A detection method and system of a MIMO-OCDM detector based on wide linear minimum mean square error

Abstract

This invention discloses a detection method and system based on a wide linear minimum mean square error (WL-MMSE) detector for MIMO-OCDM. The method includes demodulating the signal received by the receiving antenna using discrete Fresnel transformation to obtain the observed signal in the MIMO-OCDM communication system. A WL-MMSE detector is constructed to detect the observed signal and obtain the corresponding estimated signal. The estimated signal is then processed using the LAS algorithm, and the optimal solution is searched in the neighborhood of the estimated signal through successive sign flips and likelihood function optimization to complete the detection. This invention achieves a high signal-to-noise ratio gain, significantly reduces the actual computational complexity, and is more suitable for high-speed, low-latency, and high-capacity 6G application scenarios.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and specifically to a detection method and system based on a MIMO-OCDM detector with wide linear minimum mean square error. Background Technology

[0002] Chirp signals possess high processing gain, low power consumption, and robustness against channel impairments, along with good autocorrelation characteristics, making them widely used in radar and communication fields. Currently, there are many ways to utilize chirp signals for communication. One direct method is non-overlapping linear frequency modulated pulses (FSK) modulation, widely used in radar detection. However, this method leads to problems such as low spectral efficiency. To address this issue, chirp signals can be superimposed in the time or frequency domain using their orthogonality, thereby improving spectral efficiency and data rate—this is the OCDM system.

[0003] With the development of 6G communication networks, MIMO-OCDM (Multiple-Input Multiple-Output Orthogonal Chirp-Division Multiplexing) has attracted much attention due to its anti-interference characteristics in multipath channel environments, efficient utilization of spectrum resources, and low peak-to-average power ratio. However, high-dimensional MIMO-OCDM systems face a trade-off between complexity and bit error rate when designing detectors. While maximum likelihood detection can provide optimal detection performance, its high computational complexity limits its application in large-scale MIMO systems. Traditional MMSE (Minimum-mean-square-error) detectors have relatively low complexity, but their performance is limited in non-circular signal processing. Summary of the Invention

[0004] The purpose of this invention is to provide a detection method and system based on a MIMO-OCDM detector with wide linear minimum mean square error (WL-MMSE) and a Likelihood Ascent Search (LAS) algorithm. By improving detection accuracy and reducing computational complexity, this detector is more applicable to 6G massive MIMO scenarios, meets the communication requirements of low latency and high accuracy, and can achieve near-optimal detection results while maintaining low complexity.

[0005] The present invention adopts the following technical solution:

[0006] A detection method based on a wide linear minimum mean square error MIMO-OCDM detector includes the following steps:

[0007] S1. The signal received by the receiving antenna is demodulated by discrete Fresnel transformation to obtain the observation signal in the MIMO-OCDM communication system.

[0008] S2. Construct a WL-MMSE detector and use it to detect the observed signal in step S1 to obtain the corresponding estimated signal.

[0009] S3. The estimated signal in step S2 is processed using the LAS algorithm. By successively flipping the sign and optimizing the likelihood function, the optimal solution is searched in the neighborhood of the estimated signal to complete the detection.

[0010] Furthermore, in step S1, the observed signal includes the following:

[0011] S101, Configure the MIMO-OCDM communication system to include M T One transmitting antenna and M R There are N receiving antennas, and each transmitting antenna transmits N orthogonal Chirp subcarriers. The symbol sequence s of the subcarrier transmission is denoted as... in Indicates the Mth T The transpose of the symbol sequence transmitted by the subcarriers of each transmitting antenna.

[0012] S102. OCDM modulation is performed on the modulation symbol data signal using DFnT to generate orthogonal linear frequency modulated signals, and a cyclic prefix is ​​added. The orthogonal linear frequency modulated signal on the q-th transmitting antenna is... Represented as:

[0013]

[0014] Among them, s q Φ represents the symbol sequence transmitted by the subcarrier of the q-th transmit antenna. q Let I represent the set of diagonal IDFnT matrices for the q-th transmitting antenna. (m,n) represents Φ q The number of elements in Φ q The inverse transform of .

