CN116582208B - RIS-assisted space-dependent de-honeycomb large-scale MIMO system optimization method - Google Patents

RIS-assisted space-dependent de-honeycomb large-scale MIMO system optimization method Download PDF

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CN116582208B
CN116582208B CN202310851042.5A CN202310851042A CN116582208B CN 116582208 B CN116582208 B CN 116582208B CN 202310851042 A CN202310851042 A CN 202310851042A CN 116582208 B CN116582208 B CN 116582208B
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access point
ris
reconfigurable
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user
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CN116582208A (en
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杨龙祥
屠鑫
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Nanjing University of Posts and Telecommunications
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Nanjing University of Posts and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0426—Power distribution
    • H04B7/0434—Power distribution using multiple eigenmodes
    • H04B7/0443—Power distribution using multiple eigenmodes utilizing "waterfilling" technique
    • 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
    • H04B7/0617—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 for beam forming
    • 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
    • H04B7/0632—Channel quality parameters, e.g. channel quality indicator [CQI]
    • 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
    • H04B7/0837—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using pre-detection combining
    • H04B7/0842—Weighted combining
    • H04B7/086—Weighted combining using weights depending on external parameters, e.g. direction of arrival [DOA], predetermined weights or beamforming
    • 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

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

Abstract

The application relates to a RIS-assisted spatial correlation de-honeycomb large-scale MIMO system optimization method. The method comprises the following steps: establishing a channel model of the RIS-assisted spatial correlation de-cellular massive MIMO system, performing channel estimation to obtain estimated channel information, deducing an expression of a downlink reachable rate of the RIS-assisted spatial correlation de-cellular massive MIMO system, establishing a joint optimization problem and a constraint condition of an access point active beam and a reconfigurable intelligent surface passive beam, converting the joint optimization problem into an access point transmitting power optimization problem and a reconfigurable intelligent surface phase shift optimization problem, solving the access point transmitting power optimization problem and the reconfigurable intelligent surface phase shift optimization problem, obtaining an optimization result, and adjusting the transmitting power of the access point and the phase shift of the reconfigurable intelligent surface according to the optimization result so as to realize that the RIS-assisted spatial correlation de-cellular massive MIMO system maximizes the sum rate of users and improves the performance of the de-cellular massive MIMO system.

Description

RIS-assisted space-dependent de-honeycomb large-scale MIMO system optimization method
Technical Field
The application relates to the technical field of communication, in particular to a RIS-assisted space-related de-honeycomb large-scale MIMO system optimization method.
Background
In recent years, due to the dramatic increase in users and applications, the demand for data throughput has grown exponentially. To meet this demand, de-cellular massive MIMO has received extensive attention as one of the key technologies underlying 6G. As one implementation of network MIMO, de-cellular massive MIMO deploys hundreds or even thousands of randomly distributed Access Points (APs) within a wide area, with a large number of APs serving a small number of users in the network at the same time in the same frequency resource. Furthermore, by providing multiple antennas at the AP, de-cellular massive MIMO inherits the important advantage of massive MIMO channel hardening.
Smart reflective surfaces (RIS) are also one of the 6G-enabled technologies. In general, RIS is a plane formed by a large number of passive reflecting elements, each passive reflecting element can independently change the amplitude and phase of an incident signal, and by densely deploying RIS in a wireless network, a transmission channel between a transmitter and a receiver can be flexibly reconfigured, so that fading loss and interference of the wireless channel are reduced, and improvement of wireless communication capacity and reliability is realized.
However, when correlation between AP antennas is high in the current de-cellular massive MIMO system, antenna selective fading is reduced, spatial multiplexing capability is limited, and interference is increased, so that performance of the de-cellular massive MIMO system is poor.
Disclosure of Invention
Based on this, it is necessary to provide an RIS-assisted spatial correlation massive MIMO system optimization method capable of improving the performance of the massive MIMO system for cell removal, in view of the above-mentioned technical problems.
