Method and device for improving performance of communication system

Abstract

The invention discloses a method for improving the performance of a communication system. The method comprises the following steps that a sending end obtains original input information; the sending end encodes the original input information and sends encoded information; the coding information comprises a check bit; a receiving end receives the coded information; the receiving end decodes the encoding information to determine first decoding information; the receiving end outputs judgment information for determining whether the first decoding information is correct or not according to the check bit; and the receiving end corrects the first decoding information through the artificial intelligence network to determine second decoding information in response to the fact that the judgment information is wrong. The invention further comprises a device for implementing the method. The problem of low data transmission reliability of mobile communication systems such as 5G / 6G and the like is solved, and the method is particularly suitable for enhancing the performance of the communication system by the AI technology.

Description

Technical Field

[0001] This application relates to the field of wireless communication technologies, and in particular, to a method and device for improving the performance of a communication system. Background Art

[0002] Mobile communication networks contain a large amount of data resources. Existing wireless communication systems (such as 5G) rely on forward error correction (FEC) and automatic repeat request (ARQ) mechanisms to handle transmission errors. When the channel conditions are poor, multiple retransmissions are required, resulting in increased latency and reduced spectral efficiency. Traditional coding and decoding (such as LDPC, Polar codes) are limited in performance in low signal-to-noise ratio (SNR) scenarios and are difficult to meet the requirements of ultra-reliable low-latency communication (URLLC). AI models (such as DNN, CNN) can learn channel characteristics and noise patterns and recover the original information from the incorrectly decoded results, reducing the number of retransmissions. The rational utilization and exploration of 5G and 6G data resources using artificial intelligence (AI) technology can effectively improve the efficiency of mobile communication systems. The problems faced in mobile communication systems are complex and diverse. A large number of studies have shown that the performance of the mobile communication network side and the wireless side can be effectively improved through AI-based algorithms. Therefore, using AI technology to improve the performance of mobile systems has become the main direction of future network design. However, the current communication standards (such as 3GPP) do not define the standardized processes for AI model training, activation, and coordination with the terminal / network side, resulting in the difficulty of large-scale deployment of AI-enhanced functions.

[0003] When a wireless communication system uses AI technology to enhance system performance, it involves the management of AI models. To ensure the efficiency of system operation, the AI model on the terminal side needs to transfer AI model-related function information to the network side before use. The network side confirms the function status of the AI model used on the terminal side based on the transferred information, so as to better utilize AI technology to improve system performance. Therefore, a method and device that focus on the problem of transferring AI model-related function information in a wireless communication system and support the efficient transfer of AI model-related function information in a communication system are needed. Summary of the Invention

[0004] This application proposes a method and device for improving the performance of a communication system, which solves the problem of low data transmission reliability in mobile communication systems such as 5G / 6G, and is particularly suitable for enhancing the performance of a communication system using AI technology.

[0005] In a first aspect, this application proposes a method for improving the performance of a communication system, including the following steps: The sending end obtains the original input information; The sending end encodes the original input information and sends the encoded information; the encoded information includes check bits; The receiving end receives the encoded information; The receiving end decodes the encoded information to determine the first decoded information; The receiving end determines a judgment message on whether the first decoded information is correct according to the check bit output; In response to the judgment message being incorrect, the receiving end corrects the first decoded information through an artificial intelligence network to determine the second decoded information.

[0006] In a second aspect, the present application also proposes a method for improving the performance of a communication system, which is used for a sending end and includes the following steps: Obtain the original input information; Encode the original input information and send the encoded information; the encoded information includes check bits.

[0007] Furthermore, it further includes the step of: Receive the first information and send the encoded information; the first information is an instruction for the sending end to re-send the encoded information.

[0008] In a third aspect, the present application also proposes a method for improving the performance of a communication system, which is used for a receiving end and includes the following steps: Receive the encoded information; Decode the encoded information to determine the first decoded information; Determine a judgment message on whether the first decoded information is correct according to the check bits; In response to the judgment message being incorrect, correct the first decoded information through an artificial intelligence network to determine the second decoded information.

