Channel training method and program product

Abstract

Embodiments of the present application provide a channel training method and program product, the method comprising: in response to a first update instruction and first equalizer state information sent by a first device, receiving end adaptive equalization is carried out on equalization coefficient through a software interface; according to the receiving end adaptive equalization result, the second update instruction and the second equalizer state information are sent to the first device, so that the first device modifies the specified equalization coefficient, receives the third equalizer state information and the third update instruction sent by the first device after updating; in response to the third equalizer state information and the third update instruction, hardware adaptation is carried out on the receiving end equalization coefficient, and the fourth equalizer state information is sent to the first device according to the hardware adaptation result, wherein the fourth equalizer state information is used to indicate that the channel composed of the first device and the second device is trained. The problem that only hardware automatic training is used for channel training in the related art leads to unstable channel training and long time consumption.

Description

Technical Field

[0001] This application relates to the field of communications, and more specifically, to a channel training method and program product. Background Technology

[0002] With the development of high-speed data communication, signal attenuation and distortion during transmission have become increasingly prominent, especially in data centers, high-performance computing environments, and long-distance communication links. To overcome these challenges, channel training technology has become crucial in high-speed serial interface design. Channel training technology aims to dynamically adjust the equalization parameters of the transmitter and receiver to compensate for channel loss and inter-symbol interference, ensuring channel reliability and bit error rate meet standards, and enabling the channel composed of the transmitter and receiver to complete training.

[0003] In related technologies, channel training techniques mainly rely on hardware-automatic training, which automatically adjusts the equalization parameters of the receiver based on the transmission command from the transmitter until a predetermined signal quality standard is achieved. However, this mode faces challenges such as unstable training results and uncertain training time in scenarios with drastic changes in channel conditions or high insertion loss, as well as issues with high power consumption and hardware overhead. Summary of the Invention

[0004] This application provides a channel training method and program product to at least solve the problems of unstable channel training and long training time caused by relying solely on hardware automatic training in related technologies.

[0005] According to one embodiment of this application, a channel training method is provided, applied to a second device, comprising: responding to a first update command and first equalizer state information sent by a first device, performing receiver-end adaptive equalization on equalization coefficients through a software interface; sending a second update command and second equalizer state information to the first device according to the receiver-end adaptive equalization result, so that the first device modifies the specified equalization coefficients; receiving a third equalizer state information and a third update command sent by the first device after the update; responding to the third equalizer state information and the third update command, performing hardware adaptation on the receiver-end equalization coefficients; and sending a fourth equalizer state information to the first device according to the hardware adaptation result, wherein the fourth equalizer state information is used to indicate that the channel composed of the first device and the second device has completed training.

[0006] According to another embodiment of this application, a network device is also provided, the network device including a receiver, a transmitter and a processor, the network device being configured to perform the steps in the above method embodiments through at least one of the receiver, the transmitter and the processor.

[0007] According to yet another embodiment of this application, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer program, and the computer program is configured to execute the steps in the above method embodiments when it is run.

[0008] According to yet another embodiment of this application, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in the above method embodiments.

[0009] According to yet another embodiment of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0010] This embodiment implements adaptive equalization at the receiving end through a software interface, enabling the second device to flexibly adjust its equalization coefficients based on update commands and equalizer status information sent by the first device. This improves the device's adaptability to different channel characteristics, especially in complex communication environments such as high insertion loss and multipath interference, where it can more effectively compensate for channel impairments and reduce signal distortion and bit error rate. Based on the adaptive equalization results, the second device sends feedback commands to the first device—namely, a second update command and second equalizer status information—causing the first device to adjust its specified equalization coefficients. This interactive mechanism ensures that both ends of the link can work collaboratively to optimize the overall channel transmission quality, avoiding performance bottlenecks that may result from unilateral optimization. After the first device completes the equalization coefficient modification and resends the update command and equalizer status information, the second device executes a hardware adaptive process. This step not only verifies the effectiveness of software adaptation but also ensures the consistency and reliability of channel training results through hardware-level adjustments, allowing for rapid restoration to the established communication quality standard even in the event of network environment changes or device restarts. Attached Figure Description

[0011] Figure 1 This is a hardware structure block diagram of a mobile terminal for a channel training method according to an embodiment of this application;

[0012] Figure 2 This is a flowchart of the channel training method according to an embodiment of this application;

[0013] Figure 3 This is a structural block diagram of a network device according to an embodiment of this application;

[0014] Figure 4 This is a schematic diagram of the self-negotiation module structure according to an embodiment of this application;

[0015] Figure 5 This is a schematic diagram of the link training module structure according to an embodiment of this application. Detailed Implementation

[0016] The embodiments of this application will be described in detail below with reference to the accompanying drawings and examples.

