CN115226255A - Unmanned aerial vehicle auxiliary communication working mode adjusting method based on intelligent reflecting surface - Google Patents
Unmanned aerial vehicle auxiliary communication working mode adjusting method based on intelligent reflecting surface Download PDFInfo
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Abstract
An unmanned aerial vehicle auxiliary communication working mode adjusting method based on an intelligent reflector specifically comprises the steps of firstly considering channel models based on an unmanned aerial vehicle-intelligent reflector channel and an intelligent reflector-ground node, and designing parameters such as working cycles of the unmanned aerial vehicle in a static mode and a cruise mode. The system throughput is then maximized by optimizing the intelligent reflective surface phase shifts, however, since the goals of maximizing throughput and minimizing energy consumption are counter-opposing, the problem of maximizing economic efficiency of the drone is planned on this basis. Finally, through maximize economic efficiency, reach the mesh to the unmanned aerial vehicle auxiliary communication mode adjustment based on intelligent plane of reflection. Simulation results show that compared with the traditional method, the method disclosed by the invention can obtain higher performance improvement in the aspect of economic efficiency.
Description
Technical Field
The invention relates to the field of unmanned aerial vehicle auxiliary wireless sensor network communication, in particular to an unmanned aerial vehicle auxiliary communication working mode adjusting method based on an intelligent reflecting surface.
Background
The development of unmanned aerial vehicles has promoted a number of applications of unmanned aerial vehicles in military, civil and commercial fields, including aerial surveillance, cargo transportation, search and rescue, and the like. Furthermore, in contrast to ground infrastructure, drones typically fly at high altitudes, which makes the transmission link between the drone and the ground equipment dominated by a line-of-sight link. The unmanned aerial vehicle serving as a relay is beneficial to ground communication in a severe fading channel so as to improve the channel transmission performance. Wireless sensor networks have been widely used in many fields, from modern agriculture to forest fire detection, monitoring of home automation systems from human body structures. In order to prolong the service life of the wireless sensor network, the unmanned aerial vehicle is introduced to be used as a relay to carry out auxiliary communication. Unmanned aerial vehicle-assisted wireless sensor network communication can be regarded as a special mobile-based wireless sensor network, and the flexibility of the unmanned aerial vehicle can provide more effective and wider coverage for the wireless sensor network. However, drone-assisted wireless sensor network communication faces many challenges, such as: the presence of complex and uncontrollable wireless environments, particularly in crowded areas, buildings, trees, and people, among other common objects, makes the line-of-sight link more susceptible to blockage. In addition, the space and time variation of the non-stationary channel caused by the mobility of the unmanned aerial vehicle can cause the serious non-stationary of the unmanned aerial vehicle system.
With the introduction of the intelligent reflecting surface, the communication of the intelligent reflecting surface assisting the unmanned aerial vehicle has become a promising research subject. However, research studies can find that the current unmanned aerial vehicle communication research based on the assistance of the intelligent reflecting surface is mostly fixed in the aspects of the intelligent reflecting surface and a single working mode of the unmanned aerial vehicle. Deployment that unmanned aerial vehicle based on intelligence plane of reflection is supplementary provides coverage to ground as aerial basic station falls into two kinds: static unmanned aerial vehicle communication and the unmanned aerial vehicle communication that cruises based on intelligent plane of reflection. Static unmanned aerial vehicle is because far away from ground sensor node, and throughput performance can receive certain restriction, but static unmanned aerial vehicle is fixed and hovers at a certain point and does not need extra mechanical flight, so compare energy consumption lower with unmanned aerial vehicle cruise mode. Under the supplementary unmanned aerial vehicle that cruises communication scene based on intelligent plane of reflection, the unmanned aerial vehicle that cruises has shown to shorten communication distance through mechanical flight to improved system throughput, but the unmanned aerial vehicle that cruises maintains the produced energy consumption of high altitude flight also increases thereupon. Since the onboard energy of a drone is limited, how to measure the throughput performance and energy consumption of a drone system is a key challenge in drone communication research.
Disclosure of Invention
On the basis of the research, the invention researches a novel intelligent reflector auxiliary air-ground communication scene, and provides an unmanned aerial vehicle auxiliary communication working mode adjusting method based on an intelligent reflector by optimizing intelligent reflector phase shift and jointly optimizing system throughput and unmanned aerial vehicle energy consumption. Specifically, a channel model based on an unmanned aerial vehicle-intelligent reflector channel and an intelligent reflector-ground node is considered, and parameters such as the working period of the unmanned aerial vehicle in a static mode and a cruise mode are designed. The system throughput is then maximized by optimizing the intelligent reflective surface phase shifts, however, since the goals of maximizing throughput and minimizing energy consumption are counter-productive, the problem of maximizing the economic efficiency of the drone is planned based thereon. Finally, the aim of adaptively adjusting the working mode of the unmanned aerial vehicle is fulfilled by maximizing economic efficiency.
An unmanned aerial vehicle auxiliary communication working mode adjusting method based on an intelligent reflecting surface comprises the following steps:
(1) An unmanned aerial vehicle auxiliary wireless sensor network communication system based on an intelligent reflecting surface is constructed and comprises source sensor nodes, target sensor nodes, the intelligent reflecting surface and an unmanned aerial vehicle.
(2) And analyzing the channel model based on the unmanned aerial vehicle-intelligent reflector channel and the intelligent reflector-ground node and the economic efficiency of the unmanned aerial vehicle system, and designing parameters such as the working period of the unmanned aerial vehicle in a static mode and a cruising mode.
(3) And providing an unmanned aerial vehicle working mode self-adaptive algorithm based on the assistance of an intelligent reflecting surface. Firstly, the closed solution of the phase deviation of the intelligent reflecting surface of the unmanned aerial vehicle in the static and cruising working modes is deduced, and the phase alignment of the received signals of different transmission paths is realized to further improve the system throughput of the unmanned aerial vehicle. Further, through maximize unmanned aerial vehicle system economic efficiency in order to realize unmanned aerial vehicle mode adjustment.
Further, the step (1) specifically includes the following steps:
first, consider an air-to-ground wireless communication system in which a rotating wing drone and an intelligent reflective surface provide communication services for a plurality of static sensing nodes on the ground. Assuming that no direct communication link exists between each ground sensor node, the communication of the unmanned aerial vehicle auxiliary wireless sensor network in the static mode is shown in fig. 1. The unmanned aerial vehicle is used as an aerial base station to keep hovering above the sensor network at a certain height, and meanwhile, the intelligent reflecting surface is deployed to assist the unmanned aerial vehicle in communicating with the ground sensor node. Specifically, each element of the intelligent reflecting surface receives a superimposed multipath signal from a source node and then scatters the combined signal with adjustable amplitude and/or phase as a single point source.
In a Cartesian coordinate system, a sensor node 1 is taken as an origin, a connecting line between the sensor node 1 and a sensor node 2 is taken as an x axis, a plane where a wireless sensor network is located is an xoy plane, a direction perpendicular to the xoy plane is taken as a z axis to establish a coordinate system, and the coordinates of the sensor node are (x is taken as i ,y i ,z i ) I ∈ {1,2,3}, where the system is configured to have three sensor nodes. Set coordinates of the drone to (x) u ,y u ,z u ) Then projection coordinates (x) of the unmanned plane on the xoy plane u ,y u ,0). Taking the first element (i.e. the reflection unit) of the intelligent reflection surface as a reference point, the coordinate of the intelligent reflection surface is (x) k ,y k ,z k )。
Compare with unmanned aerial vehicle static mode, unmanned aerial vehicle's mobility helps realizing better air-to-ground channel under the mode of cruising to can further improve system throughput, promote network communication quality. Similarly, the smart reflective surface assists the drone in communicating with the sensor node in cruise mode as shown in fig. 2, where the smart reflective surface is mounted on the drone and can move at high speed depending on the mobility of the drone.
