CN115987727B

CN115987727B - Signal transmission method and device

Info

Publication number: CN115987727B
Application number: CN202310273098.7A
Authority: CN
Inventors: 刘毅; 朱超
Original assignee: Honor Device Co Ltd
Current assignee: Honor Device Co Ltd
Priority date: 2023-03-21
Filing date: 2023-03-21
Publication date: 2023-09-26
Anticipated expiration: 2043-03-21
Also published as: CN115987727A

Abstract

The application provides a signal transmission method and a signal transmission device, which can improve the accuracy of training a decision feedback equalizer, thereby improving the communication quality. The method comprises the following steps: receiving a signal from a transmitting end, wherein the signal comprises an actual receiving signal of a sample signal and a service signal; training a decision feedback equalizer based on the actual received signal and the sample signal; in the training process, according to a first change trend of an error between a processing result of the actual received signal by the decision feedback equalizer and a sample signal and a first corresponding relation, determining an adjustment coefficient of a parameter of the decision feedback equalizer, wherein the first corresponding relation comprises a plurality of change trends and a corresponding relation between a plurality of adjustment coefficients, and the plurality of change trends comprise a first change trend; based on the adjustment coefficient, adjusting the parameters of the decision feedback equalizer, and continuously training the decision feedback equalizer; and carrying out interference elimination processing on the service signal by utilizing the trained decision feedback equalizer.

Description

Signal transmission method and device

Technical Field

The present application relates to the field of communications technologies, and in particular, to a signal transmission method and apparatus.

Background

In the process of transmitting signals from a transmitting end to a receiving end, for example, baseband signals specified by 802.11b, multipath signal intersymbol interference exists. The receiving end can reduce the intersymbol interference of the multipath signals through the decision feedback equalizer, thereby improving the communication quality between the transmitting end and the receiving end. Before the receiving end eliminates the intersymbol interference of the multipath signals through the decision feedback equalizer, the decision feedback equalizer needs to be trained through a sample signal, so that the parameter value of the decision feedback equalizer is determined.

At present, in the training process of the decision feedback equalizer, the parameter value of the decision feedback equalizer is changed continuously according to the error between the output value and the expected value of the decision feedback equalizer until the error between the output value and the expected value of the decision feedback equalizer approaches 0.

However, the accuracy of the training of the current decision feedback equalizer is low, and the packet error rate of the data packet received by the receiving end is high, so that the communication quality between the transmitting end and the receiving end is poor, and the user experience is affected.

Disclosure of Invention

The application provides a signal transmission method and a signal transmission device, which can improve the training accuracy of a decision feedback equalizer, reduce the packet error rate of a data packet received by a receiving end, improve the communication quality between the transmitting end and the receiving end and improve the user experience.

In a first aspect, a signal transmission method is provided, including: receiving a signal from a transmitting end, wherein the signal comprises an actual receiving signal of a sample signal and a service signal; training a decision feedback equalizer based on the actual received signal and the sample signal; in the training process, according to a first change trend and a first corresponding relation of errors between the processing result of the actual received signal by the decision feedback equalizer and the sample signal, determining an adjustment coefficient of a parameter of the decision feedback equalizer, wherein the first corresponding relation comprises a plurality of change trends and a corresponding relation between a plurality of adjustment coefficients, and the plurality of change trends comprise the first change trend; based on the adjustment coefficient, adjusting the parameter of the decision feedback equalizer, and continuing to train the decision feedback equalizer; and performing interference elimination processing on the service signal by using the trained decision feedback equalizer.

According to the signal transmission method, in the training process of the decision feedback equalizer, the change trend of the error is determined continuously according to the accumulated error between the processing result of the decision feedback equalizer on the actual received signal and the sample signal, and the adjustment coefficient of the parameter of the decision feedback equalizer is further adjusted according to the change trend, so that the receiving end can adjust the parameter of the decision feedback equalizer in real time according to the change trend of the error, and in this way, under the condition that the number of the actual received signals of the decision feedback equalizer is the same, the error between the processing result of the decision feedback equalizer on the actual received signal and the sample signal can be reduced, the accuracy of the decision feedback equalizer is improved, the communication quality between the transmitting end and the receiving end is improved, and the user experience is improved.

It should be understood that the signal sent by the transmitting end to the receiving end may be a baseband signal specified by 802.11 b. The sample signal may refer to data information agreed by the transmitting end and the receiving end and used for training the decision feedback equalizer by the receiving end, that is, the signal sent by the transmitting end to the receiving end includes the sample signal. The actual received signal of the sample signal refers to the sample signal actually received by the terminal device. After the interference elimination processing is carried out on the actual received signal of the receiving end, the decision feedback equalizer has an error between the obtained output result and the sample signal, and the decision feedback equalizer is trained based on the error. The process of training the decision feedback equalizer is to continuously adjust the parameters of the decision feedback equalizer.

In certain implementations of the first aspect, the method further comprises: inputting the actual received signal to the decision feedback equalizer to obtain a processing result of the actual received signal; determining an error between a processing result of the actual received signal and the sample signal; and determining the first variation trend based on the error.

It should be understood that the error between the processing result of the actual received signal and the sample signal may refer to the difference between the processing result of the actual received signal and the sample signal. The first trend refers to a trend of the error, and thus, the first trend is determined according to at least two errors.

In certain implementations of the first aspect, the determining the first trend of change based on the error includes: each time the number of errors is accumulated to a first preset number, carrying out mean value filtering processing on the errors of the first preset number to obtain a plurality of smooth errors; determining a local change trend of each two adjacent smooth errors in the plurality of smooth errors according to the difference value between each two adjacent smooth errors in the plurality of smooth errors, wherein the local change trend comprises an ascending trend and a descending trend; and determining the first change trend according to the number of the rising trends and the number of the falling trends.

It should be appreciated that the first predetermined number may be any positive integer, such as 5, 10, etc. The average filtering processing of the first preset number of errors may mean that the first preset number of errors are averaged, and the average is a smooth error. After the receiving end starts training the decision feedback equalizer, the receiving end inputs an actual receiving signal to the decision feedback equalizer every time, and the receiving end has corresponding processing results, so that an error can be determined according to the processing results and the sample signals. In the training process of the decision feedback equalizer, the receiving end continuously inputs the actual received signal into the decision feedback equalizer, and errors can be continuously obtained. Each time the number of errors accumulates to a first preset number, the receiving end determines an average value of the first preset number of errors, thereby obtaining a smooth error. As the number of errors accumulated further increases, the number of smoothed errors also increases.

In certain implementations of the first aspect, the determining the first trend according to the number of upward trends and the number of downward trends includes: if the number of the rising trends is greater than the number of the falling trends, determining the first change trend as the rising trend; or if the number of the rising trends is equal to the number of the falling trends, determining the first change trend as a stable trend; or if the number of the rising trends is smaller than the number of the falling trends, determining the first change trend as the falling trend.

It is understood that the first variation trend includes an upward trend, a downward trend, and a steady trend. The first change trend is determined according to the number of the rising trend and the falling trend in the local change trend, so that the change trend of the error can be determined more efficiently, and the training efficiency and the training accuracy of the decision feedback equalizer can be improved.

In certain implementations of the first aspect, the determining the first trend according to the number of upward trends and the number of downward trends includes: and when the number of the plurality of smoothing errors is accumulated to a second preset number, determining the first variation trend according to the number of the rising trends and the number of the falling trends.

It should be appreciated that the second preset number may be any positive integer, for example, 3, 9, etc. For example, after each time the receiving end determines 6 smoothing errors, the receiving end determines 5 local variation trends according to every two adjacent smoothing errors in the 6 smoothing errors, and determines the first variation trend according to the number of rising trends and the number of falling trends in the 5 local variation trends.

In certain implementations of the first aspect, the first trend is derived based on a first portion of the actual received signal; after the adjusting the parameters of the decision feedback equalizer based on the adjustment coefficients, the method further comprises: determining a second variation trend of an error between a processing result of the adjusted decision feedback equalizer on a second part of signals in the actual received signals and the sample signals; determining readjustment coefficients of parameters of the decision feedback equalizer based on the second variation trend and the first correspondence; and based on the readjustment coefficient, readjusting the parameter of the decision feedback equalizer, and continuing training the decision feedback equalizer.

It should be understood that the first partial signal and the second partial signal in the actual received signal may be partial signals in the actual received signal or may be all signals in the actual received signal. When the first partial signal and the second partial signal are partial signals in the actual received signal, the actual received signal may further include a third partial signal, a fourth partial signal, and the like. The second trend may also be one of an upward trend, a downward trend, or a steady trend. The receiving end may determine the second variation trend according to the same method as the first variation trend. The steps are continuously and circularly carried out until the preset stopping condition is met.

In a second aspect, a signal transmission device is provided for performing the method in any of the possible implementations of the first aspect. In particular, the apparatus comprises means for performing the method in any one of the possible implementations of the first aspect described above.

In a third aspect, the present application provides a further signal transmission device comprising a processor coupled to a memory operable to execute instructions in the memory to implement a method as in any one of the possible implementations of the first aspect. Optionally, the apparatus further comprises a memory. Optionally, the apparatus further comprises a communication interface, the processor being coupled to the communication interface.

