A beamforming method for simultaneous communication and interference monitoring

Abstract

The present invention discloses a beamforming method for simultaneously performing communication and interference monitoring, comprising the following steps: First, construct a multi-user multiple-input multiple-output system; Second, perform communication and interference monitoring at a full-duplex base station; Third, during the communication and interference monitoring at the full-duplex base station, obtain channel state information through pilot signals; Fourth, construct a beamforming vector optimization model during the communication and interference monitoring at the full-duplex base station; Fifth, convert the beamforming vector optimization model from non-convex optimization to convex optimization to obtain the converted beamforming vector optimization model; Sixth, obtain the optimized transmit beamforming vector. The present invention is reasonably designed to solve the problem of joint beamforming optimization for simultaneously performing communication and active interference monitoring in a multi-user multiple-input multiple-output system, aiming to maximize the weighted sum communication capacity of all legitimate communication users, and to realize the optimization of the transmit beamforming vectors of active interference signals and communication signals.

Description

Technical Field

[0001] The present invention belongs to the technical field of beamforming, and particularly 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 required to avoid endangering public safety or leaking secret information. However, traditional passive monitoring is difficult to effectively decode suspicious information in the case of long distance or poor channel quality. 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 due to its good performance. Specifically, a legitimate monitor reduces the channel capacity of a suspicious link by transmitting an active interference signal, forcing the suspicious transmitter to reduce its information transmission rate. When its rate is reduced to the channel capacity of the monitoring channel, the legitimate monitor can correctly decode the suspicious information it sends, that is, information monitoring is achieved. However, the active interference signal used in this method also reduces the communication quality of legitimate communication users in the area. In this regard, beamforming, as a method of directionally transmitting signals in space, can effectively solve the interference problem between multiple co-frequency signals. In addition, deploying additional devices specifically for active interference monitoring is not only easy to expose but also wastes financial and material resources. And communication base stations, as important infrastructure for providing communication services, have strong regional coverage. If the active monitoring function can be integrated into the base station and the transmission beamforming of the active interference signal and the communication signal can be reasonably designed at the base station, it is very necessary not only to reduce the interference received by communication users during monitoring but also to effectively save energy and spectrum resources.

[0003] Therefore, a beamforming method for simultaneously performing communication and interference monitoring with reasonable design is needed to solve the problem of joint beamforming optimization for simultaneously performing communication and active interference monitoring in a multi-user multiple-input multiple-output system, aiming to maximize the weighted sum communication capacity of all legitimate communication users, and to optimize the transmission beamforming vectors of the active interference signal and the communication signal. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a beamforming method for simultaneously performing communication and interference monitoring in view of the above deficiencies in the prior art. The method steps are simple and reasonably designed, solving the problem of joint beamforming optimization for simultaneously performing communication and active interference monitoring in a multi-user multiple-input multiple-output system, aiming to maximize the weighted sum communication capacity of all legitimate communication users, and to optimize the transmission beamforming vectors of the active interference signal and the communication signal.

[0005] To solve the above technical problems, the technical solution adopted by the present invention is: a beamforming method for simultaneously performing communication and interference monitoring, the method comprising the following steps:

[0006] Step 1, constructing a multi-user multiple-input multiple-output system:

[0007] The multi-user multiple-input multiple-output system includes a full-duplex base station, a plurality of legitimate communication users, a suspicious transmitter, and a suspicious receiver; wherein, a transmitting array antenna and a receiving array antenna are provided on the full-duplex base station, both the transmitting array antenna and the receiving array antenna are ULA linear arrays, and the number of the transmitting array antennas is and the number of the receiving array antennas is , , and are all positive integers, and ; the legitimate communication users, the suspicious transmitter, and the suspicious receiver are all equipped with single antennas;

[0008] Step 2, communication and interference monitoring of the full-duplex base station:

[0009] During the communication between the full-duplex base station and a plurality of legitimate communication users, the full-duplex base station also sends an active interference signal to the suspicious receiver to enable the full-duplex base station to monitor the suspicious transmitter; wherein, when the suspicious transmitter transmits a signal to the suspicious receiver, it will interfere with a plurality of legitimate communication users;

[0010] Step 3, obtaining channel state information through pilot signals during the communication and interference monitoring process of the full-duplex base station; wherein, the channel state information includes the channel response vector of the communication link between the full-duplex base station and the th legitimate communication user, the channel response vector of the monitoring link between the full-duplex base station and the suspicious transmitter, the channel response of the interference link between the suspicious transmitter and the th legitimate communication user, the channel response vector between the full-duplex base station and the suspicious receiver, and the channel response of the suspicious communication link between the suspicious transmitter and the suspicious receiver; is a positive integer, and ;

[0011] Step 4, constructing a beamforming vector optimization model during the communication and interference monitoring process of the full-duplex base station:

[0012] Input the channel state information. Using a computer, with the goal of maximizing the weighted sum channel capacity of all legitimate communication users and with the total transmit signal power of the full-duplex base station and successful listening as constraints, construct a beamforming vector optimization model:

