WO2023010735A1 - 随机接入方法、网络设备及ue - Google Patents

随机接入方法、网络设备及ue Download PDF

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Publication number
WO2023010735A1
WO2023010735A1 PCT/CN2021/136000 CN2021136000W WO2023010735A1 WO 2023010735 A1 WO2023010735 A1 WO 2023010735A1 CN 2021136000 W CN2021136000 W CN 2021136000W WO 2023010735 A1 WO2023010735 A1 WO 2023010735A1
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Prior art keywords
information
model
random access
value
network device
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English (en)
French (fr)
Inventor
杨蓓
朱剑驰
佘小明
谢伟良
王庆扬
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China Telecom Corp Ltd
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China Telecom Corp Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W56/00—Synchronisation arrangements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W74/00—Wireless channel access
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W74/00—Wireless channel access
    • H04W74/08—Non-scheduled access, e.g. ALOHA

Definitions

  • the present disclosure belongs to the technical field of communication, and in particular relates to a random access method, network equipment and UE.
  • 5G 5th Generation Mobile Communication Technology, fifth generation mobile communication technology
  • the NR New Radio, new air interface
  • UE User Equipment, user equipment
  • the 5G NR standard supports fast random access to reduce UE access delay, for example, using 2-step random access.
  • an embodiment of the present disclosure provides a random access method, which is applied to a network device, and the method includes: generating target information according to first information; sending the target information to the UE, so that the UE determines based on the target information Sending the TA value of the random access message; wherein, the target information includes at least one of the following: indication information of the first model, configuration information of model parameters of the first model, and an association relationship between the second information and TA; The UE determines the TA according to the second information.
  • an embodiment of the present disclosure provides a network device, and the network device includes: a generating module and a sending module; the generating module is configured to generate target information according to the first information; the sending module is configured to send the target information to the UE The target information generated by the generation module, so that the UE determines the TA value for sending the random access message according to the target information; wherein, the target information includes at least one of the following: indication information of the first model, model parameters of the first model The association relationship between the configuration information, the second information and the TA; the first model is used for the UE to determine the TA according to the second information.
  • an embodiment of the present disclosure provides a random access method, which is applied to a network device, and the method includes: determining a TA value for sending a random access message according to target information; sending a random access message according to the TA value; Wherein, the target information is sent by the network device; the target information includes at least one of the following items: indication information of the first model, configuration information of model parameters of the first model, association relationship between the second information and TA; the first The model is used for the UE to determine the TA.
  • an embodiment of the present disclosure provides a UE, which includes: a determining module and a sending module; the determining module is configured to determine a TA value for sending a random access message according to target information; the sending module is configured to Send a random access message according to the TA value determined by the determining module; wherein, the target information is sent by the network device; the target information includes at least one of the following: indication information of the first model, configuration information of model parameters of the first model . An association relationship between the second information and the TA; the first model is used by the UE to determine the TA.
  • an embodiment of the present disclosure provides a network device, the network device includes a processor, a memory, and a program or instruction stored in the memory and operable on the processor, the program or instruction being executed by The processor implements the steps of the random access method described in the first aspect when executing.
  • an embodiment of the present disclosure provides a UE, where the UE includes a processor, a memory, and a program or instruction stored in the memory and operable on the processor, the program or instruction being executed by the The processor implements the steps of the random access method described in the first aspect during execution.
  • an embodiment of the present disclosure provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the implementation as described in the first aspect or the third aspect is implemented. The steps of the random access method.
  • an embodiment of the present disclosure provides a chip, the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run programs or instructions, so as to implement the first aspect The random access method described above.
  • an embodiment of the present disclosure provides a computer program product containing instructions, which when run on a computer, causes the computer to execute the steps of the random access method as described in the first aspect.
  • an embodiment of the present disclosure provides a wireless communication system, including the network device described in any embodiment of the present disclosure.
  • FIG. 1 is a schematic diagram of a wireless communication system provided by an embodiment of the present disclosure
  • FIG. 2 is one of the schematic flowcharts of the random access method provided by the embodiment of the present disclosure
  • FIG. 3 is a second schematic flow diagram of a random access method provided by an embodiment of the present disclosure.
  • FIG. 4 is a third schematic flow diagram of a random access method provided by an embodiment of the present disclosure.
  • FIG. 5 is one of possible structural diagrams of a network device provided by an embodiment of the present disclosure.
  • FIG. 6 is one of possible structural diagrams of a UE provided by an embodiment of the present disclosure.
  • FIG. 7 is the second possible hardware schematic diagram of a network device provided by an embodiment of the present disclosure.
  • FIG. 8 is a second possible hardware schematic diagram of a UE provided by an embodiment of the present disclosure.
  • FIG. 9 is a schematic hardware diagram of a network device provided by an embodiment of the present disclosure.
  • FIG. 10 is a schematic diagram of hardware of a UE provided by an embodiment of the present disclosure.
  • first”, “second” and the like in the specification and claims of the present disclosure are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the present disclosure are capable of practice in sequences other than those illustrated or described herein, and references to "first”, “second”, etc. to distinguish Objects are generally of one type, and the number of objects is not limited. For example, there may be one or more first objects.
  • “and/or” in the specification and claims means at least one of the connected objects, and the character “/” generally means that the related objects are an "or” relationship.
  • LTE Long Term Evolution, long-term evolution
  • LTE-A Long-term evolution of LTE
  • CDMA Code Division Multiple Access
  • TDMA Time Division Multiple Access
  • FDMA Frequency Division Multiple Access
  • OFDMA Orthogonal Frequency Division Multiple Access
  • SC-FDMA Single-carrier Frequency-Division Multiple Access, single-carrier frequency-division multiple access
  • system and “network” in the embodiments of the present disclosure are often used interchangeably, and the described technologies can be used for the above-mentioned systems and radio technologies as well as other systems and radio technologies.
  • NR system for example purposes, and uses NR terminology in most of the following descriptions, although these techniques can also be applied to applications other than NR system applications, such as 6G (6th Generation, 6th generation) communication systems .
  • the CP in the short frame structure
  • the length of (cyclic prefix, cyclic prefix) and/or GT (Guard Time, protection time) will be shortened. If the requirements cannot be met, in the case of short frames, when performing 2-step random access or 4-step random access, random access
  • the inter-symbol interference and inter-carrier interference of the incoming message will reduce the demodulation success rate of Msg1 or MsgA, resulting in multiple retransmissions of the random access message, increasing the access delay of the UE.
  • the UE since the UE cannot know the uplink TA (Timing Advance, timing advance) before random access, it will add CP and/or GT to the frame structure to meet the delay requirement.
  • TA Timing Advance, timing advance
  • the UE In the case of a large coverage area of a cell or a complex propagation path (such as a dense urban environment), longer CPs and/or GTs are required, and the length of CPs and/or GTs in a short frame structure is short, which cannot meet this requirement. time delay requirements.
  • the UE when the coverage area of the cell is large or the propagation path is complex (such as a dense urban environment), when the UE performs random access, since the CP and/or GT in the short frame structure cannot meet the delay requirement, the random access
  • Msg1 Message 1, signaling 1
  • MsgA Message A, signaling A
  • the purpose of the embodiments of the present disclosure is to provide a random access method, network equipment and UE, which can solve the problem of prolonging the access time during the random access process.
  • Fig. 1 is a schematic diagram of a wireless communication system provided by an embodiment of the present disclosure.
  • the wireless communication system includes: UE 101 and network device 102.
  • UE 101 may also be called terminal equipment or user equipment (User Equipment, UE).
  • the UE 101 may be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer) or a notebook computer, a personal digital assistant (Personal Digital Assistant, PDA), a palmtop computer, a netbook, Ultra-mobile personal computer (UMPC), mobile Internet device (Mobile Internet Device, MID), wearable device (Wearable Device) or vehicle-mounted equipment (VUE), pedestrian terminal (PUE) and other terminal-side equipment.
  • Wearable devices include wristbands, earphones, glasses, etc.
  • the network device 102 may be a base station, a core network, or OAM (Operation Administration and Maintenance, operation, maintenance and management), where the base station may be called a node B, an evolved node B, an access point, or a BTS (Base Transceiver Station, base transceiver station ), radio base station, radio transceiver, BSS (Basic Service Set, Basic Service Set), ESS (Extended Service Set, Extended Service Set), Node B, eNB (Evolved Node B), Home Node B, Home Evolved B Node, WLAN access point, WiFi node, TRP (Transmitting Receiving Point, sending and receiving point) or some other suitable term in the field.
  • the base station is not limited to specific technical terms. It should be noted that, in the embodiment of the present disclosure, only the base station in the NR system is taken as an example, but the specific type of the base
  • Fig. 2 is a schematic flowchart of a random access method provided by an embodiment of the present disclosure.
  • the method includes the following S201 and S202:
  • the network device generates target information according to the first information.
  • the network device may be a base station or an OAM.
  • the first information may be the attribute information of the network device, such as network standard, network performance, device address, etc., or it may be the information collected by the network device, or it may be the measurement information reported by the UE in the cell received by the network device, or it may be the cell Any useful uplink signal transmitted by the internal UE.
  • the network device may statically generate the target information according to the first information, or may semi-statically generate the target information according to the first information.
  • statically generating target information includes not updating the target information after being generated.
  • the semi-static generation of target information includes updating the target information regularly or irregularly after the target information is generated.
  • the network device may generate the target information for the UE to calculate the TA once, and directly use the target information subsequently, or periodically generate the target information for the UE to calculate the TA according to the first information.
  • the network device sends target information to the UE, so that the UE determines a TA value for sending a random access message according to the target information.
  • the network device may send the target information through the system information broadcast channel.
  • the network device may also use Radio Resource Control (Radio Resource Control, RRC) signaling and/or Medium Access Control-Control Element (Medium Access Control-Control Element, MAC-CE) signaling and/or Or downlink control information (Downlink Control Information, DCI) signaling to send the target information, or the physical downlink shared channel PDSCH may be used to receive the target information through the above signaling.
  • Radio Resource Control Radio Resource Control, RRC
  • RRC Radio Resource Control
  • Medium Access Control-Control Element Medium Access Control-Control Element
  • MAC-CE Medium Access Control-Control Element
  • DCI Downlink Control Information
  • Target information may include at least one of the following (1) to (3):
  • the first model is used by the UE to determine the TA.
  • the indication information of the first model is used to instruct the UE to adopt or select the first model to calculate the TA value.
  • the first model may be an AI (Artificial Intelligence, artificial intelligence) model or an MI (Machine Learning, machine learning) model.
  • AI Artificial Intelligence, artificial intelligence
  • MI Machine Learning, machine learning
  • the first model may be: a deep spiking neural network model, a deep convolutional neural network model, a super-resolution model, or a compressed sensing model.
  • the first model for calculating TA may not be included in the UE, and the network device may send the first model to the UE and instruct the UE to use the first model to calculate TA; multiple models for calculating TA may also be included in the UE, The network device can directly indicate the model used by the UE.
  • the network device can design and enhance the transmission of the random access message based on the physical layer of the AI/MI model. For example, design enhancements can be made to the transmission of the 2-step random access Msg1 (message 1), and design enhancements can be made to the transmission of the 4-step random access MsgA (message A).
  • the network device may instruct the UE to use the first model to calculate the TA value based on the configuration information of the model parameters of the first model indicated by the network device.
  • the network device may instruct the UE to use the model parameters corresponding to the first configuration information, and may also instruct the UE to use the model parameters corresponding to the second configuration information. For example, the network device may instruct the UE to use the first set of weights, and the network device may also instruct the UE to use the second set of weights.
  • the association relationship between the second information and TA may be the association relationship between the type of the second information and TA, or the association relationship between the measurement value of the second information and TA.
  • the network device may indicate the association relationship between the second information and the TA for the UE within the coverage of the cell, so that the UE within the coverage of the cell, according to the association between the second information and the UE indicated by the network device, A TA value for sending a random access message to the network device during the random access process is determined.
  • a UE within the coverage of a cell may also be understood as a UE served by a network device.
  • the non-intelligent enhancement scheme (determining the TA through the association relationship between the second information and the TA) and the intelligent enhancement on the RAN side (determining the TA through the first model) are flexibly supported through broadcasting.
