Communication method, device and system, medium, and computer program product

Abstract

The present disclosure provides a communication method, device and system, a medium, and a computer program product. The method comprises: inputting a first data set comprising a first signal into a first model, so as to obtain a second signal output by the first model, wherein the first signal is a baseband in-phase / quadrature signal, and the second signal is a predicted pre-distorted signal; and using the second signal as an input signal of a power amplifier. The present disclosure can improve the quality of output signals of power amplifiers and improve system performance.

Description

Communication method, device, system, medium and computer program product TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of communication, and particularly relates to a communication method, device, system, medium and computer program product. BACKGROUND

[0002] Digital pre-distortion (DPD) is a technique used to compensate for the non-linear distortion of a power amplifier (PA). The non-linear distortion of the PA can cause signal distortion and spectrum expansion, thereby affecting the performance of the communication system. The DPD technique introduces pre-distortion into the input signal to offset the non-linear effects of the PA, thereby improving the quality and efficiency of the output signal. However, the function design of digital pre-distortion is relatively complex.

[0003] SUMMARY

[0004] Embodiments of the present disclosure provide a communication method, device, system, medium and computer program product.

[0005] According to a first aspect of embodiments of the present disclosure, a communication method is provided, executed by a first device, and the method comprises: inputting a first data set comprising a first signal into a first model to obtain a second signal output by the first model, wherein the first signal is a baseband in-phase / quadrature signal, and the second signal is a predicted pre-distortion processed signal; and taking the second signal as an input signal of a power amplifier.

[0006] According to a second aspect of embodiments of the present disclosure, a communication device is provided, comprising: a processing module configured to input a first data set comprising a first signal into a first model to obtain a second signal output by the first model, wherein the first signal is a baseband in-phase / quadrature signal, and the second signal is a predicted pre-distortion processed signal; and taking the second signal as an input signal of a power amplifier.

[0007] According to a third aspect of embodiments of the present disclosure, a communication device is provided, comprising: one or more processors; and a memory coupled to the processors and having stored thereon executable instructions that, when executed by the processors, cause the communication method of the first aspect to be performed.

[0008] According to a fourth aspect of embodiments of the present disclosure, a communication system is provided, comprising a terminal and a network device, wherein the terminal is configured to implement the communication method of the first aspect, and / or the network device is configured to implement the communication method of the first aspect.

[0009] According to a fifth aspect of the embodiments of the present disclosure, a storage medium is provided, which stores instructions, when the instructions are executed on a communication device, causing the communication device to perform the communication method of the first aspect.

[0010] According to a sixth aspect of the embodiments of the present disclosure, a computer program product is provided, which includes a computer program and / or instructions, when the computer program and / or instructions are executed by a communication device, implementing the communication method of the first aspect.

[0011] With the above technical solutions, at least the following beneficial technical effects can be achieved:

[0012] The first device inputs a first data set including a first signal into the first model to obtain a second signal output by the first model. The second signal is taken as an input signal of the power amplifier. Since the second signal is a signal pre-distorted on the first signal predicted by the first model, compared with directly taking the first signal as the input signal of the power amplifier, the second signal as the input signal of the power amplifier can offset part or all of the nonlinear effects of the power amplifier, thereby improving the quality of the output signal of the power amplifier and improving the system performance. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following describes the drawings required for the embodiments, and the following drawings are only some embodiments of the present disclosure, and do not specifically limit the protection scope of the present disclosure.

[0014] FIG. 1A is an exemplary schematic diagram of an architecture of a communication system according to an embodiment of the present disclosure.

[0015] FIG. 1B is a schematic diagram of a signal processing flow according to an embodiment of the present disclosure.

[0016] FIG. 1C is a schematic diagram of the effect of a combination of a pre-distorter and an amplifier according to an embodiment of the present disclosure.

[0017] FIG. 2A is an interaction schematic diagram of a communication method according to an embodiment of the present disclosure.

[0018] FIG. 2B is a schematic diagram of model training according to an embodiment of the present disclosure.

[0019] FIG. 2C is an interaction schematic diagram of a communication method according to an embodiment of the present disclosure.

[0020] FIG. 3A is a flow schematic diagram of a communication method according to an embodiment of the present disclosure.

[0021] FIG. 3B is a flow schematic diagram of a communication method according to an embodiment of the present disclosure.

[0022] FIG. 3C is a flow diagram illustrating a communication method according to an embodiment of the present disclosure.

[0023] FIG. 4 is a flow diagram illustrating a signal processing method according to an embodiment of the present disclosure.

[0024] FIG. 5 is a flow diagram illustrating a signal processing method according to an embodiment of the present disclosure.

[0025] FIG. 6 is a structural diagram of a terminal according to an embodiment of the present disclosure.

[0026] FIG. 7 is a structural diagram of a network device according to an embodiment of the present disclosure.

[0027] FIG. 8A is a structural diagram of a communication device according to an embodiment of the present disclosure.

[0028] FIG. 8B is a structural diagram of a chip according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0029] The embodiments of the present disclosure provide a communication method, device, system, medium and computer program product.

[0030] In a first aspect, the embodiments of the present disclosure provide a communication method, performed by a first device, the method comprising: inputting a first data set comprising a first signal into a first model to obtain a second signal output by the first model, wherein the first signal is a baseband in-phase / quadrature signal, and the second signal is a predicted signal after pre-distortion processing; and taking the second signal as an input signal of a power amplifier.

[0031] In the above embodiments, the first device inputs a first data set comprising a first signal into a first model to obtain a second signal output by the first model. The second signal is taken as an input signal of a power amplifier. Since the second signal is a signal after pre-distortion processing of the first signal predicted by the first model, the present disclosure takes the second signal as an input signal of a power amplifier, which can offset part or all of the nonlinear effects of the power amplifier compared with directly taking the first signal as an input signal of the power amplifier, thereby improving the quality of the output signal of the power amplifier and enhancing the system performance.

[0032] And, an AI / ML model based on Artificial Intelligence (AI) / Machine Learning (ML) can predict a very accurate result, that is, the first model can predict a very accurate second signal after pre-distortion processing for the first signal, which is simpler and faster than describing a non-linear model of the PA by a polynomial model, a Volterra series model, a memory polynomial model (Memory Polynomials), and designing a pre-distortion function based on the non-linear model of the PA, and can also obtain an accurate second signal after pre-distortion processing, which can improve the efficiency and accuracy of signal processing.

[0033] In some embodiments of the first aspect, the first data set further includes environmental information, and the environmental information includes at least one of the following:

[0034] Standing wave ratio information;

[0035] Temperature information of the power amplifier.

[0036] In the above embodiments, in the process of predicting the second signal after pre-distortion processing corresponding to the first signal by the first model, the environmental information can be used to improve the accuracy of the prediction of the first model.

[0037] In some embodiments of the first aspect, the first model is obtained by training in the following manner:

[0038] The first model to be trained is trained according to the second data set and the power amplifier, and a trained first model is obtained.

[0039] In the above embodiments, the first model to be trained and the power amplifier are collaboratively trained according to the second data set, which can simplify the training method and the difficulty of obtaining training data, and obtain the trained first model more quickly and easily.

[0040] In some embodiments of the first aspect, the second data set includes input sample data and output sample data.

[0041] The training of the first model to be trained according to the second data set and the power amplifier comprises: performing at least one first process until a model convergence condition is met, to obtain the trained first model; wherein the first process comprises: inputting the input sample data into the first model; inputting an output signal of the first model into the power amplifier; and adjusting a model parameter of the first model according to an error between an output signal of the power amplifier and the output sample data.

[0042] In the above embodiment, the model training manner described above can be used to obtain a first model with better prediction effect and higher robustness more quickly.

[0043] In combination with some embodiments of the first aspect, in some embodiments, the model convergence condition comprises at least one of the following:

[0044] The number of times of performing the first process is greater than or equal to a first threshold value;

[0045] The error is less than or equal to a second threshold value.

[0046] In the above embodiment, the model convergence condition described above can avoid overfitting of the first model on the sample data set.

[0047] In combination with some embodiments of the first aspect, in some embodiments, the input sample data comprises in-phase / quadrature signal samples.

[0048] In combination with some embodiments of the first aspect, in some embodiments, the input sample data comprises in-phase / quadrature signal samples and environmental information samples, and the environmental information samples comprise at least one of the following:

[0049] A temperature sample of the power amplifier;

[0050] A standing wave ratio sample.

