Method and device for monitoring control channel in node used for wireless communication

Abstract

The invention discloses a method and a device for monitoring a control channel in a node used for wireless communication. The first processor receives a first information block, and the first information block is used for configuring a first search space set; monitoring a control channel alternative in the first search space set; wherein the aggregation level of the first search space set and the alternative monitoring mode of the control channel in the first search space set comprise reasoning correlation. According to the invention, the monitoring efficiency of the alternative control channel is improved, and the overhead is reduced.

Description

Technical Field

[0001] This application relates to transmission methods and devices in a wireless communication system, and in particular to methods and devices related to the monitoring of control channels in a wireless communication system. Background Art

[0002] In the evolution of communication systems from 5G (Generation) to 5G-Advanced and 6G, more advanced technologies have been proposed and studied to improve various aspects of the performance of wireless communication systems and meet the requirements of more application scenarios. Typical technologies include, but are not limited to, AI (Artificial Intelligence), or ML (Machine Learning), full-duplex mode or SubBand non-overlapping Full Duplex (SBFD), and Reconfigurable Intelligent Surface (RIS).

[0003] Compared with 5G systems, a significant feature of 6G systems will be greater intelligence. AI / ML aims to significantly improve the performance of wireless communication using advanced artificial intelligence and machine learning technologies. With AI / ML technologies, 6G systems can not only intelligently provide high-quality services based on the perception and learning of the surrounding environment, such as scheduling, data reception, signal processing, encoding / decoding, measurement, and reporting, but also intelligently achieve network self-optimization and self-maintenance. In NR (New Radio) R (release) 18, the research on AI / ML technologies was approved.

[0004] Compared with traditional processing methods, AI / ML has some unique characteristics, such as being model-dependent, based on training, requiring deployment, and having different requirements for computing / processing capabilities and storage capabilities compared to traditional technologies. According to the 3GPP (3rd Generation Partner Project) standard TS38.300, AI / ML models and algorithms are outside the scope of 3GPP.

[0005] When AI / ML technologies are introduced, the existing monitoring, receiving mechanisms related to control channels, and related configuration signaling may not be able to meet the requirements of AI / ML technologies. Summary of the Invention

[0006] The inventors found through research that when inference is used for the monitoring of control channel candidates, the existing aggregation levels and their related configurations may not be able to meet the requirements of inference.

[0007] In view of the above problems, the present application discloses a solution. It should be noted that although a large number of embodiments of the present application are directed to AI / ML, the present application is also applicable to other solutions, such as traditional control channel monitoring solutions. Although the description in the specification of the present application involves some descriptions of AI / ML models and algorithms, those skilled in the art know that these descriptions are not necessary or irreplaceable for wireless cellular communication related solutions. In addition, adopting a unified solution in different scenarios (including but not limited to AI / ML-based solutions and traditional control channel monitoring solutions) helps to reduce hardware complexity and cost. Without conflict, the embodiments and features in any node of the present application can be applied to any other node. Without conflict, the embodiments and features in the embodiments of the present application can be combined with each other arbitrarily.

[0008] When needed, the interpretation of the terms in the present application refers to the definitions in the 3GPP specification protocol series TS38, or, refers to the definitions in the 3GPP specification protocol series TS28.

[0009] The present application discloses a method in a first node for wireless communication, characterized by including:

[0010] Receiving a first information block, the first information block being used to configure a first search space set; monitoring control channel candidates in the first search space set;

[0011] Wherein, whether the aggregation level of the first search space set and the monitoring manner of the control channel candidates in the first search space set includes reasoning is relevant.

[0012] As an embodiment, the problems to be solved by the present application include: when the monitoring manner of the control channel candidates in the search space set can adopt reasoning, how to determine the aggregation level; in the above method, whether the aggregation level of the search space set and the monitoring manner of the control channel candidates in the search space set include reasoning solves this problem.

[0013] As an embodiment, the essence of the above method includes: the aggregation level applicable or configured for the monitoring manner including reasoning may be different from the aggregation level applicable or configured for the monitoring manner not including reasoning.

[0014] As an embodiment, the advantages of the above method include: reasoning can be used to monitor control channel candidates, improving the monitoring efficiency.

[0015] As an embodiment, the advantages of the above method include: providing a solution for determining the aggregation level for the monitoring of control channel candidates based on AI / ML, which is beneficial to giving full play to the advantages of AI / ML technology to improve system performance.

[0016] As an embodiment, the advantages of the above method include: reducing the overhead of control channel transmission.

[0017] As an embodiment, the advantages of the above method include: good flexibility, adapting to different terminals and different application scenarios.

[0018] According to one aspect of the present application, it is characterized in that the monitoring method of the control channel alternatives in the first search space set includes the inference corresponding to the first identifier, and the aggregation level of the first search space set also depends on the first identifier.

[0019] As an embodiment, the advantages of the above method include: the aggregation level depends on the identifier corresponding to the inference, further improving the flexibility of the design.

[0020] As an embodiment, the advantages of the above method include: providing a specific aggregation level for the inferences corresponding to different identifiers, optimizing the performance of monitoring the control channel alternatives using inferences.

[0021] According to one aspect of the present application, it is characterized in that the candidate value range of the aggregation level of the first search space set is related to whether the monitoring method of the control channel alternatives in the first search space set includes inference.

[0022] As an embodiment, the advantages of the above method include: simple implementation and improved flexibility of the system at the same time.

[0023] According to one aspect of the present application, it is characterized in that when the monitoring method of the control channel alternatives in the first search space set includes inference, the candidate value range of the aggregation level of the first search space set is the first aggregation level range; when the monitoring method of the control channel alternatives in the first search space set does not include inference, the candidate value range of the aggregation level of the first search space set is the second aggregation level range; the first aggregation level range includes at least one aggregation level, the second aggregation level range includes at least one aggregation level, and the first aggregation level range and the second aggregation level range are different.

[0024] As an embodiment, the advantages of the above method include: simple implementation and small changes to the standard.

[0025] According to one aspect of the present application, it is characterized in that the monitoring method of the control channel alternatives in the first search space set includes the inference corresponding to the first identifier, and the first aggregation level range depends on the first identifier.

[0026] As an embodiment, the advantages of the above method include: further improving the flexibility of the design.

[0027] As an embodiment, the advantages of the above method include: optimizing the performance of monitoring control channel candidates using inference.

[0028] According to one aspect of the present application, it is characterized in that the first aggregation level is an aggregation level of the first set of search spaces; the monitoring method for control channel candidates for the first aggregation level in the first set of search spaces includes inference only when a first condition is satisfied; the first condition includes that the first aggregation level belongs to a reference aggregation level range; the reference aggregation level range includes one or more aggregation levels.

[0029] As an embodiment, the advantages of the above method include: simplifying the design and reducing signaling overhead.

[0030] As an embodiment, the advantages of the above method include: good flexibility and better adaptation to different terminals and different scenarios.

[0031] According to one aspect of the present application, it is characterized in that the monitoring method for control channel candidates in the first set of search spaces includes inference corresponding to a first identifier, and the reference aggregation level range depends on the first identifier.

[0032] As an embodiment, the advantages of the above method include: adopting a more targeted aggregation level range for inferences corresponding to different identifiers, improving the performance of the monitoring method including inference.

[0033] As an embodiment, the advantages of the above method include: good flexibility and better adaptation to different terminals and different scenarios.

[0034] According to one aspect of the present application, it is characterized in that the first information block indicates whether the monitoring method for control channel candidates in the first set of search spaces includes inference.

[0035] As an embodiment, the advantages of the above method include: reducing signaling overhead.

[0036] As an embodiment, the advantages of the above method include: small changes to the standard and good backward compatibility.

[0037] According to one aspect of the present application, it is characterized in that it includes:

[0038] Transmitting a second information block;

[0039] wherein, the second information block indicates whether the monitoring method for control channel candidates by the first node includes inference.

[0040] As an embodiment, the advantages of the above method include: good flexibility and better adaptation to different terminals and different scenarios.

[0041] According to one aspect of the present application, a terminal is provided, comprising:

[0042] One or more processors and a memory;

[0043] The memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the terminal to execute the method in the first node.

[0044] The present application discloses a method in a second node for wireless communication, comprising:

[0045] Sending a first information block, the first information block being used to configure a first search space set;

[0046] Wherein, the target receiver of the first information block monitors the control channel candidates in the first search space set, and whether the aggregation level of the first search space set and the monitoring manner of the control channel candidates in the first search space set include inference correlation.

[0047] According to one aspect of the present application, the monitoring manner of the control channel candidates in the first search space set includes inference corresponding to a first identifier, and the aggregation level of the first search space set also depends on the first identifier.

[0048] According to one aspect of the present application, whether the candidate value range of the aggregation level of the first search space set and the monitoring manner of the control channel candidates in the first search space set include inference correlation.

[0049] According to one aspect of the present application, when the monitoring manner of the control channel candidates in the first search space set includes inference, the candidate value range of the aggregation level of the first search space set is a first aggregation level range; when the monitoring manner of the control channel candidates in the first search space set does not include inference, the candidate value range of the aggregation level of the first search space set is a second aggregation level range; the first aggregation level range includes at least one aggregation level, the second aggregation level range includes at least one aggregation level, and the first aggregation level range and the second aggregation level range are different.

[0050] According to one aspect of the present application, the monitoring manner of the control channel candidates in the first search space set includes inference corresponding to a first identifier, and the first aggregation level range depends on the first identifier.

[0051] According to one aspect of the present application, it is characterized in that the first aggregation level is an aggregation level of the first search space set; the monitoring manner of the control channel alternative for the first aggregation level in the first search space set includes inference only when the first condition is satisfied; the first condition includes that the first aggregation level belongs to a reference aggregation level range; the reference aggregation level range includes one or more aggregation levels.

[0052] According to one aspect of the present application, it is characterized in that the monitoring manner of the control channel alternative in the first search space set includes inference corresponding to a first identifier, and the reference aggregation level range depends on the first identifier.

[0053] According to one aspect of the present application, it is characterized in that the first information block indicates whether the monitoring manner of the control channel alternative in the first search space set includes inference.

[0054] According to one aspect of the present application, it includes:

[0055] Receiving a second information block;

[0056] wherein the second information block indicates whether the monitoring manner of the control channel alternative by the sender of the second information block includes inference.

[0057] According to one aspect of the present application, it is characterized in that a base station includes:

[0058] One or more processors and a memory;

[0059] The memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the base station to execute the method in the second node.

[0060] The present application discloses a first node for wireless communication, including:

[0061] A first processor, receiving a first information block, the first information block being used to configure a first search space set; monitoring the control channel alternative in the first search space set;

[0062] wherein the aggregation level of the first search space set is related to whether the monitoring manner of the control channel alternative in the first search space set includes inference.

[0063] The present application discloses a second node for wireless communication, including:

[0064] A second processor transmits a first information block, which is used to configure a first set of search spaces;

[0065] Among them, the target receiver of the first information block monitors the control channel candidates in the first set of search spaces, and whether the aggregation level of the first set of search spaces and the monitoring method of the control channel candidates in the first set of search spaces include inference correlation.

[0066] As an embodiment, compared with the traditional solution, the present application has the following advantages:

[0067] Improve the monitoring efficiency of control channel candidates, thereby improving the overall performance of the system;

[0068] It is beneficial to give full play to the advantages of AI / ML technology to improve system performance;

[0069] Reduce the overhead of control channel transmission;

[0070] Good flexibility to adapt to different terminals and different application scenarios;

[0071] Reduce signaling overhead. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] By reading the following detailed description of non-limiting embodiments with reference to the accompanying drawings, other features, objects, and advantages of the present application will become more apparent:

[0073] Figure 1 Shows a flowchart of a first information block and a first set of search spaces according to an embodiment of the present application;

[0074] Figure 2 Shows a schematic diagram of a network architecture according to an embodiment of the present application;

[0075] Figure 3 Shows a schematic diagram of an embodiment of a radio protocol architecture of a user plane and a control plane according to an embodiment of the present application;

[0076] Figure 4 Shows a schematic diagram of a first communication device and a second communication device according to an embodiment of the present application;

[0077] Figure 5 Shows a flowchart of a transmission according to an embodiment of the present application;

[0078] Figure 6 Shows a schematic diagram of the aggregation level of a first set of search spaces depending on a first identifier according to an embodiment of the present application;

[0079] Figure 7Shows a schematic diagram of the candidate value range and inference of the aggregation level of the first search space set according to an embodiment of the present application;

[0080] Figure 8 Shows a schematic diagram of the first aggregation level range and the second aggregation level range according to an embodiment of the present application;

[0081] Figure 9 Shows a schematic diagram of the first aggregation level range depending on the first identifier according to an embodiment of the present application;

[0082] Figure 10 Shows a schematic diagram of the first condition and the reference aggregation level range according to an embodiment of the present application;

[0083] Figure 11 Shows a schematic diagram of the reference aggregation level range depending on the first identifier according to an embodiment of the present application;

[0084] Figure 12 Shows a schematic diagram of whether the monitoring method of the first information block includes inference according to an embodiment of the present application;

[0085] Figure 13 Shows a schematic diagram of the second information block according to an embodiment of the present application;

[0086] Figure 14A-14B Respectively show schematic diagrams of the models based on which the inference is deployed according to an embodiment of the present application;

[0087] Figure 15 Shows a schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of the present application;

[0088] Figure 16 Shows a schematic diagram based on artificial intelligence or machine learning according to an embodiment of the present application;

[0089] Figure 17 Shows a schematic diagram of the AI function deployment according to an embodiment of the present application;

[0090] Figure 18 Shows a schematic diagram of the AI function deployment according to an embodiment of the present application;

[0091] Figure 19 Shows a schematic diagram of the AI function deployment according to an embodiment of the present application;

[0092] Figure 20 Shows a schematic diagram of the AI function deployment according to an embodiment of the present application;

[0093] Figure 21Shows a schematic diagram of the RAN domain AI / ML function deployment according to an embodiment of the present application;

[0094] Figure 22 Shows a schematic diagram of the AI / ML function deployment of a UE according to an embodiment of the present application;

[0095] Figure 23 Shows a structural block diagram of a processing device in a first node according to an embodiment of the present application;

[0096] Figure 24 Shows a structural block diagram of a processing device in a second node according to an embodiment of the present application. Detailed implementation manners

[0097] The technical solutions of the present application will be further described in detail below with reference to the accompanying drawings. It should be noted that, without conflict, the embodiments and features in the embodiments of the present application can be combined with each other arbitrarily. Based on considerations such as flexibility, complexity, overhead, and compatibility, those skilled in the art have the motivation to flexibly combine the embodiments in different drawings on the premise of non-conflict, for example (but not limited to) the embodiments in Figure 1 and the embodiments in Figure 5 - Figure 22 and the embodiments in Figure 5 and the embodiments in Figure 6 - Figure 22 and the embodiments in, etc.

[0098] Example 1

[0099] Embodiment 1 exemplifies a flowchart of a first information block and a first search space set according to an embodiment of the present application, as shown in Figure 1 shown. In 100 shown in Figure 1 , each box represents a step. In particular, the order of the steps in the box does not represent a specific chronological relationship between the steps.

[0100] In Embodiment 1, the first node in the present application receives a first information block in step 101, and the first information block is used to configure a first search space set; in step 102, monitor the control channel candidates in the first search space set; wherein, whether the aggregation level of the first search space set and the monitoring manner of the control channel candidates in the first search space set include inference-related.

[0101] As an embodiment, the first information block is carried by higher layer signaling.

[0102] As an example, the first information block is carried by RRC (Radio Resource Control) signaling.

[0103] As an example, the first information block is carried by an RRC IE (Information Element).

[0104] As an example, the first information block is carried by at least one RRC IE.

[0105] As an example, the first information block includes information in some or all fields of at least one RRC IE.

[0106] As an example, the first information block includes information in some or all fields of each RRC IE among multiple RRC IEs.

