Network node, UE and execution method thereof, and storage medium

Abstract

The embodiment of the invention provides a network node, UE, an execution method thereof and a storage medium, and relates to the field of artificial intelligence. The method executed by a network node comprises the following steps: acquiring measurement information of an interference cell of at least one UE and / or service volume information of the interference cell; adjusting a target block error rate (BLER) for target UE in the at least one UE according to the measurement information of the interference cell and / or the service volume information of the interference cell; and sending data to the target UE based on the adjusted target BLER. Optionally, the method performed by the network node may be performed using an artificial intelligence model.

Description

Technical Field

[0001] The present disclosure relates to the field of communications, and in particular to a network node, a user equipment (UE), a method executed by the network node, a method executed by the UE, and a computer-readable storage medium. Background Art

[0002] In order to meet the increased demand for wireless data communication services since the deployment of 4G communication systems, efforts have been made to develop improved 5G or quasi-5G communication systems. Therefore, 5G or quasi-5G communication systems are also referred to as "super 4G networks" or "post-LTE systems."

[0003] 5G communication systems are implemented in higher frequency (millimeter wave, mmWave) bands, such as the 60 GHz band, to achieve higher data rates. In order to reduce the propagation loss of radio waves and increase the transmission distance, beamforming, massive multiple-input multiple-output (MIMO), full-dimensional MIMO (FD-MIMO), array antennas, analog beamforming, and massive antenna technology are discussed in 5G communication systems.

[0004] In addition, in the 5G communication system, development of system network improvements is underway based on advanced small cells, cloud radio access networks (RAN), ultra-dense networks, device-to-device (D2D) communication, wireless backhaul, mobile networks, collaborative communications, coordinated multi-point (CoMP), receiving-end interference cancellation, etc.

[0005] In 5G systems, hybrid FSK and QAM modulation (FQAM) and sliding window superposition coding (SWSC) as advanced coded modulation (ACM), and filter bank multi-carrier (FBMC), non-orthogonal multiple access (NOMA) and sparse code multiple access (SCMA) as advanced access technologies have been developed. Summary of the invention

[0006] According to a first aspect of an embodiment of the present disclosure, a method performed by a network node is provided, including: obtaining measurement information of an interfering cell of at least one UE and / or traffic information of the interfering cell; adjusting a target block error rate (BLER) for a target UE among the at least one UE according to the measurement information of the interfering cell and / or the traffic information of the interfering cell; and sending data to the target UE based on the adjusted target BLER.

[0007] Optionally, adjusting the target block error rate BLER for the target UE among the at least one UE according to the measurement information of the interfering cell and / or the traffic volume information of the interfering cell includes: determining the target UE according to the measurement information of the interfering cell and / or the traffic volume information of the interfering cell; adjusting the target BLER for the target UE according to the measurement information and / or traffic volume information of the interfering cell of the target UE.

[0008] Optionally, the method also includes: determining the at least one UE from the candidate UEs based on the measurement information of the service cell of each UE in the candidate UEs; sending signaling to the at least one UE for configuring the at least one UE to perform interference cell measurement, wherein the measurement information of the interference cell of the at least one UE is obtained by the at least one UE performing interference cell measurement based on the signaling.

[0009] Optionally, determining the at least one UE from the candidate UEs based on the measurement information of the service cells of each UE in the candidate UEs includes: determining a UE whose measurement information of the service cells among the candidate UEs satisfies a predetermined condition within a predetermined number of time slots as the at least one UE.

[0010] Optionally, determining the target UE based on the measurement information of the interfering cell and / or the traffic information of the interfering cell includes: classifying the interfering cell according to the measurement information of the interfering cell to obtain a first classification result; classifying the time slot of the UE based on the traffic information of the interfering cell and the first classification result to obtain a second classification result; and determining the target UE based on the second classification result.

[0011] Optionally, adjusting a target BLER for a target UE in the at least one UE according to the measurement information of the interfering cell and / or the traffic information of the interfering cell includes at least one of the following:

[0012] Adjusting the target BLER of all time slots for the target UE according to the measurement information of the interference cell of the target UE and the average traffic information on all time slots;

[0013] Adjusting a target BLER of the set time slot for the target UE according to measurement information of an interference cell of the target UE and traffic information on the set time slot;

[0014] A target BLER adjustment mode for the target UE is determined according to measurement information and / or traffic information of an interfering cell of the target UE, and the target BLER for the target UE is adjusted based on the determined target BLER adjustment mode.

[0015] Optionally, determining the target BLER adjustment method for the target UE based on the measurement information and / or traffic volume information of the interfering cell of the target UE includes: determining the interference intensity of the interfering cell of the target UE based on the measurement information of the interfering cell of the target UE; and determining the target BLER adjustment method for the target UE based on the interference intensity and the traffic volume information of the interfering cell of the target UE.

[0016] Optionally, the determining the target BLER adjustment method for the target UE based on the interference intensity and the traffic volume information of the interfering cell of the target UE includes: if the target UE has an interfering cell that meets a predetermined interference intensity and the traffic volume of the interfering cell that meets the predetermined interference intensity meets a predetermined traffic volume-related condition, or if the target UE does not have an interfering cell that meets the predetermined interference intensity and the traffic volume of the interfering cell of the target UE all meets the predetermined traffic volume-related condition, then determining that the target BLER adjustment method for the target UE is to adjust the target BLER for all time slots of the target UE; otherwise, determining that the target BLER adjustment method for the target UE is to adjust the target BLER for a set time slot of the target UE.

[0017] Optionally, adjusting the target BLER for all time slots of the target UE according to the measurement information of the interfering cell of the target UE and the average traffic information on all time slots includes:

[0018] Predicting the target BLER for all time slots of the target UE using a pre-trained artificial intelligence model according to the measurement information of the interfering cell of the target UE and the average traffic information on all time slots, and adjusting the target BLER for all time slots of the target UE based on the predicted target BLER; or

[0019] According to the measurement information of the interference cell of the target UE and the average traffic volume information on all time slots, the target BLER for all time slots of the target UE is determined by using a pre-established mapping relationship between the interference fluctuation level and the target BLER, and the target BLER for all time slots of the target UE is adjusted based on the determined target BLER, wherein the interference fluctuation level is determined based on the average traffic volume information.

[0020] Optionally, adjusting a target BLER of a set time slot for the target UE according to measurement information of an interfering cell of the target UE and traffic information on a set time slot includes:

[0021] Predicting a target BLER for the set time slot for the target UE using a pre-trained artificial intelligence model based on measurement information of the interfering cell of the target UE and traffic information on the set time slot, and adjusting the target BLER for the set time slot for the target UE based on the predicted target BLER; or,

[0022] According to the measurement information of the interference cell of the target UE and the traffic volume information on the set time slot, the target BLER of the set time slot for the target UE is determined by using a pre-established mapping relationship between the interference fluctuation level and the target BLER, and the target BLER of the set time slot for the target UE is adjusted based on the determined target BLER, wherein the interference fluctuation level is determined based on the traffic volume information on the set time slot.

[0023] Optionally, the method further includes: acquiring a channel fluctuation state for the target UE; and determining whether to continue using a current target BLER according to the channel fluctuation state.

[0024] Optionally, obtaining the channel fluctuation state for the target UE includes: obtaining the channel fluctuation state according to the channel difference between different time slots of the target UE, wherein the channel difference between different time slots of the target UE includes the actual BLER difference between different time slots of the target UE, or the signal to interference and noise ratio SINR difference between different time slots of the target UE.

[0025] Optionally, determining whether to continue using the current target BLER according to the channel fluctuation state includes: marking a channel fluctuation state acquisition period as an activated state or a deactivated state according to the channel fluctuation state; if the channel fluctuation state acquisition period is marked as an activated state, continuing to use the current target BLER during the channel fluctuation state acquisition period; if the channel fluctuation state acquisition period is marked as a deactivated state, using a default fixed target BLER during the channel fluctuation state acquisition period.

[0026] Optionally, the method further includes: performing signal to interference plus noise ratio (SINR) compensation according to the adjusted target BLER and the target BLER before adjustment.

[0027] Optionally, performing SINR compensation according to the adjusted target BLER and the target BLER before adjustment includes: determining a SINR difference between the adjusted target BLER and the target BLER before adjustment according to a compensation factor, and compensating the SINR based on the determined SINR difference.

[0028] Optionally, the network node is a base station, and the method further includes: receiving indication information from a radio access network intelligent controller RIC indicating whether the base station is allowed to perform target BLER adjustment; wherein adjusting the target block error rate BLER for the target UE in the at least one UE according to the measurement information of the interfering cell and / or the traffic information of the interfering cell includes: if the indication information indicates that the base station is allowed to perform target BLER adjustment, adjusting the target BLER for the target UE according to the measurement information of the interfering cell and / or the traffic information of the interfering cell.

[0029] Optionally, the indication information is sent by the RIC based on traffic information of the interfering cell.

[0030] Optionally, the network node is a base station, and adjusting the target block error rate BLER for the target UE among the at least one UE according to the measurement information of the interfering cell and / or the traffic information of the interfering cell includes: sending the measurement information of the interfering cell of the target UE to the RIC; receiving from the RIC the target BLER for the target UE determined by the RIC according to the measurement information and traffic information of the interfering cell of the target UE; and adjusting the target BLER for the target UE based on the received target BLER.

[0031] Optionally, the network node is a RIC, wherein obtaining the measurement information of the interfering cell of at least one UE includes: receiving the measurement information of the interfering cell of the at least one UE reported by the base station; wherein adjusting the target block error rate BLER for the target UE among the at least one UE according to the measurement information of the interfering cell and / or the traffic information of the interfering cell includes: determining the target BLER for the UE according to the measurement information of the interfering cell and / or the traffic information of the interfering cell, and sending the determined target BLER to the base station; wherein sending data to the target UE based on the adjusted target BLER includes: controlling the base station to send data to the target UE based on the determined target BLER.

[0032] Optionally, the controlling the base station to send data to the target UE based on the determined target BLER includes: sending indication information to the base station based on the traffic volume information, wherein the indication information indicates whether the base station is allowed to perform target BLER adjustment.

[0033] Optionally, sending indication information to the base station based on the traffic volume information includes: determining a strong interference cell among the interfering cells, wherein the strong interference cell is a cell among the interfering cells that meets a predetermined condition; determining a load type of each time slot of the strong interference cell based on the traffic volume information of the strong interference cell; determining an interference type of each time slot of the service cell of the UE according to the load type of each time slot of the strong interference cell; determining whether to allow the base station to perform target BLER adjustment based on the interference type of each time slot of the service cell, and sending the indication information to the base station according to the determination result.

[0034] According to a second aspect of an embodiment of the present disclosure, a method performed by a user equipment UE is provided, comprising: reporting measurement information of an interfering cell of the UE to a base station; receiving data sent based on a target block error rate BLER for the UE from the base station, wherein the target BLER is adjusted according to the measurement information of the interfering cell and / or the traffic information of the interfering cell.

[0035] Optionally, the method further includes: receiving signaling from the base station for configuring the UE to perform measurement of an interfering cell, wherein the measurement information of the interfering cell is obtained by the UE performing measurement of the interfering cell based on the signaling.

[0036] Optionally, the method further includes: sending measurement information of a serving cell of the UE to the base station, wherein the measurement information of the serving cell is used by the base station to determine whether to send the signaling to the UE.

[0037] According to a third aspect of an embodiment of the present disclosure, a network node is provided, including: a transceiver; and a processor coupled to the transceiver and configured to execute the method performed by the network node as described above.

[0038] Optionally, the network node includes a base station or a radio access network intelligent controller RIC.

[0039] According to a fourth aspect of an embodiment of the present disclosure, a user equipment is provided, comprising: a transceiver; and a processor coupled to the transceiver and configured to execute the method performed by the user equipment as described above.

[0040] According to a fifth aspect of an embodiment of the present disclosure, a computer-readable storage medium storing instructions is provided, which, when executed by at least one processor, prompts the at least one processor to execute any one of the methods described above.

[0041] According to the technical solution provided by the embodiments of the present disclosure, since the network node can obtain the measurement information of the interfering cell of at least one UE and / or the traffic information of the interfering cell, adjust the target block error rate BLER for the target UE in the at least one UE according to the measurement information of the interfering cell and / or the traffic information of the interfering cell, and send data to the target UE based on the adjusted target BLER, it is possible to adaptively adjust the target BLER of the target UE, thereby improving data transmission performance and user communication experience.

[0042] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings herein are incorporated in and constitute a part of the specification, illustrate exemplary embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute improper limitations on the present disclosure.

[0044] Figure 1 Schematic diagram illustrating an example wireless network 100 according to various embodiments of the present disclosure.

[0045] Figure 2a and Figure 2b Example wireless transmit and receive paths according to the present disclosure are shown.

[0046] Figure 3a An example UE 116 according to the present disclosure is shown.

[0047] Figure 3b An example gNB 102 according to the present disclosure is shown.

[0048] Figure 4 FIG. 4 is a schematic diagram showing the problem caused by having the same target BLER for all UEs.

[0049] Figure 5 is a schematic diagram showing the variation of neighboring cell interference over time.

