CN116980915A - Configuration method, device, equipment and medium for distributed beam management - Google Patents
Configuration method, device, equipment and medium for distributed beam management Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/24—Cell structures
- H04W16/28—Cell structures using beam steering
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
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Abstract
The disclosure provides a configuration method, device, equipment and medium for beam management, and relates to the technical field of wireless communication. The method comprises the following steps: the micro base station receives configuration signaling from the macro base station, wherein the configuration signaling carries initial beam configuration information, and the initial beam configuration information comprises macro base station beam parameter configuration and micro base station beam parameter configuration; the micro base station takes the initial beam configuration information as the initial state of the intelligent beam configuration decision, periodically senses the position distribution change condition of the user equipment, adopts a deep reinforcement learning algorithm to realize the maximization of the user throughput, and carries out the beam configuration decision in real time. According to the embodiment of the disclosure, the direction of the wave beam can be adjusted in real time, and high-quality communication service is provided for users. In addition, a federal learning framework is adopted between the micro base station and the macro base station, so that the privacy problem of user data is solved.
Description
Technical Field
The disclosure relates to the technical field of wireless communication, and in particular relates to a configuration method, device, equipment and medium for distributed beam management.
Background
The number of network user equipment is increased sharply at present, the data flow in the network is also increased in an explosive manner, the 5G enhanced mobile broadband (eMBB) provides requirements for the aspects of ultrahigh transmission data rate, mobility guarantee under wide coverage and the like, the low-frequency microwave frequency band used by 4G obviously cannot meet the requirements, and the millimeter wave frequency band can meet the high-speed data transmission requirement of 5G.
In order to enhance coverage, millimeter wave micro base stations can be deployed in a large quantity in partial hot spot areas within the coverage range of the original low-frequency macro base stations, so that a high-low frequency heterogeneous network is formed, and high-speed communication service is provided for users in the hot spot areas. However, the conventional beam scanning requires a lot of time, and in the high-frequency and low-frequency heterogeneous network scenario, the real-time requirement of the beam configuration cannot be met.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The present disclosure provides a configuration method, apparatus, device, and medium for distributed beam management, which at least to some extent overcomes the problem that the real-time requirement of beam configuration cannot be met due to a large amount of time required for conventional beam scanning in the related art, and the problem that user privacy is revealed when user data is transmitted in multiple base stations.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
According to one aspect of the disclosure, a configuration method suitable for wireless intelligent air interface distributed beam management is provided, a plurality of micro base stations are deployed in a hot spot area in a service range of a macro base station, a federal learning framework is adopted between the macro base station and the micro base station, a global model is deployed in the macro base station, and a local model is deployed in the micro base station; the method is applied to the micro base station and comprises the following steps:
receiving configuration signaling from a macro base station, wherein the configuration signaling carries initial beam configuration information, and the initial beam configuration information comprises macro base station beam parameter configuration and micro base station beam parameter configuration;
taking the initial beam configuration information as an initial state of the micro base station for carrying out beam configuration decision, and carrying out initial configuration of the beam decision on the micro base station;
based on the initial beam configuration information, the micro base station collects local user position distribution data through interaction with the surrounding environment;
the collected local user location distribution data is input to a pre-trained local model to determine beam configurations that meet long-term throughput requirements of the maximized network.
In one embodiment of the present disclosure, the macro base station beam parameter configuration includes at least one of a beam width and a serving micro base station upper limit number;
the micro base station beam configuration includes at least one of a sector to which each beam is directed at an initial time, a beam width, a number of sectors around a single micro base station, an available bandwidth of a macro base station occupied by all user equipment within the single micro base station, and an upper limit number of beams that can be generated by the single micro base station at one time.
In one embodiment of the present disclosure, configuration signaling is transmitted over an Xn interface.
In one embodiment of the present disclosure, the message type of the configuration signaling is an Xn setup request message or an Xn configuration update message.
In one embodiment of the present disclosure, the initial beam configuration information is determined by the macro base station according to the local cell network environment, channel conditions, user equipment geographical location, and the number of access user equipments.
In one embodiment of the present disclosure, the method further comprises:
collecting user data through interactions with the environment;
training the local model by applying the user data, and updating parameters of the local model;
after the local model converges, uploading the local model to a macro base station so that the macro base station uniformly converges the received local models and updates global model parameters;
Downloading and updating global model parameters from a macro base station;
based on the global model parameters, updating the local model to obtain the trained local model.
According to another aspect of the disclosure, a configuration method suitable for wireless intelligent air interface distributed beam management is provided, a plurality of micro base stations are deployed in a hot spot area in a service range of a macro base station, a federal learning framework is adopted between the macro base station and the micro base station, a global model is deployed in the macro base station, and a local model is deployed in the micro base station; the method is applied to the macro base station and comprises the following steps:
and sending configuration signaling to the micro base stations, wherein the configuration signaling carries initial beam configuration information, and the initial beam configuration information comprises macro base station beam parameter configuration and micro base station beam parameter configuration, so that the micro base stations take the initial beam configuration information as an initial state of the micro base stations for making beam configuration decisions, and each micro base station obtains the long-term throughput of the maximized network by collecting surrounding environment user position distribution data and inputting the surrounding environment user position distribution data into a pre-trained local model, and determines the beam configuration required by the long-term throughput of the maximized network.
