CN114615687B - Network resource configuration method for time delay perception - Google Patents
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Abstract
The invention relates to a network resource allocation method of time delay perception, which belongs to the technical field of wireless communication, and provides a two-layer downlink 5GC-RAN system based on orthogonal frequency division multiple access, wherein a user selects a distributed remote electric head according to specific time delay requirements so as to cooperatively transmit on different subcarriers; secondly, combining an effective capacity theory with orthogonal frequency division multiple access, and modeling an optimization problem as a system energy efficiency maximization problem under the constraint of average peak power and time delay requirements; finally, an algorithm based on dual and alternate optimization is provided to solve the optimization problem. The method has high calculation efficiency and high precision, can approach the optimal system performance, and obviously increases the energy efficiency of the system.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and relates to a time delay perceived network resource allocation method.
Background
With the rapid development of communication technology in recent years, providing ubiquitous communication for emerging applications in 5G wireless communication networks requires high spectral efficiency and low latency. Meanwhile, in recent years, there has been an increasing demand for ubiquitous high-speed wireless access and explosive growth of smartphones. While the ever-increasing demand has made network providers more challenging to manage and operate wireless networks to efficiently provide the required latency. The 5G cloud radio access network (Cloud Radio Access Network, C-RAN) receives a great deal of attention from the industry and academia as a promising new technology and architecture for the future 5G standard. It provides high flexibility, large capacity, wide coverage and low cost operation, mainly by combining powerful cloud computing and virtualization technologies. The C-RAN implements a centralized processing architecture in the baseband unit that provides joint signal processing for low power base station clusters, known as radio remote heads (Remote Radio Head, RRHs), and their service subscribers. It is beneficial to the realization of coordinated multi-point transmission.
There are typically two RRH cluster models, one centered on a cell, each RRH forming a cluster to serve a selected user, and the other centered on a user, allowing each user to associate a set of RRHs. The most important benefit of using user centric is that the network can provide higher throughput than cell centric. Because in the cell-centric case, the users at the cluster edge may experience considerable inter-cluster interference. However, by applying a user-centric approach, each user may be served by a single selected subset of neighboring RRHs. Therefore, the model has no explicit cell edge and performs better than the cell-centric model by allocating resources based on user groups of different service types. The C-RAN architecture is one of the architectures that implements user-centric architecture. Since the baseband unit is responsible for supporting the entire network, it controls a large number of radio units. It is thus able to form a globally optimal user-centric cluster. Thus, user-centric RRH clustering is considered in the system model.
While the C-RAN has many advantages, it faces challenges in terms of stringent latency requirements and forward link capacity. Therefore, 3GPP introduced a new method called function partitioning, by partitioning the baseband into Distributed Units (DUs) and Centralized Units (CUs), performing different functions in DUs and CUs according to the application of different rate requirements. However, guaranteeing deterministic latency is a challenge due to variations in the wireless channel. Therefore, the statistical delay, in which the end-to-end delay is limited to a certain range of offending probability, is suitable for real-time traffic on the wireless channel. Meanwhile, the statistical time delay does not need the dynamic state of the network queue to optimize the resource allocation. Thus, effective capacity is introduced, which is another performance indicator considering communication statistics delays. Therefore, it is more appropriate when both the system data rate and the communication statistics delay are optimization objectives.
Furthermore, most of the existing work not only ignores maximum peak power limitation, limited forward capacity, RRH cooperation and multiuser interference management, but also does not consider CoMP schemes. In the time delay analysis of the C-RAN network, an important constraint is CoMP selection based on the user time delay requirement, which is not studied in most of the existing works. Therefore, in the OFDMA-based C-RAN network, joint resource allocation and CoMP selection are performed in consideration of the concept of effective capacity to ensure a user's latency requirement, thereby maximizing system energy efficiency.