[0015] S103, orthogonal linear frequency modulation signal x OCDM When propagating in a multipath channel, the signal r received by the receiving antenna is represented as:

[0016]

[0017] in, Indicates the Mth R The transpose of the signals received by each receiving antenna; Λ represents the channel coefficient matrix. Indicates the Mth T The transmitting antenna to the Mth R The channel coefficients between the receiving antennas; n represents the additive white Gaussian noise vector. Indicates the Mth R Gaussian white noise received by each receiving antenna; This represents the set of diagonal IDFnT matrices for transmitting antennas. Indicates the Mth T The IDFnT matrix corresponding to each transmitting antenna.

[0018] Performing a DFnT operation on r yields the observed signal, as shown in the following formula:

[0019] r′=Φ R r

[0020] Where r′ represents the observed signal received by the receiving antenna; Φ R This represents the set of diagonal DFnT matrices for receiving antennas. Indicates the Mth R The DFnT matrix corresponding to each receiving antenna.

[0021] Furthermore, in step S2, the estimated signal includes the following:

[0022] S201. Based on channel state information and the complex conjugate information of the observed signal, and combining the MIMO-OCDM detector and the MMSE criterion, a WL-MMSE detector is constructed. The matrix expression of this detector is as follows:

[0023]

[0024] Among them, W WL-MMSE Represents the WL-MMSE detection matrix; The augmented form representing the equivalent channel matrix. Θ T Θ represents the transpose of the equivalent channel matrix. H Represents the conjugate transpose of the equivalent channel matrix; Indicates the noise variance; Represents the identity matrix.

[0025] The S202 and WL-MMSE detectors process the observed signals to obtain the corresponding estimated signals, using the following formula:

[0026]

[0027] Where y represents the estimated signal; Represents the augmented vector of the observed signal. r ′* This represents the conjugate form of the observed signal.

[0028] Furthermore, in step S3, the optimal solution includes the following:

[0029] S301. Obtain the initial vector from the output of the WL-MMSE detector. The specific formula is as follows:

[0030]

[0031] in, This represents the initial vector for the LAS algorithm. NM T Indicates the total length of the vector; [] Ω This represents the operation of quantizing the initial vector to the nearest BPSK constellation point.

[0032] S302, from the initial vector Initially, when the likelihood change from the nth iteration to the (n+1)th iteration is positive, then... Updated to The process involves iterative selection until a fixed point is reached; the specific expression is:

[0033]

[0034] in, This represents the likelihood change from the nth iteration to the (n+1)th iteration; Represents the likelihood function. Represents the detection vector. express The transpose of r′ eff =Θ H r′+(Θ H r′) * Θ eff =Θ H Θ; This represents the likelihood function value at the nth iteration. This represents the likelihood function value for the (n+1)th iteration.

[0035] definition but Further expressed as:

[0036]

[0037] Where L(n) represents the vector length; g(n) represents the gradient of the likelihood function at the i-th bit position. Represents the real part of the signal. This represents the detection vector obtained after the nth iteration at the i-th bit position. This represents the detection vector obtained after the (n+1)th iteration at the i-th bit position. Let represent the likelihood function value of the i-th bit position after the n-th iteration, where i represents the bit position in the matrix, i = 1, ..., NM. T .

[0038] When the detection vector satisfies the flipping condition, it is flipped to ensure that the likelihood function monotonically increases; the expression for the flipping condition is:

[0039]

[0040] Among them, t i (n) represents the threshold at the i-th bit position.

[0041] When the result meets the convergence condition, the iteration stops, and the optimized detection vector is obtained. This is the optimal solution.

[0042] Furthermore, this invention also proposes a detection system based on a wide linear minimum mean square error MIMO-OCDM detector, comprising:

[0043] The observation signal acquisition module demodulates the signal received by the receiving antenna through discrete Fresnel transformation to obtain the observation signal in the MIMO-OCDM communication system.

[0044] The estimated signal acquisition module is used to construct the WL-MMSE detector. This detector is used to detect the observed signals in the observed signal acquisition module to obtain the corresponding estimated signals.