In the cruising mode, the unmanned aerial vehicle flies above the ground sensor network at a specific height H within a working period T to assist wireless sensingAnd (4) communicating with the network. The intelligent reflecting surface and the unmanned aerial vehicle initial position are located above the midpoint of the sensor 1 and the sensor 2, when the source sensor and the target sensor need to transmit data, the intelligent reflecting surface and the unmanned aerial vehicle fly to the point H above the midpoint of the source sensor and the target sensor, and the source sensor node signal is reflected to the target sensor node through the intelligent reflecting surface. The coordinates of the intelligent reflecting surface and the unmanned aerial vehicle in the cruise mode are (x) u ,y u H), the sensor node 1, sensor node 2, and sensor node 3 coordinates are the same as in the static mode.
Further, the step (2) specifically includes the following steps:
channel model in static mode: under a static mode scene, an intelligent reflecting surface provided with a uniform linear array consisting of M reflecting units and an intelligent controller capable of adjusting the phase shift of each unit is arranged at a certain height, and each unit in the intelligent reflecting surface can adjust the phase shift to reflect a received signal. Firstly, modeling an intelligent reflection surface angle phase matrix, namely:
where j represents the carrier frequency of the signal.
Assume phase offset θ i Can be controlled continuously, where θ i Belongs to [0,2 pi ]), i belongs to {1, 2. The intelligent reflecting surface is deployed on the surface of a high-rise building in a static mode scene of the unmanned aerial vehicle, and the unmanned aerial vehicle hovers at high altitude, so that a link between the unmanned aerial vehicle and the intelligent reflecting surface can be assumed as a line-of-sight channel. Because the intelligent reflecting surface adopts a uniform linear array, the subsequent channel modeling adopts a multiplicative channel model, and the channel gain h between the unmanned aerial vehicle and the intelligent reflecting surface UR Is represented as follows:
wherein, d UR Indicating the distance between the unmanned plane and the intelligent reflecting surface, and alpha indicates the pairThe rho represents a unit distance D corresponding to the related path loss index of the unmanned aerial vehicle and the intelligent reflecting surface link 0 Path loss at =1, term in the above equation
The right term representing path loss represents a uniform linear array response of M elements,
cosine representing the arrival angle of the signal from the drone to the intelligent reflecting surface, d represents the antenna spacing, and μ represents the carrier wavelength.
Similarly, the link between the intelligent reflecting surface and the ground sensor node is modeled by rice fading, and then the channel gain between the intelligent reflecting surface and the ground sensor node is expressed as:
wherein
Representing the distance between the intelligent reflecting surface and the ground sensor node,
represents a deterministic line-of-sight component, namely:
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 with the channel gain expressed as:
wherein d is SD Representing the distance of the source sensor node to the target sensor node,
represents the random scatter component modeled by a Circular Symmetric Complex Gaussian (CSCG) random variable of zero mean and unit variance.
According to equations (1) - (5), then the drone receive signal-to-noise ratio is expressed as:
in the formula (.) H Hermitian matrix, p, representing the matrix or vector u For unmanned aerial vehicle transmitting power, σ 2 Representing additive white gaussian noise, the system throughput of the drone in static mode is represented as:
channel model in cruise mode: in the cruise mode scene, because the flying height of the unmanned aerial vehicle is high enough, the links between the source sensor node and the intelligent reflecting surface and between the intelligent reflecting surface and the target sensor node are all regarded as line-of-sight links. Therefore, the channel gain between the source sensor node and the intelligent reflecting surface is:
in the formula d SR The distance between the source sensor node and the intelligent reflecting surface is shown, alpha' is a path loss index between the source sensor node and the intelligent reflecting surface,
and the cosine of the arrival angle of the signal from the source sensor node to the intelligent reflecting surface.
Similarly, the channel gain of the intelligent reflecting surface to the sensor node is expressed as:
wherein d is RD Indicating the distance of the intelligent reflecting surface from the target sensor node,
and expressing the cosine of the arrival angle of the signal from the intelligent reflecting surface to the target sensor node.
According to equations (8) and (9), the signal-to-noise ratio of the smart reflective surface to the auxiliary communication of the drone in the cruise mode can be expressed as:
wherein p is s Representing source sensor node power. The system throughput in cruise mode is then expressed as:
economic efficiency: the total energy consumption for auxiliary communication of the unmanned aerial vehicle generally comprises two parts: one part is the energy consumption resulting from radiation, signal processing, etc., and the other part is the mechanical flight energy consumption required by the drone to support its maneuverability. According to relevant theories, the mechanical flight related power consumption of the unmanned aerial vehicle can be modeled as:
wherein p is 0 Representing blade section power at hover, p i Indicating the inductive power in the hovering state, U tip Representing the tip speed, v, of the blade 0 Representing average rotor speed, d, of the drone in forward flight 0 And s represents the unmanned aerial vehicle fuselage drag ratio and rotor solidity, respectively, and ρ and a represent the air density and associated area, respectively.
Design unmanned aerial vehicle duty cycle T under the static mode, it includes: collecting the reflection signal of the intelligent reflecting surface, forwarding data and transmitting a data acquisition instruction. Suppose that the time for collecting the reflection signal of the intelligent reflecting surface by the unmanned aerial vehicle is t s The time for forwarding data is t f The time of transmitting data acquisition command is t t . Because the data forwarded by the unmanned aerial vehicle is that the unmanned aerial vehicle is at time t s Internally acquired data, the time for the drone to forward the data is then equal to the time for acquiring the data, i.e. t s ＝t f . The drone remains hovering in static mode, with its speed v u =0, the propulsion power consumption p of the unmanned aerial vehicle in the static mode is obtained according to the formula (12) h ＝p 0 +p i . Then the total energy consumption of the drone in the 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)
wherein p is c Sensing power of data for unmanned aerial vehicle, p f Power for forwarding data for drone, p t And transmitting the power of the data acquisition instruction for the unmanned aerial vehicle.
Because supplementary wireless communication system of unmanned aerial vehicle uses intelligent reflection to reflect the signal under the mode of cruising, unmanned aerial vehicle need not handle and transmit the source signal to the energy consumption that unmanned aerial vehicle mechanical flight produced is usually far higher than the communication energy consumption, and the unmanned aerial vehicle total energy consumption mainly comprises the energy consumption that mechanical flight produced under the mode of cruising then. Suppose that the drone is at velocity v u Flying at uniform speed, wherein the mechanical flying energy consumption of the unmanned aerial vehicle in unit time is E slf . Designing the working period of the unmanned aerial vehicle in the cruising mode, wherein t 1 For unmanned aerial vehicle to pass through F 12 Time taken, t 2 For unmanned aerial vehicle to pass through F 23 Time required, t 3 For unmanned aerial vehicle to pass 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:
intuitively, from a throughput maximization perspective, the drone should remain stationary at the closest location to the ground node in order to maintain optimal communication channel conditions, and then fly to transmit data to the target node. However, the energy consumption generated by mechanical flight is a challenge for the drone-assisted communication system, since the drone has limited energy itself. The economic efficiency of the drone is therefore presented herein as a measure of the throughput of the drone system and the energy consumption. Firstly, according to the relevant definition of economic efficiency, ECE is used as a universal measurement, the cost and the power consumption are considered, the ECE is a good performance index for measuring the throughput and the energy consumption of the unmanned aerial vehicle, and the characteristics of the throughput and the energy consumption of the unmanned aerial vehicle can be fully embodied.