In one implementation, the apparatus is a terminal device. When the apparatus is a terminal device, the communication interface may be a transceiver, or an input/output interface.

In another implementation, the apparatus is a chip configured in a terminal device. When the apparatus is a chip configured in a terminal device, the communication interface may be an input/output interface.

In a fourth aspect, there is provided a processor comprising: input circuit, output circuit and processing circuit. The processing circuit is configured to receive a signal via the input circuit and transmit a signal via the output circuit, such that the processor performs the method of any one of the possible implementations of the first aspect.

In a specific implementation flow, the processor may be a chip, the input circuit may be an input pin, the output circuit may be an output pin, and the processing circuit may be a transistor, a gate circuit, a trigger, various logic circuits, and the like. The input signal received by the input circuit may be received and input by, for example and without limitation, a receiver, the output signal may be output by, for example and without limitation, a transmitter and transmitted by a transmitter, and the input circuit and the output circuit may be the same circuit, which functions as the input circuit and the output circuit, respectively, at different times. The embodiment of the application does not limit the specific implementation modes of the processor and various circuits.

In a fifth aspect, a processing device is provided that includes a processor and a memory. The processor is configured to read instructions stored in the memory and to receive signals via the receiver and to transmit signals via the transmitter to perform the method of any one of the possible implementations of the first aspect.

Optionally, the processor is one or more, and the memory is one or more.

Alternatively, the memory may be integrated with the processor or the memory may be separate from the processor.

In a specific implementation process, the memory may be a non-transient (non-transitory) memory, for example, a Read Only Memory (ROM), which may be integrated on the same chip as the processor, or may be separately disposed on different chips.

It should be appreciated that the related data interaction flow may be, for example, a flow of sending indication information from a processor, and the receiving capability information may be a flow of receiving input capability information by the processor. Specifically, the data output by the processing may be output to the transmitter, and the input data received by the processor may be from the receiver. Wherein the transmitter and receiver may be collectively referred to as a transceiver.

The processing means in the fifth aspect may be a chip, and the processor may be implemented by hardware or by software, and when implemented by hardware, the processor may be a logic circuit, an integrated circuit, or the like; when implemented in software, the processor may be a general-purpose processor, implemented by reading software code stored in a memory, which may be integrated in the processor, or may reside outside the processor, and exist separately.

In a sixth aspect, there is provided a computer program product comprising: a computer program (which may also be referred to as code, or instructions) which, when executed, causes a computer to perform the method of any one of the possible implementations of the first aspect.

In a seventh aspect, a computer readable storage medium is provided, which stores a computer program (which may also be referred to as code, or instructions) which, when run on a computer, causes the computer to perform the method of any one of the possible implementations of the first aspect.

Drawings

Fig. 1 is a communication system to which an embodiment of the present application is applied;

Fig. 2 is a schematic diagram of a process of performing interference cancellation processing by a decision feedback equalizer;

FIG. 3 is a schematic diagram of a training process of a decision feedback equalizer;

FIG. 4 is a schematic diagram of a convergence trend of errors in a training process of a decision feedback equalizer;

fig. 5 is a schematic flow chart of a signal transmission method according to an embodiment of the present application;

fig. 6 is a schematic diagram of convergence trend of errors before and after the mean filtering process according to an embodiment of the present application;

FIG. 7 is a schematic diagram illustrating error convergence when adjusting coefficient adaptation and no adjusting coefficient according to an embodiment of the present application;

FIG. 8 is a schematic diagram illustrating another adaptive adjustment of adjustment coefficients and corresponding convergence of smoothing errors without adjustment coefficients according to an embodiment of the present application;

FIG. 9 is a schematic diagram illustrating a comparison of adaptive adjustment of adjustment coefficients and corresponding convergence of smoothing errors without adjustment coefficients according to an embodiment of the present application;

FIG. 10 is a schematic diagram showing the comparison of adaptive adjustment of adjustment coefficients and corresponding convergence of smoothing errors without adjustment coefficients for different sample signals according to an embodiment of the present application;

FIG. 11 is a graph showing the relationship between the packet error rate and the signal-to-noise ratio when the adjustment coefficient is adaptively adjusted and the adjustment coefficient is not provided in the embodiment of the present application;

Fig. 12 is a schematic structural diagram of a signal transmission device according to an embodiment of the present application;

fig. 13 is a schematic structural diagram of another signal transmission device according to an embodiment of the present application.

Detailed Description

The technical scheme of the application will be described below with reference to the accompanying drawings.

In embodiments of the present application, the words "first," "second," and the like are used to distinguish between identical or similar items that have substantially the same function and effect. For example, the first value and the second value are merely for distinguishing between different values, and are not limited in order. It will be appreciated by those of skill in the art that the words "first," "second," and the like do not limit the amount and order of execution, and that the words "first," "second," and the like do not necessarily differ.

It should be noted that, in the embodiments of the present application, words such as "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.

In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a alone, a and B together, and B alone, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b, or c may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or plural.

The technical scheme of the embodiment of the application can be applied to various communication systems, such as: long term evolution (long term evolution, LTE) systems, LTE frequency division duplex (frequency division duplex, FDD) systems, LTE time division duplex (time division duplex, TDD), universal mobile telecommunications system (universalmobile telecommunication system, UMTS), worldwide interoperability for microwave access (worldwide interoperability for microwave access, wiMAX) telecommunications systems, fifth generation (5th generation,5G) systems or New Radio (NR), 802.11 b-specified wireless local area network (wlan) telecommunications systems, new systems that may occur in the future, and the like.

The terminal device in the embodiment of the present application may also be referred to as: a User Equipment (UE), a Mobile Station (MS), a Mobile Terminal (MT), an access terminal, a subscriber unit, a subscriber station, a mobile station, a remote terminal, a mobile device, a user terminal, a wireless communication device, a user agent, or a user equipment, etc.

The terminal device may be a device providing voice/data connectivity to a user, e.g., a handheld device with wireless connectivity, an in-vehicle device, etc. Currently, examples of some terminal devices include: a mobile phone, a tablet, a notebook, a palm, a mobile internet device (mobile internetdevice, MID), a wearable device, a Virtual Reality (VR) device, an Augmented Reality (AR) device, a wireless terminal in an industrial control (industrial control), a wireless terminal in a self-drive (self-drive), a wireless terminal in a teleoperation (remote medical surgery), a wireless terminal in a smart grid (smart grid), a wireless terminal in a transportation security (transportation safety), a wireless terminal in a smart city (smart home), a wireless terminal in a smart home (smart home), a cellular phone, a cordless phone, a session initiation protocol (sessioninitiation protocol, SIP) phone, a wireless local loop (wireless local loop, WLL) station, a personal digital assistant (personal digital assistant, PDA), a handheld device with wireless communication capabilities, a computing device or other processing device connected to a wireless modem, a vehicle device, a wearable device, a terminal device in a 5G network or a land-line communication (public land mobile network) is not limited to this application.

By way of example and not limitation, in the present application, the terminal device may be a terminal device in an internet of things (internet of things, ioT) system. The internet of things is an important component of the development of future information technology, and is mainly technically characterized in that objects are connected with a network through a communication technology, so that man-machine interconnection and an intelligent network for the interconnection of the objects are realized. The terminal device in the embodiment of the application can be a wearable device. The wearable device can also be called as a wearable intelligent device, and is a generic name for intelligently designing daily wear by applying wearable technology and developing wearable devices, such as glasses, gloves, watches, clothes, shoes and the like. A wearable device is a portable device that may be worn directly on the body or integrated into the clothing or accessories of the user. The wearable device is not only a hardware device, but also can realize powerful functions through software support and data interaction and cloud interaction. The generalized wearable intelligent device includes full functionality, large size, and may not rely on the smart phone to implement complete or partial functionality, such as: smart watches or smart glasses, etc., and focus on only certain types of application functions, and need to be used in combination with other devices, such as smart phones, for example, various smart bracelets, smart jewelry, etc. for physical sign monitoring.

By way of example, and not limitation, in embodiments of the present application, the terminal device may also be a terminal device in machine type communication (machine type communication, MTC). The terminal device may be a vehicle-mounted module, a vehicle-mounted component, a vehicle-mounted chip, a vehicle-mounted unit, or the like, which are built in the vehicle as one or more components or units, and the vehicle may implement the method provided by the present application through the built-in vehicle-mounted module, vehicle-mounted component, vehicle-mounted chip, or vehicle-mounted unit, or the like. Therefore, the embodiment of the application can also be applied to the Internet of vehicles, such as the vehicle external connection (vehicle to everything, V2X), the long-term evolution technology of workshop communication (longterm evolution-vehicle, LTE-V), the vehicle-to-vehicle (V2V) technology and the like.

The network device to which the present application relates may be a device in communication with a terminal device, which may also be referred to as an access network device or a radio access network device, may be a transmission receiving point (transmission reception point, TRP), may also be an evolved NodeB (eNB or eNodeB) in an LTE system, may also be a home base station (e.g. home evolved NodeB, or home node B, HNB), a Base Band Unit (BBU), may also be a radio controller in a cloud radio access network (cloudradio access network, CRAN) scenario, or may be a relay station, an access point, a vehicle-mounted device, a wearable device, a network device in a 5G network or a network device in a future evolved PLMN network, etc., may also be an Access Point (AP) in a WLAN, may also be a gNB in an NR system, and may also be a city base station, a micro base station, a pico base station, a femto base station, etc., the present application is not limited thereto.

