CN114501504A - Joint optimization method and system based on non-cellular network - Google Patents

Joint optimization method and system based on non-cellular network Download PDF

Info

Publication number
CN114501504A
CN114501504A CN202210166016.4A CN202210166016A CN114501504A CN 114501504 A CN114501504 A CN 114501504A CN 202210166016 A CN202210166016 A CN 202210166016A CN 114501504 A CN114501504 A CN 114501504A
Authority
CN
China
Prior art keywords
access point
user
transmission power
optimization model
joint optimization
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
CN202210166016.4A
Other languages
Chinese (zh)
Other versions
CN114501504B (en
Inventor
卢岸
范镇淇
马涛
葛红舞
何迎利
赵高峰
蒋元晨
胡江溢
王东明
卜颖澜
夏心江
王元强
张宇新
周熠
张翔
赵华
聂云杰
陈民
程程
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.)
State Grid Corp of China SGCC
Nari Information and Communication Technology Co
Information and Telecommunication Branch of State Grid Zhejiang Electric Power Co Ltd
State Grid Electric Power Research Institute
Original Assignee
State Grid Corp of China SGCC
Nari Information and Communication Technology Co
Information and Telecommunication Branch of State Grid Zhejiang Electric Power Co Ltd
State Grid Electric Power Research Institute
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 State Grid Corp of China SGCC, Nari Information and Communication Technology Co, Information and Telecommunication Branch of State Grid Zhejiang Electric Power Co Ltd, State Grid Electric Power Research Institute filed Critical State Grid Corp of China SGCC
Priority to CN202210166016.4A priority Critical patent/CN114501504B/en
Publication of CN114501504A publication Critical patent/CN114501504A/en
Application granted granted Critical
Publication of CN114501504B publication Critical patent/CN114501504B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
    • 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
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
    • H04W28/18—Negotiating wireless communication parameters

Landscapes

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

Abstract

The invention discloses a combined optimization method and a system based on a non-cellular network, wherein a combined optimization model takes matching coefficients of an access point, users and subcarriers and transmission power at the access point as optimization variables, takes the maximization of system spectrum efficiency as a target, considers user service quality constraint, transmission power constraint at the access point and backhaul constraint at the access point, can obtain the access point preferred by the users and the preferred transmission power at the preferred access point based on the combined optimization model, and can maximize the spectrum efficiency of a non-cellular carrier aggregation system under the conditions of limited power consumption and backhaul resources.

