A low-pilot-overhead underwater divergent light array MIMO channel estimation method

Abstract

The present invention relates to the field of communication technology, and in particular to a method for underwater divergent optical dense array MIMO channel estimation with low pilot overhead. By constructing an optical MIMO sub-channel CIR model in a low-quality water environment, an underwater divergent optical dense array MIMO compressed sensing channel estimation method based on a priori sub-channel CIR model and an asymmetric shear wave-orthogonal frequency division multiplexing modulation mechanism is formed. By leveraging the inherent spatial homogeneity and time-domain sparsity of underwater optical dense array MIMO sub-channels, parameter reconstruction of the sub-channel CIR common attenuation term and independent linear term is used instead of CIR coefficient reconstruction, and CIR residual is compensated, thereby reducing pilot overhead. The present invention not only gives full play to the ability of the sub-channel CIR model to suppress channel reconstruction deviation under low signal-to-noise ratio, but also can maintain good underwater optical MIMO channel estimation accuracy and efficiency under lower pilot load.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to an underwater diverging light dense array MIMO channel estimation method with low pilot overhead. Background Art

[0002] Increasing array element size is an effective method for improving optical MIMO communication capacity. However, small underwater communication devices are often limited in surface area and structural dimensions, which constrains the array element layout and spacing of optical MIMO systems. Limited array size means that increasing array element size results in smaller element spacing, increasing subchannel spatial correlation and limiting communication capacity. Furthermore, the number of subchannels increases with optical MIMO array element size, increasing pilot load, accelerating the exhaustion of limited transmission bandwidth resources, and reducing system efficiency. However, from another perspective, the high spatial correlation of optical MIMO subchannels facilitates simplified channel estimation. In particular, in underwater optical MIMO systems with densely deployed elements, since direct links share the same propagation area, the spatial correlation between channels is very strong, even exhibiting spatial homogeneity. When symbol transmission rates reach GBaud, CIR delay in underwater long-haul optical communications or low-quality short-haul optical communications can induce intersymbol interference, causing the channel to exhibit certain frequency-selective characteristics. Diverging beams exhibit significant power dispersion and limited longitudinal coverage. In the case of short-distance optical communication, this CIR delay is small and stable. Therefore, underwater optical communication channels have a natural time-domain sparsity. This means that when implementing underwater diverging optical dense array MIMO channel estimation, a certain prior sub-channel CIR model can be established. By utilizing the spatial homogeneity and time-domain sparsity of the sub-channels, the pilot overhead of channel estimation can be reduced and the channel reconstruction error in low signal-to-noise ratio conditions can be effectively suppressed, thereby improving the effectiveness and reliability of channel estimation. Summary of the Invention

[0003] The purpose of the present invention is to provide an underwater diverging light dense array MIMO channel estimation method with low pilot overhead, aiming to reduce the underwater diverging light dense array MIMO channel reconstruction error and pilot overhead under low signal-to-noise ratio conditions, and achieve improved channel estimation quality and efficiency.

[0004] To achieve the above object, the present invention provides a low pilot overhead diverging light dense array MIMO channel estimation method, comprising the following steps:

[0005] Step 1: Using the product of a Gaussian function and a linear function as the basis function, construct an underwater diverging light dense array MIMO sub-channel CIR model with a sparsity of K;

[0006] Step 2: Based on the sub-channel CIR model and ACO-OFDM modulation mechanism in step 1, an underwater diverging light dense array MIMO compressed sensing channel estimation model is constructed, where the sparsity of each sub-channel is K and the total sparsity of all sub-channel CIRs is N t K, N t is the size of the transmitting array element;

[0007] Step 3: Aggregate the sensing matrix and sub-channel CIRs of the underwater diverging light dense array MIMO compressed sensing channel estimation model to form a K-sparse compressed sensing estimation model of aggregated sub-channel CIRs. The aggregated sub-channel CIRs are then reconstructed using matching pursuit. Based on this, the attenuation term parameters common to all sub-channel CIRs are fitted.

