Data receiving method, communication device and storage medium

Abstract

The present disclosure relates to the technical field of communications, and in particular to a data receiving method, a communication device and a storage medium. The data receiving method comprises: receiving a first signal from a second communication device; and inputting the first signal into a processing model to obtain a second signal output by the processing model, wherein the second signal is a prediction result of the processing model for a sending signal of the second communication device.

Description

Data receiving method, communication device and storage medium TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of communication, and in particular, to a data receiving method, a communication device and a storage medium. BACKGROUND

[0002] In a wireless communication system, when a receiving end demodulates received data, the related demodulation method is limited by the influence of a transmission signal on a to-be-demodulated signal, and demodulation errors are prone to occur.

[0003] SUMMARY

[0004] Embodiments of the present disclosure provide a data receiving method, a communication device and a storage medium to solve the technical problem that a receiving end is prone to demodulation errors when demodulating received data in the related art.

[0005] According to a first aspect of embodiments of the present disclosure, a data receiving method is provided, executed by a first communication device, and the method comprises: receiving a first signal from a second communication device; inputting the first signal into a processing model to obtain a second signal output by the processing model; and wherein the second signal is a prediction result of the processing model on a transmission signal of the second communication device.

[0006] According to a second aspect of embodiments of the present disclosure, a data receiving apparatus is provided, comprising: a transceiver module, configured to receive a first signal from a second communication device; and a processing module, configured to input the first signal into a processing model to obtain a second signal output by the processing model; and wherein the second signal is a prediction result of the processing model on a transmission signal of the second communication device.

[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, the memory having stored thereon executable instructions that, when executed by the processors, cause the processors to invoke instructions to cause the communication device to perform the data receiving method of the first aspect.

[0008] According to a fourth aspect of embodiments of the present disclosure, a communication system is provided, comprising a first communication device and a second communication device, wherein the first communication device is configured to: receive a first signal from the second communication device; input the first signal into a processing model to obtain a second signal output by the processing model; and wherein the second signal is a prediction result of the processing model on a transmission signal of the second communication device.

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

[0010] According to the embodiments of the present disclosure, the receiving end processes the received first signal through the processing model, restores the first signal to the second signal sent by the sending end, removes the influence that the data may suffer in the channel transmission process, realizes the functions of denoising and deinterference of the data, makes the receiving end obtain more ideal data, and improves the accuracy and efficiency of data transmission in the wireless communication system. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings needed to be used in the embodiments will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and other drawings can also be obtained by those skilled in the art without creative labor.

[0012] FIG. 1 is an architecture schematic diagram of a communication system according to an embodiment of the present disclosure.

[0013] FIG. 2 is an interaction schematic diagram of a data receiving method according to an embodiment of the present disclosure.

[0014] FIG. 3A is a schematic flowchart of a data receiving method according to an embodiment of the present disclosure.

[0015] FIG. 3B is a schematic flowchart of a data receiving method according to an embodiment of the present disclosure.

[0016] FIG. 3C is a schematic flowchart of a data receiving method according to an embodiment of the present disclosure.

[0017] FIG. 3D is a schematic flowchart of a data receiving method according to an embodiment of the present disclosure.

[0018] FIG. 3E is a schematic flowchart of a data receiving method according to an embodiment of the present disclosure.

[0019] FIG. 4 is a schematic block diagram of an apparatus structure of a communication device according to an embodiment of the present disclosure.

[0020] FIG. 5 is a schematic structural diagram of a communication device according to an embodiment of the present disclosure.

[0021] FIG. 6 is a schematic structural diagram of a chip according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0022] Embodiments of the present disclosure provide a data receiving method, a communication device and a storage medium.

[0023] In a first aspect, embodiments of the present disclosure provide a data receiving method, performed by a first communication device, the method comprising: receiving a first signal from a second communication device; inputting the first signal into a processing model to obtain a second signal output by the processing model; wherein the second signal is a prediction result of the processing model on a transmitted signal of the second communication device.

[0024] In the above embodiment, the receiving end processes the received first signal through the processing model, restores the first signal to the second signal transmitted by the transmitting end, removes the influence of the data in the channel transmission process, realizes the functions of denoising and deinterference of the data, and enables the receiving end to obtain more ideal data, thereby improving the accuracy and efficiency of data transmission in the wireless communication system.

[0025] In combination with some embodiments of the first aspect, in some embodiments, the processing model is an artificial intelligence (AI) based processing model that is trained.

[0026] In combination with some embodiments of the first aspect, in some embodiments, the method further comprises: obtaining a training sample, the training sample comprising a first signal sample after transmission and a second signal sample before transmission corresponding to the first signal sample after transmission; training the processing model based on the first signal sample after transmission, the second signal sample before transmission corresponding to the first signal sample after transmission, and a loss function; wherein the loss function is used to represent the error between the second signal sample before transmission corresponding to the input of the processing model and the output of the processing model.

[0027] In combination with some embodiments of the first aspect, in some embodiments, the first signal contains the same amount of data as the second signal.

[0028] In combination with some embodiments of the first aspect, in some embodiments, the first signal is first symbol data, the first symbol data being symbol data received by the first communication device after channel transmission of second symbol data transmitted by the second communication device; and the second signal is a prediction result of the processing model on the second symbol data.

[0029] In combination with some embodiments of the first aspect, in some embodiments, the method further comprises: demodulating the second symbol data predicted by the processing model to obtain demodulated bit data.

[0030] In some embodiments of the first aspect. In some embodiments, the first signal is first bit data, the first bit data being bit data obtained by demodulating, by the first communication device, first symbol data received by the first communication device, the first symbol data being symbol data received by the first communication device after channel transmission of second symbol data transmitted by the second communication device; and the second signal is a prediction result of the processing model for second bit data, the second bit data being bit data used by the second communication device to modulate to obtain the second symbol data.

[0031] In a second aspect, a data receiving apparatus is provided. The apparatus comprises: a transceiver configured to receive a first signal from a second communication device; and a processing module configured to input the first signal into a processing model and obtain a second signal output by the processing model, wherein the second signal is a prediction result of the processing model for a signal transmitted by the second communication device.

[0032] In a third aspect, a communication device is provided. The communication device comprises: one or more processors; and a memory coupled to the processors, the memory having stored therein executable instructions that, when executed by the processors, cause the processors to invoke the executable instructions to cause the communication device to perform the data receiving method described in the first aspect and the optional embodiments of the first aspect.

[0033] In a fourth aspect, a communication system is provided. The communication system comprises: a first communication device and a second communication device; wherein the first communication device is configured to perform the method described in the first aspect and the optional embodiments of the first aspect.

