Sensitivity integration method for communication waveform in high mobile environment

Abstract

The invention discloses a communication and inductance integration method for a communication waveform in a high mobile environment. The method comprises the following steps: constructing a UAV-ISAC system based on AFDM; constructing a model of a communication signal sent by the UAV to the base station and a model of an echo signal received by the UAV and perceived by a ground user; determining the signal-to-noise ratio of each time slot communication part link and the signal-to-noise ratio of a sensing part echo based on a communication signal model and an echo signal model, thereby determining the capacity of each time slot communication link and the sensing mutual information amount of each user, and determining the total sensing mutual information amount, the total communication capacity of the UAV in the whole flight time, and the total consumed energy; constructing a total optimization problem by using the total communication capacity and the total consumed energy, and decomposing the optimization problem into three sub-problems of user scheduling optimization, transmitting power optimization and UAV trajectory optimization; and by constructing different constraint conditions and solving the three sub-problems, an optimization result is obtained. According to the invention, the energy efficiency of the UAV-ISAC system based on AFDM can be maximized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of communication system optimization in high mobility environment, and particularly relates to a method for integrating sensing and communication of communication waveform in high mobility environment. BACKGROUND

[0002] The next generation wireless system (5G / 6G and above) is expected to significantly improve the spectrum and energy efficiency, support ubiquitous connectivity of everything, and maintain reliable communication in high mobility scenarios. Integrated sensing and communication (ISAC) technology is one of the key driving factors beyond 5G / 6G, as it can simultaneously improve spectrum and energy efficiency and extract environmental information. In the ISAC system, dual-functional waveform design is crucial to achieve integration gain by sharing signaling resources for sensing and communication. Currently, OFDM has been widely studied in ISAC waveform design and signal processing methods. However, in high mobility scenarios, OFDM suffers from severe inter-carrier interference due to large Doppler shift, leading to performance degradation. SUMMARY

[0003] The purpose of the present application is to provide a method for integrating sensing and communication of communication waveform in high mobility environment, which optimizes the UAV-ISAC system based on AFDM in different dimensions to maximize the energy efficiency of the system.

[0004] To achieve the above-mentioned tasks, the present application adopts the following technical solutions:

[0005] A method for integrating sensing and communication of communication waveform in high mobility environment, comprising:

[0006] Constructing a UAV-ISAC system based on AFDM;

[0007] Building a model of the communication signal sent by the UAV to the base station, and a model of the echo signal received by the UAV for ground user sensing;

[0008] Based on the model of the communication signal and the model of the echo signal, determine the signal-to-noise ratio of each time slot communication part link and the signal-to-noise ratio of the sensing part echo, thereby determining the communication link capacity of each time slot and the sensing mutual information of each user, and determining the total sensing mutual information, the total communication capacity of the UAV in the entire flight time, and the total energy consumed;

[0009] Using the total communication capacity and the total energy consumed to construct a total optimization problem, and decomposing it into three sub-problems of user scheduling optimization, transmit power optimization, and UAV trajectory optimization; by constructing different constraint conditions and solving the three sub-problems, the optimization result is obtained.

[0010] Further, the UAV-ISAC system contains one UAV, one base station and K users, the UAV communicates with the base station while perceiving the information of the ground users; the flight time T of the UAV is fixed, the whole flight time T is divided into S time slots, the binary variable b k (s)∈{0,1} is used to represent the user scheduling of the s-th time slot, when the variable is 1, it means that the UAV perceives the k-th user in the s-th time slot, s=1,2,...,S, k=1,2,...,K; for each time slot, it is further divided into a perception part and a communication part, and the proportion of the two is defined as γ.

[0011] Further, the communication link capacity of each time slot is represented as:

[0012] R com (s)=(1-γ)log2(1+Γ com (s))

[0013] Wherein, R com (s) represents the communication link capacity of the s-th time slot, Γ com (s) represents the signal-to-noise ratio of the communication link of the s-th time slot.

[0014] The perception mutual information of the k-th user in the s-th time slot is:

[0015]

[0016] Wherein, is the signal-to-noise ratio of the echo of the perception part of the s-th time slot.

