Method for selecting modulation and coding strategy (MCS) and communication device

Abstract

This application provides a method for selecting a modulation and coding scheme (MCS). The method includes: a first communication device using a neural network model to determine the predicted block error rate (BRP) of each MCS among multiple MCSs under a transmission time interval (TTI) of 1. The prediction parameters for each MCS include channel parameters and the MCS itself. Further, based on the multiple MCSs and the predicted BRP of each MCS, the first communication device determines a target MCS for TTI 1 from among the multiple MCSs, and transmits data to a second communication device based on this target MCS during TTI 1. Through this method, the first communication device can invoke the neural network model to obtain the predicted BRP of each MCS under TTI 1 based on the channel parameters and multiple MCSs, thereby determining the MCS that matches the channel parameters of TTI 1, which is beneficial for improving the transmission performance of the current channel.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a method for selecting modulation and coding strategy (MCS) and a communication device. Background Technology

[0002] Various interference signals and fading occur during wireless communication signal transmission, causing significant fluctuations in channel quality. To improve the transmission performance of the current channel, such as increasing the transmission rate to maximize channel capacity, adaptive modulation and coding (AMC) techniques are typically used to select a modulation and coding scheme (MCS) suitable for the current channel transmission conditions to configure the physical transmission rate of the channel. The specific process of adaptive MCS selection at the signal transmitter can be described as follows: the signal transmitter obtains current channel quality information through feedback from the signal receiver or by utilizing equivalent measurements of the channel's uplink and downlink reciprocity characteristics. The signal transmitter can then select a suitable MCS based on this current channel quality information. However, due to factors such as feedback delay, measurement error, and quantization error, the measured results of the current channel quality information often deviate from the actual channel quality at the time of MCS selection.

[0003] Currently, the channel quality indicator (CQI) obtained by the transmitter can be corrected using the Euler adjustment algorithm. This involves adjusting the cumulative adjustment amount to bring the block error rate (BLER) down to a preset target BLER, thus ensuring the stability of the CQI value. However, since the preset target BLER is calculated based on experimental scenario simulations, it deviates from the optimal target BLER in real-world scenarios. Therefore, a target MCS determined based on the preset target BLER will result in lower channel transmission performance. Summary of the Invention

[0004] This application provides a method and communication device for selecting modulation and coding scheme (MCS). The selected MCS is beneficial to improving the transmission performance of the current channel.

[0005] In a first aspect, this application provides a method for selecting a modulation and coding scheme (MCS). The method includes: a first communication device predicting the block error rate (BER) of each MCS among a plurality of MCSs under a transmission time interval (TTI) of 1 using a first BER prediction model. The prediction parameters for each MCS include one or more channel parameters and the MCS itself. The first BER prediction model is a neural network model. Further, the first communication device determines a target MCS corresponding to TTI 1 from the plurality of MCSs based on the plurality of MCSs and the BER corresponding to each MCS. During TTI 1, the first communication device sends data to a second communication device based on the target MCS.

[0006] Based on the method described in the first aspect, the first communication device can invoke the first block error rate prediction model to predict the predicted block error rate under different channel conditions in real time, and then determine the MCS that optimizes the current channel transmission performance from multiple MCSs based on the predicted block error rate under different channel conditions.

[0007] In one possible implementation, among multiple MCSs, the target MCS and its corresponding prediction block error rate maximize the spectral efficiency or throughput of the TTI 1. The target MCS determined based on this possible implementation can improve the channel's spectral efficiency or throughput.

[0008] In one possible implementation, after the first communication device sends data to the second communication device based on the target MCS in TTI 1, the first communication device obtains a first block error rate and a second block error rate within a first preset time period. The first block error rate is the actual statistical block error rate within the first preset time period, and the second block error rate is the predicted block error rate within the same time period, obtained based on the first block error rate prediction model. If the difference between the first and second block error rates is greater than or equal to a first threshold, the first communication device determines the MCS for TTI 2 based on the target block error rate, where TTI 2 is later than TTI 1. Based on this possible implementation, the accuracy of the first block error rate prediction model can be periodically monitored. When the prediction result of the first block error rate prediction model is detected to be inaccurate, the MCS used for subsequent data transmissions in TTIs can be determined through other methods, improving the robustness of the channel.

[0009] In one possible implementation, the first preset time period includes multiple Time Intervals (TTIs). The first communication device acquires the decoding results of each TTI and the prediction block error rate (BER) corresponding to the target MCS under each TTI. Further, the first communication device determines the first BER based on the decoding results of each TTI and obtains the second BER based on the prediction BER corresponding to the target MCS under each TTI. Based on this possible implementation, the first BER and the second BER can be accurately determined.

[0010] In one possible implementation, after the first communication device sends data to the second communication device based on the target MCS during TTI 1, the first communication device acquires at least one sample data within a second preset time period. This sample data includes channel parameters, MCS, and a sample block error rate (BER). The sample BER is obtained from the decoding results corresponding to multiple TTIs, where the channel parameters and MCS are consistent across all TTIs. Further, the first communication device adjusts a first BER prediction model based on at least one sample data to obtain a second BER prediction model. If the parameter change of the second BER prediction model exceeds a second threshold, the first communication device obtains the predicted BER for each MCS in TTI 3, which is later than TTI 1, based on the second BER model. Based on this possible implementation, the first communication device continuously collects data from real-world application scenarios to train and adjust the first BER prediction model, avoiding mismatch between the first BER prediction model and the constantly changing scenario, thus improving the accuracy and robustness of the first BER prediction model.

[0011] In one possible implementation, the channel parameters include: the channel quality indicator for TTI 1, the change between the channel quality indicator for TTI 1 and the channel quality indicator for the previous TTI, the reference signal received power of the current cell, the precoding matrix indicator, the rank indicator, the signal transmission rate, and the transmission power; and the reference signal received power, precoding matrix indicator, rank indicator, signal transmission rate, or transmission power of neighboring cells. Based on this possible implementation, the accuracy of the first block error rate prediction model can be improved.

[0012] In one possible implementation, the first and second communication devices are communication devices in a multi-user multiple-input multiple-output (MU MIMO) system. The channel parameters also include the number of second communication devices sharing the same channel resources and the correlation coefficient between the second communication devices. Based on this possible implementation, the input channel parameters for the first block error rate prediction model in the MU MIMO system are determined, which can further improve the accuracy of the first block error rate prediction model in the MU MIMO system.

[0013] In one possible implementation, a first communication device acquires multiple candidate pairing sets under the MU MIMO system, which include pairing situations of one or more second communication devices. Further, the first communication device uses a first block error rate (BER) prediction model to predict multiple predicted BERs for each second communication device in TTI 1 under each candidate pairing set, where each candidate pairing set includes pairing situations of one or more second communication devices. For each pairing situation in each candidate pairing set, the first communication device uses the first BER prediction model to predict multiple predicted BERs for each second communication device in TTI 1 under each pairing situation, where each predicted BER corresponds one-to-one with an MCS among multiple MCSs. Further, based on the multiple MCSs and the multiple predicted BERs for each second communication device in each pairing situation in each candidate pairing set under TTI 1, the first communication device determines the target pairing set for TTI 1 from the multiple candidate pairing sets, and the target MCS for each second communication device in each pairing situation within the target pairing set. Based on this possible implementation, the first communication device can determine the pairing situations of multiple second communication devices and the target MCS for each second communication device in TTI 1, thereby improving the transmission performance of the channel.

[0014] Secondly, a communication device is provided. This device can be a first communication equipment, a component within the first communication equipment, or a device compatible with the first communication equipment. The communication device can also be a chip system. This communication device can execute the method described in the first aspect. The functions of the communication device can be implemented in hardware or by hardware executing corresponding software. The hardware or software includes one or more units corresponding to the aforementioned functions. These units can be software and / or hardware. The operations performed by the communication device and its beneficial effects are described in the first aspect above, and will not be repeated here.

[0015] Thirdly, this application provides a communication device including a processor, wherein when the processor invokes a computer program in memory, the method executed by the first communication device as described in the first aspect is executed.

[0016] Fourthly, this application provides a communication device, the communication device including a processor and a memory, the memory being used to store computer execution instructions; the processor being used to execute the computer execution instructions stored in the memory to cause the communication device to perform the method executed by the first communication device as described in the first aspect.

[0017] Fifthly, this application provides a communication device, the communication device including a processor, a memory, and a transceiver, the transceiver being used to receive or transmit signals; the memory being used to store a computer program; and the processor being used to invoke the computer program from the memory to execute the method performed by the first communication device as described in the first aspect.