Will k r And k c Expressed as revenue per bit and energy cost per joule, R, respectively ref For the relevant data rates, R represents the drone system throughput, E represents the energy consumed by the drone system, and ECE measures the profitability of the system, equal to the revenue minus the actual cost of the service provided. Then ECE is defined as follows:
where w denotes the channel parameter, w =1MHz. Further, the step (3) specifically includes the following steps:
generally speaking, throughput and energy consumption are two important indicators for measuring communication quality of the unmanned aerial vehicle. The unmanned aerial vehicle is far away from the ground sensor node in the static mode, so that the throughput performance is influenced to a certain degree. In the cruise mode, the distance between the unmanned aerial vehicle and the ground sensor node is shortened through mechanical flight, but the mechanical flight brings more energy consumption. The optimization target of the method is to optimize the energy consumption of the unmanned aerial vehicle by designing the optimal phase shift matrix to maximize the system throughput of the unmanned aerial vehicle and designing the working period of the unmanned aerial vehicle, so that the economic efficiency of the unmanned aerial vehicle is maximized and the unmanned aerial vehicle adaptively adjusts the working mode.
(1) Optimizing intelligent reflecting surface phase shift:
according to the optimization problem, firstly, in order to maximize the throughput of the intelligent reflector assisted air-ground wireless communication system, an optimal phase shift matrix phi is designed. For the purpose of subsequent discussion, the complex vector h of equation (3) will be first introduced RG Expressed as:
wherein | h RG,i Is h RG Modulo of the i-th element, w i E [0,2 π) is h RG Phase angle of the ith element.
Signal-to-noise ratio in static mode according to equation (16)
Can be rewritten as:
assuming that the signals from the different paths are coherently combined at the target sensor node, the coherently formed signals can maximize the rate at which the signals are received, thereby maximizing the system throughput. Therefore, to maximize the signal reach, the in-phase signal is superimposed next, i.e.:
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 intelligent reflecting surface should adjust when reflecting the signal is expressed as:
with the closed form solution described above, the signal-to-noise ratio in the static mode is then
Can be rewritten as:
at this time, the throughput of the intelligent reflecting surface auxiliary air-ground communication system in the static mode can obtain the maximum value.
In order to obtain the maximum system throughput of the intelligent reflecting surface auxiliary air-ground communication system in the cruise mode, the system throughput in the cruise mode can be seen, and the channel gain
In a dominant position, when h SD And
intelligent reflector assistance in cruise mode with same phaseThe air-ground communication system can obtain a maximum channel gain. Then, the signal-to-noise ratio of the intelligent reflector-assisted air-ground communication system in the cruise mode
Can be rewritten as:
therefore, the optimal reflection phase can be obtained for each element of the intelligent reflecting surface:
at this time, the throughput of the intelligent reflecting surface auxiliary air-ground communication system in the cruise mode can obtain the maximum value.
(2) And (3) switching the working modes:
can maximize unmanned aerial vehicle system throughput by above analysis, switch unmanned aerial vehicle operating mode next and carry out the analysis, unmanned aerial vehicle promotes unmanned aerial vehicle system throughput through the flight under the mode of cruising, but has also consumed more energy. Although the unmanned aerial vehicle has the advantage of high mobility, the energy of the unmanned aerial vehicle is limited, so that the unmanned aerial vehicle still faces the challenge of energy-saving deployment. The energy-rich static mode of operation is not taken into account in view of the fact that static drones do not require mechanical flight and thus have low energy consumption, and in the case of a drone with sufficient energy, in view of having the drone fly in cruise mode in order to be able to obtain higher throughput performance. Comprehensively considering the working mode of the unmanned aerial vehicle and whether the energy of the unmanned aerial vehicle is sufficient, and setting a state space as I E { S.L, M.S, M.L }, wherein 'S.L' represents a static working mode in which the energy of the unmanned aerial vehicle is insufficient, 'M.S' represents a cruise working mode in which the energy of the unmanned aerial vehicle is sufficient, and 'M.L' represents a cruise working mode in which the energy of the unmanned aerial vehicle is insufficient.
Setting the transition probability of the unmanned aerial vehicle from the static mode to the cruise mode as p m = λ, then cruise mode shifts to static modeProbability p of s And =1- λ. When the energy of the unmanned aerial vehicle is lower than a predefined threshold value xi, the working mode of the unmanned aerial vehicle is switched to a low-electricity mode, the transition probability that the electric quantity of the unmanned aerial vehicle is converted from sufficient to insufficient is recorded as p, and then the state transition probability that the energy of the unmanned aerial vehicle is converted from insufficient to sufficient is 1-p. λ (1-p) is the transition probability of the unmanned aerial vehicle from the insufficient-energy static working mode to the sufficient-energy cruise working mode, and (1- λ) p { epsilon ≦ ξ } is the transition probability of the energy-sufficient cruise working mode to the insufficient-energy static working mode, and the unmanned aerial vehicle working mode state transition is described by fig. 3.
The normalization equation governing the above system is given by:
wherein pi j Representing the stationary probability of being in state j, j ∈ { s.l, m.s, m.l }. Solving the above system of equations, i.e.:
(π 1 ,π 2 ,π 3 )＝{C 1 a,C 1 ,C 1 b}, (24)
in the formula (24), the unmanned aerial vehicle is in the state of the insufficient energy static working mode
Unmanned aerial vehicle is in under the operating mode state that cruises of insufficient energy
The state probability of the drone under different working modes can be expressed as
Therefore, the throughput of the intelligent reflector assisted air-to-ground communication system is expressed as:
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)
the total energy consumption of the system is expressed as:
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)
the system economic efficiency is expressed as:
compared with the prior art, the invention has the following beneficial effects:
(1) An unmanned aerial vehicle auxiliary wireless sensor network communication system based on an intelligent reflecting surface is constructed.
(2) The channel model based on the unmanned aerial vehicle-intelligent reflector channel and the intelligent reflector-ground node and the economic efficiency of the unmanned aerial vehicle system are analyzed, and parameters such as the working period of the unmanned aerial vehicle in a static mode and a cruising mode are designed.
(3) An unmanned aerial vehicle working mode self-adaptive algorithm based on the assistance of an intelligent reflecting surface is provided. Firstly, the closed solution of the phase deviation of the intelligent reflecting surface of the unmanned aerial vehicle in the static and cruising working modes is deduced, and the phase alignment of the received signals of different transmission paths is realized to further improve the system throughput of the unmanned aerial vehicle. Further, through maximize unmanned aerial vehicle system economic efficiency in order to realize unmanned aerial vehicle mode adjustment.
(4) Simulation results show that compared with a scheme of random phase and not adopting an intelligent reflecting surface, the method provided by the invention has greater performance advantages in the aspects of improving throughput and economic efficiency. Meanwhile, the purpose of self-adaptive adjustment of the working mode of the unmanned aerial vehicle is achieved by maximizing the economic efficiency of the unmanned aerial vehicle.
Drawings
Fig. 1 is a schematic diagram of auxiliary communication of an intelligent reflective surface in a static mode according to an embodiment of the present invention.