According to another aspect of the disclosure, a configuration device suitable for wireless intelligent air interface distributed beam management is provided, a plurality of micro base stations are deployed in a hot spot area in a service range of a macro base station, a federal learning framework is adopted between the macro base station and the micro base station, a global model is deployed in the macro base station, and a local model is deployed in the micro base station; the device is applied to micro base station, and the device includes:
The signaling receiving module is used for receiving configuration signaling from the macro base station, wherein the configuration signaling carries initial beam configuration information, and the initial beam configuration information comprises macro base station beam parameter configuration and micro base station beam parameter configuration;
the initialization configuration module is used for taking the initial beam configuration information as an initial state of the micro base station for carrying out beam configuration decision and carrying out initialization configuration of the beam decision on the micro base station;
the data collection module is used for collecting local user position distribution data through interaction with the surrounding environment by the micro base station on the basis of the initial beam configuration information;
and the beam configuration module is used for inputting the collected local user position distribution data into a pre-trained local model so as to determine the beam configuration meeting the requirement of maximizing the long-term throughput of the network.
According to another aspect of the disclosure, a configuration device suitable for wireless intelligent air interface distributed beam management is provided, a plurality of micro base stations are deployed in a hot spot area in a service range of a macro base station, a federal learning framework is adopted between the macro base station and the micro base station, a global model is deployed in the macro base station, and a local model is deployed in the micro base station; the apparatus is applied to a macro base station, and the apparatus includes:
The signaling sending module is used for sending configuration signaling to the micro base station, wherein the configuration signaling carries initial beam configuration information, the initial beam configuration information comprises macro base station beam parameter configuration and micro base station beam parameter configuration, so that the micro base station takes the initial beam configuration information as an initial state of intelligent decision of beam configuration of the micro base station, each micro base station collects local user position distribution data through interaction with surrounding environment, inputs the local user position distribution data into a pre-trained local model, obtains the long-term throughput of the maximized network, and determines the beam configuration required by the long-term throughput of the maximized network.
According to still another aspect of the present disclosure, there is provided an electronic apparatus including: a memory for storing instructions; and the processor is used for calling the instructions stored in the memory to realize the configuration method suitable for wireless intelligent air interface distributed beam management.
According to yet another aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the above-described configuration method adapted for wireless intelligent air interface distributed beam management.
According to yet another aspect of the present disclosure, there is provided a computer program product storing instructions that, when executed by a computer, cause the computer to implement the above-described configuration method applicable to wireless intelligent air interface distributed beam management.
According to yet another aspect of the present disclosure, there is provided a chip comprising at least one processor and an interface;
an interface for providing program instructions or data to at least one processor;
the at least one processor is configured to execute the program instructions to implement the configuration method applicable to wireless intelligent air interface distributed beam management described above.
The configuration method, the device, the equipment and the medium suitable for wireless intelligent air interface distributed beam management, provided by the embodiment of the disclosure, are characterized in that a plurality of micro base stations are deployed in a hot spot area in a service range of a macro base station, a federal learning framework is adopted between the macro base station and the micro base station, a global model is deployed in the macro base station, and a local model is deployed in the micro base station; the macro base station performs initial beam configuration for a plurality of micro base stations through configuration signaling, the micro base stations input collected local user position distribution data into a pre-trained local model, the long-term throughput of the maximized network is obtained, the beam configuration which meets the long-term throughput requirement of the maximized network is determined, the direction of the beam is adjusted in real time, high-quality communication service is provided for users, and the problems that a large amount of time is required for traditional beam scanning, the real-time requirement problem of the beam configuration cannot be met, the number of beams increases suddenly, and the beam management is difficult are solved.
In addition, the federal learning framework is adopted between the macro base station and the micro base station, the global model is deployed at the macro base station, the local model is deployed at the micro base station, only the model is needed to be exchanged instead of the original data in the model training process, frequent signaling interaction between the micro base stations is effectively avoided, the system overhead and the power consumption are reduced, the privacy problem of the original data in the aspect of the user position is solved, and meanwhile, the network performance can be improved from the global angle.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.
FIG. 1 illustrates a schematic diagram of a smart beam management scenario in an embodiment of the present disclosure;
FIG. 2 illustrates a flow chart of a configuration method suitable for wireless intelligent air interface distributed beam management in an embodiment of the disclosure;
FIG. 3 shows a schematic flow diagram of model training in an embodiment of the present disclosure;
FIG. 4 illustrates a process schematic of a distributed federal learning model convergence and convergence model in an embodiment of the disclosure;
fig. 5 illustrates a flowchart of another configuration method suitable for wireless intelligent air interface distributed beam management in an embodiment of the present disclosure;
fig. 6 illustrates a flowchart of yet another configuration method suitable for wireless intelligent air interface distributed beam management in an embodiment of the present disclosure;
fig. 7 illustrates a schematic diagram of a configuration apparatus suitable for wireless intelligent air interface distributed beam management in an embodiment of the present disclosure;
fig. 8 illustrates another configuration apparatus suitable for wireless intelligent air interface distributed beam management in an embodiment of the present disclosure;
fig. 9 shows a block diagram of an electronic device in an embodiment of the disclosure.
Detailed Description
Example embodiments will now be described more fully hereinafter with reference to the accompanying drawings.