[0018] In a sixth aspect, this application provides a communication device, the communication device including a processor and an interface circuit, the interface circuit being configured to receive computer execution instructions and transmit them to the processor; the processor executing the computer execution instructions to perform the method performed by the first communication device as described in the first aspect.

[0019] In a seventh aspect, this application provides a computer-readable storage medium for storing computer-executable instructions that, when executed, cause the method performed by the first communication device as described in the first aspect to be implemented.

[0020] Eighthly, this application provides a computer program product including a computer program that, when executed, causes the method performed by the first communication device as described in the first aspect to be implemented.

[0021] Ninthly, this application provides a communication system that includes the communication apparatus described in the second, third, fourth, fifth, or sixth aspects above. Attached Figure Description

[0022] Figure 1 This application provides a schematic diagram of a spatial multiplexing multiple-input multiple-output system architecture;

[0023] Figure 2 This application provides a flowchart illustrating a method for selecting modulation and coding strategies.

[0024] Figure 3 This application provides a schematic diagram of the structure of a fully connected neural network model;

[0025] Figure 4 This application provides a flowchart of a BLER prediction network.

[0026] Figure 5 This application provides a schematic diagram of a process for determining a target MCS from multiple MCSs;

[0027] Figure 6 A flowchart illustrating another modulation and coding strategy selection method provided in this application embodiment;

[0028] Figure 7 A flowchart illustrating another modulation and coding strategy selection method provided in this application embodiment;

[0029] Figure 8 A flowchart illustrating another modulation and coding strategy selection method provided in this application embodiment;

[0030] Figure 9 A flowchart illustrating another modulation and coding strategy selection method provided in this application embodiment;

[0031] Figure 10 This application provides a schematic diagram of a BLER prediction network corresponding to a MU MIMO system;

[0032] Figure 11 This application provides a schematic diagram of the structure of a communication device;

[0033] Figure 12a This is a schematic diagram of another communication device provided in an embodiment of this application;

[0034] Figure 12b This is a schematic diagram of another communication device provided in an embodiment of this application. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of this application clearer, the application will now be described in further detail with reference to the accompanying drawings.

[0036] The terms "first" and "second," etc., used in the specification, claims, and drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of operations or units is not limited to the listed operations or units, but may optionally include operations or units not listed, or may optionally include other operations or units inherent to these processes, methods, products, or apparatuses.

[0037] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0038] In this application, "at least one (item)" means one or more, "more than one" means two or more, "at least two (items)" means two or three or more, and "and / or" is used to describe the correspondence between corresponding objects, indicating that there can be three relationships. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the corresponding objects before and after it are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0039] To better understand the solution provided in this application, the system architecture of this application will be introduced below:

[0040] The method provided in this application can be applied to various communication systems, such as Internet of Things (IoT) systems, narrowband Internet of Things (NB-IoT) systems, long term evolution (LTE) systems, 5th generation (5G) communication systems, LTE and 5G hybrid architectures, 5G new radio (NR) systems, and new communication systems that will emerge in the future development of communication.

[0041] Please see Figure 1 , Figure 1 This is a schematic diagram of a spatially multiplexed multiple-input multiple-output (MIMO) system provided in an embodiment of this application. Figure 1 As shown, the communication system includes network device 101 and terminal device 102. For a MIMO system, multiple parallel data streams can be transmitted simultaneously on the same frequency domain resources; each data stream is called a spatial layer or spatial stream. It is important to know that... Figure 1The number of terminal devices 102 is merely illustrative and not specifically limited here. In other words, when the number of terminal devices 102 simultaneously transmitting data with network device 101 is 1, the MIMO system is a single-user multi-input multi-output (SU-MIMO) scenario. When the number of terminal devices 102 simultaneously transmitting data with network device 101 is greater than 1, the MIMO system is a multi-user multi-input multi-output (MU-MIMO) scenario.

[0042] It should be understood that the first communication device mentioned in this application is a signal transmitting end in a communication system, which can be used for... Figure 1 Network device 101 in the middle can also be Figure 1 Terminal device 102 in the middle.

[0043] The terminal device involved in the embodiments of this application is an entity on the user side used to receive or transmit signals. The terminal device can be a device that provides voice and / or data connectivity to the user, such as a handheld device with wireless connectivity, an in-vehicle device, etc. The terminal device can also be other processing devices connected to a wireless modem. The terminal device can communicate with a radio access network (RAN). The terminal device can also be referred to as a wireless terminal, subscriber unit, subscriber station, mobile station, mobile station, remote station, access point, remote terminal, access terminal, user terminal, user agent, user device, or user equipment (UE), etc. The terminal device can be a mobile terminal, such as a mobile phone (or "cellular" phone) and a computer with a mobile terminal, for example, a portable, pocket-sized, handheld, computer-embedded, or in-vehicle mobile device that exchanges voice and / or data with the radio access network. For example, the terminal device can also be a personal communication service (PCS) telephone, a cordless phone, a session initiation protocol (SIP) phone, a wireless local loop (WLL) station, a personal digital assistant (PDA), and other devices. Common terminal devices include, for example, automobiles, drones, robotic arms, mobile phones, tablets, laptops, PDAs, mobile internet devices (MIDs), and wearable devices such as smartwatches, smart bracelets, and pedometers, but the embodiments in this application are not limited to these.

[0044] The access network may include one or more network devices. The network devices involved in the embodiments of this application are entities on the network side used for transmitting or receiving signals. They can be used to convert received air frames and network protocol (IP) packets to each other, and act as routers between terminal devices and the rest of the access network, where the rest of the access network may include IP networks, etc. The network devices can also coordinate the attribute management of the air interface. For example, the network device may be an evolved Node B (eNB or e-NodeB) in LTE, a new radio controller (NR controller), an ng-eNB, a gNode B (gNB) in a 5G system, a centralized unit, a new radio base station, a remote radio module, a micro base station, a relay, a distributed unit, a transmission reception point (TRP) or transmission point (TP), or any other wireless access device, but the embodiments of this application are not limited to these.

[0045] This application provides a method for selecting an MCS. The method involves a first communication device using a first block error rate prediction model to predict the predicted block error rate for each of multiple MCSs within a transmission time interval (TTI) of 1. Further, based on the predicted block error rate for each MCS, the first communication device determines the target MCS corresponding to TTI 1 from among these multiple MCSs, and then sends data to a second communication device within TTI 1 based on the target MCS.

[0046] To better understand the solutions provided in this application, the relevant terms used in the embodiments of this application are introduced below:

[0047] Modulation and coding scheme (MCS): An MCS table contains multiple MCSs, each consisting of an MCS index value and corresponding MCS information. The MCS information includes at least one of the following: the modulation order, code rate, and spectral efficiency corresponding to the modulation scheme. In short, signal transmission communication equipment can determine the modulation scheme, coding rate, and spectral efficiency of the data packets to be transmitted using the MCS.

[0048] Channel Quality Indicator (CQI): The CQI table includes at least one CQI. Each CQI consists of a CQI index value and CQI information, where the CQI information includes at least one of the following: modulation scheme, code rate, spectral efficiency, and block error rate. There is a mapping relationship between CQI and MCS, and the transmitting communication equipment can determine the MCS through the channel's CQI.

[0049] Block Error Rate (BLER): In a wireless network, communication devices exchange data in the form of transport blocks. The sending device uses the data in the transport block to calculate a cyclic redundancy check (CRC) 1 and sends this CRC 1 along with the transport block to the receiving device. After receiving the transport block and CRC 1, the receiving device calculates a CRC 2 based on the data in the received transport block and compares CRC 2 with CRC 1. If CRC 2 equals CRC 1, the receiving device returns an acknowledgment (ACK) character to the sending device; if CRC 2 does not equal CRC 1, the receiving device returns a non-acknowledgment (NACK) character to the sending device, requesting the sending device to retransmit the transport block. BLER is the ratio of NACK characters to the total number of characters (ACK characters to NACK characters).

[0050] Euler adjustment algorithm: a method to achieve a target BLER by continuously adjusting the cumulative adjustment amount of CQI. For example, when the target BLER is 10%, the Euler adjustment algorithm can be expressed as formula (1).

