CN105263135A - Robustness safety design method for multiple input multiple output (MIMO) communication system - Google Patents

Abstract

The invention discloses a robustness safety design method for a multiple input multiple output (MIMO) communication system. Receiving terminals of the method are each provided with a power separator that divides the received power to realize information decoding of the receiving terminal and energy collection simultaneously. If an idle receiving terminal is a potential eavesdropper, a sending terminal adopts methods of precoding and introducing artificial noise, thereby effectively improving system security rate and transfer efficiency of system energy. When the sending terminal only knows part of channel status information of the idle receiving terminal, the robustness safety design is described as a non-convex semi-infinite optimization problem. The non-convex semi-infinite optimization problem can be changed into a positive semi-definite relaxation problem by use of first order Taylor expansion and S-Procedure theorem, and the effective robustness safety design method can be finally provided through a convex optimization tool based iterative algorithm. With safe communication of the system guaranteed, the safety rate of the sytesm is improved remarkably, the energy transfer efficiency of the system is improved, and the communication and energy demands of the user are better met.

Description

Robustness safety design method of MIMO communication system

Technical Field

The invention relates to the technical field of wireless energy transmission and wireless communication, in particular to a robust safety design method of a downlink MIMO communication system based on information energy synchronous transmission.

Background

The explosive growth of current high-speed wireless communications has increased the energy demand on communication networks. The mobile terminal is often powered by a battery with limited energy storage, which causes a bottleneck in the life cycle of the network. Therefore, communication devices with energy harvesting functionality are considered to be a promising alternative to provide self-sustainable development for energy-limited communication systems. In practice, there are many renewable energy sources available for energy harvesting, including solar, tidal, geothermal, wind, etc. However, these energy sources are often limited by geographical location, weather, climate, etc., and are not always suitable for use in indoor or enclosed environments, as well as mobile terminals, etc. On the other hand, the radio transmission technology for collecting energy from electromagnetic waves in the radio spectrum at the receiving end has received great attention from the industry and academia, that is, the surrounding radio signals (i.e., RF) can be used as a new energy collection source. Therefore, the use of RF signals for the simultaneous transmission of wireless information and energy is considered to be an effective way to extend the life cycle of the network.

Due to the openness of wireless communication networks, legitimate information carried in RF signals is easily eavesdropped by a malicious eavesdropper. The traditional secure communication method is often independent of the physical layer and has a certain defect that the calculation capacity of an eavesdropper is assumed to be limited. While physical layer security is considered to be a limited method against eavesdroppers with unlimited computational power. The principle of physical layer security is to provide perfect secure communication by using the physical characteristics of wireless fading channels. Meanwhile, a multiple input multiple output (i.e., MIMO) technology has been widely adopted as an effective method for increasing system capacity, which theoretically increases linearly as the number of antennas increases.

Disclosure of Invention

The invention aims to solve the problem of safe transmission of a downlink MIMO communication system based on synchronous information and energy transmission, and provides a robust safety design method of the MIMO communication system. The method is characterized in that power separators are configured on receivers to achieve the purpose of synchronously decoding information and collecting energy at a receiving end, and a transmitting end only knows the robustness safety design problem under the condition of partial channel information of an idle receiving end.

The technical scheme adopted by the invention for solving the technical problems is as follows: a robustness safety design method of a MIMO communication system is based on information and energy synchronous transmission and comprises the following steps:

step 1: the information signal of a transmitting end adopts a precoding design and is added with artificial noise generated by the transmitting end, wherein x is Qs + vIn order to be the information signal, the first signal,respectively a precoding matrix and an artificial noise vector.

Step 2: aiming at the condition that a transmitting end only knows the estimated channel information of an idle receiver (namely a potential eavesdropper) and the error radius thereof in the MIMO system, the system safety capacity is optimized, and the safety rate maximization problem under the worst condition is approximately converted into a given initial point by utilizing first-order Taylor approximation, S-Procedure theorem and the likeWhere W is QQ^H,V＝vv^H，t＝1/ρ_D。

And step 3: initializationSolving a transform using a convex optimization toolkitThe SDP problem after being changed is iterated and updated in sequenceAnd (3) obtaining a new safe rate in each iteration until the safe rate value converges to a certain degree, stopping the iteration, and finally providing the approximate safe rate under the worst condition of the system and a design method of a precoding matrix and an artificial noise vector.