  • the above random access message when the UE adopts 2-step random access, the above random access message may be MsgA; when the UE adopts 4-step random access, the above random access message may be Msg1.
  • the UE can send the preamble sequence through Msg1, and send the preamble sequence and payload message to the network device through MsgA.
  • the payload message may include an RRC (Radio Resource Control, referring to radio resource control) connection request, a small data packet of the UE, and the like.
  • the network device sets the initial timing advance for the UE, which can enhance the reliability of receiving the load information of the random access message 1 .
  • TA value is an initial TA value for sending a random access message by the UE, which may be recorded as TA0.
  • a slot can consist of 2, 4 or 7 OFDM symbols.
  • the network device in the scenario of short frame structure transmission, can enhance the transmission of the random access message MsgA for the UE to send the 2-step random access, or can send the random access message MsgA for the UE to send the 4-step random access.
  • the transmission of the incoming message Msg1 is enhanced.
  • the network device can align the demodulation window of the random access message by adopting the technical solution provided by the embodiment of the present disclosure, which can improve the performance of the preamble in the random access message by the network device. Demodulation success rate of sequence and uplink load.
  • 3GPP REL-17 defines the transmission of small data packets, allowing the UE to use 2-step RACH (Random Access Channel, Random Access Channel) or 4-step RACH to transmit small data packets in the RRC_INACTIVE state. It can be understood that the UE may transmit small data packets using a normal frame structure, or may transmit small data packets using a short frame structure.
  • the network device in the scenario of small data packet transmission, can enhance the transmission of the 2-step or 4-step random access message sent by the UE, so as to reduce the number of times the UE uses the random access message to transmit small data packets.
  • the random access delay can make the network devices align the demodulation window, and can improve the success rate of demodulation of small data packets by the network devices.
  • the network device can generate target information according to the first information; then the network device sends the target information to the UE, so that the UE can determine the TA value for sending the random access message according to the target information; because
  • the target information includes: at least one item of indication information of the first model, configuration information of model parameters of the first model, and an association relationship between the second information and the TA.
  • the indication information uses the first model to calculate the TA value for sending random access messages; the UE can also use the first model to calculate the TA value based on the configuration information of the model parameters of the first model indicated by the network device; the UE can also calculate the TA value based on the second information , the TA value is calculated based on the association relationship between the second information indicated by the network device and the TA; that is, when the UE performs the random access process, it first determines the TA value for sending the random access message according to the target information indicated by the network device, so as to avoid When the coverage area of the cell is large, or the propagation path is complex, the UE does not know the TA that sends the random access message, resulting in a low access success rate of the UE, and repeated access attempts lead to increased access delay.
  • the network device can enhance the sending of random access messages, thereby improving the success rate of random access and reducing the access delay of UEs, especially the random access delay of cell edge UEs.
  • Access delay improve the demodulation success rate of network equipment for random access messages.
  • the UE adopts a short frame structure or sends a small data packet if it performs 2-step random access, the demodulation success rate of the MsgA uplink load can be greatly improved, and the access delay of the UE can be reduced. Especially the random access delay of the cell edge UE.
  • the first information may include at least one of the following (a) to (e):
  • the cell coverage information may include at least one of the following: geographic coverage information, base station coordinates, geographic coverage range, electronic map information, and the like.
  • the coverage information of the cell where the network device is located may be collected by the network device, or may be sent to the network device after being collected by other devices.
  • the coverage information of the cell where the network device is located may also be preprocessed as the first information.
  • the measurement report is a conventional measurement result reported by the UE, and may also be other measurement results newly added according to actual needs.
  • the measurement result reported by the UE may be collected by the network device from the UE side, or may be collected by other devices from the UE side and sent to the network device.
  • the measurement result reported by the UE may also be preprocessed as the first information.
  • the measurement result may be a measurement result reported by other UEs in real time, or may be a measurement result previously reported by a UE performing random access.
  • the network device can generate target information according to any uplink signal that can represent the capability of the terminal. Since the uplink signal here is from the terminal, the uplink signal represents the capability of the terminal.
  • the target uplink signal may be collected by the network device from the UE side, or may be collected by other devices from the UE side and sent to the network device.
  • the target uplink signal may also be preprocessed as the first information.
  • the target uplink signal may include at least one of the following: an uplink data signal, a reference signal, and a control signal.
  • the first model may be a model configured in the network device, and may also be a model configured in the UE.
  • At least one model can be configured in either the network device or the UE, and the network device can indicate which model to use for the UE.
  • the network device may also send the first model to the UE.
  • the network device may combine the first model or the first model and input information of the first model to generate target information.
  • the input information of the model may be inference information of the model, and the model may calculate the TA value by using the user position coordinates, the received power of the user measurement reference information, and the geographical coverage information of the base station.
  • the configuration of the weight value of the model in the UE may be indicated by the network device, or may be configured by default.
  • the network device may instruct the UE to adopt model parameters with different weight values.
  • the network device may determine different model parameters according to different first information.
  • the network device may determine configuration information of different model parameters according to first information at different times.
  • the network device can generate target information based on at least one of the coverage information of the cell where the network device is located, the measurement result reported by the UE, the target uplink signal, the first model, and the configuration information of the model parameters of the first model, so that the generated
  • the target information can truly reflect the transmission parameters of the network device, which can make the TA value in the determined target information accurately match the transmission time of the network device, and avoid multiple retransmissions caused by the UE's access failure during the random access process. Improve access success rate.
  • the association between the second information and TA includes at least one of the following: the association between path loss and TA, the association between target signal and TA, the target The relationship between the value of the preset parameter of the signal and the TA.
  • the target signal may be: a downlink data signal, a control channel signal, a reference signal, or a broadcast signal, or a GPS (Global Positioning System, Global Positioning System) geographic location signal.
  • GPS Global Positioning System, Global Positioning System
  • the above-mentioned target signal is a signal received by the UE side.
  • Example 1-1 The network device can send the association relationship between path loss and TA to the UE, so that the UE can determine the TA value according to the path loss value and the association relationship between path loss and TA.
  • Example 1-2 The network device may send the association relationship between the target signal and the TA to the UE, so that the UE may determine the TA value corresponding to the target signal according to the indicated target signal.
  • signal 1 corresponds to TA 1
  • signal 2 corresponds to TA 2
  • signal 3 corresponds to TA 3.
  • Example 1-3 The network device can send the value of the preset parameter value of the target signal to the UE, so that the UE can determine the TA value corresponding to the value of the preset parameter value of the target signal according to the indicated value of the preset parameter value of the target signal .
  • the preset parameter may be transmission power.
  • the transmission power 1 of the reference signal corresponds to TA 1
  • the transmission power 2 of the reference signal corresponds to TA 2
  • the transmission power 3 of the reference signal corresponds to TA 3.
  • the network device can flexibly adopt any of the above three methods to indicate the association relationship between the second information and the TA for the UE, which can enable the UE to determine the TA according to the measured path loss, or enable the UE to determine the TA according to different
  • the type of target signal determines the TA, and the UE may also determine the TA according to the value of a preset parameter of the measured target signal.
  • the association relationship between the second information and TA includes at least one of the following: a mapping table between the second information and the TA value, a mapping table between the second information and the TA value A formula for calculating the mapping relationship, the second information, and the TA value.
  • the UE can obtain the TA value by looking up the table; if the relationship between the second information and TA in the target information is the mapping relationship between the second information and the TA value, then the UE can obtain the TA value through the relationship mapping; if the association relationship between the second information and the TA in the target information is the calculation formula of the second information and the TA value, then the UE can obtain the TA value based on the calculation formula Calculate the TA value.
  • the UE side may support multiple calculation formulas for TA values in different typical network scenarios, and the network device may notify the UE to use one of the multiple calculation formulas, and notify the specific parameter configuration values in the formula.
  • the network device can carry the association relationship between any of the above-mentioned second information and the TA in the target information, and the configuration methods are flexible and diverse, so that after receiving the target information, the UE can directly, flexibly
  • the TA value is flexibly determined according to the mapping table between the second information and the TA value, the mapping relationship between the second information and the TA value, and the calculation formula between the second information and the TA value.
  • the target information may further include: model update information or model training information.
  • the training information of the model includes: at least one of the parameter configuration of the training model and the data of the model training; wherein, the update information of the model is used for the UE to update the first model; the training information of the model allows the UE to train the first model.
  • the network device can indicate the training information of the model for the UE, so that the UE can train and calculate the model of the TA according to the training information of the model indicated by the network device, and then according to the values of other parameters indicated in the target information that can be used as model input, according to The trained model gets the TA value.
  • the network device can also instruct the UE to update the model for calculating TA. If the model calculation in the UE is not accurate enough, it can make the UE optimize the model according to the updated information of the model. For example, the network device can instruct the UE to The input data and the actual TA value make the model adjust the parameters of the model according to the input data and the actual TA value.
  • the network device can also send model update information or model training information for the UE, which can enable the UE to update the model for calculating TA according to the model update information sent by the network device, so that the UE can update the model according to the model sent by the network device.
  • Training information train the model for calculating TA.
  • the network device updates the first information.
  • the preset condition includes at least one of the following: a change in the network measurement signal of the network device, a change in the network environment of the network device, a change in the geographical environment of the network device, and a UE random access failure rate higher than a preset value.
  • the OAM detects that the network measurement signal changes, for example, the network measurement signal changes from signal 1 to signal 2 .
  • the base station detects that the operator has adjusted the network parameter configuration, the physical orientation of the base station antenna panel has changed, the inclination angle of the base station antenna has changed, a new building base station has been built within the coverage area of the base station, and a building base station has been demolished within the coverage area of the base station.
  • the base station calculates that the UE random access failure rate is higher than a preset value within a period of time.
  • the base station or OAM can automatically detect whether the preset condition is met, and timely update the first information that better matches the changed environment according to changes in network measurement signals, network environment changes, and geographical environment changes.
  • the network device can automatically detect whether the first information needs to be updated, and if the preset conditions for updating the first information are met, the network device can perform an update to obtain the first information that matches the current state, so that the network device can according to The latest first information generates target information, so that the TA determined by the UE is more in line with the current state of the network device, which increases the reliability of UE access and reduces the delay of UE access.
  • FIG. 3 is a schematic flowchart of a random access method provided by an embodiment of the present disclosure. As shown in FIG. 3, the method may include the following S301 and S302:
  • the UE determines a TA value for sending a random access message according to the target information.
  • the target information is sent by the network device.
  • the target information may be carried in a broadcast message, and the UE receives the broadcast message broadcast by the network device to determine the target information.
  • the target information includes at least one of the following: indication information of the first model, configuration information of model parameters of the first model, and an association relationship between the second information and the TA.
  • the first model is used by the UE to determine the TA value.
  • the indication information of the first model may include information that the base station instructs the UE to select a model to use, and may also include the first model.
  • the UE when the UE adopts a short frame structure for transmission or transmits a small data packet, before performing random access, the UE may perform the above S301.
  • the above random access message may be Msg 1 in the 2-step random access process, or Msg A in the 4-step random access process.
  • UE when using 2-step random access, UE can send preamble sequence and load message through Msg 1, and the load message can include RRC connection request or small data packet of UE, etc.
  • the UE When using 4-step random access, the UE can send the preamble through Msg A.
  • the UE may obtain the uplink TA value for sending Msg 1 through the above S301.
  • the UE sends a random access message according to the TA value.
  • the UE may obtain target information according to the system information of the detected broadcast channel, and determine the TA value for sending the random access message according to the target information.
  • the UE may determine, according to the indication information of the first model, that the model for calculating TA is the first model, and then calculate the TA value according to the first model and input information corresponding to the first model.
  • the UE may determine the configuration information of the model parameters in the first model for calculating the TA according to the configuration information of the model parameters of the first model, and then calculate the TA based on the first model of the configuration information of the model parameters and the input information corresponding to the first model .
  • the input information corresponding to the first model may be a value measured by the UE.
  • the UE may determine the TA value for sending the random access message according to the association relationship between the second information and the TA, and according to the specific content of the second information.
