CN116743309A - Improved algorithm for low-complexity detection based on QSM system - Google Patents

Improved algorithm for low-complexity detection based on QSM system Download PDF

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Publication number
CN116743309A
CN116743309A CN202310234982.XA CN202310234982A CN116743309A CN 116743309 A CN116743309 A CN 116743309A CN 202310234982 A CN202310234982 A CN 202310234982A CN 116743309 A CN116743309 A CN 116743309A
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algorithm
qsm
antenna
detection
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李琦
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Chongqing Technology and Business University
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Chongqing Technology and Business University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H04L1/0001—Systems modifying transmission characteristics according to link quality, e.g. power backoff
    • H04L1/0036—Systems modifying transmission characteristics according to link quality, e.g. power backoff arrangements specific to the receiver
    • 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
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • 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
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

The application discloses an improved algorithm for low-complexity detection based on a QSM system. Aiming at the problem of higher complexity of the detection algorithm of the receiving end of the orthogonal space modulation (QuadratureSpatialModulation, QSM) system, the algorithm is improved and popularized by utilizing the characteristic of sparsity of the signal of the transmitting end of the QSM system and combining with an OMP algorithm in the compression sensing detection (CompressedSensing, CS) algorithm theory. In the iteration process, S indexes which meet the condition and are most relevant to the current residual are stored in a set lambda, one antenna index number is respectively selected from the sets lambda 1 and lambda 2 to be combined, and S2 antenna combinations are stored in the antenna index set to be controlled. The new detection algorithm is close to the maximum (MaximumLikelihood, ML) detection algorithm in terms of error performance, has a complexity of about 4.7% of ML, and has higher system performance.

