Network assisted user association and mode selection joint optimization method for full duplex cell-free wireless access network

Abstract

The application discloses a network-assisted free duplex non-cell wireless access network user association and mode selection joint optimization method, which comprises the following steps: establishing a network-assisted free duplex architecture system; establishing a user QoS constraint, user antenna mode selection and EDU-UE association problem with the target of maximizing the total spectrum efficiency of the system; determining the association relationship between the user and the EDU through a matching game algorithm; and dynamically scheduling the uplink and downlink working modes of the UE antenna by using a quantum genetic algorithm; and the application has good scalability.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of wireless communication transmission, and particularly relates to a user association and mode selection joint optimization method for a network-assisted free duplex non-cellular wireless access network. BACKGROUND

[0002] Time division duplex (TDD) and frequency division duplex (FDD) are commonly used fixed duplex modes. In order to increase the utilization efficiency of system resources, in-band full duplex (IBFD) and co-channel full duplex (CCFD) technologies enable a base station to simultaneously transmit and receive data in the same frequency band, break through the limitation of traditional half duplex, and realize doubling of spectrum efficiency. However, in fact, the uplink transmitting antenna of the base station will cause extremely strong interference to the downlink receiving antenna, resulting in performance loss. The in-band wireless full duplex coordinated multipoint (CoMPflex) system uses two spatially separated and coordinated half duplex (HD) base stations to simulate a full duplex (FD) base station, and reduces the self-interference influence by separating the uplink and downlink antennas. However, in a large-scale communication scenario, the base stations are densely deployed, and the above duplex technologies still face the challenge of antenna-to-antenna interference.

[0003] In the network-assisted full duplex (NAFD) architecture, the AP can flexibly work in full duplex, CCFD and hybrid duplex modes. In the same time domain and frequency domain resources, different APs can flexibly select uplink and downlink working modes, realize simultaneous transmission and reception of signals, and alleviate the self-interference in CCFD. However, in NAFD, the duplex mode scheduling of the AP side is mainly considered, and the duplex mode selection of the user side is not considered, and the NAFD system does not have scalability. SUMMARY

[0004] The application aims to provide a user association and mode selection joint optimization method for a network-assisted free duplex non-cellular wireless access network, so as to solve the problem of lack of scalability in the prior art.

[0005] The application provides a user association and mode selection joint optimization method for a network-assisted free duplex non-cellular wireless access network, which comprises the following steps:

[0006] (1) A network-assisted free duplex architecture system is established, which comprises a central processing unit (CPU), a plurality of edge distributed units (EDUs), an access point (AP) with fixed uplink and downlink working states, and a multi-antenna user equipment (UE);

[0007] (2) A user QoS constraint, user antenna mode selection and EDU-UE association problem is established with the goal of maximizing the total spectrum efficiency of the system;

[0008] (3) The association relationship between the user and the EDU is determined through a matching game algorithm;

[0009] (4) The uplink and downlink working modes of the UE antenna are dynamically scheduled using a quantum genetic algorithm.

[0010] Furthermore, in step (1), each EDU is equipped with several uplink APs and downlink APs, and the antenna operating mode of the UE can be dynamically scheduled.

[0011] Furthermore, in step (2), the total spectral efficiency is composed of the sum of the downlink total spectral efficiency and the uplink total spectral efficiency; wherein, the downlink total spectral efficiency includes the user's useful signal, the interference signal of other downlink users, the interference signal of the uplink antenna to the downlink antenna and the channel noise; the uplink total spectral efficiency includes the target user's useful signal, the interference signal of other uplink users, the interference signal of the downlink antenna to the uplink antenna and the channel noise.

[0012] Furthermore, in step (2), the problem is modeled as follows:

[0013] ;

[0014] in, Downlink total spectral efficiency; It is the total uplink spectral efficiency; and Let K be the uplink and downlink working vectors for user k. This indicates the association between user k and EDUx; EDUx represents the association coefficient between user and EDU, with 1 indicating association and 0 indicating no association.

[0015] Furthermore, the constraints include:

[0016] ;

[0017] ;

[0018] ;

[0019] in, For the QoS constraints of downlink user k, QoS constraints for uplink user k; ; Representing user k, respectively The nth antenna operates in both uplink and downlink; r D,k r represents the downlink spectral efficiency of user k; U,k This represents the uplink spectral efficiency of user k.

