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CN111294095B - IRS (inter-range instrumentation Standard) assisted large-scale MIMO (multiple input multiple output) wireless transmission method based on statistical CSI (channel State information) - Google Patents

IRS (inter-range instrumentation Standard) assisted large-scale MIMO (multiple input multiple output) wireless transmission method based on statistical CSI (channel State information) Download PDF

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CN111294095B
CN111294095B CN202010095831.7A CN202010095831A CN111294095B CN 111294095 B CN111294095 B CN 111294095B CN 202010095831 A CN202010095831 A CN 202010095831A CN 111294095 B CN111294095 B CN 111294095B
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CN111294095A (en
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张军
刘洁
徐亚超
汪东乾
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Nanjing University of Posts and Telecommunications
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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
    • 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
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0621—Feedback content
    • H04B7/0626—Channel coefficients, e.g. channel state information [CSI]
    • 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
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0621—Feedback content
    • H04B7/063—Parameters other than those covered in groups H04B7/0623 - H04B7/0634, e.g. channel matrix rank or transmit mode selection

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Abstract

The invention provides an IRS (inter-range instrumentation system) auxiliary large-scale MIMO (multiple input multiple output) wireless transmission method based on statistical CSI (channel state information), which comprises the following steps of: 1) aiming at an IRS (intelligent resilient station) auxiliary large-scale MIMO (multiple input multiple output) wireless communication system, a reflecting surface phase shift matrix is fixed, and a sending signal covariance matrix is designed by utilizing statistical CSI (channel state information); 2) according to the covariance matrix of the sending signals obtained in the last step, the reflection phase shift matrix is designed, the method is simple to operate and is practical, the covariance matrix of the sending signals of the base station and the reflection phase shift matrix of the IRS can be designed on the premise of only knowing statistical CSI, higher communication rate is obtained under the condition of additionally increasing less energy consumption, and the method has important practical significance on the development of the IRS assisted large-scale MIMO wireless communication system.

