A method and system for multi-point channel map assisted base station handover

Abstract

The application discloses a kind of multi-point channel atlas assisted base station switching method and system.Considering the scene of large-scale antenna array base station dense deployment, in offline training phase, multiple adjacent base stations obtain the channel information of user sample in its edge area, extract dissimilarity and fuse, and then construct the multi-point channel atlas of the area;At the same time, use machine learning technology to train the SNR prediction model based on atlas;In online prediction phase, map new users to multi-point channel atlas by sampling outer expansion method, and use SNR prediction model to predict the channel SNR of its near-neighbor base station, realize efficient base station switching according to the prediction result.The application uses multi-point channel atlas, accurately describes the relative position relationship of edge area users without real position information, so as to train more accurate SNR prediction model.The application can reduce the overhead of traditional switching method, and provide more accurate correct base station switching rate.

Description

Technical Field

[0001] The present invention relates to the field of multiple-input multiple-output (MIMO) and millimeter-wave (mmWave) wireless communications using large-scale antenna arrays, and in particular to a base station switching method and system assisted by a multi-point channel spectrum. Background Art

[0002] Massive multi-input, multi-output (MIMO) and millimeter waves are key enablers for high data rates and widespread connectivity in cellular communications. While millimeter wave bands can support multi-Gb / s data rates, they suffer from significant path loss during signal propagation, necessitating dense deployment of base stations to ensure seamless coverage. Due to user mobility and densely deployed base stations, users may frequently switch between serving base stations to maintain link quality, requiring the relevant base stations to perform a handover (HO) procedure with the core network. Traditionally, HO procedures are performed based on user received signal strength measurements, which indicate the signal-to-noise ratio (SNR) of the transmission channel, i.e., link quality. This process involves additional signaling overhead and can reduce network throughput. To effectively manage base station handovers, intelligent channel SNR acquisition is required, replacing the traditional method of frequently sending measurement signals. A user's true location can be used in millimeter wave networks to assist in HO management, but obtaining user location presents practical challenges and privacy risks.

[0003] On the other hand, channel graph (CC), as an unsupervised learning method, can determine the relative position of users based entirely on channel state information (CSI). The reason behind this is that the MIMO antenna array provides high-dimensional CSI, which contains sufficient geometric information about the propagation environment. This channel graph represents the relative position information of users, thereby relaxing the requirements for actual location, and has been proven to effectively support many wireless resource management applications. However, users located at the edge of the cell or blocked by obstacles may be inaccurately mapped to the channel graph due to poor link quality and channel acquisition capabilities, thereby degrading the performance of subsequent applications. Therefore, it is necessary to study a new technology based on a channel graph that can accurately characterize the relative position relationship of edge user groups to achieve efficient base station handover management. Summary of the Invention

[0004] Purpose of the invention: In response to the shortcomings of the existing technology, the purpose of the present invention is to provide a base station switching method and system assisted by a multi-point channel map. For the scenario of densely deployed base stations in the mmWave network, a multi-point channel map is first constructed, and then the user is mapped to the map through the out-of-sampling expansion method. The channel SNR between the user and the adjacent base station is predicted based on the user's position in the map. Finally, the base station that provides the highest channel SNR is selected for active and efficient switching to reduce switching overhead and improve switching accuracy.

[0005] Technical solution: To achieve the above-mentioned purpose, the present invention adopts the following technical solution:

[0006] A base station switching method assisted by a multi-point channel map comprises the following steps:

[0007] During the offline training phase, multiple distributed base stations jointly obtain channel information for user samples in edge areas. Each base station calculates the dissimilarity between users based on a dissimilarity metric that is insensitive to small-scale channel fading, constructs a local dissimilarity matrix, and sends this local dissimilarity matrix and the measured average signal-to-noise ratio (SNR) of the channel to the central unit. The central unit then fuses the local dissimilarity matrices based on the average channel SNRs of different base stations to form a global dissimilarity matrix, and plots a multi-point channel map based on the global dissimilarity matrix. The coordinates of each user sample on the multi-point channel map are annotated with the channel SNR between it and its neighboring base stations.

[0008] During the offline training phase, machine learning techniques are used to train an SNR prediction model based on a multi-point channel map. The model input is the user's map coordinates, and the output is the channel SNR between the user and each neighboring base station.