[0013] ; where represents the weight of the th legitimate communication user, takes values from 0 to 1, and , represents the channel capacity of the th legitimate communication user, represents the transmit beamforming vector of the full-duplex base station for the th legitimate communication user, represents the transmit beamforming vector of the full-duplex base station for the active interference signal to the suspicious receiver, represents the constraint condition, represents the total transmit signal power of the full-duplex base station, represents the square of the 2-norm, represents the signal-to-noise ratio of the suspicious transmitted signal monitored at the full-duplex base station, represents the received signal-to-interference-plus-noise ratio of the suspicious receiver;

[0014] Step Five: Convert the beamforming vector optimization model from non-convex optimization to convex optimization to obtain the converted beamforming vector optimization model;

[0015] Step Six: Obtain the optimized transmit beamforming vector:

[0016] Use the block coordinate descent method to perform iterative alternating optimization on the converted beamforming vector optimization model to obtain the optimized transmit beamforming vector of the full-duplex base station for the th legitimate communication user and the optimized transmit beamforming vector of the full-duplex base station for the active interference signal to the suspicious receiver.

[0017] For the above beamforming method for simultaneous communication and interference listening, further, in Step Three, the specific process is as follows:

[0018] The full-duplex base station sends a pilot signal to the th legitimate communication user. The th legitimate communication user uses the MMSE method for channel estimation through the pilot signal to obtain the channel response vector of the communication link between the full-duplex base station and the th legitimate communication user, and feeds it back to the full-duplex base station;

[0019] When the suspicious transmitter sends a pilot signal to the suspicious receiver, the suspicious receiver uses the MMSE method to perform channel estimation through the pilot signal, and obtains the channel response of the suspicious communication link between the suspicious transmitter and the suspicious receiver During the process of feeding back the channel response of the suspicious communication link between the suspicious transmitter and the suspicious receiver 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 eavesdropping ; At the same time, the full-duplex base station receives the pilot signal, and the full-duplex base station uses the MMSE method to perform channel estimation through the pilot signal, and obtains the channel response vector of the eavesdropping link between the full-duplex base station and the suspicious transmitter , the th legitimate communication user receives the pilot signal and uses the MMSE method to perform channel estimation, and obtains the channel response of the interference link between the suspicious transmitter and the th legitimate communication user , and feeds back the channel response of the interference link between the suspicious transmitter and the th legitimate communication user to the full-duplex base station;

[0020] 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 perform channel estimation through the pilot signal, and obtains the channel response vector between the full-duplex base station and the suspicious receiver .

[0021] The above beamforming method for simultaneously performing communication and interference eavesdropping, further, step four, the specific process is as follows:

[0022] Step 401. During the communication process between the full-duplex base station and the legitimate communication users, according to , obtain the received signal-to-interference-plus-noise ratio of the th legitimate communication user ; Among them, represents conjugate transpose, represents the square of the modulus, represents the transmit beamforming vector of the full-duplex base station for the th legitimate communication user, is a positive integer, and , , represents the transmit power of the suspicious transmitter, represents the th legitimate communication user

[0023] Step 402. According to , obtain the Channel capacity of a legal communication user ; among them, represents the set bandwidth;

[0024] Step 403: According to , obtain the received signal-to-interference-plus-noise ratio of the suspicious receiver ; among them, represents the variance of the additive white Gaussian noise at the suspicious receiver;

[0025] Step 404: According to , obtain the signal-to-noise ratio of the suspicious transmission signal monitored at the full-duplex base station ; among them, represents the variance of the additive white Gaussian noise at the receive array antenna of the full-duplex base station;

[0026] Step 405: Use a computer to construct a beamforming vector optimization model with the goal of maximizing the weighted sum channel capacity of all legal communication users and with the total transmit power of the full-duplex base station and successful monitoring as constraints:

[0027] .

[0028] For the above beamforming method for simultaneous communication and interference monitoring, further, step five, the specific process is as follows:

[0029] Step 501: Use a computer to establish the received IQ signal of the th legal communication user , and ; among them, represents the transmit signal of the full-duplex base station to the th legal communication user, represents the transmit signal of the full-duplex base station to the th legal communication user, and follow a complex Gaussian distribution with a mean of 0 and a variance of 1; represents the active interference signal, and is a complex Gaussian distribution with a mean of 0 and a variance of 1, represents the transmit signal of the suspicious transmitter, and is a complex Gaussian distribution with a mean of 0 and a variance of 1, represents the additive white Gaussian noise signal at the th legal communication user;

[0030] Step 502: Use a computer to set the receive equalizer of the th legal communication user to , then according to , the mean square error of the received signals of the th legitimate communication user is obtained ; where represents the mathematical expectation, represents conjugate; is a positive integer and takes the value of ;

[0031] Step 503, use a computer to make , and the received equalizer of the th legitimate communication user is obtained , and ;

[0032] Step 504, use a computer to substitute the received equalizer of the th legitimate communication user obtained in Step 503 into Step 502 again, and the mean square error of the received signals of the th legitimate communication user is obtained , and ;

[0033] Step 505, use a computer to set the conversion weight of the th legitimate communication user, and , then the beamforming vector optimization model is equivalently transformed to obtain the beamforming vector optimization model after the first transformation:

[0034] ;

[0035] Step 506, use a computer to substitute and into the constraint conditions to obtain , and simplify to obtain ; and use a computer to set the first intermediate variable , and , the second intermediate variable , and , then ;

[0036] Step 507, use a computer to perform a first-order Taylor expansion of the first intermediate variable at to obtain the first-order Taylor expansion formula of the first intermediate variable , and ; where represents -dimensional expansion point, represents the th expansion point, represents the expansion points;

[0037] Step 508: Use a computer to , convert it to , then the linear constraint condition ;

[0038] Step 509: Use a computer to substitute the linear constraint condition, and perform a transformation on the beamforming vector optimization model after the first transformation to obtain the beamforming vector optimization model after the second transformation:

[0039] .

[0040] For the above beamforming method for simultaneously performing communication and interference monitoring, further, the equivalent transformation in Step 505 is as follows:

[0041] Step A: Convert the beamforming vector optimization model in Step 405 into the first Lagrangian function , and

[0042] ; where, represents the first multiplier of the first Lagrangian function, represents the second multiplier of the first Lagrangian function, represents the third intermediate variable, and ;

[0043] Step B: Convert the beamforming vector optimization model after the first transformation in Step 505 into the second Lagrangian function , and

[0044] ; where, represents the first multiplier of the second Lagrangian function, represents the second multiplier of the second Lagrangian function;

[0045] Step C: Substitute into the first Lagrangian function, substitute and into the second Lagrangian function to obtain the transformed formula of the first Lagrangian function , and and the transformed formula of the second Lagrangian function , and

[0046] ;

[0047] Step D: Use a computer because is a constant term, then and , so the beamforming vector optimization model is equivalently transformed to obtain the beamforming vector optimization model after the first transformation.

[0048] The above beamforming method for simultaneously performing communication and interference monitoring, further, step six, the specific process is as follows:

[0049] Step 601: Randomly initialize and . Substitute the initialized and into step 502 and step 504 to obtain the transformed weight of the initialized th legitimate communication user, the received equalizer of the initialized th legitimate communication user, and the mean square error of the received signal of the initialized th legitimate communication user;

[0050] Step 602: Use a computer to set the optimization amount , and . Substitute the transformed weight of the initialized th legitimate communication user and the mean square error of the received signal of the initialized th legitimate communication user to obtain the initial optimization amount ;

[0051] And let be assigned to , be assigned to . After that, substitute the received equalizer of the initialized th legitimate communication user into step 502 and then substitute the transformed weight of the initialized th legitimate communication user into the beamforming vector optimization model after the second transformation, and use the CVX toolbox in the MATLAB software to solve it to obtain the transmit beamforming vector of the full-duplex base station for the th legitimate communication user in the first iteration and the transmit beamforming vector of the full-duplex base station for the active interference signal to the suspicious receiver in the first iteration;

[0052] And substitute and into step 502 and step 504 to obtain the transformed weight of the th legitimate communication user in the first iteration, the The receive equalizer of a legitimate communication user and the mean square error of the received signal of the th legitimate communication user in the first iteration ;

[0053] Step 603: Use a computer to substitute , into the optimization quantity to obtain the optimization quantity of the first iteration, and determine whether holds. If it holds, the transmit beamforming vector of the full-duplex base station for the th legitimate communication user in the first iteration is used as the optimized transmit beamforming vector of the full-duplex base station for the th legitimate communication user, and the transmit beamforming vector of the full-duplex base station for the active interference signal of the suspicious receiver in the first iteration is used as the optimized transmit beamforming vector of the full-duplex base station for the active interference signal of the suspicious receiver; if it does not hold, execute Step 604; where represents a set error value, and ranges from 0.001 to 0.01; where represents the absolute value;

[0054] Step 604: According to the method of Steps 601 to 603, substitute the transmit beamforming vector of the full-duplex base station for the th legitimate communication user in the first iteration and the transmit beamforming vector of the full-duplex base station for the active interference signal of the suspicious receiver in the first iteration for secondary iteration;

[0055] Step 605: Repeat Step 604 multiple times, substitute the transmit beamforming vector of the full-duplex base station for the th legitimate communication user in the mth iteration and the transmit beamforming vector of the full-duplex base station for the active interference signal of the suspicious receiver in the mth iteration for the (m + 1)th iteration until , then the transmit beamforming vector of the full-duplex base station for the th legitimate communication user in the (m + 1)th iteration is used as the optimized transmit beamforming vector of the full-duplex base station for the th legitimate communication user, and the transmit beamforming vector of the full-duplex base station for the active interference signal of the suspicious receiver in the (m + 1)th iteration The transmit beamforming vector of the optimized full-duplex base station for the active interference signal of the suspicious receiver where m is a positive integer greater than 1.

[0056] The present invention has the following advantages compared with the prior art:

[0057] 1. The present invention studies the joint beamforming of simultaneous communication and active interference-based eavesdropping in a multi-user multiple-input multiple-output system, so as to optimize the transmit beamforming vectors of the active interference signal and the communication signal.

