Communication method, communication device, communication system, storage medium, and program product

Abstract

The present disclosure relates to a communication method, a communication device, a communication system, a storage medium, and a program product. The method comprises: receiving a first signal sent by a second device, wherein the first signal is determined on the basis of an aliased signal, the aliased signal is generated by superimposing a baseband signal with a reference signal, and the baseband signal carries data information; and determining, on the basis of a first model and the first signal, a second signal corresponding to the aliased signal. In the method of the present disclosure, the first signal sent by a sending end during signal transmission is sent on the basis of the aliased signal obtained by superimposing the baseband signal with the reference signal, thereby improving the utilization rate of transmission resources for data signals; a receiving end processes the first signal on the basis of the pre-trained first model, thereby improving the processing accuracy of the received signal while improving the utilization rate of the data signal.

Description

Communication methods, communication equipment, communication systems, storage media and software products Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to a communication method, communication device, communication system, storage medium, and program product. Background Technology

[0002] In wireless communication systems, the transmitting end can use a transmitter to send signals, and the receiving end can use a receiver to perform channel estimation, thereby recovering the transmitted signal and completing signal transmission. During signal transmission, the data and the reference signal used for channel estimation are independent or orthogonal to each other. The high resource overhead required by the reference signal may reduce the utilization rate of data transmission resources. Summary of the Invention

[0003] Based on the transmission relationship between data and reference signals, it is necessary to provide ways to improve the utilization of data transmission resources and ensure that the receiving end can receive accurately.

[0004] This disclosure provides a communication method, communication device, communication system, storage medium, and program product.

[0005] In a first aspect, this disclosure provides a communication method, executed by a first device, the method comprising:

[0006] Receive a first signal sent by a second device, wherein the first signal is sent based on an aliasing signal, wherein the aliasing signal is generated by superimposing a baseband signal and a reference signal, and the baseband signal carries data information;

[0007] Based on the first model and the first signal, determine the second signal corresponding to the aliasing signal.

[0008] Secondly, embodiments of this disclosure provide a communication method executed by a second device, the method comprising:

[0009] Based on the baseband signal and the reference signal, the superimposed aliased signal is determined, wherein the baseband signal carries data information;

[0010] Based on the aliasing signal, a first signal is sent to the first device, wherein the first signal is used by the first device to determine a second signal corresponding to the aliasing signal based on a first model.

[0011] Thirdly, embodiments of this disclosure provide a communication device, wherein the communication device is used to perform the method described in the first aspect or the second aspect.

[0012] Fourthly, embodiments of this disclosure provide a communication system, including a first device and a second device, wherein,

[0013] The first device is configured to implement the method as described in the first aspect;

[0014] The second device is configured to implement the method as described in the second aspect.

[0015] Fifthly, embodiments of this disclosure provide a storage medium storing instructions, wherein...

[0016] When the instructions are executed on the communication device, the communication device causes the communication device to perform the method as described in the first aspect or the second aspect.

[0017] In a sixth aspect, embodiments of this disclosure provide a program product, including at least one of a program and instructions, wherein when the program and instructions are executed by a communication device, they implement the method described in the first aspect or the second aspect.

[0018] In this embodiment of the present disclosure, the first signal transmitted by the transmitting end during signal transmission is based on an aliased signal obtained by superimposing the baseband signal and the reference signal, thereby improving the utilization rate of data signals to transmission resources; the receiving end processes the first signal based on a pre-trained first model to obtain the aliased signal, thereby improving the utilization rate of data signals and the accuracy of processing received signals. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings required for the description of the embodiments are introduced below. The following drawings are only some embodiments of this disclosure and do not impose specific limitations on the protection scope of this disclosure.

[0020] Figure 1 is an exemplary schematic diagram of the architecture of a communication system provided according to an embodiment of the present disclosure;

[0021] Figures 2A and 2B are exemplary interactive schematic diagrams of the method provided according to embodiments of the present disclosure;

[0022] Figure 2C is a constellation diagram of aliasing signals provided according to an embodiment of the present disclosure;

[0023] Figure 2D is a schematic diagram of a first model provided according to an embodiment of the present disclosure;

[0024] Figures 3A to 3C are exemplary interactive schematic diagrams of the method provided according to embodiments of the present disclosure;

[0025] Figure 4 is an exemplary interactive schematic diagram of the method provided according to an embodiment of the present disclosure;

[0026] Figure 5A is a schematic diagram of the structure of a first device according to an embodiment of the present disclosure;

[0027] Figure 5B is a schematic diagram of the structure of a second device according to an embodiment of the present disclosure;

[0028] Figure 6A is a schematic diagram of a communication device according to an embodiment of the present disclosure;

[0029] Figure 6B is a schematic diagram of a communication device according to an embodiment of the present disclosure. Detailed Implementation

[0030] This disclosure provides a communication method, communication device, communication system, storage medium, and program product.

[0031] In a first aspect, this disclosure provides a communication method, executed by a first device, the method comprising:

[0032] The device receives a first signal sent by a second device, wherein the first signal is sent based on an aliasing signal, wherein the aliasing signal is generated by superimposing a baseband signal and a reference signal, and the baseband signal carries data information.

[0033] Based on the first model and the first signal, determine the second signal corresponding to the aliasing signal.

[0034] In the above embodiments, the first signal transmitted by the transmitting end in signal transmission is based on the aliased signal obtained by superimposing the baseband signal and the reference signal, thereby improving the utilization rate of data signal to transmission resources; the receiving end processes the first signal based on the pre-trained first model to obtain the aliased signal, thereby improving the utilization rate of data signal and the accuracy of processing the received signal.

[0035] In conjunction with the embodiments of the first aspect, in some embodiments, the input of the first model includes information corresponding to all resource cells (REs) on the resource grid, wherein the resource grid includes the resource grids corresponding to different receiving antennas after the first device receives the first signal;

[0036] Based on the first model and the first signal, the aliasing signal is determined, including:

[0037] The first signal is deorthogonal frequency division multiplexing (OFDM) modulation to obtain the input;

[0038] Based on the first model and the input, the second signal corresponding to the aliasing signal is output.

[0039] In conjunction with the embodiments of the first aspect, in some embodiments, the method further includes:

[0040] Based on the second signal, the corresponding transmit bit stream of the baseband signal is determined; wherein, the transmit bit stream is modulated to obtain the baseband signal.

[0041] In conjunction with the embodiments of the first aspect, in some embodiments, determining the transmit bit stream corresponding to the baseband signal based on the second signal includes:

[0042] Based on the second signal, determine the third signal corresponding to the baseband signal;

[0043] Demap the third signal to determine the transmitted bit stream.

[0044] In conjunction with the embodiments of the first aspect, in some embodiments, determining a third signal corresponding to the baseband signal based on the second signal includes:

[0045] Using the second signal as input, determine the third signal output by the second model.

[0046] In conjunction with the embodiments of the first aspect, in some embodiments, demapping the baseband signal to determine the transmitted bit stream includes:

[0047] Using the third signal as input, the transmitted bit stream of the third model output is determined.

[0048] In conjunction with the embodiments of the first aspect, in some embodiments, determining the transmit bit stream corresponding to the baseband signal based on the second signal includes:

[0049] Using the second signal as input, the transmitted bit stream of the fourth model is determined.

[0050] In conjunction with the embodiments of the first aspect, in some embodiments, the method further includes:

[0051] The first model is trained based on the dataset until it converges.

[0052] In conjunction with the embodiments of the first aspect, in some embodiments, the dataset includes at least one of the following:

[0053] Information corresponding to all resource units (REs) on the resource grid;

[0054] As an aliasing signal for tag values;

[0055] The transmitted bit stream corresponding to the data information.

[0056] In conjunction with the embodiments of the first aspect, in some embodiments, only the Physical Downlink Control Channel (PDCCH), the Physical Downlink Shared Channel (PDSCH), and the reference signal are transmitted in the time slot where the aliasing signal resides; or,

[0057] In the time slot where the aliasing signal is located, only the Physical Uplink Control Channel (PUCCH), the Physical Uplink Shared channel (PUSCH), and the reference signal are transmitted.

[0058] The reference signal is the demodulation reference signal (DMRS), and the data information is either the PDSCH data information or the PUSCH data information.

[0059] Secondly, embodiments of this disclosure provide a communication method executed by a second device, the method comprising:

[0060] Based on the baseband signal and the reference signal, the superimposed aliased signal is determined, wherein the baseband signal carries data information;

[0061] Based on the aliasing signal, a first signal is sent to the first device, wherein the first signal is used by the first device to determine the second signal corresponding to the aliasing signal based on the first model.

[0062] In conjunction with the embodiments of the second aspect, in some embodiments, the method further includes:

[0063] Normalize the baseband signal and the reference signal;

[0064] Based on the power ratio of the baseband signal and the reference signal, the normalized baseband signal and the reference signal are superimposed to obtain the aliased signal.

[0065] In conjunction with embodiments of the second aspect, in some embodiments, the power ratio satisfies at least one of the following:

[0066] The power ratio is determined by the second device based on the model;

[0067] The power ratio is configured for network devices;

[0068] The power ratio is reported by the terminal.

[0069] In conjunction with the embodiments of the second aspect, in some embodiments, the input of the first model includes information corresponding to all resource units (REs) on the resource grid, wherein the resource grid includes the resource grids corresponding to different receiving antennas after the first device receives the first signal.

[0070] In conjunction with the embodiments of the second aspect, in some embodiments, the method further includes:

[0071] After modulating the transmitted bit stream, the baseband signal is determined.

[0072] In conjunction with the embodiments of the second aspect, in some embodiments, the method further includes:

[0073] The first model is trained based on the dataset until it converges.

[0074] In conjunction with embodiments of the second aspect, in some embodiments, the dataset includes at least one of the following:

[0075] Information corresponding to all resource units (REs) on the resource grid;

[0076] As an aliasing signal for tag values;

[0077] The transmitted bit stream corresponding to the data information.

[0078] In conjunction with the embodiments of the second aspect, in some embodiments, only the Physical Downlink Control Channel (PDCCH), the Physical Downlink Shared Channel (PDSCH), and the reference signal are transmitted in the time slot where the aliasing signal resides; or,

[0079] In the time slot where the aliasing signal is located, only the Physical Uplink Control Channel (PUCCH), the Physical Downlink Shared Channel (PUSCH), and the reference signal are transmitted.

[0080] The reference signal is the demodulation reference signal DM-RS, and the data information is either the PDSCH data information or the PUSCH data information.

[0081] Thirdly, embodiments of this disclosure provide a communication device, wherein the communication device is used to perform the method described in the first aspect or the second aspect.

[0082] Fourthly, embodiments of this disclosure provide a communication system, including a first device and a second device, wherein,

[0083] The first device is configured to implement the method as described in the first aspect;

[0084] The second device is configured to implement the method as described in the second aspect.

