Beam forming method for simultaneously performing communication and interference monitoring

Abstract

The invention discloses a beam forming method for simultaneously carrying out communication and interference monitoring. The beam forming method comprises the following steps of: 1, constructing a multi-user multiple-input multiple-output system; 2, communication and interference monitoring of the full duplex base station; 3, in the full duplex base station communication and interference monitoring process, channel state information is obtained through pilot signals; 4, constructing a beam forming vector optimization model in the communication and interference monitoring process of the full-duplex base station; 5, converting the beam forming vector optimization model from non-convex optimization to convex optimization to obtain a converted beam forming vector optimization model; and 6, obtaining an optimized transmitted beam forming vector. The method is reasonable in design, solves the problem of joint beam forming optimization of simultaneous communication and active interference type monitoring in a multi-user multi-input multi-output system, and realizes emission beam forming vector optimization of active interference signals and communication signals by taking maximization of weighting and communication capacity of all legal communication users as a target.

Description

Technical Field

[0001] The present invention belongs to the technical field of beamforming, and in particular relates to a beamforming method for simultaneously performing communication and interference monitoring. Background Art

[0002] With the rapid development of wireless communication technology, effective monitoring is needed to avoid endangering public safety or leaking secret information. However, traditional passive monitoring is difficult to achieve effective decoding of suspicious information when the distance is far or the channel quality is poor. In recent years, active monitoring technology has been proposed as a means to effectively monitor suspicious information. Among them, active monitoring through interference has been widely studied because of its good performance. Specifically, the legal monitor reduces the channel capacity of the suspicious link by transmitting active interference signals, forcing the suspicious transmitter to reduce its information transmission rate. When its rate is as low as the channel capacity of the monitoring channel, the legal monitor can decode the suspicious information it sends without error, that is, information monitoring is achieved. However, the active interference signal used in this method will also reduce the communication quality of legitimate communication users in the area. In this regard, beamforming, as a method of sending signals in a directional manner in space, can effectively solve the interference problem between multiple co-frequency signals. In addition, the additional deployment of equipment specifically used for active interference monitoring is both easy to be exposed and wastes financial and material resources. As an important infrastructure for providing communication services, communication base stations have strong regional coverage. It is very necessary to integrate the active monitoring function into the base station and reasonably design the transmission beams of the active interference signal and the communication signal at the base station, which can not only reduce the interference to the communication users while monitoring, but also effectively save energy and spectrum resources.

[0003] Therefore, a rationally designed beamforming method for simultaneous communication and interference monitoring is needed to solve the problem of joint beamforming optimization for simultaneous communication and active interference monitoring in a multi-user multi-input multi-output system, with the goal of maximizing the weighted and communication capacity of all legitimate communication users, and realizing the optimization of the transmit beamforming vectors of active interference signals and communication signals. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a beamforming method for simultaneous communication and interference monitoring in view of the deficiencies in the above-mentioned prior art. The method has simple steps and reasonable design, and solves the problem of joint beamforming optimization for simultaneous communication and active interference monitoring in a multi-user multi-input multi-output system, with the goal of maximizing the weighted and communication capacity of all legitimate communication users, thereby realizing the optimization of the transmit beamforming vectors of the active interference signal and the communication signal.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: a beamforming method for simultaneous communication and interference monitoring, the method comprising the following steps: Step 1: Build a multi-user multi-input multi-output system: The multi-user multiple-input multiple-output system includes a full-duplex base station, legitimate communication users, a suspicious transmitter and a suspicious receiver; wherein the full-duplex base station is provided with a transmitting array antenna and a receiving array antenna, the transmitting array antenna and the receiving array antenna are both ULA linear arrays, and the number of the transmitting array antenna is , the number of the receiving array antennas is , , and are all positive integers, and ; The legitimate communication user, the suspicious transmitter and the suspicious receiver are all equipped with a single antenna; Step 2: Communication and interference monitoring of full-duplex base stations: In full-duplex base stations and During the communication process of a legitimate communication user, the full-duplex base station also sends an active interference signal to the suspicious receiver, so that the full-duplex base station monitors the suspicious transmitter; among them, when the suspicious transmitter sends a signal to the suspicious receiver, it will Interference is caused to legitimate communication users; Step 3: During the communication and interference monitoring process of the full-duplex base station, the channel state information is obtained through the pilot signal; wherein the channel state information includes the full-duplex base station and the first The channel response vector of the communication link of the legitimate communication users , the channel response vector of the full-duplex base station and the monitoring link of the suspected transmitter , suspicious transmitters and The channel response of the interference link of a legitimate communication user , the channel response vector between the full-duplex base station and the suspected receiver , channel response of the suspicious communication link of the suspicious transmitter and the suspicious receiver ; is a positive integer, and ; Step 4: Construct a beamforming vector optimization model during the communication and interference monitoring process of the full-duplex base station: Input the channel state information, use the computer to maximize the weighted sum of channel capacity of all legal communication users, and use the total power of the transmitted signal of the full-duplex base station and the monitoring success as constraints to build a beamforming vector optimization model: ;in, Indicates The weight of legitimate communication users, The value ranges from 0 to 1, and , Indicates The channel capacity of legitimate communication users is Indicates that the full-duplex base station The transmit beamforming vector of the legitimate communication user is, represents the transmit beamforming vector of the full-duplex base station's active jamming signal to the suspected receiver, represents the constraints, Indicates the total power of the transmitted signal of the full-duplex base station. represents the square of the 2-norm, Indicates the signal-to-noise ratio of the suspicious transmission signal monitored at the full-duplex base station. Indicates the received signal-to-interference-noise ratio of the suspected receiver; Step 5: converting the beamforming vector optimization model from non-convex optimization to convex optimization to obtain a converted beamforming vector optimization model; Step 6: Get the optimized transmit beamforming vector: The block coordinate descent method is used to iteratively optimize the transformed beamforming vector optimization model to obtain the optimized full-duplex base station pair. The transmit beamforming vectors of legitimate communication users and the optimized full-duplex base station's transmit beamforming vector for the active jamming signal to the suspicious receiver .