Description

Improved algorithm for low-complexity detection based on QSM system
Technical Field
The application relates to the technical field of space-sky-land-sea integrated communication, in particular to an improved algorithm based on QSM system low-complexity detection.
Background
The fifth generation communication system (5 generation,5 g) needs to meet the service requirement that multiple terminal devices access multiple samples simultaneously, and a high-spectrum-efficiency orthogonal spatial modulation (Quadrature Spatial Modulation, QSM) scheme has been developed, but the QSM system has a special mapping manner, so that demodulation of the system becomes difficult. Among currently existing detection algorithms, the most optimal detection in the QSM system is the maximum likelihood (Maximum Likelihood, ML) detection algorithm, but the algorithm jointly searches all possible transmit antenna combinations and modulation symbols resulting in extremely high computational complexity.
The compressed sensing (Compressed Sensing, CS) based sparse signal reconstruction theory is widely used in large-scale antenna systems because of its extremely low computational complexity in terms of signal detection. Many students use the sparsity of signals in the GSM system to apply some classical CS algorithms therein, such as the orthogonal matching Pursuit (Orthogonal Matching Pursuit, OMP) algorithm, the Basic Pursuit (BP) algorithm, the compressed sampling matching Pursuit (Compressie Sampling Matching Pursuit, coSaMP) algorithm, etc.
However, the above algorithm based on CS theory is implemented in a GSM system with a fixed number of active antennas per slot, i.e. a fixed signal sparsity, and obtains the error performance of the secondary ordering block minimum mean square error algorithm (OB-MMSE) and the improved OB-MMSE (IOB-MMSE). However, in the QSM system, the number of transmitting antennas activated by the transmitting end in each time slot is changed between 1 and 2 according to different input information streams. The base tracking noise reduction algorithm is applied to the receiving end of the QSM system, and has higher calculation complexity although better error code performance is obtained.
Disclosure of Invention
Aiming at the defects in the prior art, the application provides an improved algorithm for detecting low complexity based on a QSM system.
In one aspect, the present application provides an improved algorithm for low complexity detection based on a QSM system, the method comprising the steps of:
s1: generating a channel transmission matrix H, additive Gaussian white noise n and a receiving vector y of a receiving end of the QSM system based on priori knowledge detected by the receiving end of the QSM system;
s2: input normalized channel matrixReceived signal vector->And initializing the parameter->
S3: calculating in the t-th iteration process; s indexes which meet the condition and are most relevant to the current residual error are stored in a set lambda;
s4: respectively from the set Λ 1 ，Λ 2 One antenna index number is taken and combined, and S is the same 2 The antenna combination is stored in a set Γ;
s5: detection of l in antenna index set Γ using ML R and lI Belongs to the collection Γ;
s6: output activated antenna indexDigital modulation symbol-> and />Antenna indexes respectively representing the real part and the imaginary part of the detected transmitted symbol; /> and />Representing the real and imaginary parts of the detected data modulation symbols s.
Optionally, in the calculating step in the t-th iteration, the calculation expression is:
optionally, the calculating step includes: when t=1, if λ 1 ＞N t (λ 1 ≤N t ) Then takeMiddle indexNumber is greater than N t (Small and equal to N) t ) The first S maximum correlation values of (a) are stored in a set Λ t In (a) and (b); when t=2, if λ 1 ＞N t (λ 1 ≤N t ) Get +.>The index number of the medium is less than or equal to N t (greater than N t ) The first S maximum correlation values of (a) are stored in a set Λ t In (a) and (b); let->
Optionally, from the set Λ 1 ，Λ 2 One antenna index number is taken and combined, and S is the same 2 The antennas are stored in a set Γ, and ML is used for detection in the antenna index set Γ.
Optionally, the ML is used for detecting l in the antenna index set Γ R and lI Belonging to the set Γ, its computational expression is:
optionally, the output activates an antenna indexDigital modulation symbol-> and />Antenna indexes respectively representing the real part and the imaginary part of the detected transmitted symbol; /> and />In the real and imaginary part step representing the detected data modulation symbol s, the calculation expression is:
wherein ,representing a channel transmission matrix, ">Representing additive Gao Sibao noise, H is a multiplexing telling random variable subject to mean value 0 and variance 1, H has finite equidistant characteristic, n is independent and equidistributed, mean value is 0 and variance is delta 2 Is a complex gaussian random variable; then the receiving vector of the receiving end of the QSM system +.>Is->Respectively identify the first of the channel matrix H R Column and first I Columns.
The beneficial effects of the application are as follows: the application can carry out Monte Carlo simulation comparison on the ML algorithm, the OMP algorithm, the BP algorithm, the OB-MMSE and the new improved algorithm under the same channel environment and spectrum efficiency, and can obtain the error code performance under different QSM system parameter configurations. By comparing the error rates, it can be obtained that the error performance is significantly improved with the increase of the number of the antenna indexes, but the increased computation complexity is still far lower than that of other detection algorithms and ML algorithms. By comparison of the computational complexity, the computational complexity of the new improved algorithm decreases with increasing transmit antennas as compared to the percentage of complexity that ML occupies. Proved by a new improved algorithm, the complexity of QSM system detection can be effectively reduced under the condition of ensuring error code performance, and the method has higher engineering application value. Aiming at the problem of higher complexity of a detection algorithm of a receiving end of a quadrature space modulation (Quadrature Spatial Modulation, QSM) system, the method utilizes the characteristic of sparsity of a signal of a transmitting end of the QSM system and combines an OMP algorithm in a compressive sensing detection (Compressed Sensing, CS) algorithm theory to carry out improved popularization. In the iteration process, S indexes which meet the condition and are most relevant to the current residual are stored in a set lambda, one antenna index number is respectively selected from the sets lambda 1 and lambda 2 to be combined, and S2 antenna combinations are stored in the antenna index set to be controlled. The new detection algorithm is close to the maximum likelihood (Maximum Likelihood, ML) detection algorithm in terms of error performance, has a complexity of about 4.7% of ML, and has higher system performance.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. Like elements or portions are generally identified by like reference numerals throughout the several figures. In the drawings, elements or portions thereof are not necessarily drawn to scale.
