Communication method and apparatus, and device, system, storage medium and program product

Abstract

The present disclosure relates to the technical field of communications, and relates to a communication method and apparatus, and a device, a system, a storage medium and a program product. The method comprises: determining a first threshold correction value of an artificial intelligence (AI) receiver of a terminal device, and determining a first quality threshold of the AI receiver on the basis of the first threshold correction value; or determining a first quality threshold of the AI receiver of the terminal device, wherein the first quality threshold and a quality value that is measured by the AI receiver are used for determining the execution of a network operation. The present disclosure relates to a communication method and apparatus, and a device, a system, a storage medium and a program product, which are used for improving the success rate of network operations executed by a terminal device deployed with an AI receiver.

Description

Communication methods, devices, equipment, systems, storage media and software products Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to a communication method, apparatus, device, system, storage medium, and program product. Background Technology

[0002] Currently, a communication system may include network devices and terminal devices. Network devices can send signals to terminal devices, and terminal devices can measure the received signals and determine whether to perform some network operations (such as cell handover or beam switching) based on the measured quality values.

[0003] In related technologies, the receiver deployed in the terminal device is a non-AI receiver (i.e., a traditional receiver). The terminal device measures the received signal through this non-AI receiver and calculates the quality value based on the measurement. Summary of the Invention

[0004] This disclosure provides a communication method, apparatus, device, system, storage medium, and program product to fully leverage the receiving performance advantages of AI receivers and improve the success rate of network operations performed by terminal devices deploying AI receivers.

[0005] In a first aspect, embodiments of this disclosure propose a communication method executed by a terminal device. The method includes: determining a first threshold correction value for an artificial intelligence (AI) receiver of the terminal device, and determining a first quality threshold for the AI ​​receiver based on the first threshold correction value; or, determining the first quality threshold for the AI ​​receiver of the terminal device; wherein the first quality threshold and the quality value measured by the AI ​​receiver are used to determine the execution of a network operation.

[0006] In this method, the terminal device determines a first quality threshold for the AI ​​receiver, and then determines to perform network operations based on the first quality threshold and the quality value measured by the AI ​​receiver. This can fully leverage the receiving performance advantages of the AI ​​receiver and improve the success rate of network operations performed by terminal devices that deploy AI receivers.

[0007] In this method, the terminal device determines a first threshold correction value for the AI ​​receiver, and determines a first quality threshold for the AI ​​receiver based on the first threshold correction value. Then, based on the first quality threshold and the quality value measured by the AI ​​receiver, it determines to perform network operations. This method can fully leverage the receiving performance advantages of the AI ​​receiver and improve the success rate of network operations performed by terminal devices that deploy AI receivers.

[0008] Secondly, embodiments of this disclosure provide a communication method executed by a network device. The method includes: sending a first threshold correction value of an AI receiver of the terminal device to a terminal device, wherein the first threshold correction value is used by the terminal device to determine a first quality threshold of the AI ​​receiver; or, sending the first quality threshold of the AI ​​receiver of the terminal device to the terminal device; wherein the first quality threshold and the quality value measured by the AI ​​receiver are used by the terminal device to determine whether to perform a network operation.

[0009] In this method, the network device can send the first quality threshold of the AI ​​receiver of the terminal device to the terminal device, which enables the AI ​​receiver of the terminal device to give full play to its receiving performance advantages and ensures that the network operation of the terminal device is completed normally.

[0010] In this method, the network device can send a first threshold correction value of the AI ​​receiver of the terminal device to the terminal device, so that the terminal device can determine a first quality threshold of the AI ​​receiver based on the first threshold correction value, and then determine to perform network operations based on the first quality threshold and the quality value measured by the AI ​​receiver, so as to give full play to the receiving performance advantages of the AI ​​receiver and improve the success rate of the terminal device with the AI ​​receiver performing network operations.

[0011] Thirdly, embodiments of this disclosure provide a communication device, the communication device comprising:

[0012] The processing module is used to determine a first threshold correction value of the AI ​​receiver of the terminal device, and to determine a first quality threshold of the AI ​​receiver based on the first threshold correction value; or, to determine a first quality threshold of the AI ​​receiver of the terminal device; wherein the first quality threshold and the quality value measured by the AI ​​receiver are used to determine the execution of network operations.

[0013] Fourthly, embodiments of this disclosure provide a communication device, the communication device comprising:

[0014] The transceiver module is used to send a first threshold correction value of the AI ​​receiver of the terminal device to the terminal device, wherein the first threshold correction value is used by the terminal device to determine a first quality threshold of the AI ​​receiver; or, to send the first quality threshold of the AI ​​receiver of the terminal device to the terminal device; wherein the first quality threshold and the quality value measured by the AI ​​receiver are used by the terminal device to determine to perform network operation.

[0015] Fifthly, embodiments of this disclosure provide a terminal device, including: one or more processors; wherein the terminal device is configured to execute the communication method of any of the first aspects or the communication method of any of the third aspects.

[0016] In a sixth aspect, a network device is proposed, comprising: one or more processors; wherein the network device is configured to execute the communication method of any of the second aspects, or the communication method of any of the second aspects.

[0017] The seventh aspect proposes a communication system, including: terminal equipment and / or network equipment.

[0018] The terminal device is configured to implement the communication method of any one of the first aspects;

[0019] The network device is configured to implement the communication method of either of the second aspects.

[0020] Eighthly, a storage medium is proposed, which stores instructions that, when executed on a communication device, implement a communication method as described in any of the first to second aspects.

[0021] In the ninth aspect, a program product is proposed, comprising a program and / or instructions, which, when executed by a communication device, implement a communication method as described in any of the first to second aspects.

[0022] In a tenth aspect, embodiments of this disclosure provide a computer program that, when run on a computer, causes the computer to perform a communication method as described in any of the first to second aspects.

[0023] Eleventhly, embodiments of this disclosure provide a chip or chip system. The chip or chip system includes processing circuitry configured to perform the communication methods described in any of the first to second aspects. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings required for the description of the embodiments are introduced below. The following drawings are only some embodiments of this disclosure and do not impose specific limitations on the protection scope of this disclosure.

[0025] Figure 1a is a schematic diagram of an exemplary architecture of a communication system according to an embodiment of this disclosure;

[0026] Figure 1b is a schematic diagram of the structure of the transmitting end, wireless channel and receiving end in the wireless communication system according to an embodiment of this disclosure;

[0027] Figure 2a is a schematic diagram of the measurement accuracy range provided in the embodiments of this disclosure;

[0028] Figure 2b is a schematic diagram showing the threshold values ​​for the measurement error range of different terminal devices within the signal coverage area of ​​the network device, as well as the measurement accuracy range, provided in the disclosed embodiment.

[0029] Figure 3a is an exemplary flowchart of a communication method provided in an embodiment of this disclosure;

[0030] Figure 3b is an exemplary flowchart of a communication method provided in an embodiment of this disclosure;

[0031] Figure 3c is an exemplary flowchart of a communication method provided in an embodiment of this disclosure;

[0032] Figure 3d is an exemplary flowchart of a communication method provided in an embodiment of this disclosure;

[0033] Figure 3e is an exemplary flowchart of a communication method provided in an embodiment of this disclosure;

[0034] Figure 3f is an exemplary flowchart of a communication method provided in an embodiment of this disclosure;

[0035] Figure 3g is an exemplary flowchart of a communication method provided in an embodiment of this disclosure;

[0036] Figure 3h is an exemplary flowchart of a communication method provided in an embodiment of this disclosure;

[0037] Figure 4a is an exemplary structural diagram of a communication device provided in an embodiment of this disclosure;

[0038] Figure 4b is an exemplary structural diagram of a communication device provided in an embodiment of this disclosure;

[0039] Figure 5a is a schematic diagram of the structure of the communication device proposed in an embodiment of this disclosure;

[0040] Figure 5b is a schematic diagram of the chip structure proposed in the embodiments of this disclosure. Detailed Implementation

[0041] This disclosure provides a communication method, apparatus, device, system, storage medium, and program product to fully leverage the receiving performance advantages of the AI ​​receiver in a terminal device and improve the success rate of network operations performed by the terminal device with the AI ​​receiver deployed.

[0042] In a first aspect, embodiments of this disclosure propose a communication method executed by a terminal device. The method includes: determining a first threshold correction value of an AI receiver of the terminal device, and determining a first quality threshold of the AI ​​receiver based on the first threshold correction value; or, determining the first quality threshold of the AI ​​receiver of the terminal device; wherein the first quality threshold and the quality value measured by the AI ​​receiver are used to determine the execution of a network operation.

[0043] In this embodiment, the terminal device determines a first quality threshold for the AI ​​receiver, and then determines to perform network operations based on the first quality threshold and the quality value measured by the AI ​​receiver. This can fully leverage the receiving performance advantages of the AI ​​receiver and help improve the success rate of network operations performed by terminal devices that deploy AI receivers.

[0044] In this embodiment, the terminal device determines a first threshold correction value for the AI ​​receiver, and determines a first quality threshold for the AI ​​receiver based on the first threshold correction value. Then, based on the first quality threshold and the quality value measured by the AI ​​receiver, it determines to perform network operations. This can fully leverage the receiving performance advantages of the AI ​​receiver and help improve the success rate of network operations performed by terminal devices that deploy AI receivers.

[0045] In conjunction with some embodiments of the first aspect, in some embodiments, the first threshold correction value is the threshold correction value corresponding to the AI ​​model currently used by the AI ​​receiver; the first quality threshold is the quality threshold corresponding to the AI ​​model currently used by the AI ​​receiver.

[0046] In this embodiment, the first quality threshold is the quality threshold corresponding to the AI ​​model currently used by the AI ​​receiver, which can improve the accuracy of the terminal device in determining the network operation to be performed based on the first quality threshold and the quality value measured by the AI ​​receiver.

[0047] In this embodiment, the first threshold correction value is the threshold correction value corresponding to the AI ​​model currently used by the AI ​​receiver. This can improve the accuracy of the terminal device in determining the first quality threshold based on the first threshold correction value, and further improve the accuracy of the terminal device in determining the network operation based on the first quality threshold and the quality value measured by the AI ​​receiver.

[0048] In conjunction with some embodiments of the first aspect, in some embodiments, the first threshold correction value is any one of the following:

[0049] The threshold correction value agreed upon in the agreement; or,

[0050] Threshold correction value received from the AI ​​receiver by the network device.

[0051] In this embodiment, the first threshold correction value can be a threshold correction value agreed upon in the protocol, or a threshold correction value received from the AI ​​receiver from the network device. This helps to improve the flexibility of configuring the first threshold correction value, or in other words, helps to improve the flexibility of the terminal device in obtaining the first threshold correction value.

[0052] In conjunction with some embodiments of the first aspect, in some embodiments, when there are multiple threshold correction values ​​agreed upon in the protocol, determining the first threshold correction value of the AI ​​receiver includes: determining the first threshold correction value of the AI ​​receiver among the multiple threshold correction values ​​agreed upon in the protocol.

[0053] In this embodiment, the terminal device can determine the first threshold correction value from multiple threshold correction values ​​agreed upon in the protocol, which helps to improve the flexibility of the terminal device in selecting the first threshold correction value it currently needs from multiple threshold correction values.

[0054] In conjunction with some embodiments of the first aspect, in some embodiments, when the first threshold correction value is a threshold correction value received from the AI ​​receiver from the network device, the method further includes: sending a first request message to the network device, the first request message being used to request the acquisition of the first threshold correction value of the AI ​​receiver.

[0055] In this embodiment, the terminal device can send a first request message to the network device. The first request message is used to request the acquisition of a first threshold correction value, so that the network device can send the first threshold correction value of the AI ​​receiver to the terminal device in a timely manner based on the first request message, which helps to improve the timeliness of the terminal device receiving the first threshold correction value of the AI ​​receiver.

[0056] In conjunction with some embodiments of the first aspect, in some embodiments, the first request information includes model information of the AI ​​model currently used by the AI ​​receiver.

[0057] In this embodiment, the first request information includes model information of the AI ​​model currently used by the AI ​​receiver, enabling the network device to send the first threshold correction value currently needed by the terminal device based on the model information of the currently used AI model. This helps improve the accuracy of the terminal device in determining the first quality threshold based on the first threshold correction value, thereby improving the accuracy of the terminal device in determining the network operation based on the first quality threshold and the quality value measured by the AI ​​receiver.

[0058] In conjunction with some embodiments of the first aspect, in some embodiments, when the number of threshold correction values ​​received from the AI ​​receiver from the network device is multiple, the method further includes:

[0059] Among multiple threshold correction values ​​received from the network device, a first threshold correction value for the AI ​​receiver is determined.

[0060] In this embodiment, the terminal device can determine the first threshold correction value from multiple threshold correction values ​​sent by the network device, which helps to improve the flexibility of the terminal device in selecting the first threshold correction value it currently needs from multiple threshold correction values.

[0061] In conjunction with some embodiments of the first aspect, in some embodiments, the multiple threshold correction values ​​of the AI ​​receiver are threshold correction values ​​corresponding to multiple AI models of the AI ​​receiver.

[0062] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes: sending a second request message to a network device, the second request message being used to request the acquisition of multiple threshold correction values.

[0063] In this embodiment, the terminal device sends a second request message to the network device. The second request message is used to request the acquisition of multiple threshold correction values, so that the network device can send multiple threshold correction values ​​to the terminal device in a timely manner according to the second request message, which helps to improve the timeliness of the terminal device receiving multiple threshold correction values.

[0064] In conjunction with some embodiments of the first aspect, in some embodiments, the second request information includes one or more of the following:

[0065] Model information of multiple AI models from an AI receiver; or...

[0066] Model information of the AI ​​model currently used by the AI ​​receiver.