[0051] CQI effect =CQI feedback (t)+Δ(t)

[0052]

[0053] Among them, the transmitting communication device uses the CQI corrected by the Euler adjustment algorithm. effect Determine the MCS and CQI corresponding to the current TTI. feedback Δ(t) represents the CQI feedback (also known as CQI measurement) obtained by the first communication device through equivalent measurement of the current channel using the uplink-downlink reciprocity characteristic of the channel, or by obtaining measurement feedback from the second communication device. Δ(t) represents the cumulative adjustment of CQI under the current TTI, and Δ(t-1) represents the cumulative adjustment of CQI under the previous TTI corresponding to the current TTI. Specifically, the calculation method of Δ(t) can be understood as follows: if the transmitting communication device receives the ACK decoding result from the receiving communication device, the current cumulative adjustment is increased by 0.01; if the transmitting communication device receives the NACK decoding result from the receiving communication device, the current cumulative adjustment is decreased by 0.09. Δ(t) is 0 only when the NACK decoding result received by the transmitting communication device accounts for 10% of the total received decoding results. At this time, it can be regarded as based on CQI. feedback The MCS determined by (t) is the MCS that optimizes the current channel transmission performance.

[0054] The method for selecting modulation and coding strategies provided in the embodiments of this application will be described in further detail below:

[0055] Please see Figure 2 , Figure 2 This is a flowchart illustrating a modulation and coding strategy selection method provided in an embodiment of this application. Figure 2 As shown, the method for selecting the modulation and coding strategy includes the following steps 201 to 203. Figure 2 The subject of the method shown can be the first communication device, or the chip of the first communication device. Figure 2 The method will be explained using the first communication device as an example. The execution subject of the modulation and coding scheme (MCS) selection method shown in other figures of this application is similar and will not be repeated hereafter. Wherein:

[0056] 201. The first communication device predicts the predicted block error rate for each MCS among multiple MCSs under TTI 1 using a first block error rate prediction model. The prediction parameters for the predicted block error rate for each MCS include one or more channel parameters and the MCS itself. The first block error rate prediction model is a neural network model.

[0057] The first communication device and the second communication device can be communication devices in a SU MIMO system or a MU MIMO system. When the first communication device is a terminal device, the second communication device is a network device. Alternatively, when the first communication device is a network device, the second communication device is a terminal device.

[0058] Wherein, TTI 1 is any TTI during transmission from the first communication device to the second communication device. Channel parameters include: channel quality indication (CQI) of TTI 1, the change in CQI between TTI 1 and the previous TTI 1 (ΔCQI), reference signal receiving power (RSRP) of the cell, precoding matrix indicator (PMI), rank indicator (RI), signal transmission speed, transmission power, and neighboring cell RSRP, PMI, RI, speed, and power.

[0059] In one possible implementation, the first and second communication devices are communication devices in a SU MIMO system. In this case, the first communication device acquires multiple channel parameters corresponding to MCS and TTI 1, including: CQI, ΔCQI, RSRP, neighboring cell RSRP, PMI, RI, speed, and power.

[0060] In another possible implementation, the first and second communication devices are communication devices in a MU MIMO system. In this case, the first communication device acquires channel parameters corresponding to multiple MCS and TTI 1, including: CQI, ΔCQI, RSRP, neighboring cell RSRP, PMI, RI, speed, power, the number of second communication devices sharing the same channel resource, and the correlation coefficient between the second communication devices sharing the same channel resource.

[0061] The first communication device acquires a pre-configured MCS table in the communication system, which includes multiple MCSs. Under TTI 1, the first communication device can obtain one or more channel parameters of the current channel by utilizing the uplink-downlink reciprocity characteristic of the channel to perform an equivalent measurement, or by having a second communication device measure the current channel and then feed the results back to the first communication device. Further, the first communication device can use these channel parameters and the multiple MCSs as prediction parameters, inputting them into a neural network model to predict the prediction block error rate corresponding to each MCS under TTI 1. The multiple MCSs in the prediction parameters can be all MCSs in the system-configured MCS table, or multiple MCSs included in a candidate MCS set determined from that MCS table.

[0062] In one possible implementation, the first communication device can acquire the CQI information of the current channel under TTI 1. Furthermore, the first communication device can determine a set of candidate MCSs from a pre-configured MCS table in the communication system based on this CQI information. By implementing this possible implementation, the first communication device can reduce the candidate range corresponding to the target MCS, thereby saving the computational resources of the first communication device.

[0063] For example, the MCS table pre-configured in the communication system includes at least one MCS information, which includes the MCS index value, spatial stream number, modulation method and rate in the MCS table, as detailed in Table 1. It should be noted that the MCS table here is only an example and is not intended to be limiting.

[0064] Table 1

[0065]

[0066] The first communication device utilizes the uplink and downlink reciprocity characteristics of the channel to perform equivalent measurements on the current channel and obtain the CQI information of the current channel. Assuming the first communication device determines that the MCS index value matching the CQI information is MCS8 from the pre-configured MCS table of the communication system, the first communication device can determine a candidate MCS set in the MCS table with MCS8 as the center and a preset step size. For example, when the preset step size is 5, the first communication device can determine that the MCS index values ​​of MCS8 within 5 steps before and after in the MCS table are the candidate MCS set, that is, the determined candidate MCS set includes the following MCS index values: MCS3, MCS4, MCS5, MCS6, MCS7, MCS8, MCS9, MCS10, MCS11, MCS12, and MCS13.

[0067] In one possible implementation, the first communication device acquires at least one training sample data point within a training period. This training sample data includes channel parameters, MCS (Multi-Channel System), and a training sample block error rate (BER). The BER is obtained from the decoding results (ACK or NACK) corresponding to multiple Time Interruptions (TTIs) within the training period, and the channel parameters and MCS corresponding to the multiple TTIs within the training period are consistent. Further, the first communication device trains an initial neural network model based on this at least one training sample data point to obtain a first BER prediction model. The training period is set by the developers based on experimental conditions and can be adjusted according to specific application scenarios. The initial neural network model can be a fully connected neural network model, a convolutional neural network model, or a recurrent neural network model, etc., and is not specifically limited here.

[0068] For example, such as Figure 3 As shown, the initial neural network model is a fully connected neural network model, which includes an input layer, hidden layers, and an output layer. The number of nodes in the input layer is determined based on the number of prediction parameters in the network. The number of nodes in the hidden layer is typically set to 5 to 10 times the number of nodes in the input layer, and the number of nodes in the output layer is 1. The activation functions for the hidden and output layers can be any of the sigmoid, tanh, or ReLU functions. Taking a training period of 7 days as an example, the first communication device acquires the channel parameters, MCS, and decoding results corresponding to each TTI (e.g., 10,000 TTIs) within those 7 days. The specific channel parameters, MCS, and decoding results corresponding to some TTIs within the training period can be found in Table 2, which only shows the data for the first 7 TTIs; data for other TTIs is omitted. It should be noted that this is only an example and is not intended to be limiting. The first communication device can obtain training sample data based on the channel parameters, MCS and decoding results of TTIs with the same channel parameters and MCS. For example, the first communication device can obtain a training sample data based on TTI0, TTI1 and TTI2. The training sample data includes channel parameters, MCS and training sample block error rate. The training sample block error rate is the ratio of the number of NACKs in TTI0, TTI1 and TTI2 to the decoding results of 33.3%.

[0069] Table 2

[0070] TTI Number Channel parameters MCS Index Value Decoding result TTI0 A0, B0, C0, D0 MCS0 ACK TTI 1 A0, B0, C0, D0 MCS0 ACK TTI 2 A0, B0, C0, D0 MCS0 NACK TTI 3 A0, B0, C0, D0 MCS1 NACK TTI4 A0, B0, C0, D0 MCS1 ACK TTI5 A1, B1, C1, D1 MCS2 ACK TTI6 A1, B1, C1, D1 MCS2 ACK

[0071] Furthermore, the first communication device can perform operations based on at least one training sample data within the training period, such as... Figure 3 The initial network model shown is trained to obtain the first block error rate prediction model.

[0072] In one application scenario, after the first communication device obtains a first block error rate (BRR) prediction model based on at least one training sample data within the training period, the first communication device uses the channel parameters corresponding to each of the multiple MCSs and TTI 1 as prediction parameters for the first BRR prediction model, and inputs them into the first BRR prediction model to obtain the predicted BRR for each MCS. For example... Figure 4 As shown, MCS i represents the i-th MCS among multiple MCSs, which can be regarded as any one of the aforementioned multiple MCSs. The first communication device inputs the channel parameters corresponding to the i-th MCS and TTI 1 into the first block error rate prediction model to obtain the predicted block error rate corresponding to the i-th MCS.