[0045] The detection module is used to process the estimated signal in the estimated signal acquisition module using the LAS algorithm. By successively flipping the sign and optimizing the likelihood function, it searches for the optimal solution in the neighborhood of the estimated signal to complete the detection.

[0046] Furthermore, the present invention also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the detection method based on the wide linear minimum mean square error MIMO-OCDM detector.

[0047] Furthermore, the present invention also proposes a computer-readable storage medium storing a computer program, characterized in that the computer program is executed by a processor to perform the detection method of the MIMO-OCDM detector based on wide linear minimum mean square error.

[0048] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects:

[0049] 1. The detector proposed in this invention has better BER (Bit Error Rate) performance and can achieve near-ML detection effect with low complexity.

[0050] 2. In this invention, the combination of WL-MMSE and LAS significantly improves the SNR gain of the MIMO-OCDM system, achieving a higher signal-to-noise ratio gain.

[0051] 3. This invention further optimizes the detection based on WL-MMSE by using the LAS algorithm, making it suitable for real-time detection in large-scale MIMO-OCDM systems. It greatly reduces the actual computational complexity, can obtain better detection results in fewer iterations, and effectively reduces computation time.

[0052] 4. This invention is more suitable for 6G application scenarios with high speed, low latency, and high capacity. Attached Figure Description

[0053] Figure 1 This is a flowchart illustrating the overall implementation process of the detector of this invention.

[0054] Figure 2 This is a comparison chart of the BER performance of different detectors under 4×4 and 8×8 antennas according to the present invention.

[0055] Figure 3 This is a graph showing the iterative convergence performance of the LAS algorithm under different signal-to-noise ratios in an embodiment of the present invention. Detailed Implementation

[0056] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0057] To address the unique properties of non-circular signals, the concept of Widely Linear (WL) has emerged, attracting significant research interest in wireless communication systems in recent years. The main principle of WL is to jointly optimize the signal itself and its complex conjugate, thereby providing new degrees of freedom (DoF) for system optimization design. When a communication system transmits non-circular signals, WL often yields performance gains exceeding twice that of linear processing. At the receiver, a widely linear detector based on the MMSE criterion has been proposed, demonstrating further performance improvements over linear detectors in terms of mean square error, symbol error rate, and achievable multiplexing gain.

[0058] To achieve the above objectives, this invention proposes a detection method based on a wide linear minimum mean square error MIMO-OCDM detector, such as... Figure 1 As shown, the specific steps are as follows:

[0059] S1. The signal received by the receiving antenna is demodulated using discrete Fresnel transformation to obtain the observation signal in the MIMO-OCDM communication system. The specific content is as follows:

[0060] The S101, MIMO-OCDM communication system has M T One transmitting antenna and M R There are N receiving antennas. Each transmitting antenna transmits N orthogonal Chirp subcarriers, and the symbol sequence s of all subcarriers transmitted is denoted as... in Indicates the Mth T The transpose of the symbol sequence transmitted by the subcarriers of each transmitting antenna.

[0061] S102. The modulated symbol data signal, i.e., the BPSK (Binary Phase Shift Keying) signal, is modulated using DFnT (Discrete Fresnel Transform) to perform OCDM modulation, generating orthogonal linear frequency modulated signals. A CP (Cyclic Prefix) is added to reduce the impact of multipath interference. Similar to OFDM-based systems, the CP is appended to the OCDM block to combat multipath propagation. It is assumed that the receiver can synchronously obtain channel state information through a specific channel estimation method. The orthogonal linear frequency modulated signals on the q-th transmit antenna... It can be represented as:

[0062]

[0063] Among them, s q This represents the symbol sequence transmitted by the subcarrier of the q-th transmit antenna. This shows the DFnT matrix corresponding to the q-th transmitting antenna. This indicates its inverse transformation.

[0064] Define the DFnT matrix Φ(m,n) containing (m,n) elements as:

[0065]

[0066] S103, orthogonal linear frequency modulation signal x OCDM Propagated in a multipath channel, the signal r received by the receiving antenna can therefore be expressed as:

[0067]

[0068] in, Indicates the Mth R The transpose of the signals received by each receiving antenna; Λ represents the channel coefficient matrix. Indicates the Mth T The transmitting antenna to the Mth R The channel coefficients between the receiving antennas; n represents the additive white Gaussian noise vector. Indicates the Mth R Gaussian white noise received by each receiving antenna; This represents the set of diagonal IDFnT matrices for transmitting antennas. Indicates the Mth T The IDFnT matrix corresponding to each transmitting antenna.