[0107] As an example, the first information block includes information in some or all fields of an RRC IE whose name includes CellGroupConfig.

[0108] As an example, the first information block includes information in some or all fields of an RRC IE whose name includes ServingCellConfig.

[0109] As an example, the first information block includes information in some or all fields of a ServingCellConfig IE.

[0110] As an example, the first information block includes information in some or all fields of a ServingCellConfigCommon IE.

[0111] As an example, the first information block includes information in some or all fields of an RRC IE whose name includes BWP-Downlink.

[0112] As an example, the first information block includes information in some or all fields of a BWP-DownlinkCommon IE.

[0113] As an example, the first information block includes information in some or all fields of a BWP-DownlinkDedicated IE.

[0114] As an example, the first information block includes information in some or all fields of an RRC IE whose name includes PDCCH-Config.

[0115] As an example, the first information block includes information in some or all fields in the PDCCH-Config IE.

[0116] As an example, the first information block includes information in some or all fields in the PDCCH-ConfigCommon IE.

[0117] As an example, the first information block includes information in some or all fields in the RRC IE whose name includes SearchSpace.

[0118] As an example, the first information block is carried by the RRC IE whose name includes CellGroupConfig.

[0119] As an example, the first information block is carried by the RRC IE whose name includes ServingCellConfig.

[0120] As an example, the first information block is carried by the ServingCellConfig IE.

[0121] As an example, the first information block is carried by the ServingCellConfigCommon IE.

[0122] As an example, the first information block is carried by the RRC IE whose name includes BWP-Downlink.

[0123] As an example, the first information block is carried by the BWP-DownlinkCommon IE.

[0124] As an example, the first information block is carried by the BWP-DownlinkDedicated IE.

[0125] As an example, the first information block is carried by the RRC IE whose name includes PDCCH-Config.

[0126] As an example, the first information block is carried by the PDCCH-Config IE.

[0127] As an example, the first information block is carried by the PDCCH-ConfigCommon IE.

[0128] As an example, the first information block is carried by the RRC IE whose name includes SearchSpace.

[0129] As an example, the first information block is carried by the SearchSpace IE.

[0130] As an example, the first information block is an RRC IE.

[0131] As an example, the first information block is the PDCCH-Config IE.

[0132] As an example, the first information block is the SearchSpace IE.

[0133] As an example, the first information block includes a MAC CE (Medium Access Control layer Control Element).

[0134] As an example, the first information block configures the index of the first search space set.

[0135] As an example, the first information block configures the CORESET (Control Resource Set) associated with the first search space set.

[0136] As an example, the first information block configures the control channel monitoring period (periodicity) and offset of the first search space set.

[0137] As an example, the first information block configures the control channel monitoring pattern within a time slot of the first search space set.

[0138] As an example, the first information block configures the number of time slots in which the first search space set exists.

[0139] As an example, the first information block configures the number of control channel alternatives for each CCE (Control Channel Element) aggregation level (AL) of the first search space set.

[0140] As an example, the first information block configures the search space type of the first search space set.

[0141] As an example, the first information block is used to configure at least one of the index of the first search space set, the associated CORESET, the control channel monitoring period and offset, the control channel monitoring pattern within a time slot, the number of time slots present, the number of control channel candidates for each CCE aggregation level, or the search space type.

[0142] As an example, the configuration information of the first search space set includes one or more of the above.

[0143] As an example, the control channel includes PDCCH (Physical Downlink Control Channel).

[0144] As an example, the control channel refers to PDCCH.

[0145] As an example, the control channel candidates include PDCCH candidates.

[0146] As an example, the control channel candidates refer to PDCCH candidates.

[0147] As an example, a search space set associated with a CORESET includes: the configuration information of the search space set includes the index of the CORESET.

[0148] As an example, a search space set associated with a CORESET includes: the search space set is configured with the CORESET.

[0149] As an example, a search space set associated with a CORESET includes: the search space set is configured with the index of the CORESET.

[0150] As an example, a search space set associated with a CORESET includes: the CORESET is used to determine the time-frequency resources occupied by a control channel monitoring occasion in the search space set.

[0151] As an example, a search space set associated with a CORESET includes: the CORESET includes the time-frequency resources occupied by a control channel monitoring occasion in the search space set.

[0152] As an example, an association of a search space set with a CORESET includes: the REs (Resource Elements) occupied by the CORESET include the REs occupied by a control channel monitoring occasion in the search space set.

[0153] As an example, an association of a search space set with a CORESET includes: the frequency domain resources occupied by the CORESET include the frequency domain resources occupied by a control channel monitoring occasion in the search space set.

[0154] As an example, an association of a search space set with a CORESET includes: the RBs (Resource Blocks) occupied by the CORESET in the frequency domain include the RBs occupied by a control channel monitoring occasion in the search space set.

[0155] As an example, an association of a search space set with a CORESET includes: the number of symbols occupied by the CORESET is used to determine the symbols occupied by a control channel monitoring occasion in the search space set.

[0156] As an example, an association of a search space set with a CORESET includes: the number of symbols occupied by the CORESET is the number of symbols occupied by a control channel monitoring occasion in the search space set.

[0157] As an example, an association of a search space set with a CORESET includes: any control channel candidate in the search space set is composed of one or more CCEs in the CORESET.

[0158] As an example, a CORESET includes multiple REs.

[0159] As an example, a CORESET includes at least one CCE.

[0160] As an example, a CORESET includes multiple RBs in the frequency domain and one or more symbols in the time domain.

[0161] As an example, a CORESET is composed of multiple RBs in the frequency domain and one or more symbols in the time domain.

[0162] As an example, the index of a CORESET is configured by a higher layer parameter controlResourceSetId.

[0163] As an example, a CORESET is configured by RRC signaling.

[0164] As an example, a CORESET is configured by a ControlResourceSet IE.

[0165] As an example, for the specific definition of the CORESET, refer to Section 10 of 3GPP TS 38.213.

[0166] As an example, the monitoring occasion includes a time period.

[0167] As an example, the monitoring occasion includes at least one symbol.

[0168] As an example, the monitoring occasion includes a slot.

[0169] As an example, the monitoring occasion includes at least one symbol in a slot.

[0170] As an example, the monitoring occasion includes a subframe.

[0171] As an example, the symbol is a single-carrier symbol.

[0172] As an example, the symbol is a multi-carrier symbol.

[0173] As an example, the multi-carrier symbol is an OFDM (Orthogonal Frequency Division Multiplexing) symbol.

[0174] As an example, the multi-carrier symbol is an SC-FDMA (Single Carrier-Frequency Division Multiple Access) symbol.

[0175] As an example, the multi-carrier symbol is a DFT-S-OFDM (Discrete Fourier Transform Spread OFDM) symbol.

[0176] As an example, the multi-carrier symbol is an FBMC (Filter Bank Multi Carrier) symbol.

[0177] As an embodiment, the multi-carrier symbol includes a CP (Cyclic Prefix).

[0178] As an embodiment, the first node determines the control channel monitoring occasion in the first search space set according to the configuration information of the first search space set.

[0179] As an embodiment, the first node determines the control channel monitoring occasion in the first search space set according to the control channel monitoring period and offset of the first search space set, and the control channel monitoring pattern within a time slot.

[0180] As an embodiment, the first node determines the control channel monitoring occasion in the first search space set according to the control channel monitoring period and offset of the first search space set, the control channel monitoring pattern within a time slot, and the number of time slots present.

[0181] As an embodiment, the meaning of the monitoring includes: receiving.

[0182] As an embodiment, the meaning of the monitoring includes: decoding the control channel candidate.

[0183] As an embodiment, the meaning of the monitoring includes: blindly decoding the control channel candidate.

[0184] As an embodiment, the meaning of the monitoring includes: decoding the control channel candidate and performing CRC (Cyclic Redundancy Check).

[0185] As an embodiment, the meaning of the monitoring includes: decoding the control channel candidate and performing CRC check scrambled by RNTI (Radio Network Temporary Identity).

[0186] As an embodiment, the meaning of the monitoring includes: decoding the control channel candidate based on the monitored DCI (Downlink Control Information) format(s).

[0187] As an embodiment, the meaning of the monitoring includes: decoding the control channel candidate based on one or more monitored DCI formats.

[0188] As an example, a control channel candidate is a candidate for a Physical Downlink Control Channel (PDCCH).

[0189] As an example, a control channel candidate is a Monitored PDCCH Candidate.

[0190] As an example, a control channel candidate occupies multiple Resource Elements (REs).

[0191] As an example, a control channel candidate occupies one or more Resource-Element Groups (REGs).

[0192] As an example, a control channel candidate occupies one or more Control Channel Elements (CCEs).

[0193] As an example, a control channel candidate consists of one or more CCEs.

[0194] As an example, the aggregation level of a control channel candidate is L, and the one control channel candidate consists of L CCEs.

[0195] As an example, a control channel candidate consists of L CCEs, and the aggregation level of the one control channel candidate is L.

[0196] As an example, the number of CCEs occupied by a control channel candidate is L, and the aggregation level of the one control channel candidate is L.

[0197] As an example, the aggregation level of a control channel candidate is L, and the number of CCEs occupied by the one control channel candidate is L.

[0198] As an example, L is a positive number.

[0199] As an example, L is a positive integer.

[0200] As an example, the value of L is equal to one of 1, 2, 4, 8, 16.

[0201] As an example, the aggregation level refers to the CCE aggregation level.

[0202] As an example, one CCE includes one or more REGs.

[0203] As an example, a CCE consists of one or more REGs.

[0204] As an example, a CCE consists of multiple REGs.

[0205] As an example, a CCE consists of 6 REGs.

[0206] As an example, a REG includes one or more REs.

[0207] As an example, a REG is composed of one or more REs.

[0208] As an example, a REG includes one RB on one symbol.

[0209] As an example, a REG is one RB on one symbol.

[0210] As an example, for the specific definitions of REG and CCE, refer to Section 7.3.2 of 3GPP TS 38.211.

[0211] As an example, the number of CCEs occupied by a control channel candidate is equal to one of 1, 2, 4, 8, 16.

[0212] As an example, a CCE includes 9 REGs, and a REG includes 4 REs.

[0213] As an example, a CCE includes 6 REGs, and a REG includes 12 REs.

[0214] As an example, the aggregation levels of the first search space set include one or more aggregation levels.

[0215] Typically, any two control channel candidates for the same aggregation level include the same number of REs.

[0216] Typically, any two control channel candidates for the same aggregation level include the same number of CCEs.

[0217] As an example, for one aggregation level, the first search space set is defined as a set of control channel candidates for the one aggregation level.

[0218] As an example, the aggregation level of the first search space set only includes one aggregation level, and the first search space set includes a set of control channel candidates for the one aggregation level.

[0219] As an example, the aggregation levels of the first search space set include multiple aggregation levels, and the first search space set includes a set of control channel candidates for each of the multiple aggregation levels.

[0220] As an example, the index of the first search space set is s, the first search space set is associated with CORESETp, and the control channel candidates in the first search space set for aggregation level L corresponding to the CCE index satisfies:

[0221]

[0222] where i = 0,..., L - 1, is a value related to CORESETp, N CCE,p represents the number of CCEs in CORESETp;

[0223] nCI is the value of the carrier indicator field, or nCI - Value, or equal to 0;

[0224] is the index of the control channel candidate, represents the scheduled corresponding n CI the number of configured control channel candidates in the first search space set s for aggregation level L of the serving cell;

[0225] represents all configured n CI corresponding the maximum value in.

[0226] As an example, the aggregation level of the first search space set refers to: the aggregation level configured for the first search space set.

[0227] As an example, the aggregation level of the first search space set refers to: the aggregation level targeted by the first search space set.

[0228] As an example, the aggregation level of the first search space set refers to: the aggregation level of the control channel candidates in the first search space set.

[0229] As an example, the aggregation level of the first search space set refers to: the aggregation level of the monitored control channel candidates in the first search space set.

[0230] As an example, whether the monitoring method of the control channel alternative in the first search space set includes inference includes: whether the monitoring method of the control channel alternative in the first search space set supports inference.

[0231] As an example, whether the monitoring method of the control channel alternative in the first search space set includes inference includes: whether the monitoring method of the control channel alternative in the first search space set supports inference.

[0232] As an example, whether the monitoring method of the control channel alternative in the first search space set includes inference includes: whether the monitoring method of the control channel alternative in the first search space set allows the use of inference.

[0233] As an example, whether the monitoring method of the control channel alternative in the first search space set includes inference includes: whether the monitoring method of the control channel alternative in the first search space set supports inference-based.

[0234] As an example, whether the monitoring method of the control channel alternative in the first search space set includes inference includes: whether the monitoring method of the control channel alternative in the first search space set is inference-based.

[0235] As an example, whether the monitoring method of the control channel alternative in the first search space set includes inference includes: whether the first node supports using inference to monitor the control channel alternative in the first search space set.

[0236] As an example, whether the monitoring method of the control channel alternative in the first search space set includes inference includes: whether the first node monitors the control channel alternative in the first search space set based on inference.

[0237] As an example, only when the second condition is satisfied, the aggregation level of the first search space set is related to whether the monitoring method of the control channel alternative in the first search space set includes inference.

[0238] As an example, the second condition includes: the first information block indicates that the monitoring method of the control channel alternative in one or more search space sets includes inference, and the first search space set is one of the one or more search space sets.

[0239] As an example, the second condition includes: the first information block indicates that the monitoring manner of control channel alternatives in the set of search spaces configured in the first BWP (Bandwidth Part) includes inference, and the first set of search spaces is one set of search spaces in the set of search spaces configured in the first BWP.

[0240] As a sub - example of the above example, the first BWP is one of the BWPs configured for the first node.

[0241] As a sub - example of the above example, the first BWP is one of the BWPs in one serving cell configured for the first node.

[0242] As an example, the second condition includes: the first information block indicates that the monitoring manner of control channel alternatives in the set of search spaces associated with the first CORESET includes inference, and the CORESET associated with the first set of search spaces is the first CORESET.

[0243] As an example, the second condition includes: the first information block indicates that the monitoring manner of control channel alternatives in the set of search spaces associated with a CORESET in the first CORESET pool includes inference, the first CORESET pool is identified by a coresetPoolIndex, and the CORESET associated with the first set of search spaces is a CORESET in the first CORESET pool.

[0244] As an example, the second condition includes: the first information block indicates that the monitoring manner of control channel alternatives in the set of search spaces configured in the first serving cell includes inference, and the first set of search spaces is one set of search spaces in the set of search spaces configured in the first serving cell.

[0245] As a sub - example of the above example, the first serving cell is one of the serving cells configured for the first node.

[0246] As an example, the number of aggregation levels of the first set of search spaces is related to whether the monitoring manner of control channel alternatives in the first set of search spaces includes inference.

[0247] As an example, the maximum value of the aggregation level of the first set of search spaces is related to whether the monitoring manner of control channel alternatives in the first set of search spaces includes inference.

[0248] As an example, whether the minimum value of the aggregation level of the first search space set and the monitoring method of the control channel alternative in the first search space set is related to inference.

[0249] As an example, whether the candidate value range of the aggregation level of the first search space set and the monitoring method of the control channel alternative in the first search space set is related to inference.

[0250] As an example, the inference is based on artificial intelligence or machine learning.

[0251] As an example, the inference is based on a Neural Network.

[0252] As an example, the inference is AI inference.

[0253] As an example, the inference is ML inference.

[0254] As an example, the inference is AI inference or ML inference.

[0255] As an example, the inference is based on an AI model or an ML model.

[0256] As an example, the model on which the inference is based is an AI model or an ML model.

[0257] As an example, the inference is the inference of an AI model or an ML model.

[0258] As an example, the inference is used for one or more of CSI (Channel State Information) compression, CSI prediction, and beam management.

[0259] As an example, the inference is used for data reception.

[0260] As an example, the inference is used for downlink data reception.

[0261] As an example, the inference is used for PDSCH (Physical Downlink Shared Channel) reception.