[0020] Furthermore, in step (3), the matching game algorithm includes the following steps:

[0021] (31) the user and the EDU generate a bidirectional preference list based on a utility function, and the utility function is the total spectrum efficiency of the system uplink and downlink;

[0022] (32) the free UE initiates a matching application to the EDU at the top of the preference list, and the EDU accepts or rejects the application according to the association threshold;

[0023] (33) the surplus UE initiates a secondary matching application, and the EDU determines whether to associate based on the spectrum efficiency improvement;

[0024] (34) exchange matching is carried out, and the exchange blocks the user exchange association; whether to exchange is determined based on a utility function, and the utility function is the system uplink and downlink rate.

[0025] Further, in step (4), the quantum genetic algorithm comprises the following steps:

[0026] (41) initialize the quantum population: set all amplitude values of each individual to Initialize as:

[0027] ;

[0028] Wherein, The probability that each quantum bit takes the ground state and the excited state is represented by and respectively, and satisfies ;

[0029] (42) measure the population state: measure each individual in the population once, and the state of the individual is obtained by measurement;

[0030] (43) calculate the fitness: calculate the fitness of each individual, and take the system and the rate as the fitness function;

[0031] (44) quantum rotation gate evolution: generate a new population based on the quantum rotation gate;

[0032] (45) quantum NOT gate mutation: introduce quantum NOT gate to perform quantum mutation operation on the population;

[0033] (46) repeat steps (42) to (45) until the algorithm converges or the maximum iteration number is reached.

[0034] The user association and mode selection joint optimization system of the network-assisted free duplex non-cellular wireless access network disclosed by the application comprises:

[0035] The duplex architecture module is used for establishing a network-assisted free duplex architecture system, and comprises a central processing unit (CPU), a plurality of edge distributed units (EDUs), an access point (AP) with fixed uplink and downlink working states, and a multi-antenna user equipment (UE);

[0036] The matching game algorithm module is used for determining the association between the user and the EDU by a matching game algorithm, and meeting the backhaul constraint and the user service quality QoS requirement;

[0037] The quantum genetic algorithm module is used for dynamically scheduling the uplink and downlink operation modes of the UE antenna by a quantum genetic algorithm;

[0038] The joint optimization module is used for jointly optimizing the EDU-UE association and the UE antenna mode selection with the target of maximizing the total spectrum efficiency of the system.

[0039] The electronic device provided by the application comprises a memory and a processor, the memory stores a computer program, and the processor realizes the steps of any one of the methods when executing the program.

[0040] The computer readable storage medium provided by the application stores a computer program, and the program realizes the steps of any one of the methods when executed by a processor.

[0041] Advantages: Compared with the prior art, the application has the following remarkable advantages: the application studies a network-assisted free duplex architecture based on a cell-free wireless access network. In the uplink, an AP receives a transmission signal from the uplink antenna of a UE, and then sends the transmission signal to an edge distributed unit (EDU). The EDU is responsible for signal demodulation, and sends the processed data to a central processor (CPU) for merging. Meanwhile, the CPU sends downlink data to the EDU for precoding processing, and then sends the downlink data to the downlink UE through an associated AP. The UE receives the signal by using the downlink antenna. The user can flexibly control the uplink and downlink operation modes of the antenna, fully utilize the system resources, and improve the spectrum utilization efficiency of the system. Meanwhile, the service quality of the user and the backhaul constraint of the system are considered, and the flexible matching association between the EDU and the UE is performed. Therefore, the architecture can realize the compromise between the system performance and the calculation complexity, and has good scalability. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 Fig. 1 is a schematic diagram of a communication system model of the application;

[0043] Figure 2 Fig. 4 is a performance gain diagram of an EDU-UE association method of the application;

[0044] Figure 3 Fig. 5 is a performance gain diagram of a user antenna mode selection method of the application. DETAILED DESCRIPTION

[0045] The technical solutions of the application are further described below with reference to the drawings.

[0046] As Figure 1As shown, this embodiment of the invention provides a joint optimization method for user association and mode selection in a network-assisted free-duplex non-cellular wireless access network, comprising the following steps:

[0047] S1 establishes a network-assisted free-duplex architecture system model: Let the number of EDUs in the system be X, and the number of APs connected to each EDU be... The APs in the system have fixed uplink and downlink speeds. One uplink AP, There are M downlink APs, where the number of AP antennas is M. For a single , The number of uplink APs it is connected to. The number of downlink APs it is attached to.