In one network architecture, the network devices may include Centralized Unit (CU) nodes, or Distributed Unit (DU) nodes, or RAN devices including CU nodes and DU nodes, or RAN devices including control plane CU nodes (CU-CP nodes) and user plane CU nodes (CU-UP nodes) and DU nodes.

The network device provides services for the cell, and the terminal device communicates with the cell through transmission resources (e.g., frequency domain resources, or spectrum resources) allocated by the network device, where the cell may belong to a macro base station (e.g., macro eNB or macro gNB, etc.), or may belong to a base station corresponding to a small cell (small cell), where the small cell may include: urban cells (metro cells), micro cells (micro cells), pico cells (pico cells), femto cells (femto cells) and the like, and the micro cells have the characteristics of small coverage area and low transmitting power and are suitable for providing high-rate data transmission services.

To facilitate understanding of embodiments of the present application, a communication system suitable for use in embodiments of the present application will be described in detail with reference to fig. 1.

Fig. 1 illustrates a communication system 100 in which embodiments of the present application may be used. Communication system 100 may include at least one network device, such as network device 110 shown in fig. 1; the communication system 100 may also include at least one terminal device, such as the terminal device 120 shown in fig. 1. Network device 110 and terminal device 120 may communicate via a wireless link. In one possible scenario, the network device 110 may serve as a transmitting end, the terminal device 120 may serve as a receiving end, and the network device 110 may transmit a signal to the terminal device 120; in another possible scenario, the network device 110 may act as a receiving end, the terminal device 120 may act as a transmitting end, and the terminal device 120 may transmit a signal to the network device 110.

Fig. 1 shows an exemplary network device 110 and a terminal device 120. Optionally, the communication system 100 may also include a plurality of network devices and/or a plurality of terminal devices. The network device 110 may be a router, a base station, etc., and the terminal device 120 may be a mobile phone, a tablet computer, a smart bracelet, etc., which is not limited in the embodiment of the present application.

Each of the above-described communication devices, such as the network device 110 or the terminal device 120 in fig. 1, may be configured with a plurality of antennas. The plurality of antennas may include at least one transmitting antenna for transmitting signals and at least one receiving antenna for receiving signals. In addition, each communication device may additionally include a transmitter chain and a receiver chain, each of which may include a plurality of components (e.g., processors, modulators, multiplexers, demodulators, demultiplexers, antennas, etc.) associated with the transmission and reception of signals, as will be appreciated by one skilled in the art. Thus, communication between network device 110 and terminal device 120 may be via multiple antenna techniques.

Optionally, the communication system 100 may further include other network entities such as a network controller, a mobility management entity, and the embodiment of the present application is not limited thereto.

The quality of the signal transmission between the transmitting end and the receiving end will directly affect the experience of the end user. Because the signal sent by the sending end to the receiving end, for example, the baseband signal specified by 802.11b, is interfered by multipath signal intersymbol, the packet error rate of the receiving end is higher, and the user experience is affected. The sending end is a WiFi router, the receiving end is a mobile phone, the mobile phone receives WiFi signals from the WiFi router, and under the condition that the WiFi signals received by the mobile phone are interfered, the communication quality between the mobile phone and the router is poor, so that the internet surfing experience of a mobile phone user is affected.

In order to eliminate the intersymbol interference of multipath signals, a receiving end usually restores the received aliased signals to approximate single-path signals through a decision feedback equalizer, so that the accuracy of signal transmission is improved, and the packet error rate of the receiving end is reduced. Before the decision feedback equalizer performs interference elimination processing on the received service signal, the receiving end trains the decision feedback equalizer through the sample signal. In the process of training the decision feedback equalizer at the receiving end, the parameter value of the decision feedback equalizer is changed continuously according to the error between the output value and the expected value of the decision feedback equalizer, so that the accuracy of the decision feedback equalizer is improved.

In order to facilitate understanding of the embodiments of the present application, the process of performing interference cancellation processing on a decision feedback equalizer and the training process of the decision feedback equalizer will be described in detail with reference to fig. 2 to 4.

Fig. 2 is a schematic diagram of a process of performing interference cancellation processing by a decision feedback equalizer, where the decision feedback equalizer includes a feedforward filter, a feedback filter, and a decision device as shown in fig. 2; the process of the interference cancellation process includes a process 1, a process 2, and a process 3.

In process 1, the receiving end uses the discrete data x [ n ] from n to n+L time]~x[n+L]Input to a feed forward filter. Wherein n is the moment, n is more than or equal to 0; n+L is the time after n time, n+L > n, L is a positive integer; x [ n ]]~x[n+L]Discrete data, x [ n ] in sequence from n to n+L times respectively]~x[n+L]Is a plurality of. x [ n ]]~x[n+L]The discrete data at each time point of (a) corresponds to a parameter (W ₀ ~W _-L ) For example, x [ n ]]Corresponding parameter is W ₀ ，x[n+L]Corresponding parameter is W _-L ，W ₀ ~W _-L Is any rational number. X [ n ] is passed through a feedforward filter]~x[n+L]And W is ₀ ~W _-L Weighted summation is performed to eliminate x [ n+1 ]]~x[n+L]For x [ n ]]Is given by x n]*W ₀ +x[n+1]*W _-1 +x[n+2]*W _-2 +…+x[n+L]*W _-L . The feedback filter stores the discrete data d [ n-1 ] after the decision at the times n-1 to n-M]~d[n-M]Wherein M is a positive integer, and n-1 to n-M are all smaller than n; d [ n-1 ] ]~d[n-M]The judgment discrete data of n-1 to n-M moments are respectively adopted in sequence; d [ n-1]]~d[n-M]Respectively corresponding to a parameter (W ₁ ~W _M ) D [ n-1] is fed back by a feedback filter]~d[n-M]And W is ₁ ~W _M Weighted summation is carried out to eliminate d [ n-1]]~d[n-M]The resulting crosstalk is given by the formula d n-1]*W ₁ +d[n-2]*W ₂ +d[n-3]*W ₃ +…+d[n-M]*W _M . Will x [ n ]]~x[n+L]And W is ₀ ~W _-L The sum obtained by weighted summation is summed with d [ n-1]]~d[n-M]And W is ₁ ~W _M The sum obtained by the weighted summation is summed to obtain [ n ] after eliminating intersymbol interference]Time discrete data y n]。y[n]After hard decision processing of the decision device, discrete data d [ n ] after decision at the moment n is obtained]。

In the process 2, the receiving end inputs d [ n ] into the feedback filter, and shifts d [ n-1] to d [ n-M ] rightward for performing interference elimination processing on the discrete data x [ n+1] at the time of n+1. Meanwhile, the receiving end inputs discrete data x [ n+L+1] at the time of n+L+1 into the feedforward filter, and shifts x [ n ] -x [ n+L ] rightward.

In the process 3, the discrete data x [ n+1] at the time of n+1 is subjected to interference elimination processing, and the discrete data y [ n+1] at the time of [ n+1] after intersymbol interference is eliminated is obtained. And y [ n+1] is subjected to hard decision processing by a decision device to obtain discrete data d [ n+1] after decision at the moment of [ n+1]. d [ n+1] is input into the feedback filter and then used for carrying out interference elimination processing on the discrete data after x [ n+2] and n+2 moments.

Fig. 3 is a schematic diagram of a training process of a decision feedback equalizer. As shown in fig. 3, the decision feedback equalizer performs interference cancellation processing on the discrete data x [ n ] at time n to obtain discrete data y [ n ] at time n after intersymbol interference cancellation. According to the n-time discrete data y [ n ] after eliminating intersymbol interference and the n-time sample data y [ n, e ], the receiving end can determine an error. The receiving end can adjust the parameters of the decision feedback equalizer according to the error.

It will be appreciated that the error (e _n ）=y[n]-y[n，e]Wherein y [ n, e ]]For sample data at time n, e _n Discrete data x [ n ] for decision feedback equalizer for time n]Discrete data y [ n ] obtained by eliminating intersymbol interference]Sample data y [ n, e ] at time n]Is a difference in (c).

According to the interference elimination process of the decision feedback equalizer on x [ n ], the following can be obtained:

. Minimum mean square error (LMS) of +.>Wherein E is _n E is the minimum mean square error _n ^H And e _n Is a complex conjugate number.

W in feedforward filter according to gradient descent method ₀ ~W _-L The updated formula of (2) is:wherein W is _f Is the parameter W in the feedforward filter ₀ ~W _-L ，W _f,update For updated W _f ，βTo learn rate 1 >β> 0. W in feedback filter ₁ ~W _M The updated formula of (2) is: / >Wherein W is _b For feeding back the parameter W in the filter ₁ ~W _M ，W _b,update For updated W _b ，βTo learn rate 1 >β＞0。

It can be appreciated that the parameter W in the feedforward filter during the training of the decision feedback equalizer ₀ ~W _-L And the parameter W in the feedback filter ₁ ~W _M Will be updated continuously. The training process of the decision feedback equalizer is to determine the parameter W in the feedforward filter ₀ ~W _-L And the parameter W in the feedback filter ₁ ~W _M Is a process of (2). And, in addition, the processing unit,βwhich may also be referred to as a convergence step size,βthe value of (2) will be able to determine the convergence rate of the error generated during the training of the decision feedback equalizer. The convergence of the error refers to a state in which the error obtained in the training process of the decision feedback equalizer is dynamically reduced and tends to be dynamic and stable.