The transmitting end of the invention knows the perfect channel information of the target receiving endThe idle receiving end is in an inactive state, and the transmitting end only knows the estimated value of the channel information and the estimated error radius thereof, namely: ${\mathrm{\Ω}}_m=\left\{G_m\right|G_m={\hat{G}}_m+{\mathrm{\ΔG}}_m,\left|\right|{\mathrm{\ΔG}}_m|{|}_F\≤{\mathrm{\δ}}_m\},$ whereinRepresenting the channel estimate, Δ G_mWhich is indicative of the channel error,_mrepresenting the radius of the channel uncertainty region; the signals received by the destination receiver and the mth idle receiver can be respectively expressed as: y is_D＝H^HQs+H^Hv+n_D， $y_{Em}=G_m^{H}Qs+G_m^{H}v+n_{Em},\∀m\∈\mathrm{\Π},\mathrm{\Π}=\{1,...,M\},$ Wherein n is_D，n_EmRespectively, mean obedience is 0 and variance isComplex additive white gaussian noise of (a); separating the received signals at a receiving end through a power separator, wherein one part is used for information decoding, and the other part is used for collecting energy; the safe capacity of the destination is expressed as

$R=\underset{m\∈\mathrm{\Π}}{min}\{C_D(Q,V,{\mathrm{\ρ}}_D)-C_{Em}(Q,V)\}---\left(6\right)$

Wherein

$C_D(Q,V,{\mathrm{\ρ}}_D)={\log }_2|I_{N_d}+{\left({\mathrm{\ρ}}_D\right(H^{H}VH+{\mathrm{\σ}}_D^2{I}_{N_d})+{\mathrm{\σ}}_P^2{I}_{N_d})}^{-1}{\mathrm{\ρ}}_D{H}^H{\mathrm{QQ}}^{H}H|---\left(7\right)$

$C_{Em}(Q,V)={\log }_2|I_{N_e}+{(G_m^{H}VG+{\mathrm{\σ}}_E^2{I}_{N_e}+{\mathrm{\σ}}_P^2{I}_{N_e})}^{-1}{G}_m^H{\mathrm{QQ}}^H{G}_m|---\left(8\right)$

Respectively representing the mutual information of a target receiving end and the mutual information of an mth idle receiving end; mutual information of the idle receiving end is obtained under the condition that all received energy is used for eavesdropping information, and the problem of safety capacity maximization under the worst condition of the system under certain conditions is expressed as follows:

$R^{*}=\underset{Q,V,{\mathrm{\ρ}}_D}{max}\underset{m\∈\mathrm{\Π}}{min}\underset{{\mathrm{\ΔG}}_m\∈{\mathrm{\Ω}}_m}{min}\{C_D(Q,V,{\mathrm{\ρ}}_D)-C_{Em}(Q,V)\}---\left(9a\right)$

$\begin{array}{cc}s.t.& (1-{\mathrm{\ρ}}_D)\left(Tr\right(H^H{\mathrm{QQ}}^{H}H+H^{H}VH)+{\mathrm{\σ}}_D^2{N}_d)\≥{\mathrm{\η}}_D\end{array},---\left(9b\right)$

$\underset{{\mathrm{\ΔG}}_m\∈{\mathrm{\Ω}}_m}{min}Tr(G_m^H{\mathrm{QQ}}^H{G}_m+G_m^H{\mathrm{VG}}_m)+{\mathrm{\σ}}_E^2{N}_e\≥{\mathrm{\η}}_m,\∀m\∈\mathrm{\Π},---\left(9c\right)$

Tr(QQ^H+V)≤P,0≤ρ_D≤1,V≥0.(9d)

wherein the constraint, i.e., equation 9b, indicates that the energy collected by the intended receiver is at least η_DThe constraint, equation 9c, represents the minimum energy harvesting requirement η in the presence of channel estimation error when the mth idle receiver performs energy harvesting only_m(ii) a Equation 9d represents the maximum transmit power constraint and the power splitting ratio and the artificial noise covariance constraint at the transmitting end.

The invention initializes any pointSubstituting into (11) and utilizing a convex optimization tool kit to obtain the optimal (W, V, t) at the moment to obtain a corresponding safe speed value; updating by the obtained (W, V, t)Sequentially iterating to obtain a new initial point and a corresponding safe speed value; when the safe speed value converges to a stable value, iteration is stopped, and an optimal precoding matrix, an artificial noise vector and a power separation ratio are given by using eigenvalue decomposition.

