Channel estimation method, device, equipment, medium and product

Abstract

The present invention provides a channel estimation method, apparatus, device, medium, and product, relating to the field of signal processing technology. The method includes: dividing a received signal into multiple first subbands; determining an LS channel estimation result for the first subband based on a least squares (LS) algorithm, and determining a root mean square (RMS) delay value and a signal-to-noise ratio (SNR) value for the first subband; searching an interpolation filter coefficient table based on a pilot configuration type of the received signal, the RMS delay value, and the SNR value for the first subband, to determine the interpolation filter coefficient for the first subband; the interpolation filter coefficient table includes interpolation filter coefficients corresponding to at least one subband channel parameter value group under various pilot configuration types, the subband channel parameter value group including a theoretical RMS delay value and a theoretical SNR value; and obtaining a channel estimation result for the first subband based on the LS channel estimation result and the interpolation filter coefficient for the first subband. The present invention improves channel estimation accuracy and reduces computational complexity.

Description

Technical Field

[0001] The present invention relates to the field of signal processing technology, and in particular to a channel estimation method, apparatus, device, medium and product. Background Art

[0002] Signals transmitted in wireless channels experience dual selective fading in both the time and frequency domains, resulting in signal distortion and the addition of random noise. To restore the signal without distortion, channel estimation is essential at the receiver.

[0003] Currently, channel estimation methods commonly used include least squares (LS) channel estimation and minimum mean square error (MMES) channel estimation. LS channel estimation has low computational complexity, but its accuracy is low when noise and interference cannot be ignored. MMES channel estimation, while relatively accurate, also has relatively high computational complexity. Summary of the Invention

[0004] The present invention provides a channel estimation method, device, equipment, medium and product, which are used to solve the technical problems of low accuracy or high computational complexity of channel estimation methods in the prior art, and achieve the technical effect of improving channel estimation accuracy and reducing computational complexity.

[0005] The present invention provides a channel estimation method, which is applied to a receiving end of a communication system, and the method comprises:

[0006] dividing the received signal into a plurality of first sub-bands;

[0007] For each first subband, determine an LS channel estimation result of the first subband based on a least squares (LS) algorithm, and determine a root mean square (RMS) delay value and a signal-to-noise ratio (SNR) value of the first subband;

[0008] Based on the pilot configuration type of the received signal, the root mean square delay value of the first subband, and the signal-to-noise ratio value, searching an interpolation filter coefficient table to determine the interpolation filter coefficient for the first subband; wherein the interpolation filter coefficient table includes interpolation filter coefficients corresponding to at least one subband channel parameter value group under various pilot configuration types, the subband channel parameter value group including a theoretical root mean square delay value and a theoretical signal-to-noise ratio value;

[0009] A channel estimation result of the first subband is obtained based on the LS channel estimation result of the first subband and the interpolation filter coefficient.

[0010] According to a channel estimation method provided by the present invention, the interpolation filter coefficient table is constructed in the following manner:

[0011] Configure multiple theoretical values ​​of signal-to-noise ratio and multiple theoretical values ​​of root mean square delay;

[0012] Determining, based on the pilot configuration type of the second subband, a pilot subcarrier spacing and an interpolation filter coefficient length of the second subband; wherein the first subband and the second subband have the same number of subcarriers;

[0013] Determining, based on the multiple theoretical signal-to-noise ratio values, the multiple theoretical root mean square delay values, and the pilot subcarrier spacing of the second subband of each pilot configuration type, an interpolation filter coefficient of the interpolation filter coefficient length corresponding to each subband channel parameter value group of the second subband under each pilot configuration type;

[0014] An interpolation filter coefficient table is constructed based on the interpolation filter coefficients of the interpolation filter coefficient length corresponding to each subband channel parameter value group of the second subband under various pilot configuration types.

[0015] According to a channel estimation method provided by the present invention, configuring multiple theoretical signal-to-noise ratio values ​​includes:

[0016] determining a signal-to-noise ratio range of the communication system;

[0017] The signal-to-noise ratio range of the communication system is divided into a plurality of theoretical signal-to-noise ratio values ​​using preset signal-to-noise ratio increments.