[0034] In a fifth aspect, a storage medium is provided. The storage medium stores instructions that, when executed on a communication device, cause the communication device to perform the method described in the first aspect and the optional embodiments of the first aspect.

[0035] In a sixth aspect, a program product is provided. The program product, when executed by a communication device, causes the communication device to perform the method described in the first aspect and the optional embodiments of the first aspect.

[0036] In a seventh aspect, a computer program is provided. The computer program, when executed on a computer, causes the computer to perform the method described in the first aspect and the optional embodiments of the first aspect.

[0037] It is understood that the aforementioned terminals, network devices, communication devices, communication systems, storage media, program products, and computer programs are all used to execute the methods proposed in the embodiments of this disclosure. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0038] This disclosure provides a data receiving method, a communication device, and a storage medium. In some embodiments, terms such as information sending method, information receiving method, information processing method, and communication method can be used interchangeably; terms such as terminal and network device can be used interchangeably with terms such as information processing device and communication device; and terms such as information processing system and communication system can be used interchangeably.

[0039] This disclosure is not exhaustive, but merely illustrative of some embodiments, and is not intended to limit the scope of protection of this disclosure. Unless otherwise specified, each step in a particular embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a particular embodiment can also be implemented as an independent embodiment, and the order of the steps in a particular embodiment can be arbitrarily interchanged. Furthermore, optional embodiments in a particular embodiment can be arbitrarily combined; moreover, embodiments can be arbitrarily combined, for example, some or all steps of different embodiments can be arbitrarily combined, and a particular embodiment can be arbitrarily combined with optional embodiments of other embodiments.

[0040] In each of the disclosed embodiments, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions of the embodiments are consistent and can be referenced by each other. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0041] The terminology used in the embodiments of this disclosure is for the purpose of describing particular embodiments only and is not intended to limit the scope of this disclosure.

[0042] In the embodiments of this disclosure, unless otherwise stated, elements expressed in the singular, such as “a,” “an,” “the,” “the,” “the,” “the,” “the,” “the,” “this,” etc., may mean “one and only one,” or “one or more,” “at least one,” etc.

[0043] For example, when using articles such as "a", "an", and "the" in translation, the noun following the article can be understood as either a singular or a plural form.

[0044] In the embodiments disclosed herein, "multiple" refers to two or more.

[0045] In some embodiments, the terms "at least one of," "one or more of," "a plurality of," "multiple," and the like can be used interchangeably.

[0046] In some embodiments, the recitations "at least one of A, B," "A and / or B," "in one case A, in another case B," "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 (A is executed regardless of B); in some embodiments B (B is executed regardless of A); in some embodiments, A and B are selectively executed (A and B are selectively executed); in some embodiments, A and B (A and B are executed). When there are more branches such as A, B, C, and the like, the above is similar.

[0047] In some embodiments, the recitations "A or B" and the like can include the following technical solutions according to the case: in some embodiments A (A is executed regardless of B); in some embodiments B (B is executed regardless of A); in some embodiments, A and B are selectively executed (A and B are selectively executed). When there are more branches such as A, B, C, and the like, the above is similar.

[0048] The prefix words "first", "second", and the like in the embodiments of the present disclosure are only used to distinguish different description objects, and do not constitute a limitation on the position, order, priority, quantity, or content of the description objects. The description of the description objects should refer to the description in the context of the claims or embodiments, and should not constitute an additional limitation because of the use of the prefix words.

[0049] 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" they modify are in the same message or not, nor do they 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", where 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 their types 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 their contents can be the same or different.

[0050] In some embodiments, "comprising A", "including A", "for indicating A", "carrying A" can be interpreted as directly carrying A, or can be interpreted as indirectly indicating A.

[0051] In some embodiments, the terms "in response to", "in response to determining", "in the case of", "when", "when", "if", "if" and the like can be replaced with each other.

[0052] In some embodiments, the terms "greater than", "greater than or equal to", "not less than", "more than", "more than or equal to", "not less than", "higher than", "higher than or equal to", "not lower than", "above" and the like can be replaced with each other, and the terms "less than", "less than or equal to", "not greater than", "less than", "less than or equal to", "not more than", "lower than", "lower than or equal to", "not higher than", "below" and the like can be replaced with each other.

[0053] In some embodiments, the apparatus and the like can be interpreted as physical or virtual, and the name thereof is not limited to the name described in the embodiments.

[0054] 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.

[0055] In some embodiments, "network" can be interpreted as an apparatus (for example, access network device, core network device, etc.) contained in the network.

[0056] 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 used interchangeably.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] In some embodiments, the data, information, etc. can be obtained in compliance with the laws and regulations of the country in which the location is situated.

[0061] In some embodiments, the data, information, etc. can be obtained after obtaining the consent of the user.

[0062] 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.

[0063] FIG. 1 is an architecture schematic diagram of a communication system according to an embodiment of the present disclosure.

[0064] As shown in FIG. 1, the communication system 100 includes a first communication device 101 and a second communication device 102; wherein the first communication device and the second communication device can be a terminal or a network device, wherein the network device includes at least one of the following: an access network device, a core network device.

[0065] In some embodiments, the terminal includes at least one of the following: a mobile phone, a wearable device, an Internet of Things device, a communication-capable automobile, a smart automobile, a 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, but is not limited thereto.

[0066] In some embodiments, the access network device is, for example, a node or device that accesses a terminal to a wireless network, and the access network device 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 base station (Open RAN), a cloud base station (Cloud RAN), a base station in other communication systems, an access node in a Wi-Fi system, but is not limited thereto.

[0067] In some embodiments, the core network device can be one device including one or more network elements, or can be multiple devices or device groups including all or part of the one or more network elements described above. The network element can be virtual or physical. The core network includes, for example, at least one of an evolved packet core (EPC), a 5G core network (5GCN), and a next generation core (NGC).

[0068] In some embodiments, the technical solutions of the present disclosure can be applied to an Open RAN architecture, at which 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 realized by software or programs.

[0069] 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 protocol layers are controlled by the CU, and the remaining part or all of the protocol layers are distributed in the DU and controlled by the CU, but is not limited thereto.

[0070] 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. Those skilled in the art can know that, with the evolution of system architecture and the appearance of new business scenarios, the technical solutions proposed by the embodiments of the present disclosure are also applicable to similar technical problems.

[0071] The following embodiments of the present disclosure can be applied to the communication system 100 shown in FIG. 1 or part of the subjects, but are not limited thereto. The subjects shown in FIG. 1 are exemplary, and the communication system can include all or part of the subjects in FIG. 1, or other subjects other than FIG. 1. 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.

