An unmanned aerial vehicle integrated signal transmission method for maritime rescue

Abstract

The application belongs to the field of new generation information technology, and discloses a signal transmission method for unmanned aerial vehicle (UAV) all-sensing integration for sea rescue, which comprises the following steps: step 1, defining initial data; step 2, designing a signal waveform of the UAV as a dual-integrated signal waveform based on front-haul access and all-sensing; step 3, designing a parallel duplex frame structure matched with the MDD-OFDM waveform to realize parallel transmission and reception of multiple signals; and step 4, designing a sea UAV all-sensing integrated front-haul access fusion transmission mode based on the dual-integrated signal waveform and the parallel duplex frame structure of steps 2 and 3. Through the application, real-time data communication and sensing positioning services can be efficiently provided during the execution of the sea rescue task by the UAV, and the deficiencies of the current sea rescue network in data transmission and information sensing are made up.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of new generation information technology, and relates to unmanned aerial vehicle communication and sensing integration technology, in particular to an unmanned aerial vehicle sensing and communication integrated signal transmission method for sea rescue. BACKGROUND

[0002] The existing sea rescue task mainly relies on the Global Maritime Distress and Safety System (GMDSS), which can provide real-time communication and positioning services on the sea with large area coverage by using shore-based stations and satellite nodes. However, due to the low deployment flexibility and long deployment distance, neither the shore-based nor the satellite system can meet the modern sea rescue requirements of high-speed, low-latency transmission and high-resolution sensing.

[0003] With the rapid development of unmanned aerial vehicle hardware technology, various unmanned aerial vehicle devices for sea rescue tasks have emerged. Compared with the shore-based and satellite systems, unmanned aerial vehicles are easy to deploy and can respond to rescue tasks in time, and have the ability to quickly reach the target area for high-speed broadband communication and high-resolution positioning detection. Secondly, as wireless communication and wireless sensing gradually tend to be consistent in terms of large-scale antenna array use, large-aperture antenna demand, high-frequency signal processing, etc., unmanned aerial vehicles are expected to use sensing and communication integration technology to simultaneously realize the functions of communication and sensing on a set of radio frequency devices, thereby efficiently balancing sea data transmission and sea information sensing in sea rescue tasks.

[0004] Currently, in order to realize the unmanned aerial vehicle (UAV) integrated sensing and communication (S&C) based maritime rescue network, three major technical challenges need to be solved in the signal transmission layer: (1) Although the UAV can save energy consumption and ensure reliable flight time through the S&C integration technology, the excessive mutual interference between the coupled S&C signals in the same radio frequency device will significantly reduce the communication and sensing performance. To solve this problem, existing research proposes a time-separated communication and sensing signal transmission mode, which effectively suppresses the mutual interference. However, this method requires adding a guard interval between the S&C signal transmission, which sacrifices valuable time resources. (2) In the ground network, the UAV can always forward the transmission to its adjacent ground base station through network switching technology, so it only needs to focus on the access (uplink or downlink) transmission quality. In contrast, when performing tasks at sea, the UAV can only establish a long-distance forward link with the shore base. The severe path loss makes it necessary to focus on both the forward and access transmission qualities. The existing research proposes to use a buffer mechanism to avoid dependence on the forward link, but this solution cannot be applied to the maritime rescue scenario that requires real-time communication and positioning. (3) Due to the limited energy of the UAV, it needs to strictly optimize the transmission power and flight path during the task execution at sea to maximize the communication rate and sensing accuracy. Therefore, for the maritime rescue network, the design of the optimization algorithm must consider both the network performance and complexity, and there is no existing research on the joint optimization solution for the forward and access of the S&C integrated system. SUMMARY

