CN116419331A - Switching method, switching device and storage medium - Google Patents

Abstract

The embodiment of the application provides a switching method, a switching device and a storage medium, wherein the method comprises the following steps: determining a target beam adopted by a terminal in a future period of time; and carrying out admission control and data transmission according to the target beam. According to the switching method, the switching device and the storage medium, before admission control is carried out, the target cell determines the target beam adopted by the terminal in a period of time in the future, so that the situation that the optimal transmitting/receiving beam is searched by scanning all narrow beams in a traversing manner in the beam scanning process is avoided, the switching time delay is reduced, and the switching efficiency is improved.

Description

Switching method, switching device and storage medium

Technical Field

The present disclosure relates to the field of communications technologies, and in particular, to a switching method, a device, and a storage medium.

Background

One key technology of the fifth generation mobile communications (the 5th generation mobile communication,5G) is higher frequency band communications, especially in the millimeter wave band. Communication in the millimeter wave band can fully utilize the communication band, and the communication band propagates in space in a direct wave mode, so that the wave beam is narrow, has good directivity, but can cause smaller wireless coverage range. To enhance 5G coverage, base stations employ beamforming techniques, i.e., by adjusting the amplitude and phase of multiple antennas, giving the antenna radiation patterns a specific shape and direction, focusing the wireless signal energy on narrower beams to enhance coverage and reduce interference. Generally, the narrower the beam, the greater the signal gain. However, a side effect is that once the beam is directed away from the user, the user cannot receive high quality wireless signals, especially in high speed railway scenarios where the terminal switches frequently, requiring a fast beam alignment when the terminal communicates with the base station.

During communication between the base station and the terminal, signals are transmitted on beams formed in the antenna transmission/reception direction according to a predefined time interval within the coverage of the beams. The base station transmits/receives signals on m beams in different spatial directions, the terminal listens/scans the beam transmissions from the base station on n different receiving/transmitting spaces, so there are m x n beam scans in total, and finally the base station and the terminal select the appropriate beam for communication. In the switching process, when the terminal accesses the target cell, the base station and the terminal also adopt the strategy of beam scanning in order to aim at the beam. The strategy of scanning all narrow beams to find the best transmit/receive beam generally adopted in the beam scanning process requires a great deal of beam overhead, which results in long switching time and causes problems such as data transmission interruption.

Disclosure of Invention

The embodiment of the application provides a switching method, a switching device and a storage medium, which are used for solving the technical problem of long switching time delay caused by beam scanning in the switching process in the prior art.

In a first aspect, an embodiment of the present application provides a handover method, applied to a target cell, including:

determining a target beam adopted by a terminal in a future period of time;

And carrying out admission control and data transmission according to the target beam.

In some embodiments, determining the target beam to be employed by the terminal for a period of time in the future includes:

receiving a first message sent by a source cell; the first message contains relevant information of intelligent beam prediction;

and deducing a target beam adopted by the terminal in a future period according to the relevant information of the intelligent beam prediction.

In some embodiments, the intelligent beam prediction related information is an optimal beam codeword.

In some embodiments, the first message is an inter-cell interface message.

In some embodiments, determining the target beam to be employed by the terminal for a period of time in the future includes:

receiving a second message sent by a source cell; the second message contains the target beam adopted by the terminal in a future period.

In some embodiments, the second message is an inter-cell interface message.

In a second aspect, an embodiment of the present application provides a handover method, applied to a source cell, including:

transmitting a first message to a target cell; the first message is used for the target cell to determine a target beam to be adopted by a terminal for a period of time in the future.

In some embodiments, the first message includes information related to intelligent beam prediction.

In some embodiments, the intelligent beam prediction related information is an optimal beam codeword.

In some embodiments, before the sending the first message to the target cell, the method further includes:

determining current mobility information of a terminal;

and inputting the current mobility information of the terminal into a trained machine learning model to obtain relevant information of intelligent beam prediction.

In some embodiments, before the step of inputting the current mobility information of the terminal into the trained machine learning model, the method further includes:

acquiring training sample data;

and performing model training according to the training sample data to obtain relevant parameters of the trained machine learning model.

In some embodiments, before the step of inputting the current mobility information of the terminal into the trained machine learning model, the method further includes:

receiving a model configuration message sent by a target network element; the model configuration message contains relevant parameters of the trained machine learning model.

In some embodiments, the first message is an inter-cell interface message.

In a third aspect, an embodiment of the present application provides a handover method, applied to a source cell, including:

Sending a second message to the target cell; the second message contains the target beam adopted by the terminal in a future period.

In some embodiments, before the sending the second message to the target cell, the method further includes:

and deducing the target beam adopted by the terminal in a future period by using a machine learning model.

In some embodiments, the utilizing a machine learning model to infer a target beam employed by a terminal for a period of time in the future includes:

determining current mobility information of a terminal;

inputting the current mobility information of the terminal into a trained machine learning model to obtain relevant information of intelligent beam prediction;

and deducing a target beam adopted by the terminal in a future period according to the relevant information of the intelligent beam prediction.

In some embodiments, before the step of inputting the current mobility information of the terminal into the trained machine learning model, the method further includes:

acquiring training sample data;

and performing model training according to the training sample data to obtain relevant parameters of the trained machine learning model.

In some embodiments, before the step of inputting the current mobility information of the terminal into the trained machine learning model, the method further includes:

Receiving a model configuration message sent by a target network element; the model configuration message contains relevant parameters of the trained machine learning model.

In some embodiments, the second message is an inter-cell interface message.

In a fourth aspect, embodiments of the present application provide a target cell, including a memory, a transceiver, and a processor;

a memory for storing a computer program; a transceiver for transceiving data under control of the processor; a processor for reading the computer program in the memory and performing the steps of the handover method according to the first aspect as described above.

