Decoding method, electronic device, chip system, and storage medium

Abstract

Disclosed in the present application are a decoding method, an electronic device, a chip system, and a storage medium. The method comprises: receiving a first reference signal and downlink control information (DCI) from a network device; determining a first codeword code rate on the basis of the DCI; obtaining a first scaling factor on the basis of received power of the first reference signal and the first codeword code rate; and performing normalized min-sum decoding on the basis of the first scaling factor. By means of the method, the decoding performance can be improved.

Description

Decoding methods, electronic devices, chip systems and storage media

[0001] This application claims priority to Chinese Patent Application No. 202510125986.3, filed on January 26, 2025, entitled "Decoding Method, Electronic Device, Chip System and Storage Medium", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of communication technology, and in particular to decoding methods, electronic devices, chip systems, and storage media. Background Technology

[0003] The generally accepted standard soft-decision decoding algorithm for LDPC codes is the Belief Propagation (BP) decoding algorithm. This algorithm improves the accuracy of the final decoding result by iteratively passing extrinsic information between check nodes and variable nodes, thus achieving the decoding objective. However, the BP decoding algorithm involves numerous multiplication and exponentiation operations, resulting in high complexity and making it unsuitable for hardware implementation. A simplified version of the BP algorithm, the Normalized Min-sum decoding algorithm, can significantly reduce the computational load. Improving the decoding performance of the Min-sum decoding algorithm is a pressing technical problem that needs to be solved. Summary of the Invention

[0004] This application provides a decoding method, electronic device, chip system, and storage medium that can improve decoding performance.

[0005] In a first aspect, some embodiments of this application provide a decoding method. This decoding method may include: receiving a first reference signal and downlink control information (DCI) from a network device; determining a first codeword rate based on the DCI; obtaining a first scaling factor based on the received power of the first reference signal and the first codeword rate; and performing normalized minimum and min-sum decoding based on the first scaling factor.

[0006] By taking into account the channel environment, the above method determines a suitable scaling factor, thereby enabling the performance of Min-sum decoding to effectively approximate that of LDPC BP decoding, thus improving decoding performance.

[0007] In one possible implementation, obtaining a first scaling factor based on the received power of the first reference signal and the first codeword rate includes: inputting the received power of the first reference signal and the first codeword rate into a scaling factor determination model to obtain the first scaling factor output by the scaling factor determination model.

[0008] In one possible implementation, if the average frame error rate during the first time period is higher than a preset threshold, the received power, codeword rate, and scaling factor of the reference signal determined during the first time period are reported to the network device. The received power, codeword rate, and scaling factor of the reference signal determined during the first time period are used to update the parameters of the scaling factor determination model. The updated scaling factor determination model is received from the network device.

[0009] Using the above method, for certain channel environments with high frame error rates, the scaling factor determination model is retrained, so that the updated scaling factor determination model can perform better in these channel environments with high frame error rates, thereby determining a more accurate scaling factor.

[0010] In one possible implementation, the received power, codeword rate, and scaling factor of the first reference signal are recorded, and the received power, codeword rate, and scaling factor of the earliest recorded reference signal are deleted.

[0011] By using the above method, the received power, codeword rate, and scaling factor of the reference signal recorded too early can be deleted in a timely manner, saving storage space.

[0012] In one possible implementation, the received power of the first reference signal and the first codeword rate are input into the scaling factor determination model to obtain the first scaling factor output by the scaling factor determination model. This includes: inputting the received power of the first reference signal, the first codeword rate, and the terminal hardware device capabilities into the scaling factor determination model to obtain the first scaling factor output by the scaling factor determination model.

[0013] By using the above method, and taking into account the channel environment and the capabilities of the terminal hardware, a suitable scaling factor is determined, thereby enabling the performance of Min-sum decoding to effectively approximate that of LDPC BP decoding, thus improving decoding performance.

[0014] In one possible implementation, the capabilities of the terminal hardware device are determined based on one or more of the following information: the data processing capabilities of the terminal device processor, the data processing capabilities of the coprocessor, and the storage capacity or memory size.

[0015] In one possible implementation, the accuracy of the first scaling factor determining the model output is based on the capabilities of the terminal hardware device.

[0016] In one possible implementation, the first reference signal is the DMRS reference signal.

[0017] Secondly, this application provides a decoding device, which can be an electronic device, a device within an electronic device, or a device compatible with an electronic device; wherein the device can also be a chip system, and the decoding device can execute the methods performed by the electronic device in the first aspect. The functions of the decoding device can be implemented by hardware or by hardware executing corresponding software. The hardware or software includes one or more units corresponding to the above functions. The unit can be software and / or hardware. The operations performed by the decoding device and its beneficial effects can be found in the methods and beneficial effects described in the first aspect above, and will not be repeated here.

[0018] Thirdly, this application provides an electronic device including one or more processors and one or more memories. The one or more memories are coupled to the one or more processors, and the one or more memories are used to store computer program code, which includes computer instructions. When the one or more processors execute the computer instructions, the electronic device performs the decoding method in any of the possible implementations of the first aspect described above.

[0019] Fourthly, this application provides a chip system including a processor and an interface, the processor and the interface being coupled; the interface is used to receive or output signals, and the processor is used to execute code instructions to perform the decoding method in any possible implementation of the first aspect above.

[0020] Fifthly, this application provides a computer-readable storage medium storing a computer program / instructions that, when the computer program product is run on a computer, cause the computer to execute the decoding method in any possible implementation of the first aspect described above.

