A data security transmission method for health monitoring system based on rate splitting

Abstract

The invention discloses a data security transmission method for a health monitoring system based on rate splitting, which relates to the technical field of data security transmission. The method comprises: utilizing uplink rate splitting multiple access technology and serial interference elimination technology to realize flexible management of co-channel interference and improve data transmission rate; utilizing a base station as a jammer to emit artificial noise to suppress the decoding ability of eavesdropping users in the monitoring system and avoid information leakage; constructing an accurate proxy function for a non-convex confidential transmission rate based on the idea of ​​substitution optimization, and converting the design problem of a non-convex precoding matrix and an artificial noise covariance matrix into an iterative optimization problem; proposing an alternating optimization algorithm to maximize the confidential transmission rate of legitimate users in the system and enhance the secure transmission performance of the health monitoring system.

Description

Technical Field

[0001] The present invention relates to the technical field of data security transmission, and in particular to a data security transmission method for a health monitoring system based on rate splitting. Background Art

[0002] With the increase in the number of patients with chronic diseases, severe co-channel interference is one of the main bottlenecks faced by the implementation of health monitoring systems. In order to reduce co-channel interference, the patient's monitoring equipment must comply with specific multiple access technologies in order to reasonably allocate the time and method of data upload. The current multiple access technologies mainly include orthogonal multiple access (OMA), space division multiple access (SDMA) and non-orthogonal multiple access (NOMA) technologies. Specifically, OMA only serves a single user in the same time-frequency resource block to avoid co-channel interference, but the limited spectrum resources make this technology unable to support large-scale patients to access the health monitoring system at the same time. SDMA regards all non-target signals as interference and uses the diversity gain of multiple antennas to suppress co-channel interference. However, when the number of users served by the monitoring system exceeds the spatial freedom that the transmitting antenna can support, the user's transmission rate will reach saturation. Even if the user can increase the transmission power without limit, the transmission rate tends to a fixed value, and the limited transmission rate cannot support high-speed business needs. Compared with OMA and SDMA, NOMA weakens co-channel interference by using serial interference cancellation technology to improve the transmission performance of the system. However, this technology has extremely strict requirements on the user's channel conditions and is only applicable to scenarios where the channels between users are fully aligned and the channel strength difference is large. Therefore, none of the above three multiple access technologies can simultaneously meet the high transmission rate and high access density requirements of the health monitoring system.

[0003] As a new type of non-orthogonal multiple access technology, uplink rate splitting (RS) can meet the high transmission rate requirements of large-scale monitoring equipment by reasonably setting the information splitting ratio and decoding order. By adjusting the information splitting ratio, RS summarizes SDMA and NOMA technologies as special cases. Specifically, the uplink RS splits the data collected by each monitoring device into two parts and encodes them into two independent information streams. Then, the base station uses serial interference cancellation technology to decode the received information streams in turn and reconstruct the original collected data. Through data splitting and serial interference cancellation technology, the uplink RS can flexibly manage co-channel interference, enabling the monitoring system to achieve high transmission rates in large-scale device access scenarios.

[0004] In addition, since the patient's disease information and physiological data are extremely private, it is very important to ensure the security of data transmission in the health monitoring system. However, the broadcast characteristics of the wireless channel make the transmitted medical data extremely easy to be eavesdropped by illegal users. Physical layer security uses the randomness of the wireless channel to design a transmission scheme, which can ensure the secure transmission of information without the need for any key. Compared with traditional key encryption methods, physical layer security technology significantly reduces signaling overhead, so it has become a research hotspot for ensuring the secure transmission of information.

[0005] In 2021, Xuewan Zhang et al. proposed a health monitoring system based on NOMA technology in "Sparse Vector Coding-Based Multi-Carrier NOMA for In-Home Health Networks" published in IEEE Journal on Selected Areas in Communications, and analyzed the closed-form solution of the bit error rate and achievable rate of the monitoring system. However, this model is only applicable to scenarios where the channels are fully aligned and cannot guarantee the security of information transmission.

