CN114501504B - Combined optimization method and system based on non-cellular network - Google Patents
Combined optimization method and system based on non-cellular network Download PDFInfo
- Publication number
- CN114501504B CN114501504B CN202210166016.4A CN202210166016A CN114501504B CN 114501504 B CN114501504 B CN 114501504B CN 202210166016 A CN202210166016 A CN 202210166016A CN 114501504 B CN114501504 B CN 114501504B
- Authority
- CN
- China
- Prior art keywords
- access point
- user
- optimization model
- optimization
- transmission power
- 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.)
- Active
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 177
- 238000000034 method Methods 0.000 title claims abstract description 51
- 230000001413 cellular effect Effects 0.000 title claims abstract description 35
- 230000005540 biological transmission Effects 0.000 claims abstract description 60
- 238000001228 spectrum Methods 0.000 claims abstract description 21
- 230000006870 function Effects 0.000 claims description 48
- 239000011159 matrix material Substances 0.000 claims description 36
- 230000008569 process Effects 0.000 claims description 17
- 238000009499 grossing Methods 0.000 claims description 15
- 239000013598 vector Substances 0.000 claims description 15
- 238000010276 construction Methods 0.000 claims description 9
- 230000015654 memory Effects 0.000 claims description 8
- 230000021615 conjugation Effects 0.000 claims description 6
- 238000003860 storage Methods 0.000 claims description 6
- 230000002776 aggregation Effects 0.000 abstract description 9
- 238000004220 aggregation Methods 0.000 abstract description 9
- 238000004590 computer program Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 7
- 230000003595 spectral effect Effects 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 5
- 239000000969 carrier Substances 0.000 description 3
- 238000004891 communication Methods 0.000 description 3
- 230000009466 transformation Effects 0.000 description 3
- 230000006872 improvement Effects 0.000 description 2
- 238000013468 resource allocation Methods 0.000 description 2
- 239000000654 additive Substances 0.000 description 1
- 230000000996 additive effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000005562 fading Methods 0.000 description 1
- 238000012804 iterative process Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
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 the combined optimization model takes an access point, a matching coefficient of a user and a subcarrier 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 preferred transmission power of the user at the preferred access point and the preferred transmission power at the preferred access point based on the combined optimization model, and the obtained optimization result can maximize the spectrum efficiency of the non-cellular carrier aggregation system under the conditions of limited power consumption and backhaul resources.
Description
Technical Field
The invention relates to a combined optimization method and system based on a non-cellular network, and belongs to the technical field of wireless communication.
Background
Recently, a non-cellular network technology is proposed on the basis of a distributed multi-input multi-output network, and the technology eliminates inter-user interference through cooperation among base stations, so that the user rate in a single carrier can be improved, and the technology is another thought for improving the overall spectral efficiency of a system.
Unlike collaborative heterogeneous networks, there is no cell or hierarchy in a cellular-free network, each single-antenna access point is widely distributed in a service area, and a central processor uniformly distributes and transmits signals. The non-cellular network cooperates through a plurality of access points, so that the coverage capacity of the edge of the system is enhanced, and therefore, the power distribution problem in the system also needs to be considered again, and the matching relation between the access points and the users in the non-cellular network is not always a fixed topological structure in consideration of the calculation complexity and the forward capacity requirement which linearly increase along with the number of users. In a cellular-free network, therefore, how to allocate an optimal access point for each user, and to control the transmit power at the access point, becomes a problem to be solved.
Disclosure of Invention
The invention provides a combined 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 invention adopts the following technical scheme:
a method of joint optimization based on a non-cellular network, comprising:
gain of channels between each access point in the non-cellular network and the user on each subcarrier is obtained;
taking the gain of the acquired channel as the input of a pre-constructed joint optimization model, and solving the joint optimization model to determine a user preferred access point and preferred transmission power at the preferred access point from all access points; the joint optimization model is constructed by taking the matching coefficient among the access point, the user and the sub-carrier wave and the transmission power at each access point as optimization variables, taking the maximization of the system spectrum efficiency as an optimization target and taking the service quality of the user, the transmission power at the access point and the backhaul limit at the access point as constraint conditions.
The process of constructing the joint optimization model in advance is as follows:
constructing an initial joint optimization model by taking a matching coefficient among an access point, a user and subcarriers and transmission power at each access point as optimization variables, maximizing system spectrum efficiency as an optimization target and taking user service quality, transmission power at the access point and backhaul limitation at the access point as constraint conditions;
smoothing the initial joint optimization model by adopting a penalty function method;
and (3) adopting a continuous parameter convex approximation method to perform approximate convex optimization treatment on the initial combined optimization model after the smoothing treatment to obtain a final combined optimization model.