[0072] 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, or the like. Further, a plurality of systems can be combined (for example, combination of LTE or LTE-A and 5G, or the like).

[0073] FIG. 2 is an interaction diagram illustrating a data reception method according to an embodiment of the present disclosure.

[0074] As illustrated in FIG. 2, the data reception method includes:

[0075] In step S201, the second communication device 102 sends a first message to the first communication device 101, where the first message contains a transmission signal of the second communication device.

[0076] In some embodiments, the first communication device 101 receives a first signal from the second communication device 102, where the first signal is a received signal of the first communication device 101 after the transmission signal is transmitted through a channel.

[0077] In step S201, the first communication device 101 inputs the first signal into a processing model to obtain a second signal output by the processing model; where the second signal is a prediction result of the transmission signal of the second communication device 102 by the processing model, i.e., a restoration result of the signal before the first signal is transmitted through the channel.

[0078] In some embodiments, the processing model is an artificial intelligence (AI) based processing model that is trained.

[0079] In some embodiments, before step S201, the first communication device 101 can obtain training samples, where the training samples include a first signal sample after transmission and a second signal sample before transmission corresponding to the first signal sample after transmission; and train the processing model based on the first signal sample after transmission, the second signal sample before transmission corresponding to the first signal sample after transmission, and a loss function, where the loss function is used to represent an error between the second signal sample before transmission corresponding to the input of the processing model and the output of the processing model.

[0080] In some embodiments, before step S201, other communication devices can obtain training samples, where the training samples include a first signal sample after transmission and a second signal sample before transmission corresponding to the first signal sample after transmission; and train the processing model based on the first signal sample after transmission, the second signal sample before transmission corresponding to the first signal sample after transmission, and a loss function, where the loss function is used to represent an error between the second signal sample before transmission corresponding to the input of the processing model and the output of the processing model; and deploy the trained processing model to the first communication device 101.

[0081] In some embodiments, the processing model has the same length of input and output data, i.e., the first signal contains the same amount of data as the second signal, and the data units contained in the first signal correspond one by one to the data units contained in the second signal.

[0082] In some embodiments, the first signal and the second signal are symbol data, the first signal is first symbol data, and the first symbol data is symbol data received by the first communication device 101 after the second symbol data transmitted by the second communication device 102 is transmitted through a channel; and the second signal is a prediction result of the processing model on the second symbol data, that is, a restoration result of the second symbol data before the first symbol data is transmitted through the channel.

[0083] In some embodiments, the second communication device 102 can modulate the second bit data to be transmitted based on a preset modulation mode, obtain second symbol data to be transmitted, and transmit the second symbol data to the first communication device 101.

[0084] In some embodiments, the first communication device 101 can receive first symbol data, and the first symbol data is symbol data received by the first communication device 101 after the second symbol data is transmitted through a channel. The first communication device 101 inputs the received first symbol data into the processing model, and the second signal output by the processing model is a prediction result of the second symbol data, that is, the processing model output can be considered as the predicted second symbol data.

[0085] In some embodiments, the first symbol data contains symbols of the same length as the symbols contained in the second symbol data.

[0086] In some embodiments, the first communication device 101 can use a preset demodulation mode to demodulate the predicted second symbol data output by the processing model as symbol data to be demodulated, and the bit data obtained after demodulation can be second bit data corresponding to the second symbol data.

[0087] In some embodiments, the first signal and the second signal are bit data, the first signal is first bit data, and the first bit data is symbol data received by the first communication device after the second symbol data transmitted by the second communication device 102 is transmitted through a channel; and the second signal is a prediction result of the processing model on the second bit data, and the second bit data is bit data used by the second communication device to modulate to obtain the second symbol data, that is, a restoration result of the second bit data before the first bit data is transmitted through the channel.

[0088] In some embodiments, the first communication device 101 can receive first symbol data, and use a preset demodulation mode to demodulate the first symbol data, and obtain first bit data after demodulation. The first bit data is input into the processing model, and the second bit data is predicted by the processing model.

[0089] In some embodiments, the first bit data contains bits of the same length as the bits contained in the second bit data.

[0090] The communication method related to the embodiments of the present disclosure can include at least one of steps S201 to S202. For example, step S201 can be implemented as an independent embodiment, step S202 can be implemented as an independent embodiment, steps S201+S202 can be implemented as an independent embodiment, but are not limited thereto.

[0091] In some embodiments, steps S201 and S202 can be exchanged in order or executed simultaneously.

[0092] In some embodiments, step S201 is optional, and one or more of the steps can be omitted or replaced in different embodiments.

[0093] In some embodiments, step S202 is optional, and one or more of the steps can be omitted or replaced in different embodiments.

[0094] In some embodiments, other optional embodiments described before or after the corresponding description of FIG. 2 can be referred to.

[0095] In some embodiments, in order to meet the service requirements of high-speed data transmission, large-scale traffic broadband, reliable communication in mobile scenarios, and other services in 5G and future 6G mobile communication systems, and to realize future wireless communication visions such as smart cities and smart transportation, the current wireless communication system still needs to be improved and enhanced in many aspects. Among them, accurate signal demodulation is one of the basic modules of the wireless communication system that realizes low error rate high-speed transmission, and affects the overall performance of the wireless communication system.

[0096] The demodulator in the traditional wireless communication system is generally implemented by the classical method derived from the Neyman-Pearson (N-P) theorem and the Bayesian theorem. These demodulators usually require accurate channel state information (CSI) and channel noise distribution, and their performance depends on the parameter settings of each module, including filters, phase-locked loops, product modulators, analog-to-digital converters, etc.

[0097] The main limitations of the demodulator in the related art include: 1. Large delay and complex implementation: The use of traditional demodulators usually causes significant delay, and the implementation process is relatively complex. 2. Unstable module performance: Due to factors such as module vibration, acceleration, temperature fluctuation, aging, and unstable discrete elements, the performance of the module will change, resulting in a decline in the overall performance of the receiver. 3. Poor environmental adaptability: Actual wireless communication channels may be subject to multipath fading, impulse noise, clutter, or discrete interference, etc. These undesirable conditions can significantly reduce the demodulation performance. For example, some coherent demodulators require carrier synchronization, and when there is a phase error and frequency offset in the synchronization process, it will cause demodulation errors. 4. High dependence on CSI and other prior knowledge: Traditional demodulation methods usually have a high dependence on prior knowledge, and in actual communication, especially in fast fading scenarios, it is difficult to accurately estimate CSI, so the channel model may be unknown at the receiving end. 5. Traditional demodulation methods do not fully utilize prior waveform information and time series information.