[0006] In 2021, Junaid Ahmed et al. published "On the Physical Layer Security of Federated Learningbased IoMT Networks" in IEEE Journal Biomedical and Health Informatics, proposing a federated learning-based resource allocation algorithm for health monitoring systems to enhance the security of medical data. However, the multiple access technology used cannot support a large number of patients accessing the monitoring system at the same time.

[0007] In 2022, Wen Wang et al. published "Robust Design for STAR-RIS Secured Internet of Medical Things" at the IEEE ICC 2022 Workshop on E-health Security for Further 6G. They used smart reflective surface technology to enhance the security of information transmission and proposed a rotation optimization algorithm based on the continuous convex approximation method to design precoding vectors to improve the energy efficiency of health monitoring systems. However, the multiple access technology used in the paper cannot meet the transmission rate requirements of medical data in large connection scenarios.

[0008] In 2023, Hamed Bastami et al. published "Large-scale rate-splitting multiple access in uplink UAV networks: Effective secrecy throughput maximization under limited feedback channel" in IEEE Transactions on Vehicular Technology. They designed a physical layer security transmission method based on uplink RS to enhance the security of drone transmission data. However, the scheme did not design artificial noise to suppress the decoding ability of eavesdroppers. Therefore, when the legitimate users and eavesdropping users in the system have very similar channel vectors or there are many eavesdropping users in the system, the system will not be able to completely avoid information leakage.

[0009] None of the above four health monitoring systems can guarantee the secure and high-speed transmission of medical data when a large number of monitoring devices are connected to the system. Summary of the invention

[0010] The technical problem to be solved by the present invention is to provide a data security transmission method for a health monitoring system based on rate splitting in response to the shortcomings of the background technology, utilize a physical layer security transmission scheme based on uplink RS and base station collaboration to ensure the privacy of medical data, and at the same time provide a rotation optimization algorithm to achieve precise allocation of wireless resources to improve the security transmission performance of the health monitoring system.

[0011] The present invention adopts the following technical solutions to solve the above technical problems:

[0012] A data security transmission method for a health monitoring system based on rate splitting includes a health monitoring system, wherein the health monitoring system includes K ≥ 1 patients and L eavesdropping users, and the patient's monitoring device is used to securely upload the collected medical data to a base station for further analysis and processing; the patient and the eavesdropping user set are respectively marked as and In the health monitoring system, the base station and the patient's monitoring equipment are respectively equipped with T b >1 and T u >1 transmission antenna; the base station and the eavesdropping user are equipped with A b >1 and A e >1 receiving antenna; the channel matrices from the kth patient to the base station and the kth eavesdropping user are marked as and The channel matrix from the base station to the lth eavesdropping user is denoted by Since the base station and the patient's monitoring equipment are equipped with multiple antennas, the kth patient can transmit an N-dimensional data vector Where N≤min(A b , T u ) To improve the data transmission rate, the monitoring system uses rate division multiple access technology to upload data. The encoding process of rate division multiple access technology is as follows:

[0013] Step 1: transform each data in the N-dimensional data vector of the kth patient Split into two parts, namely and where j∈{1,...,N};

[0014] Step 2: The monitoring device will Encoded as in

[0015] Step 3, N-dimensional data vector In passing After linear precoding, the monitoring device superimposes the two information streams and uploads them to the base station.