The initial joint optimization model is:
wherein,index set for user->For subcarrier index set, +.>For access point index set, R k,n For the downlink rate of the kth user on the nth subcarrier acquired based on the channel gain, R min,k For the lowest downlink rate requirement of the kth user, Ω is the variable matrix to which the matching coefficients between the access point, the user and the subcarriers belong, P is the variable matrix to which the transmit power of the access point belongs, and P k,n For the transmission power of the kth user on the nth subcarrier ρ l,k,n For the matching coefficient of the ith access point and the kth user on the nth subcarrier, P max,l For the upper transmit power limit at the first access point, C l Is the upper backhaul limit at the first access point.
The final joint optimization model is:
wherein,index set for user->For subcarrier index set, +.>For the access point index set, Ω is the variable matrix to which the matching coefficients between the access point, the user and the subcarriers belong, P is the variable matrix to which the transmit power at the access point belongs, y k,k′,n 、x k,n 、t k,n 、α l,k,n 、Are all auxiliary variables introduced in the approximate convex optimization process>Linearly expanding the lower bound function for the original penalty function F, F, < >> And->Are lower boundary functions or concave functions defined in the approximate convex optimization process>For the thermal noise power at the user ρ l,k,n For the matching coefficient of the ith access point and the kth user on the nth subcarrier, R min,k For the lowest downlink rate requirement at the kth user, P max,l For the upper transmit power limit at the first access point, (-) (m) For the mth iteration parameter, (. Cndot.) H Transposed ρ k,n For ρ l,k,n Vector form of>Is->The half positive definite matrix is composed, L is +.>Index number of access points, element->h l,k,n Channel gain on nth carrier for the ith access point to kth user, h l,k′,n Channel gain on the nth carrier for the first access point to the kth' user, (·) * For conjugation operation, ++>Is->Constituted semi-positive definite matrix, elementsp k,n For the transmission power of the kth user on the nth subcarrier, p k′,n For the transmission power of the kth user on the nth subcarrier, ρ k′,n For ρ l,k′,n Vector form, ρ l,k′,n C for the matching coefficient of the ith access point and the kth' user on the nth subcarrier l Is the upper backhaul limit at the first access point.
A cellular-free network-based joint optimization system comprising:
a channel acquisition module, configured to acquire gains of channels between each access point in the non-cellular network and each user on each subcarrier;
the optimization module is used for taking the acquired gain of the channel as the input of a pre-constructed joint optimization model, and solving the joint optimization model to determine a user preferred access point and preferred transmission power at the preferred access point from all the access points; the joint optimization model is constructed by taking the matching coefficient among the access point, the user and the sub-carrier wave and the transmission power at each access point as optimization variables, taking the maximization of the system spectrum efficiency as an optimization target and taking the service quality of the user, the transmission power at the access point and the backhaul limit at the access point as constraint conditions.
The system also comprises a joint optimization model construction module which is used for constructing a joint optimization model in advance; the combined optimization model construction module comprises:
the initial joint optimization model construction module is used for constructing an initial joint optimization model by taking the matching coefficient among the access point, the user and the subcarriers and the transmission power at each access point as optimization variables, maximizing the system spectrum efficiency as an optimization target and taking the user service quality, the transmission power at the access point and the backhaul limit at the access point as constraint conditions;
the smoothing processing module is used for carrying out smoothing processing on the initial joint optimization model;
and the approximate convex optimization processing module is used for performing 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:
wherein,index set for user->For subcarrier index set, +.>For access point index set, R k,n For the downlink rate of the kth user on the nth subcarrier acquired based on the channel gain, R min,k For the lowest downlink rate requirement of the kth user, Ω is the variable matrix to which the matching coefficients between the access point, the user and the subcarriers belong, and P is the access pointVariable matrix to which transmit power at an entry point belongs, p k,n For the transmission power of the kth user on the nth subcarrier ρ l,k,n For the matching coefficient of the ith access point and the kth user on the nth subcarrier, P max,l For the upper transmit power limit at the first access point, C l Is the upper backhaul limit at the first access point.
