Mobile communication method, mobile communication device and readable storage medium

Abstract

The application discloses a mobile communication method, a mobile communication device and a readable storage medium, and relates to the technical field of communication, and the target position of the target terminal is obtained by determining the communication sub-carrier and the positioning sub-carrier in the sub-carrier signal according to a preset signal model, updating the positioning sub-carrier based on a preset channel model and a preset beam tracking algorithm, distributing the power required by the communication sub-carrier according to the target position and a preset power distribution algorithm, obtaining the target power of the target terminal, and realizing the mobile communication of the target terminal according to the target position and the target power, so that high-quality mobile communication of the target terminal with a dynamic trajectory is realized.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a mobile communication method, a mobile communication device, and a readable storage medium. Background Technology

[0002] Conventional mobile communication technologies are mainly for point-to-point communication or fixed-track mobile communication. Base stations obtain location information such as latitude and longitude coordinates of mobile terminal users through the operator's network to determine the location of mobile terminal users.

[0003] In real-world communication applications, mobile users do not remain stationary or follow a fixed trajectory. Their communication paths are random, and conventional mobile communication technologies do not account for this randomness. Consequently, conventional mobile communication technologies cannot fully utilize the high bandwidth, high temporal resolution, and high angular resolution of frequency bands for mobile users with dynamic trajectories. In other words, mobile users with dynamic trajectories cannot obtain high-quality communication resources, resulting in lower communication quality compared to point-to-point or fixed-trajectory mobile users. Summary of the Invention

[0004] The main objective of this invention is to provide a mobile communication method, a mobile communication device, and a readable storage medium, aiming to solve the technical problem that conventional mobile communication technologies cannot provide high-quality communication resources for mobile terminal users with dynamic trajectories.

[0005] To achieve the above objective, the present invention provides a mobile communication method, the mobile communication method comprising the following steps:

[0006] The communication subcarrier and the positioning subcarrier in the subcarrier signal are determined according to the preset signal model;

[0007] The positioning subcarrier is updated based on a preset channel model and a preset beam tracking algorithm to obtain the target location of the target terminal;

[0008] The power required by the communication subcarrier is allocated according to the target location and the preset power allocation algorithm to obtain the target power of the target terminal;

[0009] Mobile communication of the target terminal is achieved based on the target location and the target power.

[0010] Optionally, before the step of determining the communication subcarrier and the positioning subcarrier in the subcarrier signal according to the preset signal model, the mobile communication method further includes:

[0011] A preset signal model is constructed, wherein the preset signal model is constructed based on frequency division multiplexing technology. The preset signal model consists of a configuration subframe, a communication positioning subframe, and a feedback subframe in the time domain. In the frequency domain, a number of subcarrier signals corresponding to the communication positioning subframe are divided into the communication subcarrier and the positioning subcarrier.

[0012] Optionally, before the step of determining the communication subcarrier and the positioning subcarrier in the subcarrier signal according to the preset signal model, the mobile communication method further includes:

[0013] The preset channel model is generated based on the channel state information of the sample subcarrier signal, the beamforming vector of the sample subcarrier signal, and the noise transmitted from the sample subcarrier signal to the sample terminal. The preset channel model is generated based on orthogonal frequency division multiplexing (OFDM) technology.

[0014] Optionally, before the step of updating the positioning subcarrier based on a preset channel model and a preset beam tracking algorithm to obtain the target location of the target terminal, the mobile communication method further includes:

[0015] The updated parameters output based on the preset channel model are input into the near-end policy optimization algorithm to update the action network and evaluation network in the near-end policy optimization algorithm.

[0016] When the channel gain obtained based on the updated near-end policy optimization algorithm is detected to be the target channel gain, the preset beam tracking algorithm is generated according to the updated near-end policy optimization algorithm.

[0017] Optionally, the step of inputting the updated parameters output based on the preset channel model into the near-end policy optimization algorithm to update the action network and evaluation network in the near-end policy optimization algorithm includes:

[0018] The signal-to-noise ratio (SNR) of the sample subcarrier signal and the differential SNR of the sample subcarrier signal obtained based on the preset channel model are used as update parameters and input into the near-end policy optimization algorithm to obtain the action loss function for updating the action network and the evaluation loss function for updating the evaluation network.

[0019] The action network and the evaluation network are updated based on the action loss function and the evaluation loss function, respectively, wherein the updated action network and the updated evaluation network constitute the updated proximal policy optimization algorithm.

[0020] Optionally, after the step of inputting the updated parameters based on the preset channel model output into the near-end policy optimization algorithm to update the action network and evaluation network in the near-end policy optimization algorithm, the mobile communication method further includes:

[0021] When it is detected that the channel gain obtained based on the updated near-end policy optimization algorithm is not the target channel gain, a new signal-to-noise ratio, a new differential signal-to-noise ratio, and a new beam angle are obtained as new update parameters and input into the near-end policy optimization algorithm to obtain a new action loss function for updating the updated action network again and a new evaluation loss function for updating the updated evaluation network again.

[0022] The updated action network and the updated evaluation network are updated again based on the new action loss function and the new evaluation loss function, respectively. It is then determined whether the channel gain obtained based on the updated near-end policy optimization algorithm is the target channel gain. The updated action network and the updated evaluation network constitute the updated near-end policy optimization algorithm.

[0023] Optionally, before the step of allocating the power required by the communication subcarrier according to the target location and a preset power allocation algorithm to obtain the target power of the target terminal, the mobile communication method further includes:

[0024] Construct power constraints and communication interruption constraints based on the sample subcarrier signals;

[0025] By modeling the power constraints and the communication interruption constraints, a robust power allocation problem is obtained.

[0026] The robust power allocation problem is transformed into a convex optimization problem for solution, resulting in the preset power allocation algorithm.

