Communication optimization method and system for wireless energy-carrying security communication system in far and near mixed field

Abstract

The invention relates to the technical field of wireless communication networks, and discloses a communication optimization method and system for a wireless energy-carrying safety communication system in a far and near mixed field. The method comprises the following specific steps: constructing a wireless energy-carrying safety communication system in a far-near hybrid field, wherein the wireless energy-carrying safety communication system comprises a base station, an energy collection receiver with K near-field single antennas and an information decoding receiver with M far-field single antennas; constructing a mixed field channel model; constructing a signal model comprising the information signal and the digital beam forming matrix thereof, and the artificial noise and the digital beam forming matrix thereof; based on the signal model and the mixed field channel model, a maximization problem objective function of the minimum safety rate weighted sum of the information decoding receiver is formulated; and solving a maximization problem to obtain an optimal communication scheme. The method solves the problem that vector optimization of beam forming is lacked in the prior art, and has the characteristic that the information transmission security of the system can be improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication network technology, and more specifically, to a communication optimization method and system for a wireless energy-carrying safety communication system in a mixed long- and short-range environment. Background Art

[0002] With the gradual deployment and development of 5G wireless communication networks, users' demands for high-speed, low-latency, and large-capacity communications continue to grow. At the same time, with the rapid development of emerging applications such as smart devices, virtual reality, augmented reality, and autonomous driving, traditional wireless communication technologies have gradually exposed their shortcomings in large-scale connections, high-speed data transmission, and low-latency services. In particular, in the face of the high demand for future 6G networks, existing technologies are difficult to meet higher performance requirements. Therefore, ultra-large-scale multiple-input multiple-output (XL-MIMO) technology has emerged and has become one of the key technologies for future 6G networks.

[0003] XL-MIMO technology can achieve higher spectral efficiency and spatial resolution in communication systems by deploying antenna arrays on a large scale. Compared with traditional large-scale multiple-input multiple-output technology, XL-MIMO greatly improves the capacity and reliability of the system by further increasing the number of antennas, usually reaching hundreds or even thousands of antennas. At the same time, with the increase in the number of antennas, the channel characteristics have also changed significantly. First, it changes from far-field uniform plane wave transmission to near-field non-uniform spherical wave transmission, and second, it changes from traditional spatial stationarity to spatial non-stationarity. This puts higher requirements on the beamforming and channel estimation of the XL-MIMO system.

[0004] The booming development of the Internet of Things has enabled a large number of wireless communication devices to access the network. These devices are usually powered by batteries, and the cost of replacing batteries is high or difficult to achieve in some scenarios. Therefore, wireless power transfer (WPT) technology, as a key solution, has received increasing attention. WPT technology uses radio frequency signals to carry energy, allowing devices to be wirelessly powered at a relatively long distance. Wireless power transmission (SWIPT) technology can achieve the synchronous transmission of information and energy, and also provides the possibility of extending the service life of IoT devices and achieving self-powering. However, when the SWIPT system realizes the synchronous transmission of information and energy, due to the broadcast characteristics of the wireless network, the information signal may be intercepted by the energy receiving device, resulting in information leakage. Specifically, when the energy harvesting (EH) device is closer to the base station, the channel conditions it is in are relatively good, and it can obtain energy from the radio frequency signal more efficiently, and it is also easier to eavesdrop on the information sent to the information decoding (ID) device.

[0005] The prior art has an algorithm that can effectively estimate such channels. When modeling the near-field channel, the presence of the visible region (VR) is taken into account. In addition, there is also prior art that discusses the physical layer security issues of the MISO SWIPT system, using energy beams as artificial noise to maximize the confidentiality rate of the information decoding receiver under the premise of satisfying the energy collection constraints of the energy harvesting receiver. However, the SWIPT system considered in the prior art is a single information decoding receiver mode, and assumes that all receivers are in the far field range, but with the deployment of XL-MIMO, the significant increase in the number of XL-MIMO antennas and the array aperture size will lead to a significant increase in the Rayleigh distance, so that the position of the receiver may fall into the near field area. There is also prior art that considers the mixed field SWIPT scenario, under the constraints of the maximum sum rate and base station transmission power, by jointly designing the base station beam scheduling and power allocation, the weighted total power collected by all EH receivers is maximized. However, this prior art does not pay attention to the physical layer security issues of the SWIPT system, and optimizes the power allocation under the premise of given beamforming, without optimizing the beamforming vector.

[0006] In summary, the existing wireless energy-carrying safety communication technology in mixed near and far fields lacks the problem of vector optimization of beamforming. Therefore, how to invent a communication optimization method that takes into account the digital beamforming matrix is ​​a technical problem that urgently needs to be solved in this technical field. Summary of the invention

[0007] In order to solve the problem that the prior art lacks vector optimization for beamforming, the present invention provides a communication optimization method and system for a wireless energy-carrying secure communication system under a mixed far and near field, which has the characteristic of being able to improve the information transmission security of the system.

