CN115733547A - An OSIC detection method under the misalignment of transmitting and receiving in underwater optical Massive MIMO communication system - Google Patents

Abstract

The invention discloses an OSIC detection method under the misalignment of transmission and reception in an underwater optical Massive MIMO communication system. After estimating the gain of each sub-channel by using the LS channel estimation algorithm, the transmission and reception end can be judged according to the magnitude of each DC gain in the channel gain matrix. The offset direction, and then determine the detection column sequence and the column sequence according to the offset direction, and perform interference elimination to complete the signal detection of each channel. The present invention sorts according to the offset direction, firstly detects signals without interference, and then detects signals with interference, which greatly reduces the error propagation caused by sorting, makes it more accurate to subtract the detected signals in the subsequent stage, and makes it more accurate for subsequent The remaining signals in the phase have less interference, thereby improving the performance of the bit error rate, so as to solve the problem that the bit error rate of the UOWC system becomes larger due to misalignment of the transceiver end, offset and diffusion of the imaging spot in the underwater imaging optical Massive MIMO communication system question.

Description

OSIC detection under the misalignment of transmitting and receiving in an underwater optical Massive MIMO communication system method

technical field

The invention relates to the technical field of underwater optical Massive MIMO communication systems, in particular to an OSIC (Ordered Successive Interference Cancellation) detection method under the misalignment of sending and receiving in an underwater optical Massive MIMO communication system.

Background technique

my country has a vast ocean area. Activities such as underwater disaster warning, resource exploration, environment, and pollution monitoring all need to use underwater communication technology to transmit data to the water surface in real time or quasi-real time, and then forward it to shore or satellite. However, there are deficiencies in underwater acoustic communication and underwater radio frequency communication, which are suitable for long-distance underwater communication. Among them, underwater acoustic communication has the problems of narrow bandwidth and long delay, and underwater radio frequency communication has the problem of rapid propagation attenuation. Considering that the underwater environment has relatively little attenuation of blue-green light with a wavelength of 450nm to 550nm, the underwater wireless optical communication technology based on the blue-green light band can be a powerful supplement for underwater communication. With the increasing development of underwater data transmission, building a MIMO system based on light source arrays can make full use of the space-division multiplexing gain to improve system capacity and anti-interference capabilities. However, due to the rapidly increasing demand for underwater transmission, the traditional small-scale optical MIMO communication system is no longer sufficient to meet the needs of marine information transmission.

In order to expand the channel capacity, improve the bit error rate performance of the communication system, and increase the transmission distance of the communication system, it is considered to introduce the massive MIMO communication technology into the underwater optical communication system, as shown in Figure 1. However, the underwater communication link environment changes (such as the movement of the transmitter and/or receiver of the underwater vehicle due to autonomy, ocean currents and other turbulence sources, and changes in the refractive index of underwater materials due to water depth, temperature and salinity, etc.) It will lead to link inaccuracy of underwater wireless optical communication, and then lead to inaccuracy of sending and receiving of underwater optical Massive MIMO communication system. However, under the misalignment of sending and receiving in the underwater optical Massive MIMO communication system, the separated light spots formed by the imaging lens group on the detection surface will not be able to accurately fall on the detector array, and relative horizontal and/or horizontal shifts will occur ( As shown in Figure 2), resulting in greater interference between optical paths, stronger sub-channel correlation, and signal detection becomes difficult and complicated, which in turn increases the bit error rate of the underwater wireless optical communication system.

Compared with the common MIMO communication system, the underwater optical Massive MIMO communication system has a very serious problem of misalignment of sending and receiving. Therefore, if the signal detection algorithm of the ordinary MIMO communication system is used, the underwater optical Massive MIMO communication cannot be successfully completed. System signal detection. For example, the traditional OSIC detection algorithm used in common MIMO communication systems performs detection sorting based on the power of received signals, and then completes signal demodulation based on the detection order. Although the traditional OSIC detection algorithm can complete signal detection for ordinary MIMO communication systems with small transceiver misalignment and small MIMO scale, but for underwater optical Massive MIMO communication with serious transceiver misalignment and large MIMO scale For the system, the detector at the receiving end will receive multiple light spots at the same time, which causes the detector with the highest light power received by the detector at the receiving end to also have the greatest interference between beams. Therefore, traditional OSIC detection algorithms cannot be applied to underwater optical Massive MIMO communication systems.