[0009] Furthermore, it further includes the step of: Determine whether the second decoded information is correct according to the check bits; In response to the second decoded information being correct, determine the second decoded information; In response to the second decoded information being incorrect, send the first information; the first information is an instruction for the sending end to re-send the encoded information.

[0010] In one embodiment, the training process of the artificial intelligence network specifically includes the following steps: The sending end sends an encoded training information stream to the receiving end, and the content of the information stream is known to both the sending end and the receiving end; The receiving end decodes the training information stream to obtain a decoding result; In response to the decoding result not matching the known content, the decoding result is used as a piece of training data in the training data set of the artificial intelligence model; Repeat the training process until the training data reaches a set threshold to complete the construction of the training data set; Train the artificial intelligence model through the training data set.

[0011] Fourth aspect, the present application also proposes a transmitting device for improving the performance of a communication system, which is used to implement the method described in any one of the first aspect, the second aspect or the third aspect of the present application, and includes a first receiving module, a first determining module and a first transmitting module. The first receiving module is used to obtain the original input information. The first determining module is used to encode the original input information. The first transmitting module is used to transmit the encoded information; the encoded information includes parity bits.

[0012] Fifth aspect, the present application also proposes a receiving device for improving the performance of a communication system, which is used to implement the method described in any one of the first aspect, the second aspect or the third aspect of the present application, and includes a second receiving module, a second determining module and a second transmitting module. The second receiving module is used to receive the encoded information. The second determining module is used to decode the encoded information to determine the first decoded information; it is also used to determine whether the first decoded information is correct according to the parity bits; it is also used to correct the first decoded information to obtain the second decoded information; it is also used to output the judgment information for determining whether the first decoded information is correct. The second transmitting module is used to transmit the second decoded information.

[0013] The present application also proposes a communication device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the computer program is executed by the processor, it implements the steps of the method described in any embodiment of the first aspect of the present application.

[0014] The present application also proposes a computer-readable medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the method described in any embodiment of the first aspect of the present application.

[0015] The present application also proposes a mobile communication system, including at least one transmitting device as described in any embodiment of the present application and / or at least one receiving device as described in any embodiment of the present application.

[0016] The above at least one technical solution adopted in the embodiments of the present application can achieve the following beneficial effects: The technical solution of this application significantly improves communication reliability by introducing an artificial intelligence-assisted error correction mechanism. Through the intelligent correction ability of the AI model, it can recover data closer to the original information from the incorrect decoding results. Complex environment adaptability: The AI model can learn the noise characteristics in different channel environments (such as urban multipath, high-speed movement, millimeter-wave propagation) and dynamically adjust the error correction strategy, so as to maintain high communication quality in scenarios where traditional coding schemes are difficult to handle. Reducing the ARQ trigger frequency: In traditional communication systems, once decoding fails, the receiving end needs to send a NACK feedback request to the sending end to retransmit the data, resulting in additional signaling overhead and delay. Through the AI error correction mechanism in this application, it can attempt to repair autonomously when the first decoding fails, and only trigger retransmission when the check fails after AI correction. This mechanism can reduce many retransmission requests and significantly improve spectrum utilization and system throughput. Supporting high-density network deployment: In the 5G / 6G ultra-dense networking (UDN) scenario, reducing retransmissions means reducing inter-cell interference, which is especially suitable for massive connections in the Internet of Things (IoT) or low-latency applications such as URLLC. In summary, through the deep integration of AI and communication technologies, this application brings improvements in terms of performance, efficiency, compatibility, and future scalability. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of this application, form a part of this application, and the schematic embodiments and descriptions thereof are used to explain this application, and do not constitute an improper limitation to this application. In the drawings: Figure 1 is a flowchart of an embodiment of the method of this application; Figure 2 is a block diagram of the implementation of the AI model in the information flow of the method of this application; Figure 3 is a schematic diagram of the AI dataset construction process of the method of this application; Figure 4 is a flowchart of an embodiment of the method of this application for a sending-end device; Figure 5 is a flowchart of an embodiment of the method of this application for a receiving-end device; Figure 6 is a schematic diagram of an embodiment of a sending-end device; Figure 7 is a schematic diagram of an embodiment of a receiving-end device; Figure 8 is a schematic diagram of the structure of a network-side device according to another embodiment of the present invention; Figure 9 is a block diagram of a terminal-side device according to another embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following will clearly and completely describe the technical solutions of this application in combination with the specific embodiments of this application and the corresponding drawings. Obviously, the described embodiments are only a part rather than all of the embodiments of this application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts belong to the scope of protection of this application.