In a fifth aspect, embodiments of the present application provide a source cell including a memory, a transceiver, and a processor;

a memory for storing a computer program; a transceiver for transceiving data under control of the processor; a processor for reading the computer program in the memory and performing the steps of the handover method according to the second aspect as described above.

In a sixth aspect, embodiments of the present application provide a source cell including a memory, a transceiver, and a processor;

a memory for storing a computer program; a transceiver for transceiving data under control of the processor; a processor for reading the computer program in the memory and performing the steps of the handover method according to the third aspect as described above.

In a seventh aspect, an embodiment of the present application provides a switching device, including:

the first determining module is used for determining a target beam adopted by the terminal in a future period of time;

and the processing module is used for carrying out admission control and data transmission according to the target beam.

In an eighth aspect, an embodiment of the present application provides a switching device, including:

the first sending module is used for sending a first message to the target cell; the first message is used to determine a target beam to be employed by the terminal for a period of time in the future.

In a ninth aspect, an embodiment of the present application provides a switching device, including:

a second sending module, configured to send a second message to the target cell; the second message is used to determine a target beam to be employed by the terminal for a period of time in the future.

In a tenth aspect, embodiments of the present application further provide a computer-readable storage medium storing a computer program for causing a computer to execute the steps of the handover method according to the first, second or third aspects as described above.

According to the switching method, the switching device and the storage medium, before admission control is carried out, the target cell determines the target beam adopted by the terminal in a period of time in the future, so that the situation that the optimal transmitting/receiving beam is searched by scanning all narrow beams in a traversing manner in the beam scanning process is avoided, the switching time delay is reduced, and the switching efficiency is improved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief description will be given below of the drawings that are needed in the embodiments or the prior art descriptions, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic flow chart of a handover method according to an embodiment of the present application;

fig. 2 is a schematic diagram of a source cell and a target cell provided in an embodiment of the present application located in different base stations;

fig. 3 is a schematic diagram of a source cell and a target cell provided in an embodiment of the present application located in the same base station;

fig. 4 is one of signaling interaction diagrams of handover provided in an embodiment of the present application;

FIG. 5 is a second signaling diagram of a handover according to an embodiment of the present disclosure;

FIG. 6 is a second flow chart of a switching method according to the embodiment of the present application;

FIG. 7 is a third flow chart of a switching method according to the embodiment of the present disclosure;

fig. 8 is a schematic structural diagram of a target cell according to an embodiment of the present application;

fig. 9 is one of schematic structural diagrams of a source cell according to an embodiment of the present application;

Fig. 10 is a second schematic structural diagram of a source cell according to an embodiment of the present disclosure;

fig. 11 is a schematic structural diagram of a switching device according to an embodiment of the present disclosure;

FIG. 12 is a second schematic diagram of a switching device according to the embodiment of the present disclosure;

fig. 13 is a third schematic structural diagram of a switching device according to an embodiment of the present disclosure.

Detailed Description

Mobility optimization in 5G networks is one of the problems of interest of the third generation partnership project (3rd Generation Partnership Project,3GPP) standard organization, and different schemes of applying artificial intelligence (Artificial Intelligence, AI) technology to mobility optimization, such as predicting a motion track of a terminal by using AI technology and adjusting parameter configuration in a handover algorithm, have been proposed by each equipment manufacturer and operator in RAN3 group, but no mobility optimization scheme based on beam prediction or technical scheme has been proposed in the prior art.

How to quickly aim the beam at the user is the main content of the research of beam management technology in 5G technology. In order to rapidly align the beams, a beam scanning strategy is employed. The commonly employed strategy of traversing through all narrow beams to find the best transmit/receive beam requires a significant amount of beam overhead, which is not compatible with the 5G desired user experience. In particular, the terminal is often in a mobile state, and high-frequency signals (especially millimeter waves) are easily affected by a wireless environment, so that beam signals cannot reach the terminal easily. In order to ensure continuous seamless coverage of wireless signals, a terminal can rapidly and uninterruptedly receive an optimal beam signal sent by a base station in a switching scene, and the existing beam management needs beam alignment, but needs a large amount of beam overhead and has larger instruction issuing time delay. The beam management process may thus be further optimized for the handover process by the beam prediction algorithm.

The mobile communication in the high-speed railway scene adopts a cellular mobile communication system, and the terminal equipment can be frequently switched from one cell to the adjacent cell, and correspondingly, frequent beam alignment and tracking processes are required. The mobile scenario under large-scale antennas, especially high-speed railway scenarios, frequent beam alignment and tracking, user handoff causes a significant amount of beam training overhead and significant instruction issuing delay. Therefore, reducing both beam training overhead and instruction issue latency is currently critical in mobile wireless communications.

In the prior art, algorithms such as parameter estimation, data fusion, nonlinear mapping and the like are adopted for beam prediction, but only the algorithm is studied. In order to solve the problem of beam prediction for mobility optimization, a system-level architecture design and a wireless air interface signaling design need to be proposed.

In order to solve the technical problems, the embodiments of the present application provide a handover method, apparatus, and storage medium, where before performing admission control, a target cell determines a target beam adopted by a terminal in a period of time in the future, so as to avoid scanning all narrow beams to find an optimal transmit/receive beam in a beam scanning process, reduce a handover delay, and improve a handover efficiency.

For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.

Fig. 1 is one of flow diagrams of a handover method provided in the embodiment of the present application, and as shown in fig. 1, the embodiment of the present application provides a handover method, where an execution body of the handover method may be a target cell, for example, a target cell of a 5G base station. The method comprises the following steps:

and 101, determining a target beam adopted by the terminal in a future period of time.

Specifically, in the embodiment of the present application, the target beam used by the terminal is determined for a period of time in the future, i.e. the target beam (to be) used when the terminal accesses the target cell is determined. The target beam used by the terminal for some time in the future refers to the beam used (to be) when switching to (accessing) the target cell, i.e. the terminal accesses the target cell via the target beam during the execution of the switching.