[0069] Performing a DFnT operation on r yields the observed signal, as shown in the following formula:

[0070]

[0071] Where r′ represents the observed signal received by the receiving antenna; Φ R This represents the set of diagonal DFnT matrices for receiving antennas. Indicates the Mth R The DFnT matrix corresponding to each receiving antenna; Θ represents the equivalent channel matrix. n′ represents Gaussian white noise in the Fresnel domain.

[0072] S2. Construct a WL-MMSE detector and use it to detect the observed signal to obtain the corresponding estimated signal. The specific steps are as follows:

[0073] S201. Based on channel state information and the complex conjugate information of the observed signal, and combining the MIMO-OCDM detector and the MMSE criterion, a WL-MMSE detector is constructed. The matrix expression of this detector is as follows:

[0074]

[0075] Among them, W WL-MMSE Represents the WL-MMSE detection matrix; The augmented form of the equivalent channel matrix is ​​expressed as follows: Where Θ T Θ represents the transpose of the equivalent channel matrix. H Represents the conjugate transpose of the equivalent channel matrix; Indicates the noise variance; Represents the identity matrix.

[0076] The S202 and WL-MMSE detectors process the observed signals to obtain the corresponding estimated signals, using the following formula:

[0077]

[0078] Where y represents the estimated signal; Represents the augmented vector of the observed signal. r ′* This represents the conjugate form of the observed signal.

[0079] S3. Neighborhood Search Using the LAS Algorithm. The estimated signal from step S2 is processed using the LAS algorithm. Through successive sign flipping and likelihood function optimization, the optimal solution is progressively searched within the neighborhood of the estimated signal. The specific steps are as follows:

[0080] S301. The initial vector can be obtained from the output of the WL-MMSE detector, expressed as:

[0081]

[0082] in, Let [ ] represent the initial vector for the LAS algorithm. Ω This represents the operation of quantizing the initial vector to the nearest BPSK constellation point.

[0083] S302, from the initial vector Initially, the LAS algorithm... Updated to We iterate through the iterations until a fixed point is reached. The update rule for LAS is that when the likelihood changes from step n to n+1... When the value is positive, perform an update, that is:

[0084]

[0085] in, This represents the likelihood change from the nth iteration to the (n+1)th iteration; Represents the likelihood function. r′ eff =Θ H r′+(Θ H r′) * Θ eff =Θ H Θ, Represents the detection vector. express transpose; This represents the likelihood function value at the nth iteration. This represents the likelihood function value for the (n+1)th iteration.

[0086] definition thereby It can be rewritten as:

[0087]

[0088] Where L(n) represents the vector length; g(n) represents the gradient of the likelihood function at the i-th bit position. Represents the real part of the signal. This represents the detection vector obtained after the nth iteration. i represents the bit position in the matrix, i = 1, ..., NM. T NM T This represents the total length of the vector.

[0089] The update rules of the LAS algorithm can be systematically constructed to ensure that the likelihood function monotonically increases (i.e., (This is provided that there is at least one bit s) i (n) is flipped, and the expression for the flipping condition is:

[0090]

[0091] Among them, t i (n) represents the threshold at the i-th bit position.

[0092] The convergence property of the LAS algorithm ensures that the detection process is completed in a short time. Iteration stops when the result meets the convergence condition, indicating that only a specific subset of bits continuously flips between +1 and -1. The optimized sign vector is output as the optimal solution.