[0073] For example, the first communication device obtains the channel parameters corresponding to TTI 1, including CQI, ΔCQI, local RSRP, and PMI. The first communication device obtains multiple MCSs, namely MCS1, MCS2, and MCS3. Further, the first communication device calls a first block error rate prediction model to obtain a predicted BLER1 based on the channel parameters CQI, ΔCQI, local RSRP, PMI, and MCS1; the first communication device calls the first block error rate prediction model to obtain a predicted BLER2 based on the channel parameters CQI, ΔCQI, local RSRP, PMI, and MCS2; the first communication device calls the first block error rate prediction model to obtain a predicted BLER3 based on the channel parameters CQI, ΔCQI, local RSRP, PMI, and MCS3.

[0074] 202. The first communication device determines the target MCS corresponding to TTI 1 from multiple MCSs based on multiple MCSs and the prediction error rate corresponding to each MCS.

[0075] like Figure 5 The diagram illustrates the process by which a first communication device determines a target MCS from multiple MCSs. After the first communication device calls a first block error rate prediction model to predict the predicted block error rate for each MCS, it can determine the target MCS corresponding to TTI 1 from the multiple MCSs based on evaluation criteria or an evaluation function. The evaluation criteria or evaluation function are set by developers according to the application scenario and can be adjusted accordingly; this application does not impose specific limitations on them.

[0076] In one possible implementation, among the aforementioned multiple MCSs, the target MCS and the prediction error rate corresponding to the target MCS maximize the spectral efficiency or throughput of TTI 1.

[0077] For example, after the first communication device calls the first block error rate prediction model to predict the predicted block error rate of each MCS among multiple MCSs, it can calculate the spectral efficiency of each MCS according to formula (2) and the predicted block error rate of each MCS.

[0078] Eff MCS (BLER MCS )=η MCS ×(1-BLER MCS (2)

[0079] Among them, BLER MCS The BLER is the prediction block error rate corresponding to the MCS. Generally, the larger the MCS index value, the higher the BLER. MCS The larger the value, the greater the efficiency. MCS (BLER MCS η represents the spectral efficiency corresponding to this MCS. MCS This corresponds one-to-one with the MCS (Mean Cross-Sectional Scale), representing the MCS learning rate. Generally, the larger the MCS index value, the higher the learning rate η. MCS The larger the value, the better. Furthermore, the first communication device determines the target MCS based on the spectral efficiency of each MCS among the multiple MCSs.

[0080] For example, the first communication device acquires multiple MCSs, namely MCS1, MCS2, and MCS3, and obtains the predicted BLER1 corresponding to MCS1, the predicted BLER2 corresponding to MCS2, and the predicted BLER3 corresponding to MCS3 based on the first block error rate prediction model. Further, the first communication device can, according to the above formula (2), the η corresponding to MCS1... MCS1 The spectral efficiency value Eff corresponding to MCS1 is obtained from BLER1. MCS1 According to the above formula (2), η corresponding to MCS2 MCS2 The spectral efficiency value Eff corresponding to MCS2 is obtained from BLER2. MCS2 According to the above formula (2), η corresponding to MCS3 MCS3 The spectral efficiency value Eff corresponding to MCS3 is obtained from BLER3. MCS3 If Eff MCS2 For Eff MCS1 Eff MCS2 and Eff MCS3 If the maximum value in is found, then the first communication device will use Eff. MCS2 The corresponding MCS2 is determined as the target MCS.

[0081] 203. In TTI 1, the first communication device sends data to the second communication device based on the target MCS.

[0082] In TTI 1, the first communication device modulates and encodes the data based on the MCS information of the target MCS. That is, the first communication device modulates and encodes the data according to the modulation method, coding rate and spectral efficiency configured for the target MCS, and then sends the data to the second communication device.

[0083] It is evident that through implementation Figure 2 The described modulation and coding strategy selection method for MCS involves a first communication device using a neural network model to predict the prediction block error rate (MRR) of each MCS based on the channel state measurement corresponding to TTI 1. Then, based on the MRR of each MCS, the target MCS for improving channel transmission performance can be determined from multiple candidate MCSs.

[0084] Please see Figure 6 , Figure 6 This is a flowchart illustrating a modulation and coding strategy selection method provided in an embodiment of this application. Figure 6 As shown, the method for selecting the modulation and coding strategy includes the following steps 601 to 605, wherein:

[0085] 601. The first communication device predicts the predicted block error rate for each MCS among multiple MCSs under TTI 1 using a first block error rate prediction model. The prediction parameters for the predicted block error rate for each MCS include one or more channel parameters and the MCS. The first block error rate prediction model is a neural network model.

[0086] 602. The first communication device determines the target MCS corresponding to TTI 1 from multiple MCSs based on multiple MCSs and the prediction error rate corresponding to each MCS.

[0087] 603. In TTI 1, the first communication device sends data to the second communication device based on the target MCS.

[0088] The specific implementation methods of steps 601 to 603 can be found in the specific implementation methods of steps 201 to 203 of the aforementioned embodiments, and will not be repeated here.

[0089] 604. The first communication device acquires a first block error rate and a second block error rate within a first preset time period. The first block error rate is the actual statistical block error rate within the first preset time period, and the second block error rate is the predicted block error rate within the first preset time period obtained based on the first block error rate prediction model.

[0090] The first preset time period was obtained by the developers based on the experimental scenario, and can be adjusted accordingly based on the specific application scenario. This application does not impose any specific limitations on this.

[0091] In one possible implementation, the first preset time period includes multiple Time Intervals (TTIs). The first communication device can acquire the decoding results of each TTI and the prediction block error rate corresponding to the target MCS under each TTI. Further, the first communication device can determine the first block error rate based on the decoding results of each TTI, and obtain the second block error rate based on the prediction block error rate corresponding to the target MCS under each TTI.

[0092] For example, the first communication device obtains the decoding results of each of the eight TTIs included in the first preset time period and the prediction error rate of the target MCS under each TTI as shown in Table 3.

[0093] Table 3

[0094] TTI Number Decoding result Prediction error rate corresponding to the target MCS TTI 1 ACK 20％ TTI 2 ACK 20％ TTI 3 ACK 20％ TTI4 ACK 23％ TTI5 NACK 25％ TTI6 ACK 25％ TTI7 NACK 18％ TTI8 ACK 24％

[0095] The first communication device can determine the actual statistical block error rate (i.e., the first block error rate) within the first preset time period as the ratio of 25% to the sum of 8 decoding results (2) of the number of NACKs within the first preset time period. The first communication device can determine the average value of 21.9% of the predicted block error rates corresponding to the target MCS of each TTI within the first preset time period as the predicted block error rate (i.e., the second block error rate) obtained according to the first block error rate prediction model within the first preset time period.

[0096] 605. If the difference between the first block error rate and the second block error rate is greater than or equal to the first threshold, the first communication device determines the MCS corresponding to TTI 2 based on the target block error rate, where TTI 2 is later than TTI 1.

[0097] The first threshold was set by the developers based on experimental data, and can be adjusted according to specific application scenarios. No specific limit is set here.

[0098] The first communication device determines whether to determine the MCS corresponding to the next Time Period (TTI) (i.e., TTI 2 mentioned above) based on the difference between the first block error rate (i.e., the actual statistical block error rate) and the second block error rate (the predicted block error rate obtained according to the first block error rate prediction model) within a first preset time period. Specifically, when the difference between the first block error rate (i.e., the actual statistical block error rate) and the second block error rate (the predicted block error rate obtained according to the first block error rate prediction model) is less than a first threshold, the first communication device continues to determine the target MCS corresponding to TTI 2 according to the aforementioned first block error rate prediction algorithm. When the difference is greater than or equal to the first threshold, the first communication device determines the MCS corresponding to TTI 2 based on the target block error rate.

[0099] For example, if the first threshold is 10%, in such a case, if the first communication device obtains a first block error rate (i.e., the actual statistical block error rate) of 30% within a first preset time period and obtains a second block error rate (the predicted block error rate obtained according to the first block error rate prediction model) of 15% within the first preset time period, it can be seen that the difference between the first block error rate and the second block error rate is 15%, which is greater than the first threshold of 10%. Then, the first communication device can select the MCS corresponding to the next TTI (i.e., TTI 2 mentioned above) according to the target BLER and Euler (OLLA) adjustment algorithm.

