Methods, devices and computer equipment for covert communication of unmanned aerial vehicles (UAVs)

Abstract

This application relates to a method, apparatus, and computer device for covert communication between unmanned aerial vehicles (UAVs). The method includes: performing channel modeling on the communication channel between a transmitter, a reflector architecture, at least one receiver, and corresponding malicious terminals in a communication system, obtaining channel modeling results; determining a joint optimization model for resource allocation based on the channel modeling results and preset constraints, and decoupling the joint optimization model to obtain resource allocation parameters; using the resource allocation parameters, virtually partitioning each reflector unit in the reflector architecture to obtain the number of reflector units in each sub-region and determining the reflection power of each sub-region, so that the receiver and malicious terminals can communicate with the transmitter through the reflector units and reflection power of each sub-region. This method can improve the security of the source UAV's location information during UAV missions.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) communication technology, and in particular to a method, apparatus, and computer device for UAV location covert communication. Background Technology

[0002] The rapid development of Unmanned Aerial Vehicle (UAV) technology has driven its widespread application in many emerging fields, such as search and rescue, emergency communications, relay communications, and aerial reconnaissance. To ensure the smooth execution of UAV swarm missions, a reliable, stable, and efficient wireless communication network needs to be established between the UAVs.

[0003] However, due to the openness and broadcasting nature of wireless communication, UAV communication networks face severe challenges in terms of security and reliability. During communication between UAVs, malicious UAVs may use received signals to infer the location information of the source UAV, leading to the leakage of the source UAV's location privacy and seriously threatening the integrity and confidentiality of UAV missions. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, and computer equipment for UAV location covert communication that can improve the security of the source UAV's location information during UAV missions, in order to address the aforementioned technical problems.

[0005] In a first aspect, this application provides a method for covert communication of unmanned aerial vehicles (UAVs), applied to a communication system, the communication system including a transmitter, a reflective surface architecture, and at least one receiver; the method includes:

[0006] Channel modeling is performed on the communication channels between the transmitter, reflector architecture, at least one receiver, and each malicious terminal corresponding to the communication system in the communication system, and the channel modeling results are obtained.

[0007] Based on the channel modeling results and preset constraints, a joint optimization model for resource allocation is determined, and the joint optimization model for resource allocation is decoupled to obtain the resource allocation parameters of the joint optimization model for resource allocation under the condition that the channel modeling results and preset constraints are satisfied.

[0008] The resource allocation parameters are used to virtually partition each reflective unit in the reflective surface architecture to obtain the number of reflective units in each sub-region and determine the reflection power of each sub-region, so that the receiving end and the malicious terminal can communicate with the sending end through the reflective units and reflection power of each sub-region.

[0009] In one embodiment, the sub-region includes a communication enhancement sub-region and multiple location interference sub-regions, the location interference sub-region including a preset noise module, the preset noise module being used to transmit noise interference signals to the malicious terminal;

[0010] The communication channels between the transmitter, the reflector architecture, at least one receiver, and each malicious terminal corresponding to the communication system in the communication system include at least: a first communication channel between the transmitter and the receiver, a second communication channel between the transmitter and the communication enhancement sub-region, a third communication channel between the transmitter and each of the positioning interference sub-regions, a fourth communication channel between the transmitter and the malicious terminal, a fifth communication channel between the communication enhancement sub-region and each of the receivers, a sixth communication channel between the communication enhancement sub-region and the malicious terminal, and a seventh communication channel between each of the positioning interference sub-regions and the malicious terminal.

[0011] In one embodiment, the process of performing channel modeling on the communication channel between the transmitter, the reflector architecture, at least one receiver, and each malicious terminal corresponding to the communication system in the communication system, and obtaining the channel modeling result, includes:

[0012] For each of the receiving ends, the communication quality parameters of the receiving end are determined based on the ratio of the target signal power to the interference signal power received by the receiving end. The target signal power includes at least the signal power transmitted through the first communication channel, the signal power transmitted through the second communication channel, and the signal power transmitted through the fifth communication channel. The interference signal power includes at least the interference signal power of each of the other receiving ends, the self-interference signal power of the communication enhancement sub-region, and the interference signal power of the external environment.

[0013] For each malicious terminal, based on the ratio of the interference signal power of the malicious terminal to the useful signal power received by the malicious terminal by the preset noise module, the interference communication parameters of the malicious terminal are determined. The interference signal power includes at least the signal power transmitted through the seventh communication channel, and the useful signal power includes at least the signal power transmitted through the fourth communication channel, the sixth communication channel, the second communication channel, the seventh communication channel, and the third communication channel.

[0014] The received signal strength of each malicious terminal is modeled and processed to obtain the lower limit threshold of the positioning error of the malicious terminal to the sending end;

[0015] Based on the aforementioned communication quality parameters, the aforementioned interference communication parameters, and the aforementioned lower limit threshold for positioning error, the channel modeling result is determined.

[0016] Secondly, this application also provides a concealed communication device for unmanned aerial vehicles (UAVs), applied to a communication system, the communication system including a transmitter, a reflective surface architecture, and at least one receiver; including:

[0017] The channel modeling module is used to perform channel modeling on the communication channels between the transmitter, reflector architecture, at least one receiver, and each malicious terminal corresponding to the communication system in the communication system, and to obtain the channel modeling results.

[0018] The processing module is used to determine a joint optimization model for resource allocation based on the channel modeling results and preset constraints, and to decouple the joint optimization model for resource allocation to obtain the resource allocation parameters of the joint optimization model for resource allocation under the condition that the channel modeling results and the preset constraints are satisfied.

[0019] The partitioning module is used to virtually partition each reflective unit contained in the reflective surface architecture using the resource allocation parameters, to obtain the number of reflective units in each sub-region, and to determine the reflection power of each sub-region, so that the receiving end and the malicious terminal can communicate with the sending end through the reflective units and reflection power of each sub-region.

[0020] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0021] Channel modeling is performed on the communication channels between the transmitter, reflector architecture, at least one receiver, and each malicious terminal corresponding to the communication system in the communication system, and the channel modeling results are obtained.

[0022] Based on the channel modeling results and preset constraints, a joint optimization model for resource allocation is determined, and the joint optimization model for resource allocation is decoupled to obtain the resource allocation parameters of the joint optimization model for resource allocation under the condition that the channel modeling results and preset constraints are satisfied.

[0023] The resource allocation parameters are used to virtually partition each reflective unit in the reflective surface architecture to obtain the number of reflective units in each sub-region and determine the reflection power of each sub-region, so that the receiving end and the malicious terminal can communicate with the sending end through the reflective units and reflection power of each sub-region.

[0024] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0025] Channel modeling is performed on the communication channels between the transmitter, reflector architecture, at least one receiver, and each malicious terminal corresponding to the communication system in the communication system, and the channel modeling results are obtained.

[0026] Based on the channel modeling results and preset constraints, a joint optimization model for resource allocation is determined, and the joint optimization model for resource allocation is decoupled to obtain the resource allocation parameters of the joint optimization model for resource allocation under the condition that the channel modeling results and preset constraints are satisfied.

[0027] The resource allocation parameters are used to virtually partition each reflective unit in the reflective surface architecture to obtain the number of reflective units in each sub-region and determine the reflection power of each sub-region, so that the receiving end and the malicious terminal can communicate with the sending end through the reflective units and reflection power of each sub-region.

[0028] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0029] Channel modeling is performed on the communication channels between the transmitter, reflector architecture, at least one receiver, and each malicious terminal corresponding to the communication system in the communication system, and the channel modeling results are obtained.

[0030] Based on the channel modeling results and preset constraints, a joint optimization model for resource allocation is determined, and the joint optimization model for resource allocation is decoupled to obtain the resource allocation parameters of the joint optimization model for resource allocation under the condition that the channel modeling results and preset constraints are satisfied.

[0031] The resource allocation parameters are used to virtually partition each reflective unit in the reflective surface architecture to obtain the number of reflective units in each sub-region and determine the reflection power of each sub-region, so that the receiving end and the malicious terminal can communicate with the sending end through the reflective units and reflection power of each sub-region.