An optimization method of an RIS-assisted spatial correlation de-cellular massive MIMO system, which is applied to the RIS-assisted de-cellular massive MIMO system, the system comprises:Meach access point AP,KIndividual users and randomly distributed within a given areaZA reconfigurable intelligent surface RIS, wherein each access point AP is provided withNEach reconfigurable smart surface RIS is provided with an antennaA plurality of reflective units, the method comprising:
establishing a channel model of an RIS-assisted spatial correlation de-cellular large-scale MIMO system, wherein the channels are divided into direct channels and indirect channels;
according to the channel model, performing channel estimation on the direct-connection channel by adopting a mode of closing reflection units of all the reconfigurable intelligent surface RIS, and performing channel estimation by adopting a mode of opening single reflection units of single reconfigurable intelligent surface RIS one by one to obtain estimated channel information;
deducing an expression of a downlink reachable rate of the RIS-assisted spatial correlation de-cellular large-scale MIMO system according to the estimated channel information;
according to the expression of the downlink reachable rate, establishing a joint optimization problem of an access point AP active beam and a reconfigurable intelligent surface RIS passive beam, and taking an access point AP transmitting power constraint and a reconfigurable intelligent surface RIS unit modulus constraint as constraint conditions of the joint optimization problem;
according to the characteristics of the active beam and the passive beam, the joint optimization problem is converted into an access point AP transmitting power optimization problem and a phase shift optimization problem of a reconfigurable intelligent surface RIS;
solving an access point AP transmitting power optimization problem by adopting a water injection algorithm, and solving a phase shift optimization problem of the reconfigurable intelligent surface RIS by adopting a convex optimization solver to obtain an optimization result;
and adjusting the transmitting power of the access point AP and the phase shift of the reconfigurable intelligent surface RIS according to the optimization result so as to realize the maximization of the sum rate of the users of the RIS-assisted space-dependent cellular-removal large-scale MIMO system.
In one embodiment, the expression of the channel model is:
；
wherein:
，
，
，
；
wherein, representing the slave access point->To the user->Is>，，Represents the firstmAccess point->Representing the slave access point->To the user->Is used for the direct channel of (a),representing the slave access point->Through reconfigurable Smart surface->Is>The reflection units are +.>Is used for the indirect channel of (a),Zrepresenting the number of reconfigurable intelligent surface RIS, < >>Representing a reconfigurable intelligent surface->The number of reflecting units, < >>Represents the firsttReconfigurable intelligent surface->Represents a deterministic channel of known amplitude, < +.>For a channel subject to Rayleigh distribution, < - > for example>Representing access point->To the user->Is used for the video transmission channel of the (a),eis a natural constant which is used for the production of the high-temperature-resistant ceramic material,jis imaginary unit, ++>Is a reconfigurable intelligent surface->Is>Phase shift reflection coefficient of the individual reflection units, +.>Representing a self-reconfigurable intelligent surface->Is>The reflection units are +.>Channel of->Representing the slave access point->To reconfigurable Smart surface->Is>Channel of the individual reflection units->Representing the slave access point->Through reconfigurable Smart surface->Is>To the user by reflecting unitsIs a video transmission channel, ">Representing the slave access point->Through reconfigurable Smart surface->Is>The reflection units are +.>Is not line of sight transmission channel of>Representing a reconfigurable intelligent surface->To the user->Is a video transmission channel, ">Representing a reconfigurable intelligent surface->To the user->Is not line of sight transmission channel of>Representing the slave access point->To reconfigurable Smart surface->Is>The line-of-sight transmission channel of the individual reflection units, +.>Representing the slave access point->To reconfigurable smart surfacesIs>Non-line-of-sight transmission channel of a reflection unit, < >>For access point->To the user->Is used for the large-scale fading coefficients of (a),is a reconfigurable intelligent surface->To the user->Large scale fading coefficients of ∈, < ->For access point->To reconfigurable Smart surface->Large scale fading coefficients of ∈, < ->For access point->To the user->Is a Lei's factor, < >>Is a reconfigurable intelligent surfaceTo the user->Is a Lei's factor, < >>For access point->To reconfigurable Smart surface->Is a rice factor of (c).
In one embodiment, the expression of the estimated channel information is:
，
；
wherein, is->Is (are) estimated channel->Is->Is used for the estimation of the channel of (a),，for access point->To the user->Is used for the large-scale fading coefficients of (a),，for access point->Through reconfigurable Smart surface->To the user->Large scale fading coefficients of ∈, < ->For access point->Through reconfigurable Smart surface->To the user->Is used for the large-scale fading coefficients of (a),for the length of the pilot sequence, +.>Is the normalized signal-to-noise ratio of each pilot symbol, < >>For being->To the user->Direct channel of->For allocation to users +.>Is the conjugate transpose of the pilot sequence,/->For allocation to users +.>Is used for the pilot sequence of (a),representing the slave access point->To the user->Additive noise of->Representing the slave access point->Through the reconfigurable intelligent surfaceIs>The reflection units are +.>Additive noise of (a) is added.