[0016] According to the rate splitting encoding process, the signal transmitted by the kth patient can be expressed as x k =W k，1 x k，1 +W k， 2x k，2 To ensure the security of information transmission, the artificial noise x emitted by the base station an Depend on Linear precoding is performed, so the signal received by the receiver is:

[0017]

[0018] in, is additive Gaussian white noise; in equation (1), when When A m =A e ; After receiving the signal, the receiver uses serial interference cancellation technology to decode the data streams in sequence; the signal-to-interference-noise ratio of the nth data stream transmitted by the kth patient decoded by the receiver m is:

[0019]

[0020] in, is the interference power, expressed as:

[0021]

[0022] In equation (3), when m = b, δ m=0, otherwise δ m =1;π i,j >π k，n Represents in the information flow x k，n The information flow decoded afterwards; according to Shannon's formula, the decoding rate is:

[0023]

[0024] Based on this, according to the calculation method of the confidentiality rate of physical layer security, the confidentiality rate can be obtained: The expression is

[0025]

[0026] The symbol [x] + =max(x,0);

[0027] In order to improve the security transmission performance of medical data, the precoding and artificial noise covariance matrix are jointly designed to maximize the confidentiality rate of the health monitoring system; the optimization problem (6) modeled is as follows:

[0028]

[0029] Among them, P u and P th are the maximum transmission power thresholds of the patient monitoring device and the base station, respectively.

[0030] As a further preferred solution of the data security transmission method for a health monitoring system based on rate splitting of the present invention, the three constraints of the modeled optimization problem are respectively the transmission power constraint of the patient monitoring device, the transmission power constraint of the base station and the constraint of ensuring the transmission security of all data streams.

[0031] As a further preferred solution of the data security transmission method of a health monitoring system based on rate splitting of the present invention, a rotation optimization algorithm is used to solve the optimal precoding and artificial noise covariance matrix to maximize the confidentiality rate. The solving steps of the rotation optimization algorithm are as follows:

[0032] 1) To eliminate the product form between covariance matrices, introduce the semi-positive definite matrix and Based on the introduced semi-positive definite matrix, the confidentiality rate can be transformed into

[0033]

[0034] in

[0035]

[0036] 2) To overcome non-smoothness, introduce auxiliary variables Eliminating the maximum and minimum signs, formula (6) can be reconstructed into problem (10):

[0037]

[0038] in, Contains the variable {Q k，n , Q an}; Since the introduction of semi-positive definite matrix needs to satisfy Rank(Q x )≤Rank(W x ), so we add the matrix rank restrictions (10.5) and (10.6), and the problem (10) and The details are as follows

[0039]

[0040] 3) The following Lemma 1 is proposed to construct an accurate proxy function and transform problem (10) into a rotation optimization problem;

[0041] Among them, Lemma 1: By introducing the function f(T) = -Tr(TX) + logdet(T) + N, where and Available

[0042]

[0043] And the optimal solution on the right side of the equation is T * =X -1 ;

[0044] Proof: Since f(T) is a concave function with respect to T, by calculating the first-order partial derivative It can be solved to maximize the function f(T) * , that is, T * =X -1 ; T * Substituting into the function f(T) we get f(T * )=-log|X|, so Lemma 1 holds;

[0045] Apply Lemma 1 to equation (11) The first item and set The substitution function is:

[0046]

[0047] in, is an auxiliary variable introduced; according to Lemma 1, The substitution function of the second term can be expressed as:

[0048]

[0049] Combining equations (11), (13) and (14), problem (10) can be restructured into problem (15):

[0050]

[0051] in The details are as follows

[0052]

[0053] 4) Divide the variables in problem (15) into and Ignoring restrictions (10.5) and (10.6), a rotation optimization algorithm is proposed to iteratively optimize the covariance matrix and auxiliary variables in, and Solve using the convex optimization toolbox and closed-form solution respectively; auxiliary variables The closed-form solution is

[0054]

[0055] 5) If the obtained If the matrix rank constraints (10.5) and (10.6) can be satisfied, the eigenvalue decomposition method is used to obtain the optimal precoding and artificial noise covariance matrix, otherwise the randomization and scaling method is used to obtain the suboptimal precoding and artificial noise covariance matrix.

