CN111479239A - Sensor emission energy consumption optimization method of multi-antenna unmanned aerial vehicle data acquisition system - Google Patents

Abstract

The invention discloses a sensor emission energy consumption optimization method of a multi-antenna unmanned aerial vehicle data acquisition system, which is suitable for an Internet of things communication scene. The data acquisition system comprises a single multi-antenna unmanned aerial vehicle and a plurality of sensors, and the single multi-antenna unmanned aerial vehicle serves a plurality of ground sensor nodes as an air base station, and the method comprises the following steps: establishing a channel fading model and a corresponding system model according to the geographic positions of the sensor nodes and the unmanned aerial vehicle and an air-to-ground wireless channel model; establishing an optimization problem according to the flight time constraint of the unmanned aerial vehicle, the transmitting power constraint of the sensor nodes and the link transmission rate constraint, wherein the optimization problem minimizes the maximum energy consumption of the sensor nodes by optimizing the speed of the unmanned aerial vehicle and the signal transmitting power of the sensor nodes; and solving the optimization problem to obtain the optimal navigational speed of the unmanned aerial vehicle and a power distribution scheme of the sensor nodes. The invention can minimize energy consumption and prolong the cruising ability of the sensor node equipment.

Description

Sensor emission energy consumption optimization method of multi-antenna unmanned aerial vehicle data acquisition system

Technical Field

The invention belongs to the technical field of wireless communication, and particularly relates to a sensor transmission energy consumption optimization method of a multi-antenna unmanned aerial vehicle data acquisition system.

Background

The construction and implementation of communication networks currently relies mainly on terrestrial base stations or relays and other fixed communication equipment. Although these devices may provide relatively stable communication needs, their inability to move results in deployment limitations and higher costs. In the internet of things environment, energy efficiency, ultra-low latency, reliability and high speed uplink communication become major challenges. IoT devices are very battery-limited and, due to their energy limitations, are generally unable to transmit over long distances. Unmanned aerial vehicles are an effective means to address many of the challenges associated with the internet of things. At present, the research of using a multi-antenna unmanned aerial vehicle as an air base station to serve a plurality of ground sensor nodes to provide energy-saving communication service is still blank. Given that internet of things devices may be deployed in environments without a terrestrial wireless infrastructure (e.g., mountainous and desert areas), energy replenishment is difficult. Therefore, research on the multi-antenna unmanned aerial vehicle serving as an air base station to serve a plurality of ground sensor nodes to provide energy-saving communication services is of great significance.

Disclosure of Invention

The purpose of the invention is as follows: the invention aims to provide a sensor transmitting energy consumption optimization method of a multi-antenna unmanned aerial vehicle data acquisition system, which reduces the maximum transmitting power consumption of sensor nodes by optimizing the flight speed of an unmanned aerial vehicle and the signal transmitting power of each sensor node under the conditions of limited push power, limited flight time and minimum link throughput constraint.

The technical scheme is as follows: a method for optimizing energy consumption for sensor transmissions of a multi-antenna drone data acquisition system, the data acquisition system including a single multi-antenna drone and a plurality of sensors, the single multi-antenna drone serving as an airborne base station to serve a plurality of ground sensor nodes, the method comprising the steps of:

(1) establishing a channel fading model and a corresponding system model according to the geographic positions of the sensor nodes and the unmanned aerial vehicle and an air-to-ground wireless channel model;

(2) establishing an optimization problem according to the flight time constraint of the unmanned aerial vehicle, the transmitting power constraint of the sensor nodes and the link transmission rate constraint, wherein the optimization problem minimizes the maximum energy consumption of the sensor nodes by optimizing the speed of the unmanned aerial vehicle and the signal transmitting power of the sensor nodes;

(3) and (3) solving the optimization problem obtained in the step (2) to obtain the optimal speed of the unmanned aerial vehicle and the power distribution scheme of the sensor nodes.

Further, the channel fading model in step 1 is:

wherein, β₀Representing the channel gain when the drone is 1m away from the sensor; d_kRepresenting the distance between the drone and the sensor node k;

adopting a uniform equidistant linear array to generate a guide vector for an unmanned aerial vehicle antenna array, wherein lambda is a wavelength corresponding to a signal center frequency f, and d is a distance between array vibration elements; j represents the jth road section on the unmanned aerial vehicle track, and the road section j is equally divided into N_jA path gap, n_jN-th indicating a link j_jA path gap; theta_jk[n_j]For unmanned aerial vehicle lie in n of highway section j_jWhen the path is separated, the included angle between the sensor node k and the array normal is formed; q. q.s_j[n_j]For the position of the drone, with a path gap n_jIs approximately represented by the midpoint of (a), w_kIs the horizontal coordinate position of the sensor node k; h is unmanned aerial vehicle's flightHeight.