The convergence tendency of the error in the training process of the decision feedback equalizer is described in detail below with reference to fig. 4.

Fig. 4 is a schematic diagram of a convergence trend of errors in a training process of a decision feedback equalizer. As shown in fig. 4, in the three convergence trend diagrams, the abscissa indicates the number of errors, i.e., the number of iterations in the training process of the decision feedback equalizer, or the number of sample signals, and the ordinate indicates the error. In the case where the number of sample signals is 1330, the convergence steps are different, and the convergence speeds of the errors are also different. As shown in the convergence trend (a) in fig. 4, in the case where the convergence step size is moderate, the convergence speed of the error is also moderate; as shown in the convergence trend (b) in fig. 4, in the case where the convergence step size is small, the convergence speed of the error is also slow; as shown in the convergence trend (c) in fig. 4, in the case where the convergence step is faster, the convergence speed of the error is also faster. However, when the error convergence speed is low, the error still has a decreasing trend, which means that there is still a convergence space for the error. Under the condition of higher error convergence speed, the convergence trend of the error is stabilized quickly, but the convergence error has larger oscillation, which easily leads to poor training precision.

However, the training effect of the current decision feedback equalizer is poor, so that the packet error rate of the data packet received by the receiving end is high, which results in poor communication quality between the transmitting end and the receiving end, and influences the user experience.

In order to solve the technical problems, the present application provides a signal transmission method and apparatus, in which, in the training process of a decision feedback equalizer, the change trend of the error is determined continuously according to the accumulated error between the processing result of the preset number of decision feedback equalizer on the actual received signal and the sample signal, and the adjustment coefficient of the parameter of the decision feedback equalizer is further adjusted according to the change trend, so that the receiving end can adjust the parameter of the decision feedback equalizer in real time according to the change trend of the error, and in this way, under the condition that the number of the actual received signals of the decision feedback equalizer is the same, the error between the processing result of the decision feedback equalizer on the actual received signal and the sample signal is reduced, so that the accuracy of the decision feedback equalizer can be improved, thereby improving the communication quality between the transmitting end and the receiving end and improving the user experience.

The signal transmission method of the present application will be described in detail with reference to fig. 5 to 11. The embodiment of the application shows the information transmission method provided by the application from the perspective of equipment interaction. The specific form and number of the devices shown therein are only examples and should not be construed as limiting the practice of the method provided by the present application in any way. The information transmission method according to the embodiment of the present application will be described in detail below by taking the transmitting end and the receiving end as execution subjects.

It should be understood that, in the embodiment of the present application, the transmitting end is a device for transmitting a signal, which may be a terminal device or a network device itself, or may be a chip, a chip system or a processor for supporting the terminal device or the network device to implement a signal transmission method, or may be a logic module or software capable of implementing all or part of the terminal device or the network device; the receiving end in the embodiment of the application is equipment for receiving signals, which can be terminal equipment or network equipment, a chip system or a processor for supporting the terminal equipment or the network equipment to realize a signal transmission method, and a logic module or software for realizing all or part of the terminal equipment or the network equipment; the present application is not particularly limited thereto.

Fig. 5 is a flowchart of a signal transmission method 500 according to an embodiment of the present application. Method 500 is applicable to communication system 100, method 500 comprising the steps of:

s501, a transmitting end transmits signals to a receiving end, wherein the signals comprise actual received signals of sample signals and service signals. Correspondingly, the receiving end receives the signal from the transmitting end.

It should be understood that the receiving end may be a network device or a terminal device provided with a decision feedback equalizer, such as a mobile phone, a smart band, a WiFi router, etc. The signal sent from the sending end to the receiving end may be a baseband signal specified by 802.11 b. The sample signal may refer to data information agreed by the transmitting end and the receiving end and used for training the decision feedback equalizer by the receiving end, that is, the signal sent by the transmitting end to the receiving end includes the sample signal. The actual received signal of the sample signal refers to the sample signal actually received by the terminal device. It can be understood that, after the sample signal is transmitted from the transmitting end to the receiving end, the sample signal actually received by the receiving end is affected by inter-symbol interference of multipath signals, and the like, that is, the actual received signal of the sample signal received by the receiving end may be different from the sample signal. Illustratively, the sample signal transmitted from the transmitting end to the receiving end is "1234", and the actual received signal of the sample signal received by the receiving end is the sample signal that is subject to intersymbol interference of the multipath signal, which may be "2224", for example. The service signal is service data that the receiving end needs to acquire, however, in the process that the receiving end needs to acquire service data that the sending end transmits to the receiving end, the service data that the receiving end needs to acquire is affected by intersymbol interference of multipath signals and the like, so that the service data that the receiving end needs to acquire changes, namely, the service signal received by the receiving end. Under the condition that the receiving end needs to acquire the correct service signal, the receiving end can perform interference elimination processing on the actually received service signal through a trained decision feedback equalizer.

In one possible implementation, the signal includes header information (header) and a physical layer service data unit (physical service data unit, PSDU), wherein the header information is an actual received signal of the sample signal and the PSDU is a traffic signal.

S502, training a decision feedback equalizer based on an actual received signal and a sample signal.

It should be understood that the sample signal is an accurate signal agreed by the transmitting end and the receiving end, and the actual received signal is an actual received signal of the receiving end. After the interference elimination processing is carried out on the actual received signal of the receiving end, an error exists between the obtained output result and the sample signal, and the decision feedback equalizer is trained based on the error. The process of training the decision feedback equalizer is the process of continuously adjusting the parameters of the decision feedback equalizer.

S503, in the training process, according to a first change trend of the error between the processing result of the actual received signal by the decision feedback equalizer and the sample signal and a first corresponding relation, determining an adjustment coefficient of the parameter of the decision feedback equalizer, wherein the first corresponding relation comprises a plurality of change trends and a corresponding relation between a plurality of adjustment coefficients, and the plurality of change trends comprise a first change trend.

It should be understood that the error between the processing result of the actual received signal by the decision feedback equalizer and the sample signal may refer to the difference between the processing result and the sample signal. The first trend may refer to a trend of at least two errors. Illustratively, the receiving end continuously obtains three errors, and the first variation trend can be determined according to the three errors.

The parameters of the decision feedback equalizer refer to the parameters in the feedforward filter and the parameters in the feedback filter, e.g., W in process 1 shown in FIG. 2 ₀ ~W _-L And W is ₁ ~W _M . The adjustment coefficients of the parameters of the decision feedback equalizer are coefficients for adjusting the parameters of the decision feedback equalizer.

The first correspondence relationship includes correspondence relationships between a plurality of variation trends and a plurality of adjustment coefficients, and illustratively, the first correspondence relationship includes a variation trend 1, a variation trend 2, and a variation trend 3; also comprises an adjustment coefficient 1, an adjustment coefficient 2 and an adjustment coefficient 3. Wherein, the variation trend 1 corresponds to the adjustment coefficient 1, the variation trend 2 corresponds to the adjustment coefficient 2, and the variation trend 3 corresponds to the adjustment coefficient 3. In the case where the first variation trend is variation trend 1, the receiving end may determine the adjustment coefficient as adjustment coefficient 1.

S504, adjusting parameters of the decision feedback equalizer based on the adjustment coefficient, and continuing to train the decision feedback equalizer.

It should be understood that the error between the processing result of the actual received signal by the decision feedback equalizer and the sample signal will be continuously obtained in the training process of the decision feedback equalizer, and the number of errors is the number of sample signals. And in the training process of the decision feedback equalizer, the parameters of the decision feedback equalizer are continuously adjusted. Continuing to train the decision feedback equalizer means that after adjusting the parameters of the decision feedback equalizer, S503 is repeated continuously, and training is performed on the decision feedback equalizer until the stop condition is satisfied.

In one possible implementation, the stop condition may be that the decision feedback equalizer training is completed with the number of iterations of the decision feedback equalizer equal to the number of actual received signals. Illustratively, if the actual received signal includes the first signal, the second signal, and the third signal, the decision feedback equalizer is trained iteratively three times by the first signal, the second signal, and the third signal, respectively, at which time training of the decision feedback equalizer is completed.

