Data processing method and device, storage medium, chip and electronic equipment

Abstract

The invention provides a data processing method and device, a storage medium, a chip and electronic equipment, and relates to the technical field of communication. The method comprises the following steps: receiving soft value information, wherein the soft value information is output information of signal detection; determining a mean value of absolute values of the soft value information, and determining a target interval to which the soft value information belongs according to the mean value; and performing compression operation on the soft value information according to a target compression rule corresponding to the target interval to obtain first compression information. According to the method and the device, the mean value of the soft value information output by the received signal detection is calculated in real time, and the corresponding compression rule is selected to compress the soft value information, so that the problem of high soft value storage overhead in signal detection and decoding is solved, and the soft value compression performance in an interference scene is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of communication technologies, and in particular, to a data processing method, apparatus, storage medium, chip, and electronic device. Background Art

[0002] In mobile communication, after a user receives modulation information transmitted from a base station, in order to improve the transmission quality, soft value information needs to be obtained through a signal detection module and then demodulated. Since the soft value information output by signal detection needs to cover both strong signal fields and weak signal fields, it needs to cover a large dynamic range brought by strong and weak fields and has a large bit width, resulting in a large storage resource and area required, thus increasing the chip cost.

[0003] The prior art mainly compresses the above soft value information based on information such as signal-to-noise ratio estimation or modulation coding method. When compressed to a certain bit width, it will bring a large performance loss. In addition, since the interference scenario cannot be recognized through signal-to-noise ratio estimation or modulation coding method, the prior art has poor performance in the non-stationary interference scenario of colored noise. Summary of the Invention

[0004] The purpose of the embodiments of the present disclosure is to provide a data processing method, apparatus, storage medium, chip, and electronic device, thereby solving to a certain extent the problems in the related art that the soft value information can only be compressed to a certain bandwidth and has poor performance in the non-stationary interference scenario of colored noise.

[0005] According to a first aspect of the present disclosure, there is provided a data processing method, including: receiving soft value information, where the soft value information is the output information of signal detection; determining the mean value of the absolute value of the soft value information, and determining the target interval to which the soft value information belongs according to the mean value; performing a compression operation on the soft value information according to the target compression rule corresponding to the target interval to obtain first compressed information.

[0006] In an exemplary embodiment of the present disclosure, the determining the target interval to which the soft value information belongs according to the mean value includes: comparing the mean value of the absolute value with the endpoint values of each preset interval determined in advance, and determining the preset interval to which the mean value belongs as the target interval, where the preset interval is determined by a plurality of preset threshold values.

[0007] In an exemplary embodiment of the present disclosure, the operation of compressing the soft value information according to the target compression rule corresponding to the target interval to obtain the first compressed information includes: querying the corresponding preset compression rule from the pre-stored relationship as the target compression rule according to the target interval, where the pre-stored relationship is used to store the corresponding relationship between the preset interval and the preset compression rule; intercepting the corresponding bit positions of the soft value information according to the target compression rule to obtain the first compressed soft value information.

[0008] In an exemplary embodiment of the present disclosure, after the operation of compressing the soft value information according to the target compression rule corresponding to the target interval to obtain the first compressed information, the method further includes: compressing the first compressed information to a target number of bits based on a preset non-linear compression method to obtain the corresponding second compressed information.

[0009] In an exemplary embodiment of the present disclosure, the operation of compressing the first compressed information to a target number of bits based on a preset non-linear compression method includes: obtaining a gear correspondence table of the target number of bits, where the gear correspondence table corresponds the first compressed information to the gear determined by the target number of bits; obtaining the second compressed information of the first compressed information at the target number of bits according to the gear correspondence table.

[0010] In an exemplary embodiment of the present disclosure, the method further includes: restoring the second compressed information to a third compressed information according to the gear correspondence table, where the third compressed information is the first compressed information with compression loss; decoding the third compressed information to obtain the demodulation output of the soft value information.

[0011] In an exemplary embodiment of the present disclosure, the method further includes: decoding the first compressed information to obtain the demodulation output of the soft value information.

[0012] According to a second aspect of the present disclosure, there is provided a data processing device, including: a signal detection and reception module, configured to receive soft value information, where the soft value information is the output information of signal detection; a target interval determination module, configured to determine the mean value of the absolute value of the soft value information, and determine the target interval to which the soft value information belongs according to the mean value; a first soft value compression module, configured to perform a compression operation on the soft value information according to the target compression rule corresponding to the target interval to obtain the first compressed information.

