Communication methods, devices, communication system, communication device and storage medium

Abstract

The present disclosure belongs to the technical field of communications, and relates to communication methods, devices, a communication system, a communication device and a storage medium. A method comprises: a first device receiving indication information sent by a second device; and, on the basis of the indication information, executing a model-based operation. By means of indication information, consistent understanding of model use between two devices is ensured.

Description

A communication method and device, a communication system, a communication device, and a storage medium. Technical Field

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

[0002] Artificial intelligence (AI) and machine learning (ML) are becoming increasingly important components of 5G communication technology, playing a crucial role in the research and application of 5G and 6G communication standards. Machine learning can generate models from large amounts of training data, and these models can be used to predict events. Wireless communication networks can utilize AI for prediction and reasoning, thereby improving system performance.

[0003] Summary of the Invention

[0004] This disclosure presents a communication method, device, system, equipment, and storage medium, which can be used in the field of communication technology to solve the problem of achieving consistency in understanding and use of AI models between the UE and the base station.

[0005] According to a first aspect of the present disclosure, a communication method is proposed, executed by a first device, comprising: receiving indication information sent by a second device; and performing a model-based operation based on the indication information.

[0006] According to a second aspect of the present disclosure, a communication method is proposed, executed by a second device, comprising: sending instruction information to a first device, the instruction information being used to assist the first device in performing a model-based operation.

[0007] According to a third aspect of the present disclosure, a first device is provided, comprising: a transceiver module for receiving indication information sent by a second device; and a processing module for performing a model-based operation based on the indication information.

[0008] According to a fourth aspect of the present disclosure, a second device is provided, including a transceiver module for sending instruction information to a first device, the instruction information being used to assist the first device in performing a model-based operation.

[0009] According to a fifth aspect of the present disclosure, a communication device is provided, including one or more processors; wherein the one or more processors are configured to invoke instructions to cause the communication device to perform the methods described in either the first or second aspect.

[0010] According to a sixth aspect of the present disclosure, a communication system is provided, including a first device and a second device, wherein the first device is configured to implement the communication method of the first aspect, and the second device is configured to implement the communication method of the second aspect.

[0011] According to a seventh aspect of the present disclosure, a storage medium is provided that stores instructions which, when executed on a communication device, cause the communication device to perform the method described in any one of the first and second aspects.

[0012] According to the communication method proposed in this disclosure, the first device receives instruction information sent by the second device; based on the instruction information, it performs model-based operations to achieve consistency in the understanding of AI model usage between the two devices. Attached Figure Description

[0013] 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.

[0014] Figure 1 is a schematic diagram of the architecture of a communication system provided according to an embodiment of the present disclosure;

[0015] Figure 2 is an interactive schematic diagram of the communication method provided according to an embodiment of the present disclosure;

[0016] Figure 3 is an interactive schematic diagram of the communication method provided according to an embodiment of the present disclosure;

[0017] Figure 4 is an interactive schematic diagram of the communication method provided according to an embodiment of the present disclosure;

[0018] Figure 5A is a schematic flowchart of a communication method for a first device according to an embodiment of the present disclosure;

[0019] Figure 5B is a schematic flowchart of a communication method for a first device according to an embodiment of the present disclosure;

[0020] Figure 6A is a schematic flowchart of a communication method for a second device according to an embodiment of the present disclosure;

[0021] Figure 6B is a schematic flowchart of a communication method for a second device according to an embodiment of the present disclosure;

[0022] Figure 7 is an interactive schematic diagram of the communication method provided according to an embodiment of the present disclosure;

[0023] Figure 8A is a schematic diagram of the structure of a first device provided according to an embodiment of the present disclosure;

[0024] Figure 8B is a schematic diagram of the structure of a second device provided according to an embodiment of the present disclosure;

[0025] Figure 9A is a schematic diagram of the structure of a communication device according to an embodiment of the present disclosure;

[0026] Figure 9B is a schematic diagram of the chip structure proposed in an embodiment of this disclosure. Detailed Implementation

[0027] This disclosure provides a communication method and device, a communication system, a communication device, and a storage medium.

[0028] In a first aspect, embodiments of this disclosure provide a communication method, which is executed by a first device and includes: receiving indication information sent by a second device; and performing a model-based operation based on the indication information.

[0029] In the above embodiments, by receiving instruction information to perform model operations, consistency in the understanding of model usage between the two sides is achieved.

[0030] In conjunction with some embodiments of the first aspect, in some embodiments, the indication information satisfies at least one of the following: the indication information is used to indicate the type of the model; the indication information is used to indicate the training set of the training model; the indication information is used to indicate the type of the input data of the model.

[0031] In the above embodiments, the first device can determine the model type, training set, and input data type based on the instruction information sent by the second device, so as to achieve consistency in the understanding of the model usage between the two devices.

[0032] In conjunction with some embodiments of the first aspect, in some embodiments, the type of the model includes at least one of the following: a model for text; a model for images; a model for video; and a hybrid data model.

[0033] In the above embodiments, the two devices can achieve a consistent understanding of the use of the same model based on the instructions of the other device, and the type of model can be diverse.

[0034] In conjunction with some embodiments of the first aspect, in some embodiments, the type of input data includes at least one of the following: text; image; video; mixed data.

[0035] In the above embodiments, by indicating the type of input data, the counterpart device can determine the corresponding model, so as to achieve a consistent understanding of the use of the same model.

[0036] In conjunction with some embodiments of the first aspect, in some embodiments, receiving indication information sent by the second device includes: receiving first information sent by the second device, wherein the indication information is indicated by the indication field of the first information.

[0037] In the above embodiments, the second device indicates the model's instruction information through the instruction field of the first information, so that the first device can achieve a consistent understanding of the use of the same model.

[0038] In conjunction with some embodiments of the first aspect, in some embodiments, the first information includes at least one of the following: first control information, which is carried by a data channel between a first device and a second device; and second control information, which is carried by a control channel between the first device and the second device.

[0039] In the above embodiments, the second device sends first information through a data channel or a control channel to enable the first device to determine the model and achieve consistency in the understanding and use of the model.

[0040] In conjunction with some embodiments of the first aspect, in some embodiments, receiving indication information sent by the second device includes: receiving signaling sent by the second device, wherein the indication information is included in the signaling.

[0041] In the above embodiments, the second device sends instruction information to the first device via signaling so that the first device can determine the model and achieve consistency in understanding and use of the model.

[0042] In conjunction with some embodiments of the first aspect, in some embodiments, the instruction information is applied to semantic communication.

[0043] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes: sending second information to a second device, wherein the second information is used to indicate the capabilities of the first device.

[0044] In the above embodiments, the first device can send its own capabilities to the second device so that the second device can determine the type of model indicated by the instruction information sent to the first device, so as to achieve consistency in the understanding of model usage between the two devices.

[0045] In conjunction with some embodiments of the first aspect, in some embodiments, the second information includes at least one of the following: the type of model supported by the first device; the type of input data for the model supported by the first device; the training set of the training model supported by the first device; the first time required for the first device to train the model; and the second time required for the first device to perform operations using the model.

[0046] In the above embodiments, instruction information is used to ensure that the first device and the second device have a consistent understanding of the use of the model.

[0047] Secondly, embodiments of this disclosure provide a communication method executed by a second device, comprising: sending indication information to a first device, the indication information being used to assist the first device in performing a model-based operation.

[0048] In the above embodiment, by sending instruction information to the first device, the two devices can reach a consensus on the use of the model.

[0049] In conjunction with some embodiments of the second aspect, in some embodiments, the indication information satisfies at least one of the following: the indication information is used to indicate the type of the model; the indication information is used to indicate the training set of the training model; the indication information is used to indicate the type of input data of the model.

[0050] In the above embodiments, the first device can determine the model type, training set, and input data type based on the instruction information sent by the second device, so as to achieve consistency in the understanding of the model usage between the two devices.

[0051] In conjunction with some embodiments of the second aspect, in some embodiments, the model type includes at least one of the following: a text-based model; an image-based model; a video-based model; and a hybrid data model.