[0058] 2. The present invention aims to maximize the weighted sum channel capacity of all legitimate communication users, and constructs a beamforming vector optimization model with the total transmit signal power of the full-duplex base station and successful eavesdropping as constraints. It can improve the data transmission rate and reliability under the condition of satisfying active interference-based eavesdropping, so as to meet the needs of multiple users.

[0059] 3. The beamforming vector optimization model of the present invention is converted from non-convex optimization to convex optimization to obtain the converted beamforming vector optimization model, so as to realize subsequent iterative solution.

[0060] 4. The present invention uses the block coordinate descent method to iteratively optimize the converted beamforming vector optimization model, and obtains the transmit beamforming vector of the optimized full-duplex base station for the th legitimate communication user and the transmit beamforming vector of the optimized full-duplex base station for the active interference signal of the suspicious receiver, reduces the computational complexity, and gradually approaches the global optimal solution.

[0061] In summary, the method steps of the present invention are simple and reasonably designed, solve the problem of joint beamforming optimization of simultaneous communication and active interference-based eavesdropping in a multi-user multiple-input multiple-output system, and aim to maximize the weighted sum communication capacity of all legitimate communication users, so as to optimize the transmit beamforming vectors of the active interference signal and the communication signal.

[0062] Next, through the drawings and embodiments, the technical solutions of the present invention will be further described in detail. Description of the Drawings

[0063] Figure 1 It is a flow chart of the method of the present invention. Detailed Embodiments

[0064] As Figure 1 shown, a beamforming method for simultaneous communication and interference eavesdropping includes the following steps:

[0065] Step 1. Construct a multi-user multiple-input multiple-output system:

[0066] The multi - user multiple - input multiple - output system includes a full - duplex base station, legitimate communication users, a suspicious transmitter, and a suspicious receiver; among them, a transmitting array antenna and a receiving array antenna are arranged on the full - duplex base station, both the transmitting array antenna and the receiving array antenna are ULA linear arrays, the number of the transmitting array antennas is , and the number of the receiving array antennas is , , and are all positive integers, and ; the legitimate communication users, the suspicious transmitter, and the suspicious receiver are all equipped with single - element antennas;

[0067] Step 2: Communication and interference monitoring of the full - duplex base station:

[0068] During the communication between the full - duplex base station and legitimate communication users, the full - duplex base station also sends an active interference signal to the suspicious receiver to enable the full - duplex base station to monitor the suspicious transmitter; among them, when the suspicious transmitter transmits a signal to the suspicious receiver, it will interfere with legitimate communication users;

[0069] Step 3: During the communication and interference monitoring of the full - duplex base station, obtain the channel state information through pilot signals; among them, the channel state information includes the channel response vector of the communication link between the full - duplex base station and the th legitimate communication user, the channel response vector of the monitoring link between the full - duplex base station and the suspicious transmitter, the channel response of the interference link between the suspicious transmitter and the th legitimate communication user, the channel response vector between the full - duplex base station and the suspicious receiver, and the channel response of the suspicious communication link between the suspicious transmitter and the suspicious receiver; is a positive integer, and ;

[0070] Step 4: Construct a beamforming vector optimization model during the communication and interference monitoring of the full - duplex base station:

[0071] Input the channel state information, and use a computer to construct a beamforming vector optimization model with the goal of maximizing the weighted sum channel capacity of all legitimate communication users and with the total transmission power of the full - duplex base station and successful monitoring as constraints:

[0072] ; among them, represents the weight of the th legitimate communication user, takes values from 0 to 1, and , represents the channel capacity of the th legitimate communication user, represents the transmit beamforming vector of the full-duplex base station for the th legitimate communication user, represents the transmit beamforming vector of the full-duplex base station for the active interference signal to the suspicious receiver, represents the constraint condition, represents the total transmit power of the full-duplex base station, represents the square of the 2-norm, represents the signal-to-noise ratio of the suspicious transmitted signal monitored at the full-duplex base station, represents the received signal-to-interference-plus-noise ratio of the suspicious receiver;

[0073] Step 5: Convert the beamforming vector optimization model from non-convex optimization to convex optimization to obtain the converted beamforming vector optimization model;

[0074] Step 6: Obtain the optimized transmit beamforming vector:

[0075] Use the block coordinate descent method to perform iterative alternating optimization on the converted beamforming vector optimization model to obtain the optimized transmit beamforming vector of the full-duplex base station for the th legitimate communication user and the optimized transmit beamforming vector of the full-duplex base station for the active interference signal to the suspicious receiver .