The final joint optimization model is:
wherein,index set for user->For subcarrier index set, +.>For the access point index set, Ω is the variable matrix to which the matching coefficients between the access point, the user and the subcarriers belong, P is the variable matrix to which the transmit power at the access point belongs, y k,k′,n 、x k,n 、t k,n 、α l,k,n 、Are all auxiliary variables introduced in the approximate convex optimization process>Linearly expanding the lower bound function for the original penalty function F, F, < >> And->Are lower boundary functions or concave functions defined in the approximate convex optimization process>For the thermal noise power at the user ρ l,k,n For the matching coefficient of the ith access point and the kth user on the nth subcarrier, R min,k For the lowest downlink rate requirement at the kth user, P max,l For the upper transmit power limit at the first access point, (-) (m) For the mth iteration parameter, (. Cndot.) H Transposed ρ k,n For ρ l,k,n Vector form of>Is thatThe half positive definite matrix is composed, L is +.>Index number of access points, element->h l,k,n Channel gain on nth carrier for the ith access point to kth user, h l,k′,n Channel gain on the nth carrier for the first access point to the kth' user, (·) * For conjugation operation, ++>Is->A half positive definite matrix of the composition, element->p k,n For the transmission power of the kth user on the nth subcarrier, p k′,n For the kth' useTransmission power of user on nth subcarrier ρ k′,n For ρ l,k′,n Vector form, ρ l,k′,n C for the matching coefficient of the ith access point and the kth' user on the nth subcarrier l Is the upper backhaul limit at the first 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, wherein one or more programs are stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing a joint optimization method.
The invention has the beneficial effects that: the joint optimization model 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 into account the user service quality constraint, the transmission power constraint at the access point and the backhaul constraint at the access point, and can obtain the preferred transmission power of the user preferred access point and the preferred transmission power at the preferred access point based on the joint optimization model, and the obtained optimization result can maximize the spectrum efficiency of the non-cellular carrier aggregation system under the conditions of limited power consumption and backhaul resources.
Drawings
FIG. 1 is a flow chart of the joint optimization method of the present invention;
FIG. 2 is a schematic diagram of a non-cellular network system;
fig. 3 is a graph comparing the total spectral efficiency of a non-cellular carrier aggregation system corresponding to a backhaul constraint from 80Mbps to 200Mbps with a single carrier non-cellular system under 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 more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
As shown in fig. 1, a joint optimization method based on a non-cellular network includes:
step 1, gain of channels between each access point in a non-cellular network and a user on each subcarrier is obtained;
step 2, taking the gain of the acquired channel as the input of a pre-constructed joint optimization model, and solving the joint optimization model to determine a user preferred access point and preferred transmission power at the preferred access point from all access points; the joint optimization model is constructed by taking the matching coefficient among the access point, the user and the sub-carrier wave and the transmission power at each access point as optimization variables, taking the maximization of the system spectrum efficiency as an optimization target and taking the service quality of the user, the transmission power at the access point and the backhaul limit at the access point as constraint conditions.
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 maximum of the system spectrum efficiency into account the user service quality constraint, the transmission power constraint at the access point and the backhaul constraint at the access point, and can obtain the preferred transmission power of the user preferred access point and the preferred transmission power at the access point based on the joint optimization model, and the obtained optimization results can maximize the spectrum efficiency of the non-cellular carrier aggregation system under the conditions of limited power consumption and backhaul resources.
The method can be implemented for the system shown in fig. 2, and the communication system comprises a central processing unit, a single-antenna remote access point (referred to as an "access point") and users, wherein each access point cooperates to provide services for the users, the transmitted signal vectors and power are uniformly allocated by the central processing unit, and the matching relationship between the users and the access points and the adopted communication channels are also determined by the central processing unit.
Consider the joint spectral efficiency optimization problem for the downlink of a non-cellular system in a time division duplex mode. Assuming that the channels are orthogonal on each subcarrier, the flat fading is obeyed and the channel state is perfectly known, the channel state information remains unchanged during the coherence time. The CPU loads each sub-carrierThe downlink pre-coding on the wave is sent 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 user are single antenna, the system has L remote access points, works on N sub-carriers, and provides service for K users. The access point, the subcarrier and the user index set are respectivelyIs->Considering the balance between the system performance and the computation complexity, MRT is adopted as a precoding scheme.
The received signal of the user on the single carrier can be written as:
wherein the parameters areChannel gain on the nth carrier for the ith access point to kth user,Is a complex set (·) * For conjugate operation ρ l,k,n For the matching coefficient of the ith access point and the kth user on the nth subcarrier, ρ l,k,n ∈{0,1}，p k,n Transmit power on the nth subcarrier for the kth user, +.> As a real number set, v k For additive white gaussian noise at kth user, +.> Thermal noise power for the user;
is provided withFor all sets of access points on the nth subcarrier that serve the kth user, the matching coefficient values will be according to the following rules:
Let s be k For normalized data information sent to the kth user, the information is not related to each other and the mean value is zero,i.e. the normalized information average power is 1, the SINR expression of the user received signal on a single carrier can be listed in consideration of the inter-user interference:
wherein,and (3) matching the cascade matrix of the coefficients on the nth subcarrier for the kth user and each access point.