[0052] In the above embodiments, the two devices can achieve a consistent understanding of the use of the same model based on the instructions of the other device, and the type of model can be diverse.

[0053] In conjunction with some embodiments of the second aspect, in some embodiments, the type of input data includes at least one of the following: text; image; video; mixed data.

[0054] In the above embodiments, by indicating the type of input data, the counterpart device can determine the corresponding model, so as to achieve a consistent understanding of the use of the same model.

[0055] In conjunction with some embodiments of the second aspect, in some embodiments, sending indication information to the first device includes: sending first information to the first device, wherein the indication information is indicated by the indication field of the first information.

[0056] In the above embodiments, the second device indicates the model's instruction information through the instruction field of the first information, so that the first device can achieve a consistent understanding of the use of the same model.

[0057] In conjunction with some embodiments of the second aspect, in some embodiments, the first information includes at least one of the following: first control information, which is carried by a data channel between the first device and the second device; and second control information, which is carried by a control channel between the first device and the second device.

[0058] In the above embodiments, the second device sends first information through a data channel or a control channel to enable the first device to determine the model and achieve consistency in the understanding and use of the model.

[0059] In conjunction with some embodiments of the second aspect, in some embodiments, sending indication information to the first device includes: sending signaling to the first device, wherein the indication information is included in the signaling.

[0060] In the above embodiments, the second device sends instruction information to the first device via signaling so that the first device can determine the model and achieve consistency in understanding and use of the model.

[0061] In conjunction with some embodiments of the second aspect, in some embodiments, the indication information is applied to semantic communication.

[0062] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes: receiving second information sent by the first device, wherein the second information is used to indicate the capabilities of the first device.

[0063] In the above embodiments, the first device can send its own capabilities to the second device so that the second device can determine the type of model indicated by the instruction information sent to the first device, so as to achieve consistency in the understanding of model usage between the two devices.

[0064] In conjunction with some embodiments of the second aspect, in some embodiments, the second information includes at least one of the following: the type of model supported by the first device; the type of input data for the model supported by the first device; the training set for training the model by the first device; the first time required for the first device to train the model; and the second time required for the first device to perform operations using the model.

[0065] In the above embodiment, the second device sends instruction information to the first device so that the two devices can reach a consensus on the use of the model.

[0066] Thirdly, embodiments of this disclosure provide a first device, including: a transceiver module for receiving indication information sent by a second device; and a processing module for performing model-based operations based on the indication information.

[0067] Fourthly, embodiments of this disclosure provide a second device, including: a transceiver module, configured to send instruction information to a first device, the instruction information being used to assist the first device in performing model-based operations.

[0068] Fifthly, embodiments of this disclosure provide a communication device, including: one or more processors; wherein the one or more processors are configured to invoke instructions to cause the communication device to perform the method described in any one of the embodiments of the first and second aspects.

[0069] In a sixth aspect, embodiments of this disclosure provide a communication system, including: a first device and a second device, wherein the first device is configured to perform the method described in any embodiment of the first aspect of this disclosure; and the second device is configured to perform the method described in any embodiment of the second aspect of this disclosure.

[0070] In a seventh aspect, embodiments of this disclosure provide a storage medium storing instructions that, when executed on a communication device, cause the communication device to perform the method described in any one of the embodiments of the first or second aspect of this disclosure.

[0071] Eighthly, embodiments of this disclosure provide a program product that, when executed by a communication device, causes the communication device to perform the method as described in the optional implementation of the first or second aspect.

[0072] In a ninth aspect, embodiments of this disclosure provide a computer program that, when run on a computer, causes the computer to perform the methods described in an optional implementation of the first or second aspect.

[0073] In a tenth aspect, embodiments of this disclosure provide a chip or chip system. The chip or chip system includes processing circuitry configured to perform the method described according to an optional implementation of the first or second aspect above.

[0074] It is understood that the aforementioned terminals, network devices, 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.

[0075] This disclosure provides a communication method and apparatus, a communication system, a communication device, and a storage medium. In some embodiments, terms such as communication method and information processing method can be used interchangeably; terms such as first apparatus and second apparatus can be used interchangeably with terms such as information processing device and communication device; and terms such as information processing system and communication system can be used interchangeably.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] In this disclosure, unless otherwise stated, elements expressed in the singular form, such as "a," "an," "the," "the aforementioned," "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 or a plural expression.

[0080] In the embodiments disclosed herein, "multiple" refers to two or more.

[0081] In some embodiments, the terms “at least one of”, “at least one of”, “at least one of”, “one or more”, “a plurality of”, “multiple”, etc., may be used interchangeably.

[0082] The descriptions in this disclosure, such as "at least one of A, B, C..." or "A and / or B and / or C...", include the case where any one of A, B, C... exists alone, as well as the case where any combination of any of A, B, C... exists alone. Each case can exist alone. For example, "at least one of A, B, C" includes the cases of A alone, B alone, C alone, A and B combination, A and C combination, B and C combination, and A and B and C combination. For example, A and / or B includes the cases of A alone, B alone, and A and B combination.

[0083] In some embodiments, the notation "in one case A, in another case B" or "in response to one case A, in response to another case B" may include the following technical solutions depending on the situation: A is executed regardless of B, i.e., A is executed in some embodiments; B is executed regardless of A, i.e., B is executed in some embodiments; A and B are selectively executed, i.e., A and B are selected for execution in some embodiments; A and B are both executed, i.e., A and B are executed in some embodiments. The same applies when there are more branches such as A, B, and C.

[0084] 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 is 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 a "field," the ordinal numbers preceding "field" in "first field" and "second field" do not restrict the position or order of the "fields." "First" and "second" do not restrict whether the "fields" they modify are in the same message, nor do they restrict the order of "first field" and "second field." Similarly, if the descriptive object is a "level," the ordinal numbers preceding "level" in "first level" and "second level" do not restrict the priority between "levels." Furthermore, the number of descriptive objects is not limited by ordinal numbers and can be one or more. For example, in "first device," the number of "devices" can be one or more. Furthermore, the objects modified by different prefixes can be the same or different. For example, if the object being described is "device", then "first device" and "second device" can be the same device or different devices, and their types can be the same or different. Similarly, if the object being described is "information", then "first information" and "second information" can be the same information or different information, and their content can be the same or different.

[0085] In some embodiments, “including A,” “containing A,” “for indicating A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0086] In some embodiments, terms such as "time / frequency" and "time-frequency domain" refer to the time domain and / or frequency domain.

[0087] In some embodiments, the terms “in response to…”, “in response to determining…”, “in the case of…”, “when…”, “if…”, “if…”, etc., can be used interchangeably.

[0088] 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”.

[0089] In some embodiments, devices, etc., can be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. Terms such as “device”, “equipment”, “circuit”, “network element”, “node”, “function”, “unit”, “section”, “system”, “network”, “chip”, “chip system”, “entity”, and “subject” can be used interchangeably.

[0090] In some embodiments, "network" can be interpreted as devices included in a network (e.g., access network devices, core network devices, etc.).

[0091] In some embodiments, the terms "access network device (AN device)," "radio access network device (RAN device)," "base station (BS)," "radio base station," "fixed station," "node," "access point," "transmission point (TP)," "reception point (RP)," "transmission / reception point (TRP)," "panel," "antenna panel," "antenna array," "cell," "macro cell," "small cell," "femto cell," "pico cell," "sector," "cell group," "carrier," "component carrier," and "bandwidth part (BWP)" can be used interchangeably.

[0092] In some embodiments, the terms "terminal", "terminal device", "user equipment (UE)", "user terminal", "mobile station (MS)", "mobile terminal (MT)", "subscriber station", "mobile unit", "subscriber unit", "wireless unit", "remote unit", "mobile device", "wireless device", "wireless communication device", "remote device", "mobile subscriber station", "access terminal", "mobile terminal", "wireless terminal", "remote terminal", "handheld device", "user agent", "mobile client", and "client" can be used interchangeably.

