A method for adjusting the working mode of UAV-assisted communication based on intelligent reflective surface

Abstract

A method for adjusting the working mode of UAV-assisted communication based on intelligent reflective surface is proposed. Specifically, the channel model based on UAV-intelligent reflective surface channel and intelligent reflective surface-ground node is first considered, and the parameters such as the UAV working cycle in static mode and cruise mode are designed. Then, the system throughput is maximized by optimizing the phase offset of the intelligent reflective surface. However, since the goals of maximizing throughput and minimizing energy consumption are contradictory, the problem of maximizing the economic efficiency of the UAV is planned based on this. Finally, the purpose of adjusting the working mode of UAV-assisted communication based on intelligent reflective surface is achieved by maximizing economic efficiency. Simulation results show that compared with traditional methods, the method of the present invention can achieve higher performance improvement in economic efficiency.

Description

Technical Field

[0001] The invention relates to the field of unmanned aerial vehicle (UAV) assisted wireless sensor network communication, in particular to a method for adjusting an unmanned aerial vehicle (UAV) assisted communication working mode based on an intelligent reflective surface. Background Art

[0002] The development of drones has promoted a large number of applications in military, civil and commercial fields, including aerial surveillance, cargo transportation, search and rescue, etc. In addition, compared with ground infrastructure, drones usually fly at high altitudes, which makes the transmission link between drones and ground equipment mainly based on line-of-sight links. The deployment of drones as relays helps ground communication in severely fading channels to improve channel transmission performance. Wireless sensor networks have been widely used in many fields, from modern agriculture to forest fire detection, from human structure monitoring to home automation systems. In order to extend the life of wireless sensor networks, drones are introduced as relays for auxiliary communication. Drone-assisted wireless sensor network communication can be regarded as a special type of mobile-based wireless sensor network. The flexibility of drones can provide more effective and wider coverage for wireless sensor networks. However, drone-assisted wireless sensor network communication faces many challenges, such as: complex and uncontrollable wireless environments, especially in crowded areas, and the presence of common objects such as buildings, trees and human bodies makes line-of-sight links more easily blocked. In addition, the spatial and temporal variations of non-stationary channels caused by the mobility of drones will lead to serious non-stationarity in drone systems.

[0003] With the introduction of smart reflective surfaces, smart reflective surface-assisted UAV communication has become a promising research topic. However, through investigation, it can be found that the current research on UAV communication based on smart reflective surface assistance is mostly fixed on smart reflective surfaces and single working mode of UAVs. The deployment of UAVs assisted by smart reflective surfaces as air base stations to provide coverage to the ground can be divided into two categories: static UAV communication based on smart reflective surfaces and cruise UAV communication based on smart reflective surfaces. Static UAVs are far away from ground sensor nodes, so the throughput performance will be limited to a certain extent. However, static UAVs are fixed at a certain point and hover without additional mechanical flight, so the energy consumption is lower than that of the cruise working mode of UAVs. In the scenario of cruise UAV communication assisted by smart reflective surfaces, the cruise UAV significantly shortens the communication distance through mechanical flight, thereby improving the system throughput, but the energy consumption generated by the cruise UAV to maintain high-altitude flight also increases. Since the on-board energy of UAVs is limited, how to measure the throughput performance and energy consumption of UAV systems is a key challenge in UAV communication research. Summary of the invention

[0004] On the basis of the above research, the present invention studies a new intelligent reflective surface assisted air-to-ground communication scenario, and proposes a method for adjusting the working mode of UAV assisted communication based on intelligent reflective surface by optimizing the phase shift of the intelligent reflective surface and jointly optimizing the system throughput and UAV energy consumption. Specifically, the channel model based on the UAV-intelligent reflective surface channel and the intelligent reflective surface-ground node is first considered, and parameters such as the UAV working cycle in static mode and cruise mode are designed. Then, the system throughput is maximized by optimizing the phase shift of the intelligent reflective surface. However, since the goals of maximizing throughput and minimizing energy consumption are contradictory, the problem of maximizing the economic efficiency of the UAV is planned based on this. Finally, by maximizing economic efficiency, the purpose of adaptively adjusting the working mode of the UAV is achieved.

[0005] A method for adjusting a UAV-assisted communication working mode based on an intelligent reflective surface comprises the following steps:

[0006] (1) Construct a UAV-assisted wireless sensor network communication system based on smart reflective surface, which consists of source sensor nodes, target sensor nodes, smart reflective surface and UAV.

[0007] (2) Analyze the channel model based on the UAV-intelligent reflective surface channel and the intelligent reflective surface-ground node and the economic efficiency of the UAV system, and design parameters such as the UAV working cycle in static mode and cruise mode.

[0008] (3) An adaptive algorithm for UAV working mode based on intelligent reflective surface is proposed. Firstly, the closed-form solution of the phase offset of the intelligent reflective surface when the UAV is in static and cruising working modes is derived, and the phase alignment of the received signals of different transmission paths is achieved to further improve the throughput of the UAV system. Furthermore, the UAV working mode adjustment is achieved by maximizing the economic efficiency of the UAV system.

[0009] Furthermore, step (1) specifically includes the following contents:

[0010] First, consider an air-to-ground wireless communication system in which a rotary-wing UAV and an intelligent reflective surface provide communication services for multiple static sensor nodes on the ground. Assuming that there is no direct communication link between the ground sensor nodes, the UAV-assisted wireless sensor network communication in static mode is as follows: Figure 1 As shown in the figure, the UAV acts as an aerial base station and hovers above the sensor network at a certain height. At the same time, a smart reflector is deployed to assist the UAV in communicating with the ground sensor nodes. Specifically, each element of the smart reflector receives the superimposed multipath signal from the source node, and then scatters the combined signal with adjustable amplitude and / or phase like a single point source.

[0011] In the Cartesian coordinate system, sensor node 1 is taken as the origin, the line connecting sensor node 1 and sensor node 2 is the x-axis, the plane where the wireless sensor network is located is the xoy plane, and the direction perpendicular to the xoy plane is the z-axis to establish a coordinate system. The coordinates of the sensor nodes are (x i ,y i ,z i ),i∈{1,2,3}, where the system is assumed to have three sensor nodes. Set the coordinates of the drone to (x u ,y u ,z u ), then the projection coordinates of the drone on the xoy plane (x u ,y u ,0). Taking the first element of the smart reflective surface (i.e., the reflection unit) as the reference point, the coordinates of the smart reflective surface are (x k ,y k ,z k ).

[0012] Compared with the static mode of the drone, the maneuverability of the drone in the cruise mode helps to achieve a better air-to-ground channel, which can further improve the system throughput and enhance the network communication quality. Similarly, in the cruise mode, the intelligent reflective surface assists the drone to communicate with the sensor nodes. Figure 2 As shown, the smart reflective surface is installed on a drone and can move at high speed relying on the maneuverability of the drone.

