CN115119278A - User demand-oriented mobility-considered virtual cell transmission node updating method, device and medium - Google Patents
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Abstract
The invention provides a virtual cell transmission node updating method, equipment and medium which are oriented to user requirements and consider mobility. The invention provides a virtual cell updating method based on a rate factor (R factor) and a position factor (P factor) by taking user requirements as a core and considering the load of surrounding base stations and user mobility. The method considers the signal-to-interference-and-noise ratio of transmission nodes around a user and the number of attached users, models the average rate which can be provided by the transmission nodes for the user as an R factor, and selects the minimum number of transmission node clusters which can meet the requirements of the user on the basis of the R factor; and calculating a P factor by considering the mobility of the user, and further predicting the preferred transmission node by utilizing the mobility. The virtual cell dynamic cluster updating method combining the R factor and the P factor can reduce the interruption probability, improve the user satisfaction rate and reduce the signaling overhead.
Description
Technical Field
The invention belongs to the technical field of communication, and particularly relates to a method, equipment and medium for updating a virtual cell transmission node, which are oriented to user requirements and consider mobility.
Background
With the development of the fifth generation mobile communication technology (5G), the focus of attention of researchers has gradually shifted to B5G and 6G networks. Ultra-Dense networks (UDNs) are an effective solution to the explosive growth of data traffic in future B5G and 6G networks. The super-dense network can greatly improve the spatial multiplexing rate of frequency spectrum resources by densely deploying a large number of wireless access points in a hot spot area, thereby effectively improving the system capacity of the 5G network. However, in the UDN centering on the base station, the characteristics of miniaturization and densification of the cell structure, multiple network architecture levels and the like also bring problems and challenges of complex network topology, serious interference of adjacent cells, frequent cell switching and the like, thereby affecting system throughput and user experience. In order to meet the requirements of users in different scenes and solve the problems of serious inter-node interference, frequent mobile switching and the like caused by dense overlapping and heterogeneous coverage of the UDN, under the idea of taking users as centers, the academic world provides the concept of Virtual Cells (VC) to provide required services for the users in a mode closer to the users, and the spectrum efficiency and the user experience of the network are improved.
A user-centric virtual cell may be defined as: the user is always positioned in the center of the virtual cell, and access points around the user form the virtual cell and provide cooperative communication service for the virtual cell. The user center virtual cell changes the traditional cell design concept taking a base station as the center, and combines a plurality of small transmission nodes into a virtual large-small cell in control, so that the plurality of transmission nodes are taken as the resource of the virtual cell to be scheduled, a user can obtain services from more nodes, meanwhile, the multi-connection architecture can ensure the service continuity of the user, and the interruption of the single-connection architecture in the switching process is avoided.
The virtual cell dynamic cluster refers to a user and a transmission node set associated with the user in a virtual cell, and the virtual cell cluster is dynamic and moves along with the user; the user moves and the transmission nodes in the dynamic cluster are updated, similar to the switching process of the traditional base station center network. A virtual cell dynamic cluster comprises a control node and a plurality of transmission nodes. In the invention, the transmission node is a micro base station, and the control node is a macro base station, namely, a data plane is transmitted in the micro base station, and a control plane is transmitted in the macro base station. However, in a base station centric network, the user's "handoff" process is to select a micro base station, rather than a cluster of micro base stations, and this selection typically does not take into account user needs; therefore, the base station centric mobility management scheme in conventional cellular networks will no longer be suitable for the user centric mobility management mode. The problem is further exacerbated by the fact that the transport nodes moving with the user may make mobility management based on virtual cells complex and difficult, that there may be more than one transport node in a virtual cell, that the user may be exposed to frequent inter-base station handovers and a lot of signaling overhead, and that especially for users with higher moving speeds, the transport node updates may be more frequent. In addition, the UDN based on virtual cell is a user-centric network architecture, and the mobility management scheme needs to consider more the behavior characteristics and requirements of users, such as the moving track and the service requirement. Therefore, the mobility management scheme based on virtual cells in UDN needs to be redesigned and optimized to provide better service for users.