[0051] In the above embodiment, the first model is trained by adding environmental information samples, which can improve the prediction accuracy of the first model.

[0052] In combination with some embodiments of the first aspect, in some embodiments, the output sample data comprises output signal samples of the power amplifier.

[0053] In combination with some embodiments of the first aspect, in some embodiments, the number of the second data sets is a plurality, and at least one of the environmental information samples, the in-phase / quadrature signal samples, and the output sample data in different data sets is different.

[0054] In the above embodiment, the robustness of the first model can be improved by training the first model through a rich sample data set.

[0055] With reference to some embodiments of the first aspect, in some embodiments, the first device comprises at least one of:

[0056] a terminal;

[0057] a network device.

[0058] In the above embodiment, the type of the first device performing the above method is specified.

[0059] With reference to some embodiments of the first aspect, in some embodiments, the method further comprises: receiving a first message sent by a second device, the first message comprising a first data packet, the first data packet being an installation data packet of the first model, wherein the model training capability of the second device is higher than that of the first device; and installing the first model according to the first data packet.

[0060] In the above embodiment, a first model with better effect, for example, higher accuracy, can be trained due to the stronger model training capability. The use of the first model with better effect trained by the second device with stronger model training capability on the first device can improve the signal pre-distortion capability of the first device.

[0061] With reference to some embodiments of the first aspect, in some embodiments, the method further comprises: monitoring the performance of the first model, sending a second message to the second device, the second message being used to indicate the monitored performance of the first model; receiving a third message sent by the second device, the third message comprising an operation instruction determined by the second device according to the performance of the first model; and performing a corresponding operation according to the operation instruction.

[0062] In the above embodiment, by monitoring and reporting the performance of the first model, measures can be taken in time when the performance of the first model does not meet the requirements, so as to guarantee the communication quality as much as possible.

[0063] With reference to some embodiments of the first aspect, in some embodiments, the operation comprises at least one of:

[0064] updating the first model;

[0065] stopping using the first model;

[0066] replacing the first model with a pre-distortion algorithm;

[0067] reinstalling the first model.

[0068] In the above embodiments, by performing the at least one operation, the signal transmission quality of the first device can be guaranteed.

[0069] In some embodiments of the first aspect, the operation instruction includes a second data packet for updating the first model, and the updating the first model includes:

[0070] updating a parameter configuration of the first model according to the second data packet.

[0071] In the above embodiments, the purpose of optimizing the first model can be achieved by updating the parameter configuration of the first model.

[0072] In some embodiments of the first aspect, the operation instruction includes a third data set, and the updating the first model includes: retraining the first model according to the third data set to obtain a trained first model, wherein a data amount of the third data set is determined by the second device according to a model training capability of the first device.

[0073] In the above embodiments, the first device can further train the first model through the third data set to optimize the first model and improve the pre-distortion effect.

[0074] In a second aspect, the embodiments of the present disclosure provide a communication device, which includes at least one of a transceiver module and a processing module, and the communication device is configured to perform the optional implementation manners of the first aspect.

[0075] In a third aspect, the embodiments of the present disclosure provide a communication device, which includes one or more processors, and the communication device is configured to perform the optional implementation manners of the first aspect.

[0076] In a fourth aspect, the embodiments of the present disclosure provide a communication system, which includes a terminal and a network device, wherein the terminal is configured to perform the method described in the optional implementation manners of the first aspect, and / or the network device is configured to perform the method described in the optional implementation manners of the first aspect.

[0077] In a fifth aspect, the embodiments of the present disclosure provide a storage medium, which stores instructions, and when the instructions run on a communication device, the communication device is caused to perform the method described in the optional implementation manners of the first aspect.

[0078] In a sixth aspect, the embodiments of the present disclosure provide a program product, and when the program product is executed by a communication device, the communication device is caused to perform the method described in the optional implementation manners of the first aspect.

[0079] In a seventh aspect, the embodiments of the present disclosure provide a computer program which, when running on a computer, causes the computer to perform the method described in the optional implementation manner of the first aspect.

[0080] In an eighth aspect, the embodiments of the present disclosure provide a chip or chip system. The chip or chip system comprises processing circuitry configured to perform the method described in the optional implementation manner of the first aspect.

[0081] It can be understood that the first device, the communication device, the terminal, the network device, the communication system, the storage medium, the program product, the computer program, the chip or the chip system are all used to perform the method proposed in the embodiments of the present disclosure. Therefore, the beneficial effects that can be achieved are referred to the beneficial effects in the corresponding method, which will not be described here.

[0082] The embodiments of the present disclosure propose a communication method, device, system, medium and computer program product. In some embodiments, the terms of the communication method, information processing method, and AI / ML-based power amplifier digital pre-distortion processing method can be replaced with each other, the terms of the communication device, information processing device, and AI / ML-based power amplifier digital pre-distortion processing device can be replaced with each other, and the terms of the communication system, information processing system, and AI / ML-based power amplifier digital pre-distortion processing system can be replaced with each other.

[0083] The embodiments of the present disclosure are not exhaustive, but only illustrate some embodiments, and are not specific limitations on the protection scope of the present disclosure. In the case of no contradiction, each step in an embodiment can be implemented as an independent embodiment, and the steps can be combined arbitrarily, for example, the scheme after removing some steps in an embodiment can also be implemented as an independent embodiment, and the order of the steps in an embodiment can be exchanged arbitrarily, in addition, the optional implementation manners in an embodiment can be combined arbitrarily; in addition, the embodiments can be combined arbitrarily, for example, some or all steps of different embodiments can be combined arbitrarily, and an embodiment can be combined with the optional implementation manners of other embodiments.

[0084] In the embodiments of the present disclosure, the terms and / or descriptions between the embodiments are consistent if there is no special description and logical conflict, and can be referred to each other, and the technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationship.

[0085] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments, and not as a limitation on the present disclosure.

[0086] In the embodiments of the present disclosure, an element expressed in singular form, such as "a", "an", "the", "said", "the aforementioned", "the foregoing", "this", and the like, unless otherwise specified, can represent "one and only one", or can represent "one or more", "at least one", and the like. For example, in the case of using an article such as "a", "an", "the" in English, the noun after the article can be understood as a singular expression, or can be understood as a plural expression.

[0087] In the embodiments of the present disclosure, "plurality" refers to two or more.

[0088] In some embodiments, the terms "at least one of", "one or more", "a plurality of", "multiple", and the like can be replaced with each other.

[0089] In some embodiments, the description manner such as "at least one of A, B", "A and / or B", "A in one case and B in another case", "in response to a case A, in response to a case B", and the like can include the following technical solutions according to the case: in some embodiments, A is executed regardless of B; in some embodiments, B is executed regardless of A; in some embodiments, A and B are selectively executed from A and B; in some embodiments, A and B are executed (A and B are both executed). When there are more branches such as A, B, C, and the like, it is similar to the above.

[0090] In some embodiments, the description manner such as "A or B" and the like can include the following technical solutions according to the case: in some embodiments, A is executed regardless of B; in some embodiments, B is executed regardless of A; in some embodiments, A and B are selectively executed from A and B. When there are more branches such as A, B, C, and the like, it is similar to the above.

[0091] The prefix words of "first", "second" and the like in the embodiments of the present disclosure are merely used to distinguish different description objects, and do not constitute limitation on the position, order, priority, quantity or content of the description objects. The description objects are described in the claims or embodiments, and should not be construed as redundant limitation because of the use of the prefix words. For example, the description object is "field", and the ordinal words before "field" in "first field" and "second field" do not limit the position or order between "fields", and "first" and "second" do not limit whether the "fields" modified thereby are in the same message or not, nor limit the order of "first field" and "second field". For another example, the description object is "level", and the ordinal words before "level" in "first level" and "second level" do not limit the priority between "levels". For another example, the quantity of the description object is not limited by the ordinal words, and can be one or more. For example, "first device", wherein the quantity of "device" can be one or more. In addition, the objects modified by different prefix words can be the same or different, for example, the description object is "device", and "first device" and "second device" can be the same device or different devices, and the types thereof can be the same or different. For another example, the description object is "information", and "first information" and "second information" can be the same information or different information, and the contents thereof can be the same or different.

[0092] In some embodiments, "including A", "containing A", "for indicating A", "carrying A" can be interpreted as directly carrying A, or indirectly indicating A.

[0093] In some embodiments, the terms of "in response to", "in response to determining", "in the case of", "when", "when", "if", "if" and the like can be replaced with each other.