Further, the system model in step 1 is:

in the flight process of the unmanned aerial vehicle, only the sensor nodes at two ends of all road sections send information to the unmanned aerial vehicle, and the nth road section j is 1_jAt each path gap, the uplink reachable rate of the sensor node j is:

n-th route in section j ═ K +1_jAnd when the path is in a gap, the uplink reachable rate of the sensor node j-1 is as follows:

when the road section j is 2, the signal reception is performed by the unmanned aerial vehicle by using the ZF scheme, and the nth road section j is_jAt each path gap, the uplink reachable rates of the sensor nodes j-1 and j are respectively as follows:

wherein w_1j[n_j]And w_2j[n_j]For the receiving matrix of the drone, when j is 1, w_1j[n_j]Receiving a receive matrix for the drone receiving the 1 st sensor signal, when j ═ K +1, w_1j[n_j]A receive matrix for the drone to receive the kth sensor signal, when j 2_1j[n_j]And w_2j[n_j]Receiving matrices, p, for the unmanned aerial vehicle receiving the jth-1 and jth sensor signals, respectively_jj-1[n_j]For sensor node j-1 at nth section j_jSignal power at each path gap;

is the noise power spectral density.

Further, the optimization problem established in step 2 is as follows:

0≤0＜v_j[n_j]≤v_max,j＝1,...,K+1, (1.4)

0≤p_j+1j[n_j+1]≤p_max,j＝1,...,K, (1.5)

0≤p_jj[n_j]≤p_max,j＝1,...,K, (1.6)

where min represents the minimization operation, S_jRepresents the length of the jth path, v represents the speed of the drone, v_maxFor the maximum flight speed of the unmanned aerial vehicle, p represents the transmitting power of the sensor, and p_maxFor the maximum transmit power of the sensor, J represents the energy consumption of all sensors, T represents the flight time, s.t represents the constraint condition, (1.1) represents the upstream capacity constraint of the sensor node, (1.2) represents the energy consumption constraint of the sensor node, (1.3) limits the flight time of the drone, (1.4) represents the speed constraint of the drone, and (1.5-1.6) represents the power constraint of the sensor node.

Further, the step 3 comprises:

a) giving a set of feasible initial speed plans of the unmanned aerial vehicle and a power distribution scheme of the sensor nodes;

b) establishing a corresponding convex optimization problem aiming at the non-convex optimization problem in the step 2, updating the speed plan of the unmanned aerial vehicle and the power distribution scheme of the sensor node, and taking the scheme as a new initial value;

c) and c, iteratively executing the step b until the algorithm is converged, and using the output value as a power distribution scheme of the unmanned aerial vehicle speed plan and the sensor node.

Has the advantages that: in the multi-antenna unmanned aerial vehicle uplink communication system, under the conditions of limited push power, limited flight time and minimum link throughput constraint, the maximum power consumption of sensor nodes is reduced by optimizing the flight speed of the unmanned aerial vehicle and the signal transmitting power of each sensor node; the minimization of energy consumption is realized, and the cruising ability of the sensor node equipment is prolonged. The method is suitable for the communication scene of the Internet of things, and the sensor nodes can be deployed in the environment without ground wireless infrastructure, such as mountainous areas and desert areas, and are difficult to supplement energy.

Drawings

Fig. 1 is a schematic view of a communication system for information transmission between an unmanned aerial vehicle and a ground user during flight;

FIG. 2 is a flow chart of a method for optimizing energy consumption for sensor transmission in a data acquisition system of a multi-antenna unmanned aerial vehicle;

FIG. 3 is a schematic diagram of an optimal velocity plan obtained under different time-of-flight constraints;

FIG. 4 is a schematic diagram of optimal signal power distribution of a sensor node;

FIG. 5 is a schematic diagram of the performance of the solving algorithm of the present invention;

fig. 6 is a graph of target power consumption values as a function of maximum flight time T of the drone.

Detailed Description

The technical scheme of the invention is further explained by combining the attached drawings.