[0009] In the online prediction stage, the graph coordinates of new users are obtained through the sampling expansion method, and the SNR prediction model is used to predict the channel SNR between the new user and the neighboring base stations based on the position of the new user in the graph. The next serving base station is selected based on the SNR prediction results to achieve active and efficient base station switching.

[0010] Furthermore, multiple distributed base stations deploy MIMO-OFDM systems, operating in time division duplex (TDD) or frequency division duplex (FDD) modes.

[0011] Furthermore, the dissimilarity is defined as d ★ , whose expression is The minimization operation is used to overcome the drastic phase change of the channel due to small-scale fading, h l 、h m are the channel vectors of the lth and mth edge area users respectively, and ||·||2 is the binary norm operation of the vector.

[0012] Furthermore, each distributed base station constructs a local dissimilarity matrix D b ,[D b ] l,m =d ★ (h l b ,h m b ), where l,m∈{1,…,N UE} is the index of users in the edge area, NUE is the number of users, h l b 、h m b are the channel vectors of the lth and mth edge area users obtained by base station b respectively; the average channel SNR of the kth edge area user calculated by base station b in To find the expected operation, σ 2 is the noise energy at the base station receiver, The channel vector of the kth edge area user obtained by base station b.

[0013] Furthermore, the central unit fuses the local dissimilarity according to the average channel SNR of different base stations to form the global dissimilarity D, which is expressed as in ξ is the weight index, B is the number of base stations, The average channel SNRs of the lth and mth edge area users calculated for base station b respectively.

[0014] Furthermore, the central unit performs a dimensionality reduction operation on the global dissimilarity matrix to obtain the virtual position coordinate z of each user sample on the multi-point map, and draws it into a multi-point channel map

[0015] Furthermore, the out-of-sampling expansion method calculates the dissimilarity between the new user and the existing user samples based on the channel information of the new user in the current serving base station, and calculates the coordinates of the new user in the map by averaging the corresponding coordinates of the closest preset number of user samples in the map.

[0016] Furthermore, a deep neural network (DNN) is used to train a graph-based channel SNR prediction model. The trained model is stored in the central unit. The central unit selects the base station that provides the highest channel SNR as the next serving base station based on the prediction results, and informs the relevant base stations to perform the switching process.

[0017] The present invention also provides a base station switching system assisted by a multi-point channel map, comprising:

[0018] An offline training module is used in the offline training phase. Multiple distributed base stations calculate the dissimilarity between users based on the channel information of user samples obtained from edge areas, using a dissimilarity metric that is insensitive to small-scale channel fading. A local dissimilarity matrix is ​​constructed and sent to the central unit along with the measured average signal-to-noise ratio (SNR) of the channel. The central unit fuses the local dissimilarity matrices to form a global dissimilarity matrix based on the average channel SNRs of different base stations, and plots a multi-point channel map based on the global dissimilarity matrix. The coordinates of each user sample on the multi-point channel map are annotated with the channel SNR between the user and its neighboring base stations. A machine learning technique is then used to train an SNR prediction model based on the multi-point channel map. The model input is the user's map coordinates, and the output is the channel SNR between the user and each neighboring base station.

[0019] The online prediction module is used in the online prediction phase to obtain the new user's spectrum coordinates through the out-of-sample expansion method, and uses the SNR prediction model to predict the channel SNR between the new user and the neighboring base stations based on the new user's position in the spectrum;

[0020] and a base station switching module, which is used by the central unit to select the next serving base station based on the SNR prediction result.

[0021] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the offline training phase and / or the steps of the online prediction phase in the multi-point channel map-assisted base station switching method.