FIG. 2 is a schematic diagram of the auxiliary communication of the intelligent reflective surface in the cruise mode according to the embodiment of the invention.
Fig. 3 is a diagram illustrating a state transition of the working mode switching of the unmanned aerial vehicle according to the embodiment of the present invention.
Fig. 4 shows the gain of the intelligent reflecting surface according to the number of reflecting elements in the embodiment of the invention.
FIG. 5 is a graph of throughput versus path loss exponent for an embodiment of the present invention;
fig. 6 is a graph illustrating throughput as a function of the number of smart reflective surface elements in an embodiment of the present invention.
Fig. 7 shows the change of the economic efficiency of the drone system with λ in the embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the drawings in the specification.
The invention discloses an unmanned aerial vehicle auxiliary communication work mode adjusting method based on an intelligent reflector, which comprises the following steps:
s1, an unmanned aerial vehicle auxiliary wireless sensor network communication system based on an intelligent reflecting surface is constructed. The system consists of source sensor nodes, target sensor nodes, an intelligent reflecting surface and an unmanned aerial vehicle.
First, consider an air-to-ground wireless communication system in which a rotating wing drone and an intelligent reflective surface provide communication services for a plurality of static sensing nodes on the ground. Assuming that no direct communication link exists between each ground sensor node, the communication of the unmanned aerial vehicle auxiliary wireless sensor network in the static mode is shown in fig. 1. The unmanned aerial vehicle is used as an aerial base station to keep hovering above the sensor network at a certain height, and meanwhile, the intelligent reflecting surface is deployed to assist the unmanned aerial vehicle to communicate with the ground sensor node. Specifically, each element of the intelligent reflecting surface receives a superimposed multipath signal from a source node and then scatters the combined signal with adjustable amplitude and/or phase as a single point source.
In a Cartesian coordinate system, a sensor node 1 is taken as an origin, a connecting line of the sensor node 1 and a sensor node 2 is taken as an x axis, a plane where a wireless sensor network is located is an xoy plane, and the xoy plane is perpendicular toThe direction of (2) is used as a z-axis to establish a coordinate system, and the coordinates of the sensor node are (x) i ,y i ,z i ) I is equal to {1,2,3}. Set coordinates of the drone to (x) u ,y u ,z u ) Then the projection coordinates (x) of the drone on the xoy plane u ,y u ,0). The first element of the intelligent reflecting surface is taken as a reference point, and then the coordinate of the intelligent reflecting surface is (x) k ,y k ,z k )。
Compare with unmanned aerial vehicle static mode, unmanned aerial vehicle's mobility helps realizing better air-to-ground channel under the mode of cruising to can further improve system throughput, promote network communication quality. Similarly, the smart reflective surface in cruise mode assists the drone in communicating with the sensor nodes as shown in fig. 2, where the smart reflective surface is mounted on the drone and can move at high speed depending on the mobility of the drone.
In cruise mode the drone flies over the ground sensor network at a particular altitude H for a duty cycle T to assist wireless sensor network communications. The intelligent reflecting surface and the unmanned aerial vehicle are located above the midpoint of the nodes of the sensor 1 and the sensor 2, when the source sensor and the target sensor need to transmit data, the intelligent reflecting surface and the unmanned aerial vehicle fly to the H position above the midpoint of the source sensor and the target sensor, and the node signal of the source sensor is reflected to the node of the target sensor through the intelligent reflecting surface. The coordinates of the intelligent reflecting surface and the unmanned aerial vehicle in the cruise mode are (x) u ,y u H), the sensor node 1, sensor node 2, and sensor node 3 coordinates are the same as in the static mode.
S2, firstly analyzing a channel model based on an unmanned aerial vehicle-intelligent reflector channel and an intelligent reflector-ground node and the economic efficiency of an unmanned aerial vehicle system, and designing parameters such as the working period of the unmanned aerial vehicle in a static mode and a cruise mode. The details are as follows:
channel model in static mode: in a static mode scene, an intelligent reflecting surface provided with a uniform linear array consisting of M reflecting units and an intelligent controller capable of adjusting the phase shift of each unit is arranged at a certain height, and each unit in the intelligent reflecting surface can adjust the phase shift to reflect a received signal. The intelligent reflection surface angular phase matrix is first modeled, i.e.
Where j represents the carrier frequency of the signal.
Assume phase offset θ i Can be controlled continuously, where θ i E [0,2 π), i e {1, 2. The intelligent reflecting surface is deployed on the surface of a high-rise building in a static mode scene of the unmanned aerial vehicle, and the unmanned aerial vehicle hovers at high altitude, so that a link between the unmanned aerial vehicle and the intelligent reflecting surface can be assumed as a line-of-sight channel. Because the intelligent reflecting surface adopts a uniform linear array, the subsequent channel modeling adopts a multiplicative channel model, and the channel gain h between the unmanned aerial vehicle and the intelligent reflecting surface UR Is shown below
Wherein d is UR Representing the distance between the drone and the intelligent reflecting surface, alpha representing the relative path loss index corresponding to the link between the drone and the intelligent reflecting surface, and rho representing the unit distance D 0 Path loss at =1, term in the above equation
Representing the path loss (the right term represents the uniform linear array response of M elements,
cosine representing the arrival angle of the signal from the drone to the intelligent reflecting surface, d represents the antenna spacing, and μ represents the carrier wavelength.
Similarly, the link between the intelligent reflecting surface and the ground sensor node is modeled by rice fading, and then the channel gain between the intelligent reflecting surface and the ground sensor node is expressed as
Wherein
Representing the distance between the intelligent reflecting surface and the ground sensor node,
representing a deterministic line-of-sight component, i.e.
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, with the channel gain expressed as
Wherein d is SD Representing the distance of the source sensor node to the target sensor node,
represents the random scatter component modeled by a Circular Symmetric Complex Gaussian (CSCG) random variable of zero mean and unit variance.
According to equations (1) - (5), the drone receive signal-to-noise ratio is then expressed as
In the formula (.) H Hermitian matrix, p, representing the matrix or vector u For unmanned aerial vehicle transmitting power, σ 2 When additive white Gaussian noise is represented, the throughput of the unmanned aerial vehicle system in the static mode is represented as
Channel model in cruise mode: in the cruise mode scene, because the flying height of the unmanned aerial vehicle is high enough, the links between the source sensor node and the intelligent reflecting surface and between the intelligent reflecting surface and the target sensor node are all regarded as line-of-sight links. Thus, the channel gain between the source sensor node and the intelligent reflecting surface is
In the formula d SR The distance between the source sensor node and the intelligent reflecting surface is shown, alpha' is a path loss index between the source sensor node and the intelligent reflecting surface,
and the cosine of the arrival angle of the signal from the source sensor node to the intelligent reflecting surface.
Similarly, the channel gain from the intelligent reflecting surface to the sensor node is expressed as
Wherein d is RD Expressing the distance between the intelligent reflecting surface and the target sensor node, and alpha is the path loss index between the source sensor node and the intelligent reflecting surface
And expressing the cosine of the arrival angle of the signal from the intelligent reflecting surface to the target sensor node.