[0013] In an exemplary embodiment of the present disclosure, the soft value device further includes a second soft value compression module; the second soft value compression module is configured to: compress the first compressed information to a target number of bits based on a preset non-linear compression method to obtain the corresponding second compressed information.

[0014] According to a third aspect of the present disclosure, there is provided an electronic device, including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the method described in any one of the above by executing the executable instructions.

[0015] According to a fourth aspect of the present disclosure, there is provided a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method described in any one of the above is implemented.

[0016] According to a fifth aspect of the present disclosure, there is provided a chip, including a processor and an interface, and the above processor is used to read instructions to execute the method described in the first aspect.

[0017] The exemplary embodiments of the present disclosure may have some or all of the following beneficial effects:

[0018] In the data processing method provided by the exemplary embodiment of the present disclosure, soft value information is received, and the soft value information is output information of signal detection; the mean value of the absolute value of the soft value information is determined, and the target interval to which the soft value information belongs is determined according to the mean value; the soft value information is subjected to a compression operation according to the target compression rule corresponding to the target interval to obtain first compression information. The present disclosure improves the performance of soft value compression by determining the mean value of the soft value information output by the received signal detection, and selecting different corresponding rules to compress the soft value information based on the mean value for different interference scenarios. In addition, the present disclosure can compress the soft value information to a lower bit width, saving storage resources and reducing costs.

[0019] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure. Obviously, the accompanying drawings in the following description are only some embodiments of the present disclosure, and those of ordinary skill in the art can obtain other drawings according to these drawings without creative efforts.

[0021] Figure 1 Schematically shows a flowchart of a data processing method according to an embodiment of the present disclosure;

[0022] Figure 2 Schematically shows an application scenario diagram of a data processing method according to an embodiment of the present disclosure;

[0023] Figure 3A flowchart schematically showing a data processing method according to an embodiment of the present disclosure;

[0024] Figure 4 A block diagram schematically showing a data processing apparatus according to an embodiment of the present disclosure;

[0025] Figure 5 A schematic diagram schematically showing an electronic device according to an embodiment of the present disclosure. Detailed implementation manners

[0026] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The features, structures, or characteristics described may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present disclosure. However, those skilled in the art will recognize that the technical solutions of the present disclosure can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be used. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring the various aspects of the present disclosure.

[0027] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0028] In mobile communication, after a user receives the modulated information transmitted from a base station, in order to improve the transmission quality, soft value information needs to be obtained through a signal detection module and then demodulated. Since the soft value information output by the signal detection needs to cover both strong signal fields and weak signal fields, it needs to cover the large dynamic range brought by the strong and weak fields and has a relatively large bit width, resulting in a relatively large required storage resource and area, increasing the chip cost.

[0029] The prior art mainly compresses the above-mentioned soft value information based on information such as SNR estimation (signal-to-noise ratio estimation) or DCI (modulation and coding scheme). Exemplarily, the above process can be implemented as follows: According to the MCS (Modulation and Coding Scheme) information obtained by SNR estimation or DCI parsing, the soft value information is intercepted with a fixed bit width in a certain manner, that is, starting from a preset starting interception bit, a corresponding number of bits are fixedly intercepted and output to the decoding module. For example, the length of the received soft value information is 12 bit positions, and the preset rule is to truncate the 8th bit to the 3rd bit, and compress the soft value information to 6 bit positions. That is, for all the received soft value information, the method of truncating the 8th bit to the 3rd bit is adopted for compression.

[0030] Although the above method has a low implementation complexity, there are the following problems: (1) The compression ratio cannot be higher than the threshold, usually only compressed to 6 bits, and the compression ratio is not high, resulting in a still relatively large storage area; (2) Since the interference scenario cannot be identified by SNR or MCS, the method has poor performance in the non-stationary interference scenario of colored noise.

