Unmanned aerial vehicle relay system energy efficiency maximization method based on physical layer network coding

Abstract

The invention discloses an unmanned aerial vehicle relay system energy efficiency maximization method based on physical layer network coding, and the method comprises the steps: carrying out the mathematical modeling of an energy efficiency maximization problem of an unmanned aerial vehicle relay system, and converting the energy efficiency maximization problem into a mixed integer non-convex optimization problem, decoupling the non-convex optimization problem into a sub-problem of solving a transmission scheduling and association variable A, a sub-problem of transmission power variables P of the unmanned aerial vehicle and the UD, and a sub-problem of a trajectory variable Q of the unmanned aerial vehicle; and solving the sub-problem by combining a block coordinate descent algorithm, a continuous convex optimization method and a Dinkelbach algorithm. According to the energy efficiency maximization scheme provided by the invention, the relay throughput of the unmanned aerial vehicle and the energy consumption caused by flight of the unmanned aerial vehicle can be considered, and the communication throughput is improved to the greatest extent while the flight energy consumption is reduced as much as possible.

Description

Technical Field

[0001] The present invention relates to the technical fields of wireless communications and mobile communications, and in particular provides a method for maximizing energy efficiency of an unmanned aerial vehicle relay system based on physical layer network coding. Background Art

[0002] In recent years, UAVs (Unmanned Aerial Vehicles) have attracted extensive attention from academia and industry due to their unique characteristics. Compared with traditional wireless communication systems, UAV wireless communication systems have many advantages. First, UAVs are highly flexible, can be deployed quickly, and can be adjusted and reconfigured on demand. Second, UAVs usually have good line-of-sight links, so the link quality is better and the signal transmission is more reliable. In addition, the high maneuverability of UAVs enables them to adapt to various environments and application scenarios, thereby achieving more flexible data communication services. With the development of technology, UAVs are gradually becoming an indispensable part of modern life. UAVs can not only be used for various scientific research and emergency rescue missions, but also in agriculture, environmental monitoring, urban planning, security monitoring and other fields.

[0003] With the rapid growth of data demand in 5G and beyond wireless communication systems, the use of drones as mobile relays is considered a promising technology for providing network coverage. However, most previous studies have focused on traditional static relays, in which drones are considered as static relay devices. For example, drones as static relays that receive information through free-space optical links and forward information through radio frequency links have been studied. In order to fully utilize the flexibility of drones in mobile networks and study the throughput maximization problem in drone-assisted mobile relay networks, drones are used as mobile relay platforms to transmit information from one ground user device to another ground user device. Different from the scenario in which only one source ground user device transmits to another destination ground user device in the above studies, the scenario in which drones collect information from multiple source ground user devices and transmit it to only one destination ground user device has also been studied. Obviously, these existing studies mainly focus on the transmission of information between a single pair of ground user devices assisted by drone relays, which is inconsistent with the scenario in which multiple source ground user devices transmit information to multiple destination ground user devices in actual situations. Moreover, some studies have not fully utilized the flexibility and mobility of drones.

[0004] In order to make the scenario closer to reality and to make full use of the flexibility of drones, the problem of maximizing the energy efficiency of the system where multiple source ground user devices send information to the base station through drones has been studied. On the other hand, in the scenario where drones act as mobile relays to help multiple ground user devices transmit information, the problem of maximizing the minimum average transmission rate of each ground user device pair under the power constraints of the source ground user devices and drone relays has been studied. In addition, in order to ensure the security of transmission, the transmission power optimization problem of drone relays and ground user devices sending information has been studied. Different from this study, in order to maximize the minimum average secure transmission rate, the joint optimization problem of the trajectory and schedule of drone relays with cache capabilities has been studied. However, these studies all focus on unidirectional communication, that is, information is only transmitted from the ground user device that sends information to the ground user device that receives information, and the party that sends information does not need to receive messages from the other party. But in actual scenarios, the two parties in communication often have the need to pass messages to each other, which is not considered in the above studies.

[0005] In summary, some current research on UAV communication systems only considers the scenario where a single source ground user device sends information to a single destination ground user device, which is different from the usual scenario where multiple ground user devices have communication needs, and two-way communication between a pair of ground user devices is rarely considered. Therefore, how to use UAVs as relays to provide two-way communication for multiple pairs of ground user devices in an energy-efficient manner is an urgent problem to be solved. Summary of the invention

[0006] The present invention provides a method for maximizing the energy efficiency of an unmanned aerial vehicle relay system based on physical layer network coding, which realizes high-energy-efficiency two-way information relay and transmits more data while saving system energy.

[0007] In order to achieve the above object, the present invention provides a method for maximizing energy efficiency of a drone relay system based on physical layer network coding, comprising:

[0008] Mathematically model the energy efficiency maximization problem of the UAV relay system, transform the energy efficiency maximization problem into a mixed integer non-convex optimization problem, and decouple the non-convex optimization problem into sub-problems of solving transmission scheduling and associated variables A, sub-problems of solving transmission power variables P of UAVs and UDs, and sub-problems of solving UAV trajectory variables Q;

[0009] Energy efficiency is defined as: the ratio of the minimum downlink data volume transmitted by the UAV to all ground user devices within the mission time to the energy consumed by the UAV flight; the minimum downlink data volume is in Mbit, and the energy consumed by the UAV flight is in J;

[0010] The subproblems are solved by combining the block coordinate descent algorithm, the continuous convex optimization method and the Dinkelbach algorithm.

