A method for physical layer security communication transmission of uncellular massive MIMO

Abstract

The application belongs to the technical field of wireless communication, and discloses a kind of de-cell large-scale MIMO physical layer security communication transmission methods, this method constructs the de-cell large-scale MIMO system model by M access point AP, K users and a listener;According to the de-cell large-scale MIMO system model, the physical layer security model between access point AP, user and listener is constructed;According to the de-cell large-scale MIMO system model, the physical layer security model, the maximum target of communication security rate and power distribution optimization condition are constructed;Based on the maximum target of communication security rate and power distribution optimization condition, inequality and path tracking algorithm are used to solve, and the de-cell large-scale MIMO physical layer security communication method is constructed.The application provides stronger received signal strength, and has the characteristics of high security and large-scale connection.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a method for secure communication transmission at the physical layer of decellularized massive MIMO. Background Technology

[0002] Due to the broadcast nature of wireless communication, information transmitted over wireless media is highly susceptible to eavesdropping. Traditional information security methods are generally based on network layer key technology, requiring significant computer processing power to decipher. As computer processing speeds increase, the likelihood of such breaches also rises considerably. Physical layer security technology ensures the security of communication information through the physical characteristics of the wireless channel, enabling data transmission in the wireless communication system to be processed at the lower level, thus enhancing data confidentiality during transmission. In contrast, in decellularized massive MIMO architectures, all access points (APs) can communicate with each other through a central processing unit to perform beamforming transmission tasks, thereby obtaining an effective channel to ensure information security. Wireless physical layer security has significant application prospects in high-speed data transmission encryption in 5G and lightweight encryption in 5G IoT scenarios. To further realize the application of physical layer security in 5G, a decellularized massive MIMO physical layer secure communication transmission method is proposed. Summary of the Invention

[0003] The purpose of this invention is to address the shortcomings of existing technologies by proposing a cellular-free massive MIMO physical layer secure communication transmission method.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: a cellular-free massive MIMO physical layer secure communication transmission method, comprising the following steps,

[0005] Step 101: Construct a decellularized massive MIMO system model consisting of M access points (APs), K users, and one eavesdropper. During the uplink pilot transmission phase, the eavesdropper and the eavesdropped user use the same pilot. During the downlink data transmission phase, the access points (APs) treat the estimated channel as the real channel for conjugate beamforming.

[0006] Step 102: Based on the decellularized massive MIMO system model, construct a physical layer security model between the access point (AP), the user, and the eavesdropper;

[0007] Step 103: Based on the decellularized massive MIMO system model and physical layer security model, construct the communication confidentiality maximization target and power allocation optimization conditions;

[0008] Step 104: Based on the goal of maximizing communication security and the power allocation optimization conditions, solve the problem using inequalities and path tracking algorithms, and construct a cellular-free large-scale MIMO physical layer secure communication method.

[0009] Preferably, in step 101, a decellularized massive MIMO system model is constructed, consisting of M access points (APs), K users, and one eavesdropper. During the uplink pilot transmission phase, the eavesdropper and the eavesdropped user use the same pilot. During the downlink data transmission phase, the access points (APs) treat the estimated channel as the real channel for conjugate beamforming.

[0010] During the uplink pilot transmission phase, the pilot signals are publicly available. Taking advantage of this, an eavesdropper can obtain the pilot information and transmit the same pilot sequence as the target user. All users and the eavesdropper simultaneously send pilot signals to all access points (APs). The signal received by the m-th access point (AP) is:

[0011]

[0012] Where p l p is the transmit power of the l-th user. E H represents the transmission power of the eavesdropper. ml Let the channel between the m-th access point (AP) and the l-th user follow a constant with a mean of 0 and a variance of R. ml The normal distribution, H mE Let the channel between the m-th access point (AP) and the eavesdropper follow a constant with a mean of 0 and a variance of R. mE The normal distribution, where R0 is the normal distribution. ml R represents the large-scale fading of the channel between the m-th access point (AP) and the l-th user. mE Ω represents the large-scale fading of the channel between the m-th access point (AP) and the eavesdropper, and is related to shadowing fading and path loss. l and Ω E The power allocation matrices for the l-th user and the eavesdropper are respectively, Φ l This is the pilot signal sent by the l-th user, and it is assumed that the eavesdropper is eavesdropping on the signal of target user 1. It is additive white Gaussian noise with a mean of 0 and a variance of σ. 2 I N The normal distribution, where σ 2 Noise power;

[0013] The access point (AP) performs signal estimation based on the received pilot signals, using the minimum mean square error (MMSE) for channel estimation.