In a specific example, the actual received signal includes a first signal, a second signal, and a third signal; the sample signals include a first sample signal, a second sample signal, and a third sample signal. The first signal is an actual received signal of the first sample signal; the second signal is an actual received signal of the second sample signal; the third signal is the actual received signal of the third sample signal. The initial state of the parameters of the decision feedback equalizer is a, after a receiving end inputs a first signal into the decision feedback equalizer, a first processing result is obtained, and a first error is determined according to the first processing result and a first sample signal; adjusting the decision inverse based on the first error The parameters of the feedback equalizer are adjusted by the first error to a ₁ . The receiving end inputs the second signal as a parameter ₁ And (3) a second processing result is obtained, and a second error is determined according to the second processing result and the second sample signal. The receiving end can determine a first change trend according to the second error and the first error, then determine an adjustment coefficient according to the first change trend and the first corresponding relation, and adjust the parameter of the decision feedback equalizer to be a through the adjustment coefficient ₂ . The receiving end inputs the third signal as a parameter ₂ And (3) a third processing result is obtained, and a third error is determined according to the third processing result and the third sample signal. The receiving end can determine a second change trend according to the third error and the second error, then determine an adjustment coefficient according to the second change trend and the first corresponding relation, and adjust the coefficient of the decision feedback equalizer to be a through the adjustment coefficient ₃ 。

In one possible implementation, the parameters of the decision feedback equalizer are determined based on the error, adjustment coefficient, and learning rate between the processing result of the actual received signal and the sample signal. Optionally, the relation between the adjustment coefficient and the parameter of the decision feedback equalizer is as follows: ；/>Wherein, the method comprises the steps of, wherein,Rfor adjusting the coefficients, they can also be represented by ratio; w (W) _f Is the parameter W in the feedforward filter ₀ ~W _-L ，W _f,update For updated W _f ，W _b For feeding back the parameter W in the filter ₁ ~W _M ，W _b,update For updated W _b ，βTo learn rate 1 >β＞0。

It should be understood that the receiving end determines the adjustment coefficient through the first variation trend of the error, so that the parameters of the decision feedback equalizer can be changed in real time according to the error, thereby improving the training efficiency of the decision feedback equalizer, improving the training effect of the decision feedback equalizer, reducing the packet error rate of the receiving end, improving the communication quality of the transmitting end and the receiving end, and improving the user experience.

S505, the trained decision feedback equalizer is utilized to perform interference elimination processing on the service signal.

It should be appreciated that the trained decision feedback equalizer is the decision feedback equalizer that determines the good parameters. In one possible implementation, the receiving end includes a decision feedback equalizer, and after the receiving end trains the decision feedback equalizer through the sample signal, the trained decision feedback equalizer is obtained. In another possible implementation manner, the receiving end includes a decision feedback equalizer for training and a decision feedback equalizer for interference cancellation processing, after the receiving end completes training the decision feedback equalizer for training through the sample signal, the decision feedback equalizer for training sends the determined parameters to the decision feedback equalizer for interference cancellation processing, and the decision feedback equalizer for interference cancellation processing is the decision feedback equalizer after adopting the parameters of the decision feedback equalizer for training.

In one possible implementation manner, the parameters of the trained decision feedback equalizer may be parameters corresponding to the time when the error between the processing result of the actual received signal by the decision feedback equalizer and the sample signal is minimum in the training process. Illustratively, the decision feedback equalizer has a parameter c of ₁ When the error between the obtained processing result and the sample signal is g ₁ The method comprises the steps of carrying out a first treatment on the surface of the The parameter of the decision feedback equalizer is c ₂ When the error between the obtained processing result and the sample signal is g ₂ The method comprises the steps of carrying out a first treatment on the surface of the The parameter of the decision feedback equalizer is c ₃ When the error between the obtained processing result and the sample signal is g ₃ . At g ₁ ＞g ₂ ＞g ₃ In the case of (a), the receiving end can determine that the parameter of the decision feedback equalizer after training is c ₃ 。

In another possible implementation, the parameters of the trained decision feedback equalizer may also be the parameters used in the last iteration of the decision feedback equalizer.Illustratively, the decision feedback equalizer has a parameter c of ₁ When the error between the obtained processing result and the sample signal is g ₁ The method comprises the steps of carrying out a first treatment on the surface of the The parameter of the decision feedback equalizer is c ₂ When the error between the obtained processing result and the sample signal is g ₂ The method comprises the steps of carrying out a first treatment on the surface of the The parameter of the decision feedback equalizer is c ₃ When the error between the obtained processing result and the sample signal is g ₃ At g ₁ ＞g ₃ ＞g ₂ In the case of (a), the receiving end can determine that the parameter of the decision feedback equalizer after training is c ₃ 。

According to the signal transmission method, in the training process of the decision feedback equalizer, the change trend of the error is determined according to the processing result of the accumulated preset number of the decision feedback equalizer on the actual received signals and the error between the sample signals, and the adjustment coefficient of the parameters of the decision feedback equalizer is further adjusted according to the change trend, so that the receiving end can adjust the parameters of the decision feedback equalizer in real time according to the change trend of the error, and in this way, under the condition that the number of the actual received signals of the decision feedback equalizer is the same, the error between the processing result of the decision feedback equalizer on the actual received signals and the sample signals can be reduced, the accuracy of the decision feedback equalizer is improved, the communication quality between the transmitting end and the receiving end is improved, and the user experience is improved.

As an alternative embodiment, the method 500 further comprises: the receiving end inputs the actual received signal to a decision feedback equalizer to obtain a processing result of the actual received signal; determining an error between a processing result of the actual received signal and the sample signal; based on the error, a first trend is determined.

In one possible embodiment, determining the first trend of change based on the error includes: when the number of errors is accumulated to a first preset number, carrying out mean value filtering processing on the first preset number of errors to obtain a plurality of smooth errors; according to the difference value between every two adjacent smooth errors in the plurality of smooth errors, determining the local change trend of every two adjacent smooth errors in the plurality of smooth errors, wherein the local change trend comprises an ascending trend and a descending trend; and determining a first change trend according to the number of the rising trends and the number of the falling trends.

It should be appreciated that the first predetermined number may be any positive integer, such as 5, 10, etc. Alternatively, the first preset number may also be represented by a window length (window length). The average filtering of the first preset number of errors may mean that the first preset number of errors is averaged, which is a smoothed error (smoothed error). After the receiving end starts training the decision feedback equalizer, the receiving end inputs an actual receiving signal to the decision feedback equalizer every time, and the receiving end has corresponding processing results, so that an error can be determined according to the processing results and the sample signals. In the training process of the decision feedback equalizer, the receiving end continuously inputs the actual received signal into the decision feedback equalizer, and errors can be continuously obtained. Each time the number of errors accumulates to a first preset number, the receiving end determines an average value of the first preset number of errors, thereby obtaining a smooth error. As the number of errors accumulated further increases, the number of smoothed errors also increases.

The mean filtering process is further described below in conjunction with fig. 6.

Fig. 6 is a schematic diagram of convergence trend of errors before and after the mean filtering process according to an embodiment of the present application. As shown in the convergence trend (a) of the errors in fig. 6, the number of errors is 1365, i.e., 1365 iterations are performed during the training of the decision feedback equalizer. The first preset number is 21, that is, in the training process of the decision feedback equalizer, each time 21 errors are obtained, average filtering processing is performed on the 21 errors to obtain a smooth error, after the training of the decision feedback equalizer is finished, 65 smooth errors are obtained in total, and the convergence trend of the 65 smooth errors is shown as convergence trend (b) in fig. 6. As can be seen from fig. 6, the mean value filtering processing is performed on the first preset number of errors, so that the receiving end is helped to determine the variation trend of the errors, and thus, the convergence efficiency of the errors is helped to be improved, and the training effect of the decision feedback equalizer is improved.

The adjacent smoothing errors refer to two smoothing errors continuously determined by the receiving end, and a plurality of errors for determining the adjacent smoothing errors are continuously output by the decision feedback equalizer. Illustratively, in the convergence trend (B) as in fig. 6, each adjacent two points are adjacent smoothing errors, for example, the smoothing error corresponding to the point a and the smoothing error corresponding to the point B are adjacent smoothing errors. The local variation trend may be adjacent smoothing errors according to smoothing errors corresponding to the adjacent B and smoothing errors corresponding to the point C. The local change trend can be determined according to the difference value of the adjacent smooth errors, and the receiving end can determine that the local change trend is a descending trend under the condition that the latter smooth error is larger than the former smooth error in the adjacent smooth errors; in the case where the latter one of the adjacent smoothing errors is smaller than the former one, the receiving end may determine that the local variation trend is an ascending trend. Illustratively, in the convergence trend (B) as in fig. 6, the smoothing error corresponding to the point a is 1.1, the smoothing error corresponding to the point B is 0.8, and the smoothing error corresponding to the point C is 0.9. The point A and the point B are adjacent smooth errors, wherein the point B is the latter smooth error in the two adjacent smooth errors, the point A is the former smooth error, and the smooth error corresponding to the point B is smaller than the smooth error corresponding to the point A, so that a local change trend determined by the receiving end according to the adjacent smooth errors is a descending trend. Similarly, the point B and the point C are adjacent smoothing errors, and according to the two adjacent smoothing errors, the receiving end can determine that a local variation trend is an ascending trend.

In one possible implementation manner, the local variation trend further includes a stabilizing trend, and in the case that the difference between the adjacent smoothing errors is less than or equal to the first preset threshold, the local variation trend of the adjacent smoothing errors is determined to be the stabilizing trend; determining that the local variation trend of the adjacent smooth error is an ascending trend under the condition that the difference value between the adjacent smooth errors is larger than a first preset threshold value and the later smooth error in the adjacent smooth errors is larger than the former smooth error; and determining that the local variation trend of the adjacent smooth errors is a descending trend under the condition that the difference value between the adjacent smooth errors is larger than a first preset threshold value and the later smooth error in the adjacent smooth errors is smaller than the former smooth error.

