Communication control method and device, equipment, chip and medium

Abstract

The invention provides a communication control method and device, equipment, a chip and a medium, and the method comprises the steps: obtaining scheduling information needed by packaging, the scheduling information is obtained through prediction based on an artificial intelligence AI model, and the scheduling information at least comprises first uplink authorization information; and pre-packaging the to-be-processed data packet according to the first uplink authorization information to obtain a target data packet. The technical problems that the packing effect cannot be ensured and the communication performance is influenced in the related technology are solved.

Description

Technical Field

[0001] The present disclosure relates to the field of communication technology, and in particular to a communication control method, device, equipment, chip and medium. Background Art

[0002] In some versions of the communication protocol, the Media Access Control (MAC) layer starts packet assembly only after receiving a scheduling instruction from the physical layer. The time required for packet assembly is limited by the offset of the Physical Downlink Control Channel (PDCCH) scheduling time on the network side relative to the air interface time of sending the Physical Uplink Shared Channel (PUSCH). Summary of the invention

[0003] The present disclosure aims to solve one of the technical problems in the related art at least to some extent.

[0004] To this end, the present disclosure proposes a communication control method, apparatus, communication equipment, chip and storage medium, which can reduce the performance requirements for devices, effectively ensure the packet assembly effect, and improve communication performance.

[0005] The first aspect of the present disclosure proposes a communication control method, including: obtaining scheduling information required for packet assembly, wherein the scheduling information is obtained based on an artificial intelligence (AI) model prediction, and the scheduling information includes at least: first uplink authorization information; pre-assembling a data packet to be processed according to the first uplink authorization information to obtain a target data packet.

[0006] The second aspect embodiment of the present disclosure proposes a communication control device, including: an acquisition module, used to obtain scheduling information required for packet assembly, wherein the scheduling information is obtained based on an artificial intelligence AI model prediction, and the scheduling information at least includes: first uplink authorization information; a control module, used to pre-assemble a data packet to be processed according to the first uplink authorization information to obtain a target data packet.

[0007] The third aspect embodiment of the present disclosure proposes a communication device, including: a processor, and a memory connected to the processor in communication; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the communication control method proposed in the first aspect embodiment of the present disclosure.

[0008] The fourth aspect embodiment of the present disclosure proposes a chip, which includes a processing circuit and an interface circuit; wherein the interface circuit is used to read instructions, and the interface circuit sends the instructions to the processing circuit so that the processing circuit executes the communication control method proposed in the first aspect embodiment of the present disclosure.

[0009] The fifth aspect of the present disclosure provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the communication control method as described above.

[0010] The communication control method, apparatus, communication equipment, chip and storage medium provided by the present disclosure obtain the scheduling information required for packet assembly, wherein the scheduling information is obtained based on the prediction of the AI ​​model, and the scheduling information at least includes: the first uplink authorization information, and the data packet to be processed is pre-assembled according to the first uplink authorization information to obtain the target data packet. Since the packet assembly is not performed in response to the scheduling instruction of the physical layer, but the possible first uplink authorization information on the network side is predicted based on the AI ​​model, and the data packet to be processed is pre-assembled based on the predicted first uplink authorization information, the performance requirements for the device can be reduced, the packet assembly effect can be effectively ensured, and the communication performance can be improved.

[0011] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description or learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The above and / or additional aspects and advantages of the present disclosure will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0013] Figure 1 is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure;

[0014] Figure 2 A flow chart of a communication control method provided by an embodiment of the present disclosure;

[0015] Figure 3 A flowchart of another communication control method provided by an embodiment of the present disclosure;

[0016] Figure 4 A flowchart of another communication control method provided by an embodiment of the present disclosure;

[0017] Figure 5 is a schematic diagram of an application flow in an embodiment of the present disclosure;

[0018] Figure 6 is the prediction result of using ARIMA to predict the network side uplink scheduling opportunity in the embodiment of the present disclosure;

[0019] Figure 7 is a schematic diagram of using ARIMA to predict the uplink grant size of a fixed-point scenario in an embodiment of the present disclosure;

[0020] Figure 8is a schematic diagram of using ARIMA to predict the size of uplink grant in a mobile scenario in an embodiment of the present disclosure;

[0021] Fig. 9 A schematic diagram of the structure of a communication control device provided by an embodiment of the present disclosure;

[0022] Fig.10 A block diagram of an exemplary communication device suitable for implementing the embodiments of the present disclosure is shown;

[0023] Fig.11 is a schematic diagram of the structure of a chip proposed in an embodiment of the present disclosure;

[0024] Fig.12 It is a schematic diagram of the structure of another chip proposed in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0025] Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0026] In the embodiments of the present disclosure, the communication device may be, for example, a terminal or a chip, which is not limited.