[0048] Considering the scenario with multiple antennas per user, assuming there are a total of K users, each with 2 antennas, and all users are in full-duplex mode, meaning each user has one antenna operating for uplink and the other for downlink. , Indicates user The nth antenna operates in uplink (downlink) mode and must satisfy the following conditions: , and Let be the uplink and downlink working vectors for user k.

[0049] The specific process of downlink transmission is as follows: The precoding vector based on the local channel state information is... ,in for Mounted A precoded vector between a downlink AP and user k. for The downlink channel between user k and user k, where for Mounted The channel between a downlink AP and user k. The signal received by user k is:

[0050] ;

[0051] in, ;in, ; for The signal sent to downlink user k, where for The transmit power to user k. For users The channel between user k and user k. ,in, , For users The transmission power. It is additive white Gaussian noise;

[0052] This refers to the interference from the uplink antenna to the downlink antenna for full-duplex users, and the remaining error after interference cancellation. In the final equation, the first term on the right-hand side of the equation represents the useful signal of user k, the second term represents the downlink interference signal of other users, the third term represents the uplink interference signal of other users, the fourth term represents the channel noise, and the fifth term represents the residual interference error between the uplink antenna and the downlink antenna in full-duplex user k after interference cancellation.

[0053] Therefore, the downlink spectral efficiency calculated at user k is:

[0054] ;

[0055] ;

[0056] Downlink total spectral efficiency for: .

[0057] The specific process of uplink transmission is as follows: The uplink signal received at the location is: ;in, for Uplink channel to user k for Mounted The channel between an uplink AP and user k. express Downlink signals transmitted by the mounted downlink AP Interference generated by the uplink AP, matrix express Mounted downlink AP and Channels between mounted uplink APs. It is additive white Gaussian noise.

[0058] Considering that some EDUs can share data, define... In order to be able to The complement of the shared EDU set For cannot be with A shared set of EDUs. If Can be with To share data, Know the signal Therefore, IAI interference cancellation can be performed. However, considering channel estimation errors, the interference cannot be completely eliminated. is the channel estimation error of IAI, and follows . The received uplink signal can be rewritten as

[0059] ;

[0060] At , the receiver , demodulates the received uplink signal to obtain the user ; , the spectral efficiency of user k at the CPU is

[0061] ;

[0062]

[0063] ;

[0064] The total uplink spectral efficiency is .

[0065] To maximize the total spectral efficiency of the system, the EDU-UE association and UE antenna mode selection are jointly optimized as follows: the optimization problem is modeled as

[0066] ;

[0067] where is the total downlink spectral efficiency; is the total uplink spectral efficiency; and are the uplink and downlink working vectors of user k; denotes the association between user k and EDU x; EDU x denotes the association number between user and EDU, equal to 1 indicating association, and equal to 0 indicating no association.

[0068] The constraint conditions include:

[0069] ;

[0070] ;

[0071] ;

[0072] where is the QoS constraint of downlink user k, is the QoS constraint of uplink user k; ; denote that the nth antenna of user k, r D,kDownlink spectral efficiency of user k; r U,k Downlink spectral efficiency of user k; r

[0073] S2 determines the association between users and EDUs by matching game algorithm; the specific algorithm steps are as follows:

[0074] (21) Each user will form a preference list according to the descending order of the utility function value of each EDU, wherein the utility function is the total spectral efficiency of uplink and downlink, and each EDU will also form a preference list according to the descending order of the utility function value of each user.

[0075] (22) Define the user not associated with any EDU as a free UE, and all free UEs send a matching application to the first EDU in the respective preference list, form an application list for each EDU, and delete these EDUs from the preference list of the corresponding UE.

[0076] (23) Traverse all EDUs, if the total number of associated UEs plus the UEs applying for matching of the current EDU does not exceed the association threshold, the EDU will receive all matching applications and update the matching list. Otherwise, the EDU will select the UEs ranked in the first (threshold number - associated number) of the EDU preference list from the UEs applying for matching and update the matching list. If the number of EDU associations has reached the threshold, all applications are rejected.