[0032] The aforementioned method, apparatus, and computer equipment for covert UAV communication, through channel modeling of the communication channels between the transmitter, reflector architecture, at least one receiver, and each malicious terminal corresponding to the communication system, obtains channel modeling results. Based on the channel modeling results and preset constraints, a joint optimization model for resource allocation is constructed, establishing a joint optimization framework for communication enhancement and location interference. The joint optimization model for resource allocation is then decoupled and solved to obtain resource allocation parameters. These parameters are used to dynamically divide the reflector units of the reflector architecture module and allocate power to each sub-region. This enhances the communication rate between the source UAV and the receiver UAV while simultaneously interfering with the malicious UAV's interference with the source UAV, ensuring the security of the communication system during communication. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is an application environment diagram of a UAV location concealment communication method in one embodiment;

[0035] Figure 2 This is a flowchart illustrating a method for covert communication of UAV location in one embodiment;

[0036] Figure 3 This is a flowchart illustrating a method for covert communication of UAV location in one embodiment;

[0037] Figure 4 This is a flowchart illustrating a method for covert communication of UAV location in one embodiment;

[0038] Figure 5 This is a structural block diagram of a drone location concealment communication device in one embodiment;

[0039] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0041] The UAV location concealment communication method provided in this application embodiment can be applied to, for example... Figure 1 The communication system shown includes a transmitter, a reflector architecture, and at least one receiver. The transmitter can be a source UAV (SU) equipped with M antennas; the receiver can be K valid single-antenna valid receiving UAVs (RU), denoted as […]. The reflector architecture can have N t The active reconfigurable intelligent surface (ARIS) architecture consists of multiple reflective units. The reflective surface architecture is divided into multiple sub-regions, including a communication enhancement sub-region and multiple location jamming sub-regions, each corresponding to a malicious terminal.

[0042] The transmitter, receiver, and reflector architecture can share location information. The communication system operates in an open wireless communication environment, where malicious terminals, such as malicious UAVs (MUs), may attempt to locate the source UAV by receiving signals.

[0043] The communication system also includes a processing end, which transmits signals with the transmitting end, reflector architecture, and receiving end, and processes the signal data. The processing end can be a base station, server, intelligent processor, field programmable gate array (FPGA), and other devices.

[0044] The reflector architecture may also include a control unit, which receives processing signals from the processing unit, performs virtual partitioning of the reflector architecture, etc. The control unit can be a RIS controller. The reflector architecture for locating interference sub-regions may include phase shift circuits, reflective amplifiers, and controllable artificial noise sources.

[0045] Optionally, the positions of the transmitter, reflector architecture, and each receiver are as follows: the position of the transmitter is... The location of the reflector structure is The position of the kth receiver is Assume that these three types of entities can share location information. Furthermore, in an open wireless communication environment, there are E malicious nodes (Malicious UAVs, MUs) attempting to illegally locate SU by analyzing received signals. The set of malicious nodes is denoted as... The corresponding coordinates are Assuming that the distances between SU ​​and RU and all MU are sufficiently large, a plane wave approximation can be used.

[0046] In one exemplary embodiment, such as Figure 2 As shown, a method for covert communication of UAV location is provided. This embodiment applies this method to... Figure 1 Taking a communication system as an example, the following steps are included:

[0047] Step 201: Perform channel modeling on the communication channels between the transmitter, reflector architecture, at least one receiver, and each malicious terminal in the communication system to obtain the channel modeling results.

[0048] The channel modeling results reflect the channel quality of each receiver's communication channel and the channel state information of a malicious terminal's communication channel. The receiver's communication channel includes the direct path communication channel between the transmitter and receiver, and the reflection path communication channel through the reflector architecture. Similarly, the malicious terminal's communication channel includes the direct path communication channel between the transmitter and receiver, and the reflection path communication channel through the reflector architecture. A malicious terminal corresponding to the communication system is a terminal that may receive signals from the communication system, maliciously locate the system, and interfere with its normal operation; that is, a receiver outside the communication system.

[0049] Specifically, considering the communication channel between the transmitter, reflector architecture, and at least one receiver in the communication system, channel modeling is performed based on the communication channel to obtain the channel modeling results for each receiver; considering the communication channel between the transmitter, reflector architecture, and the malicious terminal corresponding to the communication system, channel modeling is performed based on the communication channel to obtain the channel modeling results for each malicious terminal.

[0050] Step 202: Based on the channel modeling results and preset constraints, obtain the resource allocation joint optimization model, and decouple the resource allocation joint optimization model to obtain the resource allocation parameters of the resource allocation joint optimization model under the condition that the channel modeling results and preset constraints are satisfied.

[0051] The preset constraints can ensure that the total transmit power of the transmitter does not exceed a transmit power threshold, the total resource allocation of the reflector architecture does not exceed a total resource threshold, and the channel modeling result does not exceed a channel threshold. The joint optimization model for resource allocation reflects the communication rate of the receiver and the interference intensity to malicious terminals. Resource allocation parameters include the virtual partitioning ratio of the reflector architecture's reflective units and the power allocation ratio of the partitioned reflector architecture.

[0052] Specifically, the control module of the communication system determines the joint optimization model for resource allocation based on channel modeling results and preset constraints. The joint optimization model is then decoupled in the physical domain to obtain the virtual partitioning ratio of the reflector units in the reflector architecture, and the power allocation ratio of the partitioned reflector architecture, under the condition that the channel modeling results and preset constraints are satisfied.

[0053] Step 203: By using resource allocation parameters, the reflective units contained in the reflective surface architecture are virtually partitioned to obtain the number of reflective units in each sub-region and to determine the reflection power of each sub-region, so that the receiving end and the malicious terminal can communicate with the sending end through the reflective units and reflection power of each sub-region respectively.

[0054] The reflector architecture includes multiple reflective elements, with each sub-region containing at least one reflective element. The reflective elements in each sub-region shape the beam by altering the propagation direction of the beam signal through phase modulation.

[0055] Specifically, by using resource allocation parameters, the reflective units within the reflective surface architecture are virtually partitioned, and corresponding reflective units and reflective powers are allocated to each sub-region of the reflective surface architecture. Both the receiving end and malicious terminals can communicate with the sending end through the reflective units and reflective powers of each sub-region.

[0056] For example, Figure 1 The green, red, and yellow solid-line squares in the diagram represent reflective elements. Reflective elements of the same color constitute a sub-region. The green solid-line squares represent reflective elements within the communication enhancement sub-region, while the red and yellow solid-line squares represent different location interference sub-regions. This should be understood. Figure 1 The results of partitioning the reflective surface architecture are for illustrative purposes only and do not constitute specific limitations. The specific results of partitioning the reflective units are determined by the actual calculated resource allocation parameters.

[0057] In addition, the receiving end and the malicious terminal can also communicate with the sending end through a direct communication path (direct path).

[0058] The aforementioned UAV covert communication method performs channel modeling on the communication channels between the transmitter, reflector architecture, at least one receiver, and each malicious terminal corresponding to the communication system. Based on the channel modeling results and preset constraints, a joint optimization model for resource allocation is constructed, establishing a joint optimization framework for communication enhancement and location interference. This model is then decoupled and solved to obtain resource allocation parameters. These parameters are used to dynamically divide the reflector units of the reflector architecture module and allocate power to each sub-region. This enhances the communication rate between the source UAV and the receiver UAV while simultaneously interfering with the malicious UAV's interference with the source UAV, ensuring the security of the communication system during communication.

[0059] In one exemplary embodiment, the sub-region includes a communication enhancement sub-region and multiple location interference sub-regions. The location interference sub-region includes a preset noise module, which is used to transmit noise interference signals to malicious terminals.

[0060] Each sub-region includes at least one reflection unit, and each reflection unit in each positioning interference sub-region includes a preset noise module, which can be a controllable artificial noise source (AN) module.

[0061] Specifically, to enhance the communication performance of the communication system and protect the privacy of the SU's location information, an AN module is introduced into ARIS to interfere with the MU. For example... Figure 1 The diagram illustrates the proposed ARIS architecture integrating the AN module. Based on this architecture, a virtual partitioning-based ARIS optimization method is proposed, mainly dividing the architecture into the following parts: a communication enhancement sub-region (ARIS partition for Communication Enhancement, ARIS-CE) and multiple localization interference sub-regions (ARIS partition for localization interference, ARIS-LI). ARIS-CE is the ARIS partition used to enhance communication between the transmitter and receiver; ARIS-LI is the ARIS partition used to interfere with the localization of the transmitter by malicious terminals.

[0062] Each ARIS-LI region introduces an AN module, such as Figure 1 The circuit diagram shown is transmitted to the malicious terminal through the ARIS-LI region. When the transmitting end sends the signal, it will be processed by the AN module to generate an interference signal, which is sent to the corresponding malicious terminal together with the modulated signal of the reflection unit of the positioning interference sub-region.

[0063] The reflector architecture can share channel state information and noise interference signals of the reflector architecture in advance with each receiver through a dedicated channel, and each receiver can filter out the noise interference signals from the received signal.