Disclosure of Invention
The invention aims to solve the problems of increased interruption probability, reduced user service satisfaction rate, increased system signaling overhead and the like caused by improper selection of transmission nodes in the process of moving along with a user in a virtual cell dynamic cluster in an ultra-dense network. Therefore, the invention provides a virtual cell dynamic cluster updating algorithm which considers user service, mobility and network load based on a rate factor and a position factor. The dynamic cluster update in the invention only relates to the update of the transmission node in the dynamic cluster. The invention provides a virtual cell transmission node updating method, equipment and medium for considering mobility facing to user requirements.
The invention is realized by the following technical scheme, and provides a virtual cell transmission node updating method considering mobility facing to user requirements, which specifically comprises the following steps:
step A: the user equipment periodically measures the reference signals of the TRP of the surrounding transmission receiving points, the position information P of the user equipment and the guaranteed stream bit rate GFBR of the service of the user equipment, and stores the reference signals and the position information P in an internal memory; meanwhile, the user equipment estimates SINR of surrounding TRP through a TRP reference signal, the surrounding TRP reference signal comprises the number L of the TRP current service users, and the bit rate GFBR of the user stream is obtained by controlling the TRP;
and B: calculating R factors of TRP in the current virtual cell, and if the sum of the R factors is less than a threshold GFBR (1+ G) min ) If yes, triggering dynamic cluster updating to enter the step C, otherwise not triggering updating to return to the step A;
and C: calculating R factors of surrounding TRPs (total transmission power points) including TRPs (total transmission power points) in the current virtual cell, and accumulating the R factors from large to small until (1) the number of transmission nodes is equal to the maximum number N of transmission nodes in the virtual cell trp_max Or (2) the sum of the current accumulated R factors is greater than GFBR (1+ G) r )；
Step D: (1) if the sum of R factors of TRPs is less than GFBR (1+ G) r ) Then all currently accumulated TRPs are selected as a target transmission node set, the number of the TRPs is N trp_max Entering step H; (2) if the sum of the R factors is greater than GFBR (1+ G) max ) Then obtain the current cell number N trp ，N trp ≤N trp_max Entering the step E;
step E: if the condition (2) in the step D is satisfied, finding out that the sum of all satisfied R factors is larger than GFBR (1+ G) max ) And the number of TRP is N trp If there are only 1 group, then the 1 group is the target transmission node set, and go to step H; if there is more than one group, then record these N r ，N r >1 group TRP, entering step F;
step F: predicting the moving direction of a user by using a mobility prediction method, acquiring the coordinates of surrounding TRPs through a source TRP, calculating the distance d between the user and the surrounding TRPs, calculating the included angle theta between the moving direction of the user and each TRP direction, and calculating a P factor according to d and theta;
step G: calculating N r The sum of P factors of the TRP groups, and selecting a group of TRP groups with the largest P factors as a target transmission node set;
step H: comparing the target transmission node set with the current virtual cell transmission node, accessing TRPs which are present in the target set but not present in the current set into the virtual cell, and releasing TRPs which are not present in the target set but not present in the current set.
Further, the step a specifically includes:
step A1: m TRPs periodically transmit reference signals, wherein the number L of current service users i (i ═ 1,2, …, M) carried in the reference signal by special resource block locations or using special sequences;
step A2: user measures surrounding TRP reference signal, demodulates information and obtains L i (i ═ 1,2, …, M), and the SINR of each TRP was measured i (i＝1,2,…,M)；
Step A3: in the user virtual cell, one TRP is a control node, performs signaling interaction with a core network, and acquires a user service GFBR through the core network; or the user himself measures a period of time T win The average throughput in the cell is taken as GFBR.
Further, the step B specifically includes:
step B1: assume that the TRP set in the current user virtual cell is T ═ TRP 1 ,…,TRP n N total TRP with TRP serial number T no ＝{t 1 ,…,t n R factor was calculated using the formula:
wherein
And
is made of SINR i Mapping to CQI i Then mapping is carried out to obtain; n is a radical of rb 、T s And f ud For the system to fix the parameters, L i Acquiring through a reference signal;
step B2: calculating the sum of R factors of TRPs in the current virtual cell, and judging if the R factors meet the following conditions:
entering the step C; otherwise, returning to the step A.
