Combined filtering method and system

Abstract

The embodiment of the invention provides a combined filtering method and system, and relates to the technical field of wireless communication. The method comprises the following steps: carrying out coarse estimation on a frequency domain signal to generate a coarse estimation result; determining a filtering coefficient based on the rough estimation result; performing fine filtering operation on the coarse estimation result based on the filtering coefficient to obtain a fine filtering result of a first subcarrier of a first sequence number, and performing interpolation operation on a second subcarrier of a second sequence number; wherein the second subcarrier is the rest of the to-be-processed carriers except the first subcarrier. According to one embodiment of the invention, the method at least solves a problem that a large amount of calculation is still needed to guarantee the filtering performance meeting the requirements in the related technology, and achieves an effect of reducing the amount of calculation on the basis of enabling the filtering performance to meet the requirements.

Description

Combined Filtering Methods and Systems Technical Field

[0001] This application relates to the technical field of wireless communication, and more specifically, to a combined filtering method and system. Background Technology

[0002] Fine filtering is widely used in the channel estimation modules of OFDM (Orthogonal Frequency Division Multiplexing) systems in modern wireless communications (including but not limited to NR, LTE, and WIFI systems). Since the reference pilot positions in OFDM systems are distributed two-dimensionally in the time and frequency domains, two-dimensional (2D) fine filtering, which comprehensively considers time-frequency domain correlation, should theoretically achieve the best results. However, considering hardware implementation pipelining and complexity, fine filtering can, for example, adopt a 2x1D filtering approach, i.e., performing frequency domain filtering first and then time domain filtering. Numerous simulation results also show that, with appropriate reference resource location settings, there is no significant difference in performance between the 2x1D filtering method and the 2D filtering method.

[0003] When performing frequency domain filtering, the frequency response of all frequency domain subcarriers (REs) can be obtained through fine filtering. However, because fine filtering involves a large number of complex multiplications and additions, to reduce computational complexity, a combination of interpolation and interval filtering is generally used to obtain the frequency response of all subcarriers. That is, the frequency response of equally spaced subcarriers (REs) is obtained through fine filtering, then time domain filtering is performed, and finally, the frequency response of the remaining subcarriers is obtained through interpolation using the finely filtered response. For example, the frequency response of subcarriers numbered 0, 3, 6, 9, 12, 15, 18, ... is obtained through fine filtering, while the frequency response of the remaining subcarriers is obtained through interpolation using adjacent subcarriers.

[0004] However, when a combination of fine filtering and linear interpolation is used in a wireless communication system to obtain the frequency domain response, a large amount of computation is still required to ensure that the required filtering performance is met. Summary of the Invention

[0005] This application provides a combined filtering method and system, which at least solves the problem in related technologies that still require a large amount of computation to ensure the required filtering performance.

[0006] According to one embodiment of this application, a combined filtering method is provided, comprising:

[0007] A coarse estimate is performed on the frequency domain signal to generate a coarse estimate result;

[0008] The filter coefficients are determined based on the coarse estimation results.

[0009] Based on the filtering coefficients, a fine filtering operation is performed on the coarse estimation result to obtain the fine filtering result of the first subcarrier with the first sequence number, and an interpolation operation is performed on the second subcarrier with the second sequence number; wherein, the second subcarrier is the remaining carrier among the carriers to be processed excluding the first subcarrier.

[0010] In one implementation, determining the filter coefficients based on the coarse estimation result includes:

[0011] The channel parameters are calculated based on the coarse estimation results, and the first sequence number and the second sequence number are determined based on the channel parameters.

[0012] In one implementation, determining the filter coefficients based on the coarse estimation result includes:

[0013] The correlation of the subcarrier spacing is calculated based on the coarse estimation result, and the first sequence number and the second sequence number are determined based on the correlation.

[0014] In one implementation, calculating channel parameters based on the coarse estimation result, and determining the first sequence number and the second sequence number based on the channel parameters, includes:

[0015] When the channel parameter is greater than or equal to the first threshold, the first sequence number and the second sequence number are determined based on the first interval;

[0016] When the channel parameter is less than a first threshold and greater than a second threshold, the first sequence number and the second sequence number are determined based on the second interval.