[0262] As an example, the inference is used for the monitoring of control channel alternatives.

[0263] As an example, the inference is used for PDCCH reception.

[0264] As an example, the inference is used for one or more of channel estimation, MIMO (Multiple Input Multiple Output) reception, demodulation, channel decoding, and CRC (Cyclic Redundancy Check) check.

[0265] As an example, the inference is used for positioning.

[0266] As an example, the inference is used for scheduling.

[0267] As an example, the inference is used for semantic-based error correction.

[0268] As an example, the inference is training-based.

[0269] As an example, the model on which the inference is based is obtained through training.

[0270] As an example, the training of the model on which the inference is based is performed by the first node.

[0271] As an example, the training of the model on which the inference is based is performed by the sender of the first information block.

[0272] As an example, the training of the model on which the inference is based is performed by the core network.

[0273] As an example, the training of the model on which the inference is based is performed by an AI function.

[0274] As an example, the training of the model on which the inference is based is performed by the AI function deployed on the first node.

[0275] As an example, the training of the model on which the inference is based is performed by an AI entity.

[0276] As an example, the training of the model on which the inference is based is performed by the AI entity deployed on the first node.

[0277] As an example, the training of the model on which the inference is based is performed by an MnS producer.

[0278] As an example, the training of the model on which the inference is based is performed by an MDA (Management Data Analytics Function) function.

[0279] As an example, the training of the model on which the inference is based is performed by a producer of MDAS (Management Data Analytics Service).

[0280] As an example, the training of the model on which the inference is based is performed by NWDAF (Network Data Analytics Function).

[0281] As an example, the model on which the inference is based includes an AI entity.

[0282] As an example, the model on which the inference is based includes a part for inference in an AI entity.

[0283] As an example, the inference is performed by an AI entity or an AI function.

[0284] As an example, the inference is performed by an AI entity or an AI function deployed on the first node.

[0285] As an example, the AI function includes an AI inference function.

[0286] As an example, the AI function includes an AI training function.

[0287] As an example, the AI function includes an AI management function.

[0288] As an example, the AI includes ML.

[0289] As an example, the AI includes AI and ML.

[0290] As an example, the AI includes AI or ML.

[0291] As an example, the model on which the inference is based needs to be deployed.

[0292] As an example, the model on which the inference is based is obtained by loading.

[0293] As an example, the model on which the inference is based is obtained by loading from the serving cell of the first node.

[0294] As an example, the model on which the inference is based is obtained by loading from the core network.

[0295] As an example, the model on which the inference is based does not require deployment.

[0296] The inference or the AI model on which the inference is based is determined by the hardware device manufacturer; nevertheless, the first node and the sender of the first information block may still need to reach some consensus on the inference or the AI model on which the inference is based to facilitate the deployment of the AI model. The following are some non-limiting implementation manners.

[0297] As an example, the inference or the AI model on which the inference is based is associated with a dataset; the dataset is a training dataset for training the AI model on which the inference is based, or a dataset for monitoring the performance of the inference output of the AI model on which the inference is based.

[0298] As an example, the dataset is stored on a server, and both the first node and the sender of the first information block can access the server to obtain the dataset.

[0299] As an example, part or all of the dataset is stored locally on the first node.

[0300] As an example, the inference or the AI model on which the inference is based is associated with a set of configuration parameters; the set of configuration parameters includes, for example, the configuration information of the first search space set, etc.; the configuration information of the first search space set indicates the monitoring timing, monitoring method, candidate models of the model on which the inference is based, the output of the inference, etc. in the first search space set.

[0301] As an example, the inference or the AI model on which the inference is based is associated with an identifier.

[0302] As a sub-example of the above example, the identifier is used to identify at least one of the dataset or the set of configuration parameters.

[0303] As an example, the first node and the sender of the first information block reach a consensus on the identifier.

[0304] As an example, the identifier is a first identifier.

[0305] Example 2

[0306] Embodiment 2 exemplifies a schematic diagram of a network architecture according to an embodiment of the present application, as shown in the appendix Figure 2 as follows.

[0307] Appendix Figure 2The network architecture 200 is described. The network architecture 200 is a 5G NR (New Radio) / LTE (Long-Term Evolution) / LTE-A (Long-Term Evolution Advanced) system, or the network architecture 200 is a network architecture of 5G+, or the network architecture 200 is a network architecture of 6G, or the network architecture 200 is a network architecture adopted in the future continuous evolution of 3GPP; the network architecture 200 can be referred to as 5GS (5G System) / EPS (Evolved Packet System), or the network architecture 200 can be referred to as 6GS (6G System); the network architecture 200 includes at least one of UE (User Equipment) 201, RAN (Radio Access Network) 202, core network 210, HSS (Home Subscriber Server) / UDM (Unified Data Management) 220, and Internet service 230. The network architecture 200 can be interconnected with other access networks, but these entities / interfaces are not shown for simplicity. As shown, the network architecture 200 provides packet switching services. However, those skilled in the art will easily understand that the various concepts presented throughout this application can be extended to networks providing circuit switching services or other cellular networks. The RAN includes node 203. The RAN may also include other nodes 204. Node 203 provides user and control plane protocol termination towards UE 201. Node 203 can be connected to other nodes 204 via the Xn interface (e.g., backhaul) / X2 interface. Node 203 can also be referred to as a base station, base transceiver station, radio base station, radio transceiver, transceiver function, basic service set (BSS), extended service set (ESS), TRP (Transmit Receive Point), or some other appropriate term. The core network 210 is 5GC (5G Core Network) / EPC (Evolved Packet Core), or the core network 210 is 6GC; node 203 provides an access point for UE 201 to the core network 210.Examples of the UE 201 include cellular phones, smart phones, Session Initiation Protocol (SIP) phones, laptop computers, personal digital assistants (PDAs), satellite radios, non-terrestrial base station communications, satellite mobile communications, global positioning systems, multimedia devices, video devices, digital audio players (e.g., MP3 players), cameras, game consoles, drones, aircraft, narrowband Internet of Things devices, machine type communication devices, land vehicles, automobiles, wearable devices, or any other similar functional devices. Those skilled in the art may also refer to the UE 201 as a mobile station, 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, handset, user agent, mobile client, client, or some other suitable term. The node 203 is connected to the core network 210 through the S1 / NG interface. The core network 210 includes a Mobility Management Entity (MME) / Authentication Management Field (AMF) / Session Management Function (SMF) 211, other MMEs / AMFs / SMFs 214, a Service Gateway (S-GW) / User Plane Function (UPF) 212, and a Packet Date Network Gateway (P-GW) / UPF 213. The MME / AMF / SMF 211 is a control node that processes the signaling between the UE 201 and the core network 210. Generally, the MME / AMF / SMF 211 provides bearer and connection management. All user Internet Protocol (IP) packets are transmitted through the S-GW / UPF 212, which is itself connected to the P-GW / UPF 213. The P-GW provides UE IP address allocation and other functions. The P-GW / UPF 213 is connected to the Internet service 230. The Internet service 230 includes carrier-corresponding Internet protocol services, specifically including the Internet, intranet, IP Multimedia Subsystem (IMS), and packet switching services.

[0308] As an embodiment, the first node in the present application includes the UE 201.

[0309] As an embodiment, the second node in the present application includes the node 203.

[0310] As an example, the radio link between the UE 201 and the node 203 includes a cellular network link.

[0311] As an example, the sender of the first information block includes the node 203.

[0312] As an example, the receiver of the first information block includes the UE 201.

[0313] As an example, the monitor of the control channel candidates in the first search space set includes the UE 201.

[0314] As an example, the receiver of the second information block includes the node 203.

[0315] As an example, the sender of the second information block includes the UE 201.

[0316] As an example, the UE 201 supports AI or ML-based operations.

[0317] As an example, the node 203 supports AI or ML-based operations.

[0318] Example 3

[0319] Embodiment 3 exemplifies a schematic diagram of an embodiment of a radio protocol architecture for a user plane and a control plane according to an embodiment of the present application, as shown in the appendix Figure 3 as shown.

[0320] Embodiment 3 shows a schematic diagram of an embodiment of a radio protocol architecture for a user plane and a control plane according to the present application, as shown in the appendix Figure 3 as shown. Figure 3 is a schematic diagram illustrating an embodiment of a radio protocol architecture for a user plane 350 and a control plane 300, Figure 3Show the radio protocol architecture of the control plane 300 for the first communication node device (UE, gNB or RSU in V2X) and the second communication node device (gNB, UE or RSU in V2X), or between two UEs, with three layers: Layer 1, Layer 2, and Layer 3. Layer 1 (L1 layer) is the lowest layer and implements various PHY (Physical Layer) signal processing functions. Layer 1 will be referred to as PHY301 in this text. Layer 2 (L2 layer) 305 is above PHY301 and is responsible for the link between the first communication node device and the second communication node device, or between two UEs. The L2 layer 305 includes a MAC (Medium Access Control) sublayer 302, an RLC (Radio Link Control) sublayer 303, and a PDCP (Packet Data Convergence Protocol) sublayer 304, and these sublayers terminate at the second communication node device. The PDCP sublayer 304 provides multiplexing between different radio bearers and logical channels. The PDCP sublayer 304 also provides security by encrypting data packets, and provides handover support for the first communication node device between the second communication node devices. The RLC sublayer 303 provides segmentation and reassembly of upper layer data packets, retransmission of lost data packets, and reordering of data packets to compensate for disordered reception due to HARQ. The MAC sublayer 302 provides multiplexing between logical and transport channels. The MAC sublayer 302 is also responsible for allocating various radio resources (e.g., resource blocks) in a cell between the first communication node devices. The MAC sublayer 302 is also responsible for HARQ operations. The RRC (Radio Resource Control) sublayer 306 in Layer 3 (L3 layer) of the control plane 300 is responsible for obtaining radio resources (i.e., radio bearers) and configuring the lower layers using RRC signaling between the second communication node device and the first communication node device. The radio protocol architecture of the user plane 350 includes Layer 1 (L1 layer) and Layer 2 (L2 layer). For the radio protocol architecture of the first communication node device and the second communication node device in the user plane 350, the physical layer 351, the PDCP sublayer 354 in the L2 layer 355, the RLC sublayer 353 in the L2 layer 355, and the MAC sublayer 352 in the L2 layer 355 are generally the same as the corresponding layers and sublayers in the control plane 300, but the PDCP sublayer 354 also provides header compression for upper layer data packets to reduce radio transmission overhead.The L2 layer 355 in the user plane 350 further includes an SDAP (Service Data Adaptation Protocol) sub-layer 356, and the SDAP sub-layer 356 is responsible for the mapping between QoS flows and data radio bearers (DRBs) to support service diversity. Although not shown, the first communication node device may have several upper layers above the L2 layer 355, including a network layer (e.g., IP layer) that terminates at the P-GW on the network side and an application layer that terminates at the other end of the connection (e.g., a remote UE, a server, etc.).

[0321] As an example, the Figure 3 radio protocol architecture in is applicable to the first node in this application.

[0322] As an example, the Figure 3 radio protocol architecture in is applicable to the second node in this application.

[0323] As an example, the higher layer in this application refers to the layer above the physical layer.

[0324] As an example, the first information block is generated in the RRC sub-layer 306.

[0325] As an example, the first information block is generated in the MAC sub-layer 302 or the MAC sub-layer 352.

[0326] As an example, the second information block is generated in the RRC sub-layer 306.

[0327] As an example, the second information block is generated in the MAC sub-layer 302 or the MAC sub-layer 352.

[0328] As an example, the second information block is generated in the PHY 301 or the PHY 351.

[0329] Example 4

[0330] Example 4 illustrates a schematic diagram of a first communication device and a second communication device according to an embodiment of this application, as shown in the appendix Figure 4 shown. The appendix Figure 4 is a block diagram of a first communication device 410 and a second communication device 450 that communicate with each other in an access network.

[0331] The first communication device 410 includes a controller / processor 475, a memory 476, a receiving processor 470, a transmitting processor 416, a multi-antenna receiving processor 472, a multi-antenna transmitting processor 471, a transmitter / receiver 418, and an antenna 420.

[0332] The second communication device 450 includes a controller / processor 459, a memory 460, a data source 467, a transmitting processor 468, a receiving processor 456, a multi-antenna transmitting processor 457, a multi-antenna receiving processor 458, a transmitter / receiver 454, and an antenna 452.

[0333] In the transmission from the first communication device 410 to the second communication device 450, at the first communication device 410, upper layer data packets from the core network are provided to the controller / processor 475. The controller / processor 475 implements the functions of the L2 layer. In the DL (DownLink), the controller / processor 475 provides header compression, encryption, packet segmentation and reordering, multiplexing between logical and transport channels, and radio resource allocation to the second communication device 450 based on various priority metrics. The controller / processor 475 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the second communication device 450. The transmitting processor 416 and the multi-antenna transmitting processor 471 implement various signal processing functions for the L1 layer (i.e., the physical layer). The transmitting processor 416 implements coding and interleaving to facilitate forward error correction (FEC) at the second communication device 450, and constellation mapping based on various modulation schemes (e.g., binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), M-phase shift keying (M-PSK), M-quadrature amplitude modulation (M-QAM)). The multi-antenna transmitting processor 471 performs digital space precoding on the coded and modulated symbols, including codebook-based precoding and non-codebook-based precoding, and beamforming processing, to generate one or more parallel streams. The transmitting processor 416 then maps each parallel stream to subcarriers, multiplexes the modulated symbols with reference signals (e.g., pilots) in the time domain and / or frequency domain, and then uses the inverse fast Fourier transform (IFFT) to generate a physical channel carrying a time-domain multi-carrier symbol stream. Subsequently, the multi-antenna transmitting processor 471 performs transmit analog precoding / beamforming operations on the time-domain multi-carrier symbol stream. Each transmitter 418 converts the baseband multi-carrier symbol stream provided by the multi-antenna transmitting processor 471 into a radio frequency stream, and then provides it to different antennas 420.

[0334] In the transmission from the first communication device 410 to the second communication device 450, at the second communication device 450, each receiver 454 receives signals via its respective antenna 452. Each receiver 454 recovers the information modulated onto the RF carrier and converts the RF stream into a baseband multi-carrier symbol stream provided to the receive processor 456. The receive processor 456 and the multi-antenna receive processor 458 perform various signal processing functions of the L1 layer. The multi-antenna receive processor 458 performs receive analog precoding / beamforming operations on the baseband multi-carrier symbol stream from the receivers 454. The receive processor 456 uses the Fast Fourier Transform (FFT) to convert the baseband multi-carrier symbol stream after the receive analog precoding / beamforming operations from the time domain to the frequency domain. In the frequency domain, the physical layer data signal and the reference signal are demultiplexed by the receive processor 456, where the reference signal will be used for channel estimation, and the data signal is recovered in the multi-antenna receive processor 458 for any parallel streams destined for the second communication device 450 after multi-antenna detection. The symbols on each parallel stream are demodulated and recovered in the receive processor 456, and soft decisions are generated. Subsequently, the receive processor 456 decodes and deinterleaves the soft decisions to recover the upper layer data and control signals transmitted by the first communication device 410 on the physical channel. Subsequently, the upper layer data and control signals are provided to the controller / processor 459. The controller / processor 459 performs the functions of the L2 layer. The controller / processor 459 may be associated with a memory 460 that stores program code and data. The memory 460 may be referred to as a computer-readable medium. In the DL, the controller / processor 459 provides demultiplexing between the transmission and the logical channels, packet reassembly, decryption, header decompression, control signal processing to recover the upper layer data packets from the core network. Subsequently, the upper layer data packets are provided to all protocol layers above the L2 layer. Various control signals may also be provided to the L3 for L3 processing. The controller / processor 459 is also responsible for error detection using the acknowledgement (ACK) and / or negative acknowledgement (NACK) protocols to support HARQ operations.

