Method and apparatus for implementing modulation signal equalization

Abstract

The present invention provides a method and apparatus for realizing modulation signal equalization. The method includes: obtaining a received signal, and determining a first training sequence according to the received signal; performing power compensation on the received signal and the first training sequence to obtain an adjusted signal and a second training sequence; determining an autocorrelation matrix according to the second training sequence; performing matrix correlation operation on the second training sequence based on a known training sequence to determine a cross-correlation matrix; performing iterative solution based on the steepest descent method according to the autocorrelation matrix and the cross-correlation matrix to obtain equalization filter parameters; and performing signal equalization on the received signal according to the equalization filter parameters and the adjusted signal. The present invention uses the steepest descent method to iteratively solve the equalization filter parameters, obtains equalization filter parameters with similar accuracy, significantly reduces the complex resource occupation in the implementation process of the least mean square error method, and realizes high-efficiency equalization of the received signal.

Description

Technical Field

[0001] The present invention relates to the field of communication technologies, and in particular, to a method and an apparatus for realizing modulation signal equalization. Background Art

[0002] With the continuous progress of society, the demand for large-capacity and highly reliable communication systems has further increased. In typical application scenarios of wireless communication systems, inter-symbol interference caused by multipath propagation has become one of the key factors reducing the quality of wireless communication. To improve the communication quality, it is necessary to estimate the interference in the signal propagation process to recover the ideal signal.

[0003] Signal equalization technology is a key technology for eliminating inter-symbol interference. It estimates the transmission channel at the receiving end by sending a known training sequence and recovers the ideal signal, thereby improving the communication quality. Among existing equalization algorithms, the least mean square error algorithm has a fast estimation speed and high accuracy, but has a high computational complexity; the adaptive equalization method is relatively simple to calculate, but has a slow convergence speed and high requirements for the length of the training sequence; the Bussgang-type blind equalization algorithm does not rely on the training sequence, but has a slow convergence speed and poor convergence accuracy. Therefore, it is necessary to propose a modulation signal equalization method that reduces its computational complexity without affecting the estimation accuracy. Summary of the Invention

[0004] The present invention provides a method and an apparatus for realizing modulation signal equalization, which solve the technical defect that the computational complexity cannot be reduced without affecting the estimation accuracy in the prior art.

[0005] The present invention provides a method for realizing modulation signal equalization, including:

[0006] Obtaining a received signal, and determining a first training sequence according to the received signal, where the received signal includes a frame synchronization sequence;

[0007] Performing power compensation on the received signal and the first training sequence to obtain an adjusted signal and a second training sequence;

[0008] Determining the autocorrelation matrix of the second training sequence according to the second training sequence;

[0009] Performing matrix correlation operation on the second training sequence based on a pre-determined known training sequence to determine the cross-correlation matrix between the known training sequence and the second training sequence;

[0010] Based on the autocorrelation matrix and the cross-correlation matrix, performing iterative solution based on the steepest descent method to obtain equalization filter parameters;

[0011] Perform signal equalization on the received signal according to the equalization filter parameters and the adjustment signal.

[0012] According to a method for realizing modulation signal equalization provided by the present invention, the determining a first training sequence according to the received signal includes:

[0013] Perform correlation calculation on the received signal based on the frame synchronization sequence to determine the start time of the training sequence, where the frame synchronization sequence is a ZC sequence or a Barker code;

[0014] Identify the first training sequence in the received signal according to the start time of the training sequence.

[0015] According to a method for realizing modulation signal equalization provided by the present invention, the determining the autocorrelation matrix of the second training sequence according to the second training sequence includes:

[0016] Determine the autocorrelation matrix of the second training sequence according to the second training sequence at multiple moments and the Hermitian matrix of the second training sequence.

[0017] According to a method for realizing modulation signal equalization provided by the present invention, perform matrix correlation operation on the second training sequence based on a pre-determined known training sequence to determine the cross-correlation matrix between the known training sequence and the second training sequence, including:

[0018] Determine the cross-correlation matrix between the known training sequence and the second training sequence according to the second training sequence at multiple moments and the Hermitian matrix of the known training sequence.