[0094] In some embodiments, the terms of "greater than", "greater than or equal to", "not less than", "more than", "more than or equal to", "not less than", "higher than", "higher than or equal to", "not lower than", "above" and the like can be replaced with each other, and the terms of "less than", "less than or equal to", "not greater than", "less than", "less than or equal to", "not more than", "lower than", "lower than or equal to", "not higher than", "below" and the like can be replaced with each other.

[0095] In some embodiments, an apparatus or the like can be interpreted as an entity, and can also be interpreted as virtual, and the name thereof is not limited to the name described in the embodiments, and the terms "apparatus", "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", "subject" and the like can be replaced with each other.

[0096] In some embodiments, a "network" can be interpreted as an apparatus (for example, an access network device, a core network device, and the like) included in the network.

[0097] In some embodiments, the terms "access network device (AN device)", "radio access network device (RAN device)", "base station (BS)", "radio base station", "fixed station", "node", "access point", "transmission point (TP)", "reception point (RP)", "transmission / reception point (TRP)", "panel", "antenna panel", "antenna array", "cell", "macro cell", "small cell", "femto cell", "pico cell", "sector", "cell group", "serving cell", "carrier", "component carrier", "bandwidth part (BWP)" and the like can be replaced with each other.

[0098] In some embodiments, the terms "terminal," "terminal device," "user equipment (UE)," "user terminal," "mobile station (MS)," "mobile terminal (MT)," "subscriber station," "mobile unit," "subscriber unit," "wireless unit," "remote unit," "mobile device," "wireless device," "wireless communication device," "remote device," "mobile subscriber station," "access terminal," "mobile terminal," "wireless terminal," "remote terminal," "handset," "user agent," "mobile client," "client," and so on can be replaced with each other.

[0099] In some embodiments, the access network device, the core network device, or the network device can be replaced with a terminal. For example, the embodiments of the present disclosure can also be applied to a structure in which communication between the access network device, the core network device, or the network device and the terminal is replaced with communication between a plurality of terminals (e.g., device-to-device (D2D), vehicle-to-everything (V2X), etc.). In this case, the terminal can also be configured to have all or part of the functions of the access network device. In addition, the terms "uplink," "downlink," and the like can also be replaced with terms corresponding to the inter-terminal communication (e.g., "side"). For example, the uplink channel, the downlink channel, and the like can be replaced with the side channel, and the uplink, the downlink, and the like can be replaced with the sidelink.

[0100] In some embodiments, the terminal can be replaced with the access network device, the core network device, or the network device. In this case, the access network device, the core network device, or the network device can also be configured to have all or part of the functions of the terminal.

[0101] In some embodiments, obtaining data, information, and the like can comply with laws and regulations of the country where the location is.

[0102] In some embodiments, data, information, and the like can be obtained after obtaining the consent of the user.

[0103] In addition, each element, each row, or each column in the table of the embodiments of the present disclosure can be implemented as an independent embodiment, and any combination of any element, any row, or any column can also be implemented as an independent embodiment.

[0104] FIG. 1A is a schematic diagram of an architecture of a communication system according to an embodiment of the present disclosure. As shown in FIG. 1A, the communication system 100 can include a terminal 101 and a network device 102.

[0105] In some embodiments, the terminal 101 includes at least one of a mobile phone, a wearable device, an Internet of Things device, a car with communication function, a smart car, a tablet computer (Pad), a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in smart grid, a wireless terminal device in transportation safety, a wireless terminal device in smart city, a wireless terminal device in smart home, and the like, but is not limited thereto.

[0106] In some embodiments, the network device 102 can include at least one of an access network device and a core network device.

[0107] Optionally, the network device 102 is an access network device. Optionally, the access network device is at least one of a node or device that accesses a terminal to a wireless network, and can include at least one of an evolved NodeB (eNB) in a 5G communication system, a next generation eNB (ng-eNB), a next generation NodeB (gNB), a node B (NB), a home node B (HNB), a home evolved node B (HeNB), a wireless backhaul device, a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a base band unit (BBU), a mobile switching center, a base station in a 6G communication system, an Open RAN, a Cloud RAN, a base station in other communication systems, an access node in a Wi-Fi system, but is not limited thereto.

[0108] In some embodiments, the network device 102 is a base station. Optionally, the base station is at least one of a macro base station, a micro base station (also referred to as a small station), a relay station, an access point, a 5G base station or a future base station, a satellite, a Transmitting and Receiving Point (TRP), a Transmitting Point (TP), a mobile switching center, or other devices that perform a base station function in a communication system, etc., and the embodiments of the present disclosure are not limited thereto. For convenience of description, in all embodiments of the present disclosure, devices that provide a wireless communication function for a terminal device are collectively referred to as network devices or base stations.

[0109] In some embodiments, the network device 102 is a core network device. Optionally, the core network device can be one device including all or part of a first network element, a second network element, etc., or can be a plurality of devices or device groups including all or part of the first network element, the second network element, etc. The network element can be virtual or physical. The core network includes at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), a Next Generation Core (NGC), etc.

[0110] In some embodiments, the technical solutions of the present disclosure can be applied to an Open RAN architecture, at this time, the interfaces between or within the access network devices involved in the embodiments of the present disclosure can become internal interfaces of the Open RAN, and the processes and information interactions between these internal interfaces can be implemented through software or programs.

[0111] In some embodiments, the access network device can be composed of a central unit (CU) and a distributed unit (DU), where the CU can also be referred to as a control unit. The CU-DU structure can split the protocol layers of the access network device, and some of the functions of the protocol layers are controlled by the CU, and the remaining or all of the functions of the protocol layers are distributed in the DU and controlled by the CU. However, the present disclosure is not limited thereto.

[0112] It can be understood that the communication system described in the embodiments of the present disclosure is for more clearly illustrating the technical solutions of the embodiments of the present disclosure, and does not constitute a limitation on the technical solutions proposed by the embodiments of the present disclosure. It can be known by those skilled in the art that, as the system architecture evolves and new business scenarios appear, the technical solutions proposed by the embodiments of the present disclosure are also applicable to similar technical problems.

[0113] The following embodiments of the present disclosure can be applied to the communication system 100 shown in FIG. 1A or part of the subject, but are not limited thereto. The subjects shown in FIG. 1A are exemplary, and the communication system can include all or part of the subjects in FIG. 1A, or other subjects other than FIG. 1A. The number and form of each subject is arbitrary, each subject can be physical or virtual, the connection relationship between each subject is exemplary, each subject can not be connected or can be connected, the connection can be in any way, can be direct connection or indirect connection, can be wired connection or wireless connection.

[0114] Embodiments of the present disclosure can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G new radio (NR), Future Radio Access (FRA), New-Radio Access Technology (RAT), New Radio (NR), New radio access (NX), Future generation radio access (FX), Global System for Mobile communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (Bluetooth (registered trademark)), Public Land Mobile Network (PLMN) network, Device-to-Device (D2D) system, Machine to Machine (M2M) system, Internet of Things (IoT) system, Vehicle-to-Everything (V2X), system using other communication methods, next-generation system expanded based thereon, and the like. Further, a plurality of systems can be applied in combination (for example, combination of LTE or LTE-A and 5G, and the like).

[0115] In some embodiments, a conventional DPD solution describes the nonlinear model of the PA by a polynomial model, a Volterra series model, a memory polynomial model, and the like. A predistortion function is designed based on the nonlinear model of the PA, and the design purpose of the predistortion function is to restore the output signal after the PA to a linear state. Referring to FIGS. 1B and 1C, the input signal V1 is pre-distorted by using a predistorter (predistortion function F(V2)) to obtain a signal V2, and the nonlinear effect of the power amplifier distorts the signal V2 to V0, and V0 output by the power amplifier is the amplifier output signal corresponding to V1.

[0116] In some embodiments, the embodiments of the present disclosure propose that the performance of the predistorter can be improved based on AI technology and ML technology, a more accurate predistorter is established by a deep learning model such as a deep neural network, and the parameters of the predistorter are optimized and adjusted through deep learning in real time to improve the compensation performance and effect of the predistortion.

[0117] FIG. 2A is an interaction schematic diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG. 2A, the embodiments of the present disclosure relate to a communication method, which is performed by the communication system 100, and the above method comprises:

[0118] In step S2101, the terminal 101 inputs a first data set comprising a first signal into a first model to obtain a second signal output by the first model.

[0119] In some embodiments, the first signal is a signal to be input into a power amplifier for power amplification. The power amplifier can be referred to as an amplifier for short.