Contents of the invention

The present invention aims to solve the problem of difficulty and complexity in signal detection caused by misalignment of transmission and reception in underwater optical Massive MIMO communication systems, and provides an OSIC detection method under the misalignment of transmission and reception of underwater optical Massive MIMO communication systems.

In order to solve the above problems, the present invention is achieved through the following technical solutions:

An OSIC detection method under the misalignment of sending and receiving of underwater optical Massive MIMO communication system, comprising the following steps:

Step 1, using the LS channel estimation method to estimate the DC gain of each sub-channel of the received electrical signal sent by the detector array to obtain a channel gain matrix;

Step 2. Take out each column of the current channel gain matrix in turn, and compare the channel gains of each row of sub-channels in the current column with the channel gains of each row of sub-channels in other columns of the current channel gain matrix: if each row of the current column When the channel gain of the channel is less than the channel gain of each row of subchannels in all other columns of the current channel gain matrix, the current column is deleted from the current channel gain matrix, and the current column is selected as the detection column, and then go to step 3; Otherwise, repeat step 2;

Step 3. Sorting the channel gains of the sub-channels in each row of the detection column from small to large, thereby obtaining the in-column detection sorting of the sub-channels in each row of the detection column;

Step 4. For the jth selected detection column:

If j=1, then according to the column detection sorting of each row of sub-channels of the selected detection column for the jth time, the original signals of each row of sub-channels are sequentially sent to the subsequent signal demodulation process for signal demodulation;

If j≠1, the original signal of each row of sub-channels of the j-th selected detection column is subtracted from the product of the original signal of each row of sub-channels of the j-1th selected detection column and the channel gain , to obtain the remaining signals of each row of sub-channels of the jth selected detection column; then according to the in-column detection sorting of each row of sub-channels of the j-th selected detection column, the remaining signals of each row of sub-channels are sequentially sent to Signal demodulation is performed in the subsequent signal demodulation process.

In the above step 1, the LS channel estimation method inserts a pilot signal at an interval of 4 data for each sub-channel signal, and after performing gain estimation on the pilot position of the signal, linear interpolation is used to obtain the direct current of all sub-channels gain.

Compared with the prior art, the present invention considers that the underwater optical Massive MIMO communication system has serious misalignment of transmission and reception caused by the environment change of the underwater communication link and the characteristics of the extremely large MIMO scale. Based on this, an improved OSIC detection algorithm (I-OSIC) based on sorting with least interference is proposed. In view of the difficulty in obtaining the minimum order of interference in underwater optical Massive MIMO communication systems, and the traditional OSIC detection algorithm based on the sorting of received signal power, without considering the interference situation, the detector at the receiving end will receive multiple light spots at the same time, and the one with the largest received optical power is also The signal with the greatest interference between beams. The present invention proposes a channel and offset direction estimation method to obtain the minimum order of interference, that is, after using the LS channel estimation algorithm to estimate the gain of each sub-channel, the offset direction of the transceiver end can be judged according to the magnitude of each DC gain in the channel gain matrix , and then according to the offset direction pair to determine the sequence of the detection column and the sequence in the column, and perform interference elimination to complete the signal detection of each channel. The present invention sorts according to the offset direction, firstly detects signals without interference, and then detects signals with interference, which greatly reduces the error propagation caused by sorting, makes it more accurate to subtract the detected signals in the subsequent stage, and makes it more accurate for subsequent The remaining signals in the stage have less interference, thereby improving the performance of the bit error rate, so as to solve the problem of the increase of the bit error rate of the UOWC system caused by misalignment of the transceiver end, offset and diffusion of the imaging spot in the underwater imaging optical Massive MIMO communication system .

Description of drawings

Figure 1 is a schematic diagram of the optical part of the underwater optical Massive MIMO communication system;

Figure 2 is a schematic diagram of the distribution of imaging spots in the detector array under the misalignment of sending and receiving;

Figure 3 is a schematic diagram of an underwater optical Massive MIMO communication system;

Fig. 4 is a comparison chart of bit error rate performance between the present invention and the existing signal detection algorithm under the condition of small misalignment;

Fig. 5 is a comparison chart of bit error rate performance between the present invention and the existing signal detection algorithm under the condition of large misalignment.

Detailed ways

In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with specific examples.