[0008] In order to achieve the above-mentioned purpose of the present invention, the technical scheme adopted is as follows:

[0009] A communication optimization method for a wireless energy-carrying safety communication system in a mixed long- and short-range environment comprises the following specific steps:

[0010] Construct a wireless energy-carrying secure communication system in a near- and far-field mixed field, including a base station, K near-field single-antenna energy harvesting receivers, and M far-field single-antenna information decoding receivers;

[0011] Construct a mixed-field channel model;

[0012] Constructing a signal model including an information signal and its digital beamforming matrix, an artificial noise and its digital beamforming matrix;

[0013] Based on the signal model and the mixed field channel model, the objective function of the maximization problem of the weighted sum of the minimum safe rate of the information decoding receiver is formulated;

[0014] Solve the maximization problem and obtain the optimal communication solution.

[0015] Preferably, the base station is equipped with a uniform linear array having N antennas and a beamformer with a fully connected structure, comprising multiple RF links; the energy harvesting receiver is located in the Fresnel near-field region of the base station, and is used to collect energy from RF signals; the information decoding receiver is located in the far-field region of the base station, and is used to receive information signals.

[0016] Furthermore, a mixed field channel model is constructed, and the specific steps are as follows:

[0017] Assume that the coordinates of the nth antenna unit are (0,δ n d), the coordinates of the lth receiver are The distance from the nth antenna element to the lth receiver is expressed as:

[0018]

[0019] in, is the antenna spacing, λ is the wavelength, N is the number of antenna units, θ l represents the angle of the lth receiver relative to the base station antenna array;

[0020] Considering the superposition of the direct visual LoS path and the indirect visual NLoS path, the channel from the base station to the mth information decoding receiver is expressed as:

[0021]

[0022] Among them, g m,l is the far-field complex-valued channel gain, satisfying L m is the number of far-field paths, l = 1 indicates a LoS path; a(θ l ) is the far-field steering vector,

[0023] Also considering the superposition of the LoS path and the NLoS path, the channel from the base station to the kth energy harvesting receiver is expressed as:

[0024]

[0025] where g k,l is the near-field complex-valued channel gain, satisfying r l is the distance from the base station to the energy harvesting receiver l; k is the number of near-field paths; b(θ l ,r l ) is the near-field steering vector, The existence of the visible area is considered in the near-field channel, ⊙ is the Hadamard product, D(Υ l ) represents the visibility or invisibility of the energy harvesting receiver 1 to the BS array; l is the visible area corresponding to the energy harvesting receiver 1; D(Υ l ) is expressed as:

[0026]

[0027] Furthermore, a signal model including an information signal and its digital beamforming matrix, an artificial noise and its digital beamforming matrix is ​​constructed, and the specific steps are as follows:

[0028] Let s and Let the information signal and its digital beamforming matrix, q and are artificial noise and its digital beamforming matrix respectively, and s m ~CN(0,1),q k ~CN(0,1), the signal sent by the base station is expressed as:

[0029]

[0030] in is the analog beamforming matrix. The fixed analog beamforming matrix is ​​designed based on the maximum ratio transmission. The i-th column of the analog beamforming matrix is ​​expressed as:

[0031]

[0032] The signals received by the information decoding receiver m and the energy harvesting receiver k are expressed as:

[0033]

[0034] in and They are the antenna noises of the information decoding receiver and the energy harvesting receiver, respectively.

[0035] Assume that there are two types of information decoding receivers, of which type I receivers do not have the ability to cancel AN interference, while type II receivers have this ability. The signal-to-interference-noise ratios of type I and type II information decoding receivers m are expressed as and

[0036]

[0037] When the energy harvesting receiver k eavesdrops on the signal sent to the information decoding receiver m, the information leakage interference-to-noise ratio is expressed as:

[0038]

[0039] The worst-case secure information rate for information decoding receiver m is expressed as:

[0040]

[0041] Furthermore, the signal model also takes into account the energy of the energy harvesting receiver, which is specifically:

[0042] Considering the linear receiver model, the energy received by the energy harvesting receiver k is expressed as:

[0043]

[0044] where ξ is the energy harvesting efficiency.

[0045] Furthermore, based on the signal model and the mixed field channel model, the objective function of the maximization problem of the weighted sum of the minimum safe rate of the information decoding receiver is formulated. The specific steps are:

[0046] Based on the signal model and the mixed field channel model, the objective function of the weighted sum maximization problem of the minimum safe rate of the information decoding receiver is formulated by considering the digital beamforming matrices w and v, the energy collection constraints of the energy collection receiver, and the transmission power constraints of the base station:

[0047]

[0048] st(15b),(15c)

[0049] Among them, α m is the security rate weighting coefficient of information decoding receiver m, R m is the secure information rate of information decoding receiver m, P max is the maximum transmit power of the base station, constraint (15b) is the energy harvesting constraint of the energy harvesting receiver, and constraint (15c) is the transmit power constraint of the base station.