According to the formulas (8) and (9), the signal-to-noise ratio of the auxiliary communication between the intelligent reflecting surface and the unmanned aerial vehicle in the cruise mode can be expressed as
Wherein p is s Representing source sensor node power. The system throughput in cruise mode is then expressed as
Economic efficiency: the total energy consumption for auxiliary communication of the unmanned aerial vehicle generally comprises two parts: one part is the energy consumption resulting from radiation, signal processing, etc., and the other part is the mechanical flight energy consumption required by the drone to support its maneuverability. According to the relevant theory, the mechanical flight-related power consumption of the unmanned aerial vehicle can be modeled as
Wherein p is 0 Representing blade section power at hover, p i Indicating induced power in hovering state, U tip Representing the tip speed, v, of the blade 0 Representing average rotor speed, d, of the drone in forward flight 0 And s represents the fuselage resistance ratio and rotor solidity of the unmanned aerial vehicle, respectively, and p and A areAir density and associated area are not indicated.
Design unmanned aerial vehicle duty cycle T under the static mode, it includes: collecting the reflection signal of the intelligent reflecting surface, forwarding data and transmitting a data acquisition instruction. Suppose that the time for collecting the reflection signal of the intelligent reflecting surface by the unmanned aerial vehicle is t s The time for forwarding data is t f The time of transmitting data acquisition command is t t . Because the data forwarded by the unmanned aerial vehicle is that the unmanned aerial vehicle is at time t s Internally acquired data, the time for the drone to forward the data is then equal to the time for acquiring the data, i.e. t s ＝t f . The drone remains hovering in static mode, its speed v u =0, and the propulsion power p of the unmanned aerial vehicle in the static mode is obtained according to the formula (12) 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)
Wherein p is c For unmanned aerial vehicle to sense power of data, p f Power for forwarding data for drone, p t And transmitting the power of the data acquisition instruction for the unmanned aerial vehicle.
Because supplementary wireless communication system of unmanned aerial vehicle uses intelligent reflection to reflect the signal under the mode of cruising, unmanned aerial vehicle need not handle and transmit the source signal to the energy consumption that unmanned aerial vehicle mechanical flight produced is usually far higher than the communication energy consumption, and the unmanned aerial vehicle total energy consumption mainly comprises the energy consumption that mechanical flight produced under the mode of cruising then. Suppose that the drone is at velocity v u The unmanned aerial vehicle flies at a constant speed, and the energy consumption of the mechanical flight of the unmanned aerial vehicle in unit time is E slf . Designing the working period of the unmanned aerial vehicle in the cruising mode, wherein t 1 For unmanned aerial vehicle to pass through F 12 Time taken, t 2 For unmanned aerial vehicle to pass through F 23 Time required, t 3 For unmanned aerial vehicle to pass through F 31 The time required to return to the initial position. The energy consumption of the drone in cruise mode is then expressed as
Intuitively, from a throughput maximization perspective, the drone should remain stationary at the closest location to the ground node in order to maintain the best communication channel conditions, and then fly to transmit data to the target node. However, the energy consumption generated by mechanical flight is a challenge for the drone-assisted communication system, since the drone has limited energy itself. The economic efficiency of the drone is therefore proposed herein as a measure of the throughput of the drone system and the energy consumption. Firstly, according to the relevant definition of economic efficiency, ECE is used as a universal measurement, the cost and the power consumption are considered, the ECE is a good performance index for measuring the throughput and the energy consumption of the unmanned aerial vehicle, and the characteristics of the throughput and the energy consumption of the unmanned aerial vehicle can be fully embodied.
Will k r And k c Expressed as revenue per bit and energy cost per joule, R, respectively ref For the relevant data rates, R represents the drone system throughput, E represents the energy consumed by the drone system, and ECE measures the profitability of the system, equal to the revenue minus the actual cost of the service provided. Then ECE is defined as
Where w denotes the channel parameters, w =1MHz.
And S3, on the basis of the analysis, the invention provides an unmanned aerial vehicle working mode self-adaptive algorithm based on the assistance of an intelligent reflecting surface. Firstly, the closed solution of the phase deviation of the intelligent reflecting surface of the unmanned aerial vehicle in the static and cruising working modes is deduced, and the phase alignment of the received signals of different transmission paths is realized to further improve the system throughput of the unmanned aerial vehicle. Further, through maximize unmanned aerial vehicle system economic efficiency in order to realize unmanned aerial vehicle mode adjustment. The details are as follows:
generally, throughput and energy consumption are two important indexes for measuring communication quality of the unmanned aerial vehicle. The unmanned aerial vehicle is far away from the ground sensor node in the static mode, so that the throughput performance is influenced to a certain degree. In the cruise mode, the distance between the unmanned aerial vehicle and the ground sensor node is shortened through mechanical flight, but the mechanical flight brings more energy consumption. The optimization target of the method is to optimize the energy consumption of the unmanned aerial vehicle by designing the optimal phase shift matrix to maximize the system throughput of the unmanned aerial vehicle and designing the working period of the unmanned aerial vehicle, so that the economic efficiency of the unmanned aerial vehicle is maximized and the unmanned aerial vehicle adaptively adjusts the working mode.
(1) Intelligent reflector phase shift optimization
According to the optimization problem, firstly, in order to maximize the throughput of the intelligent reflector assisted air-ground wireless communication system, an optimal phase shift matrix phi is designed. For the purpose of subsequent discussion, the complex vector h of equation (3) will be first introduced RG Is shown as
Wherein | h RG,i Is h RG Modulo of the i-th element of (1), w i E [0,2 π) is h RG Phase angle of the ith element.
Signal-to-noise ratio in static mode according to equation (16)
Can be rewritten as
Assuming that the signals from the different paths are coherently combined at the target sensor node, the coherently formed signals can maximize the rate at which the signals are received, thereby maximizing the system throughput. Thus, in order to maximize the signal reachable rate, the in-phase signals are then superimposed, i.e.
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 intelligent reflecting surface should adjust when reflecting the signal is expressed as
With the closed form solution, the signal-to-noise ratio in the static mode is then
Can be rewritten as
At this time, the throughput of the intelligent reflecting surface auxiliary air-ground communication system in the static mode can obtain the maximum value.
In order to obtain the maximum system throughput of the intelligent reflecting surface auxiliary air-ground communication system in the cruise mode, the system throughput in the cruise mode can be seen, and the channel gain
In a dominant position, when h SD And
when the intelligent reflecting surface auxiliary air-ground communication system has the same phase, the intelligent reflecting surface auxiliary air-ground communication system can obtain the maximum channel gain in the cruise mode. Then, the signal-to-noise ratio of the intelligent reflector assisted air-ground communication system in the cruise mode
Can be rewritten as
Therefore, each element of the intelligent reflecting surface can obtain the optimal reflection phase
At this time, the throughput of the intelligent reflecting surface auxiliary air-ground communication system in the cruise mode can obtain the maximum value.
(2) Working mode switching
By above analysis can maximize unmanned aerial vehicle system throughput, follow next to switch unmanned aerial vehicle operating mode and carry out the analysis, unmanned aerial vehicle promotes unmanned aerial vehicle system throughput through the flight under the mode of cruising, but also consumed more energy. Although the unmanned aerial vehicle has the advantage of high mobility, the unmanned aerial vehicle still faces the challenge of energy-saving deployment due to the limited energy. The energy-rich static mode of operation is not taken into account, considering that static drones do not require mechanical flight and therefore have a low energy consumption, and in the case of a drone with sufficient energy, considering that the drone is flown in cruise mode in order to be able to obtain a higher throughput performance. Comprehensively considering the working mode of the unmanned aerial vehicle and whether the energy of the unmanned aerial vehicle is sufficient, and setting a state space as I E { S.L, M.S, M.L }, wherein 'S.L' represents a static working mode in which the energy of the unmanned aerial vehicle is insufficient, 'M.S' represents a cruise working mode in which the energy of the unmanned aerial vehicle is sufficient, and 'M.L' represents the cruise working mode in which the energy of the unmanned aerial vehicle is insufficient.