[0019] According to a method for realizing modulation signal equalization provided by the present invention, perform iterative solution based on the steepest descent method according to the autocorrelation matrix and the cross-correlation matrix to obtain the equalization filter parameters, including:

[0020] Determine the initial value of the equalization filter parameters and the number of iterations;

[0021] Determine the error matrix of the previous iteration according to the autocorrelation matrix, the cross-correlation matrix, and the equalization filter parameters of the previous iteration;

[0022] Obtain the equalization filter parameters of the current iteration according to the error matrix of the previous iteration, the forgetting factor, and the equalization filter parameters of the previous moment;

[0023] When the number of iterations is satisfied, determine the equalization filter parameters of the current iteration as the final equalization filter parameters.

[0024] A method for realizing modulation signal equalization provided by the present invention, wherein signal equalization of the received signal according to the equalization filter parameters and the adjustment signal includes:

[0025] Filter the received signal according to the equalization filter parameters and the adjustment signal to obtain the equalized received signal, and complete signal equalization.

[0026] A method for realizing modulation signal equalization provided by the present invention, wherein filtering the adjustment signal according to the equalization filter parameters includes:

[0027] Filter the received signal according to the conjugate value of the equalization filter parameters and the adjustment signal.

[0028] The present invention also provides a device for realizing modulation signal equalization, including:

[0029] A training sequence determination module, configured to obtain a received signal and determine a first training sequence according to the received signal, wherein the received signal includes a frame synchronization sequence;

[0030] A power compensation module, configured to perform power compensation on the received signal and the first training sequence to obtain an adjustment signal and a second training sequence;

[0031] A training sequence correlation module, configured to determine the autocorrelation matrix of the second training sequence according to the second training sequence; perform matrix correlation operations on the second training sequence based on a pre-determined known training sequence to determine the cross-correlation matrix between the known training sequence and the second training sequence;

[0032] An equalization filter parameter determination module, configured to perform iterative solution based on the steepest descent method according to the autocorrelation matrix and the cross-correlation matrix to obtain equalization filter parameters;

[0033] A signal equalization module, configured to perform signal equalization on the received signal according to the equalization filter parameters and the adjustment signal.

[0034] The present invention also provides a terminal, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the program, the method for realizing modulation signal equalization as described in any one of the above is implemented.

[0035] The present invention also provides a full-duplex communication system, including a full-duplex remote device and a full-duplex proximal device, wherein the full-duplex proximal device implements the method for realizing modulation signal equalization.

[0036] The method and device for realizing modulation signal equalization provided by the present invention use the steepest descent method to iteratively solve the equalization filter parameters, thereby avoiding the high-complexity matrix inversion operation in the least mean square error method, and at the same time obtaining equalization filter parameters with similar accuracy, significantly reducing the complex resource occupation in the implementation process of the least mean square error method. By equalizing the received signal, the high-efficiency equalization of the received modulation signal is realized, the inter-symbol interference is greatly reduced, the bit error rate level after demodulating the received modulation signal is reduced, and the estimation accuracy is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0038] Figure 1 is a schematic flowchart of the method for realizing modulation signal equalization provided by the present invention;

[0039] Figure 2 is provided by the present invention Figure 1 is a schematic flowchart of step S110 in

[0040] Figure 3 is a schematic diagram of the autocorrelation matrix calculation structure provided by the present invention;

[0041] Figure 4 is a schematic diagram of the cross-correlation matrix calculation structure provided by the present invention;

[0042] Figure 5 is a schematic flowchart of the fast iteration of the equalization filter coefficients provided by the present invention;

[0043] Figure 6 is a schematic flowchart of the signal filtering provided by the present invention;

[0044] Figure 7 is the constellation diagram before signal equalization;

[0045] Figure 8 is the constellation diagram after signal equalization;

[0046] Figure 9 is a schematic diagram of the structure of the device for realizing modulation signal equalization provided by the present invention; DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0048] In the description of the embodiments of the present application, it should be noted that the orientation or positional relationship indicated by the terms "center", "longitudinal", "transverse", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the embodiments of the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the embodiments of the present application. In addition, the terms "first", "second", and "third" are only used for descriptive purposes and should not be construed as indicating or implying relative importance.

[0049] In the description of the embodiments of the present application, it should be noted that unless otherwise clearly specified and defined, the terms "connected" and "coupled" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to specific circumstances.

[0050] In the embodiments of the present application, unless otherwise clearly specified and defined, the first feature being "on" or "under" the second feature may be that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, the first feature being "above", "over" and "on top of" the second feature may be that the first feature is directly above or obliquely above the second feature, or merely indicates that the first feature has a higher horizontal height than the second feature. The first feature being "under", "beneath" and "underneath" the second feature may be that the first feature is directly below or obliquely below the second feature, or merely indicates that the first feature has a lower horizontal height than the second feature.