FIG. 1 is a flow chart of an improved algorithm based on QSM system low complexity detection of the present application;
fig. 2 is a flow chart of the antenna information transmission of the present application.
Detailed Description
Embodiments of the technical scheme of the present application will be described in detail below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present application, and thus are merely examples, and are not intended to limit the scope of the present application.
It is noted that unless otherwise indicated, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The current algorithm based on CS theory is realized in a GSM system with fixed activated antenna number, namely fixed signal sparsity, and obtains the error code performance of a secondary ordering block minimum mean square error algorithm (OB-MMSE) and improved OB-MMSE (IOB-MMSE). However, in the QSM system, the number of transmitting antennas activated by the transmitting end in each time slot is changed between 1 and 2 according to different input information streams. The base tracking noise reduction algorithm is applied to a receiving end of a QSM system, and has higher calculation complexity although better error code performance is obtained; in order to solve the above-mentioned problems, there is a need to develop an improved algorithm based on low complexity detection of QSM systems.
The embodiment of the application provides an improved algorithm for low-complexity detection based on a QSM system, which is shown in figures 1-2 and comprises the following steps:
in step S1, based on a priori knowledge detected by the receiving end of the QSM system, a channel transmission matrix H, additive white gaussian noise n, and a receiving vector y of the receiving end of the QSM system are generated.
In step S2, a normalized channel matrix is inputReceived signal vector->And initializing parameters
In step S3, calculation is performed during the t-th iteration; and S indexes which meet the condition and are most relevant to the current residual are stored in the set lambda.
In the embodiment of the application, the iteration is performed at the t th timeIn the calculation step, the calculation expression is as follows:the calculation steps comprise:
when t=1, if λ 1 ＞N t (λ 1 ≤N t ) Then takeThe index number is greater than N t (Small and equal to N) t ) The first S maximum correlation values of (a) are stored in a set Λ t In (a) and (b);
when t=2, if λ 1 ＞N t (λ 1 ≤N t ) Then takeThe index number of the medium is less than or equal to N t (greater than N t ) The first S maximum correlation values of (a) are stored in a set Λ t In (a) and (b);
order theFinally from the set Λ 1 ，Λ 2 One antenna index number is taken and combined, and S is the same 2 The antennas are stored in a set Γ, and ML is used for detection in the antenna index set Γ.
In step S4, each is derived from the set Λ 1 ，Λ 2 One antenna index number is taken and combined, and S is the same 2 An antenna combination is stored in the set Γ.
In step S5, ML is used for detection l in the antenna index set Γ R and lI Belonging to the set Γ.
In the embodiment of the application, ML is adopted in the antenna index set Γ for detection l R and lI Belonging to the set Γ, its computational expression is:
in step S6, an active antenna index is outputDigital modulation symbol-> and />Antenna indexes respectively representing the real part and the imaginary part of the detected transmitted symbol; /> and />Representing the real and imaginary parts of the detected data modulation symbols s.
In an embodiment of the present application, the output activates an antenna indexDigital modulation symbol-> and />Antenna indexes respectively representing the real part and the imaginary part of the detected transmitted symbol; /> and />In the real and imaginary part step representing the detected data modulation symbol s, the calculation expression is:
wherein ,representing a channel transmission matrix, ">Representing additive Gao Sibao noise, H is a multiplexing telling random variable subject to mean value 0 and variance 1, H has finite equidistant characteristic, n is independent and equidistributed, mean value is 0 and variance is delta 2 Is a complex gaussian random variable; then the receiving vector of the receiving end of the QSM system +.>Is->Respectively identify the first of the channel matrix H R Column and first I Columns.
The application designs an improved algorithm based on QSM system low complexity detection, which can carry out Monte Carlo simulation comparison on the ML algorithm, the OMP algorithm, the BP algorithm, the OB-MMSE and the new improved algorithm under the same channel environment and frequency spectrum efficiency, and can obtain error code performance under different QSM system parameter configurations. By comparing the error rates, it can be obtained that the error performance is significantly improved with the increase of the number of the antenna indexes, but the increased computation complexity is still far lower than that of other detection algorithms and ML algorithms. By comparison of the computational complexity, the computational complexity of the new improved algorithm decreases with increasing transmit antennas as compared to the percentage of complexity that ML occupies. Proved by a new improved algorithm, the complexity of QSM system detection can be effectively reduced under the condition of ensuring error code performance, and the method has higher engineering application value. Aiming at the problem of higher complexity of a detection algorithm of a receiving end of a quadrature space modulation (Quadrature Spatial Modulation, QSM) system, the method utilizes the characteristic of sparsity of a signal of a transmitting end of the QSM system and combines an OMP algorithm in a compressive sensing detection (Compressed Sensing, CS) algorithm theory to carry out improved popularization. In the iteration process, S indexes which meet the condition and are most relevant to the current residual are stored in a set lambda, one antenna index number is respectively selected from the sets lambda 1 and lambda 2 to be combined, and S2 antenna combinations are stored in the antenna index set to be controlled. The new detection algorithm is close to the maximum likelihood (Maximum Likelihood, ML) detection algorithm in terms of error performance, has a complexity of about 4.7% of ML, and has higher system performance.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application 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 scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application, and are intended to be included within the scope of the appended claims and description.