[0100] In one application scenario, the first communication device determines the MCS selection algorithm corresponding to the TTI (Transmission Time Interval) as the Euler algorithm based on the target block error rate. In such a case, such as... Figure 7 The diagram shows a flowchart of another method for selecting modulation and coding strategies. Figure 7 The determination of the MCS corresponding to the TTI by the first communication device is divided into two stages: the first block error rate prediction model training stage (steps 701-703) and the first block error rate prediction model usage stage (steps 704-705). In the first block error rate prediction model training stage, as shown in step 701, the first communication device cumulatively adjusts the CQI fed back by the second communication device according to the Euler adjustment algorithm, and determines the target MCS corresponding to the TTIs included in the first block error rate prediction model training stage based on the cumulatively adjusted CQI. Further, as shown in step 702, the first communication device collects the raw data corresponding to the TTIs included in the first block error rate prediction model training stage. This raw data includes the target MCS corresponding to each TTI and the actual statistical decoding results. Steps 701 and 702 do not have mandatory execution logic; that is, steps 701 and 702 can be performed simultaneously. As shown in step 703, the first communication device obtains at least one training sample data based on the original data corresponding to the TTI included in the training phase of the first block error rate prediction model, and uses the at least one training sample data to train the initial neural network model (e.g., the aforementioned). Figure 3The fully connected network model shown is used for training. As shown in step 704, the first communication device detects whether a preset training period has been reached. If not, the implementation method of step 701 is repeated. If the preset training period has been reached, the first communication device can obtain the first block error rate prediction model and enter the first block error rate prediction model usage stage. That is, as shown in step 705, the first communication device determines the target MCS corresponding to each TTI included in the first block error rate prediction model usage stage based on the first block error rate prediction model. In order to improve the accuracy of the first communication device in determining the target MCS corresponding to the TTI, as shown in step 706, the first communication device obtains the first block error rate (i.e., the actual statistical block error rate) within the first preset time period and obtains the second block error rate (the predicted block error rate obtained according to the first block error rate prediction model) within the first preset time period. Further, the first communication device performs the operation shown in step 707, determining whether the difference between the first block error rate and the second block error rate is greater than a first threshold. If the first communication device detects that the difference between the first block error rate and the second block error rate is greater than the first threshold, the first communication device reverts to the first block error rate prediction model training stage, that is, the first communication device executes the specific implementation of step 701 again to retrain and obtain a new first block error rate prediction model.

[0101] It is evident that through implementation Figure 6 The described modulation and coding strategy selection (MCS) method allows the first communication device to periodically monitor the accuracy of the first block error rate prediction model. When the prediction result of the first block error rate prediction model is detected to be inaccurate, the MCS used for subsequent TTI data transmission can be determined through other means, thereby improving the robustness of the channel.

[0102] Please see Figure 8 , Figure 8 This is a flowchart illustrating a modulation and coding strategy selection method provided in an embodiment of this application. Figure 8 As shown, the method for selecting the modulation and coding strategy includes the following steps 801 to 806, wherein:

[0103] 801. The first communication device predicts the predicted block error rate for each MCS among multiple MCSs under TTI 1 using a first block error rate prediction model. The prediction parameters for the predicted block error rate for each MCS include one or more channel parameters and the MCS itself. The first block error rate prediction model is a neural network model.

[0104] 802. The first communication device determines the target MCS corresponding to TTI 1 from multiple MCSs based on multiple MCSs and the prediction error rate corresponding to each MCS.

[0105] 803. In TTI 1, the first communication device sends data to the second communication device based on the target MCS.

[0106] The specific implementation methods of steps 801 to 803 can be found in the specific implementation methods of steps 201 to 203 of the aforementioned embodiments, and will not be repeated here.

[0107] 804. The first communication device acquires at least one sample data within a second preset time period. The sample data includes channel parameters, MCS, and sample block error rate. The sample block error rate is obtained from the decoding results corresponding to multiple TTIs, and the channel parameters and MCS of the multiple TTIs are consistent.

[0108] The second preset time period is set by the developers based on the experimental scenario and can be adjusted accordingly based on the specific application scenario. No specific limitation is made here.

[0109] For example, when the second preset time period is 1 day, the first communication device obtains the channel parameters, MCS, and decoding results corresponding to the TTIs (e.g., 1000 TTIs) included within 1 day. The first communication device can obtain sample data based on the channel parameters, MCS, and decoding results corresponding to the TTIs with the same channel parameters and MCS. For example, if the channel parameters and MCS corresponding to TTI0, TTI1, and TTI2 included in the second preset time period are all the same, the first communication device can obtain a sample data based on TTI0, TTI1, and TTI2. The sample block error rate of this sample data is obtained based on the decoding results corresponding to TTI0, TTI1, and TTI2 respectively.

[0110] 805. The first communication device adjusts the first block error rate prediction model based on the at least one sample data to obtain a second block error rate prediction model.

[0111] After determining the target MCS based on the predicted block error rate (MCS) predicted by the first block error rate prediction model, the first communication device can periodically adjust and optimize the first block error rate prediction model according to a second preset time period, thereby improving the adaptability of the block error rate prediction model to the environment. In other words, the first communication device can use the channel parameters and MCS of each sample data within the second preset time period obtained in step 804 as input to the first block error rate prediction model, obtain the predicted block error rate of each sample data, and adjust the model parameters in the first block error rate prediction model according to the difference between the sample block error rate corresponding to each sample data and the predicted block error rate of each sample data, to obtain the second block error rate prediction model.

[0112] 806. If the parameter change of the second block error rate prediction model is greater than the second threshold, the first communication device obtains the predicted block error rate of each MCS in multiple MCSs under TTI 3 based on the second block error rate prediction model. TTI 3 is later than TTI 1.

[0113] The second threshold was calculated by the developers based on experimental data and can be adjusted according to specific application scenarios; no specific limitations are made here. The parameter change of the second block error rate prediction model specifically refers to the change in at least one model parameter corresponding to the second block error rate prediction model relative to at least one model parameter corresponding to the first block error rate prediction model.

[0114] For example, the model parameters of the second block error rate prediction model obtained by the first communication device are parameters A2, B2, and C3, and the model parameters of the first block error rate prediction model are parameters A1, B1, and C1. The first communication device can then calculate the total parameter change of the second block error rate prediction model based on the parameter changes of each model parameter. If the total parameter change of the second block error rate prediction model is greater than a second threshold, the first communication device deletes the first block error rate prediction model and obtains the predicted block error rate for each MCS among multiple MCSs under subsequent TTI 3 based on the second block error rate prediction model. Further, the first communication device can determine the target MCS corresponding to TTI 3 based on the predicted block error rate corresponding to each MCS among multiple MCSs under TTI 3.

[0115] It is important to understand that in a MIMO system, when there are multiple spatial streams between the first and second communication devices, the first communication device can use the same first block error rate prediction model or Euler algorithm to determine the target MCS corresponding to each spatial stream.

[0116] It is evident that through implementation Figure 8 The Modulation and Coding Strategy (MCS) method described herein involves the first communication device continuously collecting data from real-world application scenarios to train and adjust the first block error rate prediction model. This avoids mismatch between the first block error rate prediction model and the constantly changing scenario, thereby improving the accuracy and robustness of the first block error rate prediction model.

[0117] Please see Figure 9 , Figure 9 This is a flowchart illustrating another method for selecting the Modulation and Coding Scheme (MCS) according to an embodiment of this application. This MCS selection method is applicable to communication devices in MU MIMO systems, such as... Figure 9 As shown, the method for selecting the modulation and coding strategy (MCS) includes the following steps 901 to 904. Wherein:

[0118] 901. The first communication device acquires multiple candidate pairing sets under the MU MIMO system, and the candidate pairing sets include the pairing status of one or more second communication devices.

[0119] In a MU MIMO system, there are multiple second communication devices. The first communication device acquires multiple candidate pairing sets for these second communication devices. Each candidate pairing set includes the combined pairing information of each second communication device. It is important to understand that the combined pairing information of each second communication device refers to the pairing information of second communication devices sharing the same channel resource.

[0120] For example, in a MU MIMO system there are 3 second communication devices, namely communication device 1, communication device 2 and communication device 3. Then there will be 5 candidate pairing sets for these 3 second communication devices, as shown in Table 4.

[0121] Table 4

[0122]

[0123] 902. For each pairing situation in each candidate pairing set, the first communication device predicts multiple predicted block error rates of each second communication device in TTI 1 under the pairing situation through the first block error rate prediction model. The predicted block error rates of these multiple predicted block error rates correspond one-to-one with the MCSs in the multiple MCSs.