[0120] Optionally, the first signal is a baseband in-phase / quadrature signal.

[0121] Optionally, the first signal is a signal obtained after processing a baseband in-phase / quadrature signal, for example, a signal obtained after signal encoding, signal modulation, and the like, of the baseband in-phase / quadrature signal.

[0122] In some embodiments, the name of the first signal is not limited, which is, for example, a signal to be processed by the power amplifier, a small power signal, and the like.

[0123] In some embodiments, the first data set comprises the first signal.

[0124] In some embodiments, the first data set comprises the first signal and environmental information.

[0125] In some embodiments, the environmental information is information of an environment in which the power amplifier is located. Optionally, the environmental information comprises factors affecting the performance of the power amplifier. Optionally, the environmental information comprises at least one of the following:

[0126] standing wave ratio information;

[0127] temperature information of the power amplifier.

[0128] It should be explained that the standing wave ratio can be understood as the ratio of the highest radio frequency voltage to the minimum radio frequency voltage through the transmission line.

[0129] In some embodiments, the environmental information can include other factors affecting the performance of the power amplifier, such as humidity information, impedance information, etc., in addition to the standing wave ratio information and the temperature information of the power amplifier.

[0130] In some embodiments, the name of the first data set is not limited, which is, for example, a test data set, an application data set, etc.

[0131] For example, referring to FIG. 5, the environmental information in the first data set can be fed back by the power amplifier.

[0132] In some embodiments, the first model is used to predict the second signal after pre-distortion processing according to the first data set. The first data set includes the first signal, and the second signal is the pre-distortion processed signal corresponding to the first signal. The name of the second signal is not limited, which is, for example, a pre-distortion signal, a first model output signal, etc.

[0133] In some embodiments, the name of the first model is not limited, which is, for example, a pre-distortion processing model, a pre-distortion device, a pre-distortion network, etc.

[0134] In some embodiments, the first model is obtained by training as follows: training the first model to be trained and the power amplifier according to the second data set to obtain the trained first model.

[0135] The second data set is a training sample data set. The second data set includes input sample data and corresponding output sample data.

[0136] For example, the first model and the power amplifier are collaboratively trained according to the second data set until the expected first model is obtained. During the training process, the power amplifier can not be adjusted, that is, the power amplifier is fixed during the training process.

[0137] It should be noted that the training process of the first model can be performed in the terminal or in the network device, and the execution location of the training process of the first model is not limited in the present disclosure.

[0138] In some embodiments, the implementation of training the first model to be trained according to the second data set and the power amplifier to obtain the trained first model comprises: performing the first process at least once until a model convergence condition is met to obtain the trained first model; wherein the first process comprises: inputting the input sample data into the first model, inputting the output signal of the first model into the power amplifier, and adjusting the model parameters of the first model according to the error between the output signal of the power amplifier and the output sample data.

[0139] For example, referring to FIG. 2B, the input sample data is input into the first model, the output signal V2 of the first model is input into the power amplifier, and the model parameters of the first model are adjusted according to the error between the output signal V0 of the power amplifier and the output sample data. The error can be a mean squared error (MSE), i.e., the mean squared error can be used as the loss function of the first model.

[0140] In some embodiments, the model convergence condition comprises at least one of the following:

[0141] The number of times of performing the first process is greater than or equal to a first threshold value;

[0142] The error is less than or equal to a second threshold value.

[0143] In some embodiments, during the model training process, the model parameters can be updated by using methods such as stochastic gradient descent (SGD) to reduce the model training loss, and the training is stopped when the model loss is reduced to a certain value or the model training period reaches a certain number of times. That is, the training can be stopped when the number of times of performing the first process is greater than or equal to the first threshold value, and / or the error is less than or equal to the second threshold value, to obtain the trained first model. The first threshold value and the second threshold value are pre-set.

[0144] In some embodiments, the input sample data in the second data set comprises in-phase / quadrature signal samples.

[0145] In some embodiments, the input sample data in the second data set comprises in-phase / quadrature signal samples and environmental information samples. Optionally, the environmental information samples are information samples of the environment in which the power amplifier is located. Optionally, the environmental information samples comprise factors affecting the performance of the power amplifier. Optionally, the environmental information samples comprise at least one of the following:

[0146] Standing wave ratio information samples;

[0147] Temperature information samples of the power amplifier.

[0148] In some embodiments, the output sample data in the second data set comprises output signal samples of the power amplifier.

[0149] In some embodiments, the second data set can be constructed according to the collected historical input data and corresponding historical output data. In other embodiments, the second data set can be generated by a simulation model.

[0150] In some embodiments, the number of the second data sets is plural, and at least one of the environmental information samples, the in-phase / quadrature signal samples, and the output sample data in different data sets is different.

[0151] For example, training the first model using the second data set comprising different standing wave ratios and / or amplifier temperatures can improve the robustness of the first model in the standing wave ratio dimension and the temperature dimension.

[0152] In some embodiments, after obtaining the trained first model, the first model can be tested. In the model testing process, the test input sample is input into the first model, the output signal of the first model is input into the power amplifier, and the error between the output signal of the power amplifier and the test output sample is calculated. The error can be used as a main accuracy index for evaluating the effect of the first model, and the smaller the error is, the better the prediction effect of the first model is.

[0153] In some embodiments, the first model on the terminal can be obtained by receiving a first message sent by a second device, the first message comprising a first data packet, the first data packet being an installation data packet of the first model, and installing the first model according to the first data packet. Optionally, the model training capability of the second device is higher than that of the terminal.

[0154] For example, the second device can be an access network device, a core network device, another terminal, or a device cluster, which has a higher model training capability than the terminal. Since the model training capability is stronger, a first model with better effect, for example, higher accuracy and stronger robustness, can be trained. Therefore, using the first model with better effect trained by the second device with stronger model training capability on the terminal can improve the signal predistortion capability of the terminal.

[0155] Of course, in the case that the terminal has the capability to train the first model, the first model on the terminal can be trained by the terminal itself.

[0156] In some embodiments, the terminal can monitor the performance of the first model, send a second message to the second device, and the second message is used to indicate the monitored performance of the first model. Optionally, a third message sent by the second device is received, the third message comprising an operation instruction determined by the second device according to the performance of the first model, and the corresponding operation is performed according to the operation instruction.

[0157] The terminal can facilitate taking measures in time when the performance of the first model does not meet the requirements, so as to guarantee the communication quality as much as possible by monitoring and reporting the performance of the first model.

[0158] In some embodiments, the operation performed by the terminal indicated by the second device includes at least one of the following:

[0159] updating the first model;

[0160] stopping using the first model;

[0161] replacing the first model with a pre-distortion algorithm;

[0162] reinstalling the first model.

[0163] For example, the first model can be reinstalled according to the first data packet.

[0164] For example, the first model can be disabled, and a pre-distortion algorithm can be used to replace the first model to pre-distort the first signal to obtain the second signal.

[0165] In some embodiments, the terminal can actively request the second device to update the first model, and update the first model based on the response of the second device.

[0166] In some embodiments, if the operation indication includes a second data packet for updating the first model, the parameter configuration of the first model can be updated according to the second data packet. Of course, the second data packet can also be sent to the terminal through other signaling or messages, which is not limited in the present disclosure.

[0167] In some embodiments, if the operation indication includes a third data set, the first model can be retrained according to the third data set to obtain a trained first model. Optionally, the data amount of the third data set is determined by the second device according to the model training capability of the first device. Optionally, the data amount of the third data set is less than the data amount of the second data set. The terminal further trains the first model through the third data set, which can realize fine-tuning of the first model, so as to optimize the first model and improve the pre-distortion effect. Of course, the third data set can also be sent to the terminal through other signaling or messages, or can be measured by the terminal itself, which is not limited in the present disclosure.

[0168] In step S2102, the terminal 101 inputs the second signal as the input signal of the power amplifier.

[0169] In some embodiments, the second signal is input into the power amplifier, and the power amplifier amplifies the power of the second signal to obtain the output signal of the power amplifier.

[0170] The power amplifier includes a linear part and a nonlinear part.

[0171] At step S2103, the terminal 101 sends the output signal of the power amplifier to the network device 102.

[0172] In some embodiments, the network device receives the output signal of the power amplifier sent by the terminal. For example, the network device 102 receives the output signal of the power amplifier sent by the terminal 101.

