CN113873622B - Communication network energy saving method based on reconfigurable intelligent surface - Google Patents
Communication network energy saving method based on reconfigurable intelligent surface Download PDFInfo
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- H—ELECTRICITY
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- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/02—Power saving arrangements
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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 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-and-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, optimizing beam vectors of all base stations, a phase shift matrix of a reconfigurable intelligent surface and a switching integer variable of the base stations by a problem conversion and alternate direction multiplier method, so that the energy efficiency of the system reaches the maximum value. The invention provides an energy-saving communication method combining an intelligent reflecting surface with a base station to turn off from the aspects of actual base station energy consumption and user service quality, which aims to model an objective function as a mixed integer nonlinear programming problem, so that the energy consumption is reduced to the greatest extent while the user communication quality is ensured and even improved, and the energy efficiency is maximized.
Description
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
Under the push of the rapid development of advanced multimedia applications, the next generation wireless network must support high spectrum efficiency and large-scale connection. Because of the high data rate requirements and the large number of users, energy consumption has become a challenging problem in future wireless network designs, and building a green network, saving network energy, has become a research hotspot for wireless networks. Therefore, energy efficiency, i.e., the ratio of spectrum 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.
Smart reflective surfaces (Intelligent Reflecting Surface, IRS) are considered revolutionary technologies beyond fifth generation wireless networks. In contrast to conventional wireless relay technology, IRSs reflect only signals and operate in a low-power, full-duplex mode. By adjusting the phase of low cost passive reflective elements integrated on the IRS, reflected signal propagation can be cooperatively modified to improve communication coverage, throughput, and energy efficiency. Recently, reconfigurable smart surfaces (Reconfigurable Intelligent Surface, RIS) assisted wireless communications have been proposed as potential solutions to improve wireless network energy efficiency. RIS is a meta-surface equipped with low cost passive components, an approximate concept of IRS, that is programmable to convert a wireless channel into a partially defined space. In an 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 not only can improve the communication quality, but also can reduce the energy consumption.
As network power consumption increases, base station on/off strategies have received widespread attention over the past few years. The base station on/off strategy is defined as closing a part of baseband circuits and radio frequencies when the user quantity is reduced, unloading the users of the current base station to the adjacent base station, and reducing the transmission power of the base station to the users and the circuit loss of the base station so as to reduce the energy consumption of the system. The potential benefit of the base station on/off strategy in the aspect of saving energy is mainly influenced by factors such as site density, user load and the like, and is an important research direction of green wireless access.
In the communication scenario, introducing a reconfigurable intelligent surface can improve the communication quality and reduce the energy consumption, but this cannot solve the huge energy consumption problem caused by multiple base stations. 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. The proportion of base station energy consumption is the largest in the energy consumption composition of the wireless network, and the related report indicates that the proportion of base station energy consumption in the total energy consumption of the wireless network is more than 80 percent. Thus, the new energy-efficient communication scheme may combine aspects of base station and RIS to further reduce system power consumption while improving communication performance.
Disclosure of Invention
In order to solve the technical problems, the invention provides an energy-saving communication method for combining a reconfigurable intelligent surface with a base station to turn off from the aspects of actual base station energy consumption and user service quality, which is used for modeling an objective function as a mixed integer nonlinear programming (Mixed Integer Non-Linear Programming, MINLP) problem, ensuring even improving the user communication quality, simultaneously reducing the energy consumption to the greatest extent and maximizing the energy efficiency.