  • the UE before the random access, can determine the TA value for sending the random access message according to the target information, and then use the TA value to send the random access message when initiating random access. Since the target information includes: at least one of the indication information of the first model, the configuration information of the model parameters of the first model, and the association between the second information and the TA, the UE can be configured according to the first model indicated by the network device.
  • the instruction information uses the first model to calculate the TA value for sending random access messages; the UE can also use the first model to calculate the TA value based on the configuration information of the model parameters of the first model indicated by the network device; the UE can also calculate the TA value according to the second model Information, calculate the TA value based on the association relationship between the second information indicated by the network device and the TA; that is, when the UE performs the random access process, it first determines the TA value for sending the random access message according to the target information indicated by the network device, that is, it can Determine the TA value for sending the random access message, so as to avoid the low access success rate of the UE caused by the fact that the UE does not know the TA for sending the random access message when the cell coverage is large or the propagation path is complex.
  • the network device can enhance the sending of random access messages, thereby improving the success rate of random access and reducing the access delay of UEs, especially the random access delay of cell edge UEs.
  • Access delay improve the demodulation success rate of network equipment for random access messages. For example, if the UE adopts a short frame structure or sends a small data packet, if it performs 2-step random access, the demodulation success rate of the MsgA uplink load can be greatly improved, and the access delay of the UE can be reduced, especially Random access delay of cell edge UE.
  • the association relationship between the second information and the TA includes at least one of the following: an association relationship between the path loss and the TA, an association relationship between the target signal and the TA, and an association relationship between a preset parameter value of the target signal and the TA.
  • the value of the preset parameter can be RSRP, RSRQ or SINR.
  • the value of the preset parameter can be a linear value or a dB (gain) value, or a quantization level. In the case where the value of the preset parameter is the quantization level, the signaling load can be reduced.
  • the association between the second information and TA may be a one-to-one mapping between numerical values, or a mapping between many-to-one numerical values, or a mapping between an interval range and a numerical value.
  • the mapping may also be an association relationship mapping performed according to a calculation formula.
  • the UE may determine the TA value based on the measured path loss according to the relationship between the path loss and the TA; if the target information indicates the target information and TA, the UE can determine the TA value according to the type of the target signal; if the target information indicates the relationship between the value of the preset parameter of the target signal and the TA, the UE can determine the value of the preset parameter according to the measurement. value determines TA.
  • the target signal may be: a downlink data signal, a control channel signal, a reference signal, a broadcast signal, or a GPS geographic location signal.
  • the UE may determine the path loss value according to the obtained downlink measurement value, and then determine the TA value corresponding to the path loss value according to the path loss value and the association relationship between the path loss and the TA.
  • the UE can flexibly use different second information to determine the corresponding TA value based on the association relationship between different types of second information and the TA value according to actual usage scenarios.
  • the association relationship between the second information and TA includes at least one of the following: a mapping table between the second information and the TA value, the second information and the TA value The mapping relationship, the calculation formula of the second information and the TA value.
  • the UE can flexibly determine the TA value for sending the random access message by using different forms of the association relationship between the second information and the TA.
  • the above S301 may be specifically performed through the following S31:
  • the UE determines the TA value for sending the random access message according to the measurement value corresponding to the target information.
  • the measurement value corresponding to the target information may be at least one of a downlink measurement value obtained by the UE and other measurement information.
  • the measured values corresponding to the configuration information of the first model and the model parameters of the first model include but are not limited to Reference Signal Received Power (RSRP), Reference Signal Received Quality (Reference Signal Received Quality, RSRQ), signal Signal Noise Ratio (SINR).
  • RSRP can be obtained based on Synchronization Signal Block (SSB) measurement, or RSRP, RSRQ or
  • the SINR can also be the above-mentioned measurement value obtained by measuring a Physical Downlink Control Channel (PDCCH), a Physical Downlink Data Channel (PDSCH) or a Demodulation Reference Signal (DMRS).
  • the UE may input these measured values into the first model to obtain the TA value.
  • the measurement value corresponding to the association relationship between the second information and the TA is a measurement value corresponding to the second information.
  • the UE can perform measurement according to the target information, and determine the TA value for sending the random access message according to the measurement result.
  • the above S31 may be specifically performed through the following S311 or S312:
  • the UE determines a TA value for sending a random access message according to a measurement value corresponding to the target information.
  • the determining manner of S311 may be defined as a non-intelligent manner, and the UE may calculate and obtain the TA value for sending the random access message according to table lookup, relationship mapping or formula.
  • the UE determines the TA value for sending the random access message according to the measurement value corresponding to the target information.
  • the determination manner of S312 may be defined as an intelligent manner, and the UE may calculate and obtain the TA value for sending the random access message based on the first model and the measurement result of the UE.
  • the target information includes: a deep convolutional neural network model used by the UE, weight values of various parameters in the model, and input data types required for model calculation. Then the UE can use the weight value of each parameter in the model indicated by the target information as the weight value of the parameter of the deep convolutional neural network model indicated in the target information, and then collect the measured value of the data of the input data type indicated by the target information, Determine the TA value.
  • the UE can determine the TA value based on the association between the target information and TA, and if the network device instructs the UE to calculate the TA model or the model and model parameters configuration information, the UE can determine the TA value based on the indicated model.
  • Various and flexible determination methods enable the UE to determine the TA value for sending a random access message before initiating random access, which increases the success rate of UE random access and reduces the access delay of UE random access.
  • the target information further includes: update information of the first model; furthermore, the above S301 can be performed through the following S32 and S33:
  • the UE updates the first model based on the update information of the first model.
  • the update information of the first model may include calculation rules and input parameters in the first model, and may also include the updated first model.
  • the UE determines a TA value for sending the random access message.
  • the UE can update the model for calculating TA based on the update information of the model indicated by the network device, so that the calculation capability of the model can be optimized in time, so that the TA value calculated according to the updated model is more in line with actual needs.
  • the target information further includes: training information of the first model, and the training information of the model includes at least one of the parameter configuration of model training and the data of model training item; and then the above S301 can be executed through the following S34 and S35:
  • the UE trains the first model based on the training information of the first model.
  • the network device can configure the training information used for model training for the UE, so that when the UE needs to calculate TA, it can first train the model for calculating TA based on the training information indicated by the network device, so as to obtain the first a model.
  • the base station notifies the UE to use the deep convolutional neural network model, the weight value of each parameter in the model, the type of input data required for model calculation, and the input data and output data required for model training.
  • the input quantity type of the model may be the reference signal received power of the UE, geographic location information, and the like.
  • the input data and output data required for model training may include the reference signal received power RSRP of the UE at the previous moment, geographical location information, and the actual TA value obtained by network measurement after the random access is completed.
  • the UE determines a TA value for sending a random access message based on the trained first model.
  • the UE may infer the TA value for sending the random access message in combination with the input of the model indicated in the target information.
  • the UE can train and optimize the model in the UE according to the training information of the model indicated by the network device, so that the TA inferred by the UE according to the model is more in line with actual needs.
  • FIG. 4 is an interaction flowchart of a random access method provided by an embodiment of the present disclosure. As shown in FIG. 4, the random access method may include the following S401 to S405:
  • the network device generates target information according to the first information.
  • the network device sends target information to the UE, so that the UE determines a TA value for sending a random access message according to the target information.
  • the UE receives the target information sent by the network device.
  • the UE determines a TA value for sending the random access message according to the target information.
  • the UE sends a random access message to the network device according to the TA value.
  • the network device can generate target information according to the first information, and then send the target information to the UE.
  • the UE can determine the TA value for sending the random access message based on the target information. Then the UE can send a random access message to the network device based on the TA value. Since the target information includes: at least one item of indication information of the first model, configuration information of model parameters of the first model, and an association relationship between the second information and the TA, the UE may be configured according to the first model indicated by the network device.
  • the instruction information uses the first model to calculate the TA value for sending random access messages; the UE can also use the first model to calculate the TA value based on the configuration information of the model parameters of the first model indicated by the network device; the UE can also calculate the TA value according to the second model information, the TA value is calculated based on the association relationship between the second information indicated by the network device and the TA; that is, when the UE performs the random access process, it first determines the TA value for sending the random access message according to the target information indicated by the network device, so as to avoid In the case of a large cell coverage or a complex propagation path, the UE does not know the TA that sends the random access message, resulting in a low access success rate of the UE, and repeated access attempts lead to increased access delay.
  • the sending of random access messages can be enhanced, so that the success rate of random access can be improved, and the access delay of UE can be reduced, especially the random access of UE at the cell edge Delay, improve the demodulation success rate of network equipment for random access messages.
  • the UE adopts a short frame structure or sends a small data packet if it performs 2-step random access, the demodulation success rate of the MsgA uplink load can be greatly improved, and the access delay of the UE can be reduced, especially Random access delay of cell edge UE.
  • the execution subject may also be a random access device, or a control module in the random access device for executing the random access method.
  • the method for performing random access by a random access device is taken as an example to illustrate the random access device provided in the embodiments of the present disclosure.
  • FIG. 5 is a schematic structural diagram of a network device provided by an embodiment of the present disclosure.
  • the network device 500 includes: a generation module 501 and a sending module 502; the generation module 501 is configured to generate a target according to the first information Information; a sending module 502, configured to send the target information generated by the generating module 501 to the UE, so that the UE determines a TA value for sending a random access message according to the target information; wherein the target information includes at least one of the following: the first Indication information of a model, configuration information of model parameters of the first model, and an association relationship between the second information and the TA; the first model is used by the UE to determine the TA according to the second information.
  • the first information includes at least one of the following: coverage information of the cell where the network device is located, a measurement result reported by the UE, a target uplink signal, the first model, and a model of the first model Parameter configuration information.
  • the association between the second information and TA includes at least one of the following: an association between path loss and TA, an association between target signal and TA, and an association between the value of a preset parameter of the target signal and TA relation.
  • the association relationship between the second information and TA includes at least one of the following: a mapping table between the second information and TA values, a mapping relationship between the second information and TA values, the first 2. Calculation formula of information and TA value.
  • the target information further includes: model update information or model training information; the model training information includes: at least one of model training parameter configuration and model training data; wherein, the The update information of the model is used by the UE to update the first model; the training information of the model is used by the UE to train the first model.
  • the network device further includes: an update module; the update module is configured to update the first information when a preset condition is met; wherein the preset condition includes at least one of the following: The network measurement signal of the network device changes, the network environment of the network device changes, the geographical environment of the network device changes, and the UE random access failure rate is higher than a preset value.
  • the first model is an artificial intelligence model or a machine learning model.
  • the network device 500 provided by the embodiments of the present disclosure can implement various processes implemented by the method embodiments in FIG. 1 to FIG. 4 , and can achieve the same technical effect. To avoid repetition, details are not repeated here.
  • FIG. 6 is a schematic structural diagram of a possible UE provided by an embodiment of the present disclosure.
  • UE 600 includes: a determining module 601 and a sending module 602; The TA value of the incoming message; the sending module 502, configured to send a random access message according to the TA value determined by the determining module 601; wherein, the target information is sent by a network device; the target information includes at least one of the following: Indication information of the first model, configuration information of model parameters of the first model, and an association relationship between the second information and the TA; the first model is used by the UE to determine the TA.
  • the relationship between the second information and TA includes at least one of the following: the relationship between path loss and TA, the relationship between target signal and TA, the value of the preset parameter of the target signal and TA relationship.
  • the determining module is specifically configured to: determine the TA value for sending the random access message according to the measurement value corresponding to the target information.
  • the determining module is specifically configured to: determine the TA value for sending the random access message based on the association relationship between the target information and the TA, and according to the measurement value corresponding to the target information.
  • the determining module is specifically configured to: determine a TA value for sending a random access message based on the first model and according to a measurement value corresponding to the target information.
  • the association relationship between the second information and TA includes at least one of the following: a mapping table between the second information and TA values, a mapping relationship between the second information and TA values, the first 2. Calculation formula of information and TA value.
  • the target information further includes: update information of the first model; the determining module is specifically configured to: update the first model based on the update information of the first model; A model that determines the TA that sends the random access message.
  • the target information further includes: training information of the first model, and the training information of the model includes at least one of parameter configuration for model training and data for model training; the determining module is specifically configured to: The training information of the first model is used to train the first model; based on the trained first model, the TA for sending the random access message is determined.