[0027] Figure 1 FIG. 1 is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure. Figure 1 As shown, the communication system 100 may include a terminal 101 and a network device 102. The network device 102 may include at least one of an access network device and a core network device.

[0028] In some embodiments, the terminal 101 includes, for example, a mobile phone, a wearable device, an Internet of Things device, a car with communication function, a smart car, a tablet computer (Pad), a computer with wireless transceiver function, a virtual reality (VR) terminal, an augmented reality (AR) terminal, a wireless terminal in industrial control (industrial control), a wireless terminal in self-driving, a wireless terminal in remote medical surgery, a wireless terminal in a smart grid, a wireless terminal in transportation safety, a wireless terminal in a smart city (smart city), and at least one of a wireless terminal in a smart home (smart home), but is not limited to these.

[0029] In some embodiments, the access network device is, for example, a node or device that accesses a terminal to a wireless network. The access network device may include an evolved NodeB (eNB), a next generation evolved NodeB (ng-eNB), a next generation NodeB (gNB), a node B (NB), a home node B (HNB), a home evolved node B (HeNB), a wireless backhaul device, a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a base band unit (BBU), a mobile switching center, a base station in a 6G communication system, an open base station (Open RAN), a cloud base station (CloudRAN), a base station in other communication systems, and at least one of an access node in a WiFi system, but is not limited thereto.

[0030] In some embodiments, the technical solution of the present disclosure may be applicable to the Open RAN architecture. In this case, the interfaces between or within the access network devices involved in the embodiments of the present disclosure may become internal interfaces of the Open RAN, and the processes and information interactions between these internal interfaces may be implemented through software or programs.

[0031] In some embodiments, the access network device may be composed of a centralized unit (central unit, CU) and a distributed unit (distributed unit, DU), wherein the CU may also be referred to as a control unit (control unit). The CU-DU structure may be used to split the protocol layer of the access network device, with some functions of the protocol layer being centrally controlled by the CU, and the remaining part or all of the functions of the protocol layer being distributed in the DU, and the DU being centrally controlled by the CU, but not limited to this.

[0032] In some embodiments, the core network device may be a device including one or more network elements, or may be a plurality of devices or a group of devices, each including all or part of one or more network elements. The network element may be virtual or physical. The core network, for example, includes at least one of an Evolved Packet Core (EPC), a 5G Core Network (5G Core Network, 5G CN), and a Next Generation Core (NGC).

[0033] It can be understood that the communication system described in the embodiment of the present disclosure is for the purpose of more clearly illustrating the technical solution of the embodiment of the present disclosure, and does not constitute a limitation on the technical solution proposed in the embodiment of the present disclosure. A person of ordinary skill in the art can know that with the evolution of the system architecture and the emergence of new business scenarios, the technical solution proposed in the embodiment of the present disclosure is also applicable to similar technical problems.

[0034] The following embodiments of the present disclosure can be applied to Figure 1 The communication system 100, or a portion thereof, is shown but is not limited thereto. Figure 1 The various entities shown are examples, and the communication system may include Figure 1 All or part of the subject, and may also include Figure 1 For entities other than the above, the number and form of each entity are arbitrary, and the connection relationship between the entities is an example. The entities may be connected or disconnected, and the connection may be in any manner, which may be direct or indirect, and may be wired or wireless.

[0035] Embodiments of the present disclosure may be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G new radio (NR), 6th generation mobile communication system (6G), Future Radio Access (FRA), New Radio Access Technology (RAT), New Radio (NR), New radio access (NX), Future generation radio access (FX), Global System for Mobile communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), Public Land Mobile Network (PLMN) network, Device-to-Device (D2D) system, Machine-to-Machine (M2M) system, Internet of Things (IoT) system, Vehicle-to-Everything (V2X), systems using other communication methods, next-generation systems based on them, etc. In addition, multiple systems can also be combined (for example, a combination of LTE or LTE-A and 5G, etc.) for application.

[0036] In some versions of the communication protocol, the Media Access Control (MAC) layer starts packet assembly only after receiving a scheduling instruction from the physical layer. The time required for packet assembly is limited by the offset of the Physical Downlink Control Channel (PDCCH) scheduling time on the network side relative to the air interface time of sending the Physical Uplink Shared Channel (PUSCH). In the New Radio (NR) protocol, the offset can be as low as 1 time slot. If the uplink authorization information allocated by the network (such as the size of the transport block (TB)) is large, the data path needs to assemble a large number of data packets in a short time, which leads to higher requirements on device performance, and cannot ensure the packet assembly effect, affecting communication performance.