[0006] The above-mentioned beamforming method for simultaneous communication and interference monitoring, further, step three, the specific process is as follows: Full-duplex base station to the first A legitimate communication user sends a pilot signal, The legitimate communication user uses the MMSE method to estimate the channel through the pilot signal to obtain the full-duplex base station and the The channel response vector of the communication link of the legitimate communication users and feed it back to the full-duplex base station; When the suspicious transmitter sends a pilot signal to the suspicious receiver, the suspicious receiver uses the MMSE method to estimate the channel through the pilot signal to obtain the channel response of the suspicious communication link between the suspicious transmitter and the suspicious receiver. , the channel response of the suspicious communication link between the suspicious transmitter and the suspicious receiver In the process of feeding back to the suspicious transmitter, the full-duplex base station obtains the channel response of the suspicious communication link between the suspicious transmitter and the suspicious receiver through monitoring. At the same time, the full-duplex base station receives the pilot signal, and the full-duplex base station uses the MMSE method to estimate the channel through the pilot signal to obtain the channel response vector of the monitoring link between the full-duplex base station and the suspicious transmitter , No. A legitimate communication user receives the pilot signal and uses the MMSE method to estimate the channel, and obtains the suspicious transmitter and the The channel response of the interference link of a legitimate communication user and the suspected transmitter and The channel response of the interference link of a legitimate communication user Feedback to the full-duplex base station; The suspicious receiver sends a pilot signal to the suspicious transmitter. The full-duplex base station receives the pilot signal and uses the MMSE method to estimate the channel through the pilot signal to obtain the channel response vector between the full-duplex base station and the suspicious receiver. .

[0007] The above-mentioned beamforming method for simultaneous communication and interference monitoring further includes step 4, and the specific process is as follows: Step 401: In a full-duplex base station and In the process of legitimate communication users communicating, according to , get the The received signal-to-interference-to-noise ratio of a legitimate communication user ;in, represents the conjugate transpose, represents the square of the modulus, Indicates that the full-duplex base station The transmit beamforming vector of the legitimate communication user is is a positive integer, and , , Indicates the transmit power of the suspected transmitter, Indicates The variance of the additive Gaussian white noise at the legitimate communication users; Step 402: According to , get the Channel capacity for legitimate communication users ;in, Indicates the set bandwidth; Step 403: According to , get the received signal-to-interference-noise ratio of the suspicious receiver ;in, represents the variance of the additive white Gaussian noise at the suspected receiver; Step 404: , get the signal-to-noise ratio of the suspicious transmission signal monitored at the duplex base station ;in, represents the variance of the additive white Gaussian noise at the full-duplex base station receiving array antenna; Step 405: A beamforming vector optimization model is constructed by using a computer with the goal of maximizing the weighted sum of channel capacity of all legal communication users and the total power of the transmitted signal of the full-duplex base station and the success of monitoring as constraints: .

[0008] The above-mentioned beamforming method for simultaneous communication and interference monitoring, further, step five, the specific process is as follows: Step 501: Use a computer to establish The received IQ signal of the legitimate communication user ,and ;in, Indicates that the full-duplex base station The transmitted signal of a legitimate communication user, Indicates that the full-duplex base station The transmitted signal of a legitimate communication user, and It obeys a complex Gaussian distribution with a mean of 0 and a variance of 1; represents an active jammer signal, and is a complex Gaussian distribution with mean 0 and variance 1. Indicates the transmission signal of the suspected transmitter, and is a complex Gaussian distribution with mean 0 and variance 1. Indicates Additive Gaussian white noise signal at each legitimate communication user; Step 502: Use a computer to set The receiving equalizer of a legitimate communication user is , then according to , get the The mean square error of the received signal of the legitimate communication user ;in, represents the mathematical expectation, express conjugation of; is a positive integer and its value is ; Step 503: Use computer command , get the The receiving equalizer of a legitimate communication user ,and ; Step 504: Use a computer to convert the first The receiving equalizer of a legitimate communication user Substitute it into step 502 again, and get The mean square error of the received signal of the legitimate communication user ,and ; Step 505: Use a computer to set The conversion weight of legitimate communication users ,and , then the beamforming vector optimization model is equivalently transformed to obtain the beamforming vector optimization model after one transformation: ; Step 506: Use a computer to and Substituting the constraints, we get , and simplify to get ; and use calculation to set the first intermediate variable ,and , the second intermediate variable ,and ,but ; Step 507: Use a computer to calculate the first intermediate variable exist Perform a first-order Taylor expansion at the position to obtain the first-order Taylor expansion of the first intermediate variable ,and ;in, express Dimension expansion point, Indicates expansion points, Indicates expansion points; Step 508: Use a computer to , which is converted to , then we get the linear constraint ; Step 509: Substitute the linear constraint conditions into the primary converted beamforming vector optimization model using a computer to convert the secondary converted beamforming vector optimization model: .