[0335] In the transmission from the second communication device 450 to the first communication device 410, at the second communication device 450, a data source 467 is used to provide an upper layer data packet to a controller / processor 459. The data source 467 represents all protocol layers above the L2 layer. Similar to the transmit function at the first communication device 410 described in DL, the controller / processor 459 performs header compression, encryption, packet segmentation and reordering, and multiplexing between logical and transport channels based on the radio resource allocation of the first communication device 410, and implements L2 layer functions for the user plane and the control plane. The controller / processor 459 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the first communication device 410. A transmit processor 468 performs modulation mapping and channel coding processing. A multi-antenna transmit processor 457 performs digital multi-antenna spatial precoding, including codebook-based precoding and non-codebook-based precoding, and beamforming processing. Subsequently, the transmit processor 468 modulates the generated parallel streams into multi-carrier / single-carrier symbol streams, and after passing through analog precoding / beamforming operations in the multi-antenna transmit processor 457, provides them to different antennas 452 via a transmitter 454. Each transmitter 454 first converts the baseband symbol stream provided by the multi-antenna transmit processor 457 into a radio frequency symbol stream and then provides it to the antenna 452.

[0336] In the transmission from the second communication device 450 to the first communication device 410, the functions at the first communication device 410 are similar to the receive functions at the second communication device 450 described in the transmission from the first communication device 410 to the second communication device 450. Each receiver 418 receives a radio frequency signal through its corresponding antenna 420, converts the received radio frequency signal into a baseband signal, and provides the baseband signal to a multi-antenna receive processor 472 and a receive processor 470. The receive processor 470 and the multi-antenna receive processor 472 jointly implement L1 layer functions. A controller / processor 475 implements L2 layer functions. The controller / processor 475 may be associated with a memory 476 that stores program code and data. The memory 476 may be referred to as a computer-readable medium. The controller / processor 475 provides demultiplexing between transport and logical channels, packet reassembly, decryption, header decompression, and control signal processing to recover the upper layer data packet from the second communication device 450. The upper layer data packet from the controller / processor 475 may be provided to the core network. The controller / processor 475 is also responsible for error detection using the ACK and / or NACK protocols to support HARQ operations.

[0337] As an example, the second communication device 450 includes: at least one processor and at least one memory, where the at least one memory includes computer program code; the at least one memory and the computer program code are configured to be used with the at least one processor. The second communication device 450 is at least configured to: receive a first information block, where the first information block is used to configure a first set of search spaces; monitor control channel candidates in the first set of search spaces; where whether the aggregation level of the first set of search spaces and the monitoring manner of the control channel candidates in the first set of search spaces involve reasoning is relevant.

[0338] As an example, the second communication device 450 includes: a memory storing a computer-readable instruction program, where the computer-readable instruction program generates actions when executed by at least one processor, and the actions include: receiving a first information block, where the first information block is used to configure a first set of search spaces; monitoring control channel candidates in the first set of search spaces; where whether the aggregation level of the first set of search spaces and the monitoring manner of the control channel candidates in the first set of search spaces involve reasoning is relevant.

[0339] As an example, the first communication device 410 includes: at least one processor and at least one memory, where the at least one memory includes computer program code; the at least one memory and the computer program code are configured to be used with the at least one processor. The first communication device 410 is at least configured to: send a first information block, where the first information block is used to configure a first set of search spaces; where the target receiver of the first information block monitors control channel candidates in the first set of search spaces, and whether the aggregation level of the first set of search spaces and the monitoring manner of the control channel candidates in the first set of search spaces involve reasoning is relevant.

[0340] As an example, the first communication device 410 includes: a memory storing a computer-readable instruction program, where the computer-readable instruction program generates actions when executed by at least one processor, and the actions include: sending a first information block, where the first information block is used to configure a first set of search spaces; where the target receiver of the first information block monitors control channel candidates in the first set of search spaces, and whether the aggregation level of the first set of search spaces and the monitoring manner of the control channel candidates in the first set of search spaces involve reasoning is relevant.

[0341] As an example, the first node in the present application includes the second communication device 450.

[0342] As an example, the second node in the present application includes the first communication device 410.

[0343] As an embodiment, at least one of {the antenna 452, the receiver 454, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, the memory 460, the data source 467} is used to receive the first information block in this application; at least one of {the antenna 420, the transmitter 418, the transmitting processor 416, the multi-antenna transmitting processor 471, the controller / processor 475, the memory 476} is used to transmit the first information block in this application.

[0344] As an embodiment, at least one of {the antenna 452, the transmitter 454, the transmitting processor 468, the multi-antenna transmitting processor 457, the controller / processor 459, the memory 460} is used to transmit the second information block in this application; at least one of {the antenna 420, the receiver 418, the receiving processor 470, the multi-antenna receiving processor 472, the controller / processor 475, the memory 476} is used to receive the second information block in this application.

[0345] Example 5

[0346] Embodiment 5 exemplifies a flowchart of a transmission according to an embodiment of this application, as shown in the appendix Figure 5 as shown. In the appendix Figure 5 the first node U01 and the second node N02 are two communication nodes transmitted through an air interface, where the steps in the dashed boxes F51 and F52 are optional respectively.

[0347] For First Node U01 , in step S5101, the model on which the inference is based is deployed; in step S5102, the second information block is transmitted; in step S5103, the first information block is received; in step S5104, the control channel candidates in the first search space set are monitored.

[0348] For Second Node N02 , in step S5201, the second information block is received; in step S5202, the first information block is transmitted.

[0349] In Embodiment 5, the first information block is used to configure the first search space set; whether the aggregation level of the first search space set and the monitoring method of the control channel candidates in the first search space set include inference-related.

[0350] As an embodiment, the first node U01 is the first node in this application.

[0351] As an embodiment, the second node N02 is the second node in the present application.

[0352] As an embodiment, the air interface between the second node N02 and the first node U01 includes a wireless interface between a base station device and a user equipment.

[0353] As an embodiment, the air interface between the second node N02 and the first node U01 includes a wireless interface between a relay node device and a user equipment.

[0354] As an embodiment, the air interface between the second node N02 and the first node U01 includes a wireless interface between user equipments.

[0355] As an embodiment, the second node N02 is a serving cell maintaining base station of the first node U01.

[0356] As an embodiment, the second node N02 includes a network device.

[0357] As an embodiment, the second node N02 includes an OTT (Over-The-Top) server.

[0358] As an embodiment, the second node N02 includes OAM (Operation Administration and Maintenance).

[0359] As an embodiment, the second node N02 includes a NAS device.

[0360] As an embodiment, the second node N02 includes a core network device.

[0361] As an embodiment, if the steps in the dashed box F51 exist, the model on which the reasoning is based needs to be deployed.

[0362] As an embodiment, if the steps in the dashed box F51 do not exist, the model on which the reasoning is based does not need to be deployed.

[0363] As an embodiment, if the steps in the dashed box F51 exist, the method in the first node for wireless communication includes: deploying the model on which the reasoning is based.

[0364] As an embodiment, the model on which the reasoning is based is one of multiple models.

[0365] As a sub - embodiment of the above embodiment, the multiple models need to be deployed.

[0366] As a sub - embodiment of the above - mentioned embodiment, the multiple models do not need to be deployed.

[0367] As a sub - embodiment of the above - mentioned embodiment, at least one of the multiple models needs to be deployed.

[0368] As a sub - embodiment of the above - mentioned embodiment, at least one of the multiple models does not need to be deployed.

[0369] As an embodiment, the multiple models are respectively identified by multiple identifiers.

[0370] As an embodiment, the model based on which the inference is made is the model among the multiple models that is identified by the first identifier.

[0371] As an embodiment, the first node and the second node have a consensus on the multiple models or the identifiers of the multiple models.

[0372] As an embodiment, which model among the multiple models the inference is based on is determined by the first node itself.

[0373] As an embodiment, which model among the multiple models the inference is based on is indicated by higher - layer signaling.

[0374] As an embodiment, which model among the multiple models the inference is based on is indicated by DCI (Downlink Control Information).

[0375] As an embodiment, the steps in the dashed - line box F52 exist.

[0376] As an embodiment, the steps in the dashed - line box F52 do not exist.

[0377] As an embodiment, the steps in the dashed - line box F52 exist. The method in the first node for wireless communication includes: sending a second information block; wherein, the second information block indicates whether the monitoring manner of the control - channel alternative by the first node includes inference.

[0378] As an embodiment, the steps in the dashed - line box F52 exist. The method in the second node for wireless communication includes: receiving a second information block; wherein, the second information block indicates whether the monitoring manner of the control - channel alternative by the sender of the second information block includes inference.

[0379] As an embodiment, the monitoring manner of the control - channel alternative in the first search - space set includes inference corresponding to the first identifier, and the aggregation level of the first search - space set also depends on the first identifier.

[0380] As an example, the range of candidate values of the aggregation level of the first search space set and whether the monitoring method of the control channel alternative in the first search space set includes reasoning are related.

[0381] As an example, when the monitoring method of the control channel alternative in the first search space set includes reasoning, the range of candidate values of the aggregation level of the first search space set is the first aggregation level range; when the monitoring method of the control channel alternative in the first search space set does not include reasoning, the range of candidate values of the aggregation level of the first search space set is the second aggregation level range; the first aggregation level range includes at least one aggregation level, the second aggregation level range includes at least one aggregation level, and the first aggregation level range and the second aggregation level range are different.

[0382] As an example, the monitoring method of the control channel alternative in the first search space set includes reasoning corresponding to the first identifier, and the first aggregation level range depends on the first identifier.

[0383] As an example, the first aggregation level is an aggregation level of the first search space set; only when the first condition is satisfied, the monitoring method of the control channel alternative for the first aggregation level in the first search space set includes reasoning; the first condition includes that the first aggregation level belongs to the reference aggregation level range; the reference aggregation level range includes one or more aggregation levels.

[0384] As an example, the monitoring method of the control channel alternative in the first search space set includes reasoning corresponding to the first identifier, and the reference aggregation level range depends on the first identifier.

[0385] As an example, the first information block indicates whether the monitoring method of the control channel alternative in the first search space set includes reasoning.

[0386] As an example, the second information block is transmitted on the PUSCH (Physical Uplink Shared Channel).

[0387] As an example, the second information block is transmitted on the PUCCH (Physical Uplink Control Channel).

[0388] As an example, the first information block is transmitted on the PDSCH (Physical Downlink Shared Channel).

[0389] Example 6

[0390] Example 6 illustrates a schematic diagram of the aggregation level of the first set of search spaces according to an embodiment of the present application depending on the first identifier; as shown in the attached Figure 6 figure.

[0391] In Example 6, the monitoring method of the control channel alternatives in the first set of search spaces includes reasoning corresponding to the first identifier, and the aggregation level of the first set of search spaces also depends on the first identifier.

[0392] As an embodiment, the essence of the above method includes: reasoning corresponding to different identifiers may have different monitoring performances, and for the reasoning with different monitoring performances, corresponding aggregation levels are provided, which improves the flexibility of the system and optimizes the performance.

[0393] As an embodiment, the monitoring method of the control channel alternatives in the first set of search spaces including reasoning corresponding to the first identifier includes: the monitoring method of the control channel alternatives in the first set of search spaces supports reasoning including corresponding to the first identifier.

[0394] As an embodiment, the monitoring method of the control channel alternatives in the first set of search spaces including reasoning corresponding to the first identifier includes: the monitoring method of the control channel alternatives in the first set of search spaces supports reasoning corresponding to the first identifier.

[0395] As an embodiment, the monitoring method of the control channel alternatives in the first set of search spaces including reasoning corresponding to the first identifier includes: the monitoring method of the control channel alternatives in the first set of search spaces allows reasoning corresponding to the first identifier to be adopted.

[0396] As an embodiment, the monitoring method of the control channel alternatives in the first set of search spaces including reasoning corresponding to the first identifier includes: the monitoring method of the control channel alternatives in the first set of search spaces supports reasoning based on the corresponding first identifier.

[0397] As an embodiment, the monitoring method of the control channel alternatives in the first set of search spaces including reasoning corresponding to the first identifier includes: the monitoring method of the control channel alternatives in the first set of search spaces is based on the reasoning corresponding to the first identifier.

[0398] As an embodiment, the monitoring method of the control channel alternatives in the first set of search spaces including reasoning corresponding to the first identifier includes: the first node monitors the control channel alternatives in the first set of search spaces by using reasoning corresponding to the first identifier.

[0399] As an example, the monitoring method for the control channel alternatives in the first search space set includes the inference corresponding to the first identifier, including: the first node monitors the control channel alternatives in the first search space set based on the inference corresponding to the first identifier.

[0400] As an example, an inference corresponding to the first identifier includes: the inference is identified by the first identifier.

[0401] As an example, an inference corresponding to the first identifier includes: the model of the inference is identified by the first identifier.

[0402] As an example, an inference corresponding to the first identifier includes: the AI entity or AI function to which the inference belongs is identified by the first identifier.

[0403] As an example, an inference corresponding to the first identifier includes: the AI entity or AI function that executes the inference is identified by the first identifier.

[0404] As an example, an inference corresponding to the first identifier includes: the training of the inference is identified by the first identifier.

[0405] As an example, an inference corresponding to the first identifier includes: the training data set of the inference is identified by the first identifier.

[0406] As an example, an inference corresponding to the first identifier includes: the inference data set of the inference is identified by the first identifier.

[0407] As an example, an inference corresponding to the first identifier includes: the first identifier is used to identify at least one of the data set or the configuration parameter set associated with the inference.

[0408] As an example, an inference corresponding to the first identifier includes: the first identifier is used to identify at least one of the data set or the configuration parameter set associated with the AI model on which the inference is based.

[0409] As an example, an inference corresponding to the first identifier includes: the first identifier is used by the first node to determine the model on which the inference is based.

[0410] As an example, the first identifier is a non - negative integer.

[0411] As an example, the first identifier is a string.

[0412] As an example, the first information block indicates the first identifier.

[0413] As an embodiment, a higher layer parameter outside the first information block indicates the first identifier.

[0414] As an embodiment, a candidate value range of the aggregation level of the first search space set depends on the first identifier.

[0415] As an embodiment, the number of aggregation levels of the first search space set depends on the first identifier.

[0416] As an embodiment, the maximum value of the aggregation level of the first search space set depends on the first identifier.

[0417] As an embodiment, the minimum value of the aggregation level of the first search space set depends on the first identifier.

[0418] Example 7

[0419] Embodiment 7 exemplifies a schematic diagram of a candidate value range of the aggregation level of the first search space set and inference according to an embodiment of the present application; as shown in the appendix Figure 7 as follows.

[0420] In Embodiment 7, whether the candidate value range of the aggregation level of the first search space set and the monitoring method of the control channel alternative in the first search space set include inference-related.

[0421] As an embodiment, when the monitoring method of the control channel alternative in the first search space set includes inference, the candidate value range of the aggregation level of the first search space set is different from that when the monitoring method of the control channel alternative in the first search space set does not include inference.

[0422] As an embodiment, the aggregation level of the first search space set depends on whether the monitoring method of the control channel alternative in the first search space set is inference-based.

[0423] As an embodiment, the number of aggregation levels of the first search space set depends on whether the monitoring method of the control channel alternative in the first search space set is inference-based.

[0424] As an embodiment, the maximum value of the aggregation level of the first search space set depends on whether the monitoring method of the control channel alternative in the first search space set is inference-based.

[0425] As an example, the minimum value of the aggregation level of the first search space set depends on whether the monitoring method of the control channel alternative in the first search space set is inference-based.

[0426] As an example, the range of candidate values of the aggregation level of the first search space set depends on whether the monitoring method of the control channel alternative in the first search space set is inference-based.

[0427] As an example, when the monitoring method of the control channel alternative in the first search space set is inference-based, the range of candidate values of the aggregation level of the first search space set is different from the range of candidate values of the aggregation level of the first search space set when the monitoring method of the control channel alternative in the first search space set is not inference-based.