[0050] Figure 6 is a flow chart illustrating a method performed by a base station according to an embodiment of the present disclosure;

[0051] Figure 7 is a schematic diagram illustrating determination of candidate UEs.

[0052] Figure 8 is a schematic diagram showing the classification results of strong interfering neighboring cells.

[0053] Fig. 9 is a schematic diagram showing the classification process of strong interfering neighboring cells.

[0054] Fig.10 It is a schematic diagram showing the relationship between the traffic in the neighboring area and the interference fluctuation.

[0055] Fig.11 It is a schematic diagram showing the classification effect of time slot index neighboring area traffic.

[0056] Fig.12 It is a schematic diagram of determining the target BLER based on the output of the AI ​​model;

[0057] Fig.13 is a schematic diagram showing an example of an AI model according to an embodiment of the present disclosure.

[0058] Fig.14 It is an example of a mapping relationship table of predefined neighboring cell traffic, interference fluctuation level and target BLER according to an embodiment.

[0059] Fig.15 is a diagram showing an example of determining a target BLER according to an embodiment.

[0060] Fig.16 is a schematic diagram showing state switching of a channel fluctuation state check period according to an embodiment;

[0061] Fig.17 is a schematic diagram illustrating SINR compensation according to an embodiment.

[0062] Fig.18 is a schematic diagram illustrating determining a load type of each time slot index of a strong interfering cell according to an embodiment.

[0063] Fig.19 FIG. 1 is a schematic diagram showing a method for determining a cell-level target BLER adaptive switch according to an embodiment.

[0064] Fig. 20 is a flowchart illustrating a method performed by a UE according to an embodiment of the present disclosure.

[0065] Fig.21 FIG. 4 is a schematic diagram showing a solution for implementing target BLER adjustment according to an embodiment of the present disclosure.

[0066] Fig. 22 It is a schematic diagram showing determination of a target UE according to an embodiment of the present disclosure.

[0067] Fig.23 is a schematic diagram showing a method for determining a target BLER adjustment method according to an embodiment of the present disclosure.

[0068] Fig.24 is a schematic diagram showing the execution of channel status self-check according to an embodiment of the present disclosure.

[0069] Fig.25 FIG. 4 is a schematic diagram showing another solution for achieving target BLER adjustment according to an embodiment of the present disclosure.

[0070] Fig.26 FIG. 1 is an exemplary deployment scheme for implementing the concept of the present disclosure.

[0071] Fig. 27 is a block diagram illustrating a network node according to an embodiment of the present disclosure.

[0072] Fig.28 is a block diagram illustrating a UE according to an embodiment of the present disclosure.

[0073] Fig.29 is a schematic structural diagram showing an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0074] The following description with reference to the accompanying drawings is provided to facilitate a comprehensive understanding of the various embodiments of the present disclosure as defined by the claims and their equivalents. This description includes various specific details to facilitate understanding but should be considered as exemplary only. Therefore, one of ordinary skill in the art will recognize that various changes and modifications can be made to the various embodiments described herein without departing from the scope and spirit of the present disclosure. In addition, for the sake of clarity and conciseness, descriptions of well-known functions and structures may be omitted.

[0075] The terms and expressions used in the following specification and claims are not limited to their dictionary meanings, but are merely used by the inventor to enable a clear and consistent understanding of the present disclosure. Therefore, it should be apparent to those skilled in the art that the following description of various embodiments of the present disclosure is provided for illustration purposes only and not for the purpose of limiting the present disclosure as defined in the appended claims and their equivalents.

[0076] It should be understood that the singular forms "a", "an", and "the" may also include plural references unless the context clearly indicates otherwise. Thus, for example, reference to "a component surface" includes reference to one or more such surfaces. When we refer to an element as being "connected" or "coupled" to another element, the one element may be directly connected or coupled to the other element, or the one element and the other element may establish a connection relationship through an intermediate element. In addition, "connected" or "coupled" as used herein may include wireless connection or wireless coupling.

[0077] The term "include" or "may include" refers to the presence of the corresponding disclosed functions, operations or components that can be used in various embodiments of the present disclosure, rather than limiting the presence of one or more additional functions, operations or features. In addition, the term "include" or "have" may be interpreted as indicating certain characteristics, numbers, steps, operations, constituent elements, components or combinations thereof, but should not be interpreted as excluding the possibility of the presence of one or more other characteristics, numbers, steps, operations, constituent elements, components or combinations thereof.

[0078] The term "or" used in various embodiments of the present disclosure includes any of the listed terms and all combinations thereof. For example, "A or B" may include A, may include B, or may include both A and B. When describing multiple (two or more) items, if the relationship between the multiple items is not clearly defined, the multiple items may refer to one, multiple, or all of the multiple items. For example, the description of "parameter A includes A1, A2, A3" may be implemented as parameter A including A1 or A2 or A3, or may be implemented as parameter A including at least two of the three items A1, A2, and A3.

[0079] Unless defined differently, all terms (including technical terms or scientific terms) used in the present disclosure have the same meanings as understood by those skilled in the art described in the present disclosure. Common terms as defined in dictionaries are interpreted as having meanings consistent with the context in the relevant technical field, and should not be interpreted ideally or overly formally unless clearly defined in the present disclosure.

[0080] At least some functions of the device or electronic device provided in the embodiments of the present disclosure can be implemented by an AI model, such as at least one module among multiple modules of the device or electronic device can be implemented by an AI model. Functions associated with AI can be performed by non-volatile memory, volatile memory and processor.

[0081] The processor may include one or more processors. In this case, the one or more processors may be general-purpose processors, such as a central processing unit (CPU), an application processor (AP), etc., or pure graphics processing units, such as a graphics processing unit (GPU), a visual processing unit (VPU), and / or an AI-specific processor, such as a neural processing unit (NPU).

[0082] The one or more processors control the processing of input data according to predefined operating rules or artificial intelligence (AI) models stored in non-volatile memory and volatile memory. The predefined operating rules or artificial intelligence models are provided by training or learning.

[0083] Here, providing by learning means obtaining a predefined operating rule or an AI model with desired characteristics by applying a learning algorithm to a plurality of learning data. The learning can be performed in the device or electronic device itself in which the AI ​​according to the embodiment is executed, and / or can be implemented by a separate server / system.

[0084] The AI ​​model may include multiple neural network layers. Each layer has multiple weight values, and each layer performs neural network calculations by calculating between the input data of the layer (such as the calculation results of the previous layer and / or the input data of the AI ​​model) and the multiple weight values ​​of the current layer. Examples of neural networks include, but are not limited to, convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), restricted Boltzmann machines (RBMs), deep belief networks (DBNs), bidirectional recurrent deep neural networks (BRDNNs), generative adversarial networks (GANs), and deep Q networks.

[0085] A learning algorithm is a method of using a plurality of learning data to train a predetermined target device (e.g., a robot) to enable, allow, or control the target device to make a determination or prediction. Examples of the learning algorithm include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

[0086] According to the present disclosure, at least one step in the method performed by a network node or a user device, such as determining a target block error rate (BLER), can be implemented using an artificial intelligence model. The processor of the electronic device can perform a preprocessing operation on the data to convert it into a form suitable for use as an input to the artificial intelligence model. The artificial intelligence model can be obtained through training. Here, "obtained through training" means obtaining a predefined operating rule or artificial intelligence model configured to perform a desired feature (or purpose) by training a basic artificial intelligence model with multiple training data through a training algorithm.

[0087] The following describes several optional embodiments to illustrate the technical solutions of the embodiments of the present disclosure and the technical effects produced by the technical solutions of the present disclosure. It should be noted that the following embodiments can refer to, draw on or combine with each other, and the same terms, similar features and similar implementation steps in different embodiments will not be described repeatedly.

[0088] Figure 1 An example wireless network 100 is shown in accordance with various embodiments of the present disclosure. Figure 1 The embodiment of the wireless network 100 shown in FIG. 1 is for illustration only. Other embodiments of the wireless network 100 can be used without departing from the scope of the present disclosure.

[0089] Wireless network 100 includes gNodeB (gNB) 101, gNB 102, and gNB 103. gNB 101 communicates with gNB 102 and gNB 103. gNB 101 also communicates with at least one Internet Protocol (IP) network 130, such as the Internet, a private IP network, or other data network.

[0090] Depending on the network type, other well-known terms such as "base station" or "access point" can be used instead of "gNodeB" or "gNB". For convenience, the terms "gNodeB" and "gNB" are used in this patent document to refer to network infrastructure components that provide wireless access to remote terminals. Also, depending on the network type, other well-known terms such as "mobile station", "subscriber station", "remote terminal", "wireless terminal" or "user device" can be used instead of "user equipment" or "UE". For convenience, the terms "user equipment" and "UE" are used in this patent document to refer to a remote wireless device that wirelessly accesses a gNB, whether the UE is a mobile device (such as a mobile phone or smartphone) or a commonly thought of fixed device (such as a desktop computer or vending machine).

[0091] gNB 102 provides wireless broadband access to network 130 for a first plurality of user equipment (UE) within coverage area 120 of gNB 102. The first plurality of UEs include: UE 111, which may be located in a small business (SB); UE 112, which may be located in an enterprise (E); UE 113, which may be located in a WiFi hotspot (HS); UE 114, which may be located in a first residence (R); UE 115, which may be located in a second residence (R); UE 116, which may be a mobile device (M), such as a cellular phone, a wireless laptop, a wireless PDA, etc. gNB 103 provides wireless broadband access to network 130 for a second plurality of UEs within coverage area 125 of gNB 103. The second plurality of UEs include UE 115 and UE 116. In some embodiments, one or more of gNBs 101-103 may be capable of communicating with each other and with UEs 111-116 using 5G, long term evolution (LTE), LTE-A, WiMAX, or other advanced wireless communication technologies.

[0092] The dashed lines illustrate the approximate extents of coverage areas 120 and 125, which are shown as approximately circular for purposes of illustration and explanation only. It should be clearly understood that coverage areas associated with gNBs, such as coverage areas 120 and 125, can have other shapes, including irregular shapes, depending on the configuration of the gNB and variations in the radio environment associated with natural and man-made obstacles.

[0093] As described in more detail below, one or more of gNB 101, gNB 102, and gNB 103 include a 2D antenna array as described in embodiments of the present disclosure. In some embodiments, one or more of gNB 101, gNB 102, and gNB 103 support codebook design and structure for a system with a 2D antenna array.

[0094] although Figure 1 One example of a wireless network 100 is shown, but Figure 1 Various changes may be made. For example, wireless network 100 can include any number of gNBs and any number of UEs in any suitable arrangement. Also, gNB 101 can communicate directly with any number of UEs and provide those UEs with wireless broadband access to network 130. Similarly, each gNB 102-103 can communicate directly with network 130 and provide UEs with direct wireless broadband access to network 130. In addition, gNBs 101, 102, and / or 103 can provide access to other or additional external networks, such as an external telephone network or other type of data network.

[0095] Figure 2a and Figure 2b Example wireless transmit and receive paths according to the present disclosure are shown. In the following description, transmit path 200 can be described as being implemented in a gNB (such as gNB 102) and receive path 250 can be described as being implemented in a UE (such as UE 116). However, it should be understood that receive path 250 can be implemented in a gNB and transmit path 200 can be implemented in a UE. In some embodiments, receive path 250 is configured to support codebook design and structure for a system with a 2D antenna array as described in embodiments of the present disclosure.

[0096] The transmit path 200 includes a channel coding and modulation block 205, a serial to parallel (S to P) block 210, an N-point inverse fast Fourier transform (IFFT) block 215, a parallel to serial (P to S) block 220, an add cyclic prefix block 225, and an upconverter (UC) 230. The receive path 250 includes a downconverter (DC) 255, a remove cyclic prefix block 260, a serial to parallel (S to P) block 265, an N-point fast Fourier transform (FFT) block 270, a parallel to serial (P to S) block 275, and a channel decoding and demodulation block 280.

[0097] In the transmit path 200, the channel coding and modulation block 205 receives a set of information bits, applies coding (such as low-density parity check (LDPC) coding), and modulates the input bits (such as using quadrature phase shift keying (QPSK) or quadrature amplitude modulation (QAM)) to generate a sequence of frequency-domain modulation symbols. The serial-to-parallel (S-to-P) block 210 converts (such as demultiplexes) the serial modulation symbols into parallel data to generate N parallel symbol streams, where N is the number of IFFT / FFT points used in the gNB 102 and the UE 116. The N-point IFFT block 215 performs an IFFT operation on the N parallel symbol streams to generate a time-domain output signal. The parallel-to-serial block 220 converts (such as multiplexes) the parallel time-domain output symbols from the N-point IFFT block 215 to generate a serial time-domain signal. The add cyclic prefix block 225 inserts a cyclic prefix into the time-domain signal. The up-converter 230 modulates (such as up-converts) the output of the add cyclic prefix block 225 to an RF frequency for transmission via a wireless channel. The signal can also be filtered at baseband before conversion to RF frequency.