Further, the step C specifically includes:
calculating R factor R of peripheral TRP according to formula (1) i (i ═ 1,2, …, M); will r is i Obtaining the R factors R after sorting from big to small j (j ═ 1,2, …, M); initializing sumR as 0; n is a radical of trp ＝0；j＝0；
While sumR<GFBR(1+G max ) And N is trp <N trp_max
j＝j+1，sumR＝sumR+r j ，N trp ＝N trp +1
End。
Further, in step D: said N is trp Is the minimum number of TRPs to meet the user's needs;
If sumR>GFBR(1+G max )
acquiring the current accumulated TRP number N trp Go to step E
Else
Selecting the largest N trp_max Taking each TRP as a new TRP group, and entering the step H
End。
Further, in step F,
let the nearest N of the user's records u Each self two-dimensional coordinate is P ue ＝{p 1 ,p 2 ,…p Nu And predicting the mobility by using an LSTM network to predict a coordinate vector p of the user at the next moment next The current user coordinate vector is p now If the displacement vector is v ═ p next -p now I.e. the user movement direction vector is v; let N r Required calculation in group TRPThe TRPs of the P factor are m in total, and the surrounding TRP coordinate vector is P trp ＝{tp 1 ,tp 2 ,…tp m V, direction vector of user position to TRP i ＝p now -tp i ，d i ＝|v i |，θ i ＝arg(v i ) Then, the P-factor for the jth TRP is calculated as:
further, in step G,
let N r TRP whose set satisfies the condition is { T } 1 ,T 2 ,…,T Nr Finding the sum of P factors of each group, and the target TRP group is the group with the maximum P factor, i.e. the group with the maximum P factor
Further, in step H, let the current virtual cell TRP set be T now Target set is T target Then the set of TRPs to be added is T add ＝T now -T target The TRP set to be deleted is T del ＝T target -T now Where-represents the aggregate difference set.
The invention provides an electronic device, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the virtual cell transmission node updating method considering mobility facing to user requirements when executing the computer program.
The present invention proposes a computer readable storage medium for storing computer instructions which, when executed by a processor, implement the steps of the user demand mobility oriented virtual cell transport node update method.
The invention relates to a method, equipment and a medium for updating a transmission node of a virtual cell in consideration of mobility facing to user requirements, aiming at solving the problems of increased interruption probability, reduced user service satisfaction rate, increased system signaling overhead and the like caused by improper selection of the transmission node in a dynamic cluster of the virtual cell in an ultra-dense network in the process of moving along with a user. The method considers the signal-to-interference-and-noise ratio of transmission nodes around a user and the number of attached users, models the average rate which can be provided by the transmission nodes for the user as an R factor, and selects the minimum number of transmission node clusters which can meet the requirements of the user on the basis of the R factor; and calculating a P factor by considering the mobility of the user, and further predicting the preferred transmission node by utilizing the mobility. The virtual cell dynamic cluster updating method combining the R factor and the P factor can reduce the interruption probability, improve the user satisfaction rate and reduce the signaling overhead.
Drawings
FIG. 1 is a schematic diagram of a super-dense network virtual cell and user-centric dynamic cluster update model;
FIG. 2 is an overall flow chart of the method of the present invention;
FIG. 3 is a flow chart of the R factor TRP selection algorithm;
FIG. 4 is a flow chart of the P-factor TRP selection algorithm;
FIG. 5 is a diagram illustrating the distance d and the angle θ in the P-factor algorithm;
FIG. 6 is a comparison graph of TRP selected using only the combination of the R factor and the R factor P factor;
FIG. 7 is a graph of satisfaction rate and signaling overhead results for different average GFBRs for the method of the present invention and the comparative method, where (a) is the satisfaction rate result and (b) is the signaling overhead result;
fig. 8 shows the satisfaction rate and signaling overhead of the method and the comparison method at different average speeds, wherein (a) is the result of satisfaction rate and (b) is the result of signaling overhead.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Assuming that M TRPs surround the user, the number of available resource blocks in the current band and subcarrier spacing is N rb OFDM symbol duration of T s The ratio of uplink and downlink frames is f ud The mapping relationship between SINR and CQI is that CQI is f s2c (SINR), the mapping relation of CQI to modulation order and code rate is Q m ＝f c2q (CQI) and R c ＝f c2r (CQI); the variable settings and the mapping relationship can be known in advance through the network.