[0077] (24) The UE associated with an EDU is deleted from the free UE list.

[0078] (25) Repeat (22)-(24) until the free UE list is empty.

[0079] (26) At this time, all UEs have completed association with an EDU. Define the UE whose preference list is not empty as a surplus UE.

[0080] (27) Similarly, all surplus UEs send a matching application to the first EDU in the respective preference list, form an application list for each EDU, and delete it from the preference list of the corresponding UE.

[0081] (28) Traverse all EDUs receiving applications, if the total number of associated UEs plus the UEs applying for matching of the current EDU does not exceed the association threshold, the EDU will judge in turn: if the spectrum efficiency can be improved after association, the association is selected, otherwise the application is rejected, and the matching list is updated. Otherwise, the EDU will select the UEs ranked in the first (threshold number - associated number) of the EDU preference list from the UEs applying for matching and judge in turn, and update the matching list. If the number of EDU associations has reached the threshold, all applications are rejected.

[0082] (29) The UE whose preference list has been emptied is deleted from the surplus UE list. Repeat steps (27)-(29) until the surplus UE list is emptied. Perform exchange matching, exchange blocking for user exchange association, define the utility function as the uplink and downlink rates on the system, and if the utility is improved, allow the exchange operation to be performed, otherwise do not perform it.

[0083] As Figure 2 shown, the performance gain diagram of the EDU-UE association using the matching algorithm of the present application, from the diagram it can be seen that the matching algorithm proposed in the present application has a significant advantage in the EDU-UE association problem, when there are 7 users, the spectrum efficiency achieved by using the matching algorithm is improved by 7.4%, 9.7% and 62% compared with DCC, K-means and random association respectively, the performance advantage mainly comes from the dynamic stable double-sided matching mechanism, which optimizes the association relationship between users and EDUs in real time, while ensuring the quality of service, effectively reduces the interference between users. The matching algorithm achieves a good balance between computational complexity and performance, and is particularly suitable for real-time resource scheduling requirements in large-scale user scenarios, providing an effective solution for the scalability of the cell-free MIMO system.

[0084] S3 adopts a quantum genetic algorithm to dynamically schedule the uplink and downlink working modes of the UE antennas; the specific algorithm process is as follows:

[0085] (31) Initialize the quantum population: there are a total of quantum individuals in the population, since each user has two antennas, each antenna only works in uplink or downlink and the working modes of the two antennas are complementary, that is Therefore, when the mode of one antenna is determined, the mode of the other antenna is also determined, consider the working mode of the first antenna of each user as a individual. Therefore, each individual is composed of amplitude pairs of dimension K of the total number of users. Each individual is initialized to

[0086] ;

[0087] wherein represent the probability of each quantum bit taking the ground state and the excited state respectively, and satisfy ;

[0088] (32) Measure the population once: measure each individual in the population once, and the state of the individual is obtained ; the measurement of state satisfies the rule:

[0089] wherein, is a random variable uniformly distributed between 0 and 1.

[0090] (33) Fitness calculation: according to , the fitness of each individual is calculated, taking the system and rate as the fitness function, i.e. , where . Therefore, the state of the individual with the highest fitness value can be identified as the optimal value of the evolution of the population individuals .

[0091] (34) Quantum rotation gate evolution: a new population is generated based on the quantum rotation gate, and the quantum rotation gate function is defined as , and the obtained population is updated as , where ; is the quantum rotation angle, is the quantum rotation direction. The specific quantum rotation strategy is shown in Table 1.

[0092] Table 1 Quantum rotation strategy

[0093] ;

[0094] where, in Table 1, represents the antenna state of the kth user in each individual in the population; represents the best state of the kth user at present; represents the fitness function of the current state; represents the total state of the current individual; represents the fitness function of the current best state; represents the state with the best performance at present; represents the quantum rotation angle; , are the probabilities of the current individual taking the ground state and the excited state, respectively.

[0095] (35) Quantum mutation: in order to prevent the population from falling into a local optimal solution, a quantum NOT gate is introduced to perform quantum mutation operation on the population. The quantum NOT gate is defined as , and the mutation rule is: if , represents the mutation probability; then a pair of amplitude of the individual is randomly selected and the mutation operation is performed, and the amplitude of the individual after mutation is .

[0096] (36) Repeat steps (32) to (35) until the algorithm converges or the maximum number of iterations is reached .