[0009] The above-mentioned beamforming method for simultaneous communication and interference monitoring, further, in step 505, an equivalent conversion is as follows: Step A: Convert the beamforming vector optimization model in step 405 into the first Lagrangian function ,and ;in, represents the first multiplier of the first Lagrangian function, represents the second multiplier of the first Lagrangian function, represents the third intermediate variable, and ; Step B: converting the beamforming vector optimization model after the first conversion in step 505 into a second Lagrangian function ,and ;in, represents the first multiplier of the second Lagrangian function, represents the second multiplier of the second Lagrangian function; Step C: Substituting into the first Lagrangian function, and Substitute the second Lagrangian function to obtain the transformation formula of the first Lagrangian function ,and And the second Lagrangian function conversion formula ,and ; Step D: Use a computer because is a constant term, then and , therefore, the beamforming vector optimization model is equivalently transformed to obtain the beamforming vector optimization model after one transformation.

[0010] The above-mentioned beamforming method for simultaneous communication and interference monitoring further includes step 6, and the specific process is as follows: Step 601: and Random initialization, initialize and Substituting into step 502 and step 504, we get the initialized The conversion weight of legitimate communication users , initialized The receiving equalizer of a legitimate communication user and the initialization The mean square error of the received signal of the legitimate communication user ; Step 602: Use a computer to set the optimization amount ,and , initialize the first The conversion weight of legitimate communication users and the initialization The mean square error of the received signal of the legitimate communication user Substitute in to get the initial optimization amount ; And order Assign to , Assign to After that, initialize the The receiving equalizer of a legitimate communication user After step 502 and initialization The conversion weight of legitimate communication users Then substitute it into the beamforming vector optimization model after secondary conversion, and use the CVX toolbox in MATLAB software to solve it, and get the full-duplex base station of the first iteration to the first The transmit beamforming vectors of legitimate communication users and the transmit beamforming vector of the first iteration of the full-duplex base station's active jamming signal to the suspicious receiver ; and will and Substituting into step 502 and step 504, we get The conversion weight of legitimate communication users , the first iteration The receiving equalizer of a legitimate communication user and the first iteration The mean square error of the received signal of the legitimate communication user ; Step 603: Use a computer to , Substitute the optimization amount , get the optimization amount of the first iteration , and judge Is it true? If so, the full-duplex base station of the first iteration is The transmit beamforming vectors of legitimate communication users As an optimized full-duplex base station The transmit beamforming vectors of legitimate communication users , the transmit beamforming vector of the first iteration of the full-duplex base station's active jamming signal to the suspicious receiver The transmit beamforming vector of the optimized full-duplex base station actively jamming the signal to the suspected receiver ; If not, execute step 604; wherein, represents the set error value, and The value range is 0.001~0.01; Indicates absolute value; Step 604: According to the method of step 601 to step 603, the full-duplex base station of the first iteration is The transmit beamforming vectors of legitimate communication users and the transmit beamforming vector of the first iteration of the full-duplex base station's active jamming signal to the suspicious receiver Substitute it for the second iteration; Step 605: Repeat step 604 multiple times to pair the full-duplex base station of the mth iteration with the first The transmit beamforming vectors of legitimate communication users and the transmit beamforming vector of the mth iteration of the full-duplex base station's active jamming signal to the suspicious receiver Substitute and perform m+1 iterations until , then the full-duplex base station of the m+1th iteration is The transmit beamforming vectors of legitimate communication users As an optimized full-duplex base station The transmit beamforming vectors of legitimate communication users , the transmit beamforming vector of the m+1th iteration of the full-duplex base station's active jamming signal to the suspicious receiver The transmit beamforming vector of the optimized full-duplex base station actively jamming the signal to the suspected receiver ; Wherein, m is a positive integer greater than 1.

[0011] Compared with the prior art, the present invention has the following advantages: 1. The present invention studies the joint beamforming of simultaneous communication and active interference monitoring in a multi-user multiple-input multiple-output system, thereby realizing the optimization of the transmit beamforming vectors of the active interference signal and the communication signal.