Setting the transition probability of the unmanned aerial vehicle from the static mode to the cruise mode as p m = λ, then the probability p of a cruise mode transition to a static mode s 1- λ. When the energy of the unmanned aerial vehicle is lower than a predefined threshold value xi, the working mode of the unmanned aerial vehicle is switched to a low-electricity mode, the transition probability that the electric quantity of the unmanned aerial vehicle is converted from sufficient to insufficient is recorded as p, and then the state transition probability that the energy of the unmanned aerial vehicle is converted from insufficient to sufficient is 1-p. Lambda (1-p) is the transition probability of the unmanned aerial vehicle from the static working mode with insufficient energy to the cruise mode with sufficient energy, and (1-lambda) p { epsilon is less than or equal to xi } is the transition probability of the cruise working mode with sufficient energy to the cruise working mode with insufficient energyTransition probability of the static working mode, the unmanned aerial vehicle working mode state transition is described by fig. 3.
The normalization equation for controlling the above system is given by
Wherein pi j Representing the stationary probability of being in state j, j ∈ { s.l, m.s, m.l }. Solving the above system of equations, i.e.
(π 1 ,π 2 ,π 3 )＝{C 1 a,C 1 ,C 1 b}, (24)
In the formula (24), the unmanned aerial vehicle is in the state of the static working mode with insufficient energy
Unmanned aerial vehicle is in under the operating mode state that cruises of insufficient energy
The state probability of the unmanned aerial vehicle in different working modes can be expressed as
Thus, the throughput of the intelligent reflector assisted air-to-ground communication system is expressed as
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)
Total energy consumption of the system is expressed as
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)
The economic efficiency of the system is expressed as
To better understand the idea and connotation of the proposed solution method, the above solution process is highly summarized and condensed as follows:
1. inputting: (x) i ,y i ,z i ),j,M,ρ,α,λ,d,β,p u ,σ 2 ,p t ,p c ,p f ,T,k r , k u ；
2. And (3) outputting: r (λ), E (λ), ECE (λ), λ opt (obtained by maximizing the economic efficiency of the drone, for drone operating mode adjustment);
3. starting;
4. initializing node coordinates of a ground sensor;
5. determining the energy consumption of the unmanned aerial vehicle in two working modes according to the formulas (12) - (14);
6. determining the optimal phase shift of each element of the IRS under the static mode of the unmanned aerial vehicle according to the formulas (16) to (20);
7. obtaining the maximum throughput of the system in the static mode through the IRS optimal phase shift;
8. obtaining the optimal phase shift of each element of the IRS in the unmanned aerial vehicle cruise mode by using the formulas (21) to (22);
9. obtaining the maximum throughput of the system in the cruise mode through the optimal phase shift of each element of the IRS in the unmanned aerial vehicle cruise mode;
10. obtaining the stability probability of each working mode of the unmanned aerial vehicle by using an unmanned aerial vehicle working mode adjustment algorithm based on an intelligent reflecting surface;
11. obtaining the total throughput R (lambda) of the optimal system of the unmanned aerial vehicle according to the formulas (23) and (26) * And total energy consumption E (. Lamda.) * ；
12. Substituting the result into a formula (27) to calculate economic efficiency;
13. obtaining lambda by maximizing unmanned aerial vehicle economic efficiency opt ；
14. When lambda is less than or equal to lambda opt When the unmanned aerial vehicle works in the static mode, otherwise, the unmanned aerial vehicle works in the cruise mode;
15. and (6) ending.
Experiment is carried out, and the transmitting power p of the unmanned aerial vehicle is set u =10dBm, unmanned aerial vehicle airspeed v u =30km/h, sensor node transmitting power p s =1W, communication bandwidth B =1MHz, ground sensor node noise power spectral density N 0 = 110dBm/Hz, the corresponding noise power σ 2 = -110dBm; giving sensor 2 node coordinates (300, 0), sensor node 3 coordinates (200, 150, 0), and unmanned aerial vehicle hovering coordinates in static mode (191.64, 81.52, 100); the power of the unmanned aerial vehicle perception data is p c =4W, power of forwarded data is p f =4W, power for transmitting data acquisition command is p t =5W; unmanned aerial vehicle work cycle T =200s; the antenna spacing d = lambda/2, the relevant path loss index alpha =2.8, the rice factor beta =3dB, the path loss coefficient rho = -20dB, and the number of reflection elements of the intelligent reflecting surface M =80; revenue per bit setting related to the economic efficiency of the drone system
Energy consumption cost per joule is set as
The performance of the proposed method will be verified in terms of drone system throughput performance and drone economic efficiency, etc.
First analyzing the smart reflector gain for the cruise mode when the number of smart reflector reflective elements increases from 20 to 120 from fig. 4, it can be seen that the smart reflector gain increases as the number of smart reflector elements increases for different drone altitudes in the cruise mode. When the unmanned aerial vehicle height that carries intelligent plane of reflection reduces, intelligent plane of reflection gain reduces thereupon. In addition, it can be found that when the number of intelligent reflecting surface elements is doubled, the intelligent reflecting surface can collect more energy from the source sensor nodes and reflect more electromagnetic waves to the target sensor nodes, and the gain of the intelligent reflecting surface is increased in proportion to the number of the intelligent reflecting surface reflecting elements.
Then, the method is based on a channel model considering the auxiliary signal propagation of the intelligent reflecting surfaceThe throughput performance of the unmanned aerial vehicle system of different schemes when the path loss index related to the intelligent reflecting surface changes is researched, and the throughput performance is shown in fig. 5. Wherein, "without IRS" means that the intelligent reflective surface is not used for auxiliary communication: theta i =0; by "random phase" is meant that the phase shift of each reflective element in the intelligent reflective surface is random. It can be seen from the figure that the throughput of the proposed method and the random phase scheme decreases as the associated path loss exponent increases. This is because as the associated path loss exponent of the intelligent reflective surface becomes larger, the signal power reflected by the intelligent reflective surface becomes weaker. Furthermore, it can be seen that when the link fading associated with the intelligent reflective surface is very severe, for example, α =4, the performance improvement of the proposed method is not very different from that of the method that does not use the intelligent reflective surface for auxiliary communication. The following simulation experiment sets the relevant path loss exponent α =2.8.
Fig. 6 shows the relationship between the throughput performance of the intelligent reflector assisted drone system and the number of intelligent reflector reflective elements. From fig. 6, it can be found that, in a scenario where no intelligent reflecting surface is deployed, the change of the throughput performance of the unmanned aerial vehicle system is negligible, and the throughput performance of the unmanned aerial vehicle system can be significantly improved by using the intelligent reflecting surface to assist communication. In addition, the throughput performance of the method and the random phase scheme is improved along with the increase of the number of the elements of the intelligent reflection surface element. Compared with the adoption of the random phase of the intelligent reflecting surface for auxiliary communication, the phase shift of the intelligent reflecting surface is optimized to bring remarkable performance gain. Therefore, it can be seen that using the intelligent reflective surface to assist the drone communication can improve the communication quality from the aspect of improving the channel environment.
