Data processing method and apparatus

Abstract

The present application discloses a data processing method and apparatus. The method comprises: acquiring spatial-domain beamforming weight data corresponding to a first stream transmitted on a first resource block group; rearranging the spatial-domain beamforming weight data and performing first processing in at least one direction dimension of an antenna array plane, to obtain angle-domain beamforming weight data corresponding to the first stream; dividing the angle-domain beamforming weight data into one or more subsets, and compressing each subset on the basis of a target compression bit width corresponding to each subset; sending beamforming weight data after each subset is compressed, and bit width information corresponding to each subset. The present solution performs beam transformation on spatial-domain beamforming weight data based on a single-flow granularity, and can obtain good sparsity in the angle domain. Adaptive bit width compression is performed on angle-domain beamforming weight data, so that a high compression rate can be obtained, thereby reducing forward transmission flow. The solution can also reduce or avoid forcibly discarding some directions in the angle domain, and MU demodulation performance is considered.

Description

A data processing method and apparatus

[0001] Cross-references to related applications

[0002] This application claims priority to Chinese Patent Application No. 202410232251.6, filed on February 29, 2024, entitled "A Data Processing Method and Apparatus", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of communication technology, and in particular to a data processing method and apparatus. Background Technology

[0004] In communication systems employing Ethernet architectures (such as fronthaul communication systems using the enhanced common public radio interface (eCPRI), downlink bit data and beamforming weight data are the bottlenecks for peak fronthaul traffic when beamforming weights are calculated in the baseband unit (BBU). Both downlink bit data and beamforming weight data increase with the number of antennas, especially beamforming weight data, which exhibits a non-linear growth with the number of antennas. Existing technologies generally employ methods to transform beamforming weight data from the antenna domain to the beam domain. Based on the sparsity of beamforming weight data in the beam domain, certain beam directions are set to zero, thereby reducing the amount of beamforming weight data.

[0005] However, in massive multiple-input multiple-output (MIMO) scenarios, scheduling more users through spatial multiplexing is a key way to improve capacity. The bottleneck in fronthaul traffic is precisely when the number of scheduled users is high. Multi-user (MU) pairing consumes a significant amount of spatial degrees of freedom; therefore, the spatial sparsity of the beamforming weight data for multi-users is often not ideal after transformation from the antenna domain to the beam domain. Discarding many directions in the beam domain leads to a decrease in the compression accuracy of the beamforming weight data, resulting in a loss of MU demodulation performance.

[0006] How to reduce fronthaul traffic while maintaining MU demodulation performance is a technical problem that urgently needs to be solved. Summary of the Invention

[0007] This application provides a data processing method and apparatus that can reduce fronthaul traffic while maintaining MU demodulation performance.

[0008] Firstly, a data processing method is provided, which can be executed by a first device. The first device can be a device in an access network device, such as a BBU. Unless otherwise specified, the term "first device" in this application can refer to the first device itself (e.g., a BBU), a component in the first device (e.g., a processor, chip, or chip system), or a logic module or software that can implement all or part of the functions of the first device. The method includes: acquiring spatial beamforming weight data corresponding to a first stream transmitted on a first resource block group; rearranging the spatial beamforming weight data to obtain rearranged spatial beamforming weight data; performing a first processing on the rearranged spatial beamforming weight data in at least one directional dimension of the antenna array to convert the rearranged spatial beamforming weight data to the angular domain to obtain angular domain beamforming weight data corresponding to the first stream; dividing the angular domain beamforming weight data into one or more subsets; compressing each subset according to the target compression bit width corresponding to each subset in the one or more subsets to obtain compressed beamforming weight data for each subset; and sending the compressed beamforming weight data for each subset and the bit width information corresponding to each subset, wherein the bit width information corresponding to each subset is used to determine the target compression bit width corresponding to each subset.

[0009] In this embodiment, the spatial beamforming weight data is rearranged and beamformed from the spatial domain to the angular domain at the granularity of a single stream (e.g., the spatial beamforming weight data corresponding to the first stream is rearranged and processed first). This results in angular domain beamforming weight data with good sparsity, which helps to compress the weight data in the angular domain. Furthermore, when compressing the weight data in the angular domain, adaptive bit-width compression is performed on the weight data in the angular domain (i.e., the angular domain beamforming weight data is divided into one or more subsets; each subset is compressed according to the target compression bit-width corresponding to it). This fully utilizes the sparsity of the weight data in the angular domain to obtain a high compression ratio, which can effectively reduce the fronthaul throughput. At the same time, compression at the granularity of a single stream can avoid the limitation of MU pairing, thereby reducing or even avoiding the forced discarding of certain directions in the angular domain. Therefore, it can simultaneously reduce or even avoid MU demodulation performance loss.

[0010] In one possible design, obtaining the spatial beamforming weight data corresponding to the first stream transmitted on the first resource block group may include: obtaining spatial beamforming weight data corresponding to multiple streams transmitted on the first resource block group, wherein the spatial beamforming weight data corresponding to the multiple streams is a spatial beamforming weight matrix with dimension P*Q, where P is the number of antennas in the antenna array, Q is the number of streams transmitted on the first resource block group, and P and Q are positive integers; determining a column from the spatial beamforming weight matrix, and the determined column is the spatial beamforming weight data corresponding to the first stream.

[0011] In one possible design, rearranging the spatial beamforming weight data may include: rearranging the spatial beamforming weight data according to the structure of the antenna array, and the rearranged spatial beamforming weight data is a spatial beamforming weight matrix with a weight dimension of the number of antennas in the horizontal direction × the number of antennas in the vertical direction.

[0012] This design approach combines the structure of the antenna array to transform the spatial beamforming weight data of a single stream from the spatial domain to the angular domain, so that the transformed angular domain beamforming weight data can better exhibit sparsity.

[0013] In one possible design, if the antenna array structure is a regularly arranged antenna array, the rearranged spatial beamforming weight data is subjected to a first processing in at least one directional dimension of the antenna array, including: performing a point-based Fourier transform on the rearranged spatial beamforming weight data in at least one directional dimension of the antenna array to obtain an angular beamforming weight matrix, wherein the angular beamforming weight data corresponding to the first flow includes an angular beamforming weight matrix.

[0014] This design approach is simple to implement and easy to carry out.

[0015] In one possible design, if the antenna array structure is an irregularly arranged antenna array, the rearranged spatial beamforming weight data is processed in at least one directional dimension of the antenna array. This process includes: performing a multi-point Fourier transform on the rearranged spatial beamforming weight data in at least one directional dimension of the antenna array to obtain multiple angular beamforming weight matrices. The angular beamforming weight data corresponding to the first flow includes multiple angular beamforming weight matrices, and each angular beamforming weight matrix in the multiple angular beamforming weight matrices corresponds to a regularly arranged antenna array.

[0016] This design approach can transform the spatial beamforming weight data of irregularly arranged antenna arrays into spatial beamforming weight data of multiple regularly arranged antenna arrays through multi-point Fourier transform processing. This facilitates subsequent adaptive bit width compression and improves the applicability of the solution.

[0017] In one possible design, the target compression bit width corresponding to each subset in one or more subsets can be determined based on the target compression bit width of the angular domain beamforming weight data corresponding to the first stream and the effective bit width of each subset in one or more subsets.

[0018] In this way, it can be ensured that after compressing each subset according to the target compression bit width corresponding to each subset, the average bit width of the compressed beamforming weight data of all subsets can be consistent with the target compression bit width of the angular domain beamforming weight data corresponding to the first stream, thus improving the reliability of the scheme.

[0019] In one possible design, the target compressed bit width of the angular domain beamforming weight data corresponding to the first stream can be calculated based on a transmission time interval (TTI) scheduling information.

[0020] In one possible design, the rearranged spatial beamforming weight data undergoes a first processing step on at least one directional dimension of the antenna array. This can include: processing the rearranged spatial beamforming weight data on a first directional dimension of the antenna array to obtain angular beamforming weight data, where the first directional dimension is the dimension corresponding to the first direction; the angular beamforming weight data includes one or more angular beamforming weight matrices. Correspondingly, dividing the angular beamforming weight data into one or more subsets includes: dividing the elements of one or more angular beamforming weight matrices into at least one subset according to the rows of the matrices, where one subset corresponds to a row of the matrix.

[0021] This design approach, by performing a one-dimensional beamforming transformation from the spatial domain to the angular domain on the spatial beamforming weight data, can make the beamforming weight data have good sparsity in the angular domain. Furthermore, by performing adaptive bit-width compression on the beamforming weight data in the angular domain according to the rows of the matrix, a good compression effect can be obtained, thereby reducing the fronthaul throughput.

[0022] In one possible design, the number of antennas in the first direction is greater than or equal to the number of antennas in the second direction.

[0023] In other words, performing beamforming transformation from the spatial domain to the angular domain on the spatial beamforming weight data in the directional dimension with a large number of antennas can make the beamforming weight data achieve better sparsity in the angular domain.