[0019] The following will, in combination with the drawings, elaborate on the technical solutions provided by each embodiment of this application in detail.

[0020] Figure 1 It is a flowchart of an embodiment of the method of this application.

[0021] This application proposes a method for improving the performance of a communication system, which includes the following steps 110 to 160: Step 110: The sending end obtains the original input information; The sending end obtains the original data information to be transmitted from the data source. The original input information can be any digital data that needs to be transmitted, such as voice, video, text, etc.

[0022] For example, in a video call scenario, the original input information is the video frame data collected by the camera.

[0023] For the industrial Internet of Things, it may be the measured values such as temperature and pressure collected by sensors.

[0024] Step 120: The sending end encodes the original input information and sends the encoded information; the encoded information includes parity bits; The sending end performs channel encoding on the original information, adds parity bits to form encoded information, and sends it through the wireless channel.

[0025] Step 130: The receiving end receives the encoded information; The receiving end receives the encoded information transmitted by the sending end through the wireless interface.

[0026] For example, a mobile phone terminal receives a 5G signal sent by a base station through an antenna.

[0027] In a factory environment, it may be affected by multipath interference caused by mechanical equipment.

[0028] Step 140: The receiving end decodes the encoded information to determine the first decoded information; Step 150: The receiving end outputs a judgment information on whether the first decoded information is correct according to the determination of the parity bits; The receiving end uses the parity bits to verify the correctness of the first decoded information. It judges whether a transmission error has occurred through methods such as CRC check and outputs the judgment information.

[0029] Step 160: In response to the determination information being an error, the receiving end corrects the first decoded information through an artificial intelligence network to determine the second decoded information.

[0030] When the determination information indicates an error, correct the decoded information through a pre-trained AI network. The AI network learns the channel characteristics and error patterns and intelligently corrects the incorrectly decoded information.

[0031] Furthermore, a secondary verification is required after correction.

[0032] It should be noted that the receiving end introduces an artificial intelligence recognition enhancement unit, where the receiving end can be the network device or the terminal device. Further, the sending end can also be the network device or the terminal device.

[0033] In a communication system composed of a network device and a terminal device, a network device can simultaneously send and receive data to / from multiple terminal devices.

[0034] For example, the network device includes a network data unit and a network control unit.

[0035] The terminal device includes a terminal data unit and a terminal control unit.

[0036] The network data unit and the terminal data unit send data through the physical downlink shared channel (PDSCH) and the physical uplink shared channel (PUSCH).

[0037] The network control unit and the terminal control unit exchange control information through the synchronization signal and physical broadcast channel (SS / PBCH), the physical downlink control channel (PDCCH), the physical random access channel (PRACH), and the physical uplink control channel (PUCCH).

[0038] The SS / PBCH sends synchronization signals and broadcast information, and the terminal control unit obtains synchronization and basic system information by receiving the SS / PBCH. The PDCCH sends downlink control information (DCI) and is related to the specific transmission formats of the PDSCH, PUSCH, and PUCCH.

[0039] After the terminal data unit finishes receiving data, the terminal control unit initiates an access based on the PRACH to the network device according to the control information sent by the network control unit and the data reception situation of the terminal data unit, or feeds back ACK / NACK information indicating whether the data is correctly received, or sends data from the terminal to the network.