[0027] Optionally, the step of constructing power constraints and communication interruption constraints based on sample subcarrier signals includes:

[0028] After determining the number of communication subcarriers and their communication power in the sample communication subcarriers, as well as the number of positioning subcarriers and their positioning power in the sample positioning subcarriers, power constraints are determined based on the total power of the sample base stations.

[0029] After determining the subcarrier rate and subcarrier rate threshold of the sample subcarrier, the communication interruption constraint condition is determined based on the communication interruption probability of the sample base station.

[0030] In addition, to achieve the above objectives, the present invention also provides a mobile communication device, including a memory, a processor, and a computer processing program stored in the memory and executable on the processor, wherein the processor executes the computer processing program to implement the steps of the above-described mobile communication method.

[0031] In addition, to achieve the above objectives, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described mobile communication method.

[0032] This invention determines the communication subcarrier and positioning subcarrier in the subcarrier signal according to a preset signal model, so as to establish a connection between communication and positioning in the subcarrier signal. Based on a preset channel model and a preset beam tracking algorithm, the positioning subcarrier is updated to obtain the target position of the target terminal. The preset signal model and preset beam tracking algorithm improve the accuracy of dynamic position prediction of the target terminal, avoiding the situation where the target terminal's position is inaccurate due to movement, resulting in fluctuations in the signal-to-noise ratio received by the target terminal and poor communication quality. According to the target position and a preset power allocation algorithm, the power required by the communication subcarrier is allocated to obtain the target power of the target terminal, realizing accurate and rapid allocation of target power for the target position of the target terminal. Based on the target position and target power, the target terminal's mobile communication is realized, achieving high-quality mobile communication for target terminals with dynamic trajectories. Attached Figure Description

[0033] Figure 1 This is a schematic diagram of the terminal structure of the hardware operating environment involved in the embodiments of the present invention;

[0034] Figure 2 This is a flowchart illustrating the first embodiment of the mobile communication method of the present invention;

[0035] Figure 3 This is a flowchart illustrating the second embodiment of the mobile communication method of the present invention;

[0036] Figure 4 This is a schematic diagram of the antenna array structure;

[0037] Figure 5 This is a flowchart illustrating the third embodiment of the mobile communication method of the present invention;

[0038] Figure 6 This is a schematic diagram of the subframe structure of the preset signal model in this invention;

[0039] Figure 7 This is a flowchart illustrating the fourth embodiment of the mobile communication method of the present invention;

[0040] Figure 8 This is a schematic diagram of the near-end strategy optimization algorithm of the present invention;

[0041] Figure 9 This is a flowchart illustrating the fifth embodiment of the mobile communication method of the present invention.

[0042] The realization of the objective of this invention, its functional characteristics and advantages will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0043] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0044] like Figure 1 As shown, Figure 1 This is a schematic diagram of the terminal structure of the hardware operating environment involved in the embodiments of the present invention.

[0045] The mobile communication method of this invention is applied on a mobile communication device, such as... Figure 1 As shown, the mobile communication device may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display area and an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0046] Optionally, the mobile communication device may also include a camera, RF (Radio Frequency) circuitry, sensors, audio circuitry, WiFi module, etc. Sensors may include light sensors, motion sensors, and other sensors. Specifically, light sensors may include ambient light sensors and proximity sensors. The ambient light sensor can adjust the brightness of the display screen according to the ambient light level, while the proximity sensor can turn off the display screen and / or backlight when the mobile terminal is moved to the ear. As a type of motion sensor, a gravity accelerometer can detect the magnitude of acceleration in various directions (generally three axes). When stationary, it can detect the magnitude and direction of gravity, and can be used for applications that identify the mobile terminal's posture (such as landscape / portrait switching, related games, magnetometer posture calibration), vibration recognition functions (such as pedometers, taps), etc. Of course, the mobile terminal may also be equipped with other sensors such as gyroscopes, barometers, hygrometers, thermometers, and infrared sensors, which will not be elaborated here.

[0047] Those skilled in the art will understand that Figure 1The mobile communication device structure shown does not constitute a limitation on the mobile communication device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0048] like Figure 1 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a computer processing program.

[0049] exist Figure 1 In the terminal shown, network interface 1004 is mainly used to connect to the backend server and communicate data with it; user interface 1003 is mainly used to connect to the client (user terminal) and communicate data with it; while processor 1001 can be used to call the computer processing program stored in memory 1005 and perform the following operations:

[0050] The communication subcarrier and the positioning subcarrier in the subcarrier signal are determined according to the preset signal model;

[0051] The positioning subcarrier is updated based on a preset channel model and a preset beam tracking algorithm to obtain the target location of the target terminal;

[0052] The power required by the communication subcarrier is allocated according to the target location and the preset power allocation algorithm to obtain the target power of the target terminal;

[0053] Mobile communication of the target terminal is achieved based on the target location and the target power.

[0054] Furthermore, the processor 1001 can call a computer program stored in the memory 1005 and also perform the following operations:

[0055] Before the step of determining the communication subcarrier and the positioning subcarrier in the subcarrier signal according to the preset signal model, a preset signal model is constructed. The preset signal model is constructed based on frequency division multiplexing technology. In the time domain, the preset signal model consists of a configuration subframe, a communication positioning subframe, and a feedback subframe. In the frequency domain, the several subcarrier signals corresponding to the communication positioning subframe are divided into the communication subcarrier and the positioning subcarrier.

[0056] Furthermore, the processor 1001 can call a computer program stored in the memory 1005 and also perform the following operations:

[0057] Before the step of determining the communication subcarrier and the positioning subcarrier in the subcarrier signal according to the preset signal model, the preset channel model is generated based on the channel state information of the sample subcarrier signal, the beamforming vector of the sample subcarrier signal, and the noise transmitted from the sample subcarrier signal to the sample terminal. The preset channel model is generated based on orthogonal frequency division multiplexing technology.