In the underwater large-scale optical Massive MIMO communication system, the sending end first performs signal modulation on the binary bit data stream to be sent, and then uses the light source array composed of light sources to convert the modulated electrical signal into a multi-channel transmission optical signal to the receiving end Sending; the multi-channel sending optical signal will form a separate spot on the detection surface after passing through the imaging lens at the receiving end, and will be converted into a multi-channel receiving electrical signal by the detector array composed of detectors, and then the multi-channel receiving electrical signal will be processed After signal detection and signal demodulation, it is restored to binary bit data.

OSIC detection is based on a set of linear receivers for signal detection and interference cancellation, each receiver detects one of the parallel data streams, and at each stage can successfully subtract the detected signal components from the received signal, making it useful for subsequent The remaining signal of the stage has less interference. The order of detection can significantly affect the overall performance of OSIC detection due to error propagation caused by wrong decisions in the previous stage. Considering that the transmission and reception misalignment of the underwater optical Massive MIMO communication system is serious, and the MIMO scale is extremely large, the present invention proposes an improved OSIC detection algorithm based on the least interference sorting, which uses the LS channel estimation algorithm for each sub-channel After the gain estimation, according to the magnitude of each DC gain in the channel gain matrix, the offset direction of the transceiver can be judged, and then the order of the detection column and the sequence within the column are determined according to the offset direction, and the interference is eliminated to complete the signal detection of each channel.

Specifically, the present invention proposes an OSIC detection method under the misalignment of transmission and reception of an underwater optical Massive MIMO communication system, which specifically includes the following steps:

Step 1. Use the LS channel estimation method to estimate the DC gain of each sub-channel of the received electrical signal y sent by the detector array to obtain a channel gain matrix.

The receiving end performs LS channel estimation according to the pilot interval during OFDM modulation at the sending end. In a preferred embodiment of the present invention, the pilot interval for OFDM modulation at the transmitting end is 4, so the sub-channel signal received at the receiving end is a signal in which a pilot is inserted at an interval of 4 data. In addition, in the process of LS channel estimation, the linear interpolation algorithm is used to interpolate the frequency data of all OFDM symbols to obtain the frequency response of OFDM symbols, and then the frequency responses of OFDM symbols are statistically averaged to obtain the channel gain. According to the underwater Massive MIMO channel absorption scattering attenuation and pilot overhead reduction requirements, the LS channel estimation method inserts a pilot signal at an interval of 4 data for each sub-channel signal, and through the pilot position of the signal After gain estimation, linear interpolation is used to obtain the DC gains of all sub-channels. The channel gain matrix of LS channel estimation is

是由导频直接估计得到的，因此只能得到导频插入位置的信道增益，通过线性插值可以估计得到其他未插入导频子载波位置的信道增益，即

经过线性插值后得到

In the formula, Y is the received signal, X is the pilot signal, X ^H is the conjugate transpose matrix of X, and X ^-1 is the inverse matrix of X.

is directly estimated by the pilot, so only the channel gain of the pilot insertion position can be obtained, and the channel gain of other uninserted pilot subcarrier positions can be estimated by linear interpolation, that is,

After linear interpolation, we get

Step 2, taking out each column of the current channel gain matrix in turn, and correspondingly comparing the channel gains of the sub-channels of each row in the current column with the channel gains of the sub-channels of the other columns of the current channel gain matrix;

If the channel gain of each row of sub-channels in the current column is less than the channel gain of each row of sub-channels in all other columns of the current channel gain matrix, the current column is considered to be the column with the most serious offset in the current channel gain matrix. The current column is deleted from the current channel gain matrix, and the current column is selected as the detection column, and then go to step 3;

Otherwise, it is considered that the current column is not the column with the worst offset in the current channel gain matrix, and step 2 is repeated to reselect the column with the worst offset in the current channel gain matrix.

Step 3: Sorting the channel gains of the subchannels in each row of the detection column from small to large, thereby obtaining an intra-column detection ranking of the subchannels in each row of the detection column.