[0085] Fifthly, embodiments of this disclosure provide a storage medium storing instructions, wherein...

[0086] When the instructions are executed on the communication device, the communication device causes the communication device to perform the method as described in the first aspect or the second aspect.

[0087] In a sixth aspect, embodiments of this disclosure provide a program product, including at least one of a program and instructions, wherein when the program and instructions are executed by a communication device, they implement the method described in the first aspect or the second aspect.

[0088] In a seventh aspect, embodiments of this disclosure provide a computer program that, when run on a computer, causes the computer to perform the methods described in alternative implementations of the first and second aspects.

[0089] Eighthly, embodiments of this disclosure provide a chip or chip system. The chip or chip system includes processing circuitry configured to perform the methods described according to optional implementations of the first and second aspects above.

[0090] It is understood that the aforementioned communication devices, communication systems, storage media, program products, computer programs, chips, or chip systems 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.

[0091] 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, the optional implementation methods in a particular embodiment can be arbitrarily combined; moreover, the 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 the optional implementation methods of other embodiments. In all embodiments of this disclosure, unless otherwise specified or logically conflicting, the terminology and / or descriptions between the embodiments are consistent and can be mutually referenced. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0092] 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.

[0093] In this embodiment of the disclosure, unless otherwise stated, elements expressed in the singular form, such as "a," "an," "the," "the," "the," "the," "the," "the," "this," etc., can mean "one and only one," or "one or more," "at least one," etc. For example, when using articles such as "a," "an," "the," etc. in translation, the noun following the article can be understood as either a singular expression or a plural expression.

[0094] In the embodiments disclosed herein, "multiple" refers to two or more.

[0095] In some embodiments, the terms “at least one of A or B, at least one of A and B”, “one or more”, “a plurality of”, “multiple”, etc., may be used interchangeably.

[0096] In some embodiments, the notation "at least one of A and B", "A and / or B", "A in one case, B in another", "in response to one case A, in response to another case B", etc., may include the following technical solutions depending on the situation: in some embodiments, A (execute A regardless of whether there is a branch B); in some embodiments, B (execute B regardless of whether there is a branch A); in some embodiments, execution is selected from A and B (A and B are selectively executed); in some embodiments, both A and B are executed. The same applies when there are more branches such as A, B, C, etc.

[0097] In some embodiments, the notation "A or B" may include the following technical solutions, depending on the situation: in some embodiments, A (execute A regardless of whether a branch B exists); in some embodiments, B (execute B regardless of whether a branch A exists); in some embodiments, execution is selected from A and B (A and B are selectively executed). The same applies when there are more branches such as A, B, and C.

[0098] The prefixes "first," "second," etc., used in the embodiments of this disclosure are merely for distinguishing different descriptive objects and do not impose restrictions on the position, order, priority, quantity, or content of the descriptive objects. The description of the descriptive objects is found in the claims or the context of the embodiments, and the use of prefixes should not constitute unnecessary restrictions. For example, if the descriptive object is a "field," the ordinal numbers preceding "field" in "first field" and "second field" do not restrict the position or order of the "fields." "First" and "second" do not restrict whether the "fields" they modify are in the same message, nor do they restrict the order of "first field" and "second field." Similarly, if the descriptive object is a "level," the ordinal numbers preceding "level" in "first level" and "second level" do not restrict the priority between "levels." Furthermore, the number of descriptive objects is not limited by ordinal numbers and can be one or more. For example, in "first device," the number of "devices" can be one or more. Furthermore, the objects modified by different prefixes can be the same or different. For example, if the object being described is "device", then "first device" and "second device" can be the same device or different devices, and their types can be the same or different. Similarly, if the object being described is "information", then "first information" and "second information" can be the same information or different information, and their content can be the same or different.

[0099] In some embodiments, “including A,” “containing A,” “for indicating A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0100] In some embodiments, terms such as "time / frequency" and "time-frequency domain" refer to the time domain and / or frequency domain.

[0101] In some embodiments, terms such as “in response to…”, “in response to determining…”, “in the case of…”, “when…”, “when…”, “if…”, etc. can be used interchangeably. These descriptions all refer to the device making a corresponding action under certain objective circumstances. They do not necessarily limit the time, nor do they require the device to make a judgment action when implementing it, nor do they mean that there must be other limitations.

[0102] 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,” and “above” can be used interchangeably, as can 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,” and “below”.

[0103] In some embodiments, devices, etc., may be interpreted as physical or virtual, and their names are not limited to those described in the embodiments. Terms such as “device,” “equipment,” “circuit,” “network element,” “network function,” “network device,” “function,” “node,” “unit,” “section,” “system,” “network,” “chip,” “chip system,” “entity,” and “subject” are interchangeable.

[0104] In some embodiments, "network" can be interpreted as devices included in a network (e.g., access network devices, core network devices, etc.).

[0105] 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," and "bandwidth part (BWP)" can be used interchangeably.

[0106] 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", and "client" can be used interchangeably.

[0107] In some embodiments, access network devices, core network devices, or network devices can be replaced by terminals. For example, embodiments of this disclosure can also be applied to structures where communication between access network devices, core network devices, or network devices and terminals is replaced by communication between multiple terminals (e.g., device-to-device (D2D), vehicle-to-everything (V2X), etc.). In this case, the structure can also be configured such that the terminal has all or part of the functions of the access network device. Furthermore, terms such as "uplink" and "downlink" can be replaced with terms corresponding to communication between terminals (e.g., "sidelink"). For example, uplink channel, downlink channel, etc., can be replaced with sidelink channel, and uplink link, downlink, etc., can be replaced with sidelink link.

[0108] In some embodiments, the terminal may be replaced by an access network device, a core network device, or a network device. In this case, the access network device, core network device, or network device may also be configured to have all or some of the functions of the terminal.

[0109] In some embodiments, the acquisition of data, information, etc., may comply with the laws and regulations of the country where the location is situated.

[0110] In some embodiments, data, information, etc., may be obtained with the user's consent.

[0111] Furthermore, each element, each row, or each column in the table of this 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.

[0112] Figure 1 is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure.

[0113] As shown in Figure 1, the communication system 100 includes a first device 101 and a second device 102.

[0114] In this context, the first device 101 can be a receiving end or receiving device in communication, and the second device 102 can be a sending end or sending device in communication. For example, in uplink communication, the first device 101 is a network device, and the second device 102 is a terminal. As another example, in downlink communication, the first device 101 is a terminal, and the second device 102 is a network device.

[0115] In some embodiments, the terminal includes, but is not limited to, at least one of the following: mobile phone, wearable device, Internet of Things device, car with communication function, smart car, tablet computer, computer with wireless transceiver function, virtual reality (VR) terminal device, augmented reality (AR) terminal device, wireless terminal device in industrial control, wireless terminal device in self-driving, wireless terminal device in remote medical surgery, wireless terminal device in smart grid, wireless terminal device in transportation safety, wireless terminal device in smart city, and wireless terminal device in smart home.

[0116] In some embodiments, the network device may include at least one of an access network device and a core network device.

[0117] In some embodiments, the access network device is, for example, a node or device that connects a terminal to a wireless network. The access network device may include at least one of the following in a 5G communication system: evolved Node B (eNB), next-generation eNB (ng-eNB), next-generation Node B (gNB), node B (NB), home node B (HNB), home evolved node B (HeNB), radio backhaul device, radio network controller (RNC), base station controller (BSC), base transceiver station (BTS), base band unit (BBU), mobile switching center, base station in a 6G communication system, open RAN, cloud RAN, base station in other communication systems, and access node in a Wi-Fi system, but is not limited thereto.

[0118] In some embodiments, the technical solutions of this disclosure can be applied to the Open RAN architecture. In this case, the interfaces between or within access network devices involved in the embodiments of this disclosure can be transformed into internal interfaces of Open RAN. The processes and information interactions between these internal interfaces can be implemented by software or programs.

[0119] In some embodiments, the access network device may be composed of a central unit (CU) and a distributed unit (DU). The CU may also be called a control unit. The CU-DU structure can separate the protocol layer of the access network device. Some of the protocol layer functions are centrally controlled by the CU, while the remaining part or all of the protocol layer functions are distributed in the DU and centrally controlled by the CU. However, this is not the only possibility.

[0120] In some embodiments, a core network device may be a single device comprising one or more network elements, or it may be multiple devices or a group of devices, each comprising all or part of one or more network elements. Network elements may be virtual or physical. The core network may include, for example, at least one of the following: Evolved Packet Core (EPC), 5G Core Network (5GCN), and Next Generation Core (NGC).

[0121] In some embodiments, core network equipment includes network elements with specific functions, such as Access Management Function (AMF) and Service Management Function (SMF).

[0122] It is understood that the communication system described in this disclosure is for the purpose of more clearly illustrating the technical solutions of this disclosure, and does not constitute a limitation on the technical solutions proposed in this disclosure. As those skilled in the art will know, with the evolution of system architecture and the emergence of new business scenarios, the technical solutions proposed in this disclosure are also applicable to similar technical problems.

[0123] The following embodiments of this disclosure can be applied to the communication system 100 shown in FIG1, or to some of the main bodies, but are not limited thereto. The main bodies shown in FIG1 are illustrative. The communication system may include all or some of the main bodies in FIG1, or may include other main bodies outside of FIG1. ​​The number and form of each main body are arbitrary. Each main body may be physical or virtual. The connection relationship between the main bodies is illustrative. The main bodies may not be connected or may be connected. The connection can be in any way, it can be a direct connection or an indirect connection, it can be a wired connection or a wireless connection.

[0124] The embodiments disclosed herein 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), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), and IEEE 802.20, Ultra-Wideband (UWB), Bluetooth (a registered trademark), Public Land Mobile Network (PLMN) networks, Device-to-Device (D2D) systems, Machine-to-Machine (M2M) systems, Internet of Things (IoT) systems, Vehicle-to-Everything (V2X) systems, systems utilizing other communication methods, and next-generation systems built upon them, etc. Furthermore, multiple systems can be combined (e.g., a combination of LTE or LTE-A with 5G).

[0125] In some implementations, the signal transmission process of a wireless communication system may include:

[0126] At the transmitting end: the transmitter encodes and modulates the source bit stream to obtain modulation symbols; it generates pilot symbols (or reference signals) for channel estimation at the receiving end, so that channel estimation can be completed at the receiving end; finally, the data symbols and pilot symbols are inserted into the resource grid to form the OFDM transmission signal; the OFDM signal reaches the receiving end through the channel.

[0127] At the receiving end: the receiver can use pilot symbols to perform channel estimation, and then perform subsequent data symbol detection, demodulation, decoding and other steps to obtain the final recovered bit stream.

