CN113873622A - Communication network energy-saving method based on reconfigurable intelligent surface - Google Patents

Communication network energy-saving method based on reconfigurable intelligent surface Download PDF

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CN113873622A
CN113873622A CN202111019407.5A CN202111019407A CN113873622A CN 113873622 A CN113873622 A CN 113873622A CN 202111019407 A CN202111019407 A CN 202111019407A CN 113873622 A CN113873622 A CN 113873622A
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CN113873622B (en
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陈琪美
王蔚然
吴静
江昊
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Wuhan University WHU
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/02—Power saving arrangements
    • H04W52/0203—Power saving arrangements in the radio access network or backbone network of wireless communication networks
    • H04W52/0206—Power saving arrangements in the radio access network or backbone network of wireless communication networks in access points, e.g. base stations
    • 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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Abstract

The invention discloses a communication network energy-saving method based on a reconfigurable intelligent surface. Firstly, constructing a reconfigurable intelligent surface assisted heterogeneous network system; then, constructing a transmission signal model of the base station, a receiving signal model of the user, a signal-to-interference-noise ratio model of the user and a total rate model of the user; constructing a total power consumption model of the system; and finally, constructing an objective function, and optimizing the beam vectors of all base stations, the phase shift matrix of the reconfigurable intelligent surface and the switch integer variable of the base stations by using a problem transformation and alternating direction multiplier method to enable the energy efficiency of the system to reach the maximum value. The invention provides an energy-saving communication method combining the connection of an intelligent reflecting surface and a base station from the actual energy consumption of the base station and the service quality of a user, models an objective function into a mixed integer nonlinear programming problem, ensures and even improves the communication quality of the user, reduces the energy consumption to the maximum extent and maximizes the energy efficiency.

Description

Communication network energy-saving method based on reconfigurable intelligent surface
Technical Field
The invention belongs to the technical field of mobile internet, and particularly relates to a communication network energy-saving method based on a reconfigurable intelligent surface.
Background
Driven by the rapid development of advanced multimedia applications, next generation wireless networks must support high spectral efficiency and large-scale connectivity. Due to the high data rate requirement and a large number of users, energy consumption becomes a challenging problem in the design of a future wireless network, and the establishment of a green network and the saving of network energy consumption become a research hotspot of the wireless network. Therefore, energy efficiency, i.e. the ratio of spectral efficiency to power consumption, becomes an important performance index for deploying a green sustainable wireless network, and how to improve energy efficiency has become an important research direction.
Intelligent Reflective Surfaces (IRS) are considered to be a revolutionary technology beyond fifth generation wireless networks. In contrast to conventional wireless relay technology, IRS reflects only signals and operates in full duplex mode with low energy consumption. By adjusting the phase of low-cost passive reflective elements integrated on the IRS, reflected signal propagation can be synergistically modified to improve communication coverage, throughput, and energy efficiency. Recently, Reconfigurable Intelligent Surface (RIS) assisted wireless communication has been proposed as a potential solution to improve the energy efficiency of wireless networks. The RIS is a meta-surface equipped with low-cost passive elements, an approximation concept of the IRS, programmable to convert a wireless channel into a partially defined space. In the RIS-assisted wireless communication network, a Base Station (BS) transmits a control signal to an RIS controller to optimize characteristics of an incident wave and improve communication quality of a user. The RIS acts as a reflector and does not perform any digitizing operations. Therefore, the existence of the reconfigurable intelligent surface can not only improve the communication quality, but also reduce the energy consumption.
As network power consumption has increased, base station on/off strategies have received widespread attention over the last few years. The base station on/off strategy is defined as that a part of baseband circuits and radio frequencies are turned off when the user quantity is reduced, users of the current base station are unloaded to the adjacent base station, and the transmission power of the base station to the users and the circuit loss of the base station are reduced, so that the energy consumption of the system is reduced. The potential benefit of the base station on/off strategy in the aspect of energy saving is mainly influenced by factors such as site density and user load, and the method is an important research direction of green wireless access.