[0005] To overcome the shortcomings of the prior art, the present application provides a UAV S&C integrated signal transmission method for maritime rescue, (1) First, for the strong coupling relationship between the forward link (shore base station-UAV), communication link (UAV-maritime ship) and sensing link (UAV-maritime target), a dual-integrated signal waveform based on multi-carrier division duplex (MDD) and orthogonal frequency division multiplexing (OFDM) for forward access and S&C is designed, which can effectively eliminate the digital domain interference between communication and sensing signals. (2) Second, for the proposed MDD-OFDM waveform, an adaptive multi-path parallel frame transmission structure is designed, which enables the UAV to simultaneously perform signal transmission for the forward and S&C access links, thereby improving the time-frequency resource utilization of the network. (3) Finally, based on the MDD-OFDM waveform and parallel frame structure, a low-complexity UAV path planning and resource allocation algorithm is proposed, which maximizes the communication rate and sensing accuracy of the UAV during task execution.

[0006] To solve the above technical problems, the technical scheme adopted by the present application is:

[0007] A UAV S&C integrated signal transmission method for maritime rescue, comprising the following steps:

[0008] Step 1, define initial data:

[0009] For a marine rescue scenario, including a shore base station, a UAV and U marine single antenna communication users, the shore base station and the UAV both use OFDM modulation, a total of M subcarriers; on the nth frame of subcarrier m, the front-haul channel of the UAV-shore base station and the downlink channel of the UAV-marine user can be obtained by pilot channel estimation, wherein the shore base station and the UAV use a shared transceiving antenna array, N BS and N UAV are the number of antennas of the shore base station and the UAV respectively, is a set composed of M subcarriers;

[0010] Step 2, design the signal waveform of the UAV as a dual-integrated signal waveform based on front-haul access and sensing:

[0011] Specifically, according to the MDD-OFDM design criteria, the subcarriers within the OFDM symbol are divided into four mutually orthogonal subcarrier sets, namely and are used for information forwarding, downlink communication, target sensing and sensing feedback respectively, and then the dual-integrated signal waveform representation of the front-haul access and sensing of the UAV is obtained;

[0012] Step 3, design a parallel duplex frame structure matched with the MDD-OFDM waveform to realize parallel transmission and reception of multiple signals;

[0013] Step 4, based on the dual-integrated signal waveform and the parallel duplex frame structure of steps 2 and 3, design a marine UAV sensing-integrated front-haul access fusion transmission mode.

[0014] Further, in step 1, the coordinate position of the shore base station is c BS , the UAV flies from the initial position to the accident area after receiving the task; in the accident area, there are U marine single antenna communication users, i.e. ships that need communication services, and J marine targets, i.e. rescue targets or buoy ships in the area, with coordinates c u and c j ; the marine rescue network uses a frequency band resource with a center frequency f c and a bandwidth B c .

[0015] Further, in step 2, the subcarrier allocation process is as follows: introduce a binary variable to represent the allocation of subcarriers in the nth frame. Specifically, if then subcarrier m is allocated to set , i.e. XX∈{CD, DL, Sen, PE}, CD, DL, Sen, PE represent information forwarding, downlink communication, target sensing and sensing feedback respectively, and vice versa;

[0016] Considering that the four subcarrier sets are orthogonal to each other, The following needs to be met:

[0017]

[0018] Thus, the unmanned aerial vehicle The front access dual integrated signal waveform transmitted in the nth frame is:

[0019]

[0020] Wherein are the beamforming vectors of target sensing, downlink communication and sensing feedback respectively.

[0021] Further, in step 3, the sea task duration of the unmanned aerial vehicle is set as T op , containing N wireless frame lengths, when N is large enough, the unmanned aerial vehicle, the sea target and the user are relatively static in each wireless frame and the channel state information remains unchanged; each wireless frame contains N t OFDM symbols, the links occupying different subcarrier resource blocks can transmit corresponding signals at the same time; parallel duplex refers to: from the 4th OFDM symbol period of each frame, the information forwarding and sensing feedback signals on the front link coexist with the downlink communication and target sensing signals on the access link in the same symbol period, that is, the four kinds of MDD-OFDM based signals, information forwarding, downlink communication, target sensing and sensing feedback, are transmitted at the same time in the time domain, forming four parallel data streams.