Step 4. For the jth selected detection column:

If j=1 (i.e. the selected detection column for the first time), then according to the detection sorting in the column of each row of sub-channels of the selected detection column for the jth time, the original signals of each row of sub-channels are sequentially sent to the subsequent Signal demodulation is performed during the signal demodulation process. Since the first selected detection column is the column with the most serious offset in the entire received electrical signal (as shown in the leftmost column in Figure 2), it will not cause interference, so the original signal is directly used for signal demodulation;

If j ≠ 1 (that is, the detection column selected for the first time), the original signal of each row of sub-channels in the detection column selected for the jth time is subtracted from the detection column selected for the j-1th time The product of the original signal corresponding to each row of sub-channels and the channel gain, to obtain the remaining signal of each row of sub-channels of the jth selected detection column; Detection and sorting, the remaining signals of each row of sub-channels are sequentially sent to the subsequent signal demodulation process for signal demodulation. Since the subsequent selected detection column is not the column with the most severe offset in the entire received electrical signal (the third column from the left in Figure 2), its original signal will be affected by the adjacent column signal (the second column from the left in Figure 2 Column) interference, so it is necessary to subtract the interference of the corresponding row of the adjacent column from the original signal before demodulating the signal.

In the later stage of detection, the interference caused by the detected adjacent signals is subtracted from the original signal of each sub-channel, so that subsequent receivers contain less interference in the detection stage.

The performance of the present invention is illustrated below through a specific example.

Fig. 3 is a schematic diagram of an underwater optical Massive MIMO communication system, which includes an optical part and an electrical part.

(1) Optical part:

A 64×64 light source array is used at the sending end, and a 64×64 detector array is used at the receiving end. The imaging lens at the receiving end is composed of a convex lens and a concave lens. The main optical axes of the convex lens and the concave lens are coincident, and the convex lens is located at the front end of the concave lens. The convex lens converges the multi-channel optical signals sent from the transmitting end to the concave lens, and the concave lens performs spot separation on the converged optical signals to obtain multiple optical signals. The front and rear end surfaces of the convex lens and the concave lens of the imaging lens are both paraboloids. By designing the paraboloids of the convex lens and the concave lens, the interference caused by imaging aberration can be effectively reduced, so that it is better suitable for underwater environments. In order to achieve precise adjustment of the lens surface while simplifying the lens design, the radius of curvature and the conic coefficient of the lens parabola are set to zero while considering the first-order and second-order coefficients of the lens parabola, and the simplified expression of the surface vector height of the lens parabola is as follows :

z=αr ² +βr ⁴

α分别为透镜抛物面的一阶系数，β分别为透镜抛物面的二阶系数。通过上式对成像透镜进行优化，通过增加透镜的焦距F以及透镜直径D，获得更高的光学增益的同时降低系统信道增益矩阵的相关性。在本实施例中，凸面透镜前端面抛物线的一阶系数和二阶系数分别为α＝0.02，β＝5×10^-6，凸面透镜后端面抛物线的一阶系数和二阶系数分别为α＝0.01，β＝1×10^-6，凸面透镜的孔径(mm)为30mm，厚度为15mm；凹面透镜前端面抛物线的一阶系数和二阶系数分别为α＝-0.03，β＝-1×10^-6，凹面透镜后端面抛物线的一阶系数和二阶系数分别为α＝0.03，β＝1×10^-6，凹面透镜的孔径(mm)为20mm，厚度为2mm。双凸透镜由于表面凸起都是同一方向因此前后端表面系数符号相同，而双凹透镜由于前后端表面凸起方向都是向这透镜内部，前后端表面凸起方向相反因此凹透镜前后端表面系数符号相反。Among them, z is the sag of the lens paraboloid, r is the radial coordinate of the axis rotation symmetric lens surface,

α are the first-order coefficients of the lens paraboloid, and β are the second-order coefficients of the lens parabola. The imaging lens is optimized through the above formula, and by increasing the focal length F and lens diameter D of the lens, higher optical gain can be obtained while reducing the correlation of the system channel gain matrix. In this embodiment, the first-order coefficients and second-order coefficients of the front parabola of the convex lens are α=0.02, β=5×10 ^-6 respectively, and the first-order coefficients and second-order coefficients of the rear end parabola of the convex lens are α= 0.01, β=1×10 ^-6 , the aperture (mm) of the convex lens is 30 mm, and the thickness is 15 mm; the first-order coefficient and the second-order coefficient of the front parabola of the concave lens are α=-0.03, β=-1×10 ^-6 , the first-order coefficient and second-order coefficient of the parabola on the rear end surface of the concave lens are α=0.03, β=1×10 ^-6 , the aperture (mm) of the concave lens is 20mm, and the thickness is 2mm. Since the surface of a biconvex lens is convex in the same direction, the signs of the front and rear surface coefficients are the same, while for a biconcave lens, the front and rear surfaces are convex in the same direction, and the front and rear surfaces are convex in opposite directions, so the signs of the front and rear surface coefficients of a concave lens are opposite. .