[0093] In some embodiments, access network devices, core network devices, or network devices can be replaced by terminals. For example, embodiments of this disclosure can also be applied to structures where communication between access network devices, core network devices, or network devices and terminals is replaced by communication between multiple terminals (e.g., device-to-device (D2D), vehicle-to-everything (V2X), etc.). In this case, the structure can also be configured such that the terminal has all or part of the functions of the access network device. Furthermore, terms such as "uplink" and "downlink" can be replaced with terms corresponding to communication between terminals (e.g., "sidelink"). For example, uplink channel, downlink channel, etc., can be replaced with sidelink channel, and uplink link, downlink, etc., can be replaced with sidelink link.

[0094] In some embodiments, the terminal may be replaced by an access network device, a core network device, or a network device. In this case, the access network device, core network device, or network device may also be configured to have all or some of the functions of the terminal.

[0095] In some embodiments, the acquisition of data, information, etc., may comply with the laws and regulations of the country where the location is situated.

[0096] In some embodiments, data, information, etc., may be obtained with the user's consent.

[0097] Furthermore, each element, each row, or each column in the table of this disclosure can be implemented as an independent embodiment, and any combination of any element, any row, or any column can also be implemented as an independent embodiment.

[0098] The following is a description of the technical terms used in this disclosure:

[0099] 1. DCI: Downlink Control Information, carried by the downlink physical control channel PDCCH, is the downlink control information sent from the eNB to the UE, including uplink and downlink resource allocation, HARQ information, power control, etc.

[0100] 2. PDCCH: Physical Downlink Control Channel.

[0101] 3. PHICH: Physical Hybrid ARQ Indicator Channel.

[0102] 4. PCFICH: Physical Control Format Indicator Channel.

[0103] 5. PBCH: Physical Broadcast Channel.

[0104] 6. URLLC: Ultra-Reliable Low-Latency Communications (Ultra-reliable and ultra-low-latency communications).

[0105] 7. PDSCH: Physical Downlink Shared Channel.

[0106] 8. SPS: Semi-Persistent Scheduling.

[0107] 9. LTE Control Region: In LTE, the Control Region consists of PCFICH, PHICH, PDCCH, and Reference Symbols. The mapping order follows this sequence: first, map the Reference Symbols; then map the PCFICH and PHICH; and finally, map the PDCCH.

[0108] Reference Symbols include Downlink cell-specific reference signals that support cell communication, and PSS (Primary Synchronization Signal) and SSS (Secondary Synchronization Signal) that support initial access synchronization of terminal devices. These signals are predefined and configured in fixed time-frequency domain positions by the protocol.

[0109] The PCFICH carries a 2-bit CFI information indicating the specific number of time-domain symbols in the control region. The PCFICH is mapped to the first time-domain symbol of the downlink subframe and to 4 REGs (Resource Element Groups) in the frequency domain, distributed equally across the entire bandwidth. The specific location of the REGs is related to the PCI; different PCIs have different REG locations. The terminal device determines the PHICH resource distribution by reading the PBCH.

[0110] In an LTE cell, all terminals search for the same PDCCH resource range of the DCI. The frequency domain resources of the control region are equal to the cell system bandwidth by default, and the time domain resources are fixed to the first 1 to 3 / 2 to 4 OFDM symbols of the downlink subframe, which are dynamically indicated by the PCFICH.

[0111] The size of the DCI field information in the PDCCH is only related to the DCI format and downlink bandwidth. The same DCI selects different aggregation levels according to channel quality. The aggregation level supports 1 to 8, representing different numbers of CCEs occupied when the DCI is transmitted. The search space is divided into two main categories: UE-specific and Common. The common space starts blind detection from CCE0, while the starting position of the UE-specific space is obtained through calculation.

[0112] 10. NR PDCCH: In NR, to improve link performance through beamforming, optimize PDCCH reference signal design, simplify base station scheduling, and save power consumption of base stations and terminals, NR uses UE-specific PDCCH resources. The PDCCH monitoring range of a terminal is concentrated from the system bandwidth into a "control subband", namely the control resource set (CORESET).

[0113] NR introduces mini-slots and flexible channel structures to achieve low latency. The LTE PDCCH, which can only be transmitted in the first few symbols of a subframe, cannot meet the requirements of URLLC and low-latency eMBB services. Furthermore, LTE terminals need to monitor the PDCCH in every downlink subframe, resulting in high terminal power consumption. Therefore, NR PDCCH requires a time-domain flexible PDCCH to match the flexibility of the data channel, thereby achieving on-demand transmission. This flexibility is ultimately reflected in the design of the PDCCH search space set.

[0114] NR carrier bandwidth can reach over 100MHz, and TDM alone cannot effectively reuse PDCCH and PDSCH, resulting in a significant waste of frequency domain resources on both sides of the PDCCH. Therefore, 5G NR systems support FDM (Frequency Division Multiplexing) for PDCCH and PDSCH. From a UE's perspective, its PDCCH is confined to the Control Sub-band, while it can simultaneously receive PDSCH outside the Control Sub-band. The base station's ORESET configuration implements this scheduling. Multiplexing between the PDCCH of other UEs and the PDSCH of the current UE is more complex, requiring information on PDCCH resources occupied by other UEs and further resolution through rate matching, among other methods.

[0115] Currently, both LTE and NR use PDCCH to carry DCI (Data Channel Integration). However, PDCCH resources are limited, leading to significant congestion issues when facing frequent multi-user scheduling in the larger-scale access of 6G. Furthermore, UE blind detection has always been a major obstacle to UE energy efficiency. In NR, to support low-latency scheduling, DCI monitoring timing is configured more frequently, which is detrimental to terminal energy saving. Based on the fundamental design requirements of reducing UE blind detection and DCI blocking rate, a design for 6G is to use a data channel-carrying DCI mechanism, with SPS (Simultaneous Power Grid Switching) as the preferred method and dynamically scheduled PDSCH as the second best.

[0116] The advantages of this mechanism are as follows:

[0117] 1. The new DCI carried by PDSCH eliminates the need for blind testing by terminal devices, fundamentally reducing the number of blind tests required by the terminal.

[0118] 2. While maintaining the existing DCI in the network, it can also reduce the candidate positions of legacy DCI carried in PDCCH, further reducing the number of blind checks in PDCCH.

[0119] 3. With the number of DCIs in the network remaining unchanged, the network side can reduce the configuration of legacy DCIs carried in the PDCCH, thereby reducing the blocking probability of legacy DCIs and helping to improve system throughput.

[0120] 4. Compared to the NR / LTE mechanism, it provides more flexible DCI transmission locations, which helps to enhance scheduling flexibility;

[0121] 5. Compared to PDCCH, PDSCH can be configured with more time and frequency resources, allowing for larger DCI payloads, which means that DCI can support more diverse functions.

[0122] The potential disadvantages of this mechanism are as follows:

[0123] 1. Affects PDSCH transmission;

[0124] 2. PDSCH has lower reliability requirements than PDCCH. After its introduction, reliability needs to be enhanced to ensure the transmission of DCI.

[0125] Regarding the first disadvantage, from the perspective of system throughput, it's necessary to consider the overall control / data resources and the specific network configuration. If the PDSCH itself is redundant and the DCI is insufficient, then the key factor affecting the overall system throughput is the number of DCIs, not the number of PDSCHs. Therefore, using PDSCHs to transmit DCIs is beneficial to the PDSCH transmission itself, and the impact on PDSCH transmission can only be considered from the perspective of a single PDSCH transmission. From a system perspective, in some scenarios, it can improve system throughput. Furthermore, compared to data information, control information occupies a very small percentage of the total PDSCH time-frequency resources, so its impact on PDSCH is limited. Additionally, this mechanism does not require PDSCHs to always carry DCIs; the network side can completely disable the function of carrying DCIs on PDSCHs when PDSCH resources are scarce.

[0126] Regarding the second disadvantage, the reliability requirements for PDSCH transmission are currently lower than those for PDCCH. However, PDSCH actually has multiple reliability / coverage enhancement schemes. Similarly, if PDSCH supports carrying DCI, conventional coverage enhancement approaches can be reused to mitigate this issue.

[0127] In summary, PDSCH carrying DCI still has significant technical advantages.