[0021] Sixthly, this application provides a computer program product that, when run on a computer, causes the computer to execute the decoding method in any possible implementation of the first aspect above. Attached Figure Description

[0022] Figure 1 is a schematic diagram of a system architecture provided in an embodiment of this application;

[0023] Figure 2 is a flowchart illustrating a decoding method provided in an embodiment of this application;

[0024] Figure 3 is a flowchart illustrating another decoding method provided in an embodiment of this application;

[0025] Figure 4 is a schematic diagram of the hardware architecture of an electronic device provided in an embodiment of this application;

[0026] Figure 5A is a flowchart illustrating another decoding method provided in an embodiment of this application;

[0027] Figure 5B is a flowchart illustrating another decoding method provided in an embodiment of this application;

[0028] Figure 6 is a schematic diagram of a decoding device provided in an embodiment of this application;

[0029] Figure 7 is a schematic diagram of a chip system provided in an embodiment of this application. Detailed Implementation

[0030] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B; "and / or" in the text is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the description of the embodiments of this application, "multiple" refers to two or more than two.

[0031] It should be understood that the terms "first," "second," etc., in the specification, claims, and drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0032] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.

[0033] To facilitate understanding of the solutions provided in the embodiments of this application, the relevant concepts involved in the embodiments of this application are introduced below:

[0034] I. Modulation and Coding Scheme (MCS)

[0035] In NR systems, the MCS (Multi-Segment Classification) scheme defines a set of standards for the number of effective bits that each Resource Element (RE) can carry. The determination of the MCS level depends on the signal quality of the radio link, which directly affects the data transmission rate and efficiency. In other words, the better the signal quality, the more bits can be used to transmit data in a symbol; if the signal quality is poor, the lower the MCS, and the fewer bits can be used to transmit data in a symbol.

[0036] In different wireless environments, network devices will allocate an MCS according to the link adaptation algorithm and send it to electronic devices through downlink control information (DCI) signaling on the Physical Downlink Control Channel (PDCCH), such as DCI1_0 and DCI1_1.

[0037] In practice, the DCI sent by the network device contains an MCS index, and the electronic device performs corresponding decoding operations based on the MCS index and the MCS table (such as the 64QAM table).

[0038] MCS can indicate the codeword rate, which can be defined as the ratio of the effective bits to the total number of transmitted bits (i.e., effective bits + redundant bits). These added redundant bits are used for forward error correction. It is the ratio of the number of higher-layer physical layer information bits to the number of bits mapped to the lower-layer physical layer PDSCH. The codeword rate can also be understood as a measure of the redundant bits added by the physical layer. A lower codeword rate means more redundant bits.

[0039] II. Low-Density Parity-Check (LDPC)

[0040] LDPC code decoding is divided into hard-decision decoding and soft-decision decoding.

[0041] Hard-decision decoding, also known as algebraic decoding, is primarily represented by the bit-flipping (BF) decoding algorithm. While relatively simple to implement, its decoding performance is poor. The basic assumption of hard-decision decoding is that when the parity check equation fails, it means a bit must be erroneous. Among all potentially erroneous bits, the bit with the highest probability of failing the parity check equation is the most likely to be erroneous. In each iteration, the bit with the highest probability of error is flipped, and the decoding is performed again using the updated codeword.

[0042] Soft-decision decoding is a probability-based decoding algorithm that usually needs to be combined with iterative decoding to realize its decoding performance advantages. The basic algorithm is the belief propagation (BP) decoding algorithm, which is much more complex than algebraic decoding methods, but has better decoding performance.

[0043] III. Normalized Minimum Sum Decoding Algorithm (Min-sum)

[0044] The min-sum algorithm is a simplification of the backpropagation (BP) algorithm. It abandons multiplication and instead uses a minimum operation to approximate the probability product, thus reducing computational complexity. The choice of scaling factor in the min-sum algorithm determines the decoding performance. A suitable scaling factor can effectively approximate the performance of LDPC BP decoding, while an unsuitable scaling factor will seriously affect the normalization of LDPC codes and the performance of the decoding algorithm.

[0045] Therefore, this application proposes a decoding method that, by considering the channel environment, determines a suitable scaling factor, thereby enabling the performance of Min-sum decoding to effectively approximate the performance of LDPC BP decoding, and thus improving the decoding performance.

[0046] The basic architecture of the communication system provided in this application is described below. The communication system provided in this application may include one or more network devices and one or more terminal devices.

[0047] The following explanation uses the system architecture shown in Figure 1 as an example. In Figure 1, the communication system includes a network device 10 and an electronic device 20 that communicates with the network device 10.

[0048] It should be noted that the number of network devices and terminal devices in Figure 1 is merely illustrative and should not be considered as a specific limitation of this application. The electronic and network devices involved in the system architecture will be described in detail below.

[0049] I. Electronic Equipment

[0050] An electronic device is an entity on the user side used to receive signals, or transmit signals, or both. This electronic device can also be called a terminal device, used to provide users with one or more of voice services and data connectivity services. The electronic device can be a device that includes wireless transceiver capabilities and can cooperate with network equipment to provide communication services to users. Specifically, electronic devices can refer to: user equipment, access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, terminal, wireless communication equipment, user agent, user equipment, or roadside unit (RSU). Electronic devices can also include drones, Internet of Things (IoT) devices, stations (STs) in wireless local area networks (WLANs), cellular phones, smartphones, cordless phones, wireless data cards, tablets, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistant (PDA) devices, laptop computers, machine type communication (MTC) terminals, handheld devices with wireless communication capabilities, computing devices or other processing devices connected to a wireless modem, in-vehicle devices, wearable devices (also known as wearable smart devices), virtual reality (VR) terminals, augmented reality (AR) terminals, wireless terminals in remote medical care, wireless terminals in industrial control, wireless terminals in self-driving vehicles, wireless terminals in smart grids, and transportation safety devices. Wireless terminals in smart cities, smart homes, etc. The electronic device can also be a terminal in a 5G system or a terminal in a next-generation communication system; this application does not limit this.