[0012] 2. The present invention aims to maximize the weighted sum of channel capacity of all legitimate communication users, takes the total power of the transmitted signal of the full-duplex base station and the success of monitoring as constraints, and constructs a beamforming vector optimization model. It can improve the data transmission rate and reliability while satisfying active interference monitoring, thereby meeting the needs of multiple users.

[0013] 3. The beamforming vector optimization model of the present invention is converted from non-convex optimization to convex optimization to obtain a converted beamforming vector optimization model, thereby realizing subsequent iterative solution.

[0014] 4. The present invention uses the block coordinate descent method to iteratively optimize the converted beamforming vector optimization model to obtain the optimized full-duplex base station pair The transmit beamforming vectors of the legitimate communication users and the transmit beamforming vectors of the optimized full-duplex base station that actively interferes with the suspicious receiver signal are used to reduce the computational complexity and gradually approach the global optimal solution.

[0015] In summary, the method of the present invention has simple steps and reasonable design. It solves the problem of joint beamforming optimization for simultaneous communication and active interference monitoring in a multi-user multi-input multi-output system, with the goal of maximizing the weighted and communication capacity of all legitimate communication users, and realizes the optimization of the transmit beamforming vectors of active interference signals and communication signals.

[0016] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The figure is a flowchart of the method of the present invention. DETAILED DESCRIPTION

[0018] like Figure 1 A beamforming method for simultaneous communication and interference monitoring is shown, comprising the following steps: Step 1: Build a multi-user multi-input multi-output system: The multi-user multiple-input multiple-output system includes a full-duplex base station, legitimate communication users, a suspicious transmitter and a suspicious receiver; wherein the full-duplex base station is provided with a transmitting array antenna and a receiving array antenna, the transmitting array antenna and the receiving array antenna are both ULA linear arrays, and the number of the transmitting array antenna is , the number of the receiving array antennas is , , and are all positive integers, and ; The legitimate communication user, the suspicious transmitter and the suspicious receiver are all equipped with a single antenna; Step 2: Communication and interference monitoring of full-duplex base stations: In full-duplex base stations and During the communication process of a legitimate communication user, the full-duplex base station also sends an active interference signal to the suspicious receiver, so that the full-duplex base station monitors the suspicious transmitter; among them, when the suspicious transmitter sends a signal to the suspicious receiver, it will Interference is caused to legitimate communication users; Step 3: During the communication and interference monitoring process of the full-duplex base station, the channel state information is obtained through the pilot signal; wherein the channel state information includes the full-duplex base station and the first The channel response vector of the communication link of the legitimate communication users , the channel response vector of the full-duplex base station and the monitoring link of the suspected transmitter , suspicious transmitters and The channel response of the interference link of a legitimate communication user , the channel response vector between the full-duplex base station and the suspected receiver , channel response of the suspicious communication link of the suspicious transmitter and the suspicious receiver ; is a positive integer, and ; Step 4: Construct a beamforming vector optimization model during the communication and interference monitoring process of the full-duplex base station: Input the channel state information, use the computer to maximize the weighted sum of channel capacity of all legal communication users, and use the total power of the transmitted signal of the full-duplex base station and the monitoring success as constraints to build a beamforming vector optimization model: ;in, Indicates The weight of legitimate communication users, The value ranges from 0 to 1, and , Indicates The channel capacity of legitimate communication users is Indicates that the full-duplex base station The transmit beamforming vector of the legitimate communication user is represents the transmit beamforming vector of the full-duplex base station's active jamming signal to the suspected receiver, represents the constraints, Indicates the total power of the transmitted signal of the full-duplex base station. represents the square of the 2-norm, Indicates the signal-to-noise ratio of the suspicious transmission signal monitored at the full-duplex base station. Indicates the received signal-to-interference-noise ratio of the suspected receiver; Step 5: converting the beamforming vector optimization model from non-convex optimization to convex optimization to obtain a converted beamforming vector optimization model; Step 6: Get the optimized transmit beamforming vector: The block coordinate descent method is used to iteratively optimize the transformed beamforming vector optimization model to obtain the optimized full-duplex base station pair. The transmit beamforming vectors of legitimate communication users and the optimized full-duplex base station's transmit beamforming vector for the active jamming signal to the suspicious receiver .

[0019] In this embodiment, step three, the specific process is as follows: Full-duplex base station to the first A legitimate communication user sends a pilot signal, The legitimate communication user uses the MMSE method to estimate the channel through the pilot signal to obtain the full-duplex base station and the The channel response vector of the communication link of the legitimate communication users and feed it back to the full-duplex base station; When the suspicious transmitter sends a pilot signal to the suspicious receiver, the suspicious receiver uses the MMSE method to estimate the channel through the pilot signal to obtain the channel response of the suspicious communication link between the suspicious transmitter and the suspicious receiver. , the channel response of the suspicious communication link between the suspicious transmitter and the suspicious receiver In the process of feeding back to the suspicious transmitter, the full-duplex base station obtains the channel response of the suspicious communication link between the suspicious transmitter and the suspicious receiver through monitoring. At the same time, the full-duplex base station receives the pilot signal, and the full-duplex base station uses the MMSE method to estimate the channel through the pilot signal to obtain the channel response vector of the monitoring link between the full-duplex base station and the suspicious transmitter , No. A legitimate communication user receives the pilot signal and uses the MMSE method to estimate the channel, and obtains the suspicious transmitter and the The channel response of the interference link of a legitimate communication user and the suspected transmitter and The channel response of the interference link of a legitimate communication user Feedback to the full-duplex base station; The suspicious receiver sends a pilot signal to the suspicious transmitter. The full-duplex base station receives the pilot signal and uses the MMSE method to estimate the channel through the pilot signal to obtain the channel response vector between the full-duplex base station and the suspicious receiver. .