[0064] Record separately and For the allocation of reflection units and power in the ARIS virtual partition, the following conditions must be met:

[0065]

[0066] in, and These represent the ratio of the number of reflective units and the power allocation ratio of ARIS-CE, respectively. and These represent the proportion of the number of reflective units and the power allocation proportion of the ARIS-LI used to interfere with the positioning of the e-th MU, respectively.

[0067] Simultaneously determine and These are the sets of reflection units corresponding to ARIS-CE and the e-th ARIS-LI, respectively.

[0068] The specific expression for the reflected signal of the ARIS-CE partition can be:

[0069]

[0070] in, This represents the reflection precoding matrix of ARIS-CE. and Representing the reflecting units respectively Amplification factor and phase shift factor.

[0071] The active characteristics of ARIS will introduce corresponding dynamic noise. , The intrinsic noise includes the input noise of the ARIS and the inherent noise generated by the device itself, which satisfies , It is a fixed constant. It is a zero vector of dimension N0; It is an N0*N0 dimensional identity matrix.

[0072] The reflected signal of the e-th ARIS-LI partition can be represented as:

[0073]

[0074] in, This represents the reflection precoding matrix of ARIS-LI e.

[0075] To effectively interfere with the MU, ARIS-LI employs a reflection mechanism similar to passive RIS, using phase adjustment in conjunction with AN transmission to perform localized interference. Therefore, This represents the phase offset factor of the reflecting unit. Furthermore, AN emitted by a controllable noise source introduced in ARIS.

[0076] Consider a downlink drone communication scenario, and send to The symbolic vector of a legally receiving unmanned aerial vehicle (RUs) is represented as follows: Its satisfaction .

[0077] Record separately , , , , , , as well as Let be the channel matrices of SU and RU k, SU and MU e, SU and ARIS-CE, SU and ARIS-LI e, ARIS-CE and RU k, ARIS-LI e and RU k, ARIS-CE and MU e, and ARIS-LI i and MU e.

[0078] Furthermore, the channel gain between any two points can be modeled as: Where d is the distance between any two points, and L0 represents the path loss at a reference distance of 1m. Furthermore, This represents the path loss index.

[0079] The communication channels between the transmitter, the reflector architecture, at least one receiver, and each malicious terminal corresponding to the communication system in the communication system include at least: a first communication channel between the transmitter and the receiver, a second communication channel between the transmitter and the communication enhancement sub-region, a third communication channel between the transmitter and each positioning interference sub-region, a fourth communication channel between the transmitter and the malicious terminal, a fifth communication channel between the communication enhancement sub-region and each receiver, a sixth communication channel between the communication enhancement sub-region and the malicious terminal, and a seventh communication channel between each positioning interference sub-region and the malicious terminal.

[0080] Optionally, the processing end can pre-set the reflection precoding matrix of the communication enhancement sub-region of the reflector architecture and the reflection precoding matrix of each positioning interference sub-region. The reflection precoding matrix represents the parameter control of the reflector architecture on the reflected signal. Communication channels are established between the transmitter, the reflector architecture, and each receiver, as well as between the transmitter, the reflector architecture, and each malicious terminal.

[0081] In this embodiment, an active reconfigurable smart surface-assisted covert communication scheme based on virtual partitioning and AN mechanism is used to achieve efficient communication with the legitimate receiving UAV while ensuring the privacy of the source UAV's location information.

[0082] In one exemplary embodiment, such as Figure 3 As shown, the specific implementation process of step 201, "to perform channel modeling on the communication channel between the transmitter, reflector architecture, at least one receiver, and each malicious terminal corresponding to the communication system, and to obtain the channel modeling result," may include the following steps:

[0083] Step 301: For each receiver, determine the communication quality parameters of the receiver based on the ratio of the target signal power to the interference signal power received by the receiver.

[0084] The target signal power includes at least the signal power transmitted through the first communication channel, the second communication channel, and the fifth communication channel. The interference signal power includes at least the interference signal power from other receivers, the self-interference signal power of the communication enhancement sub-region, and the interference signal power from the external environment. The communication quality parameter can be the signal-to-dryness ratio (SDR) of the receiver. The interference signal power from other receivers is due to the interference that the transmitting end causes to the communication channel of other receivers when transmitting signals through the communication channel.

[0085] Specifically, for each receiver, the processing end determines the first channel matrix of the first communication channel of the receiver, the second channel matrix of the receiver through the second communication channel, the fifth channel matrix of the fifth communication channel, and the reflection precoding matrix of the communication enhancement sub-region in the reflector architecture between the second and second communication channels. Based on the first channel matrix, the second channel matrix, the fifth channel matrix, and the reflection precoding matrix, the processing end determines the total channel matrix of the receiver. Based on the total channel matrix of the receiver and the beamforming vectors of the transmitter and the receiver, the target signal power is determined.

[0086] The processing end can determine the beamforming vectors of other receivers and transmitters, and based on the total channel matrix of the receiver and the beamforming vectors between each other receiver and transmitter, determine the interference signal power of the beamforming vectors between each other receiver and transmitter on the channel matrix of the receiver.

[0087] The processing end can determine the channel matrix of the communication enhancement sub-region in the reflector architecture, the reflection precoding matrix of the communication enhancement sub-region, and the interference power of the communication enhancement sub-region, and determine the self-interference signal power of the communication enhancement sub-region.

[0088] The sum of the interfering signal power, the self-interfering signal power, and the background noise power, and the ratio of the target signal power to this sum, are determined as the communication quality parameters of the receiver. The specific expression for calculating the communication quality parameters can be:

[0089]

[0090] in, The signal-to-interference-plus-noise ratio at the k-th RU; It is the conjugate transpose of the channel matrices of SU and RU; It is the beamforming vector between SU ​​and the k-th RU; It is the beamforming vector of the a-th RU; It is the conjugate transpose of the channel matrix of ARIS-CE and the k-th RU; It is the reflection precoding matrix of ARIS-CE; It is the noise power introduced and amplified by the reflective amplifier in Active RIS (ARIS-CE); It is the background noise power; It is the channel matrix of SU and ARIS-CE; It is the phase adjustment factor for reflected signals in the communication enhancement sub-region; This is the total channel matrix of the k-th RU. K is the total number of RUs, and the Diag function is a diagonal matrix function.

[0091] Step 302: For each malicious terminal, determine the interference communication parameters of the malicious terminal based on the ratio of the interference signal power of the preset noise module to the useful signal power received by the malicious terminal.

[0092] The interference signal power includes at least the signal power transmitted through the seventh communication channel, and the useful signal power includes at least the signal power transmitted through the fourth, sixth, second, seventh, and third communication channels. The interference communication parameter can be the interference-to-signal ratio.

[0093] Specifically, for each malicious terminal, the processing end determines the interference signal power of the preset noise module for the malicious terminal based on the channel matrix of the malicious terminal and the corresponding positioning interference sub-region, the reflection precoding matrix of the positioning interference sub-region, and the noise interference signal of the preset noise module.

[0094] Based on the channel matrix between the transmitter and the malicious terminal, the channel matrix between the communication enhancement sub-region and the malicious terminal, the reflection precoding matrix of the communication enhancement sub-region, the channel matrix of the communication enhancement sub-region, the channel matrix between the malicious terminal and the location interference sub-region, the reflection precoding matrix of the location interference sub-region, and the channel matrix between the transmitter and the location interference sub-region, the total channel matrix of the malicious terminal is determined. Based on the total channel matrix and the waveform shaping vector of each transmitter, the useful signal power of the malicious terminal is determined. Based on the ratio of interference signal power to useful signal power, the interference communication parameters of the malicious terminal are determined.

[0095]

[0096] in, It is the signal-to-dryness ratio at the e-th MU; It is the conjugate transpose of the channel matrix of the e-th ARIS-LI and the e-th MU; This represents the reflection precoding matrix of the e-th ARIS-LI; AN is the artificial noise vector emitted by a controllable noise source introduced in ARIS; It is the conjugate transpose of the channel matrix of SU and the e-th MU; It is the channel matrix of SU and the e-th ARIS-LI; It is the beamforming vector at the transmitting end; It is the channel matrix between ARIS-CE and the e-th MU; It is the reflection precoding matrix of ARIS-CE; It is the conjugate transpose of the total channel matrix of the e-th MU.

[0097] Step 303: Model the received signal strength of each malicious terminal to obtain the lower limit threshold of the positioning error of the malicious terminal to the sending end.