When the unmanned aerial vehicle communication assisted by the intelligent reflecting surface is not adopted, the experiment can be worked out
And with
In time, the unmanned aerial vehicle system can obtain higher economic efficiency. Then the following simulation experiment sets the energy cost per bit to be
And
and compare it with the unmanned aerial vehicle communication scheme that does not adopt the assistance of intelligent plane of reflection. The revenue per bit k in economic efficiency is described by FIG. 7 r And cost of energy consumption k per joule c Impact on drone system performance. As can be seen from the figure, the economic efficiency of the unmanned aerial vehicle system based on the assistance of the intelligent reflecting surface is firstly increased and then reduced along with the increase of the lambda, and the optimal lambda exists in the middle opt Make unmanned aerial vehicle economic efficiency gain the maximum value. I.e. when lambda is greater than or equal to lambda opt When the unmanned aerial vehicle works, the unmanned aerial vehicle selects a static mode based on intelligent reflection assistance to work; when lambda is less than lambda opt And when the unmanned aerial vehicle is in the cruising mode, the unmanned aerial vehicle works based on the intelligent reflecting surface. When the lambda =0, the unmanned aerial vehicle works in a static mode; when lambda =1, the drone is operating in cruise mode. Compared with the unmanned aerial vehicle communication scheme without the assistance of the intelligent reflecting surface, the method provided by the invention can further improve the economic efficiency of the unmanned aerial vehicle system.
The above description is only a preferred embodiment of the present invention, and the scope of the present invention is not limited to the above embodiment, but equivalent modifications or changes made by those skilled in the art according to the disclosure of the present invention should be included in the scope of the present invention as set forth in the appended claims.
Claims (10)
1. An unmanned aerial vehicle auxiliary communication working mode adjusting method based on an intelligent reflecting surface is characterized in that: the method comprises the following steps:
constructing an unmanned aerial vehicle auxiliary wireless sensor network communication system based on an intelligent reflecting surface, wherein the system consists of a source sensor node, a target sensor node, the intelligent reflecting surface and an unmanned aerial vehicle;
analyzing a channel model based on an unmanned aerial vehicle-intelligent reflector channel and an intelligent reflector-ground node and the economic efficiency of an unmanned aerial vehicle system, and designing parameters such as the working period of the unmanned aerial vehicle in a static mode and a cruise mode;
step (3), an unmanned aerial vehicle working mode self-adaption method based on intelligent reflector assistance is provided, and comprises intelligent reflector phase shift optimization and working mode switching; firstly, deducing a phase offset closed solution of an intelligent reflecting surface when the unmanned aerial vehicle is in a static state and in a cruising working mode, and realizing phase alignment of receiving signals of different transmission paths to further improve the system throughput of the unmanned aerial vehicle; through maximize unmanned aerial vehicle system economic efficiency in order to realize unmanned aerial vehicle mode adjustment.
2. The method for adjusting the auxiliary communication working mode of the unmanned aerial vehicle based on the intelligent reflecting surface of claim 1, wherein the method comprises the following steps: the step (1) specifically comprises the following steps:
in the system, an unmanned aerial vehicle and an intelligent reflecting surface provide communication service for a plurality of sensing nodes on the ground; direct communication links do not exist among the ground sensor nodes; the unmanned aerial vehicle is used as an aerial base station to keep hovering above the sensor network, and meanwhile, an intelligent reflecting surface is deployed to assist the unmanned aerial vehicle to communicate with the ground sensor node; the intelligent reflecting surface is installed on the unmanned aerial vehicle, moves along with the unmanned aerial vehicle, receives superposed multipath signals from a source node, scatters the combined signals in a single-point source mode, and is adjustable in amplitude and phase in the signals.
3. The method for adjusting the auxiliary communication working mode of the unmanned aerial vehicle based on the intelligent reflecting surface of claim 2, wherein the method comprises the following steps: setting a Cartesian coordinate system, wherein the ith sensor node coordinate is (x) i ,y i ,z i ) I ∈ {1,2,.., n }, and the unmanned aerial vehicle coordinate is (x) u ,y u ,z u ) The projection coordinate of the x-axis coordinate on the xoy plane is (x) u ,y u 0), intelligenceThe coordinate of the reflective surface is (x) k ,y k ,z k )。
4. The method for adjusting the auxiliary communication working mode of the unmanned aerial vehicle based on the intelligent reflecting surface of claim 1, wherein the method comprises the following steps: in the step (2), in a static mode scene, an intelligent reflecting surface comprising a uniform linear array consisting of M reflecting units and an intelligent controller capable of adjusting the phase shift of each unit is deployed, and each reflecting unit in the intelligent reflecting surface adjusts the phase shift to reflect the received signal; firstly, modeling an intelligent reflection surface angle phase matrix, namely:
where j represents the carrier frequency of the signal;
assume phase offset θ i Is continuously controlled, where θ i Belongs to [0,2 pi ], i belongs to {1, 2. A link between the static mode scene unmanned aerial vehicle and the intelligent reflecting surface is set as a line-of-sight channel; the channel modeling adopts a multiplicative channel model, and the channel gain h between the unmanned aerial vehicle and the intelligent reflecting surface UR Is represented as follows:
wherein d is UR Representing the distance between the drone and the intelligent reflecting surface, alpha representing the relative path loss index corresponding to the link between the drone and the intelligent reflecting surface, and rho representing the unit distance D 0 Path loss at =1, in the above equation
Representing the path loss, the right term represents the uniform linear array response of the M elements,
representing no one fromCosine of the arrival angle of the signal from the machine to the intelligent reflecting surface, d represents the antenna spacing, and mu represents the carrier wavelength;
the link between the intelligent reflecting surface and the ground sensor node is modeled by rice fading, and the channel gain is represented as:
wherein
Representing the distance between the intelligent reflecting surface and the ground sensor node,
represents a deterministic line-of-sight component, namely:
the channel is modeled as rayleigh fading, with the channel gain expressed as:
wherein, d SD Representing the distance of the source sensor node to the target sensor node,
representing the random scatter component modeled by a circularly symmetric complex gaussian random variable of zero mean and unit variance.