  • the UE 600 provided by the embodiments of the present disclosure can implement the various processes implemented by the method embodiments in FIGS. 1 to 4, and can achieve the same technical effect. To avoid repetition, details are not repeated here.
  • an embodiment of the present disclosure further provides a network device 700, including a processor 701, a memory 702, and programs or instructions stored in the memory 702 and operable on the processor 701,
  • a network device 700 including a processor 701, a memory 702, and programs or instructions stored in the memory 702 and operable on the processor 701,
  • the program or instruction is executed by the processor 701
  • each process of the random access method embodiment described above can be achieved, and the same technical effect can be achieved. To avoid repetition, details are not repeated here.
  • an embodiment of the present disclosure further provides a UE 800, including a processor 801, a memory 802, and programs or instructions stored in the memory 802 and operable on the processor 801, the When the programs or instructions are executed by the processor 801, the various processes of the foregoing random access method embodiments can be achieved, and the same technical effect can be achieved. To avoid repetition, details are not repeated here.
  • FIG. 9 is a schematic diagram of a hardware structure of a network device provided by an embodiment of the present disclosure.
  • network device 900 shown in FIG. 9 is only an example, and should not limit the functions and scope of use of this embodiment of the present disclosure.
  • the network device 900 includes a central processing unit (Central Processing Unit, CPU) 901, which can be stored in a program in a ROM (Read Only Memory, read-only memory) 902 or loaded into a RAM from a storage part 908 ( Random Access Memory, the program in random access memory) 903 executes various appropriate actions and processing.
  • CPU Central Processing Unit
  • ROM Read Only Memory
  • RAM Random Access Memory
  • various programs and data necessary for system operation are also stored.
  • the CPU 901, ROM 902, and RAM 903 are connected to each other via a bus 904.
  • An I/O (Input/Output, input/output) interface 905 is also connected to the bus 904 .
  • the following components are connected to the I/O interface 905: an input part 906 including a keyboard, a mouse, etc.; an output part 907 including a CRT (Cathode Ray Tube, cathode ray tube), an LCD (Liquid Crystal Display, liquid crystal display), etc., and a speaker ; comprise the storage part 908 of hard disk etc.; And comprise the communication part 909 of the network interface card such as LAN (Local Area Network, wireless network) card, modem etc. The communication section 909 performs communication processing via a network such as the Internet.
  • a drive 910 is also connected to the I/O interface 905 as needed.
  • a removable medium 911 such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc. is mounted on the drive 910 as necessary so that a computer program read therefrom is installed into the storage section 908 as necessary.
  • embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, where the computer program includes program codes for executing the methods shown in the flowcharts.
  • the computer program may be downloaded and installed from a network via communication portion 909, and/or installed from removable media 911.
  • CPU 901 central processing unit
  • various functions defined in the system of the present disclosure are performed.
  • FIG. 10 is a schematic diagram of a hardware structure of a UE provided by an embodiment of the present disclosure.
  • the UE 1000 includes but is not limited to: a radio frequency unit 1001, a network module 1002, an audio output unit 1003, an input unit 1004, and a sensor 1005, a display unit 1006, a user input unit 1007, an interface unit 1008, a memory 1009, a processor 1010 and other components.
  • UE 1000 may also include a power supply (such as a battery) for supplying power to various components, and the power supply may be logically connected to the processor 1010 through the power management system, so as to manage charging, discharging, and power consumption through the power management system Management and other functions.
  • a power supply such as a battery
  • the structure of the electronic device shown in FIG. 10 does not constitute a limitation to the electronic device.
  • the electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange different components, and details will not be repeated here. .
  • the processor 1010 is configured to determine the TA value for sending the random access message according to the target information; the radio frequency unit 1001 is configured to send the random access message according to the TA value; wherein the target information is broadcast by the network device ;
  • the target information includes at least one of the following: indication information of the first model, configuration information of model parameters of the first model, and an association relationship between the second information and TA; the first model is used by the UE to determine T.A.
  • the UE 1000 provided by the embodiments of the present disclosure can implement each process of the UE in the above random access method embodiments, and can achieve the same technical effect. To avoid repetition, details are not repeated here.
  • the input unit 1004 may include a graphics processor (Graphics Processing Unit, GPU) 1041 and a microphone 1042, and the graphics processor 1041 is used for the image capture device (such as the image data of the still picture or video obtained by the camera) for processing.
  • the display unit 1006 may include a display panel 1061, and the display panel 1061 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like.
  • the user input unit 1007 includes a touch panel 1071 and other input devices 1072 .
  • the touch panel 1071 is also called a touch screen.
  • the touch panel 1071 may include two parts, a touch detection device and a touch controller.
  • Other input devices 1072 may include, but are not limited to, physical keyboards, function keys (such as volume control keys, switch keys, etc.), trackballs, mice, and joysticks, which will not be repeated here.
  • the memory 1009 can be used to store software programs as well as various data, including but not limited to application programs and operating systems.