[0067] In this embodiment, the second request information includes model information of multiple AI models of the AI ​​receiver, which can ensure that the multiple threshold correction values ​​received by the terminal device are all valid threshold correction values ​​(i.e., threshold correction values ​​that the AI ​​receiver will use), and can effectively prevent the terminal device from receiving invalid threshold correction values ​​(i.e., threshold correction values ​​that the AI ​​receiver will not use).

[0068] In conjunction with some embodiments of the first aspect, in some embodiments, the first quality threshold is the difference between a preset quality threshold and a first threshold correction value.

[0069] In conjunction with some embodiments of the first aspect, in some embodiments, when determining the first quality threshold of the AI ​​receiver of the terminal device, the first quality threshold is any one of the following:

[0070] The quality threshold agreed upon in the agreement; or,

[0071] The quality threshold of the AI ​​receiver received from the network device.

[0072] In conjunction with some embodiments of the first aspect, in some embodiments, when there are multiple quality thresholds agreed upon in the protocol, determining the first quality threshold of the AI ​​receiver of the terminal device includes: determining the first quality threshold of the AI ​​receiver among the multiple quality thresholds agreed upon in the protocol.

[0073] In this embodiment, the terminal device can determine a first quality threshold from multiple quality thresholds agreed upon in the protocol, which helps the terminal device to flexibly select the quality threshold (i.e., the first quality threshold) it currently needs from multiple quality thresholds.

[0074] In conjunction with some embodiments of the first aspect, in some embodiments, when the first quality threshold is a quality threshold received from the AI ​​receiver by the network device, the method further includes:

[0075] A third request message is sent to the network device. The third request message is used to request the acquisition of the first quality threshold of the AI ​​receiver.

[0076] In this embodiment, the terminal device sends a third request message to the network device. The third request message is used to request the acquisition of a first quality threshold, so that the network device can send the first quality threshold of the AI ​​receiver to the terminal device in a timely manner based on the third request message, which helps to improve the timeliness of the terminal device receiving the first quality threshold of the AI ​​receiver.

[0077] In conjunction with some embodiments of the first aspect, in some embodiments, the third request information includes model information of the AI ​​model currently used by the AI ​​receiver.

[0078] In this embodiment, the third request information includes model information of the AI ​​model currently used by the AI ​​receiver. This allows the network device to send a first quality threshold currently required by the terminal device based on the model information of the currently used AI model. This helps to improve the matching degree between the first quality threshold and the AI ​​model currently used by the AI ​​receiver, thereby improving the accuracy of the terminal device in determining the network operation based on the first quality threshold and the quality value measured by the AI ​​receiver.

[0079] In conjunction with some embodiments of the first aspect, in some embodiments, when there are multiple quality thresholds for the AI ​​receiver received from the network device, determining a first quality threshold for the AI ​​receiver of the terminal device includes: determining a first quality threshold for the AI ​​receiver among multiple quality thresholds for the AI ​​receiver received from the network device.

[0080] In this embodiment, the terminal device can determine a first quality threshold from multiple quality thresholds sent by the network device, which helps the terminal device to flexibly select the quality threshold it currently needs (i.e., the first quality threshold) from multiple quality thresholds.

[0081] In conjunction with some embodiments of the first aspect, in some embodiments, the multiple quality thresholds of the AI ​​receiver are the quality thresholds corresponding to multiple AI models of the AI ​​receiver.

[0082] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes: sending a fourth request message to a network device, the fourth request message being used to request the acquisition of multiple quality thresholds for the AI ​​receiver.

[0083] In this embodiment, the terminal device sends a fourth request message to the network device. The fourth request message is used to request the acquisition of multiple quality thresholds, so that the network device can send multiple quality thresholds to the terminal device in a timely manner according to the fourth request message, which helps to improve the timeliness of the terminal device receiving multiple quality thresholds.

[0084] In conjunction with some embodiments of the first aspect, in some embodiments, the fourth request information includes one or more of the following:

[0085] Model information of multiple AI models from an AI receiver; or...

[0086] Model information of the AI ​​model currently used by the AI ​​receiver.

[0087] In this embodiment, the fourth request information includes model information of multiple AI models of the AI ​​receiver, which can ensure that the multiple quality thresholds received by the terminal device are all valid quality thresholds (i.e., quality thresholds that the AI ​​receiver will use), and prevent the terminal device from receiving invalid quality thresholds (i.e., quality thresholds that the AI ​​receiver will not use).

[0088] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes: determining the measurement accuracy range corresponding to the AI ​​receiver based on a first quality threshold and a quality reference value; and performing network operations when the quality value measured by the AI ​​receiver is within the measurement accuracy range.

[0089] In this embodiment, the measurement accuracy range corresponding to the AI ​​receiver is determined based on the first quality threshold and the quality reference value. When the quality value measured by the AI ​​receiver is within the measurement accuracy range, network operation is performed, which can give full play to the receiving performance advantage of the AI ​​receiver and improve the success rate of network operation of the terminal device with the AI ​​receiver deployed.

[0090] In conjunction with some embodiments of the first aspect, in some embodiments, the first quality threshold is the quality threshold of the Synchronization Signal Block (SSB), and the network operation is cell handover, cell selection, beam failure recovery, or random access procedure; or,

[0091] The first quality threshold is the quality threshold corresponding to the random access procedure, and the network operation is a two-step random access procedure; or...

[0092] The first quality threshold is the quality threshold of the SSB during random access, and the network operation is to send message A during random access through the beam corresponding to the SSB.

[0093] The first quality threshold is the quality threshold corresponding to the small data transmission SDT, and the network operation is SDT initialization; or...

[0094] The first quality threshold is the quality threshold corresponding to carrier selection in SDT, and the network operation is to perform SDT via the uplink carrier; or...

[0095] The first quality threshold is the quality threshold corresponding to the beam measurement in CG-SDT, and the network operation is to perform CG-SDT through the current beam; or...

[0096] The first quality threshold is the quality threshold corresponding to beam measurement in the small data transmission RA-SDT based on random access, and the network operation is to perform SSB selection in RA-SDT; or...

[0097] The first quality threshold is the quality threshold corresponding to the RACH type of the random access channel in SDT, and the network operation is a contention-based RACH random access procedure; or,

[0098] The first quality threshold is the quality threshold corresponding to carrier selection, and the network operation is to transmit uplink data via the uplink carrier.

[0099] Secondly, embodiments of this disclosure provide a communication method executed by a network device. The method includes: sending a first threshold correction value of an AI receiver of the terminal device to a terminal device, wherein the first threshold correction value is used by the terminal device to determine a first quality threshold of the AI ​​receiver; or, sending the first quality threshold of the AI ​​receiver of the terminal device to the terminal device; wherein the first quality threshold and the quality value measured by the AI ​​receiver are used by the terminal device to determine whether to perform a network operation.

[0100] In this method, the network device can send the first quality threshold of the AI ​​receiver of the terminal device to the terminal device, which enables the AI ​​receiver of the terminal device to give full play to its receiving performance advantages and ensures that the network operation of the terminal device is completed normally.

[0101] In this method, the network device can also send a first threshold correction value of the AI ​​receiver to the terminal device, so that the terminal device can determine a first quality threshold of the AI ​​receiver based on the first threshold correction value, and then determine to perform network operations based on the first quality threshold and the quality value measured by the AI ​​receiver, so as to give full play to the receiving performance advantages of the AI ​​receiver and improve the success rate of the terminal device with the AI ​​receiver performing network operations.

[0102] In conjunction with some embodiments of the second aspect, in some embodiments, the first threshold correction value is the threshold correction value corresponding to the AI ​​model currently used by the AI ​​receiver; the first quality threshold is the quality threshold corresponding to the AI ​​model currently used by the AI ​​receiver.

[0103] In this embodiment, the first threshold correction value is the threshold correction value corresponding to the AI ​​model currently used by the AI ​​receiver of the terminal device. This can improve the accuracy of the terminal device in determining the first quality threshold based on the first threshold correction value, and thus improve the accuracy of the terminal device in determining the network operation based on the first quality threshold and the quality value measured by the AI ​​receiver.

[0104] In this embodiment, the first quality threshold is the first quality threshold corresponding to the AI ​​model currently used by the AI ​​receiver of the terminal device. This can improve the matching degree between the first quality threshold of the terminal device and the currently used AI model, thereby improving the accuracy of the terminal device in determining the network operation based on the first quality threshold and the quality value measured by the AI ​​receiver.

[0105] In conjunction with some embodiments of the second aspect, in some embodiments, when sending the first threshold correction value of the AI ​​receiver to the terminal device, the method further includes: receiving first request information from the terminal device, the first request information being used to request the acquisition of the first threshold correction value of the AI ​​receiver.

[0106] In this embodiment, the network device can receive the first request information from the terminal device and send the first threshold correction value of the AI ​​receiver to the terminal device based on the first request information, which helps to improve the timeliness of the network device sending the first threshold correction value of the AI ​​receiver to the terminal device.

[0107] In conjunction with some embodiments of the second aspect, in some embodiments, the first request information includes model information of the AI ​​model currently used by the AI ​​receiver.

[0108] In this embodiment, the first request information includes model information of the AI ​​model currently used by the AI ​​receiver, enabling the network device to send the first threshold correction value it currently needs to the terminal device based on the model information of the currently used AI model. This helps improve the accuracy of the terminal device in determining the first quality threshold based on the first threshold correction value, thereby improving the accuracy of the terminal device in determining the network operation based on the first quality threshold and the quality value measured by the AI ​​receiver.

[0109] In conjunction with some embodiments of the second aspect, in some embodiments, sending a first threshold correction value of the AI ​​receiver of the terminal device to the terminal device includes: sending multiple threshold correction values ​​of the AI ​​receiver to the terminal device, wherein the multiple threshold correction values ​​include the first threshold correction value of the AI ​​receiver.

[0110] In this embodiment, the network device can send multiple threshold correction values ​​to the terminal device, so that the terminal device can determine the first threshold correction value among the multiple threshold correction values, which helps to improve the flexibility of the terminal device in selecting the threshold correction value (i.e. the first threshold correction value) it needs from the multiple threshold correction values.

[0111] In conjunction with some embodiments of the second aspect, in some embodiments, multiple threshold correction values ​​of the AI ​​receiver are correction values ​​corresponding to multiple AI models of the AI ​​receiver.

[0112] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes: receiving second request information from a terminal device, the second request information being used to request the acquisition of multiple threshold correction values ​​of an AI receiver.

[0113] In this embodiment, the network device can receive a second request information from the terminal device and send multiple threshold correction values ​​to the terminal device according to the second request information, which helps to improve the timeliness of the network device sending multiple threshold correction values ​​to the terminal device.

[0114] In conjunction with some embodiments of the second aspect, in some embodiments, the second request information includes one or more of the following:

[0115] Model information of multiple AI models from an AI receiver; or...

[0116] Model information of the AI ​​model currently used by the AI ​​receiver.

[0117] In this embodiment, the second request information includes model information of multiple AI models of the AI ​​receiver, which can ensure that the multiple threshold correction values ​​sent by the network device to the terminal device are all valid threshold correction values ​​(i.e., threshold correction values ​​that the AI ​​receiver will use), and can effectively prevent the network device from sending invalid threshold correction values ​​(i.e., threshold correction values ​​that the AI ​​receiver will not use) to the terminal device.

[0118] In conjunction with some embodiments of the second aspect, in some embodiments, when sending a first quality threshold of the AI ​​receiver of the terminal device to the terminal device, the method further includes: receiving third request information from the terminal device, the third request information being used to request the acquisition of the first quality threshold of the AI ​​receiver.

[0119] In this embodiment, the network device can receive a third request message from the terminal device and send a first quality threshold of the AI ​​receiver to the terminal device based on the third request message, which helps to improve the timeliness of the network device sending the first quality threshold of the AI ​​receiver to the terminal device.

[0120] In conjunction with some embodiments of the second aspect, in some embodiments, the third request information includes model information of the AI ​​model currently used by the AI ​​receiver.

[0121] In this embodiment, the third request information includes model information of the AI ​​model currently used by the AI ​​receiver, enabling the network device to send the first quality threshold it currently needs to the terminal device based on the model information of the AI ​​model currently used, which helps to ensure that the first quality threshold matches the AI ​​model currently used by the AI ​​receiver.

[0122] In conjunction with some embodiments of the second aspect, in some embodiments, sending a first quality threshold of the AI ​​receiver of the terminal device to the terminal device includes:

[0123] Send multiple quality thresholds of the AI ​​receiver to the terminal device, including a first quality threshold of the AI ​​receiver.

[0124] In this embodiment, the network device can send multiple quality thresholds to the terminal device, enabling the terminal device to flexibly select the quality threshold it currently needs (i.e., the first quality threshold) from among the multiple quality thresholds.

[0125] In conjunction with some embodiments of the second aspect, in some embodiments, the multiple quality thresholds of the AI ​​receiver are the quality thresholds corresponding to multiple AI models of the AI ​​receiver.

[0126] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes:

[0127] The terminal device receives a fourth request message, which is used to request the acquisition of multiple quality thresholds for the AI ​​receiver.

[0128] In this embodiment, the network device can receive a fourth request message from the terminal device and send multiple quality thresholds to the terminal device based on the fourth request message, which helps to improve the timeliness of the network device sending multiple quality thresholds to the terminal device.

[0129] In conjunction with some embodiments of the second aspect, in some embodiments, the fourth request information includes one or more of the following:

[0130] Model information of multiple AI models from an AI receiver; or...

[0131] Model information of the AI ​​model currently used by the AI ​​receiver.

[0132] In this embodiment, the fourth request information includes model information of multiple AI models of the AI ​​receiver, which can ensure that the multiple quality thresholds sent by the network device to the terminal device are all valid quality thresholds (i.e., the quality thresholds that the AI ​​receiver will use), and prevent the network device from sending invalid quality thresholds (i.e., the quality thresholds that the AI ​​receiver will not use) to the terminal device.