[0128] In some implementations, due to the complexity and time-varying nature of the wireless channel environment, the receiver's estimation and recovery of the wireless channel directly affects the final data recovery performance. The transmitter allocates information data symbols and specific pilot symbols known to the receiver at different resource locations, such as DMRS and Phase Tracking Reference Signals (PTRS). During the channel estimation phase, the receiver can estimate the channel information at the resource location where the pilot symbol is placed based on the actual pilot symbol and the received pilot symbol; and recover the full channel information (e.g., using interpolation algorithms) based on the estimated channel information at the pilot location for subsequent data recovery. Channel estimation methods, for example, utilize Minimum Mean-Square Error (MMSE).

[0129] In some implementations, data symbols and pilot symbols are placed at different resource locations. For example, in 5G NR, data symbols and pilot symbols are independent and orthogonal on time-frequency resources; that is, only one type of resource symbol, either a data symbol or a pilot symbol, can be placed at the same resource location. Given a fixed total transmission resource, pilot and data symbols compete for transmission resources. Increased resource overhead for pilots means less resource available for data transmission, resulting in relatively low data transmission resource utilization.

[0130] In some implementations, there is a lack of ways to improve the utilization of data transmission resources, and it is necessary to ensure that the receiving end can receive data accurately.

[0131] Figure 2A is an interactive schematic diagram of a communication method according to an embodiment of the present disclosure. As shown in Figure 2A, the present disclosure relates to a communication method, which includes:

[0132] In step S2101, the second device 102 determines whether to perform aliasing transmission in the first time slot.

[0133] In some embodiments, the second device 102 is a transmitting device, which may be a terminal in uplink communication or a network device in downlink communication.

[0134] Optionally, some embodiments of this disclosure are described using the second device 102 as a network device and the first device 101 as a terminal as an example. When the second device 102 is a terminal and the first device 101 is a network device, the description of this part of the embodiments can be referred to.

[0135] In some embodiments, the first time slot represents a specific time slot, or any time slot. For example, the second device 102 can determine whether each time slot requires aliasing transmission.

[0136] In some embodiments, aliasing transmission can refer to a signal transmission method in which the second device 102 superimposes the baseband signal and the reference signal to form an aliased signal, and then transmits the first signal to the first device 101. See the description of the embodiments below for details.

[0137] In some embodiments, the second device 102 determines the information transmitted on each time slot based on the protocol definition and / or the configuration of the network device, according to the resource grid corresponding to each time slot.

[0138] Optionally, for downlink transmission, when only PDCCH, PDSCH and DMRS are transmitted in a time slot, the second device 102 can determine that the time slot needs to be aliased.

[0139] Optionally, for uplink transmission, when only PUCCH, PUSCH and DMRS are transmitted in a time slot, the second device 102 can determine that the time slot needs to be aliased.

[0140] In some embodiments, the second device 102 can indicate to the first device 101 the time slot where aliasing transmission is required, or the time slot where the aliasing signal is located, by sending indication information. For example, in downlink communication, the network device indicates to the terminal the time slot where the aliasing signal is located.

[0141] In step S2102, the second device 102 determines the baseband signal based on the transmitted bit stream.

[0142] In some embodiments, the transmitted bit stream can be obtained by source bit stream through source coding and channel coding, and is used to indicate the data or information that the sender needs to send.

[0143] In some embodiments, the second device 102 can perform constellation modulation on the transmitted bit stream to determine the baseband signal.

[0144] Optionally, the second device 102 can map the transmitted bit stream to the corresponding resource grid (or transmit resource grid) based on the protocol definition or network device configuration to obtain the baseband signal.

[0145] Optionally, several bits in the transmitted bit stream can be mapped to a resource grid (RE).

[0146] Optionally, the horizontal axis in the resource grid represents symbols, and the vertical axis represents subcarriers. PDCCH or PUCCH and baseband signals can reside on the same resource grid, where the baseband signal represents PDSCH data or PUSCH data. The first two symbols in this resource grid can be mapped to PDCCH or PUCCH, and the remaining symbols can be mapped to baseband signals.

[0147] Optionally, in the resource grid corresponding to the aliased signal, the first two symbols are still PDCCH or PUCCH, and the other symbols can be aliased symbols of baseband signal and reference signal.

[0148] In some embodiments, the baseband signal is a modulated data signal of the transmitted bit stream. Alternatively, the baseband signal is a signal carrying data information.

[0149] Optionally, the constellation diagram corresponding to the baseband signal can be referenced as shown in Figure 2C. This baseband signal can be the constellation diagram obtained after the transmitted bit stream has been modulated by 16QAM.

[0150] In some embodiments, baseband signals, data signals, data symbols, modulation symbols, and data modulation symbols may indicate the same meaning and are interchangeable. Alternatively, in the resource grid corresponding to the baseband signal, the baseband signal may include data symbols, modulation symbols, or data modulation symbols, wherein each data symbol or modulation symbol corresponds to one RE in the resource grid.

[0151] Optionally, the data symbols may correspond to PDSCH or PUSCH, or in other words, the data information carried by the baseband signal may include PDSCH or PUSCH data information.

[0152] In step S2103, the second device 102 determines the aliasing signal based on the baseband signal and the reference signal.

[0153] In some embodiments, reference signals, pilots, pilot signals, pilot symbols, etc., can indicate the same meaning and are interchangeable. Alternatively, as shown in Figure 2C, referring to the resource grid corresponding to the reference signal, the reference signal may include pilot symbols, wherein each pilot symbol corresponds to a RE in the resource grid.

[0154] In some embodiments, in the resource grid corresponding to the aliasing signal, the aliasing signal may include aliasing symbols, each aliasing symbol corresponding to a RE in the resource grid.

[0155] In some embodiments, the constellation diagram of the reference signal or aliased signal may be shown in FIG2C.

[0156] In some embodiments, the second device 102 may select the resource grid of the corresponding reference signal after mapping the transmitted bit stream to a baseband signal, based on a protocol definition or network device configuration.

[0157] In some embodiments, as shown in the constellation diagram of FIG2C, the baseband signal and the reference signal can be superimposed to obtain an aliased signal.

[0158] In some embodiments, within the resource grid, the REs containing PDSCH and DMRS in a time slot are overlaid or aliased, or the REs containing PUSCH and DMRS in a time slot are overlaid or aliased. The REs containing PDCCH or PUCCH in a time slot are not overlaid or aliased.

[0159] For example, for downlink time slots on the base station side, if only PDCCH, PDSCH, and DM-RS signals exist in the resource grid, then the PDSCH and DM-RS signals are normalized and superimposed according to the power allocation factor in the following manner to form aliased symbols and allocated on REs other than PDCCH.

[0160] In some embodiments, step S2103 may include the following steps S2103-11 to S2103-12:

[0161] Step S2103-11: Perform power normalization processing on the baseband signal and the reference signal.

[0162] In this step, power normalization processing can be performed on the data symbols in the baseband signal and the pilot symbols in the reference signal, for example, power normalization processing can be performed on the PDSCH or PUSCH and DMRS symbols.

[0163] Step S2103-12: Based on the power ratio of the baseband signal and the reference signal, perform power allocation on the baseband signal and the reference signal.

[0164] In this step, power allocation and amplitude allocation can be interchanged.

[0165] In this step, the power ratio can be expressed as α:β, where the second power allocation factor α represents the allocation factor corresponding to the reference signal or pilot symbol, and the first power allocation factor β represents the allocation factor corresponding to the baseband signal or data symbol.

[0166] Optionally, the second device 102 performs amplitude allocation between data symbols and pilot symbols based on power ratio. For example, amplitude allocation is performed between PDSCH or PUSCH and DMRS symbols based on power.

[0167] In some embodiments, after power allocation, the baseband signal and the reference signal can be superimposed or aliased.

[0168] In some embodiments, the data symbols in the baseband signal and the symbols of the reference signal are superimposed and mapped onto a first resource unit (RE) in the transmit resource grid to determine the aliasing signal; wherein, the first RE includes REs other than the REs where the PDCCH or PUCCH is located in the resource grid corresponding to the baseband signal or the aliasing signal.

[0169] For example, aliased symbols, which are superimposed on data symbols and pilot symbols, are assigned to REs in the resource grid other than PDCCH.

[0170] In some embodiments, the power ratio or the two power allocation factors can be determined in a variety of ways.

[0171] In one example, the power ratio, or two power allocation factors, are determined by a second device based on a model. This model generates power allocation factors α and β shared across all REs, or a power ratio shared across all REs, based on the overall channel conditions. In this example, the pilot and data modulation symbols are power normalized, and then a neural network model is used to generate pilot and data modulation symbol power factors shared across all REs based on the overall channel conditions. The power of both is then allocated and superimposed across all REs.

[0172] In one example, the power ratio or two power allocation factors are configured for the network device. In this example, power allocation can be performed according to a fixed configuration on the network side. Power normalization is performed on pilot and data symbols, and then power allocation is performed and superimposed on all data and pilot symbols on all REs according to the given power allocation factors α and β or the power ratio. In this example, the network device can indicate the power ratio or power allocation factors α and / or β to the terminal by sending indication or configuration information.

[0173] In one example, the power ratio or two power allocation factors are reported by the terminal. In this example, the terminal can report the power ratio or power allocation factors α and β to the network device, and the network side performs power allocation according to the information fed back by the user. The pilot symbols and data symbols are normalized in power, and then the power allocation factors α and β fed back by the user are used to allocate and superimpose the power of the pilot and data modulation symbols on all REs.

[0174] In some embodiments, the baseband signal is superimposed with the reference signal to achieve non-orthogonal transmission of pilot and data, thereby sharing the time and frequency resources of both. While ensuring the bit error rate performance of data transmission, the amount of data transmitted is increased, effectively increasing the data throughput of the link.

[0175] In step S2104, the second device 102 sends a first signal to the first device 101 based on the aliasing signal.

[0176] In some embodiments, after the second device 102 obtains the aliased signal, such as obtaining the constellation diagram of the aliased signal shown in FIG2C, the second device 102 can perform subsequent processing based on the aliased signal, such as performing digital precoding, analog precoding and OFDM modulation on the aliased signal to obtain the first signal.

[0177] Optionally, the first signal can be an OFDM signal.

[0178] In some embodiments, after receiving the first signal, the second device 102 sends the first signal to the first device 101.

[0179] In some embodiments, the first device 101 receives a first signal.

[0180] Optionally, the second device 102 transmits a first signal via an antenna, which is then transmitted through a channel and received by the antenna of the first device 101. For example, the base station antenna transmits the first signal, which is then transmitted through a channel and received by the terminal antenna.

[0181] In step S2105, the first device 101 performs OFDM modulation on the first signal.