Description

Joint optimization method and system based on non-cellular network
Technical Field
The invention relates to a joint optimization method and system based on a non-cellular network, and belongs to the technical field of wireless communication.
Background
Recently, a cellular-free network technology is proposed on the basis of a distributed multi-input multi-output network, and the technology eliminates interference among users through cooperation among base stations, can realize user rate improvement in a single carrier, and is another idea for improving the total spectrum efficiency of a system.
Different from the cooperative heterogeneous network, no cell or hierarchy exists in the non-cellular network, and the single-antenna access points are widely distributed in a service area and are uniformly allocated by the central processing unit to send signals. The wireless network enhances the edge coverage capability of the system through the cooperation of multiple access points, so the power distribution problem in the system needs to be considered again, and the matching relationship between the access points and the users in the wireless network is not a fixed topological structure in consideration of the calculation complexity and the forwarding capacity requirement which increase linearly along with the number of users. Therefore, in the cellular-free network, how to allocate an optimal access point for each user and control the transmission power at the access point become problems to be solved.
Disclosure of Invention
The invention provides a joint optimization method and a system based on a non-cellular network, which solve the problems disclosed in the background technology.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a joint optimization method based on a non-cellular network comprises the following steps:
acquiring the gain of a channel on each subcarrier between each access point and a user in a cellular-free network;
taking the obtained channel gain as the input of a pre-constructed joint optimization model, and solving the joint optimization model to determine an access point preferred by a user and preferred sending power at the preferred access point from each access point; the joint optimization model is constructed by taking matching coefficients among access points, users and subcarriers and transmission power at each access point as optimization variables, taking the maximization of system spectrum efficiency as an optimization target, and taking user service quality, the transmission power at the access points and backhaul limitation at the access points as constraints.
The process of constructing the joint optimization model in advance comprises the following steps:
taking matching coefficients among the access points, the users and the subcarriers and transmission power at each access point as optimization variables, taking the system spectrum efficiency maximization as an optimization target, and taking user service quality, the transmission power at the access points and backhaul limitation at the access points as constraint conditions to construct an initial joint optimization model;
performing smoothing treatment on the initial joint optimization model by adopting a penalty function method;
and performing approximate convex optimization processing on the initial joint optimization model after the smoothing processing by adopting a continuous parameter convex approximation method to obtain a final joint optimization model.
The initial joint optimization model is as follows:
Figure BDA0003515951840000021
Figure BDA0003515951840000022
Figure BDA0003515951840000023
Figure BDA0003515951840000024
Figure BDA0003515951840000025
wherein the content of the first and second substances,
Figure BDA0003515951840000026
a set of indices is indexed for a user,
Figure BDA0003515951840000027
for the set of sub-carrier indices,
Figure BDA0003515951840000028
for the access point index set, Rk,nObtaining the downlink rate, R, of the kth user on the nth subcarrier based on the channel gainmin,kOmega is a variable matrix to which the matching coefficients among the access point, the user and the subcarriers belong, P is a variable matrix to which the transmission power at the access point belongs, and P is the minimum downlink rate requirement at the kth userk,nTransmission power, p, on the nth subcarrier for the kth userl,k,nFor the matching coefficient of the ith access point and the kth user on the nth subcarrier, Pmax,lIs the upper limit of the transmission power at the ith access point, ClIs the backhaul ceiling at the ith access point.
The final joint optimization model is:
Figure BDA0003515951840000031
Figure BDA0003515951840000032
Figure BDA0003515951840000033
Figure BDA0003515951840000034
Figure BDA0003515951840000035
Figure BDA0003515951840000036
Figure BDA0003515951840000037
Figure BDA0003515951840000038
Figure BDA0003515951840000039
Figure BDA00035159518400000310
Figure BDA00035159518400000311
wherein the content of the first and second substances,
Figure BDA00035159518400000312
a set of indices is indexed for a user,
Figure BDA00035159518400000313
is a set of sub-carrier indices and,
Figure BDA00035159518400000314
for the access point index set, Ω is the variable matrix to which the matching coefficients among the access point, the users and the subcarriers belong, P is the variable matrix to which the transmission power at the access point belongs, yk,k′,n、xk,n、
Figure BDA00035159518400000315
tk,n、αl,k,n、
Figure BDA00035159518400000316
Are all auxiliary variables introduced in the approximate convex optimization processing process,
Figure BDA00035159518400000317
linearly developing a lower bound function for the original penalty function F, F,
Figure BDA00035159518400000318
Figure BDA00035159518400000319
And
Figure BDA00035159518400000320
both are lower bound linear functions or concave functions defined during the approximate convex optimization process,
Figure BDA00035159518400000321
for the thermal noise power at the user, pl,k,nMatching coefficient, R, of the ith access point and the kth user on the nth subcarriermin,kFor the lowest downlink rate requirement at the kth user, Pmax,lFor the upper limit of the transmission power at the ith access point, (. C)(m)For the mth iteration parameter, (.)HTo transpose, ρk,nIs rhol,k,nIn the form of a vector of (a),
Figure BDA0003515951840000041
is composed of
Figure BDA0003515951840000042
A constructed semi-positive definite matrix, L is
Figure BDA0003515951840000043
Number of medium access point indices, element
Figure BDA0003515951840000044
hl,k,nFrom the l access point to the lChannel gain, h, of k users on the nth carrierl,k′,nChannel gain on the nth carrier for the ith access point to the kth user, (. DEG)*In order to perform the conjugation operation,
Figure BDA0003515951840000045
is composed of
Figure BDA0003515951840000046
Constructed semi-positive definite matrix, elements
Figure BDA0003515951840000047
pk,nTransmission power on the nth subcarrier, p, for the kth userk′,nTransmission power, p, on the nth subcarrier for the k' th userk′,nIs rhol,k′,nIn vector form of (1), pl,k′,nMatching coefficient on nth sub-carrier for the ith access point and the kth user, ClIs the backhaul ceiling at the ith access point.
A system for joint optimization based on a cellular-free network, comprising:
a channel acquisition module for acquiring the gain of the channel on each subcarrier from each access point to the user in the cellular-free network;
the optimization module is used for solving the joint optimization model by taking the acquired channel gain as the input of the pre-constructed joint optimization model so as to determine an access point preferred by a user and preferred sending power at the preferred access point from each access point; the joint optimization model is constructed by taking matching coefficients among access points, users and subcarriers and transmission power at each access point as optimization variables, taking the maximization of system spectrum efficiency as an optimization target, and taking user service quality, the transmission power at the access points and backhaul limitation at the access points as constraints.