Description

IRS (inter-range instrumentation Standard) assisted large-scale MIMO (multiple input multiple output) wireless transmission method based on statistical CSI (channel State information)
Technical Field
The invention relates to a wireless transmission method, in particular to an IRS (inter-range instrumentation system) assisted large-scale MIMO (multiple input multiple output) wireless transmission method based on statistical CSI (channel state information), and belongs to the technical field of wireless communication.
Background
With the continuous increase of the number of mobile devices accessing the network, the explosive growth of mobile applications such as ultra-clear video streams, virtual reality, augmented reality and the like, the frequency spectrum efficiency needs to be improved in fifth-generation mobile communication and future communication, and the wireless transmission rate is increased to ensure the service quality of a large number of users. To achieve this, many technologies such as massive MIMO (Multiple-input Multiple-output), millimeter wave communication, and ultra-dense networks have been proposed in recent years. However, although the above-mentioned technology can significantly improve the spectrum utilization efficiency of the wireless network, a large number of active antennas or rf units need to be installed, which inevitably causes problems of increased energy consumption and increased hardware cost. The irs (intelligent Reflecting surface) can be regarded as a planar structure composed of many low-energy-consumption Reflecting units, and can be flexibly arranged on the surface of a building, the indoor wall and other positions, and the signal transmission path is changed to assist communication through proper position arrangement and reflection phase setting, and considerable beam forming gain is obtained at the same time. The IRS technology as a new technology can meet the requirements of serving a large number of users, low energy consumption, high spectrum efficiency and high energy efficiency in 5G and future communication. However, the application of the IRS technology has difficulties, and one of the difficult problems is the design problem of the covariance matrix of the transmitted signal and the matrix of the reflecting surface.
Disclosure of Invention
The invention aims to provide an IRS (intelligent reflection surface) assisted large-scale MIMO (Multiple-input Multiple-output) wireless transmission method based on CSI (channel State information). under the condition that the sending power of a base station is limited, a system rate is maximized, and a sending signal covariance matrix and an IRS reflection phase shift matrix are optimally designed by utilizing statistical CSI on the basis of the principle that the IRS does not have energy consumption, so that the system rate is maximized, and the energy efficiency and the spectrum utilization rate are improved.
The purpose of the invention is realized as follows: an IRS-assisted large-scale MIMO wireless transmission method based on statistical CSI is provided, which aims at an IRS-assisted large-scale MIMO wireless communication system, wherein the system comprises an IRS with a multi-antenna base station, a multi-antenna user and a multi-reflection unit, and two communication paths from the base station to the user are respectively a direct connection path and a reflection path passing through the IRS; on the premise that the total transmission power of a base station is limited, a statistical CSI is utilized to design a covariance matrix of a transmission signal at the base station and a phase shift matrix of a reflecting surface at an IRS, and finally the maximization of the system rate is realized, and the specific steps are as follows:
aiming at an IRS (intelligent resilient station) auxiliary large-scale MIMO (multiple input multiple output) wireless communication system, a reflecting surface phase shift matrix is fixed, and a base station signal transmission covariance matrix is designed according to statistical CSI (channel state information);
and step two, designing a reflecting surface phase shift matrix according to the covariance matrix of the sending signals obtained in the step one.
As a further limitation of the present invention, in the step one, the specific steps of designing the covariance matrix of the transmitted signal are as follows:
1.1) the system is assumed to comprise an N-antenna base station, a K-antenna user and an IRS integrated with L low-power-consumption reflection units; the system comprises three channels, which are respectively: direct connection channel H from base station to user0Base station to IRS channel H1IRS to user channel H2(ii) a The three channel specific expressions can be uniformly expressed as:
Figure BDA0002385223350000021
wherein，TiRepresenting the transmit antenna correlation matrix, RiWhich represents the correlation matrix of the receiving antennas,
Figure BDA0002385223350000022
represents a deterministic Line of Sight (LOS) component of the channel, T0，T1，T2，R0，R1，R2，
Figure BDA0002385223350000023
The non-negative deterministic matrixes are respectively NxN, LxL, KxK, KxN, NxL and LxK and represent statistical CSI containing large-scale fading and Rice factors; x0，X1，X2The matrix is K multiplied by N, N multiplied by L and L multiplied by K, wherein elements are subjected to independent same distribution of zero mean and unit variance;
Figure BDA0002385223350000024
square root operations representing matrices; the transmission signal s is an N × 1 column vector, and the covariance matrix is Q ═ ssHtrQ NP indicates that the base station transmission power is limited; the reflecting surface matrix is
Figure BDA0002385223350000025
θlE [0,2 pi)) represents the adjustment angle of the IRS to the signal;
1.2) given initial reflecting surface matrix Θ ═ IL，ILAnd expressing an L multiplied by L unit diagonal matrix, designing a covariance matrix of a transmission signal:
Q*＝UFΛQUF H
wherein, UFIs a unitary matrix obtained by singular value decomposition of the matrix F,
Figure BDA0002385223350000031
ΛFfor diagonal matrices obtained by singular value decomposition of the matrix F, μ is such that Q*Satisfy trQ*Parameter no more than NP, INAnd expressing an N multiplied by N unit diagonal matrix, wherein the specific expression of F is as follows:
Figure BDA0002385223350000032
wherein phi2＝σ2(IK+e2R2)，
Figure BDA0002385223350000033
Figure BDA0002385223350000034
Figure BDA0002385223350000035
Is based on the equivalent channel parameters of the system statistical CSI;
1.3) let Q ═ Q*Updating equivalent channel parameters
Figure BDA0002385223350000036
1.4) repeating steps 1.2), 1.3) until the system rate
Figure BDA0002385223350000037