[0011] In particular, the mathematical modeling of the energy efficiency maximization problem of the UAV relay system includes:

[0012] A pair of ground user equipment uses UD k,1 and UD k,2 denoted by , wherein UD represents a ground user equipment, k represents a group number of a ground user equipment pair, and “1” and “2” represent the sequence numbers of the ground user equipment in the ground user equipment pair, respectively;

[0013] in Represents the set of group numbers of ground user equipment pairs, K represents the maximum group number; UD k,j The horizontal coordinate of In other words, j represents the sequence number of the ground user equipment in the ground user equipment pair, and x k,j and k,j Represents UD k,j The horizontal and vertical coordinates of the drone are shown in Figure 1. The fixed flight altitude of the drone is represented by the letter H.

[0014] By using the time discretization method, the task time T is divided into N small time slots of equal size. The sequence number of each time slot is represented by n. in represents the set of time slot numbers, N represents the maximum time slot number; a time slot is recorded as The horizontal position of the UAV at the nth time slot is recorded as The starting and ending positions of the drone are given by q I and q F Indicates; the maximum flight speed of the drone is represented by v max The flight distance of the UAV in each time slot is represented by Δ[n]=∥q[n]-q[n-1]∥; the UAV and UD k,j The transmission power is respectively given by p d [n] and p k,j [n] represents; the transmission decision and association of each time slot are represented by two binary variables α u,k [n] and α d,k [n] indicates; when α u,k When [n] = 1, it means that the kth pair of UDs transmits data to the drone; otherwise, α u,k [n]=0; when α d,k When [n] = 1, it means that the UAV forwards data to the kth pair of UDs; otherwise, α d,k [n] = 0; the channel power gain at the reference distance is represented by β0, the channel bandwidth is represented by B, and the noise power is represented by σ 2express; order According to Shannon's formula, from UD k,j The feasible uplink transmission rate to the UAV is expressed as in represents the line of sight probability after regularization and homogeneous approximation; similarly, from drone to UD k,j The feasible downlink transmission rate is expressed as

[0015] Let m represent the total mass of the drone, g represent the acceleration of gravity, A represent the area of ​​the rotor disc, ρ represent the air density, and W = mg represent the weight of the drone relay. represents the parameterized hovering power required; in the nth time slot, the power generated due to horizontal flight is expressed as In the nth time slot, the power consumption of the blade profile is expressed as Among them C D0 is the drag coefficient of the shape that varies with the geometry of the rotor blades; the total propulsion power required for the UAV relay flight is expressed as a function of its horizontal flight speed in the nth time slot, that is, in Therefore, the total energy consumption of the UAV relay during flight is expressed as

[0016] Let A represent the transmission schedule and association P represents the transmission power of the drone and UD Q represents the drone trajectory The energy efficiency maximization problem of the UAV-assisted mobile relay system that provides two-way communication for multiple UD pairs using physical layer network coding technology is formulated as:

[0017]

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024]

[0025]

[0026] q[0]=q I ,q[N]=q F (1h)

[0027] in, is the introduced slack variable;

[0028] Relaxing the binary variable A, problem (1) is transformed into:

[0029]

[0030]

[0031] (1a), (1c), (1d), (1e), (1f), (1g), (1h). (2b)

[0032] In particular, by using the block coordinate descent algorithm, the transmission power variables P of the UAV and UD and the UAV trajectory variable Q are fixed, and the problem is transformed into a sub-problem of solving the transmission scheduling and the associated variable A:

[0033]

[0034] st(1a),(2a),(1c),(1d), (3a)

[0035] The objective function only contains the numerator of the objective function of problem (2). Problem (3) is a standard linear programming problem and is directly solved by convex optimization tools.

[0036] In particular, by using the block coordinate descent algorithm, the transmission scheduling and association variables A and the UAV trajectory variables Q are fixed, and the problem is transformed into a sub-problem of solving the transmission power variable P of the UAV and UD:

[0037]

[0038] st(1c),(1d),(1e),(1f), (4a)

[0039] The objective function only contains the numerator of the objective function of problem (2). Since the left side of constraint (1d) is non-convex with respect to pd[n], problem (4) is obviously non-convex. The slack variable Problem (4) is reformulated as:

[0040]

[0041]

[0042]

[0043]

[0044]

[0045] (1e), (1f). (5d)

[0046] Problem (4) is a standard convex problem and is solved by the convex optimization tool CVX.

