Maximum user coverage method for full-duplex sensing integrated unmanned aerial vehicle

Abstract

The invention relates to a maximum user coverage method for a full-duplex communication-sensing integrated unmanned aerial vehicle, and belongs to the technical field of communication. According to the method, a sensing fusion signal is divided into time slots at equal intervals, and each time slot comprises a sensing frame and a communication frame. The transmitted signal passes through a channel fading model of an air-to-ground communication scene, is reflected by a user side, and then is received by an unmanned aerial vehicle base station again. After the unmanned aerial vehicle receives the signal, the position information of the user and the signal-to-noise ratio of different user receiving ends are obtained through radar signal processing. And solving the optimal placement point of the unmanned aerial vehicle corresponding to the maximum coverage rate of the users in the cell through an algorithm, solving the communication rate of the unmanned aerial vehicle, and finally obtaining the maximum user coverage and throughput. According to the method, the universality of the algorithm is effectively improved, the general inductance fusion quality is improved, the operation complexity is reduced, and the system efficiency is improved. And the unmanned aerial vehicle base station can obtain a larger user coverage range and higher throughput while meeting the communication requirements of core users.

Description

Technical Field

[0001] The invention relates to a maximum user coverage method for a full-duplex synaesthesia integrated unmanned aerial vehicle, and belongs to the technical field of communications. Background Art

[0002] Drone base stations have both mobility and deployment capabilities, and are widely used in military, agriculture, perception and positioning, rescue communications, etc. In the scenario of drone-assisted multi-user communication in a cell, it is necessary to design and optimize the drone layout method to provide services to central users while maximizing the coverage probability of drones to meet the communication needs of edge users.

[0003] Most of the existing methods for optimizing the location of drones in a cell consider factors such as user location, coverage, capacity, interference, etc., transmit traditional communication signals to communicate with the user end, provide each user with the constraint of the minimum required data rate, and meet the communication quality requirements of different users. However, most of these methods assume that the user location is known, but in real scenarios, the user location coordinates are usually unknown relative to the drone base station, which makes these methods unable to remain in theory and cannot be put into practical application.

[0004] The drone base station combines integrated synaesthesia technology to perceive the user's location and other information in real time, and use this information to adjust the position of the drone to meet higher communication quality requirements. Summary of the invention

[0005] The purpose of the present invention is to solve the problems existing in the prior art and the insufficient research on user coverage algorithms in the context of synaesthesia fusion of unmanned aerial vehicles, and creatively propose a maximum user coverage method for full-duplex synaesthesia integrated unmanned aerial vehicles. This method optimizes the design of the full-duplex synaesthesia integrated algorithm carried by the unmanned aerial vehicle to maximize the number of users covered and the throughput. The unmanned aerial vehicle transmits a synaesthesia integrated signal to sense and communicate with ground users. Since the synaesthesia integrated signal can obtain the user's location information, the maximum user coverage problem is converted into a mixed integer nonlinear problem. By adjusting the frame time slot of the synaesthesia integrated signal, the maximum user coverage and throughput are obtained.

[0006] The present invention is achieved through the following technical solutions.

[0007] A maximum user coverage method for a full-duplex synaesthesia integrated drone, the main technical means include: the transmitter transmits a synaesthesia fusion signal. The signal is divided into equally spaced time slots, each of which includes a perception frame and a communication frame to meet the maximum unambiguous detection distance requirement and extract the target echo. The transmitted signal passes through the channel fading model of the air-to-ground communication scenario, is reflected by the user end, and is received again by the drone base station. After the drone receives the signal, it is processed by the radar signal to obtain the user's location information and the signal-to-noise ratio of different user receiving ends. The algorithm is used to solve the optimal placement point of the drone corresponding to the maximum coverage rate of users in the cell, and the communication rate of the drone is calculated, and finally the maximum user coverage and throughput are obtained.