[0017] Further, the total communication capacity R ct of the UAV in the whole flight time T is the sum of the communication capacities of all S time slots; the total perception mutual information is the sum of the perception mutual information of all users in all time slots.

[0018] Further, the total energy consumed by the UAV in the flight time T is:

[0019]

[0020] Wherein, p(s) is the transmission power of the UAV in the s-th time slot, and the propulsion energy consumption of the fixed-wing UAV in the s-th time slot is represented as:

[0021]

[0022] Wherein a and b are constant parameters related to the UAV; v(s) is the flight speed of the UAV in the s-th time slot, ω[s] is the acceleration of the UAV in the s-th time slot, and g is the gravitational acceleration.

[0023] Further, define the energy efficiency function EE as the total capacity R of communication over the whole flight time of the UAV ct to the total consumed energy P total , EE = R ct / P total ; the total optimization problem is as follows:

[0024]

[0025] where B is the user scheduling optimization, P is the transmit power optimization, and U is the UAV trajectory optimization; the constraints that the problem needs to satisfy are:

[0026]

[0027] C8: 0≤d x (s)≤L x , 0≤d y (s)≤L y

[0028] wherein, denotes for all, is the information amount threshold, is the signal-to-noise ratio threshold, p max is the maximum transmit power of the UAV, v min and v max respectively represent the minimum and maximum flight speed of the UAV; wherein the length and width dimensions of the flight area of the UAV are L x × L y , d x (s) and d y (s) are respectively the position coordinates of the UAV in the length and width directions of the flight area in the s-th time slot.

[0029] Further, the user scheduling optimization sub-problem can be written as:

[0030]

[0031] st.C2, C3, C4, C5

[0032] The transmit power optimization sub-problem can be expressed as:

[0033]

[0034] st.C1, C4, C5, C6

[0035] The UAV trajectory optimization sub-problem can be expressed as:

[0036]

[0037] st.C1, C4, C5, C7, C8

[0038] Solving the above three sub-problems can obtain the user scheduling b of each time slot that maximizes the energy efficiency k (s), the transmit power p(s) of the UAV, and the position coordinates [d x (s), d y (s)] of the UAV.

[0039] Further, each optimization sub-problem is iteratively solved until the energy efficiency function value of each optimization sub-problem converges to within a set error threshold, and the solution result of each optimization sub-problem at this time is taken as the final optimization result.

[0040] A terminal device comprising a processor, a memory, and a computer program stored in the memory; when the processor executes the computer program, the method for integrating sensing and communication of the communication waveform in a high mobility environment is implemented.

[0041] A computer readable storage medium, the medium storing a computer program; when the computer program is executed by a processor, the method for integrating sensing and communication of the communication waveform in a high mobility environment is implemented.

[0042] Compared with the prior art, the present application has the following technical features:

[0043] Compared with the conventional sensing network system, the present application can optimize user scheduling, transmit power, UAV trajectory and other multiple dimensions while ensuring sensing fairness, thereby maximizing the energy efficiency of the AFDM-based UAV-ISAC system. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 is a model diagram of the AFDM-based UAV-ISAC system;

[0045] Figure 2 is a comparison of flight trajectories of UAVs in different states in an embodiment of the present application;

[0046] Figure 3 is a comparison of different user radar mutual information in an embodiment of the present application;

[0047] Figure 4 is a comparison of energy efficiency between different schemes in an embodiment of the present application. DETAILED DESCRIPTION

[0048] Affine Frequency Division Multiplexing (AFDM) is a new type of affine frequency division multiplexing technology, aiming to maintain the stability of communication in high dynamic mobile environment. The core idea is to multiplex information symbols in the framework of Discrete Affine Fourier Transform (DAFT), to ensure the independence of each transmission path, and each symbol will traverse all path coefficients. Through DAFT, AFDM can generate multiple mutually orthogonal chirp signals, and adjust the parameters of these chirp signals to match the channel characteristics. This can effectively reconstruct the delay-Doppler characteristics of the channel, and achieve comprehensive diversity effect in time-frequency dispersion channel.