[0098] The received signal strength is RSS (Received Signal Strength). The lower limit threshold for positioning error can be the Cramér-Rao Lower Bound (CRLB).

[0099] Specifically, for each malicious terminal, the received signal strength measurement at MU can be modeled as follows: , Where R is the actual received signal strength at all MUs; This represents the expected value of the signal strength received by all malicious terminals; I E It is an E*E dimensional identity matrix; This represents the expected value of the signal strength received by the malicious terminal e; The measurement error variance, p, is caused by channel shadowing fading. d,e p is the signal strength of the direct path of the e-th MU. r,e It is the signal strength of the reflection path, p n,e AN represents the signal strength. The direct path refers to the direct communication channel between the SU and the e-th MU, while the reflection path refers to the indirect communication channel between the SU and the e-th MU through the reflector architecture. d,e p r,e p n,e They can be represented as:

[0100]

[0101] in, This indicates the total transmit power of the SU; This represents the AN power of the e-th ARIS-LI. e The artificial noise vector of AN emitted by a controllable noise source introduced in ARIS; d h,e It is the path distance between SU ​​and the e-th MU; It is the path loss exponent; L0 represents the path loss at a reference distance of 1m; d g,e d represents the distance between the e-th MU and ARIS. H This represents the distance between SU ​​and ARIS. is a variable to be estimated in MU. N0 is the total number of reflecting units; p n It is the magnification factor of the nth reflecting unit; N e This refers to the number of reflective elements contained in the RIS partition used to enhance the location interference of malicious terminal e.

[0102] The signal strength is represented by an auxiliary variable, the specific expression of which can be:

[0103]

[0104]

[0105] in, and All are auxiliary variables.

[0106] The Cramér-Rao Lower Bound (CRLB) for the RSS-based positioning model is as follows:

[0107]

[0108] in, This is the Craméror lower bound of the RSS-based positioning model. SSU represents the coordinate position; J represents the Fisher information matrix; I e It is the coordinate location of the malicious terminal; I R These are the coordinates of ARIS.

[0109] Step 304: Determine the channel modeling results based on each communication quality parameter, each interference communication parameter, and the lower limit threshold of the positioning error.

[0110] Specifically, the communication quality parameters of each receiving end, the interference communication parameters of each malicious terminal, and the lower limit threshold of the positioning error of each malicious terminal are determined as the channel modeling results.

[0111] Furthermore, under the optimization of ARIS reflection precoding, it is assumed that the reflection units in the ARIS-CE section are perfectly aligned with the cascaded SU-RU channel; simultaneously, the reflection units in the ARIS-LI section are aligned with the SU-MU channel, but not with the cascaded SU-RU channel. The impact of misaligned channels on RUs is ignored. However, MUs do not possess prior knowledge of ANs, so they can only be considered as interference. Also, since ARIS-LI is only aligned with its corresponding SU-MU cascaded channel, the interference effect of ANs from misaligned channels on the e-th MU is ignored. Furthermore, we consider a scenario dominated by interference, where the additive white Gaussian noise and the intrinsic noise power of ARIS-CE are extremely small compared to the interference power, and therefore can be ignored.

[0112] In this embodiment, channel modeling is performed on the communication channels between the transmitter, reflector architecture, at least one receiver, and each malicious terminal corresponding to the communication system. The quality of the received signals of each receiver and each malicious terminal is evaluated so as to adjust the quality of the received signals of the receiver.

[0113] In an exemplary embodiment, the specific implementation process of step 202, "determining the joint optimization model for resource allocation based on channel modeling results and preset constraints," may include:

[0114] Based on communication quality parameters and interference communication parameters, the objective optimization function is determined. The first constraint is that the maximum value of the interference communication parameter satisfies a preset interference threshold. The second constraint is that the maximum value of the communication quality parameter satisfies a preset communication threshold. The third constraint is that the auxiliary variable of the received signal strength is greater than or equal to a preset variable threshold. The fourth constraint is that the total transmit power of the transmitter is less than or equal to a transmit power threshold. The fifth constraint is that the sum of the signal power through the second communication channel and the noise power of the communication enhancement sub-region is less than or equal to the power threshold of the communication enhancement sub-region. The sixth constraint is that the sum of the signal power through the third communication channel and the signal power of the noise interference signal is less than or equal to the power threshold of the location interference sub-region. The seventh constraint is that the sum of the resource allocation parameters is a preset threshold. The objective optimization function, the first constraint, the second constraint, the third constraint, the fourth constraint, the fifth constraint, the sixth constraint, and the seventh constraint are used to determine the joint optimization model for resource allocation of the reflector architecture.

[0115] The objective optimization function is a joint optimization framework for communication enhancement and location interference; communication enhancement enhances communication between the transmitter and receiver, while location interference interferes with the location of malicious terminals at the transmitter. The total transmit power of the transmitter is determined by the beamforming matrix of the transmitter. Resource allocation parameters can be the optimal partition ratio of the reflector elements in the reflector architecture, and the optimal power allocation ratio for each region. The preset threshold can be 1.

[0116] Specifically, the communication rate between the transmitter and receiver is determined based on communication quality parameters, and the positioning error of the malicious terminal on the transmitter is determined based on interference communication parameters. Using the transmitter's beamforming vector, the reflection precoding matrix of the reflector architecture, the noise vector of the preset noise model, the partition ratio, and the power allocation ratio as independent variables, the optimal objective is to determine the maximum sum of the communication rate and the positioning error. The independent variables satisfy the following constraints:

[0117] The first constraint is that, for any malicious terminal, by adjusting the reflection precoding matrix of the positioning interference sub-region of the reflective surface architecture, the maximum value of the interference-to-signal ratio of the malicious terminal is greater than or equal to the preset interference threshold, and the maximum value of the interference communication parameters is determined to satisfy the preset interference threshold.

[0118] The second constraint is that, for any receiver, the reflection precoding matrix of the communication enhancement sub-region of the reflector architecture is adjusted so that the signal-to-interference-plus-noise ratio of each receiver is greater than or equal to the preset communication threshold, and the maximum value of the communication quality parameter is determined to satisfy the preset communication threshold.

[0119] The third constraint is that the auxiliary variable of the received signal strength is greater than or equal to the preset variable threshold.

[0120] The fourth constraint is that the total transmit power of the transmitter does not exceed the transmitter power threshold.

[0121] The fifth constraint is that the sum of the signal power through the second communication channel and the noise power of the communication enhancement sub-region is less than or equal to the power threshold of the communication enhancement sub-region.

[0122] The sixth constraint is that the sum of the signal power through the third communication channel and the signal power of the noise interference signal is less than or equal to the power threshold of the localization interference sub-region.

[0123] The seventh constraint is that the sum of the allocation ratios of each sub-region is 1, and the sum of the optimal power allocation ratios of each distinct region is 1.

[0124] The specific expression for the joint optimization model of resource allocation can be:

[0125]

[0126] Where Q1 is the objective optimization function; ω is the weight that balances communication enhancement and location interference. It is a preset interference threshold; It is a preset communication threshold. It is a preset variable threshold. It is the maximum value of the transmit power threshold at the transmitting end. It is the maximum value of the reflection power threshold of the reflector architecture; It is the reflection power threshold of ARIS-CE. It is the reflection power threshold of ARIS-LI It is the ratio of the number of reflective units in ARIS-CE; It is the proportion of ARIS-LI reflective units used to interfere with the positioning of the e-th MU; It is the power allocation ratio of ARIS-CE; It is the power allocation ratio of ARIS-LI used to interfere with the positioning of the e-th MU.

[0127] In this embodiment, by determining the target optimization function based on communication quality parameters and interference communication parameters, and by determining the joint optimization model for resource allocation based on the target optimization function and various constraints, the communication with the receiving end is strengthened, and the location interference of malicious terminals is increased.

[0128] In an exemplary embodiment, the specific implementation process of step 202, "decoupling the resource allocation joint optimization model to obtain the resource allocation parameters of the resource allocation joint optimization model under the condition that the channel modeling results and preset constraints are satisfied," may further include:

[0129] The joint optimization model for resource allocation is decoupled to obtain a power sub-optimization model. The power sub-optimization model includes a power sub-optimization function, a fifth constraint, and a sixth constraint. The power sub-optimization function is solved based on the fifth and sixth constraints to obtain the optimal power allocation ratio for the communication enhancement sub-region and multiple positioning interference sub-regions, respectively.

[0130] Among them, the resource allocation parameters include the optimal power allocation ratio.