[0076] In this embodiment, step 3 is specifically as follows:

[0077] The full-duplex base station sends a pilot signal to the th legitimate communication user. The th legitimate communication user performs channel estimation using the MMSE method through the pilot signal to obtain the channel response vector of the communication link between the full-duplex base station and the th legitimate communication user, and feeds it back to the full-duplex base station;

[0078] When the suspicious transmitter sends a pilot signal to the suspicious receiver, the suspicious receiver performs channel estimation using the MMSE method through the pilot signal to obtain the channel response of the suspicious communication link between the suspicious transmitter and the suspicious receiver. During the process of feeding back the channel response of the suspicious communication link between the suspicious transmitter and the suspicious receiver to the suspicious transmitter, the full-duplex base station obtains the channel response Meanwhile, the full-duplex base station receives pilot signals. The full-duplex base station uses the MMSE method to perform channel estimation based on the pilot signals, and obtains the channel response vector of the listening link between the full-duplex base station and the suspicious transmitter. , the th legitimate communication user receives the pilot signals and uses the MMSE method to perform channel estimation, and obtains the channel response of the interference link between the suspicious transmitter and the th legitimate communication user , and feeds back the channel response of the interference link between the suspicious transmitter and the th legitimate communication user to the full-duplex base station;

[0079] The suspicious receiver sends pilot signals to the suspicious transmitter. The full-duplex base station receives the pilot signals and uses the MMSE method to perform channel estimation based on the pilot signals, and obtains the channel response vector between the full-duplex base station and the suspicious receiver. .

[0080] In this embodiment, step four is specifically as follows:

[0081] Step 401, during the communication between the full-duplex base station and the legitimate communication users, according to , obtain the received signal-to-interference-plus-noise ratio of the th legitimate communication user ; where represents conjugate transpose, represents the square of the modulus, represents the transmit beamforming vector of the full-duplex base station for the th legitimate communication user, is a positive integer, and , , represents the transmit power of the suspicious transmitter, represents the th legitimate communication user's additive white Gaussian noise variance;

[0082] Step 402, according to , obtain the channel capacity of the th legitimate communication user ; where represents the set bandwidth;

[0083] Step 403, according to , obtain the received signal-to-interference-plus-noise ratio of the suspicious receiver ; where represents the additive white Gaussian noise variance at the suspicious receiver;

[0084] Step 404, according to to obtain the signal-to-noise ratio of the suspicious transmitted signal monitored at the full-duplex base station ; where represents the variance of the additive white Gaussian noise at the receiving array antenna of the full-duplex base station;

[0085] Step 405: Use a computer to construct a beamforming vector optimization model with the goal of maximizing the weighted sum channel capacity of all legitimate communication users and subject to the total transmit signal power and successful monitoring of the full-duplex base station:

[0086] .

[0087] In this embodiment, step five is specifically as follows:

[0088] Step 501: Use a computer to establish the received IQ signal of the th legitimate communication user , and ; where represents the transmit signal of the full-duplex base station to the th legitimate communication user, represents the transmit signal of the full-duplex base station to the th legitimate communication user, and follow a complex Gaussian distribution with a mean of 0 and a variance of 1; represents the active interference signal, and is a complex Gaussian distribution with a mean of 0 and a variance of 1, represents the transmit signal of the suspicious transmitter, and is a complex Gaussian distribution with a mean of 0 and a variance of 1, represents the additive white Gaussian noise signal at the th legitimate communication user;

[0089] Step 502: Use a computer to set the receive equalizer of the th legitimate communication user to , then according to , obtain the mean square error of the received signal of the th legitimate communication user; where represents the mathematical expectation, represents conjugate; is a positive integer and takes the value of ;

[0090] Step 503: Use a computer to make , obtain the receive equalizer of the , and ;

[0091] Step 504: Use a computer to substitute the receive equalizer of the th legitimate communication user obtained in Step 503 into Step 502 again to obtain the mean square error of the received signals of the th legitimate communication user , and ;

[0092] Step 505: Use a computer to set the transformation weight of the th legitimate communication user , and , then perform an equivalent transformation on the beamforming vector optimization model to obtain the beamforming vector optimization model after the first transformation:

[0093] ;

[0094] Step 506: Use a computer to substitute and into the constraint conditions to obtain , and simplify to obtain ; and use a computer to set the first intermediate variable , and , the second intermediate variable , and , then ;

[0095] Step 507: Use a computer to perform a first-order Taylor expansion of the first intermediate variable at to obtain the first-order Taylor expansion formula of the first intermediate variable , and ; where represents the -dimensional expansion point, represents the th expansion point, represents the th expansion point;

[0096] Step 508: Use a computer to convert to , then obtain the linear constraint condition ;

[0097] Step 509: Use a computer to substitute the linear constraint condition into the beamforming vector optimization model after the first transformation for conversion to obtain the beamforming vector optimization model after the second transformation:

[0098] .

[0099] In this embodiment, the equivalent conversion in step 505 is as follows:

[0100] Step A: Convert the beamforming vector optimization model in step 405 into a first Lagrangian function , and

[0101] ; where represents the first multiplier of the first Lagrangian function, represents the second multiplier of the first Lagrangian function, represents the third intermediate variable, and ;

[0102] Step B: Convert the once-converted beamforming vector optimization model in step 505 into a second Lagrangian function , and

[0103] ; where represents the first multiplier of the second Lagrangian function, represents the second multiplier of the second Lagrangian function;

[0104] Step C: Substitute into the first Lagrangian function, substitute and into the second Lagrangian function to obtain the conversion formula of the first Lagrangian function , and and the conversion formula of the second Lagrangian function , and

[0105] ;

[0106] Step D: Since is a constant term, then and , so the beamforming vector optimization model is equivalently converted to obtain the once-converted beamforming vector optimization model.

