CN113078932B - Intelligent reflection surface assisted downlink transmission precoding design method - Google Patents

Intelligent reflection surface assisted downlink transmission precoding design method Download PDF

Info

Publication number
CN113078932B
CN113078932B CN202110335359.4A CN202110335359A CN113078932B CN 113078932 B CN113078932 B CN 113078932B CN 202110335359 A CN202110335359 A CN 202110335359A CN 113078932 B CN113078932 B CN 113078932B
Authority
CN
China
Prior art keywords
base station
intelligent
cell
reflecting surface
reflection surface
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202110335359.4A
Other languages
Chinese (zh)
Other versions
CN113078932A (en
Inventor
李潇
罗才洪
金石
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Southeast University
Original Assignee
Southeast University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Southeast University filed Critical Southeast University
Priority to CN202110335359.4A priority Critical patent/CN113078932B/en
Publication of CN113078932A publication Critical patent/CN113078932A/en
Application granted granted Critical
Publication of CN113078932B publication Critical patent/CN113078932B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • 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
    • 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
    • H04B7/14—Relay systems
    • H04B7/145—Passive relay systems

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention discloses a downlink transmission precoding design method assisted by an intelligent reflection surface, wherein a system is provided with K cells and an intelligent reflection surface in total, each cell is provided with only one single-antenna user, the user cannot be covered by a base station signal, and the user needs to carry out signal transmission on the user through the intelligent reflection surface; the method comprises the following steps: each cell base station k designs a precoding vector thereof by using known statistical channel information between the cell base station k and the intelligent reflecting surface; each cell base station designs a precoding vector according to respective direct-view path information; the intelligent reflecting surface utilizes the known statistical channel information between the intelligent reflecting surface and each cell user and the statistical channel information between each cell base station and the intelligent reflecting surface to design a reflecting coefficient matrix of the intelligent reflecting surface. The invention can effectively reduce the interference among users, each base station can independently and parallelly design the precoding matrix, and can obtain higher system throughput with lower computation complexity and channel information interaction amount, thus being easy to realize.

