriting a preliminarily optimized CE precoding formula according to the optimized coding factor, wherein let z=βx and
wherein,
step 24: the complexity of the formula obtained in the step 23 is reduced by an approximate solution method, and the reduced formula is as follows:
step 25: removing the non-convex constraint of the formula obtained in step 24 to obtain the following formula:
step 26: 1-bit quantization of the precoding vector is performed according to the step 25:
the complex precoding vector is:
completing CE precoding simplification;
step 3: and coding the simplified CE precoding to obtain a precoded transmitting signal vector.
2. The single-bit quantization precoding method based on the APGM method of claim 1, wherein the specific procedure of step 22 is as follows:
step 221: developing a preliminarily optimized CE precoding formula:
step 222: the gradient of β in the formula of step 221 is equal to zero, resulting in the following formula:
step 223: setting upObtaining optimized beta:
。
3. the single-bit quantized precoding method based on the APGM method according to claim 1 or 2, wherein the specific procedure of step 25 is as follows:
by removing non-convex constraintsThe unconstrained problem of infinite norm square convex relaxation can be obtained:
。
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012154090A1 (en) * | 2011-05-06 | 2012-11-15 | Ellintech Ab | Precoder using a constant envelope constraint and a corresponding precoding method for mu-mimo communication systems |
| CN112564747A (en) * | 2020-11-26 | 2021-03-26 | 江苏科技大学 | Constant envelope precoding suitable for large-scale MU-MIMO system |
| CN113037342A (en) * | 2019-12-24 | 2021-06-25 | 清华大学 | Channel estimation and precoding method and device for single-bit millimeter wave multi-antenna system |
| CN113315732A (en) * | 2021-05-28 | 2021-08-27 | 江苏科技大学 | Low-complexity method suitable for reducing peak-to-average power ratio of MIMO-OFDM system |
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| US7702029B2 (en) * | 2006-10-02 | 2010-04-20 | Freescale Semiconductor, Inc. | MIMO precoding enabling spatial multiplexing, power allocation and adaptive modulation and coding |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012154090A1 (en) * | 2011-05-06 | 2012-11-15 | Ellintech Ab | Precoder using a constant envelope constraint and a corresponding precoding method for mu-mimo communication systems |
| CN113037342A (en) * | 2019-12-24 | 2021-06-25 | 清华大学 | Channel estimation and precoding method and device for single-bit millimeter wave multi-antenna system |
| CN112564747A (en) * | 2020-11-26 | 2021-03-26 | 江苏科技大学 | Constant envelope precoding suitable for large-scale MU-MIMO system |
| CN113315732A (en) * | 2021-05-28 | 2021-08-27 | 江苏科技大学 | Low-complexity method suitable for reducing peak-to-average power ratio of MIMO-OFDM system |
Non-Patent Citations (1)
| Title |
|---|
| 大规模MIMO***低复杂度1位量化预编码算法;袁怡圃;江丽;;泉州师范学院学报(第02期);71-77 * |
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