It should be noted that the exemplary embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein.
The inventors have found that common non-intelligent beam configurations are deterministic beam configurations, exhaustive beam configurations, etc.
The deterministic beam configuration refers to that the base station adjusts the direction of the beam according to a pre-defined direction sequence, and the defect can not adjust the beam configuration according to the real-time change of the user distribution.
The exhaustive beam configuration is that the base station scans all possible beam directions once, selects an optimal beam configuration, and has higher delay in decision process, thus being not applicable to the scene of rapid change of user distribution.
The following problems are thus faced in the background art heterogeneous networks: 1) The traditional beam scanning requires a great deal of time, and cannot meet the real-time requirement of beam configuration; 2) User data has privacy and can be prevented from being transmitted in a plurality of base stations; 3) The number of beams in the network increases dramatically, making beam management more difficult.
Reinforcement learning is widely applied in the communication field, good results are obtained, and the huge potential of reinforcement learning in the communication field is verified. The problem of beam dynamic configuration of dense millimeter wave micro base stations belongs to the problem of continuous decision making, so that the problem of reinforcement learning can be solved.
In view of the distributed nature of the network, and the privacy of user data, a method of distributed federal learning in combination with deep reinforcement learning may be employed to make intelligent decisions about beam configuration.
The present exemplary embodiment will be described in detail below with reference to the accompanying drawings and examples.
The intelligent decision of the distributed beam configuration in the embodiment of the disclosure adopts a distributed federal learning framework, and a micro base station side is combined with a deep reinforcement learning beam optimization algorithm to find a real-time and efficient beam configuration strategy.
Fig. 1 illustrates an intelligent beam management scenario, as shown in fig. 1, in a heterogeneous network in which micro base stations are densely deployed in a hot spot area, a low-frequency macro base station 101 (MBS) is responsible for global control plane signaling transmission to provide coverage services; the micro base station 102 (mSBS) is responsible for high-speed transmission of a large amount of traffic data of a data plane, providing high-quality communication services. Wherein the micro base station may be a millimeter wave micro base station. Both macro base station 101 and micro base station 102 may serve user equipment 103.
As shown in fig. 1, there may also be obstacles 104 and reflectors 105 in the intelligent beam management scenario.
The macro base station 101 can perform global static/semi-static/dynamic configuration for the micro base station 102 within the coverage area of the macro base station 101 through control signaling according to the network environment of the cell, channel conditions, geographic locations of the user equipment 103 and the number of access user equipment 103, so as to support intelligent decision of distributed beam configuration.
In some embodiments, macro base station 101 performs initialization configuration (also serves as a transmission interface for uploading and downloading of a subsequent model) to micro base station through an Xn interface, and serves as an initial state for micro base station 102 to make intelligent decision of beam configuration.
In some embodiments, the micro base station 102 performs the beam configuration decision in real time according to the global model issued by the macro base station and the current position distribution change condition of the ue 103.
Fig. 2 is a flowchart of a configuration method suitable for wireless intelligent air interface distributed beam management in an embodiment of the disclosure, where the configuration method suitable for wireless intelligent air interface distributed beam management shown in fig. 2 may be applied to an intelligent beam management scenario, where a plurality of micro base stations are deployed in a partial hot spot area within a service range of a macro base station in the intelligent beam management scenario, a federal learning framework is adopted between the macro base station and the micro base stations, a global model is deployed at the macro base station, and a local model is deployed at the micro base station.
As shown in fig. 2, the configuration method applicable to wireless intelligent air interface distributed beam management provided in the embodiment of the present disclosure includes steps S202 to S208.
In S202, the macro base station sends configuration signaling to the plurality of micro base stations, where the configuration signaling carries initial beam configuration information, and the initial beam configuration information includes macro base station beam parameter configuration and micro base station beam parameter configuration.
In some embodiments, the macro base station beam parameter configuration may include a beam width and a serving micro base station upper limit number; the micro base station beam configuration may include a sector to which each beam is directed at an initial time, a beam width, a number of sectors around a single micro base station, an available bandwidth of all user equipments within the single micro base station occupying a macro base station, and an upper number of beams that can be generated at one time of the single micro base station.
In some embodiments, the configuration signaling in S202 may be transmitted over an Xn interface.
In some embodiments, the message type of the configuration signaling may be an Xn SETUP REQUEST message (Xn SETUP REQUEST) or an Xn configuration update message (Xn Configuration Update), and may also be a custom type.
In addition, the message type of the information sent by the micro base station to the macro base station may be an Xn SETUP REQUEST message (Xn SETUP REQUEST) or an Xn configuration update message (Xn Configuration Update), or may be a custom type.
In the embodiment of the disclosure, the initialization configuration of intelligent beam management is uniformly configured by a macro base station, and the macro base station sends configuration signaling to a plurality of micro base stations for model parameter initialization configuration of intelligent decision of beam configuration by millimeter wave micro base stations under a distributed framework.
In S204, the initial beam configuration information is used as an initial state for the micro base station to perform beam configuration decision, and the micro base station is configured to perform beam decision initialization.
In S206, the micro base station collects local user location distribution data by interacting with the surrounding environment on the basis of the initial beam configuration information.