[0131] Specifically, by virtual partitioning ARIS, the joint optimization model for resource allocation is decoupled in the physical domain. The specific process of solving for the optimal power allocation ratio of ARIS may include:

[0132] The corresponding power sub-optimization model can be decoupled from the joint optimization model of resource allocation:

[0133]

[0134] Among them, P v,e It is the AN power of the e-th ARIS-LI, d g,e P is the distance between the e-th MU and ARIS. S This is the total transmit power of SU; d h,e It is the distance from the e-th MU to the SU; d is an estimable variable of MU; g,e It is the distance from the e-th MU to the SU.

[0135] By solving the power sub-optimization model, the optimal power allocation ratio for each sub-region of ARIS is determined. The expression for the optimal power allocation ratio can be:

[0136]

[0137]

[0138] in, This represents the ratio of the distance from the e-th MU to ARIS to the distance from the e-th MU to SU. This represents the maximum total power of the RIS.

[0139] In this embodiment, by decoupling the power sub-optimization model from the resource allocation joint optimization model and solving the power sub-optimization model, the optimal power allocation ratio of each sub-region of the reflector architecture is determined, which facilitates optimization and solves the problem. The optimal power is allocated to each sub-region, which can enhance communication with the receiving end and achieve the positioning interference of malicious terminals, thereby improving the security and communication efficiency of the communication system.

[0140] In one exemplary embodiment, such as Figure 4 As shown, the specific implementation process of step 202, "decoupling the joint optimization model of resource allocation to obtain the resource allocation parameters of the joint optimization model of resource allocation under the condition that the channel modeling results and preset constraints are satisfied," may include:

[0141] Step 401: For the communication enhancement sub-region, decouple the joint optimization model of resource allocation to obtain the first sub-optimization model of the communication enhancement sub-region; solve the problem based on the fifth constraint and the first sub-optimization model to obtain the number of reflection units in the communication enhancement sub-region.

[0142] The resource allocation parameters also include the optimal partition ratio.

[0143] Specifically, for ARIS-CE, the first partition sub-optimization model related to ARIS-CE partitioning is decoupled from the joint optimization model of resource allocation. The expression of the first partition sub-optimization model can be:

[0144]

[0145] By solving the first partition sub-optimization model, the number of reflection units in the ARIS-CE partition can be obtained. The specific expression for determining the number of reflection units in the ARIS-CE partition can be:

[0146]

[0147] Among them, L g,k It is the path loss from RIS to the k-th legitimate receiving drone; L H This represents the road loss from the source drone SU to RIS.

[0148] Step 402: For each positioning interference sub-region, decouple the joint optimization model of resource allocation to obtain the second sub-optimization model of the positioning interference sub-region; solve the problem based on the sixth constraint and the second sub-optimization model to obtain the number of reflection units in the positioning interference sub-region.

[0149] Specifically, for each ARIS-LI, a second sub-optimization model for the localization interference sub-region is decoupled from the joint optimization model of resource allocation. The expression for the second sub-optimization model can be:

[0150]

[0151] By solving the second partition sub-optimization model described above, the number of reflection units in the ARIS-LI partition can be determined; the specific expression for the number of reflection units in the ARIS-LI partition can be:

[0152]

[0153] Step 403: Determine the optimal partition ratio of the reflective surface architecture based on the number of reflective units in the communication enhancement sub-region, the number of reflective units in each positioning interference sub-region, and the number of reflective units in the reflective surface architecture.

[0154] Specifically, the sum of the number of reflection units in the communication enhancement sub-region and the number of reflection units in each positioning interference sub-region is determined, and this sum is compared with the number of reflection units in the reflector architecture. If the sum is greater than or equal to the number of reflection units in the reflector architecture, then the final number of reflection units in the communication enhancement sub-region and the final number of reflection units in the positioning interference sub-region are determined. Otherwise, the final number of reflection units in the communication enhancement sub-region is the difference between the number of reflection units in the reflector architecture and the number of reflection units in each positioning interference sub-region, and the final number of reflection units in the positioning interference sub-region is determined. The specific expressions for determining the final number of reflection units in the communication enhancement sub-region and the final number of reflection units in each positioning interference sub-region can be:

[0155]

[0156] in, It is the final number of reflection units in the communication enhancement sub-region; It is the final number of reflection units in the e-th localization interference sub-region; N E It represents the total number of reflection units in the localization interference sub-region.

[0157] The optimal partitioning ratio of the communication enhancement sub-region is determined based on the ratio of the final number of reflection units in the communication enhancement sub-region to the number of reflection units in the reflector architecture; the optimal partitioning ratio of each positioning interference sub-region is determined based on the ratio of the final number of reflection units in each positioning interference sub-region to the number of reflection units in the reflector architecture.

[0158] In this embodiment, ARIS needs to perform location interference on MU while ensuring enhanced communication with RU.

[0159] In one exemplary embodiment, the UAV location covert communication method further includes:

[0160] A transmission rate optimization model for the communication enhancement sub-region is determined. Based on the preset parameter alternating optimization algorithm and the transmission rate optimization model, the first variable parameters in the transmission rate optimization model are solved sequentially to obtain the values ​​of the first variable parameters. Based on the values ​​of the first variable parameters and the transmission rate optimization model, the transmission rate value of the communication enhancement sub-region is determined. If the transmission rate value does not meet the preset optimization conditions, the step of solving the first variable parameters in the transmission rate optimization model sequentially based on the preset parameter alternating optimization algorithm and the transmission rate optimization model is continued to obtain the values ​​of the first variable parameters. Otherwise, the variable parameters of the communication enhancement sub-region are optimized based on the variable parameter values.

[0161] The first variable parameter includes at least one of the following: a first auxiliary variable, a second auxiliary variable, the beamforming vector at the transmitting end, and the reflection precoding matrix of the communication enhancement sub-region. The transmission rate optimization model is a non-convex logarithmic optimization problem. The pre-defined parameter alternating optimization algorithm is an alternating optimization algorithm based on fractional programming.

[0162] Specifically, after determining the optimal partition ratio and optimal power allocation ratio for ARIS, corresponding optimization algorithms need to be designed for different partitions. For ARIS-CE, it is necessary to maximize its system and rate, and determine the initial transmission rate optimization model for the communication enhancement sub-region. The expression for the initial transmission rate optimization model can be:

[0163]

[0164] To solve the initial transmission rate optimization model, the first auxiliary variable is determined. Second auxiliary variable Based on the first and second auxiliary variables, the initial transmission rate optimization model is used to determine the transmission rate optimization model. The specific expression of the transmission rate optimization model can be:

[0165]

[0166] For the transmission rate optimization model, the alternating optimization algorithm based on fractional programming is used to optimize the variable parameters in the transmission rate optimization model. The specific process is as follows:

[0167] First, fix the first variable parameter. Optimize the first auxiliary variable The specific expression for the first auxiliary variable is:

[0168]

[0169] in, It is the first auxiliary variable after optimization.

[0170] Fixed first variable parameter Optimize the second auxiliary variable The specific expression for the second auxiliary variable is:

[0171]

[0172] in, It is the optimized second auxiliary variable.

[0173] For ease of representation, a marker is introduced. The specific expression for the marker parameter can be:

[0174]

[0175]

[0176]

[0177]

[0178] in, I K It is a K*K dimensional identity matrix.

[0179] Fixed first variable parameter Obtain the beamforming vector The relevant optimization model, specifically the expression for the optimization model related to the beamforming vector, can be:

[0180]

[0181] in, It is a function that takes the real part.

[0182] The optimal solution for the beamforming vector is obtained by solving the optimization model related to the beamforming vector using the Lagrange relaxation algorithm. The expression for the optimal solution of the optimized beamforming vector can be:

[0183]

[0184] Among them, w op It is the optimal solution for the beamforming vector. and To find the optimal Lagrange multipliers that satisfy the constraints, a binary search can be used. KM It is a KM*KM dimensional identity matrix.

[0185] Determine the diagonal matrix of the channel matrix between ARIS-CE and the k-th RU. The expression for the diagonal matrix can be:

[0186]

[0187] Among them, G k,0 It is the diagonal matrix of the channel matrix of ARIS-CE and the k-th RU.

[0188] For ease of representation, a marker parameter is introduced. The expression for the marker parameter can be:

[0189]

[0190]

[0191]

[0192] in, It is a matrix diagonalized from the channel vector g(k,0).