[0017] If the channel parameter is greater than or equal to the second threshold, the first sequence number and the second sequence number are determined based on the third interval; wherein the first interval is less than the second interval, and the second interval is less than the third interval.

[0018] In one embodiment, the fine filtering includes Wiener filtering or transform domain filtering.

[0019] In one implementation, the interpolation includes linear interpolation or polynomial interpolation.

[0020] In one implementation, the channel parameters include: root mean square delay spread or Doppler value.

[0021] According to another embodiment of this application, a combined filtering system is provided, comprising:

[0022] The filter coefficient generation module is used to determine the filter coefficients based on the coarse estimation results;

[0023] The channel estimation module is used to perform a coarse estimation of the frequency domain signal to generate the coarse estimation result; and to perform a fine filtering operation on the coarse estimation result based on the filtering coefficients to obtain a fine filtering result of the first subcarrier with a first sequence number, and to perform an interpolation operation on the second subcarrier with a second sequence number; wherein the second subcarrier is the remaining carrier in the carrier to be processed excluding the first subcarrier.

[0024] According to yet another embodiment of this application, a computer-readable storage medium is also provided, wherein a computer program is stored therein, and the computer program is configured to perform the steps in any of the above method embodiments when it is run.

[0025] According to yet another embodiment of this application, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0026] According to yet another embodiment of this application, a computer program product is also provided, including computer instructions that, when executed by a processor, implement the steps in any of the above method embodiments.

[0027] In one embodiment of this application, a coarse estimation of the frequency domain signal is first performed to generate a coarse estimation result. This coarse estimation result can reflect the actual situation of the channel (such as the correlation between subcarriers, channel flatness, etc.), thereby identifying the first subcarrier (corresponding to the first sequence number) that requires computationally complex fine filtering operations and the second subcarrier (corresponding to the remaining carriers in the carriers to be processed other than the first subcarrier) that only requires computationally simple interpolation operations. Instead of performing fine filtering indiscriminately on subcarriers at fixed intervals as in related technologies, this approach ensures that the selection of the first subcarrier is adapted to the actual channel situation to guarantee filtering performance through the coarse estimation result, while reducing unnecessary fine filtering operations (performing only on the necessary first subcarriers). By increasing the proportion of interpolation operations with lower computational load, the overall computational load is reduced while still meeting the filtering performance requirements. Attached Figure Description

[0028] The accompanying drawings, which are included to provide a further understanding of the embodiments of this application and constitute a part of the embodiments of this application, illustrate exemplary embodiments of this application and, together with their descriptions, serve to explain the embodiments of this application and do not constitute an improper limitation of the embodiments of this application. In the drawings:

[0029] Figure 1 is a hardware structure block diagram of the combined filtering method according to an embodiment of this application;

[0030] Figure 2 is a flowchart of a combined filtering method according to an embodiment of this application;

[0031] Figure 3 is a flowchart of a method for calculating channel parameters based on coarse estimation results and determining the first and second sequence numbers based on the channel parameters according to an embodiment of this application.

[0032] Figure 4 is a structural block diagram of a combined filtering system according to an embodiment of this application;

[0033] Figure 5 is a schematic diagram of the combined filtering method in two exemplary scenarios according to embodiments of this application;

[0034] Figure 6 is a simulation comparison of the channel estimation error (RawBer) of two different Wiener filter subcarrier spacing schemes in the LTE system EPA (few multipath paths, small root mean square delay spread) scenario.

[0035] Figure 7 is a simulation diagram comparing the block error rate (BLER) of two Wiener filter subcarrier spacing schemes in the EPA flat channel scenario of the LTE system.

[0036] Figure 8 is a simulation comparison of throughput in the EPA flat channel scenario of the LTE system. Detailed Implementation

[0037] The embodiments of this application will be described in detail below with reference to the accompanying drawings and examples. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of this application can be combined with each other.

[0038] It should be noted that the terms "first," "second," etc., in the specification, claims, and drawings of the embodiments of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0039] The method embodiments provided in this application can be executed in a mobile terminal, computer terminal, or similar computing device. Taking running on a computer terminal as an example, FIG1 is a hardware structure block diagram of the combined filtering method of this application embodiment. As shown in FIG1, the hardware board may include one or more (only one is shown in FIG1) processors 12 (processors 12 may include, but are not limited to, processing devices such as microprocessors MCUs or programmable logic devices) and a memory 14 for storing data. The computer terminal may also include a transmission device 16 for communication functions and an input / output device 18. Those skilled in the art will understand that the structure shown in FIG1 is only illustrative and does not limit the structure of the computer terminal. For example, the computer terminal may also include more or fewer components than shown in FIG1, or have a different configuration than shown in FIG1.