[0031] In order to solve the problems existing in the above method, the present exemplary embodiment proposes an image processing method, an image processing device, an electronic device, and a computer-readable storage medium. The technical solutions of the embodiments of the present disclosure are elaborated in detail below:

[0032] The present exemplary embodiment first provides a data processing method. Refer to Figure 1 As shown, the data processing method specifically includes the following steps:

[0033] Step S110: Receive soft value information, which is the output information of signal detection;

[0034] Step S120: Determine the mean value of the absolute value of the soft value information, and determine the target interval to which the soft value information belongs based on this mean value;

[0035] Step S130: Perform a compression operation on the soft value information according to the target compression rule corresponding to the target interval to obtain the first compressed information.

[0036] In the data processing method provided by the exemplary embodiment of the present disclosure, by calculating in real time the mean value of the soft value information output by signal detection, for different interference scenarios, different corresponding rules are selected based on this mean value to compress the soft value information, improving the performance of soft value compression. In addition, the present disclosure can compress the soft value information to a lower bit width, saving storage resources and reducing costs. Below, the above steps are described in more detail.

[0037] In step S110, soft value information is received, and the soft value information is the output information of signal detection.

[0038] The data processing method proposed in the embodiments of the present disclosure is applied to the field of mobile communication technology. The above soft value information is the output information of signal detection in mobile communication. Specifically, as Figure 2 shown, the base station sends the modulated message to the terminal device used by the user through the wireless channel. After receiving the message, the analog front end of the terminal device transmits the received message data to signal estimation and signal detection. Channel estimation estimates the model parameters of the channel model from the message data and transmits the parameters to the signal detection module. The signal detection module processes the message data in combination with the parameters to obtain soft value information and stores it. The decoding process demodulates the received message data by decoding the soft value information, thereby realizing the communication process.

[0039] In the embodiments of the present disclosure, the above soft value information is the information obtained through signal detection. The soft value information refers to the probability that each hard decision symbol is correct while the signal detection gives the decision compliance sequence. That is, the soft value information also includes probability information for judging whether each bit of the received data is 1 or 0. Exemplarily, the soft value information may be LLR (log-likelihood ratio).

[0040] It should be noted that in the embodiments of the present disclosure, the above signal detection methods include but are not limited to maximum ratio combining, zero-forcing algorithm, minimum mean square error algorithm, maximum likelihood algorithm, low-complexity maximum likelihood algorithm, etc. The embodiments of the present disclosure do not make special limitations on the above signal detection methods.

[0041] In step S120, the mean value of the absolute value of the soft value information is determined, and the target interval to which the soft value information belongs is determined based on the mean value.

[0042] The soft value of the information output by the above signal detection needs to cover strong signal fields and weak signal fields. The large dynamic range limitation brought by the strong and weak fields makes the information soft value usually need to take a value of more than 12-bit width, resulting in more storage resources and larger area occupied, which affects the chip cost. Therefore, after obtaining the above soft value information, the embodiments of the present disclosure also need to compress the soft value information.

[0043] To improve the compression performance in the scenario of colored noise non-stationary interference, the embodiments of the present disclosure calculate the mean value of the absolute value of the above soft value information in real time, and accordingly understand the noise situation of the soft value information in real time, so as to select a suitable compression rule for compression.

[0044] In an embodiment of the present disclosure, the process of determining the target interval to which the soft value information belongs based on the mean value can be implemented as follows: Compare the mean value of the absolute values with the endpoint values of each preset interval determined in advance, and determine the preset interval to which the mean value belongs as the target interval, where the preset interval is determined by a plurality of preset threshold values.

[0045] Exemplarily, assume that the above-mentioned preset threshold values are th1, th2, th3, and th4 in sequence. Then, the preset intervals determined based on the above-mentioned preset threshold values are [th1, 2048), [th2, th1), [th3, th2), and [th4, th3). If, after comparison, the mean value of the absolute value of the above-mentioned soft value information is between the threshold value th2 and the threshold value th1, then the target interval to which the soft value information belongs is [th2, th1).

[0046] In step S130, perform a compression operation on the soft value information according to the target compression rule corresponding to the target interval to obtain the first compressed information.

[0047] In an embodiment of the present disclosure, different compression rules are set for different preset intervals to adapt to different noise environments. Specifically, this process can be implemented as follows: Query the corresponding preset compression rule from the pre-stored relationship as the target compression rule according to the target interval. The pre-stored relationship is used to store the corresponding relationship between the preset interval and the preset compression rule; Intercept the corresponding bit positions of the soft value information according to the target compression rule to obtain the first compressed soft value information. Exemplarily, the corresponding relationship between the above-mentioned preset interval and the preset compression rule can be stored in a pre-stored table, and the above-mentioned target compression rule is the preset compression rule selected from the pre-stored table according to the corresponding relationship based on the target to which the soft value information belongs.