(2) Electrical part:

The signal modulation process at the sending end includes serial-to-parallel conversion, 4QAM mapping, Hermitian symmetry, IFFT, adding CP, parallel-to-serial conversion, and wave clipping. The signal demodulation process at the receiving end includes serial-to-parallel conversion, CP removal, FFT, data subcarrier extraction, 4QAM demodulation and parallel-to-serial conversion. In addition, the transmitting end needs to perform analog-to-digital conversion on the modulated signal before sending it to the light source array, and the receiving end needs to perform digital-to-analog conversion on the signal received by the detector array before performing signal detection.

At the sending end, the binary bit stream undergoes serial/parallel conversion, 4QAM mapping, Hermitian symmetry, IFFT, cyclic prefix addition, parallel/serial conversion, and clipping, and then modulates the binary bit stream into an ACO-OFDM signal. After the analog/digital conversion, it is sent to the light source array to convert the electrical signal into an optical signal and enter the channel for transmission. At the receiving end, after the detector array receives the signal, it converts it photoelectrically, and sends it into the signal detection method of the present invention after the analog/digital conversion to detect the received signal, and then passes the detected signal through the serial/parallel , Removing cyclic prefix, FFT, extracting data subcarriers, 4QAM demodulation, and parallel/serial operation to restore the signal demodulation to the original binary bit stream.

Using the underwater optical Massive MIMO communication method of the improved OSIC detection algorithm (I-OSIC) of the present invention, its process is as follows:

When the binary bit stream is input into the system, the signal modulation part at the sending end will first perform serial/parallel conversion to convert the serial bit stream into a parallel bit stream, and use 4QAM modulation for each parallel signal. After Hermitian symmetry and IFFT operations, the 4QAM star map corresponding to each channel of data is mapped to a real number form, and then the OFDM signal is obtained after digital/analog conversion. Parallel/serial conversion is performed after adding a cyclic prefix to each signal, and clipping processing is also performed in the signal processing part in order to make the subsequent optical signal suitable for underwater channel transmission. After the clipping process, the signal is sent to the 64×64 light source array through analog/digital conversion, so that the electrical signal is converted into an optical signal and enters the channel for transmission. After the optical signal is received by the 64×64 detector array at the receiving end, the analog signal is digitized by analog-to-digital conversion, and the digital signal is transmitted to the LS for channel estimation. The LS channel estimation part will perform LS channel estimation according to the pilot inserted in the transmitted signal by the sending end, and obtain the channel gain matrix and transmit it to the subsequent OSIC signal detection part. The OSIC signal detection part can calculate the DC gain of each column in the channel gain matrix, and then associate the misalignment with the size of the DC gain. When there is a column where the channel gain of the entire column is smaller than that of other columns, and the element values in the columns are not much different, it can be judged This column is the first detection column, that is, the first column in the direction opposite to the light spot offset direction is the first detection column. In the first column, select sequentially from top to bottom, the first signal is the first signal to be detected. In this column, the interference is eliminated and detected in sequence, and then the second column to the last column are determined in the direction of light source offset. , so as to obtain the detection sequence of OSIC signal detection. The OSIC signal detection part uses a set of linear receivers, each receiver detects one of the parallel data streams, and subtracts the detected data from the received data after detection, so that the subsequent detection stage has less interference. In this way, the received signal detected by the OSIC can be obtained after passing through this part. After the OSIC signal detection, the receiving end adopts the opposite operation to the sending end, that is, serial/parallel conversion, CP removal, FFT, extraction of data subcarriers, 4QAM demodulation, parallel/serial conversion, and restores the modulated signal demodulation to Raw binary bitstream.