[0014] The m-th access point (AP) places it in the pilot signal Φ of the l-th user. k Above, we get:

[0015]

[0016] in The above received signal is vectorized to obtain:

[0017]

[0018] in h ml =vec(H ml ), h mE =vec(H mE ),

[0019] Thus, the minimum mean square error (MMSE) estimate for direct link channels is derived:

[0020]

[0021] in

[0022] In downlink data transmission, all access points (APs) treat the estimated channel as the real channel for conjugate beamforming, simultaneously transmitting data signals to K users. The signals received by the k-th user and the eavesdropper are represented as follows:

[0023]

[0024]

[0025] in For the data signal sent by the m-th access point (AP), P l For the power allocation matrix of users in the downlink transmission phase, q l For the signal of the l-th user, n k and n E These are the additive noises of the k-th user and the eavesdropper, respectively.

[0026] Preferably, in step 102, a physical layer security model is constructed between the access point (AP), the user, and the eavesdropper based on the decellularized massive MIMO system model.

[0027] The signals received by the k-th user and the eavesdropper obtained in step 101 are organized as follows:

[0028]

[0029]

[0030] in and These are the expected signals for the user and the eavesdropper, respectively. To prevent interference between users For beam gain, Interference between users and eavesdroppers;

[0031] The derived user rate is expressed as:

[0032]

[0033] in

[0034] The derived eavesdropper rate is expressed as:

[0035]

[0036] in

[0037] The communication confidentiality rate is then calculated as follows:

[0038] R sec =R k -R E .

[0039] Preferably, in step 103, based on the decellularized massive MIMO system model and the physical layer security model, a communication confidentiality maximization target and power allocation optimization conditions are constructed;

[0040] Power allocation is performed at the access point (AP) to maximize communication security. The objective function is now based on power constraints. In the case of maximizing the communication confidentiality R of target user 1 sec =R k -R E , where P max Maximum transmission power for each access point (AP).

[0041] Preferably, in step 104, based on the communication security rate maximization objective and power allocation optimization conditions, inequalities and path tracking algorithms are used to solve the problem, and a decellularized massive MIMO physical layer secure communication method is constructed.

[0042] The path tracing algorithm is used to solve non-convex optimization problems. In each iteration, the path tracing algorithm calls only a simple convex quadratic procedure, thus finding at least one local optimum. The power allocation constraint follows a second-order cone constraint, making it convex; inequalities are used to handle the objective function.

[0043] The user rate R can be obtained using the following inequality. k The lower bound:

[0044]

[0045] Calculate the user rate R k lower bound f (κ) (pk ):

[0046]

[0047] in b = |∑ k |,

[0048] The eavesdropper rate R can be obtained using the following inequality. E The lower bound:

[0049]

[0050] Find the eavesdropper rate R E lower bound g (κ) (p k ):

[0051]

[0052] in

[0053]

[0054]

[0055] The final objective function is transformed into finding f (κ) (p k )-g (κ) (p k The maximum value of ) is obtained through the initial value. Substitute the values ​​into the objective function and iterate to find the optimal value. Until it converges.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] 1. The channel estimation of this invention is performed locally at the access point (AP), thus eliminating the need to transmit information to the central processing unit and reducing the pressure on the fronthaul link.

[0058] 2. The secure transmission scheme proposed in this invention significantly improves system performance through beamforming technology.

[0059] 3. This invention maximizes communication confidentiality based on power constraints, thereby improving security performance. Attached Figure Description

[0060] Figure 1 This is a flowchart of a cellular-free massive MIMO physical layer secure communication transmission method according to an embodiment of the present invention.

[0061] Figure 2This is a schematic diagram of a cellular-free massive MIMO physical layer secure communication transmission method according to an embodiment of the present invention. Detailed Implementation

[0062] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings, so that those skilled in the art can better understand the advantages and features of the present invention, thereby making a clearer definition of the scope of protection of the present invention. The embodiments described in this invention are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0063] Reference Figure 1-2 A method for secure physical layer communication transmission without cellular massive MIMO includes the following steps:

[0064] Step 101: Construct a decellularized massive MIMO system model consisting of M access points (APs), K users, and one eavesdropper. During the uplink pilot transmission phase, the eavesdropper and the eavesdropped user use the same pilot. During the downlink data transmission phase, the access points (APs) treat the estimated channel as the real channel for conjugate beamforming.

[0065] Step 102: Based on the decellularized massive MIMO system model, construct a physical layer security model between the access point (AP), the user, and the eavesdropper;

[0066] Step 103: Based on the decellularized massive MIMO system model and physical layer security model, construct the communication confidentiality maximization target and power allocation optimization conditions;

[0067] Step 104: Based on the goal of maximizing communication security and the power allocation optimization conditions, solve the problem using inequalities and path tracking algorithms, and construct a cellular-free large-scale MIMO physical layer secure communication method.