[0051] In the description of this specification, the description referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the embodiments of this application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0052] Figure 1 is a schematic flowchart of the method for implementing modulation signal equalization provided by the present invention. Referring to Figure 1 the present invention provides a method for implementing modulation signal equalization, including:

[0053] S110, obtain a received signal, and determine a first training sequence according to the received signal, where the received signal includes a frame synchronization sequence;

[0054] S120, perform power compensation on the received signal and the first training sequence to obtain an adjusted signal and a second training sequence;

[0055] S130, determine the autocorrelation matrix of the second training sequence according to the second training sequence;

[0056] S140, perform matrix correlation operation on the second training sequence based on a pre-determined known training sequence to determine the cross-correlation matrix between the known training sequence and the second training sequence;

[0057] S150, based on the autocorrelation matrix and the cross-correlation matrix, perform iterative solution based on the steepest descent method to obtain equalization filter parameters;

[0058] S160, perform signal equalization on the received signal according to the equalization filter parameters and the adjusted signal.

[0059] In step S110, the method provided by the embodiments of this application is applicable to the receiving end, and the received signal has a frame synchronization sequence and a first training sequence inserted before the modulation signal by the sending end.

[0060] The receiving end identifies the frame synchronization sequence in the received signal through downconversion, matched filtering, and timing synchronization.

[0061] Optionally, the frame synchronization sequence adopts the method of cross-correlation between the transmitting and receiving sequences.

[0062] In step S120, the receiving end performs power detection on the received signal and performs power compensation on the received signal. The power compensation coefficient remains constant to avoid signal power that is too large or too small, ensuring that the subsequent equalization filter coefficients can converge within a given time.

[0063] Optionally, the receiving end statistically analyzes the received signal power. By multiplying the time-domain received signal by the power compensation coefficient α, the received signal power is adjusted to be near the desired power value.

[0064] In step S140, the first training sequence uses a modulation signal to ensure that both the sending end and the receiving end can determine the known training sequence.

[0065] In step S160, signal equalization is performed on the received signal so that the equalized symbols fall on the desired constellation diagram, reducing the inter-symbol interference of the received signal.

[0066] It can be understood that the embodiment of the present application uses the steepest descent method to iteratively solve the equalization filter parameters, thereby avoiding the high-complexity matrix inversion operation in the least mean square error method. At the same time, equalization filter parameters with similar accuracy are obtained, significantly reducing the complex resource occupation in the implementation process of the least mean square error method. By equalizing the received signal, the high-efficiency equalization of the received modulation signal is realized, significantly reducing the inter-symbol interference, reducing the bit error rate level after demodulating the received modulation signal, and improving the estimation accuracy.

[0067] Based on the above embodiment, as an optional embodiment, as Figure 2 shown, determining the first training sequence according to the received signal includes:

[0068] S210, performing correlation calculation on the received signal based on the frame synchronization sequence to determine the start time of the training sequence, where the frame synchronization sequence is a ZC sequence or a Barker code;

[0069] S220, identifying the first training sequence in the received signal according to the start time of the training sequence.

[0070] In step S210, the ZC sequence is a Zadoff-chu sequence.

[0071] Optionally, the frame synchronization sequence uses a combined Barker code. Let the sequence bits of the n-bit Barker code be {x1, x2, x3, …, x n}, and its autocorrelation function γ(j) satisfies:

[0072]

[0073] where i and j represent symbol serial numbers, and Σ represents the summation symbol.

[0074] Optionally, the frame synchronization sequence is generated by combining Barker codes. Take two symbols s1 and s2 of the modulation signal, satisfying s1 = -s2. Take the 7-bit basic Barker sequence as m1 = {s1, s1, s1, s2, s2, s1, s2}, and correspondingly m2 = {s2, s2, s2, s1, s1, s1, s2}. Continue to generate a frame synchronization sequence containing 21 symbols {m1, m1, m2} according to the 3-bit combined Barker code. The training sequence is sent using the modulation signal to ensure that both the transmitter and receiver know the frame synchronization sequence and the training sequence.

[0075] In step S220, the receiver correlates the combined Barker code with the received signal, and the moment corresponding to the correlation peak is used as the start moment of the training sequence in the received signal.