[0056] As a further preferred solution of the data security transmission method of the health monitoring system based on rate splitting of the present invention, the rotation optimization algorithm is as follows:

[0057] a) Initialization: Set the initial and the maximum tolerable error ξ;

[0058] b)While non-convergent execution;

[0059] c) Calculate the optimal

[0060] d) Fixed Use the convex optimization toolbox to solve problem (15) and output the optimal

[0061] e) Update i=i+1;

[0062] f) Endwhile;

[0063] g) Output Calculate W k，n and Wan ;

[0064] h) Suboptimal solution for calculating the confidentiality rate

[0065] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0066] The present invention provides a data security transmission method for a health monitoring system based on rate splitting, which utilizes a physical layer security transmission scheme based on uplink RS and base station collaboration to ensure the privacy of medical data; at the same time, this patent provides a rotation optimization algorithm to achieve accurate allocation of wireless resources to improve the security transmission performance of the health monitoring system. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a schematic diagram of data transmission and eavesdropping in the health monitoring system;

[0068] Figure 2 It is a schematic diagram of the changing trend of the confidentiality rate performance of the scheme proposed by the present invention and four benchmark transmission schemes as the transmission power of the patient monitoring device increases;

[0069] Figure 3 It is a schematic diagram of the relationship between the confidentiality rate and the base station transmission power threshold;

[0070] Figure 4 This is a schematic diagram of the relationship between the confidentiality rate and the number of patients served by the same time-frequency resource block. DETAILED DESCRIPTION

[0071] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings:

[0072] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0073] like Figure 1 As shown, a schematic diagram of data transmission and eavesdropping of a health monitoring system is shown. The health monitoring system designed in this patent includes K≥1 patients and L eavesdropping users, where the patient aims to securely upload the collected medical data to a base station for further analysis and processing.

[0074] To ensure the privacy of medical data, this patent designs a cooperative transmission mechanism, where the base station acts as a jammer to emit artificial noise to weaken the decoding ability of the eavesdropping user. The sets of patients and eavesdropping users are marked as and In the designed health monitoring system, the base station and the patient's monitoring equipment are equipped with T b >1 and T u >1 transmission antenna; the base station and the eavesdropping user are equipped with A b >1 and A e > 1 receiving antenna. The channel matrices from the kth patient to the base station and the lth eavesdropping user are marked as and Similarly, the channel matrix from the base station to the lth eavesdropping user is denoted as

[0075] Since the base station and the patient's monitoring equipment are equipped with multiple antennas, the kth patient can transmit an N-dimensional data vector Where N≤min(A b , T u ). In order to improve the transmission rate, this patent designs a data upload scheme based on RS, which is as follows: First, each data in the N-dimensional data vector of the kth patient Split into two parts, namely and where j∈{1,...,N}. Secondly, the monitoring device will Encoded as in Then, the N-dimensional data vector In passing After linear precoding, the two information streams are superimposed and uploaded to the base station. Therefore, the signal transmitted by the kth patient can be expressed as x k =W k，1 x k，1 +W k，2 x k，2 Similarly, the artificial noise x emitted by the base station an Depend on Linear precoding is performed. Therefore, the signal received by the receiver (base station and eavesdropping user) is:

[0076]

[0077] in is additive Gaussian white noise. In equation (1), when When A m =A eAfter receiving the signal, the receiver uses serial interference cancellation technology to decode the data stream in sequence. In the designed transmission scheme, the artificial noise is emitted by the base station, so the base station can use SIC to decode and remove the artificial noise, but the eavesdropping user cannot remove the artificial noise. Therefore, the receiver m Decoding k The first patient transmission n The signal-to-interference-noise ratio of a data stream is:

[0078]

[0079] in is the interference power, which can be expressed as:

[0080]

[0081] In equation (3), since the base station will not be interfered by artificial noise, when m = b, δ m =0, otherwise δ m =1. i，j >π k，n Represents in the information flow x k，n The information flow decoded later. According to Shannon's formula, the decoding rate is:

[0082]

[0083] Based on this, according to the calculation method of the confidentiality rate of physical layer security, the confidentiality rate can be obtained: The expression is

[0084]

[0085] The symbol [x] + =max(x,0).