[0040] The memory 14 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the combined filtering method in this embodiment. The processor 12 executes various functional applications and implements the methods described above by running the computer programs stored in the memory 14. The memory 14 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 14 may further include memory remotely located relative to the processor 12, and these remote memories can be connected to a computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0041] The transmission device 16 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by a telecommunications provider. In one example, the transmission device 16 includes a Network Interface Controller (NIC), which can connect to other network devices via a gateway to communicate with the Internet. In another example, the transmission device 16 may be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0042] This application provides a combined filtering method. Figure 2 is a flowchart of the combined filtering method according to an embodiment of this application. As shown in Figure 2, the process includes:

[0043] Step S201: Perform a coarse estimation on the frequency domain signal to generate a coarse estimation result;

[0044] In one exemplary implementation, for example, by combining reference signals in the OFDM system (such as the shared reference signal of an LTE CRS cell), a simple channel estimation algorithm (such as pilot-based linear estimation) is used to perform preliminary calculations on the signals of all subcarriers in the frequency domain. Key statistical parameters of the channel are extracted, such as the root mean square delay spread (RMS delay spread) and Doppler value. Simultaneously, normalized correlation coefficients under a fixed subcarrier spacing (such as the amplitude and phase similarity measure of the channel response between subcarriers spaced at 3 RE intervals) are calculated, ultimately generating a coarse estimate reflecting the actual state of the channel. Therefore, accurately capturing the core characteristics of the channel allows for the determination of whether the current channel is "flat" (small RMS delay spread, high inter-subcarrier correlation), between "flat" and "non-flat" (moderate RMS delay spread and inter-subcarrier correlation), or "non-flat" (large RMS delay spread, low inter-subcarrier correlation). This provides a reliable decision-making basis for subsequent dynamic adjustment of the filtering strategy, avoiding blind filtering processing that is detached from the actual channel conditions.

[0045] In step 201, the frequency domain signals can be divided into two main categories: 1. physical channels; 2. physical signals. Physical channels are generally used to carry the information to be transmitted, and the input for channel estimation is usually physical signals. Physical signals can be further subdivided into various types: for example, NR systems may have PDSCH DMRS, PDCCH DMRS, PBCH DMRS, PTRS, CSI, TRS, etc.; LTE systems may have CRS, DMRS, CSI, etc. Taking the NR system as an example: generally, the coarse estimation results of TRS or PBCH DMRS are used as the input to the channel parameter estimation module, outputting parameters such as root mean square delay spread; while pilot signals such as PDSCH DMRS, PDCCH DMRS, and PBCH DMRS require coarse estimation first, followed by fine estimation.

[0046] Step S202: Determine the filter coefficients based on the coarse estimation results;

[0047] In one exemplary implementation, for example, based on the coarse estimation result generated in S201, if the coarse estimation result shows that the root mean square delay spread is less than or equal to the first threshold (channel flat), then the subcarrier spacing is set to 6 REs, the first sequence number is 0, 6, 12, 18..., and the second sequence number is all subcarriers between adjacent first sequence numbers (such as 1-5, 7-11, etc.); if the root mean square delay spread is between the first threshold and the second threshold (channel mild), then the subcarrier spacing is set to 4 REs, the first sequence number is 0, 4, 8, 12..., and the second sequence number is 1-3, 5-7, etc.; if the root mean square delay spread is greater than or equal to the second threshold (channel non-flat, strong frequency selectivity), then the subcarrier spacing is set to 3 REs, the first sequence number is 0, 3, 6, 9..., and the second sequence number is 1-2, 4-5, etc.