[0098] The RF signal transmitted from gNB 102 arrives at UE 116 after passing through a wireless channel, and an operation opposite to that at gNB 102 is performed at UE 116. Downconverter 255 downconverts the received signal to a baseband frequency, and remove cyclic prefix block 260 removes the cyclic prefix to generate a serial time-domain baseband signal. Serial-to-parallel block 265 converts the time-domain baseband signal into a parallel time-domain signal. N-point FFT block 270 performs an FFT algorithm to generate N parallel frequency-domain signals. Parallel-to-serial block 275 converts the parallel frequency-domain signals into a sequence of modulated data symbols. Channel decoding and demodulation block 280 demodulates and decodes the modulated symbols to recover the original input data stream.

[0099] Each of gNBs 101-103 may implement a transmit path 200 similar to that for transmitting in the downlink to UEs 111-116 and may implement a receive path 250 similar to that for receiving in the uplink from UEs 111-116. Similarly, each of UEs 111-116 may implement a transmit path 200 for transmitting in the uplink to gNBs 101-103 and may implement a receive path 250 for receiving in the downlink from gNBs 101-103.

[0100] Figure 2a and Figure 2b Each of the components in can be implemented using hardware only, or a combination of hardware and software / firmware. As a specific example, Figure 2a and Figure 2bAt least some of the components in can be implemented with software, while other components can be implemented by configurable hardware or a mixture of software and configurable hardware. For example, FFT block 270 and IFFT block 215 can be implemented as a configurable software algorithm, in which the value of the number of points N can be modified according to the implementation.

[0101] In addition, although described as using FFT and IFFT, this is illustrative only and should not be construed as limiting the scope of the present disclosure. Other types of transforms can be used, such as discrete Fourier transform (DFT) and inverse discrete Fourier transform (IDFT) functions. It should be understood that for DFT and IDFT functions, the value of variable N can be any integer (such as 1, 2, 3, 4, etc.), while for FFT and IFFT functions, the value of variable N can be any integer as a power of 2 (such as 1, 2, 4, 8, 16, etc.).

[0102] although Figure 2a and Figure 2b An example of a wireless transmit and receive path is shown, but the Figure 2a and Figure 2b Make various changes. For example, Figure 2a and Figure 2b The various components in can be combined, further subdivided or omitted, and additional components can be added according to specific needs. Figure 2a and Figure 2b It is intended to illustrate examples of the types of transmit and receive paths that can be used in a wireless network. Any other suitable architecture can be used to support wireless communications in a wireless network.

[0103] Figure 3a An example UE 116 according to the present disclosure is shown. Figure 3a The embodiment of UE 116 shown in FIG. 1 is for illustration only, and Figure 1 UEs 111-115 can have the same or similar configurations. However, UEs have a variety of configurations, and Figure 3a The scope of the present disclosure is not limited to any particular implementation of the UE.

[0104] UE 116 includes antenna 305, radio frequency (RF) transceiver 310, transmit (TX) processing circuit 315, microphone 320, and receive (RX) processing circuit 325. UE 116 also includes speaker 330, processor / controller 340, input / output (I / O) interface 345, input device(s) 350, display 355, and memory 360. Memory 360 includes operating system (OS) 361 and one or more applications 362.

[0105] The RF transceiver 310 receives incoming RF signals from the antenna 305 transmitted by the gNB of the wireless network 100. The RF transceiver 310 downconverts the incoming RF signals to generate an intermediate frequency (IF) or baseband signal. The IF or baseband signal is sent to the RX processing circuit 325, where the RX processing circuit 325 generates a processed baseband signal by filtering, decoding and / or digitizing the baseband or IF signal. The RX processing circuit 325 sends the processed baseband signal to the speaker 330 (such as for voice data) or to the processor / controller 340 (such as for web browsing data) for further processing.

[0106] The TX processing circuit 315 receives analog or digital voice data from the microphone 320, or receives other outgoing baseband data (such as network data, email, or interactive video game data) from the processor / controller 340. The TX processing circuit 315 encodes, multiplexes, and / or digitizes the outgoing baseband data to generate a processed baseband or IF signal. The RF transceiver 310 receives the outgoing processed baseband or IF signal from the TX processing circuit 315 and up-converts the baseband or IF signal to an RF signal that is transmitted via the antenna 305.

[0107] The processor / controller 340 can include one or more processors or other processing devices and execute an OS 361 stored in the memory 360 to control the overall operation of the UE 116. For example, the processor / controller 340 can control the reception of forward channel signals and the transmission of reverse channel signals through the RF transceiver 310, the RX processing circuit 325, and the TX processing circuit 315 according to well-known principles. In some embodiments, the processor / controller 340 includes at least one microprocessor or microcontroller.

[0108] The processor / controller 340 is also capable of executing other processes and programs resident in the memory 360, such as operations for channel quality measurement and reporting for a system with a 2D antenna array as described in the embodiments of the present disclosure. The processor / controller 340 is capable of moving data into or out of the memory 360 as needed for the executed process. In some embodiments, the processor / controller 340 is configured to execute applications 362 based on the OS 361 or in response to signals received from the gNB or operator. The processor / controller 340 is also coupled to the I / O interface 345, where the I / O interface 345 provides the UE 116 with the ability to connect to other devices such as laptops and handheld computers. The I / O interface 345 is the communication path between these accessories and the processor / controller 340.

[0109] Processor / controller 340 is also coupled to input device(s) 350 and display 355. An operator of UE 116 can input data into UE 116 using input device(s) 350. Display 355 can be a liquid crystal display or other display capable of presenting text and / or at least limited graphics (such as from a website). Memory 360 is coupled to processor / controller 340. A portion of memory 360 can include random access memory (RAM), while another portion of memory 360 can include flash memory or other read-only memory (ROM).

[0110] although Figure 3a An example of UE 116 is shown, but it is possible to Figure 3a Make various changes. For example, Figure 3a The various components in can be combined, further subdivided, or omitted, and additional components can be added according to specific needs. As a specific example, processor / controller 340 can be divided into multiple processors, such as one or more central processing units (CPUs) and one or more graphics processing units (GPUs). Moreover, although Figure 3a The UE 116 is shown configured as a mobile phone or smart phone, but the UE can be configured to operate as other types of mobile or stationary devices.

[0111] Figure 3b An example gNB 102 according to the present disclosure is shown. Figure 3b The embodiment of the gNB 102 shown in FIG. is for illustration only, and Figure 1 Other gNBs can have the same or similar configurations. However, gNBs have a variety of configurations, and Figure 3b The scope of the present disclosure is not limited to any particular implementation of the gNB. It should be noted that gNB 101 and gNB 103 can include the same or similar structure as gNB 102.

[0112] like Figure 3b As shown in FIG. 1 , gNB 102 includes multiple antennas 370a-370n, multiple RF transceivers 372a-372n, transmit (TX) processing circuitry 374, and receive (RX) processing circuitry 376. In some embodiments, one or more of the multiple antennas 370a-370n include a 2D antenna array. gNB 102 also includes a controller / processor 378, a memory 380, and a backhaul or network interface 382.

[0113] The RF transceivers 372a-372n receive incoming RF signals from the antennas 370a-370n, such as signals transmitted by a UE or other gNB. The RF transceivers 372a-372n downconvert the incoming RF signals to generate IF or baseband signals. The IF or baseband signals are sent to the RX processing circuitry 376, which generates processed baseband signals by filtering, decoding, and / or digitizing the baseband or IF signals. The RX processing circuitry 376 sends the processed baseband signals to the controller / processor 378 for further processing.

[0114] The TX processing circuit 374 receives analog or digital data (such as voice data, network data, email, or interactive video game data) from the controller / processor 378. The TX processing circuit 374 encodes, multiplexes, and / or digitizes the outgoing baseband data to generate a processed baseband or IF signal. The RF transceivers 372a-372n receive the outgoing processed baseband or IF signals from the TX processing circuit 374 and up-convert the baseband or IF signals to RF signals that are transmitted via the antennas 370a-370n.

[0115] The controller / processor 378 can include one or more processors or other processing devices that control the overall operation of the gNB 102. For example, the controller / processor 378 can control the reception of forward channel signals and the transmission of reverse channel signals through the RF transceivers 372a-372n, the RX processing circuitry 376, and the TX processing circuitry 374 in accordance with well-known principles. The controller / processor 378 can also support additional functionality, such as more advanced wireless communication functionality. For example, the controller / processor 378 can perform a Blind Interference Sensing (BIS) process, such as performed through a Blind Interference Sensing (BIS) algorithm, and decode a received signal with an interference signal subtracted. The controller / processor 378 can support any of a variety of other functions in the gNB 102. In some embodiments, the controller / processor 378 includes at least one microprocessor or microcontroller.

[0116] The controller / processor 378 can also execute programs and other processes resident in the memory 380, such as a basic OS. The controller / processor 378 can also support channel quality measurement and reporting for systems with 2D antenna arrays as described in embodiments of the present disclosure. In some embodiments, the controller / processor 378 supports communications between entities such as web RTC. The controller / processor 378 can move data into or out of the memory 380 as needed by the executing process.

[0117] The controller / processor 378 is also coupled to a backhaul or network interface 382. The backhaul or network interface 382 allows the gNB 102 to communicate with other devices or systems via a backhaul connection or via a network. The backhaul or network interface 382 can support communication via any suitable (multiple) wired or wireless connections. For example, when the gNB 102 is implemented as part of a cellular communication system (such as a cellular communication system supporting 5G or new radio access technology or NR, LTE or LTE-A), the backhaul or network interface 382 can allow the gNB 102 to communicate with other gNBs via a wired or wireless backhaul connection. When the gNB 102 is implemented as an access point, the backhaul or network interface 382 can allow the gNB 102 to communicate with a larger network (such as the Internet) via a wired or wireless local area network or via a wired or wireless connection. The backhaul or network interface 382 includes any suitable structure that supports communication via a wired or wireless connection, such as an Ethernet or RF transceiver.

[0118] The memory 380 is coupled to the controller / processor 378. A portion of the memory 380 can include RAM, while another portion of the memory 380 can include flash memory or other ROM. In some embodiments, a plurality of instructions such as a BIS algorithm are stored in the memory. The plurality of instructions are configured to cause the controller / processor 378 to perform the BIS process and decode the received signal after subtracting at least one interference signal determined by the BIS algorithm.

[0119] As described in more detail below, the transmit and receive paths of gNB 102 (implemented using RF transceivers 372a-372n, TX processing circuitry 374, and / or RX processing circuitry 376) support aggregated communications with FDD cells and TDD cells.

[0120] although Figure 3b An example of a gNB 102 is shown, but the Figure 3b For example, gNB 102 can include any number of Figure 3a . As a specific example, an access point can include a number of backhaul or network interfaces 382, ​​and the controller / processor 378 can support routing functions to route data between different network addresses. As another specific example, while shown as including a single instance of TX processing circuitry 374 and a single instance of RX processing circuitry 376, the gNB 102 can include multiple instances of each (such as one for each RF transceiver).

[0121] In the data transmission scheme of the existing communication system, data is always sent based on a fixed target block error rate (BLER). For example, the modulation and coding scheme of the user is always determined based on the fixed target BLER, and the data is sent according to the adjusted coding scheme determined in this way. However, the fixed target BLER is always the same for all UEs in a cell or the same fixed target BLER is used at any time, which cannot match the different degrees of neighboring interference to different UEs or UEs at different times, resulting in a decrease in transmission performance (for example, a decrease in data transmission rate and low throughput) and a poor user communication experience. For example, in the centralized scheduling scheme of small packets in the 5G communication system, the transmission time slots of small packets can be reduced through centralized scheduling of small packets, thereby improving the system throughput and network energy consumption. However, centralized scheduling of small packets will result in large differences in neighboring interference in different time slots, which will cause different UEs to experience different interference fluctuations, and the interference fluctuations will change over time. In this case, if a fixed target BLER is always used, the transmission performance will be reduced and the user communication experience will be poor.

[0122] Figure 4 is a schematic diagram showing the problem of having the same target BLER for all UEs. Figure 4 As shown in the figure, since the cell loads of neighboring cells of different UEs are different, the neighboring cell interferences on different UEs are different. If the same target BLER is used for UEs with different neighboring cell interferences, the link adaptation scheme will not be able to adapt to the interference conditions of different UEs, thereby reducing the throughput of the UE. For example, for edge users (i.e., edge UEs) in the serving cell, a fixed target BLER will cause the determined adjustment coding scheme to not match the actual channel state (for example, the adjustment coding level is too low or too high). However, compared with the ideal adjustment coding scheme, using an adjustment coding scheme that is too high or too low will result in a loss of throughput.

[0123] Figure 5 is a schematic diagram showing the change of neighboring cell interference over time. Figure 5 As shown in FIG. 1 , the neighboring cell interference to the same UE varies with time. However, if the same fixed target BLER is used at any time, the target BLER that remains unchanged cannot adapt to the interference that varies with time, thereby reducing the user's communication experience.

[0124] In view of this, the present disclosure proposes a technical solution that can adaptively adjust the target BLER for the UE, which can effectively improve data transmission performance and user communication experience.

[0125] Hereinafter, embodiments according to the present disclosure will be described in detail.

[0126] Figure 6A flow chart of a method executed by a network node according to an embodiment of the present disclosure is shown.

