RLS channel estimation method, device and equipment for single-carrier underwater acoustic communication system and medium

Abstract

The invention provides an RLS channel estimation method, device, equipment and medium for a single-carrier underwater acoustic communication system, and the RLS channel estimation algorithm combines the sparsity characteristic of an underwater acoustic channel and the diversity gain of bidirectional estimation, and can significantly improve the precision and convergence rate of channel estimation. The method is especially suitable for an underwater acoustic communication system in a complex time-varying and multi-path sparse environment.

Description

Technical Field

[0001] The present application relates to the field of underwater acoustic communication technologies, and in particular, to an RLS channel estimation method, apparatus, device, and medium for a single-carrier underwater acoustic communication system. Background Technique

[0002] With the rapid development of fields such as marine resource development, underwater exploration, intelligent equipment, and "underwater Internet of Things", underwater communication technologies have become key supporting means. Among them, underwater acoustic communication has become the mainstream technology due to its small propagation loss and wide application range in water. Single-carrier underwater acoustic communication systems have broad application prospects in medium- and long-distance underwater acoustic communication due to their simple structure and high bandwidth utilization rate.

[0003] However, due to characteristics such as severe multipath spread, frequency-selective fading, phase perturbation, and time-variation of the underwater acoustic channel, there are obvious inter-symbol interference and signal distortion in the received signal, seriously affecting the communication quality. For this reason, accurate channel estimation has become one of the key tasks at the receiving end of underwater acoustic communication. The traditional Recursive Least Square (RLS) algorithm is widely used in underwater acoustic channel estimation due to its good convergence performance and modeling ability. However, the traditional RLS algorithm is difficult to effectively model in a highly sparse environment and is sensitive to strong noise and sudden paths.

[0004] To overcome the above problems, existing research has attempted to introduce sparse priors into the RLS algorithm, improve the modeling ability for sparse channels by introducing an ℓ1-norm constraint, and proposed improved algorithms such as ℓ1-RLS, ℓp-RLS, etc. At the same time, there are also studies using a proportion factor-adjusted PRLS algorithm or a DR-RLS algorithm based on repeated data to accelerate the convergence speed. However, these improvement methods are easily affected by rapid channel changes or dynamic drift of sparse paths, and the reliability and performance of the overall communication system cannot be guaranteed. Summary of the Invention

[0005] The present application proposes an RLS channel estimation method, apparatus, device, and medium for a single-carrier underwater acoustic communication system, which can solve one of the problems existing in the background technique.

[0006] To achieve the above object, the present application adopts the following technical solutions:

[0007] In a first aspect, there is provided an RLS channel estimation method for a single-carrier underwater acoustic communication system, the method including:

[0008] Construct a sparse-aware RLS estimation model based on the transmitted symbol, received symbol, true underwater acoustic channel impulse response, and the underwater acoustic channel impulse response to be estimated;

[0009] Based on the constructed RLS adaptive filtering algorithm, iterative updates of the forward estimation result and the backward estimation result are performed. The sparse prior update gradient in the RLS adaptive filtering algorithm is derived from the l1 norm of the underwater acoustic channel impulse response to be estimated; and

[0010] Fuse the forward estimation result and the backward estimation result obtained by the iterative update as the channel estimation result.

[0011] Based on the above technical solution, this RLS channel estimation algorithm combines the sparsity characteristics of the underwater acoustic channel and the diversity gain of bidirectional estimation, and can significantly improve the accuracy and convergence speed of channel estimation, especially suitable for underwater acoustic communication systems in complex time-varying and multipath sparse environments.

[0012] In a possible design manner of the first aspect, the sparse-aware RLS estimation model is:[[]]END]]

[0013]

[0014] where is the true underwater acoustic channel impulse response,[[]]END]] is the underwater acoustic channel impulse response to be estimated, y i is the received symbol at time i, that is, y i ∈ y, x i is the transmitted symbol at time i, λ ∈ [0, 1) is the forgetting factor, γ > 0 is the sparse regularization coefficient, and ||·||1 represents the l1 norm.