In a communication scene, a reconfigurable intelligent surface is introduced to improve the communication quality and reduce the energy consumption, but the problem of huge energy consumption caused by multiple base stations cannot be solved. Generally, the total power consumption of the system is divided into three parts, namely, the transmission power of the base station to the user, the circuit loss of the base station and the circuit loss of the user. In the energy consumption composition of the wireless network, the proportion of the energy consumption of the base station is the largest, and the relevant report indicates that the proportion of the energy consumption of the base station in the total energy consumption of the wireless network exceeds 80%. Therefore, the new power saving communication scheme can further reduce system power consumption in a case of improving communication performance in conjunction with the base station and RIS aspect.
Disclosure of Invention
In order to solve the technical problems, the invention provides an energy-saving communication method for jointly reconfiguring the intelligent surface and switching off the base station from the actual base station energy consumption and the user service quality, a target function is modeled into a Mixed Integer Non-Linear Programming (MINLP) problem, and the energy consumption is reduced to the maximum extent and the energy efficiency is maximized while the user communication quality is ensured and even improved.
The technical scheme adopted by the invention is as follows: a communication network energy-saving method based on a reconfigurable intelligent surface is characterized by comprising the following steps:
step 1: constructing a reconfigurable intelligent surface assisted heterogeneous network system;
step 2: constructing a transmission signal model of each base station and a receiving signal model of each user, constructing a signal-to-interference-and-noise ratio model of each user according to the transmission signal model of each base station and the receiving signal model of each user, and constructing a total rate model of all users according to the signal-to-interference-and-noise ratio model of each user;
and step 3: constructing a total power consumption model of the system;
and 4, step 4: constructing an objective function through a total rate model of all users in the system and a total power consumption model of the system; constructing constraint conditions by combining the total power consumption of the system, a phase shift matrix of the reconfigurable intelligent surface and switch integer variables of the base station; under the limitation of constraint conditions, the beam vectors of all base stations, the phase shift matrix of the reconfigurable intelligent surface and the switch integer variables of all the base stations are optimized by taking the objective function maximization as an optimization objective through an alternating direction multiplier method, so that the system energy efficiency in the heterogeneous network reaches the maximum value, and the optimized beam vectors of all the base stations, the optimized phase shift matrix of the reconfigurable intelligent surface and the optimized switch integer variables of all the base stations are obtained;
preferably, the reconfigurable intelligent surface assisted heterogeneous network system in the step 1 comprises L base stations, K users, a base station access module and R reconfigurable intelligent surfaces;
the base station access module is sequentially connected with the L base stations in a wireless mode;
the base station access module wirelessly transmits the switch integer variable corresponding to each base station to the L base stations;
the L base stations judge whether the base stations allow access according to corresponding switch integer variables, the L base stations further determine that M base stations are opened, namely access is allowed, and the M base stations which are opened are further defined as the M accessed base stations;
each base station in the M accessed base stations is respectively connected with K users in sequence in a wireless mode;
each reconfigurable intelligent surface in the R reconfigurable intelligent surfaces is sequentially connected with M accessed base stations in a wireless mode;
each reconfigurable intelligent surface in the R reconfigurable intelligent surfaces is sequentially connected with K users in a wireless mode;
preferably, the transmission signal model of each base station in step 2 is:
Figure BDA0003241259040000031
wherein ,mlDenotes the transmission signal of the l base station, K denotes the number of users, wl，kIs the beam forming vector, s, of the kth user at the l base stationkSignal value representing the kth user, [1, L ∈]L represents the number of base stations;
step 2, the received signal model of each user is:
Figure BDA0003241259040000032
wherein ,
Figure BDA0003241259040000033
wherein L is in the range of { 1.,. L }, L represents the number of base stations, R is in the range of { 1.,. R }, R represents the number of reconfigurable intelligent surfaces, K is in the range of { L.,. K }, K represents the number of base stations, and y represents the number of base stationskRepresenting the signal received by the k-th user, xl∈{0，1}，xlDenotes the switching integer variable, x, of the l base station l1 indicates that the l-th base station is in an on state and can transmit data to the user, xl0 means that the l base station is in off state, energy consumption can be saved,