[0013] In cruise mode, the UAV flies over the ground sensor network at a specific height H within the working cycle T to assist the wireless sensor network communication. The initial position of the smart reflective surface and the UAV is located above the midpoint of sensor 1 and sensor 2 nodes. When the source sensor and the target sensor need to transmit data, the smart reflective surface and the UAV fly to the midpoint H between the source sensor and the target sensor, and the source sensor node signal is reflected to the target sensor node through the smart reflective surface. Then the coordinates of the smart reflective surface and the UAV in cruise mode are (x u ,y u ,H), the coordinates of sensor node 1, sensor node 2 and sensor node 3 are the same as those in static mode.

[0014] Furthermore, step (2) specifically includes the following contents:

[0015] Channel model in static mode: In the static mode scenario, a smart reflector with a uniform linear array of M reflective units and an intelligent controller that can adjust the phase shift of each unit is deployed at a certain height. Each unit in the smart reflector can adjust the phase shift to reflect the received signal. First, the diagonal phase matrix of the smart reflector is modeled, that is:

[0016]

[0017] Where j represents the carrier frequency of the signal.

[0018] Assuming the phase shift {θ i} can be controlled continuously, where θ i ∈[0,2π),i∈{1,2,...,M}. In the static mode of the UAV, the smart reflective surface is deployed on the surface of a high-rise building, and the UAV is hovering in the air. Therefore, the link between the UAV and the smart reflective surface can be assumed to be a line-of-sight channel. Since the smart reflective surface adopts a uniform linear array, the subsequent channel modeling adopts a multiplicative channel model. The channel gain h between the UAV and the smart reflective surface UR It is expressed as follows:

[0019]

[0020] Among them, d UR represents the distance between the UAV and the smart reflective surface, α represents the path loss index corresponding to the link between the UAV and the smart reflective surface, and ρ represents the unit distance D 0 =1, the path loss in the above formula is The right-hand side term represents the uniform linear array response of M elements, represents the cosine of the signal arrival angle from the drone to the smart reflector, d represents the antenna spacing, and μ represents the carrier wavelength.

[0021] Similarly, the link between the smart reflective surface and the ground sensor node adopts Rice fading modeling, so the channel gain between the smart reflective surface and the ground sensor node is expressed as:

[0022]

[0023] in represents the distance between the smart reflective surface and the ground sensor node, represents the deterministic sight distance component, namely:

[0024]

[0025] Represents the non-deterministic line-of-sight component. Each element of the smart emission surface is independent of each other and obeys a circularly symmetric complex Gaussian distribution with a mean of 0 and a variance of 1. represents the cosine of the signal deviation angle from the smart reflector to the ground sensor node, β represents the Rice factor, represents the elevation angle of the smart reflective surface relative to the ground sensor node, and α represents the path loss index related to the communication link between the smart reflective surface and the ground sensor node.

[0026] Although the link between the source sensor and the target sensor node may be blocked, there is still a scattered signal, so the channel is modeled as Rayleigh fading, and its channel gain is expressed as:

[0027]

[0028] Among them, d SD represents the distance from the source sensor node to the target sensor node, represents the random scattering component modeled by a Circularly Symmetric Complex Gaussian (CSCG) random variable with zero mean and unit variance.

[0029] According to formulas (1)-(5), the UAV receiving signal-to-noise ratio is expressed as:

[0030]

[0031] Where (.) H represents the Hermitian matrix of the matrix or vector, p u is the UAV transmission power, σ 2 represents additive Gaussian white noise, then the throughput of the UAV system in static mode is expressed as:

[0032]

[0033] Channel model in cruise mode: In the cruise mode scenario, since the drone is flying at a high enough altitude, the links between the source sensor node and the smart reflective surface and between the smart reflective surface and the target sensor node are considered line-of-sight links. Therefore, the channel gain between the source sensor node and the smart reflective surface is:

[0034]

[0035] Where d SR represents the distance between the source sensor node and the smart reflective surface, α′ is the path loss index between the source sensor node and the smart reflective surface, Represents the cosine of the signal arrival angle from the source sensor node to the smart reflective surface.

[0036] Similarly, the channel gain from the smart reflector to the sensor node is expressed as:

[0037]

[0038] where d RD represents the distance between the smart reflective surface and the target sensor node, Represents the cosine of the signal arrival angle from the smart reflective surface to the target sensor node.

[0039] According to formulas (8) and (9), the signal-to-noise ratio of the intelligent reflective surface and the UAV-assisted communication in cruise mode can be expressed as:

[0040]

[0041] where p s represents the source sensor node power. Then the system throughput in cruise mode is expressed as:

[0042]

[0043] Economic efficiency: The total energy consumption of drone-assisted communication usually consists of two parts: one is the energy consumption generated by radiation, signal processing, etc., and the other is the mechanical flight energy consumption required by the drone to support its maneuverability. According to relevant theories, the power consumption related to the mechanical flight of the drone can be modeled as:

[0044]

[0045] where p 0 represents the blade profile power in the hovering state, p i Indicates the induced power in the hovering state, U tip represents the tip speed of the blade, v 0 Represents the average rotor speed of the drone when it is flying forward, d 0 and s represent the drag ratio of the UAV fuselage and the rotor solidity, respectively. ρ and A represent the air density and the related area, respectively.

[0046] In static mode, the UAV working cycle T is designed, which includes: collecting the reflected signal of the intelligent reflective surface, forwarding data, and transmitting data collection instructions. Assume that the time for the UAV to collect the reflected signal of the intelligent reflective surface is t s , the forwarding data time is t f , the transmission data acquisition instruction time is t t Since the data forwarded by the drone is the data received by the drone at time t s If the data collected in the UAV is forwarded, the time it takes for the UAV to forward the data is equal to the time it takes to collect the data, that is, t s =t f In static mode, the drone keeps hovering at a speed v u = 0, according to formula (12), the UAV propulsion power consumption p in static mode is obtained h =p 0 +p i Then the total energy consumption of the drone in static mode is expressed as:

[0047] E s =P c t s +P ft f +P t (T-2t s )+p 0 +p i , (13)

[0048] Among them, p c is the power of the drone sensing data, p f is the power of the UAV forwarding data, p t The power used to transmit data collection instructions to the drone.

[0049] In cruise mode, since the UAV auxiliary wireless communication system uses intelligent reflectors to reflect signals, the UAV does not need to process and transmit the source signal, and the energy consumption generated by the mechanical flight of the UAV is usually much higher than the communication energy consumption, then the total energy consumption of the UAV in cruise mode is mainly composed of the energy consumption generated by mechanical flight. Assuming that the UAV is moving at a speed v u The mechanical flight energy consumption of the drone per unit time is E. slf The working cycle of the UAV in cruise mode is designed, where t 1 For drones passing through F 12 The time required, t 2 For drones passing through F 23 The time required, t 3 For drones passing through F 31 The time required to return to the initial position. Then, the energy consumption of the drone in cruise mode is expressed as:

[0050]

[0051] Intuitively, from the perspective of throughput maximization, the UAV should remain stationary at the closest position to the ground node in order to maintain the best communication channel conditions, and then fly to the target node to transmit data. However, due to the limited energy of the UAV itself, the energy consumption generated by mechanical flight is a challenge for the UAV-assisted communication system. Therefore, this paper proposes the economic efficiency of UAVs as a metric to measure the throughput and energy consumption of UAV systems. First, according to the relevant definition of economic efficiency, ECE, as a general metric, takes into account cost and power consumption. It is a good performance indicator for measuring the throughput and energy consumption of UAVs, which can fully reflect the characteristics of UAV throughput and energy consumption.