  • Processor 1010 may integrate an application processor and a modem processor, wherein the application processor mainly processes operating systems, user interfaces, and application programs, and the modem processor mainly processes wireless communications. It can be understood that the foregoing modem processor may not be integrated into the processor 1010 .
  • Embodiments of the present disclosure also provide a readable storage medium, where a program or instruction is stored on the readable storage medium, and when the program or instruction is executed by a processor, each process of the foregoing random access method embodiment is implemented, and can achieve The same technical effects are not repeated here to avoid repetition.
  • the processor is the processor in the electronic device described in the above embodiments.
  • the readable storage medium includes a computer-readable storage medium, such as ROM, RAM, magnetic disk or optical disk, and the like.
  • An embodiment of the present disclosure further provides a chip, the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the above random access method embodiment Each process, and can achieve the same technical effect, in order to avoid repetition, will not repeat them here.
  • chips mentioned in the embodiments of the present disclosure may also be referred to as system-on-chip, system-on-chip, system-on-chip, or system-on-chip.
  • the embodiment of the present disclosure provides a computer program product containing instructions, which, when run on a computer, enables the computer to perform the steps of the above-mentioned random access method, and can achieve the same technical effect. To avoid repetition, it is not repeated here Let me repeat.
  • the term “comprising”, “comprising” or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article or apparatus comprising a set of elements includes not only those elements, It also includes other elements not expressly listed, or elements inherent in the process, method, article, or device. Without further limitations, an element defined by the phrase “comprising a " does not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising that element.
  • the scope of the methods and apparatus in the disclosed embodiments is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved. Functions are performed, for example, the described methods may be performed in an order different from that described, and various steps may also be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.
  • the methods of the above embodiments can be implemented by means of software plus a necessary general-purpose hardware platform, and of course also by hardware, but in many cases the former is better implementation.
  • the technical solution of the present disclosure can be embodied in the form of a software product in essence or the part that contributes to the prior art, and the computer software product is stored in a storage medium (such as ROM/RAM, disk, CD) contains several instructions to enable a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in various embodiments of the present disclosure.
  • a terminal which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.

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Abstract

本公开提供了一种随机接入方法、网络设备及UE，属于通信技术领域。该方法包括：根据第一信息，生成目标信息；向UE发送该目标信息，以使得UE根据该目标信息确定发送随机接入消息的TA值；其中，该目标信息包括以下至少一项：第一模型的指示信息、第一模型的模型参数的配置信息、第二信息与TA的关联关系；第一模型用于UE根据第二信息确定TA值。基于本公开实施例提供的随机接入方法，可以基于人工智能有机器学习或者非智能方式降低随机接入过程中UE的接入时延，提高UE接入成功率。

Description

随机接入方法、网络设备及UE
相关申请的交叉引用
本申请是以CN申请号为202110891646.3，申请日为2021年8月4日的申请为基础，并主张其优先权，该CN申请的公开内容在此作为整体引入本申请中。
技术领域
本公开属于通信技术领域，具体涉及一种随机接入方法、网络设备及UE。
背景技术
随着通信技术的发展，为了满足URLLC(Ultra Reliable Low Latency Communication，超高可靠超低时延通信)场景的低时延需求，3GPP规定5G(5th Generation Mobile Communication Technology，第五代移动通信技术)的NR(New Radio，新空口)支持UE(User Equipment，用户设备)采用短帧结构传输。
相关技术中，5G NR标准支持快速随机接入以降低UE的接入时延，例如采用2步随机接入。
发明内容
第一方面，本公开实施例提供了一种随机接入方法，应用于网络设备，该方法包括：根据第一信息，生成目标信息；向UE发送该目标信息，以使得UE根据该目标信息确定发送随机接入消息的TA值；其中，该目标信息包括以下至少一项：第一模型的指示信息、第一模型的模型参数的配置信息、第二信息与TA的关联关系；第一模型用于UE根据第二信息确定TA。
第二方面，本公开实施例提供了一种网络设备，该网络设备包括：生成模块和发送模块；该生成模块，用于根据第一信息，生成目标信息；该发送模块，用于向UE发送该生成模块生成的目标信息，以使得UE根据该目标信息确定发送随机接入消息的TA值；其中，该目标信息包括以下至少一项：第一模型的指示信息、该第一模型的模型参数的配置信息、第二信息与TA的关联关系；该第一模型用于UE根据第二信息确定TA。
第三方面，本公开实施例提供了一种随机接入方法，应用于网络设备，该方法包括： 根据目标信息，确定发送随机接入消息的TA值；根据该TA值发送随机接入消息；其中，该目标信息为网络设备发送的；该目标信息包括以下至少一项：第一模型的指示信息、该第一模型的模型参数的配置信息、第二信息与TA的关联关系；该第一模型用于该UE确定TA。
第四方面，本公开实施例提供了一种UE，该UE包括：确定模块和发送模块；该确定模块，用于根据目标信息，确定发送随机接入消息的TA值；该发送模块，用于根据确定模块确定的TA值发送随机接入消息；其中，该目标信息为网络设备发送的；该目标信息包括以下至少一项：第一模型的指示信息、该第一模型的模型参数的配置信息、第二信息与TA的关联关系；该第一模型用于UE确定TA。
第五方面，本公开实施例提供了一种网络设备，该网络设备包括处理器、存储器及存储在所述存储器上并可在所述处理器上运行的程序或指令，所述程序或指令被所述处理器执行时实现如第一方面所述的随机接入方法的步骤。
第六方面，本公开实施例提供了一种UE，该UE包括处理器、存储器及存储在所述存储器上并可在所述处理器上运行的程序或指令，所述程序或指令被所述处理器执行时实现如第一方面所述的随机接入方法的步骤。
第七方面，本公开实施例提供了一种可读存储介质，所述可读存储介质上存储程序或指令，所述程序或指令被处理器执行时实现如第一方面或第三方面所述的随机接入方法的步骤。
第八方面，本公开实施例提供了一种芯片，所述芯片包括处理器和通信接口，所述通信接口和所述处理器耦合，所述处理器用于运行程序或指令，实现如第一方面所述的随机接入方法。
第九方面，本公开实施例提供了一种包含指令的计算机程序产品，当其在计算机上运行时，使得计算机执行如第一方面所述的随机接入方法的步骤。
第十方面，本公开实施例提供了一种无线通信系统，包括本公开任意实施例所述的网络设备。
附图说明
图1为本公开实施例提供的一种无线通信系统的示意图；
图2为本公开实施例提供的随机接入方法的流程示意图之一；
图3为本公开实施例提供的随机接入方法的流程示意图之二；
图4为本公开实施例提供的随机接入方法的流程示意图之三；
图5为本公开实施例提供的一种网络设备可能的结构示意图之一；
图6为本公开实施例提供的一种UE可能的结构示意图之一；
图7为本公开实施例提供的一种网络设备可能的硬件示意图之二；
图8为本公开实施例提供的一种UE可能的硬件示意图之二；
图9为本公开实施例提供的一种网络设备的硬件示意图；
图10为本公开实施例提供的一种UE的硬件示意图。
具体实施方式
下面将结合本公开实施例中的附图，对本公开实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本公开一部分实施例，而不是全部的实施例。基于本公开中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本公开保护的范围。
本公开的说明书和权利要求书中的术语“第一”、“第二”等是用于区别类似的对象，而不用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换，以便本公开的实施例能够以除了在这里图示或描述的那些以外的顺序实施，且“第一”、“第二”等所区分的对象通常为一类，并不限定对象的个数，例如第一对象可以是一个，也可以是多个。此外，说明书以及权利要求中“和/或”表示所连接对象的至少其中之一，字符“/”，一般表示前后关联对象是一种“或”的关系。
值得指出的是，本公开实施例所描述的技术不限于LTE(Long Term Evolution，长期演进型)/LTE-A(LTE-Advanced，LTE的演进)系统，还可用于其他无线通信系统，诸如CDMA(Code Division Multiple Access，码分多址)、TDMA(Time Division Multiple Access，时分多址)、FDMA(Frequency Division Multiple Access，频分多址)、OFDMA(Orthogonal Frequency Division Multiple Access，正交频分多址)、SC-FDMA(Single-carrier Frequency-Division Multiple Access，单载波频分多址)和其他系统。本公开实施例中的术语“系统”和“网络”常被可互换地使用，所描述的技术既可用于以上提及的系统和无线电技术，也可用于其他系统和无线电技术。然而，以下描述出于示例目的描述了NR系统，并且在以下大部分描述中使用NR术语，尽管这些技术也可应用于NR系统 应用以外的应用，如6G(6th Generation，第6代)通信系统。
下面结合附图，通过具体的实施例及其应用场景对本公开实施例提供的随机接入方法进行详细地说明。
相关技术中，由于UE在随机接入之前无法获知上行TA(Timing Advance，定时提前)，在小区的覆盖范围较大或者传播路径复杂(例如密集城市环境)的情况下，短帧结构中的CP(cyclic prefix，循环前缀)和/或GT(Guard Time，保护时间)的长度会缩短，若无法满足需求，短帧情况下，在进行2步随机接入或4步随机接入时，随机接入消息的符号间的干扰和载波间的干扰会显示降低Msg1或MsgA的解调成功率，从而导致多次重传随机接入消息，使得UE的接入时延增加。
即，由于UE在随机接入之前无法获知上行TA(Timing Advance，定时提前)，因此会在帧结构中增加CP和/或GT以满足时延需求。在小区的覆盖范围较大或者传播路径复杂(例如密集城市环境)的情况下，需要更长的CP和/或GT，而短帧结构中的CP和/或GT的长度短，无法满足这种情况下的时延需求。因此，在小区的覆盖范围较大或者传播路径复杂(例如密集城市环境)的情况下，UE进行随机接入时，由于短帧结构中的CP和/或GT无法满足时延需求，随机接入消息的符号间的干扰和载波间的干扰会显示降低Msg1(Message 1，信令1)或MsgA(Message A，信令A)的解调成功率，从而导致多次重传随机接入消息，使得UE的接入时延增加。