[0047] In particular, by adopting the block coordinate descent algorithm, the transmission scheduling and association variables A and the transmission power variables P of the UAV and UD are fixed, and the problem is transformed into a sub-problem of solving the UAV trajectory variable Q:

[0048]

[0049] st(5a),(5b),(5c),(1g),(1h). (6a)

[0050] Since the denominator of the objective function of problem (6) is non-convex with respect to q[n], problem (6) is a non-convex problem, and the right sides of constraints (5b) and (5c) are non-convex with respect to q[n];

[0051] Since the right side of (5b) is restricted to ||q[n]-w k,j′ || 2 Convex, so at the rth iteration, let represents a given drone trajectory;

[0052] Since the Taylor expansion of any convex function at any point is its lower bound, we can obtain R u,k,j [n] At a given point Q (r) The first-order Taylor expression for As R u,k,j [n] At a given point Q (r) The lower bound of ; among them, and is a constant, Similarly, obtain R d,k,j [n] At a given point Q (r) The first-order Taylor expression for As R d,k,j [n] At a given point Q (r) The lower bound of ; among them,

[0053] In order to deal with the non-convex terms in the expression of the total propulsion power required for the UAV relay flight Introducing slack variables in By transposing and squaring simultaneously, we get Therefore, the function of total propulsion power is reformulated as Thus, the expression of the new energy consumption function of the UAV relay is obtained: Problem (6) is reformulated as:

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060] (5a),(1g),(1h). (7e)

[0061] Obtain the first-order Taylor expansion of the right side of restriction (7c) as its lower bound:

[0062] Through the above transformation, a new optimization problem is obtained as follows:

[0063]

[0064]

[0065] (5a), (7a), (7b), (7d), (1g), (1h). (8b)

[0066] The constraints of problem (8) are all convex, and the objective function is a fraction with a concave numerator and a convex denominator. Problem (8) is solved by the Dinkelbach algorithm.

[0067] The present invention provides a method for maximizing the energy efficiency of a drone relay system based on physical layer network coding. The drone is used as a mobile relay to greatly improve the communication effect. The mobile relay can flexibly adjust its position as needed to meet the coverage requirements of different areas. The mobile relay can expand the coverage range by moving to better meet the communication needs of a wide area. The mobile relay can also adjust its position as needed to reduce the risk of interruption caused by natural disasters.

[0068] The physical layer network coding technology used in the present invention can help the relay to simultaneously receive information from a group of UD pairs, or simultaneously forward information to a group of UD pairs without being affected by interference; the physical layer network coding technology can accept the interference between transmission links and increase the amount of data transmitted.

[0069] The energy-efficiency-maximizing fair communication scheme considered by the present invention takes the minimum amount of downlink data among all UDs as the numerator of the objective function, takes the energy consumption of the UAV flight as the denominator of the objective function, and maximizes the objective function. While ensuring that the UAV can relay a considerable amount of information for each UD, it also reduces the energy consumption caused by the flight as much as possible. The energy-efficiency-maximizing scheme considered by the present invention can take into account the throughput of the UAV relay and the energy consumption caused by its flight, while reducing the flight energy consumption as much as possible, and maximizing the communication throughput. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] The present invention is further described in detail below with reference to the accompanying drawings and embodiments:

[0071] Figure 1 A schematic diagram of a UAV-assisted two-way relay system using physical layer network coding technology provided by the present invention;

[0072] Figure 2 Schematic diagram of the initial trajectory of UD distribution and UAV relay provided by the present invention;

[0073] Figure 3 Schematic diagram of the trajectory of the drone relay under different mission times for the "EE max" solution and the comparison solution provided by the present invention;

[0074] Figure 4 The system energy efficiency performance of the "EE max" solution provided by the present invention and the comparison solution under different task times;

[0075] Figure 5 The throughput performance of the "EE max" solution provided by the present invention and the comparative solution under different task times;

[0076] Figure 6 The system energy consumption performance of the "EE max" solution provided by the present invention and the comparison solution under different task times. DETAILED DESCRIPTION

[0077] The present invention will be further explained below in conjunction with specific implementation schemes, but the present invention is not limited thereto.

[0078] Some current research on UAV communication systems only considers the scenario where a single source ground user device sends information to a single destination ground user device, which is different from the usual scenario where multiple ground user devices have communication needs. In addition, two-way communication between a pair of ground user devices is rarely considered.

[0079] Physical-layer network coding technique can be used to deal with the problem of two-way communication between ground user equipment pairs very well. Although full-duplex mode communication can also be applied to two-way communication between ground user equipment pairs, the interference between uplink and downlink will reduce the amount of transmitted data. The physical-layer network coding technique can accept interference to improve throughput. By adopting the physical-layer network coding technique, the relay can convert the signals received simultaneously from the same ground user equipment pair into pre-processable output signals. After receiving the forwarded signal from the relay, the ground user equipment pair can extract the corresponding information from it.

[0080] The use of physical layer network coding techniques in combination with drones to facilitate bidirectional communication between a pair of ground user devices and to facilitate bidirectional communication between two devices in each pair of multiple ground user devices have been studied. However, the energy consumption of the systems has not been considered in the existing studies when maximizing the throughput of both systems. In addition, the transmission rate maximization problem of a drone relay system with multiple ground user device pairs using physical layer network coding techniques has been studied under a constant energy constraint. Although drones face significant challenges in terms of energy consumption, the energy conservation of drones has not been considered in this study.