[0182] In some embodiments, after the first device 101 receives the first signal through the antenna, it can perform OFDM modulation on the first signal to obtain resource grids corresponding to different receiving antennas. Each resource grid corresponding to the receiving antenna may include multiple REs.

[0183] Optionally, the resource grid corresponding to different receiving antennas can be used as input to the first model.

[0184] Optionally, the information corresponding to all REs on the resource grid corresponding to different receiving antennas can be used as input to the first model described below. The information on each RE can be represented by the corresponding real and imaginary parts.

[0185] Optionally, all symbols or all symbol-corresponding information on the resource grid corresponding to different receiving antennas can be used as input to the first model below. This step of de-OFDM modulation of the first signal can obtain the input to the first model.

[0186] In step S2106, the first device 101 determines the second signal corresponding to the aliasing signal based on the first model and the first signal.

[0187] Optionally, the second signal is the output of the first model, which may be the prediction result, output result, or inference result of the first model.

[0188] Optionally, the second signal corresponding to the aliasing signal can represent the aliasing signal in the model prediction or inference, or the aliasing signal in the model output.

[0189] In some embodiments, the first model may also be referred to as the first function, which can predict the baseband signal.

[0190] In some embodiments, the first model is a neural network model or an AI-based model.

[0191] In some embodiments, the second signal corresponding to the aliasing signal output by the first model can be adopted. express.

[0192] In some embodiments, as shown in Figure 2D, the first model may include two parts: a convolutional module and a fully connected module. The model structure and parameter adjustments can be found in the model training process shown in Figure 2B.

[0193] Alternatively, the baseband signal and the reference signal can be mixed or superimposed in the following ways:

[0194] Where Y represents the information corresponding to all resource cells (REs) on the resource grid corresponding to different receiving antennas of the first device, or the symbols on the resource grid corresponding to different receiving antennas of the first device, which can be used as the input of the first model; P represents the symbol of the reference signal on the transmitting resource grid of the second device; α is the second power allocation factor; β is the first power allocation factor; and H represents the number of hidden layers set in the first model. This indicates an aliasing signal.

[0195] In some embodiments, the first model in this step is a model that has been trained and converged, and the training method of the model can be found in the embodiment of Figure 2B.

[0196] In some embodiments, during downlink communication, the first model can be deployed on the user side, and model inference is performed on the user side. For example, the first device 101 performing this step of model inference can be a terminal.

[0197] In some embodiments, during uplink communication, the first model can be deployed on the network side, and model inference is performed on the network side. For example, the first device 101 performing this step of model inference can be a network device.

[0198] In some embodiments, the receiver can use the trained model for model inference and perform signal processing based on the model, thereby optimizing the bit error rate performance of traditional demodulation schemes.

[0199] In step S2107, the first device 101 determines the bit stream to be transmitted based on the second signal.

[0200] In some embodiments, the first device 101 may determine the transmitted bit stream directly based on AI technology or models and on aliased signals.

[0201] In this embodiment, step S2107 may include the following steps S2107-11:

[0202] In step S2107-11, the first device 101 uses the second signal as input to determine the transmit bit stream output by the fourth model.

[0203] Optionally, the fourth model can be an AI-based model that uses AI technology to perform superposition recovery and bit demodulation of the aliased signal based on the input second signal, and outputs a demapped transmit bit stream.

[0204] Optionally, the fourth model can be pre-trained and deployed on the receiver side.

[0205] In some embodiments, the first device 101 may first determine the baseband signal based on the aliasing signal, and then determine the transmit bit stream.

[0206] In this embodiment, step S2107 may include the following steps S2107-21 to S2107-22:

[0207] In step S2107-21, the first device 101 determines the third signal corresponding to the baseband signal based on the second signal.

[0208] Optionally, the third signal represents the baseband signal recovered by the first device 101 or based on model prediction.

[0209] Optionally, this step can utilize non-AI techniques for superposition recovery, such as the maximum likelihood method. For example, combining the baseband signal constellation points shown in Figure 2C, the maximum likelihood method can be used to map them onto the signal constellation points of the superimposed aliased signal. The superimposed signal constellation points are an extension based on the original 16QAM constellation points, with each 16QAM constellation point corresponding to four constellation points in the superimposed constellation diagram. By mapping them one-to-one, the baseband signal is restored.

[0210] For example, based on the formula for the aliased signal mentioned above, based on the aliasing symbol... The baseband signal X can be recovered using the following formula:

[0211] Optionally, this step can utilize AI technology for superposition recovery, inputting the equalized aliased signal into the AI ​​model and outputting a non-superimposed baseband signal through the model. For example, the first device 101 uses the second signal as the input to the second model to determine the third signal corresponding to the baseband signal output by the second model. The second model can be a pre-trained AI model deployed on the receiving end.

[0212] In steps S2107-22, the first device 101 demaps the third signal to determine the transmitted bit stream.

[0213] Optionally, after obtaining the baseband signal using AI or non-AI technology, the transmitted bit stream can be obtained based on the third signal.

[0214] Optionally, this step can utilize non-AI techniques to perform constellation demapping on the baseband signal, such as the maximum likelihood method.

[0215] Optionally, AI technology can be used for constellation demapping in this step. For example, the first device 101 uses the third signal as input to the third model to determine the transmitted bit stream output by the third model. The third model can take the equalized and superimposed baseband signal as input and output the demapped transmitted bit stream. The third model can be a pre-trained AI model deployed on the receiver side.

[0216] In some embodiments, after the first device 101 recovers the transmitted bit stream, it can perform subsequent processing on the transmitted bit stream based on the protocol definition, including but not limited to descrambling, dechannel coding, and decyclic redundancy check (CRC) coding.

[0217] In some embodiments, the above embodiments utilize a neural network model to jointly process channel estimation, equalization, and other modules at the receiving end. This can reduce the bit error rate in the demodulation process of non-orthogonal pilot and data superposition transmission. Simultaneously, the model input is flexible, enabling demodulation under multi-layer data transmission while maintaining performance, thus improving demodulation performance under multi-layer data transmission. The improved model performance compared to traditional demodulation schemes under aliased symbol transmission allows the transmitting end to process the transmitted bitstream with higher-order constellation modulation, thereby increasing the data throughput of the link while ensuring performance, meeting the service requirements of next-generation wireless networks.

[0218] In some embodiments, the names of information, etc., are not limited to the names described in the embodiments. Terms such as "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "domain", "field", "symbol", "symbol", "codebook", "codeword", "codepoint", "bit", "data", "program", and "chip" can be used interchangeably.

[0219] In some embodiments, the terms "uplink", "uplink", and "physical uplink" can be used interchangeably, as can the terms "downlink", "downlink", and "physical downlink", as well as the terms "sidelink", "sidelink", "sidelink communication", "sidelink communication", "direct connection", "direct link", "direct communication", and "direct link communication".

[0220] In some embodiments, the terms “downlink control information (DCI),” “downlink (DL) assignment,” “DL DCI,” “uplink (UL) grant,” and “UL DCI” can be used interchangeably.

[0221] In some embodiments, terms such as "physical downlink shared channel (PDSCH)" and "DL data" can be used interchangeably, as can terms such as "physical uplink shared channel (PUSCH)" and "UL data".

[0222] In some embodiments, the terms “radio”, “wireless”, “radio access network (RAN)”, “access network (AN)”, and “RAN-based” can be used interchangeably.

[0223] In some embodiments, terms such as “moment,” “point in time,” “time,” and “time location” can be used interchangeably, as can terms such as “duration,” “segment,” “time window,” “window,” and “time.”

[0224] In some embodiments, "acquire," "get," "obtain," "receive," "transmit," "bidirectional transmission," and "send and / or receive" can be used interchangeably and can be interpreted as receiving from other entities, acquiring from protocols, acquiring from higher layers, obtaining through self-processing, or autonomous implementation. Protocols include, for example, at least one of the 3GPP protocol, Wi-Fi protocol, and audio and / or video protocols.

[0225] In some embodiments, terms such as “send,” “transmit,” “report,” “distribute,” “transfer,” “bidirectional transmission,” “send and / or receive” can be used interchangeably.

[0226] In some embodiments, terms such as "certain," "preset," "default," "set," "indicated," "a certain," "any," and "first" can be used interchangeably. "Certain A," "preset A," "default A," "set A," "indicated A," "a certain A," "any A," and "first A" can be interpreted as A pre-defined in a protocol or the like, or as A obtained through setting, configuration, or instruction, or as specific A, a certain A, any A, or first A, but are not limited thereto.

[0227] In some embodiments, the determination or judgment can be made by a value represented by 1 bit (0 or 1), or by a true or false value (boolean), or by a comparison of numerical values ​​(e.g., a comparison with a predetermined value), but is not limited thereto.

[0228] In some embodiments, if an arrow in the interaction diagram representing the sending of information, signaling, etc. from one subject to another passes through other subjects, it can be interpreted as the information being forwarded from one subject to another via other subjects, or it can be interpreted as the information being sent from one subject to another without passing through other subjects.

[0229] The measurement method disclosed herein may include at least one of steps S2101 to S2107, wherein each step may be implemented as an independent embodiment, or two or more steps may be combined as an independent embodiment. For example, step S2103 may be implemented as an independent embodiment, step S2106 may be implemented as an independent embodiment, and steps S2104 and S2106 may be implemented as independent embodiments, but are not limited thereto.

[0230] In some embodiments, at least one of steps S2101 to S2105 and S2107 is optional. In different embodiments, one of these steps may be selected for execution, or one or more of these steps may be omitted or substituted in different embodiments.

[0231] In some embodiments, steps S2101, S2102, S2105, and S2107 are optional, and one of them may be performed in different embodiments, or one or more of these steps may be omitted or substituted in different embodiments.

[0232] In some embodiments, the steps and their optional implementations in other embodiments described before or after this embodiment, as well as other related parts in the specification, can be referred to, and will not be repeated here.

[0233] In this embodiment, at the transmitting end, pilot signals and data can be transmitted in a non-orthogonal manner in the time and frequency domains, thereby breaking the resource competition between the two and sharing wireless transmission resources. At the receiving end, effective data reception is achieved from the mixed transmission of pilot signals and data based on a model, replacing the channel estimation and equalization process performed by the user side after receiving the base station signal. This ensures the equivalent effect of transmission resources for data reception and improves the overall system transmission gain.

[0234] Figure 2B is an interactive schematic diagram of a communication method according to an embodiment of the present disclosure. As shown in Figure 2B, the present disclosure relates to a communication method, which includes:

[0235] In step S2201, the second device 102 determines whether to perform aliasing transmission in the first time slot.

[0236] In some embodiments, the implementation of step S2201 is described in the embodiment of step S2101 in FIG2A, and will not be repeated here.

[0237] In step S2202, the second device 102 determines the baseband signal based on the transmitted bit stream.