本公开实施例的目的是提供一种随机接入方法、网络设备及UE，能够解决在随机接入过程中接入时延长的问题。
图1为本公开实施例提供的一种无线通信系统的示意图。
如图1中所示，该无线通信系统包括：UE 101和网络设备102。UE 101也可以称作终端设备或者用户终端(User Equipment，UE)。在一些实施例中，UE 101可以是手机、平板电脑(Tablet Personal Computer)、膝上型电脑(Laptop Computer)或称为笔记本电脑、个人数字助理(Personal Digital Assistant，PDA)、掌上电脑、上网本、超级移动个人计算机(ultra-mobile personal computer，UMPC)、移动上网装置(Mobile Internet Device，MID)、可穿戴式设备(Wearable Device)或车载设备(VUE)、行人终端(PUE)等终端侧设备。可穿戴式设备包括手环、耳机、眼镜等。需要说明的是，在本公开实施例并不限定UE 101的具体类型。网络设备102可以是基站、核心网或OAM(Operation Administration and Maintenance，操作维护管理)，其中，基站可被称为节点B、演进节点B、接入点、 BTS(Base Transceiver Station，基收发机站)、无线电基站、无线电收发机、BSS(Basic Service Set，基本服务集)、ESS(Extended Service Set，扩展服务集)、B节点、eNB(演进型B节点)、家用B节点、家用演进型B节点、WLAN接入点、WiFi节点、TRP(Transmitting Receiving Point，发送接收点)或所述领域中其他某个合适的术语。只要达到相同的技术效果，所述基站不限于特定技术词汇。需要说明的是，在本公开实施例中仅以NR系统中的基站为例，但是并不限定基站的具体类型。
图2为本公开实施例提供的一种随机接入方法的流程示意图。
如图2中所示，该方法包括下述的S201和S202：
S201、网络设备根据第一信息，生成目标信息。
在一些实施例中，网络设备可以为基站，也可以为OAM。
第一信息可以为网络设备的属性信息，例如网络制式、网络性能、设备地址等，也可以为网络设备采集的信息，也可以为网络设备接收到小区内UE上报的测量信息，也可以为小区内UE传输的任意有用的上行信号。
在一些实施例中，网络设备可以根据第一信息静态生成目标信息，也可以根据第一信息半静态生成目标信息。例如，静态生成目标信息包括生成目标信息后不再更新。半静态生成目标信息包括生成目标信息后定时或不定时更新目标信息。
可以理解，网络设备可以一次生成用于UE计算TA的目标信息，后续直接使用该目标信息，也可以周期性地根据第一信息生成用于UE计算TA的目标信息。
S202、网络设备向UE发送目标信息，以使得UE根据目标信息确定发送随机接入消息的TA值。
在一些实施例中，网络设备可以通过广播信道的系统信息发送目标信息。在另一些实施例中，网络设备也可以通过无线资源控制(Radio Resource Control，RRC)信令和/或媒体接入控制-控制元素(Medium Access Control-Control Element，MAC-CE)信令和/或下行控制信息(Downlink Control Information，DCI)信令发送目标信息，或者也可以通过上述信令指示物理下行共享信道PDSCH用于目标信息的接收。
目标信息可以包括以下(1)至(3)中的至少一项:
(1)第一模型的指示信息。
第一模型用于UE确定TA。
可以理解，第一模型的指示信息用于指示UE采用或选择第一模型计算TA值。
在本公开实施例中，第一模型可以为AI(Artificial Intelligence，人工智能)模型或MI(Machine Learning，机器学习)模型。
在一些实施例中，第一模型可以为：深度脉冲神经网络模型、深度卷积神经网络模型、超分辨模型、压缩感知模型。
在一些实施例中，UE中可以不包括计算TA的第一模型，网络设备可以向UE发送第一模型，并指示UE使用第一模型计算TA；UE中也可以包括多个计算TA的模型，网络设备可以直接指示UE使用的模型。
需要说明的是，在网络设备和UE至少一个具备人工智能能力或者机器学期能力的情况下，网络设备可以基于AI/MI模型的物理层对随机接入消息的传输进行设计增强。例如，可以对2步随机接入Msg1(消息1)的传输进行设计增强，可以对4步随机接入MsgA(消息A)的传输进行设计增强。
(2)第一模型的模型参数的配置信息。
可以理解，网络设备可以指示UE基于网络设备指示第一模型的模型参数的配置信息，使用第一模型计算TA值。
在一些实施例中，网络设备可以指示UE使用第一配置信息对应的模型参数，也可以指示UE使用第二配置信息对应的模型参数。例如，网络设备可以指示UE使用第一组权值，网络设备也可以指示UE使用第二组权值。
(3)第二信息与TA的关联关系。
在一些实施例中，该第二信息与TA的关联关系可以为第二信息的类型与TA的关联关系，也可以为第二信息的测量值与TA的关联关系。
在一些实施例中，网络设备可以为小区覆盖范围内的UE指示第二信息与TA的关联关系，以使得该小区覆盖范围内的UE，根据网络设备指示的第二信息与他的关联关系，确定在随机接入过程中向网络设备发送随机接入消息的TA值。在一些实施例中，小区覆盖范围内的UE，也可以理解为网络设备服务的UE。
在上述实施例中，通过广播灵活支持非智能化增强方案(通过第二信息与TA的关联关系确定TA)和RAN侧智能化增强(通过第一模型确定TA)。
在一些实施例中，在UE采用2步随机接入时，上述的随机接入消息可以为MsgA；在UE采用4步随机接入时，上述的随机接入消息可以为Msg1。
UE可以通过Msg1发送前导序列，通过MsgA向网络设备发送前导序列和负载消息。 该负载消息可以包括RRC(Radio Resource Control，指无线资源控制)连接请求、UE的小数据包等。
可以理解，网络设备为UE设定了初始定时提前，可以增强随机接入消息1负载信息接收的可靠性。
需要说明的是，上述的TA值为UE发送随机接入消息的初始TA值，可以记为TA0。
在一些实施例中，UE在采用短帧结构传输的情况下或者传输小数据包的情况下，可以根据目标信息确定发送随机接入消息的TA值。短帧结构中，一个slot可以由2、4或7个OFDM符号组成。
在一些实施例中，在短帧结构传输的场景中，网络设备可以针对UE发送2步随机接入的随机接入消息MsgA的传输进行增强，也可以针对UE发送4步随机接入的随机接入消息Msg1的传输进行增强。通过为UE配置发送随机接入消息的初始TA，相当于补偿了短帧结构中的CP和/或GT，从而可以降低干扰的概率，降低UE接入网络设备的时延，增加了随机接入的可靠性，提高网络设备对2步或4步随机接入的随机接入消息的解调成功率。
可以理解的是，若随机接入消息中携带上行负载，采用本公开实施例提供的技术方案，网络设备可以对齐随机接入消息的解调窗口，可以提高网络设备对随机接入消息中的前导序列和上行负载的解调成功率。
3GPP REL-17中定义了小数据包的传输，允许UE在RRC_INACTIVE状态利用2-step RACH(Random Access Channel，随机接入信道)或者4-step RACH传输小数据包。可以理解，UE可以采用普通帧结构传输小数据包，也可以采用短帧结构传输小数据包。
在一些实施例中，在小数据包传输的场景中，网络设备可以对UE发送的2步或者4步随机接入消息的传输进行增强，从而可以降低UE在采用随机接入消息传输小数据包的随机接入的时延，可以使得网络设备对齐解调窗口，可以提高网络设备对小数据包的解调成功率。
需要说明的是，上述的场景仅为公开实施例提供的示例性性说明，并不对本公开的技术方案的保护范围进行限定，本公开实施例提供的随机接入方法可应用于任意一种需要对随机接入消息的发送进行增强的场景中。
本公开实施例提供的随机接入方法，网络设备可以根据第一信息，生成目标信息；然后网络设备向UE发送目标信息，以使得UE可以根据目标信息确定发送随机接入消息的 TA值；由于该目标信息包括：第一模型的指示信息、第一模型的模型参数的配置信息，第二信息与TA的关联关系中的至少一项，因此，可以使得UE根据网络设备指示的第一模型的指示信息采用第一模型计算发送随机接入消息的TA值；UE也可以采用第一模型，基于网络设备指示的第一模型的模型参数的配置信息，计算TA值；UE也可以根据第二信息，基于网络设备指示的第二信息与TA的关联关系计算TA值；即UE在进行随机接入过程时，先根据网络设备指示的目标信息确定发送随机接入消息的TA值，从而可以避免在小区覆盖范围较大，或者传播路径复杂的情况下，UE不知道发送随机接入消息的TA导致的UE的接入成功率低，多次重复尝试接入导致接入时延变大。采用本公开实施例提供的随机接入方法，网络设备可以对随机接入消息的发送进行增强，从而可以提高随机接入的成功率，降低UE的接入时延，特别是小区边缘UE的随机接入时延，提高网络设备对随机接入消息的解调成功率。在一些实施例中，UE在采用短帧结构或者发送小数据包的情况下，若进行2步随机接入，可以大幅度提高MsgA上行负载的解调成功率，降低UE的接入时延，特别是小区边缘UE的随机接入时延。
在一些实施例中，在本公开实施例提供的随机接入方法中，第一信息可以包括以下(a)至(e)中的至少一项：
(a)网络设备所在小区的覆盖信息
在一些实施例中，小区覆盖信息可以包括以下至少一项：地理覆盖信息、基站坐标、覆盖地理范围、电子地图信息等。在一些实施例中，网络设备所在小区的覆盖信息可以是网络设备采集的，也可以是其他设备采集后发送给网络设备的。例如，还可以对网络设备所在小区的覆盖信息进行预处理后作为第一信息。
(b)UE上报的测量结果
需要说明的是，该测量上报为UE上报的常规的测量结果，也可以为根据实际需要新增的其他测量结果。在一些实施例中，UE上报的测量结果可以是网络设备从UE一侧采集的，也可以是其他设备从UE一侧采集后发送给网络设备的。例如，还可以对UE上报的测量结果进行预处理后作为第一信息。在一些实施例中，该测量结果可以为其他UE实时上报的测量结果，也可以为进行随机接入的UE历史上报的测量结果。
(c)目标上行信号
需要说明的是，网络设备可以根据任何可以表征终端能力的上行信号，生成目标信息。由于此处是上行信号，上行信号来自于终端，因此其表征的是终端能力。在一些实施例中， 目标上行信号可以是网络设备从UE一侧采集的，也可以是其他设备从UE一侧采集后发送给网络设备的。例如，还可以对目标上行信号进行预处理后作为第一信息。
在一些实施例中，目标上行信号可以包括以下至少一项：上行数据信号、参考信号、控制信号。
(d)第一模型
可以理解，该第一模型可以为网络设备中配置的模型，也可以为UE中配置的模型。
需要说明的是，网络设备或UE中均可以配置至少一个模型，网络设备可以为UE指示使用哪一个模型。网络设备也可以向UE发送第一模型。
网络设备可以结合第一模型或者第一模型和第一模型的输入信息生成目标信息。
在一些实施例中，模型的输入信息可以为模型的推理信息，该模型可以使用用户位置坐标、用户测量参考信息的接收功率，基站的覆盖地理信息等计算TA值。
(e)第一模型的模型参数的配置信息
可以理解，UE中的模型的权重值的配置可以为网络设备指示的，也可以为默认配置的。网络设备可以指示UE采用不同权重值的模型参数。
例如，网络设备可以根据不同的第一信息，确定出不同的模型参数。
在一些实施例中，网络设备可以根据不同时间的第一信息，确定出不同的模型参数的配置信息。
基于该方案，网络设备可以基于网络设备所在小区的覆盖信息、UE上报的测量结果、目标上行信号、第一模型、第一模型的模型参数的配置信息中的至少一个生成目标信息，使得生成的目标信息可以真实的反应网络设备的传输参数，可以使得确定的目标信息中的TA值能准确匹配到网络设备的传输时间，避免UE在随机接入过程中接入失败导致的多次重传，提高接入成功率。
在一些实施例中，在本公开实施例提供的随机接入方法中，第二信息与TA的关联关系包括以下至少一项：路径损耗与TA的关联关系、目标信号与TA的关联关系、目标信号的预设参数的值与TA的关联关系。
在一些实施例中，目标信号可以为：下行数据信号、控制信道信号、参考信号，或广播信号、GPS(Global Positioning System，全球定位系统)地理位置信号。
需要说明的是，上述的目标信号为UE侧接收的信号。
示例1-1：网络设备可以向UE发送路径损耗与TA的关联关系，以使得UE可以根据 路径损耗值，以及路径损耗与TA的关联关系确定TA值。
示例1-2：网络设备可以向UE发送目标信号与TA的关联关系，以使得UE可以根据指示的目标信号，确定该目标信号对应的TA值。
比如，信号1对应TA 1、信号2对应TA 2、信号3对应TA 3。
示例1-3：网络设备可以向UE发送目标信号的预设参数值的值，以使得UE可以根据指示的目标信号的预设参数的值，确定目标信号的预设参数的值对应的TA值。
其中，预设参数可以为传输功率。
比如，以参考信号的传输功率为例，参考信号的传输功率1对应TA 1，参考信号的传输功率2对应TA 2，参考信号的传输功率3对应TA 3。
基于该方案，网络设备可以灵活采用上述三种方式中的任意一种方式，为UE指示第二信息与TA的关联关系，可以使得UE可以根据测量的路径损耗确定TA，也可以使得UE根据不同类型的目标信号确定TA，也可以使得UE根据测量的目标信号的预设参数的值确定TA。
在一些实施例中，在本公开实施例提供的随机接入方法中，第二信息与TA的关联关系包括以下至少一项：第二信息与TA值的映射表、第二信息与TA值的映射关系、第二信息与TA值的计算公式。
可以理解，若目标信息中第二信息与TA的关联关系为第二信息与TA值的映射表，则UE可以通过查表的方式获取TA值；若目标信息中第二信息与TA的关联关系为第二信息与TA值的映射关系，则UE可以通过关系映射获取TA值；若目标信息中第二信息与TA的关联关系为第二信息与TA值的计算公式，则UE可以基于计算公式计算TA值。
在一些实施例中，UE侧可以支持不同典型网络场景的TA值的多种计算公式，网络设备可以通知UE使用该多种计算公式中的一种，并通知公式中的具体参数配置值。
基于该方案，网络设备可以在目标信息中携带上述任一种第二信息与TA的关联关系，配置方式灵活多样，可以使得UE在接收到目标信息后，可以直接根据上述形式的关联关系，灵活根据第二信息与TA值的映射表，第二信息与TA值的映射关系、第二信息与TA值的计算公式，灵活确定TA值。
在一些实施例中，在本公开实施例提供的随机接入方法中，目标信息还可以包括：模型的更新信息或模型的训练信息。模型的训练信息包括：训练模型的参数配置和模型训练的数据中的至少一项；其中，模型的更新信息用于UE更新第一模型；模型的训练信息用 户UE训练第一模型。
可以理解，网络设备可以为UE指示模型的训练信息，使得UE可以根据网络设备指示的模型的训练信息训练计算TA的模型，然后根据目标信息中指示的其他可以作为模型输入的参数的值，根据训练好的模型得到TA值。
可以理解，网络设备还可以指示UE更新计算TA的模型，在UE中的模型计算的不够准确的情况下，可以使得UE根据模型更新的信息，对模型进行调优，例如网络设备可以为UE指示输入数据和实际的TA值，使得模型根据输入数据和实际的TA值对模型的参数进行调整。
基于该方案，网络设备还可以为UE发送模型的更新信息或者模型的训练信息，可以使得UE可以根据网络设备发送的模型更新信息，更新计算TA的模型，使得UE可以根据网络设备发送的模型的训练信息，训练计算TA的模型。