[0107] In this embodiment, step six is as follows:

[0108] Step 601: Randomly initialize and , and substitute the initialized and into step 502 and step 504 to obtain the conversion weight of the initialized th legitimate communication user, and the receive equalizer of the initialized and the mean square error of the received signal of the th legitimate communication user in the initialization ;

[0109] Step 602: Use a computer to set the optimization quantity , and , and substitute the conversion weight of the th legitimate communication user in the initialization and the mean square error of the received signal of the th legitimate communication user in the initialization to obtain the initial optimization quantity ;

[0110] And let be assigned to , be assigned to After that, substitute the receive equalizer of the th legitimate communication user in the initialization into the result after step 502 and the conversion weight of the th legitimate communication user in the initialization and then substitute it into the beamforming vector optimization model after the second conversion. Use the CVX toolbox in MATLAB software to solve it, and obtain the transmit beamforming vector of the full-duplex base station for the th legitimate communication user in the first iteration and the transmit beamforming vector of the full-duplex base station for the active interference signal of the suspicious receiver in the first iteration;

[0111] And substitute and into step 502 and step 504 to obtain the conversion weight of the th legitimate communication user in the first iteration, the receive equalizer of the th legitimate communication user in the first iteration, and the mean square error of the received signal of the th legitimate communication user in the first iteration;

[0112] Step 603: Use a computer to substitute , into the optimization quantity to obtain the optimization quantity of the first iteration, and judge whether holds. If it holds, then the transmit beamforming vector of the full-duplex base station for the th legitimate communication user in the first iteration is used as the optimized transmit beamforming vector of the full-duplex base station for the The transmit beamforming vector of a legitimate communication user , the transmit beamforming vector of the full-duplex base station for the active interference signal of the suspicious receiver in the first iteration is used as the transmit beamforming vector of the optimized full-duplex base station for the active interference signal of the suspicious receiver ; if not, execute step 604; where represents a set error value, and the value range is 0.001 to 0.01; where represents the absolute value;

[0113] Step 604: According to the method of steps 601 to 603, the transmit beamforming vector of the full-duplex base station for the th legitimate communication user in the first iteration and the transmit beamforming vector of the full-duplex base station for the active interference signal of the suspicious receiver in the first iteration are substituted for secondary iteration;

[0114] Step 605: Repeat step 604 multiple times, and substitute the transmit beamforming vector of the full-duplex base station for the th legitimate communication user in the mth iteration and the transmit beamforming vector of the full-duplex base station for the active interference signal of the suspicious receiver in the mth iteration for the (m + 1)th iteration until , then the transmit beamforming vector of the full-duplex base station for the th legitimate communication user in the (m + 1)th iteration is used as the transmit beamforming vector of the optimized full-duplex base station for the th legitimate communication user , and the transmit beamforming vector of the full-duplex base station for the active interference signal of the suspicious receiver in the (m + 1)th iteration is used as the transmit beamforming vector of the optimized full-duplex base station for the active interference signal of the suspicious receiver ; where m is a positive integer greater than 1.

[0115] In this embodiment, the MMSE method is the minimum mean square error method.

[0116] In this embodiment, in specific implementation takes the value of 16, takes the value of 4, takes the value of 3.

[0117] In this embodiment, in specific implementation, the set bandwidth can be adjusted as needed and is not specifically limited.

[0118] In this embodiment, during specific implementation, the variance of the additive white Gaussian noise at the th legitimate communication user ranges from -110 dBm / MHz to -70 dBm / MHz; the variance of the additive white Gaussian noise at the suspicious receiver ranges from -110 dBm / MHz to -70 dBm / MHz; the variance of the additive white Gaussian noise at the duplex base station receiving array antenna ranges from -110 dBm / MHz to -70 dBm / MHz.

[0119] In this embodiment, is a complex vector, is a complex vector, is a complex vector, is a complex vector, is a complex vector, is a complex vector, and are both one-dimensional complex numbers.

[0120] In summary, the method steps of the present invention are simple and reasonably designed, solving the problem of joint beamforming optimization for simultaneous communication and active interference-based eavesdropping in a multi-user multiple-input multiple-output system, aiming to maximize the weighted sum communication capacity of all legitimate communication users, and realizing the optimization of the transmit beamforming vectors of the active interference signal and the communication signal.