[0086] In order to improve the security transmission performance of medical data, this patent jointly designs the precoding and artificial noise covariance matrix to maximize the confidentiality rate of the health monitoring system. The optimization problem (6) modeled is as follows:

[0087]

[0088] Where P u and P th are the maximum transmission power thresholds of the patient monitoring device and the base station, respectively. The three constraints of problem (6) are the transmission power constraint of the patient monitoring device, the transmission power constraint of the base station, and the constraint of ensuring the transmission security of all data flows.

[0089] The objective function of problem (6) is the difference between two logarithmic functions, and each logarithmic function is a complex fractional function. Therefore, problem (6) is non-convex and non-smooth, and it is difficult to solve directly. Based on the idea of ​​substitution optimization, this patent designs a rotation optimization algorithm to solve the optimal precoding and artificial noise covariance matrix to maximize the confidentiality rate. The solution steps of the rotation optimization algorithm are as follows:

[0090] First, in order to eliminate the product form between covariance matrices, the semi-positive definite matrix is ​​introduced and Based on the introduced semi-positive definite matrix, the confidentiality rate can be transformed into

[0091]

[0092] in

[0093]

[0094] Secondly, to overcome the non-smoothness, an auxiliary variable is introduced By removing the maximum and minimum symbols, problem (6) can be restructured into problem (10):

[0095]

[0096] in Contains the variable {Q k，n , Q an}. (10.5) and (10.6) increase the restriction of matrix rank, because after the introduction of semi-positive definite matrix, Rank(Q x )≤Rank(W x ). In question (10) and The details are as follows

[0097]

[0098] Then, the following Lemma 1 is proposed to construct an accurate proxy function and transform problem (10) into a rotation optimization problem.

[0099] Lemma 1: By introducing the function f(T) = -Tr(TX) + logdet(T) + N, where and Available

[0100]

[0101] And the optimal solution on the right side of the equation is T * =X -1 .

[0102] Proof: Since f(T) is a concave function with respect to T, by calculating the first-order partial derivative We can find the T that maximizes the function f(T) * , that is, T * =X -1 . * Substituting into the function f(T) we get f(T * )=-log|X|, so Lemma 1 holds.

[0103] Apply Lemma 1 to equation (11) The first item and set The substitution function is:

[0104]

[0105] in is an auxiliary variable introduced. Similarly, The substitution function for the second term can be expressed as:

[0106]

[0107] Combining equations (11), (13) and (14), problem (10) can be restructured into problem (15):

[0108]

[0109] in for

[0110]

[0111] Then, the variables in problem (15) are divided into and Ignoring restrictions (10.5) and (10.6), a rotation optimization algorithm is proposed to iteratively optimize the covariance matrix and auxiliary variables in and The convex optimization toolbox and closed-form solution are used to solve the problem. The closed-form solution is

[0112]

[0113] Finally, if the If the matrix rank constraints (10.5) and (10.6) can be satisfied, the eigenvalue decomposition method is used to obtain the optimal precoding and artificial noise covariance matrix, otherwise the randomization and scaling method is used to obtain the suboptimal precoding and artificial noise covariance matrix.

[0114] Algorithm 1 summarizes the specific process of the rotation optimization algorithm proposed in this patent.

[0115] a) Initialization: Set the initial and the maximum tolerable error ξ;

[0116] b)While non-convergent execution;

[0117] c) Calculate the optimal

[0118] d) Fixed Use the convex optimization toolbox to solve problem (15) and output the optimal

[0119] e) Update i=i+1;

[0120] f) Endwhile;

[0121] g) Output Calculate W k，n and W an ;

[0122] h) Suboptimal solution for calculating the confidentiality rate

[0123] The specific embodiments are as follows:

[0124] The simulation test parameters of the health monitoring system are as follows: The simulation test area is a circle with a radius of 100 meters. The base station is placed at the center of the circle. Two patients and two eavesdropping users are independently and randomly distributed in the circle. The base station and each patient's monitoring device are equipped with two transmitting antennas, and the base station and the eavesdropping user are equipped with two receiving antennas. The maximum transmission power of the patient and the base station is 15dBm and 20dBm respectively. The large-scale path loss of the Rayleigh channel is 128.1+37.6log 10 (d), where d represents the distance between the transmitter and the receiver in kilometers. The background noise is -102dBm.