Figure BDA0003241259040000034
an equivalent channel representing the channel from the l base station to the k user,
Figure BDA0003241259040000035
representing the channel from the ith base station to the kth user,
Figure BDA0003241259040000036
representing the channel from the r-th reconfigurable smart surface to the k-th user, Gr，lRepresenting the channel from the ith base station to the r-th reconfigurable smart surface. m islRepresenting the transmission signal of base station l, nkIs additive white Gaussian noise,. phirA phase shift matrix representing the r-th reconfigurable smart surface,
Figure BDA0003241259040000037
θr，n∈[0，2π]for the phase shift constraint of the nth reflecting element of the r reconfigurable intelligent surface, N ∈ {1r}，NrRepresenting the number of reflective elements that the r-th reconfigurable smart surface has;
step 2, the signal to interference noise ratio model of each user is:
Figure BDA0003241259040000041
wherein L is in the range of { 1.,. and L }, L represents the number of base stations, K is in the range of { L.,. and K }, K represents the number of users, and K is in the range of [1, K }]，γkSignal to interference plus noise ratio, x, representing the kth userl∈{0，1}，xlIndicating the switching state of the l base station, x l1 indicates that the l-th base station is in an on state and can transmit data to the user, xl0 means that the l base station is in an off state,
Figure BDA0003241259040000042
equivalent channel, w, representing the channel from the l base station to the k userl，kRepresents the beamforming vector of the kth user at the ith base station, and delta represents additive white gaussian noise;
step 2, the total rate model of all users is:
Figure BDA0003241259040000043
where B denotes the bandwidth of the channel, K denotes the number of users, RtRepresenting the total rate of the user, gammakRepresenting the signal to interference plus noise ratio for the kth user.
Preferably, the total power consumption model of the system in step 3 is as follows:
Figure BDA0003241259040000044
wherein L is in the range of { 1.,. and L }, L represents the number of base stations, K is in the range of { 1.,. and K }, K represents the number of users, and P represents the number of userstRepresents the total power consumption of the system, xl∈{0，1}，xlIndicating the switching state of the l base station, x l1 indicates that the l-th base station is in an on state and can transmit data to the user, xl0 denotes that the l-th base station is in an off state, v denotes a power amplification factor of the base station, and wl，kIs the beamforming vector for user k at base station l,
Figure BDA0003241259040000045
for the transmission power consumption of the base station, the transmission power beam of the base station to the user can be expressed as a beam forming vector transpose multiplication; plIs the circuit loss, P, of the l-th base stationkIs the circuit loss for user k;
preferably, the objective function in step 4 is a maximum value that maximizes the system energy efficiency in the heterogeneous network, and is specifically defined as follows:
Figure BDA0003241259040000046
step 4, the constraint conditions are as follows:
Figure BDA0003241259040000051
Figure BDA0003241259040000052
Figure BDA0003241259040000053
Figure BDA0003241259040000054
wherein L is in the range of { 1.,. and L }, L represents the number of base stations, K is in the range of { 1.,. and K }, K represents the number of users, and w is [ w ]1，1，...，wL，1；...；w1，K，...，wL，K]Representing the beamforming vectors of all base stations,wl，kis the beamforming vector for the kth user at the l base station;
Figure BDA0003241259040000055
phase shift matrix, theta, representing reconfigurable smart surfacesr，n∈[0，2π]For the phase shift constraint of the nth reflecting element of the r reconfigurable intelligent surface, N ∈ {1r}，NrThe number of reflecting elements of the R-th reconfigurable intelligent surface is represented, R belongs to { 1., R }, and R represents the number of reconfigurable intelligent surfaces; x ═ x1，...，xL]TA switched integer variable, x, representing all base stationsl∈{0，1}，xlIndicating the switching state of the l base station, xl1 indicates that the l-th base station is in an on state and can transmit data to the user, xl0 means that the l-th base station is in an off state. EE represents the energy efficiency of the system, B represents the bandwidth of the channel, RtRepresenting the total rate of the user, gammakRepresents the signal to interference noise ratio of the kth user, v represents the power amplification factor of the base station,
Figure BDA0003241259040000056
for the transmission power consumption of the base station, PmaxRepresents the maximum transmit power of the total base station,
Figure BDA0003241259040000057
representing the total power constraint, P, of the systemlIs the circuit loss, P, of the l-th base stationkIs the circuit loss for user k and,
Figure BDA0003241259040000058
represents the equivalent channel of the channel from the ith base station to the kth user, and δ represents additive white gaussian noise.