Make each access point correspond toCascade of +.>Is->A semi-positive definite matrix of components,>
based on the above assumption, considering the general situation, the channel between the user and the access point on different subcarriers is a random complex variable only related to the distance between the two ends of the transceiver, and the specific process of pre-constructing the joint optimization model can be as follows:
11 With the matching coefficient among the access point, the user and the sub-carriers and the transmission power at each access point as optimization variables, with the maximization of the system spectrum efficiency as the optimization target, and with the user service quality, the transmission power at the access point and the backhaul limitation at the access point as constraint conditions, an initial joint optimization model is constructed.
The initial joint optimization model may be:
s.t.C1:
C2:
C3:
C4:ρ l,k,n ∈{0,1},
wherein,index set for user->For subcarrier index set, +.>For access point index set, R k,n ＝log(1+γ k,n ) For the downlink rate of the kth user on the nth subcarrier acquired based on the channel gain, R min,k For the lowest downlink rate requirement of the kth user, Ω is the variable matrix to which the matching coefficients between the access point, the user and the subcarriers belong, P is the variable matrix to which the transmit power of the access point belongs, and P k,n For the transmission power of the kth user on the nth subcarrier ρ l,k,n For the matching coefficient of the ith access point and the kth user on the nth subcarrier, the matching relationship among the users, the subcarriers and the access points in the network is completely defined by ρ l,k,n Determining P max,l For the upper transmit power limit at the first access point, C l Is the upper backhaul limit at the first access point.
12 And (3) smoothing the initial joint optimization model by adopting a penalty function method.
The initial combined optimization model is still non-convex after being smoothed, and the approximate convex optimization problem of the optimization model can be obtained by introducing auxiliary variables and replacing convex/concave upper and lower bound functions of the model target and the constraint function at the moment. After the approximate convex optimization problem is obtained, iteration solution is needed, the optimization solution results of each time are converged to the global optimal solution of the original problem in continuous iteration, the approximate convex optimization problem in the iteration comprises the result of the previous iteration optimization as a parameter, and the update is needed in the iteration process.
From the model, the 0/1 integer variable contained in the optimization problem ensures that the optimization problem is not smooth and belongs to mixed integer optimization; to make the optimization problem solvable, the model is first smoothed using a penalty function method, i.e., redefining the 0/1 variable ρ l,k,n Setting a penalty function in the objective function for the continuous variable:
C5:ρ l,k,n ∈[0,1],
wherein f (Ω) is a penalty function, ε is a parameter;
epsilon is a very small positive integer, and the transformed optimization problem solution can be consistent with the original integer programming problem through proper value, so that the original objective function is transformed into
13 Adopting a continuous parameter convex approximation method to perform approximate convex optimization treatment on the initial combined optimization model after the smoothing treatment to obtain a final combined optimization model.
The objective function is still not easily solved, and the objective function needs to be approximated, and can be obtained by linearizing f (Ω):
wherein ( (m) As a parameter of the mth iteration,linearly expanding a lower bound function for the original penalty function f;
the objective function may be converted into:
the original non-convex optimization is converted into approximate convex optimization by using a continuous parameter convex approximation method, the m-th optimization parameter is updated by the m-1-th iteration result, and the original problem optimal solution is obtained through multiple times of circulation. When the difference between the previous and the next iterative optimization solutions is smaller than a fixed value delta, ending the loop and outputting a result, wherein delta is a very small positive integer. Non-convex parts exist in the objective function and the constraint condition in the original optimization problem, and convex approximation processing is needed to be carried out respectively.
