CN111901812A - Full-duplex cellular communication network base station and intelligent reflecting surface combined control method - Google Patents

Full-duplex cellular communication network base station and intelligent reflecting surface combined control method Download PDF

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CN111901812A
CN111901812A CN202010708097.7A CN202010708097A CN111901812A CN 111901812 A CN111901812 A CN 111901812A CN 202010708097 A CN202010708097 A CN 202010708097A CN 111901812 A CN111901812 A CN 111901812A
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base station
reflecting surface
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optimization problem
user terminal
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CN111901812B (en
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李莉
张震坤
彭张节
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Shanghai Normal University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • 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
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/06—Testing, supervising or monitoring using simulated traffic
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    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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Abstract

The invention relates to a full duplex cellular communication network base station and intelligent reflecting surface combined control method, which comprises the following steps: establishing a signal-to-interference-and-noise ratio model at a base station, and acquiring an uplink rate of each user terminal; establishing a signal-to-interference-and-noise ratio model at a user terminal, and acquiring a downlink rate of each user terminal; establishing an optimization problem for maximizing transmission fairness between user terminals according to the two signal-to-interference-and-noise ratio models, the uplink rate and the downlink rate, solving the optimization problem, and obtaining an optimal base station precoding matrix and an optimal intelligent reflecting surface reflection coefficient matrix; the base station operates according to the optimal pre-coding matrix, and the intelligent reflecting surface operates according to the optimal intelligent reflecting surface reflection coefficient matrix. Compared with the prior art, the method solves the problem of fairness among users of the full-duplex cellular communication system based on the intelligent reflecting surface, improves the overall service quality of the communication system, and has the advantages of wide application scene, good applicability, lower hardware cost and higher energy efficiency.

Description

Full-duplex cellular communication network base station and intelligent reflecting surface combined control method
Technical Field
The invention relates to the technical field of wireless communication, in particular to a full-duplex cellular communication network base station and intelligent reflecting surface combined control method.
Background
In the 5G era, the access amount of wireless devices is rapidly increasing, the data rate on which applications depend is also continuously increasing, and wireless spectrum resources face serious shortage. In addition to the advanced radio access technology and coded modulation, a new full duplex communication mode is receiving wide attention. The full-duplex cellular communication network is one of the most widely used and challenging network forms of full-duplex communication, and compared with the traditional time division duplex and frequency division duplex modes, the full-duplex communication which allows devices in the network to transmit information on the same carrier frequency at the same time can double the spectrum efficiency.
However, in practical use, the existing full-duplex cellular communication network has low energy efficiency and large hardware overhead; and the conventional co-frequency full duplex network needs to overcome the extremely strong self-interference at the relay node and the backward propagation interference at the base station and the user.
With the development of micro-electromechanical systems and programmable metamaterials, the smart reflective surface has been widely regarded as a technology capable of improving the spectrum and energy efficiency of wireless systems. The intelligent reflective surface includes a plurality of passive reflective elements capable of independently reflecting signals, each element independently imparting a phase shift to the reflected signal. By reasonably adjusting the phase shift, the intelligent reflecting surface can realize directional enhancement or suppression of signals, and meanwhile, three-dimensional beam forming with fine granularity is realized, so that the effect of improving a radio propagation environment is achieved. Compared with the traditional relay node, the intelligent reflecting surface has a simple structure, so that the hardware overhead is lower. In addition, because the working mode is passive reflection, the energy consumption of wisdom plane of reflection can be ignored, and can not produce new signal and thermal noise by itself.
To improve the energy efficiency of a full-duplex cellular communication network and reduce hardware overhead, an intelligent reflective surface is combined with a full-duplex cellular communication system. The communication system usually needs to solve the problem of fairness in transmission among users, and the existing communication system solves the problem of fairness in transmission among users by adjusting power allocation or user selection of a base station and a relay node. At present, no scheme is available for solving the transmission fairness problem of a communication system formed by combining an intelligent reflecting surface and a full-duplex cellular communication system.
Disclosure of Invention
The present invention aims to overcome the defects of the prior art and provide a full-duplex cellular communication network base station and intelligent reflecting surface combined control method which can solve the problem of transmission fairness among users.