[0024] In one possible design, the rearranged spatial beamforming weight data undergoes a first processing step on at least one directional dimension of the antenna array. This can include: performing a first processing step on the rearranged spatial beamforming weight data on a first directional dimension and a second directional dimension of the antenna array, respectively, to obtain angular beamforming weight data, where the first directional dimension is the dimension corresponding to the first direction, and the second directional dimension is the dimension corresponding to the second direction. Correspondingly, the angular beamforming weight data is divided into one or more subsets, including: determining the peak data in the angular beamforming weight data; reconstructing the peak data according to the antenna array structure to obtain the main signal; and subtracting the main signal from the angular beamforming weight data to obtain the residual signal; wherein the peak data and the residual signal are two different subsets.

[0025] This design approach, by performing a two-dimensional beamformation from the spatial domain to the angular domain on the spatial beamforming weight data, can make the beamforming weight data have good sparsity in the angular domain. Furthermore, by performing adaptive bit-width compression on the beamforming weight data in the angular domain according to the main signal and residual signal, a good compression effect can be obtained, thereby reducing the fronthaul throughput.

[0026] In one possible design, the position information of the main signal on the antenna array can also be transmitted. This helps the decompression end recover the peak data.

[0027] In one possible design, beamforming weight data corresponding to multiple resource block groups can be obtained, including a first resource block group. The beamforming weight data corresponding to the multiple resource block groups is arranged according to a third-dimensional direction, where the third-dimensional direction is the dimension corresponding to the third-dimensional direction, which is different from the first and second directions. The beamforming weight data corresponding to the multiple resource block groups is then processed in the third-dimensional direction to transform it into the time-delay domain, resulting in time-delay domain beamforming weight data corresponding to the multiple resource block groups. Finally, the time-delay domain beamforming weight data is compressed.

[0028] In this way, beamforming weight data can also be compressed in a third-party dimension, further improving the compression rate of beamforming weight data and reducing fronthaul traffic.

[0029] In one possible design, the first processing is any one of the following: inverse fast fourier transform (IFFT), inverse discrete fourier transform (IDFT), or inverse discrete cosine transform (IDCT).

[0030] Secondly, a data processing method is provided, which can be executed by a second device, which can be a device in an access network device, such as an AAU. Unless otherwise specified, the term "second device" in this application can refer to the second device itself (e.g., an AAU), a component in the second device (e.g., a processor, a chip, or a chip system), or a logic module or software that can implement all or part of the functions of the second device. The method includes: receiving compressed beamforming weight data for each subset and bit width information corresponding to each subset from one or more subsets, wherein the bit width information corresponding to each subset is used to determine the target compressed bit width corresponding to each subset; decompressing each subset according to the bit width information corresponding to each subset to obtain angular domain beamforming weight data corresponding to the first stream transmitted on the first resource block group; performing a third processing on the angular domain beamforming weight data in at least one directional dimension of the antenna array to convert the angular domain beamforming weight data to the spatial domain to obtain rearranged spatial domain beamforming weight data; and restoring the rearranged spatial domain beamforming weight data to obtain spatial domain beamforming weight data corresponding to the first stream transmitted on the first resource block group.

[0031] In one possible design, the third processing is any one of the following: Fast Fourier Transform (FFT), Discrete Fourier Transform (DFT), or Discrete Cosine Transform (DCT).

[0032] Thirdly, a communication device is provided, the device comprising modules, units, or technical means for implementing the method as described in the first aspect or any possible design of the first aspect.

[0033] For example, the apparatus may include:

[0034] The processing module is used to acquire the spatial beamforming weight data corresponding to the first stream transmitted on the first resource block group; rearrange the spatial beamforming weight data to obtain rearranged spatial beamforming weight data; perform a first processing on the rearranged spatial beamforming weight data in at least one directional dimension of the antenna array to convert the rearranged spatial beamforming weight data to the angular domain to obtain the angular domain beamforming weight data corresponding to the first stream; divide the angular domain beamforming weight data into one or more subsets; and compress each subset according to the target compression bit width corresponding to each subset in the one or more subsets to obtain compressed beamforming weight data for each subset.

[0035] The transceiver module is used to send the compressed beamforming weight data of each subset and the bit width information corresponding to each subset. The bit width information corresponding to each subset is used to determine the target compressed bit width corresponding to each subset.

[0036] Fourthly, a communication device is provided, the device comprising modules, units, or technical means for implementing the method as described in the second aspect or any possible design of the second aspect.

[0037] For example, the apparatus may include:

[0038] The transceiver module is used to receive compressed beamforming weight data for each subset and bit width information for each subset from one or more subsets. The bit width information for each subset is used to determine the target compressed bit width for each subset.

[0039] The processing module is used to decompress each subset according to the bit width information corresponding to each subset to obtain the angular domain beamforming weight data corresponding to the first stream transmitted on the first resource block group; to perform a third processing on the angular domain beamforming weight data in at least one directional dimension of the antenna array to convert the angular domain beamforming weight data to the spatial domain to obtain rearranged spatial domain beamforming weight data; and to restore the rearranged spatial domain beamforming weight data to obtain the spatial domain beamforming weight data corresponding to the first stream transmitted on the first resource block group.

[0040] Fifthly, a system is provided, comprising means as described in the third aspect or any possible design of the third aspect, and means as described in the fourth aspect or any possible design of the fourth aspect.

[0041] A sixth aspect provides a communication device including a processor and an interface circuit, the interface circuit being electrically coupled to the processor, the processor causing the method described in the first aspect or any possible design of the first aspect to be executed via logic circuitry or execution code instructions, or causing the method described in the second aspect or any possible design of the second aspect to be executed.

[0042] A seventh aspect provides a communication device, comprising: at least one processor; and a communication interface communicatively connected to the at least one processor; wherein the at least one processor, by executing instructions stored in a memory, causes the communication device to perform, via the communication interface, the method described in the first aspect or any possible design of the first aspect, or the method described in the second aspect or any possible design of the second aspect.

[0043] Eighthly, a computer-readable storage medium is provided, wherein a computer program or instructions are stored therein, which, when executed, cause the method described in the first aspect or any possible design of the first aspect to be performed, or cause the method described in the second aspect or any possible design of the second aspect to be performed.

[0044] A ninth aspect provides a computer program product including instructions that, when run on a computer, cause the method described in the first aspect or any possible design of the first aspect to be executed, or cause the method described in the second aspect or any possible design of the second aspect to be executed.

[0045] For the specific designs and beneficial effects of the second to ninth aspects mentioned above, please refer to the corresponding designs and beneficial effects in the first aspect. Attached Figure Description

[0046] Figure 1 is a schematic diagram of the architecture of a communication system that can be applied to an embodiment of this application;

[0047] Figure 2 is a schematic diagram of the access network equipment architecture;

[0048] Figure 3 is a schematic diagram of base station fronthaul;

[0049] Figure 4 is a schematic diagram of a possible eCPRI fronthaul splitting method;

[0050] Figure 5 is a schematic diagram showing the ratio of downlink bit data and weight data in the fronthaul traffic;

[0051] Figure 6 is a schematic diagram of the weight compression method;

[0052] Figure 7 is a schematic diagram of bit compression;

[0053] Figure 8 is a schematic diagram of a possible eCPRI resegmentation method;

[0054] Figure 9 is a schematic diagram of the connection relationship between AAU and BBU;

[0055] Figure 10 is a schematic diagram of an access network device provided in an embodiment of this application;

[0056] Figure 11 is a flowchart of a data processing method provided in an embodiment of this application;

[0057] Figure 12 is a schematic diagram of spatial beamforming weight data;

[0058] Figure 13 is a schematic diagram of the rearranged spatial beamforming weight data corresponding to a single stream;

[0059] Figure 14 is a schematic diagram of the multi-point IFFT transform method;

[0060] Figure 15 is a schematic diagram of the effect of multi-point IFFT transformation;

[0061] Figure 16 is a schematic diagram of the angle domain beamforming weight data obtained by one-dimensional IFFT;

[0062] Figure 17 is a schematic diagram of the angle domain beamforming weight data obtained by two-dimensional IFFT;

[0063] Figure 18 is a schematic diagram of a three-dimensional IFFT;

[0064] Figure 19 is a flowchart of another data processing method provided in an embodiment of this application;

[0065] Figure 20 is a schematic diagram of a communication device provided in an embodiment of this application;

[0066] Figure 21 is a schematic diagram of another communication device provided in an embodiment of this application;

[0067] Figure 22 is a schematic diagram of another communication device provided in an embodiment of this application. Detailed Implementation

[0068] To facilitate understanding of the technical solutions provided in the embodiments of this application, some terms mentioned in the embodiments of this application will be explained and described below.

[0069] (1) In the embodiments of this application, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally indicates that the related objects before and after are in an "or" relationship. In addition, it should be understood that although the terms "first," "second," etc. may be used to describe the objects in the embodiments of this invention, these objects should not be limited to these terms. These terms are only used to distinguish the objects from each other.

[0070] The terms "comprising" and "having," and any variations thereof, used in the description of the embodiments of this application, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices. It should be noted that in the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as preferred or advantageous over other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0071] (2) Beamforming (BF): This is a technique that uses an antenna array for directional signal transmission or reception. Beamforming creates a directional beam by changing the amplitude and phase of the signals from each antenna in the array. This causes signals in some directions to experience constructive interference, while signals in other directions experience destructive interference, thereby improving communication performance. For example, beamforming can increase the received signal-to-noise ratio, effectively combating path loss. In practice, beamforming generates a directional beam by adjusting the weighting coefficients of each element (or antenna unit) in the antenna array, thus achieving significant array gain.