[0040] For example, the basic time transfer unit in the system is a symbol, and 14 symbols form a time slot. The length of a time slot is 1 / 2k ms, where k is a positive integer, corresponding to different subcarrier intervals respectively. For example, when k = 0, 1, 2, 3, 4, 5, 6, the corresponding subcarrier intervals are 15 kHz, 30 kHz, 60 kHz, 120 kHz, 240 kHz, 480 kHz, 960 kHz.

[0041] The artificial intelligence recognition enhancement unit implements further processing of the decoded information flow, specifically as Figure 2 shown: Assume that the information flow g contains the decoded result a' of the original input information a and the judgment information y on whether the decoding result is correct. When y is correct (e.g., y = 1), the information flow g is not processed and directly output, and the information flow h = g; when y is incorrect (e.g., y = 0), the input information flow a' is processed through the artificial intelligence network to output a'', where a'' is the restoration of the original information a. The obtained a'' is verified again using the existing information verification method, that is, secondary verification, to obtain the judgment information y' on whether the decoding result is correct. The a'' and y' form the information flow h to complete the output. If the decoding result obtained by the secondary verification is incorrect, a retransmission request is sent.

[0042] In one embodiment, the artificial intelligence network includes any one of the following: a deep neural network (DNN), a recurrent neural network (CNN), or a Transformer type neural network. The artificial intelligence network described in this application may also include other neural networks applicable to this application, which will not be elaborated further here.

[0043] In one embodiment, in the training process of the artificial intelligence network in step 160, the sender and the receiver confirm to initiate the artificial intelligence network training process. For example, the sender sends the first information to the receiver to trigger the artificial intelligence network training process.

[0044] The sender and the receiver complete the construction of the dataset required for the artificial intelligence model training through the sending and receiving of confirmation information, including the steps: Step 610, the sender sends the encoded training information flow to the receiver, and the content of the information flow is known to both the sender and the receiver; The sender sends the encoded information flow with pre-agreed content to the receiver for constructing the training dataset of the AI model. The content of this information flow is known to both the sender and the receiver.

[0045] Step 620, the receiver decodes the training information flow to obtain the decoding result; The receiving end performs conventional demodulation and decoding operations on the received training information flow to obtain a decoding result.

[0046] For example, the sending end sends a training information flow x, and the content of the information flow x is known to both the sending and receiving ends.

[0047] Step 630: In response to the decoding result not matching the known content, the decoding result is used as a piece of training data in the training data set of the artificial intelligence model; When the decoding result is inconsistent with the known original content, the incorrect decoding result and its corresponding correct value are stored as a piece of training data in the data set.

[0048] For example, when the receiving end finishes receiving the information flow, if the result after demodulation and decoding by the receiving end does not match the known content, the result after demodulation and decoding is used as a piece of data in the training data set of the artificial intelligence model.

[0049] Step 640: Repeat the training process until the training data reaches a set threshold, and the construction of the training data set is completed; Repeat the training process until the number of data entries in the training data set of the artificial intelligence model reaches a certain quantity, and the construction of the training data set of the artificial intelligence model is completed.

[0050] For example, repeat steps 610 - 630 until the data volume of the training data set reaches a predetermined threshold (such as 10,000 entries) to ensure coverage of typical channel scenarios.

[0051] Step 650: Train the artificial intelligence model with the training data set.

[0052] Use the constructed training data set to train the AI correction model through supervised learning.

[0053] The artificial intelligence model is trained according to the training data set of the artificial intelligence model.

[0054] Embodiment 1 Before the network device or terminal device uses the AI model, it starts constructing the training dataset of the AI model through the first information, such as control signaling (DCI). At a predefined time, such as N time slots after the first information (DCI information) is sent, the sending end (network device or terminal device) sends a sequence of all 1s or all 0s with a fixed length. The receiving end (terminal device or network device) performs normal data reception. If the data at the receiving end can be correctly decoded, that is, the decoding result matches the known content, it is not used as a sample for the AI model training dataset; if the data at the receiving end cannot be correctly decoded, that is, the decoding result does not match the known content, the decoded information is used as a sample for the AI model training dataset. When the number of samples in the AI model training dataset reaches a certain quantity, such as a predefined value M, where M is a predefined positive integer, the receiving end notifies the sending end to stop constructing the training dataset. The flowchart of the entire dataset construction process is as shown in Figure 3 shown.