[0020] In this embodiment, step 4, the specific process is as follows: Step 401: In a full-duplex base station and In the process of legitimate communication users communicating, according to , get the The received signal-to-interference-to-noise ratio of a legitimate communication user ;in, represents the conjugate transpose, represents the square of the modulus, Indicates that the full-duplex base station The transmit beamforming vector of the legitimate communication user is, is a positive integer, and , , Indicates the transmit power of the suspected transmitter, Indicates The variance of the additive Gaussian white noise at the legitimate communication users; Step 402: According to , get the Channel capacity for legitimate communication users ;in, Indicates the set bandwidth; Step 403: According to , get the received signal-to-interference-noise ratio of the suspicious receiver ;in, represents the variance of the additive white Gaussian noise at the suspected receiver; Step 404: , get the signal-to-noise ratio of the suspicious transmission signal monitored at the duplex base station ;in, represents the variance of the additive white Gaussian noise at the full-duplex base station receiving array antenna; Step 405: A beamforming vector optimization model is constructed by using a computer with the goal of maximizing the weighted sum of channel capacity of all legal communication users and the total power of the transmitted signal of the full-duplex base station and the success of monitoring as constraints: .

[0021] In this embodiment, step five, the specific process is as follows: Step 501: Use a computer to establish The received IQ signal of the legitimate communication user ,and ;in, Indicates that the full-duplex base station The transmitted signal of a legitimate communication user, Indicates that the full-duplex base station The transmitted signal of a legitimate communication user, and It obeys a complex Gaussian distribution with a mean of 0 and a variance of 1; represents an active jammer signal, and is a complex Gaussian distribution with mean 0 and variance 1. Indicates the transmission signal of the suspected transmitter, and is a complex Gaussian distribution with mean 0 and variance 1. Indicates Additive Gaussian white noise signal at each legitimate communication user; Step 502: Use a computer to set The receiving equalizer of a legitimate communication user is , then according to , get the The mean square error of the received signal of the legitimate communication user ;in, represents the mathematical expectation, express conjugation of; is a positive integer and its value is ; Step 503: Use computer command , get the The receiving equalizer of a legitimate communication user ,and ; Step 504: Use a computer to convert the first The receiving equalizer of a legitimate communication user Substitute into step 502 again, and get The mean square error of the received signal of the legitimate communication user ,and ; Step 505: Use a computer to set The conversion weight of legitimate communication users ,and , then the beamforming vector optimization model is equivalently transformed to obtain the beamforming vector optimization model after one transformation: ; Step 506: Use a computer to and Substituting the constraints, we get , and simplify to get ; and use calculation to set the first intermediate variable ,and , the second intermediate variable ,and ,but ; Step 507: Use a computer to calculate the first intermediate variable exist Perform a first-order Taylor expansion at the position to obtain the first-order Taylor expansion of the first intermediate variable ,and ;in, express Dimension expansion point, Indicates expansion points, Indicates expansion points; Step 508: Use a computer to , which is converted to , then we get the linear constraint ; Step 509: Substitute the linear constraint conditions into the primary converted beamforming vector optimization model using a computer to convert the secondary converted beamforming vector optimization model: .

[0022] In this embodiment, the equivalent conversion in step 505 is as follows: Step A: Convert the beamforming vector optimization model in step 405 into the first Lagrangian function ,and ;in, represents the first multiplier of the first Lagrangian function, represents the second multiplier of the first Lagrangian function, represents the third intermediate variable, and ; Step B: converting the beamforming vector optimization model after the first conversion in step 505 into a second Lagrangian function ,and ;in, represents the first multiplier of the second Lagrangian function, represents the second multiplier of the second Lagrangian function; Step C: Substituting into the first Lagrangian function, and Substitute the second Lagrangian function to obtain the transformation formula of the first Lagrangian function ,and And the second Lagrangian function conversion formula ,and ; Step D: Use a computer because is a constant term, then and , therefore, the beamforming vector optimization model is equivalently transformed to obtain the beamforming vector optimization model after one transformation.