[0124] In MU MIMO systems, channel parameters include not only those in SU MIMO systems but also the number of second communication devices (n) sharing the same channel resource and the correlation coefficient between these second communication devices. In other words, MU MIMO channel parameters include: the CQI of TTI 1, the ΔCQI between the CQI of TTI 1 and the CQI of the previous TTI 1, the RSRP, PMI, RI, speed, and power of the current cell, the RSRP, PMI, RI, speed, and power of neighboring cells, the number of second communication devices (n) sharing the same channel resource, and the correlation coefficient between these second communication devices. Figure 10 The diagram shows a schematic of the BLER prediction network corresponding to the MU MIMO system. The first communication device obtains the MCS table pre-configured by the communication system. The first communication device can also obtain one or more channel parameters of the channel resources corresponding to each second communication device under TTI 1 for each pairing situation in each candidate pairing set, and use the channel parameters and multiple MCS as prediction parameters to call the neural network model to predict the prediction block error rate of each second communication device for each MCS under TTI 1.

[0125] It is important to understand that when multiple second communication devices share the same channel resource, the channel parameters obtained by the first communication device for each second communication device under the current channel will also differ. For example, taking candidate pairing set 2 in Table 4 as an example, the pairing situation of this candidate pairing set is as follows: second communication device 1 and second communication device 2 share channel resource 1, and second communication device 3 occupies channel resource 2. In this case, the channel parameters that second communication device 1 and second communication device 2 have in common under TTI 1 are: the number of second communication devices (second communication device 1 and second communication device 2) sharing the same channel resource is 2, and the correlation coefficient between the second communication devices (second communication device 1 and second communication device 2) sharing the same channel resource. However, the other channel parameters corresponding to second communication device 1 under TTI 1 may be different from the other channel parameters corresponding to second communication device 2 under TTI 1. For the specific implementation method of the first communication device for the prediction block error rate corresponding to each MCS in multiple MCS of each second communication device under TTI 1, please refer to the specific implementation method of step 201 in the above embodiments, which will not be elaborated further here.

[0126] 903. The first communication device determines the target pairing set corresponding to TTI 1 from the multiple candidate pairing sets based on multiple MCSs and the multiple prediction block error rates of each second communication device in each pairing case in each candidate pairing set, as well as the target MCS of each second communication device in each pairing case in the target pairing set.

[0127] After obtaining the multiple prediction block error rates of each second communication device under each pairing condition in each candidate pairing set at TTI1, the first communication device can determine the candidate target MCS corresponding to each second communication device under TTI1 in each pairing condition in each candidate pairing set from multiple MCSs according to the first evaluation criterion or the first evaluation function. The specific implementation of the first communication device determining the candidate target MCS corresponding to each second communication device under TTI1 in each pairing condition in each candidate pairing set from multiple MCSs can be found in the relevant description of step 202 in the aforementioned embodiments, and will not be repeated here. Further, after obtaining the candidate target MCS corresponding to each second communication device under TTI1 in each pairing condition in each candidate pairing set, the first communication device can determine the target pairing set corresponding to TTI1 from multiple candidate pairing sets according to the second evaluation criterion or the second evaluation function corresponding to the first evaluation criterion or the first evaluation function.

[0128] For example, after the first communication device obtains the multiple prediction block error rates of each second communication device in each pairing case in each candidate pairing set as shown in Table 4, it calculates the spectral efficiency of each second communication device in each MCS according to the spectral efficiency function shown in the aforementioned formula (2) and the multiple prediction block error rates of each second communication device in each pairing case in each candidate pairing set as shown in Table 5.

[0129] Table 5

[0130]

[0131] Furthermore, the first communication device obtains the sum of the spectral efficiencies of each second communication device in each candidate target MCS based on the spectral efficiency of each second communication device in its corresponding candidate target MCS. For example, the sum of the spectral efficiencies of candidate pairing set 1 in Table 5 is the sum of the spectral efficiency 1 of second communication device 1 in MCS11, the spectral efficiency 2 of second communication device 2 in MCS12, and the spectral efficiency 3 of second communication device 3 in MCS13. The first communication device can determine the candidate pairing set with the largest sum of spectral efficiencies from each candidate pairing set as the target pairing set according to the spectral efficiency maximization criterion, and determine the candidate target MCS corresponding to each second communication device in the target pairing set as the target MCS corresponding to each second communication device.

[0132] 904. In TTI 1, the first communication device sends data to the second communication device based on the target MCS.

[0133] In TTI 1, the first communication device modulates and encodes the data based on the MCS information of the target MCS of each of the second communication devices, and then sends the data to each of the second communication devices. Other specific implementations of step 904 can be found in the description of step S203 in the aforementioned embodiments, and will not be described in detail here.

[0134] It is evident that through implementation Figure 9 The modulation and coding strategy selection method described herein enables the first communication device in a MUMIMO system to not only determine the MCS corresponding to each second communication device in TTI 1, but also to determine the pairing combination of each second communication device in TTI 1, thereby further improving channel transmission performance.

[0135] Please see Figure 11 , Figure 11 A schematic diagram of the structure of a communication device according to an embodiment of this application is shown. Figure 11 The communication device shown can be used to implement some or all of the functions of the first communication device in the embodiment corresponding to the above modulation and coding strategy selection method. Figure 11The communication device shown can be used to achieve the above. Figure 2 , Figure 6 , Figure 8 and Figure 9 The described method embodiments include some or all of the functions of the first communication device. The device may be the first communication device itself, a component within the first communication device, or a device compatible with the first communication device. The communication device may also be a chip system. Figure 11 The communication device shown may include a communication unit 1101 and a processing unit 1102. Wherein:

[0136] Processing unit 1102 is configured to predict the predicted block error rate (BER) of each MCS among multiple MCSs under transmission time interval TTI 1 using a first BER prediction model. The prediction parameters for the BER of each MCS include one or more channel parameters and the MCS itself. The first BER prediction model is a neural network model. Communication unit 1101 is further configured to determine the target MCS corresponding to TTI 1 from among the multiple MCSs based on the multiple MCSs and the predicted BER of each MCS. Communication unit 1101 is configured to send data to a second communication device based on the target MCS during TTI 1.

[0137] In one possible implementation, among multiple MCSs, the target MCS and the prediction error rate corresponding to that target MCS maximize the spectral efficiency or throughput of the TTI 1.

[0138] In one possible implementation, after the first communication device sends data to the second communication device based on the target MCS in TTI 1, the processing unit 1102 is further configured to obtain a first block error rate and a second block error rate within a first preset time period. The first block error rate is the actual statistical block error rate within the first preset time period, and the second block error rate is the predicted block error rate within the first preset time period obtained based on the prediction model of the first block error rate. If the difference between the first block error rate and the second block error rate is greater than or equal to a first threshold, the MCS corresponding to TTI 2 is determined based on the target block error rate, where TTI 2 is later than TTI 1.

[0139] In one possible implementation, the first preset time period includes multiple TTIs. The communication unit 1101 is specifically used to obtain the decoding result of each TTI and the prediction block error rate corresponding to the target MCS under each TTI. The processing unit 1102 is specifically used to: determine the first block error rate according to the decoding result of each TTI; and obtain the second block error rate according to the prediction block error rate corresponding to the target MCS under each TTI.

[0140] In one possible implementation, after the TTI 1 communication unit sends data to the second communication device based on the target MCS, the communication unit 1101 is further configured to: acquire at least one sample data within a second preset time period, the sample data including channel parameters, MCS and sample block error rate, the sample block error rate being obtained from the decoding results corresponding to multiple TTIs respectively, the channel parameters and MCS of multiple TTIs being consistent; the processing unit 1102 is further configured to: adjust the first block error rate prediction model based on at least one sample data to obtain a second block error rate prediction model; if the parameter change of the second block error rate prediction model is greater than a second threshold, then based on the second block error rate prediction model, obtain the predicted block error rate corresponding to each MCS among multiple MCSs under TTI 3, TTI 3 being later than TT1.

[0141] In one possible implementation, the channel parameters include: the channel quality indication of TTI 1, the change between the channel quality indication of TTI 1 and the channel quality indication of the previous TTI, the reference signal received power of the cell, the precoding matrix indication, the rank indication, the signal transmission rate, the transmission power, the reference signal received power of neighboring cells, the precoding matrix indication, the rank indication, the signal transmission rate, or the transmission power.

[0142] In one possible implementation, the first communication device and the second communication device are communication devices in a multi-user multiple-input multiple-output (MU MIMO) system. The channel parameters also include the number of second communication devices sharing the same channel resources and the correlation coefficient between the second communication devices.