Step 4, the target function is a non-convex function, Lagrange even-to-even transformation is applied to introduce an auxiliary variable gamma into a logarithmic term, and then two auxiliary variables y and z are respectively introduced into a ratio term through fractional planning in sequence, wherein the optimal solution of gamma, y and z can be solved by taking the derivative as zero;
an integer variable is involved in the access selection constraint of the base station, and an auxiliary variable d needs to be introduced for equivalent replacement;
through the introduction of gamma, y, z and d, the objective function has been optimized to be a convex optimization problem. The convex optimization problem can be solved iteratively by using a distributed algorithm based on an alternating direction multiplier method and a cvx solver, and the algorithm is proved to have convergence.
And (4) reconstructing a phase shift matrix theta on the intelligent surface after optimization based on the optimized beam vectors w of all the base stations obtained in the step (4), and reconfiguring the wireless communication environment by the system by accessing selection variables x of all the base stations after optimization, so as to adjust the number of the working base stations, reduce energy consumption, improve network communication efficiency and improve system communication performance.
Compared with the prior art, the method has the advantages that the base station on/off integer variable is introduced aiming at the problem that the conventional energy-saving communication method does not fully consider huge power consumption caused by multiple base stations after the reconfigurable intelligent surface is introduced in common scenes including intensive scenes, and the communication network energy-saving method based on the reconfigurable intelligent surface is provided. Compared with the existing energy-saving method, the energy-saving communication method combining the intelligent surface and the base station switching off can reduce energy consumption to the maximum extent on the premise of effectively guaranteeing and even improving user experience, and achieves better energy-saving effect.
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FIG. 1: is a system model diagram of an embodiment of the invention.
FIG. 2: the invention discloses a model diagram based on a reconfigurable intelligent surface.
FIG. 3: the method is a solving step diagram of the target optimization problem of the embodiment of the invention.
FIG. 4: is a flow chart of the method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention provides a communication network energy-saving method based on a reconfigurable intelligent surface technology from the aspects of actual base station energy consumption and user service quality, which comprises the following steps:
a communication network energy-saving method based on a reconfigurable intelligent surface is characterized by comprising the following steps:
step 1: constructing a reconfigurable intelligent surface assisted heterogeneous network system;
preferably, the reconfigurable intelligent surface assisted heterogeneous network system in step 1 includes 5 base stations L, 10 users K, a base station access module, and 4 reconfigurable intelligent surfaces R;
the base station access module is sequentially connected with the L base stations in a wireless mode;
the base station access module wirelessly transmits the switch integer variable corresponding to each base station to the L base stations;
the L base stations judge whether the base stations allow access according to corresponding switch integer variables, the L base stations further determine that M base stations are opened, namely access is allowed, and the M base stations which are opened are further defined as the M accessed base stations;
each base station in the M accessed base stations is respectively connected with K users in sequence in a wireless mode;
each reconfigurable intelligent surface in the R reconfigurable intelligent surfaces is sequentially connected with M accessed base stations in a wireless mode;
each reconfigurable intelligent surface in the R reconfigurable intelligent surfaces is sequentially connected with K users in a wireless mode;
step 2: constructing a transmission signal model of each base station and a receiving signal model of each user, constructing a signal-to-interference-and-noise ratio model of each user according to the transmission signal model of each base station and the receiving signal model of each user, and constructing a total rate model of all users according to the signal-to-interference-and-noise ratio model of each user;
step 2, the transmission signal model of each base station is as follows:
Figure BDA0003241259040000071
wherein ,mlDenotes the transmission signal of the l base station, K denotes the number of users, wl，kIs the beam forming vector, s, of the kth user at the l base stationkSignal value representing the kth user, [1, L ∈]L represents the number of base stations;
step 2, the received signal model of each user is:
Figure BDA0003241259040000072
wherein ,
Figure BDA0003241259040000073
wherein L is in the range of { 1.,. L }, L represents the number of base stations, R is in the range of { 1.,. R }, R represents the number of reconfigurable intelligent surfaces, K is in the range of { L.,. K }, K represents the number of base stations, and y represents the number of base stationskRepresenting the signal received by the k-th user, xl∈{0，1}，xlDenotes the switching integer variable, x, of the l base station l1 indicates that the l-th base station is in an on state and can transmit data to the user, xl0 means that the l base station is in off state, energy consumption can be saved,
Figure BDA0003241259040000074
an equivalent channel representing the channel from the l base station to the k user,
Figure BDA0003241259040000075
representing the channel from the ith base station to the kth user,