[0022] Further, the process of step 4 is as follows:

[0023] (1) Channel estimation: the unmanned aerial vehicle receives the pilot signal from the sea user and estimates the channel state information CSI of the downlink channel;

[0024] (2) Channel feedback: the unmanned aerial vehicle sends the pilot signal and the downlink CSI to the shore base station, and the base station then obtains the channel CSI from the unmanned aerial vehicle to the shore base station;

[0025] (3) Information forwarding: the shore base station sends the encoded communication data to the unmanned aerial vehicle according to the request of the sea user, and feeds back the CSI of the front link to the unmanned aerial vehicle;

[0026] (4) Downlink communication and target sensing: the unmanned aerial vehicle sends the data received from the shore base station to the sea user by decoding; at the same time, according to the prior information of the target position, the unmanned aerial vehicle performs positioning and detection sensing tasks using the downlink data;

[0027] (5) Sensing feedback: The UAV receives the sensing echo signal and feeds back to the ground base station after preprocessing to make the final sensing decision.

[0028] Further, the unmanned aerial vehicle signal transmission method for maritime rescue further comprises the following steps: 5, an optimization step: after designing the maritime unmanned aerial vehicle sensing integrated front-end access fusion transmission mode, the communication and sensing performance of the network are optimized, the optimization target is the data transmission rate of the maritime user and the current sensing mutual information of the maritime user, and the optimization variable is the flight trajectory of the unmanned aerial vehicle and the power subcarrier allocation of the unmanned aerial vehicle and the ground base station.

[0029] Further, the optimization problem is represented as (P1):

[0030]

[0031] wherein R DL [n] and R CD [n] represent the downlink communication rate and the information forwarding rate in the nth frame, respectively, ω DL [n,m] and ∑ CD [n,m] represent the sensing mutual information of the target j on the (m, m') subcarrier pair in the nth frame, ω is a weight coefficient, ∑ SEN [n,m] and ∑ SEN [n,m′] represent the transmission power of the corresponding signals, ∏ PE [n,m] and ∑ UAV [n,m] represent the received sensing signal power and the transmission power coefficient of the sensing feedback signal, c [n] is the coordinate of the unmanned aerial vehicle in the nth frame, P UAV and P TBS are the maximum transmission powers of the unmanned aerial vehicle and the base station, V max is the maximum flight speed of the unmanned aerial vehicle, T s is an OFDM symbol period.

[0032] Further, the (P1) problem is decomposed into two sub-problems (P2) and (P3), the first sub-problem (P2) is that the unmanned aerial vehicle finds the nearest task executable point, and the second sub-problem (P3) is that after the unmanned aerial vehicle arrives at the optimal initial operating point, the unmanned aerial vehicle will continue to fly to the rescue area, at this time, the unmanned aerial vehicle needs to continuously optimize the communication and sensing services until the flight task is completed.

[0033] Furthermore, the first optimization subproblem (P2) is modeled as follows:

[0034]

[0035] Since (P2) is an iterative optimization problem, the choice of the initial value of the iteration directly affects whether the algorithm can converge to the global optimal point. The iterative variables involved in (P2) include the transmission power of each signal, the subcarrier set allocation, and the coordinates of the UAV. Because the perception feedback signal needs to be amplified by the UAV before being sent to the shore-based base station, its power initialization is closely related to the location of the UAV. Therefore, an optimization problem (P2-1) is designed to find the optimal iterative initial point of the perception feedback signal power coefficient. (P2-1) is modeled as follows:

[0036]

[0037] Where x is the slack variable, is the transmission power coefficient of the perception feedback signal of user j on the m subcarrier in the first frame, is the channel gain of user j on the m subcarrier in the first frame, is the total power of the perception feedback signal transmitted by user j on the subcarrier pair (m, m′) in the first frame. The (0) superscript on all variables represents the iteration starting point of the corresponding variable.