[0093] Figure 2(a) is a BPSK constellation diagram showing the bit error rate performance obtained using different WL-MMSE-LAS detectors under a 4×4 antenna. Figure 2 (b) shows the BPSK constellation diagram with different WL-MMSE-LAS detectors under an 8×8 antenna. As can be seen from the figure, compared with the traditional MIMO-OFDM system, the combination of WL-MMSE and LAS detection methods significantly improves the SNR gain of the MIMO-OFDM system, and the WL-MMSE detector is significantly superior to the MMSE detector at a BER of 10. -3 A signal-to-noise ratio gain of 4dB was achieved. The WL-MMSE detector also exhibits superior performance compared to the WL-MMSE-LAS detector, at a BER of 10. -4 A signal-to-noise ratio gain of 4dB was achieved. This was achieved by adding an LAS receiver after the MMSE and WL-MMSE detectors, respectively, at 10... -4 It achieved signal-to-noise ratio gains of 8dB and 3dB. Furthermore, compared to other detectors, applying WL-MMSE-LAS to a MIMO-OCDM system resulted in a BER of 10... -4 A gain of 7dB was achieved, further highlighting its advantages and proving that the method proposed in this invention is more suitable for high-speed, low-latency, and high-capacity 6G application scenarios.

[0094] Figure 3 This diagram shows the iterative convergence performance of the LAS algorithm under different signal-to-noise ratios. By further optimizing the LAS algorithm based on WL-MMSE, it becomes suitable for real-time detection in large-scale MIMO-OCDM systems, significantly reducing the actual computational complexity. The LAS algorithm features fast convergence, achieving superior detection results in fewer iterations, effectively reducing computation time.

[0095] The WL-MMSE-LAS detector of this invention is suitable for large-scale MIMO-OCDM environments in high-bandwidth, high-speed, and high-capacity 6G communication systems, and is particularly suitable for the following scenarios:

[0096] High mobility scenarios: In high-speed mobile environments such as vehicle communication, drone communication and high-speed rail communication in 6G networks, the detector of this invention can adapt to rapid changes in the channel through rapid neighborhood search, ensuring the stability of signal detection.

[0097] High-density scenarios: In high-density network environments such as city centers and stadiums, traditional ML detectors have excessively high computational complexity, while the WL-MMSE-LAS detector can effectively improve bit error rate performance while maintaining low complexity, and adapt to high user density and interference environments.

[0098] Low-latency communication: The WL-MMSE-LAS detector of this invention is suitable for 6G applications with low latency requirements, such as industrial automation, remote surgery, and virtual reality (VR) communication. The fast convergence characteristic of the LAS algorithm can significantly shorten the detection latency and ensure the real-time performance of the system.

[0099] This invention also proposes a detection system based on a wide linear minimum mean square error MIMO-OCDM detector, including an observation signal acquisition module, an estimation signal acquisition module, a detection module, and a computer program executable on a processor. It should be noted that each module in the above system corresponds to a specific step of the method provided in this invention, possessing the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in this invention.

[0100] This invention also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. It should be noted that when the processor executes the computer program, it corresponds to the specific steps of the method provided in this invention, possessing the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in this invention.

[0101] This invention also proposes a computer-readable storage medium storing a computer program. It should be noted that when the computer program is executed by a processor, it corresponds to the specific steps of the method provided in this invention, possessing the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in this invention.