[0051] The embodiments of this application do not limit the form of the electronic device. The device used to implement the function of the electronic device can be an electronic device itself; it can also be a device that supports the electronic device in implementing the function, such as a chip system. This device can be installed in or used in conjunction with the electronic device. In the embodiments of this application, the chip system can be composed of chips, or it can include chips and other discrete components.

[0052] II. Network Equipment

[0053] A network device is an entity on the network side used to transmit signals, or receive signals, or both. A network device can be a means deployed in a radio access network (RAN) to provide wireless communication capabilities to terminal devices.

[0054] In one possible scenario, network equipment can be devices with base station functions, such as evolved NodeBs (eNodeBs), transmitting and receiving points (TRPs), transmitting points (TPs), next-generation NodeBs (gNBs), next-generation base stations in 6G mobile communication systems, integrated access and backhaul (IAB) nodes, and non-terrestrial network equipment, i.e., equipment that can be deployed on high-altitude platforms or satellites. Network equipment can also be transmitting and receiving points (TRPs), base stations, and various forms of control nodes, such as network controllers and wireless controllers. Specifically, network equipment can be various forms of macro base stations, micro base stations (also known as small cells) in heterogeneous network (HetNet) scenarios, relay stations, access points (APs), radio network controllers (RNCs), node Bs (NBs), base station controllers (BSCs), base transceiver stations (BTSs), home base stations (e.g., home evolved node Bs, or home node Bs (HNBs)), baseband units (BBUs) and remote radio units (RRUs) in distributed base station scenarios, transmitting and receiving points (TRPs), transmitting points (TPs), mobile switching centers, etc., and can also be base station antenna panels. Control nodes can connect to multiple base stations and configure resources for multiple terminals covered by multiple base stations. In systems employing different wireless access technologies, the names of devices with base station functions may differ. For example, it could be a gNB in ​​5G, or a network-side device in a network after 5G, or a network device in a future evolved public land mobile network (PLMN) network, or a device that performs base station functions in device-to-device (D2D) communication, machine-to-machine (M2M) communication, or vehicle-to-everything (V2X) communication, etc. This application does not limit the specific name of the network device.Network equipment can also be open RAN (O-RAN or ORAN), baseband pool (BBU pool) and RRU under cloud radio access network (CRAN), etc.

[0055] In this embodiment, the electronic device 20 can report the received power, codeword rate, and scaling factor of the reference signal to the network device 10. The electronic device 20 can also download a scaling factor determination model from the network device 10 via the data plane, and download an updated scaling factor determination model.

[0056] The decoding method provided in the embodiments of this application will be further described below with reference to Figure 2. It is understood that the method executed by an electronic device in this application can also be executed by a module applied to the electronic device (e.g., a chip, chip system, or processor), or by a logic node, logic module, or software capable of implementing all or part of the functions of the electronic device. Wherein:

[0057] 201. The electronic device receives the first reference signal and downlink control information (DCI) from the network device.

[0058] Optionally, the first reference signal is a demodulation reference signal (DMRS). This DMRS reference signal is used for channel estimation and serves the channel demodulation of the electronic device. The network device interleaves the DMRS reference signal into the time-frequency resources of various channels and sends it along with the data to the electronic device. For the electronic device, the DMRS reference signal is a known signal; based on the DMRS reference signal, the electronic device can obtain the channel feature matrix and thus parse the carried content at the corresponding location.

[0059] Optionally, the first reference signal can also be a phase noise tracking reference signal (PTRS), etc.

[0060] Optionally, the first reference signal and the DCI are correlated and real-time. That is, the first reference signal and the DCI in step 201 are correlated reference signals and DCI.

[0061] For example, over a period of time, the network device sends DCI1 to the electronic device for downlink scheduling. Upon receiving DCI1, the electronic device receives downlink data. During this process, it first receives DMRS1 and then parses the corresponding downlink data based on DMRS1. Over another period of time, the network device sends DCI2 to the electronic device for downlink scheduling. Upon receiving DCI2, the electronic device receives downlink data. During this process, it first receives DMRS2 and then parses the corresponding downlink data based on DMRS2. DCI1 and DMRS1 are related, and DCI2 and DMRS2 are also related.

[0062] For example, the first reference signal in step 201 is DMRS1 and the DCI in step 201 is DCI1, or the first reference signal is DMRS2 and the DCI in step 201 is DCI2.

[0063] 202. Electronic devices determine the first codeword rate based on DCI.

[0064] In one possible embodiment, the electronic device determines the first codeword rate based on the MCS index indicated by the DCI. Further, the electronic device determines the first codeword rate based on the MCS index indicated by the DCI and an MCS table (e.g., a 64QAM table).

[0065] For example, the following example, using Table 1 (Table 5.1.3.11-1 PDSCH MCS Index Table 1), further illustrates the point. Table 1 is shown below:

[0066] Table 1

[0067] Assuming the MCS index in the DCI sent by the network device is 2, according to Table 1 above, the codeword code rate corresponding to this MCS index is 193, and the first codeword code rate determined by the electronic device based on this DCI is 193.

[0068] Optionally, the received power of the first reference signal and the first codeword rate are correlated and real-time. For example, over a period of time, the network device sends DCI1 to the electronic device for downlink scheduling. After receiving the DCI1, the electronic device receives downlink data. During the process of receiving downlink data, it first receives DMRS1, and then parses the corresponding downlink data based on DMRS1. The electronic device determines the codeword rate as codeword rate 1 based on the DCI1, and the received power of DMRS1 and codeword rate 1 are correlated.