[0238] In some embodiments, the implementation of step S2202 is described in the embodiment of step S2102 in FIG2A, and will not be repeated here.

[0239] In some embodiments, during the model training phase, the second device 102 needs to retain the transmitted bitstream as part of the training dataset.

[0240] In step S2203, the second device 102 determines the aliasing signal based on the baseband signal and the reference signal.

[0241] In some embodiments, the implementation of step S2203 is described in the embodiment of step S2103 in FIG2A, and will not be repeated here.

[0242] In some embodiments, during the model training phase, the second device 102 needs to retain the reference signal and the aliased signal as part of the training dataset.

[0243] In some embodiments, during the model training phase, the second device 102 may optionally store the power ratio or the first power allocation factor and the second power allocation factor as part of the training dataset or as a reference.

[0244] In step S2204, the second device 102 sends a first signal to the first device 101 based on the aliasing signal.

[0245] In some embodiments, the implementation of step S2204 is described in the embodiment of step S2104 in FIG2A, and will not be repeated here.

[0246] In step S2205, the first device 101 performs OFDM modulation demodulation on the first signal.

[0247] In some embodiments, the implementation of step S2204 is described in the embodiment of step S2104 in FIG2A, and will not be repeated here.

[0248] In some embodiments, during the model training phase, the first device 101 needs to retain symbols on the resource grid of each receiving antenna as part of the training dataset.

[0249] In step S2206, the first device 101 or the second device 102 trains the first model based on the training dataset.

[0250] In some embodiments, the datasets held by the first device 101 and the second device 102 are aggregated.

[0251] In some embodiments, taking the second device 102 as a network device as an example, the second device 102 can collect the training dataset and train the first model.

[0252] Optionally, the first device 101 is a terminal at this time, and the first device 101 can report the collected training dataset to the second device 102.

[0253] In some embodiments, before training the first model, the second device 102 may first determine the input and output of the first model, thereby determining the model structure of the first model.

[0254] Optionally, referring to Figure 2D, the input of the first model includes: information (Y) corresponding to all resource cells (REs) on the resource grid corresponding to different receiving antennas of the first device, and the output includes: the second signal.

[0255] Optionally, in conjunction with the description of the foregoing embodiments, the superposition method of the reference signal and the baseband signal is as follows:

[0256] Optionally, the model structure or parameters of the training model can be determined based on the size of the resource grid (i.e., the number of subcarriers and symbols) and the specific training task characteristics and training requirements.

[0257] Optionally, the first model may include two parts: a convolutional module (or convolutional layer) and a fully connected module (or fully connected layer).

[0258] In some embodiments, the second device 102 uses a defined dataset and model structure to determine model parameters and a loss function.

[0259] Optionally, the process of determining the number of layers and nodes in the model is performed within the convolution module:

[0260] A convolutional module consists of a convolutional layer, a batch normalization (BN) layer, and an activation function layer. The input size of the convolutional layer is equal to the number of user receiving antennas (L), the number of subcarriers (C) in the resource grid, the number of symbols (S) in the resource grid, and the real and imaginary parts of the symbols in each RE, all of which are of size 2. That is, the tensor size is (L, C, S, 2). The number of two-dimensional convolutional layers is set to S, corresponding to S convolutional modules, where the number of convolutional kernels in each two-dimensional convolutional layer is set to L. i The number of convolutional kernels should be set according to factors such as model size and model generalization ability.

[0261] The process of determining the composition of the S convolutional modules and the connection methods between them involves setting the next layer after each 2D convolutional layer as a batch normalization (BN) layer to address the vanishing gradient problem, normalize model weights, and improve the network's generalization ability. After the BN layer, a layer with the ReLU activation function is added to process the output of the BN layer. Modules are connected using activation function layers to link the 2D convolutional layers. Multiple convolutional modules with different numbers of kernels can be connected in the model to enhance its learning ability on data.

[0262] Optionally, the process of determining the number of layers and nodes in the model is performed in the fully connected module:

[0263] The number of nodes in the input layer depends on the output size of the last convolutional module. The output matrix of the last convolutional module is flattened to transform it into one-dimensional data before being input into the fully connected layer. The number of nodes in the output layer depends on the size of the resource grid. Each RE corresponds to the real and imaginary parts of the baseband signal obtained from model inference on that RE. For example, the number of nodes in the output layer can be set to L*C*S*2. The number of hidden layers is set to H, and the number of nodes in each hidden layer is set to J. i The number of hidden layers and nodes needs to take into account factors such as model size and model generalization ability.

[0264] Specifically, the process of determining the connection method between fully connected layers uses a fully connected approach, with the ReLU function as the activation function. Hidden layers are also fully connected, with the ReLU function used for activation. The connection between hidden layers and the output layer is also fully connected, with the ReLU function used for activation, thus outputting the real and imaginary parts of the aliasing signal on each output node. By integrating the output results from each output node, the aliasing signal corresponding to the transmitted bitstream can be deduced.

[0265] Alternatively, the loss function can be the mean squared error (MSE) loss function, etc.

[0266] Optionally, the process of determining the hyperparameters of the network model can refer to setting the number of learning epochs to T. The setting of the number of learning epochs needs to weigh the impact on model training speed, training cost, and model training accuracy. An Adaptive Moment Estimation (Adam) optimizer is used, and the corresponding hyperparameters are set to β1, β2, and ε; random weight initialization is chosen as the weight initialization method. Here, β1 represents the exponential decay rate of the first-moment estimation, β2 represents the exponential decay rate of the second-moment estimation, and ε represents a very small number to ensure numerical computation stability.

[0267] In some embodiments, the training dataset includes at least one of the following:

[0268] Information Y corresponding to all resource elements RE on the resource grid corresponding to different receiving antennas of the first device;

[0269] The aliasing signal is used as a tag value. For example, the aliasing signal is retained or saved by the sender.

[0270] The transmitted bit stream corresponding to the data information, such as the transmitted bit stream retained or saved by the sending end.

[0271] During the training of the first model, Y is used as the input training data, and the label value corresponding to the aliasing signal is used to calculate the training loss value of the aliasing signal output by the first model; or the label value is determined based on the transmitted bit stream, and the output loss value is calculated.

[0272] For example, using MSE as the loss function to calculate the training loss value:

[0273] Where I represents the amount of training data for the model, and y i This represents the output result of the model after data i is processed. This represents the label value of data i.

[0274] In some embodiments, the second device 102 updates the model parameters based on the training loss value, the model update method, and the selected hyperparameters, such as the stochastic gradient descent (SGD) algorithm, the Adam algorithm, etc., to update the parameters of the specific model layer.

[0275] Optionally, the SGD algorithm can be used to update the model parameters:

[0276] in, This represents the demodulation model parameters to be updated in round t. This represents the demodulation model parameters after the t-th round of updates. This represents the gradient of the training loss value calculated in round t. Let t represent the learning rate in round t.

[0277] In some embodiments, after the first model has been trained, for downlink communication, the first model can be deployed on the terminal. For uplink communication, the first model can be deployed on the network device side.

[0278] Optionally, for example, after the network device 102 trains the first model and the training of the first model converges, the network device 102 can send the model parameters of the first model to the terminal 101 to assist the terminal in deploying the model.

[0279] In some embodiments, the model can flexibly configure input and output according to the number of downlink transmission layers and the size of time-frequency resources at the transmitter, and ensure demodulation performance under multi-layer transmission based on training, adapting to the demodulation process of the transmitter superimposed with the receiver in real services with large inter-layer interference.

[0280] The measurement method involved in the embodiments of this disclosure may include at least one of steps S2201 to S2206, wherein each step may be implemented as an independent embodiment, or two or more steps may be combined as an independent embodiment. For example, step S2206 may be implemented as an independent embodiment, but is not limited thereto.

[0281] In some embodiments, step S2201 may be optionally performed, or one or more of these steps may be omitted or substituted in different embodiments.

[0282] In some embodiments, the steps and their optional implementations in other embodiments described before or after this embodiment, as well as other related parts in the specification, can be referred to, and will not be repeated here.

[0283] Figure 3A is an interactive schematic diagram illustrating a communication method according to an embodiment of the present disclosure. As shown in Figure 3A, this embodiment of the present disclosure relates to a communication method, which includes:

[0284] In step S3101, the second device 102 sends a first signal to the first device 101.

[0285] In some embodiments, the implementation of step S3101 can be referred to the implementation of step S2104 in FIG2A, and will not be repeated here.

[0286] In step S3102, the first device 101 determines the second signal corresponding to the aliasing signal based on the first model and the first signal.

[0287] In some embodiments, the implementation of step S3102 can be referred to the implementation of step S2106 in FIG2A, and will not be repeated here.

[0288] In some embodiments, the steps and their optional implementations in other embodiments described before or after this embodiment, as well as other related parts in the specification, can be referred to, and will not be repeated here.

[0289] Figure 3B is an interactive schematic diagram illustrating a communication method according to an embodiment of the present disclosure. As shown in Figure 3B, this embodiment of the present disclosure relates to a communication method, which includes:

[0290] In step S3201, the second device 102 determines the aliasing signal based on the baseband signal and the reference signal.

[0291] In some embodiments, the implementation of step S3201 can be referred to the implementation of step S2103 in FIG2A, and will not be repeated here.

[0292] In step S3202, the second device 102 sends a first signal to the first device 101 based on the aliasing signal.

[0293] In some embodiments, the implementation of step S3202 can be referred to the implementation of step S2104 in FIG2A, and will not be repeated here.

[0294] In step S3203, the first device 101 determines the second signal corresponding to the aliasing signal based on the first model and the first signal.

[0295] In some embodiments, the implementation of step S3203 can be referred to the implementation of step S2106 in FIG2A, and will not be repeated here.

[0296] In some embodiments, the steps and their optional implementations in other embodiments described before or after this embodiment, as well as other related parts in the specification, can be referred to, and will not be repeated here.

[0297] Figure 3C is an interactive schematic diagram illustrating a communication method according to an embodiment of the present disclosure. As shown in Figure 3C, this embodiment of the present disclosure relates to a communication method, which includes:

[0298] In step S3301, the second device 102 sends an instruction message to the first device 101.

[0299] In some embodiments, the indication information indicates at least one of the following:

[0300] The resource grid corresponding to the reference signal;

[0301] Power ratio;

[0302] First power allocation factor;

[0303] Second power allocation factor;

[0304] The time slot where the aliased signal is located.

[0305] In some embodiments, the second device 102 may be a network device, and the first device 102 may be a terminal. That is, the network device sends instruction information to the terminal. Alternatively, the second device 102 may be a terminal, and the terminal sends instruction information to the network device.