[0017] It should be noted that the terms "first," "second," etc., in the specification, claims, and drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0018] Link training (LT) is a key technology for optimizing signal integrity in high-speed serial communications (such as Peripheral Component Interconnect Express (PCIe), Universal Serial Bus (USB), Ethernet, etc.). Its core objective is to compensate for channel loss and inter-symbol interference (ISI) by dynamically adjusting the equalization parameters (such as Finite Impulse Response filter coefficients, Continuous Time Linear Equalizer (CTLE) / Decision Feedback Equalizer (DFE) gains) of the transmitter (Tx) and receiver (Rx), thereby ensuring link reliability and meeting bit error rate (BER) standards. Although related technologies have been widely applied to high-speed links, the following key issues still exist:

[0019] Training results are not repeatable: Because adaptive algorithms (such as LMS) are sensitive to initial conditions and channel noise, and multipath effects may introduce local optima, the same link may obtain different FIR coefficients in different training iterations, which means that the system needs to test all possible results within the voltage / temperature range, increasing the verification cost.

[0020] Link establishment time is uncertain: Since iterative equalization adjustment relies on receiver feedback, and channel nonlinearity (e.g., backplane insertion loss > 35dB) will prolong convergence time. This can lead to training time being extended due to poor signal quality (e.g., > 1 second), especially in high-loss channels where multiple retries are required.

[0021] Power consumption and hardware overhead: Since high-speed SerDes relies on analog circuits (such as PLL, CTLE), it is difficult to reduce power consumption through process scaling. The decision feedback loop of DFE needs to be completed within a very short UI, resulting in high power consumption and area cost.

[0022] This application's embodiments relate to blind equalization, which enables the SerDes (serializer / deserializer) IP domain to automatically adjust the link equalization capability under different rate and channel scenarios. In addition, the link training part is designed with hardware automatic training function and software custom algorithm interface. The main components of this application's embodiments include Auto-Negotiation (AN) and link training parts.

[0023] The auto-negotiation module sits between the Physical Medium Dependent (PMD) sublayer and the Media-Independent Interface (MDI) layer. The auto-negotiation function allows Ethernet devices to inform another device at the far end of the link of their operating modes and detect the corresponding operating mode used by the peer device. The purpose of auto-negotiation is to provide a way for two devices sharing a link across the backplane to exchange information and automatically configure these two devices to maximize their capabilities, including supported Ethernet types, maximum shared rate, and forward error correction (FEC) capabilities.

[0024] After the ports in the link complete the necessary self-negotiation information exchange and successfully negotiate, the link partners will perform link training, i.e., channel training. Link training is crucial for tuning the channel to achieve optimal transmission. Symbols can interfere with each other as they pass through the cable. The LT (Linger-Linger) partially compensates for this interference through the transmitter (Tx) equalizer, adjusting the amplitude of the transmitted symbol (Main) based on the immediately preceding and following symbols. The two endpoints of the line automatically adjust the Tx equalizer during link training. The transmitter and receiver on the link communicate via the LT to adjust the equalizer settings. The adjustment algorithm is a fine-tuning based on coarse adjustment. Because the equalization coefficients in coarse adjustment vary greatly, an RX adaptive software interface is designed. RX adaptation is performed after each coarse adjustment. Through hardware and software cooperation, the equalization coefficients of the other party's Tx are adjusted to the optimal level. In one embodiment, the equalization coefficients in the coarse adjustment stage include feed-forward equalizer coefficients (FFE).

[0025] The methods and embodiments provided in this application can be executed on a mobile terminal, a computer terminal, or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a channel training method according to an embodiment of this application. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0026] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the channel training method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thus implementing the above-described method. The memory 104 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0027] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0028] This embodiment provides a channel training method applied to a second device. Figure 2 This is a flowchart of the channel training method according to an embodiment of this application, as follows: Figure 2 As shown, the process includes the following steps:

[0029] In step S202, in response to the first update command and the first equalizer status information sent by the first device, the equalization coefficients are subjected to receiver-side adaptive equalization through the software interface.