The purpose of the invention can be realized by the following technical scheme:
a method for controlling a full-duplex cellular communication system based on a smart reflector, the full-duplex cellular communication system comprising a base station, the smart reflector and a plurality of user terminals, the base station having a plurality of transmitting antennas and a plurality of receiving antennas, the smart reflector having a plurality of reflecting elements, a direct link between the base station and the user terminals being blocked by an obstacle, the method comprising:
establishing a signal-to-interference-and-noise ratio model at a base station, and acquiring an uplink rate of each user terminal;
establishing a signal-to-interference-and-noise ratio model at a user terminal, and acquiring a downlink rate of each user terminal;
establishing an optimization problem for maximizing transmission fairness between user terminals according to the two signal-to-interference-and-noise ratio models, the uplink rate and the downlink rate, solving the optimization problem, and obtaining an optimal base station precoding matrix and an optimal intelligent reflecting surface reflection coefficient matrix;
the base station operates according to the optimal pre-coding matrix, and the intelligent reflecting surface operates according to the optimal intelligent reflecting surface reflection coefficient matrix.
Preferably, the base station has NtRoot transmitting antenna and NrThe intelligent reflecting surface is provided with M reflecting elements, and the full-duplex cellular communication system comprises K user terminals.
Preferably, the signal to interference plus noise ratio model at the user terminal is: kth user equipment UEkSignal to interference and noise ratio model gamma ofD,k；γD,kThe method specifically comprises the following steps:
Figure BDA0002595463530000021
wherein h isr,kFor intelligent reflecting surface to UEkThe channel vector of phi is the reflection coefficient matrix of the intelligent reflection surface GtIs a channel matrix from the base station to the intelligent reflecting surface, fkFor a base station to a UEkOf precoding vector fnFor the base station to the nth user terminal UEnOf precoding vector, PnFor the UEnP is a self-interference coefficient, ht,nFor the UEnThe channel vector to the intelligent reflecting surface,
Figure BDA0002595463530000022
representing a UEkThe total average power of other interferers in the received signal.
Preferably, the signal-to-interference-and-noise ratio model γ at the base stationU,kComprises the following steps:
Figure BDA0002595463530000031
wherein, PkFor the k-th user terminal UEkOf the transmission power uU,kFor a base station with respect to a UEkLinear receiver vector of GrIs a channel matrix from the intelligent reflector to the base station, phi is a reflection coefficient matrix of the intelligent reflector, ht,kFor the UEkChannel vector to the intelligent reflecting surface, PnFor the nth user terminal UEnTransmit power of ht,nFor the UEnThe channel vector to the intelligent reflecting surface,
Figure BDA0002595463530000032
the total average power of the residual noise and the thermal noise is cancelled for the interference of the base station.
Preferably, the uplink rate of the user terminal is: kth user equipment UEkOf uplink rate RU,k(Φ)；RU,k(Φ) specifically is:
RU,k(Φ)＝log(1+γU,k)
wherein phi is the inverse of the intelligent reflection surfaceMatrix of radiation coefficients, gammaU,kIs a signal to interference plus noise ratio model at the base station.
Preferably, the downlink rate of the user terminal is: kth user equipment UEkOf downlink rate RD,k(F,Φ)；RD,k(F, φ) is specifically:
RD,k(F,Φ)＝log(1+γD,k)
wherein F is a precoding matrix of the base station, and F ═ F1,f2,…,fK]，fkFor a base station to a UEkPhi is the intelligent reflection surface reflection coefficient matrix, gammaD,kKth user equipment UEkSignal to interference plus noise ratio model.
Preferably, the optimization problem is as follows:
Figure BDA0002595463530000033
s.t.Tr[FΗF]≤Pmax,
Figure BDA0002595463530000034
wherein, ω isU,kFor the k-th user terminal UEkInverse of the uplink weight of (c), ωD,kFor the UEkInverse of the downlink weight of (1), PmaxIs the maximum transmit power of the base station, F is the precoding matrix of the base station, and F ═ F1,f2,...,fK]，fkFor a base station to a UEkOf precoding vectors of phim,mIs the reflection coefficient of the m-th reflection element of the intelligent reflection surface.
Preferably, the step of solving said optimization problem comprises:
converting the optimization problem into an equivalent optimization problem;
and solving the equivalent optimization problem to obtain an optimal base station precoding matrix and an optimal intelligent reflecting surface reflection coefficient matrix.
Preferably, the equivalent optimization problem is as follows:
Figure BDA0002595463530000041
s.t.Tr[FΗF]≤Pmax,
Figure BDA0002595463530000042
wherein, ω isU,kFor the k-th user terminal UEkInverse of the uplink weight of (c), ωD,kIs the inverse of the downlink weight of the UEk,
Figure BDA0002595463530000043
for the k-th user terminal UEkOf uplink rate RU,kA lower bound function of (phi),
Figure BDA0002595463530000044
for the k-th user terminal UEkOf downlink rate RD,kA lower bound function of (F, phi),
Figure BDA0002595463530000045
for a set of linear receivers of a base station,
Figure BDA0002595463530000046
for a set of user linear decoders,
Figure BDA0002595463530000047
for the set of uplink rate-aiding variables,
Figure BDA0002595463530000048
a set of secondary variables for the downlink rate.