[0133] Thirdly, embodiments of this disclosure provide a communication device, the communication device comprising:

[0134] The processing module is used to determine a first threshold correction value of the AI ​​receiver of the terminal device, and to determine a first quality threshold of the AI ​​receiver based on the first threshold correction value; or, to determine a first quality threshold of the AI ​​receiver of the terminal device; wherein the first quality threshold and the quality value measured by the AI ​​receiver are used to determine the execution of network operations.

[0135] Fourthly, embodiments of this disclosure provide a communication device, the communication device comprising:

[0136] The transceiver module is used to send a first threshold correction value of the AI ​​receiver of the terminal device to the terminal device, wherein the first threshold correction value is used by the terminal device to determine a first quality threshold of the AI ​​receiver; or, to send the first quality threshold of the AI ​​receiver of the terminal device to the terminal device; wherein the first quality threshold and the quality value measured by the AI ​​receiver are used by the terminal device to determine to perform network operation.

[0137] Fifthly, embodiments of this disclosure provide a terminal device, including: one or more processors; wherein the terminal device is configured to execute the communication method of any one of the first aspects.

[0138] In a sixth aspect, embodiments of this disclosure provide a network device, including: one or more processors; wherein the network device is configured to perform the communication method of any of the second aspects.

[0139] In a seventh aspect, embodiments of this disclosure provide a communication system, including: a terminal device and / or a network device. The terminal device is configured to implement the communication method of any of the first aspects. The network device is configured to implement the communication method of any of the second aspects.

[0140] Eighthly, embodiments of this disclosure provide a storage medium storing instructions that, when executed on a communication device, implement the communication method as described in any of the first to second aspects.

[0141] In a ninth aspect, embodiments of this disclosure provide a program product, the program product including a program and / or instructions, which, when executed by a communication device, implement a communication method as described in any of the first to second aspects.

[0142] In a tenth aspect, embodiments of this disclosure provide a computer program that, when run on a computer, causes the computer to perform a communication method as described in any of the first to second aspects.

[0143] Eleventhly, embodiments of this disclosure provide a chip or chip system. The chip or chip system includes processing circuitry configured to perform the communication methods described in any of the first to second aspects.

[0144] It is understood that the aforementioned communication devices, terminal equipment, network equipment, communication systems, storage media, program products, computer programs, chips, or chip systems are all used to execute the methods proposed in the embodiments of this disclosure. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0145] This disclosure provides a communication method, apparatus, device, system, storage medium, and program product. In some embodiments, the terms "communication method" and "bearer processing method," "information determination method," etc., can be used interchangeably; the terms "communication apparatus method" and "bearer processing apparatus," "information determination apparatus," etc., can be used interchangeably.

[0146] This disclosure is not exhaustive, but merely illustrative of some embodiments, and is not intended to limit the scope of protection of this disclosure. Unless otherwise specified, each step in a particular embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a particular embodiment can also be implemented as an independent embodiment, and the order of the steps in a particular embodiment can be arbitrarily interchanged. Furthermore, the optional implementation methods in a particular embodiment can be arbitrarily combined; moreover, the embodiments can be arbitrarily combined, for example, some or all steps of different embodiments can be arbitrarily combined, and a particular embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0147] In each of the disclosed embodiments, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions of the embodiments are consistent and can be referenced by each other. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0148] The terminology used in the embodiments of this disclosure is for the purpose of describing particular embodiments only and is not intended to limit the scope of this disclosure.

[0149] In this embodiment of the disclosure, unless otherwise stated, elements expressed in the singular form, such as "a," "an," "the," "the aforementioned," "the," "this," etc., can mean "one and only one," or "one or more," "at least one," etc. For example, when using articles such as "a," "an," "the," etc. in translation, the noun following the article can be understood as either a singular expression or a plural expression.

[0150] In the embodiments of this disclosure, "multiple" refers to two or more.

[0151] In some embodiments, the terms “at least one of”, “one or more”, “a plurality of”, “multiple”, etc., may be used interchangeably.

[0152] In some embodiments, the notation "at least one of A and B", "A and / or B", "A in one case, B in another", "in response to one case A, in response to another case B", etc., may include the following technical solutions depending on the situation: in some embodiments, A (execute A regardless of B); in some embodiments, B (execute B regardless of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); in some embodiments, A and B (both A and B are executed). The same applies when there are more branches such as A, B, C, etc.

[0153] In some embodiments, the notation "A or B" may include the following technical solutions, depending on the situation: in some embodiments, A (execution of A regardless of B); in some embodiments, B (execution of B regardless of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). The same applies when there are more branches such as A, B, C, etc.

[0154] The prefixes "first," "second," etc., used in the embodiments of this disclosure are merely for distinguishing different descriptive objects and do not impose restrictions on the position, order, priority, quantity, or content of the descriptive objects. The description of the descriptive objects should be found in the claims or the context of the embodiments, and the use of prefixes should not constitute unnecessary restrictions. For example, if the descriptive object is "measurement," then the ordinal numbers before "request information" in "first request information" and "second request information" do not restrict the position or order of the "fields," and "first" and "second" do not restrict whether the "request information" they modify is in the same message, nor do they restrict the order of "first request information" and "second request information."

[0155] In some embodiments, “including A,” “containing A,” “instructing A,” and “carrying A” can be interpreted as directly carrying A or indirectly instructing A.

[0156] In some embodiments, the terms “in response to…”, “in response to determining…”, “in the case of…”, “when…”, “if…”, “if…”, etc., can be used interchangeably.

[0157] In some embodiments, " / " means "or", for example, "association / correspondence" can be understood as association or correspondence.

[0158] In some embodiments, the terms “greater than,” “greater than or equal to,” “not less than,” “more than,” “more than or equal to,” “not less than,” “higher than,” “higher than or equal to,” “not lower than,” and “above” can be used interchangeably, as can the terms “less than,” “less than or equal to,” “not greater than,” “less than,” “less than or equal to,” “not more than,” “lower than,” “lower than or equal to,” “not higher than,” and “below”.

[0159] In some embodiments, the apparatus and device may be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. In some cases, they may also be understood as "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", "body", etc.

[0160] In some embodiments, the acquisition of data, information, etc., may comply with the laws and regulations of the country where the location is situated. In some embodiments, data, information, etc., may be acquired with the user's consent.

[0161] Figure 1a is an exemplary architecture diagram of a communication system according to an embodiment of this disclosure. As shown in Figure 1a, the communication system 100 includes a terminal device 101 and a network device 102. It should be understood that the number and form of each device shown in Figure 1a are for illustrative purposes only and do not constitute a limitation on the embodiments of this disclosure. In actual applications, it may include two or more terminal devices and two or more network devices. The communication system 100 shown in Figure 1a is only illustrated by example, including one terminal device 101 and one network device 102.

[0162] In some embodiments, terminal device 101 includes, but is not limited to, at least one of the following: mobile phone, wearable device, Internet of Things device, car with communication function, smart car, tablet computer, computer with wireless transceiver function, virtual reality (VR) terminal device, augmented reality (AR) terminal device, terminal device in industrial control, terminal device in self-driving, terminal device in remote medical surgery, terminal device in smart grid, terminal device in transportation safety, terminal device in smart city, and terminal device in smart home.

[0163] In some embodiments, network device 102 may also be referred to as access network device, such as a node or device that connects a terminal to a wireless network. Network device 102 may include, but is not limited to, at least one of the following in a 5G communication system: evolved Node B (eNB), next-generation evolved Node B (ng-eNB), next-generation Node B (gNB), node B (NB), home node B (HNB), home evolved node B (HeNB), radio backhaul device, radio network controller (RNC), base station controller (BSC), base transceiver station (BTS), base band unit (BBU), mobile switching center, base station in a 6G communication system, open RAN, cloud RAN, base station in other communication systems, and access node in a Wi-Fi system.

[0164] In some embodiments, the technical solutions of this disclosure can be applied to the Open RAN architecture. In this case, the interfaces between or within network devices involved in the embodiments of this disclosure can be transformed into internal interfaces of Open RAN. The processes and information interactions between these internal interfaces can be implemented by software or programs.

[0165] In some embodiments, the network device 102 may be composed of a central unit (CU) and a distributed unit (DU). The CU may also be called a control unit. The CU-DU structure can separate the protocol layer of the network device. Some of the protocol layer functions are centrally controlled by the CU, while the remaining part or all of the protocol layer functions are distributed in the DU and centrally controlled by the CU. However, this is not the only possibility.

[0166] It is understood that the communication system described in this disclosure is for the purpose of more clearly illustrating the technical solutions of this disclosure, and does not constitute a limitation on the technical solutions proposed in this disclosure. As those skilled in the art will know, with the evolution of system architecture and the emergence of new business scenarios, the technical solutions proposed in this disclosure are also applicable to similar technical problems.

[0167] In some embodiments, a wireless communication system includes a transmitter, a wireless channel, and a receiver. For example, the transmitter may be a network device or a transmitter within a network device, and the receiver may be a terminal device or a receiver within a terminal device; alternatively, the transmitter may be a terminal device or a transmitter within a terminal device, and the receiver may be a network device or a receiver within a network device. The following description, in conjunction with FIG1b, illustrates a wireless communication system including a transmitter, a wireless channel, and a receiver.

[0168] Figure 1b is a schematic diagram of the structure of the transmitter, wireless channel, and receiver in the wireless communication system according to an embodiment of this disclosure. As shown in Figure 1b, the transmitter includes: a source coding module, a channel coding module, a modulation module, and a radio frequency transmission module, etc. The receiver includes: a radio frequency receiving module, a channel estimation and signal detection module, an equalization module, a demodulation module, a channel decoding module, and a source decoding module, etc.

[0169] In the transmitting end, the data to be transmitted is processed sequentially by the source coding module, channel coding module, modulation module, radio frequency transmission module, etc., to obtain the signal to be transmitted. This signal is then transmitted to the receiving end via the wireless channel.

[0170] At the receiving end, the received signal is processed sequentially through the RF receiving module, channel estimation and signal detection module, equalization module, demodulation module, channel decoding module, and source decoding module to obtain the recovered data. Under ideal conditions, the data to be transmitted at the transmitting end is the same as the data recovered at the receiving end.

[0171] In related technologies, in order to improve link / system-level performance or reduce the computational complexity of the receiver, an artificial intelligence (AI)-based receiver technology is proposed. This technology aims to use neural network models in a data-driven manner to optimize individual modules of the receiver or to jointly optimize multiple modules of the receiver.

[0172] Receivers based on AI receiver technology can also be called AI receivers. Compared to the signal detection and channel estimation algorithms used in traditional receivers, AI receivers require further optimization in terms of the computational complexity of these algorithms, but they have a significant advantage in the accuracy of received signals.

[0173] In wireless communication systems, during the communication process between network devices and terminal devices, it is necessary to consider not only the downlink transmission process of network devices sending signals and terminal devices receiving signals, but also the uplink transmission process of terminal devices sending signals and network devices receiving signals.

[0174] For downlink transmission, when a traditional receiver is deployed in the terminal device, the signal strength measured by the terminal device is related to the signal strength of the network device, the antenna gain of the network device, the channel transmission path loss between the terminal device and the network device, and the antenna gain of the terminal device, among other main factors.

[0175] The signal strength measured by the terminal device satisfies the following formula 1: RxPowerUE=TxPowerBS+TxAntennaGainBS-Pathloss+RxAntennaGainUE Formula 1;

[0176] Where RxPowerUE represents the signal strength measured by the terminal device, TxPowerBS represents the signal strength of the network device, TxAntennaGainBS represents the antenna gain of the network device, Pathloss represents the channel transmission path loss between the terminal device and the network device, and RxAntennaGainUE represents the antenna gain of the terminal device.

[0177] When an AI receiver is deployed in a terminal device, during downlink transmission, the terminal device uses the AI ​​receiver to process the raw received signal, enabling it to extract data sent by the network device from the received signal with higher accuracy. In other words, a terminal device with an AI receiver can effectively suppress the influence of factors such as the wireless channel and transceiver hardware processing, thereby improving the signal strength on the terminal device side under the same transmission and reception conditions. This means that increased signal strength leads to a performance gain for the AI ​​receiver. In other words, when an AI receiver is deployed in a terminal device, the signal strength measured by the terminal device should satisfy the following formula 2: RxPowerUE AI =TxPowerBS+TxAntennaGainBS-Pathloss+RxAntennaGainUE+ AIPowerGainUE Formula 2;

[0178] Among them, RxPowerUE AI AIPowerGainUE represents the signal strength measured by a terminal device equipped with an AI receiver, while AIPowerGainUE represents the performance gain of the AI ​​receiver.

[0179] Comparing Formula 1 and Formula 2 above, it can be seen that RxPowerUE AI =RxPowerUE + AIPowerGainUE, which means that under the same channel environment, channel conditions, and the same relative positions of network equipment and terminal equipment, a terminal equipment with an AI receiver can obtain a higher signal strength (such as Reference Signal Receiving Power (RSRP)) based on the same received signal compared to a terminal equipment with a traditional receiver.

[0180] In some cases, if the signal strength TxPowerBS of the network device, the antenna gain TxGainAntennaBS of the network device, the path loss between the terminal device and the network device, and the antenna gain RxAntennaGainUE of the terminal device remain essentially unchanged, then when the terminal device does not deploy an AI receiver (i.e., a traditional receiver is deployed in the terminal device), the signal coverage area of ​​the network device is Area. A When the distance between the terminal device and the network device is equal to Distance A At that time, the signal strength RxPowerUE measured by a terminal device equipped with a conventional receiver is equal to the threshold value ThresholdPowerUE. The threshold value ThresholdPowerUE represents the minimum required signal strength for the terminal to receive the signal.