Since the terminal is in a continuously moving state, the specific duration of the future period of time depends on factors such as the moving speed of the terminal and the beam granularity of the target cell. Under the condition that other conditions are unchanged, the faster the moving speed of the terminal is, the shorter the duration of a period of time in the future is, and conversely, the longer the duration is; the finer the beam granularity of the target cell, the shorter the duration of the future period of time, and vice versa, with other conditions unchanged.

Fig. 2 is a schematic diagram of a source cell and a target cell provided in an embodiment of the present application located at different base stations, where the source cell and the target cell are located at different base stations as shown in fig. 2.

Fig. 3 is a schematic diagram of a source cell and a target cell provided in an embodiment of the present application located at the same base station, where, as shown in fig. 3, the source cell and the target cell are located at the same base station.

In some embodiments, determining the target beam to be employed by the terminal for a period of time in the future includes:

receiving a first message sent by a source cell; the first message contains relevant information of intelligent beam prediction;

and deducing a target beam adopted by the terminal for a period of time in the future according to the related information of the intelligent beam prediction.

Specifically, in the embodiment of the application, the target beam adopted by the terminal for a period of time in the future is inferred by the target cell.

The specific steps of the target cell for determining the target beam adopted by the terminal in a future period of time include:

first, the source cell sends a first message to the target cell. The first message is for the target cell to determine a target beam to be employed by the terminal for a period of time in the future.

The target cell receives a first message sent by the source cell.

In some embodiments, the first message includes information related to intelligent beam prediction.

In some embodiments, the intelligent beam prediction related information is an optimal beam codeword.

In some embodiments, the first message is an inter-cell interface message.

And the target cell acquires the relevant information of the intelligent beam prediction, and deduces a target beam adopted by the terminal in a future period according to the relevant information of the intelligent beam prediction.

Specifically, the interface information between cells may be a handover request message or a terminal context response message.

In some embodiments, determining the target beam to be employed by the terminal for a period of time in the future includes:

receiving a second message sent by a source cell; the second message contains the target beam adopted by the terminal for a period of time in the future.

Specifically, in the embodiments of the present application, a target beam employed by a terminal for a period of time in the future is inferred by a source cell.

The specific steps of the target cell for determining the target beam adopted by the terminal in a future period of time include:

the source cell sends a second message to the target cell, where the second message includes the target beam adopted by the terminal for a period of time in the future. Wherein the target beam is inferred by the source cell using a machine learning model.

The target cell receives the second message sent by the source cell, and obtains the target beam adopted by the terminal for a period of time in the future from the second message.

In some embodiments, the second message is an inter-cell interface message.

Specifically, the interface information between cells may be a handover request message or a terminal context response message.

And 102, performing admission control and data transmission according to the target beam.

Specifically, unlike the prior art in which the target cell performs admission control first, and then scans all the narrow beams in a traversing manner adopted in the beam scanning process to find the optimal transmit/receive beam, in the embodiment of the present application, the target cell determines the beam adopted by the terminal to switch to the target cell first, and then performs admission control, that is, after determining the beam adopted by the terminal to switch to the target cell, the target cell sends a switching instruction to the terminal.

According to the switching method provided by the embodiment of the application, before admission control is carried out, the target cell determines the target beam adopted by the terminal in a period of time in the future, so that the situation that the best transmitting/receiving beam is searched by scanning all narrow beams in a traversing manner in the beam scanning process is avoided, the switching time delay is reduced, and the switching efficiency is improved.

In some embodiments, before sending the first message to the target cell, further comprising:

determining current mobility information of a terminal;

and inputting the current mobility information of the terminal into a trained machine learning model to obtain the relevant information of intelligent beam prediction.

Specifically, in the embodiment of the present application, before the source cell sends the first message to the target cell, the source cell needs to determine current mobility information of the terminal, and input the current mobility information of the terminal into a trained machine learning model to obtain relevant information of intelligent beam prediction.

The current pilot signal and the measurement signal of the terminal can be acquired first, and then the current mobility information of the terminal can be determined according to the current pilot signal and the measurement signal of the terminal.

The current mobility information of the terminal comprises signal strength, projection position and speed of the mobile terminal and the like.

In some embodiments, before inputting the current mobility information of the terminal into the trained machine learning model, the method further includes:

acquiring training sample data;

model training is carried out according to the training sample data, and relevant parameters of the trained machine learning model are obtained.

Specifically, the machine learning training process can be obtained by adopting supervised learning, taking the minimum loss function mean square error as a criterion, and carrying out iterative optimization by using a gradient descent algorithm (such as MBGD).

In the embodiment of the application, the machine learning model is obtained by training a source cell.

The specific steps of the source cell training the machine learning model are as follows:

first, the source cell acquires training sample data. The training sample data includes, but is not limited to, pilot signals and/or measurement signals of the terminal, as well as the projection distance of the corresponding terminal and/or the velocity of the terminal.

And then, carrying out model training according to the training sample data to obtain relevant parameters of the trained machine learning model.

According to the embodiment of the application, the source cell performs model training, so that the expenditure of signaling can be reduced, and the resource utilization rate is improved.

In some embodiments, before inputting the current mobility information of the terminal into the trained machine learning model, the method further includes:

Receiving a model configuration message sent by a target network element; the model configuration message includes relevant parameters of the trained machine learning model.

Specifically, the machine learning training process can be obtained by adopting supervised learning, taking the minimum loss function mean square error as a criterion, and carrying out iterative optimization by using a gradient descent algorithm (such as MBGD).

In the embodiment of the application, the machine learning model is obtained by training other network elements. For example, it may be trained by an operation and maintenance management (Operation Administration and Maintenance, OAM) network element. After training the machine learning model by other network elements, obtaining relevant parameters of the trained machine learning model, and then sending the relevant parameters to the source cell through a model configuration message.