Converging to obtain the optimal transmit covariance matrix Qopt＝Q*。
As a further limitation of the present invention, in step two, the transmit signal covariance matrix is given as Q obtained in step oneoptDesigning the IRS phase shift matrix comprises the following steps:
2.1) calculating the gradient direction vector of the system velocity versus IRS reflection phase shift matrix
Figure BDA0002385223350000038
Figure BDA0002385223350000039
Representing system rate
Figure BDA00023852233500000310
To thetalCalculating a deviation derivative;
2.2) taking the step length delta to be 0.1;
2.3)Θ*biag (j × (θ + Δ · p)), where θ ═ θ { (θ + Δ · p) }1，θ2，…，θL]TIs a column vector consisting of the diagonal elements of the matrix Θ;
2.4) let Θ be Θ*Updating channel equivalent parameters
Figure BDA00023852233500000311
2.5) repeat steps 2.1) -2.4) until the rate
Figure BDA00023852233500000312
Converging to obtain an optimal reflecting surface matrix thetaopt＝Θ*。
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
(1) the method considers the joint design of the covariance matrix of the transmitted signals and the IRS reflection phase shift matrix, obtains the optimal solution of the two matrixes through iterative optimization, enables the signals to be transmitted along the optimal characteristic direction, and improves the system speed to the maximum extent;
(2) the design scheme of the covariance matrix of the transmitted signals and the IRS reflection phase shift matrix provided by the method only utilizes statistical CSI; the statistical CSI is easier to obtain than the instantaneous CSI, so that the system overhead can be effectively reduced, the method has strong practical feasibility and can be applied to actual communication scenes.
Drawings
Fig. 1 is a diagram of an IRS-assisted massive MIMO wireless transmission system.
Fig. 2 is a flowchart of an IRS-assisted massive MIMO wireless transmission method based on statistical CSI.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the attached drawings:
as shown in fig. 1, the IRS-assisted massive MIMO wireless transmission system includes an IRS with a multi-antenna base station, a multi-antenna user, and a multi-reflection unit, where two communication paths from the base station to the user are a direct connection path and a reflection path passing through the IRS.
Fig. 2 is a flowchart of an IRS-assisted massive MIMO wireless transmission method based on statistical CSI, the method comprising the following steps:
step 201: the system is supposed to comprise an N-antenna base station, a K-antenna user and an IRS integrated with L low-power consumption reflecting units; the system comprises three channels, which are respectively: direct connection channel H from base station to user0Base station to IRS channel H1IRS to user channel H2(ii) a The three channel specific expressions can be uniformly expressed as:
Figure BDA0002385223350000041
wherein, TiRepresenting the transmit antenna correlation matrix, RiWhich represents the correlation matrix of the receiving antennas,
Figure BDA0002385223350000042
represents a deterministic Line of Sight (LOS) component of the channel, T0，T1，T2，R0，R1，R2，
Figure BDA0002385223350000043
The non-negative deterministic matrixes are respectively NxN, LxL, KxK, KxN, NxL and LxK and represent statistical CSI containing large-scale fading and Rice factors; x0，X1，X2The matrix is K multiplied by N, N multiplied by L and L multiplied by K, wherein elements are represented by independent and same distribution of zero mean value and unit variance;
Figure BDA0002385223350000044
square root operations representing matrices; the transmission signal s is an N × 1 column vector, and the covariance matrix is Q ═ ssHtrQ NP indicates that the base station transmission power is limited; the reflecting surface matrix is
Figure BDA0002385223350000051
θlE [0,2 pi)) represents the adjustment angle of the intelligent reflection surface to the signal; given initial reflecting surfaceMatrix Θ ═ IL，ILRepresenting an L x L unit diagonal matrix.
Step 202: designing a covariance matrix of a transmission signal:
Q*＝UFΛQUF H
wherein, UFIs a unitary matrix obtained by singular value decomposition of the matrix F,
Figure BDA0002385223350000052
ΛFfor diagonal matrices obtained by singular value decomposition of the matrix F, μ is such that Q*Satisfy trQ*Parameter no more than NP, INAnd expressing an N multiplied by N unit diagonal matrix, wherein the specific expression of F is as follows:
Figure BDA0002385223350000053
wherein phi2＝σ2(IK+e2R2)，
Figure BDA0002385223350000054
Figure BDA0002385223350000055
Figure BDA0002385223350000056
Is based on the equivalent channel parameters of the system statistical CSI; let Q become Q*Updating equivalent channel parameters
Figure BDA0002385223350000057
Repeatedly updating equivalent channel parameters
Figure BDA0002385223350000058
And Q*Up to the system rate
Figure BDA0002385223350000059
Converging to obtain the optimal transmit covariance matrix Qopt＝Q*。
Step 203: transmit signal covariance matrix of QoptDesigning an IRS phase shift matrix, and calculating gradient direction vectors of a system velocity pair phase matrix, namely a system velocity pair partial derivative; step length delta is taken to be 0.1, theta*Biag (j × (θ + Δ · p)), where θ ═ θ { (θ + Δ · p) }1，θ2，...，θL]TIs a column vector composed of diagonal elements of matrix theta, and let theta be theta*Updating channel equivalent parameters
Figure BDA00023852233500000510
Repeatedly updating channel equivalent parameters
Figure BDA00023852233500000511
And theta*Up to the rate
Figure BDA00023852233500000513
Converging to obtain the optimal IRS reflection phase shift matrix thetaopt＝Θ*；
Step 204: step 202 and 203 are repeated until the system rate
Figure BDA00023852233500000512
And converging to obtain the optimally designed transmit signal covariance matrix and IRS reflection phase shift matrix.
In summary, the method considers the joint design of the covariance matrix of the transmitted signals and the IRS reflection phase shift matrix, and obtains the optimal values of the two matrices through an iteration method, so that the signals are transmitted along the optimal statistical characteristic mode, and the system speed is ensured to be improved to the greatest extent. In addition, only the statistical CSI is utilized in the design process of the method, and compared with the instantaneous CSI, the statistical CSI is easy to obtain, and the system overhead can be effectively reduced.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can understand that the modifications or substitutions within the technical scope of the present invention are included in the scope of the present invention, and therefore, the scope of the present invention should be subject to the protection scope of the claims.