[0058] Furthermore, the processor 1001 can call a computer program stored in the memory 1005 and also perform the following operations:

[0059] Before the step of updating the positioning subcarrier based on the preset channel model and preset beam tracking algorithm to obtain the target location of the target terminal, the update parameters output based on the preset channel model are input into the near-end policy optimization algorithm to update the action network and evaluation network in the near-end policy optimization algorithm.

[0060] When the channel gain obtained based on the updated near-end policy optimization algorithm is detected to be the target channel gain, the preset beam tracking algorithm is generated according to the updated near-end policy optimization algorithm.

[0061] Furthermore, the processor 1001 can call a computer program stored in the memory 1005 and also perform the following operations:

[0062] The step of inputting the update parameters output based on the preset channel model into the near-end policy optimization algorithm to update the action network and evaluation network in the near-end policy optimization algorithm includes: using the signal-to-noise ratio of the sample subcarrier signal and the differential signal-to-noise ratio of the sample subcarrier signal obtained based on the preset channel model as update parameters, inputting them into the near-end policy optimization algorithm to obtain the action loss function for updating the action network and the evaluation loss function for updating the evaluation network;

[0063] The action network and the evaluation network are updated based on the action loss function and the evaluation loss function, respectively, wherein the updated action network and the updated evaluation network constitute the updated proximal policy optimization algorithm.

[0064] Furthermore, the processor 1001 can call a computer program stored in the memory 1005 and also perform the following operations:

[0065] After the step of inputting the updated parameters based on the preset channel model output into the near-end policy optimization algorithm to update the action network and evaluation network in the near-end policy optimization algorithm, when it is detected that the channel gain obtained based on the updated near-end policy optimization algorithm is not the target channel gain, a new signal-to-noise ratio, a new differential signal-to-noise ratio, and a new beam angle are obtained as new updated parameters and input into the near-end policy optimization algorithm to obtain a new action loss function for updating the updated action network again and a new evaluation loss function for updating the updated evaluation network again.

[0066] The updated action network and the updated evaluation network are updated again based on the new action loss function and the new evaluation loss function, respectively. It is then determined whether the channel gain obtained based on the updated near-end policy optimization algorithm is the target channel gain. The updated action network and the updated evaluation network constitute the updated near-end policy optimization algorithm.

[0067] Furthermore, the processor 1001 can call a computer program stored in the memory 1005 and also perform the following operations:

[0068] Before the step of allocating the power required by the communication subcarrier according to the target location and the preset power allocation algorithm to obtain the target power of the target terminal, power constraints and communication interruption constraints are constructed based on the sample subcarrier signals;

[0069] By modeling the power constraints and the communication interruption constraints, a robust power allocation problem is obtained.

[0070] The robust power allocation problem is transformed into a convex optimization problem for solution, resulting in the preset power allocation algorithm.

[0071] Furthermore, the processor 1001 can call a computer program stored in the memory 1005 and also perform the following operations:

[0072] The steps of constructing power constraints and communication interruption constraints based on sample subcarrier signals include: determining the number of communication subcarriers and their communication power in the sample subcarriers, and the number of positioning subcarriers and their positioning power in the sample subcarriers, and then determining the power constraints based on the total power of the sample base stations.

[0073] After determining the subcarrier rate and subcarrier rate threshold of the sample subcarrier, the communication interruption constraint condition is determined based on the communication interruption probability of the sample base station.

[0074] Reference Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the mobile communication method of the present invention, which includes the following steps:

[0075] Step S10: Determine the communication subcarrier and positioning subcarrier in the subcarrier signal according to the preset signal model.

[0076] It should be noted that the preset signal model is a model designed based on actual application in this embodiment. This preset signal model can divide the subcarrier bandwidth of the subcarrier signal into two parts, namely the communication subcarrier part and the positioning subcarrier part, so as to obtain the target power of the target terminal by processing the communication subcarrier and obtain the target position of the target terminal by processing the positioning subcarrier, thereby realizing the integration of communication and positioning of the target terminal user.

[0077] Specifically, in this embodiment, communication and positioning of the target terminal are performed at the base station. This embodiment uses a single base station based on Massive MIMO, and uses orthogonal frequency division multiplexing technology to modulate the signal (subcarrier signal) between the single base station and the target terminal, thereby achieving high-quality positioning communication of the target terminal's dynamic trajectory.

[0078] In this embodiment, after receiving the subcarrier signal, the single base station will input the subcarrier signal into a preset signal model. The preset signal model will automatically divide the subcarrier bandwidth of the subcarrier signal to obtain the communication subcarrier and the positioning subcarrier.

[0079] In this embodiment, the communication frequency is terahertz communication.

[0080] Step S20: Update the positioning subcarrier based on the preset channel model and preset beam tracking algorithm to obtain the target location of the target terminal.

[0081] It should be noted that the preset channel model is a channel model built on orthogonal frequency division multiplexing for modulating subcarrier signals. The signal modulation between the single base station and the target terminal is performed on the basis of this preset channel model. The preset beam tracking algorithm is proposed in this embodiment to address the problem of received signal-to-noise ratio fluctuation caused by the random movement of the target terminal user in terahertz communication, which affects the communication quality of the target terminal user. It can converge the target network of the target terminal in the direction of maximizing channel gain, thereby adjusting the direction and intensity of the antenna in the single base station to locate the target terminal and improve the positioning accuracy of the target terminal.

[0082] Based on the preset channel model, a preset beam tracking algorithm is used to analyze and calculate the signal feedback of the target terminal reflected by the positioning subcarrier, thereby obtaining the target position of the target terminal and realizing accurate positioning of the dynamic trajectory of the target terminal.

[0083] Step S30: Allocate the power required by the communication subcarrier according to the target location and the preset power allocation algorithm to obtain the target power of the target terminal.

[0084] It should be noted that the preset power allocation algorithm is an algorithm used to reallocate the power required by the target terminal based on the target location of the current target terminal. In this embodiment, communication and positioning are coupled through mutual constraints of power. The higher the positioning accuracy of the target terminal, the smaller the error range, and the less power is needed for communication. Therefore, allocating the power required by the current target terminal based on the target location and the preset power allocation algorithm can reduce the power transmitted by a single base station while ensuring positioning accuracy, thereby reducing the impact on other terminals.