Disclosure of Invention
In view of the above, the present invention aims to provide a method for configuring network resources with perceived delay, which aims at the problems of strict delay requirement and limited forward link capacity of a cloud radio access network, and maximizes the system energy efficiency by optimizing subcarrier allocation, power allocation, coMP selection and relaxation variables in consideration of the network system energy efficiency.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a method for configuring network resources of delay perception comprises the following steps:
s1: determining CoMP selection of a system, and determining the number of users, the number of subcarriers and the total bandwidth of the system;
s2: and (3) system model: a subcarrier allocation strategy and a CoMP selection strategy are determined. Obtaining the effective capacity of the user k according to the effective capacity concept, and further constructing an energy efficiency problem modeling related to the effective capacity and the power required by the downlink;
s3: due to integer variable { x m,n ,y i,n The combined optimization problem is a mixed integer partial problem and is difficult to solve. Thus is beneficial toIntroducing a relaxation variable lambda by using a Dinkelbach method to convert an original mixed integer partial problem into a mixed integer nonlinear programming problem;
s4: solving the optimal power and subcarrier allocation: given a system CoMP selection, a fixed lagrangian multiplier is given to find the optimal power allocation p for that user m,i,n Subcarrier allocation x m,n And a corresponding relaxation variable λ;
s5: determining optimal CoMP selection: optimal power allocation p based on step S4 m,i,n Subcarrier allocation x m,n And a relaxation variable lambda, an optimal CoMP selection is obtained if the updated CoMP selection is such that the target value F j Increasing, the RRHs set and the target value F (A n ) Otherwise, keeping RRHs set unchanged, updating CoMP selection, judging whether the CoMP selection is converged within tolerance range epsilon or not, and obtaining power distribution p of the next round m,i,n Subcarrier allocation x m,n A relaxation variable λ;
s6: determining Lagrangian multiplier ω n ,η m,i ,χ i ,ζ i : updating lagrangian multiplier ω based on CoMP selection obtained in step S4 n ,η m,i ,χ i ,ζ i If the updated CoMP selection reaches convergence within the tolerance range ε, the Lagrangian multiplier ω is updated n ,η m,i ,χ i ,ζ i Until the lagrangian multiplier no longer converges, the entire algorithm ends.
Further, the step S2 specifically includes the following steps:
s21: a two-layer downlink 5-G C-RAN network based on OFDMA was designed. Is provided with
Represents a set of RRHs, < >>
Representing a set of users in the network. One pool of CUs with fibers is connected to I DUs, each connected to only one RRH. Furthermore, RRH collaboration is also considered. The bandwidth of the channel is BMHz, and is divided intoN orthogonal Subcarriers (SCs),
is a collection of SCs. Let x be m,n Indicating the subcarrier allocation strategy.
In view of allocating subcarriers to users for cooperative data transmission, the subcarrier allocation strategy is specifically expressed as follows:
meanwhile, each subcarrier SCn e N is allocated to no more than one user (i.e., at most to one user), then there is a formal constraint:
thus, the SCs set allocated to user m is composed of
Given, wherein->
And +.>
S22: according to the CoMP scheme, each particular user selects a subset of RRHs on each SC. Thus, each RRHi transmits data received from DUs only on a subset of SCs allocated to the user. Let y be i,n Indicating CoMP selection on SC n, there are:
then, define in
The subset of RRHs transmitted up is +.>
Thus (S)>
The RRHs in the network cooperatively transmit data to the user m through the SC n.
S23: for delay-sensitive traffic, it is difficult and impractical to guarantee deterministic delay for mobile services due to the time-varying nature of the wireless channel. The concept of effective capacity is therefore proposed. It is defined as the maximum constant arrival rate of the channel over time under statistical delay requirements, specified by the delay index μ. The effective capacity is expressed in the form shown in the following formula:
wherein the method comprises the steps of
Is related to the service rate r=log 2 Statistical expectation of (1+sinr) the signal-to-interference-plus-noise ratio is modeled as a random variable due to channel fading. Thus, the effective capacity is a generalization of shannon capacity, which comprehensively considers communication delay and reliability.