In S208, the collected local user location distribution data is input to a pre-trained local model to determine beam configurations that meet the long-term throughput requirements of the maximized network.
In some embodiments, the local model operates on the micro base station, the input is local user location distribution data collected by the micro base station at time t, the output may be maximizing long-term throughput of the network, and then the beam configuration meeting the requirement of maximizing long-term throughput of the network may be determined according to the maximizing long-term throughput of the network.
In the embodiment of the present disclosure, the initial beam configuration information may be determined by the macro base station according to the network environment of the cell, the channel condition, the geographic location of the user equipment, and the number of access user equipments.
It should be noted that in the embodiment of the present disclosure, the user device may be a wireless communication device such as a mobile phone, a tablet computer, a notebook computer, an on-board communication device, an unmanned plane, a remote control plane, an aircraft, a mini-plane, a vehicle, and an on-board communication device.
The beam configuration decision, i.e. the action taken at the maximum throughput, i.e. the direction of beam configuration, is described above.
The method and the device are suitable for wireless intelligent air interface distributed beam management, solve the problems that how the millimeter wave micro base station performs initial beam configuration and issues transmission and which configuration parameters are transmitted in a distributed network, finally provide a specific design scheme of the initial beam parameter configuration and the issued transmission, adjust the beam direction in real time, provide high-quality communication service for users, solve the problems that the traditional beam scanning needs a large amount of time, cannot meet the real-time requirement of beam configuration, the number of beams increases suddenly, and the beam management is difficult.
In some embodiments, the present disclosure further includes a model training process, where the present disclosure employs a distributed federal learning framework to run on a macro base station to complete a model aggregation and parameter delivery process, and employs a deep reinforcement learning algorithm to run on a millimeter wave micro base station to complete model training and local beam configuration decisions.
Fig. 3 illustrates a flow of model training in the present disclosure, as shown in fig. 3, the model training process includes steps S302-S310.
In S302, user data is collected through interaction with the environment.
The collected user data is used as a training sample of the local model, and the training sample is only used locally at the micro base station and is not required to be transmitted to other base station equipment, so that information leakage caused by data transmission can be avoided.
In S304, user data is applied, the local model is trained, and parameters of the local model are updated.
The local model is trained locally at the micro base station until the model converges.
In S306, after the local model converges, the local model is uploaded to the macro base station, so that the macro base station converges the received local models uniformly, and updates the global model parameters.
In some embodiments, S306 uploading the local model to the macro base station may be transmitted over the Xn interface.
In some embodiments, the type of message that the micro base station uploads the local model to the macro base station may be an Xn SETUP REQUEST message (Xn SETUP REQUEST) or an Xn configuration update message (Xn Configuration Update), or may be a custom type.
In the model training process, the micro base station does not transmit user data to the macro base station, so that the risk of user data leakage can be reduced.
In S308, the global model parameters are updated from the macro base station.
In S310, the local model is updated based on the global model parameters, resulting in a trained local model.
After updating the local model based on the global model parameters, the micro base station may apply the updated local model to infer in S204 to maximize the long-term throughput of the network.
Fig. 4 is a schematic diagram illustrating a process of model aggregation and model delivery for distributed federal learning in the present disclosure, and the process of model training described above is described below with reference to fig. 4.
As shown in fig. 4, the macro base station 401 stores two parameters, namely a global model parameter and a global control variable, and the micro base station 402 stores a local model parameter and a local control variable, and the embodiments of the present disclosure focus on how the macro base station performs initialization parameter configuration and how the millimeter wave micro base station performs transmission.
The process of model training in embodiments of the present disclosure may include the steps of:
step a, when training starts, a macro base station issues a global model to initialize a millimeter wave micro base station local model of service;
step b, initializing parameters of a millimeter wave micro base station side beam management model;
step c, each millimeter wave micro base station collects user data through interaction with the environment, so that model training of a deep reinforcement learning algorithm is performed, and local model parameters are updated;
step d, repeating the step c, and entering the step e after the local model converges;
step e, uploading all local models of the millimeter wave micro base station side to the macro base station through an Xn interface;
step f, the macro base station unifies and gathers the received models, updates the overall model parameters and issues the overall model parameters through an Xn interface;
step g, each millimeter wave micro base station downloads and updates global model parameters from the macro base station, so as to update a local model, and collects user data at the current moment to carry out beam configuration decision;
and h, returning to the step a, and waiting for the next round of beam configuration decision.
In the embodiment of the disclosure, the beam configuration problem is regarded as a Markov Decision Process (MDP), where the MDP is formed by four tuples, i.e., m= (S, a, P, R), S represents a state space, a represents an action space, P represents a state transition probability, and R represents a reward function. In some embodiments, the quaternion of the model may be specified as follows:
(1) State: the state of the network at the time t is defined as S t For the micro base station b, its state is:
wherein U is b For this moment, the user set served by the base station, pi b (t) represents the beam configuration at the moment of the base station, i.e. the sector to which the specific beams are directed,representing U b The available bandwidth of the macro base station occupied by all users within.
(2) Action: the action of the prescribed node is to configure the direction of the beam. The micro base station acts as the following at the time t
(3) Transition Probability: specifying the transition probability as P t ＝P{S t+1 |S t ，a t In state S }, i.e t Next, action a is adopted t The state becomes S t+1 Is a probability of (2).