[0048] If the determination is based on subcarrier spacing correlation, a large spacing is used when the correlation is less than or equal to the first threshold. An intermediate spacing is used when the correlation is between the first and second thresholds. A small spacing is used when the correlation is greater than or equal to the second threshold. Therefore, this approach breaks the limitation of fixed subcarrier spacing in correlation techniques, dynamically defining the scope of fine filtering and interpolation. This allows the filtering strategy to adapt to the real-time channel conditions and reduces unnecessary fine filtering coefficient generation (e.g., only 10 sets of Wiener filter coefficients are needed for a 6-RE spacing, while 20 sets are needed for a 3-RE spacing), laying the foundation for reduced computational load.

[0049] In step 202, taking PDSCH DMRS as an example, its filter coefficient density can be determined by the root mean square delay spread obtained from the channel parameter estimation module, or by correlation after coarse estimation of PDSCH DMRS; the filter coefficient density of PDCCH DMRS can also be determined by the root mean square delay spread obtained from the channel parameter estimation module, or by correlation after coarse estimation of PDCCH DMRS; other signals are similar. In step S203, a fine filtering operation is performed on the coarse estimation result based on the filter coefficients to obtain the fine filtering result of the first subcarrier with the first sequence number, and an interpolation operation is performed on the second subcarrier with the second sequence number; wherein, the second subcarrier is the remaining carrier in the carrier to be processed excluding the first subcarrier.

[0050] In one exemplary implementation, for example, based on the coarse estimation result of S201 and the filtering coefficients determined in S202, a fine filtering operation (such as 8th-order Wiener filtering or transform domain filtering) is performed on the first subcarrier corresponding to the first sequence number to ensure the frequency response accuracy of the key subcarrier; an interpolation operation (such as linear interpolation or polynomial interpolation) is performed on the second subcarrier corresponding to the second sequence number, and its frequency response is derived using the fine filtering results of adjacent first subcarriers. For example, when the channel is flat, Wiener filtering is performed on the first subcarriers with sequence numbers 0, 6, 12, etc., and linear interpolation is performed on the second subcarriers with sequence numbers 1-5, 7-11, etc.; when the channel is moderate, Wiener filtering is performed on the first subcarriers with sequence numbers 0, 4, 8, 12, etc., and linear interpolation is performed on the second subcarriers with sequence numbers 1-3, 5-7, 9-11, etc.; when the channel is not flat, Wiener filtering is performed on the first subcarriers with sequence numbers 0, 3, 6, etc., and linear interpolation is performed on the second subcarriers with sequence numbers 1-2, 4-5, etc. Therefore, while ensuring performance, the computational load is minimized: fine filtering is applied only to the necessary first subcarrier, avoiding redundant calculations indiscriminate fine filtering, while interpolation operations have a much lower computational load than fine filtering (no complex multiplication and addition are required), which greatly reduces the overall computational load.

[0051] In step 203, PDSCH DMRS is used as an example, and the inputs are the filter coefficients of PDSCH DMRS and the coarse estimate of PDSCH DMRS.

[0052] In one implementation, fine filtering includes Wiener filtering or transform domain filtering.

[0053] In one implementation, interpolation includes linear interpolation or polynomial interpolation.

[0054] Through steps S201 to S203, a coarse estimation of the frequency domain signal is first performed to generate a coarse estimation result. This coarse estimation result can reflect the actual situation of the channel (such as the correlation between subcarriers, channel flatness, etc.), thereby clarifying the first subcarrier (corresponding to the first sequence number) that needs to perform computationally complex fine filtering operations and the second subcarrier (corresponding to the remaining carriers in the carriers to be processed other than the first subcarrier) that only needs to perform computationally simple interpolation operations. Instead of performing fine filtering indiscriminately on subcarriers at fixed intervals as in related technologies, this approach ensures that the selection of the first subcarrier is adapted to the actual channel situation to guarantee filtering performance, while reducing unnecessary fine filtering operations (performing only on the necessary first subcarriers). By increasing the proportion of interpolation operations with lower computational load, the overall computational load is reduced while still meeting the filtering performance requirements.

[0055] In one implementation, determining the filter coefficients based on the coarse estimation results includes: calculating the channel parameters of the channel based on the coarse estimation results, and determining the first sequence number and the second sequence number based on the channel parameters.

[0056] In one implementation, the channel parameters include: root mean square delay spread or Doppler value.

[0057] In one implementation, determining the filter coefficients based on the coarse estimation results includes: calculating the correlation of the subcarrier spacing based on the coarse estimation results, and determining the first sequence number and the second sequence number based on the correlation.