Preferably, in the step of solving the equivalence optimization problem, the step of solving the equivalence optimization problem by using a BCD-MM algorithm includes:
consider the optimization variables of the equivalence optimization problem as four groups, where
Figure BDA0002595463530000049
And
Figure BDA00025954635300000410
the group of the Chinese medicinal materials is formed,
Figure BDA00025954635300000411
and
Figure BDA00025954635300000412
one group, F and phi;
four groups of variables are iteratively optimized by a block coordinate descent method: in each iteration, fixing three groups of variables to solve another group of variables, and substituting the newly solved variables into the next iteration, wherein the solution
Figure BDA00025954635300000413
And
Figure BDA00025954635300000414
using minimum mean square error receiver theory to give closed expression of solution, and solving
Figure BDA00025954635300000415
And
Figure BDA00025954635300000416
and respectively giving a closed expression of a solution by utilizing the mean square error expressions of the recovery signals of the base station and the user, respectively using an MM method when solving F and phi, calculating an objective function value of an original optimization problem after each iteration, terminating the iteration process when the difference between the objective functions of two adjacent iterations is less than a given threshold value, and obtaining the solution as the solution of a base station precoding matrix and an intelligent reflecting surface reflection coefficient matrix under the transmission fairness maximization criterion between users.
Preferably, in the iterative optimization of four groups of variables by a block coordinate descent method, an MM method is used for solving F and Φ respectively, and the specific steps include:
in the block coordinate descent method, other variables are set
Figure BDA00025954635300000417
When phi is taken as a constant to solve the precoding matrix, the objective function is a piecewise function of the precoding matrix, and the MM method is used for iterative solution;
in the block coordinate descent method, other variables are set
Figure BDA00025954635300000418
When F is used as a constant to solve the reflection coefficient matrix, the target function is a piecewise function of the reflection coefficient matrix, and the MM method is used for iterative solution;
when the MM method is used for iterative solution, in each iteration, a smooth upward convex function is used for approximating an objective function, the smooth upward convex function is replaced by a lower bound function of the objective function, a closed expression of a converted problem solution is given, the objective function of the next iteration is updated by using the solution, the value of the original optimization problem objective function is calculated, the solution of the problem of the mean square error minimization is terminated when the difference between the objective functions of two adjacent iterations is smaller than a given threshold, and the precoding matrix at the termination is given by other variables.
The superscript H in the present invention denotes the conjugate transpose operation.
Compared with the prior art, the invention has the following advantages:
(1) the intelligent reflecting surface is introduced into a full-duplex cellular communication network, the intelligent reflecting surface is used for assisting communication, the minimum weighting rate of all users is maximized through the combined optimization of the base station and the intelligent reflecting surface, the problem of fairness among the users is solved, the asymmetry of uplink and downlink rate requirements possibly existing in an actual cellular communication system and the inequality of user priorities can be adapted, and the overall service quality of the communication system is improved;
(2) the method establishes an optimization problem of maximizing the transmission fairness among the user terminals, obtains an equivalent optimization problem of the problem, solves the optimization problem by adopting a BCD-MM algorithm, can obtain an approximate optimal solution close to global optimum with very low operation overhead, improves the operation efficiency, and can accurately obtain an optimal base station precoding matrix and an optimal intelligent reflecting surface reflection coefficient matrix;
(3) the communication system oriented by the invention is a multi-user full-duplex cellular communication system, compared with the existing bidirectional communication technology based on an intelligent reflecting surface, the multi-user full-duplex cellular communication system has the widest application range and simultaneously relates to one-to-many and many-to-one transmission and full-duplex communication, so the communication system has wide application scenes and good applicability;
(4) the communication system adopts the intelligent reflecting surface to assist communication, the intelligent reflecting surface can restrain interference signals and enhance useful signals at a user position, and meanwhile, the intelligent reflecting surface has a simple structure, generates no new signals and consumes almost no energy, so that the hardware cost is lower and the energy efficiency is higher.
Drawings
FIG. 1 is a flow chart of the present invention;
fig. 2 is a schematic diagram of a full-duplex cellular communication system based on an intelligent reflective surface according to the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. Note that the following description of the embodiments is merely a substantial example, and the present invention is not intended to be limited to the application or the use thereof, and is not limited to the following embodiments.