[0052] K r and k c They are respectively expressed as revenue per bit and energy cost per joule, R refis the relevant data rate, R represents the throughput of the UAV system, E represents the energy consumed by the UAV system, and ECE measures the profitability of the system, which is equal to the revenue minus the actual cost of the service provided. Then ECE is defined as follows:

[0053]

[0054] Wherein w represents the channel parameter, w=1MHz. Further, step (3) specifically includes the following contents:

[0055] Generally speaking, throughput and energy consumption are two important indicators for measuring the communication quality of drones. In static mode, the drone is far away from the ground sensor nodes, which will have a certain impact on the throughput performance. In cruise mode, the drone shortens the distance between the drone and the ground sensor nodes through mechanical flight, but mechanical flight brings more energy consumption. The optimization goal of this method is to maximize the throughput of the drone system by designing the optimal phase offset matrix and optimize the energy consumption of the drone by designing the drone working cycle, so as to maximize the economic efficiency of the drone and the drone can adaptively adjust the working mode.

[0056] (1) Smart reflector phase shift optimization:

[0057] According to the above optimization problem, in order to maximize the throughput of the smart reflector-assisted air-to-ground wireless communication system, the optimal phase offset matrix Φ is designed. In order to facilitate subsequent discussion, the complex vector h in formula (3) is first RG It is expressed as:

[0058]

[0059] where |h RG,i |It is h RG The modulus of the i-th element in, w i ∈[0,2π) is h RG The phase angle of the i-th element in .

[0060] According to formula (16), the signal-to-noise ratio in static mode is can be rewritten as:

[0061]

[0062] Assuming that the signals from different paths are coherently combined at the target sensor node, the coherently constructed signal can maximize the rate of receiving the signal, thereby maximizing the system throughput. Therefore, in order to maximize the signal reachability, the in-phase signals are then superimposed, that is:

[0063]

[0064] where h is the sign defined for the value of the in-phase signal when the phase offsets are equal.

[0065] Then the phase that each element of the smart reflective surface should adjust when reflecting the signal is expressed as:

[0066]

[0067] With the above closed-form solution, the signal-to-noise ratio in static mode is can be rewritten as:

[0068]

[0069] At this time, the throughput of the intelligent reflector-assisted air-to-ground communication system in static mode can reach the maximum value.

[0070] In order to obtain the maximum system throughput of the intelligent reflector-assisted air-to-ground communication system in cruise mode, it can be seen from the system throughput in cruise mode that the channel gain In a dominant position, when h SD and When the phase is the same, the intelligent reflector-assisted air-to-ground communication system in cruise mode can obtain the maximum channel gain. Then, the signal-to-noise ratio of the intelligent reflector-assisted air-to-ground communication system in cruise mode is can be rewritten as:

[0071]

[0072] Therefore, each element of the smart reflective surface can obtain the best reflection phase:

[0073]

[0074] At this time, the throughput of the intelligent reflective surface-assisted air-to-ground communication system in cruise mode can reach its maximum value.

[0075] (2) Working mode switching:

[0076] The above analysis can maximize the throughput of the UAV system. Next, the switching of the UAV working mode is analyzed. In the cruise mode, the UAV improves the throughput of the UAV system by flying, but it also consumes more energy. Although the UAV has the advantage of high maneuverability, it still faces the challenge of energy-saving deployment of UAVs due to its limited energy. Considering that static UAVs do not need mechanical flight and thus have low energy consumption, and when the UAV has sufficient energy, it is considered to let the UAV fly in cruise mode in order to obtain higher throughput performance, so the static working mode with sufficient energy is not taken into account. Comprehensively considering the UAV working mode and whether its energy is sufficient, the state space is set to I∈{SL,MS,ML}, where "SL" represents the static working mode of the UAV with insufficient energy, "MS" represents the cruise working mode of the UAV with sufficient energy, and "ML" represents the cruise working mode of the UAV with insufficient energy.

[0077] Set the transition probability of the drone from static mode to cruise mode to be p m =λ, then the probability p of transition from cruise mode to static mode s =1-λ. When the drone energy is lower than the predefined threshold ξ, the drone's working mode will switch to low-power mode. The transition probability of the drone's power changing from sufficient to insufficient is recorded as p, and the state transition probability of the drone's energy changing from insufficient to sufficient is 1-p. λ(1-p) is the transition probability of the drone from the static working mode with insufficient energy to the cruise mode with sufficient energy, (1-λ)p{ε≤ξ} is the transition probability of the cruise working mode with sufficient energy to the static working mode with insufficient energy, which is given by Figure 3 Describes the state transition of the drone's working mode.

[0078] The normalized equation governing the above system is given by:

[0079]

[0080] where π j represents the stationary probability of being in state j, j∈{SL,MS,ML}. Solve the above equations, that is:

[0081] (π 1 ,π 2 ,π 3 )={C 1 a,C 1 ,C 1 b}, (24)

[0082] In formula (24), the UAV is in a static working mode with insufficient energy. The drone is in cruise mode with insufficient energy. The state probability of the drone in different working modes can be expressed as

[0083] Therefore, the throughput of the intelligent reflector-assisted air-to-ground communication system is expressed as:

[0084] R(λ)=R s P s.l +R m P m.l +R m P m.s =R s π 1 +R m π 2 +R m π 3 (25)

[0085] The total energy consumption of the system is expressed as:

[0086] E(λ)=E s P s.l +E m P m.l +E m P m.s =E s π 1 +E m π 2 +E m π 3 (26)

[0087] The system economic efficiency is expressed as:

[0088]

[0089] Compared with the prior art, the present invention has the following beneficial effects:

[0090] (1) A UAV-assisted wireless sensor network communication system based on intelligent reflective surfaces was constructed.

[0091] (2) The channel model based on the UAV-intelligent reflective surface channel and the intelligent reflective surface-ground node and the economic efficiency of the UAV system were analyzed, and parameters such as the UAV working cycle in static mode and cruise mode were designed.

[0092] (3) An adaptive algorithm for UAV working mode based on intelligent reflective surface is proposed. Firstly, the closed-form solution of the phase offset of the intelligent reflective surface when the UAV is in static and cruising working modes is derived, and the phase alignment of the received signals of different transmission paths is achieved to further improve the throughput of the UAV system. Furthermore, the UAV working mode adjustment is achieved by maximizing the economic efficiency of the UAV system.