[0008] Beneficial Effects

[0009] Compared with the prior art, the present invention has the following advantages:

[0010] 1. The present invention designs a communication-sensing integrated frame time slot structure, which ensures that the signal reaches the target detection distance while enabling the receiver to receive and identify the echo. The designed system frame time slot structure effectively improves the universality of the algorithm and the quality of synaesthesia fusion.

[0011] 2. The present invention simplifies the layout problem of drones into a geometric problem of maximum circle coverage, further reducing the complexity of calculations and improving system efficiency.

[0012] 3. By combining perception-assisted drone position optimization, the present invention enables the drone base station to obtain a larger user coverage and higher throughput while meeting the communication needs of core users, compared with a pure communication signal solution with the same transmission power. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a schematic diagram of a model of the present invention;

[0014] Figure 2 This is a time slot structure diagram of the synaesthesia fusion signal frame of the present invention;

[0015] Figure 3 This is a schematic diagram of drone coverage in the synaesthesia fusion scenario of the present invention;

[0016] Figure 4 This is a schematic diagram of drone coverage in a non-synaesthesia integrated scenario of the present invention;

[0017] Figure 5 Comparison of downlink throughput of UAV in the scenario of synaesthesia integration and non-synaesthesia integration of the present invention;

[0018] Figure 6 This is a curve diagram showing the relationship between the altitude of the drone and the user coverage rate of the present invention. DETAILED DESCRIPTION

[0019] The present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0020] A maximum user coverage method for a full-duplex synaesthesia integrated drone comprises the following steps:

[0021] Step 1: Design the frame time slot of the synaesthesia waveform structure at the transmitter.

[0022] The original communication subcarrier with a time length of T is divided into Q equally spaced time slots with a length of δ t ,

[0023] Each time slot is divided into a sensing frame and a communication frame. The sensing frame is divided into a non-zero power sensing signal and a zero power sensing signal. The interval of the zero power sensing signal and the number of non-zero power sensing signals depend on the maximum unambiguous detection distance. Thus, the reference signal subcarrier forms a pulse radar signal, and the receiver extracts the echo reflected by the target at the zero power sensing signal.

[0024] Step 2: Model the air-to-ground channel.

[0025] Specifically, considering the channel fading model in the air-to-ground communication scenario, due to the presence of obstacles in the actual scenario, there are NLOS (non-line-of-sight) and LOS (line-of-sight) links, whose probabilities are determined by the elevation angle between the user and the drone, expressed as:

[0026]

[0027] P NLOS =1-P LOS

[0028] Among them, P LOS represents the probability of line-of-sight link, P NLOS represents the probability of non-line-of-sight link; A and B represent environmental coefficients. Let the coordinates of any user be (x iq ,y iq ), x iq Indicates the user's horizontal coordinate, y iq represents the user's ordinate. Then the horizontal distance between the user and the drone is r iq for: x u Indicates the horizontal coordinate of the drone, y u represents the vertical coordinate of the drone; the angle θ is expressed as: θ = arctan (h / r iq ), where h represents the altitude of the drone.

[0029] Considering the large-scale and small-scale fading in the UAV communication channel, the path loss of user i under LOS and NLOS links is expressed as:

[0030]

[0031] Among them, LOS represents the path loss of the LOS link, ζ NLOS represents the path loss of the NLOS link; d u，i represents the distance between the user and the drone; h represents the height of the drone; α L , α N They represent the large-scale path loss coefficients corresponding to LOS and NLOS links respectively; g L With g N denote the small-scale channel power gains corresponding to the LOS and NLOS links respectively. Under the Nakagami-m fading assumption, g L is a Gamma distribution with shape parameter m and scale parameter 1 / m. For NLoS links, due to the Rayleigh fading assumption, g N Exponential distribution with unit mean.

[0032] Step 3: Calculate the average received power of any ground user.