[0102] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A detection method based on a wide linear minimum mean square error MIMO-OCDM detector, characterized in that, include: S1. The signal received by the receiving antenna is demodulated by discrete Fresnel transformation to obtain the observation signal in the MIMO-OCDM communication system; Specifically: S101, Configure the MIMO-OCDM communication system to include M T One transmitting antenna and M R There are N receiving antennas, and each transmitting antenna transmits N orthogonal Chirp subcarriers. The symbol sequence s of the subcarrier transmission is denoted as... in Indicates the Mth T Transpose of the symbol sequence transmitted by the subcarriers of each transmit antenna; S102. OCDM modulation is performed on the modulation symbol data signal using DFnT to generate orthogonal linear frequency modulated signals, and a cyclic prefix is ​​added. The orthogonal linear frequency modulated signal on the q-th transmitting antenna is... Represented as: Among them, s q Φ represents the symbol sequence transmitted by the subcarrier of the q-th transmit antenna. q Let I represent the set of diagonal IDFnT matrices for the q-th transmitting antenna. (m,n) represents Φ q The number of elements in Φ q inverse transform; S103, orthogonal linear frequency modulation signal x OCDM When propagating in a multipath channel, the signal r received by the receiving antenna is represented as: r=Λx OCDM +n in, Indicates the Mth R The transpose of the signals received by each receiving antenna; Λ represents the channel coefficient matrix. Indicates the Mth T The transmitting antenna to the Mth R The channel coefficients between the receiving antennas; n represents the additive white Gaussian noise vector. Indicates the Mth R Gaussian white noise received by each receiving antenna; Performing a DFnT operation on r yields the observed signal, as shown in the following formula: r′=Φ R r Where r′ represents the observed signal received by the receiving antenna; Φ R This represents the set of diagonal DFnT matrices for receiving antennas. Indicates the Mth R The DFnT matrix corresponding to each receiving antenna; S2. Construct a WL-MMSE detector and use it to detect the observed signal in step S1 to obtain the corresponding estimated signal; specifically: S201. Based on channel state information and the complex conjugate information of the observed signal, and combining the MIMO-OCDM detector and the MMSE criterion, a WL-MMSE detector is constructed. The matrix expression of this detector is as follows: Among them, W WL-MMSE Represents the WL-MMSE detection matrix; The augmented form representing the equivalent channel matrix. Θ T Θ represents the transpose of the equivalent channel matrix. H Represents the conjugate transpose of the equivalent channel matrix; Indicates the noise variance; Represents the identity matrix; The S202 and WL-MMSE detectors process the observed signals to obtain the corresponding estimated signals, using the following formula: Where y represents the estimated signal; Represents the augmented vector of the observed signal. r′ * Represents the conjugate form of the observed signal; S3. The estimated signal from step S2 is processed using the LAS algorithm. Through successive sign flipping and likelihood function optimization, the optimal solution is searched in the neighborhood of the estimated signal to complete the detection; specifically: S301. Obtain the initial vector from the output of the WL-MMSE detector. The specific formula is as follows: in, This represents the initial vector for the LAS algorithm. NM T y represents the total length of the vector; y represents the estimated signal; [ ] Ω This represents the operation of quantizing the initial vector to the nearest BPSK constellation point; S302, from the initial vector Initially, when the likelihood change from the nth iteration to the (n+1)th iteration is positive, then... Updated to The process involves iterative selection until a fixed point is reached; the specific expression is: in, This represents the likelihood change from the nth iteration to the (n+1)th iteration; Represents the likelihood function. Represents the detection vector. express Transpose of; This represents the likelihood function value at the nth iteration. This represents the likelihood function value at the (n+1)th iteration; definition but Further expressed as: Where L(n) represents the vector length; g(n) represents the gradient of the likelihood function at the i-th bit position. Represents the real part of the signal. This represents the detection vector obtained after the nth iteration at the i-th bit position. This represents the detection vector obtained after the (n+1)th iteration at the i-th bit position. This represents the likelihood function value of the i-th bit position after the n-th iteration, where i represents the bit position in the matrix, i = 1, ..., NM. T ; When the detection vector satisfies the flipping condition, it is flipped to ensure that the likelihood function monotonically increases; the expression for the flipping condition is: Among them, t i (n) represents the threshold at the i-th bit position. When the result meets the convergence condition, the iteration stops, and the optimized detection vector is obtained. This is the optimal solution.

2. A system applied to the detection method of the MIMO-OCDM detector based on wide linear minimum mean square error as described in claim 1, characterized in that, include: The observation signal acquisition module demodulates the signal received by the receiving antenna through discrete Fresnel transformation to obtain the observation signal in the MIMO-OCDM communication system. The estimated signal acquisition module is used to construct the WL-MMSE detector, which is used to detect the observed signals in the observation signal acquisition module to obtain the corresponding estimated signals. The detection module is used to process the estimated signal in the estimated signal acquisition module using the LAS algorithm. By successively flipping the sign and optimizing the likelihood function, it searches for the optimal solution in the neighborhood of the estimated signal to complete the detection.

3. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the detection method of the MIMO-OCDM detector based on wide linear minimum mean square error as described in claim 1.

4. A computer-readable storage medium storing a computer program, characterized in that, The computer program, when executed by the processor, performs the detection method of the MIMO-OCDM detector based on wide linear minimum mean square error as described in claim 1.

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