The source cell receives a model configuration message sent by other network elements, wherein the model configuration message contains relevant parameters of the trained machine learning model.

According to the embodiment of the application, the model training is performed by other network elements, so that the complexity of the source cell can be reduced.

In some embodiments, before sending the second message to the target cell, further comprising:

and deducing the target beam adopted by the terminal in a future period by using a machine learning model.

Specifically, in the embodiment of the present application, before the source cell sends the second message to the target cell, the machine learning model may also be used to infer the target beam adopted by the terminal for a period of time in the future.

In some embodiments, using a machine learning model to infer a target beam employed by a terminal for a period of time in the future, includes:

determining current mobility information of a terminal;

inputting the current mobility information of the terminal into a trained machine learning model to obtain relevant information of intelligent beam prediction;

and deducing a target beam adopted by the terminal for a period of time in the future according to the related information of the intelligent beam prediction.

Specifically, in the embodiment of the present application, the specific steps of the source cell using the machine learning model to infer the target beam adopted by the terminal for a period of time in the future include:

firstly, a source cell determines current mobility information of a terminal, then the current mobility information of the terminal is input into a trained machine learning model to obtain relevant information of intelligent beam prediction, and finally the source cell deduces a target beam adopted by the terminal in a period of time in the future according to the relevant information of intelligent beam prediction.

Specifically, the machine learning model may be obtained by training other network elements, or may be obtained by training a source cell, which is not described herein.

The method in the above embodiment is further described below with two specific examples.

The following two examples add the functions for beam prediction model training and data acquisition at the wireless network OAM function node.

When the mobile terminal performs cell-crossing handover, the source cell needs to send relevant information of intelligent beam prediction of the mobile terminal from the source cell to the target cell through an inter-cell Xn interface message, wherein the relevant information includes, but is not limited to, a projection position and a moving speed of the terminal, the interface message refers to a message of inter-cell transfer information, and the interface message can be, but is not limited to, a handover request message or a search UE context response message. The source cell and the target cell may be located in the same base station or may be located in different base stations.

The first example, the source cell and the target cell are located in the same base station.

Fig. 4 is one of signaling interaction diagrams of handover provided in the embodiment of the present application, and as shown in fig. 4, specific steps of handover are as follows:

step 1: the terminal and the base station collect data of the terminal and send the data to an operation maintenance management system or a computing platform;

step 2: executing a machine learning model training process on an operation maintenance management system or a computing platform;

Step 3: the operation maintenance management system or the computing platform deploys a machine learning model on the base station according to the training result;

step 4: the base station executes a machine learning model reasoning process to obtain information such as a projection position or a terminal speed of the terminal;

step 5: the terminal performs a handover procedure from the source cell to the target cell:

step 501: the source cell sends a switching request message to the target cell, and sends information such as the projection position of the terminal or the terminal speed to the target cell;

step 502: the target cell sends a handover request response message to the source cell.

For example, when the source cell and the target cell are located in different base stations, the mobile terminal needs to send related information of intelligent beam prediction of the mobile terminal from the source cell to the target cell through an inter-cell Xn interface message, where the related information includes, but is not limited to, a projection position and a moving speed of the terminal, and the interface message refers to a message of inter-cell transfer information, and may include, but is not limited to, a handover request message, or a search UE context response message.

Fig. 5 is a second signaling interaction diagram of handover provided in the embodiment of the present application, and as shown in fig. 5, specific steps of handover are as follows:

Step 1: the terminal and the base station collect data of the terminal and send the data to an operation maintenance management system or a computing platform;

step 2: executing a machine learning model training process on an operation maintenance management system or a computing platform;

step 3: the operation maintenance management system or the computing platform deploys a machine learning model on the base station according to the training result;

step 4: the base station executes a machine learning model reasoning process to obtain information such as projection position or/and terminal speed of the terminal; further calculating the projection position of the mobile terminal at the predicted moment;

step 5: the terminal performs a handover procedure from the source cell to the target cell: if the mobile terminal exceeds the service area of the existing base station, performing handover;

step 501: the target cell sends a request message for searching the context of the terminal to the source cell, and sends information such as the projection position of the terminal or/and the terminal speed to the target cell;

step 502: the target cell sends a search terminal context response message to the source cell;

step 6: the terminal performs a handover procedure from the source cell to the target cell.

In the embodiment of the application, the target (base station) cell selects the matched beam according to the beam of the mobile terminal to enable the terminal to access, instead of traversing all the beams to enable the terminal to access, so that the switching time is saved.

Fig. 6 is a second flowchart of a handover method according to the embodiment of the present application, as shown in fig. 6, where an execution body of the handover method may be a source cell, for example, a source cell of a 5G base station, etc. The method comprises the following steps:

transmitting a first message to a target cell; the first message is used for the target cell to determine a target beam to be adopted by a terminal for a period of time in the future.

In some embodiments, the first message includes information related to intelligent beam prediction.

In some embodiments, the intelligent beam prediction related information is an optimal beam codeword.

In some embodiments, before the sending the first message to the target cell, the method further includes:

determining current mobility information of a terminal;

and inputting the current mobility information of the terminal into a trained machine learning model to obtain relevant information of intelligent beam prediction.

In some embodiments, before the step of inputting the current mobility information of the terminal into the trained machine learning model, the method further includes:

acquiring training sample data;

and performing model training according to the training sample data to obtain relevant parameters of the trained machine learning model.

In some embodiments, before the step of inputting the current mobility information of the terminal into the trained machine learning model, the method further includes:

receiving a model configuration message sent by a target network element; the model configuration message contains relevant parameters of the trained machine learning model.

In some embodiments, the first message is an inter-cell interface message.

Specifically, the handover method provided in the embodiment of the present application may refer to the embodiment of the handover method in which the execution body is the target cell, and may achieve the same technical effects, and the parts and beneficial effects that are the same as those of the embodiment of the corresponding method in the embodiment are not described in detail herein.