[0050] Furthermore, before solving the maximization problem, the maximization problem is transformed into a standard convex optimization problem. The specific steps are as follows:

[0051] make The constraint (15a) is equivalent to:

[0052]

[0053] Introducing slack variables And set:

[0054]

[0055] The objective function (15a) is equivalent to:

[0056]

[0057] At the same time, the following constraints are introduced:

[0058]

[0059] For (20a), at the given initial point and right and Perform a first-order Taylor expansion and obtain their respective concave lower bounds:

[0060]

[0061] Substitute the concave lower bound into the original constraint and transform constraint (20a) into a convex constraint:

[0062]

[0063] Similarly, the constraint (20d) is transformed into:

[0064]

[0065] For constraints (20b) and (20c), at the given initial point and right and Performing a first-order Taylor expansion, we obtain their concave lower bounds:

[0066]

[0067] Substituting the concave lower bound into the original constraint, constraints (20b) and (20c) are transformed into:

[0068]

[0069] For the non-convexity of the objective function (16), the slack variable is reintroduced set up:

[0070]

[0071] The objective function (16) is equivalent to:

[0072]

[0073] At the same time, the constraints on λ′ and μ′ are reintroduced as follows:

[0074]

[0075] Perform SCA processing on the terms on the left side of the inequality sign of constraint (31a) and the terms on the right side of the inequality sign of constraint (31b), and transform constraints (31a) and (31b) into:

[0076]

[0077] The energy harvesting constraints are processed and the constraint (15b) is transformed into:

[0078]

[0079] Combining the above operations, problems P1 and P2 are transformed into convex optimization problems P3 and P4 respectively:

[0080]

[0081] st(15c),(23),(24),(27),(28),(34)

[0082]

[0083] st(15c),(24),(28),(32),(33),(34).

[0084] Furthermore, when solving the maximization problem, the convex optimization tool CVX is used for efficient solution.

[0085] Furthermore, to solve the maximization problem, the specific steps are:

[0086] S1. Set the safety rate weighting coefficient α m , energy harvesting threshold Q 0 , the total base station transmission power P max ; Set the cycle number i = 0, initialize the minimum safe rate weighted sum R (i) =R′ (i) =0, precision threshold ε=10 -3 , initialize the digital beamforming matrix w of the information signal (i) =w′ (i) and the digital beamforming matrix v of artificial noise (i) =v′ (i) , and initialize the slack variable τ according to formula (18c) (i) =τ′ (i) , and initialize the slack variable μ according to formulas (18b) and (29b) respectively (i) and μ′ (i) ;

[0087] S2. Given {w (i) ,v (i) ,μ (i) ,τ(i)}, solve problem (P3) to obtain {w (i+1) ,v (i+1) ,μ (i+1) ,τ (i+1) ,R (i +1)}; Given {w′ (i) ,v′ (i) ,μ′ (i) ,τ′ (i)}, solve problem (P4) to obtain {w′ (i+1) ,v′ (i+1) ,μ′ (i+1) ,τ′ (i+1) ,R′ (i+1)};

[0088] S3. Update the ordinal number i=i+1;

[0089] S4. Determine whether |R′ (i) -R′ (i-1) | / R′ (i) ≤ε, if so, the solution ends and the optimal solution w′ is obtained opt =w′ (i) ,v′ opt =v′ (i) , R′ opt =R′ (i) , otherwise repeat steps S2 to S4.

[0090] A communication optimization system for a wireless energy-carrying safety communication system under a long- and short-range mixed field, comprising a mixed field channel model building module, a signal model building module, an objective function module, and a problem solving module;

[0091] The mixed field channel model building module is used to build a signal model including an information signal and its digital beamforming matrix, artificial noise and its digital beamforming matrix;

[0092] The signal model building module is used to build a signal model including an information signal and its digital beamforming matrix, artificial noise and its digital beamforming matrix;

[0093] The objective function module is used to formulate an objective function for maximizing the weighted sum of the minimum safe rate of the information decoding receiver based on the signal model and the mixed field channel model;

[0094] The problem solving module is used to solve the maximization problem and obtain the optimal communication solution.

[0095] The beneficial effects of the present invention are as follows:

[0096] The present invention discloses a communication optimization method for a wireless energy-carrying secure communication system under a near-far mixed field. For the wireless energy-carrying communication system under a near-far mixed field, the present invention studies the beamforming design problem under the coexistence of an energy harvesting receiver and an information decoding receiver, and at the same time utilizes the versatility of artificial noise in the system, using it as a key means to interfere with the eavesdropping capability of a near-field energy harvesting receiver, and also as an important signal source for energy harvesting by the energy harvesting receiver; the present invention proposes a joint optimization algorithm for the power constraint, energy collection constraint and security rate maximization goals of the wireless energy-carrying secure communication system under a mixed field. The algorithm achieves the improvement of the system information transmission security performance by optimizing the digital beamforming of information signals and artificial noise. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 The invention is a flow chart of a communication optimization method of a wireless energy-carrying safety communication system in a mixed long- and short-range field.

[0098] Figure 2 It is a schematic diagram of the wireless energy-carrying safety communication system in a long- and short-range mixed field of the present invention in Example 2.

[0099] Figure 3 This is a schematic diagram of the mixed field channel model in Example 2.

[0100] Figure 4 It is a schematic diagram of the algorithm flow of solving the maximization problem of the present invention in Example 2.

[0101] Figure 5 This is a schematic diagram of the convergence of different algorithms in Example 3.