[0038] Furthermore, solving the second sub-problem (P3) is equivalent to solving the problem (P1) when n>1. Specifically, after obtaining the initial mission execution coordinates of the UAV, first initialize the power of the perception subcarrier set and the information forwarding signal, downlink communication signal and target perception signal, and obtain the initialization power of the perception feedback signal by solving (P2-1); let n=1; then use the quadratic deformation-continuous convex approximation algorithm to solve the problem (P3); if n <N,则根据感知性能更新子载波分配,然后对下一帧进行优化求解,即n=n+1;最后,优化过程在最后一帧停止,即n=N。

[0039] Compared with the prior art, the present invention has the following advantages:

[0040] (1) Four parallel data streams can be realized within multiple OFDM symbol periods, and resource management between fronthaul and commutation is refined to subcarriers, which can improve the network's time and frequency resource utilization.

[0041] (2) MDD-OFDM can simplify the complexity of system channel estimation. Specifically, the information forwarding and sensing feedback use different subcarriers in the same frequency band. Then, the channel reciprocity and subcarrier correlation can be used to obtain the channel information from the base station to the UAV by estimating the channel from the UAV to the base station. Of course, if the communication ship is also the sensing target, the sensed channel information can also be used to derive the downlink communication channel from the UAV to the ship.

[0042] (3) MDD-OFDM can eliminate the digital domain self-interference and cross-layer interference in the system. By mapping the orthogonal subcarriers to the front link and the sensing link, the base station interference to the communication ship, the sensing of the UAV to the communication ship, and the self-interference of the UAV downlink signal to the echo reception can be eliminated in the digital domain through the orthogonality of the subcarriers.

[0043] (4) The present application innovatively optimizes the initial operation coordinates of the UAV and the path and resource allocation in the flight process in segments based on the actual application scenario, realizes the effective coverage of the communication and sensing services of the UAV in the rescue area, and enables the UAV to efficiently provide real-time data communication and sensing positioning services during the execution of the sea rescue task, thereby making up for the deficiencies of the current sea rescue network in data transmission and information sensing. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0045] Figure 1 The parallel duplex frame structure of the present application is shown in the figure.

[0046] Figure 2 The algorithm flowchart of the optimization problem (P2) of the present application is shown in the figure.

[0047] Figure 3 The algorithm flowchart of the optimization problem (P3) of the present application is shown in the figure. DETAILED DESCRIPTION

[0048] The present application will be further described below in combination with the drawings and specific embodiments.

[0049] The present embodiment provides a UAV communication and sensing integrated signal transmission method for sea rescue, which comprises the following steps:

[0050] Step 1, define initial data.

[0051] For an ocean rescue scenario, it includes a shore-based base station, a drone, and U single-antenna communication users at sea.

[0052] Assume that the coordinate position of the shore-based base station is c BS =[x BS ,y BS , z BS ], the drone receives the mission and moves from its initial position Fly towards the accident area. In the accident area, there are U maritime single-antenna communication users (i.e., ships that need communication services) with coordinates of The coordinates of J sea targets (i.e., rescue targets or buoy ships in the area) are The maritime rescue network uses a central frequency of f c GHz bandwidth is B c MHz frequency band resources.

[0053] Both the shore-based base station and the drone use OFDM modulation, with a total of M subcarriers; on subcarrier m of the nth frame, the forward channel from the drone to the shore-based base station is and drone-maritime user downlink channel Both can be obtained through pilot channel estimation, where the shore-based base station and the UAV use a shared transmit and receive antenna array (Shared antenna array), N BS and N UAV are the number of antennas of shore-based base stations and drones, is a set of M subcarriers.

[0054] Step 2: Design the drone's signal waveform as a dual-integrated signal waveform based on fronthaul access and synaesthesia.

[0055] Considering that a maritime rescue network composed of drones needs to provide stable and timely communication and perception services to the accident area, real-time information exchange between drones and base stations is crucial. This requires drones to be able to simultaneously transmit and receive signals from shore-based base stations and transmit integrated intersensory signals. Therefore, a dual-integrated signal waveform based on fronthaul access and intersensory is proposed.