[0037] The embodiment of the present disclosure is to solve the above technical problems and provide a communication control method, by obtaining the scheduling information required for packet assembly, wherein the scheduling information is predicted based on an artificial intelligence (AI) model, and the scheduling information at least includes: first uplink authorization information, and pre-packaging the data packet to be processed according to the first uplink authorization information to obtain a target data packet. Since the packet assembly is not performed in response to the scheduling instruction of the physical layer, but the possible first uplink authorization information on the network side is predicted based on the AI ​​model, and the data packet to be processed is pre-packed based on the predicted first uplink authorization information, the performance requirements for the device can be reduced, the packet assembly effect can be effectively ensured, and the communication performance can be improved.

[0038] The communication control method in the embodiment of the present disclosure may be applied in a communication device, and the communication device may be, for example, the above-mentioned terminal, or may be, for example, a chip, without limitation.

[0039] Figure 2 A flow chart of a communication control method provided by an embodiment of the present disclosure.

[0040] like Figure 2 As shown, the communication control method includes:

[0041] Step S201: Obtain the scheduling information required for packet assembly, wherein the scheduling information is obtained based on AI model prediction, and the scheduling information at least includes: first uplink authorization information.

[0042] Optionally, in some embodiments, the scheduling information may refer to scheduling information authorized by the network device when the terminal performs packet assembly. The scheduling information may, for example, include first uplink authorization information, which refers to resource-related information authorized for sending uplink data packets, and is not limited to this.

[0043] Packetization refers to the process of combining the received scattered data packets into complete data according to certain rules to ensure that the receiving end can correctly restore the original data of the sending end. The aforementioned scattered data packets can be called data packets to be processed, and the complete data obtained by combination can be called target data packets.

[0044] In the embodiments of the present disclosure, the above-mentioned scheduling information can be obtained based on the prediction of the AI ​​model, and the AI ​​model has the ability to predict the scheduling information authorized and allocated by the network device side for packet assembly based on some input content. That is to say, the first uplink authorization information in the scheduling information is obtained based on the prediction of the AI ​​model, rather than determined based on the scheduling indication of the physical layer, thereby enabling the scheduling information required for packet assembly to be determined earlier, and giving the actual pre-packaging process more ample processing time.

[0045] Optionally, in some embodiments, the AI ​​model may be deployed on the terminal side or on the network device side, without limitation. For example, the terminal may make predictions based on the AI ​​model to obtain the scheduling information required for packet assembly, or the network device may make predictions based on the AI ​​model to obtain the scheduling information required for packet assembly, and the network device may be, for example, an access network device or a core network device, without limitation.

[0046] Optionally, in some embodiments, the AI ​​model may be pre-trained based on sample data, and the sample data may include sample input content, such as a historical scheduling data set, and annotated scheduling information corresponding to the historical scheduling data set. The initial AI model is iteratively trained at least once based on the historical scheduling data set until the trained AI model converges, and the trained AI model is used as the AI ​​model used in the actual package assembly process. The aforementioned training process may be performed by a terminal, or may also be performed by a network device, without limitation.

[0047] Optionally, in some embodiments, the first uplink authorization information includes: a transport block TB size. The transport block TB size may also be referred to as a TB size, an uplink authorization size, a network authorization size, etc., without limitation. In other words, the first uplink authorization information may be obtained based on the prediction of the AI ​​model. In the actual process of packet assembly, a plurality of data packets to be processed may be pre-assembled based on the TB size predicted by the AI ​​model.

[0048] Step S202: pre-packaging the data packet to be processed according to the first uplink authorization information to obtain a target data packet.

[0049] After obtaining the scheduling information required for packet assembly and obtaining the first uplink authorization information in the scheduling information, the first uplink authorization information can be used for pre-packaging, for example, the pre-packaging can be performed in combination with the packet assembly rule and the first uplink authorization information. For example, Layer 2 (Layer 2, L2), i.e., the data link layer, can pre-pack several data packets to be processed based on the first uplink authorization information to obtain a target data packet, and there is no limitation on this.

[0050] In this embodiment, the scheduling information required for packet assembly is obtained, wherein the scheduling information is predicted based on an artificial intelligence AI model, and the scheduling information includes at least: first uplink authorization information, and the data packet to be processed is pre-assembled according to the first uplink authorization information to obtain a target data packet. Since the packet assembly is not performed in response to the scheduling instruction of the physical layer, but the possible first uplink authorization information on the network side is predicted based on the AI ​​model, and the data packet to be processed is pre-assembled based on the predicted first uplink authorization information, the performance requirements for the device can be reduced, the packet assembly effect can be effectively ensured, and the communication performance can be improved.