[0049] In the present application, an unmanned aerial vehicle (UAV) ISAC system based on AFDM is established. AFDM maps data symbols to a more flexible discrete affine Fourier domain through DAFT for one-dimensional modulation, which can dynamically adapt to the delay and Doppler characteristics of the channel, thereby realizing full diversity in high mobility scenarios, with flexibility and robustness. The UAV has both communication and sensing functions in the system. The present application proposes a scheme that maximizes the total communication rate of the system while meeting the system sensing performance requirements. The method of the present application is as follows:

[0050] Consider an AFDM-based UAV-ISAC system as shown in Figure 1 , which includes a UAV, a base station and K users. The UAV communicates with the base station while sensing the information of the ground users; assuming that the flight height of the UAV is H and the flight time T is fixed, the entire flight time T is divided into S time slots, and binary variable b k (s)∈{0,1} is used to represent the user scheduling of the s-th time slot, where 1 represents that the UAV senses the k-th user in the s-th time slot, and 0 represents that it is not sensed; s=1,2,...,S, k=1,2,...,K; for each time slot, it is further divided into sensing part and communication part, and the proportion of the two is defined as γ.

[0051] Step 1, the communication signal y com sent by the UAV to the base station can be modeled as:

[0052]

[0053] wherein, is the gain coefficient of the channel between the UAV and the base station in the s-th time slot, is the complex gain of the channel between the UAV and the base station, G t and G crespectively, d(s) is the distance between the UAV and the base station, λ is the signal wavelength, A is the DAFT transformation matrix, the parameter superscript H denotes conjugate transpose, and the same below; H com is the channel matrix between the UAV and the base station, x com is the communication signal without DAFT transformation, is the Gaussian white noise,

[0054] Step 2, the UAV receives the echo signal of the ground user perception, which is modeled as:

[0055]

[0056] wherein is the gain coefficient of the channel between the UAV and the kth user in the st time slot, is the complex gain of the channel between the UAV and the kth user, G r is the radar receiving antenna gain of the UAV, σ RCS is the radar cross section (RCS) of the UAV, H sen is the channel matrix between the UAV and the user, is the Gaussian white noise, x sen is the echo signal without DAFT transformation.

[0057] Step 3, link index analysis; let the transmission power of the UAV in the st time slot be p(s), then the signal-to-noise ratio Γ com (s) of the communication part link in the st time slot can be expressed as:

[0058]

[0059] wherein tr(·) is the rank of a matrix, is the mean value, σ is the standard deviation of the Gaussian white noise, I N is the unit matrix, ||·|| F is the F-norm.

[0060] The signal-to-noise ratio of the perception part echo in the st time slot can be expressed as:

[0061]

[0062] wherein ||·||2 represents the L2-norm.

[0063] The communication link capacity of each time slot is:

[0064] R com (s) = (1-γ)log2(1+Γ com (s))

[0065] The amount of perceived mutual information for the kth user at time slot s is:

[0066]

[0067] The total communication capacity R of the UAV during the whole flight time T ct The sum of the communication capacity for all S time slots can be expressed as:

[0068]

[0069] The total amount of perceived mutual information The sum of the perceived mutual information for all users in all time slots is:

[0070]

[0071] Step 4, considering the propulsion energy consumed by the UAV for high-altitude flight, the propulsion energy consumption of the fixed-wing UAV at the s time slot is expressed as:

[0072]

[0073] where a and b are constant parameters related to the UAV, which can be obtained by simulation; v(s) is the flight speed of the UAV at the s time slot, ω[s] is the acceleration of the UAV at the s time slot, and g is the gravitational acceleration.

[0074] The total energy consumed by the UAV in the flight time T is defined as:

[0075]

[0076] Step 5, define the energy efficiency function EE as the ratio of the total communication capacity R of the UAV during the whole flight time ct to the total energy consumed P total , i.e. EE = R ct / P total ; according to steps 1 to 4, construct a total optimization problem that satisfies the perceived needs of different users and maximizes the system communication capacity:

[0077]

[0078] where B is the user scheduling optimization, P is the transmit power optimization, and U is the UAV trajectory optimization; the constraints that the problem needs to satisfy are:

[0079]

[0080]

[0081]

[0082]

[0083]

[0084]

[0085]