[0102] Figure 6 This is a graph showing how safety and rate vary with energy harvesting thresholds in Example 3.

[0103] Figure 7 This is a graph showing how security and rate vary with transmission power in Example 3.

[0104] Figure 8 This is a graph showing how safety and rate vary with the number of energy harvesting receivers in Example 3.

[0105] Fig. 9 This is a graph showing how security and rate vary with the number of information decoding receivers in Example 3.

[0106] Fig.10 This is a graph showing how safety and speed vary with the size of the visible area in Example 3. DETAILED DESCRIPTION

[0107] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0108] Example 1

[0109] like Figure 1 As shown, a communication optimization method for a wireless energy-carrying safety communication system in a long-distance mixed field includes the following specific steps:

[0110] Construct a wireless energy-carrying secure communication system in a near- and far-field mixed field, including a base station, K near-field single-antenna energy harvesting receivers, and M far-field single-antenna information decoding receivers;

[0111] Construct a mixed-field channel model;

[0112] Constructing a signal model including an information signal and its digital beamforming matrix, an artificial noise and its digital beamforming matrix;

[0113] Based on the signal model and the mixed field channel model, the objective function of the maximization problem of the weighted sum of the minimum safe rate of the information decoding receiver is formulated;

[0114] Solve the maximization problem and obtain the optimal communication solution.

[0115] Example 2

[0116] More specifically, Figure 2 As shown, in a specific embodiment, the base station is equipped with a uniform linear array with N antennas and a beamformer with a fully connected structure, including multiple RF links; the energy harvesting receiver is located in the Fresnel near-field area of ​​the base station, and is used to collect energy from the RF signal; the information decoding receiver is located in the far-field area of ​​the base station, and is used to receive information signals.

[0117] In this embodiment, the information decoding receiver is located at the Rayleigh distance d R =2D 2 / λ, the energy harvesting receiver is located within the Rayleigh distance and the Fresnel distance outside.

[0118] In a specific embodiment, constructing Figure 3 The mixed field channel model shown in the figure has the following specific steps:

[0119] Assume that the coordinates of the nth antenna unit are (0,δ n d), the coordinates of the lth receiver are The distance from the nth antenna element to the lth receiver is expressed as:

[0120]

[0121] in, is the antenna spacing, λ is the wavelength, N is the number of antenna units, θ lrepresents the angle of the lth receiver relative to the base station antenna array;

[0122] Considering the superposition of the direct visual LoS path and the indirect visual NLoS path, the channel from the base station to the mth information decoding receiver is expressed as:

[0123]

[0124] Among them, g m,l is the far-field complex-valued channel gain, satisfying L m is the number of far-field paths, l = 1 indicates a LoS path; a(θ l ) is the far-field steering vector,

[0125] Also considering the superposition of the LoS path and the NLoS path, the channel from the base station to the kth energy harvesting receiver is expressed as:

[0126]

[0127] where g k,l is the near-field complex-valued channel gain, satisfying r l is the distance from the base station to the energy harvesting receiver l; k is the number of near-field paths; b(θ l ,r l ) is the near-field steering vector, The existence of the visible area is considered in the near-field channel, ⊙ is the Hadamard product, D(Υ l ) represents the visibility or invisibility of the energy harvesting receiver 1 to the BS array; l is the visible area corresponding to the energy harvesting receiver 1; D(Υ l ) is expressed as:

[0128]

[0129] In a specific embodiment, a signal model including an information signal and its digital beamforming matrix, an artificial noise and its digital beamforming matrix is ​​constructed, and the specific steps are as follows:

[0130] Let s and Let the information signal and its digital beamforming matrix, q and are artificial noise and its digital beamforming matrix respectively, and s m ~CN(0,1),q k ~CN(0,1), the signal sent by the base station is expressed as:

[0131]

[0132] in is the analog beamforming matrix. A fixed analog beamforming matrix is ​​designed based on maximum ratio transmission. In order to reduce the complexity of hybrid beamforming and balance information transmission and energy collection, a fixed analog beamforming matrix is ​​designed based on maximum ratio transmission. The i-th column of the analog beamforming matrix is ​​expressed as:

[0133]

[0134] The signals received by the information decoding receiver m and the energy harvesting receiver k are expressed as:

[0135]

[0136] in and They are the antenna noises of the information decoding receiver and the energy harvesting receiver, respectively.

[0137] Assume that there are two types of information decoding receivers, of which type I receivers do not have the ability to cancel AN interference, while type II receivers have this ability. The signal-to-interference-noise ratios of type I and type II information decoding receivers m are expressed as and

[0138]

[0139] When the energy harvesting receiver k eavesdrops on the signal sent to the information decoding receiver m, the information leakage interference-to-noise ratio is expressed as:

[0140]

[0141] The worst-case secure information rate for information decoding receiver m is expressed as:

[0142]

[0143] In a specific embodiment, the energy harvesting receiver has two main sources of energy: on the one hand, it obtains energy by eavesdropping on the signal transmitted from the base station to the information decoding receiver; on the other hand, the base station provides additional energy to the energy harvesting receiver through the AN. The signal model also takes into account the energy of the energy harvesting receiver, which is specifically:

[0144] Considering the linear receiver model, the energy received by the energy harvesting receiver k is expressed as:

[0145]

[0146] where ξ is the energy harvesting efficiency.