[0030] In this embodiment, adaptive equalization at the receiving end is implemented through a software interface, enabling the second device to flexibly adjust its own equalization coefficient according to the update command and equalizer status information sent by the first device. This improves the device's adaptability to different channel characteristics, especially in complex communication environments such as high insertion loss and multipath interference, where it can more effectively compensate for channel damage and reduce signal distortion and bit error rate.

[0031] In an exemplary embodiment of this application, before step S202, the method further includes: sending a fifth update instruction and fifth equalizer status information to the first device to enable the first device to preload the equalization coefficients of the transmitting end, and receiving the first equalizer status information generated by the first device based on the update result and the first update instruction generated based on the fifth equalizer status information.

[0032] In one embodiment, the coarse-tuning process includes preloading equalization coefficients and adaptively adjusting the updated equalizer parameters through an adaptive software interface. Since the equalizer parameters change significantly during coarse-tuning, each adjustment requires re-adaptation through the adaptive software interface.

[0033] In an exemplary embodiment of this application, before sending the fifth update instruction and the fifth equalizer status information to the first device, the method further includes: sending a first frame lock signal to the first device when a frame header signal sent by the first device is detected; and confirming that the first device and the second device have completed frame locking when the first frame lock signal is received.

[0034] In an exemplary embodiment of this application, before confirming that the first device and the second device have completed frame locking, the method further includes: sending a first self-negotiation signal to the first device and receiving a second self-negotiation signal sent by the first device, wherein the first self-negotiation signal includes an operating mode supported by the second device, and the second self-negotiation signal includes an operating mode supported by the first device; determining the highest common denominator HCD rate based on the first self-negotiation signal and the second self-negotiation signal, wherein the HCD rate is used to indicate the optimal rate jointly supported by the first device and the second device; and switching the channel rate to the HCD rate.

[0035] In an exemplary embodiment of this application, sending a first auto-negotiation signal to a first device includes: sending a first auto-negotiation signal to the first device using Differential Manchester Encoding (DME).

[0036] In an exemplary embodiment of this application, receiver-side adaptive equalization of the equalization coefficients via a software interface includes: traversing the basic parameters of the analog front-end (AFE) including different equalization capabilities; fixing the despiking of the continuous-time linear equalizer within a target range based on the AFE basic parameters; setting and fixing the enhancement gain of the first-level basic parameter continuous-time linear equalizer based on the current overall gain adjustment result of the basic parameter continuous-time linear equalizer; setting and fixing the enhancement gain of the second-level basic parameter continuous-time linear equalizer based on the current overall gain adjustment result of the basic parameter continuous-time linear equalizer; and releasing the despiking of the basic parameter continuous-time linear equalizer so that the overall gain adjustment is at the target value and the adjusted hardware adjustment range can cope with temperature changes.

[0037] In one embodiment, the continuous-time linear equalizer is called CLE, the de-peaking is called Depeak, and the boost is called Boost.

[0038] Step S204: Send a second update instruction and second equalizer status information to the first device according to the adaptive equalization result of the receiving end, so that the first device can modify the specified equalization coefficient, and receive the third equalizer status information and third update instruction sent by the first device after the update.

[0039] In this embodiment, the second device sends a feedback instruction—namely, a second update instruction and second equalizer status information—to the first device based on the adaptive equalization result, prompting the first device to adjust its specified equalization coefficient. This interactive mechanism ensures that both ends of the link can work together to optimize the transmission quality of the entire channel, avoiding performance bottlenecks that may result from unilateral optimization.

[0040] In an exemplary embodiment of this application, receiving the third equalizer status information and the third update instruction sent by the first device after the first device updates includes: receiving the third equalizer status information generated by the first device based on the update result, and the third update instruction generated based on the second equalizer status information.

[0041] Step S206: In response to the third equalizer status information and the third update instruction, hardware adaptation is performed on the equalization coefficients of the receiving end, and the fourth equalizer status information is sent to the first device according to the hardware adaptation result. The fourth equalizer status information is used to indicate that the channel composed of the first device and the second device has completed training.

[0042] In one embodiment, the fine-tuning process includes hardware adaptation of the receiver equalization coefficients.

[0043] In this embodiment, after the first device completes the equalization coefficient modification and resends the update command and equalizer status information, the second device performs a hardware adaptation process. This step not only verifies the effectiveness of software adaptation but also ensures the consistency and reliability of channel training results through hardware-level adjustments, enabling rapid restoration to the established communication quality standard even in the event of network environment changes or device restarts.