[0127] Reference Figure 6 , in step S610, obtaining the measurement information of the interfering cell of at least one UE and / or the traffic volume information of the interfering cell. According to the embodiment, the network node may include a base station, a Radio Access Network Intelligent Controller (RIC) or other network nodes. For example, if the network node is a base station, obtaining the measurement information of the interfering cell of at least one UE in step S610 may include the base station receiving the measurement information of the interfering cell of at least one UE reported by at least one UE, and obtaining the traffic volume information of the interfering cell in step S610 may include the base station obtaining the traffic volume information of the interfering cell through the base station interface interaction or the RIC and the base station interface interaction, but the base station is not limited to the way of obtaining the traffic volume information of the interfering cell, which may also be received by the base station from other devices. If the network node is a RIC, obtaining the measurement information of the interfering cell of at least one UE in step S610 may include the RIC receiving the measurement information of the interfering cell reported by the base station from at least one UE, and obtaining the traffic volume information of the interfering cell in step S610 may include the RIC obtaining the traffic volume information of the interfering cell reported by the interfering cell. According to an embodiment, one RIC can manage multiple base stations, and each base station can obtain the traffic volume information of the UE's interfering cell through the interface interaction between it and the RIC, for example, the traffic volume information of the UE's neighboring cell. In addition, as an example, the above-mentioned traffic volume information can be traffic volume information obtained based on statistics of historical traffic volume, or traffic volume information obtained based on predictions of an artificial intelligence model. For example, the traffic volume information can include physical resource block (PRB) load information.

[0128] Here, the at least one UE may be a candidate UE, that is, a UE whose target BLER may need to be adjusted. According to the embodiment, all UEs may be defaulted as candidate UEs, or at least one UE may be determined from all UEs in a certain manner as the final candidate UE. If at least one UE is determined from all UEs as the final candidate UE, then Figure 6The method shown may also include: determining the at least one UE from the candidate UEs according to the measurement information of the serving cell of each UE in the candidate UEs; sending a signaling for configuring the at least one UE to perform interference cell measurement to the at least one UE, wherein the measurement information of the interference cell of the at least one UE is obtained by the at least one UE performing interference cell measurement based on the signaling. For example, if the network node is a base station, it can be determined whether the UE is a candidate UE based on the measurement information of the serving cell reported by the UE. If the UE is determined to be a candidate UE, the base station can configure it to perform interference cell measurement so that the candidate UE can report the measurement information of its interference cell. If the network node is a RIC, the RIC can determine whether the UE is a candidate UE based on the measurement information of the serving cell reported by the UE obtained from the base station, and if it is a candidate UE, control the base station to send a signaling for configuring it to perform interference cell measurement to the candidate UE.

[0129] In the present disclosure, the interfering cell of the UE may be any cell causing interference to the UE. For example, the interfering cell of the UE may be a neighboring cell of the UE, or a cell that is not a neighboring cell of the UE but causes interference to the UE.

[0130] For example, the measurement information of the interfering cell and the measurement information of the serving cell may be at least one of SS-RSRP (Synchronization Signal Reference Signal Received Power), RSRP (Reference Signal Received Power), CQI (Channel Quality Indicator), RI (Rank Indication) and PMI (Precoding Matrix Indicator), but not limited thereto. As an example, the base station may determine a candidate user based on the SS-RSRP of the serving cell reported by the UE, and send a signaling to the candidate user to configure it to perform interference cell measurement.

[0131] According to an embodiment, the determining of the at least one UE from the candidate UEs according to the measurement information of the serving cell of each UE in the candidate UEs may include: determining a UE whose measurement information of the serving cell in the candidate UEs satisfies a predetermined condition within a predetermined number of time slots as the at least one UE. For example, a prohibition timer may be set to ensure that only the candidate UEs that satisfy the predetermined condition within a predetermined number of time slots are determined as the final candidate UEs. Figure 7 is a schematic diagram showing the determination of candidate UEs. Figure 7As shown, candidate UEs can be determined in the following manner.

[0132] ① Delete UEs that meet the following condition (high SS-RSRP) from the candidate UE list (the initial candidate UE list can include all UEs), and set a prohibition timer ProhTimer = prohTimer (prohTimer is the starting value of the prohibition timer, for example, equal to a predetermined quantity) for these UEs to avoid ping-pong handovers. For each time slot passed, decrement the value of the prohibition timer by 1. Thus, it can be achieved that a handover is only performed when the predetermined number of time slots all meet the UE RSRP <ThrRSRPIn, before a handover occurs.

[0133] · UE RSRP >ThrRSRPOut

[0134] where UE RSRP represents the SS-RSRP value reported by the UE.

[0135] ② Determine the final candidate UEs as those that simultaneously meet the following conditions (low SS-RSRP and the timer value is 0).

[0136] · UE RSRP <ThrRSRPIn

[0137] · ProhTimer = 0

[0138] The parameter thresholds involved in the above process, including the values of ThrRSRPOut, ThrRSRPIn, and prohTimer, are all dynamically configurable.

[0139] After the final candidate UEs are determined, for example, the base station can send an EventTriggerConfig message to the newly added candidate UEs in the CONNECTED state through RRCReconfiguration, enabling the candidate UEs to perform additional A3 measurements to obtain measurement information of their interfering cells.

[0140] Next, in step S620, according to the measurement information of the interfering cell and / or the traffic volume information of the interfering cell, adjust the target block error rate BLER for the target UE among the at least one UE.

[0141] According to the embodiment, step S620 may include: determining the target UE according to the measurement information of the interfering cell and / or the traffic volume information of the interfering cell; adjusting the target BLER for the target UE according to the measurement information and / or traffic volume information of the interfering cell of the target UE.

[0142] Next, first introduce how to determine the target UE.

[0143] According to the embodiment, based on the measurement information of the interfering cell and the traffic information of the interfering cell, a UE with large interference fluctuation in the neighboring cell may be selected as the target UE. In this document, the neighboring cell may also be referred to as a "neighboring cell".

[0144] For example, determining the target UE based on the measurement information of the interfering cell and / or the traffic volume information of the interfering cell may include: classifying the interfering cell according to the measurement information of the interfering cell to obtain a first classification result; classifying the time slot of the UE based on the traffic volume information of the interfering cell and the first classification result to obtain a second classification result; and determining the target UE based on the second classification result.

[0145] In the case where the interfering cell is a neighboring cell, for example, the target UE may be determined in the following manner:

[0146] (1) Classifying strong interfering neighboring cells according to the measurement information reported by the at least one UE

[0147] For example, the measurement information of the neighboring cell reported by the at least one UE may be the measurement information of the strong interfering cell reported by the UE through A3 measurement, for example, the measurement value of the signal strength of the strong interfering cell. In this case, the base station may divide the interfering cells in the strong interfering neighbor cell list into extremely strong interfering cells and medium-strong interfering cells according to the measurement value of the signal strength, such as Figure 8 shown.

[0148] Fig. 9 is a schematic diagram showing the classification process of strong interfering neighboring cells. Fig. 9 As shown, initially, the strong interference neighboring cell of the UE can be classified as a medium-strong interference cell. To avoid ping-pong switching, a dual threshold classification mechanism can be set:

[0149] ① If the jth neighboring cell of the UE is a medium-strong interference cell and meets the following conditions (high signal strength), then adjust this neighboring cell to an extremely strong interference cell.

[0150] RSRP(j)>ThrRSRPHigh

[0151] RSRP(j) represents the RSRP value of the jth neighboring cell reported by the UE.

[0152] ② If the jth neighboring cell of the UE is an extremely strong interfering cell and meets the following conditions (low signal strength), then adjust this neighboring cell to a medium-strong interfering cell.

[0153] RSRP(j) <ThrRSRPLow

[0154] The parameter thresholds involved in the above process, including the values ​​of ThrRSRPHigh and ThrRSRPLow, are dynamically configurable.

[0155] (2) Classifying the time slots of the UE (also known as classifying the time slot indexes of the UE)

[0156] For example, the base station may classify all time slot indexes of the UE into medium traffic time slots and non-medium traffic time slots according to the traffic information of the neighboring cells and the classification result of the strong interference neighboring cells.

[0157] For example, the neighboring cell traffic information can be obtained through the interface interaction between base stations or the interface interaction between RIC and base stations, and can be the traffic volume on each time slot index obtained by averaging the traffic volume on the same time slot index within a period W (such as 1 hour). The calculation formula for the time slot index is:

[0158] slot index = slot%K

[0159] Here, slot represents the number of time slots, and K may be the time slot length (for example, 10) of the small packet centralized scheduling period.

[0160] The traffic information of neighboring cells can indicate the interference fluctuation degree of neighboring cells to users. Fig.10 As shown, medium business volume indicates large fluctuations in neighboring cell interference, because the business volume of the neighboring cell at the time slot number corresponding to the time slot index fluctuates, which makes the average value in the medium range; high business volume and low business volume indicate small fluctuations in neighboring cell interference, because the average value will be higher only when the business volume of the neighboring cell at the time slot number corresponding to the time slot index is mostly large. Similarly, the average value will be lower only when the business volume of the neighboring cell at the time slot number corresponding to the time slot index is mostly small.

[0161] For example, the classification mechanism for each time slot index is as follows:

[0162] ① For a certain time slot index of a certain UE, if the UE has an extremely strong interfering cell, and the traffic of at least one extremely strong interfering cell at this time slot index meets the medium traffic condition, then this time slot index of the UE is classified as a medium traffic time slot.

[0163] ② For a certain time slot index of a certain UE, if the UE does not have an extremely strong interfering cell, and the traffic volume of medium-to-strong interfering cells exceeding a predetermined proportion (for example, 50%) at this time slot index meets the medium traffic volume condition, then this time slot index of the UE is classified as a medium traffic volume time slot.

[0164] ③ If a time slot index of a certain UE does not meet the above two conditions, the time slot index of the UE is classified as a non-medium traffic time slot.

[0165] Medium traffic conditions are:

[0166] ThrLoadHigh>Traffic Volume(j)>ThrLoadLow

[0167] Among them, the traffic volume (j) represents the traffic volume of the jth neighboring cell of the UE at a certain time slot index, ThrLoadHigh and ThrLoadLow are parameter thresholds, and the threshold values ​​can be dynamically configured.

[0168] For example, the classification effect can be Fig.11 As shown, Fig.11 (a) shows the classification effect of the time slot index of the UE with extremely strong interfering cells. Fig.11 (b) shows the classification effect of the time slot index of the UE without extremely strong interfering cells. Fig.11 UL is the uplink time slot and is not considered in the downlink algorithm, so it is not classified.

[0169] (3) Determine the target UE based on the time slot classification results

[0170] If the time slot index of the UE meets the following conditions (there are more time slots with medium traffic), it means that the neighboring cell interference fluctuations to the UE are large, and the UE can be determined as a target UE, otherwise the UE is a common user.

[0171] NumMidSlot>ThrMidRatio*K

[0172] Among them, NumMidSlot represents the number of time slot indexes with medium traffic volume in the UE's neighboring cells, and ThrMidRatio is a parameter threshold, the value of which can be dynamically configured and determined according to channel conditions, quality of service (QoS) requirements, channel load, etc.

[0173] The above describes an example of determining the target UE based on both the measurement information of the interfering cell and the traffic information of the interfering cell, however, the method of determining the target UE is not limited to the above example. For example, the target UE may also be determined based only on the measurement information of the interfering cell, such as classifying the interfering cell according to the measurement information of the interfering cell of the UE, and determining whether the UE is the target UE based on the classification result, for example, if the UE has an extremely strong interfering cell, the UE is determined to be the target UE. Alternatively, the target UE may also be determined based only on the traffic information of the interfering cell of the UE, such as classifying the time slots of the UE according to the traffic information of the interfering cell of the UE, and determining whether the UE is the target UE based on the classification result, for example, if the number of medium traffic time slots of the UE exceeds a threshold, the UE is determined to be the target UE.

[0174] After the target UE is determined, the target BLER for the target UE may be adjusted according to the measurement information and / or traffic volume information of the interfering cell of the target UE.

[0175] According to an embodiment, step S620 may include at least one of the following: adjusting the target BLER of all time slots for the target UE according to the measurement information of the interference cell of the target UE and the average traffic volume information on all time slots; adjusting the target BLER of the set time slot for the target UE according to the measurement information of the interference cell of the target UE and the traffic volume information on the set time slot; determining the target BLER adjustment method for the target UE according to the measurement information and / or traffic volume information of the interference cell of the target UE, and adjusting the target BLER for the target UE based on the determined target BLER adjustment method.