[0056] Specifically, according to the MDD-OFDM design criteria, the subcarriers in the OFDM symbol are divided into four mutually orthogonal subcarrier sets, namely and They are used for information forwarding, downlink communication, target perception and perception feedback respectively, thereby obtaining the dual integrated signal waveform representation of the forward transmission access and synaesthesia transmitted by the UAV.

[0057] Specifically, the subcarrier allocation process is as follows: Introduce the binary variable Indicates the allocation of subcarriers in the nth frame. Specifically, if then the subcarrier m is allocated to the set , i.e. CD, DL, Sen, PE represent information forwarding, downlink communication, target sensing and sensing feedback, respectively, and vice versa.

[0058] Considering that the four subcarrier sets are orthogonal to each other, the following needs to be met:

[0059]

[0060] Thus, the unmanned aerial vehicle The front access dual integrated signal waveform transmitted in the nth frame is:

[0061]

[0062] where are the beamforming vectors (transmit signals) of target sensing, downlink communication and sensing feedback, respectively.

[0063] Step 3, design a parallel duplex frame structure matched with the MDD-OFDM waveform to realize parallel transmission and reception of multiple signals.

[0064] Although the integrated waveform based on MDD-OFDM can eliminate digital domain interference between different links, subcarrier segmentation will cause the frequency resource utilization rate of the system to decrease. Therefore, the application further designs an advanced parallel duplex frame structure matched with the MDD-OFDM waveform.

[0065] The parallel duplex frame structure is shown in Figure 1 , and the sea task duration of the unmanned aerial vehicle is set to T op , containing N frames (containing N wireless frame lengths), each frame containing N t OFDM symbols, and the total bandwidth is B op . When N is large enough, the unmanned aerial vehicle, the sea target and the user are relatively static in each wireless frame and the channel state information remains unchanged; each wireless frame contains N t OFDM symbols, and the links occupying different subcarrier resource blocks can simultaneously transmit corresponding signals. Parallel duplex refers to: from the 4th OFDM symbol period of each frame, the information forwarding and sensing feedback signals on the front link coexist with the downlink communication and target sensing signals on the access link in the same symbol period, i.e. the four signals based on MDD-OFDM, information forwarding, downlink communication, target sensing and sensing feedback, are simultaneously transmitted in the time domain, forming four parallel data streams.

[0066] Therefore, unlike the traditional time division multiple access (TDMA) system which can only transmit different signals in sequence in time, the application can realize parallel transmission and reception of multiple signals by relying on the parallel frame structure.

[0067] Step 4: Based on the dual-integrated signal waveform and the parallel duplex frame structure of steps 2 and 3, a maritime unmanned aerial vehicle (UAV) integrated sensing and transmission mode is designed.

[0068] Step 4 process is as follows:

[0069] (1) Channel estimation: the UAV receives the pilot signal from the maritime user and estimates the channel state information (CSI) of the downlink channel;

[0070] (2) Channel feedback: the UAV sends the pilot signal and the downlink CSI to the shore base station, and the base station then obtains the channel CSI from the UAV to the shore base station;

[0071] (3) Information forwarding: the shore base station sends the encoded communication data to the UAV according to the request of the maritime user, and feeds back the CSI of the front-haul link to the UAV;

[0072] (4) Downlink communication and target sensing: the UAV sends the data received from the shore base station to the maritime user by decoding; at the same time, according to the prior information of the target position, the UAV performs positioning and detection sensing tasks using the downlink data;

[0073] (5) Sensing feedback: the UAV receives the sensing echo signal and feeds it back to the shore base station after preprocessing for the final sensing decision.

[0074] As a preferred embodiment, the application also includes step 5, optimization: after designing the maritime UAV integrated sensing and transmission mode, the communication and sensing performance of the network are optimized, the optimization target is the data transmission rate of the maritime user and the current sensing mutual information of the maritime user, and the optimization variable is the flight trajectory of the UAV and the power subcarrier allocation of the UAV and the shore base station.

[0075] The optimization method is described in detail below.