[0093] (4) The simulation results show that the proposed method has a greater performance advantage in improving throughput and economic efficiency compared with the random phase and non-intelligent reflective surface solutions. At the same time, the purpose of adaptive adjustment of the UAV working mode is achieved by maximizing the economic efficiency of the UAV. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] Figure 1 Schematic diagram of intelligent reflective surface assisted communication in static mode in an embodiment of the present invention.

[0095] Figure 2 Schematic diagram of intelligent reflective surface assisted communication in cruise mode in an embodiment of the present invention.

[0096] Figure 3 The figure is a state transition diagram of the UAV working mode switching in an embodiment of the present invention.

[0097] Figure 4 FIG. 4 is a graph showing how the gain of the smart reflective surface varies with the number of reflective elements in an embodiment of the present invention.

[0098] Figure 5 is a change in throughput along with the path loss index in an embodiment of the present invention;

[0099] Figure 6 FIG. 4 is a graph showing how the throughput varies with the number of smart reflective surface elements in an embodiment of the present invention.

[0100] Figure 7 Graph 1 shows the change of economic efficiency of the UAV system with λ in the embodiment of the present invention. DETAILED DESCRIPTION

[0101] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings.

[0102] The present invention discloses a method for adjusting a UAV auxiliary communication working mode based on an intelligent reflective surface, comprising the following steps:

[0103] S1. A UAV-assisted wireless sensor network communication system based on intelligent reflective surface is constructed. The system consists of source sensor nodes, target sensor nodes, intelligent reflective surface and UAV.

[0104] First, consider an air-to-ground wireless communication system in which a rotary-wing UAV and an intelligent reflective surface provide communication services for multiple static sensor nodes on the ground. Assuming that there is no direct communication link between the ground sensor nodes, the UAV-assisted wireless sensor network communication in static mode is as follows: Figure 1As shown in the figure, the UAV acts as an aerial base station and hovers above the sensor network at a certain height. At the same time, a smart reflector is deployed to assist the UAV in communicating with the ground sensor nodes. Specifically, each element of the smart reflector receives the superimposed multipath signal from the source node, and then scatters the combined signal with adjustable amplitude and / or phase like a single point source.

[0105] In the Cartesian coordinate system, sensor node 1 is taken as the origin, the line connecting sensor node 1 and sensor node 2 is the x-axis, the plane where the wireless sensor network is located is the xoy plane, and the direction perpendicular to the xoy plane is the z-axis to establish a coordinate system. The coordinates of the sensor nodes are (x i ,y i ,z i ),i∈{1,2,3}. Set the coordinates of the drone to (x u ,y u ,z u ), then the projection coordinates of the drone on the xoy plane (x u ,y u ,0). Taking the first element of the smart reflective surface as the reference point, the coordinates of the smart reflective surface are (x k ,y k ,z k ).

[0106] Compared with the static mode of the drone, the maneuverability of the drone in the cruise mode helps to achieve a better air-to-ground channel, which can further improve the system throughput and enhance the network communication quality. Similarly, in the cruise mode, the intelligent reflective surface assists the drone to communicate with the sensor nodes. Figure 2 As shown, the smart reflective surface is installed on a drone and can move at high speed relying on the maneuverability of the drone.

[0107] In cruise mode, the UAV flies over the ground sensor network at a specific height H within the working cycle T to assist the wireless sensor network communication. The initial position of the smart reflective surface and the UAV is located above the midpoint of sensor 1 and sensor 2 nodes. When the source sensor and the target sensor need to transmit data, the smart reflective surface and the UAV fly to the midpoint H between the source sensor and the target sensor, and the source sensor node signal is reflected to the target sensor node through the smart reflective surface. Then the coordinates of the smart reflective surface and the UAV in cruise mode are (x u ,y u ,H), the coordinates of sensor node 1, sensor node 2 and sensor node 3 are the same as those in static mode.

[0108] S2. The present invention first analyzes the channel model based on the UAV-intelligent reflective surface channel and the intelligent reflective surface-ground node and the economic efficiency of the UAV system, and designs the parameters such as the UAV working cycle in the static mode and the cruise mode. The details are as follows:

[0109] Channel model in static mode: In the static mode scenario, a smart reflector with a uniform linear array of M reflective units and an intelligent controller that can adjust the phase shift of each unit is deployed at a certain height. Each unit in the smart reflector can adjust the phase shift to reflect the received signal. First, the diagonal phase matrix of the smart reflector is modeled, that is,

[0110]

[0111] Where j represents the carrier frequency of the signal.

[0112] Assuming the phase shift {θ i} can be controlled continuously, where θ i ∈[0,2π),i∈{1,2,...,M}. In the static mode of the UAV, the smart reflective surface is deployed on the surface of a high-rise building, and the UAV is hovering in the air. Therefore, the link between the UAV and the smart reflective surface can be assumed to be a line-of-sight channel. Since the smart reflective surface adopts a uniform linear array, the subsequent channel modeling adopts a multiplicative channel model. The channel gain h between the UAV and the smart reflective surface UR It is expressed as follows

[0113]

[0114] Among them, d UR represents the distance between the UAV and the smart reflective surface, α represents the path loss index corresponding to the link between the UAV and the smart reflective surface, and ρ represents the unit distance D 0 =1, the path loss in the above formula is represents the path loss (, and the right-hand side term represents the uniform linear array response of M elements, represents the cosine of the signal arrival angle from the drone to the smart reflector, d represents the antenna spacing, and μ represents the carrier wavelength.

[0115] Similarly, the link between the smart reflector and the ground sensor node is modeled using Rice fading, so the channel gain between the smart reflector and the ground sensor node is expressed as

[0116]

[0117] in represents the distance between the smart reflective surface and the ground sensor node, represents the deterministic sight distance component, i.e.

[0118]

[0119] Represents the non-deterministic line-of-sight component. Each element of the smart emission surface is independent of each other and obeys a circularly symmetric complex Gaussian distribution with a mean of 0 and a variance of 1. represents the cosine of the signal deviation angle from the smart reflector to the ground sensor node, β represents the Rice factor, represents the elevation angle of the smart reflective surface relative to the ground sensor node, and α represents the path loss index related to the communication link between the smart reflective surface and the ground sensor node.

[0120] Although the link between the source sensor and the target sensor node may be blocked, there is still a scattered signal, so the channel is modeled as Rayleigh fading, and its channel gain is expressed as

[0121]

[0122] Among them, d SD represents the distance from the source sensor node to the target sensor node, represents the random scattering component modeled by a Circularly Symmetric Complex Gaussian (CSCG) random variable with zero mean and unit variance.