(4) Reward: since the goal is to maximize the long-term throughput of the network, the reward function is defined as R t R (t), i.e. the reward at the current moment is the throughput of the system.
The embodiment of the disclosure enables beam configuration decisions based on distributed management by designing a configuration method of wireless intelligent air interface distributed beam management. The federal learning framework is adopted between the macro base station and the micro base station, the micro base station uploads the trained local model to the macro base station for unified aggregation and then issuing, and the process only needs to exchange the learning model instead of the original data, so that frequent signaling interaction between the micro base stations is effectively avoided, the system overhead and the power consumption are reduced, the privacy problem of the original data in the aspect of the user position is solved, and meanwhile, the network performance can be improved from the global angle.
The subsequent micro base station updates the local model according to the global model parameters issued by the macro base station, and carries out the beam configuration strategy according to the current user position information, so that the direction of the beam can be adjusted in real time, and high-quality communication service is provided for the user, thereby solving the problems that the traditional beam scanning needs a large amount of time, the real-time requirement of the beam configuration cannot be met, the number of beams is increased sharply, and the beam management is difficult.
Fig. 5 shows a flowchart of a configuration method suitable for wireless intelligent air interface distributed beam management in an embodiment of the disclosure, where the configuration method suitable for wireless intelligent air interface distributed beam management shown in fig. 5 may be applied to an intelligent beam management scenario, where a plurality of micro base stations are deployed in a partial hot spot area within a service range of a macro base station in the intelligent beam management scenario, a federal learning framework is adopted between the macro base station and the micro base stations, a global model is deployed at the macro base station, and a local model is deployed at the micro base station.
The configuration method for wireless intelligent air interface distributed beam management shown in fig. 5 is applied to a micro base station, and the method comprises steps S502-S506.
In S502, a configuration signaling from a macro base station is received, where the configuration signaling carries initial beam configuration information, and the initial beam configuration information includes macro base station beam parameter configuration and micro base station beam parameter configuration;
In S504, the initial beam configuration information is used as an initial state of the micro base station for performing beam configuration decision, and the micro base station is configured for performing the beam decision initialization;
in S506, based on the initial beam configuration information, the micro base station collects local user location distribution data through interaction with the surrounding environment;
in S508, the collected local user location distribution data is input to a pre-trained local model to determine beam configurations that meet long-term throughput requirements of the maximized network
In the embodiment of the disclosure, the micro base station can apply the local model to adjust the direction of the beam in real time, thereby providing high-quality communication service for users, and solving the problems that the traditional beam scanning needs a lot of time, the real-time requirement of beam configuration cannot be met, the number of beams is increased sharply, and the beam management is difficult.
Fig. 6 shows a flowchart of a configuration method suitable for wireless intelligent air interface distributed beam management in an embodiment of the disclosure, where the configuration method suitable for wireless intelligent air interface distributed beam management shown in fig. 6 may be applied to an intelligent beam management scenario, where a plurality of micro base stations are deployed in a partial hot spot area within a service range of a macro base station in the intelligent beam management scenario, a federal learning framework is adopted between the macro base station and the micro base stations, a global model is deployed at the macro base station, and a local model is deployed at the micro base station.
The configuration method for wireless intelligent air interface distributed beam management shown in fig. 6 is applied to a macro base station, and the method includes step S602.
In S602, a configuration signaling is sent to the micro base station, where the configuration signaling carries initial beam configuration information, where the initial beam configuration information includes macro base station beam parameter configuration and micro base station beam parameter configuration, so that the micro base station uses the initial beam configuration information as an initial state for the micro base station to make a beam configuration decision, and each micro base station obtains a long-term throughput of the maximized network by collecting surrounding environment user location distribution data and inputting the collected data to a pre-trained local model, and determines a beam configuration required to meet the long-term throughput of the maximized network.
In the embodiment of the disclosure, the macro base station uniformly configures initialization configuration of intelligent beam management for a plurality of micro base stations, and the macro base station sends configuration signaling to the plurality of micro base stations for model parameter initialization configuration of intelligent beam configuration decision of the millimeter wave micro base stations under the distributed framework.
In the presently disclosed embodiments, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The term "and/or" in this disclosure is merely one association relationship describing the associated object, and indicates that three relationships may exist, for example, a and/or B may indicate: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
Furthermore, although the steps of the methods in the present disclosure are depicted in a particular order in the drawings, this does not require or imply that the steps must be performed in that particular order or that all illustrated steps be performed in order to achieve desirable results.
In some embodiments, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc.
Based on the same inventive concept, the embodiment of the disclosure further provides a configuration device suitable for wireless intelligent air interface distributed beam management, as described in the following embodiment. Since the principle of solving the problem of the embodiment of the device is similar to that of the embodiment of the method, the implementation of the embodiment of the device can be referred to the implementation of the embodiment of the method, and the repetition is omitted.
