Underwater scattered light dense array MIMO channel estimation method with low pilot frequency overhead

Abstract

The invention relates to the technical field of communication, in particular to an underwater scattered light dense array MIMO channel estimation method with low pilot frequency overhead, which comprises the following steps: constructing an optical MIMO sub-channel CIR model in a low-quality water environment; therefore, the underwater scattered light dense array MIMO compressed sensing channel estimation method based on the prior sub-channel CIR model and the asymmetric shear-orthogonal frequency division multiplexing modulation mechanism is formed. By means of the spatial homogeneity and the time domain sparsity in an underwater light dense array MIMO sub-channel, CIR coefficient reconstruction is replaced with parameter reconstruction of a sub-channel CIR common attenuation item and an independent linear item, and CIR residual errors are compensated, so that the pilot frequency overhead is reduced. According to the underwater optical MIMO channel estimation method, the inhibition capability of the sub-channel CIR model on the channel reconstruction deviation under the low signal-to-noise ratio is exerted, and the good underwater optical MIMO channel estimation precision and efficiency can be maintained under the low pilot frequency load.

Description

Technical Field

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

[0002] Increasing the size of array elements is an effective way to improve the capacity of optical MIMO communications. However, small underwater communication devices are often limited in surface area and structural size, which restricts the array element layout and spacing of optical MIMO systems. The limited array size means that the increase in array element size will lead to a smaller array element spacing, pushing up the spatial correlation of subchannels and hindering communication capacity. At the same time, the number of subchannels will also increase with the increase in the size of optical MIMO array elements, thereby pushing up the pilot load, accelerating the exhaustion of limited transmission band resources, and reducing system effectiveness. But from another perspective, the high spatial correlation of optical MIMO subchannels provides convenience for simplifying channel estimation. In particular, for underwater optical MIMO systems with densely arranged array elements, since the direct links share the same propagation waters, the spatial correlation between channels is very strong, and even shows spatial homogeneity. When the symbol transmission rate reaches GBaud, the CIR delay of underwater long-distance optical communication or low-quality underwater short-distance optical communication will cause inter-code interference, causing the channel to exhibit certain frequency-selective characteristics. The power dispersion of the divergent beam is serious and the longitudinal coverage is limited. In the case of short-distance optical communication, this CIR delay is small and stable. Therefore, the underwater optical communication channel has natural time-domain sparsity, which means that when implementing underwater divergent optical dense array MIMO channel estimation, a certain prior sub-channel CIR model can be established, and the spatial homogeneity and time-domain sparsity of the sub-channel can be used to reduce the pilot overhead of channel estimation and effectively suppress the channel reconstruction error under low signal-to-noise ratio conditions, 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 divergent light dense array MIMO channel estimation method with low pilot overhead, aiming to reduce the underwater divergent light dense array MIMO channel reconstruction error and pilot overhead under low signal-to-noise ratio conditions, and to achieve quality and efficiency improvement of channel estimation.

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

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

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

[0007] Step 3: Aggregate the sensing matrix and each sub-channel CIR of the underwater divergent light dense array MIMO compressed sensing channel estimation model to form a K-sparse compressed sensing estimation model of aggregated sub-channel CIR, and then reconstruct the aggregated sub-channel CIR by matching pursuit, and then fit the common attenuation term parameters of all sub-channel CIRs;

[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; partial pilots are used to realize N t Reconstruction of CIR linear term parameters of sparse underwater divergent light array MIMO subchannels;

[0009] Step 5: Based on the reconstructed linear term parameters of each sub-channel CIR and the common attenuation term, all sub-channel CIRs are spliced ​​together, and a compressed sensing estimation model of the underwater divergent light array MIMO sub-channel CIR residual is established. The other part of the pilot is used to obtain the sub-channel CIR residual by matching pursuit, and then the CIR of each sub-channel is compensated.

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

[0011]

[0012] Where i and j are the transmitting and receiving antenna indexes 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 with C 1_ij express

[0013]

[0014] Optionally, the underwater divergent 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-shaped pilot with uniform distribution, and the total amount is P = N c / 8; is the sensing matrix, N t is the number of transmit antennas, is the pilot sequence transmitted by the ith 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 of the jth PD and the additive white Gaussian noise on the pilot subcarrier are independent and identically distributed respectively.