[0098] It can be seen that, since the wireless communication system in the related art is generally designed according to strict mathematical theory and accurate system model, when demodulating the data to be demodulated, the data to be demodulated is easily demodulated due to the influence of the transmission channel from the sending end to the receiving end. Moreover, due to the increasing demand for wireless services such as smart phones, virtual reality, and the Internet of Things, these systems need to handle more complex and diverse communication needs, and traditional mathematical models may not be able to cope with these challenges well. In this case, deep learning is introduced into the wireless communication system as a powerful solution. The technical solution of receiving end demodulation can be realized by training an artificial intelligence (AI) demodulation model; wherein the AI demodulation model can directly obtain the demodulated bit data stream based on information such as constellation symbols related to the data to be demodulated of the receiving end.

[0099] The actual application of the AI demodulation model needs to consider various data modulation modes existing in a wireless communication system, such as Quadrature Phase Shift Keying (QPSK), 16 Quadrature Amplitude Modulation (QAM), 64 QAM, and the like. In the case of known or unknown data modulation information of the sending end, the existing scheme needs the receiving end to first complete the judgment of the data modulation mode, and then use the corresponding AI demodulation model to complete the data demodulation. Training and applying multiple AI demodulation models will cause a large training sample collection cost and model training and storage overhead. At the same time, it is difficult for the AI demodulation model to directly learn the mapping relationship between the symbols of the continuous multiple to-be-demodulated data and the bits of the demodulated data. Therefore, a difficulty-reducing AI-based mode needs to be applied in the signal receiving and demodulation process of the receiving end device.

[0100] Embodiments of the present disclosure provide a data receiving method. FIG. 3A is a schematic flowchart of a data receiving method according to an embodiment of the present disclosure. The data receiving method shown in the present embodiment can be executed by a first communication device, wherein the first communication device is a receiving end of data in a wireless communication system, and a second communication device is a sending end of data.

[0101] As shown in FIG. 3A, the data receiving method can include the following steps:

[0102] In step S301, a first signal is received from a second communication device.

[0103] In some embodiments, the first communication device can receive the first signal from the second communication device. The first signal can be first symbol data to be demodulated, or can be first bit data obtained after the first symbol data is demodulated.

[0104] The demodulation mode of the first symbol data can be set according to actual needs, and can use hard demodulation or soft demodulation.

[0105] In step S302, the first signal is input into a processing model, and a second signal output by the processing model is obtained; wherein the second signal is a prediction result of the processing model for a sending signal of the second communication device.

[0106] In some embodiments, after the second communication device transmits the signal to the first communication device, the transmitted signal can be transformed into the first signal received by the first communication device through channel transmission. During the channel transmission, the second communication device's transmitted signal can be affected in several ways, including channel noise, interference, fading, distortion, multipath effect, etc. The interference can include inter-symbol interference and inter-carrier interference, etc.

[0107] To remove the effects of the channel on the transmitted signal and restore the second communication device's transmitted signal as much as possible, the first communication device is pre-equipped with a processing model that is used to restore the second communication device's transmitted signal corresponding to the first signal based on the first signal, to obtain the second signal corresponding to the first signal.

[0108] In some embodiments, after receiving the first signal, the first communication device can input the first signal into the processing model, and the processing model outputs the second signal corresponding to the first signal, which is the processing model's prediction result of the second communication device's transmitted signal based on the first information.

[0109] It should be noted that the embodiment shown in FIG. 3A can be implemented independently or in combination with at least one other embodiment in the present disclosure. The specific implementation can be selected as needed, and the present disclosure does not limit it.

[0110] Based on the above embodiments, the receiving end processes the received first signal through the processing model to restore the first signal to the second signal sent by the sending end, removes the effects of the data during the channel transmission, and realizes the functions of de-noising and de-interference of the data, so that the receiving end obtains more ideal data and improves the accuracy and efficiency of data transmission in the wireless communication system.

[0111] In some embodiments, the processing model that can be used to remove noise and interference during channel transmission can be various, for example, an equalizer (Equalizers) can be used to combat the frequency-selective fading introduced by the channel, including linear equalizers and nonlinear equalizers, etc.; an encoder and error correction code can also be used to detect and correct the received data based on the redundant information in the signal; or an AI-based processing model (AI processing model) can also be used, which can be trained to obtain the mapping relationship between the first signal and the second signal through a pre-set learning or algorithm, so that the trained AI-based processing model can be used to predict the second signal based on the input first signal.

[0112] In some embodiments, the network architecture of the AI-based processing model can be set according to actual needs, for example, a deep neural network (DNN) such as a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), etc., or a deformation of the above network structure, and the present disclosure does not limit this. Any network architecture that can realize the processing model of the present disclosure is applicable to the present disclosure.

[0113] In some embodiments, the processing model can be trained by a preset learning method and algorithm. During the training of the processing model, training samples can be pre-collected, and a loss function can be constructed. The training samples include the first signal sample after transmission and the second signal sample corresponding to the first signal sample before transmission. The constructed loss function is used to represent the error between the second signal sample before transmission corresponding to the input of the processing model and the output of the processing model.

[0114] The first signal sample can be the received signal of the receiving end after channel transmission, and correspondingly, the second signal sample corresponding to the first signal sample can be the sending signal of the sending end before channel transmission of the received signal. Correspondingly, the loss function is used to represent the error between the signal output by the processing model based on the input received signal and the corresponding sending signal.

[0115] During the training of the processing model based on the loss function, the following methods can be used at least one of them: the gradient of the loss function to each parameter in the processing model can be calculated by backpropagation (Backpropagation), and these gradients are reversely transmitted to adjust the parameters in the processing model; the gradient descent (Gradient Descent) can also be used to update the parameters of the processing model along the negative direction of the gradient of the loss function to minimize the loss function; optimization algorithms such as Adam, RMSProp, Adagrad, etc. can also be used to improve the gradient descent algorithm, and the learning rate is adjusted according to the gradient size and parameter update history to optimize the parameters more effectively; regularization and batch normalization can also be used to control the complexity of the processing model and avoid overfitting; and so on.

[0116] In some embodiments, the processing model can be trained based on random gradient descent and other methods until the loss function is reduced to a certain range, the training period reaches a certain number of times, or the error between the signal output by the processing model and the second signal sample reaches a certain requirement.

[0117] In some embodiments, the construction and training of the processing model can be performed by the first communication device, and the deployment of the processing model can be completed after the training is finished. In this case, the first communication device is required to have sufficient computing capability to support the training of the processing model.