In one embodiment, the expression of the downlink achievable rate is:
；
user' sSignal-to-interference-and-noise ratio->The method comprises the following steps:
，
wherein, for mathematical expectations, superscriptsTIndicating transpose,/->For all access points AP to user +.>Is used for the channel cascade of (a),，，representing the dimension of the vector>For access point->To the user->Total concatenated channel,/->，For access point->Is>For user->Is provided for the beam forming matrix of (a),，，for access point->The corresponding beamforming vector is used to determine,，is the covariance of additive white gaussian noise,For user->Is used for the beam forming matrix of the (a).
In one embodiment, the expression of the joint optimization problem is:
，
，
；
wherein, is->Phase shift matrix of>，Is->Maximum transmit power, +.>Representing aboutAnd (3) binding.
In one embodiment, the expression of the access point AP transmit power optimization problem is:
，
；
wherein, for allocation to users +.>Power of->For long-term power allocation at the access point AP,，，is the trace of matrix +.>In order to zero-forcing the pre-coding vector,，is->Is (are) estimated channel->SuperscriptHRepresenting the conjugate transpose.
In one embodiment, the expression of the phase shift optimization problem of the reconfigurable intelligent surface RIS is:
，
；
wherein, ，as an auxiliary variable, +.>，Is a reconfigurable intelligent surface->The number of reflecting units, < >>，，Is a semi-positive definite matrix, ++>Is->Is>Line->Column elementElement (L.) of (L)>，。
According to the RIS-assisted spatial correlation de-cellular massive MIMO system optimization method, a channel model of the RIS-assisted spatial correlation de-cellular massive MIMO system is established, channels are divided into direct channels and indirect channels, channel estimation is carried out on the direct channels according to the channel model by adopting a mode of closing reflection units of all the reconfigurable intelligent surface RIS, channel estimation is carried out by adopting a mode of opening single reflection units of a single reconfigurable intelligent surface RIS one by one, estimated channel information is obtained, an expression of a downlink reachable rate of the RIS-assisted spatial correlation de-cellular massive MIMO system is deduced according to the estimated channel information, an access point AP active beam and a reconfigurable intelligent surface RIS passive beam joint optimization problem is established according to the expression of the downlink reachable rate, the joint optimization problem is converted into an access point AP transmission power optimization problem and a reconfigurable intelligent surface RIS phase shift optimization problem according to the characteristics of the active beam and the passive beam, the joint optimization problem is converted into a RIS transmission power optimization problem and a phase shift optimization problem of the reconfigurable intelligent surface S, and a large-scale bit-shifting optimization result of the RIS-based on the optimal RIS is achieved by adopting an intelligent surface to achieve the optimal result of the RIS-based on the maximum-scale optimization system. Therefore, the maximization of downlink and velocity is realized, the performance loss caused by the AP correlation of the access point is obviously improved, and the performance of the large-scale MIMO system without honeycomb is improved.
Drawings
FIG. 1 is a schematic diagram of a RIS-assisted spatially correlated descellular massive MIMO system architecture in one embodiment;
FIG. 2 is a flow diagram of a method for optimization of RIS-assisted spatially correlated descellular massive MIMO systems in one embodiment;
fig. 3 is a graph of cumulative distribution function of downstream user achievable rates in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
In one embodiment, a method for optimizing an RIS-assisted spatially correlated desceliac massive MIMO system is provided, where the method is applied to an RIS-assisted desceliac massive MIMO system as shown in fig. 1, and the system includes:Meach access point AP,KIndividual users and randomly distributed within a given areaZA reconfigurable intelligent surface RIS, wherein each access point AP is provided withNEach reconfigurable smart surface RIS is provided with an antennaEach user is provided with a single antenna, and all Access Points (APs) are connected with the CPU through a backhaul link. As shown in fig. 2, the RIS-assisted spatial correlation de-cellular massive MIMO system optimization method includes the following steps:
step S220, a channel model of the RIS-assisted spatial correlation de-cellular massive MIMO system is established, and the channels are divided into direct channels and indirect channels.