[0306] In some embodiments, for network devices, the two power allocation factors during aliasing transmission can be dynamically adjusted based on terminal feedback, and the corresponding reference signal can be fed back to the terminal.

[0307] In some embodiments, a network device can provide feedback to the terminal on whether a certain time slot is an aliased transmission time slot in order to determine the signal processing scheme of the receiving end.

[0308] In some embodiments, for network devices, the instruction information may also indicate the model parameters of the first model, so that the terminal can deploy the trained model.

[0309] In step S3302, the second device 102 sends a first signal to the first device 101.

[0310] In some embodiments, the implementation of step S3302 can be referred to the implementation of step S2104 in FIG2A, and will not be repeated here.

[0311] In step S3303, the first device 101 determines the baseband signal based on the first model and the first signal.

[0312] In some embodiments, the implementation of step S3303 can be referred to the implementation of step S2106 in FIG2A, and will not be repeated here.

[0313] In some embodiments, the steps and their optional implementations in other embodiments described before or after this embodiment, as well as other related parts in the specification, can be referred to, and will not be repeated here.

[0314] This disclosure provides a communication method, specifically a receiving method based on non-orthogonal pilot and data superposition transmission using a neural network. To facilitate understanding of the method described in this disclosure, some embodiments are listed below:

[0315] Example 1:

[0316] According to the protocol, the base station determines the transmission signal and maps the resource grid for each downlink transmission time slot configured by the base station. Based on the basic resource grid allocation process, for a downlink time slot on the base station side, if the resource grid of the current time slot only contains PDCCH, PDSCH, and DM-RS signals, then the RE containing the PDCCH is not processed. For the PDSCH and DM-RS symbols, they are normalized according to a given power factor and their amplitudes are allocated according to power. Then, they are aliased and allocated to all REs in the resource grid except for the PDCCH. After completing the pilot and data overlay, the base station performs subsequent digital and analog precoding and OFDM modulation processing. After processing, the information is transmitted by the base station antenna, transmitted through the channel, and received by the user antenna.

[0317] After receiving information transmitted by the base station, the user antenna performs OFDM modulation demodulation to obtain the symbols on the resource grid of each receiving antenna.

[0318] Example 2:

[0319] Based on Example 1, one or more of the symbols, aliasing signals, and transmitted bit streams from all user receiving antennas on the resource grid are collected to form a dataset. A neural network model is trained so that the model can use the symbols on the resource grid of the receiving antennas as input and infer the equalized aliasing signal.

[0320] Example 3:

[0321] Based on Embodiments 1 and 2, the model is deployed on the user side after training. The base station can transmit aliased symbols according to the above aliasing scheme. After the user side de-modulates the received signal using OFDM, it inputs the symbols on the resource grid of the receiving antenna into the model, infers the equalized aliased signal, and performs superposition recovery and constellation mapping on the signal to recover the transmitted bit stream.

[0322] Alternatively, the process in the above embodiments can be reversed, considering the uplink transmission time slots. For time slots containing only PUCCH, PUSCH, and DM-RS signals, the user side completes the non-orthogonal pilot and data superposition and transmission, while the base station side completes the signal reception process implemented using the above-described model.

[0323] Figure 4 is an interactive schematic diagram of a communication method according to an embodiment of the present disclosure. As shown in Figure 4, this embodiment of the present disclosure relates to a communication method, which includes:

[0324] In step S4101, the base station performs resource grid mapping according to the protocol and under the base station configuration, and completes symbol aliasing of PDSCH and DM-RS in a specific time slot, and then transmits it after subsequent processing.

[0325] Optionally, step S4101 includes the following steps:

[0326] Step S4101-1: The base station determines whether to perform aliasing transmission in the time slot.

[0327] In one embodiment, the base station determines that pilot and data modulation symbol aliasing transmission should be performed in the time slot if only PDCCH, PDSCH and DM-RS signals are transmitted on the resource grid in the time slot, based on the 5G NR standard and the base station configuration.

[0328] Step S4101-2: Power allocation and superposition of pilot and data modulation symbols.

[0329] In one embodiment, power allocation is performed according to a fixed configuration. The pilot and data modulation symbols are power normalized, and then the power of the pilots and data on all REs is allocated and superimposed according to given pilot power factors and data power factors.

[0330] In one embodiment, power allocation is performed based on user-feedback information. The pilot and data modulation symbols are power normalized, and then the pilot and data modulation symbols on all REs are allocated and superimposed using the user-feedback pilot power factor and data power factor.

[0331] In one embodiment, different power allocations are performed for different REs according to a neural network model. The power of the pilot and data modulation symbols is normalized, and then the neural network model is used to generate the power factors of the pilot and data modulation symbols on each RE according to the channel conditions of each RE. The power of the two is then allocated and superimposed on each RE.

[0332] In one embodiment, power allocation for all REs is performed according to a neural network model. Power normalization is applied to the pilot and data modulation symbols. Then, using the neural network model, based on the overall channel conditions, a shared pilot and data modulation symbol power factor is generated across all REs. The power of both is then allocated and superimposed across all REs.

[0333] In step S4101-3, the base station performs further processing on the processed resource grid and transmits it downlink.

[0334] In one embodiment, the base station performs subsequent processing on the resource grid generated after power allocation and superposition as described in step 102, including but not limited to digital precoding, analog precoding, and OFDM modulation. After processing, the data is sent downlink to the user.

[0335] Step S4102: The user-side UE receives the signal and constructs a dataset by combining it with the information transmitted by the base station. The signal reception neural network model on the user side is then trained.

[0336] Optionally, step S4102 may include the following steps:

[0337] In step S4102-1, the base station performs resource grid mapping and completes symbol aliasing of PDSCH and DM-RS in a specific time slot. After subsequent processing, the data is transmitted, and the transmitted bit stream and pilot symbols are retained to form part of the dataset.

[0338] In one embodiment, after the pilot and data modulation symbol power allocation is performed in step 102, the pilot symbols and the corresponding power allocation factors on the RE are saved, the aliasing signal is saved, and the transmitted bit stream of the data modulation symbols before modulation can be saved to form part of the model training dataset.

[0339] Step S4102-2: The user side receives the signal, performs OFDM modulation demodulation, obtains the symbols on the resource grid of each antenna, and forms part of the dataset.

[0340] In one embodiment, the user side receives the signal processed and transmitted downlink by the base station, thereby obtaining the received symbols on the resource grid of each antenna on the user side, saving them to form part of the model training dataset, and providing model input for subsequent model training.

[0341] Step S4102-3: Summarize the datasets from the base station side and the user side, determine the input and output, and thus determine the model structure. Referring to Figure 2D, where Y represents the received symbol on the resource grid of the receiving antenna, P represents the pilot symbol on the resource grid, X represents the actual baseband signal of the transmitted bitstream after constellation modulation (corresponding to the baseband signal in the aforementioned embodiment), and α and β represent the power allocation factors for the pilot and data, respectively. This represents the symbol on the resource grid after X and P are superimposed according to the data and pilot allocation factor (corresponding to the aliasing signal in the aforementioned embodiment).

[0342] Referring to Figure 2C, from left to right, the first part represents the constellation diagram of the baseband signal after 16QAM modulation, the second part represents the constellation diagram of the pilot signal, and the third part represents the constellation diagram of the superimposed signal generated by superimposing the baseband signal and the pilot signal according to the power ratio.

[0343] In one embodiment, as shown in Figure 2D, the model inputs include: received symbols on the resource grid of the receiving antenna obtained by the user's OFDM modulation; the model outputs include: the aliased modulation symbols (i.e., aliased signals) from the transmitting end; the model structure is designed based on these.

[0344] Step S4102-4: Using the given dataset and model structure, determine the model parameters and loss function.

[0345] In one embodiment, as shown in Figure 2D, a neural network model is trained to infer the baseband signal (i.e., aliasing signal) corresponding to the pilot signal superimposed on the transmitted bitstream based on the received symbols on the resource grid of each antenna obtained by the user's OFDM solution. The model structure of the training model can be determined according to the size of the resource grid (i.e., the number of subcarriers and symbols) and the specific training task characteristics and training requirements.

[0346] In one embodiment, as shown in FIG2D, the model structure of the training model can be determined as follows.

[0347] The model consists of two parts: a convolutional module and a fully connected module.

[0348] The process of determining the number of layers and nodes in the model will first be explained using the convolution module.

[0349] A convolutional module consists of a convolutional layer, a batch normalization (BN) layer, and an activation function layer. Naturally, the input size of the convolutional layer is equal to the number of user receiving antennas (L), the number of subcarriers (C) in the resource grid, the number of symbols (S) in the resource grid, and the real and imaginary parts of the symbols in each RE, all of which are of size 2. That is, the tensor size is (L, C, S, 2). The number of two-dimensional convolutional layers is set to S, corresponding to S convolutional modules, where the number of convolutional kernels in each two-dimensional convolutional layer is set to L. i The number of convolutional kernels should be set according to factors such as model size and model generalization ability.

[0350] The process of determining the composition of the S convolutional modules and the connection methods between them involves setting the next layer after each 2D convolutional layer as a batch normalization (BN) layer to address the vanishing gradient problem, normalize model weights, and improve the network's generalization ability. After the BN layer, a layer with the ReLU activation function is added to process the output of the BN layer. Modules are connected using activation function layers to link the 2D convolutional layers. Multiple convolutional modules with different numbers of kernels can be connected in the model to enhance its learning ability on data.

[0351] Next, the process of determining the number of layers and nodes in the model will be explained, specifically the fully connected layer.

[0352] The number of nodes in the input layer depends on the output size of the last convolutional module. The output matrix of the last convolutional module is flattened to transform it into one-dimensional data before being input into the fully connected layer. The number of nodes in the output layer is set to Y', and its size depends on the size of the resource grid. Each RE corresponds to the real and imaginary parts of the baseband signal obtained from model inference on that RE. For example, the size of Y' can be set to L*C*S*2. The number of hidden layers is set to H, and the number of nodes in each hidden layer is set to J. i The number of hidden layers and nodes needs to take into account factors such as model size and model generalization ability.

[0353] The process of determining the connection method between fully connected layers uses a fully connected approach, with the ReLU function as the activation function. Hidden layers are also fully connected, with the ReLU function used for activation. The connection between hidden layers and the output layer is also fully connected, with the ReLU function used for activation, thus outputting the real and imaginary parts of the baseband signal on each output node. By integrating the output results from each output node, the baseband signal corresponding to the transmitted bitstream can be deduced.

[0354] The process of determining the loss function can include using the mean squared error (MSE) loss function, etc.

[0355] For determining the hyperparameters of the network model, the number of learning epochs can be set to T. The choice of learning epochs needs to consider the impact on model training speed, training cost, and model training accuracy. The Adam optimizer is used, and the corresponding hyperparameters are set to β1, β2, and ε; random weight initialization is chosen as the weight initialization method.