It should be appreciated that the first preset threshold is any value greater than or equal to 0, for example, may be 0, 0.01, etc. When the difference between the adjacent smooth errors is smaller than or equal to a first preset threshold, the two adjacent smooth errors are close, and the local change trend can be determined to be a stable trend. Illustratively, the first smoothing error and the second smoothing error are adjacent smoothing errors, the second smoothing error and the third smoothing error are adjacent smoothing errors, the first smoothing error is 1.0, the second smoothing error is 0.9, the third smoothing error is 0.91, and the first preset threshold is 0.02, the receiving end may determine that a local variation trend is a decreasing trend according to the first smoothing error and the second smoothing error; according to the second smoothing error and the third smoothing error, the receiving end can determine that a local variation trend is a stable trend.

In one possible embodiment, determining the first trend according to the number of upward trends and the number of downward trends includes: if the number of the rising trends is greater than that of the falling trends, determining the first change trend as the rising trend; or if the number of the rising trends is equal to the number of the falling trends, determining the first change trend as a stable trend; or if the number of the rising trends is smaller than the number of the falling trends, determining the first change trend as the falling trend.

In another possible embodiment, determining the first trend according to the number of upward trends and the number of downward trends includes: when the number of the plurality of smoothing errors is accumulated to a second preset number, determining a first variation trend according to the number of the rising trends and the number of the falling trends. Alternatively, the second preset number may also be expressed in terms of a count number (count_num).

Optionally, determining the first variation trend according to the number of rising trends and the number of falling trends includes: when the number of the plurality of smoothing errors is accumulated to a second preset number, if the number of the rising trends is greater than the number of the falling trends, and the difference between the number of the rising trends and the number of the falling trends is greater than or equal to a second preset threshold, determining the first variation trend as the rising trend; or if the number of the rising trends is smaller than the number of the falling trends and the difference between the number of the falling trends and the number of the rising trends is greater than or equal to a third preset threshold, determining the first change trend as the falling trend; otherwise, the first variation trend is determined as a stable trend.

It should be understood that the second preset threshold value and the third preset threshold value are any preset positive integer. The first variation trend may be an upward trend, a downward trend, or a steady trend, and in the case where the first variation trend is not an upward trend or a downward trend, the first variation trend is a steady trend. Illustratively, the second preset threshold is 2, the third preset threshold is 3, and the receiving end determines that the first variation trend is an upward trend if the number of the upward trends of the local variation trend is more than 3 than the number of the downward trends; under the condition that the number of the descending trends in the local change trend is more than 3 than the number of the ascending trends, the receiving end determines that the first change trend is the descending trend; in the case where the number of rising trends is 1 more than the number of falling trends in the local variation trend, the receiving end determines that the first variation trend is a stable trend.

In one possible embodiment, the decision feedback equalizer includes a rising accumulator and a falling accumulator, and determining a first trend according to the number of rising trends and the number of falling trends includes: each time the receiving end determines an ascending trend, the ascending accumulator accumulates a +1; and when the number of the plurality of smooth errors is accumulated to a third preset number, determining a first change trend according to the sum of the value accumulated by the rising accumulator and the value accumulated by the falling accumulator. Determining that the first change trend is an ascending trend when the sum of the value accumulated by the ascending accumulator and the value accumulated by the descending accumulator is larger than or equal to a fourth preset threshold value; when the sum of the value accumulated by the rising accumulator and the value accumulated by the falling accumulator is smaller than or equal to a fifth preset threshold value, determining that the first change trend is a falling trend; otherwise, determining the first variation trend as a stable trend. Alternatively, the value accumulated by the rising accumulator may be represented by flag_up, that is, each time the rising accumulator accumulates a local change trend as a rising trend, flag_up is added by +1; the value accumulated by the falling accumulator can be expressed by flag_down, that is, each time the rising accumulator accumulates a local change trend, the flag_down is added with-1. Alternatively, the fourth preset threshold may be represented by up_thr, and the fifth preset threshold may be represented by down_thr.

In a specific example, the third preset number is 6, the fourth preset threshold is 2, the fifth preset threshold is-3, after the receiving end determines 6 smooth errors, the local variation trend includes 3 rising trends, and the value accumulated by the rising accumulator is +3; the local change trend comprises 2 rising trends, the value accumulated by the falling accumulator is-2, the sum of the value accumulated by the rising accumulator and the value accumulated by the falling accumulator is-1, and the receiving end determines that the first change trend is a stable trend.

If the number of the rising trends is greater than the number of the falling trends and the difference between the number of the rising trends and the number of the falling trends is greater than or equal to a second preset threshold, determining the first change trend as the rising trend; or if the number of the rising trends is smaller than the number of the falling trends and the difference between the number of the falling trends and the number of the rising trends is greater than or equal to a third preset threshold, determining the first change trend as the falling trend; otherwise, the first variation trend is determined as a stable trend.

In one possible embodiment, the plurality of variation trends include an ascending trend, a descending trend, and a stabilizing trend, and the first correspondence relationship includes: under the condition that the first change trend is an ascending trend, the adjustment coefficient is p times of the initial adjustment coefficient; under the condition that the first change trend is a descending trend, the adjustment coefficient is q times of the initial adjustment coefficient; under the condition that the first change trend is a stable trend, the adjustment coefficient is s times of the initial adjustment coefficient; p, q and s are each any value greater than 0. The initial adjustment coefficient is an adjustment coefficient before adjustment according to the first change trend and the first corresponding relation.

In one specific example, p is 0.65, q is 1.5, and s is 0.85. Under the condition that the adjustment coefficient is 2, the receiving end determines 6 smooth errors, the number of rising trends is larger than that of falling trends in 5 local change trends determined according to the 6 smooth errors, and the first change trend is determined to be the rising trend at the receiving end, so that the adjustment coefficient is changed to be 1.3. Then, the receiving end determines the parameters of the decision feedback equalizer according to the changed adjustment coefficient, and based on the newly determined parameters of the decision feedback equalizer, the receiving end continues to train the decision feedback equalizer.

As an alternative embodiment, the first trend is derived based on a first part of the signals in the actual received signal; after adjusting the parameters of the decision feedback equalizer based on the adjustment coefficients, the method 500 further includes: determining a second variation trend of errors between a processing result of the adjusted decision feedback equalizer on a second part of signals in the actual received signals and the sample signals; determining readjustment coefficients of parameters of the decision feedback equalizer based on the second variation trend and the first correspondence; and based on the readjusting coefficient, readjusting the parameters of the decision feedback equalizer, and continuing training the decision feedback equalizer.

The signal transmission method of the present application is further described below with reference to fig. 7 to 10 and specific examples.

Fig. 7 is a comparison diagram of adaptive adjustment of an adjustment coefficient and error convergence corresponding to no adjustment coefficient according to an embodiment of the present application. As shown in fig. 7, in the case that the number of sample signals is 1330, the number of iterations of the training process of the decision feedback equalizer and the number of errors obtained are 1330. In the current signal transmission method, no adjustment coefficient is used, that is, the convergence step length is always 1, the convergence speed of the error is slow, and the determined convergence trend of the error is shown as the variation trend (a) of the error in fig. 7. The decision feedback equalizer is trained by the signal transmission method, and the adjustment coefficient is continuously adaptively adjusted according to the first change trend and the first corresponding relation. In the training process of the decision feedback equalizer, the initial adjustment coefficient is 2.5, the first preset number is 11, the second preset number is 6, the fourth preset threshold is 2, the fifth preset threshold is-3, and the convergence trend of the determined error is shown as the change trend (b) in fig. 7. Carrying out mean value filtering processing on 1330 errors obtained when no adjustment coefficient exists, and obtaining 65 smooth errors, wherein the variation trend of the 65 smooth errors is shown as 701 in fig. 7; the 1330 errors obtained when the adjustment coefficient is continuously adaptively adjusted are subjected to mean value filtering processing, so as to obtain 65 smooth errors, and the variation trend of the 65 smooth errors is shown as 702 in fig. 7. In the training process of the decision feedback equalizer, every time 5 smooth errors are obtained, a receiving end determines 4 local variation trends according to any two adjacent smooth errors in the 5 smooth errors, and determines a first variation trend as an ascending trend and adjusts an adjustment coefficient to be 0.65 times of an initial adjustment coefficient under the condition that the number of the ascending trends is larger than or equal to the number of the descending trends; when the number of the descending trends is greater than or equal to 3 than the number of the ascending trends, determining that the first change trend is the descending trend, and adjusting the adjustment coefficient to be 1.5 times of the initial adjustment coefficient; otherwise, determining the first variation trend as a stable trend, and adjusting the adjustment coefficient to be 0.85 times of the initial adjustment coefficient. In this way, the change process of the adjustment coefficient is made as shown in the change process (d) in fig. 7.