Fig. 7 is a third flow chart of a handover method according to the embodiment of the present application, as shown in fig. 7, where an execution body of the handover method may be a source cell, for example, a source cell of a 5G base station, etc. The method comprises the following steps:

sending a second message to the target cell; the second message contains the target beam adopted by the terminal in a future period.

In some embodiments, before the sending the second message to the target cell, the method further includes:

And deducing the target beam adopted by the terminal in a future period by using a machine learning model.

In some embodiments, the utilizing a machine learning model to infer a target beam employed by a terminal for a period of time in the future includes:

determining current mobility information of a terminal;

inputting the current mobility information of the terminal into a trained machine learning model to obtain relevant information of intelligent beam prediction;

and deducing a target beam adopted by the terminal in a future period according to the relevant information of the intelligent beam prediction.

In some embodiments, before the step of inputting the current mobility information of the terminal into the trained machine learning model, the method further includes:

acquiring training sample data;

and performing model training according to the training sample data to obtain relevant parameters of the trained machine learning model.

In some embodiments, before the step of inputting the current mobility information of the terminal into the trained machine learning model, the method further includes:

receiving a model configuration message sent by a target network element; the model configuration message contains relevant parameters of the trained machine learning model.

In some embodiments, the second message is an inter-cell interface message.

Specifically, the handover method provided in the embodiment of the present application may refer to the embodiment of the handover method in which the execution body is the target cell, and may achieve the same technical effects, and the parts and beneficial effects that are the same as those of the embodiment of the corresponding method in the embodiment are not described in detail herein.

Fig. 8 is a schematic structural diagram of a target cell according to an embodiment of the present application, as shown in fig. 8, where the target cell includes a memory 820, a transceiver 800, and a processor 810, where:

a memory 820 for storing a computer program; a transceiver 800 for transceiving data under the control of the processor 810; a processor 810 for reading the computer program in the memory 820 and performing the following operations:

determining a target beam adopted by a terminal in a future period of time;

and carrying out admission control and data transmission according to the target beam.

Specifically, the transceiver 800 is configured to receive and transmit data under the control of the processor 810.

Wherein in fig. 8, a bus architecture may comprise any number of interconnected buses and bridges, and in particular one or more processors represented by processor 810 and various circuits of memory represented by memory 820, linked together. The bus architecture may also link together various other circuits such as peripheral devices, voltage regulators, power management circuits, etc., which are well known in the art and, therefore, will not be described further herein. The bus interface provides an interface. Transceiver 800 may be a number of elements, including a transmitter and a receiver, providing a means for communicating with various other apparatus over a transmission medium, including wireless channels, wired channels, optical cables, etc. The processor 810 is responsible for managing the bus architecture and general processing, and the memory 820 may store data used by the processor 810 in performing operations.

The processor 810 may be a Central Processing Unit (CPU), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a Field programmable gate array (Field-Programmable Gate Array, FPGA) or a complex programmable logic device (Complex Programmable Logic Device, CPLD), or a multi-core architecture.

In some embodiments, determining the target beam to be employed by the terminal for a period of time in the future includes:

receiving a first message sent by a source cell; the first message contains relevant information of intelligent beam prediction;

and deducing a target beam adopted by the terminal in a future period according to the relevant information of the intelligent beam prediction.

In some embodiments, the intelligent beam prediction related information is an optimal beam codeword.

In some embodiments, the first message is an inter-cell interface message.

In some embodiments, determining the target beam to be employed by the terminal for a period of time in the future includes:

receiving a second message sent by a source cell; the second message contains the target beam adopted by the terminal in a future period.

In some embodiments, the second message is an inter-cell interface message.

Specifically, the target cell provided in the embodiment of the present application can implement all the method steps implemented by the method embodiment in which the execution body is the target cell, and can achieve the same technical effects, and the parts and beneficial effects that are the same as those of the method embodiment in the embodiment are not specifically repeated herein.

Fig. 9 is a schematic structural diagram of a source cell according to an embodiment of the present application, as shown in fig. 9, where the source cell includes a memory 920, a transceiver 900, and a processor 910, where:

a memory 920 for storing a computer program; a transceiver 900 for transceiving data under the control of the processor 910; a processor 910 for reading the computer program in the memory 920 and performing the following operations:

transmitting a first message to a target cell; the first message is used for the target cell to determine a target beam to be adopted by a terminal for a period of time in the future.

Specifically, the transceiver 900 is configured to receive and transmit data under the control of the processor 910.

Wherein in fig. 9, a bus architecture may comprise any number of interconnected buses and bridges, and in particular one or more processors represented by processor 910 and various circuits of memory represented by memory 920, linked together. The bus architecture may also link together various other circuits such as peripheral devices, voltage regulators, power management circuits, etc., which are well known in the art and, therefore, will not be described further herein. The bus interface provides an interface. Transceiver 900 may be a number of elements, including a transmitter and a receiver, providing a means for communicating with various other apparatus over a transmission medium, including wireless channels, wired channels, optical cables, etc. The processor 910 is responsible for managing the bus architecture and general processing, and the memory 920 may store data used by the processor 910 in performing operations.

The processor 910 may be a Central Processing Unit (CPU), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a Field programmable gate array (Field-Programmable Gate Array, FPGA) or a complex programmable logic device (Complex Programmable Logic Device, CPLD), or the processor may employ a multi-core architecture.

In some embodiments, the first message includes information related to intelligent beam prediction.

In some embodiments, the intelligent beam prediction related information is an optimal beam codeword.

In some embodiments, before the sending the first message to the target cell, the method further includes:

determining current mobility information of a terminal;

and inputting the current mobility information of the terminal into a trained machine learning model to obtain relevant information of intelligent beam prediction.

In some embodiments, before the step of inputting the current mobility information of the terminal into the trained machine learning model, the method further includes:

acquiring training sample data;

and performing model training according to the training sample data to obtain relevant parameters of the trained machine learning model.