The invention is applied to a downlink MIMO communication system with energy collection requirement in wireless communication, and under the condition that a sending end only knows the channel information of an eavesdropping end part, the invention prolongs the life cycle of a network and simultaneously improves the safety rate of the system.

Aiming at a downlink MIMO communication system based on information and energy synchronous transmission, the invention can greatly improve the safety rate of the system on the premise of ensuring safe transmission and simultaneously improve the energy transfer efficiency of the system under the condition that a sending end only knows channel information of a receiving end part.

Has the advantages that:

1. all receiving ends can collect certain energy, so that the transfer of system energy is promoted to supplement self energy consumption, and the life cycle of the network is prolonged.

2. The data transmission security of the MIMO communication system is ensured, so that the system can still carry out secure communication under the condition that an eavesdropper exists.

3. Compared with the traditional design method, the invention improves the system security rate of transmitting legal information to the target receiving end.

Drawings

FIG. 1 is a schematic diagram of a system model according to the present invention.

Fig. 2 is a schematic diagram of a receiving end structure.

FIG. 3 is a flow chart of the method of the present invention.

Fig. 4 is a schematic diagram of algorithm convergence.

Fig. 5 is a diagram illustrating a comparison of the worst safe rate of the present invention with the worst safe rate of the conventional method at different transmission powers.

Fig. 6 is a diagram illustrating a comparison of worst safe rates of the present invention with the existing method under different uncertainty radiuses of channel information.

Detailed Description

The invention is described in further detail below with reference to the accompanying drawings.

As shown in fig. 1, the downlink MIMO communication system of the present invention includes: one is configured with N_tA transmitting end of the root antenna and a plurality of receiving ends. The receiving end is divided into two types: one is configured with N_dA plurality of target receiving ends of the root antenna, each of which is provided with N_eIdle receiving end of root antenna. Both receiving ends are configured with power splitters as shown in fig. 2, which split the received signal power into two parts: one part is used for information decoding and the other part is used for collecting energy. When the receiver is in idle state, it is expected to collect energy to supplement its own energy consumption. At the same time, it may also become a potential eavesdropper due to malicious eavesdropping of legitimate information sent to the destination. Therefore, the invention adopts the methods of pre-coding and adding artificial noise at the sending end to improve the safe communication capacity of the system and simultaneously improve the energy transfer efficiency of the system.

The baseband signal at the transmitting end can be expressed as: x is Qs + v, whereinIn order to be the information signal, the first signal,in order to transmit the pre-coding matrix of the signal,for artificial noise vectors generated by the transmitting end and obeying a mean value of zero covariance matrix of V ═ vv^H. The signals received by the destination receiving end and the mth idle receiving end can be respectively expressed as:

y_D＝H^HQs+H^Hv+n_D(1)

$y_{Em}=G_m^{H}Qs+G_m^{H}v+n_{Em},\∀m\∈\mathrm{\Π}---\left(2\right)$

wherein n is_D，n_EmRespectively, mean obedience is 0 and variance isComplex additive white gaussian noise.

The receiving ends are each provided with a power splitter which splits the received power into two parts, one part for information decoding and one part for energy collection. The part of power of the received power used for decoding information by the target receiving end and the mth idle receiving end is respectively rho_D∈[0,1]And ρ_Em∈[0,1]Represents the corresponding 1-rho_DAnd 1-rho_EmPart of the power is used to harvest energy. Therefore, the received signals for decoding information can be equivalently expressed at the receiving end as:

$y_D^I=\sqrt{{\mathrm{\ρ}}_D}(H^{H}Qs+H^{H}v+n_D)+n_D^P---\left(3\right)$

$y_{Em}^I=\sqrt{{\mathrm{\ρ}}_{Em}}(G_m^{H}Qs+G_m^{H}v+n_{Em})+n_E^P,\∀m\∈\mathrm{\Π}---\left(4\right)$

wherein,respectively, mean-obedience-zero-covariance produced by signal processingWhite additive gaussian noise.