[0123] According to formulas (1)-(5), the UAV receiving signal-to-noise ratio is expressed as

[0124]

[0125] Where (.) H represents the Hermitian matrix of the matrix or vector, p u is the UAV transmission power, σ 2 represents additive Gaussian white noise, then the throughput of the UAV system in static mode is expressed as

[0126]

[0127] Channel model in cruise mode: In cruise mode, since the drone is flying at a high enough altitude, the links between the source sensor node and the smart reflective surface and between the smart reflective surface and the target sensor node are considered line-of-sight links. Therefore, the channel gain between the source sensor node and the smart reflective surface is

[0128]

[0129] Where d SR represents the distance between the source sensor node and the smart reflective surface, α′ is the path loss index between the source sensor node and the smart reflective surface, Represents the cosine of the signal arrival angle from the source sensor node to the smart reflective surface.

[0130] Similarly, the channel gain from the smart reflector to the sensor node is expressed as

[0131]

[0132] where d RD represents the distance between the smart reflective surface and the target sensor node, and α is the path loss index between the source sensor node and the smart reflective surface. Represents the cosine of the signal arrival angle from the smart reflective surface to the target sensor node.

[0133] According to formulas (8) and (9), the signal-to-noise ratio of the intelligent reflective surface and the UAV-assisted communication in cruise mode can be expressed as:

[0134]

[0135] where p s represents the source sensor node power. Then the system throughput in cruise mode is expressed as

[0136]

[0137] Economic efficiency: The total energy consumption of drone-assisted communication usually consists of two parts: one is the energy consumption generated by radiation, signal processing, etc., and the other is the mechanical flight energy consumption required by the drone to support its maneuverability. According to relevant theories, the power consumption related to the mechanical flight of the drone can be modeled as

[0138]

[0139] where p 0 represents the blade profile power in the hovering state, p i Indicates the induced power in the hovering state, U tip represents the tip speed of the blade, v 0 Represents the average rotor speed of the drone when it is flying forward, d 0 and s represent the drag ratio of the UAV fuselage and the rotor solidity, respectively. ρ and A represent the air density and the related area, respectively.

[0140] In static mode, the UAV working cycle T is designed, which includes: collecting the reflected signal of the intelligent reflective surface, forwarding data, and transmitting data collection instructions. Assume that the time for the UAV to collect the reflected signal of the intelligent reflective surface is t s , the forwarding data time is t f , the transmission data acquisition instruction time is t t Since the data forwarded by the drone is the data received by the drone at time t s If the data collected in the UAV is forwarded, the time it takes for the UAV to forward the data is equal to the time it takes to collect the data, that is, t s =t fIn static mode, the drone keeps hovering at a speed v u = 0, according to formula (12), the UAV propulsion power consumption p in static mode is obtained h =p 0 +p i Then the total energy consumption of the UAV in static mode is expressed as

[0141] E s =P c t s +P f t f +P t (T-2t s )+p 0 +p i , (13)

[0142] Among them, p c is the power of the drone sensing data, p f is the power of the UAV forwarding data, p t The power used to transmit data collection instructions to the drone.

[0143] In cruise mode, since the UAV auxiliary wireless communication system uses intelligent reflectors to reflect signals, the UAV does not need to process and transmit the source signal, and the energy consumption generated by the mechanical flight of the UAV is usually much higher than the communication energy consumption, then the total energy consumption of the UAV in cruise mode is mainly composed of the energy consumption generated by mechanical flight. Assuming that the UAV is moving at a speed v u The mechanical flight energy consumption of the drone per unit time is E. slf The working cycle of the UAV in cruise mode is designed, where t 1 For drones passing through F 12 The time required, t 2 For drones passing through F 23 The time required, t 3 For drones passing through F 31 The time required to return to the initial position. Then, the energy consumption of the drone in cruise mode is expressed as

[0144]

[0145] Intuitively, from the perspective of throughput maximization, the UAV should remain stationary at the closest position to the ground node in order to maintain the best communication channel conditions, and then fly to the target node to transmit data. However, due to the limited energy of the UAV itself, the energy consumption generated by mechanical flight is a challenge for the UAV-assisted communication system. Therefore, this paper proposes the economic efficiency of UAVs as a metric to measure the throughput and energy consumption of UAV systems. First, according to the relevant definition of economic efficiency, ECE, as a general metric, takes into account cost and power consumption. It is a good performance indicator for measuring the throughput and energy consumption of UAVs, which can fully reflect the characteristics of UAV throughput and energy consumption.

[0146] K r and k c They are respectively expressed as revenue per bit and energy cost per joule, R ref is the relevant data rate, R represents the throughput of the UAV system, E represents the energy consumed by the UAV system, and ECE measures the profitability of the system, which is equal to the revenue minus the actual cost of the service provided. Then ECE is defined as follows

[0147]

[0148] Wherein w represents the channel parameter, w=1MHz.

[0149] S3. Based on the above analysis, the present invention proposes an adaptive algorithm for the working mode of a UAV assisted by an intelligent reflective surface. First, the closed-form solution of the phase offset of the intelligent reflective surface when the UAV is in static and cruising working modes is derived, and the phase alignment of the received signals of different transmission paths is achieved to further improve the throughput of the UAV system. Furthermore, the UAV working mode adjustment is achieved by maximizing the economic efficiency of the UAV system. The details are as follows:

[0150] Generally speaking, throughput and energy consumption are two important indicators for measuring the communication quality of drones. In static mode, the drone is far away from the ground sensor nodes, which will have a certain impact on the throughput performance. In cruise mode, the drone shortens the distance between the drone and the ground sensor nodes through mechanical flight, but mechanical flight brings more energy consumption. The optimization goal of this method is to maximize the throughput of the drone system by designing the optimal phase offset matrix and optimize the energy consumption of the drone by designing the drone working cycle, so as to maximize the economic efficiency of the drone and the drone can adaptively adjust the working mode.

[0151] (1) Smart reflector phase shift optimization

[0152] According to the above optimization problem, in order to maximize the throughput of the smart reflector-assisted air-to-ground wireless communication system, the optimal phase offset matrix Φ is designed. In order to facilitate subsequent discussion, the complex vector h in formula (3) is first RG Expressed as

[0153]

[0154] where |h RG,i |It is h RG The modulus of the i-th element in, w i ∈[0,2π) is h RG The phase angle of the i-th element in .

[0155] According to formula (16), the signal-to-noise ratio in static mode is can be rewritten as

[0156]

[0157] Assuming that the signals from different paths are coherently combined at the target sensor node, the coherently constructed signal can maximize the rate of receiving the signal, thereby maximizing the system throughput. Therefore, in order to maximize the signal reachability, the in-phase signals are then superimposed, that is,

[0158]

[0159] where h is the sign defined for the value of the in-phase signal when the phase offsets are equal.

[0160] Then the phase that each element of the smart reflective surface should adjust when reflecting the signal is expressed as

[0161]

[0162] With the above closed-form solution, the signal-to-noise ratio in static mode is can be rewritten as

[0163]

[0164] At this time, the throughput of the intelligent reflector-assisted air-to-ground communication system in static mode can reach the maximum value.