[0121] The above are only the preferred embodiments of the present invention, and do not impose any limitations on the present invention. Any simple modifications, changes, and equivalent structural changes made to the above embodiments according to the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A beamforming method for simultaneously performing communication and interference monitoring, characterized in that, The method includes the following steps: Step 1, construct a multi-user multiple-input multiple-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, a transmitting array antenna and a receiving array antenna are provided on the full-duplex base station, both the transmitting array antenna and the receiving array antenna are ULA linear arrays, the number of the transmitting array antennas is , and the number of the receiving array antennas is , , and are all positive integers, and ; the legitimate communication users, the suspicious transmitter, and the suspicious receiver are all equipped with single antennas; Step 2, communication and interference monitoring of the full-duplex base station: In the process of communication between a full-duplex base station and a number of legitimate communication users, the full-duplex base station also sends an active interference signal to a suspicious receiver to enable the full-duplex base station to monitor a suspicious transmitter; among them, when the suspicious transmitter transmits a signal to the suspicious receiver, it will interfere with a number of legitimate communication users; Step 3: During the full-duplex base station communication and interference monitoring process, obtain channel state information through pilot signals; among them, the channel state information includes the channel response vectors of the communication links between the full-duplex base station and the th legitimate communication user , the channel response vectors of the monitoring links between the full-duplex base station and the suspicious transmitter , the channel responses of the interference links between the suspicious transmitter and the th legitimate communication user , the channel response vector between the full-duplex base station and the suspicious receiver , the channel response of the suspicious communication link between 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 of the full-duplex base station: Input the channel state information, and use a computer to construct a beamforming vector optimization model with the goal of maximizing the weighted sum channel capacity of all legitimate communication users and with the total transmit signal power of the full-duplex base station and successful monitoring as constraints: ; Among them, represents the weight of the th legitimate communication user, taking values from 0 to 1, and , represents the channel capacity of the th legitimate communication user, represents the transmit beamforming vector of the full-duplex base station for the th legitimate communication user, represents the transmit beamforming vector of the full-duplex base station for the active interference signal to the suspicious receiver, represents the constraint condition, represents the total transmit power of the full-duplex base station, represents the square of the 2-norm, represents the signal-to-noise ratio of the suspicious transmit signal monitored at the full-duplex base station, represents the received signal-to-interference-plus-noise ratio of the suspicious receiver; Step 5, convert the beamforming vector optimization model from non-convex optimization to convex optimization to obtain the converted beamforming vector optimization model; Step 6, obtain the optimized transmit beamforming vector: The block coordinate descent method is used to perform iterative alternating optimization on the transformed beamforming vector optimization model, and the optimized transmit beamforming vector of the full-duplex base station for the th legitimate communication user and the transmit beamforming vector of the optimized full-duplex base station for the active interference signal of the suspicious receiver are obtained; Solve the problem of joint beamforming optimization for simultaneous communication and active interference-based monitoring in a multi-user multiple-input multiple-output system, with the goal of maximizing the weighted sum communication capacity of all legitimate communication users, and realize the optimization of the transmit beamforming vectors of the active interference signal and the communication signal.

2. The beamforming method for simultaneous communication and interference monitoring according to claim 1, characterized in that: Step 3, the specific process is as follows: The full-duplex base station sends a pilot signal to the th legitimate communication user. The th legitimate communication user uses the MMSE method to perform channel estimation through the pilot signal, and obtains the channel response vector of the communication link between the full-duplex base station and the th legitimate communication user, and feeds 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 perform channel estimation through the pilot signal, and obtains the channel response of the suspicious communication link between the suspicious transmitter and the suspicious receiver. During the process of feeding back the channel response of the suspicious communication link between the suspicious transmitter and the suspicious receiver 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 eavesdropping. ; Meanwhile, the full-duplex base station receives the pilot signal, and the full-duplex base station uses the MMSE method for channel estimation through the pilot signal to obtain the channel response vector of the monitoring link between the full-duplex base station and the suspicious transmitter. , the th legitimate communication user receives the pilot signal and uses the MMSE method for channel estimation to obtain the channel response of the interference link between the suspicious transmitter and the th legitimate communication user. , and feeds back the channel response of the interference link between the suspicious transmitter and the th legitimate communication user 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 performs channel estimation using the MMSE method based on 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. During the communication between the full-duplex base station and legitimate communication users, according to , obtain the received signal-to-interference-plus-noise ratio (SINR) of the th legitimate communication user ; where denotes conjugate transpose, denotes the square of the modulus, denotes the transmit beamforming vector of the full-duplex base station for the th legitimate communication user, is a positive integer, and , , denotes the transmit power of the suspected transmitter, denotes the variance of the additive white Gaussian noise at the th legitimate communication user. Step 402, according to , obtain the -th channel capacity of legal communication users ; where represents the set bandwidth. Step 403, according to , obtain the received signal-to-interference-plus-noise ratio of the suspicious receiver ; among them, represents the variance of the additive white Gaussian noise at the suspicious receiver; Step 404, according to , obtain the signal-to-noise ratio of the suspicious transmission signal monitored at the duplex base station ; where represents the variance of the additive white Gaussian noise at the receiving array antenna of the full-duplex base station; Step 405, use a computer to construct a beamforming vector optimization model with the goal of maximizing the weighted sum channel capacity of all legitimate communication users and with the total transmit signal power of the full-duplex base station and successful 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 th legitimate communication user, and ; where represents the transmitted signal of the full-duplex base station to the th legitimate communication user, represents the transmitted signal of the full-duplex base station to the th legitimate communication user, and follow a complex Gaussian distribution with a mean of 0 and a variance of 1; represents the active interference signal, and is a complex Gaussian distribution with a mean of 0 and a variance of 1, represents the transmitted signal of the suspicious transmitter, and is a complex Gaussian distribution with a mean of 0 and a variance of 1, represents the additive white Gaussian noise signal at the th legitimate communication user; Step 502. Use a computer to set the receiving equalizer of the th legal communication user as . Then, according to , obtain the mean square error of the received signal of the th legal communication user ; where represents the mathematical expectation, represents conjugate; is a positive integer and its value is ; Step 503: Use a computer to make to obtain the th receiving equalizer of the th legal communication user, and ; Step 504: Use a computer to substitute the receive equalizer of the th legitimate communication user obtained in step 503 into step 502 again to obtain the mean square error of the received signal of the th legitimate communication user , and ; Step 505. Use a computer to set the conversion weight of the th legal communication user , and , then perform an equivalent conversion on the beamforming vector optimization model to obtain the beamforming vector optimization model after the first conversion: ； Step 506. Use a computer to substitute and into the constraint conditions to obtain , and simplify to obtain ; and use the calculation to set the first intermediate variable , and , the second intermediate variable , and , then ; Step 507: Use a computer to perform a first-order Taylor expansion on the first intermediate variable at to obtain the first-order Taylor expansion of the first intermediate variable , and ; where represents dimensional expansion points, represents the th expansion point, represents the th expansion point; Step 508. Use a computer to , convert it to , then obtain the linear constraint condition ; Step 509, use a computer to substitute the linear constraint conditions and convert the beamforming vector optimization model after the first conversion to obtain the beamforming vector optimization model after the second conversion: 。 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 a first Lagrangian function , and ; wherein, 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: Convert the once-converted beamforming vector optimization model in Step 505 into a second Lagrangian function , and ; wherein, represents the first multiplier of the second Lagrangian function, represents the second multiplier of the second Lagrangian function; Step C: Substitute into the first Lagrangian function, and substitute and into the second Lagrangian function to obtain the transformed expression of the first Lagrangian function, and and the transformed expression of the second Lagrangian function, and ； Step D: Using a computer because is a constant term, then and , so the beamforming vector optimization model is equivalently transformed to obtain the beamforming vector optimization model after the first transformation.