The ultra-dense network virtual cell and user center dynamic cluster update model is shown in fig. 1. A virtual cell dynamic cluster comprises a control node and a plurality of transmission nodes. In the assumption of the invention, the data plane transmission is only in the micro base station, and the macro base station data transmission is not considered. A transmission node of User Equipment (UE) in a virtual cell 1(VC1) is composed of micro base stations 1,2, and 3, but control plane signaling is transmitted in the macro base station 1. As the UE moves, the virtual cell also becomes virtual cell 2(VC2), consisting of the transport node micro base stations 3, 4, 5, 6, and the control node macro base station 1. During the movement, the virtual cell transfer node is also updated, the micro base stations 1,2 are "released" and the micro base stations 4, 5, 6 are added. As the user moves, the virtual cell is also updated to virtual cell 3(VC3), and not only the transmission node is updated, but also the control node is updated due to the coverage of the macro base station, and the macro base station 1 is changed to macro base station 2. The updating of the virtual cell transmission node under the macro base station is controlled by the macro base station, and the updating of the virtual cell transmission node and the control node between the macro base stations is controlled by a Network Processing Center (NPC). Herein, the micro base station is synonymous with the TRP in the present invention.
The overall flow chart of the method of the invention is shown in figure 2. The invention provides a virtual cell transmission node updating method for considering mobility facing to user requirements, which specifically comprises the following steps:
step A: the user equipment periodically measures the reference signals of surrounding TRP, the position information P of the user equipment and the guaranteed stream bit rate GFBR of the service of the user equipment, and stores the reference signals and the position information P in an internal memory; meanwhile, the user equipment can estimate SINR of surrounding TRP through the TRP reference signal, the surrounding TRP reference signal contains the TRP current service user number L, and the user stream bit rate GFBR can be obtained by controlling the TRP;
step A1: m TRPs periodically transmit reference signals, wherein the number L of current service users i (i ═ 1,2, …, M), which may be carried in the reference signal by special resource block locations or using special sequences;
step A2: user measures surrounding TRP reference signal, demodulates information and obtains L i (i ═ 1,2, …, M), and the SINR of each TRP was measured i (i＝1,2,…,M)；
Step A3: in the user virtual cell, one TRP is a control node, performs signaling interaction with a core network, and can acquire a user service GFBR through the core network; or the user himself measures a period of time T win The average throughput in the cell is taken as GFBR.
And B: calculating R factors of TRP in the current virtual cell, and if the sum of the R factors is less than a threshold GFBR (1+ G) min ) If yes, triggering dynamic cluster updating to enter the step C, otherwise not triggering updating to return to the step A;
step B1: assuming that the TRP set in the virtual cell of the current user is T ═ TRP 1 ,…,TRP n N total TRP with TRP serial number T no ＝{t 1 ,…,t n The R factor is calculated using the formula:
wherein
And
is determined by the SINR i Mapping to CQI i Remap to obtain the protocol specification of the former and the basis of the latterEach equipment manufacturer sets different mapping tables; n is a radical of rb 、T s And f ud Parameters are fixed for the system, L i By reference signal acquisition. Note: the formula is simplified from the estimation formula of 3GPP for the downlink rate, and according to the 3GPP protocol, the transmission rate obtained by the user in the micro base station i is estimated by the following formula:
where J is the number of aggregated component carriers in a band or band combination; in each of the carriers, a carrier is selected,
is the number of layers;
is the modulation order; f. of (j) Is a matching parameter of uplink and downlink frames, for example, if UL is DL 2:3, f is 3/(2+3) is 0.6; r (j) Is the code rate of the code, and,
is the number of PRBs used for transmission determined by the bandwidth and subcarrier spacing,
is the average OFDM symbol duration in the sub-frame under the u parameter (taking into account the cyclic prefix),
OH (j) is other overhead.
And R (j) The parameter is determined by a CQI index, the CQI index is determined by a reference SINR, a user uses CSI reference signal measurement for micro base stations which have established connection, and SSB reference signals can be used for micro base stations which do not establish connection to measure SINR and estimate possible modulation parameters. Considering only SISO transmission
The time slot ratio of each micro base station is the same without considering other overheads, namely f and OH are fixed (j) ＝0。
Step B2: and calculating the sum of R factors of TRPs in the current virtual cell and judging. If so:
entering the step C; otherwise, returning to the step A.