[0176] According to an embodiment, a fixed target BLER adjustment method may be always used for different interference scenarios, for example, a UE-level target BLER adjustment method (also referred to as a user-level target BLER adjustment method) may be always used, that is, the target BLER is adjusted by UE, wherein for the user-level target BLER adjustment method, the target BLER of all time slots of the UE is the same. Alternatively, a time slot-level target BLER adjustment method may be always used, that is, the target BLER is adjusted by time slot, for example, the target BLER of a set time slot of the UE may be adjusted, for example, the target BLER of each time slot of the UE may be adjusted. In this article, the user-level target BLER adjustment method may be to adjust the target BLER of all time slots for the target UE, and the time slot-level target BLER adjustment method may be to adjust the target BLER of a set time slot for the target UE. Optionally, the target BLER adjustment method may also be dynamically selected. According to an embodiment, different target BLER adjustment methods may be used for different interference scenarios:

[0177] - If the traffic volume of neighboring cells of all time slot indexes corresponding to the target UE is not much different, the target UE can use the user-level target BLER adjustment method. This can make the actual BLER converge to the target BLER better on the one hand, and can also reduce the amount of calculation on the other hand. For example, if the target BLER is determined based on an artificial intelligence model, the user-level target BLER adjustment method can reduce the running time of the artificial intelligence model;

[0178] - Otherwise, the target user adopts the time slot level target BLER adjustment method to obtain the maximum user throughput;

[0179] According to an embodiment, the target BLER adjustment method for the target UE can be determined according to the measurement information and / or traffic volume information of the interfering cell of the target UE. For example, the interference strength of the interfering cell of the target UE can be determined according to the measurement information of the interfering cell of the target UE, and the target BLER adjustment method for the target UE is determined based on the interference strength and the traffic volume information of the interfering cell of the target UE, for example, it is determined whether the user-level target BLER adjustment method or the time slot-level target BLER adjustment method is applicable to the target UE.

[0180] According to an embodiment, determining the target BLER adjustment method for the target UE based on the interference intensity and the traffic volume information of the interfering cell of the target UE may include: if the target UE has an interfering cell that meets the predetermined interference intensity and the traffic volume of the interfering cell that meets the predetermined interference intensity meets the predetermined traffic volume-related conditions, or if the target UE does not have an interfering cell that meets the predetermined interference intensity and the traffic volume of the interfering cell of the target UE all meets the predetermined traffic volume-related conditions, then determining that the target BLER adjustment method for the target UE is to adjust the target BLER for all time slots of the target UE; otherwise, determining that the target BLER adjustment method for the target UE is to adjust the target BLER for the set time slot of the target UE.

[0181] For example, when the interfering cell is a neighboring cell and the interfering cell is the strong interfering cell mentioned above, the target BLER adjustment method of the target UE can be determined according to the traffic volume and interference intensity of the interfering cell. The specific process is as follows:

[0182] - For a neighboring cell of the target UE, if all its time slot indexes are medium traffic time slots and the standard deviation of the traffic between all time slot indexes is less than a certain threshold, then this neighboring cell is considered to meet the traffic standard deviation judgment condition;

[0183] - If the target UE has a very strong interfering cell, then when all the very strong interfering cells meet the traffic volume standard deviation judgment condition, the target UE is determined to be applicable to the user-level target BLER adjustment method; if the target user does not have a very strong interfering cell, then if all the strong interfering cells meet the traffic volume standard deviation judgment condition, the target UE is determined to be applicable to the user-level target BLER adjustment method. Otherwise, this target UE is determined to be applicable to the time slot level target BLER adjustment method. The traffic volume standard deviation judgment condition is as follows:

[0184]

[0185] LoadingSTD(j) <ThrLoadingSTD

[0186] Among them, Loading(k,j) is the statistical traffic volume of time slot index k of the j-th neighboring cell of the target UE, MeanLoading(j) is the average traffic volume of all time slot indexes of the j-th neighboring cell, LoadingSTD(j) is the standard deviation of the traffic volume of the j-th neighboring cell, and ThrLoadingSTD is the threshold value of the traffic volume standard deviation.

[0187] After the target BLER adjustment mode of the target UE is determined, the target BLER for the target UE may be adjusted based on the determined target BLER adjustment mode.

[0188] According to an embodiment, if the target BLER adjustment method is a user-level target BLER adjustment method, the target BLER for all time slots of the target UE is adjusted according to the measurement information of the interfering cell of the target UE and the average traffic information on all time slots; if the target BLER adjustment method is a time slot-level target BLER adjustment method, the target BLER for the set time slot of the target UE is adjusted according to the measurement information of the interfering cell of the target UE and the traffic information on the set time slot.

[0189] Optionally, as described above, instead of dynamically selecting the target BLER adjustment mode for the target UE, a user-level target BLER adjustment mode may be fixedly adopted, that is, the target BLER for all time slots of the target UE is always adjusted according to the measurement information of the interference cell of the target UE and the average traffic volume information on all time slots. Alternatively, a time slot-level target BLER adjustment mode may be fixedly adopted, that is, the target BLER for the set time slot of the target UE is always adjusted according to the measurement information of the interference cell of the target UE and the traffic volume information on the set time slot.

[0190] According to the embodiment, the greater the interference fluctuation of the interfering cell, the greater the target BLER for the target UE. When the traffic volume of the interfering cell is too low or too high, the interference fluctuation of the interfering cell is relatively small, and the target UE applies a smaller target BLER; conversely, when the traffic volume of the interfering cell is at a medium level, the interference fluctuation of the interfering cell is relatively large, and the target UE applies a larger target BLER to obtain a higher performance gain.

[0191] According to the embodiment, no matter it is a user-level target BLER adjustment method or a timeslot-level target BLER adjustment method, the target BLER may be determined by using an artificial intelligence (AI) method or a non-AI method.

[0192] For example, when the target BLER adjustment method used by the target UE is a user-level target BLER adjustment method, adjusting the target BLER for all time slots of the target UE according to the measurement information of the interfering cell of the target UE and the average traffic volume information on all time slots may include: predicting the target BLER for all time slots of the target UE using a pre-trained artificial intelligence model according to the measurement information of the interfering cell of the target UE and the average traffic volume information on all time slots, and adjusting the target BLER for all time slots of the target UE based on the predicted target BLER (i.e., AI method); or, determining the target BLER for all time slots of the target UE using a pre-established mapping relationship between the interference fluctuation level and the target BLER according to the measurement information of the interfering cell of the target UE and the average traffic volume information on all time slots, and adjusting the target BLER for all time slots of the target UE based on the determined target BLER (i.e., non-AI method), wherein the interference fluctuation level is determined based on the average traffic volume information.

[0193] For example, in the case where the target BLER adjustment method used by the target UE is a time slot level target BLER adjustment method, adjusting the target BLER of the set time slot for the target UE according to the measurement information of the interference cell of the target UE and the traffic information on the set time slot may include: predicting the target BLER of the set time slot for the target UE using a pre-trained artificial intelligence model according to the measurement information of the interference cell of the target UE and the traffic information on the set time slot, and adjusting the target BLER of the set time slot for the target UE based on the predicted target BLER (i.e., AI method); or, determining the target BLER of the set time slot for the target UE according to the measurement information of the interference cell of the target UE and the traffic information on the set time slot using a pre-established mapping relationship between the interference fluctuation level and the target BLER, and adjusting the target BLER of the set time slot for the target UE based on the determined target BLER (i.e., non-AI method), wherein the interference fluctuation level is determined based on the traffic information on the set time slot.

[0194] In this disclosure, "time slot" may be used interchangeably with "time slot index".

[0195] In the following, examples of determining the target BLER based on an AI method and determining the target BLER based on a non-AI method are described respectively. In the following example, an example is given in which the interfering cell is a neighboring cell.

[0196] (I) Example of AI-based target BLER determination scheme

[0197] Since the interference strength and interference fluctuation of different neighboring cells are not the same, the comprehensive impact of the interference of all neighboring cells on the UE after superposition can be evaluated through AI to obtain the optimal target BLER for the UE.

[0198] The training data of the AI ​​model can be generated by simulating various interference scenarios through a system-level simulation platform. As an example, considering the requirements of the 5G system and the complexity of AI implementation, a predetermined number (for example, three, but not limited to) of interference fluctuation levels and their corresponding target BLERs can be pre-designed. Then, Fig.12 As shown, the probability of each of the three target BLERs can be predicted by the AI ​​model. If the maximum probability exceeds a certain threshold (such as 70%), the target BLER corresponding to the maximum probability is selected as the predicted target BLER. Otherwise, the default fixed target BLER is used.

[0199] Fig.13 is a schematic diagram showing an example of an AI model according to an embodiment of the present disclosure.

[0200] like Fig.13 As shown in the figure, the AI ​​model can be composed of three network models, namely SVR (Support Vector Regression), CNN (Convolutional Neural Network) and FCN (Fully Convolutional Network). The input of the AI ​​model may include the measurement information of the serving cell (such as RSRP, CQI, RI, PMI), as well as the measurement information of the neighboring cells (such as RSRP) and traffic volume. It should be noted that although in Fig.13 In the example of , the input of the AI ​​model includes the measurement information of the serving cell; however, the AI ​​model can also be used to predict the target BLER without the measurement information of the serving cell.

[0201] like Fig.13 As shown, the SVR network model can use the historical data of RSRP, CQI, RI, PMI of the serving cell and RSRP of the neighboring cells as input to predict the RSRP, CQI, RI, PMI of the serving cell and RSRP of the neighboring cells at the next time.

[0202] The CNN network model can be used to extract the interference change characteristics of neighboring cells, including the mean, variance, extreme value, gradient and fluctuation coefficient of interference, etc. The input of the CNN network model may include the RSRP, CQI, RI, PM of the service cell predicted and output by the SVR model, the RSRP of the neighboring cell, and the traffic volume of the neighboring cell. Among them, the RSRP of the neighboring cell is used to reflect the interference intensity of the neighboring cell, the traffic volume of the neighboring cell can be used to reflect the fluctuation size of the interference of the neighboring cell, and the RSRP, CQI, RI, and PMI of the service cell can be used to help extract the interference fluctuation characteristics. For example, if the RSRP, CQI, RI, and PMI of the service cell change rapidly, it can indicate that the target user is moving and the interference of its neighboring cell is also changing rapidly.

[0203] The FCN network model can use the neighboring cell interference change characteristics output by the CNN network as input to determine the final target BLER probability. Based on the simulation results of various interference scenarios on the system-level simulation platform, and taking into account the requirements of the 5G system and the complexity of AI implementation, three values ​​of 10%, 20%, and 30% can be selected as typical values ​​of the target BLER. However, if the AI ​​network can support higher complexity calculations in the future, it is also possible to consider supporting more typical target BLER values.

[0204] Although the target BLER can be determined by AI for both the user-level target BLER adjustment method and the timeslot-level target BLER adjustment method, the specific determination methods may be different.

[0205] For example, if the target UE is applicable to the user-level target BLER adjustment method, the average traffic volume of all time slots for each neighboring cell can be calculated first, and then the average traffic volume of each neighboring cell is used as the input of the AI ​​model to predict a user-level target BLER through the AI ​​model. The user-level target BLER is the same for all time slots of the target UE. Fig.13 As shown, if the user-level target BLER adjustment method is used, the traffic volume of the neighboring cell n may be the average traffic volume in all time slots of the neighboring cells of the target UE, and the predicted target BLER is the target BLER for all time slots of the target UE.

[0206] For example, if the target UE applies the time slot level target BLER adjustment method, the traffic volume of the set time slot of each neighboring cell can be used as the input of the AI ​​model, and the time slot level target BLER for the set time slot can be predicted by the AI ​​model. Here, the set time slot can be each time slot in all time slots, or a certain time slot in all time slots. Fig.13As shown, if the time slot level target BLER adjustment method is used, the traffic volume of the neighboring cell n may be the average traffic volume in each time slot of the neighboring cells of the target UE, and the predicted target BLER is the target BLER for each time slot of the target UE.

[0207] (II) Example of a target BLER determination scheme based on a non-AI approach:

[0208] According to the embodiment, the target BLER of the target user may be adjusted according to the fluctuation size and interference intensity of the neighboring cell interference, based on the interference impact degree of the extremely strong interference cell, or the interference impact probability of the medium-strong interference cell.

[0209] In order to obtain the mapping relationship between the neighboring cell traffic and the target BLER, various interference scenarios can be simulated through a system-level simulation platform, and a mapping relationship table of the neighboring cell traffic, interference fluctuation level and target BLER can be predefined.

[0210] Taking into account the requirements of the 5G system, for example, three interference fluctuation levels and their corresponding target BLERs may be designed, but this is not limited to this, and more interference fluctuation levels may also be designed.

[0211] For example, a specific solution for determining the target BLER based on a non-AI approach may include:

[0212] First, a mapping table of neighboring cell traffic, interference fluctuation level and target BLER is predefined. As an example, the mapping table can be as follows: Fig.14 As shown, in Fig.14 In the mapping relationship table, traffic threshold 1 to traffic threshold 4 represent the traffic threshold values ​​of neighboring cells, basic BlER is the adaptive basic target BLER, δ L2 is the BLER offset of level 2 relative to level 1, δ L3 is the BLER offset of level 3 relative to level 1.

[0213] Then, the neighboring cell interference fluctuation level and the target BLER of the target user are determined.

[0214] Although the target BLER can be determined by a non-AI method for both the user-level target BLER adjustment method and the timeslot-level target BLER adjustment method, the specific determination methods are different.