[0048] Next, the above compression process will be described in detail in a specific embodiment. Among them, the above-mentioned pre-stored table can be Table 1 below:

[0049] Table 1:

[0050]

[0051] Among them, E = mean(LLR), which is the mean value of the absolute value of the above-mentioned soft value information. As shown in Table 1, if it is determined that E belongs to the interval [th4, th3), then the truncation method of [11, 10:6] is adopted. The specific implementation is: Intercept 11-bit sign bits and bits 9 to 5, and a total of 6 bits are output. Through this process, the 12-bit soft value information output by signal detection can be compressed to 6 bits.

[0052] In addition, after obtaining the compressed soft value information, that is, the above-mentioned first compressed information, through the above process, an embodiment of the present disclosure can also decode the first compressed information to obtain the data information received by the terminal.

[0053] Preferably, in order to further improve the compression ratio, in another embodiment, after the above compression process, the embodiments of the present disclosure may further compress the obtained first compression information in a non-linear manner. As Figure 3 shown, this exemplary embodiment includes the following steps:

[0054] Step S310: Receive soft value information, which is the output information of signal detection.

[0055] Step S320: Determine the mean value of the absolute value of the soft value information, and determine the target interval to which the soft value information belongs according to this mean value.

[0056] Step S330: Perform a compression operation on the soft value information according to the target compression rule corresponding to the target interval to obtain the first compression information.

[0057] Step S340: Compress the first compression information to the target number of bits based on a preset non-linear compression method to obtain the corresponding second compression information.

[0058] In the embodiments of the present disclosure, the above non-linear compression method may adopt a method based on μ-law compression. Specifically, this process can be implemented as follows: obtain a gear correspondence table for the target number of bits, which corresponds the first compression information to the gear determined by the target number of bits; obtain the second compression information of the first compression information at the target number of bits according to the gear correspondence table.

[0059] Next, in a specific embodiment, the above process will be described in detail. In this specific embodiment, the above gear correspondence table, that is, the μ-law compression pre-stored table, can be as shown in Table 2 below:

[0060]

[0061]

[0062] As shown in Table 2, in this specific embodiment, the first compression information of 6-bit information is further compressed to 5 bits or 4 bits according to this pre-stored table. For example, when compressing 6-bit information to 5 bits, -29 to -31 in 6 bits all correspond to gear 0 of 5 bits. When compressing 6-bit information to 4 bits, then -19 to -31 in 6 bits all correspond to gear 0 of 4 bits.

[0063] In the embodiments of the present disclosure, if the soft value information is compressed in two steps, the decoding process is implemented as follows: restore the second compression information to the third compression information according to the gear correspondence table, and the third compression information is the first compression information with compression loss; decode the third compression information to obtain the demodulation output of the soft value information.

[0064] Specifically, during the communication process, the above process can be implemented as follows: After receiving the second compressed information obtained by the above two - stage compression, the decoding module selects different soft - value restoration methods and starts decoding according to whether the μ - law compression method is enabled. For example, if after the first compression, the second compression from 6 bits to 4 bits is completed using the μ - law compression, the decoding needs to restore the second compressed information to the 4 - bit actual value (i.e., the above - mentioned third compressed information) according to the 4 - bit gear and send it to the decoder to execute decoding and output the decoding result.

[0065] It should be noted that although the steps of the methods in the present disclosure are described in a specific order in the drawings, this does not require or imply that these steps must be executed in that specific order, or that all the steps shown must be executed to achieve the desired result. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be decomposed into multiple steps for execution, etc.

[0066] Furthermore, in the present exemplary embodiment, a data processing device is also provided. Referring to Figure 4 as shown, the data processing device 400 may include a signal detection and reception module 410, a target interval determination module 420, and a first soft - value compression module 430. Among them:

[0067] The signal detection and reception module 410 can be used to receive soft - value information, and the soft - value information is the output information of signal detection;

[0068] The target interval determination module 420 can be used to determine the mean value of the absolute value of the soft - value information and determine the target interval to which the soft - value information belongs based on this mean value;

[0069] The first soft - value compression module 430 can be used to perform a compression operation on the soft - value information according to the target compression rule corresponding to the target interval to obtain the first compressed information.