In some embodiments, before the step of inputting the current mobility information of the terminal into the trained machine learning model, the method further includes:

Receiving a model configuration message sent by a target network element; the model configuration message contains relevant parameters of the trained machine learning model.

In some embodiments, the first message is an inter-cell interface message.

Specifically, the source cell provided in the embodiment of the present application can implement all the method steps implemented by the method embodiment in which the execution body is the source cell, and can achieve the same technical effects, and detailed descriptions of the same parts and beneficial effects as those of the method embodiment in the embodiment are omitted herein.

Fig. 10 is a second schematic structural diagram of a source cell according to an embodiment of the present application, as shown in fig. 10, where the source cell includes a memory 1020, a transceiver 1000, and a processor 1010, where:

a memory 1020 for storing a computer program; a transceiver 1000 for transceiving data under the control of the processor 1010; a processor 1010 for reading the computer program in the memory 1020 and performing the following operations:

sending a second message to the target cell; the second message contains the target beam adopted by the terminal in a future period.

Specifically, the transceiver 1000 is configured to receive and transmit data under the control of the processor 1010.

Wherein in fig. 10, a bus architecture may comprise any number of interconnected buses and bridges, and in particular one or more processors represented by processor 1010 and various circuits of memory represented by memory 1020, linked together. The bus architecture may also link together various other circuits such as peripheral devices, voltage regulators, power management circuits, etc., which are well known in the art and, therefore, will not be described further herein. The bus interface provides an interface. Transceiver 1000 may be a number of elements, including a transmitter and a receiver, providing a means for communicating with various other apparatus over a transmission medium, including wireless channels, wired channels, optical cables, etc. The processor 1010 is responsible for managing the bus architecture and general processing, and the memory 1020 may store data used by the processor 1010 in performing operations.

The processor 1010 may be a Central Processing Unit (CPU), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a Field programmable gate array (Field-Programmable Gate Array, FPGA) or a complex programmable logic device (Complex Programmable Logic Device, CPLD), and may also employ a multi-core architecture.

In some embodiments, before the sending the second message to the target cell, the method further includes:

and deducing the target beam adopted by the terminal in a future period by using a machine learning model.

In some embodiments, the utilizing a machine learning model to infer a target beam employed by a terminal for a period of time in the future includes:

determining current mobility information of a terminal;

inputting the current mobility information of the terminal into a trained machine learning model to obtain relevant information of intelligent beam prediction;

and deducing a target beam adopted by the terminal in a future period according to the relevant information of the intelligent beam prediction.

In some embodiments, before the step of inputting the current mobility information of the terminal into the trained machine learning model, the method further includes:

acquiring training sample data;

and performing model training according to the training sample data to obtain relevant parameters of the trained machine learning model.

In some embodiments, before the step of inputting the current mobility information of the terminal into the trained machine learning model, the method further includes:

receiving a model configuration message sent by a target network element; the model configuration message contains relevant parameters of the trained machine learning model.

In some embodiments, the second message is an inter-cell interface message.

Specifically, the source cell provided in the embodiment of the present application can implement all the method steps implemented by the method embodiment in which the execution body is the source cell, and can achieve the same technical effects, and detailed descriptions of the same parts and beneficial effects as those of the method embodiment in the embodiment are omitted herein.

Fig. 11 is one of schematic structural diagrams of a switching device provided in the embodiment of the present application, as shown in fig. 11, where the embodiment of the present application provides a switching device, including a first determining module 1101 and a processing module 1102, where:

the first determining module 1101 is configured to determine a target beam to be used by the terminal for a period of time in the future; the processing module 1102 is configured to perform admission control and data transmission according to the target beam.

In some embodiments, determining the target beam to be employed by the terminal for a period of time in the future includes:

receiving a first message sent by a source cell; the first message contains relevant information of intelligent beam prediction;

and deducing a target beam adopted by the terminal in a future period according to the relevant information of the intelligent beam prediction.

In some embodiments, the intelligent beam prediction related information is an optimal beam codeword.

In some embodiments, the first message is an inter-cell interface message.

In some embodiments, determining the target beam to be employed by the terminal for a period of time in the future includes:

receiving a second message sent by a source cell; the second message contains the target beam adopted by the terminal in a future period.

In some embodiments, the second message is an inter-cell interface message.

Specifically, the switching device provided in the embodiment of the present application can implement all the method steps implemented by the method embodiment in which the execution body is the target cell, and can achieve the same technical effects, and the same parts and beneficial effects as those of the method embodiment in the embodiment are not described in detail herein.

Fig. 12 is a second schematic structural diagram of a switching device according to the embodiment of the present application, as shown in fig. 12, where the switching device includes a first sending module 1201, and the first sending module includes:

the first sending module 1201 is configured to send a first message to a target cell; the first message is used to determine a target beam to be employed by the terminal for a period of time in the future.

In some embodiments, the first message includes information related to intelligent beam prediction.

In some embodiments, the intelligent beam prediction related information is an optimal beam codeword.

In some embodiments, before the sending the first message to the target cell, the method further includes:

determining current mobility information of a terminal;

and inputting the current mobility information of the terminal into a trained machine learning model to obtain relevant information of intelligent beam prediction.

In some embodiments, before the step of inputting the current mobility information of the terminal into the trained machine learning model, the method further includes:

acquiring training sample data;

and performing model training according to the training sample data to obtain relevant parameters of the trained machine learning model.

In some embodiments, before the step of inputting the current mobility information of the terminal into the trained machine learning model, the method further includes:

receiving a model configuration message sent by a target network element; the model configuration message contains relevant parameters of the trained machine learning model.

In some embodiments, the first message is an inter-cell interface message.

Specifically, the switching device provided in the embodiment of the present application can implement all the method steps implemented by the method embodiment in which the execution body is the source cell, and can achieve the same technical effects, and the same parts and beneficial effects as those of the method embodiment in the embodiment are not described in detail herein.