[0193] Fixed first variable parameter Obtain the reflection precoding matrix of ARIS-CE. The relevant optimization model, and the reflection precoding matrix of ARIS-CE The specific expression for the relevant optimization model can be:

[0194]

[0195] The reflection precoding matrix of ARIS-CE is obtained using the Lagrange relaxation method. The reflection precoding matrix of the relevant optimization model Optimal solution, reflection precoding matrix The expression for the optimal solution can be:

[0196]

[0197] in, These are the parameters of the reflection precoding matrix. The optimal Lagrange multipliers that satisfy the constraints can be found through a binary search.

[0198] After determining the optimized first variable parameter value each time, the transmission rate value of the communication enhancement sub-region is determined based on the transmission rate optimization model. If the transmission rate value is less than the transmission rate threshold, it is determined that the transmission rate value does not meet the preset optimization conditions, and the above parameter optimization steps are continued to optimize the first variable parameter value. If the transmission rate value is greater than or equal to the transmission rate threshold, it is determined that the transmission rate value meets the preset optimization conditions, and the variable parameters of the communication enhancement sub-region are optimized based on the variable parameter value.

[0199] In this embodiment, after determining the optimal partition ratio and optimal power allocation ratio of each sub-region, the transmission rate of the communication enhancement sub-region is improved and the communication quality of the communication enhancement sub-region is enhanced by optimizing the variable parameters in each sub-region.

[0200] In one exemplary embodiment, the UAV location covert communication method further includes:

[0201] For each location interference sub-region, an optimization model for the interference communication parameters of the malicious terminal is determined. Based on the preset parameter alternation optimization algorithm and the interference communication parameter optimization model, the second variable parameters of the interference communication parameter optimization model are solved sequentially to obtain the second variable parameter values ​​corresponding to each second variable parameter. Based on the second variable parameter values ​​and the interference communication parameter optimization model, the interference communication parameter values ​​of the location interference sub-region are determined. If the interference communication parameter values ​​do not meet the preset optimization conditions, the step of solving the second variable parameters of the interference communication parameter optimization model sequentially based on the preset parameter alternation optimization algorithm and the interference communication parameter optimization model is continued to obtain the second variable parameter values ​​corresponding to each second variable parameter. Otherwise, the variable parameters of the location interference sub-region are optimized based on the interference communication parameter values.

[0202] The second variable parameter includes the third auxiliary variable, the noise vector of the preset noise module, and the reflection precoding matrix of the localization interference sub-region.

[0203] Specifically, for each positioning interference sub-region ARIS-LI, based on maximizing the ISR of the malicious terminal MU, an initial interference communication parameter optimization model is determined. The expression for the initial interference communication parameter optimization model can be:

[0204]

[0205] To solve the initial interference communication parameter optimization model, a third auxiliary variable is introduced. Solve the equation to determine the interference communication parameter optimization model. The expression for the interference communication parameter optimization model can be:

[0206]

[0207] For the interference communication parameter optimization model, the alternating optimization algorithm based on fractional programming is used to optimize the variable parameters in the interference communication parameter optimization model. The specific process is as follows:

[0208] Fixed parameters Optimize the third auxiliary variable The expression for the optimal solution of the third auxiliary variable can be:

[0209]

[0210] in, It is the optimal solution for the third auxiliary variable.

[0211] Determine the identifier matrix Fixed parameters This allows us to obtain the noise vector v of ARIS-LI. e The relevant optimization model, and the noise vector v of ARIS-LI e The specific expression for the relevant optimization model can be:

[0212]

[0213] With ARIS-LI noise vector v e The relevant optimization model is a Rayleigh quotient problem, with a noise vector v e The expression for the optimal solution can be:

[0214]

[0215] in, It is the noise vector v e The optimal solution; u max Representation matrix B e The eigenvector corresponding to the largest eigenvalue.

[0216] The diagonal matrix of the channel matrix is ​​determined as follows: For ease of representation, a marker parameter is introduced. The expression for the marker parameter can be:

[0217]

[0218]

[0219] Fixed parameters This allows us to obtain the reflection precoding matrix of ARIS-LI. The relevant optimization model, and the reflection precoding matrix of ARIS-LI The expression for the relevant optimization model can be:

[0220]

[0221] For ease of explanation, an auxiliary matrix is ​​introduced. and auxiliary variables This will be compared with the reflection precoding matrix of ARIS-LI. The relevant optimization model is used to process the data to obtain the reflection precoding matrix of ARIS-LI. The optimized model, the reflection precoding matrix of ARIS-LI The expression for the optimization model can be:

[0222]

[0223] The optimization model of the reflection precoding matrix of ARIS-LI is a semi-positive definite programming problem. Its optimal solution can be obtained by interior point method or penalty function method to determine the optimal solution of the reflection precoding matrix.

[0224] In this embodiment, after determining the optimal partition ratio and optimal power allocation ratio of each sub-region, the variable parameters in each positioning interference sub-region are optimized to improve the transmission rate of the communication enhancement sub-region while interfering with malicious terminals.

[0225] In one embodiment, the UAV location covert communication method proposed in this application significantly outperforms other baseline algorithms. When the average distance between the MU and SU is large (greater than 400m), the proposed UAV location covert communication method can increase the positioning error by approximately 37.65% while sacrificing only 3.69% of the system and rate; while when the average distance between the MU and SU is small (less than 400m), the proposed UAV location covert communication method increases the positioning error by approximately 148.53% while sacrificing only 21.21% of the system and rate. Furthermore, when the number of MUs is large, the proposed UAV location covert communication method can improve the positioning error by approximately 258.16% while sacrificing only 12.8% of the rate, indicating that the UAV location covert communication method has strong robustness.

[0226] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0227] Based on the same inventive concept, this application also provides a drone location covert communication device for implementing the drone location covert communication method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more drone location covert communication device embodiments provided below can be found in the limitations of the drone location covert communication method described above, and will not be repeated here.

[0228] In one exemplary embodiment, such as Figure 5 As shown, a UAV location concealment communication device 50 is provided, applied to a communication system. The communication system includes a transmitter, a reflector architecture, and at least one receiver, comprising: a channel modeling module 51, a processing module 52, and a partitioning module 53, wherein:

[0229] The channel modeling module 51 is used to perform channel modeling on the communication channel between the transmitter, the reflector architecture, at least one receiver, and each malicious terminal corresponding to the communication system in the communication system, and to obtain the channel modeling result.

[0230] The processing module 52 is used to determine the joint optimization model of resource allocation based on the channel modeling results and preset constraints, and to decouple the joint optimization model of resource allocation to obtain the resource allocation parameters of the joint optimization model of resource allocation under the condition that the channel modeling results and preset constraints are satisfied.

[0231] The partitioning module 53 is used to virtually partition each reflection unit contained in the reflective surface architecture through resource allocation parameters, obtain the number of reflection units in each sub-region, and determine the reflection power of each sub-region, so that the receiving end and the malicious terminal can communicate with the sending end through the reflection units and reflection power of each sub-region.

[0232] In one embodiment, the sub-region includes a communication enhancement sub-region and multiple location interference sub-regions. The location interference sub-region includes a preset noise module, which is used to transmit noise interference signals to malicious terminals.

[0233] The communication channels between the transmitter, the reflector architecture, at least one receiver, and each malicious terminal corresponding to the communication system in the communication system include at least: a first communication channel between the transmitter and the receiver, a second communication channel between the transmitter and the communication enhancement sub-region, a third communication channel between the transmitter and each positioning interference sub-region, a fourth communication channel between the transmitter and the malicious terminal, a fifth communication channel between the communication enhancement sub-region and each receiver, a sixth communication channel between the communication enhancement sub-region and the malicious terminal, and a seventh communication channel between each positioning interference sub-region and the malicious terminal.

[0234] In one embodiment, the channel modeling module 51 is specifically used to determine the communication quality parameters of each receiver based on the ratio of the target signal power and the interference signal power received by the receiver. The target signal power includes at least the signal power transmitted through the first communication channel, the signal power transmitted through the second communication channel, and the signal power transmitted through the fifth communication channel. The interference signal power includes at least the interference signal power of each other receiver, the self-interference signal power of the communication enhancement sub-region, and the interference signal power of the external environment.

[0235] For each malicious terminal, the interference communication parameters of the malicious terminal are determined based on the ratio of the interference signal power of the malicious terminal to the useful signal power received by the malicious terminal by the preset noise module. The interference signal power includes at least the signal power transmitted through the seventh communication channel, and the useful signal power includes at least the signal power transmitted through the fourth communication channel, the sixth communication channel, the second communication channel, the seventh communication channel, and the third communication channel.

[0236] The received signal strength of each malicious terminal is modeled and processed to obtain the lower limit threshold of the positioning error of the malicious terminal to the sending end;

[0237] Based on each communication quality parameter, each interference communication parameter, and the lower limit threshold of the positioning error, the channel modeling results are determined.