[0008] Step 4: Based on the fitted sub-channel CIR common attenuation term, an equivalent sensing matrix is ​​formed, and a compressed sensing estimation model of the CIR linear term parameters of each sub-channel is constructed based on this matrix; N is realized by using partial pilots. t Reconstruction of linear term parameters of CIR of sparse underwater divergent light array MIMO sub-channels;

[0009] Step 5: Based on the reconstructed linear term parameters of each sub-channel CIR and the common attenuation term, the CIRs of all sub-channels are spliced ​​together, and a compressed sensing estimation model for the CIR residuals of the underwater divergent light array MIMO sub-channels is established. The sub-channel CIR residuals are obtained by matching pursuit using another part of the pilots, and then the CIRs of each sub-channel are compensated.

[0010] Optionally, the sub-channel CIR model constructed in step 1 uses the product of a Gaussian function and a linear function as the basis function, and the CIR model structure is:

[0011]

[0012] Where i and j are the transmitting and receiving antenna indices respectively; are the linear term and attenuation term parameters of the k-th order CIR model respectively; the second-order model is used as the sub-channel CIR fitting model, and each order basis function shares a unified linear term, that is, Unified C 1_ij express

[0013]

[0014] Optionally, the underwater diverging light dense array MIMO compressed sensing channel estimation model based on the subchannel CIR model and ACO-OFDM modulation mechanism in step 2 is:

[0015]

[0016] Among them, the number of subcarriers in the ACO-OFDM system is N c , the pilot is a comb pilot with uniform distribution, the total amount is P = N c / 8; is the sensing matrix, N t is the number of transmitting antennas, is the pilot sequence transmitted by the i-th antenna, is the MQAM modulation symbol; is a submatrix of the Fourier transform matrix with row index Ω and column index 1, 2, ... L, where L is the channel length; is the CIR from the i-th light source to the j-th PD, and The received pilot sequence and pilot subcarrier of the j-th PD are independent and identically distributed additive white Gaussian noise.

[0017] Optionally, the K-sparse aggregated sub-channel CIR estimation model is

[0018]

[0019] in, It is formed by averaging the sub-matrices of the sensing matrix, that is, A i is a submatrix of A, consisting of the ((i-1)L+1)th to iLth columns of the A matrix; is the aggregated subchannel CIR,

[0020] Aggregate sub-channel Reconstructed by K-sparse matching pursuit method and fitted by sub-channel CIR model

[0021]

[0022] in, is the linear term parameter of the aggregated sub-channel CIR The reconstructed value of is the CIR attenuation term of the aggregated sub-channel The reconstructed value of is the attenuation parameter of the reconstructed subchannel CIR.

[0023] Optionally, the compressed sensing estimation model of the CIR linear term parameters of each sub-channel is:

[0024]

[0025] The pilot subcarrier index set Ω is evenly divided into two groups Σ and Λ, Ω = Λ ∪ Σ; and The received pilot sequence of the j-th PD and the independent and identically distributed additive Gaussian white noise on the pilot subcarrier of the Σ group are respectively; Indicates the N corresponding to the j-th PD t The CIR linear term parameter vector of the sub-channels; is the equivalent sensing matrix, is the Kronecker product operation, N t Level unit array;

[0026] Optionally, the sub-channel CIR reconstruction residual compensation model is

[0027]

[0028] in, is the residual error of the Λth group of pilot signals received by the jth PD, Noise vector on the Λth group of pilots; is the residual of the sub-channel CIR and its reconstructed value, then the CIR of each sub-channel of the underwater diverging light dense array MIMO is reconstructed as

[0029]

[0030] Among them, the residual Reconstructed by matching pursuit method with sparsity K / 2.

[0031] The present invention provides a low-pilot-overhead underwater diverging light array MIMO channel estimation method. By constructing an optical MIMO sub-channel model in a low-quality water environment, a low-pilot-overhead underwater diverging light array MIMO compressed sensing channel estimation method is established based on the model and the ACO-OFDM modulation mechanism. The method utilizes the spatial homogeneity and time-domain sparsity of the underwater optical dense array MIMO sub-channel, replaces the traditional sub-channel CIR coefficient estimation with the sub-channel CIR model parameter estimation, reconstructs the common attenuation term and the independent linear term with a lower pilot load, and compensates for the CIR residual. The present invention makes full use of the inherent time-domain sparsity and spatial homogeneity of the underwater diverging light dense array MIMO sub-channel, not only exerts the sub-channel CIR model's ability to suppress reconstruction deviation under low signal-to-noise ratio, but also can substantially reduce the sensitivity of the pilot overhead to the array element size, while ensuring the signal estimation accuracy, improving the underwater diverging light dense array MIMO communication channel estimation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only 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.