[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 sensor 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 Subchannel Reconstructed by K-sparse matching pursuit method and fitted by subchannel CIR model

[0021]

[0022] in, is the linear term parameter of the aggregated subchannel CIR The reconstruction value of is the CIR attenuation term of the aggregated sub-channel The reconstruction 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 equally divided into two groups, Σ and Λ, Ω = Λ ∪ Σ; and The received pilot sequence of the j-th PD and the additive Gaussian white noise of the pilot subcarriers of the j-th PD are independent and identically distributed respectively; Indicates the N corresponding to the jth PD t CIR linear term parameter vector of subchannels; is the equivalent sensing matrix, is the Kronecker product operation, N t Order 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 between the subchannel CIR and its reconstructed value, then the CIR of each subchannel of underwater divergent light 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 method for estimating underwater divergent optical dense array MIMO channels with low pilot overhead. By constructing an optical MIMO subchannel model in a low-quality water environment, based on the model and the ACO-OFDM modulation mechanism, a method for estimating underwater divergent optical dense array MIMO compressed sensing channels with low pilot overhead is established. The method utilizes the spatial homogeneity and time-domain sparsity of underwater optical dense array MIMO subchannels, replaces the traditional subchannel CIR coefficient estimation with subchannel 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 underwater divergent optical dense array MIMO subchannels, not only exerts the ability of the subchannel CIR model to suppress reconstruction deviations under low signal-to-noise ratios, but also can substantially reduce the sensitivity of the pilot overhead to the array element size, while ensuring the accuracy of signal estimation, improving the efficiency of underwater divergent optical dense array MIMO communication channel estimation. 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 drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 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 the specific embodiment of the present invention is located at position 3. DETAILED DESCRIPTION

[0038] Embodiments of the present invention are described in detail below, 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 should not 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 an underwater divergent light dense array MIMO channel estimation method with low pilot overhead, comprising the following steps:

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

[0047] Step 2: Based on the sub-channel CIR model and ACO-OFDM modulation mechanism in step 1, an underwater divergent 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 each sub-channel CIR of the underwater divergent light dense array MIMO compressed sensing channel estimation model to form a compressed sensing estimation model of the aggregated sub-channel CIR (K sparse), and then reconstruct the aggregated sub-channel CIR by matching pursuit, and then fit the common attenuation term parameters of all sub-channel CIRs;

[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 it; the underwater divergent light dense array MIMO sub-channel CIR linear term parameter (N t Sparse) reconstruction;

[0050] Step 5: Based on the reconstructed linear term parameters of each sub-channel CIR and the common attenuation term, all sub-channel CIRs are spliced ​​together, and a compressed sensing estimation model of the underwater divergent light array MIMO sub-channel CIR residual is established. The other part of the pilot is used to obtain the sub-channel CIR residual by matching pursuit, and then the CIR of each sub-channel is compensated.

[0051] The following is a further explanation combined with the implementation steps:

[0052] Step 1:

[0053] In low-quality water or long-distance optical communication scenarios, a certain amount of scattered photons will be generated. When the symbol rate is high (typically at the GBaud level), scattered photons will cause symbol delay and crosstalk. At this time, the channel CIR will show a pattern 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 indexes respectively; are the linear term and attenuation term parameters of the k-th order CIR model, respectively. Considering 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 with C 1_ij express)

[0056]

[0057] Step 2:

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

[0059] The number of subcarriers in an 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, 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 i-transmit and j-receive subcarrier index set Ω. Assume that the channel sparsity is K, satisfying 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] Since the array elements are densely arranged, the sub-channels of optical MIMO share the same propagation area, so their impulse responses can be considered to have the same model, that is, the CIR structure of each sub-channel is consistent but the intensity is different.

[0065] Step 3:

[0066] In underwater divergent 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, equation (2) can be further expressed as

[0069]

[0070] in, Indicates the N corresponding to the jth PD t The strength vector of the CIR model of the sub-channel; is the Kronecker product operation, N t Order unit array. is the equivalent sensor matrix.