[0072] (3) Beamforming weight data, also known as beamforming weights, weight data, beam weights, weights, etc., refers to the weighting coefficients of array elements (or antenna units) in an antenna array. When beamforming weight data is represented by a vector (or matrix), the weight data can also be called a weight vector (or matrix), etc.

[0073] The technical solutions provided in this application can be applied to various communication systems, such as 5th generation (5G) communication systems, 6th generation (6G) communication systems, or other future evolution systems, or other wireless communication systems employing wireless access technologies. The technical solutions provided in this application can also be applied to device-to-device (D2D) communication, vehicle-to-everything (V2X) communication, machine-to-machine (M2M) communication, machine-type communication (MTC), and Internet of Things (IoT) communication systems or other communication systems.

[0074] For example, Figure 1 is a schematic diagram of the architecture of a communication system to which this application embodiment can be applied. The communication system 1000 includes a wireless access network 100 and a core network 200. Optionally, the communication system 1000 may also include an Internet 300. The wireless access network 100 includes at least one access network device, as shown in Figure 1 (110a and 110b), and at least one terminal device, as shown in Figure 1 (120a-120j). Specifically, 110a is a base station, 110b is a micro-station, 120a, 120e, 120f, and 120j are mobile phones, 120b is a car, 120c is a fuel dispenser, 120d is a home access point (HAP) deployed indoors or outdoors, 120g is a laptop computer, 120h is a printer, and 120i is a drone. The same terminal device or access network device can provide different functions in different application scenarios. For example, the mobile phones in Figure 1 are 120a, 120e, 120f and 120j. Mobile phone 120a can access base station 110a, connect to car 120b, communicate directly with mobile phone 120e and access HAP. Mobile phone 120b can access HAP and communicate directly with mobile phone 120a. Mobile phone 120f can access micro-station 110b, connect to laptop 120g and printer 120h. Mobile phone 120j can control drone 120i.

[0075] Terminal devices are connected to access network devices, which in turn are connected to the core network. Core network devices and access network devices can be independent physical devices, or they can integrate the functions of the core network devices and the logical functions of the access network devices onto the same physical device. Alternatively, a single physical device can integrate some core network device functions and some access network device functions. Terminal devices and access network devices can be interconnected via wired or wireless means. Figure 1 is only a schematic diagram; this communication system may also include other network devices, such as wireless relay devices and wireless backhaul devices, which are not shown in Figure 1.

[0076] The terminal device can also be referred to as a terminal, user equipment (UE), mobile station, mobile terminal, etc. Terminal devices can be widely used in various scenarios, such as device-to-device (D2D), vehicle-to-everything (V2X) communication, machine-type communication (MTC), Internet of Things (IoT), virtual reality, augmented reality, industrial control, autonomous driving, telemedicine, smart grids, smart furniture, smart offices, smart wearables, smart transportation, smart cities, etc. Terminal devices can be mobile phones, tablets, computers with wireless transceiver capabilities, wearable devices, vehicles, drones, helicopters, airplanes, ships, robots, robotic arms, smart home devices, etc. The embodiments of this application do not limit the specific technologies or device forms used in the terminal devices.

[0077] Access network 100 can be configured as a 3GPP-related cellular system. For example, access network 100 can be configured as a 4G mobile communication system, a 5G mobile communication system, a WiFi system, a future-oriented evolution system (such as a 6G mobile communication system), or a communication system integrating at least two of the above systems. 5G can also be referred to as NR (New Radio). Alternatively, access network 100 can be configured as ORAN (Optical Random Access RAN) or O-RAN (Optical Random Access RAN).

[0078] Access network equipment can be a base station, an evolved NodeB (eNodeB), a transmission reception point (TRP), a next-generation NodeB (gNB) in a 5G mobile communication system, a base station in a 6G mobile communication system, a base station in a future mobile communication system, or an access node in a wireless fidelity (WiFi) system; it can also be a module or unit that performs some of the functions of a base station, for example, a central unit (CU) or a distributed unit (DU). Access network equipment can be a macro base station (as shown in Figure 1, 110a), a micro base station or an indoor station (as shown in Figure 1, 110b), a relay node or a donor node, etc. The embodiments of this application do not limit the specific technology or equipment form used in the access network equipment. In the embodiments of this application, a base station is used as an example of an access network equipment for description.

[0079] Base stations and UEs can be fixed or mobile. They can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; on water; or in the air on aircraft, balloons, and satellites. The embodiments of this application do not limit the application scenarios of the base stations and UEs.

[0080] The roles of base station and UE can be relative. For example, the helicopter or drone 120i in Figure 1 can be configured as a mobile base station. For those 120j accessing the radio access network 100 through 120i, 120i is a base station; but for base station 110a, 120i is a UE, meaning that 110a and 120i communicate via a radio interface protocol. Of course, 110a and 120i can also communicate via a base station-to-base station interface protocol. In this case, relative to 110a, 120i is also a base station. Therefore, both base station and UE can be collectively referred to as communication devices. 110a and 110b in Figure 1 can be called communication devices with base station functions, and 120a-120j in Figure 1 can be called communication devices with UE functions.

[0081] Communication between base stations and UEs, between base stations, and between UEs can be conducted using licensed spectrum, unlicensed spectrum, or both simultaneously. Communication can also be conducted using spectrum below 6 gigahertz (GHz), spectrum above 6 GHz, or both simultaneously. The embodiments of this application do not limit the spectrum resources used for wireless communication.

[0082] In one possible scenario, multiple RAN nodes collaborate to assist terminals in achieving wireless access, with different RAN nodes each implementing a portion of the base station's functions. For example, RAN nodes can be central units (CUs), distributed units (DUs), CU-control plane (CPs), CU-user plane (UPs), or radio units (RUs), etc. CUs and DUs can be configured separately or included in the same network element, such as a baseband unit (BBU). RUs can be included in radio frequency equipment or radio frequency units, such as remote radio units (RRUs), active antenna units (AAUs), or remote radio heads (RRHs).

[0083] In different systems, CU (or CU-CP and CU-UP), DU, or RU may have different names, but those skilled in the art will understand their meaning. For example, in an open radio access network (O-RAN or open RAN or ORAN) system, CU can also be called O-CU (open CU), DU can also be called O-DU, CU-CP can also be called O-CU-CP, CU-UP can also be called O-CU-UP, and RU can also be called O-RU. CU (or CU-CP and CU-UP), DU, and RU can implement different protocol layer functions. For ease of description, this application uses CU, CU-CP, CU-UP, DU, and RU as examples. Any of the units in CU (or CU-CP, CU-UP), DU, and RU in this application can be implemented through software modules, hardware modules, or a combination of software modules and hardware modules.

[0084] Communication between access network devices and terminal devices can follow a specific protocol layer structure. This protocol layer may include a control plane protocol layer and a user plane protocol layer. The control plane protocol layer may include at least one of the following: radio resource control (RRC) layer, packet data convergence protocol (PDCP) layer, radio link control (RLC) layer, media access control (MAC) layer, or physical (PHY) layer, etc. The user plane protocol layer may include at least one of the following: service data adaptation protocol (SDAP) layer, PDCP layer, RLC layer, MAC layer, or physical layer, etc.

[0085] As shown in Figure 2, the access network equipment may include at least one CU and at least one DU. This design can be referred to as CU and DU separation. One CU can be connected to one or more DUs. CU and DU can be separated according to the protocol layer of the wireless network: for example, the functions of the PDCP layer and above (e.g., RRC layer and SDAP layer, etc.) are set in the CU, and the functions of the protocol layers below the PDCP layer (e.g., RLC layer, MAC layer and PHY layer, etc.) are set in the DU; or, for example, the functions of the protocol layers above the PDCP layer are set in the CU, and the functions of the protocol layers below the PDCP layer are set in the DU, without limitation. This disclosure does not limit the names of CU and DU, for example, CU can be called the first access network element, and DU can be called the second access network element, etc.

[0086] The above division of CU and DU processing functions according to protocol layers is merely an example; other methods can also be used. For instance, CUs or DUs can be divided into those with more protocol layer functions, or into those with partial protocol layer processing functions. For example, some RLC layer functions and protocol layer functions above the RLC layer can be placed in the CU, while the remaining RLC layer functions and protocol layer functions below the RLC layer can be placed in the DU. Furthermore, the functions of CUs or DUs can be divided according to service type or other system requirements, such as latency. Functions requiring low latency can be placed in the DU, while functions not requiring this latency can be placed in the CU.

[0087] The CU can be connected to the core network. Optionally, the CU can have some of the functions of the core network.