The system also comprises a joint optimization model construction module used for constructing a joint optimization model in advance; the joint optimization model construction module comprises:
the initial joint optimization model building module is used for building an initial joint optimization model by taking matching coefficients among access points, users and subcarriers and transmission power at each access point as optimization variables, taking system spectrum efficiency maximization as an optimization target and taking user service quality, the transmission power at the access point and backhaul limitation at the access point as constraint conditions;
the smoothing module is used for smoothing the initial joint optimization model;
and the approximate convex optimization processing module is used for carrying out approximate convex optimization processing on the initial combined optimization model after the smoothing processing to obtain a final combined optimization model.
The initial joint optimization model is as follows:
Figure BDA0003515951840000051
Figure BDA0003515951840000052
Figure BDA0003515951840000053
Figure BDA0003515951840000054
Figure BDA0003515951840000055
wherein the content of the first and second substances,
Figure BDA0003515951840000056
a set of indices is indexed for a user,
Figure BDA0003515951840000057
for the set of sub-carrier indices,
Figure BDA0003515951840000058
for the access point index set, Rk,nObtaining the downlink rate, R, of the kth user on the nth subcarrier based on the channel gainmin,kOmega is a variable matrix to which the matching coefficients among the access point, the user and the subcarriers belong, P is a variable matrix to which the transmission power at the access point belongs, and P is the minimum downlink rate requirement at the kth userk,nTransmission power, p, on the nth subcarrier for the kth userl,k,nFor the matching coefficient of the ith access point and the kth user on the nth subcarrier, Pmax,lIs the upper limit of the transmission power at the ith access point, ClIs the backhaul ceiling at the ith access point.
The final joint optimization model is:
Figure BDA0003515951840000061
Figure BDA0003515951840000062
Figure BDA0003515951840000063
Figure BDA0003515951840000064
Figure BDA0003515951840000065
Figure BDA0003515951840000066
Figure BDA0003515951840000067
Figure BDA0003515951840000068
Figure BDA0003515951840000069
Figure BDA00035159518400000610
Figure BDA00035159518400000611
wherein the content of the first and second substances,
Figure BDA00035159518400000612
a set of indices is indexed for a user,
Figure BDA00035159518400000613
for the set of sub-carrier indices,
Figure BDA00035159518400000614
for the access point index set, Ω is the variable matrix to which the matching coefficients among the access point, the users and the subcarriers belong, P is the variable matrix to which the transmission power at the access point belongs, yk,k′,n、xk,n、
Figure BDA00035159518400000615
tk,n、αl,k,n、
Figure BDA00035159518400000616
Are all auxiliary variables introduced in the approximate convex optimization processing process,
Figure BDA00035159518400000617
linearly developing a lower bound function for the original penalty function F, F,
Figure BDA00035159518400000618
Figure BDA00035159518400000619
And
Figure BDA00035159518400000620
both are lower bound linear functions or concave functions defined during the approximate convex optimization process,
Figure BDA00035159518400000621
for the thermal noise power at the user, pl,k,nMatching coefficient, R, of the ith access point and the kth user on the nth subcarriermin,kFor the lowest downlink rate requirement at the kth user, Pmax,lFor the upper limit of the transmission power at the ith access point, (. C)(m)For the mth iteration parameter, (.)HTo transpose, ρk,nIs rhol,k,nIn the form of a vector of (a),
Figure BDA00035159518400000622
is composed of
Figure BDA0003515951840000071
A constructed semi-positive definite matrix, L is
Figure BDA0003515951840000072
Number of medium access point indices, element
Figure BDA0003515951840000073
hl,k,nChannel gain on the nth carrier for the ith access point to the kth user, hl,k′,nChannel gain on the nth carrier for the ith access point to the kth user, (. DEG)*In order to perform the conjugation operation,
Figure BDA0003515951840000074
is composed of
Figure BDA0003515951840000075
Constructed semi-positive definite matrix, elements
Figure BDA0003515951840000076
pk,nTransmission power on the nth subcarrier, p, for the kth userk′,nTransmission power, p, on the nth subcarrier for the k' th userk′,nIs rhol,k′,nIn vector form of (1), pl,k′,nMatching coefficient on nth sub-carrier for the ith access point and the kth user, ClIs the backhaul ceiling at the ith access point.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform a joint optimization method.
A computing device comprising one or more processors, one or more memories, and one or more programs stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs including instructions for performing a joint optimization method.
The invention achieves the following beneficial effects: the joint optimization model takes the matching coefficients of the access point, the user and the subcarriers and the transmission power at the access point as optimization variables, takes the maximization of the system spectrum efficiency as a target, considers the user service quality constraint, the transmission power constraint at the access point and the backhaul constraint at the access point, can obtain the preferred access point of the user and the preferred transmission power at the preferred access point based on the joint optimization model, and can maximize the spectrum efficiency of a system without cellular carrier aggregation under the conditions of limited power consumption and backhaul resources.
Drawings
FIG. 1 is a flow chart of a joint optimization method of the present invention;
FIG. 2 is a schematic diagram of a system without a cellular network;
fig. 3 is a comparison graph of total spectrum efficiency of a cellular carrier aggregation-free system and a single carrier cellular-free system corresponding to backhaul constraints from 80Mbps to 200Mbps under the transmission power limits of 50mw and 500 mw.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
As shown in fig. 1, a joint optimization method based on a cellular-free network includes:
step 1, gain of channels on each subcarrier between each access point and a user in a cellular network is obtained;
step 2, the obtained channel gain is used as the input of a pre-constructed joint optimization model, and the joint optimization model is solved so as to determine an access point preferred by a user and preferred sending power at the preferred access point from each access point; the joint optimization model is constructed by taking matching coefficients among access points, users and subcarriers and transmission power at each access point as optimization variables, taking the maximization of system spectrum efficiency as an optimization target, and taking user service quality, the transmission power at the access points and backhaul limitation at the access points as constraints.
The joint optimization model of the method takes the matching coefficients of the access point, the user and the subcarrier and the transmission power at the access point as optimization variables, takes the maximization of the system spectrum efficiency as a target, considers the user service quality constraint, the transmission power constraint at the access point and the backhaul constraint at the access point, can obtain the access point preferred by the user and the preferred transmission power at the access point based on the joint optimization model, and can maximize the spectrum efficiency of the system without the cellular carrier aggregation under the conditions of limited power consumption and backhaul resources.
The method can be implemented for the system shown in fig. 2, the communication system includes a central processing unit, a single-antenna remote access point (referred to as "access point") and users, each access point cooperates to provide service for the users, the transmitted signal vector and power are uniformly allocated by the central processing unit, and the matching relationship between the users and the access points and the communication channel used by the users are also determined by the central processing unit.