[0058] Figure 3 is a flowchart of a method for calculating channel parameters based on coarse estimation results and determining a first sequence number and a second sequence number based on the channel parameters according to an embodiment of this application. As shown in Figure 3, in one embodiment, calculating channel parameters based on coarse estimation results and determining a first sequence number and a second sequence number based on the channel parameters includes:

[0059] Step S301: If the channel parameter is greater than or equal to the first threshold, determine the first sequence number and the second sequence number based on the first interval;

[0060] In one exemplary implementation, for example, channel parameters (using the root mean square delay spread as an example, in μs) are first calculated based on coarse estimation results. A first threshold is set to 1.5 μs, and a first interval is set to 1 RE (resource element). When the calculated root mean square delay spread is ≥1.5 μs (channel parameters ≥ the first threshold), a first sequence number and a second sequence number are determined based on the first interval. For example, the first sequence number is the number of all 1200 subcarriers (0, 1, 2, ..., 1199) in an LTE 20MHz bandwidth system, and the second sequence number is empty (no subcarriers requiring interpolation). Therefore, for cases with severely uneven channels (such as outdoor long-distance communication scenarios with strong multipath interference and large delay spread), fine filtering is performed on all subcarriers using the minimum first interval to ensure the frequency response accuracy of each subcarrier and avoid performance loss caused by interpolation. In this case, 48 sets of Wiener filter coefficients need to be generated (adapting to 1200 subcarriers, 8th order filtering), which is the largest computational workload (approximately 1200). 8 = 9600 complex number multiplications, 1200 (7 = 8400 complex number additions), but it can fully meet the filtering performance requirements under harsh channel conditions.

[0061] Step S302: When the channel parameter is less than the first threshold and greater than the second threshold, determine the first sequence number and the second sequence number based on the second interval;

[0062] In one exemplary implementation, for example, a second threshold is set to 0.5 μs and a second interval is 3 REs. When the calculated root mean square delay spread is less than 1.5 μs and greater than 0.5 μs (channel parameters are between the first and second thresholds), the first and second sequence numbers are determined based on the second interval. For example, the first sequence number is 0, 3, 6, 9, ..., 1197 (one is selected every two subcarriers, for a total of 400), and the second sequence number is 1-2, 4-5, 7-8, ..., 1198-1199 (subcarriers between every two first sequence numbers, for a total of 800). Therefore, to adapt to moderately flat channels (such as urban suburban communication scenarios, where there is some multipath but the impact is controllable), the performance and computational load are balanced by a moderate interval: the fine filtering of the first sequence number ensures the accuracy of the core nodes, and the interpolation of the second sequence number reduces redundant calculations. At this time, 20 sets of Wiener filter coefficients need to be generated (example data in the document), and the computational load is 3208 complex multiplications and 401... The addition of 7 + 800 = 3607 complex numbers avoids the excessive calculations of full fine filtering and prevents interpolation errors caused by excessively large intervals.

[0063] Step S303: If the channel parameter is greater than or equal to the second threshold, determine the first sequence number and the second sequence number based on the third interval; wherein the first interval is less than the second interval, and the second interval is less than the third interval.

[0064] In one exemplary implementation, for example, the third interval is set to 6 REs. When the calculated root mean square delay spread is ≤0.5μs (channel parameters ≤ second threshold, adapting to the flattest channel scenario), the first and second sequence numbers are determined based on the third interval. For example, the first sequence numbers are 0, 6, 12, 18, ..., 1194 (one is selected every 5 subcarriers, for a total of 200), and the second sequence numbers are 1-5, 7-11, 13-17, ..., 1195-1199 (subcarriers between every two first sequence numbers, for a total of 1000). Therefore, for cases with extremely flat channels (such as indoor short-range communication, LTE EPA model scenarios), the maximum third interval maximizes the reduction of the number of fine filters, allowing more subcarriers to obtain responses through low-computation interpolation. In this case, only 10 sets of Wiener filter coefficients need to be generated (example data in the document), reducing the computation to 1608 complex multiplications and 201 There are 7 + 1000 = 2407 complex number additions.