[0051] Optionally, in some embodiments of the present disclosure, the AI ​​model may be deployed on the network device side, and the network device side may predict the scheduling information required for packet assembly based on the AI ​​model, and indicate the scheduling information required for packet assembly to the terminal side, without limitation. For example, the prediction based on the AI ​​model may be performed by a chip in the network device.

[0052] Figure 3 A flowchart of another communication control method provided by an embodiment of the present disclosure.

[0053] In this embodiment, the AI ​​model is deployed on the network device side as an example. The network device side obtains the scheduling information required for packet assembly based on the AI ​​model prediction, and indicates the scheduling information required for packet assembly to the terminal side. There is no restriction on this.

[0054] like Figure 3 As shown, the communication control method includes:

[0055] Step S301: Send current network information, where the current network information and the AI ​​model are used to determine the scheduling information required for package assembly.

[0056] The current network information is used to indicate the current network access status of the terminal, such as the number of scheduling times, uplink rate, etc.

[0057] Optionally, in this embodiment, in order to support the network device to accurately predict the scheduling information required for packet assembly, the terminal can send the current network information to the network device so that the network device can predict the scheduling information required for packet assembly based on the current network information and AI model sent by the terminal.

[0058] Optionally, in some embodiments, after predicting the scheduling information required for packet assembly, the network device may indicate the scheduling information required for packet assembly to the terminal.

[0059] Step S302: Receive first information, wherein the first information is used to indicate scheduling information required for packet assembly, and the scheduling information includes: first uplink authorization information and predicted scheduling opportunity, and the predicted scheduling opportunity is used to schedule a physical uplink shared channel PUSCH.

[0060] Optionally, in some embodiments, the scheduling information required for packet assembly includes at least the first uplink authorization information, and may also include a predicted scheduling opportunity, which is used to schedule the physical uplink shared channel PUSCH to send the target data packet. In other words, the predicted scheduling opportunity in the scheduling information required for packet assembly is also predicted, and the terminal can use the first uplink authorization information predicted based on the AI ​​model for pre-packaging, and can also use the predicted scheduling opportunity predicted based on the AI ​​model to schedule PUSCH, without limitation.

[0061] Optionally, in some embodiments, the network device may indicate the scheduling information required for packet assembly to the terminal at a preset number of time units before the predicted scheduling opportunity. Then the terminal may receive the first information at a reception time earlier than the predicted scheduling opportunity, wherein the first information is used to indicate the scheduling information required for packet assembly.

[0062] Optionally, in some embodiments, the first information is received earlier than the predicted scheduling opportunity, and a preset number of time units are spaced between the reception time and the predicted scheduling opportunity. Thus, the first uplink authorization information required for packet assembly and the predicted scheduling opportunity can be determined as early as possible, ensuring that the actual pre-packaging process can be completed in an orderly and correct manner.

[0063] Optionally, in some embodiments, the time unit includes: a subframe. That is, the network device may indicate the scheduling information required for packet assembly at a time before the predicted scheduling opportunity and at a time interval of a preset number of subframes, thereby, the terminal may receive the first information at a time before the predicted scheduling opportunity and at a time interval of a preset number of subframes to obtain the scheduling information required for packet assembly indicated by the network device, and there is no limitation on this.

[0064] The preset number may be, for example, 3, 4 or 5, and there is no limitation on this.

[0065] Step S303: Determine the scheduling information required for package assembly according to the first information.

[0066] Optionally, after receiving the first information, the terminal may parse the first information to obtain the scheduling information required for packet assembly, which is not limited.

[0067] Step S304: pre-packaging the data packet to be processed according to the first uplink authorization information to obtain a target data packet.

[0068] After obtaining the scheduling information required for packet assembly and obtaining the first uplink authorization information in the scheduling information, the first uplink authorization information can be used for pre-packaging, for example, the pre-packaging can be performed in combination with the packet assembly rule and the predicted TB size. For example, Layer 2 (L2), i.e., the data link layer, can pre-pack several data packets to be processed based on the predicted TB size to obtain a target data packet, and there is no limitation on this.

[0069] Step S305: When the current time meets the predicted scheduling opportunity, schedule PUSCH to transmit the target data packet.

[0070] After the target data packet is obtained through packet assembly, the current time may be monitored. If the current time reaches the predicted scheduling opportunity, the PUSCH may be scheduled to transmit the target data packet.