[0033] Figure 1 It is a schematic diagram of the ACO-OFDM-MIMO system model of the present invention.

[0034] Figure 2 It is a schematic diagram of the simulation model structure of a specific embodiment of the present invention.

[0035] Figure 3 It is a schematic diagram of comparative data of various methods when the specific embodiment of the present invention is located at position 1.

[0036] Figure 4 It is a schematic diagram of comparative data of various methods when the specific embodiment of the present invention is located at position 2.

[0037] Figure 5 It is a schematic diagram of comparative data of various methods when located at position 3 according to a specific embodiment of the present invention. DETAILED DESCRIPTION

[0038] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0039] The following is an explanation of the English abbreviations that appear in this article. Some places in the article use English abbreviations for simplicity:

[0040] MIMO: Multiple-Input Multiple-Output, multiple input multiple output, sending and receiving multiple data streams simultaneously on the same channel;

[0041] ACO-OFDM: Asymmetrically Clipped Optical-Orthogonal Frequency Division Multiplexing, asymmetrically clipped optical orthogonal frequency division multiplexing technology;

[0042] CIR: Channel Impulse Respond, channel impulse response;

[0043] PD: Photoelectric Detectors, photoelectric detectors;

[0044] MQAM: M-Order QuadratureAmplitude Modulation, M-order quadrature amplitude modulation;

[0045] The present invention provides a low pilot overhead underwater diverging light dense array MIMO channel estimation method, comprising the following steps:

[0046] Step 1: Using the product of a Gaussian function and a linear function as the basis function, construct an underwater diverging light dense array MIMO sub-channel CIR model with a sparsity of K;

[0047] Step 2: Based on the sub-channel CIR model and ACO-OFDM modulation mechanism in step 1, an underwater diverging light dense array MIMO compressed sensing channel estimation model is constructed, where the total sparsity of all sub-channel CIRs is N t K, N t is the size of the transmitting array element;

[0048] Step 3: Aggregate the sensing matrix and sub-channel CIRs of the underwater diverging light dense array MIMO compressed sensing channel estimation model to form a compressed sensing estimation model of the aggregated sub-channel CIR (K sparse). The aggregated sub-channel CIR is then reconstructed using matching pursuit, and the attenuation term parameters common to all sub-channel CIRs are obtained by fitting.

[0049] Step 4: Based on the fitted sub-channel CIR common attenuation term, an equivalent sensing matrix is ​​formed, and a compressed sensing estimation model of each sub-channel CIR linear term parameter is constructed based on this. The CIR linear term parameter (N) of the underwater divergent light dense array MIMO sub-channel is realized by using some pilots. t Sparse) reconstruction;

[0050] Step 5: Based on the reconstructed linear term parameters of each sub-channel CIR and the common attenuation term, the CIRs of all sub-channels are spliced ​​together, and a compressed sensing estimation model for the CIR residuals of the underwater divergent light array MIMO sub-channels is established. The sub-channel CIR residuals are obtained by matching pursuit using another part of the pilots, and then the CIRs of each sub-channel are compensated.

[0051] The following is a further explanation based on the implementation steps:

[0052] Step 1:

[0053] In low-quality water or long-distance optical communication scenarios, a certain scale of scattered photons will be generated. When the symbol rate is high (typically GBaud level), scattered photons will cause symbol delay and crosstalk. At this time, the channel CIR will show a form of first rising and then falling, causing the channel to produce a certain frequency-selective fading. When implementing optical communication in a short-distance low-quality water environment, the scattered photons captured by the PD will dissipate exponentially or more violently over time. In order to describe this channel CIR morphology, the present invention uses a linear function and a Gaussian function as basis functions to construct an underwater divergent optical communication link CIR model h ij (Δ t )