在一些实施例中，在本公开实施例提供的随机接入方法中，在上述的S201之前，还可以包括下述的S203：
S203、在满足预设条件的情况下，网络设备更新第一信息。
预设条件包括以下至少一项：网络设备的网络测量信号发生变化、网络设备的网络环境发生变化、网络设备的地理环境发生变化、UE随机接入失败率高于预设值。
在一些实施例中，OAM检测网络测量信号发生变化，例如网络测量信号由信号1变化为信号2。基站检测到运营商调整了网络参数配置、基站天线面板物理朝向发生变化、基站天线倾角发生变化、基站覆盖范围内新建了楼宇基站，基站覆盖范围内拆除了楼宇基站。基站统计一个时间段内UE随机接入失败率高于预设值。
在一些实施例中，基站或者OAM可以自动检测是否满足预设条件，根据网络测量信号变化、网络环境变化、地理环境变化等及时更新与变化后的环境更加匹配的第一信息。
基于该方案，网络设备可以自动检测是否需要更新第一信息，若满足第一信息更新的预设条件，则网络设备可以进行更新，得到与当前状态匹配的第一信息，以使得网络设备可以根据最新的第一信息生成目标信息，从而使得UE确定的TA更加符合网络设备的当前状态，增加了UE接入的可靠性，降低了UE接入的时延。
图3为本公开实施例提供的一种随机接入方法的流程示意图，如图3中所示，该方法可以包括下述的S301和S302：
S301、UE根据目标信息，确定发送随机接入消息的TA值。
目标信息为网络设备发送的。例如，目标信息可以为携带在广播消息中，UE接收网络设备广播的广播消息以确定目标信息。
在一些实施例中，目标信息包括以下至少一项：第一模型的指示信息、第一模型的模型参数的配置信息、第二信息与TA的关联关系。
第一模型用于UE确定TA值。
在一些实施例中，第一模型的指示信息可以包括基站指示UE选择使用的模型的信息，也可以包括第一模型。
在一些实施例中，UE在采用短帧结构传输、或者传输小数据包的情况下，在进行随机接入前，UE可以执行上述的S301。
在一些实施例中，上述随机接入消息可以为2步随机接入过程中的Msg 1，也可以为4步随机接入过程中的Msg A。其中，在采用2步随机接入时，UE可以通过Msg 1发送前导序列和负载消息，负载消息可以包括RRC连接请求或者UE的小数据包等。在采用4步随机接入时，UE可以通过Msg A发送前导序列。
在本公开实施例中，UE可以在发送Msg 1或Msg A之前，通过上述的S301获取发送Msg 1的上行TA值。
S302、UE根据该TA值发送随机接入消息。
可以理解，UE在发起随机接入过程之前，可以根据检测广播信道的系统信息获取目标信息，根据目标信息确定发送随机接入消息的TA值。
在一些实施例中，UE可以根据第一模型的指示信息，确定计算TA的模型为第一模型，然后根据第一模型和第一模型对应的输入信息计算TA值。
UE可以根据第一模型的模型参数的配置信息，确定计算TA的第一模型中的模型参数的配置信息，然后基于该模型参数的配置信息的第一模型和第一模型对应的输入信息计算TA。
第一模型对应的输入信息可以为UE测量的值。
UE可以根据第二信息与TA的关联关系，根据第二信息的具体内容确定发送随机接入消息的TA值。
本公开实施例提供的随机接入方法，在随机接入之前，UE可以根据目标信息可以确定发送随机接入消息的TA值，然后在发起随机接入时采用该TA值发送随机接入消息。由于该目标信息包括：第一模型的指示信息、第一模型的模型参数的配置信息，第二信息与TA 的关联关系中的至少一项，因此，可以使得UE根据网络设备指示的第一模型的指示信息采用第一模型计算发送随机接入消息的TA值；UE也可以采用第一模型，基于网络设备指示的第一模型的模型参数的配置信息，计算TA值；UE也可以根据第二信息，基于网络设备指示的第二信息与TA的关联关系计算TA值；即UE在进行随机接入过程时，先根据网络设备指示的目标信息确定发送随机接入消息的TA值，即可以预先确定发送随机接入消息的TA值，从而可以避免在小区覆盖范围较大，或者传播路径复杂的情况下，UE不知道发送随机接入消息的TA导致的UE的接入成功率低，多次重复尝试接入导致接入时延变大。采用本公开实施例提供的随机接入方法，网络设备可以对随机接入消息的发送进行增强，从而可以提高随机接入的成功率，降低UE的接入时延，特别是小区边缘UE的随机接入时延，提高网络设备对随机接入消息的解调成功率。示例性地，UE在采用短帧结构或者发送小数据包的情况下，若进行2步随机接入，可以大幅度提高MsgA上行负载的解调成功率，降低UE的接入时延，特别是小区边缘UE的随机接入时延。
在一些实施例中，第二信息与TA的关联关系包括以下至少一项：路径损耗与TA的关联关系、目标信号与TA的关联关系、目标信号的预设参数的值与TA的关联关系。预设参数的值可以为RSRP、RSRQ或SINR。预设参数的值可以为线性值或dB(增益)值，也可以是量化等级。在预设参数的值为量化等级的情况下，可以减少信令负荷。
在一些实施例中，上述第二信息与TA的关联关系可以是一对一的数值之间的映射，也可以是多对一的数值之间的映射，也可以是一个区间范围对一个数值的映射，还可以是根据计算公式进行的关联关系映射。
在一些实施例中，若目标信息中指示了路径损耗与TA的关联关系，则UE可以根据路径损耗与TA的关联关系，基于测量得到的路径损耗确定TA值；若目标信息中指示了目标信息与TA的关联关系，则UE可以根据目标信号的类型可以确定TA值；若目标信息中指示了目标信号的预设参数的值与TA的关联关系，则UE可以根据测量得到的预设参数的值确定TA。
在一些实施例中，目标信号可以为：下行数据信号、控制信道信号、参考信号，广播信号、或GPS地理位置信号。
例如，UE可以根据得到的下行链路测量值确定路径损耗值，然后根据该路径损耗值，以及路径损耗与TA的关联关系，确定该路径损耗值对应的TA值。
基于该方案，UE可以基于不同类型的第二信息与TA值的关联关系，灵活根据实际的 使用场景，采用不同的第二信息确定相应的TA值。
在一些实施例中，在本公开实施例提供的随机接入方法中，第二信息与TA的关联关系，包括以下至少一项：第二信息与TA值的映射表、第二信息与TA值的映射关系、第二信息与TA值的计算公式。
基于该方案，UE可以采用不同形式的第二信息与TA的关联关系，灵活确定发送随机接入消息的TA值。
在一些实施例中，在本公开实施例提供的随机接入方法中，上述的S301具体可以通过下述的S31执行：
S31、UE根据目标信息对应的测量值，确定发送随机接入消息的TA值。
在一些实施例中，目标信息对应的测量值，可以为UE获得的下行链路测量值、其他测量信息中的至少一个。
例如，第一模型及第一模型的模型参数的配置信息对应的测量值包括但不限于参考信号接收功率(Reference Signal Receiving Power，RSRP)、参考信号接收质量(Reference Signal Received Quality，RSRQ)、信干噪比(Signal Noise Ratio，SINR)。在一些实施例中，可以基于同步广播块(Synchronization Signal Block，SSB)测量获得RSRP，也可以是基于信号状态信息中的参考信号(Channel State Information Reference Signal，CSI-RS)测量获得RSRP、RSRQ或SINR，还可以是针对物理下行控制信道(Physical Downlink Control Channel，PDCCH)、物理下行数据信道(Physical Downlink Shared Channel，PDSCH)或解调参考信号(Demodulation Reference Signal，DMRS)测量得到的上述测量值。例如，UE可以将这些测量值输入到第一模型中，得到TA值。
例如，第二信息与TA的关联关系对应的测量值为与第二信息相对应的测量值。
基于该方案，UE可以根据目标信息进行测量，根据测量的结果确定发送随机接入消息的TA值。
在一些实施例中，在本公开实施例提供的随机接入方法中，上述的S31具体可以通过下述的S311或S312执行：
S311、UE基于所述目标信息和TA的关联关系，根据目标信息对应的测量值，确定发送随机接入消息的TA值。
在一些实施例中，可以将S311的确定方式定义为非智能的方式，UE可以根据查表、关系映射或者公式计算获得发送随机接入消息的TA值。
S312、UE基于第一模型，根据目标信息对应的测量值，确定发送随机接入消息的TA值。
在一些实施例中，可以将S312的确定方式定义为智能的方式，UE可以基于第一模型和UE的测量结果，计算获得发送随机接入消息的TA值。
在一些实施例中，目标信息包括：UE使用深度卷积神经网络模型、以及模型中各个参数的权重值、模型计算所需输入数据类型。则UE可以采用将目标信息指示的模型中各个参数的权重值，作为目标信息中指示的深度卷积神经网络模型的参数的权重值，然后采集目标信息指示的输入数据类型的数据的测量值，确定TA值。
基于该方案，若网络设备指示了UE目标信息和TA的关联关系，则UE可以基于目标信息和TA的关联关系确定TA值，若网络设备指示了UE计算TA的模型或者模型和模型的参数的配置信息，则UE可以基于指示的模型确定TA值。确定方式多样灵活，可以使得UE可以在发起随机接入之前，确定发送随机接入消息的TA值，增加了UE随机接入的成功率，减少了UE进行随机接入的接入时延。
在一些实施例中，在本公开实施例提供的随机接入方法中，目标信息还包括：第一模型的更新信息；进而上述的S301可以通过下述的S32和S33执行：
S32、UE基于第一模型的更新信息，更新第一模型。
在一些实施例中，第一模型的更新信息，可以包括第一模型中的计算规则、输入参数等，也可以包括更新后的第一模型。
S33、UE基于更新后的第一模型，确定发送随机接入消息的TA值。
基于该方案，UE可以基于网络设备指示的模型的更新信息，对计算TA的模型进行更新，从而可以及时优化模型的计算能力，使得根据更新后的模型计算的TA值，更加符合实际的需求。
在一些实施例中，在本公开实施例提供的随机接入方法中，目标信息还包括：第一模型的训练信息，模型的训练信息包括模型训练的参数配置和模型训练的数据中的至少一项；进而上述的S301可以通过下述的S34和S35执行：
S34、UE基于第一模型的训练信息，训练第一模型。
可以理解的是，网络设备可以为UE配置模型训练使用的训练信息，以使得UE在需要计算TA的情况下，可以先基于该网络设备指示的训练信息对计算TA的模型进行训练，从而得到第一模型。
例如，基站通知UE使用深度卷积神经网络模型、以及模型中各个参数的权重值、模型计算所需输入数据类型，模型训练所需的输入数据和输出数据。
在一些实施例中，模型的输入数量类型可以为UE的参考信号接收功率、地理位置信息等。模型训练所需的输入数据和输出数据可以包括之前时刻UE的参考信号接收功率RSRP、地理位置信息，以及随机接入完成后网络测量获得实际TA值。
S35、UE基于训练后的第一模型，确定发送随机接入消息的TA值。
需要说明的是，UE可以基于训练后的第一模型，结合目标信息中指示的模型的输入，推理出发送随机接入消息的TA值。
基于该方案，UE可以根据网络设备指示的模型的训练信息，对UE中的模型进行训练调优，可以使得UE根据模型推理的TA更加符合实际需求。
图4为本公开实施例提供的一种随机接入方法的交互流程图，如图4中所示，该随机接入方法可以包括下述的S401至S405：
S401、网络设备根据第一信息，生成目标信息。
S402、网络设备向UE发送目标信息，以使得UE根据目标信息确定发送随机接入消息的TA值。
S403、UE接收网络设备发送的目标信息。
S404、UE根据目标信息，确定发送随机接入消息的TA值。
S405、UE根据TA值，向网络设备发送随机接入消息。
需要说明的是，该方法实施例中的相关解释说明可以参考上述方法实施例中的描述，此处不再赘述。
基于该方案，网络设备可以根据第一信息生成目标信息，然后将目标信息发送给UE，UE在接收到网络设备发送的目标信息之后，可以基于该目标信息确定发送随机接入消息的TA值，然后UE可以基于该TA值，向网络设备发送随机接入消息。由于该目标信息包括：第一模型的指示信息、第一模型的模型参数的配置信息，第二信息与TA的关联关系中的至少一项，因此，可以使得UE根据网络设备指示的第一模型的指示信息采用第一模型计算发送随机接入消息的TA值；UE也可以采用第一模型，基于网络设备指示的第一模型的模型参数的配置信息，计算TA值；UE也可以根据第二信息，基于网络设备指示的第二信息与TA的关联关系计算TA值；即UE在进行随机接入过程时，先根据网络设备指示的目标信息确定发送随机接入消息的TA值，从而可以避免在小区覆盖范围较大，或者传播路 径复杂的情况下，UE不知道发送随机接入消息的TA导致的UE的接入成功率低，多次重复尝试接入导致接入时延变大。采用本公开实施例提供的随机接入方法，可以对随机接入消息的发送进行增强，从而可以提高随机接入的成功率，降低UE的接入时延，特别是小区边缘UE的随机接入时延，提高网络设备对随机接入消息的解调成功率。示例性地，UE在采用短帧结构或者发送小数据包的情况下，若进行2步随机接入，可以大幅度提高MsgA上行负载的解调成功率，降低UE的接入时延，特别是小区边缘UE的随机接入时延。
需要说明的是，本公开实施例提供的随机接入方法，执行主体还可以为随机接入装置，或者该随机接入装置中的用于执行随机接入方法的控制模块。本公开实施例中以随机接入装置执行随机接入的方法为例，说明本公开实施例提供的随机接入的装置。
图5为本公开实施例提供的一种网络设备的结构示意图，如图5中所示，网络设备500包括：生成模块501和发送模块502；生成模块501，用于根据第一信息，生成目标信息；发送模块502，用于向UE发送生成模块501生成的目标信息，以使得UE根据所述目标信息确定发送随机接入消息的TA值；其中，所述目标信息包括以下至少一项：第一模型的指示信息、所述第一模型的模型参数的配置信息、第二信息与TA的关联关系；所述第一模型用于UE根据第二信息确定TA。
在一些实施例中，所述第一信息包括以下至少一项：所述网络设备所在小区的覆盖信息、UE上报的测量结果、目标上行信号、所述第一模型、所述第一模型的模型参数的配置信息。
在一些实施例中，所述第二信息与TA的关联关系包括以下至少一项：路径损耗与TA的关联关系、目标信号与TA的关联关系、目标信号的预设参数的值与TA的关联关系。
在一些实施例中，所述第二信息与TA的关联关系，包括以下至少一项：所述第二信息与TA值的映射表、所述第二信息与TA值的映射关系、所述第二信息与TA值的计算公式。
在一些实施例中，所述目标信息还包括：模型的更新信息或模型的训练信息；所述模型的训练信息包括：模型训练的参数配置和模型训练的数据中的至少一项；其中，所述模型的更新信息用于所述UE更新所述第一模型；所述模型的训练信息用于UE训练所述第一模型。
在一些实施例中，网络设备还包括：更新模块；该更新模块，用于在满足预设条件的情况下，更新所述第一信息；其中，所述预设条件包括以下至少一项：所述网络设备的网 络测量信号发生变化、所述网络设备的网络环境发生变化、所述网络设备的地理环境发生变化、UE随机接入失败率高于预设值。
在一些实施例中，所述第一模型为人工智能模型或机器学习模型。
本公开实施例提供的网络设备500能够实现图1至图4方法实施例实现的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。
图6为本公开实施例提供的一种UE可能的结构示意图，如图6中所示，UE 600包括：确定模块601和发送模块602；确定模块601，用于根据目标信息，确定发送随机接入消息的TA值；发送模块502，用于根据确定模块601确定的所述TA值发送随机接入消息；其中，所述目标信息为网络设备发送的；所述目标信息包括以下至少一项：第一模型的指示信息、所述第一模型的模型参数的配置信息、第二信息与TA的关联关系；所述第一模型用于所述UE确定TA。