[0055] The method and device provided in this application can enable the network and the terminal to use artificial intelligence technology to improve the performance of the communication system. The present invention uses a simple dataset construction method to obtain an ideal labeled dataset, thereby improving the performance of the communication system as a whole. By implementing the correction and recovery of the transmitted information at the receiving end through an artificial intelligence network, especially when the performance of traditional decoding is poor, the artificial intelligence network can play a role, further improving the receiving accuracy while maintaining the stability of the performance of the traditional communication system.

[0056] It should be noted that the above steps are applicable to network entities in a wireless communication system, including sending end devices, receiving end devices, or other intermediate devices; the above steps are also applicable to service devices that provide information processing for the network entity devices; the above steps are also applicable to any device, system, subsystem, circuit, chip, or software entity that provides information reception, transmission, recognition, and processing for terminal-side devices or network-side devices.

[0057] Figure 4 This is a flowchart of an embodiment of the method of this application for a sending end device.

[0058] The method according to any one of the embodiments of the first aspect of this application, when used for a sending end device, includes the following steps 210 to 220: Step 210, obtain the original input information Step 220, encode the original input information and send the encoded information; the encoded information includes parity bits.

[0059] Figure 5 This is a flowchart of an embodiment of the method of this application for a receiving end device.

[0060] The method described in any embodiment of the first aspect of the present application is used for a receiving-end device and includes the following steps 310 to 340: Step 310: Receive encoded information; Step 320: Decode the encoded information to determine first decoded information; Step 330: Determine judgment information on whether the first decoded information is correct according to parity bits; Step 340: In response to the judgment information being incorrect, correct the first decoded information through an artificial intelligence network to determine second decoded information.

[0061] Figure 6 It is a schematic diagram of an embodiment of a sending-end device.

[0062] An embodiment of the present application also proposes a sending-end device for improving the performance of a communication system, which is used to implement the method in any embodiment of the present application. The sending-end device is used to: send the original input information after encoding, that is, encoded information, and the encoded information includes parity bits.

[0063] To implement the above technical solution, a sending-end device 400 proposed by the present application includes a first receiving module 401, a first determining module 402, and a first sending module 403 that are connected to each other.

[0064] The first receiving module is used to obtain the original input information.

[0065] The first determining module is used to encode the original input information.

[0066] The first sending module is used to send encoded information; the encoded information includes parity bits.

[0067] The specific methods for implementing the functions of the first receiving module, the first determining module, and the first sending module are as described in the method embodiments of the present application and will not be elaborated here.

[0068] Figure 7 It is a schematic diagram of an embodiment of a receiving-end device.

[0069] An embodiment of the present application also proposes a receiving-end device for improving the performance of a communication system, which is used to implement the method in any embodiment of the present application. The receiving-end device is used to: receive encoded information and decode it, determine whether the decoding is correct through parity bits, and correct the decoded information with decoding errors through artificial intelligence.

[0070] To implement the above technical solution, a receiving-end device 500 proposed by the present application includes a second receiving module 501, a second determining module 502, and a second sending module 503 that are connected to each other.

[0071] The second receiving module is configured to receive encoded information.

[0072] The second determining module is configured to decode the encoded information to determine first decoded information. It is further configured to determine whether the first decoded information is correct according to parity bits. It is further configured to correct the first decoded information to obtain second decoded information. It is further configured to output determination information on whether the first decoded information is correct.

[0073] Further, the second determining module includes an artificial intelligence recognition enhancement unit for correcting the first decoded information to obtain second decoded information.

[0074] The second sending module is configured to send the second decoded information.

[0075] The specific methods for implementing the functions of the second receiving module, the second determining module, and the second sending module are as described in the method embodiments of this application and will not be elaborated here.