[0088] Furthermore, some functions of the DU can be separated. As shown in Figure 2, this function can be implemented by a radio unit (RU). The RU can have radio frequency (RF) functions. This disclosure does not limit the name of the RU; for example, the RU can be called a third access network element. The DU and RU can be split or separated at the PHY layer. For example, the DU can implement higher-level functions in the PHY layer, and the RU can implement lower-level functions in the PHY layer, or implement both lower-level functions and RF functions. Higher-level functions in the PHY layer include functions closer to the MAC layer, and lower-level functions in the PHY layer include functions closer to the RF layer. The splitting method between the DU and RU can be various and is not limited. There is an interface between the DU and RU. For example, depending on the splitting method, the interface between the DU and RU can be a common public radio interface (CPRI) interface, an enhanced common public radio interface (eCPRI) interface, or a fronthaul interface in ORAN.

[0089] A 5G wireless base station consists of two parts: an active antenna unit (AAU) and a baseband unit (BBU). The AAU and BBU correspond to the RU and DU of the ORAN, respectively. The AAU contains a low-specification baseband unit (BBL), while the BBU contains a high-specification baseband unit (BBH) and a control module.

[0090] Figure 3 illustrates a base station fronthaul diagram. The AAU and BBU are connected to the optical fiber via optical modules, and the portion between the AAU and BBU is called the fronthaul. During the evolution of 5G AAU towards Massive Multiple-Input Multiple-Output (Massive MIMO), the baseband processing portion has evolved from CPRI segmentation to eCPRI segmentation to reduce fronthaul traffic requirements. However, the development speed of optical modules has not kept pace with the growth rate of fronthaul traffic under wireless Massive MIMO, making optical modules a bottleneck restricting wireless fronthaul.

[0091] Figure 4 illustrates a possible eCPRI fronthaul splitting method. In 5G eCPRI fronthaul splitting, uplink transmits equalized data, while downlink transmits downlink bit data and weighted data before constellation modulation. With the evolution from 5G to 5.5G and 6G, bandwidth has increased from 100MHz to 200MHz and 400MHz, and the number of antennas has increased from 64T to 128T and even 256T, resulting in a non-linear increase in fronthaul traffic. Therefore, downlink bit data and weighted data are the bottleneck for peak fronthaul traffic, especially the proportion of weighted data, which increases with the number of antennas. Figure 5 illustrates the ratio of downlink bit data to weighted data in fronthaul traffic. The increase in fronthaul traffic directly leads to the upgrading of optical modules; therefore, weighted data compression is key to reducing the cost of fronthaul optical modules. It is understood that Figure 4 is only an example of one possible fronthaul splitting method. In practical applications, there can be other splitting methods for uplink and downlink. As long as the fronthaul splitting involves the transmission of downlink weighted data, the problem of peak fronthaul traffic bottlenecks will exist.

[0092] To alleviate the bottleneck of peak fronthaul traffic, the weight data can be compressed. As shown in Figure 6, the weight compression method is illustrated. The BBU obtains multi-user (MU) / single-user (SU) weights through channel sounding reference signal (SRS) measurement and downlink weight calculation. Then, the weights are dimensionality reduced and transmitted to the AAU through the eCPRI interface. The AAU then upscales the received weights before performing precoding.

[0093] In one implementation, a transform domain approach can be used to reduce fronthaul bandwidth. For example, transform domain methods such as Inverse Fast Fourier Transform (IFFT), Inverse Discrete Fourier Transform (IDFT), and Inverse Discrete Cosine Transform (IDCT) can be used to transform the weight data from the antenna domain (or spatial domain) to the beam domain (or angle domain). The weight data exhibits some sparsity in the beam domain, so some beam directions can be zeroed to reduce the amount of weight data. After transform domain processing and dimensionality reduction by zeroing some beam directions, each element in the beam domain is uniformly bit-compressed to obtain the compressed weight data. Figure 7 illustrates bit compression, where compression is taken as an example from 16 bits to 8 bits; however, the actual compression is not limited to this.

[0094] However, in Massive MIMO scenarios, scheduling more users through spatial multiplexing is a key way to improve capacity. The bottleneck of eCPRI fronthaul traffic is precisely when the number of scheduled users is large. MU pairing consumes a lot of spatial degrees of freedom, and after the MU weights are transformed into the beam domain, the spatial sparsity is often not ideal. If the MU weights discard too many directions in the angular domain, it will lead to a decrease in the compression accuracy of the MU weights, which in turn will result in a loss of MU demodulation performance.

[0095] In another implementation, an eCPRI resegmentation scheme can be used to reduce fronthaul traffic. As shown in Figure 8, a possible eCPRI resegmentation scheme is illustrated. SRS measurement and MU weight calculation are moved from the BBU to the AAU, so that the MU weight data does not need to be transmitted from the BBU to the AAU, thereby reducing fronthaul traffic.

[0096] However, different AAUs correspond to different sectors, and the load on different sectors often varies significantly. When a BBU connects to multiple AAUs simultaneously, as shown in Figure 9, which illustrates a BBU connecting three AAUs, the actual implementation is not limited to this. MU weight calculation on the BBU can form an efficient resource pool, without needing to reserve computing resources based on all AAUs simultaneously reaching maximum capacity. If MU weight calculation is moved up to the AAUs, the resource pool effect cannot be achieved; each AAU would need to reserve resources for MU weight calculation at maximum capacity, thus increasing the overall base station cost.

[0097] To address one or more of the aforementioned technical problems, this application provides a technical solution that utilizes the structural characteristics of weight data to compress the weight data, thereby reducing fronthaul traffic. Simultaneously, it avoids moving the weight calculation function to the AAU, allowing the BBU to still obtain the benefits of the resource pool and preventing an increase in base station costs.

[0098] Referring to Figure 10, which is a schematic diagram of an access network device provided in an embodiment of this application, including a BBU and an AAU. It can be understood that with changes in the access network architecture, the above-mentioned BBU and AAU can also be replaced with other names, for example, BBU can also be replaced with DU, and AAU can be replaced with RU, etc.

[0099] The BBU is used to dynamically determine the weight compression switch or depth based on the current load. For example, if the current fronthaul load is high and the traffic is about to exceed the optical module's capacity, the weight compression switch is turned on (i.e., the compression method provided in this application embodiment is started to compress the weight data) to reduce the fronthaul load rate and provide more service traffic. For the calculated weight data, the compression method provided in this application embodiment is used to compress the weight data to achieve the purpose of data compression. The compressed data is then sent to the AAU, for example, by encapsulating the compressed data into an eCPRI frame and sending the eCPRI frame to the AAU.

[0100] AAU is used to decompress received weight data using a decompression method symmetrical to the BBU compression method. For example, it can decompress the weight data in an eCPRI frame and restore it to its original dimensions using a decompression method symmetrical to the BBU compression method.

[0101] In some embodiments, the BBU can flexibly switch the compression module on and off, or adjust the degree of compression, according to current performance requirements, bandwidth requirements, etc.

[0102] It is understood that there are multiple ways to split the fronthaul of the access network device, such as including but not limited to the eCPRI fronthaul splitting method shown in Figure 4. As long as the fronthaul splitting involves the transmission of downlink weight data, it is applicable.

[0103] Referring to Figure 11, which is a flowchart of a data processing method provided in an embodiment of this application, the method can be applied to a first device, which can be a device in an access network device, such as a BBU. Unless otherwise specified, the "first device" in this application can refer to the first device itself (e.g., a BBU), a component in the first device (e.g., a processor, chip, or chip system), or a logic module or software capable of implementing all or part of the functions of the first device. The method includes:

[0104] S101. Obtain the spatial beamforming weight data corresponding to the first stream transmitted on the first resource block group;

[0105] The resource block group (RBG) can include one or more resource blocks (RBs). For example, 1RB, 2RB, or 4RB, or even larger granularities, are not limited in this embodiment. The first resource block group can be any resource block group. It is understood that the same processing method can be applied to each of the multiple resource block groups in this embodiment, therefore the processing method for the first resource block group described below can also be applied to other resource block groups.

[0106] The first resource block group can transmit spatial beamforming weight data corresponding to multiple streams. These multiple streams can be streams from multiple users. In other words, the spatial beamforming weight data transmitted on the first resource block group can be MU weight data, and one user can have one or more streams transmitted on the first resource block group.

[0107] In one specific implementation, obtaining the spatial beamforming weight data corresponding to the first stream transmitted on the first resource block group may include:

[0108] First, spatial beamforming weight data corresponding to multiple streams transmitted on the first resource block group is obtained. The spatial beamforming weight data corresponding to these multiple streams is a spatial beamforming weight matrix with a weight dimension of number of streams × number of antennas. For example, it is a spatial beamforming weight matrix with dimension P*Q, where P is the number of antennas in the antenna array, Q is the number of streams transmitted on the first resource block group, and P and Q are positive integers. As shown in Figure 12, it is a schematic diagram of spatial beamforming weight data.

[0109] Then, a column is determined from the spatial beamforming weight matrix. This determined column is the spatial beamforming weight data corresponding to the first stream. This determined column can be any column in the spatial beamforming weight matrix; in other words, the first stream can be any of the multiple streams (i.e., the first stream can be any stream transmitted on the first resource block group). It is understood that the same processing method can be applied to each stream on the first resource block group in this embodiment. Therefore, the processing method for the first stream described below can also be applied to other streams.

[0110] S102. Rearrange the spatial beamforming weight data to obtain rearranged spatial beamforming weight data.