Non-cellular system under consideration of time division multiplexing duplex modeAnd (3) joint spectral efficiency optimization of a downlink. Assuming that the channels on the subcarriers are orthogonal, the channel state information is kept unchanged in the coherence time, subject to flat fading and the channel state is perfectly known. The central processor sends the downlink pre-coding on each subcarrier to each remote access point, and each access point receives the signal and forwards the signal to a downlink user through a channel. Each remote access point and the user are single antennas, the system has L remote access points in total, works on N subcarriers and provides service for K users. The access point, the subcarrier and the user index set are respectively
Figure BDA0003515951840000098
And
Figure BDA0003515951840000099
considering the balance between system performance and computational complexity, MRT is adopted as a precoding scheme.
The received signal of a user on a single carrier can be written as:
Figure BDA0003515951840000091
wherein the parameters
Figure BDA0003515951840000092
For the channel gain on the nth carrier from the nth access point to the kth user,
Figure BDA0003515951840000093
is a complex set (·)*For conjugate operation, pl,k,nMatching coefficient rho between the ith access point and the kth user on the nth subcarrierl,k,n∈{0,1}，pk,nThe transmit power on the nth subcarrier for the kth user,
Figure BDA0003515951840000094
Figure BDA00035159518400000910
is a set of real numbers, vkIs additive white gaussian noise at the kth user,
Figure BDA0003515951840000095
Figure BDA00035159518400000911
a thermal noise power at the user;
is provided with
Figure BDA0003515951840000096
For all access point sets providing service for the kth user on the nth subcarrier, the value of the matching coefficient is determined according to the following rule:
Figure BDA0003515951840000097
let skThe normalized data information sent to the kth user is the information which is not related among all users and has zero mean value,
Figure BDA0003515951840000101
that is, the normalized information average power is 1, and the SINR expression of the user received signal on the single carrier can be listed by considering the interference between users:
Figure BDA0003515951840000102
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003515951840000103
and matching the coefficient cascade matrix on the nth subcarrier for the kth user and each access point.
Make each access point correspond to
Figure BDA0003515951840000104
Is cascaded into
Figure BDA0003515951840000105
Is composed of
Figure BDA0003515951840000106
A semi-positive definite matrix is formed,
Figure BDA0003515951840000107
based on the above assumptions, considering the general case that the channels between the users and the access point on different subcarriers are random complex variables related to the distances between the two terminals, the specific process of pre-constructing the joint optimization model may be as follows:
11) and constructing an initial joint optimization model by taking matching coefficients among the access points, the users and the subcarriers and the transmission power at each access point as optimization variables, taking the system spectrum efficiency maximization as an optimization target, and taking the user service quality, the transmission power at the access points and the backhaul limit at the access points as constraint conditions.
The initial joint optimization model may be:
Figure BDA0003515951840000108
s.t.C1:
Figure BDA0003515951840000109
C2:
Figure BDA00035159518400001010
C3:
Figure BDA00035159518400001011
C4:ρl,k,n∈{0,1},
Figure BDA00035159518400001012
wherein the content of the first and second substances,
Figure BDA00035159518400001013
a set of indices is indexed for a user,
Figure BDA00035159518400001014
for the set of sub-carrier indices,
Figure BDA00035159518400001015
for the set of access point indices, Rk,n＝log(1+γk,n) Obtaining the downlink rate, R, of the kth user on the nth subcarrier based on the channel gainmin,kOmega is a variable matrix to which the matching coefficients among the access point, the user and the subcarriers belong, P is a variable matrix to which the transmission power at the access point belongs, and P is the minimum downlink rate requirement at the kth userk,nTransmission power, p, on the nth subcarrier for the kth userl,k,nThe matching coefficient of the ith access point and the kth user on the nth subcarrier is defined as rhol,k,nDetermination of Pmax,lIs the upper limit of the transmission power at the ith access point, ClIs the backhaul ceiling at the ith access point.
12) And smoothing the initial joint optimization model by adopting a penalty function method.
The initial combined optimization model is still non-convex optimization after being smoothed, convex/concave upper and lower bound function replacement is carried out on the model target and the constraint function at the moment by introducing auxiliary variables, and the approximate convex optimization problem of the optimization model can be obtained. And after the approximately convex optimization problem is obtained, iterative solution is needed, each suboptimal solution result converges to the global optimal solution of the original problem in continuous iteration, the approximately convex optimization problem in iteration contains the result of the previous iterative optimization as a parameter, and the result needs to be updated in the iteration process.
As can be seen from the model, 0/1 integer variables contained in the optimization problem make the optimization problem non-smooth and belong to mixed integer optimization; to make the optimization problem solvable, the model is first smoothed using a penalty function method, i.e. the variable ρ is redefined 0/1l,k,nSetting a penalty function in the objective function for continuous variables:
Figure BDA0003515951840000111
C5:ρl,k,n∈[0,1],
Figure BDA0003515951840000112
wherein f (Ω) is a penalty function, and ε is a parameter;
epsilon is a very small positive integer, and the solution of the transformed optimization problem can be consistent with the original integer programming problem through proper value taking, so that the original objective function is transformed into the integer programming problem
Figure BDA0003515951840000113
13) And performing approximate convex optimization processing on the initial joint optimization model after the smoothing processing by adopting a continuous parameter convex approximation method to obtain a final joint optimization model.
The objective function at this time is still a non-convex function, which is difficult to solve, and needs to be approximated, and f (Ω) is linearized to obtain:
Figure BDA0003515951840000121
wherein, (.)(m)For the parameter of the m-th iteration,
Figure BDA0003515951840000122
linearly expanding a lower bound function for the original penalty function f;
the objective function can be converted into:
Figure BDA0003515951840000123
and (4) converting the original non-convex optimization into approximate convex optimization by using a continuous parameter convex approximation method, updating the optimization parameter of the mth time according to the m-1 iteration result, and circulating for multiple times to obtain the optimal solution of the original problem. And when the difference between the optimization solutions of the two previous iterations is smaller than a fixed value delta, ending the loop and outputting a result, wherein delta is a very small positive integer. In the original optimization problem, non-convex parts exist in both the objective function and the constraint condition, and convex approximation processing needs to be carried out respectively.
First of all introduce
Figure BDA0003515951840000124
And
Figure BDA0003515951840000125
two new sets of auxiliary variables to obtain R in the objective functionk,nLower boundary of (1)
Figure BDA0003515951840000126
Approximation:
Figure BDA0003515951840000127
