CN110012509A - A resource allocation method based on user mobility in 5G small cell network - Google Patents

Abstract

The invention relates to a resource allocation method based on user mobility in a 5G small cell network, and belongs to the technical field of mobile communication. First, in view of the serious interference problem in the small cell network, after the inactive user successfully accesses the network through paging, the present invention effectively reduces the user communication interference problem by establishing a user-centered virtual cell. No handover occurs when the user moves in the virtual cell, which ensures service continuity. Secondly, in order to improve resource utilization, based on the Lyapunov optimization method, the average energy efficiency of the network is maximized. The invention fully considers the user data packet queue length and channel quality, and decomposes the problem of maximizing the average energy efficiency of the network into two sub-problems of user optimal transmission resource allocation and optimal power allocation to solve. Through the optimal allocation of network resources, the transmission quality is improved, and the stability of the system queue is ensured while maximizing the average energy efficiency of the system.

Description

A resource allocation method based on user mobility in 5G small cell network

technical field

The invention belongs to the technical field of mobile communication, and relates to a resource allocation method based on user mobility in a 5G small cell network.

Background technique

With the development of the Internet of Things and large-scale machines, the proliferation of intelligent terminals and various user equipment in the network makes the 5G communication system face the unprecedented challenge of huge data traffic demand. In order to cope with the ever-increasing demand for data services and effectively increase system capacity, ultra-dense small cell networks emerge as the times require. The 5G small cell network has the characteristics of low cost, low power consumption, self-organization and self-optimization. At the same time, its dense coverage can meet the exponential growth of data traffic demand in dense user areas such as universities and commercial areas, improve network capacity, and expand wireless networks. coverage. In addition, due to the surge in the number of users, in order to save power consumption, when there is no data transmission and reception, the user is converted to an inactive (Inactive) state of light sleep. In order to improve the paging efficiency, the users in the Inactive state will configure a location tracking area called the Radio Access Network Notification Area (RAN Notification Area, RNA). However, when small cells are densely deployed, there are also many problems:

(1) The small cell communication coverage is small, and when the user moves, it is easy to leave the coverage of the serving cell and frequently switch to the base station serving it, thereby generating a large amount of switching signaling overhead.

(2) The ultra-dense deployment of small cell base stations results in overlapping coverage of cells, and a large number of users will be on the edge of overlapping multiple cells, suffer severe inter-cell interference, and are extremely prone to the ping-pong effect.

In view of problem (1), in existing research, by clustering small cell base stations, the data plane layer and control plane layer of users in the cluster are separated, and the control plane is connected by the cluster head. Signaling overhead due to frequent handovers.

For problem (2), the representative technologies for solving the problem of co-channel interference in small cells in the existing research mainly include Inter-Cell Interference Coordination (ICIC) and Coordinated Multiple Point (CoMP) technology. . ICIC technology divides frequency resources, and adjacent cells use different frequencies to control interference, while CoMP technology divides small cells into different CoMP groups, and eliminates cells by cooperating with multiple cells in a CoMP group. Interference.

In view of the challenges faced in 5G small cell networks, the traditional base station-centric network architecture can no longer meet the communication needs of 5G users. In order to actively cope with these challenges, reduce inter-cell interference and improve user experience, 5G proposes a user-centric cell virtualization technology, that is, a virtual cell is formed around multiple physical cells around the user, and the transmission nodes in the virtual cell cooperate through cooperation. provide communication services to users. User-centric virtual cells can eliminate edge users, alleviate interference in ultra-densely deployed small cell networks, reduce user mobile handover signaling overhead, improve communication quality, and enhance network user experience.

To sum up, the present invention proposes a resource allocation scheme based on user mobility in a 5G small cell network in order to solve the problem of service continuity and interference of mobile users in a 5G small cell network. In order to alleviate the interference problem in the dense small cell network and ensure the service continuity of mobile users, a user-centered virtual cell is established, and the transmission nodes in the virtual cell cooperate to provide communication services for users. In addition, in order to optimize network resources and improve resource utilization, the present invention, based on Lyapunov optimization theory, considers the stability of user data queues, and converts the overall network average energy efficiency optimization problem into two subsections: optimal transmission resource allocation and optimal power allocation for users. The problem is to ensure the stability of the system queue while maximizing the average energy efficiency of the system.