[0111] In one possible design, the spatial beamforming weight data can be rearranged according to the antenna array structure. Specifically, the spatial beamforming weight data corresponding to the first flow is rearranged according to the antenna array shape, such as rearranging the spatial beamforming weight data according to the antenna arrangement in the horizontal and vertical directions. The rearranged spatial beamforming weight data is a spatial beamforming weight matrix with a weight dimension of the number of antennas in the horizontal direction × the number of antennas in the vertical direction, as shown in Figure 13, which is a schematic diagram of the rearranged spatial beamforming weight data corresponding to a single flow.

[0112] S103. The rearranged spatial beamforming weight data is processed in at least one directional dimension of the antenna array to convert the rearranged spatial beamforming weight data to the angular domain, thereby obtaining the angular domain beamforming weight data corresponding to the first flow.

[0113] The first processing refers to the method of converting the rearranged spatial beamforming weight data from the spatial domain to the angular domain, such as, but not limited to, any one of IFFT, IDFT, IDCT, etc.

[0114] At least one directional dimension, including but not limited to the following three: horizontal direction, vertical direction, and horizontal direction and vertical direction. In other words, embodiments of this application may perform spatial-to-angular domain conversion on the rearranged spatial beamforming weight data only in the horizontal direction, or perform spatial-to-angular domain conversion on the rearranged spatial beamforming weight data only in the vertical direction, or perform spatial-to-angular domain conversion on the rearranged spatial beamforming weight data in both the vertical and horizontal directions.

[0115] In some possible embodiments, when the antenna array structure is a regularly arranged antenna array (e.g., with M*N antennas arranged in M ​​rows and N columns), the rearranged spatial beamforming weight data can be processed by a point-based Fourier transform in at least one directional dimension of the antenna array to obtain the angular domain beamforming weight data corresponding to the first flow, which is an angular domain beamforming weight matrix.

[0116] In other possible embodiments, when the antenna array structure is an irregularly arranged antenna array, the rearranged spatial beamforming weight data can be processed by performing a multi-point Fourier transform on at least one directional dimension of the antenna array to obtain multiple angular beamforming weight matrices. The first-order angular beamforming weight data includes multiple angular beamforming weight matrices, and each angular beamforming weight matrix corresponds to a regularly arranged antenna array. For example, Figure 14 illustrates the schematic diagrams of performing 12-point and 4-point IFFT transforms on the rearranged spatial beamforming weight data, respectively. The transformed effect is shown in Figure 15, which is equivalent to performing IFFT transforms on the spatial beamforming weight data of multiple regularly arranged antenna arrays. In this way, the complexity of subsequent compression processing can be reduced, and the applicability of the scheme can be improved.

[0117] S104. Divide the angular domain beamforming weight data into one or more subsets; compress each subset according to the target compression bit width corresponding to each subset in the one or more subsets to obtain the compressed beamforming weight data of each subset.

[0118] In this embodiment of the application, when compressing angular domain beamforming weight data, adaptive bit-width compression is performed on the angular domain beamforming weight data to fully utilize the sparsity of the beamforming weight data in the angular domain and achieve a high compression ratio. For example, the angular domain beamforming weight data is divided into one or more subsets; based on the target compression bit-width of the angular domain beamforming weight data corresponding to the first stream and the effective bit-width of each subset in the one or more subsets, the target compression bit-width corresponding to each subset in the one or more subsets is determined; and each subset is compressed according to the target compression bit-width corresponding to each subset in the one or more subsets. In other words, the angular domain beamforming weight data is divided into one or more subsets, and different subsets can have different target compression bit-widths (of course, it is not excluded that different subsets can have the same target compression bit-width), and compression is performed separately for each subset.

[0119] The target compression bit width can refer to the desired bit width after data compression. For example, if 3 bits of data need to be compressed to 2 bits, then 2 bits is the target compression bit width. Alternatively, the target compression bit width can refer to the amount of compression, i.e., the difference between the compressed and uncompressed values. For example, if 3 bits of data need to be compressed to 2 bits, then 3 bits - 2 bits = 2 bits is the target compression bit width. For ease of description, this article will consistently use the term "target compression bit width" as the desired bit width after data compression.

[0120] It is understood that in the embodiments of this application, the target compression bit width can also be replaced with other descriptions, such as target compression bit.

[0121] In a specific implementation, the target compression bit width corresponding to the subset can include the target compression bit width of each element in the subset, that is, the bit width that each element should achieve after compression. The effective bit width of the subset can include the effective bit width of each element in the subset, that is, the number of bits occupied by the effective information in the element.

[0122] It is understandable that the effective bit width and the actual bit width (i.e., the actual bit width occupied, which can also be called the original bit width or default bit width, etc.) of each element can be different, with the effective bit width ≤ the actual bit width. For example, if the protocol specifies or the system agrees that the default bit width of each element is 8 bits, and the effective information of a certain element only needs to occupy 6 bits, then the element can include 6 bits of effective information and 2 bits of redundant information (such as padding with 0s). In this case, the actual bit width of the element is 8 bits, and the effective bit width is 6 bits.

[0123] In one possible implementation, the target compressed bit width of the angle domain beamforming weight data corresponding to the first stream can be calculated based on a transmission time interval (TTI) scheduling information. For example, the target compressed bit width of the angle domain beamforming weight data corresponding to the first stream satisfies the following relationship:

[0124] Where, N bit The target compressed bit width for the angular domain beamforming weight data corresponding to the first stream; T, α, β are constant coefficients; N U For the number of users; Q m,i N is the modulation order; RB,i N represents the number of RBs to be scheduled; rank,i The number of streams to be scheduled.

[0125] Here are two specific examples:

[0126] Example 1: Step S103 involves performing a first processing on the rearranged spatial beamforming weight data in one directional dimension (e.g., the first directional dimension) of the antenna array to obtain angular beamforming weight data. Here, the first directional dimension is the dimension corresponding to a first direction, which can be either horizontal or vertical, without restriction. Optionally, the number of antennas in the first direction is greater than or equal to the number of antennas in the second direction.

[0127] Figure 16 shows a schematic diagram of the corner domain beamforming weight data obtained after performing IFFT in the horizontal dimension. The corner domain beamforming weight data exhibits obvious sparsity in the horizontal dimension.

[0128] It can be understood that when the antenna array is a regularly arranged antenna array, the angular domain beamforming weight data is a single angular domain beamforming weight matrix. When the antenna array is an irregularly arranged antenna array, the angular domain beamforming weight data consists of multiple angular domain beamforming weight matrices. Therefore, the angular domain beamforming weight data includes one or more angular domain beamforming weight matrices.

[0129] For each angular domain beamforming weight matrix, the following processing is performed: the matrix is ​​divided into at least one subset according to its rows (or columns), with one subset corresponding to a row (or column) of the matrix. It can be understood that the direction of the matrix's rows (or columns) corresponds to the first directional dimension.

[0130] In other words, each angular domain beamforming weight matrix is ​​divided into rows (or columns); based on the target compression bit width of the angular domain beamforming weight data corresponding to the first stream (or the target compression bit width of the matrix) and the effective bit width of each element in each row (or column), the target compression bit width corresponding to each element in each row (or column) is determined; based on the target compression bit width corresponding to each element in each row (or column), the element is compressed.

[0131] Example 1 above transforms the spatial beamforming weight data of a single stream into the angular domain by performing a one-dimensional beamformer (such as IFFT), and then performs adaptive bit-width compression on the angular domain beamforming weight data according to the granularity of individual horizontal / vertical (i.e., row / column of the matrix) beamblocks. This fully utilizes the sparsity of the angular domain beamforming weight data, resulting in a high compression ratio and reducing the fronthaul throughput.

[0132] Example 2, step S103 involves performing a first processing step on the rearranged spatial beamforming weight data in two directional dimensions of the antenna array (e.g., the first directional dimension and the second directional dimension) to obtain angular domain beamforming weight data. Here, the first directional dimension is the dimension corresponding to the first direction, and the second directional dimension is the dimension corresponding to the second direction. The first direction can be horizontal, and the second direction can be vertical; alternatively, the second direction can be horizontal, and the first direction can be vertical, without restriction.

[0133] Figure 17 shows a schematic diagram of the corner domain beamforming weight data obtained after performing the first processing in the horizontal and vertical dimensions. The corner domain beamforming weight data exhibits obvious sparsity in both the horizontal and vertical dimensions.

[0134] It is understood that when the antenna array is a regularly arranged antenna array, the angular domain beamforming weight data is a single angular domain beamforming weight matrix (corresponding to a regular antenna array). When the antenna array is an irregularly arranged antenna array, the angular domain beamforming weight data can be multiple angular domain beamforming weight matrices (each corresponding to multiple regular antenna arrays), or it can be a single angular domain beamforming weight matrix (corresponding to an irregular antenna array). This application does not impose any limitations on this embodiment.

[0135] Taking an angular domain beamforming weight data matrix as an example, the following processing can be performed on the angular domain beamforming weight data: Determine the peak data in the angular domain beamforming weight data; reconstruct the peak data based on the structure of the antenna array corresponding to the angular domain beamforming weight data to obtain the main signal (optionally, the position information of the main signal on the antenna array surface can also be determined); subtract the main signal from the angular domain beamforming weight data to obtain the residual signal; where the peak data and the residual signal are two different subsets. The peak data can be understood as the data corresponding to the amplitude peak in the angular domain beamforming weight data. For example, the part indicated by the arrow in Figure 17 is the part containing the peak data. It can be understood that the above peak data can also be replaced with other descriptions, such as the fitted sub-path, pulse signal, or signal peak, without limitation. The position information can be a position index, such as a row index and column index, or other information representation forms, without limitation.