[0076] It can be understood that the embodiment of the present application performs channel estimation based on the minimum mean square error method, uses a combined Barker code to achieve accurate frame synchronization, inserts a training sequence after the synchronization sequence, shortens the parameter estimation time of the equalization filter, and at the same time ensures the parameter estimation accuracy of the equalization filter.

[0077] Based on the above embodiment, as an optional embodiment, the determining the autocorrelation matrix of the second training sequence according to the second training sequence includes:

[0078] Determine the autocorrelation matrix of the second training sequence according to the second training sequence at multiple moments and the Hermitian matrix of the second training sequence.

[0079] Optionally, let the second training sequence be y train , and the autocorrelation matrix of the second training sequence is E{·} represents taking the mean, and H represents taking the Hermitian matrix of the corresponding matrix.

[0080] Figure 3 This is a schematic diagram of the calculation structure of the autocorrelation matrix of the simplified second training sequence of the present invention. For an N-tap filter, y k , y (k+1) , ……, y (k+N-1) represent the training sequences received at times k, (k + 1), ……, (k + N - 1).

[0081] Using the iterative and symmetric characteristics of the matrix to design an arithmetic unit, using the conjugate of the currently received training sequence y k and the vector y trianPerform a complex multiplication operation, and the obtained result is used as the Nth column of the current autocorrelation matrix. At the same time, before each round of complex multiplication operation, the elements in the 2nd to Nth rows of the Nth column are assigned to the 1st to N - 1th rows of the (N - 1)th column, and the elements in the 2nd to N - 1th rows of the (N - 1)th column are assigned to the 1st to N - 2th rows, and so on, to obtain the current autocorrelation matrix. Additionally, take a register matrix, add the obtained autocorrelation matrix after each round of calculation, and calculate the average value at the end of the training sequence to obtain matrix R mat . For ease of display, Figure 3 Figure 207 shows the calculation structure of the autocorrelation matrix when N = 6.

[0082] It can be understood that by simplifying the calculation of the autocorrelation matrix in the embodiments of the present application, the convergence speed can be accelerated and the requirement for the length of the training sequence can be reduced.

[0083] Based on the above embodiments, as an optional embodiment, perform matrix correlation operations on the second training sequence based on a pre-determined known training sequence to determine the cross-correlation matrix between the known training sequence and the second training sequence, including:

[0084] Determine the cross-correlation matrix between the known training sequence and the second training sequence according to the Hermitian matrices of the second training sequence and the known training sequence at multiple moments.

[0085] Optionally, let the known training sequence be x train , and the cross-correlation matrix be E{·} represents taking the mean value.

[0086] Optionally, Figure 4 Figure 219 shows the simplified calculation structure of the cross-correlation matrix of the training sequence described in an embodiment of the present invention. Similar to the autocorrelation matrix calculation method, perform a complex multiplication operation on the current received training sequence and the conjugate of the corresponding original training sequence x trian to obtain the current cross-correlation matrix value. Additionally, take a register matrix, add the obtained cross-correlation matrix after each round of calculation, and calculate the average value at the end of the training sequence to obtain matrix P vec .

[0087] For ease of display, Figure 4 Figure 222 shows the calculation structure of the cross-correlation matrix when N = 6, where 36 is the conjugate of the value of the original training sequence corresponding to the current received training sequence y1, and the calculated values are stored in registers Q1 to Q6, and the mean value is calculated after accumulation to assign to matrix P vec .

[0088] It can be understood that by simplifying the calculation of the cross-correlation matrix in the embodiments of the present application, the convergence speed can be accelerated and the requirement for the length of the training sequence can be reduced.

[0089] Based on the above embodiments, as an alternative embodiment, iterative solution is performed based on the steepest descent method according to the autocorrelation matrix and the cross-correlation matrix to obtain the equalization filter parameters, including:

[0090] Determine the initial value of the equalization filter parameters and the number of iterations;

[0091] According to the autocorrelation matrix, the cross-correlation matrix, and the equalization filter parameters of the previous iteration, determine the error matrix of the previous iteration;

[0092] According to the error matrix of the previous iteration, the forgetting factor, and the equalization filter parameters of the previous moment, obtain the equalization filter parameters of the current iteration;

[0093] When the number of iterations is satisfied, determine the equalization filter parameters of the current iteration as the final equalization filter parameters.

[0094] Optionally, the steepest descent calculation method is:

[0095] W(n + 1) = W(n) - μ * (R mat * W(n) - P vec )

[0096] where W(n) is the current channel parameter value, W(n + 1) is the parameter value of the next moment obtained by iteration, and μ is the forgetting factor used to adjust the convergence speed.