Fig. 7 illustrates a configuration device suitable for wireless intelligent air interface distributed beam management in an embodiment of the disclosure, wherein a plurality of micro base stations are deployed in a hot spot area in a service range of a macro base station, a federal learning framework is adopted between the macro base station and the micro base station, a global model is deployed in the macro base station, and a local model is deployed in the micro base station; the configuration device suitable for wireless intelligent air interface distributed beam management is applied to a micro base station, as shown in fig. 7, and the configuration device 700 suitable for wireless intelligent air interface distributed beam management comprises:
the signaling receiving module 702 is configured to receive a configuration signaling from the macro base station, where the configuration signaling carries initial beam configuration information, and the initial beam configuration information includes macro base station beam parameter configuration and micro base station beam parameter configuration;
the initialization configuration module 704 is configured to use the initial beam configuration information as an initial state of the micro base station for performing beam configuration decision, and perform initialization configuration of the beam decision on the micro base station;
a data collection module 706, configured to collect local user location distribution data by interaction with the surrounding environment by the micro base station based on the initial beam configuration information;
a beam configuration module 708 for inputting the collected local user location distribution data into a pre-trained local model to determine a beam configuration that meets the long-term throughput requirements of the maximized network.
In some embodiments, the macro base station beam parameter configuration includes a beam width and a serving micro base station upper limit number;
the configuration of the beams of the micro base station comprises the sectors pointed by each beam at the initial moment, the beam width, the number of sectors around the single micro base station, the available bandwidth of all user equipment occupying the macro base station in the single micro base station and the upper limit number of the beams which can be generated at one moment of the single micro base station.
In some embodiments, the configuration signaling is transmitted over an Xn interface.
In some embodiments, the message type of the configuration signaling is an Xn setup request message or an Xn configuration update message.
In some embodiments, the initial beam configuration information is determined by the macro base station based on the local cell network environment, channel conditions, user equipment geographical location, and number of access user equipment.
In some embodiments, the configuration apparatus 700 adapted for wireless intelligent air interface distributed beam management may further include a model training module, where the model training module is configured to perform the following steps:
collecting user data through interactions with the environment;
training the local model by applying the user data, and updating parameters of the local model;
after the local model converges, uploading the local model to a macro base station so that the macro base station uniformly converges the received local models and updates global model parameters;
Downloading and updating global model parameters from a macro base station;
based on the global model parameters, updating the local model to obtain the trained local model.
Based on the same inventive concept, the embodiment of the disclosure further provides a configuration device suitable for wireless intelligent air interface distributed beam management, wherein a plurality of micro base stations are deployed in a hot spot area in the service range of a macro base station, a federal learning framework is adopted between the macro base station and the micro base stations, a global model is deployed in the macro base station, and a local model is deployed in the micro base station; the configuration device suitable for wireless intelligent air interface distributed beam management is applied to a macro base station, as shown in fig. 8, and the configuration device 800 suitable for wireless intelligent air interface distributed beam management includes:
the signaling sending module 802 is configured to send configuration signaling to the micro base station, where the configuration signaling carries initial beam configuration information, where the initial beam configuration information includes macro base station beam parameter configuration and micro base station beam parameter configuration, so that the micro base station uses the initial beam configuration information as an initial state for the micro base station to make an intelligent decision on beam configuration, each micro base station collects local user location distribution data by interacting with surrounding environments, inputs the local user location distribution data to a pre-trained local model, obtains a long-term throughput of the maximized network, and determines beam configuration required to meet the long-term throughput of the maximized network.
The terms "first," "second," and the like in this disclosure are used solely to distinguish one from another device, module, or unit, and are not intended to limit the order or interdependence of functions performed by such devices, modules, or units.
The specific manner in which the respective modules perform the operations in the configuration apparatus suitable for wireless intelligent air interface distributed beam management in the above embodiments has been described in detail in the embodiments related to the configuration method suitable for wireless intelligent air interface distributed beam management, and will not be described in detail herein.
In summary, in the configuration device suitable for wireless intelligent air interface distributed beam management provided by the embodiment of the application, a plurality of micro base stations are deployed in a hot spot area in the service range of a macro base station, a federal learning framework is adopted between the macro base station and the micro base station, a global model is deployed in the macro base station, and a local model is deployed in the micro base station; the macro base station performs initial beam configuration for a plurality of micro base stations through configuration signaling, and the micro base stations input initial beam configuration information into a pre-trained local model to obtain the long-term throughput of the maximized network; and then, on the basis of the initial beam configuration information, determining the beam configuration required by maximizing the long-term throughput of the network, adjusting the direction of the beam in real time, and providing high-quality communication service for users, thereby solving the problems that the traditional beam scanning requires a large amount of time, the real-time requirement of the beam configuration cannot be met, the number of beams is increased sharply, and the beam management is difficult.
In addition, the federal learning framework is adopted between the macro base station and the micro base station, the global model is deployed at the macro base station, the local model is deployed at the micro base station, only the model is needed to be exchanged instead of the original data in the model training process, frequent signaling interaction between the micro base stations is effectively avoided, the system overhead and the power consumption are reduced, the privacy problem of the original data in the aspect of the user position is solved, and meanwhile, the network performance can be improved from the global angle.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory.
Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.
An electronic device provided by an embodiment of the present disclosure is described below with reference to fig. 9. The electronic device 900 shown in fig. 9 is merely an example and should not be construed to limit the functionality and scope of use of embodiments of the present disclosure in any way.
Fig. 9 shows a schematic architecture diagram of an electronic device 900 according to the present disclosure. As shown in fig. 9, the electronic device 900 includes, but is not limited to: at least one processor 910, at least one memory 920.