[0181] The distance between the terminal device and the network device is greater than Distance. A When the signal strength measured by a terminal device equipped with a traditional receiver is less than the threshold value ThresholdPowerUE, the terminal device may be unable to recover data from the received signal, the downlink transmission process may fail to complete normally, and the success rate of network operations (such as cell handover, beam switching, and random access procedures) may be low due to the weak signal strength measured by the terminal device.

[0182] Compared to terminal devices without AI receivers, deploying AI receivers on terminal devices effectively reduces data transmission bit error rate, suppresses channel noise, and mitigates the impact of non-ideal factors such as transceiver hardware and software processing, resulting in data recovered by the terminal device that more closely approximates the data sent by the network device. Therefore, when a network device sends a signal to a terminal device with an AI receiver deployed within its coverage area, the terminal device can recover data with higher accuracy. For example, the signal accuracy of a terminal device with an AI receiver deployed satisfies the following formula: RxPrecisionUE AI =RxPrecisionUE+AIPrecisionGainUE Formula 3;

[0183] Among them, RxPrecisionUE AI RxPrecisionUE represents the measurement signal accuracy of a terminal device with an AI receiver deployed, RxPrecisionUE represents the measurement signal accuracy of a terminal device without an AI receiver deployed, and AIPrecisionGainUE represents the measurement signal accuracy gain value of the AI ​​receiver.

[0184] Because terminal devices equipped with AI receivers can recover data with higher accuracy (i.e., with lower error), the error between the received signal measurement and the reference value of a terminal device equipped with an AI receiver is smaller than the error between the received signal measurement and the reference value of a terminal device without an AI receiver.

[0185] For example, if the received signal measurement value RxSignalUE of a terminal device without an AI receiver is considered to meet the accuracy requirements when it falls within the range of [ReferenceUE-Threshold, ReferenceUE+Threshold], then the received signal measurement value RxSignalUE of the terminal device with the AI ​​receiver is higher because the error between the received signal measurement value and the reference value of the terminal device with the AI ​​receiver is smaller than the error between the received signal measurement value and the reference value of the terminal device without the AI ​​receiver.AI is closer to the reference value ReferenceUE, so the received signal measurement value RxSignalUE of the terminal device equipped with an AI receiver AI is within [ReferenceUE - Threshold AI , ReferenceUE + Threshold AI range, it can be considered to meet the accuracy requirement. Wherein, ReferenceUE is the received signal measurement reference value, and Threshold AI < Threshold, Threshold AI is a received signal measurement error range threshold for terminal devices equipped with an AI receiver, and Threshold is a received signal measurement error range threshold for terminal devices not equipped with an AI receiver.

[0186] The following describes the measurement accuracy range satisfied by the received signal measurement value of a terminal device not equipped with an AI receiver and the measurement accuracy range satisfied by the received signal measurement value of a terminal device equipped with an AI receiver with reference to FIG. 2a. In the embodiments of the present disclosure, the measurement accuracy range may also be referred to as a measurement accuracy range requirement.

[0187] FIG. 2a is a schematic diagram of a measurement accuracy range provided by an embodiment of the present disclosure. As shown in FIG. 2a, the measurement accuracy range satisfied by the received signal measurement value of a terminal device not equipped with an AI receiver is [ReferenceUE - Threshold, ReferenceUE + Threshold]. The measurement accuracy range satisfied by the received signal measurement value of a terminal device equipped with an AI receiver is [ReferenceUE - Threshold AI , ReferenceUE + Threshold AI .

[0188] Wherein, Threshold AI < Threshold, compared with the range of [ReferenceUE - Threshold, ReferenceUE + Threshold], the range of [ReferenceUE - Threshold AI , ReferenceUE + Threshold AI is smaller.

[0189] If the measurement accuracy range used by the terminal device with the AI ​​receiver is [ReferenceUE-Threshold, ReferenceUE+Threshold], the downlink data communication between the network device and the terminal device will still be based on the resource and data transmission configuration when the AI ​​receiver is not deployed. In this case, the terminal device will find it difficult to fully utilize the receiving performance advantages of the AI ​​receiver. Therefore, [ReferenceUE-Threshold, ReferenceUE+Threshold] is not applicable to the case where the terminal device has an AI receiver deployed. In other words, the measurement accuracy range of the existing terminal device without an AI receiver is not applicable to the case where the terminal device has an AI receiver deployed.

[0190] The following examples illustrate that the measurement accuracy range of existing terminal devices without AI receivers is not applicable when AI receivers are deployed on the terminal devices.

[0191] For example, when the terminal device does not deploy an AI receiver, the measured value of the received signal is within [ReferenceUE-Threshold, ReferenceUE+Threshold], which meets the measurement accuracy range. In this case, the network device can use, for example, 64-phase quadrature amplitude modulation (QAM) for data modulation to achieve optimal data transmission. When the terminal device deploys an AI receiver, the accuracy of the received signal measurement is improved, and the measured value of the received signal is within [ReferenceUE-Threshold]. AI ReferenceUE+Threshold AI Within the specified range, the measurement accuracy is satisfied, and the terminal device can recover data better. The network device can actually use a higher-order data modulation method (such as 256QAM). However, if the network device still uses 64QAM for data modulation, it will result in a large amount of data transmission by the network device, poor data transmission efficiency between the network device and the terminal device, difficulty in leveraging the receiving performance advantages of the AI ​​receiver, low communication gain in the downlink transmission process, and a low success rate of the terminal device performing network operations.

[0192] To fully leverage the receiving performance advantages of the AI ​​receiver and improve communication gain during downlink transmission and the success rate of network operations performed by terminal devices, the signal coverage area of ​​the network equipment is [area missing]. A Internally, configure Threshold for terminal devices deploying AI receivers. AI Configure Threshold for terminal devices that have not deployed AI receivers.

[0193] The following, with reference to Figure 2b, illustrates the signal coverage area of ​​network devices. A Within this, the threshold values ​​for the measurement error range of received signals from terminal devices with AI receivers deployed and those without are defined, as well as the measurement accuracy range.

[0194] Figure 2b is a schematic diagram illustrating the measurement error range threshold and measurement accuracy range of different terminal devices within the signal coverage area of ​​the network device, according to a disclosed embodiment. As shown in Figure 2b, the device includes: a network device, a terminal device without an AI receiver, and a terminal device with an AI receiver.

[0195] Terminal devices without AI receivers and terminal devices with AI receivers are located within the signal coverage area of ​​the network equipment. A The distance between the terminal device without an AI receiver and the network device, and both the terminal device with an AI receiver and the network device, is designated as Distance. A .

[0196] The measurement accuracy range of terminal devices without AI receivers is [ReferenceUE-Threshold, ReferenceUE+Threshold], which can also be expressed as ReferenceUE±Threshold.

[0197] The measurement accuracy range of the terminal device deploying the AI ​​receiver is [ReferenceUE-Threshold]. AI ReferenceUE+Threshold AI It can also be expressed as ReferenceUE±Threshold AI Among them, Threshold AI <Threshold。

[0198] To leverage the receiving performance advantages of AI receivers and improve communication gain during downlink transmission and the success rate of network operations performed by terminal devices, embodiments of this disclosure provide a communication method, apparatus, device, system, storage medium, and program product. In this method, a quality threshold (Threshold) is configured for the terminal device via a network device. AI Alternatively, a threshold correction value, GainAI, can be configured. For example, when GainAI is configured, the terminal device can obtain the quality threshold, Threshold, based on Threshold and the threshold correction value, GainAI. AI Furthermore, the terminal device determines the quality threshold based on the threshold. AIThe quality value RxSignalUE obtained by measuring with an AI receiver AI By determining the network operation to be performed, the receiving performance advantages of the AI ​​receiver can be leveraged to improve the communication gain during downlink transmission and the success rate of the terminal device in performing network operations.

[0199] The communication methods, apparatus, devices, systems, storage media, and program products provided in this disclosure will be described in detail below with reference to specific embodiments.

[0200] The embodiments described below can be applied to, for example, Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), Super 3G, IMT-Advanced, 5G New Radio (NR), 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), Code Division Multiple Access (CDMA), Ultra Mobile Broadband (UMB), Wi-Fi, World Interoperability for Microwave Access (WiMAX), Ultra-Wideband (UWB), Bluetooth, and Public Land Mobile Networks. Networks (PLMNs), device-to-device (D2D) systems, machine-to-machine (M2M) systems, Internet of Things (IoT) systems, vehicle-to-everything (V2X) systems, systems utilizing other communication methods, and next-generation systems built upon them. Furthermore, multiple systems can be combined for application.

[0201] Referring to Figure 3a, which is an exemplary flowchart of a communication method provided in an embodiment of this disclosure, the method includes the following steps:

[0202] Step S3101: The terminal device determines the first quality threshold of its AI receiver.

[0203] In some embodiments, the first quality threshold can be the quality threshold corresponding to the AI ​​model currently used by the AI ​​receiver; in other words, the first quality threshold corresponds to / is associated with the AI ​​model currently used by the AI ​​receiver.

[0204] In some embodiments, the first quality threshold may also be referred to as the measurement error range threshold of the received signal of the terminal device deploying the AI ​​receiver.

[0205] In some embodiments, the AI ​​receiver may correspond to one or more AI models.

[0206] When an AI receiver corresponds to an AI model, the AI ​​model currently used by the AI ​​receiver can be the AI ​​model corresponding to that AI receiver.

[0207] When an AI receiver corresponds to multiple AI models, the AI ​​model currently used by the AI ​​receiver can be the AI ​​model currently used by the AI ​​receiver among the multiple AI models.

[0208] In some embodiments, when the AI ​​receiver corresponds to multiple AI models, the terminal device can select the currently used AI model from among the multiple AI models based on the current channel environment and / or time-frequency resource configuration, etc.

[0209] In some embodiments, the first quality threshold of the AI ​​receiver can be any of the following:

[0210] A quality threshold agreed upon / preset in the agreement;

[0211] One of several quality thresholds agreed upon / preset in the agreement;

[0212] A quality threshold received by the terminal device from the network device; or,

[0213] The quality threshold is one of several quality thresholds received by the terminal device from the network device.

[0214] In some embodiments, the first quality threshold of the AI ​​receiver can be a quality threshold determined based on a first threshold correction value of the AI ​​receiver. In some embodiments, the first threshold correction value of the AI ​​receiver can be any of the following:

[0215] A threshold correction value is agreed upon / preset in the agreement;

[0216] One of several threshold correction values ​​agreed upon / preset in the agreement;

[0217] A threshold correction value received by the terminal device from the network device; or,

[0218] A threshold correction value, which is one of multiple threshold correction values ​​received by the terminal device from the network device.

[0219] In some embodiments, the terminal device may determine a first quality threshold for its AI receiver using methods 1 to 8 as follows.

[0220] Method 1: The terminal device determines a quality threshold agreed upon / preset by the protocol as the first quality threshold of its AI receiver.

[0221] Method 2: The terminal device determines the first quality threshold of its AI receiver from among multiple quality thresholds agreed upon / preset in the protocol.

[0222] Method 3: The terminal device determines the first quality threshold of its AI receiver based on a quality threshold received from the network device.

[0223] Method 4: The terminal device determines the first quality threshold of its AI receiver from among multiple quality thresholds received from the network device.

[0224] In methods 2 and 4 above, the first quality threshold of the AI ​​receiver can be the quality threshold corresponding to the AI ​​model currently used by the AI ​​receiver among multiple quality thresholds.

[0225] Method 5: The terminal device determines a threshold correction value agreed upon / preset by the protocol as the first threshold correction value of its AI receiver, and determines the first quality threshold of the AI ​​receiver based on the first threshold correction value.

[0226] Method 6: The terminal device determines the first threshold correction value of its AI receiver from multiple threshold correction values ​​agreed upon / preset in the protocol, and determines the first quality threshold of the AI ​​receiver based on the first threshold correction value.

[0227] Method 7: The terminal device determines a threshold correction value received from the network device as the first threshold correction value of its AI receiver, and determines the first quality threshold of the AI ​​receiver based on the first threshold correction value.

[0228] Method 8: The terminal device determines the first threshold correction value of its AI receiver from multiple threshold correction values ​​received from the network device, and determines the first quality threshold of the AI ​​receiver based on the first threshold correction value.

[0229] In methods 6 and 8 above, the first threshold correction value of the AI ​​receiver can be the threshold correction value corresponding to the AI ​​model currently used by the AI ​​receiver among multiple threshold correction values.

[0230] In this embodiment of the disclosure, the multiple quality thresholds may also be referred to as multiple alternative / candidate quality thresholds, and the multiple threshold correction values ​​may also be referred to as multiple alternative / candidate threshold correction values.

[0231] For a detailed explanation of methods 1 to 8 above, please refer to the following examples. They will not be described in detail here.

[0232] Step S3102: The terminal device determines the measurement accuracy range corresponding to the AI ​​receiver based on the first quality threshold and the quality reference value.

[0233] In some embodiments, the quality reference value may be a quality reference value configured by the network device for the terminal device, or a quality reference value agreed upon in the protocol.

[0234] In this embodiment of the disclosure, the quality reference value may also be referred to as the received signal measurement reference value.

[0235] In some embodiments, the difference between the quality reference value and the first quality threshold, and the sum of the quality reference value and the first quality threshold are determined; and the range formed by the difference and the sum is determined as the measurement accuracy range corresponding to the AI ​​receiver.

[0236] For example, the measurement accuracy range corresponding to the AI ​​receiver can be... ReferenceUE represents the quality reference value. This represents the first quality threshold of the AI ​​receiver.

[0237] Step S3103: The network device sends the signal to be measured to the terminal device.

[0238] In some embodiments, the signal to be measured may be a Synchronization Signal / PBCH (SSB). Alternatively, the signal to be measured may be other signals, which will not be described in detail here.

[0239] Step S3104: The terminal device receives the signal to be measured through the AI ​​receiver, obtains the received signal, measures the received signal, and obtains the quality value.

[0240] In some embodiments, the AI ​​receiver receives the signal to be measured based on the currently used AI model, obtains the received signal, and measures the received signal to obtain a quality value.