[0069] 203. The electronic device obtains a first scaling factor based on the received power of the first reference signal and the first codeword rate.

[0070] In one possible embodiment, the electronic device obtains a first scaling factor based on the received power of the first reference signal and the first codeword rate, including: the electronic device inputs the received power of the first reference signal and the first codeword rate into a scaling factor determination model to obtain the first scaling factor output by the scaling factor determination model.

[0071] The scaling factor is greater than 0 and less than 1.

[0072] Optionally, the scaling factor determination model can be downloaded from the network device via the data plane. For example, during the first power-on of an electronic device after it leaves the factory, the electronic device downloads and stores the scaling factor determination model from the network device.

[0073] Optionally, the scaling factor determination model can be initially built into the electronic device. For example, the scaling factor determination model is built into the electronic device by the manufacturer before it leaves the factory.

[0074] Optionally, the scaling factor determination model is a model pre-trained by the network device, which is trained with the goal of minimizing the frame error rate.

[0075] For example, a reference label frame error rate is pre-set. When training the scaling factor determination model, decoding is performed based on the scaling factor output by the scaling factor determination model, and the frame error rate corresponding to the decoding result is calculated. Based on the reference label frame error rate and the frame error rate corresponding to the decoding result, the parameters in the scaling factor determination model are updated until the loss value between the frame error rate corresponding to the decoding result and the reference label frame error rate meets the preset loss condition, at which point the scaling factor determination model is considered to have completed training.

[0076] 204. The electronic device performs normalized minimum and min-sum decoding based on the first scaling factor.

[0077] In one possible embodiment, after the electronic device performs Min-sum decoding, the method further includes: the electronic device verifying the decoding result after Min-sum decoding; if the verification passes, the electronic device stops decoding; if the verification fails, the electronic device determines whether decoding needs to be stopped based on the number of iterations and the preset maximum number of iterations.

[0078] Optionally, this check may include, but is not limited to, cyclic redundancy check (CRC).

[0079] Optionally, the electronic device determines whether to stop decoding based on the number of iterations and the preset maximum number of iterations, including: if the number of iterations is less than the preset maximum number of iterations, the electronic device continues to iterate and decode; if the number of iterations is greater than or equal to the preset maximum number of iterations, the electronic device stops decoding.

[0080] In one possible embodiment, if the average frame error rate of the electronic device during the first time period is higher than a preset threshold, the electronic device reports the received power, codeword rate, and scaling factor of the reference signal determined during the first time period to the network device. The received power, codeword rate, and scaling factor of the reference signal determined during the first time period are used to update the parameters of the scaling factor determination model. The electronic device receives the updated scaling factor determination model from the network device.

[0081] This first time period is negotiated by the electronic and network devices according to standards and is initially built into the electronic devices. Within this first time period, there may be multiple reference signal received power values, multiple codeword rates, and multiple scaling factors.

[0082] Optionally, the first time period may also be indicated by the network device through Radio Resource Control (RRC) signaling or DCI.

[0083] Optionally, the average frame error rate (FER) is the average of the individual frame error rates determined within the first time period. The frame error rate is determined based on the number of erroneous frames and the total number of frames, or it can be determined based on the probability distribution function of the channel and the modulation and coding scheme; this application does not impose any restrictions on this.

[0084] Optionally, the electronic device receives an update model instruction sent by the network device; in response to the update model instruction, the electronic device downloads the updated scaling factor determination model from the network device.

[0085] In one possible embodiment, the electronic device records the received power, codeword rate, and scaling factor of the reference signal in a cyclical overlay manner during a second time period, the second time period being longer than the first time period; the electronic device determines the received power, codeword rate, and scaling factor of the reference signal in the first time period based on the received power, codeword rate, and scaling factor of the reference signal recorded during the second time period.

[0086] The second time period is negotiated by electronic devices and network devices according to standards and is initially built into the electronic devices.

[0087] Optionally, the first time period can also be indicated by the network device via RRC signaling or DCI.

[0088] For example, suppose the second time period has the following times: time 1, time 2, time 3...time 10. These six times are considered the first time period. The electronic device first calculates the average frame error rate (FERR) corresponding to times 1, 2, 3...time 6. If the FERR corresponding to times 1, 2, 3...time 6 is not higher than a preset threshold, the electronic device calculates the average FERR corresponding to times 2, 3, 4...time 7. If the FERR corresponding to times 2, 3, 4...time 7 is higher than the preset threshold, the received power, codeword rate, and scaling factor of the reference signal corresponding to times 2, 3, 4...time 7 are reported to the network device for updating the scaling factor model.

[0089] Optionally, if the average frame error rate of the electronic device in the first time period is higher than a preset threshold, the electronic device reports the received power, codeword rate and scaling factor of the reference signal determined in the second time period to the network device. The received power, codeword rate and scaling factor of the reference signal determined in the second time period are used to update the parameters of the scaling factor determination model. The electronic device receives the updated scaling factor determination model from the network device.

[0090] For example, suppose the second time period has the following times: time 1, time 2, time 3...time 10. These six times are considered the first time period. The electronic device first calculates the average frame error rate (FERR) corresponding to times 5, 2, 3...time 10. If the average FERR corresponding to times 5, 2, 3...time 10 is higher than a preset threshold, the electronic device reports the received power, codeword rate, and scaling factor (received power, codeword rate, and scaling factor of all recorded reference signals) of the reference signals corresponding to times 1, 2, 3...time 10 to the network device for updating the scaling factor model.

[0091] In one possible embodiment, the electronic device records the received power, first codeword rate, and first scaling factor of the first reference signal, and deletes the received power, codeword rate, and scaling factor of the earliest recorded reference signal.