[0081] Transmitting more data while considering saving system energy is very necessary for UAV relay systems with multiple pairs of ground user equipment. Existing research does not provide a suitable solution to maximize the energy efficiency of UAV relay systems using physical layer network coding technology.

[0082] To this end, this embodiment provides a method for maximizing the energy efficiency of a drone relay system based on physical layer network coding. Figure 1As shown. There are multiple groups of ground user equipment pairs in the system, and each group of ground user equipment pairs contains two ground user equipments that need to communicate with each other. The drone, as a mobile relay, provides two-way communication for two ground user equipments belonging to a group of ground user equipment pairs by adopting physical layer network coding technology. In addition, the drone provides two-way communication for each group of ground user equipment pairs fairly during the entire mission time. In order to ensure that communication is provided fairly for each group of ground user equipment pairs, we define energy efficiency as: the ratio of the minimum downlink data volume (unit: Mbit) of the data volume transmitted by the drone for all ground user equipments during the mission time to the energy consumed by the drone flight (unit: J).

[0083] First, the energy efficiency maximization problem of the UAV relay system is mathematically modeled. Since the formulated problem is a mixed integer non-convex optimization problem, this challenging problem is solved by combining the block coordinate descent algorithm, the continuous convex optimization method and the Dinkelbach algorithm. The detailed process is as follows:

[0084] A pair of ground user equipment uses UD k,1 and UD k,2 denoted by , wherein UD represents the ground user equipment, k represents the group number of the ground user equipment pair, and “1” and “2” represent the sequence numbers of the ground user equipment in the ground user equipment pair, respectively. in Represents the set of group numbers of ground user equipment pairs, K represents the maximum group number. k,j The horizontal coordinate of In which, j represents the serial number of the ground user equipment in the ground user equipment pair, and x k,j and k,j Represents UD k,j The horizontal and vertical coordinates of the drone are shown in Figure 1. The fixed flight altitude of the drone is represented by the letter H. Through the method of time discretization, the mission time T is divided into N time slots of equal size. The serial number of each time slot is represented by n. in represents the set of time slot numbers, and N represents the maximum time slot number. A time slot is recorded as The horizontal position of the UAV at the nth time slot is recorded as The starting and ending positions of the drone are given by q I and q F The maximum flight speed of the drone is represented by v max The flight distance of the UAV in each time slot is represented by Δ[n]=∥q[n]-q[n-1]∥. UAV and UD k,j The transmission power is respectively given by p d [n] and p k,j[n] represents the transmission decision and association of each time slot. u,k [n] and α d,k [n] represents. When α u,k When [n] = 1, it means that the kth pair of UDs transmits data to the drone; otherwise, α u,k [n]=0. When α d,k When [n] = 1, it means that the UAV forwards data to the kth pair of UDs; otherwise, α d,k [n] = 0. The channel power gain at the reference distance is represented by β0, the channel bandwidth is represented by B, and the noise power is represented by σ 2 Indicates. According to Shannon's formula, from UD k,j The feasible uplink transmission rate to the UAV is expressed as in Represents the line of sight probability after regularization and homogeneous approximation. Similarly, from UAV to UD k,j The feasible downlink transmission rate is expressed as

[0085] Let m represent the total mass of the drone, g represent the acceleration of gravity, A represent the area of ​​the rotor disc, ρ represent the air density, and W = mg represent the weight of the drone relay. represents the parameterized hovering power required. In the nth time slot, the power generated by horizontal flight is expressed as In the nth time slot, the power consumption of the blade profile is expressed as Among them C D0 is the drag coefficient of the shape that varies with the geometry of the rotor blades. The total propulsion power required for the UAV relay flight is expressed as a function of its horizontal flight speed at the nth time slot, i.e. in Therefore, the total energy consumption of the UAV relay during flight is expressed as

[0086] Let A represent the transmission schedule and association P represents the transmission power of the drone and UD Q represents the drone trajectory The energy efficiency maximization problem of the UAV-assisted mobile relay system that provides two-way communication for multiple UD pairs using physical layer network coding technology is formulated as:

[0087]

[0088] q[0]=q I ,q[N]=q F (1h)

[0089] in, is the introduced slack variable.

[0090] In order to solve problem (1), relax the binary variable A, and problem (1) is transformed into

[0091]

[0092] (1a), (1c), (1d), (1e), (1f), (1g), (1h). (2b)

[0093] First, by using the block coordinate descent algorithm, the transmission power variables P of the UAV and UD and the UAV trajectory variable Q are fixed, and the problem is transformed into a sub-problem of solving the transmission scheduling and associated variable A:

[0094]

[0095] st(1a),(2a),(1c),(1d), (3a)

[0096] The objective function only contains the numerator of the objective function of problem (2). This is because the variables related to the energy consumption of the drone in problem (3) are all fixed, that is, they are treated as constants. Temporarily ignoring the denominator as a constant will not affect the final result of maximizing the entire fraction. Problem (3) is a standard linear programming problem that can be directly solved by convex optimization tools.