Description

Intelligent reflection surface assisted downlink transmission precoding design method
Technical Field
The invention relates to a downlink transmission precoding design method assisted by an Intelligent Reflection Surface (IRS) based on Channel State Information (CSI), belonging to the technical field of wireless communication.
Background
In order to achieve higher spectral and energy efficiency in communication systems, the frequencies used by the systems are increasing and the coverage radius of the cellular network cells is decreasing. In order to reduce the communication blind area, more base stations need to be deployed to enhance the cell coverage. Distributed multiple-input multiple-output (MIMO) technology can improve the coverage of a wireless communication system by deploying a large number of antennas, but the distributed MIMO system has a complex structure and requires high hardware cost and energy consumption. Recently, the IRS, a low-cost passive reflecting plate composed of a large number of passive reflecting units capable of independently adjusting the reflection phase, has attracted a great deal of attention as a new technology being proposed. IRS enables higher spectral efficiency with lower complexity and energy consumption.
When the instantaneous CSI is known, a precoding matrix of the base station and a reflection coefficient matrix of the IRS can be jointly designed by adopting an alternating optimization algorithm. However, the IRS has a large number of reflection units, so that accurate channel estimation is very difficult, and the dimensionality of the channel matrix is large, and it is difficult to complete a series of operations such as instantaneous CSI estimation, CSI feedback, precoding matrix and IRS reflection coefficient matrix design and data transmission within a coherence time. And when the mobility of the user is low, the direct path component change of the CSI estimated each time is small, channel estimation is not needed before data transmission each time, and the characteristic of counting the CSI can be described only by a few bit numbers, so that the difficulty of channel estimation can be reduced and the feedback bit number of the CSI can be reduced by designing a base station precoding matrix and an IRS reflection coefficient matrix based on the counting CSI. Meanwhile, when the statistical CSI is adopted to design a precoding matrix and an IRS reflection coefficient matrix, the requirement on the time complexity of the algorithm is weaker than that of the algorithm adopting the instantaneous CSI.
In addition, the precoding design method does not need alternate optimization of precoding matrixes at the base stations and the IRS, the optimal precoding vector of each base station can be independently designed, and mutual information is not needed among the base stations. In summary, for an IRS-assisted multi-cell communication system, a precoding matrix of a base station and a reflection coefficient matrix of an IRS are designed based on statistical CSI to make appropriate selection.
Disclosure of Invention
The invention aims to provide an intelligent reflection surface assisted downlink transmission precoding design method, which can design a precoding matrix of a base station and a reflection coefficient matrix of an IRS (interference rejection ratio) according to statistical CSI (channel state information), and each base station can design respective precoding vectors in parallel without alternative optimization, and the IRS reflection coefficient matrix is designed based on flow pattern optimization.
In order to achieve the purpose, the invention adopts the technical scheme that:
an intelligent reflection surface assisted downlink transmission precoding design method is provided, and the method is directed at the following systems: the system is provided with K cells in total and an intelligent reflection surface, each cell is provided with only one single-antenna user, the user cannot be covered by a base station signal, and the signal transmission is carried out on the user through the intelligent reflection surface; each cell base station adopts a uniform linear antenna array comprising M antenna array elements, the intelligent reflecting surface adopts a uniform plane array comprising N reflecting units, the vertical direction of the intelligent reflecting surface comprises T rows of reflecting units, and each row of P reflecting units in the horizontal direction; each cell base station only knows the statistical channel information between the cell base station and the intelligent reflection surface, and the intelligent reflection surface only knows the statistical channel information between the cell base station and each cell user and between each cell base station and each cell user;
the method comprises the following steps:
step one, each cell base station k utilizes the known statistical channel information between the cell base station k and the intelligent reflecting surface to design the precoding vector w of the cell base station kkWherein K is 1, …, K;
step two, each cell base station designs a precoding vector according to the direct-view path information of each cell base station; the intelligent reflecting surface utilizes the known statistical channel information between the intelligent reflecting surface and each cell user and the statistical channel information between each cell base station and the intelligent reflecting surface to design a reflection coefficient matrix of the intelligent reflecting surface
Figure GDA0003079429740000021
In the first step, the statistical channel information between each cell base station k and the intelligent reflection surface includes: leise factor alpha of the channel between base station k and intelligent reflecting surfacekLarge scale fading rho of the channel between base station k and the intelligent reflecting surfacekDirect path component of the channel between base station k and the intelligent reflecting surface