[0173] In some embodiments, if the first signal is not processed by signal encoding, signal modulation, etc., the terminal sends the output signal of the power amplifier to the network device in the following manner: the output signal of the power amplifier is processed by signal encoding, signal modulation, etc. before being sent.

[0174] In some embodiments, the names of information, etc. are not limited to the names described in the embodiments, and the terms of “information”, “message”, “signal”, “signaling”, “report”, “configuration”, “indication”, “instruction”, “command”, “channel”, “parameter”, “domain”, “field”, “symbol”, “symbol”, “codebook”, “codeword”, “codepoint”, “bit”, “data”, “program”, “chip”, etc. can be replaced with each other.

[0175] In some embodiments, “acquire”, “obtain”, “get”, “receive”, “transmit”, “bidirectional transmission”, “send and / or receive” can be replaced with each other, which can be interpreted as receiving from other subjects, acquiring from protocols, acquiring from higher layers, processing to obtain, autonomously implementing, etc.

[0176] In some embodiments, the terms of “send”, “transmit”, “report”, “issue”, “transmit”, “bidirectional transmission”, “send and / or receive” can be replaced with each other.

[0177] In some embodiments, the terms "certain", "preset", "pre-set", "set", "indicated", "any", "first", and the like can be replaced with each other, "certain A", "preset A", "pre-set A", "set A", "indicated A", "any A", "first A" can be interpreted as A predetermined in a protocol or the like, or can be interpreted as A obtained by setting, configuration, or indication, or can be interpreted as certain A, any A, or first A, and the like, but are not limited thereto.

[0178] The communication method related to the embodiments of the present disclosure can include at least one of steps S2101-S2103. For example, step S2101 can be implemented as an independent embodiment, step S2102 can be implemented as an independent embodiment, and step S2103 can be implemented as an independent embodiment, but are not limited thereto.

[0179] In some embodiments, the order between any two of steps S2101-S2103 can be exchanged or executed simultaneously.

[0180] In some embodiments, steps S2102 and S2103 are optional, and one or more of these steps can be omitted or replaced in different embodiments.

[0181] In some embodiments, steps S2101 and S2103 are optional, and one or more of these steps can be omitted or replaced in different embodiments.

[0182] In some embodiments, steps S2101 and S2102 are optional, and one or more of these steps can be omitted or replaced in different embodiments.

[0183] In some embodiments, other optional implementations described before or after the description corresponding to FIG. 2A can be referred to.

[0184] FIG. 2C is an interaction schematic diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG. 2C, the embodiments of the present disclosure relate to a communication method, which is executed by the communication system 100, and the above method includes:

[0185] In step S2301, the network device 102 inputs a first data set including a first signal into a first model to obtain a second signal output by the first model.

[0186] In some embodiments, the first signal is a signal to be input into a power amplifier for power amplification.

[0187] Optionally, the first signal is an in-phase / quadrature signal of a baseband.

[0188] Optionally, the first signal is a signal obtained after processing of a baseband in-phase / quadrature signal, for example, a signal obtained after processing such as signal encoding, signal modulation, etc. of a baseband in-phase / quadrature signal.

[0189] In some embodiments, the name of the first signal is not limited, which is, for example, a signal to be processed by the power amplifier, a low-power signal, etc.

[0190] In some embodiments, the first data set includes the first signal.

[0191] In some embodiments, the first data set includes the first signal and environmental information.

[0192] In some embodiments, the environmental information is information of an environment in which the power amplifier is located. Optionally, the environmental information includes factors affecting the performance of the power amplifier. Optionally, the environmental information includes at least one of the following:

[0193] standing wave ratio information;

[0194] temperature information of the power amplifier.

[0195] It should be explained that the standing wave ratio can be understood as the ratio of the highest radio frequency voltage to the minimum radio frequency voltage through the transmission line.

[0196] In some embodiments, in addition to the standing wave ratio information and the temperature information of the power amplifier, the environmental information can also include other factors affecting the performance of the power amplifier, such as humidity information, impedance information, etc.

[0197] In some embodiments, the name of the first data set is not limited, which is, for example, a test data set, an application data set, etc.

[0198] For example, referring to FIG. 5, the environmental information in the first data set can be fed back by the power amplifier.

[0199] In some embodiments, the first model is used to predict a second signal after pre-distortion processing according to the first data set. The first data set includes the first signal, and the second signal is a signal after pre-distortion processing corresponding to the first signal. The name of the second signal is not limited, which is, for example, a pre-distortion processing signal, a first model output signal, etc.

[0200] In some embodiments, the name of the first model is not limited, which is, for example, a pre-distortion processing model, a pre-distorter, a pre-distortion network, etc.

[0201] In some embodiments, the first model is obtained by training as follows: training the first model to be trained and the power amplifier according to the second data set to obtain the first model after training.

[0202] The second data set is a training sample data set. The second data set includes input sample data and corresponding output sample data.

[0203] For example, the first model and the power amplifier are collaboratively trained according to the second data set until an expected first model is obtained. During the training process, the power amplifier can not be adjusted, that is, the power amplifier is fixed during the training process.

[0204] It should be noted that the training process of the first model can be performed at the terminal or at the network device, and the disclosure does not limit the execution location of the training process of the first model.

[0205] In some embodiments, the implementation of training the first model to be trained and the power amplifier according to the second data set to obtain the trained first model includes: performing a first process at least once until a model convergence condition is met to obtain the trained first model; wherein the first process includes: inputting the input sample data into the first model, inputting the output signal of the first model into the power amplifier, and adjusting the model parameters of the first model according to the error between the output signal of the power amplifier and the output sample data.

[0206] For example, referring to FIG. 2B, the input sample data is input into the first model, the output signal V2 of the first model is input into the power amplifier, and the model parameters of the first model are adjusted according to the error between the output signal V0 of the power amplifier and the output sample data. The error can be a mean squared error (MSE), that is, the mean squared error can be used as the loss function of the first model.

[0207] In some embodiments, the model convergence condition includes at least one of the following:

[0208] The number of times of performing the first process is greater than or equal to a first threshold value;

[0209] The error is less than or equal to a second threshold value.

[0210] In some embodiments, during the model training process, the model parameters can be updated using methods such as stochastic gradient descent (SGD) to reduce the model training loss, and the training is stopped when the model loss is reduced to a certain value or the model training period reaches a certain number. That is, the training can be stopped when the number of times of performing the first process is greater than or equal to the first threshold value, and / or the error is less than or equal to the second threshold value, to obtain the trained first model. The first threshold value and the second threshold value are pre-set.

[0211] In some embodiments, the input sample data in the second data set includes in-phase / quadrature signal samples.

[0212] In some embodiments, the input sample data in the second dataset comprises in-phase / quadrature signal samples and environmental information samples. Optionally, the environmental information samples are information samples of an environment in which the power amplifier is located. Optionally, the environmental information samples comprise factors affecting the performance of the power amplifier. Optionally, the environmental information samples comprise at least one of the following:

[0213] standing wave ratio information samples;

[0214] temperature information samples of the power amplifier.

[0215] In some embodiments, the output sample data in the second dataset comprises output signal samples of the power amplifier.

[0216] In some embodiments, the second dataset can be constructed according to collected historical input data and corresponding historical output data. In other embodiments, the second dataset can be generated by a simulation model.

[0217] In some embodiments, the number of the second datasets is multiple, and at least one of the environmental information samples, the in-phase / quadrature signal samples, and the output sample data in different datasets is different.

[0218] For example, training the first model using the second dataset comprising different standing wave ratios and / or amplifier temperatures can improve the robustness of the first model in the standing wave ratio dimension and the temperature dimension.

[0219] In some embodiments, after obtaining the trained first model, the first model can be tested. In the model testing process, a test input sample is input into the first model, an output signal of the first model is input into the power amplifier, and an error between the output signal of the power amplifier and the test output sample is calculated. The error can be used as a main accuracy index for evaluating the effect of the first model, and the smaller the error is, the better the prediction effect of the first model is.

[0220] In some embodiments, the first model on the network device can be obtained by receiving a first message sent by a second device, the first message comprising a first data packet, the first data packet being an installation data packet of the first model, and installing the first model according to the first data packet. Optionally, the model training capability of the second device is higher than that of the network device.

[0221] For example, the second device can be another access network device, a core network device, a terminal, or a device cluster, which has a higher model training capability than the network device. Since the second device has a higher model training capability, a first model with better performance, e.g., higher accuracy and stronger robustness, can be trained. Therefore, using the first model with better performance trained by the second device on the network device can improve the signal predistortion capability of the network device.