[0086] C8: 0≤d x (s)≤L x ,0≤d y (s)≤L y

[0087] wherein, C1: the total perceived mutual information is greater than the information threshold to guarantee the sensing performance; C2-C3: user scheduling constraints; C4: sensing link SNR constraint, i.e., the SNR of the sensing part of each time slot needs to be greater than the SNR threshold C5: the communication link capacity R of the UAV in each time slot com (s) is greater than the perceived mutual information of each user in each time slot to guarantee the complete return of the sensing information; C6: transmit power constraint, wherein p max represents the maximum transmit power of the UAV; C7-C8: UAV speed and position constraints, wherein v min and v max respectively represent the minimum and maximum flight speeds of the UAV; wherein the UAV is in the condition of flying at a height of H, the length and width dimensions of the flight area are L x ×L y , d x (s) and d y (s) are respectively the position coordinates of the UAV in the length and width directions of the flight area in the s-th time slot.

[0088] Step 6: divide the total optimization problem in step 4 into three optimization sub-problems of user scheduling optimization, transmit power optimization, and UAV trajectory optimization; first discuss the user scheduling problem, and the user scheduling optimization sub-problem can be written as:

[0089]

[0090] st.C2,C3,C4,C5

[0091] Solving the above sub-problem can obtain the user scheduling b k (s) that maximizes the energy efficiency in each time slot.

[0092] The transmit power optimization sub-problem can be expressed as:

[0093]

[0094] st.C1,C4,C5,C6

[0095] Solving the above sub-problems can obtain the transmission power p(s) of each time slot UAV which maximizes the energy efficiency.

[0096] The UAV trajectory optimization sub-problem can be expressed as:

[0097]

[0098] st.C1,C4,C5,C7,C8

[0099] Solving the above sub-problems can obtain the position coordinates [d x (s),d y (s)] of each time slot UAV which maximizes the energy efficiency.

[0100] Step 7, repeat step 6 until the energy efficiency function EE value of each optimization sub-problem converges to within a set error threshold, and the solution of each optimization sub-problem at this time is taken as the final optimization result.

[0101] Embodiment:

[0102] As Figure 1 shown, in an embodiment of the present application, a scenario of 1 base station, 1 UAV and 8 ground users is constructed, the flight speed of the UAV is 10-50 m / s, the maximum transmission power is 1 W, the power of the Gaussian white noise is-110 dBmW, the carrier frequency used is 28 GHz, the RCS of the user is 1 m 2 , the transmit and receive antenna gains of the UAV are 15 dBi and 25 dBi respectively, the base station receiving antenna gain is 0 dBi, the flight period is 40 s, which is divided into 80 time slots, and the length of each time slot is 0.5 s.

[0103] Figure 2 The flight trajectories of the UAV in different states are shown. The trajectory optimized by the present scheme (purple trajectory) is compared with the traditional scheme without QoS (yellow trajectory); compared with the trajectory without QoS, the distance between the UAV and the user in the present scheme is shortened as much as possible, so that the QoS of each user is guaranteed.

[0104] Figure 3 The radar mutual information of each user is shown; the present scheme (blue) is compared with the scheme without QoS (orange), it can be seen that the present scheme provides more fair service for each user, enabling the user to more effectively transmit its communication information, while the latter exhibits poor fairness and even does not provide service for user 4.

[0105] In Figure 4 different maximum transmission powers p max The energy efficiency between the original scheme (yellow), the traditional scheme without QoS (green) and the present scheme (blue); it can be seen that the traditional scheme has the best performance, and the performance of the present scheme is slightly lower, which is due to the consideration of QoS; it can be observed that although the present scheme sacrifices some energy efficiency to achieve perceptual fairness, its energy efficiency is still better than the original scheme.

[0106] The above examples are only used to illustrate the technical solutions of the present application, but not limit it; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can still be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A synaesthesia integration method for high-mobility environment communication waveforms, characterized in that: include: Construct a UAV-ISAC system based on AFDM; Build a model of the communication signals sent by the UAV to the base station, and a model of the echo signals received by the UAV that are perceived by ground users; Based on the communication signal model and the echo signal model, the signal-to-noise ratio of the communication link in each time slot and the signal-to-noise ratio of the perception echo are determined, thereby determining the communication link capacity of each time slot and the perception mutual information of each user, and then determining the total perception mutual information, the total communication capacity of the UAV during the entire flight time, and the total energy consumed; The total communication capacity and the total energy consumed are used to construct the overall optimization problem, which is then decomposed into three sub-problems: user scheduling optimization, transmission power optimization, and UAV trajectory optimization. By constructing different constraints and solving the three sub-problems, the optimization results are obtained.