[0092] Optionally, the electronic device deletes the received power, codeword rate, and scaling factor of the first reference signal recorded in the second time period when it records the received power, codeword rate, and scaling factor of a new reference signal.

[0093] For example, the second time period has the following times: time 1, time 2, time 3...time 10. The electronic device records the received power, codeword rate, and scaling factor of the reference signal at times 1, 2, 3...time 10. When the electronic device records the received power, codeword rate, and scaling factor of the reference signal at time 11, it deletes the received power, codeword rate, and scaling factor of the reference signal at time 1.

[0094] Optionally, the electronic device records a fixed number of received power, codeword rate, and scaling factor. For example, the electronic device records 10 received power values, 10 codeword rates, and 10 scaling factors.

[0095] The embodiment shown in Figure 2 above determines a suitable scaling factor by considering the channel environment (received power of the first reference signal and the first codeword rate), thereby enabling the performance of Min-sum decoding to effectively approximate the performance of LDPC BP decoding, and thus improving the decoding performance.

[0096] In practice, hardware capabilities also influence the determination of the scaling factor. Electronic devices with stronger hardware capabilities can choose a more precise scaling factor, while those with weaker hardware capabilities can choose a less precise scaling factor. Therefore, this application also provides a decoding method that determines a suitable scaling factor based on the channel environment and hardware capabilities, thereby enabling the performance of the Min-sum decoding to effectively approximate the performance of LDPC BP decoding, thus improving decoding performance. See Figure 3 for details:

[0097] 301. The electronic device receives a first reference signal and DCI from the network device.

[0098] This step can be found in the description of step 201 above, and will not be repeated here.

[0099] 302. Electronic devices determine the first codeword rate based on DCI.

[0100] This step can be found in the description of step 202 above, and will not be repeated here.

[0101] 303. The electronic device inputs the received power of the first reference signal, the first code rate, and the terminal hardware device capabilities into the scaling factor determination model to obtain the first scaling factor output by the scaling factor determination model.

[0102] Optionally, the capabilities of the terminal hardware device are determined based on one or more of the following information: the data processing capability of the terminal device processor, the data processing capability of the coprocessor, the storage capacity, or the memory size.

[0103] Optionally, the terminal hardware device capability is defined as the capability value quantified from a set of hardware capabilities, which includes one or more of the following: processor, coprocessor, storage, and memory.

[0104] Optionally, the hardware capability set can be quantized using a preset quantization algorithm. For example, as shown in Table 2 below, Table 2 presents the terminal hardware device capabilities obtained after quantizing the hardware capability set. Specifically:

[0105] Table 2

[0106] Here, i3-9300 represents the processor model; different processor models typically have different data processing capabilities. GT750 represents the coprocessor model; different coprocessor models typically have different data processing capabilities. HDD represents the storage type; HDD and SDD have different storage capacities. 4G represents the RAM size. Based on these four parameters, an electronic device with an i3-9300 processor, a GT750 coprocessor, HDD storage, and 4G of RAM has a terminal hardware capability of 10.

[0107] Optionally, the hardware capabilities can be quantified using a third-party program. This third-party program can be benchmarking software, evaluation software, etc.

[0108] In one possible embodiment, the accuracy of the first scaling factor of the scaling factor determining the model output is determined based on the capabilities of the terminal hardware device.

[0109] Optionally, the precision of the first scaling factor is proportional to the decoding effect; that is, the higher the precision of the first scaling factor, the better the decoding effect.

[0110] Optionally, the precision of the first scaling factor is related to the number of decimal places in the first scaling factor. For example, if scaling factor A is 0.8125 and scaling factor B is 0.8, the precision of scaling factor A is higher than that of scaling factor B.

[0111] Optionally, the precision of the first scaling factor is proportional to the capabilities of the terminal hardware device; the more powerful the terminal hardware device, the higher the precision of the scaling factor supported by the electronic device. For example, an electronic device with an i9-12900K (processor), RTX 8000 (coprocessor), SDD (storage), and 12GB (RAM) supports a precision of 0.8125. An electronic device with an i3-9300 (processor), GT750 (coprocessor), HDD (storage), and 4GB (RAM) supports a precision of 0.8.

[0112] 304. The electronic device performs Min-sum decoding based on the first scaling factor.

[0113] 305. Verification process.

[0114] Optionally, the steps involved in the verification process can be found in the description of step 204 above, and will not be repeated here.

[0115] 306. The electronic device records the received power of the first reference signal, the first codeword rate, the terminal hardware device capability, and the first scaling factor.

[0116] In one possible embodiment, if the average frame error rate of the electronic device during the first time period is higher than a preset threshold, the electronic device reports to the network device the received power, codeword rate, terminal hardware device capability, and scaling factor of the reference signal determined during the first time period. The received power, codeword rate, terminal hardware device capability, and scaling factor of the reference signal are used to update the parameters of the scaling factor determination model. The electronic device receives the updated scaling factor determination model from the network device.

[0117] Optionally, the terminal hardware capabilities of the electronic device are fixed, so the terminal hardware capabilities of the electronic device can be recorded only once. That is, if the electronic device does not have any recorded terminal hardware capabilities, the electronic device records the received power of the first reference signal, the first codeword rate, the terminal hardware capabilities, and the first scaling factor; if the electronic device includes recorded terminal hardware capabilities, the electronic device records the received power of the first reference signal, the first codeword rate, and the first scaling factor.

[0118] In one possible embodiment, the electronic device records the received power, first codeword rate, and first scaling factor of the first reference signal, and deletes the received power, codeword rate, and scaling factor of the earliest recorded reference signal.