[0241] In this embodiment of the disclosure, the measured quality value may also be referred to as the received signal measurement value.

[0242] Step S3105: When the quality value measured by the AI ​​receiver is within the measurement accuracy range, the terminal device performs network operation.

[0243] In some embodiments, when the quality value measured by the AI ​​receiver is within the measurement accuracy range, the network operation performed can be any of the following: cell handover, cell selection, beam failure recovery, random access procedure (two-step random access procedure or four-step random access procedure), two-step random access procedure, sending message A (MsgA) through the beam corresponding to the SSB, small data transmission (SDT) initiation, performing SDT based on the uplink (UL) carrier, verifying the validity of timing advance (TA) in the configured grant-small data transmission (CG-SDT) procedure, performing CG-SDT through the current beam, performing random access-based small data transmission (RACH-Small Data Transmission (RA-SDT) through the current beam, performing random access procedure through the contention-based random access channel (RACH), and performing uplink data transmission based on the UL carrier.

[0244] The following examples illustrate the correspondence between the first quality threshold and network operations.

[0245] Example 1A: The first quality threshold is the quality threshold of the SSB. Network operations can include cell handover, cell selection, beam failure recovery, or random access procedures.

[0246] In some embodiments, in Example 1A, the mass threshold of SSB can be the SSB strength threshold (RSRP-ThresholdSSB).

[0247] In some embodiments, in Example 1A, the random access procedure can be a two-step random access procedure or a four-step random access procedure.

[0248] Example 1B: The first quality threshold is the quality threshold corresponding to the random access procedure, and the network operation can be a two-step random access procedure.

[0249] In some embodiments, in Example 1B, the quality threshold associated with the random access procedure can be the strength threshold of message A (msgA-RSRP-Threshold).

[0250] In some embodiments, in Example 1B, if the quality value measured by the AI ​​receiver is within the measurement accuracy range corresponding to the AI ​​receiver, the network operation is a two-step random access procedure; if the quality value measured by the AI ​​receiver is not within the measurement accuracy range corresponding to the AI ​​receiver, the network operation is a four-step random access procedure.

[0251] Example 1C: The first quality threshold is the quality threshold of the SSB during random access, and the network operation can be to send MsgA through the beam corresponding to the SSB.

[0252] In some embodiments, in Example 1C, the quality threshold of the SSB during random access can be the strength threshold of message A based on the SSB (msgA-RSRP-ThresholdSSB).

[0253] In some embodiments, in Example 1C, if the quality value measured by the AI ​​receiver is within the measurement accuracy range corresponding to the AI ​​receiver, the network operation is to transmit message A through the beam corresponding to the SSB; if the quality value measured by the AI ​​receiver is not within the measurement accuracy range corresponding to the AI ​​receiver, the network operation is to select the beam with the higher RSRP to transmit message A.

[0254] Example 1D: The first quality threshold is the quality threshold corresponding to the SDT process, and the network operation can be used for SDT initialization.

[0255] In some embodiments, in Example 1D, the quality threshold corresponding to SDT can be the RSRP threshold for the determination of SDT initiation.

[0256] In some embodiments, in Example 1D, if the quality value measured by the AI ​​receiver is within the measurement accuracy range corresponding to the AI ​​receiver, the network operation is SDT initialization;

[0257] Example 1E: The first quality threshold is the quality threshold corresponding to carrier selection in SDT, and the network operation can be SDT based on the UL carrier.

[0258] In some embodiments, in Example 1E, the quality threshold corresponding to carrier selection can be the RSRP threshold for carrier selection (SUL and UL) of SDT.

[0259] In some embodiments, in Example 1E, if the quality value measured by the AI ​​receiver is within the measurement accuracy range corresponding to the AI ​​receiver, the network operation is to perform SDT based on the UL carrier; if the quality value measured by the AI ​​receiver is not within the measurement accuracy range corresponding to the AI ​​receiver, the network operation is to perform SDT via the SUL carrier.

[0260] Example 1F: The first quality threshold is the quality threshold corresponding to the beam measurement in CG-SDT, and the network operation is to perform CG-SDT through the current beam.

[0261] In some embodiments, in Example 1F, the quality threshold corresponding to the beam measurement can be the intensity threshold for beam determination of CG-SDT (RSRP threshold for beam determination of CG-SDT).

[0262] In some embodiments, in Example 1F, if the quality value measured by the AI ​​receiver is within the measurement accuracy range corresponding to the AI ​​receiver, the network operation is to perform CG-SDT using the current beam; if the quality value measured by the AI ​​receiver is not within the measurement accuracy range corresponding to the AI ​​receiver, the network operation is to switch the current beam to another beam or perform a beam recovery procedure.

[0263] Example 1G: The first quality threshold is the quality threshold corresponding to the beam measurement in RA-SDT, and the network operation is to perform RA-SDT through the current beam.

[0264] In some embodiments, in Example 1G, the quality threshold corresponding to the beam measurement can be the intensity threshold for beam determination of RA-SDT (RSRP threshold for beam determination of RA-SDT).

[0265] In some embodiments, in Example 1G, if the quality value measured by the AI ​​receiver is within the measurement accuracy range corresponding to the AI ​​receiver, the network operation is to perform RA-SDT with the current beam; if the quality value measured by the AI ​​receiver is not within the measurement accuracy range corresponding to the AI ​​receiver, the network operation is to switch the current beam to another beam or perform a beam recovery procedure.

[0266] Example 1H: The first quality threshold is the quality threshold corresponding to the RACH type in the SDT, and the network operation is a random access procedure via contention-based RACH.

[0267] In some embodiments, in Example 1H, the quality threshold corresponding to the RACH type can be the intensity threshold (RSRP threshold for the RACH type selection of SDT) for SDT.

[0268] In some embodiments, in Example 1H, if the quality value measured by the AI ​​receiver is within the measurement accuracy range corresponding to the AI ​​receiver, the network operation is to perform a random access procedure via a contention-based RACH; if the quality value measured by the AI ​​receiver is not within the measurement accuracy range corresponding to the AI ​​receiver, the network operation is to perform a random access procedure, a beam recovery procedure, or a carrier handover via a non-contention-based RACH.

[0269] Example 1I: The first quality threshold is the quality threshold corresponding to carrier selection, and the network operation is to perform uplink data transmission based on the UL carrier.

[0270] In some embodiments, in Example 1I, the quality threshold corresponding to carrier selection can be the strength threshold for carrier selection (RSRP threshold for carrier selection (SUL and UL)).

[0271] In some embodiments, in Example 1I, if the quality value measured by the AI ​​receiver is within the measurement accuracy range corresponding to the AI ​​receiver, the network operation is to perform uplink data transmission based on the UL carrier; if the quality value measured by the AI ​​receiver is not within the measurement accuracy range corresponding to the AI ​​receiver, the network operation is to perform uplink data transmission via the SUL carrier.

[0272] It should be noted that the communication method involved in the embodiments of this disclosure may include at least one of steps S3101 to S3105. For example, step S3101 may be implemented as a standalone embodiment. For example, steps S3102 to S3105 may be implemented as standalone embodiments. For example, steps S3103 to S3105 may be implemented as standalone embodiments.

[0273] In some embodiments, the order of some steps S3101 to S3105 can be interchanged. For example, the order of steps S3101 to S3102 and steps S3103 to S3104 can be interchanged.

[0274] In some embodiments, some steps S3101 to S3105 may be performed simultaneously or in combination. For example, steps S3101 to S3102 may be performed simultaneously or in combination. For example, steps S3104 and S3105 may be performed simultaneously or in combination. For example, after step S3103, at least two steps from steps S3101 to S3102 and steps S3104 to S3105 may be performed simultaneously or in combination.

[0275] In some embodiments, some steps in steps S3101 to S3105 may be omitted or combined. For example, based on method 1, step S3101 may be omitted. For instance, based on method 1, if S3101 is omitted, then the first quality threshold in steps S3102 to S3105 may be replaced with a quality threshold agreed upon / preset by the protocol.

[0276] Based on Method 2, and in conjunction with Figure 3b, the communication method provided in this disclosure will be described below.

[0277] Figure 3b is an exemplary flowchart of a communication method provided in an embodiment of this disclosure. As shown in Figure 3b, the method includes the following steps:

[0278] Step S3201: The terminal device determines the first quality threshold of its AI receiver from among multiple quality thresholds agreed upon / preset in the protocol.

[0279] In some embodiments, the multiple quality thresholds are quality thresholds associated with / corresponding to multiple AI models, wherein each quality threshold is a quality threshold associated with / corresponding to one AI model. In other words, the multiple quality thresholds are associated with / correspond to the model information of multiple AI models, wherein each quality threshold is associated with / corresponds to the model information of one AI model.

[0280] In some embodiments, the terminal device may determine a first quality threshold for the AI ​​receiver among multiple quality thresholds based on the model information of the AI ​​model currently used by the AI ​​receiver, wherein the model information of the AI ​​model currently used by the AI ​​receiver is the same as the model information of an AI model associated with / corresponding to the first quality threshold.

[0281] In some embodiments, AI model information may include one or more of the following: the identifier of the AI ​​model, the input data dimension of the AI ​​model, the output data dimension of the AI ​​model, etc.

[0282] In some embodiments, the identifier of an AI model can be its model number (ID).

[0283] Step S3202: The terminal device determines the measurement accuracy range corresponding to the AI ​​receiver based on the first quality threshold and the quality reference value.

[0284] Step S3203: The network device sends the signal to be measured to the terminal device.

[0285] Step S3204: The terminal device receives the signal to be measured through the AI ​​receiver, obtains the received signal, measures the received signal, and obtains the quality value.

[0286] Step S3205: When the quality value measured by the AI ​​receiver is within the measurement accuracy range, the terminal device performs a network operation.

[0287] For example, the execution method of steps S3202 to S3205 is the same as that of steps S3102 to S3105, and will not be described again here.

[0288] It should be noted that the communication method involved in the embodiments of this disclosure may include at least one of steps S3201 to S3205. For example, step S3201 may be implemented as a standalone embodiment. For example, steps S3201 to S3202 may be implemented as standalone embodiments.

[0289] In some embodiments, the order of some steps S3201 to S3205 can be interchanged. For example, the order of steps S3201 to S3202 and steps S3203 to S3204 can be interchanged.

[0290] In some embodiments, some steps S3201 to S3205 may be performed simultaneously or in combination. For example, steps S3201 to S3202 may be performed simultaneously or in combination. For example, steps S3204 and S3205 may be performed simultaneously or in combination. For example, after step S3203, at least two steps from steps S3201 to S3202 and steps S3204 to S3205 may be performed simultaneously or in combination.

[0291] Based on Method 3, and in conjunction with Figure 3c, the communication method provided in this disclosure will be described below.

[0292] Referring to Figure 3c, which is an exemplary flowchart of a communication method provided in an embodiment of this disclosure, the method includes the following steps:

[0293] Step S3301: The terminal device sends a third request message to the network device. The third request message is used to request the acquisition of the first quality threshold of the AI ​​receiver of the terminal device.

[0294] In some embodiments, the third request information includes model information of the AI ​​model currently used by the AI ​​receiver.

[0295] In some embodiments, the third request information may further include the identifier of the terminal device. In some embodiments, the identifier of the terminal device may be a Subscription Permanent Identifier (SUPI), a Permanent Equipment Identifier (PEI), or a 5G Temporary Identifier, etc.

[0296] Step S3302: The network device sends the first quality threshold of the AI ​​receiver of the terminal device to the terminal device according to the third request information.

[0297] In some embodiments, the network device sends a first quality threshold of the AI ​​receiver to the terminal device based on the model information of the AI ​​model currently used by the AI ​​receiver in the third request information.

[0298] In some embodiments, the network device sends third response information to the terminal device based on third request information. The third response information may include at least a first quality threshold of the AI ​​receiver of the terminal device. In some embodiments, the third response information may also include the identifier of the terminal device.

[0299] Step S3303: The terminal device determines the measurement accuracy range corresponding to the AI ​​receiver based on the first quality threshold and the quality reference value.

[0300] Step S3304: The network device sends the signal to be measured to the terminal device.

[0301] Step S3305: The terminal device receives the signal to be measured through the AI ​​receiver, obtains the received signal, measures the received signal, and obtains the quality value.

[0302] Step S3306: When the quality value measured by the AI ​​receiver is within the measurement accuracy range, the terminal device performs a network operation.

[0303] For example, the execution method of steps S3302 to S3305 is the same as that of steps S3102 to S3105, and will not be described again here.

[0304] It should be noted that the communication method involved in the embodiments of this disclosure may include at least one of steps S3301 to S3306. For example, step S3302 may be implemented as a standalone embodiment. For example, steps S3302 to S3306 may be implemented as standalone embodiments. For example, steps S3303 to S3306 may be implemented as standalone embodiments.

[0305] Optionally, in different embodiments, one or more steps in steps S3301 to S3306 may be omitted or substituted. For example, step S3301 may be omitted.

[0306] In some embodiments, the order of some steps S3301 to S3306 can be interchanged. For example, the order of steps S3301 to S3303 and steps S3304 to S3305 can be interchanged. For example, the order of steps S3303 and S3304 can be interchanged.

[0307] In some embodiments, some steps S3301 to S3306 may be performed simultaneously or in combination. For example, steps S3305 to S3306 may be performed simultaneously or in combination. For example, steps S3302 and S3304 may be performed simultaneously or in combination. For example, after step S3304, at least two steps S3303 and S3305 to S3306 may be performed simultaneously or in combination.

[0308] Based on method 4, and in conjunction with Figure 3d, the communication method provided in this disclosure will be described below.

[0309] Referring to Figure 3d, which is an exemplary flowchart of a communication method provided in an embodiment of this disclosure, the method includes the following steps:

[0310] Step S3401: The terminal device sends a fourth request message to the network device. The fourth request message is used to request the acquisition of multiple quality thresholds of the AI ​​receiver of the terminal device.