[0222] Of course, in the case where the network device has the capability to train the first model, the first model on the network device can be trained by the network device itself.

[0223] In some embodiments, the network device can monitor the performance of the first model, and send a second message to the second device, where the second message is used to indicate the monitored performance of the first model. Optionally, a third message sent by the second device is received, where the third message includes an operation instruction determined by the second device according to the performance of the first model, and the corresponding operation is performed according to the operation instruction.

[0224] By monitoring and reporting the performance of the first model, the network device can take timely measures when the performance of the first model does not meet the requirements, so as to guarantee the communication quality as much as possible.

[0225] In some embodiments, the operation performed by the network device according to the indication of the second device includes at least one of the following:

[0226] updating the first model;

[0227] stopping using the first model;

[0228] replacing the first model with a predistortion algorithm;

[0229] reinstalling the first model.

[0230] For example, the first model can be reinstalled according to the first data packet.

[0231] For example, the first model can be disabled, and a predistortion algorithm can be used to replace the first model to perform predistortion processing on the first signal to obtain the second signal.

[0232] In some embodiments, the network device can actively request the second device to update the first model, and update the first model based on the response of the second device.

[0233] In some embodiments, if the operation instruction includes a second data packet for updating the first model, the parameter configuration of the first model can be updated according to the second data packet. Of course, the second data packet can also be sent to the network device through other signaling or messages, which is not limited in the present disclosure.

[0234] In some embodiments, if the operation instruction includes a third data set, the first model can be retrained according to the third data set to obtain a trained first model. Optionally, the data amount of the third data set is determined by the second device according to the model training capability of the first device. Optionally, the data amount of the third data set is less than the data amount of the second data set. The network device further trains the first model through the third data set, which can realize fine-tuning of the first model, so as to optimize the first model and improve the pre-distortion effect. Of course, the third data set can also be sent to the network device through other signaling or messages, or can be measured by the network device itself, which is not limited in the present disclosure.

[0235] In step S2302, the network device 102 inputs the second signal as an input signal of the power amplifier.

[0236] In some embodiments, the second signal is input into the power amplifier, and the power amplifier amplifies the power of the second signal to obtain an output signal of the power amplifier.

[0237] The power amplifier includes a linear part and a nonlinear part.

[0238] In step S2303, the network device 102 sends the output signal of the power amplifier to the terminal 101.

[0239] In some embodiments, the terminal receives the output signal of the power amplifier sent by the network device. For example, the terminal 101 receives the output signal of the power amplifier sent by the network device 102.

[0240] In some embodiments, if the first signal does not undergo signal encoding, signal modulation and other processes, the implementation of the network device sending the output signal of the power amplifier to the terminal can be that the output signal of the power amplifier is subjected to signal encoding, signal modulation and other processes before being sent.

[0241] In some embodiments, the names of information and the like are not limited to the names described in the embodiments, and terms such as "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "domain", "field", "symbol", "symbol", "codebook", "codeword", "codepoint", "bit", "data", "program", "chip", and the like can be replaced with each other.

[0242] In some embodiments, "acquire", "obtain", "get", "receive", "transmit", "bidirectional transmission", "send and / or receive", and the like can be replaced with each other, and can be interpreted as receiving from other subjects, acquiring from protocols, acquiring from higher layers, obtaining by self-processing, autonomously implementing, and the like.

[0243] In some embodiments, the terms "send", "transmit", "report", "issue", "transmit", "bidirectional transmission", "send and / or receive", and the like can be replaced with each other.

[0244] In some embodiments, the terms "certain", "preset", "pre-set", "set", "indicated", "certain", "arbitrary", "first", and the like can be replaced with each other, and "certain A", "preset A", "pre-set A", "set A", "indicated A", "certain A", "arbitrary A", "first A" can be interpreted as A specified in advance in protocols and the like, A obtained by setting, configuration, or indication, and the like, A specific, certain, arbitrary, or first A, but are not limited thereto.

[0245] The communication method related to the embodiments of the present disclosure can include at least one of steps S2301-S2303. For example, step S2301 can be implemented as an independent embodiment, step S2302 can be implemented as an independent embodiment, and step S2303 can be implemented as an independent embodiment, but are not limited thereto.

[0246] In some embodiments, the order between any two of steps S2301-S2303 can be exchanged or executed simultaneously.

[0247] In some embodiments, steps S2302 and S2303 are optional, and one or more of these steps can be omitted or replaced in different embodiments.

[0248] In some embodiments, steps S2301 and S2303 are optional, and one or more of these steps can be omitted or replaced in different embodiments.

[0249] In some embodiments, steps S2301 and S2302 are optional, and one or more of these steps can be omitted or replaced in different embodiments.

[0250] In some embodiments, other optional implementations can be found in the description before or after the description of FIG. 2C.

[0251] FIG. 3A is a flow diagram of a communication method according to an embodiment of the present disclosure. The communication method is performed by a first device, which is a terminal or a network device. As shown in FIG. 3A, the communication method includes:

[0252] In step S3101, a first data set including a first signal is input into a first model, and a second signal output by the first model is obtained.

[0253] Optional implementations of step S3101 can be found in the optional implementations of steps S2101 of FIG. 2A, S2301 of FIG. 2C, and other related parts in the embodiments related to FIG. 2A and FIG. 2C, which will not be repeated here.

[0254] In step S3102, the second signal is input into a power amplifier.

[0255] Optional implementations of step S3102 can be found in the optional implementations of steps S2102 of FIG. 2A, S2302 of FIG. 2C, and other related parts in the embodiments related to FIG. 2A and FIG. 2C, which will not be repeated here.

[0256] In step S3103, an output signal of the power amplifier is transmitted.

[0257] Optional implementations of step S3103 can be found in the optional implementations of steps S2103 of FIG. 2A, S2303 of FIG. 2C, and other related parts in the embodiments related to FIG. 2A and FIG. 2C, which will not be repeated here.

[0258] In some embodiments, the first device is a terminal, and the first device transmits the output signal of the power amplifier to a network device, but is not limited thereto, and can transmit the output signal of the power amplifier to other subjects.

[0259] In some embodiments, the first device is a network device, and the first device sends the output signal of the power amplifier to a terminal, but is not limited thereto, and can send the output signal of the power amplifier to other subjects.

[0260] The communication method related to the embodiments of the present disclosure can include at least one of steps S3101-S3103. For example, step S3101 can be implemented as an independent embodiment, step S3102 can be implemented as an independent embodiment, and step S2303 can be implemented as an independent embodiment, but is not limited thereto.

[0261] In some embodiments, the order between any two of steps S3101-S3103 can be exchanged or executed simultaneously.

[0262] In some embodiments, steps S3102 and S3103 are optional, and one or more of these steps can be omitted or replaced in different embodiments.

[0263] In some embodiments, steps S3101 and S3103 are optional, and one or more of these steps can be omitted or replaced in different embodiments.

[0264] In some embodiments, steps S3101 and S3102 are optional, and one or more of these steps can be omitted or replaced in different embodiments.

[0265] FIG. 3B is a flow diagram illustrating a communication method according to an embodiment of the present disclosure. The communication method is performed by a first device, which is a terminal or a network device. As shown in FIG. 3B, the communication method includes:

[0266] Step S3201 inputs a first data set including a first signal into a first model to obtain a second signal output by the first model.

[0267] Optional implementation of step S3201 can be referred to optional implementation of step S2101 of FIG. 2A, step S2301 of FIG. 2C, step S3101 of FIG. 3A, and other associated parts in the embodiments related to FIG. 2A, FIG. 2C, and FIG. 3A, which will not be repeated here.

[0268] Step S3202 takes the second signal as an input signal of a power amplifier.

[0269] Optional implementation of step S3202 can be referred to optional implementation of step S2102 of FIG. 2A, step S2302 of FIG. 2C, step S3102 of FIG. 3A, and other associated parts in the embodiments related to FIG. 2A, FIG. 2C, and FIG. 3A, which will not be repeated here.

[0270] The communication method related to the embodiments of the present disclosure can include at least one of step S3201 and step S3202. For example, step S3201 can be implemented as an independent embodiment, and step S3202 can be implemented as an independent embodiment, but is not limited thereto.

[0271] In some embodiments, the order of step S3201 and step S3202 can be exchanged or performed simultaneously.

[0272] In some embodiments, step S3201 is optional.

[0273] In some embodiments, step S3202 is optional.

[0274] In some embodiments, step S3202 can be combined with step S3103 of FIG. 3A.