[0125] Figure 2 The confidentiality rate performance of the scheme proposed in this patent and four benchmark transmission schemes was tested as the transmission power of the patient monitoring device increased. The results show that the transmission scheme proposed in this patent has three advantages over the four benchmark schemes.

[0126] (1) Compared with the transmission scheme without base station assistance, the base station transmitting artificial noise scheme proposed in this patent achieves more than twice the performance gain, which shows that artificial noise can effectively suppress the decoding ability of eavesdropping users;

[0127] (2) Compared with the space division multiple access and non-orthogonal multiple access schemes, the confidentiality rate of the uplink rate splitting scheme proposed in this patent is increased by 1.4bps / Hz and 0.7bps / Hz respectively;

[0128] (3) The rotation optimization algorithm proposed in this patent is superior to the traditional continuous convex approximation algorithm.

[0129] Figure 3 The relationship between the confidentiality rate and the base station transmission power threshold is simulated. The simulation results show that increasing the base station transmission power is beneficial to improving the confidentiality rate. However, when the base station transmission power exceeds 25dBm, the change of the confidentiality rate will tend to be flat, mainly because the artificial noise power emitted by the base station is sufficient to suppress the eavesdropping of eavesdropping users.

[0130] Figure 4 The relationship between the confidentiality rate and the number of patients served by the same time-frequency resource block was simulated. The simulation results show that the transmission scheme proposed in this patent is better than the four benchmark schemes. In addition, as the number of patients increases, the confidentiality rate of the algorithm proposed in this patent decreases slowly. It is worth noting that when the number of patients exceeds 4, the transmission scheme without base station assistance cannot guarantee the secure transmission of medical information, which once again verifies the superiority of the transmission scheme proposed in this patent.

[0131] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless defined as herein.

[0132] The above embodiments are only for explaining the technical idea of ​​the present invention, and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution according to the technical idea proposed by the present invention shall fall within the protection scope of the present invention. The above embodiments of the present invention are described in detail, but the present invention is not limited to the above embodiments. Various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention.

Claims

1. A data security transmission method for a health monitoring system based on rate splitting, characterized in that: It includes a health monitoring system, which includes K≥1 patients and L eavesdropping users. The patient's monitoring device securely uploads the collected medical data to the base station for further analysis and processing; the sets of patients and eavesdropping users are marked as and In the health monitoring system, the base station and the patient's monitoring equipment are respectively equipped with T b >1 and T u >1 transmission antenna; the base station and the eavesdropping user are equipped with A b >1 and A e >1 receiving antenna; the channel matrices from the kth patient to the base station and the lth eavesdropping user are marked as and The channel matrix from the base station to the lth eavesdropping user is denoted by Since the base station and the patient's monitoring equipment are equipped with multiple antennas, the kth patient can transmit an N-dimensional data vector Where N≤min(A b , T u ) To improve the data transmission rate, the monitoring system uses rate division multiple access technology to upload data. The encoding process of rate division multiple access technology is as follows: Step 1: transform each data in the N-dimensional data vector of the kth patient Split into two parts, namely and where j∈{1,...,N}; Step 2: The monitoring device will Encoded as in Step 3, N-dimensional data vector In passing After linear precoding, the monitoring device superimposes the two information streams and uploads them to the base station; According to the rate splitting encoding process, the signal transmitted by the kth patient can be expressed as x k =W k，1 x k，1 +W k，2 x k，2 ; To ensure the security of information transmission, the artificial noise x emitted by the base station an Depend on Linear precoding is performed; therefore, the signal received by the receiver is: in, is additive Gaussian white noise; in equation (1), when When A m =A e ; After receiving the signal, the receiver uses serial interference cancellation technology to decode the data streams in sequence; the signal-to-interference-noise ratio of the nth data stream transmitted by the kth patient decoded by the receiver m is: in, is the interference power, expressed as: In equation (3), when m = b, δ m =0, otherwise δ m =1;π i，j >π k，n Represents in the information flow x k，n The information flow decoded afterwards; according to Shannon's formula, the decoding rate is: Based on this, according to the calculation method of the confidentiality rate of physical layer security, the confidentiality rate can be obtained: The expression is The symbol [x] + =max(x,0); In order to improve the security transmission performance of medical data, the precoding and artificial noise covariance matrix are jointly designed to maximize the confidentiality rate of the health monitoring system; the optimization problem (6) modeled is as follows: Among them, P u and P th are the maximum transmission power thresholds of the patient monitoring device and the base station, respectively.