[0071] For reconstruction You need to first obtain the parameters of the sub-channel CIR attenuation item Since the subchannel space is homogeneous, the attenuation term of CIR is consistent, so all subchannels are aggregated, and the sensor 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 Subchannel Reconstructed by K-sparse matching pursuit method and fitted by subchannel CIR model

[0074]

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

[0076] Step 4:

[0077] To reconstruct the linear term parameters of each subchannel CIR, the pilot subcarrier index set Ω is equally divided into two groups, Σ and Λ, 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] In the formula, and The received pilot sequence of the jth PD and the additive Gaussian white noise of the pilot subcarriers of the Σ group are independent and identically distributed. 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 subchannel CIR and its model estimation. Then the subchannel CIR of underwater divergent optical 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 simulation 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 of 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 OMP scheme: 32;

[0097] LED spacing: 3cm;

[0098] PD spacing: 3-4cm;

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

[0100] The simulation model results are as follows Figure 2 As shown in (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 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 From the NMSE curve in , we can see that the NMSE of the OMP scheme increases steadily with the increase of SNR, while the CIR reconstruction scheme based on the 4th-order mgf and the 2nd-order Gaussian model has an inflection point around SNR=25 and no longer increases steadily with the increase of SNR. This is mainly because the CIR model is only a smooth fit and approximation of the actual channel conditions, and is not sensitive to subtle fluctuations in the channel. The limited scale of model parameters leads to the limitation of the CIR model in presenting channel details. However, in the range of SNR of 0 to 25, this smooth fit can filter out strong channel noise, showing a significant ability to suppress reconstruction deviations, making the NMSE of this type of scheme at low signal-to-noise ratios one order of magnitude lower than that of the model-free OMP scheme. In terms of model performance, when SNR<20, the performance of the CIR reconstruction scheme based on the Gaussian model is similar to that of the mgf scheme, but as the SNR increases further, the NMSE of the Gaussian model decreases further, showing better fitting performance.

[0104] What is disclosed above is only a preferred embodiment of the present invention, and it certainly cannot be used to limit the scope of 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 according to the claims of the present invention still fall within the scope of the invention.

Claims

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

2. The underwater divergent light dense array MIMO channel estimation method with low pilot overhead as claimed in claim 1, characterized in that: The subchannel 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 indexes 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 with C 1_ij express 3. The underwater divergent light dense array MIMO channel estimation method with low pilot overhead as claimed in claim 2, characterized in that: The underwater divergent light array MIMO compressed sensing channel estimation model based on the subchannel 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-shaped pilot with uniform distribution, and the total amount is P = N c / 8; is the sensing matrix, N t is the number of transmit antennas, is the pilot sequence transmitted by the ith 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 of the jth PD and the additive white Gaussian noise on the pilot subcarrier are independent and identically distributed respectively.

4. The underwater divergent light dense array MIMO channel estimation method with low pilot overhead as claimed in claim 2, characterized in that: The CIR estimation model of K sparse aggregated sub-channels is: in, It is formed by averaging the sub-matrices of the sensor 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 Subchannel Reconstructed by K-sparse matching pursuit method and fitted by subchannel CIR model in, is the linear term parameter of the aggregated subchannel CIR The reconstruction value of is the CIR attenuation term of the aggregated sub-channel The reconstruction value of is the attenuation parameter of the reconstructed subchannel CIR.

5. The underwater divergent light dense array MIMO channel estimation method with low pilot overhead as claimed in claim 2, characterized in that: The compressed sensing estimation model of the CIR linear term parameters of each sub-channel is: The pilot subcarrier index set Ω is equally divided into two groups, Σ and Λ, Ω = Λ ∪ Σ; and The received pilot sequence of the j-th PD and the additive Gaussian white noise of the pilot subcarriers of the j-th PD are independent and identically distributed respectively; Indicates the N corresponding to the jth PD t CIR linear term parameter vector of subchannels; is the equivalent sensing matrix, is the Kronecker product operation, N t Order 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 divergent light dense array MIMO channel estimation method with low pilot overhead as claimed in 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 between the subchannel CIR and its reconstructed value, then the CIR of each subchannel of underwater divergent light array MIMO is reconstructed as Among them, the residual Reconstructed by matching pursuit method with K / 2 sparsity.

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