[0065] Through the aforementioned steps S301, S302, and S303, when the channel is poor (as in step S301), performance is guaranteed with maximum computational effort; when the channel is moderate (as in step S302), performance and computational efficiency are balanced; and when the channel is flat (as in step S303), performance is maintained with minimum computational effort. This quantitative hierarchical strategy avoids the rigidity of fixed intervals in related technologies, ensuring that the division between the first and second sequences perfectly matches the actual channel conditions. This not only ensures that filtering performance meets standards in different scenarios but also minimizes unnecessary computational overhead by dynamically adjusting the fine filtering density, enabling the OFDM system's channel estimation to remain highly efficient and stable in complex and ever-changing wireless environments.

[0066] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by adding necessary general-purpose hardware platforms through software. Of course, they can also be implemented through hardware, but in many cases, the former is a better implementation method. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the embodiments of this application.

[0067] This application also provides a combined filtering system for implementing the above embodiments and preferred embodiments; details already described will not be repeated. The term "module" as used below refers to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0068] Figure 4 is a structural block diagram of a combined filtering system according to an embodiment of this application. As shown in Figure 4, the system includes:

[0069] The filter coefficient generation module 41 is used to determine the filter coefficients based on the coarse estimation results;

[0070] The channel estimation module 42 is used to perform a coarse estimation of the frequency domain signal to generate the coarse estimation result; and to perform a fine filtering operation on the coarse estimation result based on the filtering coefficients to obtain a fine filtering result of the first subcarrier with a first sequence number, and to perform an interpolation operation on the second subcarrier with a second sequence number; wherein the second subcarrier is the remaining carrier in the carrier to be processed excluding the first subcarrier.

[0071] By adopting the above technical solution, the channel estimation module 42 performs a coarse estimation of the frequency domain signal, generating a coarse estimation result that reflects the actual channel conditions (such as channel flatness, inter-subcarrier correlation, etc.). It dynamically identifies the first subcarrier (corresponding to the first index) that needs to be fine-filtered and the second subcarrier (corresponding to the second index, i.e., the remaining carriers excluding the first subcarrier) that only requires interpolation operations with less computation. Finally, based on the coarse estimation result and the filtering coefficients, the channel estimation module 42 performs fine filtering on the first subcarrier of the first index and interpolation operations on the second subcarrier of the second index, rather than performing fixed-interval indiscriminate fine filtering as in related technologies. This ensures that the selection of the first subcarrier is adapted to the actual channel conditions to guarantee filtering performance through the coarse estimation result, and effectively reduces the overall computational load while meeting the filtering performance requirements by reducing unnecessary fine filtering operations and increasing the proportion of interpolation operations.

[0072] In one embodiment, the filter coefficient generation module 41 is further configured to: calculate the channel parameters of the channel based on the coarse estimation results, and determine the first sequence number and the second sequence number based on the channel parameters.

[0073] In one embodiment, the filter coefficient generation module 41 is further configured to: calculate the correlation of the subcarrier spacing based on the coarse estimation result, so as to determine the first sequence number and the second sequence number based on the correlation.

[0074] In one embodiment, the filter coefficient generation module 41 is further configured to:

[0075] When the channel parameter is greater than or equal to the first threshold, the first sequence number and the second sequence number are determined based on the first interval;

[0076] When the channel parameter is less than the first threshold and greater than the second threshold, the first sequence number and the second sequence number are determined based on the second interval.

[0077] When the channel parameters are greater than or equal to the second threshold, the first and second serial numbers are determined based on the third interval; wherein the first interval is less than the second interval, and the second interval is less than the third interval.

[0078] In one implementation, fine filtering includes Wiener filtering or transform domain filtering.

[0079] In one implementation, interpolation includes linear interpolation or polynomial interpolation.

[0080] In one implementation, the channel parameters include: root mean square delay spread or Doppler value.

[0081] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0082] This application also provides a computer-readable storage medium storing a computer program configured to execute the steps in any of the above method embodiments when running.

[0083] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0084] This application also provides an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0085] In one exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0086] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0087] Specific examples in the embodiments of this application can be found in the examples described in the above embodiments and exemplary implementations, and will not be repeated here.