[0097] As Figure 3The performance gain figure of the greedy genetic algorithm for selecting user antenna operating modes is shown. As can be seen from the figure, the algorithm has superior performance in the UE mode selection problem. The QGA algorithm is always significantly better than the random allocation method in system performance indicators, and can approach the performance of the exhaustive method with extremely high computational complexity. Specifically, in a typical scenario, the QGA algorithm can achieve about 95% of the performance of the exhaustive method, while reducing the computational complexity from exponential to polynomial, which is suitable for large-scale communication scenarios with a large number of users and a large number of antennas.

Claims

1. A joint optimization method for user association and mode selection in a network-assisted free-duplex non-cellular wireless access network, characterized in that, Comprising the following steps: (1) Establish a network-assisted full-duplex architecture system, including a central processing unit (CPU), a plurality of edge distributed units (EDUs), access points (APs) with fixed uplink and downlink working states, and multi-antenna user equipment (UE); each EDU is mounted with a plurality of uplink APs and downlink APs, and the antenna working mode of the UE can be dynamically scheduled; (2) To maximize the total spectral efficiency of the system as the target, a user QoS constraint, user antenna mode selection, and EDU-UE association problem are established; the total spectral efficiency is composed of the total downlink spectral efficiency and the total uplink spectral efficiency; wherein the total downlink spectral efficiency includes the user's useful signal, the interference signal of other downlink users, the interference signal of the uplink antenna to the downlink antenna, and the channel noise; the total uplink spectral efficiency includes the useful signal of the target user, the interference signal of other uplink users, the interference signal of the downlink antenna to the uplink antenna, and the channel noise; (3) The association relationship between the user and the EDU is determined through a matching game algorithm; comprising the following steps: (31) Each user will sort the utility function value of each EDU in descending order to form a preference list, wherein the utility function is the total uplink and downlink spectral efficiency, and each EDU will also sort the utility function value of each user in descending order to form a preference list; (32) Define the user not associated with any EDU as a free UE, all free UEs send a matching application to the first EDU in the respective preference list, form an application list for each EDU, and delete these EDUs from the preference list of the corresponding UE; (33) Traverse all EDUs, if the total number of UEs associated with the current EDU plus the UEs applying for matching does not exceed the association threshold, the EDU will receive all matching applications and update the matching list; otherwise, the EDU will select the UEs ranked in the threshold number minus the number of associated UEs in the EDU preference list from the UEs applying for matching and associate them, and update the matching list; if the number of UEs associated with the EDU has reached the threshold, all applications are rejected; (34) The UE associated with an EDU is deleted from the free UE list; (35) Repeat (32)-(34) until the free UE list is empty; (36) All UEs have completed association with an EDU; define the UE whose preference list is not empty as a surplus UE; (37) Similarly, all surplus UEs send a matching application to the first EDU in the respective preference list, form an application list for each EDU, and delete it from the preference list of the corresponding UE; (38) Traverse all EDUs that have received applications, if the total number of UEs associated with the current EDU plus the UEs applying for matching does not exceed the association threshold, the EDU will sequentially judge: if the spectral efficiency can be improved after association, select association, otherwise reject, and update the matching list; otherwise, the EDU will select the UEs ranked in the threshold number minus the number of associated UEs in the EDU preference list from the UEs applying for matching and sequentially judge, and update the matching list; if the number of UEs associated with the EDU has reached the threshold, all applications are rejected; (39) The UE whose preference list has been emptied is deleted from the surplus UE list; repeat steps (37)-(39) until the surplus UE list is emptied; perform exchange matching, exchange blocking for user exchange association, define the utility function as the system uplink and downlink and rate, if the utility is improved, allow the exchange operation to be performed, otherwise not; (4) Adopting quantum genetic algorithm to dynamically schedule the uplink and downlink working modes of the UE antenna; comprising the following steps: (41) Initialize the quantum population: Set all amplitudes of each individual to Initialize to: ； wherein, represent the probabilities of each qubit taking the ground and excited states, respectively, and satisfy ; (42) Measuring the population state: measuring each individual in the population once to obtain the state of the individual; (43) Calculating the fitness: calculating the fitness of each individual, taking the system and rate as the fitness function; (44) Quantum rotation gate evolution: generating a new population based on the quantum rotation gate; (45) Quantum NOT gate mutation: introducing quantum NOT gate to perform quantum mutation operation on the population; (46) Repeat steps (42) to (45) until the algorithm converges or the maximum number of iterations is reached. 