Examples
A full-duplex cellular communication network base station and intelligent reflecting surface combined control method is used for controlling a full-duplex cellular communication system based on an intelligent reflecting surface, the full-duplex cellular communication system comprises a base station, the intelligent reflecting surface and a plurality of user terminals, the base station is provided with a plurality of transmitting antennas and a plurality of receiving antennas, the intelligent reflecting surface is provided with a plurality of reflecting elements, and a direct link between the base station and the user terminals is blocked by a barrier.
In this embodiment, the base station has NtRoot transmitting antenna and NrThe smart reflector has M reflective elements, and the full-duplex cellular communication system includes K user terminals.
The channel state of the full-duplex cellular communication system of the invention is quasi-static, and the channel state information can be fully acquired by the base station; the base station calculates the optimal base station precoding matrix and the optimal intelligent reflecting surface reflection coefficient matrix for transmitting information symbols to the multi-user terminal in real time, and sends control information to the intelligent reflecting surface through a special channel. The invention establishes a signal transmission model under the scene that a base station and a multi-user terminal exchange information on the same carrier frequency at the same time. The method comprises the steps of establishing an optimization model for jointly optimizing a base station precoding matrix and an intelligent reflecting surface reflection coefficient by taking the transmission fairness among maximized users as a target and the maximum transmitting power of a base station and the unit modulus of the intelligent reflecting surface reflection coefficient as constraint conditions, and solving the established optimization problem through an efficient algorithm.
The combined control method of the base station and the intelligent reflecting surface of the full-duplex cellular communication network comprises the following specific steps:
establishing a signal-to-interference-and-noise ratio model at a base station, and acquiring an uplink rate of each user terminal;
establishing a signal-to-interference-and-noise ratio model at a user terminal, and acquiring a downlink rate of each user terminal;
establishing an optimization problem for maximizing transmission fairness between user terminals according to the two signal-to-interference-and-noise ratio models, the uplink rate and the downlink rate, solving the optimization problem, and obtaining an optimal base station precoding matrix and an optimal intelligent reflecting surface reflection coefficient matrix;
the base station operates according to the optimal pre-coding matrix, and the intelligent reflecting surface operates according to the optimal intelligent reflecting surface reflection coefficient matrix.
In particular, the signal to interference and noise ratio model γ at the base stationU,kComprises the following steps:
Figure BDA0002595463530000061
wherein, PkFor the k-th user terminal UEkOf the transmission power uU,kFor a base station with respect to a UEkLinear receiver vector of GrIs a channel matrix from the intelligent reflector to the base station, phi is a reflection coefficient matrix of the intelligent reflector, ht,kFor the UEkChannel vector to the intelligent reflecting surface, PnFor the nth user terminal UEnTransmit power of ht,nFor the UEnTo the intelligent reflectionThe channel vectors of the planes are then,
Figure BDA0002595463530000071
the total average power of the residual noise and the thermal noise is cancelled for the interference of the base station.
Also, in the present embodiment, ht,kColumn vectors of M rows, GrFor M rows NrMatrix of columns, uU,kIs NrBase station of row 1 column with respect to UEkR of (A) to (B)U,k(phi) linear receiver vector, intelligent reflector reflection coefficient matrix phi ═ diag { phi [ ]1,1,φ2,2,…,φM,M}，φm,mFor the reflection coefficient of the m-th reflection element of the intelligent reflection surface, phi is a diagonal matrix, each element on the diagonal is a reflection coefficient, and the reflection coefficient of the m-th reflection element
Figure BDA0002595463530000072
Wherein theta ismIs a phase shift;
according to the signal-to-interference-and-noise ratio model gamma at the base stationU,kObtaining the uplink rate of each user terminal by using the Shannon formula, namely the kth user terminal UEkOf uplink rate RU,k(Φ)；RU,k(Φ) specifically is:
RU,k(Φ)＝log(1+γU,k)。
specifically, the signal to interference plus noise ratio model at the user terminal is: kth user equipment UEkSignal to interference and noise ratio model gamma ofD,k；γD,kThe method specifically comprises the following steps:
Figure BDA0002595463530000073
wherein h isr,kFor intelligent reflecting surface to UEkThe channel vector of phi is the reflection coefficient matrix of the intelligent reflection surface GtIs a channel matrix from the base station to the intelligent reflecting surface, fkFor a base station to a UEkOf precoding vector fnFor the base station to the nth user terminal UEnP is a self-interference coefficient,
Figure BDA0002595463530000074
representing a UEkThe total average power of other interferers in the received signal.
hr,kColumn vectors of M rows, GtFor M rows NtMatrix of columns, fkIs NtRow 1 column vector.