[0143] In one possible implementation, the communication unit 1101 is specifically used to: acquire multiple candidate pairing sets under the MU MIMO system, the candidate pairing sets including the pairing status of one or more second communication devices; the processing unit 1102 is specifically used to: for each pairing status in each candidate pairing set, predict multiple predicted block error rates of each second communication device in the pairing status at TTI 1 using a first block error rate prediction model, the predicted block error rates of the multiple predicted block error rates correspond one-to-one with the MCSs of the multiple MCSs; based on the multiple MCSs and the multiple predicted block error rates of each second communication device in each pairing status at TTI 1 in each candidate pairing set, determine the target pairing set corresponding to TTI 1 from the multiple candidate pairing sets, and the target MCS of each second communication device in each pairing status in the target pairing set.

[0144] like Figure 12aThe illustration shows a communication device 120 provided in an embodiment of this application, used to implement the function of a first communication device in the above-described uplink modulation and coding strategy selection method. This device can be a first communication device or a device for a first communication device. The device for the first communication device can be a chip system or a chip within the first communication device. The chip system can be composed of chips, or it can include chips and other discrete components.

[0145] The communication device 120 includes at least one processor 1220 for implementing the data processing function of the first communication device in the method provided in this application embodiment. The device 120 may also include a communication interface 1210 for implementing the transmit and receive operations of the first communication device in the method provided in this application embodiment. In this application embodiment, the communication interface may be a transceiver, circuit, bus, module, or other type of communication interface for communicating with other devices via a transmission medium. For example, the communication interface 1210 enables the device in the device 120 to communicate with other devices. The processor 1220 uses the communication interface 1210 to transmit and receive data and is used to implement the method described in the above method embodiment.

[0146] Device 120 may further include at least one memory 1230 for storing program instructions and / or data. Memory 1230 is coupled to processor 1220. The coupling in this embodiment is an indirect coupling or communication connection between devices, units, or modules, and may be electrical, mechanical, or other forms, for information exchange between devices, units, or modules. Processor 1220 may operate in conjunction with memory 1230. Processor 1220 may execute program instructions stored in memory 1230. At least one of the at least one memory may be included in the processor.

[0147] This application embodiment does not limit the specific connection medium between the communication interface 1210, processor 1220, and memory 1230. This application embodiment... Figure 12a The memory 1230, processor 1220, and communication interface 1210 are connected via a bus 1240. Figure 12a The connections between other components are shown in bold and are for illustrative purposes only, not as limiting information. The bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, Figure 12a The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0148] When device 120 is specifically used in a first communication device, for example, when device 120 is specifically a chip or chip system, the communication interface 1210 may output or receive baseband signals. When device 120 is specifically a first communication device, the communication interface 1210 may output or receive radio frequency signals. In the embodiments of this application, the processor may be a general-purpose processor, digital signal processor, application-specific integrated circuit, field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, which can implement or execute the methods, steps and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the methods disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or being executed by a combination of hardware and software modules in the processor.

[0149] As an example, taking the first communication device as the terminal device. Figure 12b This is a schematic diagram of the structure of a terminal device 1200 provided in an embodiment of this application. This terminal device can perform the operations executed by the first communication device in the above-described modulation and coding scheme (MCS) selection method.

[0150] For ease of explanation, Figure 12b Only the main components of the terminal communication device are shown. (For example...) Figure 12b As shown, the terminal communication device 1200 includes a processor, memory, radio frequency circuitry, antenna, and input / output devices. The processor is primarily used for processing communication protocols and data, controlling the entire terminal communication device, executing software programs, and processing software program data, such as supporting the terminal device in executing... Figure 2 or Figure 7 The described process. The memory is primarily used to store software programs and data. The radio frequency (RF) circuitry is mainly used for converting baseband signals to RF signals and processing RF signals. The antenna is mainly used for transmitting and receiving RF signals in the form of electromagnetic waves. The terminal device 1200 may also include input / output devices, such as a touchscreen, display screen, and keyboard, mainly used for receiving user input data and outputting data to the user. It should be noted that some types of first communication devices may not have input / output devices.

[0151] When the terminal device is powered on, the processor can read the software program from the storage unit, interpret and execute the software program, and process the data in the software program. When data needs to be transmitted wirelessly, the processor performs baseband processing on the data to be transmitted and outputs the baseband signal to the radio frequency (RF) circuit. The RF circuit processes the baseband signal and transmits the RF signal outward as electromagnetic waves through the antenna. When data is sent to the terminal device, the RF circuit receives the RF signal through the antenna, converts the RF signal into a baseband signal, and outputs the baseband signal to the processor. The processor converts the baseband signal back into data and processes the data.

[0152] Those skilled in the art will understand that, for ease of explanation, Figure 12b Only one memory and processor are shown. In a real first communication device, multiple processors and memories may exist. Memory may also be called storage medium or storage device, etc., and this application embodiment does not limit this.

[0153] As an optional implementation, the processor may include a baseband processor and a central processing unit (CPU). The baseband processor is mainly used to process communication protocols and communication data, while the CPU is mainly used to control the entire first communication device, execute software programs, and process the data of the software programs. Optionally, the processor may also be a network processor (NP) or a combination of a CPU and an NP. The processor may further include hardware chips. The aforementioned hardware chips may be application-specific integrated circuits (ASICs), programmable logic devices (PLDs), or combinations thereof. The aforementioned PLDs may be complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), generic array logic (GALs), or any combination thereof. Memory may include volatile memory, such as random-access memory (RAM); memory may also include non-volatile memory, such as flash memory, hard disk drive (HDD), or solid-state drive (SSD); memory may also include combinations of the above types of memory.

[0154] For example, in the embodiments of this application, such as Figure 12b As shown, the antenna and radio frequency circuit with transceiver function can be regarded as the communication unit 1201 of the terminal device 1200, and the processor with processing function can be regarded as the processing unit 1202 of the terminal device 1200.

[0155] The communication unit 1201, also known as a transceiver, transceiver device, or transceiver unit, is used to implement transmission and reception functions. Optionally, the device in the communication unit 1201 used for receiving functions can be considered a receiving unit, and the device in the communication unit 1201 used for transmitting functions can be considered a transmitting unit; that is, the communication unit 1201 includes a receiving unit and a transmitting unit. For example, the receiving unit can also be called a receiver, receiver circuit, or receiving device, and the transmitting unit can be called a transmitter, transmitter, or transmitting circuit.

[0156] In some embodiments, the communication unit 1201 and the processing unit 1202 may be integrated into one device or separated into different devices. In addition, the processor and the memory may be integrated into one device or separated into different devices.

[0157] The communication unit 1201 can be used to perform the transmit and receive operations of the terminal device in the above method embodiment. The processing unit 1202 can be used to perform the data processing operations of the terminal device in the above method embodiment.

[0158] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed, cause the method executed by the terminal device in the above method embodiments to be implemented.

[0159] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed, cause the method executed by the first communication device in the above method embodiments to be implemented.

[0160] This application also provides a computer program product, which includes a computer program that, when executed, causes the method executed by the terminal device in the above method embodiments to be implemented.

[0161] This application also provides a computer program product, which includes a computer program that, when executed, causes the method executed by the first communication device in the above method embodiments to be implemented.

[0162] This application also provides a communication system, which includes a terminal device and a first communication device. The terminal device is used to execute the method described in the above method embodiments. The first communication device is used to execute the method described in the above method embodiments.

[0163] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0164] The descriptions of the various embodiments provided in this application can be referenced mutually. Each embodiment has its own emphasis, and parts not described in detail in a certain embodiment can be referred to the relevant descriptions of other embodiments. For the sake of convenience and brevity, for example, the functions and execution steps of the various devices and equipment provided in the embodiments of this application can be referred to the relevant descriptions of the method embodiments of this application. The method embodiments and the device embodiments can also be referenced, combined or cited from each other.

[0165] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for selecting a modulation and coding strategy (MCS), characterized in that, The method includes: The first communication device predicts the predicted block error rate for each of the multiple MCSs under a transmission time interval TTI 1 using a first block error rate prediction model. The prediction parameters for the predicted block error rate for each MCS include one or more channel parameters corresponding to TTI 1 and the MCS itself. The first block error rate prediction model is a neural network model. The first communication device determines the target MCS corresponding to TTI 1 from the plurality of MCSs based on the plurality of MCSs and the prediction error rate corresponding to each MCS; In the TTI 1, the first communication device sends data to the second communication device based on the target MCS.

2. The method according to claim 1, characterized in that, Among the multiple MCSs, the target MCS and the prediction error rate corresponding to the target MCS maximize the spectral efficiency or throughput of TTI 1.