Thus, user m is at
The service rate of (2) can be expressed as:
the signal-to-interference-and-noise ratio is expressed by the following formula:
wherein g m,i,n ，p m,i,n Sum sigma 2 Representing the complex radio access channel coefficients, the power transmitted from the rrii on SC n to user m and the receiving end noise power variance, respectively.
S24: obtaining the effective capacity of the user m according to the effective capacity formula, namely
Of the formula (I)
Mu is taken as a time delay index->
A statistical average of the internal parameters for channel g is given. Each user selects its appropriate RRHs based on its tolerable delay parameter (μ) and the data queue length of the RRHs.
Notably, the choice of CoMP affects E c Is of a size of (a) and (b). Each RRH can be considered in CoMP selection if and only if the arrival rate of traffic into the RRH is less than the achievable E c When (1). If this constraint is not satisfied, no RRH is selected in the RRH cluster. Thus E is c Must be greater than the arrival rate, i.e., the average arrival rate in the system model, to ensure queuing stability at each RRH and avoid any data loss. Thus, this condition can be expressed as a form shown in the following formula:
in the method, in the process of the invention,
is the average forward rate of the rrii. In addition, the maximum total available transmit power per RRH may be limited, i.e
In the method, in the process of the invention,
is the maximum transmit power of the rrii. Next, consider the limitation of the fiber forward rate by the rrii. At each rrii, the average rate of wireless access needs to be less than the average forward rate of the fiber link, i.e.:
s25: the system aims to solve the problems of radio resource allocation and CoMP selection so as to ensure the time delay requirement of users in an OFDMA-based C-RAN architecture centered by the users and maximize the energy efficiency of the system on the premise of meeting the constraint condition of the system. Thus, the problem is modeled as:
in the method, in the process of the invention,
circuit power consumption representing RRHi, +.>
Representing the circuit power consumption of user m.
Further, the step S3 specifically includes the following steps:
s31: due to integer variable { x m,n ,y i,n The combined optimization problem is a mixed integer partial problem and is difficult to solve. First, the original mixed integer partial problem is converted into a mixed integer nonlinear programming problem by introducing a relaxation variable lambda by using a Dinkelbach method. After introducing the relaxation variable λ, the original energy efficiency optimization problem can be converted into:
wherein the auxiliary variable lambda represents the total energy efficiency of the downlink transmission.
S32: thus, a function can be defined:
f (λ) is a convex function with respect to λ and is a strictly decreasing function with respect to λ, when p m,i,n ，x m,n And y i,n When the corresponding solution is obtained, the corresponding energy efficiency value lambda can be obtained.
Further, the step S4 specifically includes the following steps:
s41: first select y for fixed CoMP i,n Subcarrier allocation x m,n And power allocation p m,i,n 。
S42: since the first sub-problem is not jointly convex for power and subcarriers, the relaxation method is used to process integer variable { x } m,n I.e. for all n and m, x will be { m,n Relaxation to continuous variable, i.e. { x } m,n }∈[0,1]. In this way, the problem becomes a joint convex problem, and can be solved by a dual method.
S43: and respectively solving subcarrier allocation and power allocation under fixed CoMP selection by using a Lagrangian dual method.
Further, the step S5 specifically includes the following steps:
s51: since the optimal solution requires an exhaustive search algorithm to search all possible CoMP selections and users. Thus, the overall complexity is
It is not easy to realize when the number of I is large. To overcome this burden, a low complexity heuristic algorithm was introduced.
To apply the algorithm described above, SINR is defined m,n (A n ) As a means ofUser m is at the selected RRHsA n The signal-to-noise ratio over the collection is expressed as:
s52: gives the selected RRHsA n The Lagrangian function on the set is:
wherein,,
heuristic objective function F (A n ) The definition is as follows:
because p is m,i,n Is fixed, each user m tends to be associated with a subset of RRHs that meet this objective function. This means that RRHi is selected if the following equation is satisfied:
F(A n ∪{i})＞F(A n ).
s53: according to step S52, after CoMP selection of all I iterations is achieved, it is determined whether the CoMP is converged within the tolerance range epsilon, if yes, step S6 is performed, otherwise, S41 is returned.