[0071] In this embodiment, by obtaining the scheduling information required for packet assembly, wherein the scheduling information is obtained based on the prediction of the artificial intelligence AI model, the scheduling information at least includes: the first uplink authorization information, and the data packet to be processed is pre-assembled according to the first uplink authorization information to obtain the target data packet. Since the packet assembly is not performed in response to the scheduling instruction of the physical layer, but the possible first uplink authorization information on the network side is predicted based on the AI ​​model, and the data packet to be processed is pre-assembled based on the predicted first uplink authorization information, the performance requirements for the device can be reduced, the packet assembly effect can be effectively ensured, and the communication performance can be improved. Therefore, by deploying the AI ​​model on the network side, the network equipment realizes the prediction of the scheduling information required for packet assembly based on the AI ​​model, which can effectively save the computing resources required on the terminal side, and benefit from the computing power of the network equipment side, the prediction efficiency of the scheduling information required for packet assembly can be greatly improved, and the efficiency of packet assembly can be improved.

[0072] Optionally, in some embodiments of the present disclosure, the AI ​​model may be deployed on the terminal side, and the terminal side may predict the scheduling information required for the package assembly based on the AI ​​model, without limitation. For example, the prediction based on the AI ​​model may be performed by a chip in the terminal.

[0073] Figure 4A flowchart of another communication control method provided by an embodiment of the present disclosure.

[0074] In this embodiment, the AI ​​model is deployed on the terminal side as an example, and the terminal side obtains the scheduling information required for package assembly based on the AI ​​model prediction, and there is no limitation on this.

[0075] like Figure 4 As shown, the communication control method includes:

[0076] Step S401: Predict based on the AI ​​model to obtain the scheduling information required for packet assembly, wherein the scheduling information includes: first uplink authorization information and predicted scheduling opportunity, and the predicted scheduling opportunity is used to schedule PUSCH.

[0077] Optionally, in this embodiment, the terminal may predict the scheduling information required for packet assembly. For example, the terminal may predict the first uplink authorization information required for packet assembly and predict the scheduling opportunity based on the AI ​​model.

[0078] Step S402: pre-packaging the data packet to be processed according to the first uplink authorization information to obtain a target data packet.

[0079] Step S403: When the current time meets the predicted scheduling opportunity, schedule PUSCH to transmit the target data packet.

[0080] In this embodiment, the scheduling information required for packet assembly is obtained, wherein the scheduling information is predicted based on an artificial intelligence AI model, and the scheduling information includes at least: first uplink authorization information, and the data packet to be processed is pre-assembled according to the first uplink authorization information to obtain a target data packet. Since the packet assembly is not performed in response to the scheduling instruction of the physical layer, but the possible first uplink authorization information on the network side is predicted based on the AI ​​model, and the data packet to be processed is pre-assembled based on the predicted first uplink authorization information, the performance requirements for the device can be reduced, the packet assembly effect can be effectively ensured, and the communication performance can be improved.

[0081] The following optional embodiments may be executed by a terminal, a network device or a chip, without limitation.

[0082] Optionally, in some embodiments, when implementing prediction based on the AI ​​model to obtain the scheduling information required for package assembly, the target scheduling data set may be determined based on the current network information, and the target scheduling data set may be predicted based on the AI ​​model to obtain the scheduling information required for package assembly. In this way, the prediction accuracy of the scheduling information required for package assembly can be ensured, and it can be applied to personalized terminal network conditions.

[0083] Optionally, in some embodiments, the current network information includes at least one of the following: scheduling times; uplink rate.

[0084] Optionally, in some embodiments, in the process of determining the target scheduling data set according to the current network information, when the scheduling times are greater than or equal to the times threshold, or the uplink rate is greater than or equal to the rate threshold, the current scheduling data set is determined as the target scheduling data set; and when the scheduling times are less than the times threshold, and the uplink rate is less than the rate threshold, the historical scheduling data set is determined as the target scheduling data set. In this way, the prediction accuracy of the scheduling information required for the package assembly can be ensured, and it can be applied to the personalized terminal network situation.

[0085] The current scheduling data set refers to a data set generated by the terminal's current use of network resources. The current scheduling data set may include network authorization and / or configuration data, or may also be, for example, signal-related data of the terminal itself, or may also be, for example, the amount of cached data at the data link layer (for example, it may be represented by a buffer status report (Buffer Status Report, BSR)), etc., without limitation.

[0086] Optionally, in some embodiments, the current scheduling data set may be authorized and / or configured by the network, or may be obtained by detecting the signal-related data of the terminal itself, or may be determined based on the BSR, without limitation.