In one embodiment, the expression of the channel model is:
；
wherein:
，
，
，
；
wherein, representing the slave access point->To the user->Is>，，Represents the firstmAccess point->Representing the slave access point->To the user->Is used for the direct channel of (a),representing the slave access point->Through reconfigurable Smart surface->Is>The reflection units are +.>Is used for the indirect channel of (a),Zrepresenting the number of reconfigurable intelligent surface RIS, < >>Representing a reconfigurable intelligent surface->The number of reflecting units, < >>Represents the firsttReconfigurable intelligent surface->Represents a deterministic channel of known amplitude, < +.>For a channel subject to Rayleigh distribution, < - > for example>Representing access point->To the user->Is used for the video transmission channel of the (a),eis a natural constant which is used for the production of the high-temperature-resistant ceramic material,jis imaginary unit, ++>Is a reconfigurable intelligent surface->Is>Phase shift reflection coefficient of the individual reflection units, +.>Representing a self-reconfigurable intelligent surface->Is>The reflection units are +.>Channel of->Representing the slave access point->To reconfigurable Smart surface->Is>Channel of the individual reflection units->Representing the slave access point->Through reconfigurable Smart surface->Is>To the user by reflecting unitsIs a video transmission channel, ">Representing the slave access point->Through reconfigurable Smart surface->Is>The reflection units are +.>Is not line of sight transmission channel of>Representing a reconfigurable intelligent surface->To the user->Is a video transmission channel, ">Representing a reconfigurable intelligent surface->To the user->Is not line of sight transmission channel of>Representing the slave access point->To reconfigurable Smart surface->Is>The line-of-sight transmission channel of the individual reflection units, +.>Representing the slave access point->To reconfigurable smart surfacesIs>Non-line-of-sight transmission channel of a reflection unit, < >>For access point->To the user->Is used for the large-scale fading coefficients of (a),is a reconfigurable intelligent surface->To the user->Large scale fading coefficients of ∈, < ->For access point->To reconfigurable Smart surface->Large scale fading coefficients of ∈, < ->For access point->To the user->Is a Lei's factor, < >>Is a reconfigurable intelligent surfaceTo the user->Is a Lei's factor, < >>For access point->To reconfigurable Smart surface->Is a rice factor of (c).
Step S240, according to the channel model, the direct connection channel is subjected to channel estimation by adopting a mode of closing the reflection units of all the reconfigurable intelligent surfaces RIS, and the channel estimation is performed by adopting a mode of opening the single reflection units of the single reconfigurable intelligent surface RIS one by one, so as to obtain estimated channel information.
In one embodiment, the expression of the estimated channel information is:
，
；
wherein, is->Is (are) estimated channel->Is->Is used for the estimation of the channel of (a),，for access point->To the user->Is used for the large-scale fading coefficients of (a),，for access point->Through reconfigurable Smart surface->To the user->Large scale fading coefficients of ∈, < ->For access point->Through reconfigurable Smart surface->To the user->Is used for the large-scale fading coefficients of (a),for the length of the pilot sequence, +.>Is the normalized signal-to-noise ratio of each pilot symbol, < >>For being->To the user->Direct channel of->For allocation to users +.>Is the conjugate transpose of the pilot sequence,/->For allocation to users +.>Is used for the pilot sequence of (a),representing the slave access point->To the user->Additive noise of->Representing the slave access point->Through the reconfigurable intelligent surfaceIs>The reflection units are +.>Additive noise of (a) is added.