SUMMARY OF THE INVENTION

In view of this, the purpose of the present invention is to provide a resource allocation method based on user mobility in a 5G small cell network. 1) In order to alleviate the interference problem in the dense small cell network and ensure the service continuity of mobile users, a user-centered virtual cell is established, and the transmission nodes in the virtual cell cooperate to provide communication services for users. 2) Secondly, in order to improve resource utilization and optimize network resources, Lyapunov optimization method is adopted, which fully considers the queue length and channel quality of user data packets to maximize the average energy efficiency of the network.

For achieving the above object, the present invention provides following technical method:

A resource allocation method based on user mobility in a 5G small cell network. According to the characteristics of the proposed network scenario, firstly, a user-centric virtual cell is established after the user accesses the network to reduce inter-cell interference and ensure mobility. business continuity for users; secondly, optimal allocation of network resources when users transmit data;

The method includes the following steps:

S1: When a data service arrives for an inactive Inactive user, it accesses the network through network paging;

S2: After the user accesses the network, a user-centric virtual cell is established;

S3: Optimizing allocation of network resources when the user transmits data.

Further, in the step S1, when there is an inactive Inactive user data packet in the network, in order to locate the user, the anchor gNB will initiate a paging process; after the user receives the paging message, it is converted to Connection status, access to the network.

Further, in the step S2, establishing a user-centered virtual cell is divided into the following three steps:

S21: After the user accesses the network, the radio access network notification area RNA when the user was in an inactive state is used as a candidate virtual cell, the user detects the RS strengths of all gNBs in the RNA and reports the measurement results to the user's current residence gNB;

S22: The gNB that the user currently resides on reports the gNB whose RS is greater than the threshold value to the anchor gNB according to the measurement result, and the anchor gNB determines the gNB that forms the virtual cell according to the load information base, and delivers the result and the configuration information of the virtual cell to the user's current resident gNB gNB left;

S23: The gNB that the user currently resides on sends the virtual cell configuration information to the user and the corresponding gNB respectively to construct a user-centered virtual cell, and the transmission nodes in the virtual cell provide communication services for the user in a cooperative manner.

Further, in the step S3, according to the user data packet queue length and channel quality, a Lyapunov-based optimization method is used to establish a resource allocation optimization objective aiming at maximizing the time-averaged energy efficiency of the network.

Further, in the step S1, when the user is in the Inactive state, the connection between the user and the network is disconnected, and there is a notification area RNA for locating the user's position during the paging process.

Further, in the step S2, due to the mobility of the user, the gNBs included in the user-centered virtual cell will change dynamically; as the user moves, new gNBs that meet the conditions will be added, and those that do not meet the requirements will be removed. The original gNB of the conditions; because the user does not switch during the process of moving, the virtual cell identification number does not change; the anchor gNB in the network controls the dynamic adjustment of the user's virtual cell to reduce frequent signaling interactions in the dense small cell base station network.

Further, in the step S3, the performance of the virtual cell system is measured by the system time-averaged energy efficiency, which is defined as the ratio of the time-averaged total data rate to the time-averaged total power consumption.

Further, in the step S3, the optimization objective is to maximize the time-averaged energy efficiency of the system, and there are constraints related to the time-averaged constraints in the constraints, and the Lyapunov optimization theory is used to convert it into an optimization problem for each time slot; Optimal resource allocation is performed by minimizing the upper bound of the sum of the Lyapunov offset function and the penalty term, thus achieving a balance between system queue stability and time-averaged energy efficiency.

Further, in the step S3, in order to reduce the solving complexity of the problem, the optimization problem of maximizing the energy efficiency in each time slot is decomposed into two equivalent sub-optimization problems: 1) the optimization problem of optimal transmission resource allocation, 2) The optimal power distribution optimization problem;

When the user-centered virtual cell performs optimal transmission resource allocation, the network dynamically selects the optimal gNB in the virtual cell for data transmission according to the user's channel conditions, and allocates the resource block RB with the best transmission quality to the corresponding gNB, So that each user can obtain the best transmission quality gNB and RB to serve it;

It is realized through three steps. First, assign an RB with the best current transmission quality to each user; second, ensure that each gNB is assigned an RB; finally, assign the remaining RBs to the most suitable users;

After the optimal transmission resource allocation, the set of gNBs and RBs that actually transmit data for users in the virtual cell is obtained, and the optimization problem is transformed into the optimal power allocation problem. Then, the Lagrange duality principle and the subgradient update method are used to solve the optimal problem power value.