Further, the step S6 specifically includes the following steps:
s61: given initialized CoMP selection y i,n Lagrangian multiplier omega n ,η m,i ,χ i ,ζ i And tolerance epsilon.
S62: when Lagrangian multiplier omega n ,η m,i ,χ i ,ζ i When convergence, go to step S41, otherwise stop Lagrangian multiplier ω n ,η m,i ,χ i ,ζ i Find the corresponding Lagrangian multiplier ω n ,η m,i ,χ i ,ζ i Optimal CoMP selection y corresponding to the lower i,n Subcarrier allocation x m,n Power distribution p m,i,n And a relaxation variable λ.
S63: updating Lagrangian multiplier ω n ,η m,i ,χ i ,ζ i Finding the optimal subcarrier allocation x according to step S41 m,n Power distribution p m,i,n A relaxation variable lambda.
S64: provided that the Lagrangian multiplier ω n ,η m,i ,χ i ,ζ i In a convergence state, continuously updating the Lagrangian multiplier until the optimal CoMP selection y meeting the non-convergence requirement of the Lagrangian multiplier is found i,n Subcarrier allocation x m,n Power distribution p m,i,n And a relaxation variable λ.
The invention has the beneficial effects that: by introducing the effective capacity concept, compared with the traditional cloud wireless access network system, the problems of strict delay requirement of the cloud wireless access network and limited forward link capacity are effectively relieved. The invention can effectively increase the energy efficiency of the system.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objects and other advantages of the invention may be realized and obtained by means of the instrumentalities and combinations particularly pointed out in the specification.
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For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in the following preferred detail with reference to the accompanying drawings, in which:
FIG. 1 is a diagram of a network architecture of the present invention;
fig. 2 is a flow chart of the method of the present invention.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the illustrations provided in the following embodiments merely illustrate the basic idea of the present invention by way of illustration, and the following embodiments and features in the embodiments may be combined with each other without conflict.
Wherein the drawings are for illustrative purposes only and are shown in schematic, non-physical, and not intended to limit the invention; for the purpose of better illustrating embodiments of the invention, certain elements of the drawings may be omitted, enlarged or reduced and do not represent the size of the actual product; it will be appreciated by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numbers in the drawings of embodiments of the invention correspond to the same or similar components; in the description of the present invention, it should be understood that, if there are terms such as "upper", "lower", "left", "right", "front", "rear", etc., that indicate an azimuth or a positional relationship based on the azimuth or the positional relationship shown in the drawings, it is only for convenience of describing the present invention and simplifying the description, but not for indicating or suggesting that the referred device or element must have a specific azimuth, be constructed and operated in a specific azimuth, so that the terms describing the positional relationship in the drawings are merely for exemplary illustration and should not be construed as limiting the present invention, and that the specific meaning of the above terms may be understood by those of ordinary skill in the art according to the specific circumstances.
Referring to fig. 1 to fig. 2, the method for configuring network resources with time delay perception according to the present invention specifically includes the following steps:
step 1: the CoMP selection of the system is determined, and the number of users, the number of subcarriers, and the total bandwidth of the system are determined.
Step 2: and (3) system model: a subcarrier allocation strategy and a CoMP selection strategy are determined. And obtaining the effective capacity of the user k according to the effective capacity concept, and further constructing the energy efficiency problem modeling related to the effective capacity and the power required by the downlink. The method specifically comprises the following steps:
step 2.1: a two-layer downlink 5-G C-RAN network based on OFDMA was designed. Is provided with
Represents a set of RRHs, < >>
Representing a set of users in the network. One pool of CUs with fibers is connected to I DUs, each connected to only one RRH. Furthermore, RRH collaboration is also considered. The channel bandwidth is BMHz, and is divided into N orthogonal Subcarriers (SCs) in average>
Is a collection of SCs. Let x be m,n Indicating the subcarrier allocation strategy.