[0165] In order to obtain the maximum system throughput of the intelligent reflector-assisted air-to-ground communication system in cruise mode, it can be seen from the system throughput in cruise mode that the channel gain In a dominant position, when h SD and When the phase is the same, the intelligent reflector-assisted air-to-ground communication system in cruise mode can obtain the maximum channel gain. Then, the signal-to-noise ratio of the intelligent reflector-assisted air-to-ground communication system in cruise mode is can be rewritten as

[0166]

[0167] Therefore, each element of the smart reflective surface can obtain the best reflection phase

[0168]

[0169] At this time, the throughput of the intelligent reflective surface-assisted air-to-ground communication system in cruise mode can reach its maximum value.

[0170] (2) Working mode switching

[0171] The above analysis can maximize the throughput of the UAV system. Next, the switching of the UAV working mode is analyzed. In the cruise mode, the UAV improves the throughput of the UAV system by flying, but it also consumes more energy. Although the UAV has the advantage of high maneuverability, it still faces the challenge of energy-saving deployment of UAVs due to its limited energy. Considering that static UAVs do not need mechanical flight and thus have low energy consumption, and when the UAV has sufficient energy, it is considered to let the UAV fly in cruise mode in order to obtain higher throughput performance, so the static working mode with sufficient energy is not taken into account. Comprehensively considering the UAV working mode and whether its energy is sufficient, the state space is set to I∈{SL,MS,ML}, where "SL" represents the static working mode of the UAV with insufficient energy, "MS" represents the cruise working mode of the UAV with sufficient energy, and "ML" represents the cruise working mode of the UAV with insufficient energy.

[0172] Set the transition probability of the drone from static mode to cruise mode to be p m =λ, then the probability p of transition from cruise mode to static mode s =1-λ. When the drone energy is lower than the predefined threshold ξ, the drone's working mode will switch to low-power mode. The transition probability of the drone's power changing from sufficient to insufficient is recorded as p, and the state transition probability of the drone's energy changing from insufficient to sufficient is 1-p. λ(1-p) is the transition probability of the drone from the static working mode with insufficient energy to the cruise mode with sufficient energy, (1-λ)p{ε≤ξ} is the transition probability of the cruise working mode with sufficient energy to the static working mode with insufficient energy, which is given by Figure 3 Describes the state transition of the drone's working mode.

[0173] The normalized equation governing the above system is given by

[0174]

[0175] where π jrepresents the stationary probability of being in state j, j∈{SL,MS,ML}. Solve the above equations, that is,

[0176] (π 1 ,π 2 ,π 3 )={C 1 a,C 1 ,C 1 b}, (24)

[0177] In formula (24), the UAV is in a static working mode with insufficient energy. The drone is in cruise mode with insufficient energy. The state probability of the drone in different working modes can be expressed as

[0178] Therefore, the throughput of the intelligent reflector-assisted air-to-ground communication system is expressed as

[0179] R(λ)=R s P s.l +R m P m.l +R m P m.s =R s π 1 +R m π 2 +R m π 3 (25)

[0180] The total energy consumption of the system is expressed as

[0181] E(λ)=E s P s.l +E m P m.l +E m P m.s =E s π 1 +E m π 2 +E m π 3 (26)

[0182] The system economic efficiency is expressed as

[0183]

[0184] In order to better understand the idea and connotation of the proposed solution method, the above solution process is highly summarized as follows:

[0185] 1. Input: (x i ,y i ,zi ),j,M,ρ,α,λ,d,β,p u ,σ 2 ,p t ,p c ,p f ,T,k r , k u ;

[0186] 2. Output: R(λ),E(λ),ECE(λ),λ opt (obtained by maximizing the economic efficiency of the drone, used for drone operating mode adjustment);

[0187] 3. Start;

[0188] 4. Initialize the ground sensor node coordinates;

[0189] 5. According to formulas (12)-(14), determine the energy consumption of the drone in the two working modes;

[0190] 6. According to formulas (16)-(20), determine the optimal phase shift of each element of the IRS in the static mode of the UAV;

[0191] 7. Obtain the maximum system throughput in static mode through the optimal IRS phase shift;

[0192] 8. Using formulas (21)-(22), the optimal phase shift of each element of the IRS in the UAV cruise mode is obtained;

[0193] 9. Obtain the maximum system throughput in cruise mode by optimizing the phase shift of each element of IRS in the cruise mode of the drone;

[0194] 10. Use the UAV working mode adjustment algorithm based on intelligent reflective surface to obtain the stability probability of each working mode of the UAV;

[0195] 11. According to formulas (23) and (26), the optimal total throughput of the UAV system R(λ) is obtained * And the total energy consumption E(λ) * ;

[0196] 12. Substitute the above results into formula (27) to calculate the economic efficiency;

[0197] 13. Obtain λ by maximizing the economic efficiency of drones opt ;

[0198] 14. When λ≤λ opt When the drone is in static mode, it will work in cruise mode.

[0199] 15. The end.

[0200] Conduct experiments and set the UAV transmission power p u =10dBm, UAV flight speed v u =30km / h, sensor node transmission power p s =1W, communication bandwidth B = 1MHz, ground sensor node noise power spectrum density N 0 =-110dBm / Hz, then the corresponding noise power σ 2 =-110dBm; given the coordinates of sensor 2 node (300,0,0), sensor node 3 coordinates (200,150,0), the hovering coordinates of the drone in static mode are (191.64,81.52,100); the power of the drone's perception data is p c =4W, the power of forwarding data is p f =4W, the power for transmitting data acquisition instructions is p t =5W; UAV working cycle T = 200s; antenna spacing d = λ / 2, related path loss index α = 2.8, Rice factor β = 3dB, path loss coefficient ρ = -20dB, number of reflective elements of smart reflective surface M = 80; the income per bit related to the economic efficiency of the UAV system is set to The cost per joule of energy consumption is set to Next, the performance of the proposed method will be verified from aspects such as the throughput performance of the UAV system and the economic efficiency of the UAV.

[0201] First by Figure 4 By analyzing the gain of the smart reflective surface when the number of reflective elements of the smart reflective surface increases from 20 to 120 in cruise mode, it can be found that for different UAV altitudes in cruise mode, the gain of the smart reflective surface increases with the increase in the number of smart reflective surface elements. When the altitude of the UAV carrying the smart reflective surface decreases, the gain of the smart reflective surface decreases accordingly. In addition, it can be found that when the number of smart reflective surface elements doubles, the smart reflective surface can collect more energy from the source sensor node and reflect more electromagnetic waves to the target sensor node, and the gain of the smart reflective surface increases proportionally with the number of reflective elements of the smart reflective surface.

[0202] Next, based on the channel model considering the intelligent reflector-assisted signal propagation, the throughput performance of the UAV system of different schemes is studied when the path loss index associated with the intelligent reflector changes, such as Figure 5 As shown in the figure, “withoutIRS” means that no intelligent reflector is used for auxiliary communication: θ i=0; "Random phase" means that the phase shift of each reflective element in the smart reflective surface is random. As can be seen from the figure, as the correlation path loss index increases, the throughput of the proposed method and the random phase scheme decreases. This is because when the correlation path loss index of the smart reflective surface increases, the signal power reflected by the smart reflective surface becomes weaker. In addition, it can be seen that when the correlation link of the smart reflective surface fades very seriously, for example, when α=4, the performance improvement of the proposed method is not much different from that of not using the smart reflective surface for auxiliary communication. The following simulation experiment sets the correlation path loss index α=2.8.