[0128] Therefore, this disclosure proposes a communication method, device, system, and storage medium to address how the UE and the base station can achieve a consistent understanding of the AI ​​model.

[0129] The method proposed in this disclosure is applicable to various communication systems, including but not limited to 4G, 5G, 5G-advance and subsequent communication technologies (such as 6G).

[0130] Figure 1 is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure. As shown in Figure 1, the communication system 100 may include a first device 101 and a second device 102.

[0131] In some embodiments, the first device 101 may be a terminal.

[0132] In some embodiments, the first device 101 may be a device that receives the first information.

[0133] In some embodiments, the first device 101 may be a device that receives signaling.

[0134] In some embodiments, the first device 101 may be a device that sends second information.

[0135] In some embodiments, the first device 101 may be a device that performs model-based operations.

[0136] In some embodiments, the name of the first device 101 is not limited, and it may be, for example, "device for receiving instruction information" or "device for performing operations".

[0137] In some embodiments, the second device 102 may be a network node or a base station.

[0138] In some embodiments, the second device 102 may be a device that sends instruction information.

[0139] In some embodiments, the second device 102 may be a device that sends the first information.

[0140] In some embodiments, the second device 102 may be a device that sends signaling.

[0141] In some embodiments, the second device 102 may be a device that receives the second information.

[0142] In some embodiments, the name of the second device 102 is not limited, and it may be, for example, "device for sending instruction information", "device for sending first information", "device for receiving second information", etc.

[0143] In some embodiments, the terminal may include at least one of, but is not limited to, a mobile phone, a wearable device, an Internet of Things device, a car with communication capabilities, a smart car, a tablet computer, a computer with wireless transceiver capabilities, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in a smart grid, a wireless terminal device in transportation safety, a wireless terminal device in a smart city, and a wireless terminal device in a smart home.

[0144] In some embodiments, the access network equipment may include at least one of the following in a 5G communication system: an evolved NodeB (eNB), a next-generation eNB (ng-eNB), a next-generation NodeB (gNB), a node B (NB), a home node B (HNB), a home evolved node B (HeNB), a radio backhaul device, a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a base band unit (BBU), a mobile switching center, a base station in a 6G communication system, an open RAN, a cloud RAN, a base station in other communication systems, and an access node in a Wi-Fi system, but is not limited thereto.

[0145] 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 access 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.

[0146] In some embodiments, the access network device 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 access 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.

[0147] In some embodiments, a core network device may be a single device comprising one or more network elements, or it may be multiple devices or a group of devices, each comprising all or part of the aforementioned one or more network elements. Network elements may be virtual or physical. The core network may include, for example, at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), and a Next Generation Core (NGC).

[0148] 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.

[0149] The following embodiments of this disclosure can be applied to the communication system 100 shown in FIG1, or to some of the main bodies, but are not limited thereto. The main bodies shown in FIG1 are illustrative. The communication system may include all or some of the main bodies in FIG1, or may include other main bodies outside of FIG1. ​​The number and form of each main body are arbitrary. The connection relationship between the main bodies is illustrative. The main bodies may not be connected to each other or may be connected in any way. The connection may be direct or indirect, wired or wireless.

[0150] The embodiments disclosed herein can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G new radio (NR), 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), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), and IEEE 802.20, Ultra-Wideband (UWB), Bluetooth (a registered trademark), Public Land Mobile Network (PLMN) networks, Device-to-Device (D2D) systems, Machine-to-Machine (M2M) systems, Internet of Things (IoT) systems, Vehicle-to-Everything (V2X) systems, systems utilizing other user plane path establishment methods, and next-generation systems extended from them, etc. Furthermore, multiple systems can be combined (e.g., a combination of LTE or LTE-A with 5G).

[0151] Figure 2 is an interactive schematic diagram of a communication method provided in an embodiment of this disclosure. As shown in Figure 2, this embodiment of the disclosure relates to a communication method that can be executed by a communication system, such as the communication system 100 shown in Figure 1. The communication system includes a first device and a second device. The interactive method may include the following steps:

[0152] Step 2101: The first device sends the second information to the second device.

[0153] In some embodiments, the second information is used to indicate the capabilities of the first device.

[0154] In some embodiments, the second information includes at least one of the following: the type of model supported by the first device; the type of input data for the model supported by the first device; the training set of the training model supported by the first device; the first time required for the first device to train the model; and the second time required for the first device to perform operations using the model.

[0155] In the above embodiments, the first device is a terminal and the second device is a base station device.

[0156] For example, the terminal device reports a first UE capability to the base station device. The first UE capability is predefined by the protocol and is used to indicate the type of AI model supported by the terminal device.

[0157] For example, the terminal device reports a second UE capability to the base station device. The second UE capability is predefined by the protocol and is used to indicate the minimum time required to complete training / give the output result of the AI ​​model.

[0158] Step 2102: The second device sends an instruction message to the first device.

[0159] In some embodiments, the indication information satisfies at least one of the following: the indication information is used to indicate the type of the model; the indication information is used to indicate the training set for training the model; the indication information is used to indicate the type of input data for the model.

[0160] In some embodiments, the indication information is used to indicate the type of model.

[0161] In some embodiments, the model type includes at least one of the following: a text-based model; an image-based model; a video-based model; and a hybrid data model.

[0162] In some embodiments, the indication information may be indicated by the indication field of the first information.

[0163] In some embodiments, the second device sends first information to the first device, wherein the indication field in the first information indicates indication information.

[0164] In some embodiments, the first information includes at least one of the following: first control information, which is carried by a data channel between the first device and the second device; and second control information, which is carried by a control channel between the first device and the second device.

[0165] In some embodiments, the first device is a terminal and the second device is a base station device. The indication field may be included in legacy-DCI or new-DCI.

[0166] For example, the protocol predefines an AI model indication field, which is used by the base station to indicate the type of AI model to the terminal. The AI ​​model indication field can be included in legacy-DCI or new-DCI.

[0167] In some embodiments, the first information may be an AI model indicator domain, and the domain name corresponding to the first information is not limited, nor is the number of bits corresponding to it.

[0168] In some embodiments, the first device is a base station device and the second device is a terminal.

[0169] For example, the protocol predefines an AI model indication field, which is used by the terminal to report the type of AI model to the base station. The AI ​​model indication field can be included in PUSCH or PUCCH.

[0170] In some embodiments, indication information may be included in the signaling.

[0171] In some embodiments, the signaling may be RRC signaling.

[0172] For example, the protocol predefines AI model indication parameters, which are used by the base station to indicate the type of AI model to the terminal device. AI model indication parameters can be included in RRC signaling.

[0173] In the above embodiments, the indication information can be applied to semantic communication.

[0174] In some embodiments, the indication information may include at least one of the indication information shown in FIG2, FIG3, and FIG4, to indicate at least one of the type of the model, the type of the training set, and the type of the input data, which is not limited herein.

[0175] Step 2103: The first device performs a model-based operation.

[0176] In some embodiments, the first device performs model operations based on the model type indicated by the indication information.

[0177] In some embodiments, the model type includes at least one of the following: a text-based model; an image-based model; a video-based model; and a hybrid data model.

[0178] In some embodiments, the first device performs model-based operations, which may be operations on a text-based model, including management, training, reasoning, etc., which are not limited in this disclosure.

[0179] In some embodiments, the first device performs model-based operations, which may be operations on a model for an image, including management, training, inference, etc., which are not limited in this disclosure.

[0180] In some embodiments, the first device performs model-based operations, which may be operations on a model for the video, including management, training, inference, and other operations, which are not limited in this disclosure.

[0181] In some embodiments, the first device performs model-based operations, which may be operations on a model for mixed data. These operations include management, training, inference, etc., and this disclosure is not limited thereto. The mixed data model may be a mixed data model for text and images, a mixed data model for text and video, a mixed data model for images and video, or a mixed data model for text, images, and video; this disclosure is not limited thereto. For example, the AI ​​model indicator parameters or indicator domain adopt an AI model type specific to the data, such as a model for text, a model for images, a model for video, a mixed data model, etc. After obtaining relevant information, the terminal device can use the corresponding AI model to process the received data to enhance the reliability and processing efficiency of the received data.