In view of allocating subcarriers to users for cooperative data transmission, the subcarrier allocation strategy is specifically expressed as follows:
meanwhile, each subcarrier SCn e N is allocated to no more than one user (i.e., at most to one user), then there is a formal constraint:
thus, the SCs set allocated to user m is composed of
Given, wherein->
And +.>
Step 2.2: according to the CoMP scheme, each particular user selects a subset of RRHs on each SC. Thus, each RRHi transmits data received from DUs only on a subset of SCs allocated to the user. Let y be i,n Indicating CoMP selection on SC n, there are:
then, define in
The subset of RRHs transmitted up is +.>
Thus (S)>
The RRHs in the network cooperatively transmit data to the user m through the SC n.
Step 2.3: for delay-sensitive traffic, the concept of effective capacity is proposed because of the time-varying nature of the wireless channel, ensuring deterministic delay for mobile services is difficult and impractical. It is defined as the maximum constant arrival rate of the channel over time under statistical delay requirements, specified by the delay index μ. The effective capacity is expressed in the form shown in the following formula:
wherein the method comprises the steps of
Is related to the service rate r=log 2 Statistical expectation of (1+sinr) the signal-to-interference-plus-noise ratio is modeled as a random variable due to channel fading. Thus, the effective capacity is a summary of shannon capacity, which considers both communication delay and reliabilitySex.
Thus, user m is at
The service rate of (2) can be expressed as:
the signal-to-interference-and-noise ratio is expressed by the following formula:
wherein g m,i,n ，p m,i,n Sum sigma 2 Representing the complex radio access channel coefficients, the power transmitted from the rrii on SC n to user m and the receiving end noise power variance, respectively.
Step 2.4: obtaining the effective capacity of the user m according to the effective capacity formula, namely
Of the formula (I)
Mu is taken as a time delay index->
A statistical average of the internal parameters for channel g is given. Each user selects its appropriate RRHs according to its tolerable delay parameter μ and the data queue length of the RRHs.
Notably, the choice of CoMP affects E c Is of a size of (a) and (b). Each RRH can be considered in CoMP selection if and only if the arrival rate of traffic into the RRH is less than the achievable E c When (1). If this constraint is not satisfied, no RRH is selected in the RRH cluster. Thus E is c Must be greater than the arrival rate, i.e., the average arrival rate in the system model, toThe queuing stability at each RRH is guaranteed and any data loss is avoided. Thus, this condition can be expressed as a form shown in the following formula:
in the method, in the process of the invention,
is the average forward rate of the rrii. In addition, the maximum total available transmit power per RRH may be limited, i.e
Wherein P is i max Is the maximum transmit power of the rrii. Next, consider the limitation of the fiber forward rate by the rrii. At each rrii, the average rate of wireless access needs to be less than the average forward rate of the fiber link, i.e.:
step 2.5: the system aims to solve the problems of radio resource allocation and CoMP selection, so as to ensure the time delay requirement of users in an OFDMA-based C-RAN architecture taking the users as the center and maximize the energy efficiency of the system on the premise of meeting the constraint condition of the system. Thus, the problem is modeled as:
in the method, in the process of the invention,
circuit power consumption representing RRHi, +.>
Representing the circuit power consumption of user m.
Step 3: due to integer variable { x m,n ,y i,n The combined optimization problem is a mixed integer partial problem and is difficult to solve. The original mixed integer partial problem is thus converted into a mixed integer nonlinear programming problem by introducing a relaxation variable λ using the Dinkelbach method. The method specifically comprises the following steps:
step 3.1: after introducing the relaxation variable λ, the original energy efficiency optimization problem can be converted into:
wherein the auxiliary variable lambda represents the total energy efficiency of the downlink transmission.
Step 3.2: thus, a function can be defined:
f (λ) is a convex function with respect to λ and is a strictly decreasing function with respect to λ, when p m,i,n ，x m,n And y i,n When the corresponding solution is obtained, the corresponding energy efficiency value lambda can be obtained.