[0088] The above process will be explained with examples below:

[0089] Figure 5 is a schematic diagram of the combined filtering method under two exemplary scenarios according to embodiments of this application. As shown in Figure 5, after coarse estimation of the frequency domain signal, positions 6, 12, 18, 24, and 30 are determined as reference resource positions. The RE indices of the frequency response obtained by Wiener filtering are positions 0, 6, 12, 18, 24, and 30, respectively. The RE indices of the frequency response obtained by interpolation are positions between 0 and 6, 6 and 12, 12 and 18, 18 and 24, and 24 and 30, respectively. This scenario is then taken as the scenario. The frequency response REs obtained by Wiener filtering are numbered 0, 3, 6, 9, 12, 15, 18, 21, 24, 27, and 30. The frequency response REs obtained by interpolation are numbered between 0 and 3, 3 and 6, 6 and 9, 9 and 12, 12 and 15, 15 and 18, 18 and 21, 21 and 24, 24 and 27, and 27 and 30, respectively. This scenario is then used as the scene.

[0090] Taking LTE CRS as a reference resource, assuming the LTE system bandwidth is 20MHz and the Wiener filter order is 8, scenario 1 only requires 10 sets of Wiener filter coefficients, while scenario 2 requires 20 sets. The computational load in the filter coefficient generation module of scenario 1 is only half that of scenario 2. Simultaneously, in the channel estimation module, scenario 1 requires approximately 1608 complex multiplications and 3608 complex additions; while scenario 2 requires approximately 3208 complex multiplications and 4808 complex additions. Clearly, the computational load required by scenario 1 is far less than that of scenario 2.

[0091] Figure 6 is a simulation comparison of the channel estimation error (RawBer) for two different Wiener filter subcarrier spacing schemes in the LTE system EPA (few multipath paths, small root mean square delay spread) scenario. As shown in Figure 6, the test parameters strictly follow the Test3 conditions in Table 8.3.2.1-4 of the 3GPP standard, specifically 10MHz bandwidth, 5Hz Doppler shift (weak channel time-varying nature), 2x2 MIMO (LOW configuration), and 64QAM modulation. The horizontal axis represents the signal-to-noise ratio (SNR, ranging from 10 to 22dB, with higher values ​​indicating better signal quality), and the vertical axis represents the normalized channel estimation error (RawBer, ranging from 1 to 22dB). ² to 1 ¹, the smaller the value, the higher the estimation accuracy. The two curves correspond to the schemes with a subcarrier spacing of 3 REs (requiring 20 sets of Wiener filter coefficients, resulting in a large computational load) and a spacing of 6 REs (requiring 10 sets of Wiener filter coefficients, with only half the computational load of the former). As can be seen from the simulation results, the two curves almost completely overlap throughout the entire SNR range, indicating that there is no significant difference in the channel estimation error between the two schemes. That is, in the EPA scenario with a flat channel, the 6-RE scheme with a larger spacing (less fine filtering and more interpolation) significantly reduces the computational load of the filter coefficient generation module and the channel estimation module (e.g., the number of complex multiplications is significantly reduced), but still maintains filtering performance comparable to the 3-RE scheme. This directly confirms the effect of the embodiment of this application in "adaptively adjusting the fine filtering density according to the actual channel conditions to reduce the computational load while ensuring performance".

[0092] Figure 7 is a simulation comparison of the block error rate (BLER) of two Wiener filter subcarrier spacing schemes under the EPA flat channel scenario of the LTE system. As shown in Figure 7, the test conditions conform to Test3 of Table 8.3.2.1-4 of the 3GPP standard, specifically 10MHz bandwidth, 5Hz Doppler frequency shift, 2×2 LOW configuration MIMO, and 64QAM modulation. The horizontal axis represents the signal-to-noise ratio (SNR, ranging from 11 to 21 dB), and the vertical axis represents the block error rate (BLER, logarithmic scale; the smaller the value, the higher the communication reliability). The blue curve corresponds to the scheme with a subcarrier spacing of 3 REs (higher computational cost), and the orange curve corresponds to the scheme with a spacing of 6 REs (only half the computational cost of the former). The results show that the two curves almost overlap across the entire SNR range, indicating that the low computational cost scheme with a spacing of 6 REs has no significant difference in block error rate performance compared to the high computational cost scheme with a spacing of 3 REs. This further confirms the effectiveness of this application in "adaptively increasing the fine-filter subcarrier spacing under flat channel conditions can significantly reduce computational cost without reducing communication reliability."