[0018] According to a channel estimation method provided by the present invention, configuring multiple theoretical root mean square delay values ​​includes:

[0019] determining a channel model of the communication system;

[0020] Based on the channel model of the communication system, a plurality of theoretical values ​​of root mean square delay are configured.

[0021] According to a channel estimation method provided by the present invention, determining the root mean square delay value of the first subband includes:

[0022] Determining an initial value of a root mean square delay of the first subband based on the LS channel estimation result of the first subband;

[0023] Determining a root mean square delay difference between the initial root mean square delay value of the first subband and each theoretical root mean square delay value in the interpolation filter coefficient table;

[0024] A minimum value among the plurality of root mean square delay differences is determined as a root mean square delay value of the first subband.

[0025] According to a channel estimation method provided by the present invention, determining the signal-to-noise ratio value of the first subband includes:

[0026] Determining a signal power average corresponding to the signal powers of all subcarriers in the first subband and a noise power average corresponding to the noise powers;

[0027] A signal-to-noise ratio value of the first subband is determined based on the average signal power value and the average noise power value.

[0028] The present invention also provides a channel estimation device, which is applied to a receiving end of a communication system, and the device includes:

[0029] A first channel estimation module, configured to divide a received signal into a plurality of first sub-bands;

[0030] a second channel estimation module, configured to determine, for each first subband, an LS channel estimation result of the first subband based on a least squares (LS) algorithm, and determine a root mean square delay value and a signal-to-noise ratio (SNR) value of the first subband;

[0031] a third channel estimation module, configured to search an interpolation filter coefficient table to determine an interpolation filter coefficient for the first subband based on the pilot configuration type of the received signal, the root mean square delay value, and the signal-to-noise ratio value of the first subband; wherein the interpolation filter coefficient table includes interpolation filter coefficients corresponding to at least one subband channel parameter value group under various pilot configuration types, the subband channel parameter value group including a theoretical root mean square delay value and a theoretical signal-to-noise ratio value;

[0032] The fourth channel estimation module is configured to obtain a channel estimation result of the first subband based on the LS channel estimation result of the first subband and the interpolation filter coefficient.

[0033] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the above-described channel estimation methods when executing the computer program.

[0034] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program implements any of the above-mentioned channel estimation methods when executed by a processor.

[0035] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements any one of the channel estimation methods described above.

[0036] The channel estimation method, apparatus, device, medium, and product provided by the present invention divide the received signal into multiple first subbands, perform preliminary channel estimation for each first subband using the LS algorithm, and then optimize the preliminary channel estimation results by searching an interpolation filter coefficient table based on the root mean square delay value and signal-to-noise ratio value. This not only improves the accuracy of channel estimation by accounting for noise interference, but also reduces computational complexity through subband division and the interpolation filter coefficient table. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 It is a flow chart of the channel estimation method provided by the present invention.

[0039] Figure 2 This is one of the schematic diagrams of DMRS pilot distribution scenarios provided by the present invention.

[0040] Figure 3 This is the second schematic diagram of the DMRS pilot distribution scenario provided by the present invention.

[0041] Figure 4 It is a structural diagram of the channel estimation device provided by the present invention.

[0042] Figure 5 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0043] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0044] When signals are transmitted between the transmitter and receiver in a communication system, they experience dual selective fading in the time and frequency domains as they travel through the wireless channel. This causes signal distortion and the addition of random noise. To restore the signal without distortion, channel estimation is essential at the receiver.

[0045] Because blind channel estimation is computationally complex, has slow convergence, and exhibits low accuracy, channel estimation is typically achieved by transmitting pilot signals. For example, in 5G systems, a Demodulation Reference Signal (DMRS) is defined as a pilot signal, employing a two-dimensional distribution structure in the time and frequency domains. Different pilot distributions are employed for different channel conditions to aid channel estimation.

[0046] Since both the receiving and transmitting ends of the communication system know the position and size of the pilot signal, it is easier to obtain the channel gain and phase distortion from the pilot signal position, and then filter or interpolate the data signal between the pilot signals to obtain the channel estimate of the data signal.

[0047] Currently, channel estimation methods commonly used include LS channel estimation and MMES channel estimation. However, when noise and interference cannot be ignored, the LS channel estimation method has low accuracy, while the MMES channel estimation method, while relatively accurate, also has relatively high computational complexity.