2.The method of Claim 1, wherein, In step (2), the problem is modeled as: ； wherein, downlink total spectral efficiency; is uplink total spectral efficiency; and is the uplink-downlink working vector for user k; denotes the association of user k with EDUx; EDUx denotes the xth EDU. 3.The method of Claim 2, wherein, The constraint conditions include: ； ； ； wherein, QoS constraint for downlink user k, QoS constraint for uplink user k; ; respectively represent the uplink and downlink of the nth antenna of user k, r D,k r U,k r 4. A system for joint optimization of user association and mode selection for network-assisted free duplex cell-less wireless access networks, the system comprising: Including: Duplex architecture module: for establishing a network-assisted free duplex architecture system, including a central processing unit CPU, a plurality of edge distributed units EDU, an access point AP with fixed uplink and downlink working states, and a multi-antenna user equipment UE; Matching game algorithm module: for determining the association relationship between users and EDUs through a matching game algorithm to meet the backhaul constraints and user quality of service QoS requirements; comprising: (31) Each user will sort the utility function values of each EDU in descending order to form a preference list, wherein the utility function is the total spectrum efficiency of the uplink and downlink, and each EDU will also sort the utility function values of each user in descending order to form a preference list; (32) Define the user not associated with any EDU as a free UE, and all free UEs send a matching application to the first EDU in their respective preference list to form an application list for each EDU, and delete these EDUs from the preference list of the corresponding UE; (33) Traverse all EDUs, if the total number of associated UEs plus the number of UEs applying for matching does not exceed the association threshold, the EDU will receive all matching applications and update the matching list; otherwise, the EDU will select the first threshold number of UEs from the EDU preference list and update the matching list; if the number of EDU associations has reached the threshold, all applications are rejected; (34) The UE associated with an EDU is deleted from the free UE list; (35) Repeat (32)-(34) until the free UE list is emptied; (36) All UEs have completed association with an EDU; define the UE whose preference list has not been emptied as a surplus UE; (37) Similarly, all surplus UEs send a matching application to the first EDU in their respective preference list to form an application list for each EDU, and delete it from the preference list of the corresponding UE; (38) EDU iteratively judges: if the total number of associated UE plus the number of application matching UE does not exceed the association threshold, then the EDU judges if the spectrum efficiency can be improved after association, and if yes, the EDU selects association, otherwise, the EDU rejects and updates the matching list; otherwise, the EDU selects the top threshold number of UE from the application matching UE and judges iteratively, and updates the matching list; if the number of associated UE has reached the threshold, then the EDU rejects all applications; (39) UE whose preference list has been emptied is deleted from the surplus UE list; repeat steps (37)-(39) until the surplus UE list is empty; perform exchange matching, exchange blocking associates with users, define the utility function as the uplink and downlink rate of the system, if the utility is improved, then the exchange operation is allowed to be performed, otherwise, the exchange operation is not performed; The quantum genetic algorithm module is configured to dynamically schedule the uplink and downlink working modes of the UE antennas by using a quantum genetic algorithm, and includes: (41) Initialize the quantum population: Set all amplitudes of each individual to Initialize to: ； wherein, represent the probability of each qubit to be in the ground state and the excited state, respectively, and satisfy ; (42) measuring the population state: measuring each individual in the population to obtain the state of the individual; (43) calculating the fitness: calculating the fitness of each individual, taking the system and rate as the fitness function; (44) quantum rotation gate evolution: generating a new population based on a quantum rotation gate; (45) quantum non-gate mutation: introducing a quantum non-gate to perform a quantum mutation operation on the population; (46) repeat steps (42) to (45) until the algorithm converges or the maximum number of iterations is reached; The joint optimization module is configured to jointly optimize the EDU-UE association and the UE antenna mode selection with the goal of maximizing the total spectrum efficiency of the system.

5. An electronic device, comprising: The memory stores a computer program, and the processor executes the program to realize the steps of the method of any one of claims 1-3.

6. A computer readable storage medium characterized by The memory stores a computer program, and the processor executes the program to realize the steps of the method of any one of claims 1-3.

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