Figure GDA0003079429740000022
Wherein the content of the first and second substances,
Figure GDA0003079429740000023
Figure GDA0003079429740000024
represents the arrival angle theta of the wave in the direction perpendicular to the direct path between the base station k and the intelligent reflection surfacekThe arrival angle of a wave in the horizontal direction of a direct path between a base station k and the intelligent reflection surface is represented, d is the distance between adjacent reflection units on the intelligent reflection surface, lambda is the carrier wave length, N is the number of the reflection units on the intelligent reflection surface, the vertical direction of the intelligent reflection surface comprises T rows of reflection units, each row of P reflection units in the horizontal direction is provided, e is a natural constant, j is an imaginary part unit, pi is the circumferential ratio, and the superscript (·)HRepresenting conjugate transpose, symbol
Figure GDA0003079429740000025
Represents the product of the kronecker reaction,
Figure GDA0003079429740000026
m denotes the number of transmit antennas per base station,
Figure GDA0003079429740000027
horizontal wave departure angle, d, representing the direct path between base station k and the intelligent reflecting surfacekThe distance between adjacent antenna units on an antenna array of a base station K, K is 1, …, K;
in the first step, a base station k precoding vector is calculated by the following formula:
Figure GDA0003079429740000028
in the second step, the channel state information statistics between the intelligent reflection surface and each cell user includes: direct path component of channel between intelligent reflecting surface and k cell user
Figure GDA0003079429740000031
Leis factor betakAnd large scale fading psikWherein K is 1, …, K; and the statistical channel information between each cell base station and the intelligent reflecting surface is the same as the statistical channel information between each cell base station and the intelligent reflecting surface in the step one.
In the second step, a reflection coefficient matrix is obtained through the following steps:
step b1, setting convergence threshold xi, initializing loop iteration number r as 0, and setting vector
Figure GDA0003079429740000032
Is an initial value of(0)＝1N×1In which 1 isN×1An N × 1-dimensional column vector representing all elements as 1;
step b2, calculating function
Figure GDA0003079429740000033
Gradient of (2)
Figure GDA0003079429740000034
Figure GDA0003079429740000035
The calculation formula of (A) is as follows:
Figure GDA0003079429740000036
wherein the content of the first and second substances,
Figure GDA0003079429740000037
Figure GDA0003079429740000038
Figure GDA0003079429740000039
Figure GDA00030794297400000310
wherein p iskRepresenting the transmission power of the kth base station, gammakIs the priority weight of the kth user,
Figure GDA00030794297400000311
the power of additive white gaussian noise received for the kth user, K ═ 1, …, K;
step b3, calculating the function f (η) by(r)) Riemann gradient of
Figure GDA00030794297400000312
Figure GDA00030794297400000313
Wherein symbol |, indicates the Hadamard product, superscript (.)*Representing the conjugate of a complex number, and Re {. is the real part of the complex number;
step b4, calculating
Figure GDA0003079429740000041
Where τ is the step size determined by the Armijo criterion;
step b5, calculating
Figure GDA0003079429740000042
Wherein unit () represents a normalization operation on each element in the vector;
step b6, determine whether the following equation holds:
Figure GDA0003079429740000043
wherein the content of the first and second substances,
Figure GDA0003079429740000044
if not, making r ═ r +1 and returning to step b2, otherwise, ending calculation and outputting wkAs a precoding vector of the base station, wherein the output Φ ═ diag { η [ ](r+1)As a reflection coefficient matrix of the intelligent reflective surface, wherein diag {. cndot } denotes a diagonal matrix with vector in brackets as diagonal element.
Has the advantages that: the invention relates to a precoding design method of an intelligent reflection surface assisted multi-cell downlink transmission system, which has the following advantages compared with the prior art:
(1) the invention is suitable for an IRS auxiliary multi-cell downlink communication system, and the obtained precoding and reflection phase design can achieve higher system and rate;
(2) the algorithm related by the invention has the advantages that each base station independently designs the precoding matrix, and compared with the existing algorithm, the algorithm has lower calculation complexity and is easy to realize;
(3) the invention designs an algorithm, only needs partial statistics of channel information, and reduces the acquisition overhead of the channel information.
Detailed Description
The technical solutions provided by the present invention will be described in detail below with reference to specific examples, and it should be understood that the following specific embodiments are only illustrative of the present invention and are not intended to limit the scope of the present invention.
The invention discloses an intelligent reflecting surface assisted downlink transmission precoding design method, which aims at the following systems: the system is provided with K cells in total and an intelligent reflection surface, each cell is provided with only one single-antenna user, the user cannot be covered by a base station signal, and the signal transmission is carried out on the user through the intelligent reflection surface; each cell base station adopts a uniform linear antenna array comprising M antenna array elements, the intelligent reflecting surface adopts a uniform plane array comprising N reflecting units, the vertical direction of the intelligent reflecting surface comprises T rows of reflecting units, and each row of P reflecting units in the horizontal direction; each cell base station only knows the statistical channel information between the cell base station and the intelligent reflection surface, and the intelligent reflection surface only knows the statistical channel information between the cell base station and each cell user and between each cell base station and each cell user;