In one embodiment, the access point is toTo the userkChannel modeling of (c) is:
；
it will be appreciated that, considering the correlation between each access point AP antenna,modeled as a deterministic channel with known amplitude, +.>Modeled as a channel obeying Rayleigh distribution and having +.>，Representing compliance mean +.>、Covariance is a circularly symmetric composite gaussian distribution. Wherein, for access point->And a userkA matrix of correlation coefficients between. From the access point->Through->Is>The reflective element is->Is modeled as:
；
wherein:
，
；
the phase shift matrix of (2) can be expressed as +.>, Representing the unit modulus constraint of the RIS reflective element. Based on access point->To the userkChannel modeling and access point->Through->Is>The reflective element is->The total concatenated channel of the system can be expressed as:
。
channel estimation is performed under consideration of the time division duplex protocol, and it is assumed that the presence of RIS does not affect channel reciprocity. Under the time division duplex protocol, each coherence interval is divided into three stages of uplink training, downlink data transmission and uplink data transmission. In the uplink training stage, all users send the AP with the length ofWherein, is allocated to the firstkThe pilot sequence of the individual user is +.>，，Representing the dimension of the vector. Dividing the channel estimation phase intoSub-stages. In the first sub-phase we estimate the direct channel +.>. Let the stage be +.>：In->Sub-phase, the indirect channel is estimated by switching on only one reflection unit on one RIS at a time>。
In the first sub-stage, MMSE estimation is employed at the AP, on the direct channelIs>The expression of (2) is:
；
wherein, normalized signal-to-noise ratio (SNR) for each pilot symbol,>，representing the power of the transmitted pilot,representing the transmit noise power.Is an access point->Additive white gaussian noise at +.>. Receive signal->Signal projected onto AP +.>The method can obtain the following steps:
；
thereby, a slave access point can be obtainedTo the user->Direct channel->Is estimated as:
；
wherein, 。
in the first placeIn the stage, will->Is>The individual elements are open and all other elements are closed.Received pilot signal->The expression of (2) is:
；
by passing throughIs>Expression of>Received signal->The expression of (2) can be obtained:
；
wherein, expressed as +.>Is provided.
Thereby, it is possible to obtainThe linear minimum mean square error estimate is:
；
wherein the method comprises the steps of。
Step S260, deducing the expression of the downlink reachable rate of the RIS auxiliary space correlation de-cellular large-scale MIMO system according to the estimated channel information.
The downlink data transmission process is modeled, and the downlink reachable rate of the user is deduced.
In one embodiment, the expression for the downstream achievable rate is:
；
user' sSignal-to-interference-and-noise ratio->The method comprises the following steps:
，
wherein, for mathematical expectations, superscriptsTIndicating transpose,/->For all access points AP to user +.>Is used for the channel cascade of (a),，，representing the dimension of the vector>For access point->To the user->Total concatenated channel,/->，For access point->Is>For user->Is provided for the beam forming matrix of (a),，，for access point->The corresponding beamforming vector is used to determine,，is the covariance of additive white gaussian noise,For user->Is used for the beam forming matrix of the (a).
In one embodiment of the present application, in one embodiment,transmitted pre-coded signal->The expression of (2) is:
；
wherein, for access point->Corresponding beamforming vector, ">，For user->Is a data symbol of (a). Furthermore->And->，Representation ofK×KIs marked with the unit matrix of (2)HRepresenting the conjugate transpose. Each AP satisfies the power constraint +.>Wherein->Is->Is provided. The user +.>Is a user->Is>The expression of (2) is:
；
wherein, is additive white gaussian noise at user k, +.>For user->Is a data symbol of (a). Thereby, the user +.>Signal-to-interference-and-noise ratio->And downstream achievable rate->The method comprises the following steps:
，
。
step S280, establishing an access point AP active beam and reconfigurable intelligent surface RIS passive beam joint optimization problem according to an expression of the downlink reachable rate, and taking the access point AP transmitting power constraint and the reconfigurable intelligent surface RIS unit modulus constraint as constraint conditions of the joint optimization problem.
In one embodiment, the hybrid beamforming is designed jointly to maximize the user and the rate, the joint optimization problem of the active beam of the access point AP and the RIS passive beam of the reconfigurable intelligent surface is established, and the transmitting power constraint of the access point AP and the RIS unit modulus constraint of the reconfigurable intelligent surface are used as constraint conditions of the joint optimization problem, and the expression of the joint optimization problem is as follows:
，
，
；
wherein, is->Phase shift matrix of>，Is->Maximum transmit power, +.>The representation is constrained.
Step S300, according to the characteristics of the active beam and the passive beam, the joint optimization problem is converted into an access point AP transmitting power optimization problem and a phase shift optimization problem of a reconfigurable intelligent surface RIS.
Wherein, in the passive beam forming part, a long-term passive beam forming device can be designed, the maximum power of the active pre-coder is minimized, and a convex optimization solver is used for solving and reconstructing the phase shift optimization problem of the RIS of the intelligent surface; in the active beam shaping part, a zero-forcing precoder is used for beam shaping design, and a water injection algorithm is used for solving the problem of optimizing the transmitting power of the access point AP.
In one embodiment, the expression for the access point AP transmit power optimization problem is:
，
；
wherein, for allocation to users +.>Power of->For long-term power allocation at the access point AP,，，is the trace of matrix +.>In order to zero-forcing the pre-coding vector,，is->Is (are) estimated channel->SuperscriptHRepresenting the conjugate transpose.