[0023] In this embodiment, step six, the specific process is as follows: Step 601: and Random initialization, initialize and Substituting into step 502 and step 504, we get the initialized The conversion weight of legitimate communication users , initialized The receiving equalizer of a legitimate communication user and the initialization The mean square error of the received signal of the legitimate communication user ; Step 602: Use a computer to set the optimization amount ,and , initialize the first The conversion weight of legitimate communication users and the initialization The mean square error of the received signal of the legitimate communication user Substitute in to get the initial optimization amount ; And order Assign to , Assign to After that, initialize the The receiving equalizer of a legitimate communication user After step 502 and initialization The conversion weight of legitimate communication users Then substitute it into the beamforming vector optimization model after secondary conversion, and use the CVX toolbox in MATLAB software to solve it, and get the full-duplex base station of the first iteration to the first The transmit beamforming vectors of legitimate communication users and the transmit beamforming vector of the first iteration of the full-duplex base station's active jamming signal to the suspicious receiver ; and will and Substituting into step 502 and step 504, we get The conversion weight of legitimate communication users , the first iteration The receiving equalizer of a legitimate communication user and the first iteration The mean square error of the received signal of the legitimate communication user ; Step 603: Use a computer to , Substitute the optimization amount , get the optimization amount of the first iteration , and judge Is it true? If so, the full-duplex base station of the first iteration is The transmit beamforming vectors of legitimate communication users As an optimized full-duplex base station The transmit beamforming vectors of legitimate communication users , the transmit beamforming vector of the first iteration of the full-duplex base station's active jamming signal to the suspicious receiver The transmit beamforming vector of the optimized full-duplex base station actively jamming the signal to the suspected receiver ; If not, execute step 604; wherein, represents the set error value, and The value range is 0.001~0.01; Indicates absolute value; Step 604: According to the method of step 601 to step 603, the full-duplex base station of the first iteration is The transmit beamforming vectors of legitimate communication users and the transmit beamforming vector of the first iteration of the full-duplex base station's active jamming signal to the suspicious receiver Substitute for the second iteration; Step 605: Repeat step 604 multiple times to pair the full-duplex base station of the mth iteration with the first The transmit beamforming vectors of legitimate communication users and the transmit beamforming vector of the mth iteration of the full-duplex base station's active jamming signal to the suspicious receiver Substitute and perform m+1 iterations until , then the full-duplex base station of the m+1th iteration is The transmit beamforming vectors of legitimate communication users As an optimized full-duplex base station The transmit beamforming vectors of legitimate communication users , the transmit beamforming vector of the m+1th iteration of the full-duplex base station's active jamming signal to the suspicious receiver The transmit beamforming vector of the optimized full-duplex base station actively jamming the signal to the suspected receiver ; Wherein, m is a positive integer greater than 1.

[0024] In this embodiment, the MMSE method is a minimum mean square error method.

[0025] In this embodiment, during the specific implementation, The value is 16. The value is 4. The value is 3.

[0026] In this embodiment, when implementing, the bandwidth is set It can be adjusted as needed and is not specifically limited.

[0027] In this embodiment, during the specific implementation, The variance of the additive Gaussian white noise at the legitimate communication users is The value is -110dBm / MHz~-70dBm / MHz; the variance of the additive white Gaussian noise at the suspected receiver The value is -110dBm / MHz~-70dBm / MHz; the variance of the additive white Gaussian noise at the duplex base station receiving array antenna The value range is -110dBm / MHz to -70dBm / MHz.

[0028] In this embodiment, for Complex vectors, for complex vector, for complex vector, for complex vector, for Complex vectors, for complex vector, and are all one-dimensional complex numbers.

[0029] In summary, the method of the present invention has simple steps and reasonable design. It solves the problem of joint beamforming optimization for simultaneous communication and active interference monitoring in a multi-user multi-input multi-output system, with the goal of maximizing the weighted and communication capacity of all legitimate communication users, and realizes the optimization of the transmit beamforming vectors of the active interference signal and the communication signal.

[0030] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent structural change made to the above embodiment based on the technical essence of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A beamforming method for simultaneous communication and interference monitoring, characterized in that: The method comprises the following steps: Step 1: Build a multi-user multi-input multi-output system: The multi-user multiple-input multiple-output system includes a full-duplex base station, legitimate communication users, a suspicious transmitter and a suspicious receiver; wherein the full-duplex base station is provided with a transmitting array antenna and a receiving array antenna, the transmitting array antenna and the receiving array antenna are both ULA linear arrays, and the number of the transmitting array antenna is , the number of the receiving array antennas is , , and are all positive integers, and ; The legitimate communication user, the suspicious transmitter and the suspicious receiver are all equipped with a single antenna; Step 2: Communication and interference monitoring of full-duplex base stations: In full-duplex base stations and During the communication process of a legitimate communication user, the full-duplex base station also sends an active interference signal to the suspicious receiver, so that the full-duplex base station monitors the suspicious transmitter; among them, when the suspicious transmitter sends a signal to the suspicious receiver, it will Interference is caused to legitimate communication users; Step 3: During the communication and interference monitoring process of the full-duplex base station, the channel state information is obtained through the pilot signal; wherein the channel state information includes the full-duplex base station and the first The channel response vector of the communication link of the legitimate communication users , the channel response vector of the full-duplex base station and the monitoring link of the suspected transmitter , suspicious transmitters and The channel response of the interference link of a legitimate communication user , the channel response vector between the full-duplex base station and the suspected receiver , channel response of the suspicious communication link of the suspicious transmitter and the suspicious receiver ; is a positive integer, and ; Step 4: Construct a beamforming vector optimization model during the communication and interference monitoring process of the full-duplex base station: Input the channel state information, use the computer to maximize the weighted sum of channel capacity of all legal communication users, and use the total power of the transmitted signal of the full-duplex base station and the monitoring success as constraints to build a beamforming vector optimization model: ;in, Indicates The weight of legitimate communication users, The value ranges from 0 to 1, and , Indicates The channel capacity of legitimate communication users is Indicates that the full-duplex base station The transmit beamforming vector of the legitimate communication user is, represents the transmit beamforming vector of the full-duplex base station's active jamming signal to the suspected receiver, represents the constraints, Indicates the total power of the transmitted signal of the full-duplex base station. represents the square of the 2-norm, Indicates the signal-to-noise ratio of the suspicious transmission signal monitored at the full-duplex base station. Indicates the received signal-to-interference-noise ratio of the suspected receiver; Step 5: converting the beamforming vector optimization model from non-convex optimization to convex optimization to obtain a converted beamforming vector optimization model; Step 6: Get the optimized transmit beamforming vector: The block coordinate descent method is used to iteratively optimize the transformed beamforming vector optimization model to obtain the optimized full-duplex base station pair. The transmit beamforming vectors of legitimate communication users and the optimized full-duplex base station's transmit beamforming vector for the active jamming signal to the suspicious receiver .