[0085] Step S40: Implement mobile communication of the target terminal based on the target location and the target power.

[0086] When the target location of the target terminal at a certain point during dynamic movement and the target power required for communication between the single base station and the target terminal at the target location are obtained through steps S20 and S30, the single base station realizes mobile communication with the dynamically moving target terminal based on the target location and target power. This broadens the application scope of mobile communication and is highly applicable to scenarios that require both communication and positioning. For example, it can provide accurate positioning of robots in factories with robot assembly workshops to complete the monitoring and precise control of robot status; it can provide assisted positioning and navigation planning based on traffic flow for smart cars; it can provide pedestrian flow monitoring for shopping malls; and it can provide accurate positioning of homeowners for smart home design to realize smart home linkage, etc.

[0087] In this embodiment, communication subcarriers and positioning subcarriers in the subcarrier signal are determined according to a preset signal model to establish a connection between communication and positioning in the subcarrier signal. The positioning subcarrier is updated based on a preset channel model and a preset beam tracking algorithm to obtain the target position of the target terminal. This avoids inaccurate positioning of the target terminal due to its movement, which could cause fluctuations in the signal-to-noise ratio received by the target terminal and result in poor communication quality. The power required by the communication subcarrier is allocated according to the target position and a preset power allocation algorithm to obtain the target power of the target terminal. This enables accurate and rapid allocation of target power for the target position of the target terminal. Based on the target position and target power, the target terminal can achieve mobile communication, realizing high-quality mobile communication for the target terminal with dynamic trajectory.

[0088] Reference Figure 3 , Figure 3This is a flowchart illustrating a second embodiment of the mobile communication method of the present invention. Before step S10, which determines the communication subcarrier and the positioning subcarrier in the subcarrier signal according to a preset signal model, the mobile communication method further includes:

[0089] Step S101: Generate the preset channel model based on the channel state information of the sample subcarrier signal, the beamforming vector of the sample subcarrier signal, and the noise transmitted from the sample subcarrier signal to the sample terminal. The preset channel model is generated based on orthogonal frequency division multiplexing (OFDM) technology.

[0090] It should be noted that the mobile communication method in this embodiment is based on the constructed terahertz communication and positioning integrated model based on Massive MIMO, which includes a preset channel model, a preset signal model, a preset beam tracking algorithm, and a preset power allocation algorithm.

[0091] First, a preset channel model based on orthogonal frequency division multiplexing (OFDM) technology needs to be constructed. It should be noted that the preset channel model consists of formulas related to subcarrier signals. The sample subcarrier signal is the subcarrier signal obtained in any propagation process. The preset channel model built based on this subcarrier signal in this embodiment can be applied to subcarrier signals obtained in other propagation processes.

[0092] The expression for determining the m-th subcarrier signal is given by Formula 1:

[0093]

[0094] Where m represents the number of orthogonal subcarrier signals, B represents the signal bandwidth, and f c f represents the fundamental frequency of the subcarrier signal in orthogonal frequency division multiplexing. m Let M represent the m-th subcarrier signal, and M represent the set of all subcarrier signals.

[0095] The channel expression corresponding to the transmission of the m-th subcarrier signal is determined by Formula 2:

[0096]

[0097] Among them, H m ρ represents the channel for the transmission of the m-th subcarrier signal. l,m This represents the large-scale path loss that occurs when the signal on the m-th subcarrier propagates through the l-th propagation path in free space. Represents the imaginary unit, θ l,m N represents the signal phase shift caused by the signal on the m-th subcarrier propagating through the l-th propagation path. b and N uThis refers to the number of antenna elements in a uniform linear antenna array for a single base station and mobile device. and This represents the antenna directional response vector of a single base station and a mobile device.

[0098] Formula 2 mentions the need to calculate the large-scale path loss and signal phase shift of the m-th subcarrier signal along the l-th propagation path. The propagation path refers to the path from the antenna on a single base station to the mobile terminal. In this embodiment, it is also necessary to calculate the path loss and signal phase shift caused by the m-th subcarrier signal propagating in free space via the line-of-sight path. The line-of-sight path refers to the direct path from the single base station to the mobile terminal. The path loss caused by propagation in free space via the line-of-sight path is expressed as Formula 3:

[0099]

[0100] Where, ρ 0,m G represents the path loss caused by the m-th subcarrier signal propagating in free space via the line-of-sight path. b G represents the antenna gain of a single base station. u d0 represents the antenna gain of the mobile terminal, c represents the speed of light, and d0 represents the distance between the single base station and the mobile terminal.

[0101] The phase shift of the m-th subcarrier signal caused by the line-of-sight path propagating in free space is expressed by Equation 4:

[0102]

[0103] Where, θ 0,m τm represents the signal phase shift caused by the propagation of the m-th subcarrier signal in free space via the line-of-sight path, and τ0 represents the flight time of the m-th subcarrier signal in free space.

[0104] Furthermore, due to the angular difference between the antenna array and the subcarrier signal transmission, the signals responded to by different antenna elements will have phase differences. Figure 4 For example, due to the antenna (i.e. Figure 4 The different path lengths of the subcarrier signals caused by the spacing between the antennas (2001) result in a certain path difference Δd between the subcarrier signals of adjacent antennas. The resulting signal phase difference is related to the incident angle φ, the antenna spacing λ, and the signal wavelength λ. c There is a relationship between them, and their impact on the signal in a single base station and a mobile terminal is reflected in the antenna directional response vectors of the single base station and the mobile device, respectively, as shown in Formula 5:

[0105]

[0106]

[0107] in, An angle factor representing the antenna directional response vector associated with the departure angle. The angle factor represents the antenna directional response vector associated with the angle of arrival, as shown in Equation 6:

[0108]

[0109]

[0110] In the original channel model H, H is a closed-form expression of ToF, AoD, and AoA, which is related to the actual location of the mobile terminal. The actual location of the mobile terminal can be represented by the estimated location and the estimation error. Therefore, the function related to the terahertz channel in this embodiment and the estimated location and estimation error is shown in Equation 7:

[0111]

[0112] Based on the relevant formulas from Formula 1 to Formula 7, the m-th subcarrier signal received by the mobile terminal from the signal of a single base station can be expressed as Formula 8.