Figure BDA0003241259040000081
representing the channel from the r-th reconfigurable smart surface to the k-th user, Gr，lMeans from the l base station to the r base stationThe channels of the smart surface are reconstructed. m islRepresenting the transmission signal of base station l, nkIs additive white Gaussian noise,. phirA phase shift matrix representing the r-th reconfigurable smart surface,
Figure BDA0003241259040000082
θr，n∈[0，2π]for the phase shift constraint of the nth reflecting element of the r reconfigurable intelligent surface, N ∈ {1r}，NrRepresenting the number of reflective elements that the r-th reconfigurable smart surface has;
step 2, the signal to interference noise ratio model of each user is:
Figure BDA0003241259040000083
wherein L is in the range of { 1.,. and L }, L represents the number of base stations, K is in the range of { L.,. and K }, K represents the number of users, and K is in the range of [1, K }].γkSignal to interference plus noise ratio, x, representing the kth userl∈{0，1}，xlIndicating the switching state of the l base station, xl1 indicates that the 1 st base station is in an on state and can transmit data to the user, xl0 means that the 1 st base station is in an off state,
Figure BDA0003241259040000084
equivalent channel, w, representing the channel from the l base station to the k userl，kRepresents the beamforming vector of the kth user at the ith base station, and delta represents additive white gaussian noise;
step 2, the total rate model of all users is:
Figure BDA0003241259040000085
where B denotes the bandwidth of the channel, K denotes the number of users, RtRepresenting the total rate of the user, gammakSignal and interference plus noise representing the kth userAnd (4) the ratio.
And step 3: constructing a total power consumption model of the system;
step 3, the total power consumption model of the system is as follows:
Figure BDA0003241259040000086
wherein L is in the range of { 1.,. and L }, L represents the number of base stations, K is in the range of { 1.,. and K }, K represents the number of users, and P represents the number of userstRepresents the total power consumption of the system, xl∈{0，1}，xlIndicating the switching state of the l base station, xl1 indicates that the l-th base station is in an on state and can transmit data to the user, xl0 denotes that the l-th base station is in an off state, v denotes a power amplification factor of the base station, and wl，kIs the beamforming vector for user k at base station l,
Figure BDA0003241259040000091
for the transmission power consumption of the base station, the transmission power beam of the base station to the user can be expressed as a beam forming vector transpose multiplication; plIs the circuit loss, P, of the l-th base stationkIs the circuit loss for user k;
and 4, step 4: constructing an objective function through a total rate model of all users in the system and a total power consumption model of the system; constructing constraint conditions by combining the total power consumption of the system, a phase shift matrix of the reconfigurable intelligent surface and switch integer variables of the base station; under the limitation of constraint conditions, the beam vectors of all base stations, the phase shift matrix of the reconfigurable intelligent surface and the switch integer variables of all the base stations are optimized by taking the objective function maximization as an optimization objective through an alternating direction multiplier method, so that the system energy efficiency in the heterogeneous network reaches the maximum value, and the optimized beam vectors of all the base stations, the optimized phase shift matrix of the reconfigurable intelligent surface and the optimized switch integer variables of all the base stations are obtained;
the objective function in step 4 is a maximum value for maximizing system energy efficiency in the heterogeneous network, and is specifically defined as follows:
Figure BDA0003241259040000092
step 4, the constraint conditions are as follows:
Figure BDA0003241259040000093
Figure BDA0003241259040000094
Figure BDA0003241259040000095
Figure BDA0003241259040000096
wherein L is in the range of { L.,. L }, L represents the number of base stations, K is in the range of { 1.,. K }, K represents the number of users, and w is [ w.,. K }, where w is in the range of [ w. ]1，1，...，wL，1；...；w1，K，...，wL，K]Representing the beamforming vectors, w, of all base stationsl，kIs the beamforming vector for the kth user at the l base station;
Figure BDA0003241259040000097
phase shift matrix, theta, representing reconfigurable smart surfacesr，n∈[0，2π]For the phase shift constraint of the nth reflective element of the r reconfigurable intelligent surface, N ∈ { 1., N, }, NrThe number of reflecting elements of the R-th reconfigurable intelligent surface is represented, R belongs to { 1., R }, and R represents the number of reconfigurable intelligent surfaces; x ═ x1，...，xL]TA switched integer variable, x, representing all base stationsl∈{0，1}，xlIndicating the switching state of the l base station, xl1 indicates that the l-th base station is in an on state and can transmit data to the user, xl0 means that the l-th base station is in an off state. EE represents the energy of the systemEfficiency, B denotes the bandwidth of the channel, RtRepresenting the total rate of the user, gammakRepresents the signal to interference noise ratio of the kth user, v represents the power amplification factor of the base station,
Figure BDA0003241259040000101
for the transmission power consumption of the base station, PmaxRepresents the maximum transmit power of the total base station,
Figure BDA0003241259040000102
representing the total power constraint, P, of the systemlIs the circuit loss, P, of the l-th base stationkIs the circuit loss for user k and,
Figure BDA0003241259040000103
represents the equivalent channel of the channel from the ith base station to the kth user, and δ represents additive white gaussian noise.