[0182] For example, this AI model indicator parameter / indicator field is applied to semantic communication. Voice communication is a task-oriented, "understand first, then transmit" communication method. Before transmitting the signal, the base station equipment selectively extracts, compresses, and transmits the original signal using a specific AI model / type of AI model, and then packages the semantic information and sends it to the terminal device. This parameter / indicator field assists the terminal device in understanding the information of the AI ​​model, processing the received data through the correct AI model, and restoring the original information; this parameter / indicator field also assists the terminal in understanding the information of the AI ​​model and further reading the semantic information based on the AI ​​model.

[0183] In the above embodiments, step 2101 is optional, and this step can be omitted or replaced in different embodiments.

[0184] The communication method involved in the embodiments of this disclosure may include at least one of steps 2101 to 2103. For example, step 2101 may be implemented as a standalone embodiment, step 2102 may be implemented as a standalone embodiment, and so on, but is not limited thereto. Steps 2101+2102, 2102+2103, and 2101+2102+2103 may be implemented as standalone embodiments, but are not limited thereto.

[0185] In this implementation or embodiment, unless there is contradiction, each step can be independent, arbitrarily combined or exchanged in order, optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other implementations or other embodiments.

[0186] In the above embodiments, the type of model indicated by the instruction information transmitted between the first device and the second device enables both devices to have a consistent understanding of the model, thereby improving system performance during AI prediction and reasoning.

[0187] Figure 3 is an interactive schematic diagram of a communication method provided in an embodiment of this disclosure. As shown in Figure 3, this embodiment of the disclosure relates to a communication method that can be executed by a communication system, such as the communication system 100 shown in Figure 1. The communication system includes a first device and a second device. The interactive method may include the following steps:

[0188] Step 3101: The first device sends the second information to the second device.

[0189] In some embodiments, the second information is used to indicate the capabilities of the first device.

[0190] In some embodiments, the second information includes at least one of the following: the type of model supported by the first device; the type of input data for the model supported by the first device; the training set of the training model supported by the first device; the first time required for the first device to train the model; and the second time required for the first device to perform operations using the model.

[0191] In the above embodiments, the first device is a terminal and the second device is a base station device.

[0192] For example, the terminal device reports a first UE capability to the base station device. The first UE capability is predefined by the protocol and is used to indicate the type of AI model supported by the terminal device.

[0193] For example, the terminal device reports a second UE capability to the base station device. The second UE capability is predefined by the protocol and is used to indicate the minimum time required to complete training / give the output result of the AI ​​model.

[0194] Step 3102: The second device sends an instruction message to the first device.

[0195] In some embodiments, the indication information satisfies at least one of the following: the indication information is used to indicate the type of the model; the indication information is used to indicate the training set for training the model; the indication information is used to indicate the type of input data for the model.

[0196] In some embodiments, the indication information is used to indicate the training set for training the model.

[0197] In some embodiments, the indication information may be indicated by the indication field of the first information.

[0198] In some embodiments, the second device sends first information to the first device, wherein the indication field in the first information indicates indication information.

[0199] In some embodiments, the first information includes at least one of the following: first control information, which is carried by a data channel between the first device and the second device; and second control information, which is carried by a control channel between the first device and the second device.

[0200] In some embodiments, the first device is a terminal and the second device is a base station device. The indication field may be included in legacy-DCI or new-DCI.

[0201] For example, the protocol predefines an AI model training set indication field, which is used by the base station to indicate the index of the AI ​​model training set to the terminal. The AI ​​model training set indication field can be included in legacy-DCI or new-DCI.

[0202] In some embodiments, the first information may be an AI model training set indicator field, and the name of the indicator field corresponding to the first information is not limited, nor is the number of bits corresponding to it.

[0203] In some embodiments, the first device is a base station device and the second device is a terminal.

[0204] For example, the protocol predefines an AI model training set indication field, which is used by the terminal to report the index of the AI ​​model training set to the base station. The AI ​​model training set indication field can be included in PUSCH or PUCCH.

[0205] In some embodiments, indication information may be included in the signaling.

[0206] In some embodiments, the signaling may be RRC signaling.

[0207] For example, the protocol predefines an AI model training set index indication parameter, which is used by the base station to indicate the index of the AI ​​model training set to the terminal device. The AI ​​model training set index indication parameter can be included in RRC signaling.

[0208] In the above embodiments, the indication information can be applied to semantic communication.

[0209] In some embodiments, the indication information may include at least one of the indication information shown in FIG2, FIG3, and FIG4, to indicate at least one of the type of the model, the type of the training set, and the type of the input data, which is not limited herein.

[0210] Step 3103: The first device performs a model-based operation.

[0211] In some embodiments, the first device performs model operations based on the training set of the training model indicated by the instruction information.

[0212] In some embodiments, the first device performs model-based operations, which may involve determining the corresponding model based on the training set and performing model-based operations, including management, training, inference, and other operations, which are not limited in this disclosure.

[0213] For example, the base station and the terminal device use the same AI model, but can obtain different training parameters through different training sets for application in actual transmission. Before transmission, the base station indicates the training set index, and the terminal device runs the general AI model according to the training set index before transmission begins to obtain a specific AI model for actual transmission. Subsequent transmission is based on this model for data processing and other operations.

[0214] In the above embodiments, step 3101 is optional, and this step can be omitted or replaced in different embodiments.

[0215] The communication method involved in the embodiments of this disclosure may include at least one of steps 3101 to 3103. For example, step 3101 may be implemented as a standalone embodiment, step 3102 may be implemented as a standalone embodiment, and so on, but is not limited thereto. Steps 3101+3102, 3102+3103, and 3101+3102+3103 may be implemented as standalone embodiments, but are not limited thereto.

[0216] In this implementation or embodiment, unless there is contradiction, each step can be independent, arbitrarily combined or exchanged in order, optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other implementations or other embodiments.

[0217] In the above embodiments, the training set of the model is indicated by the instruction information transmitted between the first device and the second device, so that the two devices can have a consistent understanding of the use of the model, thereby improving the system performance in the process of using AI for prediction and reasoning.

[0218] Figure 4 is an interactive schematic diagram of a communication method provided in an embodiment of this disclosure. As shown in Figure 4, this embodiment of the disclosure relates to a communication method that can be executed by a communication system, such as the communication system 100 shown in Figure 1. The communication system includes a first device and a second device. The interactive method may include the following steps:

[0219] Step 4101: The first device sends the second information to the second device.

[0220] In some embodiments, the second information is used to indicate the capabilities of the first device.

[0221] In some embodiments, the second information includes at least one of the following: the type of model supported by the first device; the type of input data for the model supported by the first device; the training set of the training model supported by the first device; the first time required for the first device to train the model; and the second time required for the first device to perform operations using the model.

[0222] In the above embodiments, the first device is a terminal and the second device is a base station device.

[0223] For example, the terminal device reports a first UE capability to the base station device. The first UE capability is predefined by the protocol and is used to indicate the type of AI model supported by the terminal device.

[0224] For example, the terminal device reports a second UE capability to the base station device. The second UE capability is predefined by the protocol and is used to indicate the minimum time required to complete training / give the output result of the AI ​​model.

[0225] Step 4102: The second device sends an instruction message to the first device.

[0226] In some embodiments, the indication information satisfies at least one of the following: the indication information is used to indicate the type of the model; the indication information is used to indicate the training set for training the model; the indication information is used to indicate the type of input data for the model.

[0227] In some embodiments, the indication information is used to indicate the type of input data for the model.

[0228] In some embodiments, the type of input data includes at least one of the following: text; image; video; hybrid data model.

[0229] In some embodiments, the second device sends the type of input data for the model to the first device, but does not specify the model, and the first device selects the corresponding model based on the type of input data.

[0230] In some embodiments, the indication information may be indicated by the indication field of the first information.

[0231] In some embodiments, the second device sends first information to the first device, wherein the indication field in the first information indicates indication information.