The channel state information of the destination receiving end is known by the transmitting end, but the channel state information of the idle receiving end is difficult to be completely obtained by the receiving end due to the inactive state of the idle receiving end. Thus, the present invention assumes that only part of the channel information for an idle receiver is known to the receiver, and its channel uncertainty can be determined by the following model:

${\mathrm{\Ω}}_m=\left\{G_m\right|G_m={\hat{G}}_m+{\mathrm{\ΔG}}_m,\left|\right|{\mathrm{\ΔG}}_m|{|}_F\≤{\mathrm{\δ}}_m\},---\left(5\right)$

wherein,representing the channel estimate, Δ G_mWhich is indicative of the channel error,_mrepresenting the radius of the channel uncertainty region. Considering that a free receiver may become a potential eavesdropper, the security capacity of the system at the destination receiver is expressed as:

$R=\underset{m\∈\mathrm{\Π}}{min}\{C_D(Q,V,{\mathrm{\ρ}}_D)-C_{Em}(Q,V)\}---\left(6\right)$

wherein

$C_D(Q,V,{\mathrm{\ρ}}_D)={\log }_2|I_{N_d}+{\left({\mathrm{\ρ}}_D\right(H^{H}VH+{\mathrm{\σ}}_D^2{I}_{N_d})+{\mathrm{\σ}}_P^2{I}_{N_d})}^{-1}{\mathrm{\ρ}}_D{H}^H{\mathrm{QQ}}^{H}H|---\left(7\right)$

$C_{Em}(Q,V)={\log }_2|I_{N_e}+{(G_m^{H}VG+{\mathrm{\σ}}_E^2{I}_{N_e}+{\mathrm{\σ}}_P^2{I}_{N_e})}^{-1}{G}_m^H{\mathrm{QQ}}^H{G}_m|---\left(8\right)$

Respectively representing the mutual information of the target receiving end and the mutual information of the mth idle receiving end. It should be noted that the mutual information of the idle receiving end is obtained in the case that the energy received by the idle receiving end is all used for eavesdropping on the information. The safety capacity optimization problem of the system under certain conditions can be described as:

$R^{*}=\underset{Q,V,{\mathrm{\ρ}}_D}{max}\underset{m\∈\mathrm{\Π}}{min}\underset{{\mathrm{\ΔG}}_m\∈{\mathrm{\Ω}}_m}{min}\{C_D(Q,V,{\mathrm{\ρ}}_D)-C_{Em}(Q,V)\}---\left(9a\right)$

$\begin{array}{cc}s.t.& (1-{\mathrm{\ρ}}_D)\left(Tr\right(H^H{\mathrm{QQ}}^{H}H+H^{H}VH)+{\mathrm{\σ}}_D^2{N}_d)\≥{\mathrm{\η}}_D\end{array},---\left(9b\right)$

$\underset{{\mathrm{\ΔG}}_m\∈{\mathrm{\Ω}}_m}{min}Tr(G_m^H{\mathrm{QQ}}^H{G}_m+G_m^H{\mathrm{VG}}_m)+{\mathrm{\σ}}_E^2{N}_e\≥{\mathrm{\η}}_m,\∀m\∈\mathrm{\Π},---\left(9c\right)$

Tr(QQ^H+V)≤P,0≤ρ_D≤1,V≥0.(9d)

wherein the constraint (9b) indicates that the energy collected by the intended receiver is at least η_DConstraint (9c) indicates its minimum energy harvesting requirement η in the presence of channel estimation error when only energy harvesting is performed at the mth idle receiving end_m(ii) a (9d) Representing the maximum transmit power constraint at the transmitting end as well as the covariance constraints of the power splitting ratio and the artificial noise.

Introduction of new variable W ═ QQ^HAnd the relaxation variable t is 1/rho_D,β₁,β₂. The problem (9) to be optimized is converted into:

$\begin{array}{cc}\underset{W,V,t}{max}& {\mathrm{\β}}_1-{\mathrm{\β}}_2\end{array}---\left(10a\right)$

$\begin{array}{cc}s.t.& {\log }_2|I_{N_d}+{(H^{H}VH+{\mathrm{\σ}}_D^2{I}_{N_d}+{\mathrm{t\σ}}_P^2{I}_{N_d})}^{-1}{H}^{H}WH|\≥{\mathrm{\β}}_1\end{array},---\left(10b\right)$