[0068] The cellular-free massive MIMO physical layer secure communication transmission architecture of this embodiment is as follows: Figure 2As shown in the diagram, the scenario includes M access points (APs), 1 central processing unit, K users, and one eavesdropper. The user terminals and the eavesdropper have N antennas, and each AP has L antennas. The central processing unit is connected to the APs via a fronthaul link. The entire system consists of AP 201, central processing unit 202, user terminals 203, fronthaul link 204, and eavesdropper 205. AP 201 is primarily responsible for transmitting and receiving data; central processing unit 202 is primarily responsible for baseband signal processing, user processing, and switching processing; user terminals 203 are devices for transmitting and receiving user data; fronthaul link 204 is primarily responsible for data transmission between the APs and the central processing unit; and eavesdropper 205 is primarily responsible for eavesdropping on the valid signals of target user 1.

[0069] Specifically, in the uplink pilot transmission phase:

[0070] During the uplink pilot transmission phase, the pilot signals are publicly available. Taking advantage of this, an eavesdropper can obtain the pilot information and transmit the same pilot sequence as the target user. All users and the eavesdropper simultaneously send pilot signals to all access points (APs). The signal received by the m-th access point (AP) is:

[0071]

[0072] Where p l p is the transmit power of the l-th user. E H represents the transmission power of the eavesdropper. ml Let the channel between the m-th access point (AP) and the l-th user follow a constant with a mean of 0 and a variance of R. ml The normal distribution, H mE Let the channel between the m-th access point (AP) and the eavesdropper follow a constant with a mean of 0 and a variance of R. mE The normal distribution, where R0 is the normal distribution. ml R represents the large-scale fading of the channel between the m-th access point (AP) and the l-th user. mE Ω represents the large-scale fading of the channel between the m-th access point (AP) and the eavesdropper, and is related to shadowing fading and path loss. l and Ω E The power allocation matrices for the l-th user and the eavesdropper are respectively, Φ l This is the pilot signal sent by the l-th user, and it is assumed that the eavesdropper is eavesdropping on the signal of target user 1. It is additive white Gaussian noise with a mean of 0 and a variance of σ. 2 I N The normal distribution, where σ 2 This represents noise power.

[0073] Specifically, the channel estimation stage:

[0074] The access point (AP) performs signal estimation based on the received pilot signals, using the minimum mean square error (MMSE) for channel estimation.

[0075] The m-th access point (AP) places it in the pilot signal Φ of the l-th user. k Above, we get:

[0076]

[0077] in The above received signal is vectorized to obtain:

[0078]

[0079] in h ml =vec(H ml ), h mE =vec(H mE ),

[0080] Thus, the minimum mean square error (MMSE) estimate for direct link channels is derived:

[0081]

[0082] in

[0083] Specifically, the downlink data transmission phase:

[0084] In downlink data transmission, all access points (APs) treat the estimated channel as the real channel for conjugate beamforming, simultaneously transmitting data signals to K users. The signals received by the k-th user and the eavesdropper are represented as follows:

[0085]

[0086]

[0087] in For the data signal sent by the m-th access point (AP), P l For the power allocation matrix of users in the downlink transmission phase, q l For the signal of the l-th user, n k and n E These are the additive noises of the k-th user and the eavesdropper, respectively.

[0088] Specifically, what is the communication confidentiality rate?

[0089] The signals received by the k-th user and the eavesdropper obtained in step 101 are organized as follows:

[0090]

[0091]

[0092] in and These are the expected signals for the user and the eavesdropper, respectively. To prevent interference between users For beam gain, Interference between users and eavesdroppers.

[0093] The derived user rate is expressed as:

[0094]

[0095] in

[0096] The derived eavesdropper rate is expressed as:

[0097]

[0098] in

[0099] The communication confidentiality rate is then calculated as follows:

[0100] R sec =R k -R E .

[0101] Specifically, we construct the objective of maximizing communication security and the optimization conditions for power allocation:

[0102] The objective function is under power constraints In the case of maximizing the communication confidentiality R of target user 1 sec =R k -R E , where P max Maximum transmission power for each access point (AP).

[0103] Specifically, the solution is obtained using inequalities and path tracing algorithms:

[0104] The path tracing algorithm is used to solve non-convex optimization problems. In each iteration, the path tracing algorithm calls only a simple convex quadratic procedure, thus finding at least one local optimum. The power allocation constraint follows a second-order cone constraint, making it convex. Inequalities are used to handle the objective function.