Claims (2)

1. An IRS-assisted large-scale MIMO wireless transmission method based on statistical CSI is characterized in that the method aims at an IRS-assisted large-scale MIMO wireless communication system, the system comprises a multi-antenna base station, a multi-antenna user and an IRS of a multi-reflection unit, and two communication paths from the base station to the user are respectively a direct connection path and a reflection path passing through the IRS; on the premise that the total transmission power of a base station is limited, a statistical CSI is utilized to design a covariance matrix of a transmission signal at the base station and a phase shift matrix of a reflecting surface at an IRS, and finally the maximization of the system rate is realized, and the specific steps are as follows:
the method comprises the following steps that firstly, a reflecting surface phase shift matrix is fixed and unchanged for an IRS auxiliary large-scale MIMO wireless communication system, and a base station sending signal covariance matrix is designed according to statistical CSI, and the method comprises the following steps:
1.1) the system is assumed to comprise an N-antenna base station, a K-antenna user and an IRS integrated with L low-power-consumption reflection units; the system comprises three channels, which are respectively: direct connection channel H from base station to user0Base station to IRS channel H1IRS to user channel H2(ii) a The three channel specific expressions can be uniformly expressed as:
Figure FDA0003403748570000011
wherein, TiRepresenting the transmit antenna correlation matrix, RiWhich represents the correlation matrix of the receiving antennas,
Figure FDA0003403748570000012
a deterministic line-of-sight component, T, representing the channel0，T1，T2，R0，R1，R2，
Figure FDA0003403748570000013
Respectively are NxN, LxL, KxK, KxN, NxL,an LxK deterministic non-negative matrix representing statistical CSI including large-scale fading and Rice factors; x0，X1，X2The matrix is K multiplied by N, N multiplied by L and L multiplied by K, wherein elements are subjected to independent same distribution of zero mean and unit variance;
Figure FDA0003403748570000014
square root operations representing matrices; the transmission signal s is an N × 1 column vector, and the covariance matrix is Q ═ ssHtrQ NP indicates that the base station transmission power is limited; the reflecting surface matrix is
Figure FDA0003403748570000015
θlE [0,2 pi)) represents the adjustment angle of the IRS to the signal;
1.2) given initial reflecting surface matrix Θ ═ IL，ILAnd expressing an L multiplied by L unit diagonal matrix, designing a covariance matrix of a transmission signal:
Q*＝UFΛQUF H
wherein, UFIs a unitary matrix obtained by singular value decomposition of the matrix F,
Figure FDA0003403748570000016
ΛFfor diagonal matrices obtained by singular value decomposition of the matrix F, μ is such that Q*Satisfy trQ*Parameter no more than NP, INAnd expressing an N multiplied by N unit diagonal matrix, wherein the specific expression of F is as follows:
Figure FDA0003403748570000021
wherein phi2＝σ2(IK+e2R2)，
Figure FDA0003403748570000022
Figure FDA0003403748570000023
Is based on the equivalent channel parameters of the system statistical CSI;
1.3) let Q ═ Q*Updating equivalent channel parameters
Figure FDA0003403748570000024
1.4) repeating steps 1.2), 1.3) until the system rate
Figure FDA0003403748570000025
Converging to obtain the optimal transmit covariance matrix Qopt＝Q*；
And step two, designing a reflecting surface phase shift matrix according to the covariance matrix of the sending signals obtained in the step one.
2. The method of claim 1, wherein in step two, the covariance matrix of the transmitted signal is given as the Q obtained in step oneoptDesigning the IRS phase shift matrix comprises the following steps:
2.1) calculating the gradient direction vector of the system velocity versus IRS reflection phase shift matrix
Figure FDA0003403748570000026
Representing system rate
Figure FDA0003403748570000027
To thetalCalculating a deviation derivative;
2.2) taking the step length delta to be 0.1;
2.3)Θ*biag (j × (θ + Δ · p)), where θ ═ θ { (θ + Δ · p) }1，θ2，...，θL]TIs a column vector consisting of the diagonal elements of the matrix Θ;
2.4) let Θ be Θ*Updating channel equivalent parameters
Figure FDA0003403748570000028
2.5)Repeat steps 2.1) -2.4) until the rate
Figure FDA0003403748570000029
Converging to obtain an optimal reflecting surface matrix thetaopt＝Θ*。
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