[0097] Then, by using the block coordinate descent algorithm, the transmission scheduling and associated variables A and the UAV trajectory variables Q are fixed, and the problem is transformed into a sub-problem of solving the transmission power variable P of the UAV and UD:

[0098]

[0099] st(1c),(1d),(1e),(1f), (4a)

[0100] The objective function only contains the numerator of the objective function in problem (2), which is the same as the treatment in problem (3). d [n] is non-convex. Problem (4) is obviously non-convex. Introducing slack variables Problem (4) is reformulated as:

[0101]

[0102] (1e),(1f). (5d)

[0103] Problem (4) is a standard convex problem and can be solved by the convex optimization tool CVX.

[0104] Next, by adopting the block coordinate descent algorithm, the transmission scheduling and association variables A and the transmission power variables P of the UAV and UD are fixed, and the problem is transformed into a sub-problem of solving the UAV trajectory variable Q:

[0105]

[0106] st(5a),(5b),(5c),(1g),(1h). (6a)

[0107] Since the denominator of the objective function of problem (6) is non-convex with respect to q[n], problem (6) is a non-convex problem. In addition, the right-hand sides of constraints (5b) and (5c) are non-convex with respect to q[n].

[0108] The above non-convex functions and non-convex restrictions can be solved by continuous convex optimization methods. Since the right side of the restriction (5b) is about ||q[n]-w k,j′ || 2 Convex, so at the rth iteration, let represents the given drone trajectory. Since the Taylor expansion of any convex function at any point is its lower bound, we can obtain R u,k,j [n] At a given point Q (r) The first-order Taylor expression for As R u,k,j [n] At a given point Q (r) The lower bound of . and is a constant, Similarly, we can obtain R d,k,j [n] At a given point Q (r) The first-order Taylor expression for

[0109] As R d,k,j [n] At a given point Q (r) The lower bound of .

[0110]

[0111] In order to deal with the non-convex terms in the expression of the total propulsion power required for the UAV relay flight Introducing slack variables in By transposing and squaring the terms, we get Therefore, the function of total propulsion power is reformulated as Thus, the expression of the new energy consumption function of the UAV relay can be obtained: Based on the above discussion, problem (6) is reformulated as:

[0112]

[0113] (5a),(1g),(1h). (7e)

[0114] Although the left side of restriction (7c) is convex with respect to s2[n], the right side of restriction (7c) is Regarding s2[n] being non-concave, About q[n] is non-concave. However, With respect to s2[n] convexity, Convex about q[n]. The first-order Taylor expansion of the right side of restriction (7c) can be obtained as its lower bound:

[0115] Through the above transformation, a new optimization problem can be obtained as follows:

[0116]

[0117]

[0118] (5a), (7a), (7b), (7d), (1g), (1h). (8b)

[0119] The restrictions of problem (8) are all convex, and the objective function is a fraction with a concave numerator and a convex denominator, so problem (8) can be solved by the Dinkelbach algorithm.

[0120] After that, continue to iterate and solve subproblems (3), (5) and (8) in order until the algorithm converges. The pseudo code of the algorithm is as follows:

[0121]

[0122]

[0123] Compared with the traditional fixed relay solution, the use of drones as mobile relays can greatly improve the communication effect. First of all, traditional relays are usually fixedly deployed in specific locations, such as buildings or communication towers. Since the location of traditional relays is fixed, they cannot quickly adapt to changes in network needs and can only serve specific areas. And when natural disasters occur, such as earthquakes and floods, traditional relays in fixed locations may be affected and cause equipment failure. Compared with traditional relays, mobile relays can be moved at any time and deployed in different locations. Therefore, mobile relays can flexibly adjust their positions as needed to meet the coverage requirements of different areas. In addition, mobile relays can expand coverage by moving to better meet the communication needs of a wide area. In addition, mobile relays can adjust their positions as needed to reduce the risk of interruptions caused by natural disasters.

[0124] Compared with the relay scheme using the traditional half-duplex mode, the use of physical layer network coding technology can help the relay receive information from a group of UD pairs at the same time, or forward information to a group of UD pairs at the same time, and not be affected by interference. In the relay scheme using the traditional half-duplex mode, the relay cannot send and receive at the same time, and must switch between sending and receiving. Obviously, this results in the relay scheme using the traditional half-duplex mode being inferior to the scheme using the physical layer network coding technology in terms of the amount of data transmitted. Although the full-duplex mode can also realize the simultaneous transmission of two links, that is, the simultaneous transmission of the uplink and the downlink, interference will occur between the uplink and the downlink, which will affect the amount of data transmitted. The physical layer network coding technology can accept the interference between the transmission links and increase the amount of data transmitted.