[0118] In some embodiments, the construction and training of the processing model can be performed by another communication device, and the processing model can be deployed to the first communication device after the training is finished, so that the first communication device can process the received first signal by using the processing model to obtain the second signal corresponding to the first signal. For example, the construction and training of the processing model can be performed by a base station, and the processing model can be deployed to the communication devices in the cell where the base station is located after the training is finished.

[0119] In some embodiments, the deployed processing model can be periodically fine-tuned.

[0120] In some embodiments, the deployed processing model can be fine-tuned online by the first communication device.

[0121] In some embodiments, during the training of the processing model, different processing models can be trained for different application scenarios, and the corresponding processing model can be deployed according to the current application scenario of the first communication device. Different application scenarios can include at least one of the following: the type of the device at the receiving end; the type of the device at the sending end; the type of the data transmitted; the channel state; and the like.

[0122] In some embodiments, based on the application scenario to which the processing model is applicable, the training samples related to the applicable application scenario can be obtained, including the first signal samples after transmission collected under the applicable application scenario, and the second signal samples before transmission corresponding to the first signal samples; and the processing model can be trained based on the collected training samples and the loss function to obtain the processing model applicable to the application scenario.

[0123] In some embodiments, after the multiple processing models applicable to multiple application scenarios are trained by the other communication device, the processing model corresponding to the current application scenario of the first communication device can be deployed to the first communication device based on the current application scenario of the first communication device.

[0124] In some embodiments, the first communication device can request the other communication device to deploy the processing model corresponding to the current application scenario based on the current application scenario.

[0125] In some embodiments, after the first communication device completes the training or deploys multiple processing models applicable to multiple application scenarios, the first communication device can automatically or based on a setting call a processing model corresponding to the current application scenario to implement data reception based on the current application scenario.

[0126] In some embodiments, the processing model can be obtained based on iterative training of signals when communicating based on a channel between the first communication device and the second communication device as training samples, and / or iterative training of signals transmitted based on a channel environment or an application scenario similar to conditions of the channel as training samples.

[0127] In some embodiments, the processing model can be obtained based on iterative training of signals when communicating under various conditions such as different configurations of communication devices, different application scenarios, different channel environments, different signal-to-noise ratios, etc. as training samples, and the processing model can be well applicable to communication under various conditions such as different configurations of communication devices, different application scenarios, different channel environments, different signal-to-noise ratios, etc., to achieve prediction of the signal of the sending end at the receiving end, and can also be applied when the first communication device and the second communication device communicate.

[0128] Based on the above embodiments, the processing model is trained by obtaining training samples and constructing a loss function, and the trained processing model is deployed to the receiving end to process the received first signal to obtain the second signal, which realizes the functions of denoising and interference removal of data, and enables the receiving end to obtain more ideal data, thereby improving the accuracy and efficiency of data transmission in the wireless communication system.

[0129] Since the mapping between constellation symbols and data is different under different modulation modes such as QPSK, 16QAM, and 64QAM, and one symbol of the data to be demodulated corresponds to bits of demodulated data of different lengths, for example, one symbol of the data to be demodulated corresponds to 2, 4, and 6 bits of demodulated data under QPSK, 16QAM, and 64QAM modes, respectively, a matched demodulation mode is needed for the symbol of the data to be demodulated using different modulation modes. Therefore, in the case of using an AI demodulation model to implement data reception, different modulation modes also need to be considered.

[0130] The training of the AI demodulation model needs to use input data and output data of a certain dimension, and the length of the bits of the demodulation data corresponding to a symbol of the to-be-demodulated data under different modulation modes can be different. For example, under a 16QAM modulation mode, a symbol of the to-be-demodulated data corresponds to 4 bits of demodulation data, and under a 64QAM modulation mode, a symbol of the to-be-demodulated data corresponds to 6 bits of demodulation data. Therefore, the output length of the AI demodulation model used for 16QAM and 64QAM modes should be 4 bits and 6 bits respectively, and the AI demodulation model needs to learn the mapping relationship between constellation points and data under the two modulation modes respectively.

[0131] In some embodiments, when receiving data under different modulation modes based on the AI demodulation model, the modulation mode information needs to be known. The information can be indicated to the receiving end by the sending end during data transmission as auxiliary information for selecting the AI demodulation model. As shown in FIG. 3B, the first communication device can receive to-be-demodulated data and modulation mode information, input the to-be-demodulated data into the AI demodulation model corresponding to the modulation mode based on the modulation mode indicated by the modulation mode information (such as 16QAM, 64QAM, etc.), and output the demodulation data. The input data of the AI demodulation model is n symbols of to-be-demodulated data, and the output data is n x M 0 / 1 bits of demodulation data, where M is the modulation order.

[0132] In some embodiments, in the case where the sending end does not indicate the modulation mode information, an AI-based modulation mode identification model can also be used, that is, the to-be-demodulated data and the corresponding modulation mode information are used as training data to train the AI modulation mode identification model. The AI modulation mode identification model can determine the modulation mode of the sending end based on the features of the to-be-demodulated data, and then select the AI demodulation model under the corresponding modulation mode to demodulate the data, thereby realizing the AI demodulation scheme without indicating the modulation information. As shown in FIG. 3C, after receiving the to-be-demodulated data, the first communication device can identify the to-be-demodulated data through the AI modulation mode identification model to obtain the modulation mode information of the to-be-demodulated data; then select the corresponding AI demodulation model based on the identified modulation mode information of the to-be-demodulated data, and input the to-be-demodulated data into the selected AI demodulation model to output the demodulation data.

[0133] It can be seen that, in the case of using an AI demodulation model to implement data reception, since the AI demodulation model itself cannot obtain the correspondence between the symbols of the to-be-demodulated data and the bits of the demodulated data in the model input and output data in the model training process, the difficulty of model training is greatly increased. In addition, in an actual wireless communication system, due to different application scenarios, channel environments, signal-to-noise ratios, and other conditions, the most suitable modulation mode for data transmission is different. Considering the influence of factors such as user movement and real-time changes in the wireless communication environment, the modulation mode used for data transmission may change. Training multiple AI demodulation models for multiple data modulation modes requires collecting more model training data under each modulation mode, resulting in higher data collection costs and larger model training and storage overheads.

[0134] The processing model of the present application does not function to implement demodulation, the length of the input and output data of the processing model is the same, that is, the amount of data contained in the first signal and the amount of data contained in the second signal are the same, and the data units contained in the first signal and the data units contained in the second signal correspond one by one.