[0033] Let P u represents the transmission signal power of the drone and the receiving power W of user i i，q It is expressed as:

[0034] W i，q =P LOS E(P i，LOS )+P NLOS E(P i，NLOS )

[0035] =P LOS E(P u ζ LOS )+P NLOS E(P u ξ NLOS )

[0036] Among them, E represents the mean of the function; ζ LOS represents the path loss of the LOS link, ζ NLOS represents the path loss of the NLOS link; P i，LOS represents the received power of the user under LOS transmission, P i，NLOS Indicates the user's received power in NLOS transmission.

[0037] The user received signal-to-noise ratio of the A2G propagation model is derived It is expressed as:

[0038]

[0039] Among them, N c Represents noise power; N rsi represents the power of the full-duplex residual self-interference signal; A and B represent environmental coefficients; π is the value of pi.

[0040] Assuming the shape and scale parameters of Nakagami-m fading are 1, the coverage is defined by calculating the signal-to-noise ratio, that is, if the probability average signal-to-noise ratio of the received signal at user i exceeds the predefined threshold Γ q , where q = 1, 2, it indicates that the user is covered.

[0041] Step 4: Convert the user coverage problem into a geometric problem.

[0042] In a given environment, with fixed UAV-BS altitude and transmit power, the SNR at the user receiving end is only related to r iq Related to, and is iq Therefore, the signal-to-noise ratio reaches the threshold Γ q The maximum radius corresponding to the time has one and only one unique solution R q , then the coverage area of ​​the drone is described as a (x u ,y u ) is the center of the circle and the radius is R q disc.

[0043] To meet the QoS (Quality of Service) requirements of core users in the region, the coordinates of user i (x iq ,y iq ) as the center, the feasible position of the UAV is solved, and the signal-to-noise ratio threshold Γ required by the user receiver is q , model the maximum radius of the drone, and there is only one solution R q′ Therefore, when the core user signal-to-noise ratio threshold Γ is met q Under these conditions, the feasible range of the UAV is a user coordinate (x iq ,y iq ) is the center of the circle, the height is h, and the radius is R q′ disc.

[0044] After analysis, we know that for the same user end, the signal-to-noise ratio threshold Γ q , the coverage radius R of the drone that meets the conditions q The feasible position radius R of the drone q′ are the same.

[0045] Step 5: In the synaesthesia fusion scenario, model and simplify the analysis of the optimal placement point of the drone.

[0046] After defining the position of the drone, the user is analyzed from the drone’s perspective. The minimum signal-to-noise ratio required for accurate radar detection on the user side is met. When the UAV covers an area of ​​C u It is a center (x u ,y u ) and radius R u The disk, x u Indicates the user's horizontal coordinate, y u Represents the user's ordinate. If the set U q User i is located at a distance from the center drone (x u ,y u ) Up to R q If the user i is within the distance of , then the user i is covered, q = 1, 2. Let u iq ∈{0, 1} is a binary variable such that if user i of set U is in the coverage area C u If u iq =1, otherwise u iq = 0. In this case, it can be written as:

[0047] Obviously, when u iq =1, it must satisfy

[0048] However, when u iq = 0, can be satisfied by any solution. To avoid the variable u iq 、x u and u The multiplication of and simplification of operations, using the big-M method. Therefore, the above formula is further rewritten as:

[0049]

[0050] Where M is a constant chosen large enough so that when u iq =0, the solution of the system is still not affected;

[0051] Therefore, under the premise of meeting the QoS requirements of core users, the layout of the maximum coverage of the synaesthesia integrated drone is expressed as:

[0052]

[0053] subject to

[0054]

[0055] u iq ∈{0, 1}, i=1, 2,...K, q=2.

[0056] Wherein, K represents the total number of ground users.

[0057] The problem has quadratic, exponential, and binary terms, making it a Mixed-Integer Nonlinear Programming (MINLP) problem that can be solved efficiently using a combination of the solver's interior point optimizer and binary search.

[0058] This achieves maximum user coverage and throughput.