First introduceIs->Two new auxiliary variables to obtain R in objective function k,n Lower bound of->Approximation:
wherein x is k,n 、y k,k′,n The following conditions are satisfied:
C6:
C7:
wherein,and->Similarly, is->A semi-positive definite matrix of components,>
simple changes were made to the above conditions (C6 and C7):
C8:
C9:
r is as described above k,n Still in non-concave form, which is functionally scaled using the following expression:
wherein a, b is more than or equal to 0 and is any numerical variable, R is obtained by utilizing the above formula k,n Is defined by the approximate concave lower bound of (2):
using the scaling inequalityIs->Performing convex approximation on two non-convex constraints C8 and C9; wherein c is any vector variable in the scaling inequality, d=dd H Is a semi-positive definite matrix, d is any vector constant, and a is any real variable;
respectively into two constraints C8 and C9:
C10:
C11:
the original objective function has been converted to a convex form, and the original optimization problem has been converted to the following expression:
the optimization problem still has non-convex constraints C1, C2 and C3, and the scaling inequality is used to convert C1 into its convex approximation:
C12:
by introducing a new set of optimization variablesConverting C2 to a resolvable convex form:
C13:
C14:
it is apparent that C13 is still a non-convex inequality, and can be obtained by simple term shiftingScaling the constraint convex approximation by using a scaling inequality to obtain the constraint convex approximation:
for the non-convex constraint C3, a new set of variables is introduced as wellTo replace R in C3 k,n ：
C16:
C17:
Obviously the two inequalities are still in a non-convex form;
the simple transformation of C16 can be obtained:
reintroducingIs->Two new sets of variables to get the convex upper bound:
C18:
C19:
C20:
convex transformation of C20 with first order Taylor expansion:
C21:
c18 and C19 are still non-convex, making simple variants of the two inequalities:
C22:
C23:
the first-order taylor expansion is used for obtaining a C22 convex approximation inequality:
C24:
using the scaling inequalityPerforming convex transformation on C23 to obtain:
C25:
for the non-convex constraint C17, it is approximated by a convex upper bound using the arithmetic inequality:
if and only ifThe time equal sign is true;
constraint C17 may then be approximated as:
C26:
according toUpdating parameter +.>Such that the approximation constraint is ultimately in an iterative process with ρ l,k,n t k,n Taking an equal sign.
The combined optimization model is as follows:
wherein,index set for user->For subcarrier index set, +.>For the access point index set, Ω is the variable matrix to which the matching coefficients between the access point, the user and the subcarriers belong, P is the variable matrix to which the transmit power at the access point belongs, y k,k′,n 、x k,n 、t k,n 、α l,k,n 、Are all auxiliary variables introduced in the approximate convex optimization process>Linearly expanding the lower bound function for the original penalty function F, F, < >> And->Are lower boundary functions or concave functions defined in the approximate convex optimization process>For the thermal noise power at the user ρ l,k,n For the matching coefficient of the ith access point and the kth user on the nth subcarrier, R min,k For the lowest downlink rate requirement at the kth user, P max,l For the upper transmit power limit at the first access point, (-) (m) For the mth iteration parameter, (. Cndot.) H Transposed ρ k,n For ρ l,k,n Vector form of>Is thatThe half positive definite matrix is composed, L is +.>Index number of access points, element->h lkn Channel gain on nth carrier for the ith access point to kth user, h l,k′,n Channel gain on the nth carrier for the first access point to the kth' user, (·) * For conjugation operation, ++>Is->A half positive definite matrix of the composition, element->p k,n For the transmission power of the kth user on the nth subcarrier, p k′,n For the transmission power of the kth user on the nth subcarrier, ρ k′,n For ρ l,k′,n Vector form, ρ l,k′,n C for the matching coefficient of the ith access point and the kth' user on the nth subcarrier l Is the upper backhaul limit at the first access point.
And (3) carrying out smooth processing on the original non-convex mixed integer optimization problem by using a penalty function method for the initial combined optimization model, 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.
And finally, inputting the channel into a pre-constructed joint optimization model, and determining the preferred access point of the user and the preferred transmission power at the preferred access point from the access points.
Fig. 3 shows a plot of spectral efficiency of a non-cellular carrier aggregation system (model proposed by the present invention) versus a conventional non-cellular system (control) as backhaul constraints increase, with power consumption limits of 50mw and 500 mw. From simulation results, as the backhaul limit increases from 80Mbps to 200Mbps, the system spectral efficiency increases and gradually flattens. 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 under the power constraint of 50mw or 500mw, and the total spectral efficiency of the optimal resource allocation non-cellular carrier aggregation system under the 200Mbps backhaul constraint is improved by about 5% compared with that of the traditional non-cellular system. Under the condition of the optimal allocation of resources, the system model 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 overall 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 transmitting power at the access point by solving the joint optimization model. Compared with the traditional honeycomb-free system without carrier allocation, the optimization method utilizes the difference of channels on all sub-carriers after decomposition, gives the freedom degree of user channel selection by taking the system spectrum efficiency as an optimization target, can match optimal channel resources for users and access points, further suppresses inter-user interference, and finally achieves the improvement of the system spectrum efficiency.