In one embodiment, the expression for the phase shift optimization problem for the reconfigurable intelligent surface RIS is:
，
；
wherein, ，as an auxiliary variable, +.>，Is a reconfigurable intelligent surface->The number of reflecting units, < >>，，Is a semi-positive definite matrix, ++>Is->Is>Line->Column element (s)/(S)>，。
In one embodiment, first, in terms of active beam design, the use of zero-forcing (ZF) precoding is consideredAnd a receiver, because it can eliminate inter-user interference, thereby simplifying optimal power allocation. User' sReceived signal->Can be expressed as:
；
wherein, ，，，，representing a userkAdditive noise at. Zero-forcing precoding can be followed by +.>So that the active beam shaping can be set to. There is a long-term power allocation at all APs +.>Lower, user->Can be reduced to +.>。
Constraint conditions，Can be transformed into:
。
therefore, the joint optimization problem can be converted into an Access Point (AP) transmission power optimization problem, and the expression of the AP transmission power optimization problem is as follows:
，
；/>
，，indicating +.>Corresponding beamforming vectors. The optimal solution of the access point AP transmit power optimization problem can be obtained by a water filling algorithm, then:wherein->Is->Is>Diagonal elements>Is a normalization factor, and->Can meet->。
Second, according to the proposed active beam design, the passive beamformer is found independent of the objective function, only with respect to the transmit power constraints and unit mode constraints in the joint optimization problem. Thus, a long term passive beamformer can be designed to minimize the maximum power of the active precoder. The optimization problem of minimizing the maximum power of the active precoder can be expressed as:
；
；
wherein, ，，. In practice, as the channel gain increases, the transmission power decreases, and therefore, a sub-optimization problem of maximizing the minimum average channel gain through passive beamforming is presented, where the expression of the sub-optimization problem is:
，
；
by using statistical channel state information (Channel State Information, CSI), the userCan be expressed as an average channel gain with respect to +.>Is a function of:
；
wherein, ，，。
wherein, ；
；
；
wherein, ，is->Part of the medium-range transmission channel, +.>Is->Part of the medium non-line-of-sight transmission channel, +.>，For->The product is obtained after the mathematical transformation is performed,，for userskChannel matrix to all RIS, +.>Representing a self-reconfigurable intelligent surface->To the user->Direct channel of->，，For being->To reconfigurable Smart surface->Channel of->Is->Channel matrix to all RIS, +.>Is->Part of the medium-range transmission channel, +.>Is->Part of the medium non-line-of-sight transmission channel, +.>，。Is->Is a unit matrix of (a).
Due toHalf-positive, average channel gain is +.>Is a convex function of (a). However, because the objective function is not +.>The unit modulus constraint is also not convex, so the phase shift optimization problem of the reconfigurable intelligent surface RIS is a non-convex optimization problem. The application of semi-positive relaxation (SDR) translates the access point AP transmit power optimization problem into a convex one. First, the auxiliary variable +.>The user is +.>Is rewritten as:
；
wherein, ，，is a semi-positive definite matrix. Due toLet->，And->And (5) performing half positive determination. By relaxing->The expression of the sub-optimization problem can be changed into the phase shift optimization problem of the reconfigurable intelligent surface RIS by the rank constraint, and the expression of the phase shift optimization problem of the reconfigurable intelligent surface RIS is as follows:
；
；
the phase shift optimization problem of the reconfigurable intelligent surface RIS is a convex half-positive programming, and can be effectively solved by using the existing convex optimization solver. Optimal solution to the phase shift optimization problem if reconfigurable smart surface RISIs a matrix of rank one, then the optimal solution +.>By taking the corresponding optimal solution +.>Is obtained from the feature vector of the maximum feature value of (a).
Step S320, solving the problem of optimizing the transmitting power of the AP by adopting a water injection algorithm, and solving the problem of optimizing the phase shift of the RIS of the reconfigurable intelligent surface by adopting a convex optimization solver to obtain an optimization result.
Step S340, adjusting the transmitting power of the access point AP and the phase shift of the reconfigurable intelligent surface RIS according to the optimization result, so as to realize the maximization of the sum rate of the users of the RIS-assisted space-dependent cellular large-scale MIMO system.