2. The data security transmission method of a health monitoring system based on rate splitting according to claim 1 is characterized by: The three constraints of the modeled optimization problem are the transmission power constraint of the patient monitoring device, the transmission power constraint of the base station, and the transmission security constraint of all data streams.

3. The data security transmission method of a health monitoring system based on rate splitting according to claim 2 is characterized by: The rotation optimization algorithm is used to solve the optimal precoding and artificial noise covariance matrix to maximize the confidentiality rate. The solution steps of the rotation optimization algorithm are as follows: 1) To eliminate the product form between covariance matrices, introduce the semi-positive definite matrix and Based on the introduced semi-positive definite matrix, the confidentiality rate can be transformed into in 2) To overcome non-smoothness, introduce auxiliary variables By removing the maximum and minimum symbols, problem (6) can be restructured into problem (10) as in, Contains the variable {Q k，n , Q an }; Since the introduction of semi-positive definite matrix needs to satisfy Rank(Q x )≤Rank(W x ), so we add the matrix rank restrictions (10.5) and (10.6), and the problem (10) and The details are as follows 3) The following Lemma 1 is proposed to construct an accurate proxy function and transform problem (10) into a rotation optimization problem; Among them, Lemma 1: By introducing the function f(T) = -Tr(TX) + logdet(T) + N, where and Available And the optimal solution on the right side of the equation is T * =X -1 ; Proof: Since f(T) is a concave function with respect to T, by calculating the first-order partial derivative It can be solved to maximize the function f(T) * , that is, T * =X -1 ; T * Substituting into the function f(T) we get f(T * )=-log|X|, so Lemma 1 holds; Apply Lemma 1 to equation (11) The first item and set The substitution function is: in, is an auxiliary variable introduced; using Lemma 1, The substitution function of the second term can be expressed as: Combining equations (11), (13) and (14), problem (10) can be restructured into problem (15) as: in As shown below 4) Divide the variables in problem (15) into and Ignoring restrictions (10.5) and (10.6), a rotation optimization algorithm is proposed to iteratively optimize the covariance matrix and auxiliary variables in, and Solve using the convex optimization toolbox and closed-form solution respectively; auxiliary variables The closed-form solution is 5) If the obtained If the matrix rank constraints (10.5) and (10.6) can be satisfied, the eigenvalue decomposition method is used to obtain the optimal precoding and artificial noise covariance matrix, otherwise the randomization and scaling method is used to obtain the suboptimal precoding and artificial noise covariance matrix.

4. The data security transmission method of a health monitoring system based on rate splitting according to claim 3 is characterized by: The rotation optimization algorithm is as follows: a) Initialization: Set the initial i = 1 and maximum tolerable error ξ; b)While non-convergent execution; c) Calculate the optimal d) Fixed Use the convex optimization toolbox to solve problem (15) and output the optimal e) Update i=i+1; f) End while; g) Output Calculate W k，n and W an ; h) Suboptimal solution for calculating the confidentiality rate

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