[0048] In light of this, the present invention provides a channel estimation method for use at the receiving end of the communication system described above. Specifically, the receiving end divides the received signal into multiple first subbands, performs preliminary channel estimation for each first subband using the LS algorithm, and then optimizes the preliminary channel estimation by searching an interpolation filter coefficient table based on the root mean square delay and signal-to-noise ratio. This method not only improves channel estimation accuracy by accounting for noise interference, but also reduces computational complexity through subband division and the use of an interpolation filter coefficient table.

[0049] Figure 1 It is a flow chart of the channel estimation method provided by the present invention, such as Figure 1 As shown, the method includes step 110 , step 120 , step 130 and step 140 .

[0050] Step 110: Divide the received signal into a plurality of first sub-bands.

[0051] Here, a received signal refers to a signal transmitted by a transmitting end and obtained by a receiving end from a channel in a communication system. For example, a signal transmitted by a base station and obtained by a user equipment from a physical downlink shared channel, or a signal transmitted by a user equipment and obtained by a base station from a physical uplink shared channel.

[0052] It should be understood that the received signal is represented in the frequency domain as a superposition of multiple subcarriers, each of which carries specific frequency information. Therefore, in this embodiment, to reduce the computational complexity of channel estimation, the received signal is divided into multiple first subbands at a preset granularity. Each first subband corresponds to a subcarrier in a specific frequency range in the received signal, so that independent channel estimation processing can be performed on each first subband.

[0053] Here, the preset granularity is pre-set based on the channel characteristics corresponding to the received signal. Different preset granularities can be set for different channel characteristics, and there is no limitation on this.

[0054] It's important to note that communication between base stations and user devices is achieved using over-the-air radio spectrum resources, also known as resource blocks (RBs). Each RB typically corresponds to a certain number of subcarriers (for example, an RB contains 12 subcarriers, with a spacing of 15kHz or 30kHz between subcarriers). The base station divides the transmission into resource blocks of different sizes based on different user services. For example, if the base station allocates 10 RBs to a user device, the received signal can be divided into five primary subbands at a granularity of 2 RBs, each consisting of 24 subcarriers.

[0055] Step 120 : For each first sub-band, determine the LS channel estimation result of the first sub-band based on the least squares LS algorithm, and determine the root mean square delay value and signal-to-noise ratio value of the first sub-band.

[0056] Here, RMS delay is a parameter used to describe channel delay spread, reflecting the time dispersion of a signal reaching the receiver in a multipath propagation environment. The signal-to-noise ratio (SNR) is the ratio of signal power to noise power, measuring the strength of the signal relative to the noise.

[0057] In this embodiment, in each first sub-band, based on the least squares LS algorithm, the receiving end obtains the LS channel estimation result of the first sub-band by conjugate multiplying the received pilot signal with the local pilot signal.

[0058] Furthermore, the root mean square delay value of each first sub-band is calculated by measuring the delay and power of the multipath component, and the signal-to-noise ratio of each first sub-band is calculated by measuring the signal power and noise power.

[0059] Step 130: Based on the pilot configuration type of the received signal, the root mean square delay value of the first subband, and the signal-to-noise ratio value, search an interpolation filter coefficient table to determine the interpolation filter coefficient of the first subband; wherein the interpolation filter coefficient table includes interpolation filter coefficients corresponding to at least one subband channel parameter value group under various pilot configuration types, and the subband channel parameter value group includes a theoretical root mean square delay value and a theoretical signal-to-noise ratio value.

[0060] It should be understood that in a communication system, the pilot configuration type refers to the distribution and parameter settings of the pilot signal in the frequency and time domains. Therefore, different pilot configuration types have different pilot subcarrier spacings, and thus different corresponding interpolation filter coefficients.

[0061] For example, in 5G systems, the Demodulation Reference Signal (DMRS) is a commonly used pilot signal used to demodulate data signals. Figure 2 , DMRS pilot distribution interval can be distributed more densely in the time domain and frequency domain, refer to Figure 3 , can also be distributed more sparsely in the time domain and frequency domain.