[0215] For example, for a target UE that applies the time slot level target BLER adjustment method, it can first be determined whether there is an extremely strong interfering cell based on the measurement information of the neighboring cells reported by the target UE. If it is determined that there is an extremely strong interfering cell based on the measurement information of the neighboring cells reported by the target UE, the following process is performed for each time slot index, such as Fig.15 As shown in (a):

[0216] ① For each extremely strong interference cell, according to its traffic volume, each interference fluctuation level of each extremely strong interference cell is determined through a mapping table between traffic volume and interference fluctuation level;

[0217] ② Select the maximum interference fluctuation level among all extremely strong interference cells as the interference fluctuation level of the time slot index;

[0218] ③ According to the interference fluctuation level of the time slot index, the target BLER of the time slot index is determined through the interference fluctuation level and target BLER mapping table;

[0219] For a target UE that applies the time slot level target BLER adjustment method, if it is determined that there is no extremely strong interfering cell based on the measurement information of the neighboring cells reported by the target UE, the following process is performed for each time slot index, such as Fig.15 As shown in (b):

[0220] ① For each medium-strong interference cell, according to its traffic volume, the interference fluctuation level of each medium-strong interference cell is determined through a mapping table of traffic volume and interference fluctuation level;

[0221] ② Select the interference fluctuation level with the largest number of medium and strong interference cells as the interference fluctuation level of this time slot index. If the number of medium and strong interference cells of two interference fluctuation levels is equal, select the one with the larger interference fluctuation level as the interference fluctuation level of this time slot index.

[0222] ③ According to the interference fluctuation level of the time slot index, the target BLER of the time slot index is determined through the interference fluctuation level and target BLER mapping table;

[0223] For the target UE that applies the user-level target BLER adjustment method, the traffic volume on all time slot indexes of each neighboring cell can be averaged to obtain the average traffic volume, and then the above process is performed. All time slot indexes of the target UE that applies the user-level target BLER adjustment method will use the same target BLER.

[0224] When the actual traffic volume of the interfering cell is inconsistent with the traffic volume information of the interfering cell that has been obtained, the target BLER determined based on the traffic volume information of the interfering cell will not match the actual situation, which may cause transmission performance loss. For example, when the actual traffic volume of the neighboring cell is inconsistent with the obtained statistical traffic volume of the neighboring cell, the target BLER determined based on the statistical traffic volume of the neighboring cell will not match the actual situation, which may cause performance loss. In order to cope with this situation, the present disclosure further proposes that the network node perform a channel state self-check, check the channel fluctuation state, and then determine whether to apply the target BLER determined for the target UE above.

[0225] According to an embodiment, optionally, Figure 6 The method shown may also include: acquiring a channel fluctuation state for the target UE; and determining whether to continue using the current target BLER according to the channel fluctuation state.

[0226] According to an embodiment, the acquiring of the channel fluctuation state for the target UE may include: acquiring the channel fluctuation state according to the channel difference between different time slots of the target UE, wherein the channel difference between different time slots of the target UE includes the actual BLER difference between different time slots of the target UE, or the signal to interference and noise ratio SINR difference between different time slots of the target UE. For example, the channel fluctuation state may be periodically acquired according to the channel difference between different time slots of the target UE.

[0227] For example, if the target BLER adjustment method for the target UE is a user-level target BLER adjustment method, the channel fluctuation state can be periodically obtained according to the actual BLER difference between different time slots of the target UE; if the target BLER adjustment method for the target UE is a time slot-level target BLER adjustment method, the channel fluctuation state can be periodically obtained according to the signal-to-interference-and-noise ratio (SINR) difference between different time slots of the target UE.

[0228] According to an embodiment, the above-mentioned determining whether to continue to use the current target BLER according to the channel fluctuation state may include: marking the channel fluctuation state acquisition period as an activated state or a deactivated state according to the channel fluctuation state; if the channel fluctuation state acquisition period is marked as an activated state, continuing to use the current target BLER during the channel fluctuation state acquisition period; if the channel fluctuation state acquisition period is marked as a deactivated state, using the default fixed target BLER during the channel fluctuation state acquisition period.

[0229] For example, you can perform a channel fluctuation self-check in the following way:

[0230] (1) According to the channel differences between all time slot indices, the channel fluctuation status is checked regularly to reflect the interference fluctuation in a timely manner:

[0231] ① For the time slot level target BLER adjustment method, if the signal to noise ratio (Signal to

[0232] If the Interference plus Noise Ratio (SINR) difference is less than the threshold, the cycle N is marked as deactivated, otherwise, it is marked as activated;

[0233] ② For the user-level target BLER adjustment method, if it is detected that the actual BLER difference is greater than the threshold, the cycle N is marked as deactivated, otherwise, it is marked as activated;

[0234] (2) For the active state, the period continues to use the current target BLER, for example, the target BLER determined above; for the deactivated state period, the default fixed target BLER is used.

[0235] The period N is the period of channel self-checking, which is smaller than the period W.

[0236] Specifically, for the time slot level target BLER adjustment method, the traffic volume of neighboring cells on different time slot indices should be different, so the SINR on different time slot indices should also be different. Therefore, the SINR difference between time slot indices can be used as the basis for channel status self-check. For example, the self-check can be performed in the following way:

[0237] 1) Because the larger the target BLER, the higher the corresponding SINR, the SINRs on time slot indices with different target BLERs cannot be directly compared, so the SINR compensation factor ε(k) is used to align the SINRs on each time slot index.

[0238] SINR = actual SINR - ε(k),

[0239] ε(k)=((TargetBLER(k)-TargetBLER(kmin))*BaseCompFactor, wherein TargetBLER(k) is the target BLER determined by the time slot index k, TargetBLER(kmin) is the minimum target BLER among all time slot indices, and BaseCompFactor is the basic compensation factor.

[0240] 2) Calculate the SINR difference between the maximum SINR and the minimum SINR in all time slot indices;

[0241] 3) If the SINR difference is greater than the threshold, then mark cycle N as "activated". If not, mark it as "deactivated". To prevent ping-pong switching of states, Fig.16 As shown in (a), for example, a double threshold value may be set, where the entry threshold 1 is greater than the exit threshold 1. If the SINR difference is greater than the entry threshold 1, the cycle N is marked as an "activated" state, and if the SINR difference is less than the exit threshold 1, the cycle N is marked as a "deactivated" state.

[0242] For the user-level target BLER adjustment method, the traffic volume of neighboring cells in all time slot indices should not be very different, so the actual BLER of different time slot indices should also be similar. Therefore, the actual BLER difference between time slot indices can be used as the basis for self-checking. For example, the self-check can be performed in the following way:

[0243] ① Calculate the difference between the maximum BLER and the minimum BLER in all time slot indices

[0244] ② If the BLER difference is less than the threshold, cycle N is marked as "activated", otherwise it is marked as "deactivated". In order to prevent ping-pong switching of states, Fig.16 As shown in (b), a double threshold value may be set, wherein the entry threshold value 2 is less than the exit threshold value 2. If the actual BLER difference is less than the entry threshold value 2, the cycle N is marked as an "activated" state, and if the actual BLER difference is greater than the exit threshold value 2, the cycle N is marked as a "deactivated" state.

[0245] According to an embodiment, optionally, Figure 6 The method shown may also include: performing SINR compensation according to the adjusted target BLER and the target BLER before adjustment. By performing SINR compensation, the actual BLER can be quickly converged to the adjusted target BLER. For example, Fig.17 As shown, when the target BLER is adjusted, for example, from 10% to 30%, if the SINR is compensated, the actual BLER can quickly converge to the adjusted target BLER.

[0246] Different target BLERs correspond to different ideal SINRs, and the higher the target BLER, the greater the ideal SINR. When the target BLER changes, the base station can perform SINR compensation based on the difference between the adjusted target BLER and the target BLER before adjustment. According to an embodiment, the SINR difference between the adjusted target BLER and the target BLER before adjustment can be determined based on the compensation factor, and the SINR can be compensated based on the determined SINR difference. For example, the base station can calculate the SINR difference between the new target BLER and the current target BLER based on the basic compensation factor (BaseCompFactor), and compensate the actual SINR based on the calculated SINR difference.

[0247] According to the embodiment, when the target BLER of the UE is adjusted, SINR compensation is performed. For example, when the target UE / ordinary UE state changes, when the target BLER adjustment mode of the target UE changes, when the "activation" / "deactivation" state of the channel fluctuation state check cycle of the target UE changes, the target BLER of the UE may be adjusted, and SINR compensation is performed when the target BLER is adjusted. Depending on the target BLER adjustment mode adapted by the target UE, SINR compensation may be performed by performing user-level SINR compensation or time slot-level SINR compensation.

[0248] For example, if slot-level SINR compensation is performed, then for a given slot index k, its SINR can be calculated by the following formula:

[0249] SINR(k)=SINR(k)+((CurrentTargetBLER(k)-PreviousTargetBLER(k))*BaseCompFactor

[0250] Wherein, CurrentTargetBLER(k) is the newly adjusted target BLER of the current cycle time slot index k, and PreviousTargetBLER(k) is the target BLER of the previous cycle time slot index k.

[0251] For example, if user-level SINR compensation is performed, for the target UE, its SINR can be calculated by the following formula:

[0252] SINR=SINR+((CurrentTargetBLER-PreviousTargetBLER))*BaseCompFactor

[0253] Among them, CurrentTargetBLER is the newly adjusted target BLER of the current cycle, and PreviousTargetBLER is the target BLER of the previous cycle.

[0254] According to an embodiment, the network node may be a base station. In this case, optionally, in order to further improve the algorithm performance and reduce the calculation complexity of the base station, the RIC may control whether to allow the base station to perform the target BLER adjustment, and the RIC may also perform the determination of the target BLER and then send the determination result to the base station. In this case, optionally, although not shown, Figure 6The method shown may also include: receiving indication information from the radio access network intelligent controller RIC indicating whether the base station is allowed to perform target BLER adjustment. Accordingly, step S620 may include: if the indication information indicates that the base station is allowed to perform target BLER adjustment, adjusting the target BLER for the target UE according to the measurement information of the interfering cell and / or the traffic information of the interfering cell.

[0255] For example, the indication information may be sent by the RIC based on the traffic information of the interfering cell. For example, each cell may report its own traffic to the RIC respectively. The RIC knows the neighboring cells of the UE's serving cell, and thus may obtain the traffic information of the neighboring cells of the UE, and may determine whether to allow the base station to perform the target BLER adjustment based on the traffic information of the neighboring cells, and then send the corresponding indication information. In the case where the network node is a base station, optionally, Figure 6 The method shown may also include: sending the traffic volume information of the base station to the RIC. For example, the RIC may determine whether the base station is allowed to perform the target BLER adjustment based on the traffic volume information reported by the base station and the neighboring cells, and then send corresponding indication information. However, the manner of determining whether the base station is allowed to perform the target BLER adjustment is not limited thereto. For example, the RIC may also consider other factors to determine whether the base station is allowed to perform the target BLER adjustment. The manner in which the RIC controls whether the base station is allowed to perform the target BLER adjustment will be described below when describing the method performed by the RIC, and will not be described here for the time being.

[0256] Optionally, the determination of the target BLER may be performed by the RIC, and the determination result is then sent to the base station. Figure 6 The step S620 shown may include: sending the measurement information of the interfering cell of the target UE to the RIC; receiving from the RIC the target BLER for the target UE determined by the RIC based on the measurement information and traffic volume information of the interfering cell of the target UE; and adjusting the target BLER for the target UE based on the received target BLER. Optionally, the channel fluctuation state for the UE may be further acquired, and whether to continue to use the new target BLER may be determined based on the channel fluctuation state. In addition, the manner in which the RIC determines the target BLER for the target UE based on the measurement information and traffic volume information of the interfering cell of the target UE may be the same as the manner in which the base station determines the new target BLER for the target UE based on the measurement information and traffic volume information of the interfering cell of the target UE as mentioned above, and therefore will not be described in detail.

[0257] Return to reference Figure 6After adjusting the target BLER for the target UE according to the measurement information of the interfering cell and / or the traffic information of the interfering cell, in step S630, data may be sent to the target UE based on the adjusted target BLER. For example, data may be sent to the target UE based on the channel state information reported by the target UE and the adjusted target BLER. Figure 6 In the method, since the network node can obtain the measurement information of the interfering cell of at least one UE, adjust the target BLER for the target UE in the at least one UE according to the measurement information of the interfering cell and / or the traffic information of the interfering cell, and send data to the target UE based on the adjusted target BLER, it is possible to adaptively adjust the target BLER of the target UE, thereby improving data transmission performance and user communication experience.

[0258] As described above, according to the embodiment of the present disclosure, in order to reduce the burden on the base station, the RIC may perform the determination of the target BLER and then send the determination result to the base station. Figure 6 In the described method, if the network node is a RIC, obtaining the measurement information of the interfering cell of at least one UE in step S610 may include receiving the measurement information of the interfering cell of the at least one UE reported by the base station, and obtaining the traffic volume information of the interfering cell may include the RIC receiving the traffic volume information of the interfering cell reported by the interfering cell. For example, the RIC may receive the measurement information of the neighboring cell of the target UE determined above reported by the base station.