[0113] y m =H m w m s+n m ————Formula 8

[0114] Among them, H m This represents the channel expression corresponding to the transmission of the m-th subcarrier signal, which includes the channel state information of the subcarrier signal, w. m This represents the beamforming vector designed for the m-th subcarrier signal at the transmitter. The beamforming vector is related to the path loss and signal phase offset of the m-th subcarrier signal, i.e., it is related to the parameters calculated in Equations 3 to 6. s is the transmitted symbol with unit energy. The noise received at the receiver is modeled as additive complex white Gaussian noise, i.e. It is related to Formula 7.

[0115] In this embodiment, a preset channel model is generated based on the channel state information of the sample subcarrier signal, the beamforming vector of the sample subcarrier signal, and the noise transmitted from the sample subcarrier signal to the sample terminal. Based on the preset channel model, a computational basis is provided for the positioning of the dynamic trajectory of the mobile terminal and the allocation of the required power.

[0116] Reference Figure 5 , Figure 5This is a flowchart illustrating the third embodiment of the mobile communication method of the present invention. Before the step S10, which determines the communication subcarrier and the positioning subcarrier in the subcarrier signal according to a preset signal model, the mobile communication method further includes:

[0117] Step S102: Construct a preset signal model, wherein the preset signal model is constructed based on frequency division multiplexing technology. In the time domain, the preset signal model consists of a configuration subframe, a communication positioning subframe, and a feedback subframe. In the frequency domain, the several subcarrier signals corresponding to the communication positioning subframe are divided into the communication subcarrier and the positioning subcarrier.

[0118] After building the preset channel model, by analyzing the multiplexing method of communication and positioning waveforms, an integrated communication and positioning signal model based on frequency division multiplexing is established, namely the preset signal model.

[0119] Reference Figure 6 As explained, the preset signal model proposed in this embodiment consists of three subframes forming a complete frame in the time domain: a configuration subframe, a communication and positioning subframe, and a feedback subframe. In the frequency domain, the communication and positioning subframe of this preset signal model uses frequency division multiplexing to divide the effective bandwidth of the terahertz communication and positioning integration into two parts: a communication subcarrier and a positioning subcarrier.

[0120] exist Figure 6 As can be seen, the configuration subframe is located at the beginning of the complete frame. The information transmission in this configuration subframe includes downlink and uplink. The single base station sends a configuration request to the mobile terminal through the downlink, and the mobile terminal feeds back information such as the received signal strength through the uplink. Based on the positioning results of the previous frame and the information fed back by the mobile terminal, the single base station will complete the configuration of the transmission power and beam for the current frame in the configuration subframe.

[0121] Where T represents the beam update process, F represents the signal feedback process of the mobile terminal, and PA represents the power allocation process. Beam adjustment and feedback together realize the beam tracking function in the configuration subframe.

[0122] In this embodiment, a preset signal model is constructed based on frequency division multiplexing (FDM) technology. In the time domain, the preset signal model consists of a configuration subframe, a communication and positioning subframe, and a feedback subframe. In the frequency domain, the subcarrier signals corresponding to the communication and positioning subframe are divided into communication subcarriers and positioning subcarriers. This preset signal model can divide the subcarrier bandwidth of the subcarrier signal into two parts: the communication subcarrier part and the positioning subcarrier part. This allows the target power of the target terminal to be obtained by processing the communication subcarrier and the target position of the target terminal to be obtained by processing the positioning subcarrier, thereby realizing the integration of communication and positioning for the target terminal user.

[0123] Reference Figure 7 , Figure 7 This is a flowchart illustrating the fourth embodiment of the mobile communication method of the present invention. Before step S20, which updates the positioning subcarrier based on a preset channel model and a preset beam tracking algorithm to obtain the target location of the target terminal, the mobile communication method further includes:

[0124] Step S201: Input the updated parameters based on the preset channel model output into the near-end policy optimization algorithm to update the action network and evaluation network in the near-end policy optimization algorithm.

[0125] Specifically, a beam tracking design based on near-end policy optimization is carried out based on a pre-built channel model. The near-end policy optimization algorithm includes an action network and an evaluation network. The action network is further divided into a new action network and an old action network. The action network is used as an agent to output the probability distribution of actions based on the state. The evaluation network is used as a policy to evaluate the actions. It should be noted that the state refers to the signal-to-noise ratio and differential signal-to-noise ratio, and the policy is the scheme of how the agent selects actions based on the environment.

[0126] In this embodiment, a preset channel model is used to input update parameters for updating the action network and evaluation network into the near-end policy optimization algorithm. This enables the near-end policy optimization algorithm to continuously learn for randomly moving mobile terminals in the terahertz communication and positioning integrated environment, and to converge the target network of the mobile terminal in the direction of maximizing the reward, i.e. maximizing the channel gain, thereby realizing a beam tracking algorithm based on near-end policy optimization.

[0127] Step S202: When it is detected that the channel gain obtained based on the updated near-end policy optimization algorithm is the target channel gain, the preset beam tracking algorithm is generated according to the updated near-end policy optimization algorithm.

[0128] During the continuous learning and updating process based on the updated parameters output by the preset channel model, when the channel gain output by the current update learning is detected to be the target channel gain, it indicates that the near-end policy optimization algorithm has been trained. Based on this, the near-end training optimization algorithm can update the positioning subcarrier and obtain accurate mobile terminal movement trajectory location information. Therefore, the preset beam tracking algorithm is directly generated based on the near-end policy optimization algorithm at this time.