[0428] As an example, when the monitoring method of the control channel alternative in the first search space set is inference-based, the range of candidate values of the aggregation level of the first search space set includes the range of candidate values of the aggregation level of the first search space set when the monitoring method of the control channel alternative in the first search space set is not inference-based.

[0429] As an example, when the monitoring method of the control channel alternative in the first search space set is not inference-based, the range of candidate values of the aggregation level of the first search space set is a subset of the range of candidate values of the aggregation level of the first search space set when the monitoring method of the control channel alternative in the first search space set is inference-based.

[0430] As an example, when the monitoring method of the control channel alternative in the first search space set is inference-based, any candidate value in the range of candidate values of the aggregation level of the first search space set does not belong to the range of candidate values of the aggregation level of the first search space set when the monitoring method of the control channel alternative in the first search space set is not inference-based.

[0431] As an example, when the monitoring method of the control channel alternative in the first search space set is inference-based, there is at least one candidate value in the range of candidate values of the aggregation level of the first search space set that does not belong to the range of candidate values of the aggregation level of the first search space set when the monitoring method of the control channel alternative in the first search space set is not inference-based.

[0432] As an example, when the monitoring method of the control channel alternative in the first search space set is based on inference, there is at least one candidate value in the candidate value range of the aggregation level of the first search space set that does not belong to the candidate value range of the aggregation level of the first search space set when the monitoring method of the control channel alternative in the first search space set is not based on inference; when the monitoring method of the control channel alternative in the first search space set is based on inference, there is at least one candidate value in the candidate value range of the aggregation level of the first search space set that belongs to the candidate value range of the aggregation level of the first search space set when the monitoring method of the control channel alternative in the first search space set is not based on inference.

[0433] Example 8

[0434] Example 8 illustrates a schematic diagram of the first aggregation level range and the second aggregation level range according to an embodiment of the present application; as shown in the Figure 8 accompanying drawings.

[0435] In Example 8, when the monitoring method of the control channel alternative in the first search space set includes inference, the candidate value range of the aggregation level of the first search space set is the first aggregation level range; when the monitoring method of the control channel alternative in the first search space set does not include inference, the candidate value range of the aggregation level of the first search space set is the second aggregation level range; the first aggregation level range includes at least one aggregation level, the second aggregation level range includes at least one aggregation level, and the first aggregation level range and the second aggregation level range are different.

[0436] As an example, the first aggregation level range includes one or more aggregation levels.

[0437] As an example, the second aggregation level range includes one or more aggregation levels.

[0438] As an example, the minimum value in the first aggregation level range is less than the minimum value in the second aggregation level range.

[0439] As an example, the maximum value in the first aggregation level range is less than the maximum value in the second aggregation level range.

[0440] As an example, the maximum value in the first aggregation level range is less than the minimum value in the second aggregation level range.

[0441] As an example, the maximum value in the first aggregation level range is less than or equal to the minimum value in the second aggregation level range.

[0442] As an example, the range of candidate values for the aggregation level is {1, 2, 4, 8, 16}.

[0443] As a sub - example of the above - mentioned example, the control channel alternative for the aggregation level L in the first search space set occupies L CCEs, where L is a value in {1, 2, 4, 8, 16}.

[0444] As a sub - example of the above - mentioned example, one CCE consists of a positive integer number of REGs.

[0445] As a reference example of the above - mentioned sub - example, the number of REGs that make up the one CCE is equal to 6.

[0446] As a reference example of the above - mentioned sub - example, the number of REGs that make up the one CCE is not equal to 6.

[0447] As a reference example of the above - mentioned sub - example, the number of REGs that make up the one CCE is less than 6.

[0448] As a reference example of the above - mentioned sub - example, the number of REGs that make up the one CCE is one of 1, 2, 3.

[0449] As a sub - example of the above - mentioned example, one REG includes one RB on one symbol.

[0450] As a sub - example of the above - mentioned example, one REG is one RB on one symbol.

[0451] As an example, the essence of the above - mentioned method includes: in the existing standard, one CCE consists of 6 REGs, one control channel alternative occupies one or more CCEs, and when monitoring the control channel alternative based on inference, a smaller - granularity CCE (the number of REGs that make up one CCE is less than 6) can be used.

[0452] As an example, the advantages of the above - mentioned method include: without changing the existing aggregation level, the modification to the standard is small.

[0453] As an example, the advantages of the above - mentioned method include: reducing the overhead of the control channel.

[0454] As an example, the first aggregation level range is a subset of {1, 2, 4, 8, 16}, the second aggregation level range is a subset of {1, 2, 4, 8, 16}, and the first aggregation level range and the second aggregation level range are different.

[0455] As an example, the range of the first aggregation level is J values from {1, 2, 4, 8, 16}, the range of the second aggregation level is K values from {1, 2, 4, 8, 16}, the J and the K are positive integers respectively, the J is less than 5, and the K is less than 5.

[0456] As a sub - example of the above example, the minimum value among the J values is less than the minimum value among the K values.

[0457] As a sub - example of the above example, the maximum value among the J values is less than the maximum value among the K values.

[0458] As a sub - example of the above example, the maximum value among the J values is less than the minimum value among the K values.

[0459] As a sub - example of the above example, the maximum value among the J values is less than or equal to the minimum value among the K values.

[0460] As a sub - example of the above example, the J values are the smaller J values from {1, 2, 4, 8, 16}, and the K values are the larger K values from {1, 2, 4, 8, 16}.

[0461] As a reference example of the above sub - example, the sum of the J and the K is not greater than 5.

[0462] As a reference example of the above sub - example, the sum of the J and the K is greater than 5.

[0463] As a sub - example of the above example, the first information block indicates a first numerical value, the J values are the values from {1, 2, 4, 8, 16} that are less than or equal to the first numerical value, the K values are the values from {1, 2, 4, 8, 16} that are greater than the first numerical value, and the first numerical value is a positive number.

[0464] As a sub - example of the above example, a higher - layer parameter outside the first information block indicates a first numerical value, the J values are the values from {1, 2, 4, 8, 16} that are less than or equal to the first numerical value, the K values are the values from {1, 2, 4, 8, 16} that are greater than the first numerical value, and the first numerical value is a positive number.

[0465] As an example, the range of candidate values of the aggregation level, in addition to including {1, 2, 4, 8, 16}, also includes P positive real numbers less than 1, where the P is a positive integer.

[0466] As a sub - embodiment of the above - mentioned embodiment, a control channel alternative in the first search space set for an aggregation level L occupies L CCEs, where L is one value among {1, 2, 4, 8, 16} and P positive real numbers less than 1.

[0467] As a reference embodiment of the above - mentioned sub - embodiment, when the aggregation level L is one of the P positive real numbers less than 1, the meaning that a control channel alternative occupies L CCEs includes: the one control channel alternative occupies (R×L) REGs, where (R×L) is a positive integer, R is a positive integer, and one CCE is composed of R REGs.

[0468] As a reference embodiment of the above - mentioned sub - embodiment, when the aggregation level L is one of the P positive real numbers less than 1, the meaning that a control channel alternative occupies L CCEs includes: the one control channel alternative occupies (12×R×L) REs, where (12×R×L) is a positive integer, R is a positive integer, and one CCE is composed of R REGs.

[0469] As a sub - embodiment of the above - mentioned embodiment, R is equal to 6.

[0470] As a sub - embodiment of the above - mentioned embodiment, R is not equal to 6.

[0471] As a sub - embodiment of the above - mentioned embodiment, R is a positive integer greater than 6.

[0472] As a sub - embodiment of the above - mentioned embodiment, one REG includes 12 REs.

[0473] As a sub - embodiment of the above - mentioned embodiment, one REG includes 12 consecutive REs in the frequency domain on the same symbol.

[0474] As a sub - embodiment of the above - mentioned embodiment, one REG includes one RB on one symbol.

[0475] As a sub - embodiment of the above - mentioned embodiment, one REG is one RB on one symbol.

[0476] As a sub - embodiment of the above - mentioned embodiment, the P positive real numbers less than 1 include at least one of 1 / 6, 1 / 3, 1 / 2, 2 / 3.

[0477] As an embodiment, the advantages of the above - mentioned method include: good forward compatibility.

[0478] As an embodiment, the advantages of the above - mentioned method include: reducing the overhead of the control channel.

[0479] As an example, the first aggregation level range is a subset of {1, 2, 4, 8, 16} and the P positive real numbers less than 1, the second aggregation level range is a subset of {1, 2, 4, 8, 16} and the P positive real numbers less than 1, and the first aggregation level range is different from the second aggregation level range.

[0480] As an example, the first aggregation level range is a subset of the P positive real numbers less than 1, and the second aggregation level range is a subset of {1, 2, 4, 8, 16}.

[0481] As an example, the first aggregation level range is the P positive real numbers less than 1, and the second aggregation level range is {1, 2, 4, 8, 16}.

[0482] As an example, the first aggregation level range is J values among {1, 2, 4, 8, 16} and the P positive real numbers less than 1, the second aggregation level range is K values among {1, 2, 4, 8, 16} and the P positive real numbers less than 1, J and K are positive integers respectively, J is less than (P + 5), and K is less than (P + 5).

[0483] As a sub - example of the above example, the minimum value among the J values is less than the minimum value among the K values.

[0484] As a sub - example of the above example, the maximum value among the J values is less than the maximum value among the K values.

[0485] As a sub - example of the above example, the maximum value among the J values is less than the minimum value among the K values.

[0486] As a sub - example of the above example, the maximum value among the J values is less than or equal to the minimum value among the K values.

[0487] As a sub - example of the above example, the J values are the smaller J values among {1, 2, 4, 8, 16} and the P positive real numbers less than 1, and the K values are the larger K values among {1, 2, 4, 8, 16} and the P positive real numbers less than 1.

[0488] As a reference example of the above sub - example, the sum of J and K is not greater than (P + 5).

[0489] As a reference example of the above sub - example, the sum of J and K is greater than (P + 5).

[0490] As a sub - embodiment of the above - mentioned embodiment, the first information block indicates a first numerical value, the J values are the values among {1, 2, 4, 8, 16} and the P positive real numbers less than 1 that are less than or equal to the first numerical value, the K values are the values among {1, 2, 4, 8, 16} and the P positive real numbers less than 1 that are greater than the first numerical value, and the first numerical value is a positive number.

[0491] As a sub - embodiment of the above - mentioned embodiment, a higher - layer parameter outside the first information block indicates a first numerical value, the J values are the values among {1, 2, 4, 8, 16} and the P positive real numbers less than 1 that are less than or equal to the first numerical value, the K values are the values among {1, 2, 4, 8, 16} and the P positive real numbers less than 1 that are greater than the first numerical value, and the first numerical value is a positive number.

[0492] As an embodiment, the candidate value range of the aggregation level includes, in addition to {1, 2, 4, 8, 16}, Q positive integers greater than 16, where Q is a positive integer.

[0493] As a sub - embodiment of the above - mentioned embodiment, the control channel alternative for the aggregation level L in the first search space set occupies L CCEs, where L is a value among {1, 2, 4, 8, 16} and the Q positive integers greater than 16.

[0494] As a sub - embodiment of the above - mentioned embodiment, one CCE is composed of a positive - integer number of REGs.

[0495] As a reference embodiment of the above - mentioned sub - embodiment, the number of REGs constituting the one CCE is equal to 6.

[0496] As a reference embodiment of the above - mentioned sub - embodiment, the number of REGs constituting the one CCE is not equal to 6.

[0497] As a reference embodiment of the above - mentioned sub - embodiment, the number of REGs constituting the one CCE is less than 6.

[0498] As a reference embodiment of the above - mentioned sub - embodiment, the number of REGs constituting the one CCE is one of 1, 2, 3.

[0499] As a sub - embodiment of the above - mentioned embodiment, one REG includes T REs, where T is a positive integer.

[0500] As a sub - embodiment of the above - mentioned embodiment, one REG includes the T REs that are contiguous in the frequency domain on the same symbol.

[0501] As a reference embodiment of the above - mentioned sub - embodiment, T is equal to 12.

[0502] As a reference embodiment of the above sub - embodiment, T is not equal to 12.

[0503] As a reference embodiment of the above sub - embodiment, T is less than 12.

[0504] As a reference embodiment of the above sub - embodiment, T is one of the values 3, 4, 6.

[0505] As a sub - embodiment of the above embodiment, a REG includes an RB on a symbol.

[0506] As a sub - embodiment of the above embodiment, a REG is an RB on a symbol.

[0507] As a sub - embodiment of the above embodiment, at least one of the Q positive integers greater than 16 includes one of 24, 32, 48, 64, 96.

[0508] As an embodiment, the advantages of the above method include: good forward compatibility.

[0509] As an embodiment, the advantages of the above method include: reducing the overhead of the control channel.

[0510] As an embodiment, the first aggregation level range is a subset of {1, 2, 4, 8, 16} and the Q positive integers greater than 16, the second aggregation level range is a subset of {1, 2, 4, 8, 16} and the Q positive integers greater than 16, and the first aggregation level range and the second aggregation level range are different.

[0511] As an embodiment, the first aggregation level range is a subset of {1, 2, 4, 8, 16}, and the second aggregation level range is a subset of the Q positive integers greater than 16.

[0512] As an embodiment, the first aggregation level range is {1, 2, 4, 8, 16}, and the second aggregation level range is the Q positive integers greater than 16.

[0513] As an embodiment, the first aggregation level range is J values among {1, 2, 4, 8, 16} and the Q positive integers greater than 16, the second aggregation level range is K values among {1, 2, 4, 8, 16} and the Q positive integers greater than 16, J and K are positive integers respectively, J is less than (Q + 5), and K is less than (Q + 5).

[0514] As a sub - embodiment of the above embodiment, the minimum value among the J values is less than the minimum value among the K values.

[0515] As a sub - embodiment of the above - mentioned embodiment, the maximum value among the J values is less than the maximum value among the K values.

[0516] As a sub - embodiment of the above - mentioned embodiment, the maximum value among the J values is less than the minimum value among the K values.

[0517] As a sub - embodiment of the above - mentioned embodiment, the maximum value among the J values is less than or equal to the minimum value among the K values.

[0518] As a sub - embodiment of the above - mentioned embodiment, the J values are the smaller J values among {1, 2, 4, 8, 16} and the Q positive integers greater than 16, and the K values are the larger K values among {1, 2, 4, 8, 16} and the Q positive integers greater than 16.

[0519] As a reference embodiment of the above - mentioned sub - embodiment, the sum of J and K is not greater than (Q + 5).

[0520] As a reference embodiment of the above - mentioned sub - embodiment, the sum of J and K is greater than (Q + 5).

[0521] As a sub - embodiment of the above - mentioned embodiment, the first information block indicates a first numerical value. The J values are the values among {1, 2, 4, 8, 16} and the Q positive integers greater than 16 that are less than or equal to the first numerical value, and the K values are the values among {1, 2, 4, 8, 16} and the Q positive integers greater than 16 that are greater than the first numerical value. The first numerical value is a positive number.

[0522] As a sub - embodiment of the above - mentioned embodiment, a higher - layer parameter outside the first information block indicates a first numerical value. The J values are the values among {1, 2, 4, 8, 16} and the Q positive integers greater than 16 that are less than or equal to the first numerical value, and the K values are the values among {1, 2, 4, 8, 16} and the Q positive integers greater than 16 that are greater than the first numerical value. The first numerical value is a positive number.

[0523] As an embodiment, only when the monitoring method of the control channel alternatives in the first search space set includes inference, the aggregation level of the first search space set is limited to a reference aggregation level range.

[0524] As an embodiment, the first aggregation level range and the second aggregation level range are respectively configurable.

[0525] As an embodiment, both the first aggregation level range and the second aggregation level range are explicitly configured.

[0526] As an example, the first aggregation level range is explicitly configured, and the second aggregation level range is implicitly determined.

[0527] As an example, the first aggregation level range is explicitly configured, and the second aggregation level range is the part of the candidate value range of the aggregation level excluding the first aggregation level range.