[0232] In some embodiments, the first information includes at least one of the following: first control information, which is carried by a data channel between the first device and the second device; and second control information, which is carried by a control channel between the first device and the second device.

[0233] In some embodiments, the first device is a terminal and the second device is a base station device. The indication field may be included in legacy-DCI or new-DCI.

[0234] For example, the protocol predefines an AI model input data type indicator field, which is used by the base station to indicate to the terminal the type of AI model input data. The AI ​​model input data type indicator field can be included in legacy-DCI or new-DCI.

[0235] In some embodiments, the first information may be an AI model training set indicator field, and the name of the indicator field corresponding to the first information is not limited, nor is the number of bits corresponding to it.

[0236] In some embodiments, the first device is a base station device and the second device is a terminal.

[0237] For example, the protocol predefines an AI model input data type indicator field, which is used by the terminal to report the type of AI model input data to the base station. The AI ​​model input data type indicator field can be included in PUSCH or PUCCH.

[0238] In some embodiments, indication information may be included in the signaling.

[0239] In some embodiments, the signaling may be RRC signaling.

[0240] For example, the protocol predefines an AI model input data type indication parameter, which is used by the base station to indicate the type of AI model input data to the terminal device. The AI ​​model input data type indication parameter can be included in RRC signaling.

[0241] For example, the base station equipment sends an instruction to the terminal equipment, indicating the type of input data for the model, including text, images, videos, mixed data, etc., and the terminal selects the model to perform the operation based on the type of input data.

[0242] In the above embodiments, the indication information can be applied to semantic communication.

[0243] In some embodiments, the indication information may include at least one of the indication information shown in FIG2, FIG3, and FIG4, to indicate at least one of the type of the model, the type of the training set, and the type of the input data, which is not limited herein.

[0244] Step 4103: The first device performs a model-based operation.

[0245] In some embodiments, the first device determines the model corresponding to the type of input data based on the type of input data indicated by the indication information, and performs the operation of the model.

[0246] In some embodiments, the first device performs model-based operations, which may involve determining the corresponding model based on the type of input data to perform model-based operations. These operations include management, training, inference, and other operations, which are not limited in this disclosure.

[0247] In the above embodiments, step 4101 is optional, and this step can be omitted or replaced in different embodiments.

[0248] The communication method involved in the embodiments of this disclosure may include at least one of steps 4101 to 4103. For example, step 4101 may be implemented as a standalone embodiment, step 4102 may be implemented as a standalone embodiment, and so on, but is not limited thereto. Steps 4101+4102, 4102+4103, and 4101+4102+4103 may be implemented as standalone embodiments, but are not limited thereto.

[0249] In this implementation or embodiment, unless there is contradiction, each step can be independent, arbitrarily combined or exchanged in order, optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other implementations or other embodiments.

[0250] In the above embodiments, the type of input data of the model is indicated by the instruction information transmitted between the first device and the second device, so that the two devices can have a consistent understanding of the use of the model, thereby improving system performance in the process of using AI for prediction and reasoning.

[0251] Figure 5A is a schematic flowchart of a communication method for a first device according to an embodiment of the present disclosure. This disclosure relates to a communication method, which includes:

[0252] Step 5101: Send the second information to the second device.

[0253] The optional implementation of step 5101 can be found in the optional implementations of step 2101 in Figure 2, step 3101 in Figure 3, and step 4101 in Figure 4, as well as other related parts in the embodiments involved in Figures 2, 3, and 4, which will not be repeated here.

[0254] Step 5102: Receive instruction information sent by the second device.

[0255] The optional implementation of step 5102 can be found in the optional implementations of step 2102 in Figure 2, step 3102 in Figure 3, and step 4102 in Figure 4, as well as other related parts in the embodiments involved in Figures 2, 3, and 4, which will not be repeated here.

[0256] Step 5103: Perform model-based operations.

[0257] The optional implementation of step 5103 can be found in the optional implementations of step 2103 in Figure 2, step 3103 in Figure 3, and step 4103 in Figure 4, as well as other related parts in the embodiments involved in Figures 2, 3, and 4, which will not be repeated here.

[0258] The communication method involved in the embodiments of this disclosure may include at least one of steps 5101 to 5103. For example, step 5101 may be implemented as a standalone embodiment, and step 5102 may be implemented as a standalone embodiment. And so on, but not limited thereto. Steps 5101+5102, 5102+5103, and 5101+5102+5103 may be implemented as standalone embodiments, but are not limited thereto.

[0259] In some embodiments, step 5101 is optional, and this step may be omitted or replaced in different embodiments.

[0260] Figure 5B is a schematic flowchart of a communication method for a first device according to an embodiment of the present disclosure. This disclosure relates to a communication method, which includes:

[0261] Step 5201: Receive instruction information sent by the second device.

[0262] The optional implementation of step 5201 can be found in step 2102 of Figure 2, step 3102 of Figure 3, step 4102 of Figure 4, optional implementation of step 5102 of Figure 5A, and other related parts in the embodiments involved in Figures 2, 3, 4, and 5A, which will not be repeated here.

[0263] In embodiments of this disclosure, step 5201 can be combined with step 5101 in FIG5A, and step 5201 can be combined with step 5103 in FIG5A.

[0264] Step 5202: Based on the instruction information, perform model-based operations.

[0265] The optional implementation of step 5202 can be found in step 2103 of Figure 2, step 3103 of Figure 3, step 4103 of Figure 4, optional implementation of step 5103 of Figure 5A, and other related parts in the embodiments involved in Figures 2, 3, 4, and 5A, which will not be repeated here.

[0266] In embodiments of this disclosure, step 5202 may be combined with step 5102 in FIG5A.

[0267] Figure 6A is a schematic flowchart of a communication method for a second device according to an embodiment of the present disclosure. This disclosure relates to a communication method, which includes:

[0268] Step 6101: Receive the second information sent by the first device.

[0269] The optional implementation of step 6101 can be found in step 2101 of Figure 2, step 3101 of Figure 3, step 4101 of Figure 4, and other related parts in the embodiments involved in Figures 2, 3, and 4, which will not be repeated here.

[0270] Step 6102: Send instruction information to the first device.

[0271] The optional implementations of step 6102 can be found in step 2102 of Figure 2, step 3102 of Figure 3, and step 4102 of Figure 4, as well as other related parts in the embodiments involved in Figures 2, 3, and 4, which will not be repeated here.

[0272] The communication method involved in the embodiments of this disclosure may include at least one of steps 6101 to 6102. For example, step 6101 may be implemented as a standalone embodiment, and step 6102 may be implemented as a standalone embodiment. And so on, but not limited thereto. Steps 6101 and 6102 may be implemented as standalone embodiments, but not limited thereto.

[0273] In some embodiments, step 6101 is optional and may be omitted or replaced in different embodiments.

[0274] Figure 6B is a schematic flowchart of a communication method for a second device according to an embodiment of the present disclosure. This disclosure relates to a communication method, which includes:

[0275] Step 6201: Send instruction information to the first device.

[0276] The instruction information is used to assist the first device in performing model-based operations.

[0277] The optional implementation of step 6201 can be found in step 2102 of Figure 2, step 3102 of Figure 3, step 4102 of Figure 4, optional implementation of step 6102 of Figure 6A, and other related parts in the embodiments involved in Figures 2, 3, 4, and 6A, which will not be repeated here.

[0278] In embodiments of this disclosure, step 6201 may be combined with step 6101 in FIG6A.

[0279] Figure 7 is an interactive schematic diagram of the communication method provided according to an embodiment of the present disclosure. As shown in Figure 7, the embodiments of the present disclosure relate to a communication method, which includes:

[0280] Step 7101: The first device receives the instruction information sent by the second device.

[0281] The optional implementations of step 7101 can be found in the optional implementations of steps 2102 in Figure 2, 3102 in Figure 3, 4102 in Figure 4, 5102 in Figure 5A, 5201 in Figure 5B, 6102 in Figure 6A, and 6201 in Figure 6B, as well as other related parts in the embodiments involved in Figures 2, 3, 4, 5A, 5B, 6A, and 6B, which will not be repeated here.