The unmanned aerial vehicle communication network utilizes an unmanned aerial vehicle as an aerial base station and acquires data from a sensor node in the flight process through communication equipment carried by the unmanned aerial vehicle. For traditional ground network, corresponding communication network can be established fast in unmanned aerial vehicle base station communication to can not receive the influence of ground road conditions. Aiming at the unmanned aerial vehicle data acquisition system, the invention provides a sensor emission energy consumption optimization method of a multi-antenna unmanned aerial vehicle data acquisition system.

As shown in fig. 1, in an embodiment, the multi-antenna drone data acquisition system is an uplink communication system composed of a drone with M antennas and K sensor nodes; the flight height of the unmanned aerial vehicle is H, and the unmanned aerial vehicle starts from the initial position, sequentially flies through the K nodes along a straight line and finally reaches the end position within the limited time T. During this flight, the drone collects data from the sensor nodes. Suppose that the unmanned aerial vehicle is in flight, only the sensor nodes at the two ends of the flight section send information to the unmanned aerial vehicle. Under the conditions of limited push power, limited flight time and minimum link throughput, the maximum transmission power consumption of the sensor nodes is reduced by optimizing the flight speed of the unmanned aerial vehicle and the signal transmission power of each sensor node. Referring to fig. 2, the method comprises the following specific steps:

step 1, establishing a channel fading model and a system model according to the geographical positions of the sensor nodes and the unmanned aerial vehicle and an air-to-ground wireless channel model.

The channel power gain information changes with the change of the sensor node and the geographical position of the unmanned aerial vehicle, and assuming that the geographical position of the sensor node is known by the unmanned aerial vehicle, the acquisition mode of the position of the unmanned aerial vehicle is as follows: dividing the flight path of the unmanned aerial vehicle into K +1 small road sections by K nodes, discretizing each road section, namely equally dividing the road section j into N_jA path gap such that at the n-th of the section j_j(n_j＝1,...,N_j) In each path gap, the position q of the unmanned aerial vehicle_j[n_j]Can be approximated by the midpoint of the gap, q_j[n_j]＝[x_j[n_j],y_j[n_j]]，x_j[n_j],y_j[n_j]Respectively shown at n-th of the link j_j(n_j＝1,...,N_j) In each path gap, the abscissa and ordinate of the position of the drone.

Obtaining the channel power gain of the system:

wherein, β₀Represents the channel gain when the drone is at a distance of 1m from the sensor; d_kRepresenting the distance between the drone and the sensor node k;

adopting a guide vector generated by a uniform equidistant linear array for an unmanned aerial vehicle antenna array, wherein lambda is a wavelength corresponding to a signal center frequency f; d represents the distance between the antenna elements; theta_jk[n_j]Is the n-th of the link j_jDuring each path gap, the included angle between a sensor node k and the normal line of the unmanned aerial vehicle array; w is a_kIs the horizontal coordinate position of sensor node k.

In the flight process of the unmanned aerial vehicle, only the sensor nodes at two ends of the road section send information to the unmanned aerial vehicle, so that when the unmanned aerial vehicle is located at the position of the road section j, the unmanned aerial vehicle can only serve the sensor node j-1 and the sensor node j at most. The unmanned aerial vehicle uses a Zero Forcing (ZF) scheme for signal reception, and the signal reception is carried out on the nth section of the road j_jDuring each path gap, the receiving signal of the unmanned aerial vehicle is as follows:

wherein p is_jk[n_j]For sensor node k at nth of road section j_jSignal power at each path gap; s_jk[n_j]For sensor node k at nth of road section j_jDuring a gap of one pathEmitting signals with the average value of 1; n is_r[n_j]Is complex additive white Gaussian noise, has a mean value of zero and a covariance matrix of

When j is 1, H_j[n_j]＝[h_jj[n_j]]，

When j is 2, K,

H_j[n_j]＝[h_jj-1[n_j],h_jj[n_j]]；

when j is K +1, H_j[n_j]＝[h_jj-1[n_j]]，

When the link j is 1 or K +1, the reception matrix W_j[n_j]Expressed as:

n-th route in section j ═ K +1_jAt each path gap, the uplink reachable rate of the sensor node j-1 is represented as:

n-th of 1-j_jAt each path gap, the uplink reachable rate of sensor node j is represented as:

when a road segment j is 2_j[n_j]Expressed as:

wherein w_1j[n_j]And w_2j[n_j]Receive signal s separately_jj-1[n]And s_jj[n](ii) a Then at the nth of the section j_jAt each path gap, the uplink reachable rates of the sensor nodes j-1 and j are respectively expressed as:

step 2, the maximum energy consumption of the sensor nodes is minimized by optimizing the speed of the unmanned aerial vehicle and the signal transmitting power of the sensor nodes, and the optimization problem is established as follows:

0≤0＜v_j[n_j]≤v_max,j＝1,...,K+1, (1.4)

0≤p_j+1j[n_j+1]≤p_max,j＝1,...,K, (1.5)

0≤p_jj[n_j]≤p_max,j＝1,...,K, (1.6)

wherein，S_jThe length of the jth path is represented, (1.1) the upstream capacity constraint of the sensor node is represented, (1.2) the energy consumption constraint of the sensor node is represented, (1.3) the flight time of the unmanned aerial vehicle is limited, (1.4) the speed constraint of the unmanned aerial vehicle is represented, and (1.5-1.6) the power constraint of the sensor node is represented.

Where v denotes drone speed, p denotes sensor transmit power, and J denotes energy consumption of all sensor nodes.

And 3, solving the constructed optimization problem to obtain the optimal navigational speed of the unmanned aerial vehicle and the power distribution scheme of the sensor nodes.

Since both the formula (1.1) and the formula (1.2) are non-convex constraints, the original problem P is a non-convex optimization problem and is difficult to solve. The invention provides an effective algorithm based on continuous Convex Approximation (SCA), which converts an original problem into a series of Convex optimization problems and continuously solves the Convex optimization problems so as to obtain a local optimal solution of the original problem.

The non-convex condition (1.1) is first treated by introducing an auxiliary variable t_j+1j[n_j+1],j＝1,...,K,t_jj[n_j]J 1.. K, which divides the original complex constraint into a plurality of relatively simple constraints, the formula (1.1) can be converted equivalently into the following constraints:

wherein, only restraint (1.7) and restraint (1.8) are limited by the non-convex strip piece. And both are identical in form and can be processed in the same way. First, by modifying equation (1.7), the following can be obtained:

wherein, κ_j+1j[n_j+1]＝(t_j+1j[n_j+1]-v_j+1[n_j+1])²，

(1.12) non-convex term κ in formula_j+1j[n_j+1]Concave lower boundary of

Is composed of

Wherein,

is a given initial point; using equation (1.13), the non-convex constraint (1.7) can be approximated as the following convex constraint:

likewise, an equation (1.8), which is identical in form to equation (1.7), may be approximated as follows:

then the non-protruding strip (1.2) is treated by introducing an auxiliary variable g_j+1j[n_j+1],j＝1,...,K,g_jj[n_j],j＝1,..., K, dividing the original complex constraint condition into a plurality of relatively simple constraint conditions, wherein the condition (1.2) can be equivalently converted into the following constraint conditions:

wherein, only restraint (1.16) and restraint (1.17) are limited by the non-convex strip piece. And both are identical in form and can be processed in the same way. First, by modifying equation (1.16), the following can be obtained:

wherein psi_j+1j[n_j+1]＝(g_j+1j[n_j+1]-v_j+1[n_j+1])²，μ_j+1j[n_j+1]＝(g_j+1j[n_j+1]+v_j+1[n_j+1])²(ii) a Non-convex term mu in the formula (1.21)_j+1j[n_j+1]Concave lower boundary ζ of_j+1j[n_j+1]Can be expressed as:

wherein,

using ζ_j+1j[n_j+1]De-approximating mu_j+1j[n_j+1]Then (1.16) can be approximated as:

it is clear that the formula (1.23) is a rib limitation. The same can be said that (1.7) can be approximated as follows:

after the above approximate treatment, the expressions (1.9-1.11), (1.14) and (1.15) are used to replace the expression (1.1) in the original problem P, and the expressions (1.18-1.20), (1.23) and (1.24) are used to replace the expression (1.2) in the original problem P, so as to obtain the SCA subproblem P1:

s.t(1.9)-(1.11),(1.14),(1.15)

(1.18)-(1.20),(1.23),(1.24)

(1.3)-(1.6)

the problem can be solved by using an optimization algorithm such as an interior point method. And taking the obtained optimal solution as an initial point of the next iteration, performing first-order Taylor expansion in equations (1.13) and (1.22), updating the problem P1, and continuously solving. And continuously repeating the steps until the algorithm converges. The resulting SCA complete algorithm is summarized as follows:

1: initializing speed of unmanned aerial vehicle

Sensor node transmission signal power

Intermediate variables

And

the iteration number r is 0;

2: performing iteration 3, 4;

3: performing first-order Taylor expansion in the set initial value formulas (1.13) and (1.22), and updating the problem P1, wherein r is r + 1;