[0015] In a possible design manner of the first aspect, the iterative update process of the forward estimation result is:[[]]END]]

[0016]

[0017] where is the forward estimation result,[[]]END]] is the Kalman gain in the forward process,[[]]END]] is the inverse matrix of the autocorrelation matrix in the forward estimation process, κ = γ(λ - 1) is the regularization parameter,[[]]END]] represents the sparse prior update gradient derived from the l1 norm in the forward estimation process,[[]]END]] is the prior error at time n, which is expressed as[[]]END]] sign{·} represents the sign function, and its expression is as follows:[[]]END]]

[0018]

[0019] The iterative update process of the backward estimation result is:[[]]END]]

[0020]

[0021] ← indicates the direction opposite to →.

[0022] In a possible design manner of the first aspect, the forward estimation result and the backward estimation result obtained by the iterative update are fused as the channel estimation result Specifically:

[0023]

[0024] where α for and α rev are weighted fusion weights, and TR[·] is a sequence reversal operation to ensure alignment with the channel estimated in the forward direction.

[0025] In a possible design manner of the first aspect, the method further includes:

[0026] Using the channel estimation result in a decision feedback equalizer based on channel estimation to perform a recovery decision on the transmitted symbol.

[0027] In a possible design manner of the first aspect, the following formula is used for the recovery decision on the transmitted symbol:

[0028]

[0029] where represents the vector composed of the decision symbols at the previous n time instants, y n represents the vector composed of the received symbols, g ff and g fb are the feedforward filter coefficient and the feedback filter coefficient respectively, and the solutions of the feedforward filter coefficient and the feedback filter coefficient directly depend on the channel estimation result

[0030] In a second aspect, an RLS channel estimation device for a single - carrier underwater acoustic communication system is provided, and the method includes:

[0031] A construction unit, configured to construct a sparse - sensing RLS estimation model based on the transmitted symbol, the received symbol, the true underwater acoustic channel impulse response, and the underwater acoustic channel impulse response to be estimated;

[0032] An iterative update unit, based on the constructed RLS adaptive filtering algorithm, performs iterative updates on the forward estimation result and the backward estimation result, and the sparse prior update gradient in the RLS adaptive filtering algorithm is derived from the l1 norm of the underwater acoustic channel impulse response to be estimated; and

[0033] A fusion unit, configured to fuse the forward estimation result and the backward estimation result obtained by the iterative update as the channel estimation result.

[0034] In a third aspect, an electronic device is provided. The electronic device includes: a processor, and a memory coupled to the processor. The memory is configured to store a computer program. The processor is configured to execute the computer program stored in the memory, so that the electronic device performs the method according to any possible implementation manner in the first aspect.

[0035] In a fourth aspect, a computer-readable storage medium is provided, including a computer program or instruction. When the computer program or instruction runs on a computer, the computer is caused to perform the method according to any possible implementation manner in the first aspect.

[0036] In a fifth aspect, a computer program product is provided, including: a computer program or instruction. When the computer program or instruction runs on a computer, the computer is caused to perform the method according to any possible implementation manner in the first aspect. Description of the Drawings

[0037] To more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or the related art descriptions. Obviously, the drawings in the following descriptions are only some embodiments of the embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0038] Figure 1 is the estimation flowchart provided by the embodiment of the present application;

[0039] Figure 2 is the average mean square deviation curve graph in the simulation experiment provided by the embodiment of the present application;

[0040] Figure 3 is the performance curve graph in the underwater acoustic communication experiment at sea provided by the embodiment of the present application. Detailed Embodiments

[0041] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0042] It should be noted that although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order from the module division in the device or the flowchart in the flowchart. Terms such as "first" and "second" in the description, the claims and the above drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this application belongs. The terms used herein are for the purpose of describing embodiments of this application only and are not intended to limit this application.

[0044] An RLS channel estimation method, apparatus, device, and medium for a single-carrier underwater acoustic communication system according to embodiments of this application will be exemplarily described below.