When n ≠ k, ρ is equal to 1, and when n ≠ k, ρ is greater than 0 and smaller than 1, and the specific value of ρ is determined by a self-interference cancellation module in the user receiver.
SINR model gamma at a user terminalD,kIn the denominator of (a) of (b),
Figure BDA0002595463530000075
for the average power of the multi-user interference,
Figure BDA0002595463530000076
is the average power of the back-propagating interference.
According to signal-to-interference-and-noise ratio model gamma at user terminalD,kObtaining the downlink rate of each user terminal by using the Shannon formula, namely the kth user terminal UEkOf downlink rate RD,k(F,Φ)；RD,k(F, φ) is specifically:
RD,k(F,Φ)＝log(1+γD,k)
wherein F is a precoding matrix of the base station, and F ═ F1,f2,…,fK]，fkFor the UEkThe precoding vector of (2).
According to the obtained signal-to-interference-and-noise ratio model gamma at the base stationU,kKth user equipment UEkSignal to interference and noise ratio model gamma ofD,kUplink rate R of each user terminalU,k(Φ), downlink rate R of each user terminalD,k(F, Φ), an optimization problem is established that maximizes the fairness of transmissions among users:
Figure BDA0002595463530000081
s.t.Tr[FΗF]≤Pmax,
Figure BDA0002595463530000082
wherein, ω isU,kFor the k-th user terminal UEkInverse of the uplink weight of (c), ωD,kFor the UEkInverse of the downlink weight of (1), PmaxIs the maximum transmit power of the base station.
And solving the optimization problem to obtain an optimal base station precoding matrix and an optimal intelligent reflecting surface reflection coefficient matrix.
The step of solving this optimization problem includes: converting the optimization problem into an equivalent optimization problem; and solving the equivalent optimization problem to obtain an optimal base station precoding matrix and an optimal intelligent reflecting surface reflection coefficient matrix.
Specifically, the k-th UE is obtained from the equivalence of the transmission rate maximization and the signal-to-interference-and-noise ratio maximization of the recovered signal, and the equivalence of the signal-to-interference-and-noise ratio maximization of the recovered signal and the mean square error minimization under the condition of constant signal powerkOf uplink rate RU,kLower bound function of (phi)
Figure BDA0002595463530000083
And the k-th user terminal UEkOf downlink rate RD,kLower bound function of (F, phi)
Figure BDA0002595463530000084
Figure BDA0002595463530000085
Figure BDA0002595463530000086
Wherein the content of the first and second substances,
Figure BDA0002595463530000087
for a set of linear receivers of a base station,
Figure BDA0002595463530000088
for a set of user linear decoders,
Figure BDA0002595463530000089
for the set of uplink rate-aiding variables,
Figure BDA00025954635300000810
for the set of downlink rate auxiliary variables, eU,kUE recovered for base stationkMean square error of the signal, eD,kFor the UEkThe mean square error of the signal is recovered.
And the number of the first and second electrodes,
Figure BDA00025954635300000811
uD,kfor the UEkFor a linear decoder of a received signal,
Figure BDA00025954635300000812
wU,kto a UEkThe upstream rate auxiliary variable introduced by the upstream rate,
Figure BDA00025954635300000813
wD,kto a UEkAnd a downlink rate auxiliary variable introduced by the downlink rate.
According to
Figure BDA00025954635300000814
And
Figure BDA00025954635300000815
therefore, the following steps are carried out: make it
Figure BDA00025954635300000816
Is satisfied under the condition that
Figure BDA00025954635300000817
Make it
Figure BDA00025954635300000818
Is establishedProvided that
Figure BDA00025954635300000819
And is
Figure BDA00025954635300000820
And
Figure BDA00025954635300000821
any of the variables for each is a convex function.
By using
Figure BDA0002595463530000091
Converting the optimization problem into an equivalent optimization problem:
Figure BDA0002595463530000092
s.t.Tr[FΗF]≤Pmax,
Figure BDA0002595463530000093
in this embodiment, this equivalent optimization problem is solved by using the BCD-MM algorithm, and an optimal base station precoding matrix and an optimal intelligent reflecting surface reflection coefficient matrix are obtained.