[0259] In addition, if the network node is a RIC, step S620 may include: determining a target BLER for the UE according to the measurement information of the interfering cell and / or the traffic information of the interfering cell, and sending the determined target BLER to the base station. The manner in which the RIC determines the target BLER for the UE according to the measurement information of the interfering cell and / or the traffic information of the interfering cell may be the same as the manner in which the base station determines the target BLER for the target UE according to the measurement information of the interfering cell and / or the traffic information of the interfering cell mentioned above. For example, the target BLER for the target UE may also be determined based on an AI manner, or the target BLER for the target UE may also be determined based on a non-AI manner, which will not be described in detail here.

[0260] If the network node is a RIC, step S630 may include controlling the base station to send data to the target UE based on the determined target BLER. Optionally, as mentioned above, the RIC may control whether the base station performs target BLER adjustment. Therefore, the controlling the base station to send data to the target UE based on the determined target BLER may include: sending indication information to the base station based on the traffic volume information, wherein the indication information indicates whether the base station is allowed to perform target BLER adjustment, that is, whether the target BLER adaptation function is allowed to be performed. That is, the indication information indicates whether the cell-level target BLER adaptation is on (ON) or off (OFF). For example, the RIC may predict the traffic volume at a future moment based on the traffic volume information reported by each cell, for example, using an AI module (such as SVR) to predict the traffic volume at the next moment, and making a cell-level target BLER adaptation switch judgment based on the predicted traffic volume.

[0261] According to an embodiment, sending indication information to a base station based on the traffic volume information may include: determining a strong interference cell among the interfering cells, wherein the strong interference cell is a cell among the interfering cells that meets a predetermined condition; determining a load type of each time slot of the strong interference cell based on the traffic volume information of the strong interference cell; determining an interference type of each time slot of the serving cell according to the load type of each time slot of the strong interference cell; determining whether to allow the base station to perform a target BLER adjustment based on the interference type of each time slot of the serving cell of the UE, and sending the indication information to the base station according to the determination result.

[0262] For example, taking the interfering cell as a neighboring cell, the cell-level target BLER adaptive switch judgment can be performed based on the following process, and then the corresponding indication information is sent according to the judgment result:

[0263] (1) Select strong interference neighboring area

[0264] For example, RIC can select a strong interfering neighboring cell according to the handover information of the serving cell. Here, the handover information can be the cumulative number of times a UE switches from a neighboring cell to a serving cell. If cell A is a strong interfering cell of cell B, then cell B is also a strong interfering cell of cell A.

[0265] (2) Determine the load type of each time slot index for each strong interference cell

[0266] For example, RIC classifies each time slot index into one of the following three load types according to the predicted traffic volume of each strong interference cell, such as Fig.18 As shown:

[0267] - If the traffic volume corresponding to the time slot index k of the strong interference cell > Thr_Loading_H, the load type of the time slot index k of the strong interference cell is the high traffic volume type

[0268] - If the traffic volume corresponding to the time slot index k of the strong interference cell < Thr_Loading_L, the load type of the time slot index k of the strong interference cell is the low traffic volume type

[0269] - Otherwise, the load type of the time slot index k of the strong interference cell is the medium traffic volume type, indicating that the interference fluctuation of the strong interference cell is obvious.

[0270] Among them, Thr_Loading_H and Thr_Loading_L are the high threshold and low threshold of the neighbor cell traffic volume respectively, and Thr_Loading_H is greater than Thr_Loading_L.

[0271] (3) Determine the cell-level target BLER adaptive switch

[0272] For example, the interference type of the time slot index of the serving cell can be determined first according to the load types of each strong interference cell. For a time slot index, if one of the strong interference neighbor cells is of the medium traffic volume type, then mark this time slot index as "medium", otherwise mark it as "non-medium".

[0273] Secondly, the RIC can determine the target BLER adaptive switch according to the interference type of each time slot index of the determined serving cell. For example, if NumMidLoadingType > ThrTypeNum, set the cell-level BLER adaptive switch to "ON", otherwise set it to "OFF",

[0274] Among them, NumMidLoadingType is the number of time slot indexes of the serving cell marked as "medium", and ThrTypeNum is the threshold.

[0275] For example, as Fig.19 shown, the number of time slot indexes of the medium traffic volume type in Cell 1 exceeds the threshold, then its cell-level BLER adaptive switch can be set to "ON", while for Cell 0 and Cell 2, since the number of time slot indexes of the medium traffic volume type does not exceed this threshold, their cell-level BLER adaptive switches can be set to "OFF".

[0276] According to the embodiment, only when the cell-level target BLER adaptive switch is "ON", the base station performs the target BLER adjustment.

[0277] Since the RIC determines the target BLER for the UE based on the measurement information of the interfering cell of the UE and / or the traffic information of the interfering cell reported by the base station, and sends the determined target BLER to the base station, the burden and complexity of the base station can be reduced, and the efficiency of the target BLER adjustment can be improved, thereby further improving the data transmission performance and the user communication experience. In addition, according to the embodiment of the present disclosure, the base station involves communication with the UE when performing the target BLER adjustment. Therefore, the present disclosure also provides a method performed by the UE. Fig. 20 is a flowchart illustrating a method performed by a UE according to an embodiment of the present disclosure.

[0278] Reference Fig. 20 In step S2010, the measurement information of the interfering cell of the UE is reported to the base station. In step S2020, data sent based on a target block error rate (BLER) for the UE is received from the base station, wherein the target BLER is adjusted according to the measurement information of the interfering cell and / or the traffic information of the interfering cell. Figure 6 The description of adjusting the target BLER according to the measurement information of the interfering cell and / or the traffic information of the interfering cell has been described, which will not be repeated here.

[0279] As mentioned above, if the UE is determined as a candidate UE, the base station may send a signaling for configuring the UE to perform interference cell measurement to configure the UE to perform interference cell measurement. Fig. 20 The method shown may also include: receiving signaling from a base station for configuring the UE to perform measurement of the interfering cell, wherein the measurement information of the interfering cell is obtained by the UE performing interfering cell measurement based on the signaling.

[0280] In addition, as mentioned above, the base station can determine the candidate UE based on the measurement information of the serving cell reported by the UE, and then send a signaling to the candidate UE for configuring it to perform the measurement of the interfering cell. Fig. 20 The method shown may also include: sending measurement information of a serving cell of the UE to a base station, wherein the measurement information of the serving cell is used by the base station to determine whether to send the signaling to the UE.

[0281] according to Fig. 20 In the method shown, since the UE reports the measurement information of the interfering cell of the UE to the base station and receives data sent based on the target BLER for the UE from the base station, and the target BLER is adjusted according to the measurement information of the interfering cell and / or the traffic information of the interfering cell, the data transmission performance and the user's communication experience can be improved.

[0282] Fig.21FIG. 4 is a schematic diagram showing a solution for implementing target BLER adjustment according to an embodiment of the present disclosure.

[0283] like Fig.21 As shown, the base station may first perform step 1-1 to select a candidate UE. For example, a UE that is not in the center of the cell may be selected as a candidate UE. The method for selecting a candidate UE has been described above and will not be repeated here. If a candidate UE is selected in step 1-1, the base station may send a signaling to the candidate UE for configuring the candidate UE to perform interference cell measurement, for example, sending A3 measurement configuration information. The UE performs interference cell measurement based on the received signaling, for example, performs RSRP measurement of the interference cell, and reports the measurement information. In addition, the UE may perform CSI (Channel State Information) measurement and report CSI information.

[0284] In step 1-2, the base station performs target UE determination. Fig. 22 As shown, a candidate UE with strong interference strength, large interference fluctuation, and need to adopt an adaptive target BLER can be selected as the target UE. For example, a non-central UE is selected as a candidate UE based on the expected signal strength and the prohibition timer, and the candidate UE measures the interference strength of the neighboring cell and reports the measurement result to the base station. The base station selects a UE with strong interference strength and large interference fluctuation from the candidate UE as the target UE based on the neighboring cell interference strength and the neighboring cell traffic information (for example, PRB load information).

[0285] If the target UE is determined, the base station can report the received measurement information of the interfering cell (such as CSI, RSRP) to the RIC, and in step 2, the RIC performs target BLER adaptation, that is, determines the target BLER for the target UE, for example, determines the target BLER for the target UE based on an AI method. The specific determination method has been described above and will not be repeated here. In addition, as described above, the interference intensity of the interfering cell of the target UE can be determined based on the measurement information of the interfering cell of the target UE, and the target BLER adjustment method for the target UE can be determined based on the interference intensity and the traffic information of the interfering cell of the target UE (for example, PRB load information), for example, whether to perform UE-level target BLER adjustment or time slot-level target BLER adjustment. As mentioned above, whether to perform UE-level target BLER adjustment or time slot-level target BLER adjustment can be determined based on the judgment condition of the standard deviation (Standard Deviation, STD) of the neighboring cell traffic on all time slot indices. As Fig.23As shown in FIG. 1 , the neighboring cell traffic of all time slot indexes of UE B is not much different, so the UE-level target BLER adjustment method can be used for UE B, while the neighboring cell traffic of all time slot indexes of UE A is greatly different, so the time slot-level target BLER adjustment method can be used for UE A. Fig.23 As shown, since UE-level target BLER adjustment is adopted for UE B, the target BLER of all time slots of UE B is the same, for example, 30%. Since UE A adopts time slot-level target BLER adjustment, the target BLER of all time slots of UE A is not necessarily the same, for example, the target BLER of time slot 0 may be 20%, and the target BLER of time slot 1 may be 30%.

[0286] After determining the target BLER adjustment method for the target UE, for example, the target BLER for the target UE can be determined by AI based on the measurement information of the serving cell (such as RSRP, CQI, RI, PMI), the measurement information of the neighboring cell (such as RSRP) and the PRB load information of the neighboring cell.

[0287] After determining the target BLER, the RIC may send the determined target BLER to the base station. Fig.21 , it is shown that the target BLER adaptation is performed by the RIC, but optionally, the target BLER adaptation may also be performed by the base station, for example, if the base station has sufficient AI computing capability, the target BLER adaptation may also be performed by the base station.

[0288] In step 3, the base station may perform a channel status self-check (i.e., checking the channel fluctuation status mentioned above) to determine whether to continue using the current target BLER to handle the abnormal situation where the actual neighboring cell traffic volume does not match the obtained traffic volume. Fig.24 As shown, if the target BLER adjustment method for the target UE is a UE-level target BLER adjustment method, the channel fluctuation state can be periodically obtained according to the actual BLER difference between different time slots of the target UE, and the state of the signal state acquisition period is determined to be an activated state or a deactivated state according to the obtained channel fluctuation state. If the target BLER adjustment method for the target UE is a time slot-level target BLER adjustment method, the channel fluctuation state can be periodically obtained according to the signal to interference and noise ratio SINR difference between different time slots of the target UE, and the state of the signal state acquisition period is determined to be an activated state or a deactivated state according to the obtained channel fluctuation state. If it is an activated state, it is determined to continue to use the current target BLER, and if it is a deactivated state, the default fixed target BLER is used.

[0289] Whenever the target BLER is adjusted, the base station may perform target BLER update compensation in step 4, that is, perform SINR compensation according to the adjusted target BLER and the target BLER before adjustment. Finally, the base station may determine MCS (Modulation and Coding Scheme) according to the adjusted target BLER, and send data, such as scheduling information, to the UE according to the determined MCS, and the UE performs data detection.

[0290] In order to further improve the transmission performance and reduce the computational complexity of the base station, the present disclosure proposes that the RIC performs traffic prediction of the UE's interfering cell and cell-level target BLER adaptive switching control. Fig.25 As shown, the RIC can perform traffic volume prediction, for example, perform PRB load prediction based on the traffic volume information reported by each interfering cell. Subsequently, the RIC performs cell-level function adjustment according to the predicted traffic volume, that is, performs cell-level target BLER adaptive switch judgment. Only when the cell-level target BLER adaptive is turned on (ON), the RIC sends the predicted traffic volume information of the interfering cell (such as the predicted neighboring cell PRB load) to the base station, so that the base station selects the candidate UE accordingly. Fig.25 Other operations in Fig.21 The operations already described are the same and will not be repeated here.

[0291] In the above, it is mentioned that the base station and RIC can cooperate to achieve the target BLER adjustment. Fig.26 An exemplary deployment scheme for implementing the concepts of the present disclosure is briefly described.

[0292] For example, in Fig.26 In the example of FIG. 1 , the AI ​​module and the cell-level target BLER adaptive switch judgment of the present invention can be deployed on the RIC side, and other functions can be deployed on the MAC (Medium Access Control) module of the base station distributed unit (DU), and Fig.26 In the example, it is taken that the interfering cell is a neighboring cell.

[0293] like Fig.26 As shown, the adaptive adjustment of the target BLER can be achieved through the following steps.

[0294] Step 1: MAC collects historical cell traffic information and periodically reports it to RIC.

[0295] Step 2: RIC predicts the traffic volume and determines the cell-level adaptive function switch based on the historical traffic volume information reported by MAC, and sends the judgment result and predicted traffic volume information to MAC.

[0296] Step 3: When the cell-level adaptation function switch is set to "ON", MAC determines the candidate UE, performs RRC configuration for the candidate user, and triggers it to perform neighboring cell measurement.

[0297] Step 4: MAC determines the target UE based on the measurement information of the neighboring cells reported by the candidate UE.