Memory 920 for storing instructions.
In some embodiments, memory 920 may include readable media in the form of volatile memory units, such as Random Access Memory (RAM) 9201 and/or cache memory 9202, and may further include Read Only Memory (ROM) 9203.
In some embodiments, memory 920 may also include a program/utility 9204 having a set (at least one) of program modules 9205, such program modules 9205 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
In some embodiments, memory 920 may store an operating system. The operating system may be a real-time operating system (Real Time eXecutive, RTX), LINUX, UNIX, WINDOWS or OS X like operating systems.
In some embodiments, memory 920 may also have data stored therein.
As one example, processor 910 may read data stored in memory 920, which may be stored at the same memory address as the instructions, or which may be stored at a different memory address than the instructions.
A processor 910 for invoking instructions stored in memory 920 to perform steps according to various exemplary embodiments of the present disclosure described in the above "exemplary methods" section of the present specification. For example, the processor 910 may perform the following steps of the method embodiments described above:
receiving configuration signaling from a macro base station, wherein the configuration signaling carries initial beam configuration information, and the initial beam configuration information comprises macro base station beam parameter configuration and micro base station beam parameter configuration;
taking the initial beam configuration information as an initial state of the micro base station for carrying out beam configuration decision, and carrying out initial configuration of the beam decision on the micro base station;
based on the initial beam configuration information, the micro base station collects local user position distribution data through interaction with the surrounding environment;
the collected local user location distribution data is input to a pre-trained local model to determine beam configurations that meet long-term throughput requirements of the maximized network.
Or, executing the following steps of the method embodiment:
the method comprises the steps that configuration signaling is sent to micro base stations, the configuration signaling carries initial beam configuration information, the initial beam configuration information comprises macro base station beam parameter configuration and micro base station beam parameter configuration, so that the micro base stations can conduct intelligent decision initial state of beam configuration through the initial beam configuration information as micro base stations, local user position distribution data are collected through interaction with surrounding environments, the local user position distribution data are input into a pre-trained local model, long-term throughput of a maximized network is obtained, and beam configuration needed to meet the long-term throughput of the maximized network is determined.
It should be noted that the processor 910 may be a general-purpose processor or a special-purpose processor. Processor 910 may include one or more processing cores, and processor 910 performs various functional applications and data processing by executing instructions.
In some embodiments, the processor 910 may include a central processing unit (central processing unit, CPU) and/or a baseband processor.
In some embodiments, processor 910 may determine an instruction based on a priority identification and/or functional class information carried in each control instruction.
In the present disclosure, the processor 910 and the memory 920 may be separately provided or may be integrated.
As one example, processor 910 and memory 920 may be integrated on a single board or System On Chip (SOC).
As shown in fig. 9, the electronic device 900 is embodied in the form of a general purpose computing device. The electronic device 900 may also include a bus 930.
The bus 930 may be any one or more of several types of bus structures including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.
The electronic device 900 may also communicate with one or more external devices 940 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 900, and/or any devices (e.g., routers, modems, etc.) that enable the electronic device 900 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 950.
Also, electronic device 900 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 960.
As shown in fig. 9, the network adapter 960 communicates with other modules of the electronic device 900 over the bus 930.
It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 900, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
It is to be understood that the illustrated structure of the presently disclosed embodiments does not constitute a particular limitation of the electronic device 900. In other embodiments of the present disclosure, electronic device 900 may include more or fewer components than shown in FIG. 9, or may combine certain components, or split certain components, or a different arrangement of components. The components shown in fig. 9 may be implemented in hardware, software, or a combination of software and hardware.
The present disclosure also provides a computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the configuration method applicable to wireless intelligent air interface distributed beam management described in the above method embodiments.
A computer-readable storage medium in an embodiment of the present disclosure is a computer instruction that can be transmitted, propagated, or transmitted for use by or in connection with an instruction execution system, apparatus, or device.
As one example, the computer-readable storage medium is a non-volatile storage medium.
In some embodiments, more specific examples of the computer readable storage medium in the present disclosure may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, a U disk, a removable hard disk, or any suitable combination of the foregoing.
In an embodiment of the present disclosure, a computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with computer instructions (readable program code) carried therein.
Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing.
In some examples, the computing instructions contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
The disclosed embodiments also provide a computer program product storing instructions that, when executed by a computer, cause the computer to implement the configuration method applicable to wireless intelligent air interface distributed beam management described in the above method embodiments.
The instructions may be program code. In particular implementations, the program code can be written in any combination of one or more programming languages.
The programming languages include object oriented programming languages such as Java, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages.
The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server.
In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
The embodiment of the disclosure also provides a chip comprising at least one processor and an interface;
an interface for providing program instructions or data to at least one processor;
the at least one processor is configured to execute the program instructions to implement the configuration method applicable to wireless intelligent air interface distributed beam management described in the above method embodiments.
In some embodiments, the chip may also include a memory for holding program instructions and data, the memory being located either within the processor or external to the processor.
Those of ordinary skill in the art will appreciate that all or a portion of the steps implementing the above embodiments may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein.