[0275] FIG. 3C is a flow diagram illustrating a communication method according to an embodiment of the present disclosure. The communication method is performed by a first device, which is a terminal or a network device. As shown in FIG. 3C, the communication method includes:

[0276] Step S3301, inputting a first data set including a first signal into a first model to obtain a second signal output by the first model.

[0277] The optional implementation of step S3301 can refer to the optional implementation of step S2101 of FIG. 2A, step S2301 of FIG. 2C, step S3101 of FIG. 3A, and other associated parts in the embodiments related to FIG. 2A, FIG. 2C, and FIG. 3A, which will not be repeated here.

[0278] In some embodiments, the present disclosure also provides the following interactive examples of the communication method:

[0279] Step 1, the first device receives a first message sent by a second device.

[0280] Step 2, the first device installs a first model.

[0281] Step 3, the first device monitors the performance of the first model.

[0282] Step 4, the first device sends a second message to the second device.

[0283] Step 5, the second device sends a third message to the first device

[0284] Step 6, the first device responds to the third message.

[0285] The optional implementation of steps 1-6 can be referred to the optional implementation of step S2101 of FIG. 2A, step S2301 of FIG. 2C, and other related parts in the embodiments involved in FIG. 2A and FIG. 2C, which will not be repeated here.

[0286] In some embodiments, according to the data set A (Set A is equivalent to the second data set in the foregoing embodiments), the DPD model is obtained based on the AI / ML model training, and the DPD model outputs the pre-distortion processed signal V2 satisfying the specified performance requirements, wherein the data parameters of the data set (Set A) used for model training at least include the following one parameter information:

[0287] PA input signal V1 without DPD processing, i.e., I / Q signal of baseband;

[0288] Output signal Y of PA;

[0289] Temperature information of PA;

[0290] Voltage standing wave ratio (VSWR) information.

[0291] The AI / ML model training is based on FIG. 2B.

[0292] In some embodiments, according to the AI / ML-based DPD system, i.e., the trained AI / ML model (i.e., the first model after training), the input data set B (Set B is equivalent to the first data set in the foregoing embodiments), the pre-distortion processed PA input signal (V2) is predicted and output, wherein the data parameter information of the data set (Set B) used for model prediction at least includes the following one:

[0293] PA input signal V1 without DPD processing, i.e., I / Q signal of baseband (equivalent to the first signal in the foregoing embodiments);

[0294] Temperature information of PA;

[0295] Voltage standing wave ratio (VSWR) information.

[0296] In some embodiments, the use of the trained AI / ML pre-distortion model is shown in FIG. 4.

[0297] In some embodiments, the environmental parameters are dynamically changing. For example: the change of PA operating temperature and the change of voltage standing wave ratio VSWR, based on the dynamically changing PA environment, the AI / ML-based DPD system can output the appropriate pre-distortion processed signal V2. For details, refer to FIG. 5, which shows that the environmental parameters can be dynamically fed back by the PA.

[0298] In the embodiments of the present disclosure, part or all of the steps, and optional implementation manners thereof, can be combined with part or all of the steps in other embodiments, or combined with optional implementation manners of other embodiments.

[0299] The embodiments of the present disclosure also propose a device for implementing any of the above methods, for example, a device comprising units or modules for implementing the steps performed by the terminal in any of the above methods. For another example, another device is proposed, comprising units or modules for implementing the steps performed by the network equipment (such as access network equipment, core network function node, core network equipment, etc.) in any of the above methods.

[0300] It should be understood that the division of each unit or module in the above device is only a logical function division, and all or part of them can be integrated into one physical entity, or can be physically separated. In addition, the units or modules in the device can be implemented in the form of processor calling software: for example, the device includes a processor, the processor is connected with a memory, the memory stores instructions, and the processor calls the instructions stored in the memory to implement any of the above methods or the functions of each unit or module of the device, wherein the processor is, for example, a general processor, such as a central processing unit (CPU) or a microprocessor, and the memory is a memory in the device or a memory outside the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuit, and the functions of part or all of the units or modules can be implemented by designing the hardware circuit, and the hardware circuit can be understood as one or more processors; for example, in one implementation, the hardware circuit is an application-specific integrated circuit (ASIC), and the functions of part or all of the units or modules are implemented by designing the logical relationship of elements in the circuit; for another example, in another implementation, the hardware circuit is a programmable logic device (PLD), and taking a field programmable gate array (FPGA) as an example, it can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by a configuration file, so as to implement the functions of part or all of the units or modules. All units or modules of the above device can be implemented in the form of processor calling software, or all units or modules can be implemented in the form of hardware circuit, or part of the units or modules can be implemented in the form of processor calling software, and the remaining part can be implemented in the form of hardware circuit.

[0301] In the embodiments of the present disclosure, the processor is a circuit with signal processing capability. In one implementation, the processor can be a circuit with instruction reading and running capability, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), a digital signal processor (DSP), or the like. In another implementation, the processor can implement certain functions through a logical relationship of a hardware circuit, and the logical relationship of the hardware circuit is fixed or can be reconfigured. For example, the processor is a hardware circuit implemented by an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In the reconfigurable hardware circuit, the processor loads a configuration document to implement the configuration of the hardware circuit. It can be understood that the processor loads instructions to implement the functions of the above part or all units or modules. In addition, the hardware circuit can also be designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), or the like.

[0302] FIG. 6 is a structural schematic diagram of a terminal according to the embodiments of the present disclosure. As shown in FIG. 6, the terminal 600 can include at least one of a transceiver module 601, a processing module 602, and the like. In some embodiments, the processing module 602 is configured to input a first data set including a first signal into a first model to obtain a second signal output by the first model, wherein the first signal is a baseband in-phase / quadrature signal, and the first model is configured to predict the second signal after pre-distortion processing according to the first data set; and the second signal is used as an input signal of a power amplifier. Optionally, the transceiver module 601 is configured to perform at least one of the communication steps (for example, step S2103, but not limited thereto) of the sending and / or receiving performed by the terminal 101 in any of the above methods, and details are not described herein. Optionally, the processing module 601 is configured to perform at least one of the other steps (for example, step S2101, step S2102, but not limited thereto) performed by the terminal 101 in any of the above methods, and details are not described herein.

[0303] FIG. 7 is a structural schematic diagram of a network device according to an embodiment of the present disclosure. As shown in FIG. 7, the network device 700 can include at least one of a transceiver module 701, a processing module 702, and the like. In some embodiments, the processing module 702 described above is configured to input a first data set including a first signal into a first model, to obtain a second signal output by the first model, wherein the first signal is a baseband in-phase / quadrature signal, and the first model is used to predict the second signal after pre-distortion processing according to the first data set; and use the second signal as an input signal of a power amplifier. Optionally, the transceiver module 701 described above is configured to perform at least one of the communication steps (for example, step S2303, but not limited thereto) of transmitting and / or receiving and the like performed by the network device 102 in any of the above methods, which will not be described herein again. Optionally, the processing module 702 described above is configured to perform at least one of the other steps (for example, step S2301, step S2302, but not limited thereto) performed by the network device 102 in any of the above methods, which will not be described herein again.

[0304] In some embodiments, the transceiver module can include a transmitting module and / or a receiving module, and the transmitting module and the receiving module can be separate or integrated together. Optionally, the transceiver module can be mutually replaced with a transceiver.

[0305] In some embodiments, the processing module can be one module, or can include a plurality of sub-modules. Optionally, the plurality of sub-modules perform all or part of the steps required to be performed by the processing module respectively. Optionally, the processing module can be mutually replaced with a processor.

[0306] FIG. 8A is a structural schematic diagram of a communication device 8100 according to an embodiment of the present disclosure. The communication device 8100 can be a network device (for example, an access network device, a core network device, and the like), a terminal (for example, a user equipment, and the like), a chip, a chip system, or a processor supporting the network device to implement any of the above methods, or a chip, a chip system, or a processor supporting the terminal to implement any of the above methods. The communication device 8100 can be used to implement the methods described in the above method embodiments, and specific implementation can be referred to the descriptions in the above method embodiments.

[0307] As shown in FIG. 8A, the communication device 8100 includes one or more processors 8101. The processor 8101 can be a general processor or a special-purpose processor, etc., such as a baseband processor or a central processor. The baseband processor can be used to process communication protocols and communication data, and the central processor can be used to control a communication apparatus (e.g., a base station, a baseband chip, a terminal device, a terminal device chip, a DU or a CU, etc.), execute programs, and process data of the programs. Optionally, the communication device 8100 is configured to perform any of the above methods. Optionally, the one or more processors 8101 are configured to invoke instructions to cause the communication device 8100 to perform any of the above methods.