[0070] In the embodiments of the present disclosure, the above - mentioned data processing device further includes a second soft - value compression module; the second soft - value compression module is used to: compress the first compressed information to the target number of bits based on a preset non - linear compression method to obtain the corresponding second compressed information. Specifically, this process can be implemented as follows: Obtain a gear correspondence table of the target number of bits, and the gear correspondence table corresponds the first compressed information to the gear determined by the target number of bits; obtain the second compressed information of the first compressed information at the target number of bits according to the gear correspondence table.

[0071] In the embodiments of the present disclosure, the above - mentioned target interval determination module is specifically used to: compare the mean value of the absolute value with the endpoint values of each preset interval determined in advance, and determine the preset interval to which the mean value belongs as the target interval, where the above - mentioned preset interval is determined by multiple preset threshold values.

[0072] In an embodiment of the present disclosure, the first soft value compression module is specifically configured to: query a corresponding preset compression rule from a pre-stored relationship as a target compression rule according to a target interval, where the pre-stored relationship is used to store the corresponding relationship between a preset interval and a preset compression rule; intercept corresponding bit positions of soft value information according to the target compression rule to obtain first compressed soft value information.

[0073] In an embodiment of the present disclosure, the data processing device further includes a demodulation module, which is specifically configured to: restore the second compressed information to third compressed information according to the gear correspondence table, where the third compressed information is the first compressed information with compression loss; decode the third compressed information to obtain a demodulation output of the soft value information; or, when it is determined that the soft value information has not been compressed by the second soft value compression module, directly decode the first compressed information to obtain a demodulation output of the soft value information.

[0074] The specific implementation details of the above data processing device have been described in detail at the corresponding positions of the above data processing method, so they will not be repeated here.

[0075] It should be noted that although several modules or units of a device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of the two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0076] Figure 5 It is a schematic structural diagram of an electronic device in an embodiment of the present disclosure. Specifically refer to Figure 5 below, which shows a schematic structural diagram of an electronic device 500 suitable for implementing the present disclosure. Figure 5 The electronic device shown is only an example and should not impose any limitation on the functions and usage scope of the embodiments of the present disclosure.

[0077] As Figure 5 shown, the electronic device 500 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 501, which may execute various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503 to implement the data processing method of the embodiments as described in the present disclosure. In the RAM 503, various programs and data required for the operation of the electronic device 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0078] Typically, the following devices can be connected to the I / O interface 505: an input device 506 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 507 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 508 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 509. The communication device 509 can allow the electronic device 500 to communicate with other devices wirelessly or wiredly to exchange data. Although Figure 5 the electronic device 500 with various devices is shown, it should be understood that it is not required to implement or have all the shown devices. Instead, more or fewer devices can be implemented or had.

[0079] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product that includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes program code for performing the method shown in the flowchart, thereby implementing the data processing method as described above. In such an embodiment, the computer program can be downloaded and installed from a network through the communication device 509, or installed from the storage device 508, or installed from the ROM 502. When the computer program is executed by the processing device 501, the above functions defined in the method of the embodiment of the present disclosure are executed.

[0080] It should be noted that the computer-readable medium described above can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted by any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0081] In some embodiments, the client and the server can communicate using any currently known or future-developed network protocol such as HTTP (HyperText Transfer Protocol), and can be interconnected with digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include local area networks ("LANs"), wide area networks ("WANs"), the Internet (e.g., the Internet), and end-to-end networks (e.g., ad hoc end-to-end networks), as well as any currently known or future-developed networks.

[0082] The above computer-readable medium can be included in the above electronic device; or it can exist separately without being assembled into the electronic device.

[0083] The above computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device is caused to:

[0084] Receive soft value information, which is the output information of signal detection;

[0085] Determine the mean value of the absolute value of the soft value information, and determine the target interval to which the soft value information belongs based on this mean value;

[0086] Perform a compression operation on the soft value information according to the target compression rule corresponding to the target interval to obtain the first compressed information.

[0087] Optionally, when one or more of the above programs are executed by the electronic device, the electronic device may also execute the other steps described in the above embodiments.

[0088] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0089] The units involved in the embodiments described in the present disclosure can be implemented in software or in hardware. Among them, the name of the unit does not constitute a limitation to the unit itself in some cases.