[0022] Beneficial effects: The present invention uses a multi-point channel map to predict the SNR of the channel between the user and the neighboring base station. Based on the prediction results, the central unit directly selects the switching base station. Compared with the traditional switching process of frequently sending measurement signals to obtain channel quality, the present invention reduces the switching signaling overhead of the multi-base station network and improves the throughput of the communication system. In view of the fact that the user group that needs to be switched usually appears in the edge area of ​​the base station, the multi-point channel map provides a more accurate relative position relationship of the user group, so it is expected to obtain a better SNR prediction accuracy, that is, the base station correct switching rate. The present invention further adopts a low-complexity calculation of dissimilarity and sampling expansion method to reduce the difficulty of physical layer implementation and improve the flexibility of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 Schematic diagram of the overall method flow of an embodiment of the present invention;

[0024] Figure 2 A distribution diagram of user samples participating in generating a graph in an embodiment of the present invention;

[0025] Figure 3Schematic diagram of the SNR prediction model in an embodiment of the present invention;

[0026] Figure 4 Schematic diagram of the multi-point channel map assisted base station switching process in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0028] like Figure 1 As shown, the multi-point channel map-assisted base station switching method disclosed in an embodiment of the present invention can be divided into an offline training phase and an online prediction phase. In the offline training phase, a distributed MIMO base station group collaboratively constructs a multi-point channel map for multiple cell edge collection areas and uses machine learning technology to train a map-based channel SNR prediction model. Specifically, multiple distributed base stations jointly obtain channel information of user samples in the edge area. Each base station calculates the dissimilarity between each user based on a dissimilarity measure that is insensitive to small-scale channel fading, constructs a local dissimilarity matrix, and sends the local dissimilarity matrix and the measured channel average SNR to the central unit; the central unit fuses the local dissimilarity matrices according to the channel average SNR of different base stations to form a global dissimilarity matrix, and draws a multi-point channel map based on the global dissimilarity matrix; the coordinates of each user sample on the multi-point channel map are annotated with the channel SNR between it and the neighboring base stations; the SNR prediction model trained based on the multi-point channel map is used to capture the relationship between the user's virtual position in the map and the channel quality to the neighboring base stations. The model input is the user's map coordinates, and the output is the channel SNR between the user and each neighboring base station.

[0029] During the online prediction phase, the map coordinates of new users are obtained using the out-of-sample expansion method. The SNR prediction model is then used to predict the channel SNR between the new user and neighboring base stations based on the new user's position in the map. The next serving base station is selected based on the SNR prediction results, achieving proactive and efficient base station switching. In this embodiment of the present invention, the offline training phase requires storing a large amount of user channel data and performing a series of operations to complete the construction of the multi-point channel map and the training of the SNR prediction model. Based on the results achieved during the offline training process, the online prediction phase rapidly calculates the channel map coordinates of the new user (i.e., users outside the offline training user sample) and sends them to the central unit. The central unit predicts the SNR of the channel between the new user and other neighboring base stations, thereby selecting the base station with the highest channel SNR as the base station to be switched to in the next transmission cycle. After completing the offline training phase, due to the quasi-static characteristics of the actual environment, the channel map and SNR prediction model assist in performing the functions of the online prediction phase for the next multiple transmission cycles. Therefore, the offline training process can be updated over a longer period of time, ensuring the timeliness of the method of the embodiment of the present invention.

[0030] The present invention uses multiple base stations to work together and integrates multiple different observation angles, which helps to build a more accurate multi-point channel map. The user's position in the channel map is highly correlated with the channel quality between it and the base station. This correlation is captured through classic machine learning techniques. A large number of labeled data sets can be used to train a map-based SNR prediction model to directly obtain the channel's SNR. The present invention uses a multi-point channel map to accurately depict the relative position relationship of users in edge areas without the need for real location information, thereby training a more accurate SNR prediction model. The present invention can provide a more accurate correct base station switching rate while reducing the overhead of traditional switching methods.

[0031] The method of this embodiment is described in more detail below with reference to a specific scenario.

[0032] Part 1: Building a Distributed MIMO-OFDM Base Station System Model and Dividing Base Station Handover Target User Groups

[0033] Specifically, consider a system with multiple, distributed, large-scale antenna array base stations deployed in a millimeter wave network. Each base station is equipped with dozens or more antennas, which can adopt linear, circular, flat, or other array structures. Each antenna unit can use an omnidirectional antenna or a sector antenna. When each antenna unit uses an omnidirectional antenna, a 120-degree sector antenna, or a 60-degree sector antenna, the spacing between antennas can be configured to be 1 / 2 wavelength, 1 / 3 wavelength, and 1 wavelength. Each antenna unit can use a single-polarization or multi-polarization antenna, and different base stations can use different antenna array configurations. The base stations use broadband OFDM technology and operate in time division duplex (TDD) or frequency division duplex (FDD) mode. The central unit, located in a data center or cloud computing center, is responsible for processing data from the distributed base stations and is also responsible for the management and coordination of the entire network.