2. A beamforming method for simultaneous communication and interference monitoring according to claim 1, characterized in that: Step 3: The specific process is as follows: Full-duplex base station to the first A legitimate communication user sends a pilot signal, The legitimate communication user uses the MMSE method to estimate the channel through the pilot signal to obtain the full-duplex base station and the The channel response vector of the communication link of the legitimate communication users and feed it back to the full-duplex base station; When the suspicious transmitter sends a pilot signal to the suspicious receiver, the suspicious receiver uses the MMSE method to estimate the channel through the pilot signal to obtain the channel response of the suspicious communication link between the suspicious transmitter and the suspicious receiver. , the channel response of the suspicious communication link between the suspicious transmitter and the suspicious receiver In the process of feeding back to the suspicious transmitter, the full-duplex base station obtains the channel response of the suspicious communication link between the suspicious transmitter and the suspicious receiver through monitoring. ; At the same time, the full-duplex base station receives the pilot signal, and uses the MMSE method to estimate the channel through the pilot signal to obtain the channel response vector of the monitoring link between the full-duplex base station and the suspicious transmitter. , No. A legitimate communication user receives the pilot signal and uses the MMSE method to estimate the channel, and obtains the suspicious transmitter and the The channel response of the interference link of a legitimate communication user and the suspected transmitter and The channel response of the interference link of a legitimate communication user Feedback to the full-duplex base station; The suspicious receiver sends a pilot signal to the suspicious transmitter. The full-duplex base station receives the pilot signal and uses the MMSE method to estimate the channel through the pilot signal to obtain the channel response vector between the full-duplex base station and the suspicious receiver. .