wherein x isk,n、yk,k′,nThe following conditions are satisfied:
C6:
Figure BDA0003515951840000128
C7:
Figure BDA0003515951840000129
wherein the content of the first and second substances,
Figure BDA0003515951840000131
and
Figure BDA0003515951840000132
similarly, is made of
Figure BDA0003515951840000133
A semi-positive definite matrix is formed,
Figure BDA0003515951840000134
Figure BDA0003515951840000135
simple changes to the above conditions (C6 and C7) resulted in:
C8:
Figure BDA0003515951840000136
C9:
Figure BDA0003515951840000137
r is as defined abovek,nThe lower bound approximation function of (a) is still non-concave, and is functionally scaled using the following expression:
Figure BDA0003515951840000138
wherein a and b are any numerical variables, and R is obtained by using the formulak,nApproximate concave lower bound of (c):
Figure BDA0003515951840000139
using a scaling inequality
Figure BDA00035159518400001310
And
Figure BDA00035159518400001311
convex approximation is carried out on two non-convex constraints C8 and C9; wherein, c is any vector variable in the scaling inequality, D ═ ddHThe vector is a semi-positive definite matrix, d is any vector constant, and a is any real variable;
the two constraints C8 and C9 are respectively substituted to obtain:
C10:
Figure BDA0003515951840000141
C11:
Figure BDA0003515951840000142
so far, the original objective function has been converted into a convex form, and the original optimization problem is converted into the following expression:
Figure BDA0003515951840000143
the optimization problem still has non-convex constraints C1, C2, and C3, and C1 is transformed into its convex approximation using the scaling inequality:
C12:
Figure BDA0003515951840000144
by introducing a new set of optimization variables
Figure BDA0003515951840000145
Conversion of C2 to the disintegrable form:
C13:
Figure BDA0003515951840000146
C14:
Figure BDA0003515951840000147
obviously, C13 is still a non-convex inequality, which can be obtained by simple item shifting
Figure BDA0003515951840000148
And scaling the constraint convex approximation by using a scaling inequality to obtain:
Figure BDA0003515951840000149
for the non-convex constraint C3, a new set of variables is also introduced
Figure BDA00035159518400001410
To replace R in C3k,n：
C16:
Figure BDA00035159518400001411
C17:
Figure BDA00035159518400001412
It is clear that the two inequalities remain in a non-convex form;
c16 is simply transformed to obtain:
Figure BDA0003515951840000151
is introduced again
Figure BDA0003515951840000152
And
Figure BDA0003515951840000153
two new sets of variables to get the convex upper bound of the above equation:
C18:
Figure BDA0003515951840000154
C19:
Figure BDA0003515951840000155
C20:
Figure BDA0003515951840000156
convex transformation of C20 with a first order taylor expansion:
C21:
Figure BDA0003515951840000157
c18 and C19 are still non-convex, simply deforming two inequalities:
C22:
Figure BDA0003515951840000158
C23:
Figure BDA0003515951840000159
the C22 convex approximation inequality is solved by using a first-order Taylor expansion:
C24:
Figure BDA00035159518400001510
using scaling inequalities
Figure BDA0003515951840000161
Convex transformation of C23 yields:
C25:
Figure BDA0003515951840000162
for the non-convex constraint C17, it is approximated with the arithmetic inequality as convex upper bound:
Figure BDA0003515951840000163
if and only if
Figure BDA0003515951840000164
The time equal sign is established;
constraint C17 may then be approximated as:
C26:
Figure BDA0003515951840000165
according to the formula
Figure BDA0003515951840000166
Updating parameters in the mth iteration
Figure BDA0003515951840000167
Such that the approximation constraint ends up with p in the iterative processl,k,ntk,nAnd taking an equal sign.
The combined optimization model is as follows:
Figure BDA0003515951840000171
Figure BDA0003515951840000172
Figure BDA0003515951840000173
Figure BDA0003515951840000174
Figure BDA0003515951840000175
Figure BDA0003515951840000176
Figure BDA0003515951840000177
Figure BDA0003515951840000178
Figure BDA0003515951840000179
Figure BDA00035159518400001710
Figure BDA00035159518400001711
wherein the content of the first and second substances,
Figure BDA00035159518400001712
a set of indices is indexed for a user,
Figure BDA00035159518400001713
for the set of sub-carrier indices,
Figure BDA00035159518400001714
for the access point index set, Ω is the variable matrix to which the matching coefficients among the access point, the users and the subcarriers belong, P is the variable matrix to which the transmission power at the access point belongs, yk,k′,n、xk,n、
Figure BDA00035159518400001715
tk,n、αl,k,n、
Figure BDA00035159518400001716
Are all auxiliary variables introduced in the approximate convex optimization processing process,
Figure BDA00035159518400001717
linearly developing a lower bound function for the original penalty function F, F,
Figure BDA00035159518400001718
Figure BDA00035159518400001719
And with
Figure BDA00035159518400001720
Both are lower bound linear functions or concave functions defined during the approximate convex optimization process,
Figure BDA00035159518400001721
for the thermal noise power at the user, pl,k,nMatching coefficient, R, of the ith access point and the kth user on the nth subcarriermin,kFor the lowest downlink rate requirement at the kth user, Pmax,lUpper limit of transmission power at the ith access point, ((·)(m)For the mth iteration parameter, (.)HTo transpose, ρk,nIs ρl,k,nIn the form of a vector of (a),
Figure BDA00035159518400001722
is composed of
Figure BDA0003515951840000181
A constructed semi-positive definite matrix, L is
Figure BDA0003515951840000182
Number of middle access point indices, element
Figure BDA0003515951840000183
hlknChannel gain on the nth carrier for the ith access point to the kth user, hl,k′,nChannel gain on the nth carrier for the ith access point to the kth user, (. DEG)*In order to perform the conjugation operation,
Figure BDA0003515951840000184
is composed of
Figure BDA0003515951840000185
Constructed semi-positive definite matrix, elements
Figure BDA0003515951840000186
pk,nTransmission power on the nth subcarrier, p, for the kth userk′,nTransmission power, p, on the nth subcarrier for the k' th userk′,nIs rhol,k′,nIn vector form of (1), pl,k′,nMatching coefficient on nth sub-carrier for the ith access point and the kth user, ClIs the backhaul ceiling at the ith access point.
And for the initial joint optimization model, firstly, smoothing the original non-convex mixed integer optimization problem by a penalty function method, and then converting the original non-convex problem into an approximate convex optimization problem by using a continuous parameter convex approximation method for iterative solution.
Finally, inputting the channel into a pre-constructed joint optimization model, namely determining the access point preferred by the user and the preferred transmitting power at the preferred access point from each access point.
Fig. 3 shows the spectral efficiency of a (control) cellular carrier aggregation system (modeled by the present invention) versus a conventional (control) cellular carrier aggregation system under the power consumption constraints of 50mw and 500 mw. As can be seen from the simulation results, as the backhaul limit is increased from 80Mbps to 200Mbps, the system spectrum efficiency is also increased and gradually becomes flat. No matter under the power constraint of 50mw or 500mw, the total spectral efficiency of the non-cellular carrier aggregation system using the method provided by the invention is higher than that of the traditional non-cellular system without carrier resource allocation, and the total spectral efficiency of the optimal resource allocation non-cellular carrier aggregation system under the backhaul constraint of 200Mbps is improved by about 5% compared with that of the traditional non-cellular system. This proves that under the condition of optimal resource allocation, the system model of the invention can jointly plan the optimal matching relation and channel selection between the access point and the user so as to reduce interference and realize higher code rate, thereby further improving the total spectrum efficiency of the system.