[0062] In this embodiment, the signal-to-noise ratio and root mean square delay are used as preset parameters, and corresponding values ​​are designed according to different channel conditions. Then, the interpolation filter coefficients of the parameters at different values ​​under each pilot configuration type are calculated, thereby constructing an interpolation filter coefficient table.

[0063] In practical applications, after measuring the RMS delay and SNR value of the first subband, the interpolation filter coefficients that match the RMS delay and SNR value of the first subband under the corresponding pilot configuration type are found according to the pilot configuration type of the received signal.

[0064] Here, in the process of searching the interpolation filter coefficient table, if there is a theoretical value in the interpolation filter coefficient table that completely matches the measured value, the interpolation filter coefficient corresponding to the theoretical value is directly used. If there is no theoretical value in the interpolation filter coefficient table that completely matches the measured value, the theoretical value closest to the measured value is searched and the interpolation filter coefficient corresponding to the theoretical value is used.

[0065] In addition, it should be understood that since a subband generally includes multiple subcarriers, under various pilot configuration types, the interpolation filter coefficient corresponding to each subband channel parameter value group in the interpolation filter coefficient table is a matrix with a specific interpolation filter coefficient length.

[0066] For example, a subband includes 24 subcarriers, and the pilot subcarrier spacing is twice the subcarrier spacing of the subband (that is, a pilot subcarrier is inserted after each data subcarrier). In this pilot configuration type, the interpolation filter coefficient corresponding to each subband channel parameter value group of the subband can be expressed as a 24x12 matrix, where 24 represents the number of all subcarriers, 12 represents the number of pilot subcarriers, and each row corresponds to an interpolation filter coefficient, which is used to extend the channel estimation value of the pilot subcarrier to the non-pilot subcarrier.

[0067] Step 140: Obtain a channel estimation result for the first subband based on the LS channel estimation result for the first subband and the interpolation filter coefficients.

[0068] After calculating the LS channel estimation result for the first subband, the channel estimation value for the pilot subcarriers in the first subband can be obtained. The LS channel estimation result for the first subband is then multiplied by the interpolation filter coefficient to obtain the channel estimation value for the non-pilot subcarriers in the first subband.

[0069] The channel estimation method provided by the present invention divides the received signal into multiple first subbands, performs preliminary channel estimation on each first subband using the LS algorithm, and then searches an interpolation filter coefficient table based on the root mean square delay and signal-to-noise ratio to optimize the preliminary channel estimation results. This not only improves the accuracy of channel estimation by accounting for noise interference, but also reduces computational complexity through subband division and the interpolation filter coefficient table.

[0070] In one embodiment, the interpolation filter coefficient table is constructed in the following manner:

[0071] Configure multiple theoretical values ​​of signal-to-noise ratio and multiple theoretical values ​​of root mean square delay;

[0072] Determining, based on the pilot configuration type of the second subband, a pilot subcarrier spacing and an interpolation filter coefficient length of the second subband; wherein the first subband and the second subband have the same number of subcarriers;

[0073] Determining, based on the multiple theoretical signal-to-noise ratio values, the multiple theoretical root mean square delay values, and the pilot subcarrier spacing of the second subband of each pilot configuration type, an interpolation filter coefficient of the interpolation filter coefficient length corresponding to each subband channel parameter value group of the second subband under each pilot configuration type;

[0074] An interpolation filter coefficient table is constructed based on the interpolation filter coefficients of the interpolation filter coefficient length corresponding to each subband channel parameter value group of the second subband under various pilot configuration types.

[0075] Here, the second subband is a reference subband used to construct the interpolation filter coefficient table. In this embodiment, the number of subcarriers in the second subband is the same as the number of subcarriers in the first subband used for subsequent channel estimation. This allows the channel estimation result for the first subband to be calculated directly based on the interpolation filter coefficients in the interpolation filter coefficient table.

[0076] Specifically, the final channel estimation result is defined in this embodiment as The calculation formula is as follows:

[0077] ;

[0078] in, is a constant, is the identity matrix, is the channel correlation matrix, is the signal-to-noise ratio, is the LS channel estimation result.