Step 4: solving the optimal power and subcarrier allocation: given a system CoMP selection, a fixed lagrangian multiplier is given to find the optimal power allocation p for that user m,i,n Subcarrier allocation x m,n And a corresponding relaxation variable λ. The method specifically comprises the following steps:
step 4.1: first select y for fixed CoMP i,n Subcarrier allocation x m,n And power allocation p m,i,n ；
Step 4.2: since the first sub-problem is not jointly convex for power and subcarriers, the relaxation method is used to process integer variable { x } m,n I.e. for all n and m, x will be { m,n Relaxation to continuous variable, i.e. { x } m,n }∈[0,1]. Thus, the problem becomes a joint convex problem, which can be solved by a dual method.
Step 4.3: and respectively solving subcarrier allocation and power allocation under fixed CoMP selection by using a Lagrangian dual method.
By applying the above method, the Lagrange function is as follows
The above Lagrangian function is decomposed into a master-slave problem. The problem is to maximize the transmit power of the fixed lagrangian multiplier and allocate subcarriers. The main problem is to update the lagrangian multiplier using a gradient-based approach.
For a given set of lagrangian multipliers, the optimal transmit power allocation is as follows:
wherein the method comprises the steps of
And->
Similarly, for a given set of lagrangian multipliers, the optimal subcarrier allocation is as follows:
wherein phi is n As a marginal benefit for the allocation of subcarrier n to users.
Step 5: determining optimal CoMP selection: optimal power allocation p based on step 4 m,i,n Subcarrier waveDistribution x m,n And a relaxation variable lambda, an optimal CoMP selection is obtained if the updated CoMP selection is such that the target value F j Increasing, the RRHs set and the target value F (A n ) Otherwise, keeping RRHs set unchanged, updating CoMP selection, judging whether the CoMP selection is converged within tolerance range epsilon or not, and obtaining power distribution p of the next round m,i,n Subcarrier allocation x m,n A relaxation variable. The method specifically comprises the following steps:
step 5.1: since the optimal solution requires an exhaustive search algorithm to search all possible CoMP selections and users. Thus, the overall complexity is
It is not easy to realize when the number of I is large. To overcome this burden, a low complexity heuristic algorithm was introduced.
To apply the algorithm described above, SINR is defined m,n (A n ) At selected RRHsA as user m n The signal-to-noise ratio over the collection is expressed as:
step 5.2: gives the selected RRHsA n The Lagrangian function on the set is:
wherein,,
heuristic objective function F (A n ) The definition is as follows:
because p is m,i,n Is fixed and is used for the treatment of the skin,each user m tends to be associated with a subset of RRHs that meet this objective function. This means that RRHi is selected if the following equation is satisfied:
F(A n ∪{i})＞F(A n ).
definition F l For the target value of the first iteration, first initialize
F (F) l =0. Furthermore, the channel power gains on each SC n are ordered in descending order. At each iteration l e {1,..i }, select the rri with the l-th maximum channel power gain l . Then, the above inequality is checked. If F is increased l Is the value of RRHi l Added to A n Is a subset of (c). Thus, aggregate and target value F l+1 Respectively updated to A n ＝A n ∪{i l Sum F l+1 ＝F(A n ∪{m l })。/>
Step 5.3: according to the step S52, after CoMP selection of all I iterations is achieved, it is determined whether the CoMP is converged within the tolerance range epsilon, if yes, step S6 is performed, otherwise, S41 is returned;
step 6: determining Lagrangian multiplier ω n ,η m,i ,χ i ,ζ i : updating lagrangian multiplier ω based on CoMP selection obtained in step S4 n ,η m,i ,χ i ,ζ i If the updated CoMP selection reaches convergence within the tolerance range ε, the Lagrangian multiplier ω is updated n ,η m,i ,χ i ,ζ i Until the lagrangian multiplier no longer converges, the entire algorithm ends. The method specifically comprises the following steps:
step 6.1: given initialized CoMP selection y i,n Lagrangian multiplier omega n ,η m,i ,χ i ,ζ i And tolerance epsilon.