[0097] Figure 5 This is the operation schematic diagram of the fast iteration of the equalization filter coefficients described in an embodiment of the present invention. P err = R mat * W(n - 1) - P vec is the error matrix.

[0098] Optionally, μ takes the constant 0.1. In actual operation, in combination with the foregoing mean value calculation process, it can be approximately determined as an integer power of 2 according to the product of the training sequence and 1 / μ, and simplified operation is achieved through shifting.

[0099] Regarding the calculation of the vector R mat * W(n - 1), N operations are required for each iteration, and N complex multipliers are selected for each operation. In the first operation, take 1 column of elements in the R mat matrix and multiply them by the first element in W(n - 1); in the second operation, take 2 columns of elements in the R mat matrix and multiply them by the second element in W(n - 1), and so on. After each complex multiplication, accumulate the results. After completing each round of operation, take the accumulated result as the result and clear the next round of accumulation. Subtract μ * R mat * W(n - 1) and μ * P vecIt is used as the filter parameters for the next round. After the filter coefficients converge after a set number of rounds of calculation, the filter coefficients are used for signal filtering.

[0100] It can be understood that the embodiment of the present application avoids the matrix inversion step of the least mean square error method and uses the steepest descent method to iteratively solve the filter parameters, realizing the simplification of the operation.

[0101] Based on the above embodiment, as an optional embodiment, the signal equalization of the received signal according to the equalization filter parameters and the adjustment signal includes:

[0102] Filter the received signal according to the equalization filter parameters and the adjustment signal to obtain the equalized received signal, and complete the signal equalization.

[0103] Optionally, filtering the adjustment signal according to the equalization filter parameters includes:

[0104] Filter the received signal according to the conjugate value of the equalization filter parameters and the adjustment signal.

[0105] Optionally, the filtering method is where e is the equalized sequence, is the conjugate value of the filter parameters calculated in step four, y is the received sequence adjusted in step two, and a tap filter structure is used to equalize the received signal so that the equalized symbols fall on the desired constellation diagram, reducing the inter-symbol interference of the received signal.

[0106] Figure 6 This is the operation schematic diagram of signal filtering described in an embodiment of the present invention. Filter the received signal with the equalization filter parameters obtained above to obtain the equalized constellation diagram. Among them is the inverse of the filter parameters obtained above, D represents delaying the received signal, and through the final equalization result is obtained.

[0107] It can be understood that the matlab simulation signal constellation diagram is used, the modulation method is 16QAM, and the channel is a multipath signal with a fixed delay and random phase distribution. Figure 7 is the constellation diagram before signal equalization. Figure 8 is the constellation diagram after signal equalization. It can be seen that the signal quality before equalization is extremely poor and cannot be used for demodulation. The symbols of the signal constellation diagram after equalization are ideally distributed and can be used for demodulation. After simulation, its bit error rate is 0.

[0108] Next, the device for realizing modulation signal equalization provided by the present invention will be described. The device for realizing modulation signal equalization described below can be mutually corresponding and referred to with the method for realizing modulation signal equalization described above.

[0109] Figure 9 is a schematic structural diagram of the device for realizing modulation signal equalization provided by the present invention. Referring to Figure 9 , the present invention also provides a device for realizing modulation signal equalization, including:

[0110] A training sequence determination module 910, configured to obtain a received signal and determine a first training sequence according to the received signal, where the received signal includes a frame synchronization sequence;

[0111] A power compensation module 920, configured to perform power compensation on the received signal and the first training sequence to obtain an adjusted signal and a second training sequence;

[0112] A training sequence correlation module 930, configured to determine an autocorrelation matrix of the second training sequence according to the second training sequence; perform matrix correlation operations on the second training sequence based on a pre-determined known training sequence to determine a cross-correlation matrix between the known training sequence and the second training sequence;

[0113] An equalization filter parameter determination module 940, configured to perform iterative solution based on the steepest descent method according to the autocorrelation matrix and the cross-correlation matrix to obtain equalization filter parameters;

[0114] A signal equalization module 950, configured to perform signal equalization on the received signal according to the equalization filter parameters and the adjusted signal.

[0115] In one embodiment, the training sequence determination module 910 is further configured to:

[0116] Perform correlation calculation on the received signal based on the frame synchronization sequence to determine the start time of the training sequence, where the frame synchronization sequence is a ZC sequence or a Barker code;

[0117] Identify the first training sequence in the received signal according to the start time of the training sequence.