[0045] As Figure 1 shown, this embodiment proposes a sparse-aware bidirectional joint iterative RLS channel estimation algorithm applicable to a single-carrier underwater acoustic communication system, which specifically includes the following steps:

[0046] S1. Construct a sparse-aware RLS estimation model

[0047] Considering that the underwater acoustic channel has sparse structural characteristics, first, based on the received signal y n at time n n and the transmitted signal x

[0048]

[0049] In the above formula, is the true underwater acoustic channel impulse response, is the underwater acoustic channel impulse response to be estimated. argmin is used to calculate the minimum value. T represents the transpose. y i is the received symbol at time i, that is, y i ∈ y. x i is the transmitted symbol vector at time i, λ ∈ [0, 1) is the forgetting factor, γ > 0 is the sparse regularization coefficient, and ||·||1 represents the l1 norm.

[0050] Furthermore, ||w||1 quantifies the estimated underwater acoustic channel impulse response, provides sparse prior information for the RLS estimation model, and then can guide the estimated underwater acoustic channel impulse response to converge to a sparse structure, effectively improving the estimation accuracy of the underwater acoustic channel.

[0051] S2. Forward and backward RLS joint iterative update

[0052] To introduce the backward estimation process and enhance the diversity gain, this embodiment adds a bidirectional joint iterative mechanism on the basis of the traditional RLS. It should be noted that the update methods of the forward estimation and the backward estimation use the RLS algorithm as the adaptive filtering algorithm, and at the same time consider the sparse prior of the underwater acoustic channel guided by the l1 norm, which can be specifically expressed as follows:

[0053] For the forward estimation update process:

[0054]

[0055] In the above formula, is the forward estimation result, is the Kalman gain in the forward process, and its iterative formula can be expressed as is the inverse matrix of the autocorrelation matrix in the forward estimation process, and its iterative formula can be expressed as κ = γ(λ - 1) is the regularization parameter. represents the sparse prior update gradient derived from the l1 norm in the forward estimation process. sign{·} represents the sign function, and its expression is as follows:

[0056]

[0057] More specifically, in the forward estimation, is the prior error at time n, which is expressed as

[0058] For the backward estimation update process:

[0059]

[0060] In the above formula, and have the same parameter definitions and iterative formulas as formula (2), only with differences in the estimation direction. κ = γ(λ - 1) is the regularization parameter. represents the sparse prior update gradient derived from the l1 norm in the backward estimation process.

[0061] S3. Two-way joint output feedback

[0062] The present invention uses the optimal weighting method to fuse the estimation results in the forward and backward directions to obtain the final channel estimation:

[0063]

[0064] In the above formula, α for and α rev are the weighted fusion weights, and their values can be: α for = α rev = 0.5, TR[·] is the sequence reversal operation to ensure alignment with the channel of the forward estimation. Further, to make full use of the correlation of the two-way estimation output results, the fusion estimation of the two-way joint output is respectively fed back to each one-way estimation process for use in subsequent iterative processes, specifically expressed as:

[0065]

[0066] Based on the above analysis, it can be seen that in the sparse-aware bidirectional joint iterative channel estimation algorithm proposed in this embodiment, its overall operation process can be summarized into three key steps: sparse prior quantization, bidirectional joint estimation, and feedback iterative optimization. In the first step, by introducing the l1-norm regularization term, the sparse structure of the underwater acoustic channel is modeled, and the main sparse components in the underwater acoustic channel response are quantified and strengthened, thereby enhancing the stability and physical interpretability of the estimation results. In the second step, channel estimation is performed separately in the forward and reverse directions, and on this basis, the bidirectional estimation results are fused to achieve bidirectional diversity combining. This step effectively introduces the time-reverse diversity gain, significantly improving the robustness and accuracy of channel estimation in low signal-to-noise ratio scenarios. In the third step, the algorithm uses the channel estimation result obtained by bidirectional combining in the current iteration as the initialization value for the next iteration process. Since this estimation result carries the structural prior information of the channel varying with time, it significantly enhances the adaptive ability and tracking performance of the algorithm in a time-varying channel environment.

[0067] Through the integration of the above sparse modeling, bidirectional fusion, and feedback iteration, the algorithm proposed in the present invention can continuously optimize the underwater acoustic channel estimation process and maintain high estimation accuracy and communication stability in a complex underwater acoustic communication environment.