Based on the same technical scheme, the invention also discloses a software system of the method, and a combined optimization system based on a non-cellular network, which comprises the following components:
and the channel acquisition module is used for acquiring the gain of the channel between each access point in the non-cellular network and each subcarrier between the user.
The optimization module is used for taking the acquired gain of the channel as the input of a pre-constructed joint optimization model, and solving the joint optimization model to determine a user preferred access point and preferred transmission power at the preferred access point from all the access points; the joint optimization model is constructed by taking the matching coefficient among the access point, the user and the sub-carrier wave and the transmission power at each access point as optimization variables, taking the maximization of the system spectrum efficiency as an optimization target and taking the service quality of the user, the transmission power at the access point and the backhaul limit at the access point as constraint conditions.
The combined optimization model construction module is used for constructing a combined optimization model in advance; the combined optimization model construction module comprises:
the initial joint optimization model construction module is used for constructing an initial joint optimization model by taking the matching coefficient among the access point, the user and the subcarriers and the transmission power at each access point as optimization variables, maximizing the system spectrum efficiency as an optimization target and taking the user service quality, the transmission power at the access point and the backhaul limit at the access point as constraint conditions;
the initial joint optimization model is:
wherein,index set for user->For subcarrier index set, +.>For access point index set, R k,n For the downlink rate of the kth user on the nth subcarrier acquired based on the channel gain, R min,k For the lowest downlink rate requirement of the kth user, Ω is the variable matrix to which the matching coefficients between the access point, the user and the subcarriers belong, P is the variable matrix to which the transmit power of the access point belongs, and P k,n For the transmission power of the kth user on the nth subcarrier ρ l,k,n For the matching coefficient of the ith access point and the kth user on the nth subcarrier, P max,l For the upper transmit power limit at the first access point, C l Is the upper backhaul limit at the first access point.
And the smoothing processing module is used for smoothing the initial joint optimization model by adopting a penalty function method.
The approximate convex optimization processing module is used for performing 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:
wherein,index set for user->For subcarrier index set, +.>For the access point index set, Ω is the index between the access point, user and subcarrierThe variable matrix to which the matching coefficient belongs, P is the variable matrix to which the transmission power at the access point belongs, y k,k′,n 、x k,n 、t k,n 、α l,k,n 、Are all auxiliary variables introduced in the approximate convex optimization process>Linearly expanding the lower bound function for the original penalty function F, F, < >> And->Are lower boundary functions or concave functions defined in the approximate convex optimization process>For the thermal noise power at the user ρ l,k,n For the matching coefficient of the ith access point and the kth user on the nth subcarrier, R min,k For the lowest downlink rate requirement at the kth user, P max,l For the upper transmit power limit at the first access point, (-) (m) For the mth iteration parameter, (. Cndot.) H Transposed ρ k,n For ρ l,k,n Vector form of>Is thatThe half positive definite matrix is composed, L is +.>Index number of access points, element->h l,k,n Channel gain on nth carrier for the ith access point to kth user, h l,k′,n Channel gain on the nth carrier for the first access point to the kth' user, (·) * For conjugation operation, ++>Is->A half positive definite matrix of the composition, element->p k,n For the transmission power of the kth user on the nth subcarrier, p k′,n For the transmission power of the kth user on the nth subcarrier, ρ k′,n For ρ l,k′,n Vector form, ρ l,k′,n C for the matching coefficient of the ith access point and the kth' user on the nth subcarrier l Is the upper backhaul limit at the first access point.
The division of the modules in the embodiments of the present application is schematically only one logic function division, and there may be another division manner in actual implementation, and in addition, each functional module in each embodiment of the present application may be integrated in one processor, or may exist separately and physically, or two or more modules may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules.
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 comprising instructions, which when executed by a computing device, cause the computing device to perform a joint optimization method.
Based on the same technical scheme, the invention also discloses a computing device, which comprises 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 are configured to be executed by the one or more processors, and the one or more programs comprise instructions for executing the joint optimization method.
It will be appreciated by those skilled in the art that 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 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 illustrative of the present invention and is not to be construed as limiting thereof, but rather as providing for the use of additional embodiments and advantages of all such modifications, equivalents, improvements and similar to the present invention are intended to be included within the scope of the present invention as defined by the appended claims.