[0203] Figure 6 The relationship between the throughput performance of the smart reflector-assisted UAV system and the number of reflective elements on the smart reflector is shown. Figure 6 It can be found that in the scenario where no smart reflector is deployed, the change in the throughput performance of the UAV system is negligible, while the use of smart reflectors to assist communication can significantly improve the throughput performance of the UAV system. In addition, the throughput performance of this method and the random phase scheme increases with the increase in the number of smart reflector elements. Compared with the use of smart reflectors for assisting communication, optimizing the phase shift of the smart reflector can bring significant performance gains. Therefore, it can be found that the use of smart reflectors to assist UAV communication can improve the communication quality by improving the channel environment.

[0204] When UAV communication without intelligent reflective surface assistance is not used, the experiment can be properly and The UAV system can achieve higher economic efficiency when the energy cost per bit is set to as well as And compare it with the UAV communication solution without the assistance of intelligent reflective surface. Figure 7 Describe the economic efficiency of the revenue per bit k r and the energy cost per joule k c As can be seen from the figure, the economic efficiency of the UAV system based on the intelligent reflective surface first increases and then decreases as λ increases, and there is an optimal λ in the middle. opt The economic efficiency of the drone is maximized. That is, when λ≥λ opt When λ<λ, the UAV chooses to work in static mode based on intelligent reflection assistance; when λ<λ optWhen λ=0, the UAV works in the static mode; when λ=1, the UAV works in the cruise mode. Compared with the UAV communication scheme without the assistance of the intelligent reflective surface, the method proposed in the present invention can further improve the economic efficiency of the UAV system.

[0205] The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiment. Any equivalent modifications or changes made by ordinary technicians in this field based on the contents disclosed by the present invention should be included in the protection scope recorded in the claims.

Claims

1. A method for adjusting the working mode of UAV-assisted communication based on intelligent reflective surface. Features: The steps include: Step (1), constructing a UAV-assisted wireless sensor network communication system based on an intelligent reflective surface, the system comprising a source sensor node, a target sensor node, an intelligent reflective surface and a UAV; Step (2), analyzing the channel model based on the UAV-intelligent reflective surface channel and the intelligent reflective surface-ground node and the economic efficiency of the UAV system, and designing parameters such as the UAV working cycle in static mode and cruise mode; Step (3) proposes a UAV working mode adaptation method based on the assistance of intelligent reflective surface, including intelligent reflective surface phase shift optimization and working mode switching; firstly, the closed-form solution of the phase shift of the intelligent reflective surface when the UAV is in static and cruising working modes is derived, and the phase alignment of the received signals of different transmission paths is achieved to further improve the throughput of the UAV system; the UAV working mode adjustment is achieved by maximizing the economic efficiency of the UAV system; The switching of UAV working modes is analyzed, and the UAV working mode and whether its energy is sufficient are comprehensively considered. The state space is set as I∈{SL,MS,ML}, where "SL" represents the static working mode of the UAV with insufficient energy, "MS" represents the cruising working mode of the UAV with sufficient energy, and "ML" represents the cruising working mode of the UAV with insufficient energy. Set the transition probability of the drone from static mode to cruise mode to be p m =λ, then the probability p of transition from cruise mode to static mode s =1-λ; when the drone energy is lower than the predefined threshold ξ, the drone's working mode will switch to low-power mode, and the transition probability of the drone's power changing from sufficient to insufficient is recorded as p, then the state transition probability of the drone's energy changing from insufficient to sufficient is 1-p; λ(1-p) is the static working state of the drone with insufficient energy. The transition probability of the working mode to the cruise mode with sufficient energy is (1-λ)p{ε≤ξ}, which is the transition probability of the cruise working mode with sufficient energy to the static working mode with insufficient energy. The normalized equation of the control system is given by: where π j represents the stable probability of being in state j, j∈{SL,MS,ML}; solve the above equations, that is: (p 1 ,p 2 ,p 3 )={C 1 a,C 1 ,C 1 b}, (24) In formula (24), the UAV is in a static working mode with insufficient energy. The drone is in cruise mode with insufficient energy. The state probability of the drone in different working modes is expressed as In step (3), the throughput of the intelligent reflector-assisted air-to-ground communication system is expressed as: R(λ)=R s p 1 +R m p 2 +R m p 3 (25) Among them, R s is the throughput of the UAV system in static mode, R m is the system throughput in cruise mode; The total energy consumption of the system is expressed as: E(λ)=E s π 1 +E m π 2 +E m π 3 (26) Among them, E s is the total energy consumption of the drone in static mode, E m Energy consumption of the drone in cruise mode; The system economic efficiency is expressed as: Among them, k r and k c They are respectively expressed as revenue per bit and energy cost per joule, R ref is the relevant data rate, R represents the UAV system throughput, E represents the energy consumed by the UAV system, and w represents the channel parameters.

2. According to claim 1, a method for adjusting the working mode of UAV-assisted communication based on intelligent reflective surface, Features: Step (1) specifically includes the following contents: In the system, drones and smart reflective surfaces provide communication services for multiple ground sensor nodes. It is assumed that there is no direct communication link between the ground sensor nodes. The drones act as aerial base stations and hover over the sensor network. At the same time, smart reflective surfaces are deployed to assist the drones in communicating with the ground sensor nodes. The intelligent reflective surface is installed on the drone. As the drone moves, it receives superimposed multipath signals from the source node and then scatters the combined signal in a single point source manner. The amplitude and phase of the signal are adjustable.

3. According to claim 2, a method for adjusting the working mode of UAV-assisted communication based on a smart reflective surface, Features: Set the Cartesian coordinate system, where the coordinates of the i-th sensor node are (x i ,y i ,z i ), i∈{1,2,...,n}, the coordinates of the drone are (x u ,y u ,z u ), its projection coordinates on the xoy plane are (x u ,y u ,0), the coordinates of the smart reflective surface are (x k ,y k ,z k ).