[0238] In one embodiment, the processing module 52 is specifically used to determine the target optimization function based on communication quality parameters and interference communication parameters;

[0239] The maximum value of the interference communication parameter satisfies the preset interference threshold and is determined as the first constraint condition; the maximum value of the communication quality parameter satisfies the preset communication threshold and is determined as the second constraint condition.

[0240] The auxiliary variable of the received signal strength being greater than or equal to a preset variable threshold is determined as the third constraint condition;

[0241] The fourth constraint condition is that the total transmit power of the transmitter is less than or equal to the transmit power threshold.

[0242] The sum of the signal power through the second communication channel and the noise power of the communication enhancement sub-region is less than or equal to the power threshold of the communication enhancement sub-region, which is determined as the fifth constraint condition.

[0243] The sum of the signal power through the third communication channel and the signal power of the noise interference signal is less than or equal to the power threshold of the localization interference sub-region, which is determined as the sixth constraint condition.

[0244] The sum of the resource allocation parameters is determined to be a preset threshold and is set as the seventh constraint.

[0245] The objective optimization function, the first constraint, the second constraint, the third constraint, the fourth constraint, the fifth constraint, the sixth constraint, and the seventh constraint are used to determine the joint optimization model for resource allocation of the reflector architecture.

[0246] In one embodiment, the processing module 52 is specifically used to decouple the resource allocation joint optimization model to obtain a power sub-optimization model; the power sub-optimization model includes a power sub-optimization function, a fifth constraint condition, and a sixth constraint condition.

[0247] The power sub-optimization function is solved based on the fifth and sixth constraints to obtain the optimal power allocation ratio for the communication enhancement sub-region and multiple positioning interference sub-regions, respectively.

[0248] In one embodiment, the processing module 52 is specifically used to decouple the joint optimization model of resource allocation for the communication enhancement sub-region to obtain the first sub-optimization model of the communication enhancement sub-region; and to solve the number of reflection units in the communication enhancement sub-region based on the fifth constraint and the first sub-optimization model.

[0249] For each location interference sub-region, the joint optimization model of resource allocation is decoupled to obtain the second sub-optimization model of the location interference sub-region; the number of reflection units in the location interference sub-region is obtained by solving the sixth constraint and the second sub-optimization model.

[0250] The optimal partition ratio of the reflector architecture is determined based on the number of reflector units in the communication enhancement sub-region, the number of reflector units in each positioning interference sub-region, and the number of reflector units in the reflector architecture.

[0251] In one embodiment, the device further includes:

[0252] The optimization module is used to determine the transmission rate optimization model of the communication enhancement sub-region. Based on the preset parameter alternating optimization algorithm and the transmission rate optimization model, the first variable parameters in the transmission rate optimization model are solved sequentially to obtain the first variable parameter values ​​corresponding to each first variable parameter. The first variable parameters include at least one of the first auxiliary variable, the second auxiliary variable, the beamforming vector of the transmitting end, and the reflection precoding matrix of the communication enhancement sub-region.

[0253] The determination module is used to determine the transmission rate value of the communication enhancement sub-region based on the parameter values ​​of each first variable and the transmission rate optimization model;

[0254] The judgment module is used to continue executing the step of solving the first variable parameter in the transmission rate optimization model in turn to obtain the first variable parameter value corresponding to each first variable parameter if the transmission rate value does not meet the preset optimization conditions; otherwise, it optimizes the variable parameters of the communication enhancement sub-region based on the variable parameter value.

[0255] In one embodiment, the optimization module is further configured to determine the interference communication parameter optimization model of the malicious terminal for each positioning interference sub-region, and solve the second variable parameters of the interference communication parameter optimization model in sequence based on the preset parameter alternating optimization algorithm and the interference communication parameter optimization model to obtain the second variable parameter value corresponding to each second variable parameter. The second variable parameters include a third auxiliary variable, a noise vector of a preset noise module and a reflection precoding matrix of the positioning interference sub-region.

[0256] The determination module is also used to determine the interference communication parameter values ​​of the local interference sub-region based on the optimization model of each second variable parameter value and interference communication parameters;

[0257] The judgment module is also used to continue executing the second variable parameter value of the interference communication parameter optimization model based on the preset parameter alternating optimization algorithm and the interference communication parameter optimization model if the interference communication parameter value does not meet the preset optimization conditions; otherwise, it optimizes the variable parameters of the local interference sub-region based on the interference communication parameter value.

[0258] The modules in the aforementioned UAV covert communication device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0259] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and databases. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for covert communication of a UAV's location.

[0260] Those skilled in the art will understand that Figure 6The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0261] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0262] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0263] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0264] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0265] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0266] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0267] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for covert communication of a drone's location, characterized in that, The method is applied to a communication system, which includes a transmitter, a reflector architecture, and at least one receiver; the method includes: Channel modeling is performed on the communication channels between the transmitter, reflector architecture, at least one receiver, and each malicious terminal corresponding to the communication system in the communication system, and the channel modeling results are obtained. Based on the channel modeling results and preset constraints, a joint optimization model for resource allocation is determined, and the joint optimization model for resource allocation is decoupled to obtain the resource allocation parameters of the joint optimization model for resource allocation under the condition that the channel modeling results and preset constraints are satisfied. The resource allocation parameters are used to virtually partition each reflective unit in the reflective surface architecture to obtain the number of reflective units in each sub-region and determine the reflection power of each sub-region, so that the receiving end and the malicious terminal can communicate with the sending end through the reflective units and reflection power of each sub-region. The sub-region includes a communication enhancement sub-region and multiple positioning interference sub-regions. The positioning interference sub-region includes a preset noise module, which is used to transmit noise interference signals to the malicious terminal. The communication channels between the transmitter, the reflector architecture, at least one receiver, and each malicious terminal corresponding to the communication system in the communication system include at least: a first communication channel between the transmitter and the receiver, a second communication channel between the transmitter and the communication enhancement sub-region, a third communication channel between the transmitter and each of the positioning interference sub-regions, a fourth communication channel between the transmitter and the malicious terminal, a fifth communication channel between the communication enhancement sub-region and each of the receivers, a sixth communication channel between the communication enhancement sub-region and the malicious terminal, and a seventh communication channel between each of the positioning interference sub-regions and the malicious terminal. The process involves performing channel modeling on the communication channels between the transmitter, reflector architecture, at least one receiver, and each malicious terminal corresponding to the communication system, to obtain channel modeling results, including: For each of the receiving ends, the communication quality parameters of the receiving end are determined based on the ratio of the target signal power to the interference signal power received by the receiving end. The target signal power includes at least the signal power transmitted through the first communication channel, the signal power transmitted through the second communication channel, and the signal power transmitted through the fifth communication channel. The interference signal power includes at least the interference signal power of each of the other receiving ends, the self-interference signal power of the communication enhancement sub-region, and the interference signal power of the external environment. For each malicious terminal, based on the ratio of the interference signal power of the malicious terminal to the useful signal power received by the malicious terminal by the preset noise module, the interference communication parameters of the malicious terminal are determined. The interference signal power includes at least the signal power transmitted through the seventh communication channel, and the useful signal power includes at least the signal power transmitted through the fourth communication channel, the sixth communication channel, the second communication channel, the seventh communication channel, and the third communication channel. The received signal strength of each malicious terminal is modeled and processed to obtain the lower limit threshold of the positioning error of the malicious terminal to the sending end; Based on the aforementioned communication quality parameters, the aforementioned interference communication parameters, and the aforementioned lower limit threshold for positioning error, the channel modeling result is determined.

2. The method according to claim 1, characterized in that, The determination of the joint optimization model for resource allocation based on the channel modeling results and preset constraints includes: Based on the communication quality parameters and the interference communication parameters, determine the target optimization function; The maximum value of the interference communication parameter satisfies a preset interference threshold and is determined as the first constraint condition; the maximum value of the communication quality parameter satisfies a preset communication threshold and is determined as the second constraint condition. The auxiliary variable of the received signal strength being greater than or equal to a preset variable threshold is determined as the third constraint condition; The condition that the total transmit power of the transmitting end is less than or equal to the transmit power threshold is defined as the fourth constraint condition. The sum of the signal power through the second communication channel and the noise power of the communication enhancement sub-region is less than or equal to the power threshold of the communication enhancement sub-region, which is determined as the fifth constraint condition; The sum of the signal power through the third communication channel and the signal power of the noise interference signal is less than or equal to the power threshold of the location interference sub-region, which is determined as the sixth constraint condition; The sum of the resource allocation parameters is determined to be a preset threshold and is set as the seventh constraint condition; The objective optimization function, the first constraint, the second constraint, the third constraint, the fourth constraint, the fifth constraint, the sixth constraint, and the seventh constraint are used to determine the joint optimization model for resource allocation of the reflector architecture.