Step 4, the target function is a non-convex function, Lagrange even-to-even transformation is applied to introduce an auxiliary variable gamma into a logarithmic term, and then two auxiliary variables y and z are respectively introduced into a ratio term through fractional planning in sequence, wherein the optimal solution of gamma, y and z can be solved by taking the derivative as zero;
an integer variable is involved in the access selection constraint of the base station, and an auxiliary variable d needs to be introduced for equivalent replacement;
through the introduction of gamma, y, z and d, the objective function has been optimized to be a convex optimization problem. The convex optimization problem can be solved iteratively by using a distributed algorithm based on an alternating direction multiplier method and a cvx solver, and the algorithm is proved to have convergence.
Based on the optimized beam vectors w of all the base stations obtained in the step 4, a phase shift matrix theta on the intelligent surface can be reconstructed after optimization, the access selection variables x of all the base stations after optimization are changed, the system reconfigures the wireless communication environment, the number of the working base stations is adjusted, the energy consumption is reduced, the network communication efficiency is improved, and the system communication performance is improved;
referring to FIG. 3, the optimization problem solving step is illustrated by first ordering
Figure BDA0003241259040000104
Figure BDA0003241259040000105
Applying a lagrange dual transform:
Figure BDA0003241259040000106
wherein ,γkIs the introduced auxiliary variable and gamma represents the set of all auxiliary variables. The optimum gamma can be obtained by
Figure BDA0003241259040000107
And (6) solving. The expression is as follows:
Figure BDA0003241259040000108
the original optimization problem can be transformed into the following form:
Figure BDA0003241259040000109
programming auxiliary variables y by scorekIntroducing a ratio term, then:
Figure BDA0003241259040000111
the optimization problem is updated as follows:
Figure BDA0003241259040000112
optimum ykCan pass through
Figure BDA0003241259040000113
And (6) solving.
Figure BDA0003241259040000114
Programming auxiliary variables z by scorekIntroducing a ratio term, then:
Figure BDA0003241259040000115
the optimization problem is updated as follows: p3：maxw，Φ，x，zfo(w，Φ，x，γ，y，z)
Optimum zkCan pass through
Figure BDA0003241259040000116
And (6) solving.
Figure BDA0003241259040000117
Integer variable processing:
according to the theorem: definition set
Figure BDA0003241259040000118
Figure BDA0003241259040000119
Then the vector pair (x, d) belongs to the set Φ, one can deduce that x e {0, 1}K，d∈{0，1}K，x＝d。
The optimization problem can be updated as: p4：maxw，Φ，x，z，dfo(w，Φ，x，γ，y，z)
And (3) constraint:
Figure BDA00032412590400001110
Figure BDA00032412590400001111
Figure BDA00032412590400001112
Figure BDA00032412590400001113
(2x-1)T(2x-1)＝L
Figure BDA0003241259040000121
obviously, this time P4Has been a convex optimization problem.