[0044] In this embodiment of the application, the completion of channel training composed of the first device and the second device indicates that the link training is complete.

[0045] In an exemplary embodiment of this application, hardware adaptation of the receiver equalization coefficient includes: hardware adaptation of the receiver equalization coefficient, obtaining the current signal-to-noise ratio (SNR) value through an eye diagram interface, wherein the SNR value is used to indicate the current channel quality; and determining that the hardware adaptation of the receiver equalization coefficient of the second device is completed when the SNR value meets a preset threshold.

[0046] In one embodiment, an "eye diagram" is a commonly used signal quality visualization tool that displays the signal state sampled at different time points by the receiver, visually representing the stability and integrity of the signal. Signal-to-noise ratio (SNR) is an important metric for measuring signal quality; it represents the ratio of the average power of the signal to the average power of the noise. In the field of digital communication, a high SNR means a relatively clear signal with less noise interference, which is crucial for ensuring the accuracy and reliability of data transmission.

[0047] In an exemplary embodiment of this application, the fourth equalizer status information includes a receiver preparation completion flag, which is used to indicate that the second device is in a state of waiting to receive data.

[0048] In one embodiment, the first / second / third / fourth / fifth update instructions are control instructions used to control the increase, decrease, or preloading of specified equalizer parameters by the peer device, and to request the TX encoding method from the peer device; the first / second / third / fourth / fifth equalizer status information is a response to the update instructions, informing the peer device of the equalizer status information of the local device, including whether the update instructions have been received and executed, whether the equalizer parameters have reached the boundary values, the TX encoding method, the RX frame lock flag, and the RX READY (receiver ready) flag; the update instructions and equalizer status information can be packaged into a single data frame for transmission.

[0049] In this embodiment of the application, the channel training method described above can also be applied to the first device.

[0050] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0051] This application also provides a network device. Figure 3 This is a structural block diagram of a network device according to an embodiment of this application, such as... Figure 3 As shown, the network device 300 includes a receiver 301, a transmitter 302, and a processor 303. The network device 300 is used to perform the steps of the above-described channel training method embodiment through at least one of the receiver 301, transmitter 302, and processor 303.

[0052] Embodiments of this application also provide a computer-readable storage medium storing a computer program, wherein the computer program is configured to execute the steps in any of the above method embodiments when run.

[0053] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0054] Embodiments of this application also provide an electronic device including a memory and a processor, the memory storing a computer program and the processor being configured to run the computer program to perform the steps in any of the above method embodiments.

[0055] In one exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0056] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0057] In one exemplary embodiment, the computer program product described above includes a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the methods described in various embodiments of this application.

[0058] Specific examples in this embodiment can be found in the examples described in the above embodiments and exemplary implementations, and will not be repeated here.

[0059] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0060] This application embodiment also provides a channel training method, which includes: a first device updating the equalizer parameters of the first device according to an update instruction sent by a second device, the update causing the first device to generate equalizer status information to indicate the updated equalizer status of the first device; obtaining the current equalizer status of the second device according to the equalizer status information sent by the second device, and generating an update instruction accordingly; and then the first device sending a new update instruction and equalizer status information to the second device.

[0061] During the coarse adjustment phase, the update command sent by the second device is to control the preloading of all TX equalization coefficients of the first device. After receiving the equalizer status information of the first device, the second device performs adaptive equalization on the RX parameters of the second device based on the updated peer TX data through the adaptive software interface. After completing the RX adaptive equalization, the current SNR value is obtained through the eye diagram interface to indicate the current channel quality, and then the next update command is sent to the first device. During the coarse adjustment phase, the TX equalization changes significantly, so the software RX adaptive equalization must be performed again.

[0062] During the fine-tuning phase, the update command sent by the second device controls the addition or subtraction of the TX equalization coefficient specified by the first device, so that the TX equalization coefficient is adjusted near the coarse-tuning result. After receiving the equalizer status information from the first device, the second device waits for a very short time for the hardware to automatically perform RX adaptation, and then obtains the current SNR value through the eye diagram interface to indicate the current channel quality, and obtains the optimal TX equalization coefficient.

[0063] It should be noted that the update command is a control command that controls the increase, decrease, or preloading of specified equalizer parameters on the peer device, and requests the peer device's TX encoding method. The equalizer status information is a response to the update command, informing the peer device about the equalizer status of the local device, including whether the update command has been received and executed, whether the equalizer parameters have reached the boundary values, the TX encoding method, the RX frame lock flag, and the RX READY (receiver ready) flag. The update command and equalizer status information are packaged together in a data frame and sent.