The beneficial effects of the present invention are:

1) The optimal transmission resource allocation optimization problem. When the user-centered virtual cell performs optimal transmission resource allocation, the network will dynamically select the optimal gNB in the virtual cell for data transmission according to the user's channel conditions, and allocate the RB with the best transmission quality to the corresponding gNB, so that Each user can obtain the gNB and RB with the best transmission quality to serve it.

2) The optimal power distribution optimization problem. After the optimal transmission resource allocation, the set of gNBs and RBs that actually transmit data for users in the virtual cell can be obtained, and the optimization problem is transformed into an optimal power allocation problem. Then, the optimal power value is solved by using the Lagrangian duality principle and the subgradient update method.

The invention fully considers the user data packet queue length and channel quality, and decomposes the problem of maximizing the average energy efficiency of the network into two sub-problems of user optimal transmission resource allocation and optimal power allocation to solve. Through the optimal allocation of network resources, the transmission quality is improved, and the stability of the system queue is ensured while maximizing the average energy efficiency of the system.

Other advantages, objects, and features of the present invention will be set forth in the description that follows, and will be apparent to those skilled in the art based on a study of the following, to the extent that is taught in the practice of the present invention. The objectives and other advantages of the present invention may be realized and attained by the following description.

Description of drawings

In order to make the objects, technical methods and advantages of the present invention clearer, the present invention will be preferably described in detail below in conjunction with the accompanying drawings, wherein:

Fig. 1 is a user-centric virtual cell network scenario diagram;

Fig. 2 is a flow chart for establishing a virtual cell;

Fig. 3 is the optimal transmission resource allocation algorithm diagram;

Figure 4 is a diagram of an optimal power allocation algorithm.

Detailed ways

The embodiments of the present invention are described below through specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the drawings provided in the following embodiments are only used to illustrate the basic idea of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

Among them, the accompanying drawings are only used for exemplary description, and represent only schematic diagrams, not physical drawings, and should not be construed as limitations of the present invention; in order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings will be omitted, The enlargement or reduction does not represent the size of the actual product; it is understandable to those skilled in the art that some well-known structures and their descriptions in the accompanying drawings may be omitted.

The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there are terms “upper”, “lower”, “left” and “right” , "front", "rear" and other indicated orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the indicated device or element must be It has a specific orientation, is constructed and operated in a specific orientation, so the terms describing the positional relationship in the accompanying drawings are only used for exemplary illustration, and should not be construed as a limitation of the present invention. situation to understand the specific meaning of the above terms.

Referring to FIG. 1, FIG. 1 is a user-centric virtual cell network scenario diagram. The network consists of gNBs and users, and the users are randomly distributed in the network. It is assumed that a resource block (Resource Block, RB) is allocated to only one gNB, and is allocated to only one user in the gNB for use. The total bandwidth of the system is divided into N RBs of equal bandwidth, and the bandwidth of each RB is B=W/N. In the downlink, the user can accept transmission signals from multiple gNBs in the virtual cell, that is, the gNBs in the virtual cell use CoMP to provide services for the user, and the cooperating gNBs use different RBs to transmit user data. This chapter will build user-centric virtual cells to reduce user interference and service continuity issues in dense small cell networks.

When an Inactive user in the network arrives with a data packet, the user accesses the network through the currently residing gNB and converts it to a connected state. According to the signal strength of all gNBs detected by the user and the gNB load, the user is screened by the anchor gNB and established to connect to the user. as the center of the virtual cell. In order to improve resource utilization and reduce energy consumption, virtual cells will perform optimal transmission resource allocation, and only gNBs that meet the transmission conditions will cooperate in the form of CoMP to serve users.

Referring to FIG. 2, FIG. 2 is a flowchart of establishing a virtual cell. The user-centered virtual cell can not only reduce the interference of other gNBs to users, avoid frequent handover of users, but also eliminate edge users and improve communication quality. There is a location notification area RNA when the user is in the Inactive state. When an Inactive user has a data transmission requirement, the currently residing gNB is converted to a connected state, and RNA is used as a candidate virtual cell. The user detects the signal strength of all gNBs in the RNA, and establishes a user-centered virtual cell after screening by the anchor gNB. .