[0136] In other words, the angular domain beamforming weight data is divided into main signal and residual signal; the main signal and residual signal can be compressed and transmitted with different target compression bit widths. Among them, only the peak data of the main signal can be compressed.

[0137] Example 2 above transforms the spatial beamforming weight data of a single stream into the angular domain by performing a two-dimensional beamformer (such as IFFT), and then compresses the peak data and residual signal according to different target compression bit widths, which can achieve a better compression effect and reduce the forward transmission throughput.

[0138] S105. Send the compressed beamforming weight data for each subset and the bit width information corresponding to each subset.

[0139] In this implementation, the bit width information corresponding to each subset is used to determine the target compression bit width for each subset. In one implementation, the bit width information corresponding to each subset is the target compression bit width for that subset. In another implementation, the bit width information corresponding to each subset is an indicator (e.g., a flag) that can indicate the target compression bit width for each subset. Of course, these two implementations are merely examples and are not limited to specific implementations.

[0140] Specifically, the compressed beamforming weight data of each subset and the corresponding bit width information of each subset are sent to the second device through the fronthaul network.

[0141] For Example 1 above, the target compressed bit width for each row of each matrix, along with the compressed element data for each row, can be packaged and sent from the first device (e.g., BBU) to the second device (e.g., AAU). Optionally, the number of subsets, i.e., the number of rows M1 of each matrix, can also be sent. Optionally, the number of elements N1 of each subset (i.e., the number of elements in each row of each matrix) can also be sent.

[0142] For example, taking the beamforming weight data of each RBG as containing L (number of streams) × Ntx (number of transmitting antennas) complex elements, where L and Ntx are positive integers, the number of data elements in the beamforming weight data of each RBG remains unchanged after compression using the above method, i.e., each stream has Ntx complex elements. The BBU and AAU can agree on the number of subsets to be divided (e.g., M1, where M1 is a positive integer) and the number of elements in each subset (it can be understood that the number of elements in different subsets can be different). Then, in S105, the transmitted data can be:

[0143] 1) The compressed complex elements of each row of each matrix;

[0144] 2) The bit width indicator W for each row of each matrix (W includes the target compressed bit width corresponding to each element in the row. The bit width indicators W for different rows can be different, such as W1, W2...Wn, etc., all of which are positive integers). The real and imaginary parts of each element in each row can use different bit width indicators.

[0145] 3) Number of subsets (e.g., M1). Of course, M1 does not have to be transmitted from BBU to AAU; for example, the number of subsets can be agreed upon between BBU and AAU.

[0146] 4) The number of elements in each subset. Of course, the number of elements in each subset does not have to be transmitted from BBU to AAU. For example, the number of elements in each subset can be agreed upon between BBU and AAU.

[0147] For Example 2 above, the compressed peak data, the target compressed bit width of the compressed peak data, the compressed residual signal, and the target compressed bit width of the compressed residual signal can be packaged and sent from the first device (e.g., BBU) to the second device (e.g., AAU). Optionally, the location information of the peak data can also be sent. Optionally, the data volume of the compressed peak data (e.g., M² complex elements) and the data volume of the compressed residual signal (e.g., N² complex elements) can also be sent.

[0148] For example, taking the beamforming weight data of each RBG as containing L (number of streams) × Ntx (number of transmitting antennas) complex elements, where L and Ntx are positive integers, the number of data elements in each RBG's beamforming weight data remains unchanged after compression using the above method, i.e., each stream has Ntx complex elements. The BBU and AAU can agree to divide the data into two categories: the first category (corresponding to peak data) has M2 complex elements, and the second category (corresponding to residual signals) has N2 complex elements, where M2 and N2 are positive integers. Then, in S105, the transmitted data can be:

[0149] 1) Each complex element in the first type of data after compression;

[0150] [Corrected according to Rule 91 03.03.2025] 2) Each complex element in the second type of data after compression;

[0151] 3) Bit width indication for each complex element of the first type of data, for example, using W21(1,2,…,M2). Each complex element of the first type of data can be indicated by two indices, I2(1,2,…,M2) and J2(1,2,…,M2), and both the real and imaginary parts of the complex elements are quantized using W21(1,2,…,M2);

[0152] 4) The bit width indicator for each complex element of the second type of data, for example, is indicated by W22(1,2,...,N2), and both the real and imaginary parts of the complex elements are quantized using W22(1,2,...,N2);

[0153] 5) Position information of each element in the first type of data, such as I2(1,2,…,M2) and J2(1,2,…,M2);

[0154] 6) The number of elements in the first type of data (M2). Of course, M2 does not have to be transmitted from BBU to AAU. For example, M2 can be agreed upon between BBU and AAU.

[0155] 7) The number of elements in the second type of data (N2). Of course, N2 does not have to be transmitted from BBU to AAU. For example, N2 can be agreed upon between BBU and AAU.

[0156] This application embodiment performs beamforming transformation from the spatial domain to the angular domain on the spatial domain beamforming weight data according to the single-stream granularity and combined with the antenna array structure. This results in the obtained angular domain beamforming weight data having good sparsity, which facilitates subsequent compression. During the compression of the angular domain beamforming weight data, the data is classified (e.g., by row, or by main signal and residual signal) and adaptive bit-width compression is performed (i.e., different classes can be compressed according to different target compression bit widths). This fully utilizes the sparsity of the angular domain beamforming weight data to achieve a high compression ratio, effectively reducing fronthaul throughput. Furthermore, it can reduce or even avoid forcibly discarding certain directions in the angular domain, thus reducing or even avoiding MU demodulation performance loss.

[0157] In one possible design, the elements corresponding to the weight data of multiple RBGs can be transformed in the third dimension of the beam domain (such as IFFT) to transform the third dimension from the frequency domain to the time delay domain. Combined with the first two dimensions (such as the horizontal and vertical dimensions mentioned above), a three-dimensional beam transformation and bit width compression can be formed, which can achieve better sparsity and compression ratio.

[0158] For example, beamforming weight data corresponding to multiple resource block groups are obtained, including a first resource block group; the beamforming weight data corresponding to the multiple resource block groups are arranged according to a third-direction dimension, where the third-direction dimension is the dimension corresponding to the third direction. The multiple resource block groups are arranged in the third direction, i.e., the third direction is the frequency domain dimension. The third direction is different from the first direction and the second direction. For example, as shown in Figure 18, the frequency domain dimension is perpendicular to the plane containing the horizontal antenna dimension and the vertical antenna dimension; the beamforming weight data corresponding to the multiple resource block groups is subjected to a second processing in the third-direction dimension to convert the beamforming weight data corresponding to the multiple resource block groups from the frequency domain to the time delay domain, obtaining the time delay domain beamforming weight data corresponding to the multiple resource block groups; the time delay domain beamforming weight data is then compressed.

[0159] The second processing step can refer to the first processing step, and may include, but is not limited to, any one of IFFT, IDFT, IDCT, etc. In other words, the transformation and compression methods in the third dimension can refer to the transformation and compression methods in Example 1 or Example 2 above, and will not be repeated here.

[0160] The compression method has been introduced above; the decompression method will be introduced below.

[0161] Referring to Figure 19, which is a flowchart of another data processing method provided in an embodiment of this application, the method can be applied to a second device, which can be a device in an access network device, such as an AAU. Unless otherwise specified, the term "second device" in this application can refer to the second device itself (e.g., an AAU), a component within the second device (e.g., a processor, chip, or chip system), or a logic module or software capable of implementing all or part of the functions of the second device. The method includes:

[0162] S201. Receive the compressed beamforming weight data of each subset in one or more subsets and the bit width information corresponding to each subset. The bit width information corresponding to each subset is used to determine the target compressed bit width corresponding to each subset.

[0163] It is understandable that the content received by the second device in S201 corresponds to the content sent by the first device in S105 above. Therefore, the specific description of the content received by the second device in S201 is the same as the specific description of the content sent by the first device in S105 above, and will not be repeated here.

[0164] S202. Decompress each subset according to the bit width information corresponding to each subset to obtain the corner domain beamforming weight data corresponding to the first stream transmitted on the first resource block group.

[0165] For example, the second device can determine the original bit width of each subset (such as the original bit width of each element in each subset, for example, the size of the original bit width of each element is specified by the protocol or the system). The second device can determine the target compressed bit width corresponding to each subset based on the bit width information corresponding to each subset (such as the target compressed bit width of each element in each subset, i.e., how much the bit width of each element is after compression), and decompress each subset based on the original bit width and the target compressed bit width (for example, restoring each element from the compressed bit width to the original bit width). The specific decompression method is not limited in this application.

[0166] S203. Perform a third processing on the angular domain beamforming weight data in at least one directional dimension of the antenna array to convert the angular domain beamforming weight data to the spatial domain, and obtain rearranged spatial domain beamforming weight data.