[0356] Step S4102-5: Train the model.

[0357] In one embodiment, as shown in Figure 2D, the received symbols on the resource grid of the user's receiving antenna are taken as input to the model from the dataset; the output of the model is the baseband signal X, which is the superposition of the pilot signals and all REs on the resource grid, i.e., the aliasing signal.

[0358] In one embodiment, the training loss value is calculated based on the model's output and label information, for example, using the mean squared error (MSE) function.

[0359] Where I represents the amount of training data for the model, and y i This represents the output result of the model after data i is processed. This represents the label value of data i.

[0360] In one embodiment, the base station updates the model parameters based on the training loss value, the model update method, and the selected hyperparameters, such as stochastic gradient descent (SGD) or the Adam algorithm, to update the parameters of specific model layers. For example, the SGD algorithm is used to update the model parameters.

[0361] in, This represents the demodulation model parameters to be updated in round t. This represents the demodulation model parameters after the t-th round of updates. This represents the gradient of the training loss value calculated in round t. Let t represent the learning rate in round t.

[0362] Step S4102-6: After completing the model training, deploy the model on the user side for subsequent invocation.

[0363] In one embodiment, for the downlink superimposed signal transmitted by the base station, the model is deployed on the user side to complete the reception.

[0364] In one embodiment, for user uplink transmission of superimposed signals, the model is deployed at the base station to complete the reception. In this embodiment, the aforementioned PDCCH, PDSCH, and DM-RS signals are replaced with PUCCH, PUSCH, and DM-RS signals.

[0365] In step S4103, after receiving the signal, the user performs OFDM decoding to obtain the symbols on the resource grid of each antenna. The received symbols are then input into the model, and the model infers the equalized baseband signal superimposed on the pilot signal, i.e., the aliasing signal.

[0366] Optionally, step S4103 may include the following steps:

[0367] Step S4103-1: The user receives the signal sent by the base station and performs OFDM demodulation on the user side to obtain the symbols on the resource grid of each receiving antenna.

[0368] In one embodiment, the user side receives the signal that has been superimposed and downlinked by the base station, and performs OFDM modulation on it to obtain the received symbols on the resource grid of each antenna on the user side.

[0369] In step S4103-2, the user inputs the symbols on the resource grid of each receiving antenna into the demodulation model, and uses the pilot symbols as an aid to infer the equalized baseband signal superimposed on the pilot, i.e., the aliasing signal.

[0370] In one embodiment, taking Figure 2D as an example, the model is deployed on the user side, and model inference is completed on the user side. The user inputs the received symbols and pilots on the resource grid of the receiving antenna into the model. The trained model is used to equalize the non-orthogonal pilot and data superimposed transmission signals to recover the equalized baseband signal superimposed with the pilots, i.e., the aliased signal.

[0371] In step S4104, the user performs superposition recovery on the equalized baseband signal superimposed with the pilot signal to restore the modulated baseband signal from the transmitting end. Through demapping, the bitstream from the transmitting end is restored.

[0372] Step S4104-1: The user performs superposition and recovery on the obtained baseband signal superimposed with the pilot signal to restore the baseband signal.

[0373] In one embodiment, the user performs superposition recovery using non-AI techniques, such as the maximum likelihood method. The baseband signal constellation points inferred from the model are mapped onto the superimposed signal constellation points using the maximum likelihood method. The superimposed signal constellation points are an extension of the original 16QAM constellation points; each 16QAM constellation point corresponds to four constellation points in the superimposed constellation diagram. These are mapped one-to-one to complete the baseband signal reconstruction.

[0374] In one embodiment, the user uses AI technology to perform superposition recovery, inputting the equalized baseband signal superimposed with the pilot signal into the AI ​​model, and outputting a non-superimposed baseband signal through the model.

[0375] Step S4104-2: The user demaps the superimposed and recovered baseband signal to restore the bit stream from the transmitting end.

[0376] In one embodiment, the user uses non-AI techniques for constellation demapping, such as maximum likelihood estimation.

[0377] In one embodiment, the user uses AI technology to perform constellation demapping, inputting the equalized and superimposed baseband signal into the AI ​​model, and outputting the demapped bitstream through the model.

[0378] In one embodiment, the user combines steps S4104-1 and S4104-2, and uses AI technology for superposition recovery and bit demodulation. The equalized baseband signal superimposed on the pilot is input into the AI ​​model, and the model outputs the demapped bitstream.

[0379] Step S4104-3: The user performs subsequent processing on the received bit stream based on the protocol, including but not limited to descrambling, dechannel coding, and deCRC coding.

[0380] In one embodiment, the neural network model proposed in this invention replaces the channel estimation and equalization process on the user side after receiving base station signals in the NR standard. After inferring the equalized baseband signal using the model and recovering the bit stream, subsequent processing can be performed according to the NR standard, thereby recovering the transmitted data blocks at the receiving end.

[0381] In some embodiments, a neural network model trained using AI technology optimizes the bit error rate performance of the traditional NR scheme in pilot and data modulation symbol overlay transmission at the receiver. This replaces the channel estimation and equalization steps at the receiver in the traditional NR scheme, reducing the signal processing complexity of the receiver. The model can flexibly configure input and output according to the number of downlink transmission layers and the available time-frequency resources at the transmitter, ensuring demodulation performance under multi-layer transmission and adapting to the transmitter-overlay transmission and receiver demodulation process in real-world services with significant inter-layer interference.

[0382] In some embodiments, the base station needs to dynamically adjust the power allocation factor for both pilot and data modulation symbols during aliased transmission based on user feedback, and provide feedback to the user on the corresponding pilot symbols.

[0383] In some embodiments, the base station needs to provide feedback to the user whether the time slot is a pilot and data modulation symbol aliasing transmission time slot in order to determine the signal processing scheme of the receiver.

[0384] In some embodiments, the base station, based on a protocol, completes the aliasing transmission of pilot symbols and data symbols on time-frequency resources, enabling them to share resources and increasing the amount of data transmitted. Furthermore, at the user-side receiver, a neural network-trained model is used to optimize the bit error rate performance of traditional demodulation schemes.

[0385] Optionally, at the receiving end, channel estimation, equalization, and other modules can be jointly processed using a neural network model to reduce the bit error rate during demodulation. Simultaneously, the model input is flexible, enabling demodulation under multi-layer data transmission while maintaining demodulation performance. The improved performance of its neural network model compared to traditional demodulation schemes in aliased symbol transmission allows the transmitting end to process the transmitted bitstream using higher-order constellation modulation, thereby increasing the data throughput of the link while ensuring performance, to meet the service requirements of next-generation wireless networks.

[0386] In some embodiments, in the receiving method for non-orthogonal pilot and data superposition transmission based on neural networks, the time and frequency resources of both can be shared, effectively increasing the data throughput of the link while ensuring the bit error rate performance of data transmission.

[0387] In some embodiments, the steps and their optional implementations in other embodiments described before or after this embodiment, as well as other related parts in the specification, can be referred to, and will not be repeated here.

[0388] This disclosure also proposes an apparatus (also referred to as a communication device, etc.) for implementing any of the above methods. For example, an apparatus is proposed that includes units or modules for implementing the steps performed by the terminal in any of the above methods. Furthermore, another apparatus is proposed that includes units or modules for implementing the steps performed by a network device (e.g., an access network device, a core network functional node, a core network device, etc.) in any of the above methods.

[0389] It should be understood that the division of units or modules in the above device is only a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, the units or modules in the device can be implemented by a processor calling software: for example, the device includes a processor connected to a memory containing instructions. The processor calls the instructions stored in the memory to implement any of the above methods or to implement the functions of the units or modules in the above device. The processor can be, for example, a general-purpose processor, such as a Central Processing Unit (CPU) or a microprocessor, and the memory can be internal or external to the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits. The functionality of some or all of the units or modules can be achieved through the design of these hardware circuits, which can be understood as one or more processors. For example, in one implementation, the hardware circuit is an application-specific integrated circuit (ASIC). The functionality of some or all of the units or modules is achieved through the design of the logical relationships between the components within the circuit. In another implementation, the hardware circuit can be implemented using a programmable logic device (PLD). Taking a field-programmable gate array (FPGA) as an example, it can include a large number of logic gates. The connection relationships between the logic gates are configured through configuration files, thereby achieving the functionality of some or all of the units or modules. All units or modules of the above device can be implemented entirely through processor-called software, entirely through hardware circuits, or partially through processor-called software with the remaining parts implemented through hardware circuits.

[0390] In this embodiment, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction read and execute capabilities, such as a Central Processing Unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), or a digital signal processor (DSP). In another implementation, the processor can implement certain functions through the logical relationships of hardware circuits. The logical relationships of the aforementioned hardware circuits are fixed or reconfigurable. For example, the processor is a hardware circuit implemented using an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document and configuring the hardware circuit can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. Furthermore, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a Neural Network Processing Unit (NPU), a Tensor Processing Unit (TPU), or a Deep Learning Processing Unit (DPU).

[0391] Figure 5A is a schematic diagram of a first device according to an embodiment of this disclosure. The first device 5100 is used to perform any of the above methods. In some embodiments, as shown in Figure 5A, the first device 5100 may include at least one of a transceiver module 5101, a processing module 5102, etc. In some embodiments, the transceiver module 5101 is used to receive a first signal sent by a second device, wherein the first signal is determined based on an aliasing signal, the aliasing signal being generated by superimposing a baseband signal and a reference signal, the baseband signal carrying data information; the processing module 5102 is used to determine a second signal corresponding to the aliasing signal based on a first model and the first signal.

[0392] Optionally, the transceiver module 5101 is used to perform at least one of the communication steps such as sending and / or receiving performed by the first device in any of the above methods, which will not be described in detail here. Optionally, the processing module 5102 is used to perform at least one of the other steps performed by the first device in any of the above methods, which will not be described in detail here.

[0393] Figure 5B is a schematic diagram of the structure of the second device proposed in an embodiment of this disclosure. The second device 5200 is used to perform any of the above methods. In some embodiments, as shown in Figure 5B, the second device 5200 may include at least one of a transceiver module 5201, a processing module 5202, etc. In some embodiments, the processing module 5202 is used to determine an aliased signal after superposition based on a baseband signal and a reference signal, wherein the baseband signal carries data information; the transceiver module 5201 is used to send a first signal to a first device based on the aliased signal, wherein the first signal is used by the first device to determine a second signal corresponding to the aliased signal based on a first model.

[0394] Optionally, the transceiver module 5201 is used to perform at least one of the communication steps such as sending and / or receiving performed by the core network device 103 in any of the above methods, which will not be described in detail here. Optionally, the processing module 5202 is used to perform at least one of the other steps performed by the core network device 103 in any of the above methods, which will not be described in detail here.