[0147] In a specific embodiment, based on the signal model and the mixed field channel model, an objective function of maximizing the weighted sum of the minimum safe rate of the information decoding receiver is formulated, and the specific steps are:

[0148] Based on the signal model and the mixed field channel model, the objective function of the weighted sum maximization problem of the minimum safe rate of the information decoding receiver is formulated by considering the digital beamforming matrices w and v, the energy collection constraints of the energy collection receiver, and the transmission power constraints of the base station:

[0149]

[0150] st(15b),(15c)

[0151] Among them, α m is the security rate weighting coefficient of information decoding receiver m, R m is the secure information rate of information decoding receiver m, P max is the maximum transmit power of the base station, constraint (15b) is the energy harvesting constraint of the energy harvesting receiver, and constraint (15c) is the transmit power constraint of the base station.

[0152] In a specific embodiment, before solving the maximization problem, the maximization problem is also converted into a standard convex optimization problem, and the specific steps are:

[0153] set up[] + The sign does not affect the optimal solution. The constraint (15a) is equivalent to:

[0154]

[0155] Introducing slack variables And set:

[0156]

[0157] The objective function (15a) is equivalent to:

[0158]

[0159] At the same time, the following constraints are introduced:

[0160]

[0161] At this time, the objective function is a concave function, but the introduced constraints (20a)-(20d) are still non-convex, so the SCA method is used to deal with them. For (20a), at the given initial point and right and Perform a first-order Taylor expansion and obtain their respective concave lower bounds:

[0162]

[0163] Substitute the concave lower bound into the original constraint and transform constraint (20a) into a convex constraint:

[0164]

[0165] Similarly, the constraint (20d) is transformed into:

[0166]

[0167] For constraints (20b) and (20c), the terms on the right side of their inequalities are convex, and at a given initial point and right and Performing a first-order Taylor expansion, we obtain their concave lower bounds:

[0168]

[0169] Substituting the concave lower bound into the original constraint, constraints (20b) and (20c) are transformed into:

[0170]

[0171]

[0172] For the non-convexity of the objective function (16), the treatment method is similar to the above method, reintroducing the slack variable set up:

[0173]

[0174] The objective function (16) is equivalent to:

[0175]

[0176] At the same time, the constraints on λ′ and μ′ are reintroduced as follows:

[0177]

[0178] Similarly, the terms on the left side of the inequality sign of constraint (31a) and the terms on the right side of the inequality sign of constraint (31b) are processed by SCA, and constraints (31a) and (31b) are converted to:

[0179]

[0180] The energy harvesting constraints are processed and the constraint (15b) is transformed into:

[0181]

[0182] Combining the above operations, problems P1 and P2 are transformed into convex optimization problems P3 and P4 respectively:

[0183]

[0184] st(15c),(23),(24),(27),(28),(34)

[0185]

[0186] st(15c),(24),(28),(32),(33),(34).

[0187] In a specific embodiment, when solving the maximization problem, a convex optimization tool CVX is used for efficient solution.

[0188] In a specific embodiment, Figure 4 As shown, the specific steps to solve the maximization problem are as follows;

[0189] S1. Set the safety rate weighting coefficient α m , energy harvesting threshold Q 0 , the total base station transmission power P max ; Set the cycle number i = 0, initialize the minimum safe rate weighted sum R (i) =R′ (i) =0, precision threshold ε=10 -3 , initialize the digital beamforming matrix w of the information signal (i) =w′ (i) and the digital beamforming matrix v of artificial noise (i) =v′ (i) , and initialize the slack variable τ according to formula (18c) (i) =τ′ (i) , and initialize the slack variable μ according to formulas (18b) and (29b) respectively (i) and μ′ (i) ;

[0190] S2. Given {w (i) ,v (i) ,μ (i) ,τ (i)}, solve problem (P3) to obtain {w (i+1) ,v (i+1) ,μ (i+1) ,τ (i+1) ,R (i +1)}; Given {w′ (i),v′ (i) ,μ′ (i) ,τ′ (i)}, solve problem (P4) to obtain {w′ (i+1) ,v′ (i+1) ,μ′ (i+1) ,τ′ (i+1) ,R′ (i+1)};

[0191] S3. Update the ordinal number i=i+1;

[0192] S4. Determine whether |R′ (i) -R′ (i-1) | / R′ (i) ≤ε, if so, the solution ends and the optimal solution w′ is obtained opt =w′ (i) ,v′ opt =v′ (i) , R′ opt =R′ (i) , otherwise repeat steps S2 to S4.

[0193] In this embodiment, the present invention has the following advantages:

[0194] 1) The present invention considers deploying XL-MIMO in base stations. Compared with the traditional large-scale multiple-input multiple-output technology, XL-MIMO can achieve higher spectrum efficiency and spatial resolution in the communication system by further increasing the number of antennas, usually reaching hundreds or even thousands of antennas.