[0093] Figure 8 shows a simulation comparison of throughput in the LTE system under the EPA flat channel scenario. As shown in Figure 8, the test conditions follow Test3 of Table 8.3.2.1-4 of the 3GPP standard, specifically 10MHz bandwidth, 5Hz Doppler shift, 2×2 LOW configuration MIMO, and 64QAM modulation. The horizontal axis represents the signal-to-noise ratio (SNR, ranging from 10 to 22dB), and the vertical axis represents the system throughput (Mbps, with higher values ​​indicating better transmission efficiency). The blue curve corresponds to the high computational complexity scheme with a subcarrier spacing of 3 REs, and the orange curve corresponds to the low computational complexity scheme with a spacing of 6 REs. The results show that the two curves almost completely overlap across the entire SNR range, indicating that although the 6 RE spacing scheme significantly reduces computational complexity (halving the number of filter coefficients and reducing complex number operations), it shows no significant difference in throughput performance compared to the 3 RE spacing scheme. This further verifies the effectiveness of this application's claim that "adaptively expanding the fine-filter subcarrier spacing based on channel flatness can optimize computational complexity without sacrificing core system communication performance" from the perspective of "transmission efficiency."

[0094] Obviously, those skilled in the art should understand that the modules or steps of the embodiments of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the embodiments of this application are not limited to any particular combination of hardware and software.

[0095] The above description is merely a preferred embodiment of the present application and is not intended to limit the embodiments of the present application. For those skilled in the art, various modifications and variations can be made to the embodiments of the present application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the embodiments of the present application should be included within the protection scope of the embodiments of the present application.

Claims

1. A combined filtering method, characterized in that, include: A coarse estimate is performed on the frequency domain signal to generate a coarse estimate result; The filter coefficients are determined based on the coarse estimation results. Based on the filtering coefficients, a fine filtering operation is performed on the coarse estimation result to obtain the fine filtering result of the first subcarrier with the first sequence number, and an interpolation operation is performed on the second subcarrier with the second sequence number; wherein, the second subcarrier is the remaining carrier among the carriers to be processed excluding the first subcarrier.

2. The method according to claim 1, characterized in that, Determining the filter coefficients based on the coarse estimation results includes: calculating the channel parameters of the channel based on the coarse estimation results, and determining the first sequence number and the second sequence number based on the channel parameters.

3. The method according to claim 1, characterized in that, Determining the filter coefficients based on the coarse estimation results includes: calculating the correlation of the subcarrier spacing based on the coarse estimation results, and determining the first sequence number and the second sequence number based on the correlation.

4. The method according to claim 2, characterized in that, The channel parameters of the channel are calculated based on the coarse estimation result, and the first sequence number and the second sequence number are determined based on the channel parameters, including: when the channel parameters are greater than or equal to a first threshold, the first sequence number and the second sequence number are determined based on a first interval; when the channel parameters are less than the first threshold but greater than a second threshold, the first sequence number and the second sequence number are determined based on a second interval; when the channel parameters are greater than or equal to the second threshold, the first sequence number and the second sequence number are determined based on a third interval; wherein the first interval is less than the second interval, and the second interval is less than the third interval.

5. The method according to claim 1, characterized in that, The fine filtering includes Wiener filtering or transform domain filtering.

6. The method according to claim 1, characterized in that, The interpolation includes linear interpolation or polynomial interpolation.

7. The method according to claim 2, characterized in that, The channel parameters include: root mean square delay spread or Doppler value.

8. A combined filtering system, characterized in that, include: The filter coefficient generation module is used to determine the filter coefficients based on the coarse estimation results; A channel estimation module is used to coarsely estimate the frequency domain signal to generate the coarse estimation result; Furthermore, based on the filtering coefficients, a fine filtering operation is performed on the coarse estimation result to obtain the fine filtering result of the first subcarrier with the first sequence number, and an interpolation operation is performed on the second subcarrier with the second sequence number; wherein, the second subcarrier is the remaining carrier among the carriers to be processed excluding the first subcarrier.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, wherein the computer program is executed by a processor to perform the method described in any one of claims 1 to 7.

10. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the method of any one of claims 1 to 7.

11. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the method of any one of claims 1 to 7.

Images