[0087] That is to say, if the number of scheduling times is greater than or equal to the number threshold, or the uplink rate is greater than or equal to the rate threshold, it means that the terminal's current access to the network is relatively stable and is in the normal Internet access stage. At this time, the current scheduling data set related to the terminal can be used to guide the AI ​​model to predict the scheduling information required for packet assembly. If the number of scheduling times is less than the number threshold, and the uplink rate is less than the rate threshold, it means that the terminal's current access to the network is relatively unstable and is in the initial network stage. At this time, the reference value of the current scheduling data set related to the terminal may not be very high, so the historical scheduling data set in the network can be used to guide the AI ​​model to predict the scheduling information required for packet assembly. "Historical scheduling data set" can be a historical scheduling data set for the terminal, or a scheduling data set of other reference terminals, or a scheduling data set measured experimentally or pre-marked, without restriction.

[0088] Optionally, in some embodiments, the current scheduling data set includes at least one of the following: second uplink authorization information; air interface time for sending PUSCH; network configuration information; signal quality information. Of course, the current scheduling data set may also include any other possible data, such as the amount of cached data at the data link layer, which is not limited.

[0089] The second uplink authorization information may be the uplink authorization information allocated by the network device for the terminal to access the network this time, and there is no restriction on this. Network configuration information may be used to configure the rate. Network configuration information may be, for example, the modulation and coding scheme (MCS). Signal quality information may be used to describe the quality of the network signal. Signal quality information may be, for example, the signal to interference plus noise ratio (Signal to Interference plus NoiseRatio), the reference signal receiving power (Reference Signal Receiving Power, RSRP), and the signal-to-noise ratio (Signal-to-Noise Ratio, SNR), and there is no restriction on this.

[0090] The examples for the above embodiment are as follows:

[0091] Optionally, in some embodiments, an AI model may be used to perform modeling learning based on a scheduling data set to predict possible scheduling opportunities (an optional example of predicting scheduling opportunities) and uplink authorization sizes (an optional example of first uplink authorization information). Figure 5 As shown, Figure 5 It is a schematic diagram of the application process in the embodiment of the present disclosure. It can be divided into two stages:

[0092] The first is the initial resident network stage: two options can be selected at this time. One is to use the normal packetization process for packetization, and normal signaling can be sent and received during this stage. In addition, the network scheduling mode at this stage is relatively simple, and the historical scheduling data set in the network can be learned for predictive packetization. The second is the stable Internet access stage, and the number of scheduling, uplink rate, etc. can be selected as the trigger threshold (that is, if the number of scheduling or the uplink rate meets some thresholds, the prediction is based on the prediction method in the second stage, and if the number of scheduling and the uplink rate do not meet some thresholds, the prediction is based on the prediction method in the first stage). In the second stage, the physical layer (PHY) can notify the AI ​​model of the uplink authorization size (an optional example of the second uplink authorization information) and the air interface time sent by PUSCH. The AI ​​model instructs L2 to pre-packaging before the possible predicted scheduling opportunity and at an interval of k (an optional example of a preset number) subframes. The AI ​​model mentioned above can be deployed on the terminal side or the network device side, without restriction. The AI ​​model can use various common machine learning algorithms for predicting timing sequences, including but not limited to regression models, neural network models, etc.

[0093] Take the AI ​​model as an example based on the Autoregressive Integrated Moving Average Model (ARIMA). Figure 6 , Figure 7 , Figure 8 As shown, Figure 6 This is the prediction result of using ARIMA to predict the network-side uplink scheduling opportunity in the embodiment of the present disclosure, with an accuracy rate of 84.7%. Figure 7 3 is a schematic diagram of using ARIMA to predict the uplink authorization size of a fixed-point scenario in an embodiment of the present disclosure, with a root mean square error of 8. Figure 8 is a schematic diagram of using ARIMA to predict the size of uplink authorization in a mobile scenario in an embodiment of the present disclosure, with a root mean square error of 106. Figure 6 In the figure, the horizontal axis represents the time unit, such as milliseconds (ms), time slots, or symbols, and the vertical axis represents the predicted scheduling occasion (the predicted scheduling occasion can also be called the transmission occasion (Tx occasion), the unit is Bool). Actual tx occasion represents the actual scheduling occasion, and Predicted txoccasion represents the predicted scheduling occasion. Figure 7 In the example, the vertical axis represents the predicted TB size (TBS for short) in a fixed-point scenario, in bytes. The horizontal axis represents the time unit, in units such as milliseconds (ms), or time slots, or symbols. Figure 8 In the figure, the vertical axis represents the predicted TB size (TBS for short) in the mobile scenario, in bytes. The horizontal axis represents the time unit, in milliseconds (ms), or time slots, or symbols. Figure 7 and Figure 8 In the figure, Actual TBS refers to the actual TBS, and Predicted TBS refers to the predicted TBS.