[0528] As an example, the second aggregation level range is explicitly configured, and the first aggregation level range is implicitly determined.

[0529] As an example, the second aggregation level range is explicitly configured, and the first aggregation level range is the part of the candidate value range of the aggregation level excluding the second aggregation level range.

[0530] As an example, the first aggregation level range is default.

[0531] As an example, the first aggregation level range is fixed.

[0532] As an example, the first aggregation level range is predefined.

[0533] As an example, the second aggregation level range is default.

[0534] As an example, the second aggregation level range is fixed.

[0535] As an example, the second aggregation level range is predefined.

[0536] As an example, the first information block indicates the first aggregation level range.

[0537] As an example, the first information block indicates each aggregation level in the first aggregation level range.

[0538] As an example, the first information block indicates a first value, the first value is a positive number, the first aggregation level range includes the aggregation level candidate values less than or equal to the first value in the candidate value range of the aggregation level, and the second aggregation level range includes the aggregation level candidate values greater than the first value in the candidate value range of the aggregation level.

[0539] As an example, the first aggregation level range depends on the capability information reported by the first node.

[0540] As an example, the first information block indicates the first aggregation level range, and the first aggregation level range depends on the capability information reported by the first node.

[0541] As an example, the first information block indicates the first aggregation level range according to the capability information reported by the first node.

[0542] As an example, the first aggregation level range is reported by the first node.

[0543] As an example, the advantages of the above method include: better flexibility to adapt to different terminals.

[0544] Example 9

[0545] Example 9 illustrates a schematic diagram of the first aggregation level range depending on the first identifier according to an embodiment of the present application; as shown in the appendix Figure 9 as shown.

[0546] In Example 9, the monitoring method for the control channel alternatives in the first search space set includes reasoning corresponding to the first identifier, and the first aggregation level range depends on the first identifier.

[0547] As an example, the essence of the above method includes: reasoning corresponding to different identifiers may have different monitoring performances. For the reasoning with different monitoring performances, an aggregation level range adapted to its performance is provided, which improves the flexibility of the system and optimizes the system performance.

[0548] As an example, N aggregation level ranges correspond to N1 identifiers, where N and N1 are positive integers greater than 1 respectively; the first aggregation level range is one of the N aggregation level ranges corresponding to the first identifier.

[0549] As an example, N is less than N1, any one of the N aggregation level ranges corresponds to at least one of the N1 identifiers, and each of the N1 identifiers corresponds to one of the N aggregation level ranges.

[0550] As an example, the monitoring of the control channel alternatives in the first search space set is based on the reasoning corresponding to the first identifier. The candidate value range of the aggregation level of the first search space set is the first aggregation level range, and the first aggregation level range is one of the N aggregation level ranges corresponding to the identifier including the first identifier.

[0551] As an example, each of the N1 identifiers is a non - negative integer.

[0552] As an example, each of the N1 identifiers is a string.

[0553] As an example, the first identifier is one of the N1 identifiers.

[0554] As an example, N aggregation level ranges respectively correspond to N identifiers, where N is a positive integer greater than 1; the first aggregation level range is one of the N aggregation level ranges corresponding to the first identifier.

[0555] As an example, the monitoring of the control channel candidates in the first search space set is based on the inference corresponding to the first identifier, the candidate value range of the aggregation level of the first search space set is the first aggregation level range, and the first aggregation level range is one of the N aggregation level ranges corresponding to the first identifier.

[0556] As an example, each of the N identifiers is a non - negative integer.

[0557] As an example, each of the N identifiers is a string.

[0558] As an example, the first identifier is one of the N identifiers.

[0559] As an example, the N aggregation level ranges are default.

[0560] As an example, the N aggregation level ranges are fixed.

[0561] As an example, the N aggregation level ranges are predefined.

[0562] As an example, the N aggregation level ranges are configurable.

[0563] As an example, the N aggregation level ranges are configured by higher - layer signaling.

[0564] As an example, the N aggregation level ranges are indicated by higher - layer signaling.

[0565] As an example, the first information block indicates the N aggregation level ranges.

[0566] As an example, the first information block indicates each of the N aggregation level ranges.

[0567] As an example, the first information block indicates the N aggregation level ranges and the corresponding N identifiers.

[0568] As an example, the first information block indicates the N aggregation level ranges and the corresponding N1 identifiers.

[0569] As an example, the N aggregation level ranges depend on the capability information reported by the first node.

[0570] As an example, the first information block indicates the N aggregation level ranges, and the N aggregation level ranges depend on the capability information reported by the first node.

[0571] As an example, the first information block indicates the N aggregation level ranges according to the capability information reported by the first node.

[0572] As an example, the N aggregation level ranges are reported by the first node.

[0573] As an example, the first information block indicates a first value, the first value is a positive number, and any one of the N aggregation level ranges is a subset of the aggregation level candidate values less than or equal to the first value in the candidate value range of the aggregation level.

[0574] As an example, the first information block indicates a first value, the first value is a positive number, and any one of the N aggregation level ranges is a proper subset of the aggregation level candidate values less than or equal to the first value in the candidate value range of the aggregation level.

[0575] Example 10

[0576] Example 10 exemplifies a schematic diagram of a first condition and a reference aggregation level range according to an embodiment of the present application; as shown in the appendix Figure 10 as shown.

[0577] In Example 10, the first aggregation level is an aggregation level of the first search space set; only when the first condition is satisfied, the monitoring method for the control channel alternative for the first aggregation level in the first search space set includes inference; the first condition includes that the first aggregation level belongs to the reference aggregation level range; the reference aggregation level range includes one or more aggregation levels.

[0578] As an example, only when the first condition is satisfied, the monitoring method for the control channel alternative for the first aggregation level in the first search space set supports inference.

[0579] As an example, when only the first condition is satisfied, the monitoring method for the control channel candidates in the first search space set for the first aggregation level is inference.

[0580] As an example, when only the first condition is satisfied, the monitoring method for the control channel candidates in the first search space set for the first aggregation level is inference-based.

[0581] As an example, when only the first condition is satisfied, the first node uses inference to monitor the control channel candidates in the first search space set for the first aggregation level.

[0582] As an example, when only the first condition is satisfied, the first node monitors the control channel candidates in the first search space set for the first aggregation level based on inference.

[0583] As an example, the first condition is that the first aggregation level belongs to the reference aggregation level range.

[0584] As an example, when the first aggregation level does not belong to the reference aggregation level range, the first condition is not satisfied, and the monitoring method for the control channel candidates in the first search space set for the first aggregation level does not include inference.

[0585] As an example, the reference aggregation level range is default.

[0586] As an example, the reference aggregation level range is fixed.

[0587] As an example, the reference aggregation level range is predefined.

[0588] As an example, the reference aggregation level range is configurable.

[0589] As an example, the reference aggregation level range is configured by higher layer signaling.

[0590] As an example, the reference aggregation level range is indicated by higher layer signaling.

[0591] As an example, the first information block indicates the reference aggregation level range.

[0592] As an example, the first information block indicates each aggregation level in the reference aggregation level range.

[0593] As an example, the reference aggregation level range depends on the capability information reported by the first node.

[0594] As an example, the first information block indicates the reference aggregation level range, and the reference aggregation level range depends on the capability information reported by the first node.

[0595] As an example, the first information block indicates the reference aggregation level range according to the capability information reported by the first node.

[0596] As an example, the reference aggregation level range is reported by the first node.

[0597] As an example, the first information block indicates a first value, the first value is a positive number, and the reference aggregation level range includes the aggregation level candidate values less than or equal to the first value in the candidate value range of the aggregation level.

[0598] As an example, the first information block indicates a first value, the first value is a positive number, and the reference aggregation level range is a subset of the aggregation level candidate values less than or equal to the first value in the candidate value range of the aggregation level.

[0599] As an example, the first information block indicates a first value, the first value is a positive number, and the reference aggregation level range is a proper subset of the aggregation level candidate values less than or equal to the first value in the candidate value range of the aggregation level.

[0600] Example 11

[0601] Example 11 exemplifies a schematic diagram of the reference aggregation level range depending on the first identifier according to an embodiment of the present application; as shown in the appendix Figure 11 as shown.

[0602] In Example 11, the monitoring method of the control channel alternatives in the first search space set includes reasoning corresponding to the first identifier, and the reference aggregation level range depends on the first identifier.

[0603] As an example, N aggregation level ranges correspond to N1 identifiers, where N and N1 are positive integers greater than 1 respectively; the reference aggregation level range is one of the N aggregation level ranges corresponding to the first identifier.

[0604] As an example, N is less than N1, any one of the N aggregation level ranges corresponds to at least one of the N1 identifiers, and each of the N1 identifiers corresponds to one of the N aggregation level ranges.

[0605] As an example, the monitoring of the control channel alternatives in the first search space set is based on the inference corresponding to the first identifier, and the candidate value range of the aggregation level of the first search space set is the reference aggregation level range, and the reference aggregation level range is one of the N aggregation level ranges corresponding to the identifier including the first identifier among the N aggregation level ranges.

[0606] As an example, each of the N1 identifiers is a non - negative integer.

[0607] As an example, each of the N1 identifiers is a string.

[0608] As an example, the first identifier is one of the N1 identifiers.

[0609] As an example, N aggregation level ranges respectively correspond to N identifiers, where N is a positive integer greater than 1; the reference aggregation level range is one of the N aggregation level ranges corresponding to the first identifier among the N aggregation level ranges.

[0610] As an example, the monitoring of the control channel alternatives in the first search space set is based on the inference corresponding to the first identifier, and the candidate value range of the aggregation level of the first search space set is the reference aggregation level range, and the reference aggregation level range is one of the N aggregation level ranges corresponding to the first identifier among the N aggregation level ranges.

[0611] As an example, each of the N identifiers is a non - negative integer.

[0612] As an example, each of the N identifiers is a string.

[0613] As an example, the first identifier is one of the N identifiers.

[0614] As an example, the example of the N aggregation level ranges refers to Example 9.

[0615] Example 12

[0616] Example 12 illustrates a schematic diagram of whether the indication of the first information block for the monitoring method of the control channel alternatives according to an embodiment of the present application includes inference; as shown in the appendix Figure 12 as follows.

[0617] In Example 12, the first information block indicates whether the monitoring method of the control channel alternatives in the first search space set includes inference.

[0618] As an example, the first information block indicates that the monitoring manner of the control channel alternatives in the first search space set includes inference.

[0619] As an example, the first information block indicates that the monitoring manner of the control channel alternatives in the first search space set does not include inference.

[0620] As an example, the first information block explicitly indicates whether the monitoring manner of the control channel alternatives in the first search space set includes inference.

[0621] As an example, the first information block directly indicates whether the monitoring manner of the control channel alternatives in the first search space set includes inference.

[0622] As an example, a field in the first information block indicates whether the monitoring manner of the control channel alternatives in the first search space set includes inference.

[0623] As an example, a higher layer parameter in the first information block indicates whether the monitoring manner of the control channel alternatives in the first search space set includes inference.

[0624] As an example, the first information block implicitly indicates whether the monitoring manner of the control channel alternatives in the first search space set includes inference.

[0625] As an example, the first information block indicates whether the monitoring manner of the control channel alternatives in the first search space set includes inference by indicating other information.

[0626] As an example, the first information block indicates the first aggregation level range, and the monitoring manner of the control channel alternatives in the first search space set includes inference; the first information block does not indicate the first aggregation level range, and the monitoring manner of the control channel alternatives in the first search space set does not include inference.

[0627] As an example, the first information block indicates the reference aggregation level range, and the monitoring manner of the control channel alternatives in the first search space set includes inference; the first information block does not indicate the reference aggregation level range, and the monitoring manner of the control channel alternatives in the first search space set does not include inference.

[0628] As an example, the first information block indicates the first value, and the monitoring manner of the control channel alternatives in the first search space set includes inference; the first information block does not indicate the first value, and the monitoring manner of the control channel alternatives in the first search space set does not include inference.

[0629] Example 13

[0630] Embodiment 13 exemplifies a schematic diagram of a second information block according to an embodiment of the present application; as shown in the attached Figure 13 figure.

[0631] In Embodiment 13, the first node sends a second information block; the second information block indicates whether the monitoring mode of the first node for the control channel alternative includes inference.

[0632] As an embodiment, the second information block directly indicates whether the monitoring mode of the first node for the control channel alternative includes inference.

[0633] As an embodiment, the second information block explicitly indicates whether the monitoring mode of the first node for the control channel alternative includes inference.

[0634] As an embodiment, the second information block implicitly indicates whether the monitoring mode of the first node for the control channel alternative includes inference.

[0635] As an embodiment, whether the monitoring mode of the first node for the control channel alternative includes inference includes: whether the monitoring mode of the first node for the control channel alternative supports including inference.

[0636] As an embodiment, whether the monitoring mode of the first node for the control channel alternative includes inference includes: whether the monitoring mode of the first node for the control channel alternative supports inference.

[0637] As an embodiment, whether the monitoring mode of the first node for the control channel alternative includes inference includes: whether the monitoring mode of the first node for the control channel alternative supports inference-based.

[0638] As an embodiment, whether the monitoring mode of the first node for the control channel alternative includes inference includes: whether the first node can use inference to monitor the control channel alternative.

[0639] As an embodiment, whether the monitoring mode of the first node for the control channel alternative includes inference includes: whether the first node uses inference to monitor the control channel alternative.

[0640] As an embodiment, the second information block indicates whether the monitoring mode of the control channel alternative in the first search space set includes inference.

[0641] As an embodiment, the second information block is carried by a higher layer message.

[0642] As an embodiment, the second information block is carried by an RRC (Radio Resource Control) message.

[0643] As an embodiment, the second information block is carried by RRC signaling.

[0644] As an embodiment, the second information block is borne by higher layer signaling.

[0645] As an embodiment, the second information block includes one or more fields in one or more RRC IEs (information elements).

[0646] As an embodiment, the second information block includes a MAC CE.

[0647] As an embodiment, the second information block includes control information.

[0648] As an embodiment, the second information block includes UCI (Uplink Control Information).

[0649] As an embodiment, the second information block is borne by physical layer signaling.

[0650] As an embodiment, the second information block is borne by physical layer uplink signaling.

[0651] As an embodiment, the second information block is transmitted on a physical layer channel.

[0652] As an embodiment, the second information block is transmitted on a physical layer uplink channel.

[0653] As an embodiment, the second information block is transmitted on a PUCCH (Physical Uplink Control Channel).

[0654] As an embodiment, the second information block is transmitted on a PUSCH (Physical Uplink Shared Channel).

[0655] As an embodiment, the first information block includes a capability report of the first node.

[0656] As an embodiment, the first information block includes the UE processing capability of the first node.

[0657] As an example, the first information block includes the UE capability indication of the first node.

[0658] As an example, the second information block belongs to the capability information of the first node.

[0659] As an example, the second information block includes the capability information of the first node.

[0660] As an example, the second information block includes one or more capability parameters of the first node.

[0661] As an example, the second information block includes UE (User Equipment) capability information.

[0662] As an example, the second information block is carried by a UE (User Equipment) capability IE (information element).

[0663] As an example, the second information block includes one or more fields in a UE (User Equipment) capability IE (information element).

[0664] As an example, the second information block includes one or more fields in one or more UE (User Equipment) capability IE (information element)s.

[0665] As an example, the second information block includes one or more parameters in one or more UE capability IEs.

[0666] As an example, after receiving a UE Capability Enquiry from the network, the first node transmits the capability information of the first node, and the second information block belongs to the capability information of the first node.

[0667] As an example, the capability information of the first node includes UECapabilityInformation.

[0668] As an example, the capability information of the first node includes the radio access capability of the first node.

[0669] Example 14A-14B

[0670] Examples 14A - 14B illustrate a schematic diagram of deploying a model on which inference is based according to an embodiment of the present application; as shown in the appendix Figure 14A-14B as follows.