[0195] 2) The present invention adopts a mixed near-field and far-field multipath channel model. When XL-MIMO is deployed at the base station, the locations of some receivers may fall into the near-field area. Therefore, the present invention establishes a near-field multipath channel model for the energy collection receiver that is closer, and at the same time establishes a far-field multipath channel model for the information decoding receiver. This modeling is closer to the actual communication environment.

[0196] 3) The present invention utilizes the versatility of artificial noise in the system, using it as a key means to interfere with the eavesdropping ability of the near-field energy harvesting receiver, and also as an important signal source for energy harvesting by the energy harvesting receiver. By optimizing the beamforming of the information signal and artificial noise, the energy collection requirements of the energy harvesting receiver are guaranteed, and its eavesdropping behavior is effectively suppressed, thereby improving the information transmission security of the system.

[0197] Example 3

[0198] In this embodiment, the number of base station antennas N is set to 128, and the total transmission power P max =1W, energy harvesting threshold Q 0 =0.005uW, safety rate weighting coefficient Precision threshold ε=10 -3 ,The performance of the communication optimization method of the present invention under different ,schemes is compared through computer simulation. The specific scheme names are shown in Table 1.

[0199] Table 1: Chinese and English comparison table of the comparison schemes used in the simulation:

[0200]

[0201]

[0202] like Figure 5 As shown in the figure, the convergence behavior of all methods is plotted. The results show that under different energy harvesting thresholds considered, the algorithm usually converges within 5 iterations, and the convergence speed is fast. In addition, under the same energy harvesting threshold, the system performance of the type II receiver is better than that of the type I receiver, because the type II information decoding receiver can offset the interference of artificial noise, thereby improving the security and rate of the system, which is in line with expectations.

[0203] like Figure 6 As shown, with the increase of the energy collection threshold, the system safety and rate of the five schemes all decrease. This is because the larger the energy collection threshold, the base station will allocate more power to artificial noise to meet the energy collection requirements, thereby allocating less power to the information signal, resulting in a decrease in the system safety and rate. In addition, the system safety performance of the scheme of the present invention is better than that of the scheme without artificial noise, and with the increase of the energy collection threshold, the speed of the safety and rate decrease is slightly slower than that of the scheme without artificial noise, which proves the effectiveness and necessity of the use of artificial noise in improving the safety of the hybrid field wireless energy communication system. However, the system safety performance of the proposed scheme is inferior to that of the all-digital beamforming scheme, and with the increase of the energy collection threshold, the speed of the safety and rate decrease is faster than that of the all-digital beamforming scheme. This is because in order to reduce the computational complexity, the hybrid beamforming method adopted by the proposed scheme is fixed analog beamforming and only optimizes digital beamforming, while all-digital beamforming has higher flexibility and optimization capability, and can more effectively improve the safety performance of the system, but all-digital beamforming requires an independent RF link to be configured for each antenna, and the hardware cost and system complexity increase significantly with the increase in the number of antennas.

[0204] like Figure 7As shown in the figure, with the increase of base station transmission power, the system security and rate of the five schemes are significantly improved. This is because the increase of transmission power increases the strength of information signals and artificial noise, which can not only increase the information rate of legitimate information decoding receivers, but also aggravate the artificial noise interference suffered by eavesdropping receivers. Although the eavesdropping rate may increase with the increase of transmission power, the security and rate of the system still increase with the increase of transmission power because the signal gain received by the legitimate receiver is more significant.

[0205] like Figure 8 As shown in the figure, as the number of energy harvesting receivers increases, the system security and rate of the five schemes decrease. This is because the number of energy harvesting receivers increases, the number of potential eavesdroppers in the system increases, and the information signal is more easily received and eavesdropped by these receivers, resulting in a decrease in the security and rate of the system. In addition, as the number of energy harvesting receivers increases, the security and rate of the proposed scheme and the scheme without artificial noise decrease faster than the full digital beamforming scheme. This is because the full digital beamforming method provides a larger optimization space and can more flexibly adjust the transmission signal of each antenna, thereby improving the system's ability to resist eavesdropping.

[0206] like Fig. 9 As shown, as the number of information decoding receivers increases, the system security and rate of the five schemes increase accordingly. This is because the increase in the number of information decoding receivers increases the number of information beams, thereby improving the coverage and transmission rate of the information.

[0207] like Fig.10As shown, when the number of antennas in the visible area of ​​each energy harvesting receiver does not exceed 32, the visible areas of each energy harvesting receiver are independent of each other and do not overlap. Under this condition, as the number of antennas in the visible area increases, the channel conditions of the energy harvesting receiver are improved, and the energy collection efficiency is improved, so that the system allocates more power to the information signal, while reducing the power allocation of artificial noise, thereby improving the security and rate of the system. When the number of antennas in the visible area of ​​the energy harvesting receiver exceeds 32, the visible areas of each energy harvesting receiver begin to overlap. This overlap increases the interference to the energy harvesting receiver, thereby effectively suppressing the eavesdropping ability of the energy harvesting receiver, and further accelerating the growth trend of system security and rate. In addition, the scheme of the present invention is designed based on a non-stationary channel, and in the comparative stable channel scheme, since the visible area of ​​each energy harvesting receiver covers the entire antenna array, its channel condition is significantly better than that of the non-stationary channel, so that the system can allocate power more effectively and improve the security and rate of the system. At the same time, in the stable channel scheme, the visible areas of each energy harvesting receiver completely overlap, resulting in significantly enhanced interference between energy harvesting receivers. Although this interference may have a certain impact on the energy harvesting efficiency, it can effectively suppress the eavesdropping capability of the energy harvesting receiver, thereby further enhancing the security of system information transmission.