The process of solving the equivalence optimization problem by using the BCD-MM algorithm comprises the following steps:
(1) consider the optimization variables of the equivalence optimization problem as four groups, where
Figure BDA0002595463530000094
And
Figure BDA0002595463530000095
the group of the Chinese medicinal materials is formed,
Figure BDA0002595463530000096
and
Figure BDA0002595463530000097
f and phi are respectively and independently grouped into one group;
(2) four groups of variables are iteratively optimized by a block coordinate descent method: in each iteration, fixing three groups of variables to solve another group of variables, and substituting the newly solved variables into the next iteration, wherein the solution
Figure BDA0002595463530000098
And
Figure BDA0002595463530000099
using minimum mean square error receiver theory to give closed expression of solution, and solving
Figure BDA00025954635300000910
And
Figure BDA00025954635300000911
and respectively giving a closed expression of a solution by utilizing the mean square error expressions of the recovery signals of the base station and the user, using an MM method when solving F and phi, calculating an objective function value of an original optimization problem after each iteration, terminating the iteration process when the difference between the objective functions of two adjacent iterations is less than a given threshold value, and obtaining the solution as the solution of a base station precoding matrix and an intelligent reflecting surface reflection coefficient matrix under the transmission fairness maximization criterion between the users.
The inner layer iteration method for solving the precoding matrix and the intelligent reflecting surface reflection coefficient matrix based on the MM method comprises the following steps:
in the block coordinate descent method, other variables are set
Figure BDA00025954635300000912
When phi is taken as a constant to solve the precoding matrix, the objective function is a piecewise function of the precoding matrix, and the MM method is used for iterative solution;
in the block coordinate descent method, other variables are set
Figure BDA00025954635300000913
When F is used as a constant to solve the reflection coefficient matrix, the target function is a piecewise function of the reflection coefficient matrix, and the MM method is used for iterative solution;
when the MM method is used for iterative solution, in each iteration, a smooth upward convex function is used for approximating an objective function, the smooth upward convex function is replaced by a lower bound function of the objective function, a closed expression of a converted problem solution is given, the objective function of the next iteration is updated by using the solution, the value of the original optimization problem objective function is calculated, the solution of the problem of the mean square error minimization is terminated when the difference between the objective functions of two adjacent iterations is smaller than a given threshold, and the precoding matrix at the termination is given by other variables.
In particular, it is most preferred
Figure BDA00025954635300000914
And
Figure BDA00025954635300000915
given by formula (1) and formula (2), respectively:
Figure BDA0002595463530000101
Figure BDA0002595463530000102
eU,kand eD,kCalculated from equations (3) and (4), respectively:
Figure BDA0002595463530000103
Figure BDA0002595463530000104
Figure BDA0002595463530000105
when Φ is determined, the sub-problem of the equivalence optimization problem with respect to F can be obtained:
Figure BDA0002595463530000106
Figure BDA0002595463530000107
when F is determined, the sub-problem of the equivalence optimization problem about phi can be obtained:
Figure BDA0002595463530000108
definition of
Figure BDA0002595463530000109
The steps for solving the equivalence optimization problem by using the BCD-MM algorithm are as follows:
BCD-MM algorithm flow:
1. initializing the current cycle number l to 0 and the initial feasible solution F0And phi0Calculate Obj (F)0,Φ0) Setting the maximum number of cycles lmaxAnd margin of errore；
2. Given FlAnd philUpdating base station linear receiver using equation (1)
Figure BDA00025954635300001010
And updating the user linear decoder using equation (2)
Figure BDA00025954635300001011
3. Given Fl、Φl、
Figure BDA00025954635300001012
And
Figure BDA00025954635300001013
using formulae (3) and (4) and
Figure BDA00025954635300001014
and
Figure BDA00025954635300001015
updating a set of auxiliary variables
Figure BDA00025954635300001016
And
Figure BDA00025954635300001017
4. given phil、
Figure BDA00025954635300001018
And
Figure BDA00025954635300001019
with FlIteratively solving the problem (5) using the MM algorithm to update the precoding matrix F for an initial feasible solutionl+1；
5. Given Fl+1、
Figure BDA00025954635300001020
And
Figure BDA00025954635300001021
at philFor the initial feasible solution, the MM algorithm is used to solve the problem (6) iteratively to update the reflection coefficient matrix phil+1；
6. Calculate Obj (F)l+1,Φl+1)
7. If | Obj (F)l+1,Φl+1)-Obj(Fl,Φl)|<eObj(Fl,Φl) Or l is not less than lmaxAnd ending the algorithm; otherwise, l ═ l +1 and jump to step 2.