the method comprises the following steps:
step one, each cell base station k utilizes the known statistical channel information between the cell base station k and the intelligent reflecting surface to design the precoding vector w of the cell base station kkWherein K is 1, …, K;
wherein, the statistical channel information between each cell base station k and the intelligent reflection surface comprises: leise factor alpha of the channel between base station k and intelligent reflecting surfacekLarge scale fading rho of the channel between base station k and the intelligent reflecting surfacekDirect path component of the channel between base station k and the intelligent reflecting surface
Figure GDA0003079429740000051
Wherein the content of the first and second substances,
Figure GDA0003079429740000052
Figure GDA0003079429740000053
represents the arrival angle theta of the wave in the direction perpendicular to the direct path between the base station k and the intelligent reflection surfacekThe arrival angle of a wave in the horizontal direction of a direct path between a base station k and the intelligent reflection surface is represented, d is the distance between adjacent reflection units on the intelligent reflection surface, lambda is the carrier wave length, N is the number of the reflection units on the intelligent reflection surface, the vertical direction of the intelligent reflection surface comprises T rows of reflection units, each row of P reflection units in the horizontal direction is provided, e is a natural constant, j is an imaginary part unit, pi is the circumferential ratio, and the superscript (·)HRepresenting conjugate transpose, symbol
Figure GDA0003079429740000054
Represents the product of the kronecker reaction,
Figure GDA0003079429740000055
m denotes the number of transmit antennas per base station,
Figure GDA0003079429740000056
horizontal wave departure angle, d, representing the direct path between base station k and the intelligent reflecting surfacekThe distance between adjacent antenna units on an antenna array of a base station K, K is 1, …, K;
the base station k precoding vector is calculated by:
Figure GDA0003079429740000057
step two, each cell base station designs a precoding vector according to the direct-view path information of each cell base station; the intelligent reflecting surface utilizes the known statistical channel information between the intelligent reflecting surface and each cell user and the statistical channel information between each cell base station and the intelligent reflecting surface to design a reflection coefficient matrix of the intelligent reflecting surface
Figure GDA0003079429740000058
Wherein, the statistical channel state information between the intelligent reflecting surface and each cell user comprises: intelligent reflecting surface and kth cell user communicationDirect path component of the track
Figure GDA0003079429740000059
Leis factor betakAnd large scale fading psikWherein K is 1, …, K; the statistical channel information between each cell base station and the intelligent reflection surface is the same as the statistical channel information between each cell base station and the intelligent reflection surface in the step one;
the reflection coefficient matrix is designed and obtained through the following steps:
step b1, setting convergence threshold xi, initializing loop iteration number r as 0, and setting vector
Figure GDA0003079429740000061
Is an initial value of(0)＝1N×1In which 1 isN×1An N × 1-dimensional column vector representing all elements as 1;
step b2, calculating function
Figure GDA0003079429740000062
Gradient of (2)
Figure GDA0003079429740000063
Figure GDA0003079429740000064
The calculation formula of (A) is as follows:
Figure GDA0003079429740000065
wherein the content of the first and second substances,
Figure GDA0003079429740000066
Figure GDA0003079429740000067
Figure GDA0003079429740000068
Figure GDA0003079429740000069
wherein p iskRepresenting the transmission power of the kth base station, gammakIs the priority weight of the kth user,
Figure GDA00030794297400000610
the power of additive white gaussian noise received for the kth user, K ═ 1, …, K;
step b3, calculating the function f (η) by(r)) Riemann gradient of
Figure GDA00030794297400000611
Figure GDA00030794297400000612
Wherein symbol |, indicates the Hadamard product, superscript (.)*Representing the conjugate of a complex number, and Re {. is the real part of the complex number;
step b4, calculating
Figure GDA00030794297400000613
Where τ is the step size determined by the Armijo criterion;
step b5, calculating
Figure GDA0003079429740000071
Wherein unit () represents a normalization operation on each element in the vector;
step b6, determine whether the following equation holds:
Figure GDA0003079429740000072
wherein the content of the first and second substances,
Figure GDA0003079429740000073
if not, making r ═ r +1 and returning to step b2, otherwise, ending calculation and outputting wkAs a precoding vector of the base station, wherein the output Φ ═ diag { η [ ](r+1)As a reflection coefficient matrix of the intelligent reflective surface, wherein diag {. cndot } denotes a diagonal matrix with vector in brackets as diagonal element.
In conclusion, the invention can effectively reduce the interference among users, each base station can independently and parallelly design the precoding matrix, and can obtain higher system throughput with lower computation complexity and channel information interaction amount, thus being easy to realize.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.