3. The method according to claim 1, characterized in that, After the first communication device in TTI 1 sends data to the second communication device based on the target MCS, the method further includes: The first communication device acquires a first block error rate and a second block error rate within a first preset time period. The first block error rate is the actual statistical block error rate within the first preset time period, and the second block error rate is the predicted block error rate within the first preset time period obtained based on the first block error rate prediction model. If the difference between the first block error rate and the second block error rate is greater than or equal to the first threshold, the first communication device determines the MCS corresponding to TTI 2 based on the target block error rate, wherein TTI 2 is later than TTI 1.

4. The method according to claim 3, characterized in that, The first preset time period includes multiple TTIs. The first communication device acquires the first block error rate and the second block error rate within the first preset time period, including: The first communication device acquires the decoding result of each TTI in the plurality of TTIs and the prediction error rate corresponding to the target MCS under each TTI; The first communication device determines the first block error rate based on the decoding results of each TTI; The first communication device obtains the second block error rate based on the predicted block error rate corresponding to the target MCS under each TTI.

5. The method according to any one of claims 1-4, characterized in that, After the first communication device in TTI 1 sends data to the second communication device based on the target MCS, the method further includes: The first communication device acquires at least one sample data within a second preset time period. The sample data includes channel parameters, MCS, and sample block error rate. The sample block error rate is obtained from the decoding results corresponding to multiple TTIs, and the channel parameters and MCS of the multiple TTIs are consistent. The first communication device adjusts the first block error rate prediction model based on the at least one sample data to obtain a second block error rate prediction model; If the parameter change of the second block error rate prediction model is greater than the second threshold, the first communication device obtains the predicted block error rate for each MCS in multiple MCSs under TTI 3 based on the second block error rate prediction model, wherein TTI 3 is later than TTI 1.

6. The method according to any one of claims 1-4, characterized in that, The channel parameters include: channel quality indication for TTI 1, the change between the channel quality indication for TTI 1 and the channel quality indication for the previous TTI, the reference signal received power of the current cell, precoding matrix indication, rank indication, signal transmission rate, transmission power, and the reference signal received power, precoding matrix indication, rank indication, signal transmission rate, or transmission power of neighboring cells.

7. The method according to claim 6, characterized in that, The first communication device and the second communication device are communication devices in a multi-user multiple-input multiple-output (MU MIMO) system. The channel parameters also include the number of the second communication devices sharing the same channel resources and the correlation coefficient between the second communication devices.

8. The method according to claim 7, characterized in that, The first communication device predicts the predicted block error rate for each of the multiple MCSs under a transmission time interval TTI 1 using a first block error rate prediction model, including: The first communication device acquires multiple candidate pairing sets under the MU MIMO system, and the candidate pairing sets include the pairing status of one or more of the second communication devices; For each pairing in each candidate pairing set, the first communication device predicts multiple predicted block error rates for each of the second communication devices in the TTI 1 using a first block error rate prediction model. The predicted block error rates among the multiple predicted block error rates correspond one-to-one with the MCSs among the multiple MCSs. The first communication device determines the target MCS corresponding to TTI 1 from the plurality of MCSs based on the plurality of MCSs and the prediction block error rate corresponding to each MCS, including: The first communication device determines, from the plurality of candidate pairing sets, the target pairing set corresponding to TTI 1, and the target MCS of each second communication device in each pairing case in each candidate pairing set, based on the plurality of MCS and the plurality of prediction block error rates of each second communication device in each pairing case in each candidate pairing set.

9. A communication device, characterized in that, The communication device includes: The processing unit is configured to predict the predicted block error rate (BRR) of each MCS among multiple MCSs under a transmission time interval (TTI) 1 using a first BRR prediction model. The prediction parameters for the predicted BRR of each MCS include one or more channel parameters corresponding to TTI 1 and the MCS itself. The first BRR prediction model is a neural network model. The processing unit is further configured to determine the target MCS corresponding to TTI 1 from the plurality of MCSs based on the plurality of MCSs and the prediction error rate corresponding to each MCS in the plurality of MCSs; A communication unit is used to send data to a second communication device based on the target MCS in the TTI 1.

10. The apparatus according to claim 9, characterized in that, Among the multiple MCSs, the target MCS and the prediction error rate corresponding to the target MCS maximize the spectral efficiency or throughput of TTI 1.

11. The apparatus according to claim 9, characterized in that, After the communication unit sends data to the second communication device based on the target MCS, the processing unit is further configured to: Obtain the first error block rate and the second error block rate within the first preset time period. The first error block rate is the actual statistical error block rate within the first preset time period, and the second error block rate is the predicted error block rate within the first preset time period obtained based on the first error block rate prediction model. If the difference between the first block error rate and the second block error rate is greater than or equal to the first threshold, then the MCS corresponding to TTI 2 is determined according to the target block error rate, wherein TTI 2 is later than TTI 1.

12. The apparatus according to claim 11, characterized in that, The first preset time period includes multiple TTIs. The communication unit is specifically used to: obtain the decoding result of each TTI in the plurality of TTIs and the prediction error rate corresponding to the target MCS under each TTI; The processing unit is specifically used to: determine a first block error rate based on the decoding results of each TTI; and obtain a second block error rate based on the predicted block error rate corresponding to the target MCS under each TTI.

13. The apparatus according to any one of claims 9-12, characterized in that, After the communication unit in TTI 1 sends data to the second communication device based on the target MCS... The communication unit is further configured to: acquire at least one sample data within a second preset time period, the sample data including channel parameters, MCS and sample block error rate, the sample block error rate being obtained from the decoding results corresponding to multiple TTIs respectively, and the channel parameters and MCS of the multiple TTIs being consistent; The processing unit is further configured to: adjust the first block error rate prediction model according to the at least one sample data to obtain a second block error rate prediction model; if the parameter change of the second block error rate prediction model is greater than a second threshold, then obtain the predicted block error rate corresponding to each MCS in multiple MCSs under TTI 3 based on the second block error rate prediction model, wherein TTI 3 is later than TTI 1.

14. The apparatus according to any one of claims 9-12, characterized in that, The channel parameters include the channel quality indication of TTI 1, the change between the channel quality indication of TTI 1 and the channel quality indication of the previous TTI, the reference signal received power of the current cell, the precoding matrix indication, the rank indication, the signal transmission rate, the transmission power, the reference signal received power of the neighboring cell, the precoding matrix indication, the rank indication, the signal transmission rate, or the transmission power.

15. The apparatus according to claim 14, characterized in that, The communication device and the second communication equipment are communication devices in a multi-user multiple-input multiple-output (MU MIMO) system. The channel parameters also include the number of the second communication equipment sharing the same channel resources and the correlation coefficient between the second communication equipment.

16. The apparatus according to claim 15, characterized in that, The communication unit is specifically used to: acquire multiple candidate pairing sets under the MU MIMO system, wherein the candidate pairing sets include the pairing status of one or more of the second communication devices; The processing unit is specifically configured to: for each pairing situation in each candidate pairing set, predict multiple predicted block error rates for each of the second communication devices in the pairing situation at TTI 1 using a first block error rate prediction model, wherein the predicted block error rates among the multiple predicted block error rates correspond one-to-one with the MCSs among the multiple MCSs; based on the multiple MCSs and the multiple predicted block error rates for each of the second communication devices in each pairing situation in each candidate pairing set at TTI 1, determine the target pairing set corresponding to TTI 1 from the multiple candidate pairing sets, and the target MCS for each of the second communication devices in each pairing situation in the target pairing set.

17. A communication device comprising a processor, wherein the method of any one of claims 1-8 is executed when the processor executes a computer program in memory.

18. A communication device, characterized in that, Including processor and memory; The memory is used to store computer-executed instructions; The processor is configured to execute computer execution instructions stored in the memory to cause the communication device to perform the method as described in any one of claims 1-8.

19. A communication device, characterized in that, Includes processor, memory, and transceiver; The transceiver is used to receive or send signals; The memory is used to store computer programs; The processor is configured to invoke the computer program from the memory to perform the method as described in any one of claims 1-8.

20. A communication device, characterized in that, Includes processor and interface circuitry; The interface circuit is configured to receive computer execution instructions and transmit them to the processor; the processor executes the computer execution instructions to perform the method as described in any one of claims 1-8.

21. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store computer-executable instructions that, when executed, cause the method as described in any one of claims 1-8 to be implemented.

22. A computer program product, characterized in that, The computer program product includes a computer program that, when executed, causes the method as described in any one of claims 1-8 to be implemented.

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