[0059] Example

[0060] First, model the system. Figure 1 As shown, a circular area containing a group of stationary or slowly moving users is considered, and a four-rotor low-altitude quasi-stationary drone is used. The drone is equipped with a complete full-duplex hardware platform that can simultaneously send and receive synaesthesia signals and provide stable and persistent information transmission services for ground users.

[0061] Assume the radius of the circular area is R, the height of the drone is h, and its horizontal position coordinates are (x u ,y u ), which can provide wireless coverage for ground users in a certain area. The total number of ground users is K, which is represented by a set U. According to the QoS requirements of different users, the set is divided into U1 and U2, corresponding to the high and low user receiver signal-to-noise ratio requirements, and there are Users are distributed using a Poisson point process, and the horizontal position of any user is expressed as (x iq ,y iq ), i=1, 2, ...K, q=1 or 2, i represents the user number, q represents the QoS requirement category; since adjacent users use different frequencies to communicate with drones, there is no mutual interference between users, and users and drones in this area are not affected by external signals.

[0062] Step 2: Calculate the communication and perception performance of the drone.

[0063] The time slot format of the drone transmission signal is as follows Figure 2 shown.

[0064] Radar estimation rate of the drone for user i in time slot t in the synaesthesia scenario It is expressed as:

[0065]

[0066] Among them, β represents the time slot factor of the synaesthesia fusion signal; Indicates the user received signal-to-noise ratio of the A2G propagation model.

[0067] In this case, the communication rate for user i is It is expressed as:

[0068]

[0069] Downlink throughput T of the UAV in the synaesthesia scenario isac It is expressed as:

[0070]

[0071] Among them, B w represents the signal bandwidth, and M represents the number of users that the synesthesia drone can cover.

[0072] Accordingly, the downlink throughput T in the communication scenario is obtained com for:

[0073]

[0074] Among them, J represents the number of users that the drone can cover in the communication scenario; Indicates the communication rate in the UAV communication scenario.

[0075] Next, the performance of the UAV base station system in a circular area is simulated, and the above theoretical analysis results and algorithms are verified.

[0076] Figure 3 The figure shows the schematic diagram of drone coverage in the synaesthesia fusion scenario. The drone obtains the optimal placement point through the optimization algorithm, where C1 represents the coverage area of ​​the drone, and area C2 represents the feasible area of ​​the drone's horizontal coordinates (indicated by ·) under the premise of meeting the QoS requirements of the core users (indicated by *). That is, if the coordinates of the drone are within the blue area C2, it means that the QoS requirements of the core users are met. The figure also marks the users who are successfully covered by the drone and the users who are not successfully covered by the drone.

[0077] In order to compare with the situation of drone synaesthesia, Figure 4 A schematic diagram of drone coverage in a scenario without synesthesia is drawn. Since the drone cannot perceive at this time, it does not know the specific location of the core user, so the placement point is any random position in the disk. Therefore, the horizontal coordinate position of the drone in the scenario without synesthesia is random in the area. C3 represents the coverage area of ​​the drone. It can be seen that the number of users it covers is significantly less than that of the drone with synesthesia function.

[0078] Analyzing the above two figures, it can be seen that although the drone in the synaesthesia fusion scenario loses some frame efficiency, it obtains the user's location information. By solving the mixed integer nonlinear problem, the optimal drone placement point is obtained, and finally a larger number of user coverage is obtained.

[0079] The following is a theoretical analysis and performance simulation of this method.

[0080] Figure 5 The downlink throughput of the UAV in the synaesthesia and non-synaesthesia scenarios varies with the perception time slot allocation factor. β The curve of the change of , where the value of each point is the average value taken after 500 Monte Carlo simulations. Analysis shows that the throughput of the drone without perception-assisted communication maintains a relatively stable value. For drones with full-duplex ISAC, when β is small, that is, a part of the communication signal time slot is divided for perception, the user's location information can be obtained, thereby obtaining a larger user coverage rate, and its throughput is significantly greater than that of drones in pure communication scenarios. As β increases, the radar estimation rate of the drone increases, the perception information and accuracy of the drone increase, but the information it carries decreases, so the throughput decreases accordingly, and eventually it is less than that of drones in pure communication scenarios.