Step 6.2: when Lagrangian multiplier omega n ,η m,i ,χ i ,ζ i When convergence, go to step S41, otherwise stop Lagrangian multiplier ω n ,η m,i ,χ i ,ζ i Find the corresponding Lagrangian multiplier ω n ,η m,i ,χ i ,ζ i Optimal CoMP selection y corresponding to the lower i,n Subcarrier allocation x m,n Power distribution p m,i,n And a relaxation variable λ.
Step 6.3: updating Lagrangian multiplier ω n ,η m,i ,χ i ,ζ i Finding the optimal subcarrier allocation x according to step S41 m,n Power distribution p m,i,n A relaxation variable lambda.
Step 6.4: provided that the Lagrangian multiplier ω n ,η m,i ,χ i ,ζ i In a convergence state, continuously updating the Lagrangian multiplier until the optimal CoMP selection y meeting the non-convergence requirement of the Lagrangian multiplier is found i,n Subcarrier allocation x m,n Power distribution p m,i,n And a relaxation variable λ.
Finally, it is noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the present invention, which is intended to be covered by the claims of the present invention.
Claims (1)
1. A network resource allocation method for time delay perception is characterized in that: the method comprises the following steps:
s1: determining CoMP selection of a system, and determining the number of users, the number of subcarriers and the total bandwidth of the system;
s2: constructing a system model: determining a subcarrier allocation strategy and a CoMP selection strategy to obtain the effective capacity of a user k, and constructing energy efficiency problem modeling related to the effective capacity and the power required by downlink;
s3: introducing a relaxation variable lambda by using a Dinkelbach method to convert an original mixed integer partial problem into a mixed integer nonlinear programming problem;
s4: solving for optimumPower and subcarrier allocation: given a system CoMP selection, a fixed lagrangian multiplier is given to find the optimal power allocation p for that user m,i,n Subcarrier allocation x m,n And a corresponding relaxation variable λ;
s5: determining optimal CoMP selection: optimal power allocation p based on step S4 m,i,n Subcarrier allocation x m,n And a relaxation variable lambda, an optimal CoMP selection is obtained if the updated CoMP selection is such that the target value F j Increasing, the RRHs set and the target value F (A n ) Otherwise, keeping RRHs set unchanged, updating CoMP selection, judging whether the CoMP selection is converged within tolerance range epsilon or not, and obtaining power distribution p of the next round m,i,n Subcarrier allocation x m,n A relaxation variable λ;
s6: determining Lagrangian multiplier ω n ,η m,i ,χ i ,ζ i : updating lagrangian multiplier ω based on CoMP selection obtained in step S4 n ,η m,i ,χ i ,ζ i If the updated CoMP selection reaches convergence within the tolerance range ε, the Lagrangian multiplier ω is updated n ,η m,i ,χ i ,ζ i Until the Lagrangian multiplier no longer converges, the whole algorithm ends;
the step S2 specifically includes the following steps:
s21: two-layer downlink 5G C-RAN network based on OFDMA is designed and provided with
Represents a set of RRHs that are associated with the set,
representing the set of users in the network, a CU pool with fibers is connected to I DUs, each DU is connected to only one RRH, the channel bandwidth is BMHz considering RRH cooperation, and is equally divided into N orthogonal Subcarriers (SCs), where->
For the set of SCs, let x m,n Representing subcarrier divisionA strategy is matched;
the subcarrier allocation strategy is specifically shown as follows:
each subcarrier SCn e N is allocated to at most one user, then there is a formula constraint:
the SCs set allocated to user m is composed of
Given, wherein->
And
S22: according to the CoMP scheme, each particular user selects a subset of RRHs on each SC, each RRHi transmitting data received from DUs only on a subset of SCs allocated to the user; let y be i,n Indicating CoMP selection on SC n, there are:
is defined in
The subset of RRHs transmitted up is +.>
The RRHs in the network transmit data to a user m through SC n cooperation;
s23: the effective capacity is expressed in the form shown in the following formula:
the effective capacity is the maximum constant arrival rate of a channel which changes with time under the requirement of statistical time delay and is specified by a time delay index mu; in the middle of
Is related to the service rate r=log 2 Statistical expectation of (1+sinr), signal-to-interference-plus-noise ratio is modeled as a random variable;
user m is at
The service rate of (2) is expressed as:
the signal-to-interference-and-noise ratio is expressed by the following formula:
wherein g m,i,n ，p m,i,n Sum sigma 2 Respectively representing complex wireless access channel coefficients, the power transmitted from RRHi on SC n to user m and the noise power variance of receiving end;
s24: obtaining the effective capacity of the user m according to the effective capacity formula, namely
Of the formula (I)
Mu is taken as a time delay index->
Giving a statistical average of the internal parameters for channel g; each user selects the proper RRHs according to the tolerable delay parameter mu and the data queue length of the RRHs;
E c greater than the average arrival rate in the system model, expressed as:
in the method, in the process of the invention,
is the average forward rate of RRHi;
the maximum total available transmit power per RRH may be limited, namely:
wherein P is i max Is the maximum transmit power of the RRHi;
at each RRHi, the average rate of wireless access is less than the average forward rate of the fiber link, i.e.:
s25: modeling the energy efficiency problem as:
Subject to:C1-C6.