[0105] The user rate R can be obtained using the following inequality. k The lower bound:

[0106]

[0107] Calculate the user rate R k lower bound f (κ) (p k ):

[0108]

[0109] in b = |∑ k |,

[0110] The eavesdropper rate R can be obtained using the following inequality. E The lower bound:

[0111]

[0112] Find the eavesdropper rate R E lower bound g (κ) (p k ):

[0113]

[0114] in

[0115]

[0116]

[0117] The final objective function is transformed into finding f (κ) (p k )-g (κ) (p k The maximum value of ) is obtained through the initial value. Substitute the values ​​into the objective function and iterate to find the optimal value. Until it converges.

[0118] The descriptions and practices disclosed in this invention are readily apparent and understandable to those skilled in the art, and various modifications and refinements can be made without departing from the principles of this invention. Therefore, any modifications or improvements made without departing from the spirit of this invention should also be considered within the scope of protection of this invention.

Claims

1. A method for secure physical layer communication transmission without cellular massive MIMO, characterized in that, Includes the following steps: Step 101: Construct a decellularized massive MIMO system model consisting of M access points (APs), K users, and one eavesdropper. During the uplink pilot transmission phase, the eavesdropper and the eavesdropped user use the same pilot. During the downlink data transmission phase, the access points (APs) treat the estimated channel as the real channel for conjugate beamforming. Step 102: Based on the decellularized massive MIMO system model, construct a physical layer security model between the access point (AP), the user, and the eavesdropper; Step 103: Based on the decellularized massive MIMO system model and physical layer security model, construct the communication confidentiality maximization target and power allocation optimization conditions; Step 104: Based on the goal of maximizing communication security and the power allocation optimization conditions, solve the problem using inequalities and path tracking algorithms, and construct a cellular-free large-scale MIMO physical layer secure communication method; In step 101, during the uplink pilot transmission phase, the pilot is publicly available. The eavesdropper obtains the pilot information and transmits the same pilot sequence as the target user. All users and the eavesdropper simultaneously send pilot signals to all access points (APs). The signals received by each access point (AP) are: in For the transmit power of the l-th user, The eavesdropper's transmission power, Let m be the channel between the m-th access point (AP) and the l-th user, and let its mean be . The variance is The normal distribution Let m be the channel between the m-th access point (AP) and the eavesdropper, and let its mean be . The variance is The normal distribution, where This represents the large-scale fading of the channel between the m-th access point (AP) and the l-th user. This represents the large-scale fading of the channel between the m-th access point (AP) and the eavesdropper, and is related to shadowing fading and path loss. and These are the power allocation matrices for the l-th user and the eavesdropper, respectively. This is the pilot signal sent by the l-th user, and it is assumed that the eavesdropper is eavesdropping on the signal of target user 1. It is additive white Gaussian noise with a mean of . The variance is The normal distribution, where Noise power; The access point (AP) performs signal estimation based on the received pilot signal and uses the minimum mean square error (MMSE) for channel estimation. The m-th access point (AP) places it in the pilot signal of the l-th user. Above, we get: in The received signal is vectorized to obtain: Thus, the minimum mean square error (MMSE) estimate for direct link channels is derived: During the downlink data transmission phase, all access points (APs) treat the estimated channel as the real channel for conjugate beamforming, simultaneously transmitting data signals to K users. The signals received by the k-th user and the eavesdropper are represented as follows: in The data signal sent by the m-th access point (AP). This is the power allocation matrix for users during the downlink transmission phase. For the signal of the l-th user, and These are the additive noises of the k-th user and the eavesdropper, respectively.

2. The method for secure physical layer communication transmission of decellularized massive MIMO according to claim 1, characterized in that: In step 102, the signals received by the k-th user and the eavesdropper obtained in step 101 are organized as follows: The derived user rate is expressed as: The derived eavesdropper rate is expressed as: The communication confidentiality rate is then calculated as follows: 。 3. The method for secure physical layer communication transmission of decellularized massive MIMO according to claim 2, characterized in that: In step 103, power allocation is performed at the access point (AP) to maximize communication security. The objective function is based on power constraints. In this case, maximize the communication confidentiality of target user 1. ,in Maximum transmission power for each access point (AP).

4. The method for secure physical layer communication transmission of decellularized massive MIMO according to claim 1, characterized in that: In step 104, the path tracing algorithm is used to solve the non-convex optimization problem. The path tracing algorithm only calls a simple convex quadratic procedure in each iteration, which can find at least one local optimum. The power allocation constraint is a second-order cone constraint, so it is convex. Inequalities are used to process the objective function. The user rate can be obtained using the following inequality. The lower bound: The eavesdropper's speed can be obtained using the following inequality. The lower bound: in

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