[0064] During the training completion phase, the first device receives equalizer status information sent by the second device. If the information includes an RX READY flag, it confirms that the channel composed of the first device's TX and the second device's RX has completed training. The RX READY flag in the equalizer status information indicates that the second device is ready to receive normal data. Following the same procedure, if the equalizer status information sent by the first device includes the RX READY flag, it confirms that the channel composed of the second device's TX and the first device's RX has completed training.

[0065] The handshake after training is completed: the equalizer status information sent by the first device includes RX READY, and the equalizer status information received from the second device also includes RX READY, confirming that the first device has ended training; based on the same logic, the handshake confirms that the second device has ended training.

[0066] Figure 4 This is a schematic diagram of the self-negotiation module structure according to an embodiment of this application. The self-negotiation module is used to execute the negotiation process in the above embodiments, such as... Figure 4 As shown, the auto-negotiation module includes an auto-negotiation transmission module (an_transmit module) responsible for DME transmission, an auto-negotiation reception module (an_receive module) responsible for DME reception, an auto-negotiation arbitration module (an_arbit module) responsible for auto-negotiation state control, and an auto-negotiation configuration interface module (an_cfgi module) responsible for register reading and writing. When the auto-negotiation module negotiates the Highest Commom Denominator (HCD) rate, other firmware will read the HCD rate through the auto-negotiation configuration interface module and perform link training after the rate switching is completed.

[0067] Figure 5 This is a schematic diagram of the link training module structure according to an embodiment of this application. The link training module is used to execute the channel training process in the above embodiments, such as... Figure 5As shown, in the link training module, the hardware control link training receiver equalization control module (lt_rxeq module) and the software (sofware_ifc) interface mux control the link training send module (lt_tx module). The link training receiver module (lt_rx module), the response module, and the software interface mux respond to the commands received by the link training receiver module (lt_rx). The training controller module (train_ctrl) is used to implement the main state machine and overall control flow of the link training module. In one embodiment, lt_txeq uses hardware mode, and the link training receiver equalization algorithm module (lt_rxeq_alg module) uses software mode to customize the algorithm for adjusting the peer's FFE.

[0068] In this embodiment, AN is AN73, and the application scenarios include backplanes or high-speed copper cable interfaces such as KR / KX / CR / CX (e.g., 25G / 100G Ethernet). It adopts Differential Manchester Encoding (DME), which improves negotiation efficiency by detecting edge transitions to transmit information. It supports richer technical capability fields (such as FEC function and multi-rate negotiation), including Base-R FEC (Base-R based forward error correction code), RS-FEC (error control code), and RS-FEC-int (integrated error control code). It is suitable for 25G, 50G, and 100G single-channel requirements, and the rate covers multiple protocols from 2.5G to 800G (e.g., 25G BASE-KR (Kr-based interface), 800G BASE-CR8).

[0069] The link training LT modules used in the embodiments of this application are based on IEEE 802.3+ck+df, namely CL72, CL92, CL136, and CL162.

[0070] This application implements auto-negotiation and link training functions based on IEEE 802.3. The sending and receiving devices for link training are designed with hardware automatic training mode and software training mode respectively, and support the customization of link training algorithms.

[0071] This application embodiment designs a receiver adaptive software interface for the link training LT module. When the equalization coefficient changes significantly, receiver adaptive behavior is initiated to make the channel indicators accurate. The channel indicators can be customized by the software. The waiting time during receiver adaptive behavior can also be set as needed.

[0072] The embodiments of this application achieve repeatable training results and predictable link establishment time by performing fine-tuning based on pre-coarse tuning.

[0073] In this embodiment, the self-negotiation supports negotiation of multiple protocol rates and multiple forward error correction capabilities. Link training supports single-lane link training of 25g, 50g, and 100g.

[0074] The application scenarios of this application include at least: data centers and high-performance computing; interconnection of 400G / 800G / 1.6T Ethernet switches and servers; 5G / 6G base stations and edge computing; dynamic training channels for ANLT training to adapt to temperature fluctuations and ensure the stability of base station and core network links; high insertion loss (>30dB) for long-distance transmission; AN / LT+ anti-radiation FPGA architecture, with dynamic rebinding channels to adapt to signal attenuation.