[0076] The sending device and the receiving device described in this application may both refer to a network-side device or a terminal-side device. That is, if the sending end is a network-side device, the receiving end may be a terminal-side device, and vice versa.

[0077] The network-side device may refer to a base station facility, a network-side device connected to the base station or a server. It may also be a system that provides services for the above devices, or any system, subsystem, module, circuit, chip or software running device that provides information reception, transmission, recognition, and processing for the above devices.

[0078] The terminal-side device may refer to a user equipment (UE), a personal mobile terminal, a smart terminal, a mobile phone, a computer with a communication function. It may also be a system that provides services for the above devices, or any system, subsystem, module, circuit, chip or software running device that provides information reception, transmission, recognition, and processing for the above devices.

[0079] Figure 8The structural schematic diagram of the network - side device according to another embodiment of the present invention is shown. As shown in the figure, the network - side device 600 includes a processor 601, a wireless interface 602, and a memory 603. Among them, the wireless interface can be multiple components, that is, it includes a transmitter and a receiver, and provides a unit for communicating with various other devices on the transmission medium. The wireless interface realizes the communication function with the terminal - side device, processes wireless signals through the receiving and transmitting devices, and the data carried by its signals communicates with the memory or the processor via the internal bus structure. The memory 603 contains a computer program for implementing any embodiment of the present application, and the computer program runs or changes on the processor 601. When the memory, the processor, and the wireless interface circuit are connected through a bus system, the bus system includes a data bus, a power bus, a control bus, and a status signal bus, which will not be elaborated here.

[0080] Figure 9 The block diagram of the terminal - side device according to another embodiment of the present invention. The terminal - side device 700 includes at least one processor 701, a memory 702, a user interface 703, and at least one network interface 704. Each component in the terminal - side device 700 is coupled together through a bus system. The bus system is used to realize the connection and communication between these components. The bus system includes a data bus, a power bus, a control bus, and a status signal bus.

[0081] The user interface 703 may include a display, a keyboard, or a pointing device, for example, a mouse, a trackball, a touchpad, or a touch screen, etc.

[0082] The memory 702 stores executable modules or data structures. The memory may store an operating system and application programs. Among them, the operating system contains various system programs, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware - based tasks. The application programs contain various application programs, such as a media player, a browser, etc., for implementing various application services.

[0083] In the embodiment of the present invention, the memory 702 contains a computer program for implementing any embodiment of the present application, and the computer program runs or changes on the processor 701.

[0084] The memory 702 contains a computer - readable storage medium. The processor 701 reads the information in the memory 702 and combines its hardware to complete the steps of the above - mentioned method. Specifically, a computer program is stored on the computer - readable storage medium, and when the computer program is executed by the processor 701, it realizes each step of the method embodiment as described in any of the above - mentioned embodiments.

[0085] The processor 701 may be an integrated circuit chip with the ability to process signals. In the implementation process, the steps of the method of the present application can be completed by the integrated logic circuit of the hardware in the processor 701 or the instructions in the form of software. The processor 701 may be a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present invention can be directly embodied as being executed and completed by the hardware decoding processor, or executed and completed by the combination of the hardware and software modules in the decoding processor.

[0086] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. In a typical configuration, the device of the present application includes one or more processors (CPUs), an input / output user interface, a network interface, and a memory.

[0087] In addition, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0088] Therefore, the present application also proposes a computer-readable medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the method described in any one of the embodiments of the present application. For example, the memories 603, 702 of the present invention may include non-permanent memories in the computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM).

[0089] Based on the embodiments of the above device of the present application, the present application also proposes a mobile communication system, which includes at least one embodiment of any one of the sending-end devices in the present application and / or at least one embodiment of any one of the receiving-end devices in the present application.

[0090] It should be noted that the specific mobile communication technology described in the present invention is not limited, and it can be WCDMA, CDMA2000, TD-SCDMA, WiMAX, LTE / LTE-A, LAA, MuLTEfire, and the fifth-generation, sixth-generation, Nth-generation mobile communication technologies that may appear in the future.