${\log }_2|I_{N_e}+{(G_m^H{\mathrm{VG}}_m+{\mathrm{\σ}}_E^2{I}_{N_e}+{\mathrm{\σ}}_P^2{I}_{N_e})}^{-1}{G}_m^H{\mathrm{WG}}_m|\≤{\mathrm{\β}}_2,\∀\mathrm{\Δ}{G}_m\∈{\mathrm{\Ω}}_m,m\∈\mathrm{\Π},---\left(10c\right)$

$Tr(H^{H}WH+H^{H}WH)+{\mathrm{\σ}}_D^2{N}_d\≥(1+\frac{1}{t-1}){\mathrm{\η}}_D,---\left(10d\right)$

$Tr(G_m^H{\mathrm{WG}}_m+G_m^H{\mathrm{VG}}_m)+{\mathrm{\σ}}_E^2{N}_e\≥{\mathrm{\η}}_m,\∀{\mathrm{\ΔG}}_m\∈{\mathrm{\Ω}}_m,m\∈\mathrm{\Π},---\left(10e\right)$

Tr(W+V)≤P,W≥0,V≥0,t≥1.(10f)

for the semi-infinite constraint (10e), its left-hand equivalent is:

$Tr(G_m^H{\mathrm{WG}}_m+G_m^H{\mathrm{VG}}_m)=g_m^H{F}_1{g}_m+2\mathrm{Re}\left\{g_m^H{F}_1{e}_m\right\}+e_m^H{F}_1{e}_m---\left(11\right)$

wherein, $g_m=vec\left({\hat{G}}_m\right),$ e_m＝vec(ΔG_m)， $F_1=I_{N_e}\⊗(W+V).$

considering the partial channel state information constraint (1) and using the S-Procedure theorem, the constraint (10e) can be transformed into the following linear matrix inequality:

$\left[\begin{array}{cc}{a}_m{I}_{N_t\×N_e}+F_1& F_1{g}_m\\ g_m^H{F}_1& g_m^H{F}_1{g}_m+{\mathrm{\σ}}_E^2{N}_e-a_m{\mathrm{\δ}}_m^2-{\mathrm{\η}}_m\end{array}\right]>0---\left(12\right)$

wherein a is_mAnd m ∈ pi is a relaxation variable being equal to or more than 0.

Constraining (10b) at a given point according to a first order Taylor expansionCan be approximately expressed as:

it should be noted here thatWhen the value is close to the optimum point,can be well approximated to C_D。

Conservative estimates are made for the constraint (10d) and using the first order Taylor expansion and S-Procedure theorem at a given pointCan be approximately converted into:

β₂+log₂β_3m-β_4m≥0(14)

$\left[\begin{array}{cc}{b}_m{I}_{N_t\×N_e}+F_2& F_2{g}_m\\ g_m^H{F}_2& \mathrm{\Γ}\end{array}\right]>0,---\left(15\right)$

$\left[\begin{array}{cc}{c}_m{I}_{N_t\×N_e}-F_{3m}& -F_{3m}{g}_m\\ -g_m^H{F}_{3m}& \mathrm{\Φ}\end{array}\right]>0---\left(16\right)$

β therein_3m,β_4m,b_mandc_mRepresents a relaxation variable; $\mathrm{\Γ}=g_m^H{F}_2{g}_m+\mathrm{\σ}-{\mathrm{\σ}}^{1-N_e}{\mathrm{\β}}_{3m}-b_m{\mathrm{\δ}}_m^2,A_m={\left({\hat{G}}_m^H\right(\tilde{W},\tilde{V})\hat{G}m+{\mathrm{\σI}}_{N_e})}^{-1},$ ${\mathrm{\α}}_m=-{\log }_2|{\hat{G}}_m^H(\tilde{W},\tilde{V}){\hat{G}}_m+{\mathrm{\σI}}_{N_e}|+\frac{1}{ln2}Tr\left(A_m{G}_m^H\right(\tilde{W}+\tilde{V}\left)G_m\right),F_{3m}=A_m^T\⊗(W+V),$ $\mathrm{\Φ}=-c_m{\mathrm{\δ}}_m^2-g_m^H{F}_{3m}{g}_m+({\mathrm{\β}}_{4m}+{\mathrm{\α}}_m)ln2.$

based on the above transformation, the optimization problem (10) is at a given pointHere, it can be approximately expressed as the following SDP problem:

$\begin{array}{c}\underset{W,V,t,{\mathrm{\β}}_1,{\mathrm{\β}}_2,{\mathrm{\β}}_{3m},{\mathrm{\β}}_{4m},a_m,b_m,c_m}{\max }{\mathrm{\β}}_1-{\mathrm{\β}}_2\\ s.t.\left(5d\right),\left(5f\right),\left(6\right),\left(7\right),\left(8\right),\left(9\right),\left(10\right)\\ a_m\≥0,b_m\≥0,c_m\≥0,{\mathrm{\β}}_{3m}\≥0.\end{array},---\left(17\right)$

the SDP problem is a convex optimization problem that can be used to quickly derive an optimal solution with the help of a convex optimization toolkit.