[0034] In this embodiment, the user terminal uses a single antenna. Consider deploying B base stations, each equipped with A antennas, with a total of S subcarriers evenly distributed at the center frequency f C The total bandwidth occupied on both sides is W. The user sends an uplink pilot signal, and each base station obtains the corresponding uplink channel based on the received signal. The uplink channel vector between user k and base station b is recorded as Assuming that the signal propagation follows the plane wave model, the channel information includes the multipath arrival angle and propagation delay. Represents the channel on the a-th antenna and the s-th subcarrier.

[0035] Figure 2 This is a schematic diagram of the distribution of user samples participating in the generation of the map. The selected user samples are located in the edge areas of the service of each base station, which can also be interpreted as the boundary area of ​​multiple adjacent cells.

[0036] In order to accurately capture the relative positions of users in the multi-cell boundary area and build a complete channel map, it is necessary to divide the base station switching target user groups. Let the serviceable user set covered by base station s be This set includes not only the user groups of this cell, but also the user groups of some neighboring base stations. The user groups located in the boundary area of ​​multiple cells can be expressed as where {t1,...,t p} is the neighboring cell of base station s. In addition, by calculating the cumulative probability distribution function of the SNR of all user channel samples, the threshold γ is set th The 10% threshold of the cumulative probability distribution function of SNR is used to classify user samples below this threshold as the target user group. This approach also includes users who are not in the base station's service edge area but have poor communication channel quality with the base station (due to factors such as obstruction) in the training data set to ensure data integrity.

[0037] Part II: Offline Training and Online Prediction in Efficient Base Station Handover Methods

[0038] Based on the multi-point architecture of multiple base stations with large-scale antenna arrays working together and the division of base station handover target groups, all channel samples of user groups in the multi-cell boundary area at their neighboring base stations are obtained in the offline training phase, namely where N UE is the number of user samples, and B is the number of base stations.

[0039] Each distributed base station performs the construction of the local dissimilarity matrix D b The operation is as follows: [D b ] l,m =d ★ (h l b ,h m b ).d ★ Characterizes a dissimilarity measure that is insensitive to small-scale channel fading, and its expression is The minimization operation is used to overcome the drastic phase change of the channel due to small-scale fading, so that the dissimilarity can effectively reflect the actual physical distance between users. Further, the objective function is rewritten as and use Get d ＊ The equivalent analytical expression is Where |·| is the modulo operation, and ||·||2 is the vector norm operation. Calculate the channel average SNR The expectation operation This can be accomplished by averaging multiple channels on coherent time resources or frequency resources, σ 2 is the noise energy at the base station receiving end. After completing the above local operations, each distributed base station unit will calculate its local dissimilarity matrix D b and the measured channel average Send to the central unit.

[0040] The central unit fuses the local dissimilarity according to the channel observation quality of different base stations to form a global dissimilarity matrix D, which is expressed as in ξ is the weight index. The weight index shows a certain robustness in the dissimilarity fusion process and is generally set to 2 or 3. For the special case where a user sample n cannot connect to the base station b due to severe channel fading or blocking, the SNRγ is set n b =0 and T Inf is a large positive constant.

[0041] The Isomap algorithm is used to reduce the dimension of the global dissimilarity matrix. Specifically, the matrix D is regarded as a weighted graph containing the distances of each point, and its k-nearest neighbor graph G is found. k (k nearest neighbors). k Use Dijkstra's algorithm to search for the shortest path, and add the shortest path distance between any two nodes in the graph to the final dissimilarity matrix D f In, based on D f Implementing dimensionality scaling means solving the following problem:

[0042]

[0043] Among them, z k That is, the virtual position coordinates of user sample k on the multi-point map, and the multi-point channel map is expressed as After the multi-point channel map is constructed at the central unit, the central unit sends the map data to each distributed base station and stores it in the local unit.

[0044] The above process of constructing a multi-point map can be summarized as follows:

[0045] Step S1: The distributed base station calculates the local dissimilarity matrix and SNR, and sends the calculation results to the central unit.

[0046] Step S2: The central unit performs data fusion to form a global dissimilarity matrix.