And C: calculating R factors of surrounding TRP, and accumulating the R factors from large to small until (1) the number of transmission nodes is equal to the maximum number N of transmission nodes in the virtual cell trp_max Or (2) the sum of the current accumulated R factors is larger than GFBR (1+ G) max )；
Step C1:
1) calculating R factor R of peripheral TRP according to formula (1) i (i＝1,2,…,M)
2) Will r is i Obtaining the R factor R after sequencing from big to small j (j＝1,2,…,M)
3) Initializing sumR-0; n is a radical of hydrogen trp ＝0；j＝0
4)While sumR<GFBR(1+G max ) And N is trp <N trp_max
5)j＝j+1，sumR＝sumR+r j ，N trp ＝N trp +1
6)End
Step D: (1) if the sum of R factors of TRPs is less than GFBR (1+ G) r ) Then all currently accumulated TRPs are selected as the target set of transmission nodes (number N) trp_max ) Entering step H; (2) if the sum of the R factors is greater than GFBR (1+ G) max ) I.e. by
Then the current cell number N is obtained trp (N trp ≤N trp_max ) At this time N trp The minimum TRP number which can meet the user requirement is obtained, and the step E is carried out;
step D1:
1)If sumR>GFBR(1+G max )
2) acquiring the current accumulated TRP number N trp Go to step E
3)Else
4) Selecting the largest N trp_max Taking each TRP as a new TRP group, and entering the step H
5)End
Step E: if the condition (2) in the step D is satisfied, finding out that the sum of all satisfied R factors is larger than GFBR (1+ G) max ) And the number of TRP is N trp If there are only 1 group, then the 1 group is the target transmission node set, and go to step H; if there is more than one group, then record these N r (N r >1) Grouping TRP, entering step F;
step E1: select N r Number of groups N trp And sumR>GFBR(1+G max ) Combinations of TRP
Step E2:
1)If N r ＝1
2) outputting the set of TRPs to step H
3)Else
4) Outputting a plurality of groups of TRP to be selected, and entering the step F
5)End
The above steps A to E are called "R factor selection algorithm", as shown in FIG. 3.
Step F: predicting the moving direction of a user by using a mobility prediction method, acquiring the coordinates of surrounding TRPs through a source TRP, calculating the distance d between the user and the surrounding TRPs, calculating the included angle theta between the moving direction of the user and each TRP direction, and calculating a P factor according to d and theta;
step F1: let the nearest N of the user's records u Each self two-dimensional coordinate is P ue ＝{p 1 ,p 2 ,…p Nu It can use LSTM network to make mobility prediction, which is a commonly used mobility prediction method to predict the coordinate vector p of user at next time next The current user coordinate vector is p now If the displacement vector is v ═ p next -p now (vector subtraction), user movement direction is v; let N r The total m TRPs needing to calculate P factors in the group TRP are provided, and the coordinate vector of the surrounding TRP is P trp ＝{tp 1 ,tp 2 ,…tp m V, direction vector of user position to TRP i ＝p now -tp i ，d i ＝|v i |，θ i ＝arg(v i ) Then, the P-factor for the jth TRP is calculated as:
wherein the schematic diagrams of d and θ are shown in fig. 5.
Step G: calculating N r The sum of P factors of the TRP groups, and selecting a group of TRP groups with the largest P factors as a target transmission node set;
step G1: let N r TRP whose set satisfies the condition is { T } 1 ,T 2 ,…,T Nr Finding the sum of P factors of each group, and the target TRP group is the group with the maximum P factor, i.e. the group with the maximum P factor
The above steps F to G are called "P-factor selection algorithm", as shown in FIG. 4.
Step H: comparing the target transmission node set with the current virtual cell transmission node, accessing TRPs which are present in the target set but not present in the current set into the virtual cell, and releasing TRPs which are not present in the target set but not present in the current set.
Step H1: setting current virtual cell TRP set as T now Target set is T target Then the set of TRPs to be added is T add ＝T now -T target The TRP set to be deleted is T del ＝T target -T now Where-represents the difference set of the aggregation.
Step H2: the existing networks with addition and deletion operations have related processes, which do not belong to the considered scope of the invention, and the method of the invention is finished.
The invention provides an electronic device, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the virtual cell transmission node updating method considering mobility facing to user requirements when executing the computer program.