[0167] The third processing is the inverse processing of the first processing, such as any one of the Fast Fourier Transform (FFT), Discrete Fourier Transform (DFT), or Discrete Cosine Transform (DCT).

[0168] S204. Restore the rearranged spatial beamforming weight data to obtain the spatial beamforming weight data corresponding to the first stream transmitted on the first resource block group.

[0169] It is understandable that the second device uses the opposite method to the first device for decompression. Therefore, the decompression process can be referred to the compression process above, and will not be elaborated here.

[0170] Of course, the above is only one possible decompression process example, and the actual decompression method is not limited to this. The embodiments of this application do not limit the decompression method.

[0171] It is understood that the above embodiments can be implemented individually or in combination, without limitation.

[0172] The methods provided by the embodiments of this application have been described above with reference to the accompanying drawings. The apparatus provided by the embodiments of this application will be described below with reference to the accompanying drawings.

[0173] This application provides a communication device 210, which may be, for example, a satellite, a base station, a terminal, or an access point, or a device within a satellite, base station, terminal, or access point. The device 210 includes modules, units, or means that perform the method steps described in the above method embodiments. These functions, units, or means can be implemented in software, hardware, or by hardware executing corresponding software.

[0174] For example, referring to FIG20, device 210 may include processing module 211 and transceiver module 212. The transceiver module 212 may include only a sending module, only a receiving module, or both a sending module and a receiving module, without limitation.

[0175] When device 210 is in the first device:

[0176] Processing module 211 is used to acquire spatial beamforming weight data corresponding to the first stream transmitted on the first resource block group; rearrange the spatial beamforming weight data to obtain rearranged spatial beamforming weight data; perform a first processing on the rearranged spatial beamforming weight data in at least one directional dimension of the antenna array to convert the rearranged spatial beamforming weight data to the angular domain to obtain angular domain beamforming weight data corresponding to the first stream; divide the angular domain beamforming weight data into one or more subsets; and compress each subset according to the target compression bit width corresponding to each subset in the one or more subsets to obtain compressed beamforming weight data for each subset.

[0177] The transceiver module 212 is used to send the compressed beamforming weight data of each subset and the bit width information corresponding to each subset. The bit width information corresponding to each subset is used to determine the target compressed bit width corresponding to each subset.

[0178] In one possible design, when the processing module 211 acquires the spatial beamforming weight data corresponding to the first stream transmitted on the first resource block group, it is specifically used to: acquire the spatial beamforming weight data corresponding to multiple streams transmitted on the first resource block group, wherein the spatial beamforming weight data corresponding to multiple streams is a spatial beamforming weight matrix with dimensions of P*Q, where P is the number of antennas in the antenna array, Q is the number of streams transmitted on the first resource block group, and P and Q are positive integers; and determine a column from the spatial beamforming weight matrix, wherein the determined column is the spatial beamforming weight data corresponding to the first stream.

[0179] In one possible design, the processing module 211 rearranges the spatial beamforming weight data, specifically by rearranging the spatial beamforming weight data according to the structure of the antenna array. The rearranged spatial beamforming weight data is a spatial beamforming weight matrix with a weight dimension of the number of antennas in the horizontal direction × the number of antennas in the vertical direction.

[0180] In one possible design, when processing module 211 performs a first processing on the rearranged spatial beamforming weight data in at least one directional dimension of the antenna array, it specifically performs the following: if the antenna array structure is a regularly arranged antenna array, the rearranged spatial beamforming weight data is processed by a point-based Fourier transform in at least one directional dimension of the antenna array to obtain an angular beamforming weight matrix, and the angular beamforming weight data corresponding to the first flow includes an angular beamforming weight matrix; or, if the antenna array structure is an irregularly arranged antenna array, the rearranged spatial beamforming weight data is processed by multiple point-based Fourier transforms in at least one directional dimension of the antenna array to obtain multiple angular beamforming weight matrices, and the angular beamforming weight data corresponding to the first flow includes multiple angular beamforming weight matrices, each of the multiple angular beamforming weight matrices corresponding to a regularly arranged antenna array.

[0181] In one possible design, the processing module 211 can also be used to: determine the target compression bit width corresponding to each subset in one or more subsets based on the target compression bit width of the angular domain beamforming weight data corresponding to the first stream and the effective bit width of each subset in one or more subsets.

[0182] In one possible design, when processing module 211 performs a first processing on the rearranged spatial beamforming weight data in at least one directional dimension of the antenna array, it is specifically used to: perform a first processing on the rearranged spatial beamforming weight data in a first directional dimension of the antenna array to obtain angular beamforming weight data, wherein the first directional dimension is the dimension corresponding to the first direction; the angular beamforming weight data includes one or more angular beamforming weight matrices;

[0183] When dividing the angular domain beamforming weight data into one or more subsets, the processing module 211 is specifically used to divide the elements in one or more angular domain beamforming weight matrices into at least one subset according to the rows of the matrix, wherein one subset corresponds to a row of the matrix.

[0184] In one possible design, the number of antennas in the first direction is greater than or equal to the number of antennas in the second direction.

[0185] In one possible design, when the processing module 211 performs a first processing on the rearranged spatial beamforming weight data in at least one directional dimension of the antenna array, it is specifically used to: perform a first processing on the rearranged spatial beamforming weight data in the first directional dimension and the second directional dimension of the antenna array respectively to obtain angular beamforming weight data, wherein the first directional dimension is the dimension corresponding to the first direction and the second directional dimension is the dimension corresponding to the second direction;

[0186] When dividing the angular domain beamforming weight data into one or more subsets, the processing module 211 is specifically used to: determine the peak data in the angular domain beamforming weight data; reconstruct the peak data according to the structure of the antenna array to obtain the main signal; subtract the main signal from the angular domain beamforming weight data to obtain the residual signal; wherein the peak data and the residual signal are two different subsets.

[0187] In one possible design, the transceiver module 212 can also be used to transmit the position information of the main signal on the antenna array.

[0188] In one possible design, the processing module 211 can also be used to: calculate the target compressed bit width of the angular domain beamforming weight data corresponding to the first stream based on a TTI scheduling information.

[0189] In one possible design, the processing module 211 can also be used to: acquire beamforming weight data corresponding to multiple resource block groups, the multiple resource block groups including a first resource block group; arrange the beamforming weight data corresponding to the multiple resource block groups according to a third-dimensional direction, the third-dimensional direction being the dimension corresponding to the third-dimensional direction, the third-dimensional direction being different from the first and second directions; perform a second processing on the beamforming weight data corresponding to the multiple resource block groups in the third-dimensional direction to convert the beamforming weight data corresponding to the multiple resource block groups to the time-delay domain, obtaining time-delay domain beamforming weight data corresponding to the multiple resource block groups; and compress the time-delay domain beamforming weight data.

[0190] In one possible design, the first processing is any one of IFFT, IDFT, or IDCT.

[0191] When device 210 is located in the second device:

[0192] The transceiver module 212 is used to receive compressed beamforming weight data and bit width information for each subset in one or more subsets. The bit width information corresponding to each subset is used to determine the target compressed bit width corresponding to each subset.

[0193] The processing module 211 is used to decompress each subset according to the bit width information corresponding to each subset to obtain the angular domain beamforming weight data corresponding to the first stream transmitted on the first resource block group; to perform a third processing on the angular domain beamforming weight data in at least one directional dimension of the antenna array to convert the angular domain beamforming weight data to the spatial domain to obtain rearranged spatial domain beamforming weight data; and to restore the rearranged spatial domain beamforming weight data to obtain the spatial domain beamforming weight data corresponding to the first stream transmitted on the first resource block group.

[0194] In one possible design, the third processing is any one of FFT, DFT, or DCT.

[0195] It should be understood that all relevant content of each step involved in the above method embodiments can be referenced from the functional description of the corresponding functional module, and will not be repeated here.

[0196] Based on the same technical concept, referring to Figure 21, this application embodiment also provides a communication device 220, including:

[0197] At least one processor 221; and a communication interface 223 communicatively connected to the at least one processor 221; the at least one processor 221 executes instructions stored in the memory 222, causing the device to perform the method steps in the above method embodiments through the communication interface 223. The communication interface 223 can be used to perform the functions of the transceiver module 212, and the processor 221 can be used to perform the functions of the processing module 211.

[0198] Optionally, the memory 222 is located outside the device 220.

[0199] Optionally, the device 220 includes the memory 222, which is connected to the at least one processor 221 and stores instructions executable by the at least one processor 221. Figure 21 shows, with dashed lines, that the memory 222 is optional for the device 220.

[0200] The processor 221 and the memory 222 can be coupled through an interface circuit or integrated together; no restriction is imposed here.

[0201] This embodiment does not limit the specific connection medium between the processor 221, memory 222, and communication interface 223. In Figure 21, the processor 221, memory 222, and communication interface 223 are connected via a bus 224, which is represented by a thick line. The connection methods between other components are for illustrative purposes only and are not intended to be limiting. The bus can be categorized as an address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used in Figure 21, but this does not indicate that there is only one bus or one type of bus.