[0135] In the case where the first signal input to the processing model is first symbol data, the second signal output is second symbol data, wherein the symbol length contained in the first symbol data and the symbol length contained in the second symbol data are the same, and the symbol bits contained in the first symbol data and the symbol bits contained in the second symbol data correspond one by one.

[0136] In the case where the first signal input to the processing model is first bit data, the second signal output is second bit data, wherein the bit length contained in the first bit data and the bit length contained in the second bit data are the same, and the bit bits contained in the first bit data and the bit bits contained in the second bit data correspond one by one.

[0137] Since the amount of data contained in the first signal and the amount of data contained in the second signal are the same, and the data units contained correspond one by one, it is more conducive for the processing model to learn the mapping relationship between each data unit of the first signal and the second signal in the training process, and the processing model trained in this way can be applied to different modulation modes, and one processing model can complete data reception under different modulation modes.

[0138] In some embodiments, the first signal and the second signal are both symbol data, and accordingly, the processing model is a processing model for symbol data.

[0139] In some embodiments, the second communication device can modulate the to-be-sent second bit data based on a preset modulation mode to obtain to-be-sent second symbol data, and send the second symbol data to the first communication device.

[0140] In some embodiments, as shown in FIG. 3D, the first communication device can receive the first symbol data, which is the symbol data received by the first communication device after the second symbol data is transmitted through the channel. The first communication device inputs the received first symbol data into the processing model, and the second signal output by the processing model is the prediction result of the second symbol data, that is, the second symbol data predicted by the processing model.

[0141] In some embodiments, the first communication device can take the second symbol data predicted by the processing model as the symbol data to be demodulated, and use a preset demodulation method to demodulate, so as to obtain the second bit data corresponding to the second symbol data.

[0142] The demodulation method can use a traditional hard demodulation or soft demodulation method.

[0143] In some embodiments, the input of the AI-based processing model for symbol data is the symbol data to be demodulated received by the receiving end, and the output data is the sending symbol data modulated by the corresponding sending end. Due to various interferences and noises in the channel, there is a certain difference between the symbol data to be demodulated received by the receiving end and the sending symbol data modulated by the sending end. The AI-based processing model learns the mapping relationship between the symbol data to be demodulated actually received by the receiving end and the corresponding sending symbol data modulated by the sending end through training, so as to realize the data denoising and interference removing functions, and output more ideal symbol data to be demodulated by the processing model. At the same time, the symbol contained in the symbol data to be demodulated input by the processing model is one-to-one corresponding to the symbol contained in the processed symbol data to be demodulated output by the processing model, which is more conducive to the processing model to learn the corresponding mapping relationship in the training process. Moreover, the AI-based processing model trained in this way can be applied to different modulation methods, and one processing model can complete the processing of the symbol data to be demodulated under different modulation methods.

[0144] In the training process of the AI-based processing model, the pre-collected training data includes the symbol data to be demodulated received by the receiving end and the sending symbol data modulated by the sending end; the symbol data to be demodulated in the training data is input into the processing model, the error between the processed symbol data to be demodulated actually output by the processing model and the sending symbol data modulated by the sending end in the training data is taken as a loss function, and the model is trained through a random gradient descent method or the like until the loss function is reduced to a certain range, the training period reaches a certain number of times, or the error between the symbol data output by the processing model and the sending symbol data reaches a certain requirement.

[0145] Then, the trained processing model can be deployed at the receiving end, and input the received symbol data (x, y) to be demodulated, and output the symbol data (x', y') to be demodulated processed by the processing model. The subsequent process is the same as the existing data processing process of the receiving end, that is, the bit data after demodulation is obtained through hard decision or soft decision on the symbol data (x', y').

[0146] In some embodiments, the first signal and the second signal are both bit data, and correspondingly, the processing model is a processing model for bit data.

[0147] In some embodiments, the second communication device can modulate the second bit data to be sent based on a preset modulation mode, obtain the second symbol data to be sent, and send the second symbol data to the first communication device.

[0148] In some embodiments, as shown in FIG. 3E, the first communication device can receive the first symbol data, which is the symbol data received by the first communication device after the second symbol data is transmitted through the channel; the first communication device can first demodulate the received first symbol data based on a preset demodulation mode to obtain the first bit data, and input the first bit data into the processing model, and the second signal output by the processing model is the prediction result of the second bit data, that is, the processing model output can be considered as the predicted second bit data, that is, the predicted second bit data to be sent by the second communication device.

[0149] In some embodiments, the demodulation mode can be a traditional hard demodulation or soft demodulation.

[0150] In some embodiments, the input of the AI-based processing model for bit data is the bit data obtained after the receiving end demodulates the received symbol data to be demodulated, and the output data is the bit data to be sent before modulation by the corresponding sending end. Due to various interferences and noises in the channel, there is a certain difference between the symbol data to be demodulated received by the receiving end and the sending symbol data modulated by the sending end, and correspondingly, there is also a certain difference between the bit data obtained after demodulating the symbol data to be demodulated and the bit data to be sent before modulation by the sending end. The AI-based processing model learns the mapping relationship between the bit data demodulated by the actual receiving end and the bit data to be sent before modulation by the corresponding sending end through training, so as to realize the data denoising and deinterference function, and output more ideal demodulated bit data by the processing model. At the same time, the demodulated bit data input by the processing model contains bits corresponding to the bits contained in the processed bit data to be demodulated output by the processing model, which is more conducive to the processing model learning the corresponding mapping relationship during the training process. Moreover, the AI-based processing model trained in this way can be applied to different modulation modes, and one processing model can complete the processing of demodulated bit data under different modulation modes.

[0151] In the training process of the AI-based processing model, the pre-acquired training data includes: demodulated bit data obtained after the receiving end demodulates the received symbol data to be demodulated and bit data to be sent before modulation by the sending end; the demodulated bit data in the training data is input into the processing model, the error between the processed bit data actually output by the processing model and the bit data to be sent before modulation by the sending end in the training data is taken as a loss function, and the model is trained by random gradient descent or the like until the loss function decreases to a certain range, the training period reaches a certain number, or the error between the bit data output by the processing model and the bit data to be sent reaches a certain requirement.

[0152] Then, the trained processing model can be deployed at the receiving end, and the demodulated bit data (a1, a2; b1, b2) obtained after the receiving end demodulates the received symbol data (x, y) is input, wherein the symbol x is demodulated to obtain bits (a1, a2), and the symbol y is demodulated to obtain bits (b1, b2); the bit data (a1', a2'; b1', b2') processed by the processing model is output, and the subsequent process is the same as the existing data processing process of the receiving end.