[0076] The optimization problem is represented as (P1):

[0077]

[0078] wherein R DL [n] and R CD [n] represent the downlink communication rate and the information forwarding rate of the nth frame, respectively, represents the perceptual mutual information of target j on the (m, m′) subcarrier pair in the nth frame, ω is the weight coefficient, ∑ DL [n, m], ∑ CD [n, m] and ∑ SEN [n, m] represent the transmission power of the corresponding signal, SEN [n, m′] and ∑ PE [n, m] represent the received sensing signal power and the transmit power coefficient of the sensing feedback signal, respectively. UAV [n] is the coordinate of the drone in the nth frame, and are the minimum communication rate and perceptual mutual information requirements, P UAV and P TBS are the maximum transmission power of the UAV and the base station, V max is the maximum flight speed of the UAV, T s is an OFDM symbol period.

[0079] Problem (P1) is decomposed into two sub-problems (P2) and (P3). The first sub-problem (P2) is: the UAV searches for the nearest mission executable point. The second sub-problem (P3) is: after arriving at the optimal initial operating point, the UAV will continue to fly towards the rescue area. At this time, the UAV needs to continuously optimize the communication and perception services until the flight mission is completed.

[0080] Specifically, the first optimization subproblem (P2) is modeled as follows:

[0081]

[0082] Combine Figure 2 As shown in Figure 2, the solution process of sub-problem (P2) is as follows: first determine the take-off coordinates of the UAV; then initialize the power of the sensing sub-carrier set and the information forwarding signal, downlink communication signal and target sensing signal, and obtain the initialization power of the sensing feedback signal by solving (P2-1); then, assign the initial iteration coordinates of the UAV to the center point of the rescue area (c EA ), determine whether (P2) has a solution, if so, retain the initial iteration coordinates of the drone If there is no solution, use step size l step Update the initial iteration coordinates until (P2) is solved; finally, using the known initial values ​​of all variables, solve (P2) again until the problem converges, and obtain the final UAV's nearest mission execution coordinates c UAV [1].

[0083] Since (P2) is an iterative optimization problem, the choice of the initial value of the iteration directly affects whether the algorithm can converge to the global optimal point. The iterative variables involved in (P2) include the transmission power of each signal, the subcarrier set allocation, and the coordinates of the UAV. Because the perception feedback signal needs to be amplified by the UAV before being sent to the shore-based base station, its power initialization is closely related to the location of the UAV. Therefore, an optimization problem (P2-1) is designed to find the optimal iterative initial point of the perception feedback signal power coefficient. (P2-1) is modeled as follows:

[0084]

[0085] Where χ is the slack variable, is the transmission power coefficient of the perception feedback signal of user j on the m subcarrier in the first frame, is the channel gain of user j on the m subcarrier in the first frame, is the total power of the perception feedback signal transmitted by user j on the subcarrier pair (m, m′) in the first frame. The (0) superscript on all variables represents the iteration starting point of the corresponding variable.

[0086] Solving the second sub-problem (P3) is equivalent to solving the problem (P1) when n>1, combined with Figure 3 As shown in Figure 2, the solution process of the second optimization sub-problem (P3) is as follows: after obtaining the initial task execution coordinates of the UAV, first initialize the power of the sensing sub-carrier set and the information forwarding signal, downlink communication signal and target sensing signal, and obtain the initialization power of the sensing feedback signal by solving (P2-1); let n = 1; then use the quadratic deformation-continuous convex approximation algorithm to solve (P3); if n <N,则根据感知性能更新子载波分配,然后对下一帧进行优化求解,即n=n+1;最后,优化过程在最后一帧停止,即n=N。

[0087] In summary, the present invention provides a drone synesthesia integrated signal transmission method for maritime rescue, proposes a maritime rescue network based on drone synesthesia integration, and designs an access fronthaul fusion transmission method that can be used for the network, which enables drones to efficiently provide real-time data communication and perception positioning services during the execution of maritime rescue missions, making up for the shortcomings of the current maritime rescue network in data transmission and information perception.