wherein P is i CIR,RRH Representing the circuit power consumption of the rrii,
representing the circuit power consumption of user m;
the step S3 specifically comprises the following steps:
s31: introducing a relaxation variable lambda by using a Dinkelbach method to convert an original mixed integer partial problem into a mixed integer nonlinear programming problem; after introducing the relaxation variable lambda, the original energy efficiency optimization problem is converted into:
wherein the auxiliary variable λ represents the total energy efficiency of the downlink transmission;
s32: defining a function:
f (λ) is a convex function with respect to λ and is a strictly decreasing function with respect to λ, when p m,i,n ，x m,n And y i,n When a corresponding solution is obtained, a corresponding energy efficiency value lambda is obtained;
the step S4 specifically includes the following steps:
s41: selection y for fixed CoMP i,n Subcarrier allocation x m,n And power allocation p m,i,n ；
S42: processing integer variables { x }, using relaxation m,n I.e. for all n and m, x will be { m,n Relaxation to continuous variable, i.e. { x } m,n }∈[0,1]；
S43: respectively solving subcarrier allocation and power allocation under fixed CoMP selection by using a Lagrangian dual method;
the step S5 specifically includes the following steps:
s51: overall complexity is
When the number of I is large, a low-complexity heuristic algorithm is introduced; definition of the definition
At selected RRHsA as user m n The signal-to-noise ratio over the collection is expressed as:
s52: gives the selected RRHsA n The Lagrangian function on the set is:
wherein,,
heuristic objective function F (A n ) The definition is as follows:
RRHi is selected if the following equation is satisfied:
F(A n ∪{i})＞F(A n )
s53: after the CoMP selection of all the iterations is realized, judging whether the CoMP is converged in the tolerance range epsilon, if yes, performing step S6, otherwise returning to S41;
the step S6 specifically includes the following steps:
s61: given initialized CoMP selection y i,n Lagrangian multiplier omega n ,η m,i ,χ i ,ζ i Tolerance epsilon;
s62: when Lagrangian multiplier omega n ,η m,i ,χ i ,ζ i When convergence, go to step S41, otherwise stop Lagrangian multiplier ω n ,η m,i ,χ i ,ζ i Find the corresponding Lagrangian multiplier ω n ,η m,i ,χ i ,ζ i Optimal CoMP selection y corresponding to the lower i,n SonCarrier allocation x m,n Power distribution p m,i,n And a relaxation variable λ;
s63: updating Lagrangian multiplier ω n ,η m,i ,χ i ,ζ i Finding the optimal subcarrier allocation x according to step S41 m,n Power distribution p m,i,n A relaxation variable λ;
s64: provided that the Lagrangian multiplier ω n ,η m,i ,χ i ,ζ i Is in a convergence state, the Lagrangian multiplier is continuously updated until the optimal CoMP selection y meeting the non-convergence requirement of the Lagrangian multiplier is found i,n Subcarrier allocation x m,n Power distribution p m,i,n And a relaxation variable λ.
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