Claims (1)

1. An intelligent reflection surface assisted downlink transmission precoding design method is characterized in that: the method is directed to the following system: the system is provided with K cells in total and an intelligent reflection surface, each cell is provided with only one single-antenna user, the user cannot be covered by a base station signal, and the signal transmission is carried out on the user through the intelligent reflection surface; each cell base station adopts a uniform linear antenna array comprising M antenna array elements, the intelligent reflecting surface adopts a uniform plane array comprising N reflecting units, the vertical direction of the intelligent reflecting surface comprises T rows of reflecting units, and each row of P reflecting units in the horizontal direction; each cell base station only knows the statistical channel information between the cell base station and the intelligent reflection surface, and the intelligent reflection surface only knows the statistical channel information between the cell base station and each cell user and between each cell base station and each cell user;
the method comprises the following steps:
step one, each cell base station k designs the pre-programming by using the known statistical channel information between the cell base station k and the intelligent reflecting surfaceCode vector wkWherein K is 1, …, K;
wherein, the statistical channel information between each cell base station k and the intelligent reflection surface comprises: leise factor alpha of the channel between base station k and intelligent reflecting surfacekLarge scale fading rho of the channel between base station k and the intelligent reflecting surfacekDirect path component of the channel between base station k and the intelligent reflecting surface
Figure FDA0003504700380000011
Wherein the content of the first and second substances,
Figure FDA0003504700380000012
Figure FDA0003504700380000013
represents the arrival angle theta of the wave in the direction perpendicular to the direct path between the base station k and the intelligent reflection surfacekThe arrival angle of a wave in the horizontal direction of a direct path between a base station k and the intelligent reflection surface is represented, d is the distance between adjacent reflection units on the intelligent reflection surface, lambda is the carrier wave length, N is the number of the reflection units on the intelligent reflection surface, the vertical direction of the intelligent reflection surface comprises T rows of reflection units, each row of P reflection units in the horizontal direction is provided, e is a natural constant, j is an imaginary part unit, pi is the circumferential ratio, and the superscript (·)HRepresenting conjugate transpose, symbol
Figure FDA0003504700380000014
Represents the product of the kronecker reaction,
Figure FDA0003504700380000015
m indicates the number of antenna elements employed by each base station,
Figure FDA0003504700380000016
horizontal wave departure angle, d, representing the direct path between base station k and the intelligent reflecting surfacekThe distance between adjacent antenna units on an antenna array of a base station K, K is 1, …, K;
the base station k precoding vector is calculated by:
Figure FDA0003504700380000017
secondly, the intelligent reflecting surface designs a reflecting coefficient matrix of the intelligent reflecting surface by utilizing the known statistical channel information between the intelligent reflecting surface and each cell user and the statistical channel information between each cell base station and the intelligent reflecting surface
Figure FDA0003504700380000018
Wherein, the statistical channel state information between the intelligent reflecting surface and each cell user comprises: direct path component of channel between intelligent reflecting surface and k cell user
Figure FDA0003504700380000021
Leis factor betakAnd large scale fading psikWherein K is 1, …, K; the statistical channel information between each cell base station and the intelligent reflection surface is the same as the statistical channel information between each cell base station and the intelligent reflection surface in the step one;
the reflection coefficient matrix is designed and obtained through the following steps:
step b1, setting convergence threshold xi, initializing loop iteration number r as 0, and setting vector
Figure FDA0003504700380000022
Is an initial value of(0)＝1N×1In which 1 isN×1An N × 1-dimensional column vector representing all elements as 1;
step b2, calculating function
Figure FDA0003504700380000023
Gradient of (2)
Figure FDA0003504700380000024
Figure FDA0003504700380000025
The calculation formula of (A) is as follows:
Figure FDA0003504700380000026
wherein the content of the first and second substances,
Figure FDA0003504700380000027
Figure FDA0003504700380000028
Figure FDA0003504700380000029
Figure FDA00035047003800000210
wherein p iskRepresenting the transmission power of the kth base station, gammakIs the priority weight of the kth user,
Figure FDA00035047003800000211
the power of additive white gaussian noise received for the kth user, K ═ 1, …, K;
step b3, calculating the function f (η) by(r)) Riemann gradient of
Figure FDA00035047003800000212
Figure FDA00035047003800000213
Wherein symbol |, indicates the Hadamard product, superscript (.)*Representing the conjugate of a complex number, and Re {. is the real part of the complex number;
step b4, calculating
Figure FDA0003504700380000031
Where τ is the step size determined by the Armijo criterion;
step b5, calculating
Figure FDA0003504700380000032
Wherein unit () represents a normalization operation on each element in the vector;
step b6, determine whether the following equation holds:
Figure FDA0003504700380000033
wherein the content of the first and second substances,
Figure FDA0003504700380000034
if not, making r ═ r +1 and returning to step b2, otherwise, ending calculation and outputting wkAs a precoding vector of the base station, wherein the output Φ ═ diag { η [ ](r+1)As a reflection coefficient matrix of the intelligent reflective surface, wherein diag {. cndot } denotes a diagonal matrix with vector in brackets as diagonal element.
CN202110335359.4A 2021-03-29 2021-03-29 Intelligent reflection surface assisted downlink transmission precoding design method Active CN113078932B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110335359.4A CN113078932B (en) 2021-03-29 2021-03-29 Intelligent reflection surface assisted downlink transmission precoding design method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110335359.4A CN113078932B (en) 2021-03-29 2021-03-29 Intelligent reflection surface assisted downlink transmission precoding design method