According to the RIS-assisted spatial correlation de-cellular massive MIMO system optimization method, a channel model of the RIS-assisted spatial correlation de-cellular massive MIMO system is established, channels are divided into direct channels and indirect channels, channel estimation is carried out on the direct channels according to the channel model by adopting a mode of closing reflection units of all the reconfigurable intelligent surface RIS, channel estimation is carried out by adopting a mode of opening single reflection units of a single reconfigurable intelligent surface RIS one by one, estimated channel information is obtained, an expression of a downlink reachable rate of the RIS-assisted spatial correlation de-cellular massive MIMO system is deduced according to the estimated channel information, an access point AP active beam and a reconfigurable intelligent surface RIS passive beam joint optimization problem is established according to the expression of the downlink reachable rate, the joint optimization problem is converted into an access point AP transmission power optimization problem and a reconfigurable intelligent surface RIS phase shift optimization problem according to the characteristics of the active beam and the passive beam, the joint optimization problem is converted into a RIS transmission power optimization problem and a phase shift optimization problem of the reconfigurable intelligent surface S, and a large-scale bit-shifting optimization result of the RIS-based on the optimal RIS is achieved by adopting an intelligent surface to achieve the optimal result of the RIS-based on the maximum-scale optimization system. Therefore, the maximization of downlink and velocity is realized, the performance loss caused by the AP correlation of the access point is obviously improved, and the performance of the large-scale MIMO system without honeycomb is improved.
In one embodiment, the performance of the RIS-assisted spatially correlated descellular massive MIMO system optimization method of the present application is further described in connection with simulation experiments.
The cumulative distribution function diagram of the downlink user reachable rate shown in fig. 3 can intuitively reflect the system performance. Simulation parameter acquisitionK=4，M=4，N=5，Z=3，=20. As shown in fig. 3, the system performance after optimization in the space correlation and space uncorrelation systems is greatly improved, and it can be obviously seen that the optimization algorithm can greatly improve the influence of the space correlation on the system performance.
In summary, the application performs joint optimization on the RIS passive beam and the AP active beam, and can greatly improve the user reachable rate in the system by means of the convex optimization solver and the water injection algorithm.
It should be understood that, although the steps in the flowchart of fig. 2 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 2 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.

Claims (7)

1. An optimization method of an RIS-assisted spatial correlation de-cellular massive MIMO system, which is applied to the RIS-assisted de-cellular massive MIMO system, the system comprises:Meach access point AP,KIndividual users and randomly distributed within a given areaZA reconfigurable intelligent surface RIS, wherein each access point AP is provided withNEach reconfigurable smart surface RIS is provided with an antennaA reflective element, the method comprising:
establishing a channel model of an RIS-assisted spatial correlation de-cellular large-scale MIMO system, wherein the channels are divided into direct channels and indirect channels;
according to the channel model, performing channel estimation on the direct-connection channel by adopting a mode of closing reflection units of all the reconfigurable intelligent surface RIS, and performing channel estimation by adopting a mode of opening single reflection units of single reconfigurable intelligent surface RIS one by one to obtain estimated channel information;
deducing an expression of a downlink reachable rate of the RIS-assisted spatial correlation de-cellular large-scale MIMO system according to the estimated channel information;
according to the expression of the downlink reachable rate, establishing a joint optimization problem of an access point AP active beam and a reconfigurable intelligent surface RIS passive beam, and taking an access point AP transmitting power constraint and a reconfigurable intelligent surface RIS unit modulus constraint as constraint conditions of the joint optimization problem;
according to the characteristics of the active beam and the passive beam, the joint optimization problem is converted into an access point AP transmitting power optimization problem and a phase shift optimization problem of a reconfigurable intelligent surface RIS;
solving an access point AP transmitting power optimization problem by adopting a water injection algorithm, and solving a phase shift optimization problem of the reconfigurable intelligent surface RIS by adopting a convex optimization solver to obtain an optimization result;
and adjusting the transmitting power of the access point AP and the phase shift of the reconfigurable intelligent surface RIS according to the optimization result so as to realize the maximization of the sum rate of the users of the RIS-assisted space-dependent cellular-removal large-scale MIMO system.