3. The method according to claim 2, characterized in that, The resource allocation parameters include the optimal power allocation ratio. The decoupling process of the joint resource allocation optimization model to obtain the resource allocation parameters of the joint resource allocation optimization model under the condition that it satisfies the channel modeling results and the preset constraints includes: The resource allocation joint optimization model is decoupled to obtain a power sub-optimization model; the power sub-optimization model includes a power sub-optimization function, the fifth constraint condition, and the sixth constraint condition. The power sub-optimization function is solved based on the fifth and sixth constraints to obtain the optimal power allocation ratio for the communication enhancement sub-region and multiple positioning interference sub-regions, respectively.

4. The method according to claim 2, characterized in that, The resource allocation parameters also include the optimal partition ratio. The decoupling process of the joint resource allocation optimization model to obtain the resource allocation parameters of the joint resource allocation optimization model under the condition that it satisfies the channel modeling results and the preset constraints includes: For the communication enhancement sub-region, the resource allocation joint optimization model is decoupled to obtain the first partition sub-optimization model of the communication enhancement sub-region; the number of reflection units in the communication enhancement sub-region is obtained by solving the fifth constraint and the first partition sub-optimization model. For each of the aforementioned positioning interference sub-regions, the resource allocation joint optimization model is decoupled to obtain a second partition sub-optimization model for the positioning interference sub-region; the number of reflection units in the positioning interference sub-region is obtained by solving the sixth constraint and the second partition sub-optimization model. The optimal partition ratio of the reflective surface architecture is determined based on the number of reflective units in the communication enhancement sub-region, the number of reflective units in each of the positioning interference sub-regions, and the number of reflective units in the reflective surface architecture.

5. The method according to claim 1, characterized in that, The method further includes: A transmission rate optimization model for the communication enhancement sub-region is determined. Based on the preset parameter alternating optimization algorithm and the transmission rate optimization model, the first variable parameters in the transmission rate optimization model are solved sequentially to obtain the first variable parameter values ​​corresponding to each first variable parameter. The first variable parameters include at least one of the first auxiliary variable, the second auxiliary variable, the beamforming vector of the transmitting end, and the reflection precoding matrix of the communication enhancement sub-region. Based on the values ​​of each of the first variable parameters and the transmission rate optimization model, the transmission rate value of the communication enhancement sub-region is determined; If the transmission rate value does not meet the preset optimization conditions, the step of continuing to execute the preset parameter alternating optimization algorithm and the transmission rate optimization model, and sequentially solving the first variable parameter in the transmission rate optimization model to obtain the first variable parameter value corresponding to each first variable parameter; otherwise, the variable parameters of the communication enhancement sub-region are optimized based on the variable parameter value.

6. The method according to claim 1, characterized in that, The method further includes: For each of the aforementioned positioning interference sub-regions, an interference communication parameter optimization model for the malicious terminal is determined. Based on the preset parameter alternation optimization algorithm and the interference communication parameter optimization model, the second variable parameters of the interference communication parameter optimization model are solved sequentially to obtain the second variable parameter values ​​corresponding to each second variable parameter. The second variable parameters include a third auxiliary variable, the noise vector of the preset noise module, and the reflection precoding matrix of the positioning interference sub-region. Based on the values ​​of each of the second variable parameters and the interference communication parameter optimization model, the interference communication parameter values ​​of the positioning interference sub-region are determined; If the interference communication parameter value does not meet the preset optimization conditions, the step of continuing to execute the preset parameter alternating optimization algorithm and the interference communication parameter optimization model, and sequentially solving the second variable parameter of the interference communication parameter optimization model to obtain the second variable parameter value corresponding to each second variable parameter; otherwise, the variable parameters of the positioning interference sub-region are optimized based on the interference communication parameter value.

7. A concealed communication device for unmanned aerial vehicles (UAVs), characterized in that, The device is applied to a communication system, which includes a transmitter, a reflector architecture, and at least one receiver; the device includes: The channel modeling module is used to perform channel modeling on the communication channels between the transmitter, reflector architecture, at least one receiver, and each malicious terminal corresponding to the communication system in the communication system, and to obtain the channel modeling results. The processing module is used to determine a joint optimization model for resource allocation based on the channel modeling results and preset constraints, and to decouple the joint optimization model for resource allocation to obtain the resource allocation parameters of the joint optimization model for resource allocation under the condition that the channel modeling results and the preset constraints are satisfied. The partitioning module is used to virtually partition each reflective unit contained in the reflective surface architecture using the resource allocation parameters, to obtain the number of reflective units in each sub-region, and to determine the reflection power of each sub-region, so that the receiving end and the malicious terminal can communicate with the sending end through the reflective units and reflection power of each sub-region; The sub-region includes a communication enhancement sub-region and multiple positioning interference sub-regions. The positioning interference sub-region includes a preset noise module, which is used to transmit noise interference signals to the malicious terminal. The communication channels between the transmitter, the reflector architecture, at least one receiver, and each malicious terminal corresponding to the communication system in the communication system include at least: a first communication channel between the transmitter and the receiver, a second communication channel between the transmitter and the communication enhancement sub-region, a third communication channel between the transmitter and each of the positioning interference sub-regions, a fourth communication channel between the transmitter and the malicious terminal, a fifth communication channel between the communication enhancement sub-region and each of the receivers, a sixth communication channel between the communication enhancement sub-region and the malicious terminal, and a seventh communication channel between each of the positioning interference sub-regions and the malicious terminal. The channel modeling module is used to determine the communication quality parameters of each receiver based on the ratio of the target signal power to the interference signal power received by the receiver. The target signal power includes at least the signal power transmitted through the first communication channel, the signal power transmitted through the second communication channel, and the signal power transmitted through the fifth communication channel. The interference signal power includes at least the interference signal power of each of the other receivers, the self-interference signal power of the communication enhancement sub-region, and the interference signal power of the external environment. For each malicious terminal, based on the ratio of the interference signal power of the malicious terminal to the useful signal power received by the malicious terminal by the preset noise module, the interference communication parameters of the malicious terminal are determined. The interference signal power includes at least the signal power transmitted through the seventh communication channel, and the useful signal power includes at least the signal power transmitted through the fourth communication channel, the sixth communication channel, the second communication channel, the seventh communication channel, and the third communication channel. The received signal strength of each malicious terminal is modeled and processed to obtain the lower limit threshold of the positioning error of the malicious terminal to the sending end; Based on the aforementioned communication quality parameters, the aforementioned interference communication parameters, and the aforementioned lower limit threshold for positioning error, the channel modeling result is determined.

8. The apparatus according to claim 7, characterized in that, The processing module is used to determine the target optimization function based on the communication quality parameters and the interference communication parameters; The maximum value of the interference communication parameter satisfies a preset interference threshold and is determined as the first constraint condition; the maximum value of the communication quality parameter satisfies a preset communication threshold and is determined as the second constraint condition. The auxiliary variable of the received signal strength being greater than or equal to a preset variable threshold is determined as the third constraint condition; The condition that the total transmit power of the transmitting end is less than or equal to the transmit power threshold is defined as the fourth constraint condition. The sum of the signal power through the second communication channel and the noise power of the communication enhancement sub-region is less than or equal to the power threshold of the communication enhancement sub-region, which is determined as the fifth constraint condition; The sum of the signal power through the third communication channel and the signal power of the noise interference signal is less than or equal to the power threshold of the location interference sub-region, which is determined as the sixth constraint condition; The sum of the resource allocation parameters is determined to be a preset threshold and is set as the seventh constraint condition; The objective optimization function, the first constraint, the second constraint, the third constraint, the fourth constraint, the fifth constraint, the sixth constraint, and the seventh constraint are used to determine the joint optimization model for resource allocation of the reflector architecture.

9. The apparatus according to claim 8, characterized in that, The resource allocation parameters include the optimal power allocation ratio. The processing module is used to decouple the joint optimization model of resource allocation to obtain a power sub-optimization model. The power sub-optimization model includes a power sub-optimization function, the fifth constraint condition, and the sixth constraint condition. The power sub-optimization function is solved based on the fifth and sixth constraints to obtain the optimal power allocation ratio for the communication enhancement sub-region and multiple positioning interference sub-regions, respectively.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

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