[0208] Example 4

[0209] A communication optimization system for a wireless energy-carrying safety communication system under a long- and short-range mixed field, comprising a mixed field channel model building module, a signal model building module, an objective function module, and a problem solving module;

[0210] The mixed field channel model building module is used to build a signal model including an information signal and its digital beamforming matrix, artificial noise and its digital beamforming matrix;

[0211] The signal model building module is used to build a signal model including an information signal and its digital beamforming matrix, artificial noise and its digital beamforming matrix;

[0212] The objective function module is used to formulate an objective function for maximizing the weighted sum of the minimum safe rate of the information decoding receiver based on the signal model and the mixed field channel model;

[0213] The problem solving module is used to solve the maximization problem and obtain the optimal communication solution.

[0214] Obviously, the above embodiments of the present invention are only examples for clearly explaining the present invention, and are not intended to limit the implementation methods of the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention shall be included in the protection scope of the claims of the present invention.

Claims

1. A communication optimization method for a wireless energy-carrying safety communication system in a mixed long- and short-range environment, characterized in that: The specific steps include: Construct a wireless energy-carrying secure communication system in a near- and far-field mixed field, including a base station, K near-field single-antenna energy harvesting receivers, and M far-field single-antenna information decoding receivers; Constructing a mixed-field channel model; Constructing a signal model including an information signal and its digital beamforming matrix, an artificial noise and its digital beamforming matrix; Based on the signal model and the mixed field channel model, the objective function of the maximization problem of the weighted sum of the minimum safe rate of the information decoding receiver is formulated; Solve the maximization problem and obtain the optimal communication solution.

2. The communication optimization method of the wireless energy-carrying safety communication system in a long- and short-range mixed field according to claim 1 is characterized in that: The base station is equipped with a uniform linear array with N antennas and a beamformer with a fully connected structure, including multiple radio frequency links; the energy harvesting receiver is located in the Fresnel near-field area of ​​the base station and is used to collect energy from radio frequency signals; the information decoding receiver is located in the far-field area of ​​the base station and is used to receive information signals.

3. The communication optimization method of the wireless energy-carrying safety communication system in a long- and short-range mixed field according to claim 2 is characterized in that: Construct a mixed field channel model. The specific steps are: Assume that the coordinates of the nth antenna unit are (0,δ n d), the coordinates of the lth receiver are The distance from the nth antenna element to the lth receiver is expressed as: in, is the antenna spacing, λ is the wavelength, n=0,…,N-1, N is the number of antenna units, θ l represents the angle of the lth receiver relative to the base station antenna array; Considering the superposition of the direct visual LoS path and the indirect visual NLoS path, the channel from the base station to the mth information decoding receiver is expressed as: Among them, g m,l is the far-field complex-valued channel gain, satisfying L m is the number of far-field paths, l = 1 indicates a LoS path; a(θ l ) is the far-field steering vector, Also considering the superposition of the LoS path and the NLoS path, the channel from the base station to the kth energy harvesting receiver is expressed as: where g k,l is the near-field complex-valued channel gain, satisfying r l is the distance from the base station to the energy harvesting receiver l; k is the number of near-field paths; b(θ l ,r l ) is the near-field steering vector, The existence of the visible area is considered in the near-field channel, ⊙ is the Hadamard product, D(Υ l ) represents the visibility or invisibility of the energy harvesting receiver 1 to the BS array; l is the visible area corresponding to the energy harvesting receiver 1; D(Υ l ) is expressed as:

4. The communication optimization method of the wireless energy-carrying safety communication system in a long- and short-range mixed field according to claim 3 is characterized in that: Construct a signal model including the information signal and its digital beamforming matrix, artificial noise and its digital beamforming matrix. The specific steps are as follows: Let s and Let the information signal and its digital beamforming matrix, q and are artificial noise and its digital beamforming matrix respectively, and s m ~CN(0,1),q k ~CN(0,1), the signal sent by the base station is expressed as: in is the analog beamforming matrix. The fixed analog beamforming matrix is ​​designed based on the maximum ratio transmission. The i-th column of the analog beamforming matrix is ​​expressed as: The signals received by the information decoding receiver m and the energy harvesting receiver k are expressed as: in and They are the antenna noises of the information decoding receiver and the energy harvesting receiver, respectively. Assume that there are two types of information decoding receivers, of which type I receivers do not have the ability to cancel AN interference, while type II receivers have this ability. The signal-to-interference-noise ratios of type I and type II information decoding receivers m are expressed as and When the energy harvesting receiver k eavesdrops on the signal sent to the information decoding receiver m, the information leakage interference-to-noise ratio is expressed as: The worst-case secure information rate for information decoding receiver m is expressed as:

5. The communication optimization method of the wireless energy-carrying safety communication system in a long- and short-range mixed field according to claim 4 is characterized in that: The signal model also takes into account the energy of the energy harvesting receiver, which is specifically: Considering the linear receiver model, the energy received by the energy harvesting receiver k is expressed as: where ξ is the energy harvesting efficiency.