[0153] In some embodiments, the names of information and the like 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", and the like can be replaced with each other.

[0154] In some embodiments, the terms of "time", "time point", "time", "time position", and the like can be replaced with each other, and the terms of "time length", "time period", "time window", "window", "time", and the like can be replaced with each other.

[0155] In some embodiments, the terms of "component carrier (CC)", "cell", "frequency carrier", "carrier frequency", and the like can be replaced with each other.

[0156] In some embodiments, "acquire", "obtain", "get", "receive", "transmit", "bidirectional transmission", "send and / or receive", and the like can be replaced with each other, which can be interpreted as receiving from other subjects, obtaining from protocols, obtaining from higher layers, obtaining by self-processing, and the like.

[0157] In some embodiments, the terms of "send", "transmit", "report", "issue", "transmit", "bidirectional transmission", "send and / or receive", and the like can be replaced with each other.

[0158] Corresponding to the foregoing embodiments of the data receiving method, the present disclosure also provides embodiments of a terminal and a network device.

[0159] FIG. 4 is a schematic block diagram of an apparatus structure of a communication device according to an embodiment of the present disclosure. As shown in FIG. 4, the communication device can be a data receiving apparatus, and the apparatus includes a processing module 401 and a transceiver module 402.

[0160] In some embodiments, the transceiving module 402 is configured to receive a first signal from a second communication device; the processing module 401 is configured to input the first signal into a processing model, and obtain a second signal output by the processing model; wherein the second signal is a prediction result of the processing model on a signal transmitted by the second communication device.

[0161] In some embodiments, the processing model is an artificial intelligence (AI) based processing model that is trained.

[0162] In some embodiments, the processing module 401 is further configured to: obtain a training sample, the training sample comprising a first signal sample after transmission, and a second signal sample before transmission corresponding to the first signal sample after transmission; train the processing model based on the first signal sample after transmission, the second signal sample before transmission corresponding to the first signal sample after transmission, and a loss function; wherein the loss function is used to represent an error between the second signal sample before transmission corresponding to an input of the processing model and an output of the processing model.

[0163] In some embodiments, the first signal contains the same amount of data as the second signal.

[0164] In some embodiments, the first signal is first symbol data, the first symbol data being symbol data received by the first communication device after channel transmission of second symbol data transmitted by the second communication device; and the second signal is a prediction result of the processing model on the second symbol data.

[0165] In some embodiments, the processing module 401 is further configured to demodulate the second symbol data predicted by the processing model to obtain demodulated bit data.

[0166] In some embodiments, the first signal is first bit data, the first bit data being bit data obtained by demodulating first symbol data received by the first communication device, the first symbol data being symbol data received by the first communication device after channel transmission of second symbol data transmitted by the second communication device; and the second signal is a prediction result of the processing model on second bit data, the second bit data being bit data used by the second communication device to modulate to obtain the second symbol data.

[0167] It should be noted that the modules included in the communication device are not limited to the modules described in the above embodiments, and can also include other modules, such as a storage module, a display module, etc.

[0168] For the apparatus embodiment, since it basically corresponds to the method embodiment, the relevant part can be seen from the part of the method embodiment. The apparatus embodiment described above is only illustrative, wherein the modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical modules, i.e., can be located in one place or distributed to multiple network modules. Part or all of the modules can be selected to achieve the purpose of the embodiment according to actual needs. Those skilled in the art can understand and implement it without creative labor.

[0169] The embodiment of the present disclosure also provides a communication device, comprising: one or more processors; a memory coupled to the processor, wherein the memory stores executable instructions, and when the executable instructions are executed by the processor, the processor invokes the executable instructions to enable the communication device to perform the data receiving method in the optional embodiment.

[0170] The embodiment of the present disclosure also provides a communication system, comprising a first communication device and a second communication device, wherein the first communication device is configured to implement the data receiving method in the optional embodiment.

[0171] The embodiment of the present disclosure also provides a storage medium, wherein the storage medium stores instructions, and when the instructions are executed on the communication device, the communication device performs the data receiving method in the optional embodiment.

[0172] The embodiment of the present disclosure also provides an apparatus for implementing any of the above methods, for example, an apparatus comprising units or modules for implementing the steps performed by the terminal in any of the above methods. For another example, another apparatus is provided, comprising units or modules for implementing the steps performed by the network device (such as an access network device, a core network function node, a core network device, etc.) in any of the above methods.

[0173] It should be understood that the division of each unit or module in the above apparatus is only a logical function division, and all or part of them can be integrated into a physical entity or physically separated in actual implementation. In addition, the units or modules in the apparatus can be implemented in the form of processor calling software: for example, the apparatus 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 realize any of the above methods or realize the functions of each unit or module of the above apparatus, wherein the processor is a general processor such as a central processing unit (CPU) or a microprocessor, and the memory is a memory in the apparatus or a memory outside the apparatus. Alternatively, the units or modules in the apparatus can be implemented in the form of hardware circuit, and the functions of part or all of the units or modules can be realized by the design of hardware circuit. The above hardware circuit can be understood as one or more processors; for example, in one implementation, the above hardware circuit is an application-specific integrated circuit (ASIC), and the functions of part or all of the units or modules are realized by the design of the logical relationship of elements in the circuit; for another example, in another implementation, the above hardware circuit is a programmable logic device (PLD), and a field programmable gate array (FPGA) is taken as an example, which 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 realize the functions of part or all of the above units or modules. All units or modules of the above apparatus can be all implemented in the form of processor calling software, or all implemented in the form of hardware circuit, or part implemented in the form of processor calling software and the remaining part implemented in the form of hardware circuit.

[0174] 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), and 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 reconfigurable. 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), and the like.

[0175] FIG. 5 is a structural schematic diagram of a communication device 5100 according to the embodiments of the present disclosure. The communication device 5100 can be a network device (for example, an access network device, a core network device, and the like), or a terminal (for example, a user equipment, and the like), or 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 5100 can be used to implement the methods described in the above method embodiments, and details can be referred to the descriptions in the above method embodiments.

[0176] As shown in FIG. 5, the communication device 5100 includes one or more processors 5101. The processor 5101 can be a general processor or a special-purpose processor, etc., for example, a baseband processor or a central processor. The baseband processor can be used to process communication protocols and communication data, 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. The processor 5101 is configured to invoke instructions to enable the communication device 5100 to perform any of the above methods.

[0177] In some embodiments, the communication device 5100 further includes one or more memories 5102 configured to store instructions. Optionally, all or part of the memory 5102 can also be outside the communication device 5100.