Claims (6)

1. An improved algorithm for low complexity detection based on a QSM system, the method comprising the steps of:
s1: generating a channel transmission matrix H, additive Gaussian white noise n and a receiving vector y of a receiving end of the QSM system based on priori knowledge detected by the receiving end of the QSM system;
s2: input normalized channel matrixReceived signal vector->And initializing the parameter->
S3: calculating in the t-th iteration process; s indexes which meet the condition and are most relevant to the current residual error are stored in a set lambda;
s4: respectively from the set Λ 1 ，Λ 2 One antenna index number is taken and combined, and S is the same 2 The antenna combination is stored in a set Γ;
s5: detection of l in antenna index set Γ using ML R and lI Belongs to the collection Γ;
s6: output activated antenna indexDigital modulation symbol-> and />Antenna indexes respectively representing the real part and the imaginary part of the detected transmitted symbol; /> and />Representing the real and imaginary parts of the detected data modulation symbols s.
2. The method of claim 1, wherein in the calculating step in the t-th iteration, the calculation expression is:
3. the improved algorithm for low complexity detection based on a QSM system according to claim 2, wherein the calculating step includes:
when t=1, if λ 1 ＞N t (λ 1 ≤N t ) Then takeThe index number is greater than N t (Small and equal to N) t ) The first S maximum correlation values of (a) are stored in a set Λ t In (a) and (b);
when t=2, if λ 1 ＞N t (λ 1 ≤N t ) Then takeThe index number of the medium is less than or equal to N t (greater than N t ) The first S maximum correlation values of (a) are stored in a set Λ t In (a) and (b);
order the
4. An improved algorithm based on low complexity detection of QSM systems according to claim 3, wherein the algorithm is selected from the set Λ 1 ，Λ 2 One antenna index number is taken and combined, and S is the same 2 The antennas are stored in a set Γ, and ML is used for detection in the antenna index set Γ.
5. The improved algorithm for low complexity detection based on a QSM system according to claim 1, wherein ML is used for detection in the antenna index set Γ R and lI Belonging to the set Γ, its computational expression is:
6. an improved algorithm for low complexity detection based on a QSM system according to claim 1, wherein said output activates an antenna indexDigital modulation symbol-> and />Antenna indexes respectively representing the real part and the imaginary part of the detected transmitted symbol; /> and />In the real and imaginary part step representing the detected data modulation symbol s, the calculation expression is:
wherein ,representing a channel transmission matrix, ">Representing additive Gao Sibao noise, H is a multiplexing telling random variable subject to mean value 0 and variance 1, H has finite equidistant characteristic, n is independent and equidistributed, mean value is 0 and variance is delta 2 Is a complex gaussian random variable; then the receiving vector of the receiving end of the QSM system +.>Is->Identifying channel moments respectivelyFirst of array H R Column and first I Columns.
CN202310234982.XA 2023-03-12 2023-03-12 Improved algorithm for low-complexity detection based on QSM system Pending CN116743309A (en)

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