The process of establishing a virtual cell is as follows:

201. When an Inactive user data packet arrives in the network, in order to locate the user, the anchor gNB will initiate a paging process. After receiving the paging message, the user switches to the connected state through the currently resident gNB and accesses the network;

202. After the user accesses the network, the radio access network notification area RNA when the user was previously inactive is used as a candidate virtual cell, the user detects the RS strengths of all gNBs in the RNA and reports the measurement results to the gNB where the user currently resides;

203. The gNB that the user currently resides on reports the gNB whose RS is greater than the threshold to the anchor point gNB according to the measurement result, and the anchor point gNB determines the gNB that forms the virtual cell according to the load information base, and delivers the result and the configuration information of the virtual cell to the user currently camping on. gNB left;

204. The gNB where the user currently resides sends the virtual cell configuration information to the user and the corresponding gNB respectively to construct a user-centered virtual cell, and the transmission nodes in the virtual cell provide communication services for the user in a cooperative manner.

The present invention measures the performance of the virtual cell system by the system time-averaged energy efficiency, which is defined as the ratio of the time-averaged total data rate to the time-averaged total power consumption.

The total user rate of the system at time t can be expressed as:

Among them, _ak,m ∈{0,1} represents the connection indicator variable between user k and gNB m in the virtual cell, _ak,m =1 represents the connection between user k and gNB m, otherwise _ak,m =0. _bn,m ∈{0,1} represents the allocation indicator variable between RB n and gNB m, _bn,m represents that RB n is allocated to gNB m, otherwise _bn,m =0. r _k,n,m (t) is the transmission rate of user k on RB n by gNB m.

Further, it can be concluded that the system time-averaged total rate is:

The power consumption of all gNBs in the system is the sum of data transmission power consumption and circuit power consumption, so the system power consumption model of the present invention is shown in the following formula:

Among them, τ is the power amplification efficiency of the gNB, M is the number of gNBs, and p _cir is the power consumption of the gNB generation circuit when it is in an idle state. The system time-averaged total power consumption can be expressed as:

Assuming that the downlink queue length of user k is Q _k (t), the packet queue length on the gNB side can be expressed as:

Queue length at time t+1 = queue length at time t - number of packets sent by link at time t + arrival number of data packets at time t, then the update process of user k's downlink queue is expressed as:

Q _k (t+1)=max{Q _k (t)-D _k (t),0}+A _k (t) (5)

Among them, Q _k (0)=0, A _k (t) is the arrival number of data packets of user k in time t, which obeys Poisson distribution. D _k (t) is the number of data packets sent by user k at time t, the size of each data packet is L, and the unit is bit.

Then the resource allocation optimization problem with the goal of maximizing the time-averaged energy efficiency can be expressed as:

Among them, R _min is the minimum transmission rate of each user, and P ^max is the maximum transmit power of a single gNB. Constraint C ₁ is the requirement of maximizing the time-averaged energy efficiency of the system while ensuring the stability of the queue of each user, Constraint C ₂ is the minimum rate requirement constraint of a single user, Constraint C ₃ is the maximum transmission power constraint of a single gNB, Constraints C ₄ is the transmission power constraint of a single user. Constraint C ₅ means that each multiple gNBs can cooperate to serve one user, and one gNB can be allocated to multiple users. Constraint C ₆ means that one RB can only be allocated to one gNB , but each gNB can be assigned multiple different RBs.

Further, based on Lyapunov optimization modeling, the steps are as follows:

1) Define the Lyapunov function of the system:

2) Define the Lyapunov transfer function:

ΔQ(t)=E{L(Q(t+1))-L(Q(t))|Q(t)} (9)

3) Define the Lyapunov penalty term:

VU(t)=V(R _tot (t)-qP _tot (t)) (10)

4) System optimization problem transformation:

In order to reduce the complexity of solving the problem, the optimization problem is decomposed into two equivalent sub-optimization problems: 1) optimization problem of optimal transmission resource allocation and 2) optimization problem of optimal power distribution, and the Lagrangian duality principle and The subgradient update method is used to solve it.

Referring to FIG. 3, FIG. 3 is a diagram of an optimal transmission resource allocation algorithm. First initialize the settings. Assuming that there are K users, N RBs, and M gNBs in the system, K N×M three-dimensional channel gain matrices H(K, N, M) can be formed in the network, that is, K N rows of M Columns of the channel gain matrix. It is assumed that the number of RBs is greater than the number of gNBs and the number of users, and the number of gNBs is smaller than the number of users, that is, M≤K≤N. First, assign an RB with the best current transmission quality to each user, secondly, ensure that each gNB is assigned an RB, and finally assign the remaining RBs to the user with the best performance. The optimal transmission resource allocation scheme can be divided into the following three steps:

301. First allocate an RB with the best current transmission quality to each user. Traverse the channel gain matrix H(K, N, M), assign the gNB corresponding to h _{k, n, m} with the largest channel gain to the corresponding user, and assign the RB to the gNB. Delete the row of the channel gain matrix corresponding to the RB, and delete the entire channel gain matrix corresponding to the user. If the gNB associated with this RB is not in the user's virtual cell, the resource allocation will be abandoned, and an appropriate transmission resource will be re-selected for the user. Continue to traverse the remaining channel gain values, and repeat the above allocation steps until all users are served by one gNB and one RB.