[0118] In one embodiment, the training sequence correlation module 930 is further configured to:

[0119] Determine the autocorrelation matrix of the second training sequence according to the second training sequence at multiple moments and the Hermitian matrix of the second training sequence.

[0120] In one embodiment, the training sequence correlation module 930 is further configured to:

[0121] Determine the cross-correlation matrix between the known training sequence and the second training sequence according to the second training sequence at multiple moments and the Hermitian matrix of the known training sequence.

[0122] In one embodiment, the equalization filter parameter determination module 940 is further configured to:

[0123] Determine the initial value of the equalization filter parameter and the number of iterations;

[0124] Determine the error matrix of the previous round according to the autocorrelation matrix, the cross-correlation matrix and the equalization filter parameter of the previous round of iteration;

[0125] Obtain the equalization filter parameter of the current round according to the error matrix of the previous round, the forgetting factor and the equalization filter parameter of the previous moment;

[0126] When the number of iterations is satisfied, determine the equalization filter parameter of the current round as the final equalization filter parameter.

[0127] In one embodiment, the signal equalization module 950 is further configured to:

[0128] Filter the received signal according to the equalization filter parameter and the adjustment signal to obtain the equalized received signal, and complete signal equalization.

[0129] In one embodiment, the signal equalization module 950 is further configured to:

[0130] Filter the received signal according to the conjugate value of the equalization filter parameter and the adjustment signal.

[0131] The present invention also provides a full-duplex communication system, including a full-duplex remote device and a full-duplex proximal device, and the full-duplex proximal device implements the method for realizing modulation signal equalization.

[0132] Full-duplex step one: The full-duplex proximal device sends a self-interference channel training sequence and receives it at the same frequency at the receiving end for self-interference channel estimation.

[0133] The self-interference channel estimation method includes a training sequence estimation method, an adaptive estimation method, and an adaptive digital domain self-interference cancellation method with pre-estimation of the training sequence. In order to ensure the fast convergence of the self-interference channel estimation while retaining the adaptive characteristics of the channel estimation, preferably, the self-interference channel estimation method uses the adaptive digital domain self-interference cancellation method with pre-estimation of the training sequence.

[0134] Full-duplex step two: The full-duplex remote device sends an expected modulation signal, and the full-duplex proximal device receives the expected signal. The received signal includes a self-interference signal and an expected signal. The full-duplex proximal device reconstructs the self-interference signal according to the self-interference channel parameter obtained in the full-duplex step one and the known transmission sequence, and subtracts the reconstructed self-interference signal from the received signal to obtain the expected signal.

[0135] Full-duplex Step 3: The full-duplex proximal device equalizes the desired signal obtained in Full-duplex Step 2 for subsequent demodulation of the modulated signal.

[0136] The channel equalization method includes the training sequence method, the adaptive equalization method, the blind equalization method, and a method for realizing the equalization of the modulated signal disclosed in the present invention.

[0137] Since the channel estimation in Full-duplex Step 1 cannot perfectly restore the self-interference channel characteristics, the desired signal obtained in Full-duplex Step 2 still contains the self-interference signal components that have not been estimated. This part, as a noise component, further reduces the signal-to-noise ratio of the desired signal and increases the inter-symbol interference of the received signal. In order to achieve signal equalization more precisely and quickly, preferably, a method for realizing the equalization of the modulated signal disclosed in the present invention is used as the equalization method for the desired signal after full-duplex self-interference cancellation.

[0138] It can be understood that a method and device for realizing the equalization of the modulated signal disclosed in the present invention can also be applied to interference cancellation in simultaneous and co-frequency full-duplex. After the full-duplex proximal device performs self-interference cancellation, the restored desired signal is equalized to further reduce the self-interference component as inter-symbol interference in the desired signal, reduce the bit error rate level after demodulation of the received modulated signal, and further improve the communication quality of simultaneous and co-frequency full-duplex.