[0119] The embodiment shown in Figure 3 above determines a suitable scaling factor by considering the channel environment (received power of the first reference signal and the first codeword rate) and hardware capabilities (terminal hardware device capabilities), thereby enabling the performance of Min-sum decoding to effectively approximate the performance of LDPC BP decoding, and thus improving the decoding performance.

[0120] The hardware structure of electronic devices is described below:

[0121] Please refer to Figure 4, which is a schematic diagram of the hardware structure of the electronic device 100 provided in the embodiment of this application.

[0122] Electronic device 100 may include processor 110, coprocessor 120, memory 121, storage 122, universal serial bus (USB) interface 130, charging management module 140, power management module 141, battery 142, antenna 1, antenna 2, mobile communication module 150, wireless communication module 160, channel state monitoring module 170, scaling factor adjustment module 171, LDPC code decoding module 172, data storage module 173, and terminal capability query module 174.

[0123] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on the electronic device 100. In other embodiments of this application, the electronic device 100 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0124] Processor 110 may include one or more processing units, such as: application processor (AP), modem processor, graphics processing unit (GPU), image signal processor (ISP), controller, memory, video codec, digital signal processor (DSP), baseband processor, and / or neural network processing unit (NPU), etc. Different processing units may be independent devices or integrated into one or more processors.

[0125] The controller can be the nerve center and command center of the electronic device 100. The controller can generate operation control signals according to the instruction opcode and timing signals to complete the control of fetching and executing instructions.

[0126] The processor 110 may also include a memory for storing instructions and data. In some embodiments, the memory in the processor 110 is a cache memory. This memory can store instructions or data that the processor 110 has just used or that are used repeatedly. If the processor 110 needs to use the instruction or data again, it can directly retrieve it from the memory. This avoids repeated accesses, reduces the waiting time of the processor 110, and thus improves the efficiency of the system. The processor 110 retrieves the instructions or data stored in the memory, causing the electronic device 100 to execute the imaging method performed by the electronic device in the following method embodiments.

[0127] In some embodiments, the processor 110 may include one or more interfaces. Interfaces may include an inter-integrated circuit (I2C) interface, an inter-integrated circuit sound (I2S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a subscriber identity module (SIM) interface, and / or a universal serial bus (USB) interface, etc.

[0128] A coprocessor

[0120] , also known as a graphics processing unit, is a processor specifically designed for graphics and computation processing. It was originally designed to accelerate graphics rendering, but has gradually evolved into a general-purpose computing device capable of handling various complex computational tasks.

[0129] Memory

[0121] is mainly used to temporarily store the computational data in the CPU and the data exchanged with external storage devices such as hard disks. It is the place where instructions and data are temporarily stored when the computer runs programs.

[0130] Storage

[0122] is used to preserve data and programs for a long time, and can retain information whether the computer is running or shut down, so that it can be read and used at any time.

[0131] The charging management module 140 is used to receive charging input from the charger. The charger can be a wireless charger or a wired charger.

[0132] The power management module 141 is used to connect the battery 142, the charging management module 140, and the processor 110. The power management module 141 receives input from the battery 142 and / or the charging management module 140 to power the processor 110, internal memory 121, external memory, display 194, camera 193, and wireless communication module 160, etc. In some other embodiments, the power management module 141 may also be located in the processor 110.

[0133] The wireless communication function of electronic device 100 can be realized through antenna 1, antenna 2, mobile communication module 150, wireless communication module 160, modem processor and baseband processor, etc.

[0134] Antenna 1 and antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in electronic device 100 can be used to cover one or more communication frequency bands. Different antennas can also be multiplexed to improve antenna utilization. For example, antenna 1 can be multiplexed as a diversity antenna for a wireless local area network. In some other embodiments, the antennas can be used in conjunction with tuning switches.

[0135] The mobile communication module 150 can provide solutions for wireless communication, including 2G / 3G / 4G / 5G, applied to the electronic device 100. The mobile communication module 150 may include at least one filter, switch, power amplifier, low noise amplifier (LNA), etc. The mobile communication module 150 can receive electromagnetic waves via antenna 1, and perform filtering, amplification, and other processing on the received electromagnetic waves before transmitting them to a modem processor for demodulation. The mobile communication module 150 can also amplify the signal modulated by the modem processor and convert it into electromagnetic waves for radiation via antenna 1. In some embodiments, at least some functional modules of the mobile communication module 150 may be housed in the processor 110. In some embodiments, at least some functional modules of the mobile communication module 150 and at least some modules of the processor 110 may be housed in the same device.

[0136] A modem processor may include a modulator and a demodulator. The modulator modulates the low-frequency baseband signal to be transmitted into a mid-to-high frequency signal. The demodulator demodulates the received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then transmits the demodulated low-frequency baseband signal to the baseband processor for processing. After processing by the baseband processor, the low-frequency baseband signal is transmitted to the application processor.

[0137] The wireless communication module 160 can provide solutions for wireless communication applications on the electronic device 100, including wireless local area networks (WLAN) (such as Wi-Fi), Bluetooth (BT), BLE broadcasting, global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR). The wireless communication module 160 can be one or more devices integrating at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via antenna 2, performs frequency modulation and filtering of the electromagnetic wave signal, and sends the processed signal to processor 110. The wireless communication module 160 can also receive signals to be transmitted from processor 110, perform frequency modulation and amplification, and convert them into electromagnetic waves for radiation via antenna 2.

[0138] In some embodiments, antenna 1 of electronic device 100 is coupled to mobile communication module 150, and antenna 2 is coupled to wireless communication module 160, so that electronic device 100 can communicate with networks and other devices through wireless communication technology.

[0139] The channel state monitoring module

[0170] is used to detect the DMRS signal power and calculate the codeword rate.