[0075] This application's embodiments achieve decoupling and modular design of link training. Both sending and receiving in link training can be individually configured in hardware or custom software modes. The training process and receiver adaptation can work well together, channel metrics can be customized, and the training algorithm can be flexibly set. Furthermore, training time is shortened; the algorithm's pre-coarse tuning followed by fine tuning reduces training time, ensuring the stability and repeatability of training results.

[0076] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A channel training method, characterized in that, Applied to a second device, including: In response to the first update command and the first equalizer status information sent by the first device, the equalization coefficients are subjected to receiver adaptive equalization through a software interface. Based on the adaptive equalization result of the receiving end, a second update instruction and second equalizer status information are sent to the first device so that the first device can modify the specified equalization coefficients. The third equalizer status information and the third update instruction are then sent by the first device after the update. In response to the third equalizer status information and the third update instruction, the equalization coefficients at the receiving end are hardware-adapted, and the fourth equalizer status information is sent to the first device according to the hardware adaptation result. The fourth equalizer status information is used to indicate that the channel composed of the first device and the second device has completed training. The receiver-side adaptive equalization of the equalization coefficients via a software interface includes: traversing the basic parameters of the analog front-end (AFE) with different equalization capabilities; fixing the despiking of the continuous-time linear equalizer within the target range based on the basic AFE parameters; setting and fixing the enhancement gain of the first-level basic parameter continuous-time linear equalizer based on the current overall gain adjustment result of the basic parameter continuous-time linear equalizer; setting and fixing the enhancement gain of the second-level basic parameter continuous-time linear equalizer based on the current overall gain adjustment result of the basic parameter continuous-time linear equalizer; and releasing the despiking of the basic parameter continuous-time linear equalizer to ensure that the overall gain adjustment is at the target value and that the adjusted hardware adjustment range can cope with temperature changes.

2. The method according to claim 1, characterized in that, Before performing receiver-end adaptive equalization on the equalization coefficients of the second device via the software interface, in response to the first update command and first equalizer status information sent by the first device, the process further includes: Send a fifth update command and fifth equalizer status information to the first device so that the first device preloads the equalization coefficient of the transmitter and receives the first equalizer status information generated by the first device based on the update result and the first update command generated based on the fifth equalizer status information.

3. The method according to claim 1, characterized in that, The step of receiving the third equalizer status information and the third update instruction sent by the first device after the update includes: The device receives third equalizer status information generated by the first device based on the update result, and a third update instruction generated based on the second equalizer status information.

4. The method according to claim 1, characterized in that, The hardware adaptive equalization coefficient at the receiver includes: The equalization coefficients at the receiver are hardware-adaptive, and the current signal-to-noise ratio (SNR) value is obtained through the eye diagram interface, wherein the SNR value is used to indicate the current channel quality. When the SNR value meets the preset threshold, the hardware adaptive determination of the equalization coefficient of the receiver of the second device is completed.

5. The method according to claim 1, characterized in that, in, The fourth equalizer status information includes a receiver preparation completion flag, which indicates that the second device is in a state of waiting to receive data.

6. The method according to claim 2, characterized in that, Before sending the fifth update command and the fifth equalizer status information to the first device, the method further includes: Upon detecting the frame header signal sent by the first device, a first frame lock signal is sent to the first device; Upon receiving the first frame lock signal sent by the first device, it is confirmed that the first device and the second device have completed frame locking.

7. The method according to claim 6, characterized in that, Before confirming that the first device and the second device have completed frame locking, the method further includes: Send a first self-negotiation signal to the first device and receive a second self-negotiation signal sent by the first device, wherein the first self-negotiation signal includes the operating modes supported by the second device, and the second self-negotiation signal includes the operating modes supported by the first device; The highest common denominator HCD rate is determined based on the first self-negotiation signal and the second self-negotiation signal, wherein the HCD rate is used to indicate the optimal rate jointly supported by the first device and the second device; Switch the channel rate to the HCD rate.

8. The method according to claim 7, characterized in that, Sending the first self-negotiation signal to the first device includes: sending the first self-negotiation signal to the first device using Differential Manchester Encoding (DME).

9. A network device, characterized in that, The network device includes a receiver, a transmitter, and a processor, and the network device is configured to perform the steps of the method according to any one of claims 1 to 8 via at least one of the receiver, the transmitter, and the processor.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the method described in any one of claims 1 to 8.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 8.

12. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method described in any one of claims 1 to 8.

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