[0282] Step 7102: The first device performs a model-based operation based on the instruction information.

[0283] The optional implementation of step 7102 can be found in the optional implementations of step 2103 in Figure 2, step 3103 in Figure 3, step 4103 in Figure 4, step 5103 in Figure 5A, and step 5202 in Figure 5B, as well as other related parts in the embodiments involved in Figures 2, 3, 4, 5A, and 5B, which will not be repeated here.

[0284] In some embodiments, the above method may include the methods described in the embodiments of the first device side and the second device side, which will not be repeated here.

[0285] In this implementation or embodiment, unless there is contradiction, each step can be independent, arbitrarily combined or exchanged in order, optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other implementations or other embodiments.

[0286] The following is a specific embodiment of a communication method provided by this disclosure, which specifically includes the following steps:

[0287] Example 1:

[0288] In a network, base station equipment and terminal equipment can process data based on AI models. The base station equipment sends instruction information to the terminal equipment, and the terminal equipment performs corresponding AI operations based on the instruction information.

[0289] The methods for determining the instruction information include at least one of the following:

[0290] Method 1:

[0291] The protocol predefines an AI model indication field, which is used by the base station to indicate the type of AI model to the terminal device. This AI model indication field can be included in legacy DCI or new DCI.

[0292] Method 2:

[0293] The protocol predefines AI model indication parameters, which are used by the base station to indicate the type of AI model to the terminal device. These AI model indication parameters may be included in RRC signaling.

[0294] Example 1

[0295] This parameter / indicator field indicates the type of AI model used for the specific data, such as a model for text, a model for images, a model for video, or a mixed data model. After obtaining the relevant information, the terminal device can use the corresponding AI model to process the received data, thereby enhancing the reliability and processing efficiency of the received data.

[0296] Example 2

[0297] This parameter / indicator field is applied to semantic communication. Semantic communication is a task-oriented, "understand first, then transmit" communication method. Before transmitting signals, the base station equipment selectively extracts, compresses, and transmits features of the original signal using a specific AI model / type, and then packages the semantic information and sends it to the terminal device. 1) This parameter / indicator field assists the terminal device in understanding the information of the AI ​​model, processing the received data through the correct AI model, and restoring the original information. 2) This parameter / indicator field assists the terminal device in understanding the information of the AI ​​model, and further reads the semantic information based on the AI ​​model (decoding).

[0298] Optionally, alternative implementations of Embodiment 1 can be found in the alternative implementations of the embodiments shown in Figure 2.

[0299] Example 2:

[0300] In a network, base station equipment and terminal equipment can process data based on AI models. The base station equipment sends instruction information to the terminal equipment, and the terminal equipment performs corresponding AI operations based on the instruction information.

[0301] The methods for determining the instruction information include at least one of the following:

[0302] Method 1:

[0303] The protocol predefines an AI model training set index indication field, which is used by the base station to indicate the index of the AI ​​model training set to the terminal device. This AI model training set index indication field can be included in legacy DCI or new DCI.

[0304] Method 2:

[0305] The protocol predefines an AI model training set index indication parameter, which is used by the base station to indicate the index of the AI ​​model training set to the terminal device. This AI model training set index indication parameter may be included in RRC signaling.

[0306] Example 1

[0307] The base station and terminal equipment use the same AI model, but can obtain different training parameters through different training sets for application in actual transmission. Before transmission, the base station indicates the training set index, and the terminal equipment runs the general AI model according to the training set index before transmission begins to obtain a specific AI model for actual transmission. Subsequent transmission uses this model for data processing and other operations.

[0308] Optionally, alternative implementations of Embodiment 2 can be found in the alternative implementations of the embodiments shown in Figure 3.

[0309] Example 3:

[0310] In a network, base station equipment and terminal equipment can process data based on AI models. The base station equipment sends instruction information to the terminal equipment, and the terminal equipment performs corresponding AI operations based on the instruction information.

[0311] The methods for determining the instruction information include at least one of the following:

[0312] Method 1:

[0313] The protocol predefines an AI model input data type indicator field, which is used by the base station to indicate the type of AI model input data to the terminal device. This AI model input data type indicator field can be included in legacy DCI or new DCI.

[0314] Method 2:

[0315] The protocol predefines an AI model input data type indication parameter, which is used by the base station to indicate the type of AI model input data to the terminal device. This AI model input data type indication parameter may be included in RRC signaling.

[0316] Optionally, alternative implementations of embodiment 3 can be found in the alternative implementations of the embodiment shown in Figure 4.

[0317] Example 4:

[0318] In a network, base station equipment and terminal equipment can process data based on AI models. The base station equipment sends instruction information to the terminal equipment, and the terminal equipment performs corresponding AI operations based on the instruction information.

[0319] The protocol predefines a first UE capability, which indicates the types of AI models supported by the terminal device. This first UE capability is reported by the terminal device to the base station device.

[0320] The protocol predefines a second UE capability, which indicates the minimum time required to complete training / output the AI ​​model. This second UE capability is reported by the terminal device to the base station device.

[0321] Optionally, alternative implementations of Embodiment 4 can be found in the alternative implementations of the embodiments shown in steps 2101 in Figure 2, 3101 in Figure 3, and 4101 in Figure 4.

[0322] 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.

[0323] This disclosure also provides 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 in any of the above methods. Alternatively, another apparatus is provided that includes units or modules for implementing the steps performed by a network device (e.g., an access network device, a core network functional node, a core network device, etc.) in any of the above methods.

[0324] 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), and 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), such as a field-programmable gate array (FPGA), which 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.

[0325] 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. In addition, it can also be hardware circuits designed for artificial intelligence, which can be understood as ASICs, such as Neural Network Processing Units (NPUs), Tensor Processing Units (TPUs), and Deep Learning Processing Units (DPUs).

[0326] Figure 8A is a schematic diagram of the structure of a terminal provided according to an embodiment of the present disclosure. As shown in Figure 8A, the first device 8100 includes a transceiver module 8101 and a processing module 8102. In some embodiments, the transceiver module is used to receive instruction information sent by the second device.

[0327] Optionally, the transceiver module is used to perform at least one of the communication steps such as sending and / or receiving performed by the first device 8100 in any of the above methods (e.g., steps 2101, 2102, 3101, 3102, 4101, 4102, 5101, 5102, 5201, 7101, but not limited thereto), which will not be elaborated here.

[0328] 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.

[0329] In some embodiments, the processing module 8102 described above is used to perform model-based operations based on indication information.

[0330] Optionally, the above processing module is used to execute at least one of the other communication steps (such as steps 2103, 3103, 4103, 5103, 5202, and 7102, but not limited thereto) executed by the first device 8100 in any of the above methods, which will not be elaborated here.

[0331] Figure 8B is a schematic diagram of the structure of a second device 8200 provided according to an embodiment of the present disclosure. As shown in Figure 8B, the second device 8200 includes a transceiver module 8201.

[0332] In some embodiments, the transceiver module 8201 is used to send instruction information to the first device, the instruction information being used to assist the first device in performing model-based operations.

[0333] Optionally, the transceiver module is used to perform at least one of the communication steps such as sending and / or receiving performed by the second device 8200 in any of the above methods (e.g., steps 2101, 2102, 3101, 3102, 4101, 4102, 6101, 6102, 6201, 7101, but not limited thereto), which will not be elaborated here.

[0334] 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.

[0335] In some embodiments, the second device further includes a processing module for performing at least one of the other communication steps performed by the second device 8200 in any of the above methods, which will not be described in detail here.

[0336] Figure 9A is a schematic diagram of the structure of a communication device 9100 provided according to an embodiment of this disclosure. The communication device 9100 can be a network device (e.g., access network device, core network device, etc.), a terminal (e.g., user equipment, etc.), a chip, chip system, or processor that supports the network device in implementing any of the above methods, or a chip, chip system, or processor that supports the terminal in implementing any of the above methods. The communication device 9100 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.

[0337] As shown in Figure 9A, the communication device 9100 includes one or more processors 9101. The processor 9101 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 9100 can be used to execute any of the above methods. Optionally, one or more processors 9101 can be used to invoke instructions to cause the communication device 9100 to execute any of the above methods.