Comparing 701 and 702, it can be seen that by the signal transmission method of the present application, the convergence efficiency of the error can be improved, so that the accuracy of training the decision feedback equalizer can be improved under the condition of determining the number of sample signals, and thus, the packet error rate of the receiving end can be reduced, the communication quality between the transmitting end and the receiving end can be improved, and the user experience sense can be improved by the decision feedback equalizer with higher accuracy.

Fig. 8 is a comparison diagram of adaptive adjustment of an adjustment coefficient and corresponding convergence of a smoothing error without an adjustment coefficient according to another embodiment of the present application. As shown in fig. 8, the actual received signal of the receiving end comes from the B channel, the receiving end determines 50 smooth errors in total, and in the condition that there is no adjustment coefficient in the training process of the decision feedback equalizer of the receiving end, the convergence trend of the smooth errors is shown as 801, and in the case of 35 smooth errors, the smooth errors tend to converge; in the process of training the decision feedback equalizer at the receiving end, under the condition that the adjustment coefficient is adaptively adjusted, the first preset number is 11, the convergence trend of the smoothing errors is shown as 802, and when the smoothing errors are 10, the smoothing errors tend to converge. Comparing 801 and 802, it can be seen that the signal transmission method of the present application shortens the convergence time of the training of the decision feedback equalizer to 27% of the convergence time of the training of the decision feedback equalizer without adjustment coefficients. Therefore, the signal transmission method of the application adaptively adjusts the adjustment coefficient in the training process of the decision feedback equalizer, thus being capable of improving the training efficiency and the accuracy of the decision feedback equalizer, thereby improving the accuracy of the equalization result of the decision feedback equalizer.

Fig. 9 is a schematic diagram showing still another adaptive adjustment of an adjustment coefficient and corresponding convergence of a smoothing error without the adjustment coefficient according to an embodiment of the present application. As shown in fig. 9, the receiving end determines 75 smooth errors in total, and in the process of training the decision feedback equalizer of the receiving end, under the condition of no adjustment coefficient, the convergence trend of the smooth errors is shown as 901; in the process of training the decision feedback equalizer at the receiving end, under the condition of adaptive adjustment of the adjustment coefficient, the first preset number is 11, and the convergence trend of the smoothing error is shown as 902. Comparing 901 and 902, it can be seen that by the signal transmission method according to the embodiment of the present application, training efficiency of the decision feedback equalizer can be improved. Partially enlarging the region 903 in the convergence trend (a) of the smoothing error in fig. 9, it can be seen that, after convergence of the smoothing error, when there is no adjustment coefficient, the convergence trend of the smoothing error is shown as 904; in the case of adaptive adjustment of the adjustment coefficient after convergence of the smoothing error, the convergence trend of the smoothing error is shown as 904.

Illustratively, in an embodiment of the present application, the actual received signal at the receiving end may be from one or more of a B channel, a C channel, a D channel, an E channel, and an F channel. The B channel, the C channel, the D channel, the E channel and the F channel are 5 analog channels which are simulated by a matrix laboratory (matrix laboratory, MATLAB), and the 5 channels respectively have different degrees of intersymbol interference, so that the sample signals can receive different degrees of intersymbol interference in the process of transmitting the sample signals from a transmitting end to a receiving end. Therefore, after receiving the actual received signals from the 5 channels, the receiving end trains the decision feedback equalizer based on the actual received signals, and performs interference cancellation processing on the service signals through the trained decision feedback equalizer.

Fig. 10 is a schematic diagram of comparison between adaptive adjustment of adjustment coefficients and corresponding convergence of smoothing errors without adjustment coefficients for different sample signals according to an embodiment of the present application. As shown in fig. 10, the number of smoothing errors determined by the receiving end is 50. Further, diagrams of convergence trends of the smoothing errors when the decision feedback equalizer is trained on the actual received signals from the B channel, the C channel, the D channel, the E channel, and the F channel are shown in (a) to (E) of fig. 10, respectively. When the actual received signals of the receiving end come from the B channel, under the condition of no adjustment coefficient, the convergence trend of the smoothing error is shown as 1001; in the case of adjustment of the adjustment coefficient, the first preset number is 11 above, and the convergence trend of the smoothing error is shown as 1002. When the actual received signals of the receiving end come from the C channel, under the condition of no adjustment coefficient, the convergence trend of the smoothing error is shown as 1003; in the case of adaptive adjustment of the adjustment coefficients, the first preset number is 11 above, and the convergence trend of the smoothing error is shown as 1004. When the actual received signals of the receiving end come from the D channel, under the condition of no adjustment coefficient, the convergence trend of the smoothing error is shown as 1005; in the case of adaptive adjustment of the adjustment coefficients, the first preset number is 11, and the convergence trend of the smoothing error is shown as 1006. When the actual received signals of the receiving end come from the E channel, under the condition of no adjustment coefficient, the convergence trend of the smoothing error is shown as 1007; in the case of adjustment of the adjustment coefficient, the first preset number is 11 above, and the convergence trend of the smoothing error is shown as 1008. When the actual received signals of the receiving end come from the F channel, under the condition of no adjustment coefficient, the convergence trend of the smoothing error is shown as 1009; in the case of adaptive adjustment of the adjustment coefficients, the first preset number is 11 above, and the convergence trend of the smoothing error is shown as 1010. Comparing the convergence trend 1001 with the convergence trend 1002, the convergence trend 1003 with the convergence trend 1004, the convergence trend 1005 with the convergence trend 1006, the convergence trend 1007 with the convergence trend 1008, and the convergence trend 1009 with the convergence trend 1010 respectively, it can be seen that the signal transmission method of the application adaptively adjusts the adjustment coefficient in the training process of the decision feedback equalizer, so that the training efficiency and the accuracy of the decision feedback equalizer can be improved for different actually received signals, thereby improving the accuracy of the equalization result of the decision feedback equalizer.

Fig. 11 is a schematic diagram of a relationship between packet error rate and signal-to-noise ratio corresponding to adaptive adjustment of adjustment coefficients and no adjustment coefficients provided in an embodiment of the present application. As shown in fig. 11, the packet error rate (PER-PSDU) tends to decrease with an increase in the signal-to-noise ratio (signal to noise ratio, SNR) regardless of whether the adjustment coefficient is adaptively adjusted. In the case where there is no adjustment coefficient, a relationship between the packet error rate and the signal-to-noise ratio is shown as 1101, and in the case where the parameter of the feedback equalizer is adaptively adjusted based on the adjustment coefficient, a relationship between the packet error rate and the signal-to-noise ratio is shown as 1102. Comparing 1101 and 1102, the corresponding packet error rate in 1102 is lower under the condition of the same signal to noise ratio. For example, when the signal-to-noise ratio is 32dB, the packet error rate corresponding to 1101 is 0.173, and the packet error rate corresponding to 1102 is 0.112, and by the signal transmission method according to the embodiment of the present application, the packet error rate of the receiving end is reduced by about 35%.

It should be understood that the sequence numbers of the above methods do not mean the order of execution, and the order of execution of the methods should be determined by their functions and internal logic.

The signal transmission method according to the embodiment of the present application is described in detail above with reference to fig. 2 to 11, and the signal transmission device according to the embodiment of the present application is described in detail below with reference to fig. 12 and 13.

Fig. 12 is a schematic structural diagram of a signal transmission device 1200 according to an embodiment of the present application. As shown in fig. 12, the apparatus 1200 includes: a transceiver module 1201 and a processing module 1202.

The apparatus 1200 is configured to implement steps corresponding to the receiving end in the above method embodiment.

A transceiver module 1201, configured to receive a signal from a transmitting end, where the signal includes an actual received signal of the sample signal and a traffic signal;

a processing module 1202, configured to train the decision feedback equalizer based on the actual received signal and the sample signal; in the training process, according to a first change trend of an error between a processing result of the actual received signal by the decision feedback equalizer and a sample signal and a first corresponding relation, determining an adjustment coefficient of a parameter of the decision feedback equalizer, wherein the first corresponding relation comprises a plurality of change trends and a corresponding relation between a plurality of adjustment coefficients, and the plurality of change trends comprise a first change trend; based on the adjustment coefficient, adjusting the parameters of the decision feedback equalizer, and continuously training the decision feedback equalizer; and carrying out interference elimination processing on the service signal by utilizing the trained decision feedback equalizer.

Optionally, the processing module 1202 is further configured to: inputting the actual received signal to a decision feedback equalizer to obtain a processing result of the actual received signal; determining an error between a processing result of the actual received signal and the sample signal; based on the error, a first trend is determined.

Optionally, the processing module 1202 is specifically configured to: when the number of errors is accumulated to a first preset number, carrying out mean value filtering processing on the first preset number of errors to obtain a plurality of smooth errors; according to the difference value between every two adjacent smooth errors in the plurality of smooth errors, determining the local change trend of every two adjacent smooth errors in the plurality of smooth errors, wherein the local change trend comprises an ascending trend and a descending trend; and determining a first change trend according to the number of the rising trends and the number of the falling trends.

Optionally, the processing module 1202 is specifically configured to: if the number of the rising trends is greater than that of the falling trends, determining the first change trend as the rising trend; or if the number of the rising trends is equal to the number of the falling trends, determining the first change trend as a stable trend; or if the number of the rising trends is smaller than the number of the falling trends, determining the first change trend as the falling trend.