[0298] Step 5: MAC reports the determination result of the target UE and the measurement information of the neighboring cells of the target UE to the RIC, such as CSI.

[0299] Step 6: RIC determines the adaptive target BLER and sends the result to MAC.

[0300] Step 7: MAC performs channel status self-check and target BLER update compensation (ie, performs SINR compensation), and controls the physical layer (PHY-C, Physical Layer-Control) to perform corresponding data transmission.

[0301] The method according to the embodiment of the present disclosure has been described above. For example, the embodiment of the present disclosure can be applied to at least the following scenarios:

[0302] Scenario 1: Neighboring cell interference in the serving cell, the traffic volume in the neighboring cells corresponding to different time slot indices varies greatly. In this scenario, the neighboring cell interference fluctuates greatly, and the interference fluctuations corresponding to different time slot indices are different. If the traditional solution is adopted, a fixed target BLER solution is used for all UEs in the serving cell, which cannot adapt to the fluctuations in neighboring cell interference, resulting in limited user transmission performance and serious impact on user experience. For this scenario, the "time slot level" adaptive target BLER adjustment in the present disclosure is adopted. For the target UE, different target BLERs are used in different time slots, which can accurately match the interference fluctuations in different time slots of the neighboring cells, effectively improve the transmission performance of the target UE, and also improve the average performance of the cell to a certain extent. The simulation results show that the average performance of the target users in the cell is improved by about 25%, and the average performance of all UEs in the cell is also improved by about 6%.

[0303] Scenario 2: Neighboring cell interference in the serving cell, the traffic volume in the neighboring cells is moderate, and the traffic volume in the neighboring cells corresponding to different time slot indices does not change much. In this scenario, the neighboring cell interference fluctuates greatly, but the interference fluctuations corresponding to different time slot indices are relatively small. If the traditional solution is adopted, the same target BLER solution is used for all UEs in the serving cell, which cannot adapt to the fluctuation of neighboring cell interference, resulting in limited user transmission performance and serious impact on user experience. For this scenario, the "user-level" adaptive target BLER adjustment in the present disclosure is adopted. For the target UE, different target BLERs are used, which can accurately match the fluctuation of neighboring cell interference and enable the target UE to quickly converge to the adaptive target BLER, effectively improving the transmission performance of the target user, and also improving the average performance of the cell to a certain extent. The simulation results show that the performance of users at the edge of the cell is improved by about 37% on average, and the average performance of all UEs in the cell is also improved by about 11%.

[0304] The following briefly describes a network node and a user equipment according to an embodiment of the present disclosure.

[0305] Fig. 27 is a block diagram showing a network node according to an embodiment of the present disclosure. Fig. 27 The network node 2700 may include a transceiver 2701 and a processor 2702, wherein the processor 2702 is coupled to the transceiver 2701 and configured to execute the method performed by the network node described above. According to an embodiment, the network node may include a base station or a RIC, but is not limited thereto.

[0306] Fig.28 is a block diagram showing a UE according to an embodiment of the present disclosure. Fig.28 UE 2800 may include a transceiver 2801 and a processor 2802, wherein the processor 2802 is coupled to the transceiver 2801 and is configured to execute the method performed by the UE as described above.

[0307] An electronic device is also provided in an embodiment of the present disclosure, which includes at least one processor and, optionally, may also include at least one transceiver and / or at least one memory coupled to the at least one processor, and the at least one processor is configured to execute the steps of the method provided in any optional embodiment of the present disclosure.

[0308] Fig.29 A schematic diagram of the structure of an electronic device applicable to an embodiment of the present invention is shown in FIG. Fig.29 As shown, Fig.29The electronic device 4000 shown includes: a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, such as through a bus 4002. Optionally, the electronic device 4000 may also include a transceiver 4004, which may be used for data interaction between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, each of the processor 4001, the memory 4003 and the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present disclosure. Optionally, the electronic device may be a first network node, a second network node or a third network node.

[0309] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of the present invention. Processor 4001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0310] The bus 4002 may include a path to transmit information between the above components. The bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 4002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.29 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0311] The memory 4003 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), magnetic disk storage media, other magnetic storage devices, or any other medium that can be used to carry or store computer programs and can be read by a computer, without limitation herein.

[0312] The memory 4003 is used to store computer programs or executable instructions for executing the embodiments of the present disclosure, and the execution is controlled by the processor 4001. The processor 4001 is used to execute the computer programs or executable instructions stored in the memory 4003 to implement the steps shown in the above method embodiments.

[0313] An embodiment of the present disclosure provides a computer-readable storage medium having a computer program or instructions stored thereon. When the computer program or instructions are executed by at least one processor, the steps and corresponding contents of the aforementioned method embodiment can be executed or implemented.

[0314] The embodiments of the present disclosure also provide a computer program product, including a computer program, which can implement the steps and corresponding contents of the aforementioned method embodiments when executed by a processor.

[0315] The terms "first", "second", "third", "fourth", "1", "2", etc. (if any) in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than that shown or described in the text.

[0316] It should be understood that, although the flowchart of the embodiment of the present disclosure indicates each operation step by arrows, the implementation order of these steps is not limited to the order indicated by the arrows. Unless clearly stated herein, in some implementation scenarios of the embodiment of the present disclosure, the implementation steps in each flowchart can be executed in other orders according to demand. In addition, some or all of the steps in each flowchart may include multiple sub-steps or multiple stages based on the actual implementation scenario. Some or all of these sub-steps or stages can be executed at the same time, and each sub-step or stage in these sub-steps or stages can also be executed at different times. In scenarios with different execution times, the execution order of these sub-steps or stages can be flexibly configured according to demand, and the embodiment of the present disclosure does not limit this.

[0317] The above text and drawings are provided only as examples to help readers understand the present disclosure. They are not intended and should not be interpreted as limiting the scope of the present disclosure in any way. Although certain embodiments and examples have been provided, based on the contents disclosed herein, it is obvious to those skilled in the art that the embodiments and examples shown can be changed without departing from the scope of the present disclosure, and other similar implementation means based on the technical ideas of the present disclosure are adopted, which also fall within the protection scope of the embodiments of the present disclosure.

Claims

1. A method performed by a network node, comprising: Obtaining measurement information of an interfering cell of at least one UE and / or traffic information of the interfering cell; Adjusting a target block error rate BLER for a target UE in the at least one UE according to the measurement information of the interfering cell and / or the traffic information of the interfering cell; Data is sent to the target UE based on the adjusted target BLER.

2. According to the method of claim 1, adjusting the target block error rate (BLER) for the target UE in the at least one UE according to the measurement information of the interfering cell and / or the traffic information of the interfering cell comprises: Determine the target UE according to the measurement information of the interfering cell and / or the traffic information of the interfering cell; The target BLER for the target UE is adjusted according to the measurement information and / or the traffic volume information of the interfering cell of the target UE.

3. The method according to claim 1, further comprising: Determine the at least one UE from the candidate UEs according to measurement information of a serving cell of each UE among the candidate UEs; sending, to the at least one UE, signaling for configuring the at least one UE to perform interference cell measurement, The measurement information of the interfering cell of the at least one UE is obtained by the at least one UE performing interfering cell measurement based on the signaling.

4. The method according to claim 3, wherein: The determining the at least one UE from the candidate UEs according to the measurement information of the serving cell of each UE in the candidate UEs includes: A UE among the candidate UEs, whose measurement information of the serving cell satisfies a predetermined condition within a predetermined number of time slots, is determined as the at least one UE.

5. The method according to claim 2, wherein: The determining the target UE according to the measurement information of the interfering cell and / or the traffic information of the interfering cell includes: Classifying the interfering cell according to the measurement information of the interfering cell to obtain a first classification result; Classifying the time slots of the UE based on the traffic volume information of the interfering cell and the first classification result to obtain a second classification result; The target UE is determined according to the second classification result.

6. The method according to claim 1, wherein: The adjusting, according to the measurement information of the interfering cell and / or the traffic information of the interfering cell, the target BLER for the target UE in the at least one UE comprises at least one of the following: Adjusting the target BLER of all time slots for the target UE according to the measurement information of the interference cell of the target UE and the average traffic information on all time slots; Adjusting a target BLER of the set time slot for the target UE according to measurement information of an interference cell of the target UE and traffic information on the set time slot; A target BLER adjustment mode for the target UE is determined according to measurement information and / or traffic information of an interfering cell of the target UE, and the target BLER for the target UE is adjusted based on the determined target BLER adjustment mode.

7. The method according to claim 6, wherein: The determining, according to the measurement information and / or traffic volume information of the interfering cell of the target UE, a target BLER adjustment method for the target UE includes: Determine the interference intensity of the interfering cell of the target UE according to the measurement information of the interfering cell of the target UE; A target BLER adjustment mode for the target UE is determined based on the interference intensity and traffic information of the interfering cell of the target UE.

8. The method according to claim 7, wherein: The determining a target BLER adjustment method for the target UE based on the interference intensity and the traffic volume information of the interfering cell of the target UE includes: If the target UE has an interfering cell that meets the predetermined interference intensity and the traffic volume of the interfering cell that meets the predetermined interference intensity meets the predetermined traffic volume-related condition, or if the target UE does not have an interfering cell that meets the predetermined interference intensity and the traffic volume of the interfering cell of the target UE all meets the predetermined traffic volume-related condition, it is determined that the target BLER adjustment method for the target UE is to adjust the target BLER of all time slots for the target UE; otherwise, it is determined that the target BLER adjustment method for the target UE is to adjust the target BLER of a set time slot for the target UE.

9. The method according to claim 6, wherein: The adjusting the target BLER for all time slots of the target UE according to the measurement information of the interference cell of the target UE and the average traffic information on all time slots includes: Predicting the target BLER for all time slots of the target UE using a pre-trained artificial intelligence model according to the measurement information of the interfering cell of the target UE and the average traffic information on all time slots, and adjusting the target BLER for all time slots of the target UE based on the predicted target BLER; or According to the measurement information of the interference cell of the target UE and the average traffic volume information on all time slots, the target BLER for all time slots of the target UE is determined by using a pre-established mapping relationship between the interference fluctuation level and the target BLER, and the target BLER for all time slots of the target UE is adjusted based on the determined target BLER, wherein the interference fluctuation level is determined based on the average traffic volume information.

10. The method according to claim 6, wherein: The adjusting the target BLER of the set time slot for the target UE according to the measurement information of the interference cell of the target UE and the traffic information on the set time slot includes: Predicting a target BLER for the set time slot for the target UE using a pre-trained artificial intelligence model based on measurement information of the interfering cell of the target UE and traffic information on the set time slot, and adjusting the target BLER for the set time slot for the target UE based on the predicted target BLER; or, According to the measurement information of the interference cell of the target UE and the traffic volume information on the set time slot, the target BLER of the set time slot for the target UE is determined by using a pre-established mapping relationship between the interference fluctuation level and the target BLER, and the target BLER of the set time slot for the target UE is adjusted based on the determined target BLER, wherein the interference fluctuation level is determined based on the traffic volume information on the set time slot.

11. The method according to claim 1, wherein: Also includes: Acquire a channel fluctuation state for the target UE; Whether to continue using the current target BLER is determined according to the channel fluctuation state.

12. The method according to claim 11, wherein: The acquiring of the channel fluctuation state for the target UE includes: The channel fluctuation state is obtained according to the channel difference between different time slots of the target UE, wherein the channel difference between different time slots of the target UE includes an actual BLER difference between different time slots of the target UE, or a signal to interference plus noise ratio SINR difference between different time slots of the target UE.

13. The method according to claim 12, wherein: The determining whether to continue to use the current target BLER according to the channel fluctuation state includes: Marking a channel fluctuation state acquisition period as an activation state or a deactivation state according to the channel fluctuation state; If the channel fluctuation state acquisition period is marked as an activated state, the current target BLER continues to be used during the channel fluctuation state acquisition period; If the channel fluctuation status acquisition period is marked as a deactivated state, a default fixed target BLER is used during the channel fluctuation status acquisition period.

14. A method performed by a user equipment UE, comprising: Reporting measurement information of the interfering cell of the UE to the base station; Data transmitted based on a target block error rate (BLER) for the UE is received from the base station, wherein the target BLER is adjusted according to measurement information of the interfering cell and / or traffic information of the interfering cell.

15. The method according to claim 14, further comprising: receiving, from the base station, signaling for configuring the UE to perform measurement of an interfering cell, The measurement information of the interfering cell is obtained by the UE performing interfering cell measurement based on the signaling.

16. The method according to claim 15, further comprising: Sending measurement information of a serving cell of the UE to the base station, wherein the measurement information of the serving cell is used by the base station to determine whether to send the signaling to the UE.

17. A network node, comprising: Transceiver; A processor is coupled to the transceiver and configured to execute the method according to any one of claims 1 to 13.

18. The network node according to claim 17, wherein: The network node includes a base station or a radio access network intelligent controller RIC.

19. A user equipment, comprising: Transceiver; A processor is coupled to the transceiver and configured to execute the method according to any one of claims 14 to 16.

20. A computer-readable storage medium storing instructions, which, when executed by at least one processor, cause the at least one processor to perform the method of any one of claims 1 to 16.

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