[0094] Fig. 9 A schematic diagram of the structure of a communication control device provided in an embodiment of the present disclosure.

[0095] like Fig. 9 As shown, the communication control device 90 includes:

[0096] The acquisition module 901 is used to obtain the scheduling information required for packet assembly, wherein the scheduling information is obtained based on the prediction of the artificial intelligence AI model, and the scheduling information at least includes: first uplink authorization information.

[0097] The control module 902 is used to pre-packetize the data packet to be processed according to the first uplink authorization information to obtain a target data packet.

[0098] It should be noted that the above explanation of the communication control method embodiment is also applicable to the communication control device of this embodiment, and will not be repeated here.

[0099] In this embodiment, the scheduling information required for packet assembly is obtained, wherein the scheduling information is predicted based on an artificial intelligence AI model, and the scheduling information includes at least: first uplink authorization information, and the data packet to be processed is pre-assembled according to the first uplink authorization information to obtain a target data packet. Since the packet assembly is not performed in response to the scheduling instruction of the physical layer, but the possible first uplink authorization information on the network side is predicted based on the AI ​​model, and the data packet to be processed is pre-assembled based on the predicted first uplink authorization information, the performance requirements for the device can be reduced, the packet assembly effect can be effectively ensured, and the communication performance can be improved.

[0100] In order to implement the above embodiments, the present disclosure also proposes a communication device, including: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the above embodiments.

[0101] Fig.10 A block diagram of an exemplary communication device suitable for implementing embodiments of the present disclosure is shown. Fig.10 The communication device 12 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure. The communication device may be, for example, a terminal, which is not limited.

[0102] like Fig.10 As shown, the communication device 12 is implemented in the form of a general purpose computing device. Components of the communication device 12 may include, but are not limited to: one or more processors or processing units 16, memory 28, and a bus 18 that connects various system components (including the memory 28 and the processing unit 16).

[0103] The bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnection (PCI) bus.

[0104] The communication device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the communication device 12, including volatile and non-volatile media, removable and non-removable media.

[0105] The memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache 32. The communication device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Fig.10 Not shown, often called a "hard drive").

[0106] although Fig.10 Not shown in the figure, a disk drive for reading and writing a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing a removable non-volatile optical disk (e.g., a compact disc read only memory (hereinafter referred to as: CD-ROM), a digital versatile disc read only memory (hereinafter referred to as: DVD-ROM) or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present disclosure.

[0107] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28, such program modules 42 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described in the present disclosure.

[0108] The communication device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a human body to interact with the communication device 12, and / or any device that enables the communication device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication may be performed through an input / output (I / O) interface 22. In addition, the communication device 12 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 20. As shown, the network adapter 20 communicates with other modules of the communication device 12 through a bus 18. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the communication device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0109] The processing unit 16 executes various functional applications and data processing by running the programs stored in the memory 28, such as implementing the methods mentioned in the above embodiments.

[0110] In order to implement the above embodiments, the present disclosure further proposes a chip, including: the chip includes a processing circuit, and the processing circuit is configured to execute the method provided by the above embodiments.

[0111] Fig.11 is a schematic diagram of the structure of a chip proposed in an embodiment of the present disclosure. Fig.11 The structure of the chip 1100 is shown, but is not limited to this.

[0112] The chip 1100 includes a processing circuit 1101 and an interface circuit 1102. The interface circuit 1102 is used to read instructions. The interface circuit 1102 sends the instructions to the processing circuit 1101 so that the processing circuit 1101 executes the above method.

[0113] Alternatively, if Fig.12 As shown, Fig.12The chip 1100 may further include: a memory 1103 for storing instructions, and the interface circuit 1102 may be used to read the instructions stored in the memory 1103 .

[0114] Optionally, the interface circuit 1102 is connected to the memory 1103, and the interface circuit 1102 can be used to receive signals from the memory 1103 or other devices, and the interface circuit 1102 can be used to send signals to the memory 1103 or other devices. For example, the interface circuit 1102 can read the instructions stored in the memory 1103 and send the instructions to the processing circuit 1101.

[0115] Optionally, the number of the memory 1103 may be one or more. The number of the interface circuit 1102 may also be one or more.

[0116] In some embodiments, the interface circuit 1102 performs at least one of the communication steps such as sending and / or receiving in the above method, and the processing circuit 1101 performs other steps.

[0117] In some embodiments, terms such as interface circuit, interface, transceiver pin, and transceiver may be used interchangeably.