4. According to claim 1, a method for adjusting the working mode of UAV-assisted communication based on intelligent reflective surface, Features: In step (2), in a static mode scenario, an intelligent reflective surface is deployed with a uniform linear array consisting of M reflective units and an intelligent controller capable of adjusting the phase shift of each unit. Each reflective unit in the intelligent reflective surface adjusts the phase shift to reflect the received signal. First, the diagonal phase matrix of the intelligent reflective surface is modeled, that is: Where j represents the carrier frequency of the signal; Assuming the phase shift {θ i } is continuously controlled, where θ i ∈[0,2π),i∈{1,2,...,M}; the link between the UAV and the intelligent reflective surface in the static mode scene is defined as a line-of-sight channel; the channel modeling adopts a multiplicative channel model, and the channel gain h between the UAV and the intelligent reflective surface UR It is expressed as follows: Among them, d UR represents the distance between the UAV and the smart reflective surface, α represents the path loss index corresponding to the link between the UAV and the smart reflective surface, and ρ represents the unit distance D 0 =1, the path loss in the above formula represents the path loss, and the right-hand side term represents the uniform linear array response of M elements, represents the cosine of the signal arrival angle from the drone to the smart reflector, d represents the antenna spacing, and μ represents the carrier wavelength; The link between the smart reflector and the ground sensor node adopts Rice fading modeling, and the channel gain is expressed as: in represents the distance between the smart reflective surface and the ground sensor node, represents the deterministic sight distance component, namely: represents the non-deterministic line-of-sight component. Each reflective unit of the smart emitting surface is independent of each other and obeys a circularly symmetric complex Gaussian distribution with a mean of 0 and a variance of 1. represents the cosine of the signal deviation angle from the smart reflective surface to the ground sensor node, β represents the Rice factor, and α represents the path loss index related to the communication link between the smart reflective surface and the ground sensor node; The channel is modeled as Rayleigh fading, and its channel gain is expressed as: Among them, d SD represents the distance from the source sensor node to the target sensor node, h represents the random scattering component modeled by a cyclically symmetric complex Gaussian random variable with zero mean and unit variance; According to formulas (1)-(5), the UAV receiving signal-to-noise ratio is expressed as: Where (.) H represents the Hermitian matrix of the matrix or vector, p u is the UAV transmission power, σ 2 represents additive Gaussian white noise, then the throughput of the UAV system in static mode is expressed as:

5. According to claim 1, a method for adjusting the working mode of UAV-assisted communication based on intelligent reflective surface, Features: In step (2), in the cruise mode scenario, the links between the source sensor node and the smart reflective surface and between the smart reflective surface and the target sensor node are considered as line-of-sight links; the channel gain between the source sensor node and the smart reflective surface is: Where d SR represents the distance between the source sensor node and the smart reflective surface, α′ is the path loss index between the source sensor node and the smart reflective surface, represents the cosine of the signal arrival angle from the source sensor node to the smart reflective surface; The channel gain from the smart reflector to the sensor node is expressed as: where d RD represents the distance between the smart reflective surface and the target sensor node, represents the cosine of the signal arrival angle from the smart reflective surface to the target sensor node; According to formulas (8) and (9), the signal-to-noise ratio of the intelligent reflective surface and the UAV-assisted communication in cruise mode is expressed as: where p s represents the source sensor node power, σ 2 represents additive white Gaussian noise; the system throughput in cruise mode is expressed as:

6. According to claim 1, a method for adjusting the working mode of UAV-assisted communication based on intelligent reflective surface, Features: In step (2), the economic efficiency of the system is based on the total energy consumption of UAV-assisted communication, including energy consumption and mechanical flight energy consumption; for a flight speed of v u The mechanical flight energy consumption of the UAV is modeled as: where p 0 represents the blade profile power in the hovering state, p i Indicates the induced power in the hovering state, U tip represents the tip speed of the blade, v 0 Represents the average rotor speed of the drone when it is flying forward, d 0 and s represent the drag ratio of the UAV fuselage and the solidity of the rotor, respectively, and ρ and A represent the air density and the associated area, respectively; In static mode, the UAV working cycle T is designed, which includes: collecting the reflected signal of the intelligent reflective surface, forwarding data, and transmitting data collection instructions; assuming that the time for the UAV to collect the reflected signal of the intelligent reflective surface is t s , the forwarding data time is t f , the transmission data acquisition instruction time is t t ; Since the data forwarded by the drone is the data of the drone at time t s If the data collected in the UAV is t, then the time for the UAV to forward the data is equal to the time for collecting the data, that is, t s =t f ; In static mode, the drone keeps hovering, and its speed v u = 0, according to formula (12), the UAV propulsion power consumption p in static mode is obtained h =p 0 +p i ; Then the total energy consumption of the drone in static mode is expressed as: E s =P c t s +P f t f +P t (T-2t s )+p 0 +p i , (13) Among them, p c is the power of the drone sensing data, p f is the power of the UAV forwarding data, p t Power to transmit data collection instructions to the drone; In the cruise mode, the UAV working cycle is designed. For a total of l cruise points, t 1 The path F between the first and second cruising points of the drone 1,2 The time required, t 2 For drones passing through F 2,3 The time required, by analogy, is t l For drones passing through F l1 The time required to return to the initial position, the energy consumption of the drone in cruise mode is expressed as: Define ECE as a metric that takes into account both cost and power consumption; r and k c They are respectively expressed as revenue per bit and energy cost per joule, R ref is the relevant data rate, R represents the UAV system throughput, E represents the energy consumed by the UAV system, and ECE measures the profitability of the system, which is equal to the revenue minus the actual cost of the service provided; then ECE is defined as follows: Wherein w represents the channel parameter, w=1MHz.

7. According to claim 1, a method for adjusting the working mode of UAV-assisted communication based on intelligent reflective surface, Features: In step (3), the optimization goal is set to design the optimal phase offset matrix to maximize the UAV system throughput and design the UAV working cycle to optimize the UAV energy consumption, thereby maximizing the UAV economic efficiency and the UAV adaptively adjusts the working mode.

8. According to claim 7, a method for adjusting the working mode of UAV-assisted communication based on intelligent reflective surface, Features: In the phase shift optimization of the smart reflector, according to the optimization goal, firstly, in order to maximize the throughput of the smart reflector-assisted air-to-ground wireless communication system, the optimal phase shift matrix Φ is designed; firstly, the complex vector h in formula (3) is RG It is expressed as: where |h RG,i |It is h RG The modulus of the i-th element in, w i ∈[0,2π) is h RG The phase angle of the i-th element in ; According to formula (16), the signal-to-noise ratio in static mode is Rewritten as: Assuming that the signals from different paths are coherently combined at the target sensor node, the coherently constructed signal maximizes the rate of receiving the signal, thereby maximizing the system throughput; in order to maximize the signal reachability, the in-phase signals are superimposed, that is: where h is the sign defined for the value of the in-phase signal when the phase offsets are equal; The phase that each element of the smart reflective surface should adjust when reflecting the signal is expressed as: Signal-to-noise ratio in static mode is rewritten as: At this time, the throughput of the intelligent reflector-assisted air-to-ground communication system in static mode reaches the maximum value; In order to obtain the maximum system throughput of the intelligent reflector-assisted air-to-ground communication system in cruise mode, the signal-to-noise ratio of the intelligent reflector-assisted air-to-ground communication system Rewritten as: Therefore, each reflection unit of the smart reflection surface obtains the optimal reflection phase: At this time, the throughput of the intelligent reflective surface-assisted air-to-ground communication system in cruise mode reaches its maximum value.

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