It should be understood that the above description of the preferred embodiments is given for clarity and not for any purpose of limitation, and that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (5)

1. A communication network energy-saving method based on a reconfigurable intelligent surface is characterized by comprising the following steps:
step 1: constructing a reconfigurable intelligent surface assisted heterogeneous network system;
step 2: constructing a transmission signal model of each base station and a receiving signal model of each user, constructing a signal-to-interference-and-noise ratio model of each user according to the transmission signal model of each base station and the receiving signal model of each user, and constructing a total rate model of all users according to the signal-to-interference-and-noise ratio model of each user;
and step 3: constructing a total power consumption model of the system;
and 4, step 4: constructing an objective function through a total rate model of all users in the system and a total power consumption model of the system; constructing constraint conditions by combining the total power consumption of the system, a phase shift matrix of the reconfigurable intelligent surface and switch integer variables of the base station; under the limitation of constraint conditions, the beam vectors of all base stations, the phase shift matrix of the reconfigurable intelligent surface and the switch integer variables of all the base stations are optimized by taking the objective function maximization as an optimization objective through an alternating direction multiplier method, so that the system energy efficiency in the heterogeneous network reaches the maximum value, and the optimized beam vectors of all the base stations, the optimized phase shift matrix of the reconfigurable intelligent surface and the optimized switch integer variables of all the base stations are obtained.
2. The communication network energy-saving method based on the reconfigurable intelligent surface according to claim 1, wherein the reconfigurable intelligent surface assisted heterogeneous network system in the step 1 comprises L base stations, K users, a base station access module and R reconfigurable intelligent surfaces;
the base station access module is sequentially connected with the L base stations in a wireless mode;
the base station access module wirelessly transmits the switch integer variable corresponding to each base station to the L base stations;
the L base stations judge whether the base stations allow access according to corresponding switch integer variables, the L base stations further determine that M base stations are opened, namely access is allowed, and the M base stations which are opened are further defined as the M accessed base stations;
each base station in the M accessed base stations is respectively connected with K users in sequence in a wireless mode;
each reconfigurable intelligent surface in the R reconfigurable intelligent surfaces is sequentially connected with M accessed base stations in a wireless mode;
and each reconfigurable intelligent surface in the R reconfigurable intelligent surfaces is sequentially connected with K users in a wireless mode.
3. The reconfigurable intelligent surface-based communication network energy saving method according to claim 1,
step 2, the transmission signal model of each base station is as follows:
Figure FDA0003241259030000021
wherein ,mlDenotes the transmission signal of the l base station, K denotes the number of users, wl，kIs the beam forming vector, s, of the kth user at the l base stationkSignal value representing the kth user, [1, L ∈]L represents the number of base stations;
step 2, the received signal model of each user is:
Figure FDA0003241259030000022
wherein ,
Figure FDA0003241259030000023
wherein L is in the range of { 1.,. L }, L represents the number of base stations, R is in the range of { 1.,. R }, R represents the number of reconfigurable intelligent surfaces, K is in the range of { 1.,. K }, K represents the number of base stations, and y represents the number of base stationskRepresenting the signal received by the k-th user, xl∈{0，1}，xlDenotes the switching integer variable, x, of the l base stationl1 indicates that the l-th base station is in an on state and can transmit data to the user, xl0 means that the l base station is in off state, energy consumption can be saved,
Figure FDA0003241259030000024
an equivalent channel representing the channel from the l base station to the k user,
Figure FDA0003241259030000025
representing the channel from the ith base station to the kth user,
Figure FDA0003241259030000026
representing the channel from the r-th reconfigurable smart surface to the k-th user, Gr，lRepresenting the l base station to the r reconfigurable intelligenceA channel of the surface; m islRepresenting the transmission signal of base station l, nkIs additive white Gaussian noise,. phirA phase shift matrix representing the r-th reconfigurable smart surface,
Figure FDA0003241259030000027
θr，n∈[0，2π]for the phase shift constraint of the nth reflecting element of the r reconfigurable intelligent surface, N ∈ {1r}，NrRepresenting the number of reflective elements that the r-th reconfigurable smart surface has;
step 2, the signal to interference noise ratio model of each user is:
Figure FDA0003241259030000028
wherein L is in the range of { 1.,. and L }, L represents the number of base stations, K is in the range of { 1.,. and K }, K represents the number of users, and K is in the range of [ 1.,. and K }, respectively]，γkSignal to interference plus noise ratio, x, representing the kth userl∈{0，1}，xlIndicating the switching state of the l base station, xl1 indicates that the l-th base station is in an on state and can transmit data to the user, xl0 means that the l base station is in an off state,
Figure FDA0003241259030000031
equivalent channel, w, representing the channel from the l base station to the k userl，kRepresents the beamforming vector of the kth user at the ith base station, and delta represents additive white gaussian noise;
step 2, the total rate model of all users is:
Figure FDA0003241259030000032
where B denotes the bandwidth of the channel, K denotes the number of users, RtRepresenting the total rate of the user, gammakRepresents the k-th userSignal to interference plus noise ratio.