The present invention proposes a computer readable storage medium for storing computer instructions which, when executed by a processor, implement the steps of the user demand oriented mobility aware virtual cell transport node update method.
P factor selection results are different after R factor selection than using only R factor, as explained in connection with fig. 6.
Fig. 6 illustrates the difference between using the R factor selection algorithm and the R factor P factor in combination with the selection algorithm. The R-factor selection algorithm will select the TRP cluster with the largest R-factor at the current time, such as "maxRF VC" in the figure, whose number 3 is also the current minimum number of connections. However, at the minimum number of connections of 3, there are other TRP clustering schemes that can meet the user's needs, as indicated by "RF & PF VC", which are not R-factor-optimal but are indeed P-factor-optimal. The P factor selection considers a distance factor, the smaller the distance, the larger the absolute value of the factor, and also considers the moving direction of the user, the farther the direction, the negative value of the factor, and the larger the P factor, the better the service which is possibly provided for the user in the future period of time represented by the TRP.
2. The introduction of P-factor selection is necessary and efficient, as illustrated in connection with fig. 7 and 8.
In order to illustrate the effectiveness of the algorithm, simulation is carried out, and user satisfaction rate and signaling overhead (with penalty) are used as key indexes. The algorithm based on R factor only is called "RF only" algorithm, the algorithm combining R factor and P factor is called "RF & PF" (invention) algorithm, considering that the contrast algorithm is based on optimal RSRP and SINR gain of adding TRP, similar to the conventional handover algorithm, and is hereinafter referred to as "RSRP based" algorithm; and a comparison algorithm using mobility prediction, taking into account both signal strength and user mobility, hereinafter referred to as a "mobility prediction based" algorithm.
The performance of the algorithm at different average guaranteed stream bit rates (GFBR) was first simulated under the condition that the UE/TRP ratio was 1 and the user average moving speed was 1m/s, as shown in fig. 7. Although the satisfaction rate of the RF & PF algorithm is reduced compared with the RFonly algorithm, the transmission nodes of the RF & PF algorithm have a longer average duration in the virtual cell, and the situation of repeatedly adding and deleting the same transmission node is reduced, so the operations of adding and deleting the TRP are reduced, and the overall signaling overhead is reduced.
The performance of the algorithm at different user average rates was simulated with a UE/TRP ratio of 1 and a user demand rate of 30Mbps, as shown in fig. 8. Under the scene of higher speed, the method of the invention can realize better satisfaction rate and lower signaling overhead.
The method, the device and the medium for updating the transmission node of the virtual cell, which are provided by the invention and are oriented to user requirements and considering mobility, are introduced in detail, and a specific example is applied in the text to explain the principle and the implementation mode of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A method for updating a transmission node of a virtual cell considering mobility for user demand, the method comprising:
step A: the user equipment periodically measures the reference signals of the TRP of the surrounding transmission receiving points, the position information P of the user equipment and the guaranteed stream bit rate GFBR of the service of the user equipment, and stores the reference signals and the position information P in an internal memory; meanwhile, the user equipment estimates SINR of surrounding TRP through a TRP reference signal, the surrounding TRP reference signal comprises the number L of the TRP current service users, and the bit rate GFBR of the user stream is obtained by controlling the TRP;
and B: calculating R factors of TRP in the current virtual cell, and if the sum of the R factors is less than a threshold GFBR (1+ G) min ) If yes, triggering dynamic cluster updating to enter step C, otherwise, not triggering updating to return to step A;
and C: calculating R factors of surrounding TRPs (potential transmission points), wherein the surrounding TRPs comprise TRPs in the current virtual cell, and accumulating the R factors from large to small until (1) the number of transmission nodes is equal to the maximum number N of transmission nodes in the virtual cell trp_max Or (2) the sum of the current accumulated R factors is greater than GFBR (1+ G) r )；
Step D: (1) if the sum of R factors of TRPs is less than GFBR (1+ G) r ) Then all currently accumulated TRPs are selected as a target transmission node set, and the number is N trp_max Entering step H; (2) if the sum of the R factors is greater than GFBR (1+ G) max ) Then obtain the current cell number N trp ，N trp ≤N trp_max Entering the step E;
and E, step E: if the condition (2) in the step D is satisfied, finding out that the sum of all satisfied R factors is larger than GFBR (1+ G) max ) And the number of TRPs is N trp If there are only 1 group, then the 1 group is the target transmission node set, and go to step H; if there is more than one group, then record these N r ，N r >1 group TRP, go to step F;
step F: predicting the moving direction of a user by using a mobility prediction method, acquiring the coordinates of surrounding TRPs through a source TRP, calculating the distance d between the user and the surrounding TRPs, calculating the included angle theta between the moving direction of the user and each TRP direction, and calculating a P factor according to d and theta;
step G: calculating N r The sum of P factors of the TRP groups, and selecting a group of TRP groups with the largest P factors as a target transmission node set;
step H: comparing the target transmission node set with the current virtual cell transmission node, accessing TRPs which are present in the target set but not present in the current set into the virtual cell, and releasing TRPs which are not present in the target set but not present in the current set.