[0068] S4. Combine a decision feedback equalizer based on channel estimation to achieve underwater acoustic symbol recovery

[0069] The estimated underwater acoustic channel impulse response can be used in a decision feedback equalizer based on channel estimation to perform recovery decision on the transmitted symbols. At this time, the recovered symbol at the nth moment can be specifically expressed as follows:

[0070]

[0071] In the above formula, represents the vector composed of the decision symbols in the previous n moments, y n represents the vector composed of the received symbols. g ff and g fb are the feedforward and feedback filter coefficients respectively, and the solution of these two coefficients directly depends on the underwater acoustic channel estimation result By improving the underwater acoustic channel estimation accuracy, the symbol error rate is reduced and the output signal-to-noise ratio is improved.

[0072] To verify the performance of the present invention, comparative experiments were conducted in simulation and actual sea trials respectively. In the simulation, a time-varying underwater acoustic channel with a length of 128 was adopted, and 2 and 64 non-zero taps were set respectively, corresponding to sparse and non-sparse channel scenarios, and the signal-to-noise ratio ranged from 1 - 25 dB. The results show that when the signal-to-noise ratio is higher than 15 dB, the average mean square deviation of the algorithm of the present invention is reduced by 3 - 7 dB compared with the traditional algorithm, showing better algorithm performance. The experimental results are as Figure 2 shown.

[0073] In the underwater acoustic communication experiment carried out in Xiamen Port in October 2022, the communication distance was 2 km, the carrier frequency was 15.5 kHz, and the transmitted signal was a 2.5 k symbol rate QPSK signal. The experimental results are as Figure 3 shown. The decision feedback equalizer based on channel estimation driven by the algorithm of the present invention still maintains good bit error performance when the channel changes violently. Its average bit error rate is only 0.0832%, and the output signal-to-noise ratio is as high as 12.60 dB, which is significantly better than the comparative algorithms such as Bi-Ji-RLS and l1-RLS.

[0074] In short, this embodiment relates to a sparse-aware bidirectional joint iterative recursive least squares (RLS) channel estimation method, which is applicable to the receiver channel estimation of a single-carrier underwater acoustic communication system. This method combines the sparsity and bidirectional time structure of the underwater acoustic channel, and improves the accuracy and robustness of channel estimation by introducing the l1-norm sparse regularization term and the bidirectional joint iterative mechanism. The algorithm first performs forward and backward sparse RLS iterations respectively, and then fuses the estimated channels in the two directions through optimal weights to obtain the final channel estimation result. This method can be embedded in the decision feedback equalizer based on channel estimation to achieve accurate recovery of underwater acoustic symbols. This embodiment shows excellent performance in both simulation and actual sea trials, effectively reducing the bit error rate, increasing the system output signal-to-noise ratio, and having good application prospects.

[0075] The channel estimation method proposed in this embodiment can be seamlessly integrated into the existing underwater acoustic receivers based on the CE-DFE architecture.

[0076] This application embodiment also provides an RLS channel estimation device for a single-carrier underwater acoustic communication system, and the method includes:

[0077] A construction unit, configured to construct a sparse-aware RLS estimation model based on the transmitted symbol, the received symbol, the true underwater acoustic channel impulse response, and the underwater acoustic channel impulse response to be estimated;

[0078] An iterative update unit, configured to perform iterative update of the forward estimation result and the backward estimation result based on the constructed RLS adaptive filtering algorithm, and the sparse prior update gradient in the RLS adaptive filtering algorithm is derived from the l1-norm of the underwater acoustic channel impulse response to be estimated; and

[0079] A fusion unit, configured to fuse the forward estimation result and the backward estimation result obtained by the iterative update as a channel estimation result.

[0080] An embodiment of the present application further provides an electronic device, including: a processor, and a memory coupled to the processor, where the memory is configured to store a computer program; the processor is configured to execute the computer program stored in the memory, so that the electronic device executes the method described in any one of the foregoing embodiments.

[0081] The electronic device may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The electronic device may include, but is not limited to, a processor and a memory.

[0082] The so-called processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor may also be any conventional processor, etc. The processor is the control center of the electronic device, and connects various parts of the entire device through various interfaces and lines.

[0083] The memory may be configured to store the computer program, and the processor realizes various functions of the electronic device by running or executing the computer program stored in the memory and calling the data stored in the memory.