[0047] Step S3: The central unit uses the Isomap algorithm on the global dissimilarity matrix to generate a multi-point channel map.

[0048] The coordinates of each sample on the map are marked with the SNR of the channel between it and the neighboring base station, forming a labeled dataset to train the SNR prediction model. The loss function is defined as the error between the predicted SNR and the actual SNR.

[0049] Figure 3 This is a schematic diagram of the SNR prediction model. The model uses a deep neural network structure with two hidden layers consisting of a fully connected layer with a RELU activation function. The input dimension is consistent with the channel map coordinate dimension, and the output dimension is consistent with the number of neighboring base stations.

[0050] Figure 4 This is a schematic diagram of the multi-point channel map-assisted base station switching process, in which the multi-point channel map and SNR prediction model implemented in the offline training phase are saved in the central unit and applied in the online prediction phase.

[0051] After completing offline training, when the next switching cycle arrives, the current serving base station b first obtains the CSI of the new user q, which is recorded as According to the CSI, the out-of-sample expansion method is implemented to map the user to the global map. Specifically, p user samples (whose CSI is has the smallest dissimilarity), forming a set The coordinates of the new user in the graph are the average of the coordinates of the samples in the set, and the calculation formula is: The current serving base station will The data is sent to the central unit and input into the SNR training model. The central unit then obtains the SNR prediction value of the channel between user q and other neighboring base stations. Based on the result, the central unit determines the base station that currently provides the highest channel SNR, selects it as the base station to be switched, and informs the relevant base stations to execute the switching process.

[0052] An embodiment of the present invention discloses a base station switching system assisted by a multi-point channel map, including:

[0053] An offline training module is used in the offline training phase. Multiple distributed base stations calculate the dissimilarity between users based on the channel information of user samples obtained from edge areas, using a dissimilarity metric that is insensitive to small-scale channel fading. A local dissimilarity matrix is ​​constructed and sent to the central unit along with the measured average signal-to-noise ratio (SNR) of the channel. The central unit fuses the local dissimilarity matrices to form a global dissimilarity matrix based on the average channel SNRs of different base stations, and plots a multi-point channel map based on the global dissimilarity matrix. The coordinates of each user sample on the multi-point channel map are annotated with the channel SNR between the user and its neighboring base stations. A machine learning technique is then used to train an SNR prediction model based on the multi-point channel map. The model input is the user's map coordinates, and the output is the channel SNR between the user and each neighboring base station.

[0054] The online prediction module is used in the online prediction phase to obtain the new user's spectrum coordinates through the out-of-sample expansion method, and uses the SNR prediction model to predict the channel SNR between the new user and the neighboring base stations based on the new user's position in the spectrum;

[0055] and a base station switching module, which is used by the central unit to select the next serving base station based on the SNR prediction result.

[0056] An embodiment of the present invention discloses a computer program product, including a computer program. When executed by a processor, the computer program implements the steps of the offline training phase and / or the steps of the online prediction phase in the multi-point channel map-assisted base station handover method. Any details not described in this invention are well known to those skilled in the art.

[0057] The above describes in detail the preferred embodiments of the present invention. It should be understood that those skilled in the art can make numerous modifications and variations based on the concepts of the present invention without inventive effort. Therefore, any technical solutions that can be derived by those skilled in the art through logical analysis, reasoning, or limited experimentation based on the concepts of the present invention and the prior art should be within the scope of protection defined by the claims.

Claims

1. A base station switching method assisted by a multi-point channel map, characterized in that: The steps include: During the offline training phase, multiple distributed base stations jointly obtain channel information of user samples in edge areas. Each base station calculates the dissimilarity between users based on a dissimilarity metric that is insensitive to small-scale channel fading, constructs a local dissimilarity matrix, and sends the local dissimilarity matrix and the measured channel average signal-to-noise ratio (SNR) to the central unit. The central unit fuses the local dissimilarity matrices based on the average channel SNRs of different base stations to form a global dissimilarity matrix, and then plots a multi-point channel map based on the global dissimilarity matrix. The coordinates of each user sample on the multi-point channel map are marked with the channel SNR between it and the neighboring base stations. During the offline training phase, machine learning techniques are used to train an SNR prediction model based on a multi-point channel map. The model input is the user's map coordinates, and the output is the channel SNR between the user and each neighboring base station. In the online prediction stage, the out-of-sampling expansion method is used to obtain the map coordinates of the new user, and the SNR prediction model is used to predict the channel SNR between the new user and the neighboring base stations based on the position of the new user in the map, and the next serving base station is selected according to the SNR prediction result; the out-of-sampling expansion method is to calculate the dissimilarity between the new user and the existing user samples based on the channel information of the new user in the current serving base station, and calculate the coordinates of the new user in the map by averaging the corresponding coordinates of the closest preset number of user samples.