[0671] In Example 14A, the first node sends a request to a first producer to load the model on which the inference is based, and obtains the model on which the inference is based from the first producer.

[0672] As an example, the model on which the inference is based needs to be deployed.

[0673] As an example, the deployment includes obtaining the model on which the inference is based.

[0674] As an example, the deployment includes obtaining an AI entity.

[0675] As an example, the deployment includes obtaining an AI entity that performs the inference.

[0676] As an example, the deployment includes obtaining an AI entity that includes an AI function for performing the inference.

[0677] As an example, the deployment includes obtaining an AI function.

[0678] As an example, the deployment includes obtaining an AI function for performing the inference.

[0679] As an example, the deployment includes loading the model on which the inference is based.

[0680] As an example, the deployment includes sending a request to load the model on which the inference is based.

[0681] As an example, the appendix Figure 14A The request in is the request sent by the first node to load the model on which the inference is based.

[0682] As an example, the appendix Figure 14A The response in is the response to the request sent by the first node to load the model on which the inference is based.

[0683] As an example, the first node passes through the appendix Figure 14AThe inference is obtained from the response therein.

[0684] As an example, the first node obtains, through attachment, Figure 14A the model on which the inference is based from the response therein.

[0685] As an example, the first node obtains, through attachment, Figure 14A the AI entity including the AI function that executes the inference from the response therein.

[0686] As an example, the first node obtains, through attachment, Figure 14A the AI function that executes the inference from the response therein.

[0687] As an example, the first producer provides the inference to the first node through attachment of Figure 14A the response therein.

[0688] As an example, the first producer provides, through attachment, Figure 14A the model on which the inference is based to the first node from the response therein.

[0689] As an example, the first producer provides, through attachment, Figure 14A the AI entity including the AI function that executes the inference to the first node from the response therein.

[0690] As an example, the first producer provides, through attachment, Figure 14A the AI function that executes the inference to the first node from the response therein.

[0691] As an example, the deployment is completed by an AI function.

[0692] As an example, the deployment is completed by the AI function deployed on the first node.

[0693] As an example, the deployment is completed by an AI deployment function.

[0694] As an example, the deployment is completed by the AI deployment function deployed on the first node.

[0695] As an example, the deployment is completed by an AI inference function.

[0696] As an example, the deployment is completed by the AI inference function deployed on the first node.

[0697] As an example, the deployment is completed by an AI entity.

[0698] As an example, the deployment is completed by an AI entity deployed on the first node.

[0699] As an example, the deployment is completed by an AI entity having a deployment function.

[0700] As an example, the deployment is completed by an AI entity having a deployment function and deployed on the first node.

[0701] As an example, the deployment is completed by an AI entity having an inference function.

[0702] As an example, the deployment is completed by an AI entity having an inference function and deployed on the first node.

[0703] As an example, the deployment includes obtaining the model on which the inference is based from a first producer.

[0704] As an example, the deployment includes sending a request to the first producer to load the model on which the inference is based.

[0705] As an example, the deployment includes loading the model on which the inference is based from the first producer.

[0706] As an example, the first producer generates and provides an AI model.

[0707] As an example, the first producer generates and provides an AI entity.

[0708] As an example, the first producer generates and provides an AI function.

[0709] As an example, the first producer is the producer of the model on which the inference is based.

[0710] As an example, the first producer is the producer of the training of the model on which the inference is based.

[0711] As an example, the first producer includes an AI entity producer.

[0712] As an example, the first producer includes an AI function producer.

[0713] As an example, the first producer includes an AI deployment producer.

[0714] As an example, the first producer includes an AI loading producer.

[0715] As an example, the first producer includes an AI training producer.

[0716] As an example, the first producer includes an AI inference producer.

[0717] As an example, the first producer includes a producer for the training of an AI model.

[0718] As an example, the first producer includes a MnS (Management Service) producer.

[0719] As an example, the first producer is the serving cell of the first node.

[0720] As an example, the first producer is the base station that maintains the serving cell of the first node.

[0721] As an example, the first producer is the core network.

[0722] As an example, the training of the model on which the inference is based is performed by the first producer.

[0723] As an example, the entity or function that performs the training of the model on which the inference is based is different from the first producer.

[0724] In Embodiment 14B, the first node sends a request to the second producer to load the model on which the inference is based, and obtains the model on which the inference is based from the first producer.

[0725] As an example, the deployment includes obtaining the model on which the inference is based.

[0726] As an example, the deployment includes obtaining the AI entity or AI function that performs the inference.

[0727] As an example, the deployment includes loading the model on which the inference is based.

[0728] As an example, the deployment includes sending a request to load the model on which the inference is based.

[0729] As an example, the deployment is completed by an AI function deployed on the first node.

[0730] As an example, the deployment is completed by an AI deployment function deployed on the first node.

[0731] As an example, the deployment is completed by an AI entity having a deployment function.

[0732] As an example, the second producer generates and provides an AI entity or an AI function.

[0733] As an example, the second producer includes an MnS (Management Service) producer.

[0734] As an example, the second producer includes a producer for the training of an AI model.

[0735] As an example, the second producer is the target receiver of the second information block.

[0736] As an example, the second producer is different from the target receiver of the second information block.

[0737] As an example, the second producer is the serving cell of the first node.

[0738] As an example, the second producer is the maintenance base station of the serving cell of the first node.

[0739] As an example, the second producer is the core network.

[0740] As an example, the model on which the inference is based is loaded and obtained from the serving cell of the first node.

[0741] As an example, the model on which the inference is based is loaded and obtained from the maintenance base station of the serving cell of the first node.

[0742] As an example, the model on which the inference is based is loaded and obtained from the core network.

[0743] As an example, the training for obtaining the model on which the inference is based is performed by the second producer.

[0744] As an example, the second producer is different from the first producer.

[0745] As an example, the first producer generates and provides an AI entity.

[0746] As an example, the first producer generates and provides AI capabilities.

[0747] As an example, the first producer is the producer of the model on which the inference is based.

[0748] As an example, the first producer includes an AI entity producer.

[0749] As an example, the first producer includes an AI capabilities producer.

[0750] As an example, the first producer includes an AI deployment producer.

[0751] As an example, the first producer includes an AI loading producer.

[0752] As an example, the first producer includes an AI training producer.

[0753] As an example, the first producer includes an AI inference producer.

[0754] As an example, the first producer includes a producer of the deployment of an AI entity.

[0755] As an example, the first producer includes a producer of the loading of an AI entity.

[0756] As an example, the first producer includes a MnS (Management Service) producer.

[0757] Example 15

[0758] Example 15 illustrates a schematic diagram of an artificial intelligence or machine learning-based processing system according to an embodiment of the present application; as shown in the appendix Figure 22 As shown. In Example 15, the third processor sends a first data set to the fourth processor and a second data set to the fifth processor; the fourth processor generates a target first type of parameter set based on the first data set, and the fourth processor sends the generated target first type of parameter set to the fifth processor; the fifth processor processes the second data set using the target first type of parameter set to obtain a first type of output, and the fifth processor sends the first type of output to the sixth processor. In the appendix Figure 15 First type of feedback and second type of feedback are optional; the fourth processor includes an ML training function; the fifth processor includes an inference function.

[0759] As an example, the sixth processor includes an ML testing function.

[0760] As an example, the sixth processor includes performance monitoring / evaluation of the ML model.

[0761] As an example, the fifth processor sends a first type of feedback to the fourth processor, and the first type of feedback is used to trigger recalculation or update of the target first type of parameter group, that is, to trigger ML initial training or ML retraining.

[0762] As an example, the sixth processor sends a second type of feedback to the third processor, and the second type of feedback is used to generate the first data set or the second data set, or the second type of feedback is used to trigger the sending of the first data set or the sending of the second data set.

[0763] As an example, the third processor generates the first data set and the second data set based on measurements of the reference signal.

[0764] As an example, the fifth processor belongs to the first node.

[0765] As an example, the sixth processor belongs to the first node or the second node.

[0766] As an example, the fifth processor performs the inference.

[0767] As an example, the second data set includes measurements of the reference signal.

[0768] As an example, the first data set includes Training Data.

[0769] As an example, the fourth processor is used to train the ML model, and the trained model is described by the target first type of parameter group.

[0770] As an example, the fourth processor is located in the first node.

[0771] The above embodiments avoid transferring the first data set to the second node.

[0772] As an example, the fourth processor is located in the second node.

[0773] The above embodiments support joint training and optimize system performance.

[0774] As an example, the fourth processor is located in the core network.

[0775] The above embodiments support full-network joint training and further optimize system performance.

[0776] As an example, the second data set includes Inference Data.

[0777] As an example, the fifth processor is located at the first node.

[0778] As an example, the fifth processor constructs a model according to the target first type of parameter group, and then inputs the second data set into the constructed model to obtain the first type of output.

[0779] As an example, the fifth processor compares the true measurement result with the first type of output, and the obtained error is used to generate the first type of feedback.

[0780] As an example, the fifth processor generates the first type of feedback through performance monitoring.

[0781] As an example, the first type of feedback is used to reflect the performance of the trained model; when the performance of the trained model does not meet the requirements, the fourth processor recalculates the target first type of parameter group.

[0782] As an example, the sixth processor compares the true measurement result with the first type of output, and the obtained error is used to generate the second type of feedback.

[0783] As an example, the sixth processor generates the second type of feedback through performance monitoring.

[0784] As an example, the second type of feedback is used to reflect the performance of the trained model; when the performance of the trained model does not meet the requirements, the third processor sends the first data set to trigger or assist the fourth processor to recalculate the target first type of parameter group.

[0785] As an example, when the error is too large or there is no update for too long, the performance of the trained model is considered not to meet the requirements.

[0786] As an example, the target first type of parameter group includes one or more of: convolution kernel size, number of convolution layers, convolution stride, pooling kernel size, pooling kernel stride, pooling function, activation function, or number of feature maps.

[0787] As an example, the target first type of parameter group includes one or more of: convolution kernel, pooling kernel, pooling function, activation function, parameters of the pooling function, or parameters of the activation function.

[0788] As an example, the ML includes AI.

[0789] As an example, the ML includes ML and AI.

[0790] Example 16

[0791] Example 16 exemplifies a schematic diagram based on artificial intelligence or machine learning according to an embodiment of the present application; as shown in the appendix Figure 16 as shown. Appendix Figure 16 Includes a second operation, a third operation, a fourth operation, a fifth operation, and a sixth operation. In Example 16, the second operation and the third operation belong to the first phase, the fourth operation belongs to the second phase, the fifth operation belongs to the third phase, and the sixth operation belongs to the fourth phase. In the appendix Figure 16 The arrowed lines indicate the order of the process.

[0792] As an example, the second operation includes ML training, the third operation includes ML testing, the fourth operation includes ML emulation, the fifth operation includes ML entity loading, and the sixth operation includes inference.

[0793] As an example, the first phase includes a training phase, the second phase includes an emulation phase, the third phase includes a deployment phase, and the fourth phase includes an inference phase.

[0794] As an example, the first phase includes ML model training.

[0795] As an example, the first phase includes ML model training and ML testing.

[0796] As an example, the ML model training includes initial training and re-training of one or a group of ML models.

[0797] As an example, the ML model training depends on training data.

[0798] As an example, the ML model training includes ML entity validation.

[0799] As an example, the ML entity validation is used to evaluate the performance of the ML entity.

[0800] As an example, the ML entity verification relies on verification data.

[0801] As an example, if the result of the ML entity verification does not meet the expectation, the ML model will be retrained.

[0802] As an example, the ML testing includes testing the verified ML entity to estimate the performance of the trained ML model.

[0803] As an example, if the result of the ML testing meets the expectation, the ML entity proceeds to the next stage; otherwise, the ML model will be retrained.

[0804] As an example, the ML testing relies on test data.

[0805] As an example, the second stage includes ML simulation, and the ML simulation performs inference of the ML entity in a simulation environment.

[0806] As an example, the ML simulation estimates the performance of the ML entity inference in a simulation environment before using the ML entity.

[0807] As an example, the second stage is optional.

[0808] As an example, the third stage includes ML entity loading, and the ML entity loading is to obtain the trained ML entity to obtain the desired AI inference function.

[0809] As an example, the third stage is optional.

[0810] As an example, when the training function and the inference function are co-located, the third stage is no longer required.

[0811] As an example, the fourth stage includes AI inference or ML inference.

[0812] As an example, the ML includes AI.

[0813] As an example, the AI includes ML.

[0814] Example 17

[0815] Example 17 exemplifies a schematic diagram of the AI function deployment according to an embodiment of the present application; as shown in the appendix Figure 17 as follows.

[0816] In Embodiment 17, the AI training function in the RAN (Radio Access Network) domain is located in the 3GPP RAN domain-specific management function, while the AI inference function is located in the UE.

[0817] In Embodiment 17, the RAN domain-specific management function provides the management capabilities for the AI training function and the AI inference function.

[0818] Example 18

[0819] Embodiment 18 illustrates a schematic diagram of the AI function deployment according to an embodiment of the present application; as shown in the appendix Figure 18 as follows.

[0820] In Embodiment 18, the AI training function is located in the RAN domain-specific management function, and the AI inference function is located locally in the UE.

[0821] In Embodiment 18, the management capability of the AI training function is provided by the RAN domain-specific management function, and the management capability of the AI inference is provided locally by the UE.

[0822] In the appendix Figure 18 MnF refers to the Management Function.

[0823] Example 19

[0824] Embodiment 19 illustrates a schematic diagram of the AI function deployment according to an embodiment of the present application; as shown in the appendix Figure 19 as follows.

[0825] In Embodiment 19, both the AI training function and the AI inference function are located in the UE, where the UE provides the capabilities for training and inference.

[0826] In Embodiment 19, the RAN domain-specific management function provides the management capabilities for the AI training function and the AI inference function.

[0827] Example 20

[0828] Embodiment 20 illustrates a schematic diagram of the AI function deployment according to an embodiment of the present application; as shown in the appendix Figure 20 as follows.

[0829] In Embodiment 20, both the AI training function and the AI inference function are located in the UE.

[0830] In Embodiment 20, the management capabilities of both the AI training function and the AI inference function are provided locally by the UE.

[0831] In the appendix Figure 20 MnF refers to Management Function.

[0832] Example 21

[0833] Embodiment 21 illustrates a schematic diagram of the deployment of RAN (Radio Access Network) domain AI / ML functions according to an embodiment of the present application; as shown in the appendix Figure 21 shown. The gNB in Embodiment 21 can be replaced with network devices such as an eNB, or a 6G base station, etc.

[0834] AI / ML-related functions include an ML training function (also referred to as AI training, or AI / ML training), an ML testing function, an ML inference function (also referred to as AI inference, or AI / ML inference), etc. The ML training function, the ML testing function, and the ML inference function can be deployed independently or co-located. The deployment of AI / ML-related functions can be achieved through software, such as the download and / or running of executable files; it can also be achieved through a combination of software and hardware, such as accelerating a specific computing unit through hardware to improve the operation speed or save power consumption.

[0835] For the ML training function, it can be deployed in a cross-domain management system or a domain-specific management system; the domain-specific management system is used to manage the RAN domain or the CN (Core Network) domain. For example, the ML training function for MDA (Management Data Analytics) can be deployed in the MDAF (MDA function); the ML training for network data analysis can be deployed in the NWDAF (Network Data Analytics Function), that is, the ML training function is the MTLF (Model Training logical function).

[0836] For the ML inference function, it can also be deployed in a cross-domain management system or a domain-specific management system; for example, the ML inference function is an MDAF, or the ML inference function is an AnLF (Analytics logical function) located in the NWDAF.

[0837] Similarly, the ML testing function can also be deployed in a cross-domain management system or a domain-specific management system.

[0838] In Embodiment 21, the RAN domain ML training function 2102 is located in the RAN domain management function 2103; while the ML inference function is located in the base station, that is, the AI / ML inference function 2104 is located in the gNB 2105, the AI / ML inference function 2106 is located in the gNB 2107,....