The invention combines the advantages of the non-cellular network and the carrier aggregation technology, combines the multi-access point cooperation and the multi-carrier aggregation scheme in the non-cellular network, and obtains the preferred access point and the preferred sending power of the access point by solving the joint optimization model. Compared with the traditional non-cellular system without carrier allocation, the optimization method has the advantages that the diversity of channels on each sub-carrier after decomposition is utilized, the degree of freedom of user channel selection is given by taking the system spectrum efficiency as an optimization target, the optimal channel resources can be matched for users and access points, the interference among the users is further inhibited, and the improvement of the system spectrum efficiency is finally realized.
Based on the same technical scheme, the invention also discloses a software system of the method, and a combined optimization system based on a cellular-free network comprises the following steps:
and the channel acquisition module is used for acquiring the gain of the channel on each subcarrier from each access point to the user in the cellular-free network.
The optimization module is used for solving the joint optimization model by taking the obtained channel gain as the input of the pre-constructed joint optimization model so as to determine the access point preferred by the user and the preferred sending power at the preferred access point from the access points; the joint optimization model is constructed by taking matching coefficients among access points, users and subcarriers and transmission power at each access point as optimization variables, taking the maximization of system spectrum efficiency as an optimization target, and taking user service quality, the transmission power at the access points and backhaul limitation at the access points as constraints.
The joint optimization model building module is used for building a joint optimization model in advance; the joint optimization model building module comprises:
the initial joint optimization model building module is used for building an initial joint optimization model by taking matching coefficients among access points, users and subcarriers and transmission power at each access point as optimization variables, taking system spectrum efficiency maximization as an optimization target and taking user service quality, the transmission power at the access point and backhaul limitation at the access point as constraint conditions;
the initial joint optimization model is as follows:
Figure BDA0003515951840000201
Figure BDA0003515951840000202
Figure BDA0003515951840000203
Figure BDA0003515951840000204
Figure BDA0003515951840000205
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003515951840000206
a set of indices is indexed for a user and,
Figure BDA0003515951840000207
for the set of sub-carrier indices,
Figure BDA0003515951840000208
for the access point index set, Rk,nObtaining the downlink rate of the k user on the nth sub-carrier based on the channel gain, Rmin,kOmega is a variable matrix to which the matching coefficients among the access point, the user and the subcarriers belong, P is a variable matrix to which the transmission power at the access point belongs, and P is the minimum downlink rate requirement at the kth userk,nTransmission power, p, on the nth subcarrier for the kth userl,k,nFor the matching coefficient of the ith access point and the kth user on the nth subcarrier, Pmax,lIs the upper limit of the transmission power at the ith access point, ClIs the backhaul ceiling at the ith access point.
And the smoothing processing module is used for smoothing the initial combined optimization model by adopting a penalty function method.
The approximate convex optimization processing module is used for carrying out approximate convex optimization processing on the initial combined optimization model after the smoothing processing by adopting a continuous parameter convex approximation method to obtain a final combined optimization model;
the final joint optimization model is:
Figure BDA0003515951840000211
Figure BDA0003515951840000212
Figure BDA0003515951840000213
Figure BDA0003515951840000214
Figure BDA0003515951840000215
Figure BDA0003515951840000216
Figure BDA0003515951840000217
Figure BDA0003515951840000218
Figure BDA0003515951840000219
Figure BDA00035159518400002110
Figure BDA00035159518400002111
wherein the content of the first and second substances,
Figure BDA00035159518400002112
a set of indices is indexed for a user,
Figure BDA00035159518400002113
for the set of sub-carrier indices,
Figure BDA00035159518400002114
for the access point index set, Ω is the match between access point, user and subcarrierA variable matrix to which the coefficients belong, P being the variable matrix to which the transmission power at the access point belongs, yk,k′,n、xk,n、
Figure BDA00035159518400002115
tk,n、αl,k,n、
Figure BDA00035159518400002116
Are all auxiliary variables introduced in the approximate convex optimization processing process,
Figure BDA00035159518400002117
linearly developing a lower bound function for the original penalty function F, F,
Figure BDA00035159518400002118
Figure BDA00035159518400002119
And
Figure BDA00035159518400002120
both are lower bound linear functions or concave functions defined during the approximate convex optimization process,
Figure BDA00035159518400002121
for the thermal noise power at the user, pl,k,nMatching coefficient, R, of the ith access point and the kth user on the nth subcarriermin,kFor the lowest downlink rate requirement at the kth user, Pmax,lFor the upper limit of the transmission power at the ith access point, (. C)(m)For the mth iteration parameter, (.)HTo transpose, ρk,nIs rhol,k,nIn the form of a vector of (a),
Figure BDA00035159518400002122
is composed of
Figure BDA0003515951840000221
A constructed semi-positive definite matrix, L is
Figure BDA0003515951840000222
Number of medium access point indices, element
Figure BDA0003515951840000223
hl,k,nChannel gain on the nth carrier for the ith access point to the kth user, hl,k′,nChannel gain on the nth carrier for the ith access point to the kth user, (. DEG)*In order to perform the conjugation operation,
Figure BDA0003515951840000224
is composed of
Figure BDA0003515951840000225
Constructed semi-positive definite matrix, elements
Figure BDA0003515951840000226
pk,nTransmission power on the nth subcarrier, p, for the kth userk′,nTransmission power, p, on the nth subcarrier for the k' th userk′,nIs rhol,k′,nIn vector form of (1), pl,k′,nMatching coefficient on nth sub-carrier for the ith access point and the kth user, ClIs the backhaul ceiling at the ith access point.
The division of the modules in the embodiments of the present application is schematic, and only one logical function division is provided, and in actual implementation, there may be another division manner, and in addition, each functional module in each embodiment of the present application may be integrated in one processor, may also exist alone physically, or may also be integrated in one module by two or more modules. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
Based on the same technical solution, the present invention also discloses a computer-readable storage medium storing one or more programs, the one or more programs including instructions, which when executed by a computing device, cause the computing device to perform a joint optimization method.
Based on the same technical solution, the present invention also discloses a computing device comprising one or more processors, one or more memories, and one or more programs, wherein the one or more programs are stored in the one or more memories and configured to be executed by the one or more processors, and the one or more programs comprise instructions for executing the joint optimization method.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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 invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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 present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.