Fig. 13 is a third schematic structural diagram of a switching device provided in the embodiment of the present application, as shown in fig. 13, where the embodiment of the present application provides a switching device, including a second sending module 1301, where:

the second sending module 1301 is configured to send a second message to the target cell; the second message is used to determine a target beam to be employed by the terminal for a period of time in the future.

In some embodiments, before the sending the second message to the target cell, the method further includes:

and deducing the target beam adopted by the terminal in a future period by using a machine learning model.

In some embodiments, the utilizing a machine learning model to infer a target beam employed by a terminal for a period of time in the future includes:

determining current mobility information of a terminal;

inputting the current mobility information of the terminal into a trained machine learning model to obtain relevant information of intelligent beam prediction;

and deducing a target beam adopted by the terminal in a future period according to the relevant information of the intelligent beam prediction.

In some embodiments, before the step of inputting the current mobility information of the terminal into the trained machine learning model, the method further includes:

acquiring training sample data;

And performing model training according to the training sample data to obtain relevant parameters of the trained machine learning model.

In some embodiments, before the step of inputting the current mobility information of the terminal into the trained machine learning model, the method further includes:

receiving a model configuration message sent by a target network element; the model configuration message contains relevant parameters of the trained machine learning model.

In some embodiments, the second message is an inter-cell interface message.

Specifically, the switching device provided in the embodiment of the present application can implement all the method steps implemented by the method embodiment in which the execution body is the source cell, and can achieve the same technical effects, and the same parts and beneficial effects as those of the method embodiment in the embodiment are not described in detail herein.

It should be noted that the division of the units/modules in the embodiments of the present application is merely a logic function division, and other division manners may be implemented in practice. In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a processor-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution, in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

In some embodiments, there is also provided a computer-readable storage medium storing a computer program for causing a computer to execute the steps of the handover method provided by the above-described method embodiments.

Specifically, the computer readable storage medium provided in the embodiment of the present application can implement all the method steps implemented by the embodiments of the present application and achieve the same technical effects, and the parts and beneficial effects that are the same as those of the embodiments of the present application are not described in detail herein.

It should be noted that: the computer readable storage medium may be any available medium or data storage device that can be accessed by a processor including, but not limited to, magnetic memory (e.g., floppy disks, hard disks, magnetic tape, magneto-optical disks (MOs), etc.), optical memory (e.g., CD, DVD, BD, HVD, etc.), and semiconductor memory (e.g., ROM, EPROM, EEPROM, nonvolatile memory (NAND FLASH), solid State Disk (SSD)), etc.

In addition, it should be noted that: the terms "first," "second," and the like in the embodiments of the present application are used for distinguishing between similar objects and not for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application are capable of operation in sequences other than those illustrated or otherwise described herein, and that the terms "first" and "second" are generally intended to be used in a generic sense and not to limit the number of objects, for example, the first object may be one or more.

In the embodiment of the application, the term "and/or" describes the association relationship of the association objects, which means that three relationships may exist, for example, a and/or B may be represented: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.

The term "plurality" in the embodiments of the present application means two or more, and other adjectives are similar thereto.

The technical scheme provided by the embodiment of the application can be suitable for various systems, in particular to a 5G system. For example, suitable systems may be global system for mobile communications (global system of mobile communication, GSM), code division multiple access (code division multiple access, CDMA), wideband code division multiple access (Wideband Code Division Multiple Access, WCDMA) universal packet Radio service (general packet Radio service, GPRS), long term evolution (long term evolution, LTE), LTE frequency division duplex (frequency division duplex, FDD), LTE time division duplex (time division duplex, TDD), long term evolution-advanced (long term evolution advanced, LTE-a), universal mobile system (universal mobile telecommunication system, UMTS), worldwide interoperability for microwave access (worldwide interoperability for microwave access, wiMAX), 5G New air interface (New Radio, NR), and the like. Terminal devices and network devices are included in these various systems. Core network parts such as evolved packet system (Evloved Packet System, EPS), 5G system (5 GS) etc. may also be included in the system.

The terminal device according to the embodiments of the present application may be a device that provides voice and/or data connectivity to a user, a handheld device with a wireless connection function, or other processing device connected to a wireless modem, etc. The names of the terminal devices may also be different in different systems, for example in a 5G system, the terminal devices may be referred to as User Equipment (UE). The wireless terminal device may communicate with one or more Core Networks (CNs) via a radio access Network (Radio Access Network, RAN), which may be mobile terminal devices such as mobile phones (or "cellular" phones) and computers with mobile terminal devices, e.g., portable, pocket, hand-held, computer-built-in or vehicle-mounted mobile devices that exchange voice and/or data with the radio access Network. Such as personal communication services (Personal Communication Service, PCS) phones, cordless phones, session initiation protocol (Session Initiated Protocol, SIP) phones, wireless local loop (Wireless Local Loop, WLL) stations, personal digital assistants (Personal Digital Assistant, PDAs), and the like. The wireless terminal device may also be referred to as a system, subscriber unit (subscriber unit), subscriber station (subscriber station), mobile station (mobile), remote station (remote station), access point (access point), remote terminal device (remote terminal), access terminal device (access terminal), user terminal device (user terminal), user agent (user agent), user equipment (user device), and the embodiments of the present application are not limited.