[0311] In some embodiments, the multiple quality thresholds are quality thresholds corresponding to multiple AI models of the AI ​​receiver.

[0312] In some embodiments, the fourth request information includes one or more of the following: model information of multiple AI models of the AI ​​receiver, or model information of the AI ​​model currently used by the AI ​​receiver.

[0313] In some embodiments, the fourth request information may also include the identifier of the terminal device.

[0314] In some embodiments, the model information of multiple AI models can be arranged sequentially according to the priority order of the multiple AI models.

[0315] Step S3402: The network device sends multiple quality thresholds of the AI ​​receiver of the terminal device to the terminal device according to the fourth request information.

[0316] In some embodiments, the network device sends multiple quality thresholds of the AI ​​receiver to the terminal device based on the model information of multiple AI models of the AI ​​receiver in the fourth request information.

[0317] In some embodiments, the multiple quality thresholds are quality thresholds associated with / corresponding to multiple AI models, wherein each quality threshold is a quality threshold associated with / corresponding to one AI model. In other words, the multiple quality thresholds are associated with / correspond to the model information of multiple AI models, wherein each quality threshold is associated with / corresponds to the model information of one AI model.

[0318] In some embodiments, the correlation between multiple quality thresholds and model information of multiple AI models can be indicated implicitly or explicitly.

[0319] Example 2A: When the correlation between multiple quality thresholds and the model information of multiple AI models is implicitly indicated, the network device can send multiple quality thresholds of the AI ​​receiver to the terminal device according to the order of the model information of multiple AI models in the fourth request information.

[0320] For example, the model information of multiple AI models is arranged in the following order: model information of AI model 1, model information of AI model 2, and model information of AI model 3. Correspondingly, the quality thresholds are arranged in the following order: quality threshold 1, quality threshold 2, and quality threshold 3. Wherein, quality threshold 1 is associated with the model information of AI model 1, quality threshold 2 is associated with the model information of AI model 2, and quality threshold 3 is associated with the model information of AI model 3.

[0321] Building upon Example 2A, the network device can also send a fourth response message to the terminal device based on the fourth request information. This fourth response message may include multiple quality thresholds for the terminal device's AI receiver, with the order of these quality thresholds corresponding to the order of the model information for multiple AI models. For example, the fourth response message may also include the identifier of the terminal device.

[0322] Example 2B: When indicating the correlation between multiple quality thresholds and model information of multiple AI models through a display method, the network device can also send multiple quality thresholds and the correlation between the multiple quality thresholds and model information of multiple AI models to the terminal device.

[0323] Based on Example 2B, in some embodiments, the network device sends a fourth response information to the terminal device according to the fourth request information. The fourth response information includes multiple quality thresholds of the AI ​​receiver of the terminal device, and the correlation between the multiple quality thresholds and the model information of multiple AI models.

[0324] For example, the correlation between multiple quality thresholds and model information of multiple AI models is shown in Table 1 below.

[0325] Table 1

[0326] Step S3403: The terminal device determines the first quality threshold of the AI ​​receiver among multiple quality thresholds.

[0327] In some embodiments, based on Example 2A, the terminal device determines a first quality threshold for the AI ​​receiver based on the position of the model information of the currently used AI model among the model information of multiple AI models.

[0328] For example, when the model information of multiple AI models is arranged in the order of AI model 1, AI model 2, and AI model 3, if the model information of the currently used AI model is AI model 2, then the position of the currently used AI model's model information among the multiple AI models' model information can be determined as 2. When the arrangement of multiple quality thresholds is quality threshold 1, quality threshold 2, and quality threshold 3, the quality threshold at position 2 (quality threshold 2) among the multiple quality thresholds can be determined as the first quality threshold of the AI ​​receiver.

[0329] In some embodiments, based on Example 2B, the terminal device determines a first quality threshold for the AI ​​receiver based on the model information of the currently used AI model and the correlation between multiple quality thresholds and the model information of multiple AI models.

[0330] For example, based on the correlation between multiple quality thresholds and the model information of multiple AI models as shown in Table 1, if the model information of the currently used AI model is the model information of AI model 2, the quality threshold 1 in this correlation can be determined as the first quality threshold of the AI ​​receiver.

[0331] Step S3404: The terminal device determines the measurement accuracy range corresponding to the AI ​​receiver based on the first quality threshold and the quality reference value.

[0332] Step S3405: ​​The network device sends the signal to be measured to the terminal device.

[0333] Step S3406: The terminal device receives the signal to be measured through the AI ​​receiver, obtains the received signal, measures the received signal, and obtains the quality value.

[0334] Step S3407: When the quality value measured by the AI ​​receiver is within the measurement accuracy range, the terminal device performs a network operation.

[0335] For example, the execution methods of S3404 to S3407 are the same as those of S3102 to S3105, and will not be described again here.

[0336] It should be noted that the communication method involved in the embodiments of this disclosure may include at least one of steps S3401 to S3407. For example, step S3402 may be implemented as a standalone embodiment. For example, steps S3402 to S3407 may be implemented as standalone embodiments. For example, steps S3403 to S3407 may be implemented as standalone embodiments.

[0337] Optionally, in different embodiments, one or more steps S3401 to S3407 may be omitted or substituted. For example, step S3401 may be omitted. For example, steps S3401 to S3402 may also be omitted.

[0338] In some embodiments, the order of some steps S3401 to S3407 can be interchanged. For example, the order of steps S3401 to S3404 and steps S3405 to S3406 can be interchanged. For example, the order of steps S3405 and steps S3403 to S3404 can be interchanged.

[0339] In some embodiments, some steps S3401 to S3407 may be performed simultaneously or in combination. For example, steps S3406 to S3407 may be performed simultaneously or in combination. For example, steps S3402 and S3405 may be performed simultaneously or in combination. For example, after step S3405, at least two steps from steps S3403 to S3404 and steps S3407 to S3407 may be performed simultaneously or in combination.

[0340] Based on method 5, and in conjunction with Figure 3e, the communication method provided in this disclosure will be described below.

[0341] Referring to Figure 3e, which is an exemplary flowchart of a communication method provided in an embodiment of this disclosure, the method includes the following steps:

[0342] Step S3501: The terminal device determines a threshold correction value agreed upon / preset by the protocol as the first threshold correction value of its AI receiver.

[0343] Step S3502: The terminal device determines the first quality threshold of the AI ​​receiver based on the first threshold correction value of the AI ​​receiver.

[0344] In some embodiments, the terminal device determines the first quality threshold of the AI ​​receiver based on the first threshold correction value and the preset quality threshold.

[0345] In some embodiments, the terminal device can use a preset quality threshold Threshold and a first threshold correction value. The difference is determined as the first quality threshold for the AI ​​receiver. Right now

[0346] In some embodiments, the preset quality threshold may be the quality threshold configured by the network device for the non-AI receiver corresponding to the terminal device, or the quality threshold for the non-AI receiver agreed upon by the protocol.

[0347] In some embodiments, the preset quality threshold may also be referred to as the measurement error range threshold for the received signal of a terminal device without an AI receiver deployed.

[0348] Step S3503: The terminal device determines the measurement accuracy range corresponding to the AI ​​receiver based on the first quality threshold and the quality reference value.

[0349] Step S3504: The network device sends the signal to be measured to the terminal device.

[0350] Step S3505: The terminal device receives the signal to be measured through the AI ​​receiver, obtains the received signal, and measures the received signal to obtain the quality value.

[0351] Step S3506: When the quality value measured by the AI ​​receiver is within the measurement accuracy range, the terminal device performs a network operation.

[0352] For example, the execution method of steps S3503 to S3506 is the same as the execution method of steps S3102 to S3105, and will not be described again here.

[0353] It should be noted that the communication method involved in the embodiments of this disclosure may include at least one of steps S3501 to S3506. For example, step S3501 may be implemented as a standalone embodiment. For example, step S3502 may be implemented as a standalone embodiment. For example, step S3506 may be implemented as a standalone embodiment.

[0354] Optionally, in different embodiments, one or more steps in steps S3501 to S3506 can be omitted or substituted. For example, step S3501 can be omitted. For example, based on method 5, if S3501 is omitted, then the first threshold correction value of the AI ​​receiver in step S3502 can be replaced with a threshold correction value agreed upon / preset by the protocol.

[0355] In some embodiments, the order of some steps S3501 to S3506 can be interchanged. For example, the order of steps S3501 to S3503 and step S3504 can be interchanged.

[0356] In some embodiments, some steps in steps S3501 to S3506 may be executed simultaneously or in combination. For example, any two adjacent steps in steps S3501 to S3503 may be executed simultaneously or in combination. For example, steps S3505 and S3506 may be executed simultaneously or in combination. For example, after step S3504, at least two steps in steps S3501 to S3503 and steps S3505 to S3506 may be executed simultaneously or in combination.

[0357] Based on method 6, and in conjunction with Figure 3g, the communication method provided in this disclosure will be described below.

[0358] Referring to Figure 3f, which is an exemplary flowchart of a communication method provided in an embodiment of this disclosure, as shown in Figure 3g, the method includes the following steps:

[0359] Step S3601: The terminal device determines the first threshold correction value of its AI receiver from among multiple threshold correction values ​​agreed upon / preset in the protocol.

[0360] In some embodiments, the multiple threshold correction values ​​are threshold correction values ​​corresponding to multiple AI models.

[0361] In some embodiments, the terminal device may determine a first threshold correction value for the AI ​​receiver from among multiple threshold correction values ​​based on the model information of the AI ​​model currently used by the AI ​​receiver, wherein the model information of the AI ​​model currently used by the AI ​​receiver is the same as the model information of an AI model associated with / corresponding to the first threshold correction value.

[0362] In some embodiments, AI model information may include one or more of the following: the identifier of the AI ​​model, the input data dimension of the AI ​​model, the output data dimension of the AI ​​model, etc.

[0363] Step S3602: The terminal device determines the first quality threshold of the AI ​​receiver based on the first threshold correction value of the AI ​​receiver.

[0364] Step S3603: The terminal device determines the measurement accuracy range corresponding to the AI ​​receiver based on the first quality threshold and the quality reference value.

[0365] Step S3604: The network device sends the signal to be measured to the terminal device.

[0366] Step S3605: The terminal device receives the signal to be measured through the AI ​​receiver, obtains the received signal, and measures the received signal to obtain the quality value.

[0367] Step S3606: When the quality value measured by the AI ​​receiver is within the measurement accuracy range, the terminal device performs network operation.

[0368] It is worth noting that the execution methods of steps S3602 to S3606 are the same as those of steps S3502 to S3506, and will not be repeated here.

[0369] It should be noted that the communication method involved in the embodiments of this disclosure may include at least one of steps S3601 to S3606. For example, step S3601 may be implemented as a standalone embodiment. For example, step S3602 may be implemented as a standalone embodiment. For example, step S3606 may be implemented as a standalone embodiment.

[0370] Optionally, in different embodiments, one or more steps in steps S3601 to S3606 may be omitted or substituted.

[0371] In some embodiments, the order of some steps S3601 to S3606 can be interchanged. For example, the order of steps S3601 to S3603 and step S3604 can be interchanged.

[0372] In some embodiments, some steps in steps S3601 to S3606 may be executed simultaneously or in combination. For example, any two adjacent steps in steps S3601 to S3603 may be executed simultaneously or in combination. For example, steps S3605 and S3606 may be executed simultaneously or in combination. For example, after step S3604, at least two steps in steps S3601 to S3603 and steps S3605 to S3606 may be executed simultaneously or in combination.

[0373] The following description, using method 7 as an example and referring to Figure 3g, illustrates the communication method provided in this disclosure.

[0374] Referring to Figure 3g, which is an exemplary flowchart of a communication method provided in an embodiment of this disclosure, the method includes the following steps:

[0375] Step S3701: The network device sends a first request message to the terminal device. The first request message is used to request the acquisition of the first threshold correction value of the AI ​​receiver of the terminal device.

[0376] In some embodiments, the first request information may include model information of the AI ​​model currently used by the AI ​​receiver.

[0377] In some embodiments, the first request information may also include the identifier of the terminal device.

[0378] In some embodiments, the AI ​​receiver may correspond to one or more AI models.

[0379] When an AI receiver corresponds to an AI model, the AI ​​model currently used by the AI ​​receiver can be the AI ​​model corresponding to that AI receiver.

[0380] When an AI receiver corresponds to multiple AI models, the AI ​​model currently used by the AI ​​receiver can be the AI ​​model currently used by the AI ​​receiver among the multiple AI models.

[0381] In some embodiments, the first threshold correction value can be the threshold correction value corresponding to the AI ​​model currently used by the AI ​​receiver. In other words, the first threshold correction value corresponds to / is associated with the AI ​​model currently used by the AI ​​receiver.

[0382] Step S3702: The network device sends the first threshold correction value of the AI ​​receiver of the terminal device to the terminal device according to the first request information.

[0383] In some embodiments, the network device sends a first threshold correction value of the AI ​​receiver to the terminal device based on the model information of the AI ​​model currently used by the AI ​​receiver in the first request information.

[0384] In some embodiments, the network device sends a first response message to the terminal device based on the first request message. The first response message may include at least a first threshold correction value of the AI ​​receiver of the terminal device. In some embodiments, the first response message may also include the identifier of the terminal device.

[0385] Step S3703: The terminal device determines the first quality threshold of the AI ​​receiver based on the first threshold correction value.

[0386] Step S3704: The terminal device determines the measurement accuracy range corresponding to the AI ​​receiver based on the first quality threshold and the quality reference value.

[0387] Step S3705: The network device sends the signal to be measured to the terminal device.

[0388] Step S3706: The terminal device receives the signal to be measured through the AI ​​receiver, obtains the received signal, and measures the received signal to obtain the quality value.