4. The reconfigurable intelligent surface-based communication network energy saving method according to claim 1,
step 3, the total power consumption model of the system is as follows:
Figure FDA0003241259030000033
wherein L is in the range of { 1.,. and L }, L represents the number of base stations, K is in the range of { 1.,. and K }, K represents the number of users, and P represents the number of userstRepresents the total power consumption of the system, xl∈{0，1}，xlIndicating the switching state of the l base station, xl1 indicates that the l-th base station is in an on state and can transmit data to the user, xl0 denotes that the l-th base station is in an off state, v denotes a power amplification factor of the base station, and wl，kIs the beamforming vector for user k at base station l,
Figure FDA0003241259030000034
for the transmission power consumption of the base station, the transmission power beam of the base station to the user can be expressed as a beam forming vector transpose multiplication; plIs the circuit loss, P, of the l-th base stationkIs the circuit loss for user k.
5. The reconfigurable intelligent surface-based communication network energy saving method according to claim 1,
the objective function in step 4 is a maximum value for maximizing system energy efficiency in the heterogeneous network, and is specifically defined as follows:
Figure FDA0003241259030000035
step 4, the constraint conditions are as follows:
Figure FDA0003241259030000041
Figure FDA0003241259030000042
Figure FDA0003241259030000043
Figure FDA0003241259030000044
wherein L is in the range of { 1.,. and L }, L represents the number of base stations, K is in the range of { 1.,. and K }, K represents the number of users, and w is [ w ]1，1，...，wL，1；...；w1，K，...，wL，K]Representing the beamforming vectors, w, of all base stationsl，kIs the beamforming vector for the kth user at the l base station;
Figure FDA0003241259030000048
phase shift matrix, theta, representing reconfigurable smart surfacesr，n∈[0，2π]For the phase shift constraint of the nth reflecting element of the r reconfigurable intelligent surface, N ∈ {1r}，NrThe number of reflecting elements of the R-th reconfigurable intelligent surface is represented, R belongs to { 1., R }, and R represents the number of reconfigurable intelligent surfaces; x ═ x1，...，xL]TA switched integer variable, x, representing all base stationsl{0，1}，xlIndicating the switching state of the l base station, xl1 indicates that the l-th base station is in an on state and can transmit data to the user, xl0 means that the l-th base station is in an off state; EE represents the energy efficiency of the system, B represents the bandwidth of the channel, RtRepresenting the total rate of the user, gammakRepresents the signal to interference noise ratio of the kth user, v represents the power amplification factor of the base station,
Figure FDA0003241259030000045
for the transmission power consumption of the base station, PmaxRepresents the maximum transmit power of the total base station,
Figure FDA0003241259030000046
representing the total power constraint, P, of the systemlIs the circuit loss, P, of the l-th base stationkIs the circuit loss for user k and,
Figure FDA0003241259030000047
represents the equivalent channel of the channel from the ith base station to the kth user, and delta represents additive white gaussian noise;
step 4, the target function is a non-convex function, Lagrange even-to-even transformation is applied to introduce the auxiliary variable into a logarithmic term, and then two auxiliary variables y and z are respectively introduced into a ratio term through fractional planning, wherein the optimal solution of gamma, y and z can be solved by taking the derivative as zero;
an integer variable is involved in the access selection constraint of the base station, and an auxiliary variable d needs to be introduced for equivalent replacement;
through the introduction of gamma, y, z and d, the objective function is optimized into a convex optimization problem; the convex optimization problem can be solved iteratively by using a distributed algorithm based on an alternating direction multiplier method and a cvx solver, and the algorithm is proved to have convergence;
and (4) reconstructing a phase shift matrix theta on the intelligent surface after optimization based on the optimized beam vectors w of all the base stations obtained in the step (4), and reconfiguring the wireless communication environment by the system by accessing selection variables x of all the base stations after optimization, so as to adjust the number of the working base stations, reduce energy consumption, improve network communication efficiency and improve system communication performance.
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