[0308] In some embodiments, the communication device 8100 further includes one or more transceivers 8102. When the communication device 8100 includes the one or more transceivers 8102, the transceiver 8102 performs at least one of the communication steps (e.g., step S2103, step S2303, but not limited to) in the above methods, and the processor 8101 performs at least one of the other steps (e.g., step S2101, step S2102, step S2301, step S2302, but not limited to). In optional embodiments, the transceiver can include a receiver and / or a transmitter, which can be separate or integrated together. Optionally, the terms transceiver, transceiving unit, transceiver, transceiving circuit, interface circuit, interface, etc. can be replaced with each other, and the terms transmitter, transmitting unit, transmitter, transmitting circuit, etc. can be replaced with each other, and the terms receiver, receiving unit, receiver, receiving circuit, etc. can be replaced with each other.

[0309] In some embodiments, the communication device 8100 further includes one or more memories 8103 for storing data. Optionally, all or part of the memory 8103 can also be outside the communication device 8100. In optional embodiments, the communication device 8100 can include one or more interface circuits 8104. Optionally, the interface circuit 8104 is connected to the memory 8103, and the interface circuit 8104 can be used to receive data from the memory 8103 or other devices, and can be used to send data to the memory 8103 or other devices. For example, the interface circuit 8104 can read data stored in the memory 8103 and send the data to the processor 8101.

[0310] The communication device 8100 described in the above embodiments may be a network device or a terminal, but the scope of the communication device 8100 described in this disclosure is not limited thereto, and the structure of the communication device 8100 may not be limited by FIG8A. The communication device may be a standalone device or may be part of a larger device. For example, the communication device may be: (1) a standalone integrated circuit IC, or chip, or chip system or subsystem; (2) a collection of one or more ICs, optionally, the IC collection may also include storage components for storing data and programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, terminal device, smart terminal device, cellular phone, wireless device, handheld device, mobile unit, vehicle device, network device, cloud device, artificial intelligence device, etc.; (6) others, etc.

[0311] Figure 8B is a schematic diagram of the structure of chip 8200 according to an embodiment of this disclosure. For cases where the communication device 8100 can be a chip or a chip system, please refer to the schematic diagram of chip 8200 shown in Figure 8B, but it is not limited thereto.

[0312] Chip 8200 includes one or more processors 8201. Chip 8200 is used to perform any of the above methods.

[0313] In some embodiments, chip 8200 further includes one or more interface circuits 8202. Optionally, terms such as interface circuit, interface, and transceiver pin can be used interchangeably. In some embodiments, chip 8200 further includes one or more memories 8203 for storing data. Optionally, all or part of the memories 8203 may be located outside of chip 8200. Optionally, interface circuit 8202 is connected to memory 8203, and interface circuit 8202 can be used to receive data from memory 8203 or other devices, and interface circuit 8202 can be used to send data to memory 8203 or other devices. For example, interface circuit 8202 can read data stored in memory 8203 and send the data to processor 8201.

[0314] In some embodiments, the interface circuit 8202 performs at least one of the communication steps such as sending and / or receiving in the above method (e.g., steps S2103, S2303, but not limited thereto). For example, the interface circuit 8202 performing the communication steps such as sending and / or receiving in the above method means that the interface circuit 8202 performs data interaction between the processor 8201, the chip 8200, the memory 8203, or the transceiver device. In some embodiments, the processor 8201 performs at least one of other steps (e.g., steps S2101, S2102, S2301, S2302, but not limited thereto).

[0315] The modules and / or devices described in the various embodiments, such as virtual devices, physical devices, and chips, can be combined or separated arbitrarily as needed. Optionally, some or all steps can also be performed collaboratively by multiple modules and / or devices, which is not limited here.

[0316] This disclosure also proposes a storage medium storing instructions that, when executed on a communication device 8100, cause the communication device 8100 to perform any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but not limited thereto; it may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but not limited thereto; it may also be a temporary storage medium.

[0317] This disclosure also provides a program product that, when executed by the communication device 8100, causes the communication device 8100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0318] This disclosure also proposes a computer program that, when run on a computer, causes the computer to perform any of the above methods.

Claims

1. A communication method characterized by comprising: The method is executed by a first device, and the method comprises: inputting a first data set comprising a first signal into a first model to obtain a second signal output by the first model, wherein the first signal is a baseband in-phase / quadrature signal, and the second signal is a predicted pre-distortion processed signal; inputting the second signal as an input signal of a power amplifier.

2. The method of claim 1, wherein, The first data set further comprises environmental information, and the environmental information comprises at least one of: standing wave ratio information; temperature information of the power amplifier.

3. The method according to claim 1 or 2, characterized in that, The first model is obtained by training in the following manner: training the first model to be trained according to a second data set and the power amplifier to obtain the first model after training.

4. The method of claim 3, wherein, The second data set comprises input sample data and output sample data. The training of the first model to be trained according to the second data set and the power amplifier to obtain the first model after training comprises: performing at least one first process until a model convergence condition is met to obtain the first model after training; The first process comprises: inputting the input sample data into the first model; inputting an output signal of the first model into the power amplifier; adjusting model parameters of the first model according to an error between an output signal of the power amplifier and the output sample data.

5. The method of claim 4, wherein, The model convergence condition comprises at least one of: a number of times of performing the first process is greater than or equal to a first threshold value; the error is less than or equal to a second threshold value.

6. The method according to claim 4 or 5, characterized in that, The input sample data comprises in-phase / quadrature signal samples.

7. The method according to claim 4 or 5, characterized in that, The input sample data comprises in-phase / quadrature signal samples and environmental information samples, and the environmental information samples comprise at least one of: temperature samples of the power amplifier; standing wave ratio samples.

8. The method according to any one of claims 4-7, characterized in that, The output sample data comprises output signal samples of the power amplifier.

9. The method of claim 7, wherein, The number of the second data sets is a plurality, and at least one of the environmental information samples, the in-phase / quadrature signal samples and the output sample data in different data sets is different.

10. The method according to any one of claims 1-9, characterized in that, The first device comprises at least one of: a terminal; a network device.

11. The method according to any one of claims 1-10, characterized in that, The method further comprises: receiving a first message sent by a second device, wherein the first message comprises a first data packet, the first data packet is an installation data packet of the first model, and a model training capability of the second device is higher than a model training capability of the first device; installing the first model according to the first data packet.

12. The method of claim 11, wherein, The method further comprises: monitoring a performance of the first model, sending a second message to the second device, wherein the second message is used to indicate the monitored performance of the first model; receiving a third message sent by the second device, wherein the third message comprises operation instructions determined by the second device according to the performance of the first model; performing corresponding operations according to the operation instructions.

13. The method of claim 12, wherein, The operations comprise at least one of: updating the first model; stopping using the first model; replacing the first model with a pre-distortion algorithm; reinstalling the first model.

14. The method of claim 13, wherein, The operation instructions comprise a second data packet used to update the first model, and the updating of the first model comprises: updating a parameter configuration of the first model according to the second data packet.

15. The method of claim 13, wherein, The operation indication comprises a third data set, and the updating the first model comprises: retraining the first model according to the third data set to obtain a trained first model, wherein a data amount of the third data set is determined by the second device according to a model training capability of the first device.

16. A communication device, characterized by comprising: a processing module, configured to input a first data set comprising a first signal into a first model to obtain a second signal output by the first model, wherein the first signal is a baseband in-phase / quadrature signal, and the second signal is a predicted signal after pre-distortion processing; taking the second signal as an input signal of a power amplifier.

17. A communication device, characterized by comprising: one or more processors; a memory coupled to the processors, the memory having stored thereon executable instructions that, when executed by the processors, cause the communication method according to any one of claims 1-15 to be performed.

18. A communication system, characterized by comprising a terminal and a network device, wherein the terminal is configured to implement the communication method according to any one of claims 1-15, and / or the network device is configured to implement the communication method according to any one of claims 1-15.

19. A storage medium, the storage medium storing instructions, wherein, The instructions, when executed on the communication device, cause the communication device to perform the communication method according to any one of claims 1-15.

20. A computer program product comprising computer programs and / or instructions, characterized in that, The computer program and / or instructions, when executed on the communication device, implement the communication method according to any one of claims 1-15.

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