As shown in fig. 3, any initial point is initializedAnd (17) substituting the optimal (W, V, t) value at the moment by using a convex optimization tool package to obtain a corresponding safe speed value. Updating by the obtained (W, V, t)And iteration is carried out in sequence to obtain a new initial point and a corresponding safe speed value. Meanwhile, since (W, V, t) generated by each iteration is a feasible point of the next iteration, the safe rate value obtained by each iteration is monotonously non-decreasing, which ensures that the iterative algorithm finally converges to a stable point, as shown in fig. 4.

When the iteration converges, the optimal solution is obtainedAnd the precoding matrix, the generated artificial noise vector and the power separation ratio can be obtained according to the eigenvalue decomposition method. It should be noted that, because a safety approximation is involved in the solution process, this optimal solution is not the optimal solution of the original optimization problem (9), and it can only ensure that the problem (9) is feasible and obtains good performance gain.

The effects of the present invention can be further illustrated by the following simulations:

setting simulation parameters, wherein the number of antennas configured at the equipment end is respectively as follows: n is a radical of_t＝5，N_d＝N_eThe number of idle receivers is 2. The rest related parametersThe number distribution is 0.02, P2 d BETA, η_D＝22dΒm，η₁＝…＝η_M23 dBm. Channel state informationA composite gaussian distribution with a mean of zero and a variance of 0.01(0.1) was obeyed.

As shown in fig. 4, the convergence of the algorithm under random channel implementation is described. As can be seen from the figure, the safe rate value obtained by each iteration is monotonously non-reduced and finally converges to a stable point, so that the realizability of the scheme is ensured.

As shown in fig. 5, a comparison of the proposed inventive method and the conventional method under different transmit power conditions is described. It can be seen from the figure that the worst-case safe rate of the system in the robust safe transmission design method provided by the invention increases with the increase of the transmission power, and the performance of the robust safe transmission design method is obviously better than that of the traditional design method. Meanwhile, it can be seen from the figure that the method provided by the invention improves the energy transfer efficiency of the system. Here, artificial noise has two applications: the safety performance of the system is improved; as a power source for receiving end energy collection.

As shown in fig. 6: the change situation of the safety rate along with the radius of the uncertain region of the channel state information under the worst condition in the robustness safety design method provided by the invention is described. It can be seen from the figure that when the uncertainty radius is small, the method proposed in the present invention can well approximate the upper performance bound in the perfect case. Meanwhile, under different uncertain area radiuses, the method provided by the invention is obviously superior to other traditional methods.

The examples described herein are only preferred embodiments and are not intended to limit the scope of the present invention, and any modifications or equivalent substitutions made based on the spirit of the present invention should be included within the scope of the present invention without departing from the spirit and scope of the present invention.

Claims (6)

1. A robustness safety design method of a MIMO communication system is characterized by comprising the following steps:

step 1: the information signal of a transmitting end adopts a precoding design and is added with artificial noise generated by the transmitting end, wherein x is Qs + vIn order to be the information signal, the first signal,respectively a precoding matrix and an artificial noise vector;

step 2: for a transmitting end in a MIMO system, only known idle receivers are: the potential eavesdropper estimates the channel information and the error radius thereof, optimizes the system security capacity, and approximately converts the problem of solving the security rate maximization under the worst condition into a given initial point by utilizing the first-order Taylor approximation, the S-Procedure theorem and the likeWhere W is QQ^H,V＝vv^H，t＝1/ρ_D；

And step 3: initializationSolving the converted SDP problem by using a convex optimization toolkit, and sequentially iterating and updatingAnd (3) obtaining a new safe rate in each iteration until the safe rate value converges to a certain degree, stopping the iteration, and finally providing the approximate safe rate under the worst condition of the system and a design method of a precoding matrix and an artificial noise vector.