[0839] Appendix Figure 21 In, the management of the ML inference functions of multiple base stations is completed by the RAN domain management function 2103, that is, data interaction is performed with the RAN domain MnS (Management Service) consumer / cross-domain management 2101 (as shown by the dotted arrow in Appendix Figure 21 ).

[0840] Optionally, the management of the ML inference function can also be completed by the base station itself, that is, each base station can independently perform data interaction with the RAN domain MnS user / cross-domain management 2101.

[0841] It should be noted that Embodiment 21 is merely a non-limiting implementation; optionally, the RAN domain ML training function may also be deployed in the base station; or optionally, some base stations deploy the ML inference function and the RAN domain ML training function, while some base stations only deploy the ML inference function.

[0842] As an embodiment, one gNB (or base station) in Embodiment 21 is the second node of the present application.

[0843] As an embodiment, the second processor in the present application includes an AL / ML inference function in Appendix Figure 21 , that is, 2104 or 2106.

[0844] Example 22

[0845] Embodiment 22 illustrates a schematic diagram of the AI / ML function deployment of a UE according to an embodiment of the present application; as shown in Appendix Figure 22 . The RAN domain ML training function 2205 in Appendix Figure 22 is optional.

[0846] The UE function 2204 is deployed in the first node of this application. The UE function 2204 includes an AI / ML inference function 2206. The AI / ML inference function 2206 uses an ML model (also known as an AI model) for inference. An ML model usually needs to be trained before being used for AI / ML inference.

[0847] As an embodiment, the first processor in this application includes an Figure 22 AL / ML inference function 2206 among them.

[0848] As an embodiment, the UE function 2204 includes a RAN domain ML training function 2205. The RAN domain ML training function 2205 runs training data through an ML model to obtain relevant losses, and adjusts the parameters of the ML model based on the calculated losses. The ML training includes at least one of ML initial training, ML re-training, and reinforcement learning.

[0849] The above embodiments can reduce the complexity of the base station, or save the radio interface resources caused by reporting training data. However, the above embodiments place relatively high requirements on the processing capabilities of the UE side.

[0850] Optionally, the UE function 2204 further includes a CN domain ML training function ( Figure 22 not included in it).

[0851] Optionally, the UE function 2204 further includes an AI / ML deployment function - Figure 22 not included in it, for loading the ML model and data.

[0852] As an embodiment, the first node indicates whether it supports the ML training function (RAN domain or CN domain) through capability reporting. The capability reporting is RRC signaling or NAS (Non-Access Stratum) signaling.

[0853] As an embodiment, the ML model and related metadata are loaded by the first node from a network device or a remote server.

[0854] Optionally, the UE function 2204 is an MnS (Management Service) Producer that provides data for management or analysis to the CN domain MnF (Management Function) 2201, and / or the RAN domain MnF 2202, and / or the cross-domain management system 2203 (as shown by the double arrow 2207).

[0855] Optionally, the UE function 2204 is an MnS Consumer that loads data from the CN domain MnF (Management Function) 2201, and / or the RAN domain MnF 2202, and / or the cross-domain management system 2203 for AI / ML-related management, such as management data requests, ML model activation, and / or ML training, etc. (as shown by the double arrow 2207).

[0856] As an example, the ML model is based on a Neural Network.

[0857] As an example, the ML model is based on a CNN (Convolutional Neural Networks).

[0858] As an example, the ML model is based on a Transformer architecture.

[0859] Example 23

[0860] Embodiment 23 exemplifies a structural block diagram of a processing device in a first node according to an embodiment of the present application; as shown in the appendix Figure 23 shown. In the appendix Figure 23 the processing device 2300 in the first node includes a first processor 2301.

[0861] As an example, the first node is a user equipment.

[0862] As an example, the user equipment is a terminal.

[0863] As an example, the first node is a terminal.

[0864] As an example, the first node is a relay node device.

[0865] As an example, the first processor 2301 includes at least one of {antenna 452, receiver 454, receive processor 456, multi-antenna receive processor 458, controller / processor 459, memory 460, data source 467} in Embodiment 4.

[0866] As an example, the first processor 2301 includes at least one of {antenna 452, transmitter 454, transmit processor 468, multi-antenna transmit processor 457, controller / processor 459, memory 460, data source 467} in Embodiment 4.

[0867] The first processor 2301 receives a first information block, where the first information block is used to configure a first set of search spaces; monitors control channel candidates in the first set of search spaces.

[0868] In Embodiment 23, is the aggregation level of the first set of search spaces and the monitoring manner of control channel candidates in the first set of search spaces related to inference?

[0869] As an example, the monitoring manner of control channel candidates in the first set of search spaces includes inference corresponding to a first identifier, and the aggregation level of the first set of search spaces also depends on the first identifier.

[0870] In an example, is the candidate value range of the aggregation level of the first set of search spaces and the monitoring manner of control channel candidates in the first set of search spaces related to inference?

[0871] As an example, when the monitoring manner of control channel candidates in the first set of search spaces includes inference, the candidate value range of the aggregation level of the first set of search spaces is a first aggregation level range; when the monitoring manner of control channel candidates in the first set of search spaces does not include inference, the candidate value range of the aggregation level of the first set of search spaces is a second aggregation level range; the first aggregation level range includes at least one aggregation level, the second aggregation level range includes at least one aggregation level, and the first aggregation level range and the second aggregation level range are different.

[0872] As an example, the monitoring manner of control channel candidates in the first set of search spaces includes inference corresponding to a first identifier, and the first aggregation level range depends on the first identifier.

[0873] As an example, the first aggregation level is an aggregation level of the first set of search spaces; the monitoring mode for the control channel candidates in the first set of search spaces includes inference only when a first condition is satisfied; the first condition includes that the first aggregation level belongs to a reference aggregation level range; the reference aggregation level range includes one or more aggregation levels.

[0874] As an example, the monitoring mode for the control channel candidates in the first set of search spaces includes inference corresponding to a first identifier, and the reference aggregation level range depends on the first identifier.

[0875] As an example, the first information block indicates whether the monitoring mode for the control channel candidates in the first set of search spaces includes inference.

[0876] As an example, it includes:

[0877] The first processor 2301 sends a second information block;

[0878] Wherein, the second information block indicates whether the monitoring mode for the control channel candidates by the first node includes inference.

[0879] Example 24

[0880] Embodiment 24 exemplifies a structural block diagram of a processing device in a second node according to an embodiment of the present application; as shown in the appendix Figure 24 shown. In the appendix Figure 24 the processing device 2400 in the second node includes a second processor 2401.

[0881] As an example, the second node is a base station.

[0882] As an example, the second node is a base station device.

[0883] As an example, the second node is a user equipment.

[0884] As an example, the second node is a relay node device.

[0885] As an example, the second node includes an OTT server (Over-The-Top server).

[0886] As an example, the second node includes an OAM (Operation Administration and Maintenance).

[0887] As an example, the second node includes a NAS device.

[0888] As an embodiment, the second node includes a core network device.

[0889] As an embodiment, the second processor 2401 includes at least one of {antenna 420, transmitter 418, transmit processor 416, multi-antenna transmit processor 471, controller / processor 475, memory 476} in Embodiment 4.

[0890] As an embodiment, the second processor 2401 includes at least one of {antenna 420, receiver 418, receive processor 470, multi-antenna receive processor 472, controller / processor 475, memory 476} in Embodiment 4.

[0891] The second processor 2401 sends a first information block, and the first information block is used to configure a first search space set.

[0892] In Embodiment 24, the target receiver of the first information block monitors the control channel candidates in the first search space set, whether the aggregation level of the first search space set and the monitoring manner of the control channel candidates in the first search space set include inference correlation.

[0893] As an embodiment, the monitoring manner of the control channel candidates in the first search space set includes inference corresponding to a first identifier, and the aggregation level of the first search space set also depends on the first identifier.

[0894] As an embodiment, the candidate value range of the aggregation level of the first search space set and whether the monitoring manner of the control channel candidates in the first search space set include inference correlation.

[0895] As an embodiment, when the monitoring manner of the control channel candidates in the first search space set includes inference, the candidate value range of the aggregation level of the first search space set is a first aggregation level range; when the monitoring manner of the control channel candidates in the first search space set does not include inference, the candidate value range of the aggregation level of the first search space set is a second aggregation level range; the first aggregation level range includes at least one aggregation level, the second aggregation level range includes at least one aggregation level, and the first aggregation level range and the second aggregation level range are different.

[0896] As an embodiment, the monitoring manner of the control channel candidates in the first search space set includes inference corresponding to a first identifier, and the first aggregation level range depends on the first identifier.

[0897] As an example, the first aggregation level is an aggregation level of the first set of search spaces; the monitoring mode of the control channel candidates in the first set of search spaces for the first aggregation level includes inference only when a first condition is satisfied; the first condition includes that the first aggregation level belongs to a reference aggregation level range; the reference aggregation level range includes one or more aggregation levels.

[0898] As an example, the monitoring mode of the control channel candidates in the first set of search spaces includes inference corresponding to a first identifier, and the reference aggregation level range depends on the first identifier.

[0899] As an example, the first information block indicates whether the monitoring mode of the control channel candidates in the first set of search spaces includes inference.

[0900] As an example, it includes:

[0901] Receiving a second information block;

[0902] wherein, the second information block indicates whether the monitoring mode of the control channel candidates by the sender of the second information block includes inference.

[0903] Those of ordinary skill in the art can understand that all or part of the steps in the above methods can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a hard disk, or an optical disc, etc. Optionally, all or part of the steps of the above embodiments can also be implemented using one or more integrated circuits. Correspondingly, each module unit in the above embodiments can be implemented in a hardware form or in the form of a software function module. This application is not limited to any specific form of the combination of software and hardware. The user equipment, terminal, and UE in this application include but are not limited to drones, communication modules on drones, remote control airplanes, aircraft, small airplanes, mobile phones, tablet computers, notebooks, vehicle-mounted communication devices, wireless sensors, network cards, Internet of Things terminals, RFID terminals, NB-IOT terminals, MTC (Machine Type Communication) terminals, eMTC (enhanced MTC) terminals, data cards, network cards, vehicle-mounted communication devices, low-cost mobile phones, low-cost tablet computers, and other wireless communication devices. The base stations or system devices in this application include but are not limited to macrocell base stations, microcell base stations, home base stations, relay base stations, gNB (NR Node B), TRP (Transmitter Receiver Point), GNSS, relay satellites, satellite base stations, aerial base stations, RSU (Road Side Unit), drones, test equipment (such as transceiver devices or signaling testers that simulate some functions of base stations), and other wireless communication devices.

[0904] The above is only a preferred embodiment of the present application and is not used to limit the protection scope of the present application. Any changes and modifications made based on the embodiments described in the specification, if they can obtain similar partial or all technical effects, should be regarded as obvious and fall within the protection scope of the present invention.

Claims

1. A method in a first node for wireless communication, characterized in that: include: receiving a first information block, wherein the first information block is used to configure a first search space set; Monitoring control channel candidates in the first search space set; Among them, the aggregation level of the first search space set and whether the alternative monitoring method of the control channel in the first search space set includes inference correlation.

2. The method in the first node according to claim 1, characterized in that: The alternative monitoring method of the control channel in the first search space set includes reasoning corresponding to the first identifier, and the aggregation level of the first search space set also depends on the first identifier.

3. The method in the first node according to claim 1 or 2, characterized in that: The candidate value range of the aggregation level of the first search space set and whether the alternative monitoring mode of the control channel in the first search space set includes inference correlation.

4. The method in the first node according to any one of claims 1 to 3, characterized in that: When the alternative monitoring method for control channels in the first search space set includes inference, the candidate value range of the aggregation level of the first search space set is a first aggregation level range; when the alternative monitoring method for control channels in the first search space set does not include inference, the candidate value range of the aggregation level of the first search space set is a second aggregation level range; the first aggregation level range includes at least one aggregation level, the second aggregation level range includes at least one aggregation level, and the first aggregation level range and the second aggregation level range are different.

5. The method in the first node according to claim 4, characterized in that: The alternative monitoring method of the control channel in the first search space set includes reasoning corresponding to the first identifier, and the first aggregation level range depends on the first identifier.

6. The method in the first node according to any one of claims 1 to 3, characterized in that: The first aggregation level is an aggregation level of the first search space set; the alternative monitoring mode for the control channel of the first aggregation level in the first search space set includes inference only when a first condition is satisfied; the first condition includes that the first aggregation level belongs to a reference aggregation level range; The reference aggregation level range includes one or more aggregation levels.

7. The method in the first node according to claim 6, characterized in that: The alternative monitoring method of the control channel in the first search space set includes an inference corresponding to a first identifier, and the reference aggregation level range depends on the first identifier.

8. The method in the first node according to any one of claims 1 to 7, characterized in that: The first information block indicates whether the monitoring method of the control channel alternatives in the first search space set includes inference.

9. The method in the first node according to any one of claims 1 to 7, characterized in that: include: sending a second information block; The second information block indicates whether the alternative monitoring method of the control channel by the first node includes reasoning.

10. A terminal, characterized in that: The terminal comprises: one or more processors and memory; The memory is coupled to the one or more processors, and the memory is used to store computer program codes, where the computer program codes include computer instructions. The one or more processors call the computer instructions to enable the terminal to execute the method according to any one of claims 1 to 9.

11. A method in a second node for wireless communication, characterized in that: include: Sending a first information block, where the first information block is used to configure a first search space set; Among them, the target receiver of the first information block monitors the control channel candidates in the first search space set, and whether the aggregation level of the first search space set and the monitoring method of the control channel candidates in the first search space set include inference correlation.

12. The method in the second node according to claim 11, characterized in that: The alternative monitoring method of the control channel in the first search space set includes reasoning corresponding to the first identifier, and the aggregation level of the first search space set also depends on the first identifier.

13. The method in the second node according to claim 11 or 12, characterized in that: The candidate value range of the aggregation level of the first search space set and whether the alternative monitoring mode of the control channel in the first search space set includes inference correlation.

14. The method in the second node according to any one of claims 11 to 13, characterized in that: When the alternative monitoring method for control channels in the first search space set includes inference, the candidate value range of the aggregation level of the first search space set is a first aggregation level range; when the alternative monitoring method for control channels in the first search space set does not include inference, the candidate value range of the aggregation level of the first search space set is a second aggregation level range; the first aggregation level range includes at least one aggregation level, the second aggregation level range includes at least one aggregation level, and the first aggregation level range and the second aggregation level range are different.

15. The method in the second node according to claim 14, characterized in that: The alternative monitoring method of the control channel in the first search space set includes reasoning corresponding to the first identifier, and the first aggregation level range depends on the first identifier.

16. The method in the second node according to any one of claims 11 to 13, characterized in that: The first aggregation level is an aggregation level of the first search space set; the alternative monitoring mode for the control channel of the first aggregation level in the first search space set includes inference only when a first condition is satisfied; the first condition includes that the first aggregation level belongs to a reference aggregation level range; The reference aggregation level range includes one or more aggregation levels.

17. The method in the second node according to claim 16, characterized in that: The alternative monitoring method of the control channel in the first search space set includes an inference corresponding to a first identifier, and the reference aggregation level range depends on the first identifier.

18. The method in the second node according to any one of claims 11 to 17, characterized in that: The first information block indicates whether the monitoring method of the control channel alternatives in the first search space set includes inference.

19. The method in the second node according to any one of claims 11 to 17, characterized in that: include: receiving a second information block; The second information block indicates whether the sender of the second information block includes inference in the alternative monitoring method of the control channel.

20. A base station, characterized in that: The base station comprises: one or more processors and memory; The memory is coupled to the one or more processors, and the memory is used to store computer program codes, where the computer program codes include computer instructions, and the one or more processors call the computer instructions to enable the base station to perform the method according to any one of claims 11 to 19.

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