[0091] The terminal described in the present invention refers to a terminal-side product that can support the communication protocols of land mobile communication systems, specifically a communication modem module (Wireless Modem), which can be integrated into various types of terminal forms such as mobile phones, tablets, and data cards to complete communication functions.

[0092] For ease of description, the fourth-generation mobile communication system LTE / LTE-A and its derivative MulteFire are used as examples. Among them, the mobile communication terminal can be represented as UE (User Equipment), and the access device on the network side can be represented as a base station or an access point.

[0093] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, commodity or device including the said element.

[0094] Those skilled in the art of the present technology can understand that, unless specifically stated otherwise, the singular forms "a", "an", "the" and "said" used may also include the plural form. It should be understood that when a device or component is "connected" to another device or component, it can be directly connected to other devices or components, or there may also be intermediate devices or components. In addition, the "connection" used here can include partial wireless connection and can also include partial wired connection.

[0095] In the description of the present application, it should be understood that the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances. In addition, in the description of the present application, unless otherwise stated, "a plurality of" means two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after.

[0096] The above description is only for the embodiments of the present application and is not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for improving the performance of a communication system, characterized in that, It includes the following steps: The sending end obtains the original input information; The sending end encodes the original input information and sends the encoded information; the encoded information includes parity bits; The receiving end receives the encoded information; The receiving end decodes the encoded information to determine the first decoded information; The receiving end outputs a judgment message on whether the first decoded information is correct according to the parity bits; In response to the judgment message being incorrect, the receiving end corrects the first decoded information through an artificial intelligence network to determine the second decoded information.

2. A method for improving the performance of a communication system, which is used for a sending end, characterized in that, It includes the following steps: Obtain the original input information; Encode the original input information and send the encoded information; the encoded information includes parity bits.

3. The method according to claim 2, wherein It also includes the step: Receive the first message and send the encoded information; the first message is an instruction for the sending end to resend the encoded information.

4. A method for improving the performance of a communication system, for use at a receiving end, characterized in that, It includes the following steps: Receive the encoded information; Decode the encoded information to determine the first decoded information; Output a judgment message on whether the first decoded information is correct according to the parity bits; In response to the judgment message being incorrect, correct the first decoded information through an artificial intelligence network to obtain the second decoded information.

5. The method according to claim 4, wherein It also includes the step: Determine whether the second decoded information is correct according to the parity bits; In response to the second decoded information being correct, determine the second decoded information; In response to the second decoded information being incorrect, send the first message; the first message is an instruction for the sending end to resend the encoded information.

6. The method according to any one of claims 1 to 5, characterized in that, The training process of the artificial intelligence network specifically includes the steps: The sending end sends the encoded training information flow to the receiving end, and the content of the information flow is known to the sending end and the receiving end; The receiving end decodes the training information flow to obtain the decoding result; In response to the decoding result not matching the known content, the decoding result is used as a piece of training data in the training data set of the artificial intelligence model; Repeat the training process until the training data reaches the set threshold to complete the construction of the training data set; Train the artificial intelligence model through the training data set.

7. A sending end device for improving the performance of a communication system, used to implement the method according to any one of claims 1 to 6, characterized in that It includes a first receiving module, a first determining module and a first sending module; The first receiving module is used to obtain the original input information; The first determining module is used to encode the original input information; The first sending module is used to send the encoded information; the encoded information includes parity bits.

8. A receiving end device for improving the performance of a communication system, used to implement the method according to any one of claims 1 to 6, characterized in that The receiving end device includes a second receiving module, a second determining module and a second sending module; The second receiving module is used to receive the encoded information; The second determining module is used to decode the encoded information to determine the first decoded information; is also used to determine whether the first decoded information is correct according to the parity bits; is also used to correct the first decoded information to obtain the second decoded information; is also used to output a judgment message on whether the first decoded information is correct; The second sending module is used to send the second decoded information.

9. A communication device, characterized in that, It includes: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

11. A mobile communication system comprising at least one transmitting-end device according to claim 7 and / or at least one receiving-end device according to claim 8.

Images