[0129] Optionally, the step of inputting the updated parameters based on the preset channel model output into the near-end policy optimization algorithm in step S201 to update the action network and evaluation network in the near-end policy optimization algorithm includes:

[0130] Step A1: The signal-to-noise ratio (SNR) of the sample subcarrier signal obtained based on the preset channel model and the differential SNR of the sample subcarrier signal are used as update parameters and input into the near-end policy optimization algorithm to obtain the action loss function for updating the action network and the evaluation loss function for updating the evaluation network.

[0131] by Figure 8 Let's take an example to illustrate, by Figure 8 It can be seen that action networks are divided into new action networks (i.e., Figure 8 (2002) and the old action network (i.e. Figure 8 The 2003 (in the text) is composed of three different neuron groups, namely the common neuron (i.e., the 2003). Figure 8 (in the public), mean-trained neurons (i.e.) Figure 8 The mean) and variance training neurons (i.e. Figure 8 (variance in the data).

[0132] The new action network acts as an intelligent agent, with its input being the state s obtained from environmental perception. t (i.e., the signal-to-noise ratio of the sample subcarrier signal output by the preset channel model and the differential signal-to-noise ratio of the sample subcarrier signal), state s t The input is fed into the new action network through a common neuron, and the mean μ of the action distribution is output by the mean training neuron and the variance training neuron, respectively. a and variance This generates state s t The corresponding Gaussian distribution, and the action a obtained by sampling the Gaussian distribution. t (sampling in) Figure 8 Completed in 2005), that is Where λ σ ∈(0,1) is a random exploration discount factor used to reduce the variance of action selection in order to adjust the stability of network convergence in the later stages of training.

[0133] The old action network stores the policy learned from the training of the new action network. It perceives the environment based on this policy and periodically copies the new policy from the new action network. It should be noted that the old action network also receives the state s obtained from the environment perception. t It outputs the mean μ through common neurons, mean-trained neurons, and variance-trained neurons. a and variance Generate a Gaussian distribution, and obtain action a by sampling the Gaussian distribution. t .

[0134] Evaluation network (i.e.) Figure 8 The 2004 load action evaluation, through the input state s t The value v is obtained through evaluation.t Combined with the reward r obtained from the current action t Advantage A in generating this set of states and actions t This is represented by Formula 9:

[0135] A t =δ t +λγδ t+1 +...+...+(λγ) T-t+1 δ T-1 ————Formula 9

[0136] Where T represents the length of a training cycle, λ and γ are discount factors, and δ t Defined as:

[0137] δ t =r t +γV(s t+1 )-V(s t )

[0138] Figure 8 The parameter updates in the PPO algorithm include updates to the new action network, the old action network, and the evaluation network. Advantage A t The input is fed into the PPO algorithm, and the output is the action loss function L used to update the new action network. A Its expression is shown in Formula 10:

[0139] L A =exp(-E[min(r) t A t ,clip(r t A t ])————Formula 10

[0140] Where E represents the expectation, clip represents the importance sampling function, corresponding to the PPO algorithm, and the reward r t The expression is Formula 11:

[0141]

[0142] Among them, G(a t |s t G represents the action distribution function output by the new action network, i.e., the Gaussian distribution generated based on the new action network. old (a t |s t ) represents the action distribution function output by the action network, i.e., the Gaussian distribution generated based on the old action network, and ε is used to prevent the importance sampling coefficients from having infinite hyperparameters.

[0143] Evaluate network output based on state s t value vt Therefore, the network output V(s) can be directly evaluated. t ) and true value The root mean square error is used as the evaluation loss function L C Update the evaluation network and evaluate the loss function L. C As shown in Formula 12:

[0144]

[0145] In this embodiment, the signal-to-noise ratio (SNR) and differential signal-to-noise ratio (DSR) of the subcarrier signals output by the preset channel model are taken as the environmental state s. t As shown in Formula 13:

[0146] s t =[SNR t ,ΔSNR t ] T ————Formula 13

[0147] Wherein, ΔSNR t =SNR t -SNR t-1 Differential signal-to-noise ratio (SNR) represents the change in the channel's SNR in each adjacent time slot as the mobile terminal moves.

[0148] In this embodiment, action a t The update amount is designed as the beam angle ψ, as shown in Equation 14:

[0149] ψ t =ψ t-1 +a t ————Formula 14

[0150] And each action of beam tracking a t Reward r t The average channel gain is designed to be affected by the beam angle, i.e., Equation 15:

[0151]

[0152] Step A2: Update the action network and the evaluation network based on the action loss function and the evaluation loss function, respectively. The updated action network and the updated evaluation network constitute the updated proximal policy optimization algorithm.

[0153] After outputting the action loss function and the evaluation loss function, the action loss function is input into the new action network of the action network to update the new action network, and the evaluation loss function is input into the evaluation network to update the evaluation network. This process aims to converge the target network, composed of the action network and the evaluation network, towards maximizing the reward, i.e., maximizing the channel gain. This improves the accuracy of the subsequent beam tracking algorithm based on the near-end policy optimization algorithm for locating the mobile terminal.

[0154] Optionally, after step S201, where the updated parameters based on the preset channel model are input into the near-end policy optimization algorithm to update the action network and evaluation network in the near-end policy optimization algorithm, the mobile communication method further includes:

[0155] Step A3: When it is detected that the channel gain obtained based on the updated near-end policy optimization algorithm is not the target channel gain, a new signal-to-noise ratio, a new differential signal-to-noise ratio, and a new beam angle are obtained as new update parameters and input into the near-end policy optimization algorithm to obtain a new action loss function for updating the updated action network and a new evaluation loss function for updating the updated evaluation network.