Claims (10)

1. A joint optimization method based on a cellular-free network is characterized by comprising the following steps:
acquiring the gain of a channel on each subcarrier between each access point and a user in a cellular-free network;
taking the obtained channel gain as the input of a pre-constructed joint optimization model, and solving the joint optimization model to determine an access point preferred by a user and preferred sending power at the preferred access point from each access point; the joint optimization model is constructed by taking matching coefficients among access points, users and subcarriers and transmission power at each access point as optimization variables, taking the maximization of system spectrum efficiency as an optimization target, and taking user service quality, the transmission power at the access points and backhaul limitation at the access points as constraints.
2. The method of claim 1, wherein the process of constructing the joint optimization model comprises:
taking matching coefficients among access points, users and subcarriers and transmission power at each access point as optimization variables, taking the maximization of system spectrum efficiency as an optimization target, and taking user service quality, the transmission power at the access points and backhaul limitation at the access points as constraint conditions to construct an initial joint optimization model;
carrying out smoothing treatment on the initial joint optimization model;
and performing approximate convex optimization processing on the initial joint optimization model after the smoothing processing to obtain a final joint optimization model.
3. The method of claim 2, wherein the initial joint optimization model is:
Figure FDA0003515951830000021
Figure FDA0003515951830000022
Figure FDA0003515951830000023
Figure FDA0003515951830000024
Figure FDA0003515951830000025
wherein the content of the first and second substances,
Figure FDA0003515951830000026
a set of indices is indexed for a user,
Figure FDA0003515951830000027
for the set of sub-carrier indices,
Figure FDA0003515951830000028
for the access point index set, Rk,nFor the k user based on channel gain acquisition in the n sub-carrierDownstream rate on wave, Rmin,kOmega is a variable matrix to which the matching coefficients among the access point, the user and the subcarriers belong, P is a variable matrix to which the transmission power at the access point belongs, and P is the minimum downlink rate requirement at the kth userk,nTransmission power, p, on the nth subcarrier for the kth userl,k,nFor the matching coefficient of the ith access point and the kth user on the nth subcarrier, Pmax,lIs the upper limit of the transmission power at the ith access point, ClIs the backhaul ceiling at the ith access point.
4. The method of claim 2, wherein the final joint optimization model is:
Figure FDA0003515951830000031
Figure FDA0003515951830000032
Figure FDA0003515951830000033
Figure FDA0003515951830000034
Figure FDA0003515951830000035
Figure FDA0003515951830000036
Figure FDA0003515951830000037
Figure FDA0003515951830000038
Figure FDA0003515951830000039
Figure FDA00035159518300000310
Figure FDA00035159518300000311
wherein the content of the first and second substances,
Figure FDA00035159518300000312
a set of indices is indexed for a user,
Figure FDA00035159518300000313
for the set of sub-carrier indices,
Figure FDA00035159518300000314
for the access point index set, Ω is the variable matrix to which the matching coefficients among the access point, the users and the subcarriers belong, P is the variable matrix to which the transmission power at the access point belongs, yk,k′,n、xk,n、
Figure FDA00035159518300000315
tk,n、αl,k,n、
Figure FDA00035159518300000316
Are all approximately convex in the optimization processing processThe auxiliary variable of the input is changed,
Figure FDA00035159518300000317
linearly developing a lower bound function for the original penalty function F, F,
Figure FDA00035159518300000318
Figure FDA00035159518300000319
And
Figure FDA00035159518300000320
both are lower bound linear functions or concave functions defined in the approximate convex optimization process,
Figure FDA00035159518300000321
for the thermal noise power at the user, pl,k,nMatching coefficient, R, of the ith access point and the kth user on the nth subcarriermin,kFor the lowest downlink rate requirement at the kth user, Pmax,lFor the upper limit of the transmission power at the ith access point, (. C)(m)For the mth iteration parameter, (.)HTo transpose, ρk,nIs rhol,k,nIn the form of a vector of (a),
Figure FDA00035159518300000322
is composed of
Figure FDA0003515951830000041
A constructed semi-positive definite matrix, L is
Figure FDA0003515951830000042
Number of medium access point indices, element
Figure FDA0003515951830000043
hl,k,nChannel gain on the nth carrier for the ith access point to the kth user, hl,k′,nFrom the l access point to the k' userChannel gain on the nth carrier, (-)*In order to perform the conjugation operation,
Figure FDA0003515951830000044
is composed of
Figure FDA0003515951830000045
Constructed semi-positive definite matrix, elements
Figure FDA0003515951830000046
pk,nTransmission power on the nth subcarrier, p, for the kth userk′,nTransmission power, p, on the nth subcarrier for the k' th userk′,nIs rhol,k′,nVector form of (1), pl,k′,nMatching coefficient on nth sub-carrier for the ith access point and the kth user, ClIs the backhaul ceiling at the ith access point.
5. A system for joint optimization based on a cellular-free network, comprising:
a channel acquisition module for acquiring the gain of the channel on each subcarrier from each access point to the user in the cellular-free network;
the optimization module is used for solving the joint optimization model by taking the acquired channel gain as the input of the pre-constructed joint optimization model so as to determine an access point preferred by a user and preferred sending power at the preferred access point from each access point; the joint optimization model is constructed by taking matching coefficients among access points, users and subcarriers and transmission power at each access point as optimization variables, taking the maximization of system spectrum efficiency as an optimization target, and taking user service quality, the transmission power at the access points and backhaul limitation at the access points as constraints.
6. The joint optimization system based on the cellular-free network according to claim 5, further comprising a joint optimization model construction module for constructing a joint optimization model in advance; the joint optimization model building module comprises:
the initial joint optimization model building module is used for building an initial joint optimization model by taking matching coefficients among access points, users and subcarriers and transmission power at each access point as optimization variables, taking the maximization of the system spectrum efficiency as an optimization target and taking the user service quality, the transmission power at the access point and the backhaul limitation at the access point as constraint conditions;
the smoothing module is used for smoothing the initial joint optimization model;
and the approximate convex optimization processing module is used for carrying out approximate convex optimization processing on the initial combined optimization model after the smoothing processing to obtain a final combined optimization model.
7. The system of claim 6, wherein the initial joint optimization model is:
Figure FDA0003515951830000051
Figure FDA0003515951830000052
Figure FDA0003515951830000053
Figure FDA0003515951830000054
Figure FDA0003515951830000055
wherein the content of the first and second substances,
Figure FDA0003515951830000056
a set of indices is indexed for a user,
Figure FDA0003515951830000057
is a set of sub-carrier indices and,
Figure FDA0003515951830000058
for the access point index set, Rk,nObtaining the downlink rate of the k user on the nth sub-carrier based on the channel gain, Rmin,kOmega is a variable matrix to which the matching coefficients among the access point, the user and the subcarriers belong, P is a variable matrix to which the transmission power at the access point belongs, and P is the minimum downlink rate requirement at the kth userk,nTransmission power, p, on the nth subcarrier for the kth userl,k,nFor the matching coefficient of the ith access point and the kth user on the nth subcarrier, Pmax,lIs the upper limit of the transmission power at the ith access point, ClIs the backhaul ceiling at the ith access point.
8. The system of claim 6, wherein the final joint optimization model is:
Figure FDA0003515951830000061
Figure FDA0003515951830000062
Figure FDA0003515951830000063
Figure FDA0003515951830000064
Figure FDA0003515951830000065
Figure FDA0003515951830000066
Figure FDA0003515951830000067
Figure FDA0003515951830000068
Figure FDA0003515951830000069
Figure FDA00035159518300000610
Figure FDA00035159518300000611
wherein the content of the first and second substances,
Figure FDA00035159518300000612
a set of indices is indexed for a user and,
Figure FDA00035159518300000613
for the set of sub-carrier indices,
Figure FDA00035159518300000614
for the access point index set, omega for the access pointA variable matrix to which the matching coefficient between the user and the subcarrier belongs, P is the variable matrix to which the transmission power at the access point belongs, yk,k′,n、xk,n、
Figure FDA00035159518300000615
tk,n、αl,k,n、
Figure FDA00035159518300000616
Are all auxiliary variables introduced in the approximate convex optimization processing process,
Figure FDA00035159518300000617
linearly developing a lower bound function for the original penalty function F, F,
Figure FDA00035159518300000618
Figure FDA00035159518300000619
And
Figure FDA00035159518300000620
both are lower bound linear functions or concave functions defined during the approximate convex optimization process,
Figure FDA00035159518300000621
for the thermal noise power at the user, pl,k,nMatching coefficient, R, of the ith access point and the kth user on the nth subcarriermin,kFor the lowest downlink rate requirement at the kth user, Pmax,lFor the upper limit of the transmission power at the ith access point, (. C)(m)For the mth iteration parameter, (.)HTo transpose, ρk,nIs rhol,k,nIn the form of a vector of (a),
Figure FDA00035159518300000622
is composed of
Figure FDA0003515951830000071
A constructed semi-positive definite matrix, L is
Figure FDA0003515951830000072
Number of medium access point indices, element
Figure FDA0003515951830000073
hl,k,nChannel gain on the nth carrier for the ith access point to the kth user, hl,k′,nFor the channel gain on the nth carrier from the ith access point to the kth user, (. cndot.)*In order to perform the conjugation operation,
Figure FDA0003515951830000074
is composed of
Figure FDA0003515951830000075
Constructed semi-positive definite matrix, elements
Figure FDA0003515951830000076
pk,nTransmission power on the nth subcarrier, p, for the kth userk′,nTransmission power, p, on the nth subcarrier for the k' th userk′,nIs rhol,k′,nIn vector form of (1), pl,k′,nMatching coefficient on nth sub-carrier for the ith access point and the kth user, ClIs the backhaul ceiling at the ith access point.
9. A computer readable storage medium storing one or more programs, characterized in that: the one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods of claims 1-4.
10. A computing device, comprising:
one or more processors, one or more memories, and one or more programs stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods of claims 1-4.
CN202210166016.4A 2022-02-23 2022-02-23 Combined optimization method and system based on non-cellular network Active CN114501504B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210166016.4A CN114501504B (en) 2022-02-23 2022-02-23 Combined optimization method and system based on non-cellular network