6. The 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. Initialize and randomly, and substitute the initialized and into Step 502 and Step 504 to obtain the conversion weights of the initialized th legal communication user, the receive equalizer of the initialized th legal communication user, and the mean square error of the received signal of the initialized th legal communication user; Step 602: Use a computer to set the optimization amount , and , substitute the conversion weight of the th initialized legal communication user and the mean square error of the received signal of the th initialized legal communication user to obtain the initial optimization amount ; And let be assigned to , be assigned to After that, the receive equalizer of the th initialized legitimate communication user is substituted into the result after step 502 and the conversion weight of the th initialized legitimate communication user and then substituted into the beamforming vector optimization model after the second conversion. The CVX toolbox in MATLAB software is used for solution to obtain the transmit beamforming vector of the full-duplex base station for the th legitimate communication user in the first iteration and the transmit beamforming vector of the full-duplex base station for the active interference signal of the suspicious receiver in the first iteration ; And substitute and into Step 502 and Step 504 to obtain the conversion weight of the th legitimate communication user in the first iteration , the receiving equalizer of the th legitimate communication user in the first iteration and the mean square error of the received signal of the th legitimate communication user in the first iteration ; Step 603. Use a computer to substitute , into the optimization amount to obtain the optimization amount of the first iteration, and determine whether holds. If it holds, the transmit beamforming vector of the full-duplex base station for the -th legitimate communication user in the first iteration is used as the optimized transmit beamforming vector of the full-duplex base station for the -th legitimate communication user, and the transmit beamforming vector of the full-duplex base station for the active interference signal of the suspicious receiver in the first iteration is used as the optimized transmit beamforming vector of the full-duplex base station for the active interference signal of the suspicious receiver; if it does not hold, execute Step 604; where represents the set error value, and ranges from 0.001 to 0.01; where represents the absolute value. Step 604: According to the method of Steps 601 to 603, substitute the transmit beamforming vector of the full-duplex base station in the first iteration for the th legitimate communication user and the transmit beamforming vector of the active interference signal of the full-duplex base station in the first iteration for the suspicious receiver into the formula for secondary iteration; ​ Step 605. Repeat step 604 multiple times. Substitute the transmit beamforming vector of the full-duplex base station for the th legitimate communication user in the m-th iteration and the transmit beamforming vector of the active interference signal of the full-duplex base station for the suspected receiver in the m-th iteration into the formula for the (m + 1)-th iteration until . Then, the transmit beamforming vector of the full-duplex base station for the th legitimate communication user in the (m + 1)-th iteration is used as the optimized transmit beamforming vector of the full-duplex base station for the th legitimate communication user, and the transmit beamforming vector of the full-duplex base station for the active interference signal of the suspected receiver in the (m + 1)-th iteration is used as the optimized transmit beamforming vector of the full-duplex base station for the active interference signal of the suspected receiver ; where m is a positive integer greater than 1.​

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