[0156] During the continuous learning and updating process based on the updated parameters output by the preset channel model, when it is detected that the channel gain output by the current update learning is not the target channel gain, it indicates that the near-end policy optimization algorithm has not yet been fully trained. The preset beam tracking algorithm based on the output of the near-end policy optimization algorithm at this time cannot guarantee the accuracy of the mobile terminal's positioning. Therefore, at this point, it is necessary to obtain new action loss functions and new evaluation loss functions to update the action network and evaluation network again. The process of obtaining the new action loss function and the new evaluation loss function is shown in step A1 and will not be elaborated here.

[0157] Step A4: Based on the new action loss function and the new evaluation loss function, update the updated action network and the updated evaluation network again, and determine whether the channel gain obtained based on the updated near-end policy optimization algorithm is the target channel gain. The updated action network and the updated evaluation network constitute the updated near-end policy optimization algorithm.

[0158] The specific implementation process of step A4 is shown in step A2, and will not be repeated here.

[0159] In this embodiment, the updated parameters based on the output of the preset channel model are input into the near-end policy optimization algorithm to update the action network and evaluation network in the near-end policy optimization algorithm. When the channel gain obtained based on the updated near-end policy optimization algorithm is detected to be the target channel gain, a preset beam tracking algorithm is generated according to the updated near-end policy optimization algorithm. This enables the near-end policy optimization algorithm to continuously learn for randomly moving mobile terminals in the terahertz communication and positioning integrated environment, and to converge the target network of the mobile terminal in the direction of maximizing the reward, that is, maximizing the channel gain, thereby realizing the beam tracking algorithm based on near-end policy optimization.

[0160] Reference Figure 9 , Figure 9 This is a flowchart illustrating the fifth embodiment of the mobile communication method of the present invention. Before step S30, in which the power required by the communication subcarrier is allocated according to the target location and a preset power allocation algorithm to obtain the target power of the target terminal, the mobile communication method further includes:

[0161] Step S301: Construct power constraints and communication interruption constraints based on the sample subcarrier signals.

[0162] To address the issue of limited total power of a single base station in integrated communication and positioning systems, this embodiment designs a robust power allocation algorithm based on convex optimization by relaxing the interruption probability constraint using the Bernstein inequality. This algorithm, known as the preset power allocation algorithm, avoids the need to retrain the mobile terminal's position after each movement. Compared to the cumbersome and inconvenient nature of existing deep learning-based power allocation methods that require retraining based on the mobile terminal's position after each movement, the preset power allocation algorithm in this embodiment is more convenient.

[0163] Specifically, only one sample subcarrier signal needs to be acquired. Based on the signal information of the sample subcarrier signal, power constraints and communication interruption constraints are constructed. The power constraints are used to prevent the power of the signal corresponding to the transmitted subcarrier signal from exceeding the total power of a single base station. The communication interruption constraints are used to reduce the probability of signal transmission interruption between a single base station and a mobile terminal.

[0164] Based on the constructed power constraints and communication interruption constraints, we can ensure effective power supply under the limited total power of a single base station, which can guarantee the continuity of signals between the single base station and the mobile terminal.

[0165] Optionally, the step of constructing power constraints and communication interruption constraints based on the sample subcarrier signals in step S301 includes:

[0166] Step B1: After determining the number of communication subcarriers and their communication power, as well as the number of positioning subcarriers and their positioning power, in the sample subcarriers, determine the power constraint conditions based on the total power of the sample base station.

[0167] Step B2: After determining the subcarrier rate and subcarrier rate threshold of the sample subcarrier, determine the communication interruption constraint condition based on the communication interruption probability of the sample base station.

[0168] Obtain the number M of communication subcarriers in the sample subcarriers. c and communication power P c And the number M of sample positioning subcarriers in the sample subcarriers. p and positioning power P p Based on the number of communications M c Communication power P c Number of locations M p and positioning power P p The power constraint conditions are as shown in Equation 16:

[0169] M c P c +M p P p ≤ξ total ————Formula 16

[0170] Where, ξ total This represents the total power of a single base station.

[0171] Simultaneously, the subcarrier rate R of the sample subcarrier signal is obtained. c and subcarrier rate threshold R th Based on carrier rate R c and subcarrier rate threshold R th The communication interruption constraint conditions are as shown in Formula 17:

[0172] Pr{R c ≤R th}≤P out ————Formula 17

[0173] Among them, P out The upper limit probability of signal transmission interruption between a single base station and a mobile terminal is represented by Pr, which represents the probability of signal transmission interruption between a single base station and a mobile terminal. By constraining the probability of signal transmission interruption between a single base station and a mobile terminal to be less than or equal to the upper limit probability of signal transmission interruption between a single base station and a mobile terminal, the stability of communication between a single base station and a mobile terminal is improved.

[0174] It should be noted that the subcarrier rate R c Represented as Formula 18:

[0175]

[0176] Among them, B c Indicates communication bandwidth. e represents an estimate of the distance between a single base station and a mobile terminal. d The distance estimation error can be characterized by CRB.

[0177] Step S302: Model the power constraint and the communication interruption constraint to obtain a robust power allocation problem.

[0178] After obtaining the power constraint and the communication interruption constraint, we model the power constraint and the communication interruption constraint to obtain the robust power allocation problem:

[0179]

[0180] stPr{R c ≤R th}≤P out

[0181] M c P c +M p P p ≤ξ tatal

[0182] ψ=ψ PPO

[0183] Where, ψ PPO This represents the beam angle obtained by the preset beam tracking algorithm generated by the near-end strategy optimization algorithm.

[0184] Pr{R} represents minimizing the lower bound of the localization theory. c ≤R th}≤P out M represents the rate constraint. c P c +M p P p ≤ξ tatal Representing the total power constraint, ψ=ψ PPO Indicates a fixed beam direction.

[0185] Step S303: The robust power allocation problem is transformed into a convex optimization problem and solved to obtain the preset power allocation algorithm.

[0186] After obtaining the modeled robust power allocation problem, the constraints of the robust power allocation problem are relaxed to transform it into a convex optimization problem for solution.