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210166016.4A CN114501504B (en) 2022-02-23 2022-02-23 Combined optimization method and system based on non-cellular network

Publications (2)

Publication Number Publication Date
CN114501504A true CN114501504A (en) 2022-05-13
CN114501504B CN114501504B (en) 2024-02-13

Family

ID=81481452

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210166016.4A Active CN114501504B (en) 2022-02-23 2022-02-23 Combined optimization method and system based on non-cellular network

Country Status (1)

Country Link
CN (1) CN114501504B (en)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109905918A (en) * 2019-02-25 2019-06-18 重庆邮电大学 A kind of NOMA honeycomb car networking dynamic resource scheduling method based on efficiency
CN111447631A (en) * 2020-03-05 2020-07-24 南京邮电大学 Satellite-ground combined beam forming and power distribution method based on non-orthogonal multiple access technology
WO2020223593A1 (en) * 2019-05-01 2020-11-05 Northeastern University Operating system for software-defined cellular networks
WO2021128608A1 (en) * 2019-12-26 2021-07-01 重庆邮电大学 Multi-carrier resource allocation method employing wirelessly powered backscatter communication network

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109905918A (en) * 2019-02-25 2019-06-18 重庆邮电大学 A kind of NOMA honeycomb car networking dynamic resource scheduling method based on efficiency
WO2020223593A1 (en) * 2019-05-01 2020-11-05 Northeastern University Operating system for software-defined cellular networks
WO2021128608A1 (en) * 2019-12-26 2021-07-01 重庆邮电大学 Multi-carrier resource allocation method employing wirelessly powered backscatter communication network
CN111447631A (en) * 2020-03-05 2020-07-24 南京邮电大学 Satellite-ground combined beam forming and power distribution method based on non-orthogonal multiple access technology

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
刘超;张顺颐;: "基于遗传算法的认知无线电网络共同信道和功率最优分配", 南京邮电大学学报(自然科学版), no. 06, pages 76 - 81 *

Also Published As

Publication number Publication date
CN114501504B (en) 2024-02-13

Similar Documents

Publication Publication Date Title
Saleem et al. Latency minimization for D2D-enabled partial computation offloading in mobile edge computing
Liu et al. Resource allocation for energy-efficient MEC in NOMA-enabled massive IoT networks
Zhang et al. Dynamic task offloading and resource allocation for mobile-edge computing in dense cloud RAN
Zhou et al. Fronthaul compression and transmit beamforming optimization for multi-antenna uplink C-RAN
CN112105062B (en) Mobile edge computing network energy consumption minimization strategy method under time-sensitive condition
Wang et al. Joint task offloading and caching for massive MIMO-aided multi-tier computing networks
Liu et al. Max-min energy balance in wireless-powered hierarchical fog-cloud computing networks
Rajapaksha et al. Deep learning-based power control for cell-free massive MIMO networks
Chen et al. Energy efficient resource allocation for IRS assisted CoMP systems
CN108990071B (en) NOMA-based two-step power distribution method in CR network system
Kim et al. Joint optimization of signal design and resource allocation in wireless D2D edge computing
Zhao et al. Context-aware TDD configuration and resource allocation for mobile edge computing
Chen et al. Hybrid fog/cloud computing resource allocation: Joint consideration of limited communication resources and user credibility
CN113473499A (en) Energy efficiency fair non-cellular network resource allocation and pre-coding method and device
CN101854725A (en) Resource distribution and power distribution method for cellular multi-cell orthogonal frequency division multiple access (OFDMA) system
Dinh et al. Energy efficient resource allocation optimization in fog radio access networks with outdated channel knowledge
CN114189936A (en) Cooperative edge computing task unloading method based on deep reinforcement learning
Li et al. Energy efficiency maximization oriented resource allocation in 5G ultra-dense network: Centralized and distributed algorithms
Luo et al. Energy-efficient mobile edge computing in RIS-aided OFDM-NOMA relay networks
Nguyen et al. Energy Efficiency Maximization for C-RANs: Discrete Monotonic Optimization, Penalty, and $\ell _ {0} $-Approximation Methods
Gao et al. Energy-efficient resource block assignment and power control for underlay device-to-device communications in multi-cell networks
Hassan et al. Joint throughput-power optimization of fog-RAN using rate-splitting multiple access and reinforcement-learning based user clustering
Han et al. Over-the-air computation of large-scale nomographic functions in MapReduce over the edge cloud network
Wang et al. Average rate maximization for mobile STAR-RIS-aided NOMA system
Ni et al. User association and hybrid beamforming designs for cooperative mmWave MIMO systems

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