[0090] The functions described above herein can be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that can be used include: Field Programmable Gate Array (FPGA), Application Specific Integrated Circuit (ASIC), Application Specific Standard Product (ASSP), System on Chip (SOC), Complex Programmable Logic Device (CPLD), and so on.

[0091] In the context of the present disclosure, a machine-readable medium may be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0092] An embodiment of the present disclosure also provides a chip, which includes a processor and an interface, wherein the processor is configured to read instructions for a data processing method mentioned in the embodiment of the present disclosure.

[0093] The above description is only a preferred embodiment of the present disclosure and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the disclosure involved in the present disclosure is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosure concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) having similar functions disclosed in the present disclosure.

[0094] In addition, although the operations are depicted in a particular order, this should not be construed as requiring that the operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features described in the context of separate embodiments may also be implemented combinatorially in a single embodiment. Conversely, the various features described in the context of a single embodiment may also be implemented separately or in any suitable sub-combination in multiple embodiments.

[0095] Although the subject matter has been described in language specific to structural features and / or methodological acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are merely example forms of implementing the claims.

Claims

1. A data processing method, characterized in that, it includes: Receiving soft value information, where the soft value information is the output information of signal detection; Determining the mean value of the absolute value of the soft value information, and determining the target interval to which the soft value information belongs according to the mean value; Performing a compression operation on the soft value information according to the target compression rule corresponding to the target interval to obtain first compression information.

2. The data processing method according to claim 1, characterized in that, The determining the target interval to which the soft value information belongs according to the mean value includes: Comparing the mean value of the absolute value with the endpoint values of each preset interval determined in advance, and determining the preset interval to which the mean value belongs as the target interval, where the preset interval is determined by multiple preset threshold values.

3. The data processing method according to claim 2, characterized in that, The performing a compression operation on the soft value information according to the target compression rule corresponding to the target interval to obtain first compression information includes: Querying the corresponding preset compression rule from the pre-stored relationship as the target compression rule according to the target interval, where the pre-stored relationship is used to store the corresponding relationship between the preset interval and the preset compression rule; Intercepting the corresponding bit positions of the soft value information according to the target compression rule to obtain the first compressed soft value information.

4. The data processing method according to any one of claims 1-3, characterized in that, After performing a compression operation on the soft value information according to the target compression rule corresponding to the target interval to obtain first compression information, the method further includes: Compressing the first compression information to a target number of bits based on a preset non-linear compression method to obtain corresponding second compression information.

5. The data processing method according to claim 4, characterized in that, The compressing the first compression information to a target number of bits based on a preset non-linear compression method includes: Obtaining a gear correspondence table of the target number of bits, where the gear correspondence table corresponds the first compression information to the gear determined by the target number of bits; Obtaining the second compression information of the first compression information at the target number of bits according to the gear correspondence table.

6. The data processing method according to claim 5, characterized in that, The method further includes: Restoring the second compression information to third compression information according to the gear correspondence table, where the third compression information is the first compression information with compression loss; Decoding the third compression information to obtain the demodulation output of the soft value information.

7. The data processing method according to claim 3, characterized in that, The method further includes: Decoding the first compression information to obtain the demodulation output of the soft value information.

8. A data processing device, characterized in that, it includes: A signal detection receiving module, configured to receive soft value information, where the soft value information is the output information of signal detection; A target interval determination module, configured to determine the mean value of the absolute value of the soft value information, and determine the target interval to which the soft value information belongs according to the mean value; A first soft value compression module, configured to perform a compression operation on the soft value information according to a target compression rule corresponding to the target interval, so as to obtain first compressed information.

9. The data processing device according to claim 8, wherein, the soft value device further includes a second soft value compression module; the second soft value compression module is configured to: compress the first compressed information to a target number of bits based on a preset non-linear compression method, so as to obtain corresponding second compressed information.

10. A computer-readable storage medium, on which a computer program is stored, wherein, the computer program, when executed by a processor, implements the method according to any one of claims 1-7.

11. A chip, wherein, it includes a processor and an interface; the processor is configured to read instructions to execute the method according to any one of claims 1-7.

12. An electronic device, wherein, it includes: a processor; a memory, configured to store executable instructions of the processor; wherein, the processor is configured to execute the method according to any one of claims 1-7 by executing the executable instructions.

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