[0140] The scaling factor adjustment module

[0171] is used to determine the scaling factor based on the DMRS signal power and codeword rate. The scaling factor adjustment module includes a scaling factor determination model.

[0141] The LDPC code decoding module

[0172] is used to store the DMRS signal power, codeword rate and scaling factor.

[0142] The data storage module

[0173] is used for normalized minimum and min-sum decoding, as well as CRC verification.

[0143] The terminal capability query module

[0174] is used to query the capabilities of the terminal hardware device.

[0144] Furthermore, an operating system runs on top of the aforementioned components. Examples include iOS and Android. The operating system of electronic device 100 can adopt a layered architecture, event-driven architecture, microkernel architecture, microservice architecture, or cloud architecture. This application embodiment uses the layered architecture Android system as an example to exemplify the software structure of electronic device 100. It should be noted that although this application embodiment uses the Android system as an example for illustration, its basic principles are equally applicable to electronic devices with other operating systems.

[0145] Based on the modules shown in Figure 4 above, the decoding method provided in this application will be further described below with reference to Figure 5A. As shown in Figure 5A:

[0146] First, the channel state monitoring module monitors the received power of the reference signal and calculates the codeword rate.

[0147] Optionally, the channel state monitoring module monitors the received power of the reference signal in real time and calculates the real-time codeword rate.

[0148] The channel state monitoring module transmits the received power of the reference signal and the codeword rate to the scaling factor adjustment module, which includes a scaling factor determination module.

[0149] The channel state monitoring module transmits the received power of the reference signal and the codeword rate to the data storage module.

[0150] The scaling factor adjustment module determines the scaling factor based on the received power of the received reference signal and the codeword rate.

[0151] Optionally, the scaling factor determination model in the scaling factor adjustment module determines the scaling factor based on the received power of the received reference signal and the codeword rate.

[0152] The scaling factor adjustment module passes the determined scaling factor to the LDPC code decoding module.

[0153] The scaling factor adjustment module passes the determined scaling factor to the data storage module.

[0154] The LDPC code decoding module decodes based on this scaling factor.

[0155] Optionally, the decoding algorithm used by this LDPC code decoding module is normalized minimum sum Min-sum decoding.

[0156] The LDPC code decoding module passes the decoding result to the data storage module.

[0157] The data storage module saves the received power, codeword rate, and scaling factor of the reference signal. Optionally, the data storage module can also save the decoding results.

[0158] Optionally, the data storage module may decide whether to report the received power, codeword rate, and scaling factor of the stored reference signal to the network device based on the decoding error rate over a period of time.

[0159] The following section, with reference to Figure 5B, further describes another decoding method provided in this application. As shown in Figure 5B:

[0160] First, the channel state monitoring module monitors the received power of the reference signal and calculates the codeword rate.

[0161] Optionally, the channel state monitoring module monitors the received power of the reference signal in real time and calculates the real-time codeword rate.

[0162] The channel state monitoring module transmits the received power of the reference signal and the codeword rate to the scaling factor adjustment module, which includes a scaling factor determination module.

[0163] The channel state monitoring module transmits the received power of the reference signal and the codeword rate to the data storage module.

[0164] The terminal capability query module determines the capabilities of the terminal hardware device.

[0165] The terminal capability query module transmits the terminal hardware device capabilities to the scaling factor adjustment module, and the terminal capability query module also transmits the terminal hardware device capabilities to the data storage module.

[0166] The scaling factor adjustment module determines the scaling factor based on the received power of the received reference signal, the codeword rate, and the capabilities of the terminal hardware.

[0167] Optionally, the scaling factor determination model in the scaling factor adjustment module determines the scaling factor based on the received power of the received reference signal and the codeword rate.

[0168] The scaling factor adjustment module passes the determined scaling factor to the LDPC code decoding module.

[0169] The scaling factor adjustment module passes the determined scaling factor to the data storage module.

[0170] The LDPC code decoding module decodes based on this scaling factor.

[0171] Optionally, the decoding algorithm used by this LDPC code decoding module is normalized minimum sum Min-sum decoding.

[0172] The LDPC code decoding module passes the decoding result to the data storage module.

[0173] The data storage module stores the received power, codeword rate, scaling factor, and terminal hardware capabilities of the reference signal. Optionally, the data storage module can also store the decoding results.

[0174] Optionally, the data storage module may decide whether to report the received power, codeword rate, scaling factor, and terminal hardware capabilities of the stored reference signal to the network device based on the decoding error rate over a period of time.

[0175] Please refer to Figure 6, which is a schematic diagram of the structure of a decoding device 600 provided in an embodiment of this application. The decoding device shown in Figure 6 can be an electronic device, a device within an electronic device, or a device that can be used in conjunction with an electronic device. The decoding device shown in Figure 6 may include a receiving unit 601 and a processing unit 602. Wherein:

[0176] The receiving unit 601 is used to receive a first reference signal and downlink control information (DCI) from the network device;

[0177] The processing unit 602 is used to determine the first codeword rate based on DCI; obtain a first scaling factor based on the received power of the first reference signal and the first codeword rate; and perform normalized minimum and min-sum decoding based on the first scaling factor.

[0178] In one possible embodiment, the processing unit 602 is further configured to input the received power based on the first reference signal and the first codeword rate into the scaling factor determination model to obtain the first scaling factor output by the scaling factor determination model.

[0179] In one possible embodiment, the processing unit 602 is further configured to report the received power, codeword rate and scaling factor of the reference signal determined in the first time period to the network device when the average frame error rate in the first time period is higher than a preset threshold. The received power, codeword rate and scaling factor of the reference signal determined in the first time period are used to update the parameters of the scaling factor determination model.