6. The communication optimization method of the wireless energy-carrying safety communication system in a long- and short-range mixed field according to claim 5 is characterized in that: Based on the signal model and the mixed field channel model, the objective function of the maximization problem of the weighted sum of the minimum safe rate of the information decoding receiver is formulated. The specific steps are: Based on the signal model and the mixed field channel model, the objective function of the weighted sum maximization problem of the minimum safe rate of the information decoding receiver is formulated by considering the digital beamforming matrices w and v, the energy collection constraints of the energy collection receiver, and the transmission power constraints of the base station: (P1)： s.t. (P2)： st(15b),(15c) Among them, α m is the security rate weighting coefficient of information decoding receiver m, R m is the secure information rate of information decoding receiver m, P max is the maximum transmit power of the base station, constraint (15b) is the energy harvesting constraint of the energy harvesting receiver, and constraint (15c) is the transmit power constraint of the base station.

7. The communication optimization method of the wireless energy-carrying safety communication system in a long- and short-range mixed field according to claim 6 is characterized in that: Before solving the maximization problem, the maximization problem is transformed into a standard convex optimization problem. The specific steps are as follows: make The constraint (15a) is equivalent to: Introducing slack variables And set: The objective function (15a) is equivalent to: At the same time, the following constraints are introduced: For (20a), at the given initial point and right and Perform a first-order Taylor expansion and obtain their respective concave lower bounds: Substitute the concave lower bound into the original constraint and transform constraint (20a) into a convex constraint: Similarly, the constraint (20d) is transformed into: For constraints (20b) and (20c), at the given initial point and right and Performing a first-order Taylor expansion, we obtain their concave lower bounds: Substituting the concave lower bound into the original constraint, constraints (20b) and (20c) are transformed into: For the non-convexity of the objective function (16), the slack variable is reintroduced set up: The objective function (16) is equivalent to: At the same time, the constraints on λ′ and μ′ are reintroduced as follows: Perform SCA processing on the terms on the left side of the inequality sign of constraint (31a) and the terms on the right side of the inequality sign of constraint (31b), and transform constraints (31a) and (31b) into: The energy harvesting constraints are processed and the constraint (15b) is transformed into: Combining the above operations, problems P1 and P2 are transformed into convex optimization problems P3 and P4 respectively: (P3)： st(15c),(23),(24),(27),(28),(34) (P4)： st(15c),(24),(28),(32),(33),(34).

8. The communication optimization method of the wireless energy-carrying safety communication system in a long- and short-range mixed field according to claim 7 is characterized in that: When solving the maximization problem, the convex optimization tool CVX is used for efficient solution.

9. The communication optimization method of the wireless energy-carrying safety communication system in a long- and short-range mixed field according to claim 7 is characterized in that: To solve the maximization problem, the specific steps are: S1. Set the safety rate weighting coefficient α m , energy collection threshold Q0, base station transmission total power P max ; set up Cycle number i = 0, initialize the minimum safe rate weighted sum R (i) =R ′(i) =0, precision threshold ε=10 -3 , initialize the digital beamforming matrix w of the information signal (i) =w ′(i) and the digital beamforming matrix v of artificial noise (i) =v ′(i) , and initialize the slack variable τ according to formula (18c) (i) =τ ′(i) , and initialize the slack variable μ according to formulas (18b) and (29b) respectively (i) and μ ′(i) ; S2. Given {w (i) ,v (i) ,μ (i) ,τ (i) }, solve problem (P3) to obtain {w (i+1) ,v (i+1) ,μ (i+1) ,τ (i+1) ,R (i+1) }; Given {w ′(i) ,v ′(i) ,μ ′(i) ,τ ′(i) }, solve problem (P4) to obtain {w ′(i+1) ,v ′(i+1) ,μ ′(i+1) ,τ ′(i+1) ,R ′(i+1) }; S3. Update the ordinal number i=i+1; S4. Determine whether |R ′(i) -R ′(i-1) | / R ′(i) ≤ε, if so, the solution ends and the optimal solution w is obtained ′opt =w ′(i) ,v ′opt =v ′(i) , R ′opt =R ′(i) , otherwise repeat steps S2 to S4.

10. A communication optimization system for a wireless energy-carrying safety communication system in a mixed long- and short-range environment, characterized in that: It includes a mixed field channel model building module, a signal model building module, an objective function module, and a problem solving module; The mixed field channel model building module is used to build a signal model including an information signal and its digital beamforming matrix, artificial noise and its digital beamforming matrix; The signal model building module is used to build a signal model including an information signal and its digital beamforming matrix, artificial noise and its digital beamforming matrix; The objective function module is used to formulate an objective function for maximizing the weighted sum of the minimum safe rate of the information decoding receiver based on the signal model and the mixed field channel model; The problem solving module is used to solve the maximization problem and obtain the optimal communication solution.

Images