[0088] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Any changes, modifications, additions or substitutions made by ordinary technicians in this technical field within the essential scope of the present invention should fall within the scope of protection of the present invention.

Claims

1. A method for transmitting integrated signals of UAV synaesthesia for maritime rescue, characterized in that: The following steps are involved: Step 1. Define initial data: For a marine rescue scenario, including a shore-based base station, a drone, and U single-antenna communication users at sea, both the shore-based base station and the drone use OFDM modulation with a total of M subcarriers; on subcarrier m of the nth frame, the forward channel from the drone to the shore-based base station is and drone-maritime user downlink channel Both can be obtained through pilot channel estimation, where the shore-based base station and the UAV use a shared transmit and receive antenna array, N BS and N UAV are the number of antennas of shore-based base stations and drones, is a set of M subcarriers; Step 2: Design the drone's signal waveform as a dual-integrated signal waveform based on forward access and synaesthesia: Specifically, according to the MDD-OFDM design criteria, the subcarriers in the OFDM symbol are divided into four mutually orthogonal subcarrier sets, namely and They are used for information forwarding, downlink communication, target perception and perception feedback, respectively, to obtain the dual-integrated signal waveform representation of the forward transmission access and synaesthesia of the UAV transmission; Step 3: Design a parallel duplex frame structure that matches the MDD-OFDM waveform to achieve parallel transmission and reception of multiple signals. In step 3, set the duration of the UAV sea mission to T op , contains N wireless frame lengths. When N is large enough, the UAV, maritime targets, and users are relatively stationary within each wireless frame and the channel state information remains unchanged. Each wireless frame contains N t OFDM symbols, links occupying different subcarrier resource blocks can transmit corresponding signals simultaneously. Parallel duplexing means that starting from the fourth OFDM symbol period of each frame, the information forwarding and perception feedback signals on the fronthaul link coexist with the downlink communication and target perception signals on the access link in the same symbol period. In other words, the four MDD-OFDM-based signals—information forwarding, downlink communication, target perception, and perception feedback—are transmitted simultaneously in the time domain, forming four parallel data streams. Step 4: Based on the dual-integrated signal waveform and parallel duplex frame structure of steps 2 and 3, design the integrated fronthaul access fusion transmission mode for maritime UAVs. The process of step 4 is as follows: (1) Channel estimation: The UAV receives the pilot signal from the maritime user and estimates the channel state information (CSI) of the downlink channel; (2) Channel feedback: The UAV sends the pilot signal and downlink CSI to the shore-based base station, which then obtains the channel CSI from the UAV to the shore-based base station; (3) Information forwarding: The shore-based base station sends the encoded communication data to the UAV according to the request of the maritime user, and at the same time feeds back the CSI of the forward link to the UAV; (4) Downlink communication and target perception: The UAV decodes the data received from the shore-based base station and sends it to the user at sea. At the same time, based on the prior information of the target location, the UAV uses the downlink data to perform positioning, detection and perception tasks. (5) Perception feedback: The UAV receives the perception echo signal and feeds it back to the shore-based base station through the forward link after pre-processing for the final perception decision.

2. The method for transmitting integrated signals of a UAV for maritime rescue according to claim 1, characterized in that: In step 1, the shore-based base station coordinate position is c BS After receiving the mission, the UAV flies from the initial position to the accident area. In the accident area, there are U maritime single-antenna communication users, i.e., ships that need communication services, and J maritime targets, i.e., rescue targets or buoy ships in the area. Their coordinates are c u and c j ; The central frequency used by the maritime rescue network is f c , bandwidth is B c frequency band resources.