[0054]

[0055] Where i and j are the transmit and receive antenna indices respectively; are the linear term and attenuation term parameters of the k-th order CIR model respectively. Taking into account the model accuracy, computational complexity and fitting delay, the second-order CIR channel model is used as the channel estimation fitting model, and each order basis function shares a unified linear term (i.e. Unified C 1_ij express)

[0056]

[0057] Step 2:

[0058] See also Figure 1 Based on the sub-channel CIR model and ACO-OFDM modulation mechanism, a scale of N t ×N r Underwater diverging light dense array MIMO compressed sensing channel estimation model.

[0059] The number of subcarriers in the OFDM system is N c , the subcarrier index sets of pilot and data symbols are denoted by Ω and Ξ respectively, Ω∪Ξ={2u+1|u∈[0,N c / 4-1]}, the pilot and data symbols sent by the i-th transmitting array element are expressed as and The pilot observation vector received by the jth PD is for

[0060]

[0061] in, is the MQAM modulation symbol, It is a submatrix of the Fourier transform matrix with row index Ω, and L is the channel length. is the channel impulse response from the i-th light source to the j-th PD, and is the frequency response of the subchannel corresponding to the transmission and reception of i and j in the subcarrier index set Ω. Assume that the channel sparsity is K, and K<<L. is the additive Gaussian white noise of independent and identical distribution on the pilot subcarrier. Assuming that the channel characteristics remain unchanged within an OFDM symbol, the above formula can be expressed as

[0062]

[0063] Where A is the sensing matrix,

[0064] Because the array elements are densely deployed, the optical MIMO sub-channels share the same propagation area, so their impulse responses can be considered to have the same model, meaning that the CIR structure of each sub-channel is consistent but the intensity varies.

[0065] Step 3:

[0066] In underwater diverging light dense array MIMO, the CIR intensity difference of each subchannel can be characterized by the linear term parameter of each subchannel CIR; the structural uniformity of each subchannel CIR can be characterized by the Gaussian attenuation term of the subchannel CIR model. That is, the linear term of each subchannel CIR is different but the attenuation term is the same.

[0067]

[0068] in, N t The parameter of the common attenuation term of the CIR of the sub-channels. Therefore, formula (2) can be further expressed as

[0069]

[0070] in, Indicates the N corresponding to the j-th PD t The intensity vector of the CIR model of the sub-channel; is the Kronecker product operation, N t Level unit array. is the equivalent sensing matrix.

[0071] For reconstruction You need to first obtain the parameters of the sub-channel CIR attenuation item Since the sub-channel space is homogeneous, the attenuation term of CIR is consistent, so all sub-channels are aggregated, and the sensing matrix is ​​also the same. A i is a submatrix of A, consisting of the ((i-1)L+1)th to iLth columns of the A matrix; is the aggregated subchannel CIR, At this time, the sub-channel CIR attenuation parameter estimation model is:

[0072]

[0073] Aggregate sub-channel Reconstructed by K-sparse matching pursuit method and fitted by sub-channel CIR model

[0074]

[0075] in, is the linear term parameter of the aggregated sub-channel CIR The reconstruction value of is the sub-channel common CIR model The reconstructed value of is the attenuation parameter of the reconstructed sub-channel CIR model.

[0076] Step 4:

[0077] To reconstruct the linear term parameters of each subchannel CIR, the pilot subcarrier index set Ω is divided into two groups, Σ and Λ, where Ω = Λ ∪ Σ. The linear term parameter reconstruction process of the subchannel CIR uses the Σ group of pilots, so the compressed sensing estimation model of the linear term parameters of the subchannel CIR model is:

[0078]

[0079] Where, and The jth PD receives the Σ group pilot sequence and the Σ group pilot subcarriers with independent and identically distributed additive Gaussian white noise. At this time, the linear term parameter vector of the CIR channel model of the subchannel is Refactored to

[0080]

[0081] in, for The pseudo-inverse matrix of each sub-channel CIR is given by and Refactored to

[0082]

[0083] Step 5:

[0084] Due to the complexity and variability of the underwater optical communication environment, the reconstruction method based on the subchannel CIR model may produce a certain degree of deviation. Therefore, it is necessary to perform residual compensation on each reconstructed subchannel CIR. To this end, the subchannel CIR residual is estimated using the Λth group of pilots. The specific residual compensation model is:

[0085]

[0086] in, is the residual error of the Λth group of pilot signals received by the jth PD, Noise vector on the Λth group of pilots; is the residual of the sub-channel CIR and its model estimation. Then the sub-channel CIR of underwater diverging light dense array MIMO can be reconstructed as

[0087]

[0088] Among them, the residual Reconstructed by matching pursuit method with sparsity K / 2.

[0089] Furthermore, the present invention also conducts experimental simulations through embodiments and compares the results with corresponding channel estimation methods. Figures 2 to 5 :

[0090] The environmental parameters are as follows:

[0091] Optical MIMO scale: 4×4

[0092] Number of OFDM subcarriers: 512;

[0093] Channel length: 32;

[0094] Cyclic prefix length: 44;

[0095] The number of pilots in the method of the present invention is 24, of which Σ group pilots are 8 and Λ group pilots are 16, all of which are orthogonal pilots.

[0096] Number of pilots for the OMP scheme: 32;

[0097] LED spacing: 3cm;

[0098] PD spacing: 3-4 cm;

[0099] Transmitter and receiver element spacing: 12m;

[0100] The simulation model results are as follows Figure 2 As shown in the figure (the horizontal axis is the signal-to-noise ratio (SNR), and the vertical axis is the normalized mean square error (NMSE) of the reconstructed channel CIR), the specific comparison schemes are: the model-free orthogonal matching pursuit (OMP) channel estimation method; the channel model-assisted channel estimation scheme based on 4th-order multi-gamma (mgf), 2nd-order dual gamma (dgf) and 2nd-order Gaussian, and the corresponding results are shown in the figure. Figures 3 to 5 As shown. The mgf and dgf models are

[0101]

[0102] Among them, C1, C2…C5 are CIR model parameters.

[0103] from Figures 3 to 5 The NMSE curves in Figure 2 show that the NMSE of the OMP scheme increases steadily with increasing SNR, while the CIR reconstruction scheme based on a 4th-order MGF and a 2nd-order Gaussian model shows an inflection point around SNR=25, where it no longer increases steadily with increasing SNR. This is primarily because the CIR model only smooths and approximates the actual channel conditions and is insensitive to subtle channel fluctuations. The limited size of the model parameters limits the CIR model's ability to capture channel details. However, within the SNR range of 0-25, this smoothing can filter out strong channel noise, demonstrating significant reconstruction bias suppression. This results in the NMSE of this scheme being an order of magnitude lower than that of the model-free OMP scheme at low SNRs. Regarding model performance, the Gaussian model-based CIR reconstruction scheme performs similarly to the MGF scheme when the SNR is less than 20. However, as the SNR increases further, the NMSE of the Gaussian model decreases further, demonstrating superior fitting performance.

[0104] The above disclosure is only a preferred embodiment of the present invention, and certainly cannot be used to limit the scope of the rights of the present invention. Ordinary technicians in this field can understand that all or part of the processes of the above embodiment and equivalent changes made in accordance with the claims of the present invention are still within the scope of the invention.

Claims

1. A low pilot overhead underwater diverging light dense array MIMO channel estimation method, characterized in that: The following steps are involved: Step 1: Using the product of a Gaussian function and a linear function as the basis function, construct an underwater diverging light dense array MIMO sub-channel CIR model with a sparsity of K; Step 2: Based on the sub-channel CIR model and ACO-OFDM modulation mechanism in step 1, an underwater diverging light dense array MIMO compressed sensing channel estimation model is constructed, where the sparsity of each sub-channel is K and the total sparsity of all sub-channel CIRs is N t K, N t is the size of the transmitting array element; Step 3: Aggregate the sensing matrix and sub-channel CIRs of the underwater diverging light dense array MIMO compressed sensing channel estimation model to form a K-sparse compressed sensing estimation model of aggregated sub-channel CIRs. The aggregated sub-channel CIRs are then reconstructed using matching pursuit. Based on this, the attenuation term parameters common to all sub-channel CIRs are fitted. Step 4: Based on the fitted sub-channel CIR common attenuation term, an equivalent sensing matrix is ​​formed, and a compressed sensing estimation model of the CIR linear term parameters of each sub-channel is constructed based on this matrix; N is realized by using partial pilots. t Reconstruction of linear term parameters of CIR of sparse underwater divergent light array MIMO sub-channels; Step 5: Based on the reconstructed linear terms of each sub-channel CIR and the common attenuation term parameters, the CIRs of all sub-channels are spliced ​​together, and a compressed sensing estimation model for the CIR residuals of the underwater divergent light array MIMO sub-channels is established. The sub-channel CIR residuals are obtained by matching pursuit using another part of the pilots, and then the CIRs of each sub-channel are compensated.