[0081] Figure 6 The curve shows the relationship between the height of the drone and the user coverage rate. As the height of the drone increases, its user coverage rate also increases until it reaches a peak, and then gradually decreases. This is because when the drone is at a low altitude, the angle between the drone and the user is small, P NLOS The probability of channel loss is higher, resulting in greater path attenuation, which leads to a smaller coverage area of ​​the drone and lower user coverage. As the drone altitude increases, P LOS As the number of channels increases, the drone will have a larger coverage radius, and the user coverage rate will reach a peak. As the drone's altitude continues to increase, the distance between the drone and the user will cause serious path loss, and the user coverage rate will decrease. Figure 6 It can also be seen that since the drone using the synesthesia signal can sense the user's location information to move to the best coverage point, compared with the scenario where only communication signals are transmitted, the user coverage rate of the synesthesia drone at any altitude has a significant performance improvement.

Claims

1. A maximum user coverage method for a full-duplex synaesthesia integrated drone, characterized in that: The synaesthesia fusion signal is divided into equally spaced time slots, each of which includes a perception frame and a communication frame to meet the maximum unambiguous detection distance requirement and extract the target echo; the transmitted signal passes through the channel fading model of the air-to-ground communication scenario, is reflected by the user end, and is received again by the drone base station; after the drone receives the signal, it is processed by the radar signal to obtain the user's location information and the signal-to-noise ratio of different user receiving ends; The algorithm is used to find the best placement point of the drone corresponding to the maximum coverage rate of users in the cell, and the communication rate of the drone is calculated, ultimately achieving the maximum user coverage and throughput; The following steps are involved: Step 1: Design the frame time slot of the synaesthesia waveform structure of the transmitter; The original communication subcarrier with a time length of T is divided into Q equally spaced time slots with a length of δ t , Among them, each time slot is divided into a sensing frame and a communication frame; the sensing frame is divided into a non-zero power sensing signal and a zero power sensing signal; the interval of the zero power sensing signal and the number of the non-zero power sensing signals depend on the maximum unambiguous detection distance; thus, the reference signal subcarrier forms a pulse radar signal, and the receiver extracts the echo reflected by the target at the zero power sensing signal; Step 2: Model the air-to-ground channel; Step 3: Calculate the average received power of any ground user; Step 4: Convert the user coverage problem into a geometric problem; Let the coordinates of any user be (x iq ,y iq ), x iq Indicates the user's horizontal coordinate, y iq Represents the user's ordinate, and its horizontal distance from the drone r iq for: x u Indicates the horizontal coordinate of the drone, y u represents the vertical coordinate of the UAV; in a given environment, with fixed UAV-BS height and transmit power, the signal-to-noise ratio at the user receiving end is only related to r iq Related to, and is iq The signal-to-noise ratio decreases monotonically with the increase of q The maximum radius corresponding to the time has one and only one unique solution R q , then the coverage area of ​​the drone is described as a (x u ,y u ) is the center of the circle and the radius is R q of discs; For the core users in the area, if their QoS requirements are to be met, the coordinates of user i (x iq ,y iq ) as the center, the feasible position of the UAV is solved, and the signal-to-noise ratio threshold Γ required by the user receiver is q , model the maximum radius of the drone, and there is only one solution R q′ , when the core user signal-to-noise ratio threshold Γ is met q Under these conditions, the feasible range of the UAV is a user coordinate (x iq ,y iq ) is the center of the circle, the height is h, and the radius is R q′ of discs; The signal-to-noise ratio threshold Γ for the same user end q , the coverage radius R of the drone that meets the conditions q The feasible position radius R of the drone q′ same; Step 5: In the synaesthesia fusion scenario, model and simplify the analysis of the optimal placement of drones to achieve maximum user coverage and throughput.