3. The method for transmitting integrated signals of drones for maritime rescue according to claim 1, characterized in that: In step 2, the subcarrier allocation process is as follows: Introduce binary variables Indicates the allocation of subcarriers in the nth frame; specifically, if Then subcarrier m is allocated to the set In, that is CD, DL, Sen, and PE represent information forwarding, downlink communication, target perception, and perception feedback, respectively, and vice versa; Considering that the four subcarrier sets are orthogonal to each other, Need to meet: So the waveform of the forward transmission access dual integrated signal transmitted by UAV u in the nth frame is: in are the beamforming vectors for target perception, downlink communication, and perception feedback, respectively. They are target perception signal, downlink communication signal, and perception feedback signal respectively.

4. The method for transmitting integrated signals of a UAV for maritime rescue according to claim 1, characterized in that: It also includes step 5, the optimization step: after the design of the integrated fronthaul access fusion transmission mode of the maritime UAV is completed, the communication and perception performance of the network is optimized. The optimization target is the data transmission rate of the maritime users and the current perception mutual information at sea. The optimization variables are the flight trajectory of the UAV and the power subcarrier allocation between the UAV and the shore-based base station.

5. The method for transmitting integrated signals of a UAV for maritime rescue according to claim 4, characterized in that: The optimization problem is expressed as (P1): in are the total data transmission rate and perceptual mutual information of the network in the nth frame, R DL [n] and R CD [n] represents the downlink communication rate and information forwarding rate of the nth frame respectively, represents the perceptual mutual information of target j on the (m,m′) subcarrier pair in the nth frame, ω is the weight coefficient, ∑ DL [n,m],∑ CD [n,m] and ∑ SEN [n,m] represent the transmission power of the corresponding signal, π SEN [n,m′] and ∑ PE [n,m] represent the received sensing signal power and the transmission power coefficient of the sensing feedback signal, respectively. UAV [n] is the coordinate of the drone in the nth frame, and are the minimum communication rate and perceptual mutual information requirements, P UAV and P TBS are the maximum transmission power of the UAV and the base station, V max is the maximum flight speed of the UAV, T s is an OFDM symbol period.

6. The method for transmitting integrated signals of a UAV for maritime rescue according to claim 5, characterized in that: Problem (P1) is decomposed into two sub-problems (P2) and (P3). The first sub-problem (P2) is: the UAV searches for the nearest mission executable point. The second sub-problem (P3) is: after arriving at the optimal initial operating point, the UAV will continue to fly towards the rescue area. At this time, the UAV needs to continuously optimize the communication and perception services until the flight mission is completed.

7. The method for transmitting integrated signals of a UAV for maritime rescue according to claim 6, characterized in that: The first optimization subproblem (P2) is modeled as follows: Since (P2) is an iterative optimization problem, the choice of the initial value of the iteration directly affects whether the algorithm can converge to the global optimal point. The iterative variables involved in (P2) include the transmission power of each signal, the subcarrier set allocation, and the coordinates of the UAV. Because the perception feedback signal needs to be amplified by the UAV before being sent to the shore-based base station, its power initialization is closely related to the location of the UAV. Therefore, an optimization problem (P2-1) is designed to find the optimal iterative initial point of the perception feedback signal power coefficient. (P2-1) is modeled as follows: Where χ is the slack variable, is the transmission power coefficient of the perception feedback signal of user j on the m subcarrier in the first frame, is the channel gain of user j on the m subcarrier in the first frame, is the total power of the perception feedback signal transmitted by user j on the subcarrier pair (m, m′) in the first frame. The (0) superscript on all variables represents the iteration starting point of the corresponding variable.

8. The method for transmitting integrated signals of a UAV for maritime rescue according to claim 7, characterized in that: Solving the second sub-problem (P3) is equivalent to solving the (P1) problem when n > 1, specifically: after obtaining the initial task execution coordinates of the UAV, first initialize the power of the sensing sub-carrier set, information forwarding signal, downlink communication signal, and target sensing signal, and obtain the initial power of the sensing feedback signal by solving (P2-1); let n = 1; then use the quadratic transformation - successive convex approximation algorithm to solve the (P3) problem; if n < N, update the sub-carrier allocation according to the sensing performance, and then perform optimization and solution for the next frame, that is, n = n + 1; finally, the optimization process stops at the last frame, that is, n = N.

Images