Claims (8)
1. A method of joint optimization based on a non-cellular network, comprising:
gain of channels between each access point in the non-cellular network and the user on each subcarrier is obtained;
taking the gain of the acquired channel as the input of a pre-constructed joint optimization model, and solving the joint optimization model to determine a user preferred access point and preferred transmission power at the preferred access point from all access points; the joint optimization model is constructed by taking a matching coefficient among an access point, a user and subcarriers and transmission power at each access point as optimization variables, maximizing system spectrum efficiency as an optimization target and taking user service quality, transmission power at the access point and backhaul limitation at the access point as constraint conditions;
the joint optimization model is as follows:
wherein,index set for user->For subcarrier index set, +.>For the access point index set, Ω is the variable matrix to which the matching coefficients between the access point, the user and the subcarriers belong, P is the variable matrix to which the transmit power at the access point belongs, y k,k′,n 、x k,n 、t k,n 、α l,k,n 、Are all auxiliary variables introduced in the approximate convex optimization process>Linearly expanding the lower bound function for the original penalty function F, F, < >> And->Are lower boundary functions or concave functions defined in the approximate convex optimization process>For the thermal noise power at the user ρ l,k,n For the matching coefficient of the ith access point and the kth user on the nth subcarrier, R min,k For the lowest downlink rate requirement at the kth user, P max,l For the upper transmit power limit at the first access point, (-) (m) For the mth iteration parameter, (. Cndot.) H Transposed ρ k,n For ρ l,k,n Vector form of>Is->The half positive definite matrix is composed, L is +.>Index number of access points, element->h l,k,n Channel gain on nth carrier for the ith access point to kth user, h l,k′,n Channel gain on the nth carrier for the first access point to the kth' user, (·) * For conjugation operation, ++>Constituted semi-positive definite matrix, elementsp k,n For the transmission power of the kth user on the nth subcarrier, p k′,n For the transmission power of the kth user on the nth subcarrier, ρ k′,n For ρ l,k′,n Vector form, ρ l,k′,n C for the matching coefficient of the ith access point and the kth' user on the nth subcarrier l Is the upper backhaul limit at the first access point.
2. The method for joint optimization based on a non-cellular network according to claim 1, wherein the process of constructing the joint optimization model is as follows:
constructing an initial joint optimization model by taking a matching coefficient among an access point, a user and subcarriers and transmission power at each access point as optimization variables, maximizing system spectrum efficiency as an optimization target and taking user service quality, transmission power at the access point and backhaul limitation at the access point as constraint conditions;
smoothing the initial joint optimization model;
and performing approximate convex optimization on the initial combined optimization model after the smoothing treatment to obtain a final combined optimization model.
3. The method of joint optimization based on a non-cellular network according to claim 2, wherein the initial joint optimization model is:
wherein R is k,n Is the downlink rate of the kth user on the nth subcarrier acquired based on the channel gain.
4. A cellular-free network-based joint optimization system, comprising:
a channel acquisition module, configured to acquire gains of channels between each access point in the non-cellular network and each user on each subcarrier;
the optimization module is used for taking the acquired gain of the channel as the input of a pre-constructed joint optimization model, and solving the joint optimization model to determine a user preferred access point and preferred transmission power at the preferred access point from all the access points; the joint optimization model is constructed by taking a matching coefficient among an access point, a user and subcarriers and transmission power at each access point as optimization variables, maximizing system spectrum efficiency as an optimization target and taking user service quality, transmission power at the access point and backhaul limitation at the access point as constraint conditions;
the joint optimization model is as follows:
wherein,index set for user->For subcarrier index set, +.>For the access point index set, Ω is the variable matrix to which the matching coefficients between the access point, the user and the subcarriers belong, P is the variable matrix to which the transmit power at the access point belongs, y k,k′,n 、x k,n 、t k,n 、α l,k,n 、Are all auxiliary variables introduced in the approximate convex optimization process>Linearly expanding the lower bound function for the original penalty function F, F, < >> And->Are lower boundary functions or concave functions defined in the approximate convex optimization process>For the thermal noise power at the user ρ l,k,n For the matching coefficient of the ith access point and the kth user on the nth subcarrier, R min,k For the lowest downlink rate requirement at the kth user, P max,l For the upper transmit power limit at the first access point, (-) (m) For the mth iteration parameter, (. Cndot.) H Transposed ρ k,n For ρ l,k,n Vector form of>Is->The half positive definite matrix is composed, L is +.>Index number of access points, element->h l,k,n Channel gain on nth carrier for the ith access point to kth user, h l,k′,n Channel gain on the nth carrier for the first access point to the kth' user, (·) * For conjugation operation, ++>Is->Constituted semi-positive definite matrix, elementsp k,n For the transmission power of the kth user on the nth subcarrier, p k′,n For the transmission power of the kth user on the nth subcarrier, ρ k′,n For ρ l,k′,n Vector form, ρ l,k′,n For the ith access point and the kth' user on the nth subcarrierMatching coefficient, C l Is the upper backhaul limit at the first access point.