2. The underwater diverging light dense array MIMO channel estimation method with low pilot overhead according to claim 1, characterized in that: The sub-channel CIR model constructed in step 1 uses the product of Gaussian function and linear function as the basis function. The CIR model structure is: Where i and j are the transmitting and receiving antenna indices respectively; are the linear term and attenuation term parameters of the k-th order CIR model respectively; the second-order model is used as the sub-channel CIR fitting model, and each order basis function shares a unified linear term, that is, Unified C 1_ij express 3. The underwater diverging light dense array MIMO channel estimation method with low pilot overhead according to claim 2, characterized in that: The underwater diverging light dense array MIMO compressed sensing channel estimation model based on the sub-channel CIR model and ACO-OFDM modulation mechanism in step 2 is: Among them, the number of subcarriers in the ACO-OFDM system is N c , the pilot is a comb pilot with uniform distribution, the total amount is P = N c / 8; is the sensing matrix, N t is the number of transmitting antennas, is the pilot sequence transmitted by the i-th antenna, is the MQAM modulation symbol; is a submatrix of the Fourier transform matrix with row index Ω and column index 1, 2, ... L, where L is the channel length; is the CIR from the i-th light source to the j-th PD, and The received pilot sequence and pilot subcarrier of the j-th PD are independent and identically distributed additive white Gaussian noise.

4. The underwater diverging light dense array MIMO channel estimation method with low pilot overhead according to claim 2, wherein: The CIR estimation model of K sparse aggregated sub-channels is: in, It is formed by averaging the sub-matrices of the sensing matrix, that is, A i is a submatrix of A, consisting of the ((i-1)L+1)th to iLth columns of the A matrix; is the aggregated subchannel CIR, Aggregate sub-channel Reconstructed by K-sparse matching pursuit method and fitted by sub-channel CIR model in, is the linear term parameter of the aggregated sub-channel CIR The reconstructed value of is the CIR attenuation term of the aggregated sub-channel The reconstructed value of is the attenuation parameter of the reconstructed subchannel CIR.

5. The underwater diverging light dense array MIMO channel estimation method with low pilot overhead according to claim 2, wherein: The compressed sensing estimation model of the CIR linear term parameters of each sub-channel is: The pilot subcarrier index set Ω is evenly divided into two groups Σ and Λ, Ω = Λ ∪ Σ; and The received pilot sequence of the j-th PD and the independent and identically distributed additive Gaussian white noise on the pilot subcarrier of the Σ group are respectively; Indicates the N corresponding to the j-th PD t The CIR linear term parameter vector of the sub-channels; is the equivalent sensing matrix, is the Kronecker product operation, N t Level unit array; Subchannel CIR linear term parameter vector Refactored to in, for The pseudo-inverse matrix of each sub-channel CIR is and Refactored to 6. The underwater diverging light dense array MIMO channel estimation method with low pilot overhead according to claim 2, characterized in that: The sub-channel CIR reconstruction residual compensation model is: in, is the residual error of the Λth group of pilot signals received by the jth PD, Noise vector on the Λth group of pilots; is the residual of the sub-channel CIR and its reconstructed value, then the CIR of each sub-channel of the underwater diverging light dense array MIMO is reconstructed as Among them, the residual Reconstructed by matching pursuit method with K / 2 sparsity.

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