[0395] In some embodiments, the transceiver module may include a transmitting module and / or a receiving module, which may be separate or integrated. Optionally, the transceiver module may be interchangeable with a transceiver.

[0396] In some embodiments, the processing module may be a single module or may include multiple sub-modules. Optionally, the multiple sub-modules may each perform all or part of the steps required by the processing module.

[0397] In some embodiments, the processing module can be replaced by the processor, and the transceiver module can be replaced by the transceiver.

[0398] Figure 6A is a schematic diagram of the structure of the communication device 6100 proposed in an embodiment of this disclosure. The communication device 6100 can be a network device (e.g., access network device, core network device, etc.), a terminal (e.g., user equipment, etc.), a chip, chip system, or processor that supports the network device in implementing any of the above methods, or a chip, chip system, or processor that supports the terminal in implementing any of the above methods. The communication device 6100 can be used to implement the methods described in the above method embodiments; for details, please refer to the descriptions in the above method embodiments.

[0399] As shown in Figure 6A, the communication device 6100 is used to execute any of the above methods. In some embodiments, the communication device 6100 includes one or more processors 6101. The processor 6101 may be a general-purpose processor or a special-purpose processor, such as a baseband processor or a central processing unit. The baseband processor may be used to process communication protocols and communication data, and the central processing unit may be used to control communication devices (e.g., base stations, baseband chips, terminal devices, terminal device chips, DUs or CUs, etc.), execute programs, and process program data. Optionally, the communication device 6100 is used to execute any of the above methods. Optionally, one or more processors 6101 are used to invoke instructions to cause the communication device 6100 to execute any of the above methods.

[0400] In some embodiments, the communication device 6100 further includes one or more transceivers 6102. When the communication device 6100 includes one or more transceivers 6102, the transceiver 6102 performs at least one of the communication steps such as sending and / or receiving in the above-described method, and the processor 6101 performs at least one of the other steps. In optional embodiments, the transceiver may include a receiver and / or a transmitter, which may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, transceiver circuit, interface circuit, interface, etc., can be used interchangeably; the terms transmitter, transmitting unit, transmitter, transmitting circuit, etc., can be used interchangeably; the terms receiver, receiving unit, receiver, receiving circuit, etc., can be used interchangeably.

[0401] In some embodiments, the communication device 6100 further includes one or more memories 6103 for storing data and / or instructions. Optionally, one or more processors 6101 are used to invoke instructions stored in the memory 6103 to cause the communication device 6100 to perform any of the above methods. Optionally, all or part of the memory 6103 may also be located outside the communication device 6100. In optional embodiments, the communication device 6100 may include one or more interface circuits 6104. Optionally, the interface circuit 6104 is connected to the memory 6103 and can be used to receive data and / or instructions from the memory 6103 or other devices, and can be used to send data and / or instructions to the memory 6103 or other devices. For example, the interface circuit 6104 can read data and / or instructions stored in the memory 6103 and can be used to send data and / or instructions to the memory 6103 or other devices. For example, the interface circuit 6104 can read data and / or instructions stored in the memory 6103 and send the data and / or instructions to the processor 6101.

[0402] The communication device 6100 described in the above embodiments may be a network device or a terminal, but the scope of the communication device 6100 described in this disclosure is not limited thereto, and the structure of the communication device 6100 may not be limited by FIG. 6A. 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, programs and / or instructions; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (6) 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.; (7) others, etc.

[0403] Figure 6B is a schematic diagram of the structure of chip 6200 according to an embodiment of this disclosure. For cases where the communication device 6100 can be a chip or a chip system, please refer to the schematic diagram of chip 6200 shown in Figure 6B, but it is not limited thereto.

[0404] Chip 6200 includes one or more processors 6201. Chip 6200 is used to perform any of the methods described above.

[0405] In some embodiments, chip 6200 further includes one or more interface circuits 6202. Optionally, terms such as interface circuit, interface, and transceiver pin can be used interchangeably. In some embodiments, chip 6200 further includes one or more memories 6203 for storing data and / or instructions. Optionally, all or part of the memories 6203 may be located outside of chip 6200. Optionally, interface circuit 6202 is connected to memory 6203, and interface circuit 6202 can be used to receive data and / or instructions from memory 6203 or other devices, and interface circuit 6202 can be used to send data and / or instructions to memory 6203 or other devices. For example, interface circuit 6202 can read data and / or instructions stored in memory 6203 and send the data and / or instructions to processor 6201.

[0406] In some embodiments, the interface circuit 6202 performs at least one of the communication steps, such as sending and / or receiving, in the above-described method. For example, the interface circuit 6202 performing the communication steps, such as sending and / or receiving, in the above-described method means that the interface circuit 6202 performs data and / or instruction interaction between the processor 6201, the chip 6200, the memory 6203, or the transceiver device. In some embodiments, the processor 6201 performs at least one of the other steps.

[0407] 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.

[0408] This disclosure also proposes a storage medium storing instructions that, when executed on the communication device 6100, cause the communication device 6100 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.

[0409] This disclosure also proposes a program product, including a program and / or instructions, which, when executed by the communication device 6100, cause the communication device 6100 to perform any of the above methods. Optionally, the program product is a computer program product. Optionally, the program product is stored on the storage medium.

[0410] This disclosure also proposes a computer program that, when run on a computer, causes the computer to perform any of the above methods. Industrial applicability

[0411] In signal transmission, the first signal transmitted by the transmitting end is based on the aliased signal obtained by superimposing the baseband signal and the reference signal, thereby improving the utilization rate of data signal to transmission resources; the receiving end processes the first signal based on the pre-trained first model, thereby improving the utilization rate of data signal and the accuracy of processing the received signal.

Claims

1. A communication method, performed by a first device, the method comprising: The device receives a first signal sent by a second device, wherein the first signal is determined based on an aliasing signal, the aliasing signal being generated by superimposing a baseband signal and a reference signal, and the baseband signal carrying data information. Based on the first model and the first signal, determine the second signal corresponding to the aliasing signal.

2. The method as described in claim 1, wherein, The input of the first model includes information corresponding to all resource cells (REs) on the resource grid, wherein the resource grid includes the resource grids corresponding to different receiving antennas after the first device receives the first signal; The step of determining the second signal corresponding to the aliasing signal based on the first model and the first signal includes: The first signal is deorthogonal frequency division multiplexing (OFDM) modulation to obtain the input; Based on the first model and the input, the second signal corresponding to the aliasing signal is output.

3. The method of claim 1, wherein, The method further includes: Based on the second signal, the corresponding transmit bit stream of the baseband signal is determined; wherein the transmit bit stream is modulated to obtain the baseband signal.

4. The method of claim 3, wherein, The step of determining the transmitted bit stream corresponding to the baseband signal based on the second signal includes: Based on the second signal, determine the third signal corresponding to the baseband signal; The third signal is demapped to determine the transmitted bit stream.

5. The method of claim 4, wherein, The step of determining the third signal corresponding to the baseband signal based on the second signal includes: Using the second signal as input, the third signal output by the second model is determined.

6. The method of claim 4, wherein, The demapping of the third signal to determine the transmitted bit stream includes: Using the third signal as input, the transmitted bit stream output by the third model is determined.

7. The method of claim 3, wherein, The step of determining the transmitted bit stream corresponding to the baseband signal based on the second signal includes: Using the second signal as input, the transmitted bit stream output by the fourth model is determined.

8. The method of any one of claims 1 to 7, wherein, The method further includes: The first model is trained based on the dataset until it converges.

9. The method of claim 8, wherein, The dataset includes at least one of the following: Information corresponding to all resource units (REs) on the resource grid; The aliasing signal is the tag value; The transmitted bit stream corresponding to the data information.

10. The method according to any one of claims 1 to 9, wherein, The time slot containing the aliased signal only transmits the Physical Downlink Control Channel (PDCCH), the Physical Downlink Shared Channel (PDSCH), and the reference signal; or... The time slot containing the aliasing signal only transmits the Physical Uplink Control Channel (PUCCH), the Physical Downlink Shared Channel (PUSCH), and the reference signal. The reference signal is the demodulation reference signal DM-RS, and the data information is the data information of the PDSCH or the data information of the PUSCH.

11. A communication method performed by a second device, the method comprising: Based on the baseband signal and the reference signal, the superimposed aliased signal is determined, wherein the baseband signal carries data information; Based on the aliasing signal, a first signal is sent to the first device, wherein the first signal is used by the first device to determine a second signal corresponding to the aliasing signal based on a first model.

12. The method of claim 11, wherein, The method further includes: The power of the baseband signal and the reference signal is normalized. The power of the baseband signal and the reference signal is allocated according to the power ratio of the baseband signal and the reference signal.

13. The method of claim 12, wherein, The power ratio satisfies at least one of the following: The power ratio is determined by the second device based on the model; The power ratio is configured for the network device; The power ratio is reported by the terminal.

14. The method as claimed in any one of claims 11 to 13, wherein, The input to the first model includes information corresponding to all resource units (REs) on the resource grid, wherein the resource grid includes the resource grids corresponding to different receiving antennas after the first device receives the first signal.

15. The method as claimed in any one of claims 11 to 13, wherein, The method further includes: After modulating the transmitted bit stream, the baseband signal is determined.

16. The method as claimed in any one of claims 11 to 15, wherein, The method further includes: The first model is trained based on the dataset until it converges.

17. The method of claim 16, wherein, The dataset includes at least one of the following: Information corresponding to all resource units (REs) on the resource grid; The aliasing signal serves as the tag value; The transmitted bit stream corresponding to the data information.

18. The method as claimed in any one of claims 11 to 17, wherein, The time slot containing the aliased signal only transmits the Physical Downlink Control Channel (PDCCH), the Physical Downlink Shared Channel (PDSCH), and the reference signal; or... The time slot containing the aliasing signal only transmits the Physical Uplink Control Channel (PUCCH), the Physical Downlink Shared Channel (PUSCH), and the reference signal. The reference signal is the demodulation reference signal DM-RS, and the data information is the data information of the PDSCH or the data information of the PUSCH.

19. A communication device, wherein, The communication device is used to perform the method according to any one of claims 1 to 10 or any one of claims 11 to 18.

20. A communication system comprising a first device and a second device, wherein, The first device is configured to implement the method as described in any one of claims 1 to 10; The second device is configured to implement the method as described in any one of claims 11 to 18.

21. A storage medium storing instructions, wherein, When the instructions are executed on the communication device, the communication device performs the method as described in any one of claims 1 to 10 or any one of claims 11 to 18.

22. A program product comprising at least one of a program and instructions, wherein, When at least one of the programs or instructions is executed by a communication device, it implements the method as described in any one of claims 1 to 10 or any one of claims 11 to 18.

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