[0178] In some embodiments, the communication device 5100 further includes one or more transceivers 5103. When the communication device 5100 includes one or more transceivers 5103, the communication steps in the above methods, such as sending and receiving, are performed by the transceiver 5103, and the other steps are performed by the processor 5101.

[0179] In some embodiments, the transceiver can include a receiver and a transmitter, which can be separate or integrated together. Optionally, the terms transceiver, transceiving unit, transceiver, transceiving circuit, etc. can be replaced by each other, the terms transmitter, transmitting unit, transmitter, transmitting circuit, etc. can be replaced by each other, and the terms receiver, receiving unit, receiver, receiving circuit, etc. can be replaced by each other.

[0180] Optionally, the communication device 5100 further includes one or more interface circuits 5104 connected to the memory 5102. The interface circuit 5104 can be used to receive signals from the memory 5102 or other devices, and can be used to send signals to the memory 5102 or other devices. For example, the interface circuit 5104 can read instructions stored in the memory 5102 and send the instructions to the processor 5101.

[0181] The communication device 5100 described in the above embodiments may be a network device or a terminal, but the scope of the communication device 5100 described in this disclosure is not limited thereto, and the structure of the communication device 5100 may not be limited by FIG. 5. The communication device may be an independent device or may be part of a larger device. For example, the communication device may be: (1) an independent 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.

[0182] Figure 6 is a schematic diagram of the structure of the chip 6200 proposed in an embodiment of this disclosure. For cases where the communication device 5100 can be a chip or a chip system, the schematic diagram of the chip 6200 shown in Figure 6 can be referenced, but is not limited thereto.

[0183] Chip 6200 includes one or more processors 6201, which are used to invoke instructions to cause chip 6200 to perform any of the above methods.

[0184] In some embodiments, the chip 6200 further includes one or more interface circuits 6202, which are connected to the memory 6203. The interface circuits 6202 can be used to receive signals from the memory 6203 or other devices, and can also be used to send signals to the memory.

[0185] 6203 or other devices send signals. For example, interface circuit 6202 can read instructions stored in memory 6203 and send those instructions to processor 6201. Optionally, the terms interface circuit, interface, transceiver pin, transceiver, etc., can be used interchangeably.

[0186] In some embodiments, chip 6200 further includes one or more memories 6203 for storing instructions. Optionally, all or part of the memories 6203 may be located outside of chip 6200.

[0187] The present disclosure further provides a storage medium having stored instructions which, when executed on the communication device 5100, cause the communication device 5100 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 is not limited thereto and can also be a storage medium readable by other apparatuses. Optionally, the storage medium can be a non-transitory storage medium, but is not limited thereto and can also be a transitory storage medium.

[0188] The present disclosure further provides a program product which, when executed by the communication device 5100, causes the communication device 5100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0189] The present disclosure further provides a computer program which, when executed on a computer, causes the computer to perform any of the above methods.

Claims

1. A data receiving method characterized by comprising: The method is performed by a first communication device, and the method comprises: receiving a first signal from a second communication device; inputting the first signal into a processing model to obtain a second signal output by the processing model; wherein the second signal is a prediction result of the processing model on a signal transmitted by the second communication device.

2. The method of claim 1, wherein, The processing model is a trained artificial intelligence (AI) based processing model.

3. The method according to claim 1 or 2, characterized in that, The method further comprises: obtaining training samples, wherein the training samples comprise a first signal sample after transmission and a second signal sample before transmission corresponding to the first signal sample after transmission; training the processing model based on the first signal sample after transmission, the second signal sample before transmission corresponding to the first signal sample after transmission, and a loss function; wherein the loss function is used to represent an error between the second signal sample before transmission corresponding to the input of the processing model and the output of the processing model.

4. The method according to any one of claims 1 to 3, characterized in that, The first signal comprises first symbol data, which is symbol data received by the first communication device after transmission of second symbol data transmitted by the second communication device; and the second signal comprises a prediction result of the processing model on the second symbol data.

5. The method of claim 4, wherein, The method further comprises: demodulating the second symbol data predicted by the processing model to obtain demodulated bit data.

6. The method of any one of claims 1-3, wherein, The first signal comprises first bit data, which is bit data obtained by demodulating the first symbol data received by the first communication device, wherein the first symbol data is symbol data received by the first communication device after transmission of second symbol data transmitted by the second communication device; and the second signal comprises a prediction result of the processing model on second bit data, which is bit data used by the second communication device to modulate to obtain the second symbol data.

7. A data receiving apparatus characterized by comprising: The method comprises: a transceiver module configured to receive a first signal from a second communication device; a processing module configured to input the first signal into a processing model to obtain a second signal output by the processing model; wherein the second signal is a prediction result of the processing model on a signal transmitted by the second communication device.

8. The apparatus of claim 7, wherein, The processing model is a trained artificial intelligence (AI) based processing model.

9. The apparatus of claim 7 or 8, wherein, The processing module is further configured to: obtain training samples, wherein the training samples comprise a first signal sample after transmission and a second signal sample before transmission corresponding to the first signal sample after transmission; train the processing model based on the first signal sample after transmission, the second signal sample before transmission corresponding to the first signal sample after transmission, and a loss function; wherein the loss function is used to represent an error between the second signal sample before transmission corresponding to the input of the processing model and the output of the processing model. The first signal is first symbol data, which is symbol data received by the first communication device after channel transmission of second symbol data transmitted by the second communication device; and the second signal is a prediction result of the processing model on the second symbol data.

10. The apparatus of any one of claims 7-9, wherein, ​ 11. The apparatus of claim 10, wherein, The processing module is further configured to demodulate the second symbol data predicted by the processing model to obtain demodulated bit data.

12. The apparatus of any one of claims 7-9, wherein, The first signal is first bit data, the first bit data being bit data obtained by demodulating first symbol data received by the first communication device, the first symbol data being symbol data received by the first communication device after channel transmission of second symbol data transmitted by the second communication device; and the second signal is a prediction result of the processing model on second bit data, the second bit data being bit data used by the second communication device to modulate to obtain the second symbol data.

13. 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 processors to invoke instructions to cause the communication device to perform the data receiving method of any one of claims 1-6.

14. A communication system, characterized by The first communication device is configured to receive a first signal from the second communication device; input the first signal into a processing model to obtain a second signal output by the processing model; and the second signal is a prediction result of the processing model on a signal transmitted by the second communication device.

15. A storage medium, the storage medium storing instructions, wherein, When the instructions are executed on the communication device, the communication device performs the data receiving method of any one of claims 1-6.

Images