5. The cellular-free network-based joint optimization system of claim 4, further comprising a joint optimization model construction module configured to construct a joint optimization model in advance; the combined optimization model construction module comprises:
the initial joint optimization model construction module is used for constructing an initial joint optimization model by taking the matching coefficient among the access point, the user and the subcarriers and the transmission power at each access point as optimization variables, maximizing the system spectrum efficiency as an optimization target and taking the user service quality, the transmission power at the access point and the backhaul limit at the access point as constraint conditions;
the smoothing processing module is used for carrying out smoothing processing on the initial joint optimization model;
and the approximate convex optimization processing module is used for performing approximate convex optimization processing on the initial combined optimization model after the smoothing processing to obtain a final combined optimization model.
6. The cellular-free network-based joint optimization system of claim 5, wherein the initial joint optimization model is:
wherein R is k,n Is the downlink rate of the kth user on the nth subcarrier acquired based on the channel gain.
7. A computer readable storage medium storing one or more programs, characterized by: the one or more programs include instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-3.
8. 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, the one or more programs comprising instructions for performing any of the methods of claims 1-3.
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 CN114501504A (en) | 2022-05-13 |
| CN114501504B true 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) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115884227A (en) * | 2022-11-28 | 2023-03-31 | 网络通信与安全紫金山实验室 | Method and assembly for determining frequency spectrum efficiency of NAFD uRLLC system |
Citations (4)
| 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 |
-
2022
- 2022-02-23 CN CN202210166016.4A patent/CN114501504B/en active Active
Patent Citations (4)
| 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)
| Title |
|---|
| 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;刘超;张顺颐;;南京邮电大学学报(自然科学版)(06);76-81 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114501504A (en) | 2022-05-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Liu et al. | Max-min energy balance in wireless-powered hierarchical fog-cloud computing networks | |
| Liu et al. | Resource allocation for energy-efficient MEC in NOMA-enabled massive IoT networks | |
| Saleem et al. | Latency minimization for D2D-enabled partial computation offloading in mobile edge computing | |
| Zaher et al. | Learning-based downlink power allocation in cell-free massive MIMO systems | |
| Zhang et al. | Deep reinforcement learning for multi-agent power control in heterogeneous networks | |
| Zhao et al. | Energy-saving offloading by jointly allocating radio and computational resources for mobile edge computing | |
| Wang et al. | Joint interference alignment and power control for dense networks via deep reinforcement learning | |
| CN112105062B (en) | Mobile edge computing network energy consumption minimization strategy method under time-sensitive condition | |
| 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 | |
| Ma et al. | Cooperative beamforming for RIS-aided cell-free massive MIMO networks | |
| Kim et al. | Joint optimization of signal design and resource allocation in wireless D2D edge computing | |
| Shang et al. | Computation offloading and resource allocation in NOMA–MEC: A deep reinforcement learning approach | |
| Wu et al. | Intelligent resource allocation for IRS-enhanced OFDM communication systems: A hybrid deep reinforcement learning approach | |
| CN107613556B (en) | Full-duplex D2D interference management method based on power control | |
| Chen et al. | Hybrid fog/cloud computing resource allocation: Joint consideration of limited communication resources and user credibility | |
| Dinh et al. | Energy efficient resource allocation optimization in fog radio access networks with outdated channel knowledge | |
| CN110493804A (en) | A kind of wave beam and power distribution method of millimeter-wave systems | |
| Liu et al. | SCMA-based multiaccess edge computing in IoT systems: An energy-efficiency and latency tradeoff | |
| Forouzanmehr et al. | Energy efficiency maximization for IRS-assisted uplink systems: Joint resource allocation and beamforming design | |
| CN114501504B (en) | Combined optimization method and system based on non-cellular network | |
| CN110677175A (en) | Sub-channel scheduling and power distribution joint optimization method based on non-orthogonal multiple access system | |
| Zhao et al. | Computation offloading and beamforming optimization for energy minimization in wireless-powered IRS-assisted MEC | |
| Hassan et al. | Joint throughput-power optimization of fog-RAN using rate-splitting multiple access and reinforcement-learning based user clustering | |
| Gao et al. | Energy-efficient resource block assignment and power control for underlay device-to-device communications in multi-cell networks |
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 |