2. The method according to claim 1, wherein step a is specifically:
step A1: m TRPs periodically transmit reference signals, wherein the number L of current service users i (i ═ 1,2, …, M), carried in the reference signal by a special resource block location or using a special sequence;
step A2: user measures surrounding TRP reference signal, demodulates information and obtains L i (i ═ 1,2, …, M), and the SINR of each TRP was measured i (i＝1,2,…,M)；
Step A3: in the user virtual cell, one TRP is a control node, performs signaling interaction with a core network, and acquires a user service GFBR through the core network; or the user himself measures a period of time T win The average throughput in the cell is taken as GFBR.
3. The method according to claim 2, wherein step B is specifically:
step B1: assuming that the TRP set in the virtual cell of the current user is T ═ TRP 1 ,…,TRP n A total of n TRPs with TRP number T no ＝{t 1 ,…,t n R factor was calculated using the formula:
wherein
And
is made of SINR i Mapping to CQI i Then mapping is carried out to obtain; n is a radical of rb 、T s And f ud For the system to fix the parameters, L i Acquiring through a reference signal;
step B2: calculating the sum of R factors of TRPs in the current virtual cell, and judging if the R factors meet the following conditions:
entering the step C; otherwise, returning to the step A.
4. The method according to claim 3, wherein step C is specifically:
calculating R factor R of peripheral TRP according to formula (1) i (i ═ 1,2, …, M); will r is i Obtaining the R factor R after sequencing from big to small j (j ═ 1,2, …, M); initializing sumR as 0; n is a radical of trp ＝0；j＝0；
While sumR<GFBR(1+G max ) And N is trp <N trp_max
j＝j+1，sumR＝sumR+r j ，N trp ＝N trp +1
End。
5. Method according to claim 4, characterized in that in step D: said N is trp Is the minimum number of TRPs to meet the user's needs;
If sumR>GFBR(1+G max )
acquiring the current accumulated TRP number N trp Go to step E
Else
Selecting the largest N trp_max Taking each TRP as a new TRP group, and entering step H
End。
6. The method according to claim 5, wherein, in step F,
let the nearest N of the user's records u Each self two-dimensional coordinate is P ue ＝{p 1 ,p 2 ,…p Nu And predicting the mobility by using an LSTM network to predict a coordinate vector p of the user at the next moment next The current user coordinate vector is p now If the displacement vector is v ═ p next -p now I.e. the user movement direction vector is v; let N r The total m TRPs needing to calculate P factors in the group TRP are provided, and the coordinate vector of the surrounding TRP is P trp ＝{tp 1 ,tp 2 ,…tp m H, direction vector v of user position to TRP i ＝p now -tp i ，d i ＝|v i |，θ i ＝arg(v i ) P-factorization of jth TRPThe formula is as follows:
7. the method according to claim 6, wherein, in step G,
let N r TRP whose set satisfies the condition is { T } 1 ,T 2 ,…,T Nr Finding the sum of P factors of each group, wherein the target TRP group is the group with the maximum P factor, namely
8. The method according to claim 7, characterized in that in step H, the current set of virtual cell TRPs is set to T now Target set is T target Then the set of TRPs to be added is T add ＝T now -T target The TRP set to be deleted is T del ＝T target -T now Where-represents the aggregate difference set.
9. An electronic device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method according to any one of claims 1-8 when executing the computer program.
10. A computer-readable storage medium storing computer instructions, which when executed by a processor implement the steps of the method of any one of claims 1 to 8.
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