The technical scheme adopted by the invention is as follows: the communication network energy saving method based on the reconfigurable intelligent surface is characterized by comprising the following steps of:
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;
step 3: constructing a total power consumption model of the system;
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 the switch integer variable of the base station; under the constraint condition, optimizing the beam vectors of all the base stations, the phase shift matrix of the reconfigurable intelligent surface and the switch integer variable of all the base stations by using an objective function maximization as an optimization target through an alternating direction multiplier method, so that the energy efficiency of the system in the heterogeneous network reaches the maximum value, and obtaining the optimized beam vectors of all the base stations, the phase shift matrix of the reconfigurable intelligent surface after optimization and the switch integer variable of all the base stations after optimization;
preferably, the heterogeneous network system assisted by the reconfigurable intelligent surface 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 connected with the L base stations in sequence 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 in a wireless mode;
the L base stations judge whether the base stations allow access according to the corresponding switch integer variables, the state of M base stations in the L base stations is further determined to be open, namely the access is allowed, and the M base stations with the open states are further defined as M access 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:
wherein ,ml Representing the transmission signal of the first base station, K representing the number of users, w l，k Is the beam forming vector of the kth user at the ith base station, s k Signal value representing kth user, l e 1, l]L represents the number of base stations;
the received signal model of each user in step 2 is as follows:
wherein ,
where L e {1,., L }, L denotes the number of base stations, R e {1,., R }, R represents the number of reconfigurable intelligent surfaces, k.epsilon. { L.. The number of base stations, K }, K represents the number of base stations, y k Representing the signal received by the kth user, x l ∈{0，1}，x l A switch integer variable, x, representing the first base station l =1 indicates that the first base station is in an on state, and can transmit data to the user, x l =0 represents the first baseThe station is in an off state, which can save energy consumption,equivalent channel representing the channel from the first base station to the kth user,/for the channel from the kth base station to the kth user>Representing the channel from the first base station to the kth user,/for the kth user>Representing the channel from the (r) th reconfigurable intelligent surface to the (k) th user, G r，l Representing the channel from the first base station to the r reconfigurable intelligent surface. m is m l Representing the transmission signal of base station/n k Is additive white Gaussian noise, phi r Representing the phase shift matrix of the r-th reconfigurable intelligent surface,
θ r，n ∈[0，2π]for the phase shift constraint of the nth reflective element of the nth reconfigurable intelligent surface, N e { 1.. r }，N r Representing the number of reflective elements that the r-th reconfigurable intelligent surface has;
the signal-to-interference-and-noise ratio model of each user in step 2 is as follows:
where L e {1,., L }, L represents the number of base stations, K is { l..A., K }, K represents the number of users, k.epsilon.1, K]，γ k Representing the signal to interference plus noise ratio, x, of the kth user l ∈{0，1}，x l Indicating the switch state of the first base station, x l =1 indicates that the first base station is in an on state, and can transmit data to the user, x l =0 indicates that the first base station is in the off state,equivalent channel, w, representing the channel from the first base station to the kth user l，k Representing the beam forming vector of the kth user at the ith base station, delta representing additive white gaussian noise;
and 2, the total rate model of all users in the step 2 is as follows:
wherein B represents the bandwidth of the channel, K represents the number of users, R t Indicating the total rate of the user, gamma k Representing the signal to interference plus noise ratio of the kth user.
Preferably, the total power consumption model of the system described in step 3:
where L e {1,., L }, L represents the number of base stations, K is {1,., K }, K represents the number of users, P t Representing the total power consumption of the system, x l ∈{0，1}，x l Indicating the switch state of the first base station, x l =1 indicates that the first base station is in an on state, and can transmit data to the user, x l =0 indicates that the first base station is in an off state, v indicates the power amplification factor of the base station, w l，k Is the beamforming vector for user k at base station i,for the transmit power consumption of the base station, the transmit power beam of the base station to the user may be represented as a beam forming vector transpose multiplication; p (P) l Is the circuit loss of the first base station, P k Is the circuit loss for user k;
preferably, the objective function in step 4 is a maximum value for maximizing the energy efficiency of the system in the heterogeneous network, and is specifically defined as follows:
the constraint conditions in the step 4 are as follows:
where L e {1,., L }, L represents the number of base stations, K is {1,., K }, K represents the number of users, w= [ w ] 1，1 ，...，w L，1 ；...；w 1，K ，...，w L，K ]Representing beamforming vectors, w, for all base stations l，k Is the beam forming vector of the kth user at the kth base station;phase shift matrix, θ, representing a reconfigurable smart surface r，n ∈[0，2π]For the phase shift constraint of the nth reflective element of the nth reconfigurable intelligent surface, N e { 1.. r }，N r Representing the number of reflective elements the R-th reconfigurable intelligent surface has, R e { 1..r }, R representing the number of reconfigurable intelligent surfaces; x= [ x ] 1 ，...，x L ] T Representing the switch integer variable, x, of all base stations l ∈{0，1}，x l Indicating the switch state of the first base station, x l =1 indicates that the first base station is in an on state, and can transmit data to the user, x l =0 indicates that the first base station is in the off state. EE represents the energy efficiency of the system, B represents the channelBandwidth, R t Indicating the total rate of the user, gamma k Representing the signal-to-interference-and-noise ratio of the kth user, v representing the power amplification factor of the base station,/->For transmitting power consumption of base station, P max Representing the maximum transmit power of the total base station, +.>Representing the total power constraint of the system, P l Is the circuit loss of the first base station, P k Is the circuit loss of user k, +.>Representing the equivalent channel of the channel from the first base station to the kth user, delta representing additive white gaussian noise.
Step 4, the objective function is a non-convex function, lagrange dual 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 programming in sequence, wherein the optimal solutions of gamma, y and z can be solved by leading the derivative to be zero;
the access selection constraint of the base station involves integer variables, and auxiliary variables d are required to be introduced for equivalent replacement;
through the introduction of γ, y, z and d, the objective function has been optimized as 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 has proved to have convergence.
Based on the beam vector w of all the optimized base stations obtained in the step 4, the phase shift matrix theta on the optimized reconfigurable intelligent surface is shown, the access selection variable x of all the optimized base stations is shown, the system reconfigures the wireless communication environment, adjusts the number of working base stations, reduces the energy consumption, improves the network communication efficiency and improves the communication performance of the system.
Compared with the prior art, the method has the beneficial effects that aiming at the problem that the existing energy-saving communication method in a general scene including a dense scene does not fully consider the huge power consumption caused by a plurality of base stations after the reconfigurable intelligent surface is introduced, the method introduces a base station on/Guan Zhengshu variable, and provides the communication network energy-saving method based on the reconfigurable intelligent surface. Compared with the existing energy-saving method, the energy-saving communication method combining the intelligent surface and the base station turn-off can reduce the energy consumption to the greatest extent on the premise of effectively guaranteeing or even improving the user experience, and achieves a better energy-saving effect.
Drawings
Fig. 1: is a system model diagram of an embodiment of the present invention.
Fig. 2: is a model diagram based on a reconfigurable intelligent surface according to an embodiment of the invention.
Fig. 3: is a solving step diagram of the target optimization problem in the embodiment of the invention.
Fig. 4: is a flow chart of the method of the invention.
Detailed Description
The present invention 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 invention 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 invention.
The invention provides a communication network energy-saving method based on a reconfigurable intelligent surface technology, which starts from the actual base station energy consumption and the user service quality, and comprises the following steps:
the communication network energy saving method based on the reconfigurable intelligent surface is characterized by comprising the following steps of:
step 1: constructing a reconfigurable intelligent surface-assisted heterogeneous network system;
preferably, the heterogeneous network system assisted by the reconfigurable intelligent surface in step 1 includes l=5 base stations, k=10 users, a base station access module, and r=4 reconfigurable intelligent surfaces;
the base station access module is connected with the L base stations in sequence 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 in a wireless mode;
the L base stations judge whether the base stations allow access according to the corresponding switch integer variables, the state of M base stations in the L base stations is further determined to be open, namely the access is allowed, and the M base stations with the open states are further defined as M access 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;
the transmission signal model of each base station in step 2 is as follows:
wherein ,ml Representing the transmission signal of the first base station, K representing the number of users, w l，k Is the beam forming vector of the kth user at the ith base station, s k Signal value representing kth user, l e 1, l]L represents the number of base stations;
the received signal model of each user in step 2 is as follows:
wherein ,
where L e {1,., L }, L denotes the number of base stations, R e {1,., R }, R represents the number of reconfigurable intelligent surfaces, k.epsilon. { L.. The number of base stations, K }, K represents the number of base stations, y k Representing the signal received by the kth user, x l ∈{0，1}，x l A switch integer variable, x, representing the first base station l =1 indicates that the first base station is in an on state, and can transmit data to the user, x l =0 indicates that the first base station is in an off state, which can save energy consumption,equivalent channel representing the channel from the first base station to the kth user,/for the channel from the kth base station to the kth user>Representing the channel from the first base station to the kth user,/for the kth user>Representing the channel from the (r) th reconfigurable intelligent surface to the (k) th user, G r，l Representing the channel from the first base station to the r reconfigurable intelligent surface. m is m l Representing the transmission signal of base station/n k Is additive white Gaussian noise, phi r Representing the phase shift matrix of the r-th reconfigurable intelligent surface,
θ r，n ∈[0，2π]for the phase shift constraint of the nth reflective element of the nth reconfigurable intelligent surface, N e { 1.. r }，N r Representing the number of reflective elements that the r-th reconfigurable intelligent surface has;
the signal-to-interference-and-noise ratio model of each user in step 2 is as follows:
where L e {1,., L }, L represents the number of base stations, K is { l..A., K }, K represents the number of users, k.epsilon.1, K].γ k Representing the signal to interference plus noise ratio, x, of the kth user l ∈{0，1}，x l Indicating the switch state of the first base station, x l =1 indicates that the 1 st base station is in an on state, and can transmit data to the user, x l =0 means that the 1 st base station is in the off state,equivalent channel, w, representing the channel from the first base station to the kth user l，k Representing the beam forming vector of the kth user at the ith base station, delta representing additive white gaussian noise;
and 2, the total rate model of all users in the step 2 is as follows:
wherein B represents the bandwidth of the channel, K represents the number of users, R t Indicating the total rate of the user, gamma k Representing the signal to interference plus noise ratio of the kth user.
Step 3: constructing a total power consumption model of the system;
step 3, a total power consumption model of the system:
where L e {1,., L }, L represents the number of base stations, K is {1,., K }, K represents the number of users, P t Representing the total power consumption of the system, x l ∈{0，1}，x l Indicating the switch state of the first base station, x l =1 indicates that the first base station is in an on state, and can transmit data to the user, x l =0 indicates that the first base station is in an off state, v indicates the power amplification factor of the base station, w l，k Is the beamforming vector for user k at base station i,for the transmit power consumption of the base station, the transmit power beam of the base station to the user may be represented as a beam forming vector transpose multiplication; p (P) l Is the circuit loss of the first base station, P k Is the circuit loss for user k;
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 the switch integer variable of the base station; under the constraint condition, optimizing the beam vectors of all the base stations, the phase shift matrix of the reconfigurable intelligent surface and the switch integer variable of all the base stations by using an objective function maximization as an optimization target through an alternating direction multiplier method, so that the energy efficiency of the system in the heterogeneous network reaches the maximum value, and obtaining the optimized beam vectors of all the base stations, the phase shift matrix of the reconfigurable intelligent surface after optimization and the switch integer variable of all the base stations after optimization;
the objective function in step 4 is the maximum value of the energy efficiency of the system in the maximum heterogeneous network, and is specifically defined as follows:
the constraint conditions in the step 4 are as follows:
wherein, L is { L, the number of the first and second layers, L, L represents the number of base stations and, K is {1,., K }, K represents the number of users, w= [ w ] 1，1 ，...，w L，1 ；...；w 1，K ，...，w L，K ]Representing beamforming vectors, w, for all base stations l，k Is the beam forming vector of the kth user at the kth base station;phase shift matrix, θ, representing a reconfigurable smart surface r，n ∈[0，2π]For the phase shift constraint of the nth reflective element of the nth reconfigurable intelligent surface, N is { 1., -, N, N }, N r Representing the number of reflective elements the R-th reconfigurable intelligent surface has, R e { 1..r }, R representing the number of reconfigurable intelligent surfaces; x= [ x ] 1 ，...，x L ] T Representing the switch integer variable, x, of all base stations l ∈{0，1}，x l Indicating the switch state of the first base station, x l =1 indicates that the first base station is in an on state, and can transmit data to the user, x l =0 indicates that the first base station is in the off state. EE represents the energy efficiency of the system, B represents the bandwidth of the channel, R t Indicating the total rate of the user, gamma k Representing the signal-to-interference-and-noise ratio of the kth user, v representing the power amplification factor of the base station,/->For transmitting power consumption of base station, P max Representing the maximum transmit power of the total base station, +.>Representing the total power constraint of the system, P l Is the circuit loss of the first base station, P k Is the circuit loss of user k, +.>Equivalent channel representing channel from the first base station to the kth userDelta represents additive gaussian white noise.
Step 4, the objective function is a non-convex function, lagrange dual 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 programming in sequence, wherein the optimal solutions of gamma, y and z can be solved by leading the derivative to be zero;
the access selection constraint of the base station involves integer variables, and auxiliary variables d are required to be introduced for equivalent replacement;
through the introduction of γ, y, z and d, the objective function has been optimized as 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 has proved to have convergence.
Based on the beam vectors w of all the optimized base stations obtained in the step 4, the phase shift matrix theta on the optimized reconfigurable intelligent surface is shown, the access selection variable x of all the optimized base stations is shown, the system reconfigures the wireless communication environment, adjusts the number of working base stations, reduces the energy consumption, improves the network communication efficiency and improves the communication performance of the system;
please refer to fig. 3, which is a diagram illustrating a solving step of the optimization problem, firstly, let
The lagrangian dual transform is applied:
wherein ,γk Is an introduced auxiliary variable, and γ represents the set of all auxiliary variables. Optimal gamma can be achieved byAnd (5) solving. The expression is as follows:
the original optimization problem can be converted into the following form:
assist variable y by score planning k Introducing a ratio term, then:
the optimization problem is updated as follows:
optimal y k Can pass throughAnd (5) solving.
Auxiliary variable z by score planning k Introducing a ratio term, then:
the optimization problem is updated as follows: p (P) 3 ：max w，Φ，x，z f o (w，Φ，x，γ，y，z)
Optimal z k Can pass throughAnd (5) solving.
Integer variable processing:
according to the theorem: definition set Then the vector pair (x, d) belongs to the set Φ, x ε {0,1}, can be deduced K ，d∈{0，1} K ，x＝d。
The optimization problem can be updated as: p (P) 4 ：max w，Φ，x，z，d f o (w，Φ，x，γ，y，z)
Constraint:
(2x-1) T (2x-1)＝L
obviously, at this time P 4 Has been a convex optimization problem.
It should be understood that the foregoing description of the preferred embodiments is not intended to limit the scope of the invention, but rather to limit the scope of the claims, and that those skilled in the art can make substitutions or modifications without departing from the scope of the invention as set forth in the appended claims.
Claims (5)
1. The communication network energy saving method based on the reconfigurable intelligent surface is characterized by comprising the following steps of:
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;
step 3: constructing a total power consumption model of the system;
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 the switch integer variable of the base station; under the constraint condition, the beam vectors of all the base stations, the phase shift matrix of the reconfigurable intelligent surface and the switch integer variable of all the base stations are optimized through an alternating direction multiplier method by maximizing the objective function, so that the energy efficiency of the system in the heterogeneous network reaches the maximum value, and the beam vectors of all the base stations after optimization, the phase shift matrix of the reconfigurable intelligent surface after optimization and the switch integer variable of all the base stations after optimization are obtained.
2. The method for saving energy in a communication network based on a reconfigurable intelligent surface according to claim 1, wherein the heterogeneous network system assisted by the reconfigurable intelligent surface in step 1 comprises L base stations, K users, a base station access module, and R reconfigurable intelligent surfaces;
the base station access module is connected with the L base stations in sequence 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 in a wireless mode;
the L base stations judge whether the base stations allow access according to the corresponding switch integer variables, the state of M base stations in the L base stations is further determined to be open, namely the access is allowed, and the M base stations with the open states are further defined as M access 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.
3. The method for power saving in a communication network based on a reconfigurable intelligent surface of claim 1,
the transmission signal model of each base station in step 2 is as follows:
wherein ,ml Representing the transmission signal of the first base station, K representing the number of users, w l,k Is the beam forming vector of the kth user at the ith base station, s k Signal value representing kth user, l e 1, l]L represents the number of base stations;
the received signal model of each user in step 2 is as follows:
wherein ,
where L e {1,., L }, L denotes the number of base stations, R e {1,., R }, R represents the number of reconfigurable intelligent surfaces, k.epsilon. { 1.. The number of base stations, K }, y k Representing the signal received by the kth user, x l ∈{0,1}，x l A switch integer variable, x, representing the first base station l =1 indicates that the first base station is in an on state, and can transmit data to the user, x l =0 indicates that the first base station is in an off state, which can save energy consumption,equivalent channel representing the channel from the first base station to the kth user,/for the channel from the kth base station to the kth user>Representing the channel from the first base station to the kth user,/for the kth user>Representing the channel from the (r) th reconfigurable intelligent surface to the (k) th user, G r,l Representing the channel from the first base station to the r reconfigurable intelligent surface; m is m l Representing the transmission signal of base station/n k Is additive white Gaussian noise, phi r Phase shift matrix representing the r-th reconfigurable smart surface, W l,j Is the beam forming vector of the jth user at the ith base station;
for the phase shift constraint of the nth reflective element of the nth reconfigurable intelligent surface, N e { 1.. r }，N r Representing the number of reflective elements that the r-th reconfigurable intelligent surface has;
the signal-to-interference-and-noise ratio model of each user in step 2 is as follows:
where L e {1,., L }, L represents the number of base stations, K is {1,., K }, K represents the number of users, k.epsilon.1, K]，γ k Representing the signal to interference plus noise ratio, x, of the kth user l ∈{0,1}，x l Indicating the switch state of the first base station, x l =1 indicates that the first base station is in an on state, and can transmit data to the user, x l =0 indicates that the first base station is in the off state,equivalent channel, w, representing the channel from the first base station to the kth user l,k Representing the beam forming vector of the kth user at the ith base station, delta representing additive white gaussian noise;
and 2, the total rate model of all users in the step 2 is as follows:
wherein B represents the bandwidth of the channel, K represents the number of users, R t Indicating the total rate of the user, gamma k Representing the signal to interference plus noise ratio of the kth user.
4. The method for power saving in a communication network based on a reconfigurable intelligent surface of claim 1,
step 3, a total power consumption model of the system:
where L e {1,., L }, L represents the number of base stations, K is {1,., K }, K represents the number of users, P t Representing the total power consumption of the system, x l ∈{0,1}，x l Indicating the switch state of the first base station, x l =1 indicates that the first base station is in an on state, and canTransmitting data to a user x l =0 indicates that the first base station is in an off state, v indicates the power amplification factor of the base station, w l,k Is the beamforming vector for user k at base station i,for the transmit power consumption of the base station, the transmit power beam of the base station to the user may be represented as a beam forming vector transpose multiplication; p (P) l Is the circuit loss of the first base station, P k Is the circuit loss for user k.
5. The method for power saving in a communication network based on a reconfigurable intelligent surface of claim 1,
the objective function in step 4 is the maximum value of the energy efficiency of the system in the maximum heterogeneous network, and is specifically defined as follows:
the constraint conditions in the step 4 are as follows:
where L e {1,., L }, L represents the number of base stations, K e {1,., K },k represents the number of users, w= [ w ] 1,1 ,...,w L,1 ；...；w 1,K ,...,w L,K ]Representing beamforming vectors, w, for all base stations l,k Is the beam forming vector of the kth user at the kth base station;phase shift matrix, θ, representing a reconfigurable smart surface r,n ∈[0,2π]For the phase shift constraint of the nth reflective element of the nth reconfigurable intelligent surface, N e { 1.. r }，N r Representing the number of reflective elements the R-th reconfigurable intelligent surface has, R e { 1..r }, R representing the number of reconfigurable intelligent surfaces; x= [ x ] 1 ,...,x L ] T Representing the switch integer variable, x, of all base stations l ∈{0,1}，x l Indicating the switch state of the first base station, x l =1 indicates that the first base station is in an on state, data can be transmitted to the user, and x=0 indicates that the first base station is in an off state; EE represents the energy efficiency of the system, B represents the bandwidth of the channel, R t Indicating the total rate of the user, gamma k Representing the signal-to-interference-and-noise ratio of the kth user, v representing the power amplification factor of the base station, P max Representing the maximum transmit power of the total base station, +.>Representing the total power constraint of the system, P l Is the circuit loss of the first base station, P k Is the circuit loss of user k, +.>Equivalent channel representing the channel from the first base station to the kth user, delta representing additive white gaussian noise;
step 4, the objective function is a non-convex function, lagrange dual 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 programming in sequence, wherein the optimal solutions of gamma, y and z can be solved by leading the derivative to be zero;
the access selection constraint of the base station involves integer variables, and auxiliary variables d are required to be introduced for equivalent replacement;
through the introduction of gamma, y, z and d, the objective function has been optimized as a convex optimization problem; the convex optimization problem can be solved iteratively by using a distributed algorithm based on an alternate direction multiplier method and a cvx solver, and the algorithm has convergence;
based on the beam vector w of all the optimized base stations obtained in the step 4, the phase shift matrix theta on the optimized reconfigurable intelligent surface is shown, the access selection variable x of all the optimized base stations is shown, the system reconfigures the wireless communication environment, adjusts the number of working base stations, reduces the energy consumption, improves the network communication efficiency and improves the communication performance of the system.
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