The flow of solving the subproblems (5) and (6) by using the MM algorithm is the same, taking the subproblem (5) as an example, the flow of the MM algorithm is as follows:
define ObjMM(F)＝Obj(F,Φl) And (3) solving the subproblem (5) by using an MM algorithm:
1. setting an initial feasible solution
Figure BDA0002595463530000111
Maximum number of cycles lmaxAnd margin of errore；
2. Approximating the objective function of the subproblem (5) with a derivable smoothing function f (f);
3. construct a smoothing function in
Figure BDA0002595463530000112
Lower bound function of
Figure BDA0002595463530000113
4. Replacing the target function of the subproblem (5) by using a lower bound function to obtain a replacement problem;
5. solving substitution problem update solution
Figure BDA0002595463530000114
6. If it is
Figure BDA0002595463530000115
Or l is not less than lmaxAnd ending the algorithm; otherwise, l ═ l +1 and jump to step 2.
After the optimal base station precoding matrix and the optimal intelligent reflecting surface reflection coefficient matrix are obtained by using the BCD-MM algorithm, the base station operates according to the optimal precoding matrix, and the intelligent reflecting surface operates according to the optimal intelligent reflecting surface reflection coefficient matrix, in this embodiment, an intelligent reflecting surface controller is arranged in the intelligent reflecting surface, specifically:
the base station adjusts the transmitting beam forming according to the optimal pre-coding matrix and transmits a control signal to the intelligent reflecting surface through a special channel according to the optimal reflection coefficient matrix;
the intelligent reflecting surface controller adjusts the phase shift of each reflecting element of the intelligent reflecting surface according to the received control signal;
the user terminal receives the reflection signal of the intelligent reflection surface.
The above embodiments are merely examples and do not limit the scope of the present invention. These embodiments may be implemented in other various manners, and various omissions, substitutions, and changes may be made without departing from the technical spirit of the present invention.

Claims (10)

1. A method for jointly controlling a base station and a smart reflective surface of a full-duplex cellular communication network, the method being used for controlling a full-duplex cellular communication system based on the smart reflective surface, the full-duplex cellular communication system comprising the base station, the smart reflective surface and a plurality of user terminals, the base station having a plurality of transmitting antennas and a plurality of receiving antennas, the smart reflective surface having a plurality of reflective elements, a direct link between the base station and the user terminals being blocked by an obstacle, the method comprising:
establishing a signal-to-interference-and-noise ratio model at a base station, and acquiring an uplink rate of each user terminal;
establishing a signal-to-interference-and-noise ratio model at a user terminal, and acquiring a downlink rate of each user terminal;
establishing an optimization problem for maximizing transmission fairness between user terminals according to the two signal-to-interference-and-noise ratio models, the uplink rate and the downlink rate, solving the optimization problem, and obtaining an optimal base station precoding matrix and an optimal intelligent reflecting surface reflection coefficient matrix;
the base station operates according to the optimal pre-coding matrix, and the intelligent reflecting surface operates according to the optimal intelligent reflecting surface reflection coefficient matrix.
2. The method as claimed in claim 1, wherein the base station has NtRoot transmitting antenna and NrThe intelligent reflecting surface is provided with M reflecting elements, and the full-duplex cellular communication system comprises K user terminals.
3. The method as claimed in claim 2, wherein the SINR model at the UE is: kth user equipment UEkSignal to interference and noise ratio model gamma ofD,k；γD,kThe method specifically comprises the following steps:
Figure FDA0002595463520000011
wherein h isr,kFor intelligent reflecting surface to UEkThe channel vector of phi is the reflection coefficient matrix of the intelligent reflection surface GtIs a channel matrix from the base station to the intelligent reflecting surface, fkFor a base station to a UEkOf precoding vector fnFor the base station to the nth user terminal UEnOf precoding vector, PnFor the UEnP is a self-interference coefficient, ht,nFor the UEnThe channel vector to the intelligent reflecting surface,
Figure FDA0002595463520000012
representing a UEkThe total average power of other interferers in the received signal.
4. The method as claimed in claim 2, wherein the SINR model γ at the base station is a model of the SINR of the full-duplex cellular communication networkU,kComprises the following steps:
Figure FDA0002595463520000021
wherein, PkFor the k-th user terminal UEkOf the transmission power uU,kFor a base station with respect to a UEkLinear receiver vector of GrIs a channel matrix from the intelligent reflector to the base station, phi is a reflection coefficient matrix of the intelligent reflector, ht,kFor the UEkChannel vector to the intelligent reflecting surface, PnFor the nth user terminal UEnTransmit power of ht,nFor the UEnThe channel vector to the intelligent reflecting surface,
Figure FDA0002595463520000022
the total average power of the residual noise and the thermal noise is cancelled for the interference of the base station.