[0125] In the mobile relay scheme that only considers throughput maximization, the drone will always fly to the position closest to the UD, that is, the air directly above the UD to relay information for the UD. This is because the position in the air directly above the UD is closest to the UD. According to Shannon's formula, the shorter the communication distance, the greater the amount of data transmitted per unit time in the communication. Therefore, at the position in the air directly above the UD, the drone can receive the most data from the UD or transmit the most data to the UD. In order to maintain high-data-volume communication, the drone will hover at this position to maintain the shortest communication distance with the UD. However, from the total propulsion power expression of the drone, it can be seen that when the horizontal movement distance of the drone is 0, the propulsion power caused by the drone flight reaches the maximum value. Moreover, in the case of multiple UDs, if the drone needs to fly above each UD when relaying information for each UD, the flight mileage of the drone will be greatly increased, resulting in an increase in its propulsion energy consumption. In addition, factors such as the size, weight and load capacity of the drone limit the amount of energy it carries for flight. Therefore, the design of the drone hovering above each UD and relaying information for it can greatly increase the amount of data transmitted, but it also increases the energy required for the drone to fly. The huge flight energy consumption is usually a thorny challenge for drones. In addition to energy consumption, traditional throughput maximization schemes often use the total throughput or average throughput relayed by the drone for each UD as the objective function. This may cause the drone to only provide communication for one or several UDs close to itself, while ignoring UDs that are far away from itself. For all UDs in the scene, such communication conditions are obviously not ideal. The fair communication scheme that maximizes energy efficiency that we are considering uses the minimum amount of downlink data among all UDs as the numerator of the objective function, the drone's flight energy consumption as the denominator of the objective function, and maximizes the objective function. In this way, while ensuring that the drone can relay a considerable amount of information for each UD, it also minimizes the energy consumption caused by flight.

[0126] In the mobile relay scheme that only considers energy minimization, the drone always flies along the straight path connecting the starting point to the end point at the most energy-efficient speed. However, according to Shannon's formula, this will cause the amount of information relayed by the drone to UDs that are farther away from this trajectory to be relatively small. Compared with the energy minimization scheme, the energy efficiency maximization scheme we consider can take into account the throughput of the drone relay and the energy consumption caused by its flight, while minimizing the flight energy consumption and maximizing the communication throughput.

[0127] The energy efficiency maximization method of the UAV relay system based on physical layer network coding in this implementation scheme is simulated by using Python programming language and toolkits such as NumPy, Matplotlib, and CVXPY. At the same time, in order to reflect the superiority of the scheme proposed in the present invention, the throughput maximization scheme, energy minimization scheme, initial flight scheme, and energy efficiency maximization scheme in half-duplex mode are used as comparison schemes, and simulation experiments are also carried out on the comparison schemes.

[0128] First, the drone is designed to fly from the starting point at the maximum flight speed, along the line between the starting point and the destination point, and hover at the midpoint of the line, which is also the center of the entire area. Before the mission time expires, the drone ends its hovering flight, and continues to fly from the center of the area along the line between the starting point and the destination point to the destination point at the maximum speed, and arrives at the destination point when the mission time expires. The drone's hovering flight time is the total mission time minus the time it takes for the drone to fly from the starting point to the destination point at the maximum speed. The drone relays information for all UDs during the flight. When the mission duration is 200 seconds, the drone's initial trajectory is as follows: Figure 2 shown.

[0129] Figure 3 The figure shows the trajectories of the drone relay under three different mission times of 160 seconds, 200 seconds and 240 seconds, according to different flight plans. The blue line represents the trajectory obtained by the energy efficiency maximization plan "EE max" proposed by the present invention, the red line represents the trajectory obtained by the throughput maximization plan "Throughput max", the green line represents the trajectory obtained by the energy minimization plan "Energy min", and the purple line represents the trajectory obtained by the half-duplex mode energy efficiency maximization plan "Half Duplex". The trajectory obtained by the initial flight plan "Initial Flight" is the same as the trajectory obtained by the initial flight plan "Initial Flight". Figure 2 Similar to the trajectory in Figure 3 The display is in order to avoid too many lines making it difficult to distinguish each solution. Figure 3 As can be seen in (a), when the throughput maximization scheme is adopted, the drone tends to fly to the airspace near each UD to relay information to it. This is because the shorter the communication distance, the greater the amount of data transmitted per unit time in the communication. This conclusion can be easily obtained based on the Shannon formula. As the mission time increases, this rule becomes more obvious. Figure 3 (b) and Figure 3 In (c), we can see that the trajectory points of the drone near each UD are very dense, which means that in order to increase the amount of relayed data under sufficient mission time, the drone hovers in the dense trajectory points. Figure 3It can be seen that no matter how the mission time changes, the drone trajectory points are evenly distributed on the straight line connecting the drone's take-off point to the destination point. This shows that when executing the energy minimization scheme, the drone flies at an energy-saving speed and uniformly along the shortest path between the take-off point and the destination point, and relays information for each UD pair during the flight. In the initial flight plan, no matter how long the drone's mission is, the drone flies from the starting point to the center of the communication area at the maximum speed to hover, and then flies to the destination at the maximum speed after the hovering ends. Therefore, the trajectories generated by the drone flying at different mission times under the initial plan are the same. Figure 2 The trajectories of the medium task with a duration of 200 seconds are very similar. Figure 3 The trajectory of the drone flight under this scheme is not shown. In the energy efficiency maximization scheme in half-duplex mode and the energy efficiency maximization scheme using physical layer network coding technology proposed in the present invention, the flight trajectory of the drone has a tendency to approach the UD that deviates from the initial trajectory, but there are also some trajectories that are very close to the trajectory of the energy minimization scheme. This is because the energy efficiency maximization scheme must take into account both increasing the amount of data relayed and reducing the energy consumed by flight. Therefore, the drone cannot simply reduce the communication distance between itself and the UD in order to increase the amount of data relayed, fly over every UD, or only fly along the trajectory generated by the energy minimization scheme.