According to equations (1) - (5), then the drone receive signal-to-noise ratio is expressed as:
in the formula (.) H Hermitian matrix, p, representing the matrix or vector u For unmanned aerial vehicle transmit power, σ 2 Representing additive white gaussian noise, the system throughput of the drone in static mode is represented as:
5. the method for adjusting the auxiliary communication working mode of the unmanned aerial vehicle based on the intelligent reflecting surface of claim 1, wherein the method comprises the following steps: in the step (2), in a cruise mode scene, taking links between a source sensor node and an intelligent reflecting surface as well as links between the intelligent reflecting surface and a target sensor node as line-of-sight links; the channel gain between the source sensor node and the intelligent reflecting surface is as follows:
in the formula d SR Representing source sensor nodesThe distance between the source sensor node and the intelligent reflecting surface, alpha' is a path loss index between the source sensor node and the intelligent reflecting surface,
cosine representing the arrival angle of the signal from the source sensor node to the intelligent reflecting surface;
the channel gain from the intelligent reflecting surface to the sensor node is expressed as:
wherein d is RD Indicating the distance of the intelligent reflecting surface from the target sensor node,
representing cosine of arrival angle of a signal from the intelligent reflecting surface to the target sensor node;
according to the formulas (8) and (9), the signal-to-noise ratio of the intelligent reflecting surface and the auxiliary communication signal-to-noise ratio of the unmanned aerial vehicle in the cruise mode is expressed as follows:
wherein p is s Representing source sensor node power, σ 2 Representing additive white gaussian noise; the system throughput in cruise mode is expressed as:
6. the method for adjusting the auxiliary communication working mode of the unmanned aerial vehicle based on the intelligent reflecting surface of claim 1, wherein the method comprises the following steps: in the step (2), the economic efficiency of the system is based on the total energy consumption of the unmanned aerial vehicle auxiliary communication, including energy consumption and mechanical flight energy consumption; for a flight velocity v u The unmanned aerial vehicle has mechanical flight energy consumption modeling as follows:
wherein p is 0 Representing blade section power at hover, p i Indicating the inductive power in the hovering state, U tip Representing the tip speed, v, of the blade 0 Representing average rotor speed, d, of the drone in forward flight 0 And s represents the fuselage drag ratio and rotor solidity of the drone, respectively, and ρ and a represent the air density and associated area, respectively;
design unmanned aerial vehicle duty cycle T under the static mode, it includes: collecting the reflection signals of the intelligent reflecting surface, forwarding data and transmission data acquisition instructions; it is t to establish the time for unmanned aerial vehicle to collect the reflection signal of the intelligent reflection surface s Time of forwarding data is t f The time of transmitting data acquisition command is t t (ii) a Because the data forwarded by the unmanned aerial vehicle is that the unmanned aerial vehicle is at time t s Internally acquired data, the time for the drone to forward the data is then equal to the time for acquiring the data, i.e. t s ＝t f (ii) a The drone remains hovering in static mode, with its speed v u =0, the propulsion power consumption p of the unmanned aerial vehicle in the static mode is obtained according to the formula (12) h ＝p 0 +p i (ii) a Then the total energy consumption of the drone in the 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)
wherein p is c For unmanned aerial vehicle to sense power of data, p f Power, p, for forwarding data for drone t Transmitting power of a data acquisition instruction for the unmanned aerial vehicle;
designing the working cycle of the unmanned aerial vehicle in the cruising mode, wherein t is the total number of cruising points 1 For the unmanned plane to pass through a path F between a 1 st cruise point and a 2 nd cruise point 1,2 Time taken, t 2 For unmanned aerial vehicle to pass through F 2,3 Required timeAnd so on is t l For unmanned aerial vehicle to pass through F l1 The time required to return to the initial position, the energy consumption of the drone in cruise mode is expressed as:
defining ECE as a measure for comprehensively considering cost and power consumption; will k is r And k c Expressed as revenue per bit and energy cost per joule, R, respectively ref For the relevant data rates, R represents the unmanned aerial vehicle system throughput, E represents the energy consumed by the unmanned aerial vehicle system, and ECE measures the profitability of the system, equal to revenue minus the actual cost of the service provided; then ECE is defined as follows:
where w denotes the channel parameter, w =1MHz.
7. The method for adjusting the auxiliary communication working mode of the unmanned aerial vehicle based on the intelligent reflecting surface of claim 1, wherein the method comprises the following steps: in the step (3), an optimization target is set to design an optimal phase offset matrix to maximize the system throughput of the unmanned aerial vehicle and design the working period of the unmanned aerial vehicle to optimize the energy consumption of the unmanned aerial vehicle, so that the economic efficiency of the unmanned aerial vehicle is maximized and the unmanned aerial vehicle adaptively adjusts the working mode.
8. The method for adjusting the auxiliary communication working mode of the unmanned aerial vehicle based on the intelligent reflecting surface of claim 7, wherein the method comprises the following steps: in the phase shift optimization of the intelligent reflecting surface, firstly, an optimal phase shift matrix phi is designed in order to maximize the throughput of the intelligent reflecting surface auxiliary air-ground wireless communication system according to an optimization target; firstly, the complex vector h of formula (3) RG Expressed as:
wherein | h RG,i Is h RG Modulo of the i-th element, w i E.g. 0,2 π) is h RG Phase angle of the ith element;
signal-to-noise ratio in static mode according to equation (16)
The rewrite is:
assuming that the signals from the different paths are coherently combined at the target sensor node, the coherently formed signals achieve a maximization of the rate of the received signal, thereby maximizing the system throughput; in order to maximize the signal reachability, the in-phase signals are superimposed, i.e.:
wherein h is a symbol defined for the value of the in-phase signal when the phase offsets are equal;
the phase that each element of the intelligent reflecting surface should adjust when reflecting the signal is expressed as:
signal to noise ratio in quiescent mode
Is rewritten as:
at the moment, the throughput of the intelligent reflecting surface auxiliary air-ground communication system in the static mode obtains the maximum value;
in order to obtain the maximum system throughput of the intelligent reflector auxiliary air-ground communication system in the cruising mode, the signal-to-noise ratio of the intelligent reflector auxiliary air-ground communication system
The rewrite is:
therefore, the optimal reflection phase is obtained for each reflection unit of the intelligent reflection surface:
at the moment, the intelligent reflecting surface assists the air-ground communication system throughput to obtain the maximum value in the cruising mode.
9. The method for adjusting the auxiliary communication working mode of the unmanned aerial vehicle based on the intelligent reflecting surface of claim 7, wherein the method comprises the following steps: analyzing the switching of the working modes of the unmanned aerial vehicle, comprehensively considering whether the working modes of the unmanned aerial vehicle and the energy of the unmanned aerial vehicle are sufficient, and setting a state space as I E { S.L, M.S, M.L }, wherein S.L ' represents a static working mode in which the energy of the unmanned aerial vehicle is insufficient, M.S ' represents a cruise working mode in which the energy of the unmanned aerial vehicle is sufficient, and M.L ' represents a cruise working mode in which the energy of the unmanned aerial vehicle is insufficient;
setting the transition probability of the unmanned aerial vehicle from the static mode to the cruise mode as p m = λ, probability p of transition from cruise mode to static mode s =1- λ; when the energy of the unmanned aerial vehicle is lower than a predefined threshold value xi, the working mode of the unmanned aerial vehicle is switched to a low-electricity mode, the transition probability that the electricity of the unmanned aerial vehicle is converted from sufficient to insufficient is recorded as p, and then no energy is availableThe state transition probability of converting the insufficient human-machine energy into the sufficient state is 1-p; λ (1-p) is the transition probability of the unmanned aerial vehicle from the insufficient-energy static working mode to the sufficient-energy cruise mode, and (1- λ) p { epsilon ≦ ξ } is the transition probability of the sufficient-energy cruise working mode to the insufficient-energy static working mode;
the normalization equation for the control system is given by:
wherein pi j Representing the stationary probability of being in state j, j ∈ { S.L, M.S, M.L }; solving the above equation system, namely:
(π 1 ,π 2 ,π 3 )＝{C 1 a,C 1 ,C 1 b}, (24)
in the formula (24), the unmanned aerial vehicle is in the state of the insufficient energy static working mode
Unmanned aerial vehicle is in under the operating mode state that cruises of insufficient energy
The state probability of the unmanned aerial vehicle in different working modes is expressed as
10. The method for adjusting the auxiliary communication working mode of the unmanned aerial vehicle based on the intelligent reflecting surface of claim 1, wherein the method comprises the following steps: in the step (3), the throughput of the intelligent reflecting surface auxiliary air-ground communication system is represented as follows:
R(λ)＝R s P s.l +R m P ml +R m P m.s
＝R s π 1 +R m π 2 +R m π 3 (25)
the total energy consumption of the system is expressed as:
E(λ)＝E s P s.l +E m P ml +E m P m.s
＝E s π 1 +E m π 2 +E m π 3 (26)
the system economic efficiency is expressed as:
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| CN115802313A (en) * | 2022-11-16 | 2023-03-14 | 河南大学 | Air-ground mobile network energy-carrying fair communication method based on intelligent reflecting surface |
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