2. The base station switching method assisted by a multi-point channel map according to claim 1, characterized in that: Multiple distributed base stations deploy MIMO-OFDM systems, operating in time division duplex (TDD) or frequency division duplex (FDD) mode.

3. The base station switching method assisted by a multi-point channel map according to claim 1, characterized in that: The dissimilarity is defined as , whose expression is , where a minimization operation is used to overcome the drastic phase changes of the channel due to small-scale fading, 、 Respectively 、 The channel vector of the edge area user is It is the two-norm operation of the vector.

4. The base station switching method assisted by a multi-point channel map according to claim 1, characterized in that: Each distributed base station builds a local dissimilarity matrix , ,in An index for users in marginal areas. is the number of users, is the dissimilarity, 、 Base stations b Obtained 、 Channel vector of users in edge areas; base station b Calculate the average channel SNR of the kth edge area user ,in To find the expectation operation, is the noise energy at the base station receiver, For base stations b The channel vector of the kth user in the edge area is obtained, It is the two-norm operation of the vector.

5. The base station switching method assisted by a multi-point channel map according to claim 1, characterized in that: The central unit fuses the local dissimilarity to form the global dissimilarity based on the average SNR of the channels of different base stations. , whose expression is ,in , is the weight index, B is the number of base stations, For base stations b The local dissimilarity matrix constructed, An index for users in marginal areas. is the number of users, 、 Base stations b Calculated 、 The average channel SNR of users in the edge area.

6. The base station switching method assisted by a multi-point channel map according to claim 1, characterized in that: The central unit performs dimensionality reduction on the global dissimilarity matrix to obtain the virtual position coordinate z of each user sample on the multi-point map and draws it into a multi-point channel map. , is the number of users.

7. The base station switching method assisted by a multi-point channel map according to claim 1, characterized in that: A deep neural network is used to train a graph-based channel SNR prediction model. The trained SNR prediction model is stored in the central unit. Based on the prediction results, the central unit selects the base station that provides the highest channel SNR as the next serving base station and informs the relevant base stations to perform the switching process.

8. A base station switching system assisted by a multi-point channel map, characterized in that: include: The offline training module is used in the offline training phase. Multiple distributed base stations calculate the dissimilarity between users based on the channel information of user samples obtained from edge areas, using a dissimilarity metric that is insensitive to small-scale channel fading. They then construct a local dissimilarity matrix and send the local dissimilarity matrix and the measured channel average signal-to-noise ratio (SNR) to the central unit. The central unit fuses the local dissimilarity matrices based on the average channel SNRs of different base stations to form a global dissimilarity matrix, and then plots a multi-point channel map based on the global dissimilarity matrix. The coordinates of each user sample on the multi-point channel map are annotated with the channel SNR between the user and its nearest neighboring base stations. Machine learning techniques are then used to train an SNR prediction model based on the multi-point channel map. The model input is the user's map coordinates, and the output is the channel SNR between the user and each nearest neighboring base station. An online prediction module, used in the online prediction phase, obtains the graph coordinates of new users using an out-of-sample expansion method, and uses an SNR prediction model to predict the channel SNR between the new user and its neighboring base stations based on the new user's position in the graph. The out-of-sample expansion method calculates the dissimilarity between the new user and existing user samples based on the channel information of the new user at the current serving base station, and calculates the coordinates of the new user in the graph by averaging the corresponding coordinates of a preset number of the closest user samples in the graph. and a base station switching module, which is used by the central unit to select the next serving base station based on the SNR prediction result.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program implements the steps of the offline training phase and / or the steps of the online prediction phase in the multi-point channel map-assisted base station switching method according to any one of claims 1 to 7.

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