[0130] Figure 4 It shows the energy efficiency of each solution at different times. The blue column represents the energy efficiency of the energy efficiency maximization solution "EE max" proposed by us, the red column represents the energy efficiency of the throughput maximization solution "Throughput max", the green column represents the energy efficiency of the energy minimization solution "Energy min", the yellow column represents the energy efficiency of the initial flight solution "Initial Flight", and the purple column represents the energy efficiency of the solution "Half Duplex" that maximizes energy efficiency in half-duplex mode. Figure 4 It can be seen that the energy efficiency maximization scheme proposed by the present invention performs better in energy efficiency than other schemes at any mission time. Although the energy minimization scheme also performs well in energy efficiency, second only to the scheme proposed by the present invention, the energy minimization scheme improves energy efficiency by reducing energy consumption. Under this scheme, the amount of information relayed by the drone for the UD is not optimistic. Therefore, the energy minimization scheme cannot be called an ideal scheme.

[0131] Figure 5The data volume relayed by each scheme at different task times is shown. Among them, the blue line represents the data volume relayed by the energy efficiency maximization scheme "EE max" proposed by us, the red line represents the data volume relayed by the throughput maximization scheme "Throughputmax", the green line represents the data volume relayed by the energy minimization scheme "Energy min", the yellow line represents the data volume relayed by the initial flight scheme "Initial Flight", and the purple line represents the data volume relayed by the scheme "Half Duplex" that maximizes energy efficiency in half-duplex mode. It can be seen that the performance of the scheme proposed by the present invention in terms of the amount of data relayed is second only to the throughput maximization scheme, and is better than the other three schemes.

[0132] Figure 6 It shows the system energy consumed by each solution at different task times. Among them, the blue line represents the energy consumption of the energy efficiency maximization solution "EE max" proposed by the present invention, the red line represents the energy consumption of the throughput maximization solution "Throughputmax", the green line represents the energy consumption of the energy minimization solution "Energy min", the yellow line represents the energy consumption of the initial flight solution "Initial Flight", and the purple line represents the energy consumption of the solution "HalfDuplex" that maximizes energy efficiency in half-duplex mode. It is not difficult to see that the energy consumption curve of the solution proposed by the present invention almost coincides with the energy consumption curve of the energy minimization solution, and is much lower than the energy consumption curve of the throughput maximization solution.

[0133] Combination Figure 5 and Figure 6 It can be seen that the energy efficiency maximization scheme proposed by the present invention is not the best in terms of both the amount of relayed data and the system energy consumption, but it is a suboptimal scheme. When comprehensively considering the amount of relayed data and the system energy consumption, the scheme proposed by the present invention is the best scheme.

[0134] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention.

Claims

1. A method for maximizing energy efficiency of UAV relay system based on physical layer network coding, characterized in that: include: Mathematically model the energy efficiency maximization problem of the UAV relay system, transform the energy efficiency maximization problem into a mixed integer non-convex optimization problem, and decouple the non-convex optimization problem into sub-problems of solving transmission scheduling and associated variables A, sub-problems of solving transmission power variables P of UAVs and UDs, and sub-problems of solving UAV trajectory variables q; Energy efficiency is defined as: the ratio of the minimum downlink data volume transmitted by the UAV to all ground user devices within the mission time to the energy consumed by the UAV flight; the minimum downlink data volume is in Mbit, and the energy consumed by the UAV flight is in J; The subproblems are solved by combining the block coordinate descent algorithm, the continuous convex optimization method and the Dinkelbach algorithm.