3. A beamforming method for simultaneous communication and interference monitoring according to claim 1, characterized in that: Step 4: The specific process is as follows: Step 401: In a full-duplex base station and In the process of legitimate communication users communicating, according to , get the The received signal-to-interference-to-noise ratio of a legitimate communication user ;in, represents the conjugate transpose, represents the square of the modulus, Indicates that the full-duplex base station The transmit beamforming vector of the legitimate communication user is, is a positive integer, and , , Indicates the transmit power of the suspected transmitter, Indicates The variance of the additive Gaussian white noise at the legitimate communication users; Step 402: According to , get the Channel capacity for legitimate communication users ;in, Indicates the set bandwidth; Step 403: According to , get the received signal-to-interference-noise ratio of the suspicious receiver ;in, represents the variance of the additive white Gaussian noise at the suspected receiver; Step 404: , get the signal-to-noise ratio of the suspicious transmission signal monitored at the duplex base station ;in, represents the variance of the additive white Gaussian noise at the full-duplex base station receiving array antenna; Step 405: A beamforming vector optimization model is constructed by using a computer with the goal of maximizing the weighted sum of channel capacity of all legal communication users and the total power of the transmitted signal of the full-duplex base station and the success of monitoring as constraints: 。 4. A beamforming method for simultaneous communication and interference monitoring according to claim 3, characterized in that: Step 5: The specific process is as follows: Step 501: Use a computer to establish The received IQ signal of the legitimate communication user ,and ;in, Indicates that the full-duplex base station The transmitted signal of a legitimate communication user, Indicates that the full-duplex base station The transmitted signal of a legitimate communication user, and It obeys a complex Gaussian distribution with a mean of 0 and a variance of 1; represents an active jammer signal, and is a complex Gaussian distribution with mean 0 and variance 1. Indicates the transmission signal of the suspected transmitter, and is a complex Gaussian distribution with mean 0 and variance 1. Indicates Additive Gaussian white noise signal at each legitimate communication user; Step 502: Use a computer to set The receiving equalizer of a legitimate communication user is , then according to , get the The mean square error of the received signal of the legitimate communication user ;in, represents the mathematical expectation, express conjugation of; is a positive integer and its value is ; Step 503: Use computer command , get the The receiving equalizer of a legitimate communication user ,and ; Step 504: Use a computer to convert the first The receiving equalizer of a legitimate communication user Substitute into step 502 again, and get The mean square error of the received signal of the legitimate communication user ,and ; Step 505: Use a computer to set The conversion weight of legitimate communication users ,and , then the beamforming vector optimization model is equivalently transformed to obtain the beamforming vector optimization model after one transformation: ； Step 506: Use a computer to and Substituting the constraints, we get , and simplify to get ; and use calculation to set the first intermediate variable ,and , the second intermediate variable ,and ,but ; Step 507: Use a computer to calculate the first intermediate variable exist Perform a first-order Taylor expansion at the position to obtain the first-order Taylor expansion of the first intermediate variable ,and ;in, express Dimension expansion point, Indicates expansion points, Indicates expansion points; Step 508: Use a computer to , which is converted to , then we get the linear constraint ; Step 509: Substitute the linear constraint conditions into the primary converted beamforming vector optimization model using a computer to convert the secondary converted beamforming vector optimization model: 。 5. A beamforming method for simultaneous communication and interference monitoring according to claim 4, characterized in that: The equivalent conversion in step 505 is as follows: Step A: Convert the beamforming vector optimization model in step 405 into the first Lagrangian function ,and ;in, represents the first multiplier of the first Lagrangian function, represents the second multiplier of the first Lagrangian function, represents the third intermediate variable, and ; Step B: converting the beamforming vector optimization model after the first conversion in step 505 into a second Lagrangian function ,and ;in, represents the first multiplier of the second Lagrangian function, represents the second multiplier of the second Lagrangian function; Step C: Substituting into the first Lagrangian function, and Substitute the second Lagrangian function to obtain the transformation formula of the first Lagrangian function ,and And the second Lagrangian function conversion formula ,and ； Step D: Use a computer because is a constant term, then and , therefore, the beamforming vector optimization model is equivalently transformed to obtain the beamforming vector optimization model after one transformation.

6. A beamforming method for simultaneous communication and interference monitoring according to claim 5, characterized in that: Step 6: The specific process is as follows: Step 601: and Random initialization, initialize and Substituting into step 502 and step 504, we get the initialized The conversion weight of legitimate communication users , initialized The receiving equalizer of a legitimate communication user and the initialization The mean square error of the received signal of the legitimate communication user ; Step 602: Use a computer to set the optimization amount ,and , initialize the first The conversion weight of legitimate communication users and the initialization The mean square error of the received signal of the legitimate communication user Substitute in to get the initial optimization amount ; And order Assign to , Assign to After that, initialize the The receiving equalizer of a legitimate communication user After step 502 and initialization The conversion weight of legitimate communication users Then substitute it into the beamforming vector optimization model after secondary conversion, and use the CVX toolbox in MATLAB software to solve it, and get the full-duplex base station of the first iteration to the first The transmit beamforming vectors of legitimate communication users and the transmit beamforming vector of the first iteration of the full-duplex base station's active jamming signal to the suspicious receiver ; and will and Substituting into step 502 and step 504, we get The conversion weight of legitimate communication users , the first iteration The receiving equalizer of a legitimate communication user and the first iteration The mean square error of the received signal of the legitimate communication user ; Step 603: Use a computer to , Substitute the optimization amount , get the optimization amount of the first iteration , and judge Is it true? If so, the full-duplex base station of the first iteration is The transmit beamforming vectors of legitimate communication users As an optimized full-duplex base station The transmit beamforming vectors of legitimate communication users , the transmit beamforming vector of the first iteration of the full-duplex base station's active jamming signal to the suspicious receiver The transmit beamforming vector of the optimized full-duplex base station actively jamming the signal to the suspected receiver ; If not, execute step 604; wherein, represents the set error value, and The value range is 0.001~0.01; Indicates absolute value; Step 604: According to the method of step 601 to step 603, the full-duplex base station of the first iteration is The transmit beamforming vectors of legitimate communication users and the transmit beamforming vector of the first iteration of the full-duplex base station's active jamming signal to the suspicious receiver Substitute for the second iteration; Step 605: Repeat step 604 multiple times to pair the full-duplex base station of the mth iteration with the first The transmit beamforming vectors of legitimate communication users and the transmit beamforming vector of the mth iteration of the full-duplex base station's active jamming signal to the suspicious receiver Substitute and perform m+1 iterations until , then the full-duplex base station of the m+1th iteration is The transmit beamforming vectors of legitimate communication users As an optimized full-duplex base station The transmit beamforming vectors of legitimate communication users , the transmit beamforming vector of the m+1th iteration of the full-duplex base station's active jamming signal to the suspicious receiver The transmit beamforming vector of the optimized full-duplex base station actively jamming the signal to the suspected receiver ; Wherein, m is a positive integer greater than 1.

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