The network device according to the embodiment of the present application may be a base station, where the base station may include a plurality of cells for providing services for a terminal. A base station may also be called an access point or may be a device in an access network that communicates over the air-interface, through one or more sectors, with wireless terminal devices, or other names, depending on the particular application. The network device may be operable to exchange received air frames with internet protocol (Internet Protocol, IP) packets as a router between the wireless terminal device and the rest of the access network, which may include an Internet Protocol (IP) communication network. The network device may also coordinate attribute management for the air interface. For example, the network device according to the embodiments of the present application may be a network device (Base Transceiver Station, BTS) in a global system for mobile communications (Global System for Mobile communications, GSM) or code division multiple access (Code Division Multiple Access, CDMA), a network device (NodeB) in a wideband code division multiple access (Wide-band Code Division Multiple Access, WCDMA), an evolved network device (evolutional Node B, eNB or e-NodeB) in a long term evolution (long term evolution, LTE) system, a 5G base station (gNB) in a 5G network architecture (next generation system), a home evolved base station (Home evolved Node B, heNB), a relay node (relay node), a home base station (femto), a pico base station (pico), and the like. In some network structures, the network device may include a Centralized Unit (CU) node and a Distributed Unit (DU) node, which may also be geographically separated.

Multiple-input Multiple-output (Multi Input Multi Output, MIMO) transmissions may each be made between a network device and a terminal device using one or more antennas, and the MIMO transmissions may be Single User MIMO (SU-MIMO) or Multiple User MIMO (MU-MIMO). The MIMO transmission may be 2D-MIMO, 3D-MIMO, FD-MIMO, or massive-MIMO, or may be diversity transmission, precoding transmission, beamforming transmission, or the like, depending on the form and number of the root antenna combinations.

It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-executable instructions. These computer-executable instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These processor-executable instructions may also be stored in a processor-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the processor-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These processor-executable instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.

Claims (26)

1. A handover method, applied to a target cell, comprising:

determining a target beam adopted by a terminal in a future period of time;

and carrying out admission control and data transmission according to the target beam.

2. The handover method according to claim 1, wherein determining the target beam to be employed by the terminal for a period of time in the future comprises:

receiving a first message sent by a source cell; the first message contains relevant information of intelligent beam prediction;

and deducing a target beam adopted by the terminal in a future period according to the relevant information of the intelligent beam prediction.

3. The handover method according to claim 2, wherein the intelligent beam prediction related information is an optimal beam codeword.

4. The handover method according to claim 2, wherein the first message is an inter-cell interface message.

5. The handover method according to claim 1, wherein determining the target beam to be employed by the terminal for a period of time in the future comprises:

receiving a second message sent by a source cell; the second message contains the target beam adopted by the terminal in a future period.

6. The handover method according to claim 5, wherein the second message is an inter-cell interface message.

7. A handover method, applied to a source cell, comprising:

transmitting a first message to a target cell; the first message is used for the target cell to determine a target beam to be adopted by a terminal for a period of time in the future.

8. The handover method according to claim 7, wherein the first message includes information related to intelligent beam prediction.

9. The handover method according to claim 8, wherein the intelligent beam prediction related information is an optimal beam codeword.

10. The handover method according to claim 8, wherein before the sending the first message to the target cell, further comprising:

determining current mobility information of a terminal;

and inputting the current mobility information of the terminal into a trained machine learning model to obtain relevant information of intelligent beam prediction.

11. The handover method according to claim 10, wherein before the current mobility information of the terminal is input into the trained machine learning model, further comprising:

acquiring training sample data;

and performing model training according to the training sample data to obtain relevant parameters of the trained machine learning model.

12. The handover method according to claim 10, wherein before the current mobility information of the terminal is input into the trained machine learning model, further comprising:

receiving a model configuration message sent by a target network element; the model configuration message contains relevant parameters of the trained machine learning model.

13. The handover method according to claim 7, wherein the first message is an inter-cell interface message.

14. A handover method, applied to a source cell, comprising:

sending a second message to the target cell; the second message contains the target beam adopted by the terminal in a future period.

15. The handover method according to claim 14, wherein before the sending the second message to the target cell, further comprising:

and deducing the target beam adopted by the terminal in a future period by using a machine learning model.

16. The handover method according to claim 15, wherein the using the machine learning model to infer the target beam to be used by the terminal for a period of time in the future comprises:

determining current mobility information of a terminal;

inputting the current mobility information of the terminal into a trained machine learning model to obtain relevant information of intelligent beam prediction;

And deducing a target beam adopted by the terminal in a future period according to the relevant information of the intelligent beam prediction.

17. The handover method according to claim 16, wherein before the current mobility information of the terminal is input into the trained machine learning model, further comprising:

acquiring training sample data;

and performing model training according to the training sample data to obtain relevant parameters of the trained machine learning model.

18. The handover method according to claim 17, wherein before the current mobility information of the terminal is input into the trained machine learning model, further comprising:

receiving a model configuration message sent by a target network element; the model configuration message contains relevant parameters of the trained machine learning model.

19. The handover method according to claim 14, wherein the second message is an inter-cell interface message.

20. A target cell, comprising a memory, a transceiver, and a processor;

a memory for storing a computer program; a transceiver for transceiving data under control of the processor; a processor for reading the computer program in the memory and performing the steps of the handover method according to any of claims 1 to 6.

21. A source cell comprising a memory, a transceiver, and a processor;

a memory for storing a computer program; a transceiver for transceiving data under control of the processor; a processor for reading the computer program in the memory and performing the steps of the handover method according to any of claims 7 to 13.

22. A source cell comprising a memory, a transceiver, and a processor;

a memory for storing a computer program; a transceiver for transceiving data under control of the processor; a processor for reading the computer program in the memory and performing the steps of the handover method according to any of claims 14 to 19.

23. A switching device, comprising:

the first determining module is used for determining a target beam adopted by the terminal in a future period of time;

and the processing module is used for carrying out admission control and data transmission according to the target beam.

24. A switching device, comprising:

the first sending module is used for sending a first message to the target cell; the first message is used to determine a target beam to be employed by the terminal for a period of time in the future.

25. A switching device, comprising:

a second sending module, configured to send a second message to the target cell; the second message is used to determine a target beam to be employed by the terminal for a period of time in the future.

26. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for causing a computer to execute the handover method according to any one of claims 1 to 19.

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