[0180] The receiving unit 601 is also used to receive the updated scaling factor determination model from the network device.

[0181] In one possible embodiment, the processing unit 602 is further configured to record the received power, the first codeword rate, and the first scaling factor of the first reference signal, and delete the received power, codeword rate, and scaling factor of the earliest recorded reference signal.

[0182] In one possible embodiment, the processing unit 602 is further configured to input the received power of the first reference signal, the first code rate, and the terminal hardware device capabilities into the scaling factor determination model to obtain the first scaling factor output by the scaling factor determination model.

[0183] In one possible embodiment, the capabilities of the terminal hardware device are determined based on one or more of the following information: the ability of the terminal device processor to process data, the ability of the coprocessor to process data, storage capacity, or memory size.

[0184] In one possible embodiment, the accuracy of the first scaling factor of the scaling factor determining the model output is determined based on the capabilities of the terminal hardware device.

[0185] In one possible embodiment, the first reference signal is a DMRS reference signal.

[0186] When the decoding device can be a chip or a chip system, refer to the schematic diagram of the chip structure shown in Figure 7. The chip 700 shown in Figure 7 includes a processor 701 and an interface 702. Optionally, it may also include a memory 703. The number of processors 701 can be one or more, and the number of interfaces 702 can be multiple.

[0187] For cases where the chip is used to implement the electronic device in the embodiments of this application:

[0188] The interface 702 is used to receive or output signals;

[0189] The processor 701 is used to perform data processing operations of the electronic device.

[0190] It is understood that the processor in the embodiments of this application 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, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.

[0191] The method steps in the embodiments of this application can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. Additionally, the ASIC can reside in a network device or a terminal device. Alternatively, the processor and storage medium can exist as discrete components in the terminal device or network device.

[0192] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this application are performed entirely or partially. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer program or instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; it can also be an optical medium, such as a DVD; or it can be a semiconductor medium, such as a solid-state disk (SSD).

[0193] In the various embodiments of this application, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions of different embodiments are consistent and can be referenced by each other. The technical features of different embodiments can be combined to form new embodiments according to their inherent logical relationship.

[0194] It is understood that the various numerical designations used in the embodiments of this application are merely for descriptive convenience and are not intended to limit the scope of the embodiments of this application. The order of the process numbers described above does not imply the order of execution; the execution order of each process should be determined by its function and internal logic.

[0195] This application also provides a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are executed, the method executed by the electronic device or network device in the above method embodiments is implemented.

[0196] This application also provides a computer program product, which includes a computer program that, when executed, causes the method executed by the terminal device or network device in the above method embodiments to be implemented.

[0197] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0198] The descriptions of the various embodiments provided in this application can be referenced mutually. Each embodiment has its own emphasis, and parts not described in detail in a certain embodiment can be referred to the relevant descriptions of other embodiments. For the sake of convenience and brevity, for example, the functions and execution steps of the various devices and equipment provided in the embodiments of this application can be referred to the relevant descriptions of the method embodiments of this application. The method embodiments and the device embodiments can also be referenced, combined or cited from each other.

[0199] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A decoding method, comprising: The method includes: Receive the first reference signal and downlink control information (DCI) from the network device; The code rate of the first codeword is determined based on the DCI. Based on the received power of the first reference signal and the first codeword rate, a first scaling factor is obtained; Normalized minimum and min-sum decoding are performed based on the first scaling factor.

2. The method of claim 1, wherein, Based on the received power of the first reference signal and the first codeword rate, a first scaling factor is obtained, including: The scaling factor determination model is obtained by inputting the received power of the first reference signal and the code rate of the first codeword into the scaling factor determination model, and the first scaling factor output by the scaling factor determination model is obtained.

3. The method of claim 2, wherein, The method further includes: If the average frame error rate during the first time period is higher than a preset threshold, the received power, codeword rate, and scaling factor of the reference signal determined during the first time period are reported to the network device. The received power, codeword rate, and scaling factor of the reference signal determined during the first time period are used to update the parameters of the scaling factor determination model. Receive the updated scaling factor determination model from the network device.

4. The method of claim 3, wherein, The method further includes: Record the received power of the first reference signal, the codeword rate of the first codeword, and the scaling factor of the first codeword, and delete the received power, codeword rate, and scaling factor of the earliest recorded reference signal.

5. The method according to claim 2, characterized in that, The step of inputting the received power of the first reference signal and the codeword rate into the scaling factor determination model to obtain the first scaling factor output by the scaling factor determination model includes: The received power of the first reference signal, the first code rate, and the terminal hardware capabilities are input into the scaling factor determination model to obtain the first scaling factor output by the scaling factor determination model.

6. The method according to claim 5, characterized in that, The capabilities of the terminal hardware device are determined based on one or more of the following information: the data processing capability of the terminal device processor, the data processing capability of the coprocessor, and the storage capacity or memory size.

7. The method according to claim 5 or 6, characterized in that, The accuracy of the first scaling factor output by the scaling factor determination model is determined based on the capabilities of the terminal hardware device.

8. The method according to any one of claims 1-7, characterized in that, The first reference signal is the DMRS reference signal.

9. An electronic device comprising one or more memories and one or more processors, characterized in that, The memory is used to store a computer program; the processor is used to invoke the computer program, causing the electronic device to perform the method of any one of claims 1-8.

10. A chip system for use in electronic devices, characterized in that, The chip system includes at least one processor and an interface for receiving instructions and transmitting them to the at least one processor; the at least one processor executes the instructions to cause the electronic device to perform the method as described in any one of claims 1-8.

11. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method as described in any one of claims 1-8.

12. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method as described in any one of claims 1-8.

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