5. The method as claimed in claim 2, wherein the uplink rate of the ue is: for the k thUser Equipment (UE)kOf uplink rate RU,k(Φ)；RU,k(Φ) specifically is:
RU,k(Φ)＝log(1+γU,k)
where phi is the reflection coefficient matrix of the intelligent reflection surface, gammaU,kIs a signal to interference plus noise ratio model at the base station.
6. The method as claimed in claim 2, wherein the downlink rate of the ue is: kth user equipment UEkOf downlink rate RD,k(F,Φ)；RD,k(F, φ) is specifically:
RD,k(F,Φ)＝log(1+γD,k)
wherein F is a precoding matrix of the base station, and F ═ F1,f2,...,fK]，fkFor a base station to a UEkPhi is the intelligent reflection surface reflection coefficient matrix, gammaD,kKth user equipment UEkSignal to interference plus noise ratio model.
7. The method as claimed in claim 2, wherein the optimization problem is:
Figure FDA0002595463520000023
s.t.Tr[FΗF]≤Pmax,
Figure FDA0002595463520000024
wherein, ω isU,kFor the k-th user terminal UEkInverse of the upstream weight of (1), RU,k(Φ) is kth user equipment UEkOf the uplink rate, ωD,kFor the UEkInverse of the downlink weight of (1), RD,k(F, phi) is the kth user terminal UEkUpper speed ofRate, PmaxFor the maximum transmit power of the base station, Φ is the reflection coefficient matrix of the intelligent reflection surface, F is the precoding matrix of the base station, and F ═ F1,f2,…,fK]，fkFor a base station to a UEkOf precoding vectors of phim,mIs the reflection coefficient of the m-th reflection element of the intelligent reflection surface.
8. The method as claimed in claim 7, wherein the step of solving the optimization problem comprises:
converting the optimization problem into an equivalent optimization problem;
and solving the equivalent optimization problem to obtain an optimal base station precoding matrix and an optimal intelligent reflecting surface reflection coefficient matrix.
9. The method as claimed in claim 8, wherein the equivalence optimization problem is:
Figure FDA0002595463520000031
s.t.Tr[FΗF]≤Pmax,
Figure FDA0002595463520000032
wherein, ω isU,kFor the k-th user terminal UEkInverse of the uplink weight of (c), ωD,kFor the UEkThe inverse of the downstream weight of (a),
Figure FDA0002595463520000033
for the k-th user terminal UEkIs determined as a function of the lower bound of the upstream rate,
Figure FDA0002595463520000034
for the k-th user terminal UEkPhi is the reflection coefficient matrix of the intelligent reflection surface, F is the precoding matrix of the base station, and F ═ F is the lower bound function of the downlink rate of (1)1,f2,...,fK]，fkFor a base station to a UEkThe precoding vector of (a) is determined,
Figure FDA0002595463520000035
for a set of linear receivers of a base station,
Figure FDA0002595463520000036
for a set of user linear decoders,
Figure FDA0002595463520000037
for the set of uplink rate-aiding variables,
Figure FDA0002595463520000038
for the set of downlink rate auxiliary variables, PmaxIs the maximum transmission power of the base station, phim,mIs the reflection coefficient of the m-th reflection element of the intelligent reflection surface.
10. The method as claimed in claim 9, wherein the step of solving the equivalence optimization problem comprises using a BCD-MM algorithm to solve the equivalence optimization problem, and the step of using the BCD-MM algorithm to solve the equivalence optimization problem comprises:
consider the optimization variables of the equivalence optimization problem as four groups, where
Figure FDA0002595463520000039
And
Figure FDA00025954635200000310
the group of the Chinese medicinal materials is formed,
Figure FDA00025954635200000311
and
Figure FDA00025954635200000312
one group, F and phi;
four groups of variables are iteratively optimized by a block coordinate descent method: in each iteration, fixing three groups of variables to solve another group of variables, and substituting the newly solved variables into the next iteration, wherein the solution
Figure FDA00025954635200000313
And
Figure FDA00025954635200000314
using minimum mean square error receiver theory to give closed expression of solution, and solving
Figure FDA00025954635200000315
And
Figure FDA00025954635200000316
and respectively giving a closed expression of a solution by utilizing the mean square error expressions of the recovery signals of the base station and the user, respectively using an MM method when solving F and phi, calculating an objective function value of an original optimization problem after each iteration, terminating the iteration process when the difference between the objective functions of two adjacent iterations is less than a given threshold value, and obtaining the solution as the solution of a base station precoding matrix and an intelligent reflecting surface reflection coefficient matrix under the transmission fairness maximization criterion between users.
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