[0084] The memory may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system, application programs required for at least one function, etc.; the data storage area may store data created according to the use of the mobile phone, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0085] The embodiments of the present application further provide a storage medium, which is a computer-readable storage medium. The computer program is stored in the computer-readable storage medium. When the computer program is executed by a processor, the steps of the above various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0086] The embodiments of the present application further provide a computer program product, including: a computer program or instruction. When the computer program or instruction runs on a computer, the computer is enabled to execute the method of any one of the above possible implementation manners.

[0087] The above is the preferred implementation manner of the present application. It should be noted that for those of ordinary skill in the art in the technical field of the present application, without departing from the principle of the present application, several improvements and refinements can be made, and these improvements and refinements are also regarded as the protection scope of the present application.

Claims

1. A RLS channel estimation method for a single - carrier underwater acoustic communication system, characterized in that The method includes: Based on the transmitted symbol, received symbol, true underwater acoustic channel impulse response, and the underwater acoustic channel impulse response to be estimated, constructing a sparse-sensing RLS estimation model; Based on the constructed RLS adaptive filtering algorithm, performing iterative updates of the forward estimation result and the backward estimation result, where the sparse prior update gradient in the RLS adaptive filtering algorithm is derived from the l1 norm of the underwater acoustic channel impulse response to be estimated; and Fusing the forward estimation result and the backward estimation result obtained by the iterative update as the channel estimation result.

2. The method according to claim 1, wherein The sparse-sensing RLS estimation model is: Among them, is the true underwater acoustic channel impulse response, is the underwater acoustic channel impulse response to be estimated, and y i is the received symbol at time i, that is, y i ∈ y, x i is the transmitted symbol at time i, λ ∈ [0, 1) is the forgetting factor, γ > 0 is the sparse regularization coefficient, and ||·||1 represents the l1 norm.

3. The method according to claim 2, characterized in that, The iterative update process of the forward estimation result is: Among them, is the forward estimation result, is the Kalman gain in the forward process, is the inverse matrix of the autocorrelation matrix in the forward estimation process, κ = γ(λ - 1) is the regularization parameter, represents the sparse prior update gradient derived from the l1 norm in the forward estimation process, is the prior error at time n, which is expressed as sign{·} represents the sign function, and its expression is as follows: The iterative update process of the backward estimation result is: ← indicates the direction opposite to →.

4. The method according to claim 3, characterized in that, Fuse the forward estimation result and the backward estimation result obtained by the iterative update as the channel estimation result Specifically: where α for and α rev are weighted fusion weights, and TR[·] is the sequence reversal operation to ensure alignment with the channel estimated in the forward direction.

5. The method according to claim 4, characterized in that, The method further includes: Using the channel estimation result in a decision feedback equalizer based on channel estimation to perform recovery decision on the transmitted symbol.

6. The method according to claim 5, characterized in that The following formula is used to perform the recovery decision on the transmitted symbol: Among them, represents the vector composed of the decision symbols in the previous n time instants, y n represents the vector composed of the received symbols, g ff and g fb are the feedforward filter coefficient and the feedback filter coefficient respectively. The solutions of the feedforward filter coefficient and the feedback filter coefficient directly depend on the channel estimation result 7. An RLS channel estimation device for a single - carrier underwater acoustic communication system, characterized in that, The method includes: A construction unit, configured to construct a sparse-sensing RLS estimation model based on the transmitted symbol, received symbol, true underwater acoustic channel impulse response, and the underwater acoustic channel impulse response to be estimated; An iterative update unit, based on the constructed RLS adaptive filtering algorithm, performing iterative updates of the forward estimation result and the backward estimation result, where the sparse prior update gradient in the RLS adaptive filtering algorithm is derived from the l1 norm of the underwater acoustic channel impulse response to be estimated; and A fusion unit, configured to fuse the forward estimation result and the backward estimation result obtained by the iterative update as the channel estimation result.

8. An electronic device, characterized in that, The electronic device includes: a processor, and a memory coupled to the processor, The memory is configured to store a computer program; and The processor is configured to execute the computer program stored in the memory, so that the electronic device executes the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a computer program or instruction, and when the computer program or instruction runs on a computer, the computer is caused to execute the method according to any one of claims 1-6.

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