Publications (2)

Publication Number Publication Date
CN113078932A CN113078932A (en) 2021-07-06
CN113078932B true CN113078932B (en) 2022-04-01

Family

ID=76611378

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110335359.4A Active CN113078932B (en) 2021-03-29 2021-03-29 Intelligent reflection surface assisted downlink transmission precoding design method

Country Status (1)

Country Link
CN (1) CN113078932B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117882315A (en) * 2021-09-10 2024-04-12 联想(北京)有限公司 RIS capability reporting
WO2023092602A1 (en) * 2021-11-29 2023-06-01 北京小米移动软件有限公司 Precoding method and apparatus, user equipment, ris array, base station and storage medium
CN116830471A (en) * 2022-01-07 2023-09-29 北京小米移动软件有限公司 Precoding method, device and equipment and storage medium

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110855338A (en) * 2019-10-28 2020-02-28 东南大学 FD-MIMO downlink self-adaptive transmission method based on two-layer precoding

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102983935B (en) * 2011-09-07 2017-10-27 株式会社Ntt都科摩 Precoding, pre-coding/pre-decoding method and emitter and mobile terminal based on interference alignment
CN111246491B (en) * 2020-03-10 2021-06-08 电子科技大学 Intelligent reflection surface assisted terahertz communication system design method
CN111818533B (en) * 2020-06-04 2021-08-17 浙江大学 Wireless communication system design method based on intelligent reflecting surface
CN111953391B (en) * 2020-07-09 2021-06-01 东南大学 Intelligent reflector assisted multi-user MIMO uplink energy efficiency and spectrum efficiency combined optimization method
CN112564758B (en) * 2020-11-25 2022-07-05 东南大学 Broadband wireless transmission method assisted by distributed intelligent reflecting surface

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110855338A (en) * 2019-10-28 2020-02-28 东南大学 FD-MIMO downlink self-adaptive transmission method based on two-layer precoding

Also Published As

Publication number Publication date
CN113078932A (en) 2021-07-06

Similar Documents

Publication Publication Date Title
CN113078932B (en) Intelligent reflection surface assisted downlink transmission precoding design method
CN1695271B (en) Antenna array including virtual antenna elements
CN100593288C (en) Wireless communication system, wireless communication device, wireless communication method and computer program
CN105577587B (en) Mimo system channel estimation methods and device under a kind of 60GHz indoor scenes
CN113163325B (en) Wireless communication method and system assisted by multiple intelligent reflecting surfaces
CN110430150B (en) Receiver design method of cellular mobile communication system based on neural network
CN109714091B (en) Iterative hybrid precoding method based on hierarchical design in millimeter wave MIMO system
CN104600873A (en) Large-scale antenna array oriented wireless energy information transmission network power control method
CN101557367A (en) Method for precoding multi-point limited cooperative multiple-input-multiple-output communication system
CN107086886A (en) The double-deck Precoding Design of extensive mimo system fusion ZF and Taylor series expansion
Wu Research on massive MIMO key technology in 5G
Qiu et al. Downlink performance analysis of cell-free massive MIMO over spatially correlated Rayleigh channels
CN102223170A (en) Sequential multi-antenna selectable transmitting method for code division multi-path channel
CN104868945A (en) Multi-user downlink three-dimensional statistics beam forming adaptive transmission method using channel mean information
CN101483467A (en) Method for MIMO multiple access channel throughput maximization
Bromberg et al. The LEGO approach for achieving max-min capacity in reciprocal multipoint networks
CN113098575B (en) Intelligent reflection surface assisted multi-cell downlink transmission design method for improving edge rate
CN110445519B (en) Method and device for resisting inter-group interference based on signal-to-interference-and-noise ratio constraint
CN104821840A (en) Anti-interference method of massive multiple input multiple output downlink system
CN109347528B (en) 3D-MIMO downlink multi-user scheduling and self-adaptive transmission method
Liu et al. A distributed detection algorithm for uplink massive MIMO systems
Amin et al. Quantized power allocation algorithms in cell-free massive MIMO systems
Zhao et al. Learning beamforming for RIS-aided systems with permutation equivariant graph neural networks
Shi et al. Deep learning based robust precoder design for massive MIMO downlink
CN110855338A (en) FD-MIMO downlink self-adaptive transmission method based on two-layer precoding

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant