CN116471613A - Energy efficiency optimization method and device for multi-user MIMO system - Google Patents

Energy efficiency optimization method and device for multi-user MIMO system Download PDF

Info

Publication number
CN116471613A
CN116471613A CN202310480508.5A CN202310480508A CN116471613A CN 116471613 A CN116471613 A CN 116471613A CN 202310480508 A CN202310480508 A CN 202310480508A CN 116471613 A CN116471613 A CN 116471613A
Authority
CN
China
Prior art keywords
user
users
leader
ris
channel
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202310480508.5A
Other languages
Chinese (zh)
Other versions
CN116471613B (en
Inventor
陈美娟
祝泓秀
朱晓荣
唐志敏
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing University of Posts and Telecommunications
Original Assignee
Nanjing University of Posts and Telecommunications
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nanjing University of Posts and Telecommunications filed Critical Nanjing University of Posts and Telecommunications
Priority to CN202310480508.5A priority Critical patent/CN116471613B/en
Publication of CN116471613A publication Critical patent/CN116471613A/en
Application granted granted Critical
Publication of CN116471613B publication Critical patent/CN116471613B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • 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
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0426—Power distribution
    • 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
    • H04B7/0452—Multi-user MIMO systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/08—Load balancing or load distribution
    • H04W28/09—Management thereof
    • H04W28/0917—Management thereof based on the energy state of entities
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/08—Load balancing or load distribution
    • H04W28/09—Management thereof
    • H04W28/0958—Management thereof based on metrics or performance parameters
    • 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

Landscapes

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

Abstract

The invention discloses a multi-user MIMO system energy efficiency optimization method and a device, wherein in a wireless communication network, an artificial controllable D2D technology for realizing a wireless communication environment by using RIS is used for relieving the load of a base station side. And selecting a proper user as a Leader user according to the channel quality of the user, and under the condition that the wireless communication environment based on RIS assistance is controllable, preferentially distributing resources for the Leader user by the system, meeting the rate requirement of the Leader user, enabling other users to communicate by using a D2D technology, and enabling the D2D user to promote the rate of the user by multiplexing the resources of the Leader user, so that more terminals can be accommodated in the existing system while energy consumption is not increased. In order to improve the energy efficiency of the system, an optimization target for realizing the maximization of the energy efficiency of the system is provided, and because the problem is an NP-hard problem, the method adopts an alternating optimization method (AO) to divide the problem into three sub-problems for solving, and the sub-problems are respectively a sub-carrier allocation method based on the channel intensity, a power allocation method based on SCA and a phase shift optimization method based on Riemann gradient. The invention can improve the energy efficiency of the system under the condition of limited resources.

Description

Energy efficiency optimization method and device for multi-user MIMO system
Technical Field
The invention belongs to the technical field of mobile communication, and particularly relates to a multi-user MIMO system energy efficiency optimization method and device.
Background
With the continuous updating of the application scene and service demand of the 6G network in the future, the global mobile data traffic will show an explosive growth trend, and the global Internet protocol data traffic is expected to increase by 55% each year from the beginning of 2020 to the end of 2023, and finally reaches 5016EB per month, and the data rate can reach 1TB/s. On the premise of limited resources, how to accommodate more wireless terminals is one of the problems to be solved in the existing 5G communication technology.
The RIS can realize intelligent reconstruction of the wireless propagation environment, and in a future 6G network, the RIS has wider application scenes in the aspects of improving the network capacity, enhancing the degree of freedom of the wireless propagation environment, eliminating network coverage blind areas, safety communication and the like; the D2D (Device-to-Device) can alleviate centralized traffic brought by a large number of terminal devices accessing the network for the base station, reduce the load of the base station, realize sharing of spectrum resources, and further improve the spectrum utilization rate.
The prior art researches are only based on the improvement of the energy efficiency of the RIS-assisted wireless communication network, and the user terminal can be used as a signal source to communicate with other users, so that the load of a base station is reduced, the energy efficiency of a system is improved, and more network terminals can be allowed to be accessed on the premise of limited network resources.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a multi-user MIMO system energy efficiency optimization method and device, which can effectively optimize the energy efficiency of the multi-user MIMO system.
In order to achieve the above purpose, the invention is realized by adopting the following technical scheme:
in a first aspect, the present invention provides a method for optimizing energy efficiency of a multi-user MIMO system, including the steps of:
step 1, establishing a multi-area user communication system model, and establishing a channel model among users, RIS and base stations;
step 2, selecting a proper user as a leader according to the channel quality of the user based on the channel model;
step 3, based on the determined leader user, under the condition of RIS auxiliary communication, maximizing the energy efficiency of the system by means of the resource allocation mode of the SAPA, and establishing an optimization problem with the aim of maximizing the energy efficiency of the system;
and 4, decomposing the constructed optimization problem into three sub-problems to solve, wherein the sub-problems comprise sub-carrier allocation, power allocation and phase shift optimization problems, and obtaining a sub-carrier allocation result, a power allocation result and a phase shift optimization result, namely an optimization result.
Further, in step 1, a multi-zone user communication system model is first established,
let the horizontal distance D from the user equipment to RIS be D, and center on the user equipment 1 In the area of radius, the user equipment accepts the RIS reflected signal,
according to the different position distribution, different areas are defined, users in the different areas select RIS nearby to communicate, a proper Leader user is selected to perform subcarrier allocation according to the channel quality of the users in the areas, the rate requirement of the Leader user is preferentially met, other users in the group communicate in a D2D communication mode, RX users in the D2D pair forward through the power of TX users and multiplex subcarriers of the Leader user, and the rate of the RX users is improved.
In the orthogonal D2D communication mode, that is, for spectrum resources not allocated to the Leader user, the D2D device can be used, in which case complex interference due to multiplexing of spectrum resources can be avoided.
Suppose that users are divided into T areas according to different positions, and the number of users in the areas is differentSelecting different numbers of Leader, and communicating other users in D2D mode, wherein each region is provided with I 1 ，I 2 ，...，I T Each user selects its nearest RIS to communicate, i.e., let the set of RIS be r= {1,2,..r }, each RIS contains N reflecting units. The antenna number of the base station is M t Defining the channel for the Leader to reach the BSThe channel link for the Leader to reach RIS is +.>The link of RIS to BS can be expressed as +.>Among D2D users, TX i With Rx i The channel between them can be expressed as +.>The channel between Txi and Rxi' can be denoted as h i，i' E C, txi channel to BS isThe channel between Txi and Leader is +.>Taking the leader of the t-th area as an example, the channel of the leader user in the RIS-assisted network can be expressed as:
wherein the channel of the D2D pair can be expressed as:
for a distance from the userThe channels of the remote RIS can be approximated by the CSCG distribution, namely h s ～CN(0，Ng s，b g l，s ) The channel gain due to interference caused by scattering can be expressed as:
the channel from the base station directly to the user is subject to rayleigh fading, the RIS-assisted channel link is subject to rice fading, the channel of the RIS-BS and the Leader-RIS:
wherein L is 1 And L 2，l Is the path loss parameter, epsilon is the Rician factor,θ is an angle variable, ++>And->Representing NLOS, subject to CNf (0, 1).
Further, in step 2, according to the number of users in the area, a proper number of leader is selected, the channel quality is centered, the other users communicate in a D2D communication mode, the base station preferentially allocates resources for the leader users, and the D2D users multiplex the resources of the leader users.
Further, in step 3, in the case of RIS assisted communication, the energy efficiency of the system is maximized by means of the resource allocation method of SAPA, and in order to achieve this objective, an optimization problem is established with the aim of maximizing the energy efficiency of the system.
According to channel models among different users, the signal to noise ratio of a Leader user and a D2D user in receiving the subcarrier m is as follows:
according to the shannon formula, the rates of the Leader user and the D2D user can be obtained as follows:
representing the transmission power of the Leader user on subcarrier m,/for>The transmission power of the D2D user at the subcarrier m is indicated, and a single subcarrier can be used by only one Leader user. P (P) s |h s | 2 Representing interference signals generated by RIS scattering in other areas, < >>Indicating interference caused by the D2D users multiplexing the subcarriers of the header users within the region,representing interference of leader users on D2D users in an area, +.>Indicating interference generated by other D2D users within the region, where ρ is a 0,1 variable indicating whether the sub-carriers are used by the users.
In summary, the optimization problem is:
s.t.|Θ n，n |＝1 (1a)
wherein constraint (1 a) is a unit mode constraint of RIS, (1 b) (1 c) is a rate constraint of a Leader user and a D2D user respectively, aiming at meeting QoS requirements of the users, constraint (1 e) (1 f) is a power constraint, and constraint (1 h) is a coefficient constraint allocated to subcarriers, wherein a single subcarrier can only be occupied by one Leader user.
Further, in step 4, according to the optimization problem set forth in step 3, the problem is an NP-hard problem, and the problem is decomposed into three sub-problems to be solved respectively, where the three sub-problems are respectively: subcarrier allocation, power allocation and phase shift optimization problems.
Further, the subcarrier allocation is mainly based on the channel strength of the user, and it is assumed that the power of each subcarrier is the same, that is:
under the condition that the power is known, sub-carriers are preferentially allocated to the Leader users so as to meet the rate requirements of the Leader users, the basic principle is that sub-carriers with larger gains are preferentially allocated to the Leader users with larger deviation from the basic rate, if the sub-carriers are remained, the remained sub-carriers are allocated to the D2D users, meanwhile, the D2D users multiplex the sub-carriers of the Leader users, but not all users can multiplex the sub-carriers of the Leader users, and only the D2D users have better channel conditions on the sub-carriers and do not generate interference on other users can multiplex.
In order to verify that the subcarrier of the D2D user multiplexing header user can realize the rate improvement, omega is defined l Indicating that the Leader user is using the set of subcarriers,indicating the set of leader users using subcarrier m, Ω i Representing the set of sub-carriers used by the D2D user,/->Representing a set of D2D users using subcarrier m, Ω m Representing the set of users using subcarrier m, the rate change after D2D user multiplexing of the subcarriers can be obtained as:
the content in the first bracket is the rate that the D2D user can realize on the subcarrier m, the content in the second bracket is the rate that the D2D user multiplexes the subcarriers, and the content in the third bracket is the rate that the D2D user does not multiplex the subcarriers.
Further, the subcarrier allocation method includes the following steps:
initializing: setting up
Setting available
Subcarrier set m= {1,2,..
Step 1: sub-carriers are allocated to the leader users:
step 1-1 traverses each Leader user (l=1, 2, …, L) within the T region (t=1, 2, …, T);
step 1-2 selectionA corresponding user l;
step 1-3 arbitrary m.epsilon.M ifOmega. Then l ＝Ω l U { M }, M=M- { M }, calculate +.>UpdatingUpdate->Ω m ，/>
Step 1-4Or->
Step 2: sub-carriers are allocated to the D2D user:
step 2-1 if
Step 2-2, arbitrary m.epsilon.M, ifOmega. Then i ＝Ω i U { M }, M=M- { M }, calculate +.>Update->Ω m ，/>
Step 2-3
Step 3: D2D user multiplexing subcarriers:
step 3-1 traversing the subcarrier set Ω of the Leader user l
Step 3-2 arbitrary mεΩ l If (if)Then sub-carriers are allocated to user i, update +.>Ω m Calculate->
Step 3-3 ifAnd->Returning to step 1
Further, the power allocation is solved mainly by means of continuous convex optimization (SCA) and Dinkelbach methods. Given the subcarrier allocation variables, for a Leader user, multiplexing subcarriers of the Leader user by the D2D user may cause interference to the Leader user, and for the Leader user and the D2D user in the region t, the rate may be expressed as follows:
on the premise that the subcarrier allocation situation is known, the power of a Leader user and the power of a D2D user are optimized, and the optimization problem is changed into (P2):
the optimization problem (P2) is an NP-hard problem, the solution is carried out by adopting the SCA method, the constraint (2 a) (2 b) is a non-convex constraint, the constraint can be converted into a difference value form of two concave functions, then one of the constraint can be converted into a standard convex function form by using a first-order convex approximation mode, and the conversion process is as follows:
derivative of variable p:
assume an iterative sequence { p } j Using the first order Taylor expansion constraint (2 a) (2 b) can be approximated as:
namely:
the optimization problem P2 can be converted into (P3):
through transformation, constraint conditions of the optimization problem (P3) are convex, but an optimization objective function is a concave-convex fraction planning problem, and the Dinkelbach method is adopted to transform the problems. Assuming that the energy efficiency parameter η is introduced, the optimization objective function becomes (P4):
the above power allocation method includes the steps of:
initializing: known omega l ，Ω i ，Ω m Setting initialization power P, calculating initial R, epsilon 1 ，ε 2 ，num＝1
And (3) circulation:
step 1: calculation of
Step 2: introducing the variable eta
Step 3: solving optimization problem with cvx (P4)
Step 4: num=num+1, p num ＝p opt
Step 5: calculation of
Until:
furthermore, the phase shift matrix after RIS optimization is mainly solved by using a Riemann gradient method. Under the condition that subcarrier allocation and power allocation are known, solving a phase shift matrix of the RIS, wherein users using RIS to assist communication are Leader users and TX users in a D2D pair, under the condition that energy efficiency is allowed, maximizing the sum rate of the Leader users and the RX users in the D2D pair, and the optimization problem becomes as follows:
s.t|Θ n，n |＝1 (5a)
to make the expression clearer, the following variables are defined:
θ＝[θ 1 ，θ 2 ，...，θ N ] H ，and then can obtain
The optimization problem of the unit mode constraint (5 a) can be solved by adopting a Riemann manifold method.
First, a Riemann gradient is calculated, and the objective function is assumed to be the projection of the fRiemann gradient or the Euclidean gradient on manifold space:
wherein the Euclidean gradient is:
determining the search direction according to the calculated Riemann gradient:
d j+1 ＝-gradf+τ 1 T(d j ) (29)
projecting the tangent vector onto the manifold space:
the Riemann gradient method comprises the following steps:
it is known that: setting an initial point u 1 Convergence tolerance epsilon, step size delta j (j＝1)
According to the known conditionsComputing Riemann gradients
Repeating:
step 1: determining an initial search direction d j Selecting a suitable step size tau 1
Step 2: determining search direction d from Riemann gradient j+1 ＝-gradf+τ 1 T(d j )
Step 3: selection of the appropriate τ 2 Projecting tangent vectors onto manifold space
Step 4: j=j+1
Until:
setting: Θ=diag (θ)
Further, the method further comprises:
step 5: according to the method, a method flow is provided, and the feasibility of the method is verified through a simulation experiment.
In a second aspect, the present invention provides an apparatus for optimizing energy efficiency of a multi-user MIMO system based on RIS and D2D assistance, the apparatus comprising a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to the first aspect.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a multi-user MIMO system energy efficiency optimization method and device, which adopt RIS and D2D technology to assist users to communicate in urban scenes, and improve the energy efficiency of the users on the premise of limited resources.
Experimental results show that compared with a system without RIS and without assistance of a D2D technology, the system based on RIS and D2D technology assisted communication provided by the invention has better energy efficiency on the premise of limited resources, and in addition, the resource allocation mode of the SAPA is better than other resource allocation modes.
The RIS can enable the wireless propagation environment to be intelligent and controllable, the D2D technology is a technology for relieving the load of a base station and realizing communication between adjacent devices, the two technologies are jointly applied to a wireless communication network, and the energy efficiency of a user is improved on the premise of limited resources. In future 6G communication, a green and environment-friendly communication mode is attracting attention, and how to meet the rate requirement of more users under the premise of limited resources is one of the key problems of future research, and the two modes are used for assisting the existing communication network together according to the passive characteristic of RIS and the characteristic that the D2D technology user does not directly communicate with the base station, so that the goal of green communication can be well realized.
Drawings
FIG. 1 is a diagram of a model of a communication system based on multiple RIS and D2D assistance;
FIG. 2 is a flow chart of a method;
fig. 3 shows sum rate of D2D users (D2D user number fixed) under different mechanisms;
fig. 4 shows the sum rate (fixed number of leader users) of D2D users under different mechanisms;
fig. 5 shows D2D user energy efficiency (leader user number fixed) for different D2D user numbers;
fig. 6D 2D user energy efficiency change (D2D user number fixed) for different Leader users;
fig. 7 illustrates the energy efficiency of the user in different communication modes.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
Embodiment one:
in the invention, the figure 1 is taken as a research scene which consists of three parts of multiple users, multiple RISs and a base station, and the energy efficiency of a system is researched on the premise of RIS and D2D auxiliary communication, and the analysis and optimization of the total energy efficiency of the system brought by adopting a SAPA resource allocation mode and RIS are emphasized.
Specifically, the multi-user MIMO system energy efficiency optimization method based on RIS and D2D assistance comprises the following steps:
step 1: establishing a multi-area user communication system model, and establishing a channel model among users, RIS and base stations;
FIG. 1 is a diagram of an application scenario of the present invention, where the horizontal distance D between a user device and a RIS is set to be D, and the user device is taken as the center 1 In the area of radius, the user equipment receives the RIS reflected signal, for the area D 1 ≤d≤D 2 RIS in range, the user equipment of its main service is located at the left side of the upper diagram and is not in the range of the area under study, but the scattered signal generated by RIS will generate a certain interference to the area under study when the distance is larger than D 2 When this area user equipment no longer receives the RIS reflected or scattered signal. According to the different position distribution, different areas are defined, users in the different areas select RIS nearby to communicate, a proper Leader user is selected to perform subcarrier allocation according to the channel quality of the users in the areas, the rate requirement of the Leader user is preferentially met, other users in the group communicate in a D2D communication mode, RX users in the D2D pair forward through the power of TX users and multiplex subcarriers of the Leader user, and the rate of the RX users is improved. The invention adopts an orthogonal (coverage) D2D communication mode, i.e. for spectrum resources not allocated to the Leader user, the D2D device can be used, in which case complex interference due to multiplexing of spectrum resources can be avoided.
Assuming that users are divided into T areas according to different positions, different numbers of Leader are selected according to different numbers of users in the areas, other users communicate in a D2D mode, and each area is provided with I 1 ，I 2 ，...，I T Each user selects its nearest RIS to communicate, i.e., let the set of RIS be r= {1,2,..r }, each RIS contains N reflecting units. The antenna number of the base station is M t Defining the channel for the Leader to reach the BSThe channel link for the Leader to reach RIS is +.>The link of RIS to BS can be expressed as +.>Among D2D users, TX i With Rx i The channel between them can be expressed as +.>The channel between Txi and Rxi' can be denoted as h i，i' E C, txi channel to BS isThe channel between Txi and Leader is +.>Taking the leader of the t-th area as an example, the channel of the leader user in the RIS-assisted network can be expressed as:
wherein the channel of the D2D pair can be expressed as:
for RIS far from users, the users mainly served by the RIS are on the left side in the upper diagram, and the reflected signals are provided for the left side users, and meanwhile, scattered signals with random phases are generated to generate interference for the users in the area, the channels can be approximated by CSCG (Circularly Symmetric Complex Gaussian) distribution, namely h s ～CN(0，Ng s，b g l，s ) The channel gain due to interference caused by scattering can be expressed as:
assuming that the channel from the base station directly to the user is subject to Rayleigh fading, the RIS-assisted channel link is subject to Rayleigh fading, taking the channels of RIS-BS and Leader-RIS as examples:
wherein L is 1 And L 2，l Is the path loss parameter, epsilon is the Rician factor,θ is an angle variable, ++>And->Representing NLOS, subject to CNf (0, 1).
Step 2: selecting a proper user as a leader according to the channel quality of the user;
according to the number of users in the area, a proper number of leader is selected, the channel quality is centered, the rest of users communicate in a D2D communication mode, the base station preferentially allocates resources for the leader users, and the D2D users multiplex the resources of the leader users.
Step 3: by means of the resource allocation mode of the SAPA and the assistance of the RIS, the energy efficiency of the system is improved, the energy efficiency of the system is maximized by constructing an optimization problem, and the optimization variables are determined to be subcarrier allocation coefficients, power allocation and a phase shift matrix of the RIS; forming constraint conditions according to the rate requirements of users, power constraint and RIS phase shift unit mode constraint:
in the case of RIS assisted communication, the energy efficiency of the system is maximized by means of the resource allocation manner of SAPA, and in order to achieve this objective, an optimization problem is established with the aim of maximizing the energy efficiency of the system. According to channel models among different users, the signal to noise ratio of a Leader user and a D2D user in receiving the subcarrier m is as follows:
according to the shannon formula, the rates of the Leader user and the D2D user can be obtained as follows:
representing the transmission power of the Leader user on subcarrier m,/for>The transmission power of the D2D user at the subcarrier m is indicated, and a single subcarrier can be used by only one Leader user. P (P) s |h s | 2 Representing interference signals generated by RIS scattering in other areas, < >>Indicating interference caused by the D2D users multiplexing the subcarriers of the header users within the region,representing interference of leader users on D2D users in an area, +.>Indicating interference generated by other D2D users within the region, where ρ is a 0,1 variable indicating whether the sub-carriers are used by the users.
In summary, the optimization problem is:
/>
s.t.|Θ n，n |＝1 (1a)
wherein constraint (1 a) is a unit mode constraint of RIS, (1 b) (1 c) is a rate constraint of a Leader user and a D2D user respectively, aiming at meeting QoS requirements of the users, constraint (1 e) (1 f) is a power constraint, and constraint (1 h) is a coefficient constraint allocated to subcarriers, wherein a single subcarrier can only be occupied by one Leader user. The problem is an NP-hard problem, which is solved by adopting an alternative optimization method, and the optimization problem is divided into three sub-problems to be solved respectively.
Step 4: decomposing the constructed NP-hard problem into three sub-problems for solving, wherein the sub-problems are mainly decomposed into sub-carrier allocation, power allocation and phase shift optimization problems, the sub-carrier allocation is mainly performed based on the channel intensity of users, the power allocation is mainly performed by means of continuous convex optimization (SCA) and Dinkelbach methods, the phase shift matrix after RIS optimization is mainly performed by means of Riemann gradient methods, and the method is shown in a flow chart of figure 2
According to the optimization problem in the step 3, the problem is an NP-hard problem, and in order to solve the optimization problem, the optimization problem is decomposed into three sub-problems to be respectively solved, wherein the three sub-problems are respectively: subcarrier allocation, power allocation and phase shift optimization problems. The subcarrier allocation is mainly based on the channel strength of the user, and it is assumed that the power of each subcarrier is the same, that is:
under the condition that the power is known, sub-carriers are preferentially allocated to the Leader users so as to meet the rate requirements of the Leader users, the basic principle is that sub-carriers with larger gains are preferentially allocated to the Leader users with larger deviation from the basic rate, if the sub-carriers are remained, the remained sub-carriers are allocated to the D2D users, meanwhile, the D2D users multiplex the sub-carriers of the Leader users, but not all users can multiplex the sub-carriers of the Leader users, and only the D2D users have better channel conditions on the sub-carriers and do not generate interference on other users can multiplex. Multiplexing a header for use in order to authenticate a D2D userThe subcarrier of the user can realize the rate improvement, and the omega is defined l Indicating that the Leader user is using the set of subcarriers,indicating the set of leader users using subcarrier m, Ω i Representing the set of sub-carriers used by the D2D user,/->Representing a set of D2D users using subcarrier m, Ω m Representing the set of users using subcarrier m, the rate change after D2D user multiplexing of the subcarriers can be obtained as: />
The content in the first bracket is the rate that the D2D user can realize on the subcarrier m, the content in the second bracket is the rate that the D2D user multiplexes the subcarriers, and the content in the third bracket is the rate that the D2D user does not multiplex the subcarriers.
The main content of the method is as shown in table 1:
TABLE 1 subcarrier allocation method
/>
The power allocation is mainly solved by means of a continuous convex optimization (SCA) method and a Dinkelbach method. Given the subcarrier allocation variables, for a Leader user, multiplexing subcarriers of the Leader user by the D2D user may cause interference to the Leader user, and for the Leader user and the D2D user in the region t, the rate may be expressed as follows:
on the premise that the subcarrier allocation situation is known, the power of a Leader user and the power of a D2D user are optimized, and the optimization problem is changed into (P2):
the optimization problem (P2) is an NP-hard problem, the solution is carried out by adopting the SCA method, the constraint (2 a) (2 b) is a non-convex constraint, the constraint can be converted into a difference value form of two concave functions, then one of the constraint can be converted into a standard convex function form by using a first-order convex approximation mode, and the conversion process is as follows:
/>
derivative of variable p:
assume an iterative sequence { p } j Using the first order Taylor expansion constraint (2 a) (2 b) can be approximated as:
namely:
the optimization problem P2 can be translated into:
through transformation, constraint conditions of the optimization problem (P3) are convex, but an optimization objective function is a concave-convex fraction planning problem, and the Dinkelbach method is adopted to transform the problems. Assuming that the energy efficiency parameter η is introduced, the optimization objective function becomes:
/>
the basic contents of the above power allocation method are shown in table 2:
TABLE 2 Power distribution method
The phase shift matrix after RIS optimization is mainly solved by a Riemann gradient method. Under the condition that subcarrier allocation and power allocation are known, the section solves a phase shift matrix of the RIS, under the scene of the invention, users using RIS to assist communication are Leader users and TX users in a D2D pair, and under the condition that the invention wants to maximize the sum rate of the Leader users and the RX users in the D2D pair under the condition that energy efficiency is allowed, so in the section, the optimization problem becomes as follows:
s.t|Θ n，n |＝1 (5a)
to make the expression clearer, the following variables are defined:
θ＝[θ 1 ，θ 2 ，...，θ N ] H ，
and then can obtain
The optimization problem of the unit mode constraint (5 a) can be solved by adopting a Riemann manifold method.
First, a Riemann gradient is calculated, and the objective function is assumed to be the projection of the fRiemann gradient or the Euclidean gradient on manifold space:
wherein the Euclidean gradient is:
determining the search direction according to the calculated Riemann gradient:
d j+1 ＝-gradf+τ 1 T(d j ) (29)
projecting the tangent vector onto the manifold space:
the main contents of the Riemann gradient method are shown in Table 3:
table 3 phase shift optimization method
To illustrate the effectiveness of the method of the present invention, an example is given below. The invention uses matlabR2020a simulation to carry out experimental verification on the proposed method, and under the experimental scene, 1 base station, 3 RIS and different numbers of users are assumed.
Table 4 is the simulated system parameters:
table 4 simulation parameters
Parameters (parameters) Value of
Route loss for RIS auxiliary links 35.6+22.01gd
Direct link path loss 32.6+36.7lgd
Noise density -174dBm/Hz
User maximum power constraint 20dBm
Subcarrier bandwidth 15kHz
Number of subcarriers 30
Embodiment two:
the embodiment provides an energy efficiency optimization device of a multi-user MIMO system based on RIS and D2D assistance, which comprises a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to embodiment one.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.

Claims (10)

1. A multi-user MIMO system energy efficiency optimization method based on RIS and D2D assistance is characterized in that: the method comprises the following steps:
establishing a multi-area user communication system model, and establishing a channel model among users, RIS and base stations;
based on the channel model, selecting a proper user as a leader according to the channel quality of the user;
based on the determined leader user, under the condition of RIS auxiliary communication, the energy efficiency of the system is maximized by means of the resource allocation mode of the SAPA, and an optimization problem is established with the aim of maximizing the energy efficiency of the system;
and decomposing the constructed optimization problem into three sub-problems to solve, wherein the three sub-problems comprise sub-carrier allocation, power allocation and phase shift optimization problems, and obtaining a sub-carrier allocation result, a power allocation result and a phase shift optimization result, namely an optimization result.
2. The method for optimizing energy efficiency of a multi-user MIMO system based on RIS and D2D assistance according to claim 1, wherein: establishing a multi-zone user communication system model, establishing a channel model between a user, a RIS and a base station, comprising
Let the horizontal distance D from the user equipment to RIS be D, and center on the user equipment 1 In the area of radius, the user equipment accepts the RIS reflected signal,
according to different position distribution, different areas are defined, users in different areas select RIS nearby to communicate, appropriate Leader users are selected to perform subcarrier allocation according to channel quality of the users in the areas, the rate requirements of the Leader users are preferentially met, other users in the group communicate in a D2D communication mode, RX users in the D2D pair forward through power of TX users and multiplex subcarriers of the Leader users, and the rate of the RX users is improved;
adopting an orthogonal D2D communication mode, namely, for spectrum resources which are not allocated to a Leader user, D2D equipment can be used;
assuming that users are divided into T areas according to different positions, different numbers of Leader are selected according to different numbers of users in the areas, other users communicate in a D2D mode, and each area is provided with I 1 ，I 2 ，...，I T Each user selects an RIS close to the user to communicate, namely, the set of RIS is set as r= {1,2,..r }, and each RIS contains N reflecting units; the antenna number of the base station is M t Defining the channel for the Leader to reach the BSThe channel link for the Leader to reach RIS is +.>The link of RIS to BS can be expressed as +.>Among D2D users, TX i With Rx i The channel between them can be expressed as +.>The channel between Txi and Rxi' can be denoted as h i,i' E C, txi channel to BS isTxi and LeaderThe channel between is->Taking the leader of the t-th area as an example, the channel of the leader user in the RIS-assisted network can be expressed as:
wherein the channel of the D2D pair can be expressed as:
for RIS far from user, its channel can be approximated by CSCG distribution, i.e. h s ～CN(0,Ng s,b g l,s ) The channel gain due to interference caused by scattering can be expressed as:
the channel from the base station directly to the user is subject to rayleigh fading, the RIS-assisted channel link is subject to rice fading, the channel of the RIS-BS and the Leader-RIS:
wherein L is 1 And L 2,l Is the path loss parameter, epsilon is the Rician factor,is an angle variable +.>And->Representing NLOS, subject to->
3. The method for optimizing energy efficiency of a multi-user MIMO system based on RIS and D2D assistance according to claim 1, wherein: based on the channel model, the method for selecting the proper user as the leader according to the channel quality of the user comprises the following steps:
according to the number of users in the area, a proper number of leader is selected, the channel quality is centered, the rest of users communicate in a D2D communication mode, the base station preferentially allocates resources for the leader users, and the D2D users multiplex the resources of the leader users.
4. The method for optimizing energy efficiency of a multi-user MIMO system based on RIS and D2D assistance according to claim 1, wherein: in the case of RIS assisted communication, maximizing the energy efficiency of the system by means of the resource allocation of SAPA creates an optimization problem targeting the maximization of the energy efficiency of the system, including:
according to channel models among different users, the signal to noise ratio of a Leader user and a D2D user in receiving the subcarrier m is as follows:
according to the shannon formula, the rates of the Leader user and the D2D user can be obtained as follows:
representing the transmission power of the Leader user on subcarrier m,/for>Representing the transmission power of the D2D user in the subcarrier m, wherein a single subcarrier can only be used by one Leader user; p (P) s h s 2 Representing interference signals generated by RIS scattering in other areas,indicating interference caused by multiplexing subcarriers of Leader users by D2D users in the region, +.>Representing interference of leader users on D2D users in an area, +.>Representing interference generated by other D2D users within the region, where ρ is 0,1A variable indicating whether subcarriers are used by a user;
in summary, the optimization problem is:
s.t.|Θ n,n |＝1(1a)
wherein constraint (1 a) is a unit mode constraint of RIS, (1 b) (1 c) is a rate constraint of a Leader user and a D2D user respectively, aiming at meeting QoS requirements of the users, constraint (1 e) (1 f) is a power constraint, and constraint (1 h) is a coefficient constraint allocated to subcarriers, wherein a single subcarrier can only be occupied by one Leader user.
5. The method for optimizing energy efficiency of multi-user MIMO system based on RIS and D2D assistance according to claim 4, wherein: the optimization problem constructed above is an NP-hard problem, and the NP-hard problem is decomposed into three sub-problems to be solved respectively, wherein the three sub-problems are respectively: subcarrier allocation, power allocation and phase shift optimization problems.
6. The method for optimizing energy efficiency of a multi-user MIMO system based on RIS and D2D assistance of claim 5, wherein: the subcarrier allocation is allocated based on the channel strength of the user, assuming that the power of each subcarrier is the same, i.e.:
under the condition that the power is known, sub-carriers are preferentially allocated to the Leader users so as to meet the rate requirements of the Leader users, wherein a basic principle is to preferentially allocate the sub-carriers with larger gains to the Leader users with larger deviation from the basic rate, if the sub-carriers are remained, the remained sub-carriers are allocated to the D2D users, and meanwhile, the D2D users multiplex the sub-carriers of the Leader users, but not all users can multiplex the sub-carriers of the Leader users, and only the D2D users have better channel conditions on the sub-carriers and can multiplex the sub-carriers when no interference is generated to other users;
in order to verify that the subcarrier of the D2D user multiplexing header user can realize the rate improvement, omega is defined l Indicating that the Leader user is using the set of subcarriers,indicating the set of leader users using subcarrier m, Ω i Representing the set of sub-carriers used by the D2D user,/->Representing a set of D2D users using subcarrier m, Ω m Representing the set of users using subcarrier m, the rate change after D2D user multiplexing of the subcarriers can be obtained as:
the content in the first bracket is the rate that the D2D user can realize on the subcarrier m, the content in the second bracket is the rate that the D2D user multiplexes the subcarrier, and the content in the third bracket is the rate that the D2D user does not multiplex the subcarrier;
initializing: setting up
Setting available
Subcarrier set m= {1,2,..
Step 1: sub-carriers are allocated to the leader users:
step 1-1 traverses each Leader user (l=1, 2, …, L) within the T region (t=1, 2, …, T);
step 1-2 selectionA corresponding user l;
step 1-3 arbitrary m.epsilon.M ifOmega. Then l ＝Ω l U { M }, M=M- { M }, calculate +.>UpdatingUpdate->Ω m ，/>
Step 1-4Or->
Step 2: sub-carriers are allocated to the D2D user:
step 2-1 if
Step 2-2, arbitrary m.epsilon.M, ifOmega. Then i ＝Ω i U { M }, M=M- { M }, calculate +.>Update->Ω m ，
Step 2-3
Step 3: D2D user multiplexing subcarriers:
step 3-1 traversing the subcarrier set Ω of the Leader user l
Step 3-2 arbitrary mεΩ l If (if)Then sub-carriers are allocated to user i, update +.>Ω m Calculation of
Step 3-3 ifAnd->Returning to step 1.
7. The method for optimizing energy efficiency of a multi-user MIMO system based on RIS and D2D assistance of claim 5, wherein: the power allocation is solved mainly by means of a continuous convex optimization (SCA) method and a Dinkelbach method; given the subcarrier allocation variables, for a Leader user, multiplexing subcarriers of the Leader user by the D2D user may cause interference to the Leader user, and for the Leader user and the D2D user in the region t, the rate may be expressed as follows:
on the premise that the subcarrier allocation situation is known, the power of a Leader user and the power of a D2D user are optimized, and the optimization problem is changed into (P2):
the optimization problem (P2) is an NP-hard problem, the solution is carried out by adopting the SCA method, the constraint (2 a) (2 b) is a non-convex constraint, the constraint can be converted into a difference value form of two concave functions, then one of the constraint can be converted into a standard convex function form by using a first-order convex approximation mode, and the conversion process is as follows:
derivative of variable p:
assume an iterative sequence { p } j Using the first order Taylor expansion constraint (2 a) (2 b) can be approximated as:
namely:
the optimization problem P2 can be translated into:
through transformation, constraint conditions of the optimization problem (P3) are convex, but an optimization objective function is a concave-convex fraction planning problem, and the Dinkelbach method is adopted to transform the problems; assuming that the energy efficiency parameter η is introduced, the optimization objective function becomes:
the power distribution method comprises the following steps:
initializing: known omega l ,Ω i ,Ω m Setting initialization power P, calculating initial R, epsilon 1 ,ε 2 ，num＝1
And (3) circulation:
step 1: calculation of
Step 2: introducing the variable eta
Step 3: solving optimization problem with cvx (P4)
Step 4: num=num+1, p num ＝p opt
Step 5: calculation of
Until:
8. the method for optimizing energy efficiency of a multi-user MIMO system based on RIS and D2D assistance of claim 5, wherein: the phase shift matrix after RIS optimization is mainly solved by a Riemann gradient method; under the condition that subcarrier allocation and power allocation are known, solving a phase shift matrix of the RIS, wherein users using RIS to assist communication are Leader users and TX users in a D2D pair, under the condition that energy efficiency is allowed, maximizing the sum rate of the Leader users and the RX users in the D2D pair, and the optimization problem becomes as follows:
s.t|Θ n,n |＝1 (5a)
to make the expression clearer, the following variables are defined:
θ＝[θ 1 ,θ 2 ,...,θ N ] H ，
and then can obtain
The optimization problem of the unit mode constraint (5 a) can be solved by adopting a Riemann manifold method;
first, a Riemann gradient is calculated, and the objective function is assumed to be the projection of the fRiemann gradient or the Euclidean gradient on manifold space:
wherein the Euclidean gradient is:
determining the search direction according to the calculated Riemann gradient:
d j+1 ＝-gradf+τ 1 T(d j ) (29)
projecting the tangent vector onto the manifold space:
the Riemann gradient method comprises the following steps:
it is known that: setting an initial point u 1 Convergence tolerance epsilon, step size delta j (j＝1)
According to the known conditionsComputing Riemann gradients
Repeating:
step 1: determining an initial search direction d j Selecting a suitable step size tau 1
Step 2: determining search direction d from Riemann gradient j+1 ＝-gradf+τ 1 T(d j )
Step 3: selection of the appropriate τ 2 Projecting tangent vectors onto manifold space
Step 4: j=j+1
Until:
setting: Θ=diag (θ).
9. The method for optimizing energy efficiency of a multi-user MIMO system based on RIS and D2D assistance according to claim 1, wherein: the method further comprises the steps of:
the feasibility of the method is verified through simulation experiments.
10. An energy efficiency optimizing device of a multi-user MIMO system based on RIS and D2D assistance is characterized by comprising a processor and a storage medium;
the storage medium is used for storing instructions;
the processor being operative according to the instructions to perform the steps of the method according to any one of claims 1 to 9.
CN202310480508.5A 2023-04-28 2023-04-28 Energy efficiency optimization method and device for multi-user MIMO system Active CN116471613B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310480508.5A CN116471613B (en) 2023-04-28 2023-04-28 Energy efficiency optimization method and device for multi-user MIMO system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310480508.5A CN116471613B (en) 2023-04-28 2023-04-28 Energy efficiency optimization method and device for multi-user MIMO system

Publications (2)

Publication Number Publication Date
CN116471613A true CN116471613A (en) 2023-07-21
CN116471613B CN116471613B (en) 2023-10-10

Family

ID=87175281

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310480508.5A Active CN116471613B (en) 2023-04-28 2023-04-28 Energy efficiency optimization method and device for multi-user MIMO system

Country Status (1)

Country Link
CN (1) CN116471613B (en)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113423112A (en) * 2021-06-18 2021-09-21 东南大学 RIS assisted multi-carrier NOMA transmission system parameter optimization method
CN113596784A (en) * 2021-07-24 2021-11-02 浙江以正通信技术有限公司 Robustness transmission design method of intelligent reflection surface assisted D2D communication system
CN114051225A (en) * 2021-11-16 2022-02-15 郑州大学 Resource allocation method and device based on RIS (RIS) assisted D2D secure communication
CN115412936A (en) * 2022-07-22 2022-11-29 郑州大学 IRS (intelligent resource management) assisted D2D (device-to-device) system resource allocation method based on multi-agent DQN (differential Quadrature reference network)
WO2023037243A1 (en) * 2021-09-07 2023-03-16 King Abdullah University Of Science And Technology Reconfigurable intelligent surface enabled leakage suppression and signal power maximization system and method

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113423112A (en) * 2021-06-18 2021-09-21 东南大学 RIS assisted multi-carrier NOMA transmission system parameter optimization method
CN113596784A (en) * 2021-07-24 2021-11-02 浙江以正通信技术有限公司 Robustness transmission design method of intelligent reflection surface assisted D2D communication system
WO2023037243A1 (en) * 2021-09-07 2023-03-16 King Abdullah University Of Science And Technology Reconfigurable intelligent surface enabled leakage suppression and signal power maximization system and method
CN114051225A (en) * 2021-11-16 2022-02-15 郑州大学 Resource allocation method and device based on RIS (RIS) assisted D2D secure communication
CN115412936A (en) * 2022-07-22 2022-11-29 郑州大学 IRS (intelligent resource management) assisted D2D (device-to-device) system resource allocation method based on multi-agent DQN (differential Quadrature reference network)

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
HONGXIA ZHENG等: ""Data Rate Maximization in RIS-Assisted D2D Communication with Transceiver Hardware Impairments"", ELECTRONICS *

Also Published As

Publication number Publication date
CN116471613B (en) 2023-10-10

Similar Documents

Publication Publication Date Title
Zhang et al. Resource allocation in NOMA-based fog radio access networks
CN106912079B (en) Combined user access selection and resource allocation method in cache heterogeneous network
CN107071914B (en) Dynamic mode selection and energy distributing method in a kind of energy capture D2D network
CN103889061B (en) D2D user resource distribution method based on multicarrier communication
CN112737837A (en) Method for allocating bandwidth resources of unmanned aerial vehicle cluster under high dynamic network topology
CN105451322B (en) A kind of channel distribution and Poewr control method based on QoS in D2D network
CN103260244B (en) D2D uplink resource multiplexing mode switching method in a kind of cellular system
CN110493854B (en) WPT-MEC network uplink and downlink resource allocation and power control mechanism based on optimization theory
CN103379502B (en) The frequency spectrum resource allocation method of D2D technology is introduced in a kind of cellular network
CN111586646B (en) Resource allocation method for D2D communication combining uplink and downlink channels in cellular network
CN104703270B (en) User&#39;s access suitable for isomery wireless cellular network and power distribution method
CN104540139A (en) Allocation method and allocation system for heterogeneous convergence network resources
CN106792451A (en) A kind of D2D communication resource optimization methods based on Multiple-population Genetic Algorithm
CN109788540B (en) Power control and channel allocation method based on energy collection in D2D system
CN110809259A (en) Social relationship-based NOMA enabled D2D communication resource gaming method
CN101164301B (en) Medium time allocation and scheduling using iso-zone structured superframe for qos provisioning in wireless networks
CN109451571A (en) A kind of federated resource distribution method in NOMA relay system
CN109039494A (en) A kind of 5G resource assignment method of communication system based on improvement harmonic search algorithm
CN103582105A (en) Optimization method for system efficiency maximization in large-scale heterogeneous cellular network
CN106162753B (en) A kind of resource allocation methods and device
CN105792370B (en) A kind of Resource co-allocation method in two-way more relayed OFDM systems
CN106714090A (en) Return-based resource mapping method in network virtualization LTE (Long Term Evolution) superposing D2D
CN116471613B (en) Energy efficiency optimization method and device for multi-user MIMO system
CN103686743B (en) D2D communication channel resource allocation method based on graph theory
CN106162855B (en) It communicates in conjunction with more D2D of subcarrier distribution and power control to resource allocation methods

Legal Events

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