[0139] This application also discloses a terminal, which may include: a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete communication with each other through the communication bus. The processor can call the logical instructions in the memory to execute the method for realizing the equalization of the modulated signal, and the method includes:

[0140] Obtain a received signal, and determine a first training sequence according to the received signal, where the received signal includes a frame synchronization sequence;

[0141] Perform power compensation on the received signal and the first training sequence to obtain an adjusted signal and a second training sequence;

[0142] Determine the autocorrelation matrix of the second training sequence according to the second training sequence;

[0143] Perform matrix correlation operation on the second training sequence based on a pre-determined known training sequence to determine the cross-correlation matrix between the known training sequence and the second training sequence;

[0144] Based on the autocorrelation matrix and the cross-correlation matrix, perform iterative solution based on the steepest descent method to obtain the equalization filter parameters;

[0145] Perform signal equalization on the received signal according to the equalization filter parameters and the adjustment signal.

[0146] In addition, when the logic instructions in the above-mentioned memory are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0147] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the method for realizing modulation signal equalization provided by the above-mentioned various methods. The method includes:

[0148] Obtain a received signal, and determine a first training sequence according to the received signal, where the received signal includes a frame synchronization sequence;

[0149] Perform power compensation on the received signal and the first training sequence to obtain an adjustment signal and a second training sequence;

[0150] According to the second training sequence, determine the autocorrelation matrix of the second training sequence;

[0151] Perform matrix correlation operations on the second training sequence based on a pre-determined known training sequence to determine the cross-correlation matrix between the known training sequence and the second training sequence;

[0152] Based on the autocorrelation matrix and the cross-correlation matrix, perform iterative solution based on the steepest descent method to obtain equalization filter parameters;

[0153] Perform signal equalization on the received signal according to the equalization filter parameters and the adjustment signal.

[0154] In another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements a method for realizing modulation signal equalization provided by the above-mentioned various methods. The method includes:

[0155] Obtain a received signal, and determine a first training sequence according to the received signal, wherein the received signal includes a frame synchronization sequence;

[0156] Perform power compensation on the received signal and the first training sequence to obtain an adjusted signal and a second training sequence;

[0157] According to the second training sequence, determine the autocorrelation matrix of the second training sequence;

[0158] Perform matrix correlation operation on the second training sequence based on a pre-determined known training sequence to determine the cross-correlation matrix between the known training sequence and the second training sequence;

[0159] Based on the autocorrelation matrix and the cross-correlation matrix, perform iterative solution based on the steepest descent method to obtain equalization filter parameters;

[0160] Perform signal equalization on the received signal according to the equalization filter parameters and the adjusted signal.

[0161] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0162] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0163] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for realizing modulation signal equalization, characterized in that, Including: Obtain a received signal, and determine a first training sequence according to the received signal, where the received signal includes a frame synchronization sequence; Perform power compensation on the received signal and the first training sequence to obtain an adjusted signal and a second training sequence; Determine the autocorrelation matrix of the second training sequence according to the second training sequence; Perform matrix correlation operation on the second training sequence based on a pre-determined known training sequence to determine the cross-correlation matrix between the known training sequence and the second training sequence; Based on the autocorrelation matrix and the cross-correlation matrix, perform iterative solution based on the steepest descent method to obtain equalization filter parameters; Perform signal equalization on the received signal according to the equalization filter parameters and the adjusted signal; Based on the autocorrelation matrix and the cross-correlation matrix, perform iterative solution based on the steepest descent method to obtain equalization filter parameters, including: Determine the initial value of the equalization filter parameters and the number of iterations; Determine the error matrix of the previous iteration according to the autocorrelation matrix, the cross-correlation matrix, and the equalization filter parameters of the previous iteration; Obtain the equalization filter parameters of the current iteration according to the error matrix of the previous iteration, the forgetting factor, and the equalization filter parameters of the previous moment; When the number of iterations is satisfied, determine the equalization filter parameters of the current iteration as the final equalization filter parameters; The steepest descent calculation method is: W(n + 1) = W(n) - μ * (R mat * W(n) - P vec ) Where W(n) is the current channel parameter value, W(n + 1) is the parameter value of the next moment obtained by iteration, and μ is the forgetting factor used to adjust the convergence speed; P err = R mat *W(n - 1)-P vec is the error matrix; Regarding vector R mat *To obtain W(n - 1), N operations are required for each round of iteration. Each operation selects N complex multipliers. In the first operation, take one column of elements from the R mat matrix and multiply it by the first element in W(n - 1); in the second operation, take two columns of elements from the R mat matrix and multiply it by the second element in W(n - 1), and so on. After each complex multiplication, accumulate the results. After completing each round of operations, take the accumulated value as the result and clear the next-round accumulation; subtract μ * R mat *W(n - 1) and μ * P vec from the current filter coefficients and use them as the filter parameters for the next round. After calculating for the set number of rounds, the filter coefficients converge. At this time, use the filter coefficients for signal filtering.