302. Ensure that RBs are allocated to each gNB. Through step 1), since one gNB can serve multiple users and can be repeatedly selected by users, there are Mx (1≤x≤M) gNBs that have not been allocated RB resources, and NK RBs remain to be allocated. Based on this, a new three-dimensional channel gain matrix H'(K, NK, Mx) is generated, that is, a matrix of K rows and Mx columns. Continue to traverse the channel gain matrix H', allocate h'_k',n',m' with the largest channel gain and the corresponding gNB to the corresponding user, and allocate the RB to the gNB. Delete the rows and columns of the channel gain matrix corresponding to the RB and gNB, and determine whether the gNB allocated to the user belongs to the user's virtual cell. If the gNB does not belong to the user's virtual cell, the resource allocation is abandoned. The above steps are repeated until each gNB is allocated with at least RB, and thus the gNB set G _k , k∈{1,2,...K} to which each user belongs can be obtained.

303. Allocate the remaining RBs to the users. Through steps 1) and 2), it can be known that NK-M+x RBs remain unallocated. Since gNBs can be allocated to multiple users, there are still M gNBs that can be allocated. Based on this, a three-dimensional matrix H"(K,NK-M+x,M) is generated, which is a matrix of K NK-M+x rows and M columns. Also based on the matrix H", the maximum gain value h"_k",n",m" and its corresponding gNBs are allocated to the corresponding users, RBs are allocated to the gNBs, and the allocated RBs are deleted until all RBs are allocated, thus the RB set of each gNB can be obtained as

Through the optimal transmission resource allocation algorithm, the set of gNBs and RBs that actually transmit data for users in the virtual cell can be obtained, then the constraints C5, C6, and C7 in the optimization problem (11) have been satisfied, so the original optimization problem can be equivalently transformed into The new optimization problem is as follows:

When the initial energy efficiency value q is given, in order to obtain the optimal power distribution, the Lagrangian dual method is used to obtain the Lagrangian function as follows:

where α _k and β _m are the Lagrange multipliers corresponding to the constraints C ₂ and C ₃ , respectively, and Both α _k ≥ 0 and β _m ≥ 0 are satisfied.

Suppose there is an optimal solution The objective function of equation (12) is optimized and all constraints are satisfied. According to the KKT conditions, the optimal power distribution can be solved by the Lagrangian function L(p,α _k ,β _m ) for the derivation equation of p _k,n,m (t), and the optimal power Available:

where [X] ⁺ =max{0,X}. In the process of Lagrangian solution, the Lagrangian multiplier is first fixed by using the KKT condition, so as to obtain the local optimal power distribution, and then the Lagrangian multiplier is updated by the sub-gradient method. When the iterative process When the convergence conditions are satisfied, the approximate optimal solution of Eq. (15) can be obtained.

Referring to FIG. 4, FIG. 4 is a diagram of an optimal power allocation algorithm. First initialize Lagrangian multipliers α, β, control parameter value V, queue length Q _k (t) at time t, Initial energy efficiency value q, error tolerance threshold ε. The optimal power allocation scheme can be divided into specific steps as follows:

401. According to formula (14), calculate the optimal power of user k at time t

402. Calculate the energy efficiency of the system at time t

403. Determine whether |R _tot (p ^* )-qP _tot (p ^* )|≤ε is established;

404. |R _tot (p ^* )-qP _tot (p ^* )|≥ε algorithm does not converge, and the updated initial energy efficiency is Update the Lagrangian factors α _k (t+1) and β _m (t+1), update the iteration parameter t=t+1, and return to step 401;

405. |R _tot (p ^* )-qP _tot (p ^* )|≤ε algorithm converges, and outputs optimal power distribution p ^* and The algorithm ends.

Finally, it should be noted that the above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiment, those of ordinary skill in the art should understand that the technical method of the present invention can be carried out. Modifications or equivalent substitutions, without departing from the spirit and scope of the technical method, should all be included in the scope of the claims of the present invention.