[0079] Based on the above formula, the interpolation filter coefficient of the subcarrier can be determined for:

[0080] ;

[0081] Next, To make it concrete, we get the following formula:

[0082] ;

[0083] in, is the data subcarrier index, is the pilot subcarrier index, is the RMS delay, is the pilot subcarrier spacing, It is a plural unit.

[0084] In this embodiment, based on the above formula, combined with multiple pre-configured theoretical signal-to-noise ratio values ​​and multiple theoretical root mean square delay values, interpolation filter coefficients corresponding to different sub-band channel parameter value groups (i.e., theoretical signal-to-noise ratio values ​​and theoretical root mean square delay values) under various pilot configuration types are calculated.

[0085] It should be noted that the second subband includes multiple subcarriers. Therefore, under various pilot configuration types, the interpolation filter coefficient corresponding to each subband channel parameter value group in the interpolation filter coefficient table is a matrix with a specific interpolation filter coefficient length.

[0086] For example, the second subband includes 24 subcarriers, and the pilot subcarrier spacing is twice the subcarrier spacing of the subband (that is, a pilot subcarrier is inserted after each data subcarrier). Then, under this pilot configuration type, the interpolation filter coefficient corresponding to each subband channel parameter value group of the second subband can be expressed as a 24x12 matrix, where 24 represents the number of all subcarriers, 12 represents the number of pilot subcarriers, and each row corresponds to an interpolation filter coefficient, which is used to extend the channel estimation value of the pilot subcarrier to the non-pilot subcarrier.

[0087] In one example, a signal-to-noise ratio range of a communication system is determined; and the signal-to-noise ratio range of the communication system is divided into a plurality of theoretical signal-to-noise ratio values ​​in preset signal-to-noise ratio increments.

[0088] It should be understood that when designing a communication system, the signal-to-noise ratio range needs to be considered to ensure the performance of the communication system in different environments. Therefore, in this embodiment, the signal-to-noise ratio range of the communication system is divided into multiple theoretical signal-to-noise ratio values ​​based on the signal-to-noise ratio range of the communication system and at preset signal-to-noise ratio increments.

[0089] For example, if the signal-to-noise ratio range of a communication system is 0 dB to 30 dB, then the preset signal-to-noise ratio increment can be 2 dB, and the obtained theoretical signal-to-noise ratio values ​​are 0 dB, 2 dB, 4 dB, ..., 30 dB.

[0090] In one example, a channel model of a communication system is determined; and based on the channel model of the communication system, a plurality of theoretical root mean square delay values ​​are configured.

[0091] It should be understood that a channel model is a mathematical model used in communication systems to describe the physical and statistical characteristics of a signal during transmission. It reflects the various influences on a signal during transmission in a channel, such as multipath effects. RMS delay is a key parameter for measuring multipath effects. It represents the RMS value of the delay differences along each path after a signal propagates through multiple paths.

[0092] Different channel models correspond to different root mean square delays. Therefore, in this embodiment, different root mean square delay theoretical values ​​are configured according to different channel models of the communication system.

[0093] For example, according to different channel models, the theoretical value of the root mean square delay includes 0nm, 15nm, 30nm, 100nm, 150nm, 300nm, 1000nm, and 3000nm.

[0094] The channel estimation method provided by the present invention uses the signal-to-noise ratio and root mean square delay as preset parameters, designs corresponding values ​​based on different channel conditions, and then calculates the interpolation filter coefficients for different parameter values ​​under various pilot configuration types. This constructs an interpolation filter coefficient table, allowing subsequent channel estimation to directly retrieve the relevant interpolation filter coefficients from the interpolation filter coefficient table. This significantly reduces the amount of computation and computational complexity compared to traditional channel estimation algorithms.

[0095] In some embodiments, determining a root mean square delay value of a first subband includes:

[0096] Determining an initial value of a root mean square delay of the first subband based on the LS channel estimation result of the first subband;

[0097] Determining a root mean square delay difference between the initial root mean square delay value of the first subband and each theoretical root mean square delay value in the interpolation filter coefficient table;

[0098] A minimum value among the plurality of root mean square delay differences is determined as a root mean square delay value of the first subband.