2. The method for realizing modulation signal equalization according to claim 1, wherein The determining the first training sequence according to the received signal includes: Perform correlation calculation on the received signal based on the frame synchronization sequence to determine the start time of the training sequence, where the frame synchronization sequence is a ZC sequence or a Barker code; Identify the first training sequence in the received signal according to the start time of the training sequence.

3. The method for realizing modulation signal equalization according to claim 1, characterized in that, The determining the autocorrelation matrix of the second training sequence according to the second training sequence includes: Determine the autocorrelation matrix of the second training sequence according to the second training sequence at multiple moments and the Hermitian matrix of the second training sequence.

4. The method for implementing modulation signal equalization according to claim 1, characterized in that, Performing matrix correlation operation on the second training sequence based on a pre-determined known training sequence to determine the cross-correlation matrix between the known training sequence and the second training sequence includes: Determine the cross-correlation matrix between the known training sequence and the second training sequence according to the second training sequence at multiple moments and the Hermitian matrix of the known training sequence.

5. The method for realizing modulation signal equalization according to claim 1, wherein The performing signal equalization on the received signal according to the equalization filter parameters and the adjusted signal includes: Perform filtering on the received signal according to the equalization filter parameters and the adjusted signal to obtain the equalized received signal, and complete signal equalization.

6. The method for realizing modulation signal equalization according to claim 5, characterized in that, The performing filtering on the adjusted signal according to the equalization filter parameters includes: Perform filtering on the received signal according to the conjugate value of the equalization filter parameters and the adjusted signal.

7. A device for realizing modulation signal equalization, characterized in that, Including: A training sequence determination module, configured to obtain a received signal and determine a first training sequence according to the received signal, where the received signal includes a frame synchronization sequence; A power compensation module, configured to perform power compensation on the received signal and the first training sequence to obtain an adjusted signal and a second training sequence; A training sequence correlation module, configured to determine an autocorrelation matrix of the second training sequence according to the second training sequence; perform matrix correlation operations on the second training sequence based on a pre-determined known training sequence to determine a cross-correlation matrix between the known training sequence and the second training sequence; An equalization filter parameter determination module, configured to perform iterative solution based on the steepest descent method according to the autocorrelation matrix and the cross-correlation matrix to obtain equalization filter parameters; A signal equalization module, configured to perform signal equalization on the received signal according to the equalization filter parameters and the adjusted signal; Performing iterative solution based on the steepest descent method according to the autocorrelation matrix and the cross-correlation matrix to obtain equalization filter parameters, including: Determining an initial value of the equalization filter parameters and the number of iterations; Determining an error matrix of the previous iteration according to the autocorrelation matrix, the cross-correlation matrix, and the equalization filter parameters of the previous iteration; Obtaining the equalization filter parameters of the current iteration according to the error matrix of the previous iteration, a forgetting factor, and the equalization filter parameters of the previous moment; When the number of iterations is satisfied, determining the equalization filter parameters of the current iteration as the final equalization filter parameters; The steepest descent calculation method is: W(n + 1) = W(n) - μ * (R mat * W(n) - P vec ) wherein, W(n) is the current channel parameter value, W(n + 1) is the parameter value of the next moment iterated, and μ is the forgetting factor for adjusting the convergence speed; P err = R mat *W(n - 1)-P vec is the error matrix; Regarding vector R mat *To obtain W(n - 1), N operations are required for each round of iteration. Each operation selects N complex multipliers. In the first operation, take one column of elements from the R mat matrix and multiply it by the first element in W(n - 1); in the second operation, take two columns of elements from the R mat matrix and multiply it by the second element in W(n - 1), and so on. After each complex multiplication, accumulate the results. After completing each round of operations, take the accumulated result as the final result and clear the accumulation for the next round; subtract μ * R mat *W(n - 1) and μ * P vec from the current filter coefficients and use them as the filter parameters for the next round. After calculating for the set number of rounds, the filter coefficients converge. At this time, use the filter coefficients for signal filtering.

8. A terminal, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for realizing modulation signal equalization according to any one of claims 1 to 6.

9. A full-duplex communication system, comprising a full-duplex remote device and a full-duplex proximal device, characterized in that, The full-duplex proximal device implements the method for realizing modulation signal equalization according to any one of claims 1 to 6.

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