2. The method as claimed in claim 1, wherein the method comprises: in a downlink MIMO communication system, a sending end is configured with N_tRoot antenna, and there are two types of receiving ends: one is configured with N_dA plurality of target receiving ends of the root antenna, each of which is provided with N_eThe idle receiving end of root antenna, two kinds of receiving ends all dispose power splitter, and it divides received signal power into two parts: one part is used for information decoding, and the other part is used for collecting energy; if the idle receiving end becomes a malicious eavesdropper, a precoding design and an artificial noise introduction method are adopted at the transmitting end, and the baseband signal of the transmitting end is expressed as follows: x is equal to Qs + v,whereinIn order to be the information signal, the first signal,respectively a precoding matrix and an artificial noise vector.

3. The method as claimed in claim 1, wherein the method comprises: perfect channel information of transmitting end and destination receiving endThe idle receiving end is in an inactive state, and the transmitting end only knows the estimated value of the channel information and the estimated error radius thereof, namely:whereinRepresenting the channel estimate, Δ G_mWhich is indicative of the channel error,_mrepresenting the radius of the channel uncertainty region; the signals received by the destination receiver and the mth idle receiver can be respectively expressed as: y is_D＝H^HQs+H^Hv+n_D，N ═ 1,. said, M, where n is_D，n_EmRespectively, mean obedience is 0 and variance isComplex additive white gaussian noise of (a); separating the received signals at a receiving end through a power separator, wherein one part is used for information decoding, and the other part is used for collecting energy; the safe capacity of the destination is expressed as

Wherein

Respectively representing the mutual information of a target receiving end and the mutual information of an mth idle receiving end; mutual information of the idle receiving end is obtained under the condition that all received energy is used for eavesdropping information, and the problem of safety capacity maximization under the worst condition of the system under certain conditions is expressed as follows:

Tr(QQ^H+V)≤P,0≤ρ_D≤1,V≥0.(9d)

wherein the constraint, i.e., equation 9b, indicates that the energy collected by the intended receiver is at least η_DThe constraint, equation 9c, represents the minimum energy harvesting requirement η in the presence of channel estimation error when the mth idle receiver performs energy harvesting only_m(ii) a Equation 9d represents the maximum transmit power constraint and the power splitting ratio and the artificial noise covariance constraint at the transmitting end.

4. The robust security design method of the MIMO communication system as claimed in claim 1, wherein: introduction of a new variable W＝QQ^HAnd the relaxation variable t is 1/rho_D,β₁,β₂And converting the problem (4) to be optimized into:

Tr(W+V)≤P,W≥0,V≥0,t≥1.(10f)

using the S-Procedure theorem, the constraint (10e) can be transformed into the following linear matrix inequality:

whereine_m＝vec(ΔG_m)；a_mNot less than 0, m ∈ pi is a relaxation variable;

constraining (5b) at a given point according to a first order Taylor expansionThe process is represented as:

when the point is onWhen the value is close to the optimum point,can be well approximated to C_D；

For constraints, namely: equation 5d is a conservative estimate and uses the first order Taylor expansion and S-Procedure theorem at a given pointTo convert into:

β₂+log₂β_3m-β_4m≥0(14)

β therein_3m,β_4m,b_mandc_mRepresents a relaxation variable;

based on the above transformation, the optimization problem (4) is at a given pointHere, expressed as the SDP problem:

s.t.(5d)，(5f)，(6)，(7)，(8)，(9)，(10)，(17)

a_m≥0,b_m≥0,c_m≥0,β_3m≥0.

the SDP problem is a convex optimization problem that quickly derives the optimal solution with the help of a convex optimization toolkit.

5. The method as claimed in claim 1, wherein the method comprises: initializing any pointSubstituting into (11) and utilizing a convex optimization tool kit to obtain the optimal (W, V, t) at the moment to obtain a corresponding safe speed value; updating by the obtained (W, V, t)Sequentially iterating to obtain a new initial point and a corresponding safe speed value; when the safe speed value converges to a stable value, iteration is stopped, and an optimal precoding matrix, an artificial noise vector and a power separation ratio are given by using eigenvalue decomposition.

6. The method as claimed in claim 1, wherein the method is applied to a downlink MIMO communication system with energy harvesting requirement in wireless communication.