This disclosure is intended to cover any adaptations, uses, or adaptations of the disclosure following the general principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (11)
1. The configuration method suitable for wireless intelligent air interface distributed beam management is characterized in that a plurality of micro base stations are deployed in a hot spot area in the service range of a macro base station, a federal learning framework is adopted between the macro base station and the micro base stations, a global model is deployed in the macro base station, and a local model is deployed in the micro base station; the method is applied to a micro base station, and comprises the following steps:
receiving configuration signaling from the macro base station, wherein the configuration signaling carries initial beam configuration information, and the initial beam configuration information comprises macro base station beam parameter configuration and micro base station beam parameter configuration;
taking the initial beam configuration information as an initial state of the micro base station for carrying out beam configuration decision, and carrying out initial configuration of the beam decision on the micro base station;
based on the initial beam configuration information, the micro base station collects local user position distribution data through interaction with the surrounding environment;
the collected local user location distribution data is input to a pre-trained local model to determine beam configurations that meet long-term throughput requirements of the maximized network.
2. The method of claim 1, wherein the macro base station beam parameter configuration comprises at least one of a beam width and a serving micro base station upper limit number;
The beam configuration of the micro base station comprises at least one of a sector pointed by each beam at the initial moment, a beam width, the number of sectors around a single micro base station, the available bandwidth of all user equipment occupied by a macro base station in the single micro base station and the upper limit number of the beam number which can be generated at one moment of the single micro base station.
3. The method of claim 2, wherein the configuration signaling is transmitted over an Xn interface.
4. A method according to claim 3, characterized in that the message type of the configuration signaling is an Xn setup request message or an Xn configuration update message.
5. The method of claim 2, wherein the initial beam configuration information is determined by the macro base station based on the local cell network environment, channel conditions, user equipment geographical location, and number of access user equipment.
6. The method according to any one of claims 1-5, further comprising:
collecting user data through interactions with the environment;
training the local model by applying the user data, and updating parameters of the local model;
after the local model converges, uploading the local model to a macro base station so that the macro base station uniformly converges the received local models and updates global model parameters;
Downloading and updating global model parameters from a macro base station;
and updating the local model based on the global model parameters to obtain a trained local model.
7. The configuration method suitable for wireless intelligent air interface distributed beam management is characterized in that a plurality of micro base stations are deployed in a hot spot area in the service range of a macro base station, a federal learning framework is adopted between the macro base station and the micro base stations, a global model is deployed in the macro base station, and a local model is deployed in the micro base station; the method is applied to a macro base station, and comprises the following steps:
transmitting configuration signaling to micro base stations, wherein the configuration signaling carries initial beam configuration information, the initial beam configuration information comprises macro base station beam parameter configuration and micro base station beam parameter configuration, so that the micro base stations can use the initial beam configuration information as an initial state of beam configuration decision of the micro base stations, each micro base station collects local user position distribution data through interaction with surrounding environment, inputs the local user position distribution data into a pre-trained local model, obtains long-term throughput of a maximized network, and determines beam configuration required by the long-term throughput of the maximized network.
8. The configuration device suitable for wireless intelligent air interface distributed beam management is characterized in that a plurality of micro base stations are deployed in a hot spot area in the service range of a macro base station, a federal learning framework is adopted between the macro base station and the micro base stations, a global model is deployed in the macro base station, and a local model is deployed in the micro base station; the apparatus is applied to a micro base station, and the apparatus comprises:
The signaling receiving module is used for receiving configuration signaling from the macro base station, wherein the configuration signaling carries initial beam configuration information, and the initial beam configuration information comprises macro base station beam parameter configuration and micro base station beam parameter configuration;
the initialization configuration module is used for taking the initial beam configuration information as an initial state of the micro base station for carrying out beam configuration decision and carrying out initialization configuration of the beam decision on the micro base station;
the data collection module is used for collecting local user position distribution data through interaction with the surrounding environment by the micro base station on the basis of the initial beam configuration information;
and the beam configuration module is used for inputting the collected local user position distribution data into a pre-trained local model so as to determine the beam configuration meeting the requirement of maximizing the long-term throughput of the network.
9. The configuration device suitable for wireless intelligent air interface distributed beam management is characterized in that a plurality of micro base stations are deployed in a hot spot area in the service range of a macro base station, a federal learning framework is adopted between the macro base station and the micro base stations, a global model is deployed in the macro base station, and a local model is deployed in the micro base station; the apparatus is applied to a macro base station, and the apparatus includes:
The system comprises a signaling sending module, a micro base station and a pre-training local model, wherein the signaling sending module is used for sending configuration signaling to the micro base station, the configuration signaling carries initial beam configuration information, the initial beam configuration information comprises macro base station beam parameter configuration and micro base station beam parameter configuration, so that the micro base station takes the initial beam configuration information as an initial state of intelligent decision of beam configuration of the micro base station, each micro base station collects local user position distribution data through interaction with surrounding environment, the local user position distribution data is input into the pre-training local model, long-term throughput of a maximized network is obtained, and beam configuration needed to be carried out for meeting the long-term throughput of the maximized network is determined.
10. An electronic device, comprising:
a memory for storing instructions;
a processor, configured to invoke the instructions stored in the memory, and implement the configuration method applicable to wireless intelligent air interface distributed beam management according to any one of claims 1-7.
11. A computer readable storage medium having stored thereon computer instructions, which when executed by a processor, implement the configuration method for wireless intelligent air interface distributed beam management of any of claims 1-7.
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