[0338] In some embodiments, the communication device 9100 further includes one or more transceivers 9102. When the communication device 9100 includes one or more transceivers 9102, the transceivers 9102 perform at least one of the communication steps such as sending and / or receiving in the above-described method (e.g., steps 2101, 2102, 3101, 3102, 4101, 4102, 5101, 5102, 5201, 6101, 6102, 6201, 7101, but not limited thereto), and the processor 9101 performs at least one of other steps (e.g., steps 2103, 3103, 4103, 5103, 5202, 7102, but not limited thereto). In optional embodiments, the transceivers may include a receiver and / or a transmitter, which may be separate or integrated together. Optionally, terms such as transceiver, transceiver unit, transceiver, transceiver circuit, interface circuit, and interface can be used interchangeably; terms such as transmitter, transmitting unit, transmitter, and transmitting circuit can be used interchangeably; and terms such as receiver, receiving unit, receiver, and receiving circuit can be used interchangeably.

[0339] In some embodiments, the communication device 9100 further includes one or more memories 9103 for storing data. Optionally, all or part of the memories 9103 may be located outside the communication device 9100. In optional embodiments, the communication device 9100 may include one or more interface circuits 9104. Optionally, the interface circuit 9104 is connected to the memory 9102 and can be used to receive data from the memory 9102 or other devices, and can be used to send data to the memory 9102 or other devices. For example, the interface circuit 9104 can read data stored in the memory 9102 and send the data to the processor 9101.

[0340] In some embodiments, the processor 9101 may store a computer program 9105, which runs on the processor 9101 and enables the communication device 9000 to perform the methods described in the above method embodiments. The computer program 9105 may be embedded in the processor 9101, in which case the processor 9101 may be implemented in hardware.

[0341] The communication device 9100 described in the above embodiments may be a network device or a terminal, but the scope of the communication device 9100 described in this disclosure is not limited thereto, and the structure of the communication device 9100 may not be limited by FIG. 9A. 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.

[0342] Figure 9B is a schematic diagram of the structure of the chip 9200 proposed in an embodiment of this disclosure. For cases where the communication device 9100 can be a chip or a chip system, the schematic diagram of the chip 9200 shown in Figure 9B can be referred to, but is not limited thereto.

[0343] Chip 9200 includes one or more processors 9201. Chip 9200 is used to perform any of the methods described above.

[0344] In some embodiments, chip 9200 further includes one or more interface circuits 9202. Optionally, terms such as interface circuit, interface, and transceiver pin can be used interchangeably. In some embodiments, chip 9200 further includes one or more memories 9203 for storing data. Optionally, all or part of the memories 9203 may be located outside chip 9200. Optionally, interface circuit 9202 is connected to memory 9203, and interface circuit 9202 can be used to receive data from memory 9203 or other devices, and interface circuit 9202 can be used to send data to memory 9203 or other devices. For example, interface circuit 9202 can read data stored in memory 9203 and send the data to processor 9201.

[0345] In some embodiments, the interface circuit 9202 performs at least one of the communication steps such as sending and / or receiving in the above-described method (e.g., steps 2101, 2102, 3101, 3102, 4101, 4102, 5101, 5102, 5201, 6101, 6102, 6201, 7101, but not limited thereto). The interface circuit 9202 performing the communication steps such as sending and / or receiving in the above-described method refers, for example, to the interface circuit 9202 performing data interaction between the processor 9201, the chip 9200, the memory 9203, or the transceiver device. In some embodiments, the processor 9201 performs at least one of other steps (e.g., steps 2103, 3103, 4103, 5103, 5202, 7102, but not limited thereto).

[0346] 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.

[0347] This disclosure also proposes a storage medium storing instructions that, when executed on the communication device 9100, cause the communication device 9100 to perform any of the above methods. 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.

[0348] This disclosure also provides a program product that, when executed by the communication device 9100, causes the communication device 9100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0349] This disclosure also proposes a computer program that, when run on a computer, causes the computer to perform any of the above methods.

Claims

1. A communication method, characterized in that, The method is performed by a first device, and the method includes: Receive instruction information sent by the second device; Based on the indicated information, perform model-based operations.

2. The method according to claim 1, characterized in that, The indication information satisfies at least one of the following: The indication information is used to indicate the type of the model; The indication information is used to indicate the training set for training the model; The indication information is used to indicate the type of input data for the model.

3. The method according to claim 2, characterized in that, The model type includes at least one of the following: A model for text; Models for images; Models for videos; Hybrid data model.

4. The method according to claim 2 or 3, characterized in that, The type of input data includes at least one of the following: Word; image; video; Mixed data.

5. The method according to at least one of claims 1 to 4, characterized in that, The receipt of indication information sent by the second device includes: receiving first information sent by the second device, wherein the indication information is indicated by the indication field of the first information.

6. The method according to claim 5, characterized in that, The first information includes at least one of the following: First control information, which is carried by the data channel between the first device and the second device; The second control information is carried by the control channel between the first device and the second device.

7. The method according to at least one of claims 1 to 4, characterized in that, The receipt of indication information sent by the second device includes: receiving signaling sent by the second device, wherein the indication information is included in the signaling.

8. The method according to any one of claims 1 to 7, characterized in that, The indication information is used in semantic communication.

9. The method according to any one of claims 1 to 8, characterized in that, The method further includes: Send a second message to the second device, wherein the second message is used to indicate the capabilities of the first device.

10. The method according to claim 9, characterized in that, The second information includes at least one of the following: The types of models supported by the first device; The types of input data for the model supported by the first device; The training set supported by the first device for training the model; The first time required for the first device to train the model; The second time required for the first device to perform the operation using the model.

11. A communication method, characterized in that, The method is performed by a second device, and the method includes: Send instruction information to the first device, the instruction information being used to assist the first device in performing model-based operations.

12. The method according to claim 11, characterized in that, The indication information satisfies at least one of the following: The indication information is used to indicate the type of the model; The indication information is used to indicate the training set for training the model; The indication information is used to indicate the type of input data for the model.

13. The method according to claim 12, characterized in that, The model type includes at least one of the following: A model for text; Models for images; Models for videos; Hybrid data model.

14. The method according to claim 12 or 13, characterized in that, The type of input data includes at least one of the following: Word; image; video; Mixed data.

15. The method according to at least one of claims 11 to 14, characterized in that, The sending of instruction information to the first device includes: Send first information to the first device, wherein the indication information is indicated by the indication field of the first information.

16. The method according to claim 15, characterized in that, The first information includes at least one of the following: First control information, which is carried by the data channel between the first device and the second device; The second control information is carried by the control channel between the first device and the second device.

17. The method according to at least one of claims 11 to 14, characterized in that, The sending of instruction information to the first device includes: A signaling message is sent to the first device, and the indication information is included in the signaling message.

18. The method according to any one of claims 11 to 17, characterized in that, The indication information is used in semantic communication.

19. The method according to any one of claims 11 to 18, characterized in that, The method further includes: Receive second information sent by the first device, wherein the second information is used to indicate the capabilities of the first device.

20. The method according to claim 19, characterized in that, The second information includes at least one of the following: The types of models supported by the first device; The types of input data for the model supported by the first device; The training set supported by the first device for training the model; The first time required for the first device to train the model; The second time required for the first device to perform the operation using the model.

21. A first device, characterized in that, include: The transceiver module is used to receive indication information sent by the second device; The processing module is used to perform model-based operations based on the indicated information.

22. A second device, characterized in that, include: The transceiver module is used to send instruction information to the first device, the instruction information being used to assist the first device in performing model-based operations.

23. A communication device, wherein, include: transceiver; Memory; The processor, connected to the transceiver and the memory respectively, is configured to control the wireless signal transmission and reception of the transceiver by executing computer-executable instructions on the memory, and is capable of implementing the method of any one of claims 1-10 or 11-20.

24. A computer storage medium, wherein, The computer storage medium stores computer-executable instructions; when executed by a processor, the computer-executable instructions can implement the method of any one of claims 1-10 or 11-20.

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