[0099] Here, the explanation is given by taking the case where the number of subcarrier data of the first subband is 120, the pilot subcarrier spacing is twice the subcarrier spacing of the subband (that is, a pilot subcarrier is inserted after each data subcarrier), and there are 8 theoretical values ​​of the root mean square delay configured in the interpolation filter coefficient table as representatives.

[0100] In this pilot configuration type for the first subband, the LS channel estimation result for the first subband is a complex vector of length 60. Based on this, the 2nd to 60th values ​​in the LS channel estimation result for the first subband are conjugate multiplied with the 1st to 59th values, respectively, to obtain 59 conjugate multiplication results. These 59 conjugate multiplication results are then averaged, and the corresponding average value is used as the initial RMS delay value for the first subband.

[0101] Next, the initial RMS delay value of the first subband is subtracted from the eight RMS delay theoretical values ​​configured in the interpolation filter coefficient table to obtain eight RMS delay differences.

[0102] Finally, the sum of the square of the real part and the square of the imaginary part corresponding to each RMS delay difference is calculated respectively, and the minimum value among the eight sums is taken as the RMS delay value of the first subband.

[0103] In some embodiments, determining the signal-to-noise ratio value of the first subband includes:

[0104] Determining a signal power average corresponding to the signal powers of all subcarriers in the first subband and a noise power average corresponding to the noise powers;

[0105] A signal-to-noise ratio value of the first subband is determined based on the average signal power value and the average noise power value.

[0106] In this embodiment, the signal power of all subcarriers in the first subband is averaged to obtain an average signal power value, which reflects the average strength of the data signal in the first subband. Furthermore, the noise power of all subcarriers in the first subband is averaged to obtain an average noise power value, which reflects the average strength of the noise signal in the first subband. Finally, the signal-to-noise ratio (SNR) of the first subband is obtained based on the ratio of the average signal power value to the average noise power value.

[0107] The channel estimation method provided by the present invention determines the root mean square delay value and signal-to-noise ratio value of the first subband respectively in the above manner, so that the corresponding interpolation filter coefficient can be found from the interpolation filter coefficient table in combination with the root mean square delay value and the signal-to-noise ratio value, thereby reducing the computational complexity of the channel estimation.

[0108] The channel estimation device provided by the present invention is described below. The channel estimation device provided by the present invention is applied to a receiving end of a communication system. The channel estimation device described below and the channel estimation method described above can be referenced to each other.

[0109] like Figure 4 As shown, the channel estimation apparatus includes: a first channel estimation module 410 , a second channel estimation module 420 , a third channel estimation module 430 and a fourth channel estimation module 440 .

[0110] The first channel estimation module 410 is configured to divide the received signal into a plurality of first sub-bands.

[0111] The second channel estimation module 420 is configured to determine, for each first sub-band, an LS channel estimation result of the first sub-band based on a least squares (LS) algorithm, and determine a root mean square (RMS) delay value and a signal-to-noise ratio (SNR) value of the first sub-band.

[0112] The third channel estimation module 430 is configured to search an interpolation filter coefficient table to determine the interpolation filter coefficient for the first subband based on the pilot configuration type of the received signal, the root mean square delay value of the first subband, and the signal-to-noise ratio value; wherein the interpolation filter coefficient table includes interpolation filter coefficients corresponding to at least one subband channel parameter value group under various pilot configuration types, and the subband channel parameter value group includes a theoretical root mean square delay value and a theoretical signal-to-noise ratio value.

[0113] The fourth channel estimation module 440 is configured to obtain a channel estimation result for the first subband based on the LS channel estimation result for the first subband and the interpolation filter coefficients.

[0114] The channel estimation device provided by the present invention divides the received signal into multiple first subbands, performs preliminary channel estimation for each first subband using the LS algorithm, and then searches an interpolation filter coefficient table based on the root mean square delay and signal-to-noise ratio to optimize the preliminary channel estimation results. This not only improves the accuracy of channel estimation by accounting for noise interference, but also reduces computational complexity through subband division and the interpolation filter coefficient table.