[0187] Taking the rate constraint in the robust power allocation problem as an example, by transforming the rate constraint using the Bernstein inequality, we can obtain the following convex optimization problem:

[0188]

[0189] The transformed convex optimization problem was solved using MATLAB's CVX tool to obtain a solution with rate constraints.

[0190] Similarly, the total power constraint in the robust power allocation problem is transformed using the Bernstein inequality, and the transformed total power constraint is solved using MATLAB's CVX tool to obtain the solution to the total power constraint. These two solutions yield the preset power allocation algorithm.

[0191] In this embodiment, power constraints and communication interruption constraints are constructed based on the sample subcarrier signals. The power constraints and communication interruption constraints are modeled to obtain a robust power allocation problem. The robust power allocation problem is then transformed into a convex optimization problem for solution, resulting in a preset power allocation algorithm. This approach ensures the accuracy of power allocation while avoiding the tediousness and inconvenience of retraining the mobile terminal's position after each movement.

[0192] Furthermore, this embodiment of the invention also proposes a mobile communication device, which includes a memory, a processor, and a computer processing program stored in the memory and executable on the processor. When the processor executes the computer processing program, it implements the steps of the above-described mobile communication method.

[0193] Furthermore, the present invention also proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described mobile communication method.

[0194] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0195] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0196] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0197] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A mobile communication method, characterized in that, The mobile communication method includes the following steps: A preset channel model is generated based on the channel state information of the sample subcarrier signal, the beamforming vector of the sample subcarrier signal, and the noise transmitted from the sample subcarrier signal to the sample terminal. The preset channel model is generated based on orthogonal frequency division multiplexing technology. The communication subcarrier and the positioning subcarrier in the subcarrier signal are determined according to the preset signal model; The positioning subcarrier is updated based on the preset channel model and preset beam tracking algorithm to obtain the target location of the target terminal; The power required by the communication subcarrier is allocated according to the target location and the preset power allocation algorithm to obtain the target power of the target terminal; Mobile communication of the target terminal is achieved based on the target location and the target power.

2. The mobile communication method as described in claim 1, characterized in that, Before the step of determining the communication subcarrier and the positioning subcarrier in the subcarrier signal according to the preset signal model, the mobile communication method further includes: A preset signal model is constructed, wherein the preset signal model is constructed based on frequency division multiplexing technology. The preset signal model consists of a configuration subframe, a communication positioning subframe, and a feedback subframe in the time domain. In the frequency domain, a number of subcarrier signals corresponding to the communication positioning subframe are divided into the communication subcarrier and the positioning subcarrier.

3. The mobile communication method as described in claim 1, characterized in that, Before the step of updating the positioning subcarrier based on a preset channel model and a preset beam tracking algorithm to obtain the target location of the target terminal, the mobile communication method further includes: The updated parameters output based on the preset channel model are input into the near-end policy optimization algorithm to update the action network and evaluation network in the near-end policy optimization algorithm. When the channel gain obtained based on the updated near-end policy optimization algorithm is detected to be the target channel gain, the preset beam tracking algorithm is generated according to the updated near-end policy optimization algorithm.

4. The mobile communication method as described in claim 3, characterized in that, The step of inputting the updated parameters output based on the preset channel model into the near-end policy optimization algorithm to update the action network and evaluation network in the near-end policy optimization algorithm includes: The signal-to-noise ratio (SNR) of the sample subcarrier signal and the differential SNR of the sample subcarrier signal obtained based on the preset channel model are used as update parameters and input into the near-end policy optimization algorithm to obtain the action loss function for updating the action network and the evaluation loss function for updating the evaluation network. The action network and the evaluation network are updated based on the action loss function and the evaluation loss function, respectively, wherein the updated action network and the updated evaluation network constitute the updated proximal policy optimization algorithm.

5. The mobile communication method as described in claim 4, characterized in that, After the step of inputting the updated parameters output based on the preset channel model into the near-end policy optimization algorithm to update the action network and evaluation network in the near-end policy optimization algorithm, the mobile communication method further includes: When it is detected that the channel gain obtained based on the updated near-end policy optimization algorithm is not the target channel gain, a new signal-to-noise ratio, a new differential signal-to-noise ratio, and a new beam angle are obtained as new update parameters and input into the near-end policy optimization algorithm to obtain a new action loss function for updating the updated action network again and a new evaluation loss function for updating the updated evaluation network again. The updated action network and the updated evaluation network are updated again based on the new action loss function and the new evaluation loss function, respectively. It is then determined whether the channel gain obtained based on the updated near-end policy optimization algorithm is the target channel gain. The updated action network and the updated evaluation network constitute the updated near-end policy optimization algorithm.

6. The mobile communication method as described in claim 1, characterized in that, Before the step of allocating the power required by the communication subcarrier according to the target location and a preset power allocation algorithm to obtain the target power of the target terminal, the mobile communication method further includes: Construct power constraints and communication interruption constraints based on the sample subcarrier signals; By modeling the power constraints and the communication interruption constraints, a robust power allocation problem is obtained. The robust power allocation problem is transformed into a convex optimization problem for solution, resulting in the preset power allocation algorithm.

7. The mobile communication method as described in claim 6, characterized in that, The steps of constructing power constraints and communication interruption constraints based on sample subcarrier signals include: After determining the number of communication subcarriers and their communication power in the sample communication subcarriers, as well as the number of positioning subcarriers and their positioning power in the sample positioning subcarriers, power constraints are determined based on the total power of the sample base stations. After determining the subcarrier rate and subcarrier rate threshold of the sample subcarrier, the communication interruption constraint condition is determined based on the communication interruption probability of the sample base station.

8. A mobile communication device, characterized in that, The mobile communication device includes a memory, a processor, and a computer processing program stored in the memory and executable on the processor. When the processor executes the computer processing program, it implements the steps of the mobile communication method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the mobile communication method according to any one of claims 1 to 7.

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