Optionally, the processing module 1202 is specifically configured to: when the number of the plurality of smoothing errors is accumulated to a second preset number, determining a first variation trend according to the number of the rising trends and the number of the falling trends.

Optionally, the first trend is derived based on a first part of signals in the actual received signal; the processing module 1202 is also configured to: determining a second variation trend of errors between a processing result of the adjusted decision feedback equalizer on a second part of signals in the actual received signals and the sample signals; determining readjustment coefficients of parameters of the decision feedback equalizer based on the second variation trend and the first correspondence; and based on the readjusting coefficient, readjusting the parameters of the decision feedback equalizer, and continuing training the decision feedback equalizer.

It should be appreciated that the apparatus 1200 herein is embodied in the form of functional modules. The term module herein may refer to an application specific integrated circuit (application specific integrated circuit, ASIC), an electronic circuit, a processor (e.g., a shared, dedicated, or group processor, etc.) and memory that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that support the described functionality. In an alternative example, it will be understood by those skilled in the art that the apparatus 1200 may be specifically configured as the receiving end in the foregoing embodiment, and the apparatus 1200 may be configured to perform each flow and/or step corresponding to the receiving end in the foregoing method embodiment, which is not described herein for avoiding repetition.

The apparatus 1200 has a function of implementing the corresponding steps executed by the receiving end in the method; the above functions may be implemented by hardware, or may be implemented by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the functions described above.

In an embodiment of the present application, the apparatus 1200 in fig. 12 may also be a chip, for example: SOC. Correspondingly, the processing module 1202 may be a transceiver circuit of the chip, which is not limited herein.

Fig. 13 is a schematic structural diagram of a signal transmission device 1300 according to an embodiment of the present application. The apparatus 1300 includes a processor 1301, a transceiver 1302, and a memory 1303. The processor 1301, the transceiver 1302 and the memory 1303 communicate with each other through an internal connection path, the memory 1303 is configured to store instructions, and the processor 1301 is configured to execute the instructions stored in the memory 1303, so as to control the transceiver 1302 to transmit signals and/or receive signals.

It should be understood that the apparatus 1300 may be specifically configured as the receiving end in the foregoing embodiment, and may be configured to perform the steps and/or flows corresponding to the receiving end in the foregoing method embodiment. The memory 1303 may optionally include read-only memory and random access memory, and provide instructions and data to the processor. A portion of the memory may also include non-volatile random access memory. For example, the memory may also store information of the device type. The processor 1301 may be configured to execute instructions stored in a memory, and when the processor 1301 executes instructions stored in the memory, the processor 1301 is configured to perform the steps and/or flows of the method embodiments described above. The transceiver 1302 may include a transmitter that may be used to implement various steps and/or processes for performing transmit actions corresponding to the transceiver described above, and a receiver that may be used to implement various steps and/or processes for performing receive actions corresponding to the transceiver described above.

It should be appreciated that in embodiments of the present application, the processor may be a central processing unit (central processing unit, CPU), the processor may also be other general purpose processors, digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in the processor for execution. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor executes instructions in the memory to perform the steps of the method described above in conjunction with its hardware. To avoid repetition, a detailed description is not provided herein.

The present application also provides a computer readable storage medium for storing a computer program for implementing the method shown in the above-described method embodiments.

The present application also provides a computer program product comprising a computer program (which may also be referred to as code, or instructions) which, when run on a computer, performs the method as shown in the method embodiments described above.

Those of ordinary skill in the art will appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described system, apparatus and module may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.

In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.

The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module.

The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

The foregoing is merely a specific implementation of the present application, but the scope of the embodiments of the present application is not limited thereto, and any person skilled in the art may easily think about changes or substitutions within the technical scope of the embodiments of the present application, and all changes and substitutions are included in the scope of the embodiments of the present application. Therefore, the protection scope of the embodiments of the present application shall be subject to the protection scope of the claims.

Claims

1. A method of signal transmission, comprising:

receiving a signal from a transmitting end, wherein the signal comprises an actual receiving signal of a sample signal and a service signal, the signal transmitted to the receiving end by the transmitting end comprises the sample signal, the sample signal is data information agreed by the transmitting end and the receiving end and used for training a decision feedback equalizer by the receiving end, and the actual receiving signal of the sample signal is the sample signal actually received by the receiving end;

training the decision feedback equalizer based on the actual received signal and the sample signal;

in the training process, according to a first change trend and a first corresponding relation of errors between the processing result of the actual received signal by the decision feedback equalizer and the sample signal, determining an adjustment coefficient of a parameter of the decision feedback equalizer, wherein the first corresponding relation comprises a plurality of change trends and a corresponding relation between a plurality of adjustment coefficients, and the plurality of change trends comprise the first change trend;

based on the adjustment coefficient, adjusting the parameter of the decision feedback equalizer, and continuing to train the decision feedback equalizer, wherein the parameter of the decision feedback equalizer is determined according to the error between the processing result of the actual received signal and the sample signal, the adjustment coefficient and the learning rate, and the relation between the adjustment coefficient and the parameter of the decision feedback equalizer is shown in the following formula: Wherein R is an adjustment coefficient, < >>Is a parameter in a feed forward filter +.>，/>For update +.>，/>For the parameters in the feedback filter +.>，/>For updated->，/>For learning rate, 1 >)>＞0；

Performing interference elimination processing on the service signal by using the trained decision feedback equalizer;

inputting the actual received signal to the decision feedback equalizer to obtain a processing result of the actual received signal;

determining an error between a processing result of the actual received signal and the sample signal;

each time the number of errors is accumulated to a first preset number, carrying out mean value filtering processing on the errors of the first preset number to obtain a plurality of smooth errors;

determining a local change trend of each two adjacent smooth errors in the plurality of smooth errors according to the difference value between each two adjacent smooth errors in the plurality of smooth errors, wherein the local change trend comprises an ascending trend and a descending trend;

determining the first variation trend according to the number of the rising trends and the number of the falling trends;

the determining the first variation trend according to the number of rising trends and the number of falling trends includes:

If the number of the rising trends is greater than the number of the falling trends, determining the first change trend as the rising trend;

if the number of the rising trends is equal to the number of the falling trends, determining the first change trend as a stable trend;

and if the number of the rising trends is smaller than the number of the falling trends, determining the first change trend as the falling trend.

2. The method of claim 1, wherein the determining the first trend from the number of upward trends and the number of downward trends comprises:

and when the number of the plurality of smoothing errors is accumulated to a second preset number, determining the first variation trend according to the number of the rising trends and the number of the falling trends.

3. The method of claim 1, wherein the first trend is derived based on a first portion of the actual received signal;

after the adjusting the parameters of the decision feedback equalizer based on the adjustment coefficients, the method further comprises:

determining a second variation trend of an error between a processing result of the adjusted decision feedback equalizer on a second part of signals in the actual received signals and the sample signals;

Determining readjustment coefficients of parameters of the decision feedback equalizer based on the second variation trend and the first correspondence;

and based on the readjustment coefficient, readjusting the parameter of the decision feedback equalizer, and continuing training the decision feedback equalizer.

4. A signal transmission device, comprising:

the receiving and transmitting module is used for receiving signals from a transmitting end, wherein the signals comprise actual receiving signals and service signals of sample signals, the signals transmitted to a receiving end by the transmitting end comprise the sample signals, the sample signals are data information agreed by the transmitting end and the receiving end and used for training a decision feedback equalizer by the receiving end, and the actual receiving signals of the sample signals are sample signals actually received by the receiving end;

a processing module, configured to train the decision feedback equalizer based on the actual received signal and the sample signal; in the training process, according to a first change trend and a first corresponding relation of errors between the processing result of the actual received signal by the decision feedback equalizer and the sample signal, determining an adjustment coefficient of a parameter of the decision feedback equalizer, wherein the first corresponding relation comprises a plurality of change trends and a corresponding relation between a plurality of adjustment coefficients, and the plurality of change trends comprise the first change trend; based on the adjustment coefficient, adjusting the parameter of the decision feedback equalizer, and continuing to train the decision feedback equalizer, wherein the parameter of the decision feedback equalizer is determined according to the error between the processing result of the actual received signal and the sample signal, the adjustment coefficient and the learning rate, and the relation between the adjustment coefficient and the parameter of the decision feedback equalizer is shown in the following formula:

Wherein R is an adjustment coefficient, < >>Is a parameter in a feed forward filter +.>，/>For updated->，/>For the parameters in the feedback filter +.>，/>For updated->，/>For learning rate, 1 >)>> 0; performing interference elimination processing on the service signal by using the trained decision feedback equalizer;

the processing module is further configured to:

the processing module is specifically configured to:

5. The apparatus of claim 4, wherein the processing module is specifically configured to:

6. The apparatus of claim 4, wherein the first trend is derived based on a first portion of the actual received signal;

the processing module is further configured to:

7. A signal transmission device, comprising: a processor coupled to a memory for storing a computer program which, when invoked by the processor, causes the apparatus to perform the method of any one of claims 1 to 3.

8. A computer readable storage medium storing a computer program comprising instructions for implementing the method of any one of claims 1 to 3.