[0115] Figure 5 An example of a physical structure diagram of an electronic device is shown below. Figure 5 As shown, the electronic device may include: a processor 510, a communication interface 520, a memory 530, and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other via the communication bus 540. The processor 510 may call the logic instructions in the memory 530 to execute the channel estimation method, which includes:

[0116] dividing the received signal into a plurality of first sub-bands;

[0117] For each first subband, determine an LS channel estimation result of the first subband based on a least squares (LS) algorithm, and determine a root mean square (RMS) delay value and a signal-to-noise ratio (SNR) value of the first subband;

[0118] Based on the pilot configuration type of the received signal, the root mean square delay value of the first subband, and the signal-to-noise ratio value, searching an interpolation filter coefficient table to determine the interpolation filter coefficient for the first subband; wherein the interpolation filter coefficient table includes interpolation filter coefficients corresponding to at least one subband channel parameter value group under various pilot configuration types, the subband channel parameter value group including a theoretical root mean square delay value and a theoretical signal-to-noise ratio value;

[0119] A channel estimation result of the first subband is obtained based on the LS channel estimation result of the first subband and the interpolation filter coefficient.

[0120] Furthermore, the logic instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the 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, server, or 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 capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0121] On the other hand, the present invention further provides a computer program product, comprising a computer program. The computer program may be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform the channel estimation method provided by the above methods, wherein the method comprises:

[0122] dividing the received signal into a plurality of first sub-bands;

[0123] For each first subband, determine an LS channel estimation result of the first subband based on a least squares (LS) algorithm, and determine a root mean square (RMS) delay value and a signal-to-noise ratio (SNR) value of the first subband;

[0124] Based on the pilot configuration type of the received signal, the root mean square delay value of the first subband, and the signal-to-noise ratio value, searching an interpolation filter coefficient table to determine the interpolation filter coefficient for the first subband; wherein the interpolation filter coefficient table includes interpolation filter coefficients corresponding to at least one subband channel parameter value group under various pilot configuration types, the subband channel parameter value group including a theoretical root mean square delay value and a theoretical signal-to-noise ratio value;

[0125] A channel estimation result of the first subband is obtained based on the LS channel estimation result of the first subband and the interpolation filter coefficient.

[0126] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the channel estimation method provided by each of the above methods is implemented, the method comprising:

[0127] dividing the received signal into a plurality of first sub-bands;

[0128] For each first subband, determine an LS channel estimation result of the first subband based on a least squares (LS) algorithm, and determine a root mean square (RMS) delay value and a signal-to-noise ratio (SNR) value of the first subband;

[0129] Based on the pilot configuration type of the received signal, the root mean square delay value of the first subband, and the signal-to-noise ratio value, searching an interpolation filter coefficient table to determine the interpolation filter coefficient for the first subband; wherein the interpolation filter coefficient table includes interpolation filter coefficients corresponding to at least one subband channel parameter value group under various pilot configuration types, the subband channel parameter value group including a theoretical root mean square delay value and a theoretical signal-to-noise ratio value;

[0130] A channel estimation result of the first subband is obtained based on the LS channel estimation result of the first subband and the interpolation filter coefficient.

[0131] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0132] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0133] 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A channel estimation method, characterized in that: Applied to a receiving end of a communication system, the method comprises: dividing the received signal into a plurality of first sub-bands; For each first subband, determine an LS channel estimation result of the first subband based on a least squares (LS) algorithm, and determine a root mean square (RMS) delay value and a signal-to-noise ratio (SNR) value of the first subband; Based on the pilot configuration type of the received signal, the root mean square delay value of the first subband, and the signal-to-noise ratio value, searching an interpolation filter coefficient table to determine the interpolation filter coefficient for the first subband; wherein the interpolation filter coefficient table includes interpolation filter coefficients corresponding to at least one subband channel parameter value group under various pilot configuration types, the subband channel parameter value group including a theoretical root mean square delay value and a theoretical signal-to-noise ratio value; Obtaining a channel estimation result for the first subband based on the LS channel estimation result for the first subband and the interpolation filter coefficients; Determining the root mean square delay value of the first subband includes: Determining an initial value of a root mean square delay of the first subband based on the LS channel estimation result of the first subband; Determining a root mean square delay difference between the initial root mean square delay value of the first subband and each theoretical root mean square delay value in the interpolation filter coefficient table; Determine a minimum value among the plurality of root mean square delay differences as a root mean square delay value of the first subband; The interpolation filter coefficient table is constructed in the following way: Configure multiple theoretical values ​​of signal-to-noise ratio and multiple theoretical values ​​of root mean square delay; Determining, based on the pilot configuration type of the second subband, a pilot subcarrier spacing and an interpolation filter coefficient length of the second subband; wherein the first subband and the second subband have the same number of subcarriers; Determining, based on the multiple theoretical signal-to-noise ratio values, the multiple theoretical root mean square delay values, and the pilot subcarrier spacing of the second subband of each pilot configuration type, an interpolation filter coefficient of the interpolation filter coefficient length corresponding to each subband channel parameter value group of the second subband under each pilot configuration type; An interpolation filter coefficient table is constructed based on the interpolation filter coefficients of the interpolation filter coefficient length corresponding to each subband channel parameter value group of the second subband under various pilot configuration types.