2. The method for maximizing energy efficiency of a drone relay system based on physical layer network coding according to claim 1 is characterized in that: The mathematical modeling of the energy efficiency maximization problem of the UAV relay system includes: A pair of ground user equipment uses UD k,1 and UD k,2 denoted by, wherein UD represents a ground user equipment, k represents a group number of a ground user equipment pair, and "1" and "2" represent the sequence numbers of the ground user equipment in the ground user equipment pair, respectively; in Represents the set of group numbers of ground user equipment pairs, K represents the maximum group number; UD k,j The horizontal coordinate of In other words, j represents the sequence number of the ground user equipment in the ground user equipment pair, and x k,j and k,j Represents UD k,j The horizontal and vertical coordinates of the drone are shown in Figure 1. The fixed flight altitude of the drone is represented by the letter H. By using the time discretization method, the task time T is divided into N small time slots of equal size. The sequence number of each time slot is represented by n. in represents the set of time slot numbers, N represents the maximum time slot number; a time slot is recorded as The horizontal position of the UAV at the nth time slot is recorded as The starting and ending positions of the drone are given by q I and q F Indicates; the maximum flight speed of the drone is represented by v max The flight distance of the UAV in each time slot is represented by Δ[n] = ||q[n]-q[n-1]||; the UAV and UD k,j The transmission power is respectively given by p d [n] and p k,j [n] represents; the transmission decision and association of each time slot are represented by two binary variables α u,k [n] and α d,k [n] indicates; when α u,k When [n] = 1, it means that the kth pair of UDs transmits data to the drone; otherwise, α u,k [n]=0; when α d,k When [n] = 1, it means that the UAV forwards data to the kth pair of UDs; otherwise, α d,k [n] = 0; the channel power gain at the reference distance is represented by β0, the channel bandwidth is represented by B, and the noise power is represented by σ 2 express; order According to Shannon's formula, from UD k,j The feasible uplink transmission rate to the UAV is expressed as in represents the line of sight probability after regularization and homogeneous approximation; similarly, from drone to UD k,j The feasible downlink transmission rate is expressed as Let m represent the total mass of the drone, g represent the acceleration of gravity, A represent the area of ​​the rotor disc, ρ represent the air density, and W = mg represent the weight of the drone relay. represents the parameterized hovering power required; in the nth time slot, the power generated due to horizontal flight is expressed as In the nth time slot, the power consumption of the blade profile is expressed as Among them C D0 is the drag coefficient of the shape that varies with the geometry of the rotor blades; the total propulsion power required for the UAV relay flight is expressed as a function of its horizontal flight speed in the nth time slot, that is, in Therefore, the total energy consumption of the UAV relay during flight is expressed as Let A represent the transmission schedule and association P represents the transmission power of the drone and UD Q represents the drone trajectory The energy efficiency maximization problem of the UAV-assisted mobile relay system that provides two-way communication for multiple UD pairs using physical layer network coding technology is formulated as: q[0]=q I ,q[N]=q F (1h) in, is the introduced slack variable; Relaxing the binary variable A, problem (1) is transformed into: (1a), (1c), (1d), (1e), (1f), (1g), (1h). (2b).

3. The method for maximizing energy efficiency of a drone relay system based on physical layer network coding according to claim 2 is characterized in that: Through the block coordinate descent algorithm, the transmission power variables P of the UAV and UD and the UAV trajectory variable Q are fixed, and the problem is transformed into a sub-problem of solving the transmission scheduling and the associated variable A: st(1a),(2a),(1c),(1d), (3a) The objective function only contains the numerator of the objective function of problem (2). Problem (3) is a standard linear programming problem and is directly solved by convex optimization tools.

4. The method for maximizing energy efficiency of a drone relay system based on physical layer network coding according to claim 2 is characterized in that: Through the block coordinate descent algorithm, the transmission scheduling and association variables A and the UAV trajectory variables Q are fixed, and the problem is transformed into a sub-problem of solving the transmission power variable P of the UAV and UD: st(1c),(1d),(1e),(1f), (4a) The objective function only contains the numerator of the objective function of problem (2). Since the left side of constraint (1d) is about p d [n] Non-convex. Problem (4) is obviously non-convex; introduce slack variables Problem (4) is reformulated as: (1e),(1f). (5d) Problem (5) is a standard convex problem and is solved by the convex optimization tool CVX.

5. The method for maximizing energy efficiency of a drone relay system based on physical layer network coding according to claim 2 is characterized in that: By adopting the block coordinate descent algorithm, the transmission scheduling and association variables A and the transmission power variables P of the UAV and UD are fixed, and the problem is transformed into a sub-problem of solving the UAV trajectory variable Q: st(5a),(5b),(5c),(1g),(1h). (6a) Since the denominator of the objective function of problem (6) is non-convex with respect to q[n], problem (6) is a non-convex problem, and the right sides of constraints (5b) and (5c) are non-convex with respect to q[n]; Since the right side of (5b) is restricted to ||q[n]-w k,j′ || 2 Convex, so at the rth iteration, let represents a given drone trajectory; Since the Taylor expansion of any convex function at any point is its lower bound, we can obtain R u,k,j [n] At a given point Q (r) The first-order Taylor expression for As R u,k,j [n] At a given point Q (r) The lower bound of ; among them, and is a constant, Similarly, obtain R d,k,j [n] At a given point Q (r) The first-order Taylor expression for As R d,k,j [n] At a given point Q (r) The lower bound of ; among them, In order to deal with the non-convex terms in the expression of the total propulsion power required for the UAV relay flight Introducing slack variables in By transposing and squaring simultaneously, we get Therefore, the function of total propulsion power is reformulated as Thus, the expression of the new energy consumption function of the UAV relay is obtained: Problem (6) is reformulated as: (5a),(1g),(1h). (7e) Obtain the first-order Taylor expansion of the right side of restriction (7c) as its lower bound: Through the above transformation, a new optimization problem is obtained as follows: (5a),(7a),(7b),(7d),(1g),(1h). (8b) The constraints of problem (8) are all convex, and the objective function is a fraction with a concave numerator and a convex denominator. Problem (8) is solved by the Dinkelbach algorithm.

Images