在一些实施例中，所述第二信息与TA的关联关系包括以下至少一项：路径损耗与TA的关联关系、目标信号与TA的关联关系、所述目标信号的预设参数的值与TA的关联关系。
在一些实施例中，确定模块具体用于：根据目标信息对应的测量值，确定发送随机接入消息的TA值。
在一些实施例中，确定模块具体用于：基于所述目标信息和TA的关联关系，根据所述目标信息对应的测量值，确定发送随机接入消息的TA值。
在一些实施例中，确定模块具体用于：基于所述第一模型，根据所述目标信息对应的测量值，确定发送随机接入消息的TA值。
在一些实施例中，所述第二信息与TA的关联关系，包括以下至少一项：所述第二信息与TA值的映射表、所述第二信息与TA值的映射关系、所述第二信息与TA值的计算公式。
在一些实施例中，所述目标信息还包括：第一模型的更新信息；确定模块具体用于：基于所述第一模型的更新信息，更新所述第一模型；基于更新后的所述第一模型，确定发送随机接入消息的TA。
在一些实施例中，所述目标信息还包括：第一模型的训练信息，所述模型的训练信息包括模型训练的参数配置和模型训练的数据中的至少一项；确定模块具体用于：基于所述第一模型的训练信息，训练所述第一模型；基于训练后的所述第一模型，确定发送随机接入消息的TA。
本公开实施例提供的UE 600能够实现图1至图4方法实施例实现的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。
在一些实施例中，如图7所示，本公开实施例还提供一种网络设备700，包括处理器701，存储器702，存储在存储器702上并可在处理器701上运行的程序或指令，该程序或指令被处理器701执行时实现上述随机接入方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。
在一些实施例中，如图8所示，本公开实施例还提供一种UE 800，包括处理器801，存储器802，存储在存储器802上并可在处理器801上运行的程序或指令，该程序或指令被处理器801执行时实现上述随机接入方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。
图9为本公开实施例提供的一种网络设备的硬件结构示意图。
需要说明的是，图9示出的网络设备900仅是一个示例，不应对本公开实施例的功能和使用范围带来任何限制。
如图9所示，网络设备900包括中央处理单元(Central Processing Unit，CPU)901，其可以根据存储在ROM(Read Only Memory，只读存储器)902中的程序或者从存储部分908加载到RAM(Random Access Memory，随机访问存储器)903中的程序而执行各种适当的动作和处理。在RAM 903中，还存储有系统操作所需的各种程序和数据。CPU 901、ROM 902以及RAM 903通过总线904彼此相连。I/O(Input/Output，输入/输出)接口905也连接至总线904。
以下部件连接至I/O接口905：包括键盘、鼠标等的输入部分906；包括诸如CRT(Cathode Ray Tube，阴极射线管)、LCD(Liquid Crystal Display，液晶显示器)等以及扬声器等的输出部分907；包括硬盘等的存储部分908；以及包括诸如LAN(Local Area Network，无线网络)卡、调制解调器等的网络接口卡的通信部分909。通信部分909经由诸如因特网的网络执行通信处理。驱动器910也根据需要连接至I/O接口905。可拆卸介质911，诸如磁盘、光盘、磁光盘、半导体存储器等等，根据需要安装在驱动器910上，以便于从其上读出的计算机程序根据需要被安装入存储部分908。
特别地，根据本公开的实施例，下文参考流程图描述的过程可以被实现为计算机软件程序。例如，本公开的实施例包括一种计算机程序产品，其包括承载在计算机可读介质上的计算机程序，该计算机程序包含用于执行流程图所示的方法的程序代码。在这样的实施 例中，该计算机程序可以通过通信部分909从网络上被下载和安装，和/或从可拆卸介质911被安装。在该计算机程序被中央处理单元(CPU 901)执行时，执行本公开的系统中限定的各种功能。
图10为本公开实施例提供的一种UE的硬件结构示意图，如图10中所示，该UE 1000包括但不限于：射频单元1001、网络模块1002、音频输出单元1003、输入单元1004、传感器1005、显示单元1006、用户输入单元1007、接口单元1008、存储器1009、以及处理器1010等部件。
本领域技术人员可以理解，UE 1000还可以包括给各个部件供电的电源(比如电池)，电源可以通过电源管理系统与处理器1010逻辑相连，从而通过电源管理系统实现管理充电、放电、以及功耗管理等功能。图10中示出的电子设备结构并不构成对电子设备的限定，电子设备可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置，在此不再赘述。
其中，处理器1010，用于根据目标信息，确定发送随机接入消息的TA值；射频单元1001，用于根据所述TA值发送随机接入消息；其中，所述目标信息为网络设备广播的；所述目标信息包括以下至少一项：第一模型的指示信息、所述第一模型的模型参数的配置信息、第二信息与TA的关联关系；所述第一模型用于所述UE确定TA。
可以理解，本公开实施例提供的UE 1000可以实现上述随机接入方法实施例中UE的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。
应理解的是，本公开实施例中，输入单元1004可以包括图形处理器(Graphics Processing Unit，GPU)1041和麦克风1042，图形处理器1041对在视频捕获模式或图像捕获模式中由图像捕获装置(如摄像头)获得的静态图片或视频的图像数据进行处理。显示单元1006可包括显示面板1061，可以采用液晶显示器、有机发光二极管等形式来配置显示面板1061。用户输入单元1007包括触控面板1071以及其他输入设备1072。触控面板1071，也称为触摸屏。触控面板1071可包括触摸检测装置和触摸控制器两个部分。其他输入设备1072可以包括但不限于物理键盘、功能键(比如音量控制按键、开关按键等)、轨迹球、鼠标、操作杆，在此不再赘述。存储器1009可用于存储软件程序以及各种数据，包括但不限于应用程序和操作系统。处理器1010可集成应用处理器和调制解调处理器，其中，应用处理器主要处理操作系统、用户界面和应用程序等，调制解调处理器主要处理无线通信。可以理解的是，上述调制解调处理器也可以不集成到处理器1010中。
本公开实施例还提供一种可读存储介质，所述可读存储介质上存储有程序或指令，该程序或指令被处理器执行时实现上述随机接入方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。
所述处理器为上述实施例中所述的电子设备中的处理器。所述可读存储介质，包括计算机可读存储介质，如ROM、RAM、磁碟或者光盘等。
本公开实施例另提供了一种芯片，所述芯片包括处理器和通信接口，所述通信接口和所述处理器耦合，所述处理器用于运行程序或指令，实现上述随机接入方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。
应理解，本公开实施例提到的芯片还可以称为系统级芯片、系统芯片、芯片系统或片上系统芯片等。
本公开实施例提供了一种包含指令的计算机程序产品，当其在计算机上运行时，使得计算机执行如上述的随机接入方法的步骤，且能达到相同的技术效果，为避免重复，这里不再赘述。
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者装置不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者装置所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者装置中还存在另外的相同要素。此外，需要指出的是，本公开实施方式中的方法和装置的范围不限按示出或讨论的顺序来执行功能，还可包括根据所涉及的功能按基本同时的方式或按相反的顺序来执行功能，例如，可以按不同于所描述的次序来执行所描述的方法，并且还可以添加、省去、或组合各种步骤。另外，参照某些示例所描述的特征可在其他示例中被组合。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本公开的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端(可以是手机，计算机，服务器，空调器，或者网络设备等)执行本公开各个实施例所述的方法。
上面结合附图对本公开的实施例进行了描述，但是本公开并不局限于上述的具体实施 方式，上述的具体实施方式仅仅是示意性的，而不是限制性的，本领域的普通技术人员在本公开的启示下，在不脱离本公开宗旨和权利要求所保护的范围情况下，还可做出很多形式，均属于本公开的保护之内。

Claims (22)

  1. 一种随机接入方法，应用于网络设备，所述方法包括：
    根据第一信息，生成目标信息；
    向用户设备UE发送所述目标信息，以使得UE根据所述目标信息确定发送随机接入消息的定时提前TA值；
    其中，所述目标信息包括以下至少一项：第一模型的指示信息、所述第一模型的模型参数的配置信息、第二信息与TA的关联关系；所述第一模型用于UE根据第二信息确定TA。
  2. 根据权利要求1所述的方法，其中，所述第一信息包括以下至少一项：所述网络设备所在小区的覆盖信息、UE上报的测量结果、目标上行信号、所述第一模型、所述第一模型的模型参数的配置信息。
  3. 根据权利要求1或2所述的方法，其中，所述第二信息与TA的关联关系包括以下至少一项：路径损耗与TA的关联关系、目标信号与TA的关联关系、目标信号的预设参数的值与TA的关联关系。
  4. 根据权利要求1至3任一项所述的方法，其中，所述第二信息与TA的关联关系，包括以下至少一项：所述第二信息与TA值的映射表、所述第二信息与TA值的映射关系、所述第二信息与TA值的计算公式。
  5. 根据权利要求1至4任一项所述的方法，其中，所述目标信息还包括：模型的更新信息或模型的训练信息；所述模型的训练信息包括：模型训练的参数配置和模型训练的数据中的至少一项；
    其中，所述模型的更新信息用于UE更新所述第一模型；所述模型的训练信息用于UE训练所述第一模型。
  6. 根据权利要求1至5任一项所述的方法，其中，所述根据第一信息，生成目标信息 之前，所述方法还包括：
    在满足预设条件的情况下，更新所述第一信息；
    其中，所述预设条件包括以下至少一项：所述网络设备的网络测量信号发生变化、所述网络设备的网络环境发生变化、所述网络设备的地理环境发生变化、UE随机接入失败率高于预设值。
  7. 根据权利要求1至6中任一项所述的方法，其中，所述第一模型为人工智能模型或机器学习模型。
  8. 一种随机接入方法，应用于用户设备UE，所述方法包括：
    根据网络设备发送的目标信息，确定发送随机接入消息的定时提前TA值；
    根据所述TA值发送随机接入消息；
    其中，所述目标信息包括以下至少一项：第一模型的指示信息、所述第一模型的模型参数的配置信息、第二信息与TA的关联关系；所述第一模型用于所述UE确定TA。
  9. 根据权利要求8所述的方法，其中，所述第二信息与TA的关联关系包括以下至少一项：路径损耗与TA的关联关系、目标信号与TA的关联关系、所述目标信号的预设参数的值与TA的关联关系。
  10. 根据权利要求8或9所述的方法，其中，所述根据目标信息，确定发送随机接入消息的TA值，包括：
    根据目标信息对应的测量值，确定发送随机接入消息的TA值。
  11. 根据权利要求10所述的方法，其中，所述根据目标信息对应的测量值，确定发送随机接入消息的TA值，包括：
    基于所述目标信息和TA的关联关系，根据所述目标信息对应的测量值，确定发送随机接入消息的TA值。
  12. 根据权利要求10所述的方法，其中，所述根据目标信息对应的测量值，确定发送 随机接入消息的TA值，包括：
    基于所述第一模型，根据所述目标信息对应的测量值，确定发送随机接入消息的TA值。
  13. 根据权利要求8至12任一项所述的方法，其中，所述第二信息与TA的关联关系，包括以下至少一项：所述第二信息与TA值的映射表、所述第二信息与TA值的映射关系、所述第二信息与TA值的计算公式。
  14. 根据权利要求8至13任一项所述的方法，其中，所述目标信息还包括：第一模型的更新信息；所述根据目标信息，确定发送随机接入消息的TA值，包括：
    基于所述第一模型的更新信息，更新所述第一模型；
    基于更新后的所述第一模型，确定发送随机接入消息的TA值。
  15. 根据权利要求8至13任一项所述的方法，其中，所述目标信息还包括：第一模型的训练信息，所述模型的训练信息包括模型训练的参数配置和模型训练的数据中的至少一项；所述根据目标信息，确定发送随机接入消息的TA值，包括：
    基于所述第一模型的训练信息，训练所述第一模型；
    基于训练后的所述第一模型，确定发送随机接入消息的TA值。
  16. 一种网络设备，包括：生成模块和发送模块；
    所述生成模块，用于根据第一信息，生成目标信息；
    所述发送模块，用于向用户设备UE发送所述生成模块生成的目标信息，以使得UE根据所述目标信息确定发送随机接入消息的定时提前TA值；
    其中，所述目标信息包括以下至少一项：第一模型的指示信息、所述第一模型的模型参数的配置信息、第二信息与TA的关联关系；所述第一模型用于UE根据第二信息确定TA。
  17. 一种用户设备UE，包括：确定模块和发送模块；
    所述确定模块，用于根据网络设备发送的目标信息，确定发送随机接入消息的定时提 前TA值；
    所述发送模块，用于根据确定模块确定的TA值发送随机接入消息；
    其中，所述目标信息包括以下至少一项：第一模型的指示信息、所述第一模型的模型参数的配置信息、第二信息与TA的关联关系；所述第一模型用于所述UE确定TA。
  18. 一种网络设备，包括处理器，存储器及存储在所述存储器上并可在所述处理器上运行的程序或指令，所述程序或指令被所述处理器执行时实现如权利要求1至7中任一项所述的随机接入方法的步骤。
  19. 一种用户设备UE，包括处理器，存储器及存储在所述存储器上并可在所述处理器上运行的程序或指令，所述程序或指令被所述处理器执行时实现如权利要求8至15中任一项所述的随机接入方法的步骤。
  20. 一种可读存储介质，其中，所述可读存储介质上存储程序或指令，所述程序或指令被处理器执行时实现如权利要求1至15中任一项所述的随机接入方法的步骤。
  21. 一种无线通信系统，包括：
    如权利要求16或18任一项所述的网络设备。
  22. 根据权利要求21所述的无线通信系统，其中，所述网络设备包括基站或操作维护管理OAM设备。
PCT/CN2021/136000 2021-08-04 2021-12-07 随机接入方法、网络设备及ue Ceased WO2023010735A1 (zh)

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