The relative offset of the transceiver end of an underwater optical Massive MIMO communication system can be divided into 8 directions. Taking a single offset to the right as an example, the algorithm proposed in this scenario can also be applied to a single offset such as upward, downward, and left. Move the system down. Figure 4 and Figure 5 show the specific comparison of the bit error rate performance between the OSIC detection method of the present invention and the existing algorithm under misalignment conditions. FIG. 4 is a simulation diagram of the bit error rate of the system when the horizontal offset error is small, and FIG. 5 is a simulation diagram of the bit error rate of the system with a large horizontal offset error. As the relative offset error of the transceiver end increases, the channel correlation also increases. At this time, the SVD precoding detection algorithm has limited improvement in the bit error performance of the system in a poor channel environment. The ZF detection algorithm will amplify the additive noise weighting. The MMSE detection algorithm is improved on the basis of the ZF detection algorithm, which requires noise variance estimation, but the improvement of the system bit error rate is relatively limited, especially in the case of large offsets. These shortcomings also make the ZF detection algorithm and MMSE The bit error rate performance of the detection algorithm is not as good as that of the OSIC detection algorithm under the same SNR environment. Compared with the traditional OSIC algorithm (OSIC) based on the power sorting at the receiving end, the improved OSIC detection algorithm (I-OSIC) based on the minimum interference sorting of the present invention estimates the spot offset direction according to the channel gain matrix, and selects the spot offset direction opposite The first column of the direction is used as the first column of detection, so that there is no interference of other light spots in the first column of detection, the detected signal is subtracted and then the column with interference is detected, and the bit error is under the same signal-to-noise ratio Performance has been improved. In addition, the time complexity of SVD detection algorithm is O(min(m ² n,mn ² )), the time complexity of ZF detection algorithm and MMSE detection algorithm is O(k ³ ), and the time complexity of traditional OSIC detection algorithm is O(k ² ), where k represents the number of transmitting antennas, m and n represent the number of rows and columns of the matrix, respectively. Since the present invention is improved on the basis of the traditional OSIC detection algorithm, its time complexity is also small.

To sum up, the OSIC detection method for underwater optical Massive MIMO communication system under the misalignment of transmission and reception proposed by the present invention combines channel estimation, channel coding, signal detection and other technologies to meet the requirements of misalignment at the transmission and reception ends of underwater imaging optical systems. Under the conditions of enhancement of inter-signal interference caused by imaging spot shift and diffusion, and misalignment of communication links, it is necessary to improve the communication rate and bit error rate index of the UOWC system, and at the same time avoid the influence of existing technical solutions by misalignment of light sources Larger, poor bit error rate performance after misalignment, etc.

It should be noted that although the above-mentioned embodiments of the present invention are illustrative, they are not intended to limit the present invention, so the present invention is not limited to the above specific implementation manners. Without departing from the principles of the present invention, all other implementations obtained by those skilled in the art under the inspiration of the present invention are deemed to be within the protection of the present invention.

Claims (2)

1. An OSIC detection method under misalignment of receiving and transmitting of an underwater optical Massive MIMO communication system is characterized by comprising the following steps:

step 1, estimating the direct current gain of each path of sub-channel of a received electric signal sent by a detector array by using an LS channel estimation method to obtain a channel gain matrix;

step 2, sequentially taking out each column of the current channel gain matrix, and correspondingly comparing the channel gain of each row of sub-channels of the current column with the channel gain of each row of sub-channels of other columns of the current channel gain matrix respectively: if the channel gain of each row of sub-channels of the current column is smaller than the channel gain of each row of sub-channels of all other columns of the current channel gain matrix, deleting the current column from the current channel gain matrix, selecting the current column as a detection column, and turning to the step 3; otherwise, repeating the step 2;

step 3, sorting the channel gains of the sub-channels in each row of the detection column from small to large, thereby obtaining the in-column detection sorting of the sub-channels in each row of the detection column;

step 4, for the detection column selected at the jth time:

if j =1, sequentially sending the original signals of each row of sub-channels into a subsequent signal demodulation process for signal demodulation according to the in-row detection sorting of each row of sub-channels of the j-th selected detection row;

if j is not equal to 1, subtracting the product of the original signal of each sub-channel of each corresponding row of the j-1 th selected detection column from the original signal of each sub-channel of each row of the j-th selected detection column and the channel gain to obtain the residual signal of each sub-channel of each row of the j-th selected detection column; and then sequentially sending the residual signals of the sub-channels in each row into a subsequent signal demodulation process for signal demodulation according to the in-column detection sequencing of the sub-channels in each row of the jth selected detection column.

2. The OSIC detection method under the condition of misalignment of transceiving of the underwater optical Massive MIMO communication system as claimed in claim 1, wherein the LS channel estimation method of step 1 is to insert a pilot signal for each sub-channel signal at an interval of 4 data, and after gain estimation is performed on the pilot positions of the signals, linear interpolation is adopted to obtain the direct current gain of all sub-channels.

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