[0389] Step S3707: When the quality value measured by the AI ​​receiver is within the measurement accuracy range, the terminal device performs network operation.

[0390] For example, the execution methods of S3703 to S3707 are the same as those of S3502 to S3506, and will not be described again here.

[0391] It should be noted that the communication method involved in the embodiments of this disclosure may include at least one of steps S3701 to S3707. For example, step S3702 may be implemented as a standalone embodiment. For example, steps S3702 to S3707 may be implemented as standalone embodiments. For example, steps S3703 to S3707 may be implemented as standalone embodiments.

[0392] Optionally, in different embodiments, one or more steps S3701 to S3707 may be omitted or substituted. For example, step S3701 may be omitted. For example, steps S3701 to S3702 may also be omitted.

[0393] In some embodiments, the order of some steps S3701 to S3707 can be interchanged. For example, the order of steps S3701 to S3704 and steps S3705 to S3706 can be interchanged. For example, the order of steps S3705 and steps S3703 to S3704 can be interchanged.

[0394] In some embodiments, some steps S3701 to S3707 may be performed simultaneously or in combination. For example, steps S3706 to S3707 may be performed simultaneously or in combination. For example, steps S3702 and S3705 may be performed simultaneously or in combination. For example, after step S3705, at least two steps from steps S3703 to S3704 and steps S3706 to S3707 may be performed simultaneously or in combination.

[0395] Based on method 8, and in conjunction with Figure 3h, the communication method provided in this disclosure will be described below.

[0396] Referring to Figure 3h, which is an exemplary flowchart of a communication method provided in an embodiment of this disclosure, the method includes the following steps:

[0397] Step S3801: The network device sends a second request message to the terminal device. The second request message is used to request to obtain multiple threshold correction values ​​corresponding to the AI ​​receiver of the terminal device.

[0398] In some embodiments, the second request information includes one or more of the following: model information of multiple AI models of the AI ​​receiver, or model information of the AI ​​model currently used by the AI ​​receiver.

[0399] In some embodiments, the model information of multiple AI models can be arranged sequentially according to the priority order of the multiple AI models.

[0400] In some embodiments, the second request information may also include the identifier of the terminal device.

[0401] In some embodiments, the multiple threshold correction values ​​corresponding to the AI ​​receiver are the threshold correction values ​​associated with / corresponding to multiple AI models of the AI ​​receiver, wherein one threshold correction value is the threshold correction value associated with / corresponding to one AI model of the AI ​​receiver.

[0402] Step S3802: The network device sends multiple threshold correction values ​​corresponding to the AI ​​receiver of the terminal device to the terminal device according to the second request information.

[0403] In some embodiments, the network device sends multiple threshold correction values ​​of the AI ​​receiver to the terminal device based on the model information of multiple AI models of the AI ​​receiver in the second request information.

[0404] In some embodiments, multiple threshold correction values ​​are threshold correction values ​​associated with / corresponding to multiple AI models, wherein each threshold correction value is a threshold correction value associated with / corresponding to one AI model. In other words, multiple threshold correction values ​​are associated with / correspond to model information of multiple AI models, wherein one threshold correction value is associated with / corresponds to model information of one AI model.

[0405] In some embodiments, the association between multiple threshold correction values ​​and model information of multiple AI models can be indicated implicitly or explicitly.

[0406] Example 3A: When the correlation between multiple threshold correction values ​​and the model information of multiple AI models is implicitly indicated, the network device can send multiple threshold correction values ​​of the AI ​​receiver to the terminal device according to the order of the model information of multiple AI models in the second request information.

[0407] For example, the model information of multiple AI models is arranged in the following order: model information of AI model 1, model information of AI model 2, and model information of AI model 3. Correspondingly, the threshold correction values ​​are arranged in the following order: threshold correction value 1, threshold correction value 2, and threshold correction value 3. Specifically, threshold correction value 1 is associated with the model information of AI model 1, threshold correction value 2 is associated with the model information of AI model 2, and threshold correction value 3 is associated with the model information of AI model 3.

[0408] Building upon Example 3A, the network device can also send a second response message to the terminal device based on the second request information. This second response message may include multiple threshold correction values ​​from the terminal device's AI receiver, with the sorting order of these threshold correction values ​​corresponding to the sorting order of the model information from multiple AI models. For example, the second response message may also include the identifier of the terminal device.

[0409] Example 3B: When indicating the association between multiple threshold correction values ​​and model information of multiple AI models through a display method, the network device can also send multiple threshold correction values ​​and the association between the multiple threshold correction values ​​and model information of multiple AI models to the terminal device.

[0410] Based on Example 3B, in some embodiments, the network device sends a second response information to the terminal device according to the second request information. The second response information includes multiple threshold correction values ​​of the AI ​​receiver of the terminal device, and the correlation between the multiple threshold correction values ​​and the model information of multiple AI models.

[0411] For example, the correlation between multiple threshold correction values ​​and model information of multiple AI models is shown in Table 2 below.

[0412] Table 2

[0413] Step S3803: The terminal device determines the first threshold correction value of the AI ​​receiver among multiple threshold correction values.

[0414] In some embodiments, based on Example 3A, the terminal device determines the first threshold correction value of the AI ​​receiver according to the position of the model information of the currently used AI model among the model information of multiple AI models.

[0415] For example, when the model information of multiple AI models is arranged in the following order: model information of AI model 1, model information of AI model 2, and model information of AI model 3, if the model information of the currently used AI model is the model information of AI model 2, then the position of the model information of the currently used AI model in the model information of multiple AI models can be determined as 2. When the arrangement of multiple threshold correction values ​​is: threshold correction value 1, threshold correction value 2, and threshold correction value 3, the threshold correction value at position 2 (threshold correction value 2) can be determined as the first threshold correction value of the AI ​​receiver.

[0416] In some embodiments, based on Example 3B, the terminal device determines the first threshold correction value of the AI ​​receiver according to the model information of the currently used AI model and the correlation between the multiple threshold correction values ​​and the model information of the multiple AI models.

[0417] For example, based on the correlation between multiple threshold correction values ​​and the model information of multiple AI models as shown in Table 1, if the model information of the currently used AI model is the model information of AI model 2, the threshold correction value 1 in the correlation can be determined as the first threshold correction value of the AI ​​receiver.

[0418] Step S3804: The terminal device determines the first quality threshold of the AI ​​receiver based on the first threshold correction value.

[0419] Step S3805: The terminal device determines the measurement accuracy range corresponding to the AI ​​receiver based on the first quality threshold and the quality reference value.

[0420] Step S3806: The network device sends the signal to be measured to the terminal device.

[0421] Step S3807: The terminal device receives the signal to be measured through the AI ​​receiver, obtains the received signal, and measures the received signal to obtain the quality value.

[0422] Step S3808: When the quality value measured by the AI ​​receiver is within the measurement accuracy range, the terminal device performs a network operation.

[0423] For example, the execution methods of S3804 to S3808 are the same as those of S3502 to S3506, and will not be described again here.

[0424] It should be noted that the communication method involved in the embodiments of this disclosure may include at least one of steps S3801 to S3808. For example, step S3802 may be implemented as a standalone embodiment. For example, steps S3802 to S3808 may be implemented as standalone embodiments. For example, steps S3803 to S3808 may be implemented as standalone embodiments.

[0425] Optionally, in different embodiments, one or more steps in steps S3801 to S3808 may be omitted or substituted. For example, step S3801 may be omitted. For example, steps S3801 to S3802 may also be omitted.

[0426] In some embodiments, the order of some steps S3801 to S3808 can be interchanged. For example, the order of steps S3801 to S3805 and step S3806 can be interchanged. For example, the order of steps S3806 and steps S3804 to S3805 can be interchanged.

[0427] In some embodiments, some steps S3801 to S3808 may be performed simultaneously or in combination. For example, steps S3807 to S3808 may be performed simultaneously or in combination. For example, steps S3802 and S3806 may be performed simultaneously or in combination. For example, after step S3806, at least two steps from steps S3803 to S3805 and steps S3807 to S3808 may be performed simultaneously or in combination.

[0428] In the embodiments disclosed herein, some or all of the steps and their optional implementations may be arbitrarily combined with some or all of the steps in other embodiments, or may be arbitrarily combined with the optional implementations in other embodiments.

[0429] This disclosure also provides embodiments of an apparatus for implementing any of the above methods. For example, an apparatus is provided that includes units or modules for implementing the steps performed by the terminal device in any of the above methods. Furthermore, another apparatus is provided that includes units or modules for implementing the steps performed by the network device in any of the above methods.

[0430] It should be understood that the division of units or modules in the above device is only a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, the units or modules in the device can be implemented by a processor calling software: for example, the device includes a processor connected to a memory containing instructions. The processor calls the instructions stored in the memory to implement any of the above methods or to implement the functions of the units or modules in the above device. The processor can be, for example, a general-purpose processor, such as a Central Processing Unit (CPU) or a microprocessor, and the memory can be internal or external to the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits. The functionality of some or all of the units or modules can be achieved through the design of these hardware circuits, which can be understood as one or more processors. For example, in one implementation, the hardware circuit is an application-specific integrated circuit (ASIC). The functionality of some or all of the units or modules is achieved through the design of the logical relationships between the components within the circuit. In another implementation, the hardware circuit can be implemented using a programmable logic device (PLD). Taking a field-programmable gate array (FPGA) as an example, it can include a large number of logic gates. The connection relationships between the logic gates are configured through configuration files, thereby achieving the functionality of some or all of the units or modules. All units or modules of the above device can be implemented entirely through processor-called software, entirely through hardware circuits, or partially through processor-called software with the remaining parts implemented through hardware circuits.

[0431] In this embodiment, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction read and execute capabilities, such as a Central Processing Unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), or a digital signal processor (DSP). In another implementation, the processor can implement certain functions through the logical relationships of hardware circuits. The logical relationships of the aforementioned hardware circuits are fixed or reconfigurable. For example, the processor is a hardware circuit implemented using an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document and configuring the hardware circuit can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. Furthermore, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a Neural Network Processing Unit (NPU), a Tensor Processing Unit (TPU), or a Deep Learning Processing Unit (DPU).

[0432] Figure 4a is an exemplary structural diagram of a communication device provided in an embodiment of this disclosure. The communication device can be a terminal device, or a chip or chip system within a terminal device. As shown in Figure 4a, the communication device 4100 includes at least one of a transceiver module 4101 and a processing module 4102.

[0433] In some embodiments, the transceiver module 4101 is configured to receive a first quality threshold from the network device via the artificial intelligence (AI) receiver of the terminal device, wherein the first quality threshold and the quality value measured by the AI ​​receiver are used to determine the execution of network operations.

[0434] Optionally, the transceiver module 4101 is used to perform at least one of the communication steps such as sending and / or receiving performed by the terminal device in any of the above methods (e.g., the receiving steps corresponding to steps S3301, S3401, S3701, and S3801, or the receiving steps corresponding to steps S3302, S3402, S3702, and S3802, but not limited thereto), which will not be elaborated here.

[0435] Optionally, the processing module 4102 is used to execute at least one of the other steps executed by the terminal device in any of the above methods (e.g., steps S3101-S3102, steps S3104-S3105, steps S3201-S3202, steps S3204-S3205, steps S3303, steps S3305-S3306, steps S3403-S3404, steps S3406-S3407, steps S3501-S3503, steps S3505-S3506, steps S3601-S3603, steps S3605-S3606, steps S3703-S3704, steps S3706-S3707, steps S3803-S3805, steps S3807-S3808, but not limited thereto), which will not be elaborated here.

[0436] Figure 4b is an exemplary structural diagram of a communication device provided in an embodiment of this disclosure. The communication device can be a network device, or a chip or chip system within a network device. As shown in Figure 4b, the communication device 4200 includes a transceiver module 4201.

[0437] In some embodiments, the transceiver module 4201 is configured to send a first quality threshold of the AI ​​receiver of the terminal device to the terminal device, wherein the first quality threshold and the quality value measured by the AI ​​receiver are used by the terminal device to determine to perform network operations.

[0438] Optionally, the transceiver module 4201 is used to perform at least one of the communication steps such as sending and / or receiving performed by the terminal device in any of the above methods (e.g., steps S3103, S3203, S3302, S3304, S3402, S3405, S3504, S3604, S3702, S3705, S3802, S3806, but not limited thereto), which will not be elaborated here.

[0439] In some embodiments, the transceiver module may include a transmitting module and / or a receiving module, which may be separate or integrated. Optionally, the transceiver module may be interchangeable with a transceiver.

[0440] In some embodiments, the processing module may be a single module or may include multiple sub-modules. Optionally, the multiple sub-modules may each perform all or part of the steps required by the processing module. Optionally, the processing module may be interchangeable with a processor.

[0441] Figure 5a is a schematic diagram of the structure of the communication device 5100 proposed in an embodiment of this disclosure. The communication device 5100 can be a network device, a terminal device, a chip, chip system, or processor that supports the implementation of any of the above methods in a network device, or a chip, chip system, or processor that supports the implementation of any of the above methods in a terminal device. The communication device 5100 can be used to implement the methods described in the above method embodiments; for details, please refer to the descriptions in the above method embodiments.

[0442] As shown in Figure 5a, the communication device 5100 includes one or more processors 5101. The processor 5101 can be a general-purpose processor or a dedicated processor, such as a baseband processor or a central processing unit (CPU). The baseband processor can be used to process communication protocols and communication data, while the CPU can be used to control communication devices (e.g., base stations, baseband chips, terminal devices, terminal device chips, DUs or CUs, etc.), execute programs, and process program data. Optionally, the communication device 5100 can be used to execute any of the above methods. Optionally, one or more processors 5101 can be used to invoke instructions to cause the communication device 5100 to execute any of the above methods.