Claims (9)

1. A resource allocation method based on user mobility in a 5G small cell network, characterized in that: According to the characteristics of the proposed network scenario, the method firstly establishes a user-centric virtual cell after the user accesses the network to reduce inter-cell interference and ensure the service continuity of the mobile user; secondly, when the user transmits data, the network resource make optimal allocations; The method includes the following steps: S1: When a data service arrives for an inactive Inactive user, it accesses the network through network paging; S2: After the user accesses the network, a user-centric virtual cell is established; S3: Optimizing allocation of network resources when the user transmits data. 2. The resource allocation method based on user mobility in a 5G small cell network according to claim 1, wherein in the step S1, when there is an inactive Inactive user data packet arriving in the network, it is To locate the user, the anchor gNB will initiate the paging process; after the user receives the paging message, the user switches to the connected state through the currently resident gNB and accesses the network. 3. The resource allocation method based on user mobility in a 5G small cell network according to claim 1, wherein in the step S2, establishing a user-centered virtual cell is divided into the following three steps : S21: After the user accesses the network, the radio access network notification area RNA when the user was in an inactive state is used as a candidate virtual cell, the user detects the RS strengths of all gNBs in the RNA and reports the measurement results to the user's current residence gNB; S22: The gNB that the user currently resides on reports the gNB whose RS is greater than the threshold value to the anchor gNB according to the measurement result, and the anchor gNB determines the gNB that forms the virtual cell according to the load information base, and delivers the result and the configuration information of the virtual cell to the user's current resident gNB gNB left; S23: The gNB that the user currently resides on sends the virtual cell configuration information to the user and the corresponding gNB respectively to construct a user-centered virtual cell, and the transmission nodes in the virtual cell provide communication services for the user in a cooperative manner. 4. the resource allocation method based on user mobility in a kind of 5G small cell network according to claim 1, is characterized in that: in described step S3, according to user data packet queue length and channel quality, adopt Lyapunov-based The optimization method establishes a resource allocation optimization objective with the goal of maximizing the time-averaged energy efficiency of the network. 5. The resource allocation method based on user mobility in a 5G small cell network according to claim 2, wherein in the step S1, when the user is in an Inactive state, the connection between the user and the network is disconnected, And there is a notification area RNA for locating the user's location during the paging process. 6. The resource allocation method based on user mobility in a 5G small cell network according to claim 3, characterized in that: in the step S2, due to the mobility of the user, the virtual cell centered on the user The included gNBs will change dynamically; as the user moves, new gNBs that meet the conditions will be added, and the original gNBs that do not meet the conditions will be removed; since the user does not switch during the movement process, the virtual cell ID remains unchanged ; The anchor point gNB in the network controls the dynamic adjustment of the user virtual cell to reduce the frequent signaling interaction in the dense small cell base station network. 7. The resource allocation method based on user mobility in a 5G small cell network according to claim 4, wherein in the step S3, the performance of the virtual cell system is measured by the system time average energy efficiency, The system time-averaged energy efficiency is defined as the ratio of the time-averaged total data rate to the time-averaged total power consumption. 8. The resource allocation method based on user mobility in a 5G small cell network according to claim 4, wherein in the step S3, the optimization goal is to maximize the time-averaged energy efficiency of the system, and There are constraints related to time averaging in the constraints, and Lyapunov optimization theory is used to convert it into an optimization problem for each time slot; the optimal resource allocation is performed by minimizing the upper bound of the sum of the Lyapunov offset function and the penalty term, so that A balance is achieved between system queue stability and time-averaged energy efficiency. 9. The resource allocation method based on user mobility in a 5G small cell network according to claim 8, wherein in the step S3, in order to reduce the complexity of solving the problem, each time slot is maximized The energy-efficiency optimization problem within the system is decomposed into two equivalent sub-optimization problems: 1) the optimal transmission resource allocation optimization problem, and 2) the optimal power allocation optimization problem; When the user-centered virtual cell performs optimal transmission resource allocation, the network dynamically selects the optimal gNB in the virtual cell for data transmission according to the user's channel conditions, and allocates the resource block RB with the best transmission quality to the corresponding gNB, So that each user can obtain the best transmission quality gNB and RB to serve it; It is realized through three steps. First, assign an RB with the best current transmission quality to each user; second, ensure that each gNB is assigned an RB; finally, assign the remaining RBs to the most suitable users; After the optimal transmission resource allocation, the set of gNBs and RBs that actually transmit data for users in the virtual cell is obtained, and the optimization problem is transformed into the optimal power allocation problem. Then, the Lagrange duality principle and the subgradient update method are used to solve the optimal problem power value.