2. The method of claim 1, wherein the expression of the channel model is:
；
wherein:
，
，
，
；
wherein, representing the slave access point->To the user->Is>，，Represents the firstmAccess point->Representing the slave access point->To the user->Direct channel of->Representing slave access pointsThrough reconfigurable Smart surface->Is>The reflection units are +.>Is used for the indirect channel of (a),Zrepresenting the number of reconfigurable intelligent surface RIS, < >>Representing a reconfigurable intelligent surface->The number of reflecting units, < >>Represents the firsttReconfigurable intelligent surface->Represents a deterministic channel of known amplitude, < +.>For a channel subject to Rayleigh distribution, < - > for example>Representing access point->To the user->Is used for the video transmission channel of the (a),eis a natural constant which is used for the production of the high-temperature-resistant ceramic material,jis imaginary unit, ++>Is a reconfigurable intelligent surface->Is>Phase shift reflection coefficient of the individual reflection units, +.>Representing a self-reconfigurable intelligent surface->Is>The reflection units are +.>Channel of->Representing the slave access point->To reconfigurable Smart surface->Is>The channels of the individual reflecting units,representing the slave access point->Through reconfigurable Smart surface->Is>The reflection units are +.>Is a video transmission channel, ">Representing the slave access point->Through reconfigurable Smart surface->Is>The reflection units are +.>Is not line of sight transmission channel of>Representing a reconfigurable intelligent surface->To the user->Is a video transmission channel, ">Representing a reconfigurable intelligent surface->To the user->Is not line of sight transmission channel of>Representing the slave access point->To reconfigurable Smart surface->Is the first of (2)The line-of-sight transmission channel of the individual reflection units, +.>Representing the slave access point->To reconfigurable Smart surface->Is>Non-line-of-sight transmission channel of a reflection unit, < >>For access point->To the user->Large scale fading coefficients of ∈, < ->Is a reconfigurable intelligent surface->To the user->Large scale fading coefficients of ∈, < ->For access point->To reconfigurable Smart surface->Large scale fading coefficients of ∈, < ->For access point->To the user->Is a Lei's factor, < >>Is a reconfigurable intelligent surface->To the user->Is a Lei's factor, < >>For access point->To reconfigurable Smart surface->Is a rice factor of (c).
3. The method of claim 2, wherein the expression of the estimated channel information is:
，
；
wherein, is->Is (are) estimated channel->Is->Is used for the estimation of the channel of (a),，for access point->To the user->Is used for the large-scale fading coefficients of (a),，for access point->Through reconfigurable Smart surface->To the user->Large scale fading coefficients of ∈, < ->For access point->Through reconfigurable Smart surface->To the user->Is used for the large-scale fading coefficients of (a),for the length of the pilot sequence, +.>Is the normalized signal-to-noise ratio of each pilot symbol, < >>For being->To the user->Direct channel of->For allocation to users +.>Is the conjugate transpose of the pilot sequence,/->For allocation to users +.>Is used for the pilot sequence of (a),representing the slave access point->To the user->Additive noise of->Representing the slave access point->Through the reconfigurable intelligent surfaceIs>The reflection units are +.>Additive noise of (a) is added.
4. A method according to claim 3, wherein the expression for the downstream achievable rate is:
；
user' sSignal-to-interference-and-noise ratio->The method comprises the following steps:
，
wherein, for mathematical expectations, superscriptsTIndicating transpose,/->For all access points AP to user +.>Is used for the channel cascade of (a),，，representing the dimension of the vector>For access point->To the user->Total concatenated channel,/->，For access point->Is>For user->Is provided for the beam forming matrix of (a),，，for access point->The corresponding beamforming vector is used to determine,，is the covariance of additive white gaussian noise,For user->Is used for the beam forming matrix of the (a).
5. The method of claim 4, wherein the expression of the joint optimization problem is:
，
，
；
wherein, is->Phase shift matrix of>，Is->Maximum transmit power, +.>The representation is constrained.
6. The method of claim 5, wherein the access point AP transmit power optimization problem is expressed as:
，
；
wherein, for allocation to users +.>Power of->For long-term work at access point APThe rate at which the data is to be distributed,，，is the trace of matrix +.>In order to zero-forcing the pre-coding vector,，is->Is (are) estimated channel->SuperscriptHRepresenting the conjugate transpose.
7. The method of claim 6, wherein the expression of the phase shift optimization problem for the reconfigurable intelligent surface RIS is:
，
；
wherein, ，as an auxiliary variable, +.>，Is a reconfigurable intelligent surface->The number of reflecting units, < >>，，Is a semi-positive definite matrix, ++>Is->Is>Line->Column element (s)/(S)>，。
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