[0443] In some embodiments, the communication device 5100 further includes one or more transceivers 5102. When the communication device 5100 includes one or more transceivers 5102, the transceiver 5102 performs at least one of the communication steps such as sending and / or receiving in the above method (e.g., steps S3301, S3401, S3701, S3801, or, for example, the receiving steps corresponding to steps S3302, S3402, S3702, and S3802, such as steps S3103, S3203, S3302, S3304, S3402, S3405, S3504, S3604, S3702, S3705, S3802, and S3806, but not limited thereto), and processes... Processor 5101 performs at least one of other steps (e.g., steps S3101-S3102, steps S3104-S3105, steps S3201-S3202, steps S3204-S3205, steps S3303, steps S3305-S3306, steps S3403-S3404, steps S3406-S3407, steps S3501-S3503, steps S3505-S3506, steps S3601-S3603, steps S3605-S3606, steps S3703-S3704, steps S3706-S3707, steps S3803-S3805, steps S3807-S3808, but not limited thereto).

[0444] In optional embodiments, the transceiver may include a receiver and / or a transmitter, which may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, transceiver circuit, interface circuit, interface, etc., can be used interchangeably; the terms transmitter, transmitting unit, transmitter, transmitting circuit, etc., can be used interchangeably; and the terms receiver, receiving unit, receiver, receiving circuit, etc., can be used interchangeably.

[0445] In some embodiments, the communication device 5100 further includes one or more memories 5103 for storing data. Optionally, all or part of the memories 5103 may be located outside the communication device 5100. In optional embodiments, the communication device 5100 may include one or more interface circuits 5104. Optionally, the interface circuits 5104 are connected to the memories 5102, and the interface circuits 5104 can be used to receive data from the memories 5102 or other devices, and can be used to send data to the memories 5102 or other devices. For example, the interface circuits 5104 can read data stored in the memories 5102 and send the data to the processor 5101.

[0446] The communication device 5100 described in the above embodiments may be a network device or a terminal, but the scope of the communication device 5100 described in this disclosure is not limited thereto, and the structure of the communication device 5100 may not be limited by FIG. 5a. The communication device may be a standalone device or a part of a larger device. For example, the communication device may be: (1) a standalone integrated circuit IC, or chip, or chip system or subsystem; (2) a collection of one or more ICs, optionally, the IC collection may also include storage components for storing data and programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, terminal device, smart terminal device, cellular phone, wireless device, handheld device, mobile unit, vehicle device, network device, cloud device, artificial intelligence device, etc.; (6) others, etc.

[0447] Figure 5b is a schematic diagram of the structure of the chip 5200 proposed in an embodiment of this disclosure. For cases where the communication device 5100 can be a chip or a chip system, please refer to the schematic diagram of the chip 5200 shown in Figure 5b, but it is not limited thereto.

[0448] Chip 5200 includes one or more processors 5201. Chip 5200 is used to perform any of the methods described above.

[0449] In some embodiments, chip 5200 further includes one or more interface circuits 5202. Optionally, terms such as interface circuit, interface, and transceiver pin can be used interchangeably. In some embodiments, chip 5200 further includes one or more memories 5203 for storing data. Optionally, all or part of the memories 5203 may be located outside of chip 5200. Optionally, interface circuit 5202 is connected to memory 5203, and interface circuit 5202 can be used to receive data from memory 5203 or other devices, and interface circuit 5202 can be used to send data to memory 5203 or other devices. For example, interface circuit 5202 can read data stored in memory 5203 and send the data to processor 5201.

[0450] In some embodiments, the interface circuit 5202 performs at least one of the communication steps such as sending and / or receiving in the above method (e.g., steps S3301, S3401, S3701, S3801, or, for example, the receiving steps corresponding to steps S3302, S3402, S3702, and S3802, such as steps S3103, S3203, S3302, S3304, S3402, S3405, S3504, S3604, S3702, S3705, S3802, and S3806). The interface circuit 5202 performing the communication steps such as sending and / or receiving in the above method refers, for example, to the interface circuit 5202 performing data interaction between the processor 5201, the chip 5200, the memory 5203, or the transceiver device.

[0451] In some embodiments, the processor 5201 performs at least one of other steps (e.g., steps S3101-S3102, steps S3104-S3105, steps S3201-S3202, steps S3204-S3205, step S3303, steps S3305-S3306, steps S3403-S3404, steps S3406-S3407, steps S3501-S3503, steps S3505-S3506, steps S3601-S3603, steps S3605-S3606, steps S3703-S3704, steps S3706-S3707, steps S3803-S3805, steps S3807-S3808, but is not limited thereto).

[0452] The modules and / or devices described in the various embodiments, such as virtual devices, physical devices, and chips, can be combined or separated arbitrarily as needed. Optionally, some or all steps can also be performed collaboratively by multiple modules and / or devices, which is not limited here.

[0453] This disclosure also proposes a storage medium storing instructions that, when executed on a communication device 5100, cause the communication device 5100 to perform any of the methods described above. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but not limited thereto; it may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but not limited thereto; it may also be a temporary storage medium.

[0454] This disclosure also proposes a program product, including a program and / or instructions, which, when executed by the communication device 5100, cause the communication device 5100 to perform any of the above methods. Optionally, the above program product is a computer program product.

[0455] This disclosure also proposes a computer program that, when run on a computer, causes the computer to perform any of the above methods.

[0456] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0457] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0458] The above are merely specific embodiments of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A communication method characterized by comprising: The method is performed by a terminal device, and the method comprises: determining a first threshold correction value of an artificial intelligence (AI) receiver of the terminal device, and determining a first quality threshold of the AI receiver according to the first threshold correction value; or determining a first quality threshold of an artificial intelligence (AI) receiver of the terminal device; The first quality threshold and a quality value measured by the AI receiver are used to determine whether to perform a network operation.

2. The method of claim 1, wherein, The first threshold correction value is a threshold correction value corresponding to an AI model currently used by the AI receiver. The first quality threshold is a quality threshold corresponding to the AI model currently used by the AI receiver.

3. The method according to claim 1 or 2, characterized in that, The first threshold correction value is any one of the following: a protocol-agreed threshold correction value; or a threshold correction value of the AI receiver received from a network device.

4. The method of claim 3, wherein, When the number of protocol-agreed threshold correction values is multiple, the determination of the first threshold correction value of the AI receiver comprises: determining a first threshold correction value of the AI receiver from the multiple protocol-agreed threshold correction values.

5. The method of claim 3, wherein, When the first threshold correction value is a threshold correction value of the AI receiver received from the network device, the method further comprises: sending first request information to the network device, the first request information being used to request to obtain the first threshold correction value of the AI receiver.

6. The method of claim 5, wherein, The first request information comprises model information of an AI model currently used by the AI receiver.

7. The method of claim 3, wherein, When the number of threshold correction values of the AI receiver received from the network device is multiple, the method further comprises: determining a first threshold correction value of the AI receiver from the multiple threshold correction values of the AI receiver received from the network device.

8. The method of claim 7, wherein, The multiple threshold correction values of the AI receiver are threshold correction values corresponding to multiple AI models of the AI receiver.

9. The method according to claim 7 or 8, characterized in that, The method further comprises: sending second request information to the network device, the second request information being used to request to obtain the multiple threshold correction values.

10. The method of claim 9, wherein, The second request information comprises one or more of the following: model information of the multiple AI models of the AI receiver; or model information of an AI model currently used by the AI receiver.

11. The method according to any one of claims 3-10, characterized in that, The first quality threshold is a difference between a preset quality threshold and the first threshold correction value.

12. The method of claim 1 or 2, wherein, When determining the first quality threshold of the artificial intelligence (AI) receiver of the terminal device, the first quality threshold is any one of the following: a protocol-agreed quality threshold; or a quality threshold of the AI receiver received from a network device.

13. The method of claim 12, wherein, When the number of protocol-agreed quality thresholds is multiple, the determination of the first quality threshold of the artificial intelligence (AI) receiver of the terminal device comprises: determining the first quality threshold of the AI receiver from the multiple protocol-agreed quality thresholds.

14. The method of claim 12, wherein, When the first quality threshold is a quality threshold of the AI receiver received from the network device, the method further comprises: sending third request information to the network device, the third request information being used to request to obtain the first quality threshold of the AI receiver.

15. The method of claim 14, wherein, The third request information includes model information of an AI model currently used by the AI receiver.

16. The method of claim 12, wherein, When the quality threshold of the AI receiver received from the network device is multiple, the method further includes: Among the multiple quality thresholds of the AI receiver received from the network device, the first quality threshold of the AI receiver is determined.

17. The method of claim 16, wherein, The multiple quality thresholds of the AI receiver are quality thresholds corresponding to multiple AI models of the AI receiver.

18. The method of claim 16 or 17, wherein, The method further includes: sending fourth request information to the network device, the fourth request information being used to request to obtain the multiple quality thresholds of the AI receiver.

19. The method of claim 18, wherein, The fourth request information includes one or more of the following: model information of the multiple AI models of the AI receiver; or model information of an AI model currently used by the AI receiver.

20. The method of any one of claims 1-19, wherein, The method further includes: determining a measurement accuracy range corresponding to the AI receiver according to the first quality threshold and a quality reference value; when a quality value measured by the AI receiver is located in the measurement accuracy range, performing the network operation.

21. The method of claim 20, wherein, The first quality threshold is a quality threshold of a synchronization signal block (SSB), and the network operation is cell switching, cell selection, beam failure recovery, or a random access procedure; or The first quality threshold is a quality threshold corresponding to a random access procedure, and the network operation is a two-step random access procedure; or The first quality threshold is a quality threshold of an SSB in a random access procedure, and the network operation is to send a message A in the random access procedure through a beam corresponding to the SSB; The first quality threshold is a quality threshold corresponding to small data transmission (SDT), and the network operation is SDT initialization; or The first quality threshold is a quality threshold corresponding to carrier selection in SDT, and the network operation is SDT through an uplink carrier; or The first quality threshold is a quality threshold corresponding to beam measurement in CG-SDT, and the network operation is CG-SDT through a current beam; or The first quality threshold is a quality threshold corresponding to beam measurement in random access-based small data transmission (RA-SDT), and the network operation is SSB selection in RA-SDT; or The first quality threshold is a quality threshold corresponding to a random access channel (RACH) type in SDT, and the network operation is a random access procedure based on a contention-based RACH; or The first quality threshold is a quality threshold corresponding to carrier selection, and the network operation is uplink data transmission through an uplink carrier.

22. A method of communication, comprising: The method is performed by a network device, and the method includes: sending, to a terminal device, a first threshold correction value of an AI receiver of the terminal device, wherein the first threshold correction value is used by the terminal device to determine a first quality threshold of the AI receiver; or sending, to a terminal device, a first quality threshold of an AI receiver of the terminal device; The first quality threshold and a quality value measured by the AI receiver are used by the terminal device to determine to perform a network operation.

23. The method of claim 22, wherein, The first threshold correction value is a threshold correction value corresponding to an AI model currently used by the AI receiver; The first quality threshold value is a quality threshold value corresponding to an AI model currently used by the AI receiver.

24. The method of claim 22 or 23, wherein, When the first threshold correction value of the AI receiver is sent to the terminal device, the method further comprises: receiving first request information from the terminal device, the first request information being used to request to obtain the first threshold correction value of the AI receiver.

25. The method of claim 24, wherein, The first request information comprises model information of an AI model currently used by the AI receiver.

26. The method of claim 22 or 23, wherein, The sending of the first threshold correction value of the AI receiver of the terminal device to the terminal device comprises: sending a plurality of threshold correction values of the AI receiver to the terminal device, the plurality of threshold correction values comprising the first threshold correction value of the AI receiver.

27. The method of claim 26, wherein, The plurality of threshold correction values of the AI receiver are correction values corresponding to a plurality of AI models of the AI receiver.

28. The method of claim 26 or 27, wherein, The method further comprises: receiving second request information from the terminal device, the second request information being used to request to obtain a plurality of threshold correction values of the AI receiver.

29. The method of claim 28, wherein, The second request information comprises one or more of the following: model information of a plurality of AI models of the AI receiver; or model information of an AI model currently used by the AI receiver.

30. The method of claim 22 or 23, wherein, When the first quality threshold value of the AI receiver of the terminal device is sent to the terminal device, the method further comprises: receiving third request information from the terminal device, the third request information being used to request to obtain the first quality threshold value of the AI receiver.

31. The method of claim 30, wherein, The third request information comprises model information of an AI model currently used by the AI receiver.

32. The method of claim 22 or 23, wherein, The sending of the first quality threshold value of the AI receiver of the terminal device to the terminal device comprises: sending a plurality of quality threshold values of the AI receiver to the terminal device, the plurality of quality threshold values comprising the first quality threshold value of the AI receiver.

33. The method of claim 32, wherein, The plurality of quality threshold values of the AI receiver are quality threshold values corresponding to a plurality of AI models of the AI receiver.

34. The method of claim 32 or 33, wherein, The method further comprises: receiving fourth request information from the terminal device, the fourth request information being used to request to obtain a plurality of quality threshold values of the AI receiver.

35. The method of claim 34, wherein, The fourth request information comprises one or more of the following: model information of a plurality of AI models of the AI receiver; or model information of an AI model currently used by the AI receiver.

36. A communications device, characterized by The communication device is configured to perform the communication method of any one of claims 1 to 21 or any one of claims 22 to 35.

37. A communication system, characterized by comprises: a terminal device and a network device; The terminal device is configured to implement the communication method of any one of claims 1 to 21; The network device is configured to implement the communication method of any one of claims 22 to 35.

38. A storage medium, the storage medium storing instructions, wherein, When the instructions run on the communication device, the communication device is caused to perform the communication method of any one of claims 1 to 21 or any one of 22 to 35.

39. A program product comprising a program and / or instructions, characterized in that The program and / or instructions are executed by the communication device to implement the communication method of any one of claims 1 to 21 or any one of claims 23 to 35.

Images