2. The channel estimation method according to claim 1, wherein The configuring of multiple theoretical signal-to-noise ratio values ​​includes: determining a signal-to-noise ratio range of the communication system; The signal-to-noise ratio range of the communication system is divided into a plurality of theoretical signal-to-noise ratio values ​​using preset signal-to-noise ratio increments.

3. The channel estimation method according to claim 1, wherein Configuring multiple root mean square delay theoretical values ​​includes: determining a channel model of the communication system; Based on the channel model of the communication system, a plurality of theoretical values ​​of root mean square delay are configured.

4. The channel estimation method according to claim 1, wherein The determining the signal-to-noise ratio value of the first subband includes: Determining a signal power average corresponding to the signal powers of all subcarriers in the first subband and a noise power average corresponding to the noise powers; A signal-to-noise ratio value of the first subband is determined based on the average signal power value and the average noise power value.

5. A channel estimation device, characterized in that: Applied to a receiving end of a communication system, the device comprises: A first channel estimation module, configured to divide a received signal into a plurality of first sub-bands; a second channel estimation module, configured to determine, for each first subband, an LS channel estimation result of the first subband based on a least squares (LS) algorithm, and determine a root mean square delay value and a signal-to-noise ratio (SNR) value of the first subband; a third channel estimation module, configured to search an interpolation filter coefficient table to determine an interpolation filter coefficient for the first subband based on the pilot configuration type of the received signal, the root mean square delay value, and the signal-to-noise ratio value of the first subband; wherein the interpolation filter coefficient table includes interpolation filter coefficients corresponding to at least one subband channel parameter value group under various pilot configuration types, the subband channel parameter value group including a theoretical root mean square delay value and a theoretical signal-to-noise ratio value; a fourth channel estimation module, configured to obtain a channel estimation result for the first subband based on the LS channel estimation result for the first subband and the interpolation filter coefficient; Determining the root mean square delay value of the first subband includes: Determining an initial value of a root mean square delay of the first subband based on the LS channel estimation result of the first subband; Determining a root mean square delay difference between the initial root mean square delay value of the first subband and each theoretical root mean square delay value in the interpolation filter coefficient table; Determine a minimum value among the plurality of root mean square delay differences as a root mean square delay value of the first subband; The interpolation filter coefficient table is constructed in the following way: Configure multiple theoretical values ​​of signal-to-noise ratio and multiple theoretical values ​​of root mean square delay; Determining, based on the pilot configuration type of the second subband, a pilot subcarrier spacing and an interpolation filter coefficient length of the second subband; wherein the first subband and the second subband have the same number of subcarriers; Determining, based on the multiple theoretical signal-to-noise ratio values, the multiple theoretical root mean square delay values, and the pilot subcarrier spacing of the second subband of each pilot configuration type, an interpolation filter coefficient of the interpolation filter coefficient length corresponding to each subband channel parameter value group of the second subband under each pilot configuration type; An interpolation filter coefficient table is constructed based on the interpolation filter coefficients of the interpolation filter coefficient length corresponding to each subband channel parameter value group of the second subband under various pilot configuration types.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the channel estimation method according to any one of claims 1 to 4 is implemented.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the channel estimation method according to any one of claims 1 to 4 is implemented.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the channel estimation method according to any one of claims 1 to 4 is implemented.

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