[0202] This embodiment does not limit the specific connection medium between the processor 221, memory 222, and communication interface 223. In Figure 21, the processor 221, memory 222, and communication interface 223 are connected via a bus 224, which is represented by a thick line. The connection methods between other components are for illustrative purposes only and are not intended to be limiting. The bus can be categorized as an address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used in Figure 21, but this does not indicate that there is only one bus or one type of bus.

[0203] Based on the same technical concept, this application also provides a communication device 230. Referring to FIG22, the communication device 230 includes a processor 231 and an interface circuit 232. The interface circuit 232 is electrically coupled to the processor 231. The processor 231 executes the method steps in the above method embodiments through logic circuits or executable code instructions. Optionally, the communication device 230 also includes a memory. The interface circuit 232 can be used to execute the functions of the transceiver module 212, and the processor 231 can be used to execute the functions of the processing module 211.

[0204] It should be understood that the processor mentioned in the embodiments of this application can be implemented in hardware or software. When implemented in hardware, the processor can be a logic circuit, integrated circuit, etc. When implemented in software, the processor can be a general-purpose processor, implemented by reading software code stored in memory.

[0205] For example, the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0206] It should be understood that the memory mentioned in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate Synchronous DRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0207] It should be noted that when the processor is a general-purpose processor, DSP, ASIC, FPGA, or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, the memory (storage module) can be integrated into the processor.

[0208] It should be noted that the memories described herein are intended to include, but are not limited to, these and any other suitable types of memories.

[0209] Based on the same technical concept, embodiments of this application also provide a computer-readable storage medium, including a program or instructions, which, when run on a computer, cause the methods in the above method embodiments to be executed.

[0210] Based on the same technical concept, embodiments of this application also provide a computer program product, including instructions that, when run on a computer, cause the methods in the above method embodiments to be executed.

[0211] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0212] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more blocks of the flowchart illustrations and / or one or more blocks of the block diagrams.

[0213] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0214] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

Claims

1. A data processing method, characterized by, include: Acquire the spatial beamforming weight data corresponding to the first stream transmitted on the first resource block group; The spatial beamforming weight data is rearranged to obtain rearranged spatial beamforming weight data; the rearranged spatial beamforming weight data is subjected to a first processing in at least one directional dimension of the antenna array to convert the rearranged spatial beamforming weight data to the angular domain to obtain the angular domain beamforming weight data corresponding to the first stream. The angular domain beamforming weight data is divided into one or more subsets; Based on the target compression bit width corresponding to each subset in the one or more subsets, each subset is compressed to obtain the compressed beamforming weight data of each subset; The compressed beamforming weight data of each subset and the bit width information corresponding to each subset are sent. The bit width information corresponding to each subset is used to determine the target compressed bit width corresponding to each subset.

2. The method of claim 1, wherein, The step of obtaining the spatial beamforming weight data corresponding to the first stream transmitted on the first resource block group includes: Acquire spatial beamforming weight data corresponding to multiple streams transmitted on the first resource block group, wherein the spatial beamforming weight data corresponding to the multiple streams is a spatial beamforming weight matrix with dimension P*Q, where P is the number of antennas of the antenna array, Q is the number of streams transmitted on the first resource block group, and P and Q are positive integers. A column is determined from the spatial beamforming weight matrix, and the determined column is the spatial beamforming weight data corresponding to the first stream.

3. The method of claim 1 or 2, wherein, The spatial beamforming weight data is rearranged, including: The spatial beamforming weight data is rearranged according to the structure of the antenna array. The rearranged spatial beamforming weight data is a spatial beamforming weight matrix with a weight dimension of the number of antennas in the horizontal direction × the number of antennas in the vertical direction.

4. The method according to any one of claims 1 to 3, characterized in that, The first processing of the rearranged spatial beamforming weight data in at least one directional dimension of the antenna array includes: If the antenna array structure is a regularly arranged antenna array, then the rearranged spatial beamforming weight data is subjected to a point-based Fourier transform in at least one directional dimension of the antenna array to obtain an angular beamforming weight matrix, wherein the angular beamforming weight data corresponding to the first stream includes the angular beamforming weight matrix; or, If the antenna array structure is an irregularly arranged antenna array, the rearranged spatial beamforming weight data is subjected to Fourier transform processing with multiple points in at least one directional dimension of the antenna array to obtain multiple angular beamforming weight matrices. The angular beamforming weight data corresponding to the first stream includes the multiple angular beamforming weight matrices, and each angular beamforming weight matrix in the multiple angular beamforming weight matrices corresponds to a regularly arranged antenna array.

5. The method according to any one of claims 1 to 4, wherein The method further includes: Based on the target compression bit width of the angular domain beamforming weight data corresponding to the first stream and the effective bit width of each subset in one or more subsets, the target compression bit width corresponding to each subset in the one or more subsets is determined.

6. The method according to any one of claims 1-5, characterized in that, The first processing of the rearranged spatial beamforming weight data in at least one directional dimension of the antenna array includes: The rearranged spatial beamforming weight data is processed in the first directional dimension of the antenna array to obtain angular beamforming weight data, wherein the first directional dimension is the dimension corresponding to the first direction; the angular beamforming weight data includes one or more angular beamforming weight matrices. The step of dividing the angular domain beamforming weight data into one or more subsets includes: The elements in the one or more angular domain beamforming weight matrices are divided into at least one subset according to the rows of the matrix, where one subset corresponds to a row of the matrix.

7. The method of claim 6, wherein, The number of antennas in the first direction of the antenna array is greater than or equal to the number of antennas in the second direction of the antenna array.

8. The method of any one of claims 1-5, wherein, The first processing of the rearranged spatial beamforming weight data in at least one directional dimension of the antenna array includes: The rearranged spatial beamforming weight data is subjected to a first processing on the first and second directional dimensions of the antenna array to obtain angular beamforming weight data, wherein the first directional dimension is the dimension corresponding to the first direction and the second directional dimension is the dimension corresponding to the second direction. The step of dividing the angular domain beamforming weight data into one or more subsets includes: Determine the peak data in the angular domain beamforming weight data; reconstruct the peak data according to the structure of the antenna array to obtain the main signal; subtract the main signal from the angular domain beamforming weight data to obtain the residual signal; wherein the peak data and the residual signal are two different subsets.

9. The method of claim 8, wherein, The method further includes: The main signal is transmitted to correspond to the position information on the antenna array.

10. The method of claim 5, wherein, The method further includes: The target compressed bit width of the angular domain beamforming weight data corresponding to the first stream is calculated based on a transmission time interval (TTI) scheduling information.

11. The method of any one of claims 1-10, wherein, Also includes: Acquire beamforming weight data corresponding to multiple resource block groups, wherein the multiple resource block groups include the first resource block group; The beamforming weight data corresponding to multiple resource block groups are arranged according to a third dimension, wherein the third dimension is the dimension corresponding to the third direction, and the third direction is different from the first direction and the second direction. The beamforming weight data corresponding to the multiple resource block groups are subjected to a second processing on the third direction dimension to transform the beamforming weight data corresponding to the multiple resource block groups into the time delay domain, thereby obtaining the time delay domain beamforming weight data corresponding to the multiple resource block groups. The time-delay domain beamforming weight data is compressed.

12. The method according to any one of claims 1-11, characterized in that, The first processing is any one of Inverse Fast Fourier Transform (IFFT), Inverse Discrete Fourier Transform (IDFT), or Inverse Discrete Cosine Transform (IDCT).

13. A data processing method, characterized in that, include: Receive compressed beamforming weight data for each subset and bit width information corresponding to each subset from one or more subsets, wherein the bit width information corresponding to each subset is used to determine the target compressed bit width corresponding to each subset; Based on the bit width information corresponding to each subset, each subset is decompressed to obtain the corner domain beamforming weight data corresponding to the first stream transmitted on the first resource block group; The angular domain beamforming weight data is subjected to a third processing in at least one directional dimension of the antenna array to convert the angular domain beamforming weight data to the spatial domain, thereby obtaining rearranged spatial domain beamforming weight data. The rearranged spatial beamforming weight data is restored to obtain the spatial beamforming weight data corresponding to the first stream transmitted on the first resource block group.

14. The method as described in claim 13, characterized in that, The third processing method is any one of Fast Fourier Transform (FFT), Discrete Fourier Transform (DFT), or Discrete Cosine Transform (DCT).

15. A system, characterized in that, include: A first device is configured to perform the method as described in any one of claims 1-12; A second device is used to perform the method as described in claim 13 or 14.

16. A communication device, characterized in that, It includes a module for performing the method as described in any one of claims 1-12, or includes a module for performing the method as described in claim 13 or 14.

17. A communication device, characterized in that, It includes a processor and an interface circuit, the interface circuit being electrically coupled to the processor, the processor causing the method as described in any one of claims 1-12 to be executed via logic circuitry or execution code instructions, or causing the method as described in claim 13 or 14 to be executed.

18. A computer-readable storage medium, characterized in that, The storage medium stores a computer program or instructions that, when executed, cause the method as described in any one of claims 1-12 to be performed, or cause the method as described in claim 13 or 14 to be performed.

19. A computer program product, characterized in that, Includes instructions that, when run on a computer, cause the method as described in any one of claims 1-12 to be performed, or cause the method as described in claim 13 or 14 to be performed.

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