2. A maximum user coverage method for a full-duplex synaesthesia integrated drone as claimed in claim 1, characterized in that: In step 2, the probability of NLOS and LOS links is determined by the elevation angle between the user and the drone, expressed as: P NLOS =1-P LOS Among them, P LOS represents the probability of line-of-sight link, P NLOS represents the probability of non-line-of-sight link; A and B represent environmental coefficients; let the coordinates of any user be (x iq ,y iq ), x iq Indicates the user's horizontal coordinate, y iq Represents the user's ordinate, and its horizontal distance from the drone r iq for: x u Indicates the horizontal coordinate of the drone, y u represents the vertical coordinate of the drone; the angle θ is expressed as: θ = arctan (h / r iq ), where h represents the altitude of the drone; The path loss of user i under LOS and NLOS links is expressed as: Among them, LOS represents the path loss of the LOS link, ζ NLOS represents the path loss of the NLOS link; d u,i represents the distance between the user and the drone; h represents the height of the drone; α L , α N They represent the large-scale path loss coefficients corresponding to LOS and NLOS links respectively; g L With g N denote the small-scale channel power gains corresponding to the LOS and NLOS links respectively. Under the Nakagami-m fading assumption, g L is a Gamma distribution with shape parameter m and scale parameter 1 / m; for NLoS links, due to the Rayleigh fading assumption, g N Exponential distribution with unit mean.

3. A maximum user coverage method for a full-duplex synaesthesia integrated drone as claimed in claim 1, characterized in that: In step 3, let P u represents the transmission signal power of the drone and the receiving power W of user i i,q It is expressed as: W i,q =P LOS E(P i,LOS )+P NLOS E(P i,NLOS ) =P LOS E(P u ζ LOS )+P NLOS E(P u ζ NLOS ) Where E represents the mean of the function; ζ LOS represents the path loss of the LOS link, ζ NLOS represents the path loss of the NLOS link; P i,LOS represents the received power of the user under LOS transmission, P i,NLOS Indicates the user's received power under NLOS transmission; The user received signal-to-noise ratio of the A2G propagation model is derived It is expressed as: Among them, N c Represents noise power; N rsi represents the power of the full-duplex residual self-interference signal; A and B represent the environmental coefficients; π is the value of pi; r iq Indicates the horizontal distance between the user and the drone; h indicates the height of the drone; Assume that the shape and scale parameters of Nakagami-m fading are 1. The coverage range is defined by calculating the signal-to-noise ratio. If the probability average signal-to-noise ratio of the received signal at user i exceeds the predefined threshold Γ q , where q = 1, 2, it means that the user is covered.

4. A maximum user coverage method for a full-duplex synaesthesia integrated drone as claimed in claim 1, characterized in that: In step 5, after the position of the drone is limited, the user is analyzed from the perspective of the drone; Minimum signal-to-noise ratio to meet the user's precise radar detection When the UAV covers an area of ​​C u It is a center (x u ,y u ) and radius R u The disk, x u Indicates the user's horizontal coordinate, y u Represents the user's ordinate; if the set U q User i is located at a distance from the center drone (x u ,y u ) Up to R q If the user i is within the distance of , then the user i is covered, q = 1,2; let u iq ∈{0,1} is a binary variable such that if user i of set U is in the coverage area C u If u iq =1, otherwise u iq =0; in this case, it is written as: When u iq =1, it must satisfy When u iq = 0, Can be satisfied by any solution; to avoid the variable u iq 、x u and u The multiplication and simplification of the operation, using the big-M method, the above formula is rewritten as: Where M is a constant chosen large enough so that when u iq =0, the solution of the system is still not affected; Under the premise of meeting the QoS requirements of core users, the layout of the maximum coverage of the synaesthesia integrated drone is expressed as: subject to u iq ∈{0,1},i=1,2,...K,q=2. Wherein, K represents the total number of ground users.

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