2. The synaesthesia integration method of high-mobility environment communication waveform according to claim 1, characterized in that: The UAV-ISAC system includes a UAV, a base station and K users. The UAV communicates with the base station while sensing the ground user information. The flight time T of the UAV is fixed. The entire flight time T is divided into S time slots, and the binary variable b is referenced. k (s)∈{0,1} represents the user scheduling of the s-th time slot. When the variable is 1, it means that the UAV senses the k-th user in the s-th time slot, s=1,2,...,S,k=1,2,...,K. For each time slot, it is divided into the sensing part and the communication part, and the ratio of the two is defined as γ.

3. The synaesthesia integration method of high-mobility environment communication waveform according to claim 1, characterized in that: The communication link capacity of each time slot is expressed as: R com (s)=(1-γ)log2(1+Γ com (s)) Among them, R com (s) represents the communication link capacity of the sth time slot, Γ com (s) represents the signal-to-noise ratio of the communication link in the sth time slot; The perceived mutual information of the kth user in time slot s is: in, is the signal-to-noise ratio of the partial echo sensed in the sth time slot.

4. The synaesthesia integration method of high-mobility environment communication waveform according to claim 1, characterized in that: The total communication capacity R of the UAV during the entire flight time T ct is the sum of the communication capacity of all S time slots; the total perceptual mutual information is the sum of the perceived mutual information of all users in all time slots.

5. The synaesthesia integration method of high-mobility environment communication waveform according to claim 1, characterized in that: The total energy consumed by the drone during the flight time T is: Where p(s) is the transmit power of the UAV in the sth time slot, and the propulsion energy consumption of the fixed-wing UAV in the sth time slot is expressed as: where a and b are constant parameters related to the UAV; v(s) is the flight speed of the UAV in the sth time slot, ω[s] is the acceleration of the UAV in the sth time slot, and g is the acceleration due to gravity.

6. The synaesthesia integration method of high-mobility environment communication waveform according to claim 1, characterized in that: Define the energy efficiency function EE as the total communication capacity R of the UAV during the entire flight time ct The total energy consumed P total The ratio of EE = R ct / P total ; The overall optimization problem is as follows: Among them, B is user scheduling optimization, P is transmit power optimization, and U is UAV trajectory optimization. The constraints that need to be satisfied in this problem are: in, It means that for all, is the information threshold, is the signal-to-noise ratio threshold, p max is the maximum transmission power of UAV, v min and v max They represent the minimum and maximum flight speeds of the UAV respectively; the length and width of the flight area of ​​the UAV is L x ×L y , d x (s) and d y (s) are respectively the position coordinates of the UAV in the length and width directions of the UAV in the flight area at the sth time slot.

7. The synaesthesia integration method of high-mobility environment communication waveform according to claim 6, characterized in that: The user scheduling optimization subproblem can be written as: The transmit power optimization sub-problem can be expressed as: The UAV trajectory optimization sub-problem can be expressed as: Solving the above three sub-problems can yield the user scheduling b for each time slot that maximizes energy efficiency. k (s), UAV transmission power p(s) and UAV position coordinates [d x (s),d y (s)].

8. The synaesthesia integration method of high-mobility environment communication waveform according to claim 7, characterized in that: Iteratively solve each optimization sub-problem until the energy efficiency function value of each optimization sub-problem converges to the set error threshold, and the solution result of each optimization sub-problem at this time is used as the final optimization result.

9. A terminal device comprising a processor, a memory, and a computer program stored in the memory; characterized in that: When the processor executes the computer program, the synaesthesia integration method of the high-mobility environment communication waveform according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium storing a computer program; wherein: When the computer program is executed by a processor, the synaesthesia integration method of a high-mobility environment communication waveform according to any one of claims 1 to 8 is implemented.

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