[0118] Optionally, all or part of the memory 1103 may also be located outside the chip 1100 .

[0119] In order to implement the above embodiments, the present disclosure further proposes a non-transitory computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method proposed in the above embodiments of the present disclosure is implemented.

[0120] In order to implement the above embodiments, the present disclosure further proposes a computer program product. When instructions in the computer program product are executed by a processor, the method proposed in the above embodiments of the present disclosure is executed.

[0121] The collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in this disclosure shall comply with the relevant laws and regulations and shall not violate public order and good morals.

[0122] It should be noted that personal information from users should be collected for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign the agreement / authorization including authorization of relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others who have access to personal information data comply with its privacy policy and procedures.

[0123] The present disclosure anticipates providing implementation schemes for users to selectively block the use or access of personal information data. That is, the present disclosure anticipates providing hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, risks can be minimized by limiting data collection and deleting the data. In addition, when applicable, such personal information is de-identified to protect the privacy of the user.

[0124] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0125] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present disclosure, "plurality" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0126] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present disclosure includes additional implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present disclosure belong.

[0127] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0128] It should be understood that the various parts of the present disclosure can be implemented in hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0129] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0130] In addition, each functional unit in each embodiment of the present disclosure may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0131] The storage medium mentioned above may be a read-only memory, a disk or an optical disk, etc. Although the embodiments of the present disclosure have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present disclosure. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present disclosure.

Claims

1. A communication control method, characterized in that: include: Obtaining scheduling information required for packet assembly, wherein the scheduling information is obtained based on an artificial intelligence (AI) model prediction, and the scheduling information at least includes: first uplink authorization information; The data packet to be processed is pre-packed according to the first uplink authorization information to obtain a target data packet.

2. The method according to claim 1, characterized in that The scheduling information further includes: a predicted scheduling opportunity, where the predicted scheduling opportunity is used to schedule a physical uplink shared channel PUSCH; wherein the method further includes: When the current time meets the predicted scheduling opportunity, the PUSCH is scheduled to transmit the target data packet.

3. The method according to claim 2, characterized in that The obtaining of the scheduling information required for the package assembly includes: receiving first information, wherein the first information is used to indicate scheduling information required for the package assembly; The scheduling information required for the package assembly is determined according to the first information.

4. The method according to claim 3, characterized in that The receiving time of the first information is earlier than the predicted scheduling opportunity, and the receiving time and the predicted scheduling opportunity are separated by a preset number of time units.

5. The method according to claim 4, characterized in that The time unit includes: a subframe.

6. The method according to claim 3, characterized in that The method further comprises: Sending current network information, wherein the current network information and the AI ​​model are used to determine the scheduling information required for the package assembly.

7. The method according to claim 2, characterized in that The obtaining of the scheduling information required for the package assembly includes: Predictions are made based on the AI ​​model to obtain the scheduling information required for the package assembly.

8. The method according to claim 7, characterized in that The prediction based on the AI ​​model to obtain the scheduling information required for the package assembly includes: Determine the target scheduling data set based on the current network information; The target scheduling data set is predicted based on the AI ​​model to obtain the scheduling information required for the package assembly.

9. The method according to claim 8, characterized in that The current network information includes at least one of the following: Dispatch times; Uplink rate.

10. The method according to claim 9, characterized in that The step of determining the target scheduling data set according to the current network information includes: When the scheduling number is greater than or equal to the number threshold, or the uplink rate is greater than or equal to the rate threshold, determining the current scheduling data set as the target scheduling data set; When the scheduling times are less than the times threshold and the uplink rate is less than the rate threshold, the historical scheduling data set is determined as the target scheduling data set.

11. The method according to claim 10, characterized in that The current scheduling data set includes at least one of the following: Second uplink authorization information; The air interface time of sending the PUSCH; Network configuration information; Signal quality information.

12. The method according to any one of claims 1 to 11, characterized in that: The first uplink grant information includes: a transport block TB size.

13. A communication control device, characterized in that: include: An acquisition module is used to acquire scheduling information required for packet assembly, wherein the scheduling information is obtained based on the prediction of the artificial intelligence AI model, and the scheduling information at least includes: first uplink authorization information; The control module is used to pre-pack the data packets to be processed according to the first uplink authorization information to obtain the target data packets.

14. A communication device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 12.

15. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 12 when executed by a processor.

16. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 12 when being executed by a processor.

17. A chip, characterized in that: The chip includes a processing circuit and an interface circuit; wherein the interface circuit is used to read instructions, and the interface circuit sends the instructions to the processing circuit so that the processing circuit executes the method according to any one of claims 1-12.

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