4: obtaining a new unmanned aerial vehicle speed plan by solving problems by using algorithms such as an interior point method

And signal transmission power scheme of sensor node

And using it as new initial value;

5: returning to the optimal speed plan until the algorithm converges

And power allocation

Based on the detailed step description of the sensor emission energy consumption optimization method of the multi-antenna unmanned aerial vehicle data acquisition system, the performance of the method is verified through a simulation example, as shown in fig. 2 to 5, the simulation example simulates the scene by using MAT L AB, the height H of the unmanned aerial vehicle is 100m, the number of antennas is 4, and the maximum speed V is_max30m/s, unmanned aerial vehicle's home position is (0, 0), and the terminal point coordinate is (800, 500), and unmanned aerial vehicle maximum flight time T is 200 s. There are 3 sensor nodes, whose positions are (200, 150), (500, 150), and (800, 300), respectively. Maximum transmitting power p of sensor node_smax0.1W, noise power spectral density σ²160dBm, the system bandwidth is 10MHz, each road segment is divided into 50 equal-length path gaps, and the threshold value η of the uplink capacity of each sensor node is 4.5 × 10⁹And (6) bit. The initial speed of the unmanned aerial vehicle is set to be the uniform speed in the whole course, the unmanned aerial vehicle just reaches the terminal point after the time T, and the sensorThe node initial signal power is set to 0.1 w.

Fig. 3 shows the velocity profile of the drone at different path gaps at total flight times of 180s, 200s, 220s, respectively, with an x-axis length of 200, since the flight path is divided into four segments in total, each segment being equally divided into 50 segments, respectively. Where the x-axis represents path gap, the y-axis represents velocity, and the sensor node positions are at 50, 100, and 150, respectively, of the x-axis. It can be seen that the trend of the velocity is consistent for different flight times T.

Fig. 4 shows a power distribution diagram of each sensor node at different path gaps in the flight process, and it can be seen that the power variation trends of each sensor node are consistent, and are increased and then decreased, when the unmanned aerial vehicle approaches the sensor node, the sensor node increases the signal power, and the unmanned aerial vehicle decreases the speed, so that the maximization of the power effect is realized, and the total power consumption of the task is reduced.

Fig. 5 shows a graph of the variation of the target value with the variation of the number of iterations for the proposed algorithm of the present invention. The x axis represents the iteration number of the algorithm, and the y axis represents the minimum maximum power consumption value of the sensor node optimized by the algorithm. When the x-axis coordinate is zero, the value of the y-axis represents the minimum maximum power consumption value at initial setup. It can be seen that the algorithm only needs several iterations to reach the convergence condition, and compared with the MRC (Maximum Ratio Transmission) scheme, the ZF receiving scheme has a faster convergence speed and a better optimization effect.

FIG. 6 illustrates when the system throughput requirement is 16 × 10⁹And when the unmanned aerial vehicle is in bit, a curve graph of the total power consumption changing along with the maximum flight time T of the unmanned aerial vehicle is used by using a ZF receiving scheme. It can be seen that as the maximum flight time T becomes larger, the total power consumption is gradually becoming smaller. This is because the total throughput of the system is fixed, and as the maximum flight time T becomes larger, the rate requirement of the system decreases, resulting in less power consumption. It can also be seen that as the number of antennas increases, the overall power consumption also decreases. This is because the array antenna technology is used in the invention, and the multiple antennas can increase the coherence of signals, thereby obtaining array gain and improving the system performance. But when T is large enough, the number of antennas isThe effect of the increase on the results becomes less pronounced.

Claims (5)

1. A sensor emission energy consumption optimization method of a multi-antenna unmanned aerial vehicle data acquisition system is characterized in that the data acquisition system comprises a single multi-antenna unmanned aerial vehicle and a plurality of sensors, and the single multi-antenna unmanned aerial vehicle serves a plurality of ground sensor nodes as an aerial base station, and the method comprises the following steps:

(1) establishing a channel fading model and a corresponding system model according to the geographic positions of the sensor nodes and the unmanned aerial vehicle and an air-to-ground wireless channel model;

(2) establishing an optimization problem according to the flight time constraint of the unmanned aerial vehicle, the transmitting power constraint of the sensor nodes and the link transmission rate constraint, wherein the optimization problem minimizes the maximum energy consumption of the sensor nodes by optimizing the speed of the unmanned aerial vehicle and the signal transmitting power of the sensor nodes;

(3) and (3) solving the optimization problem obtained in the step (2) to obtain the optimal speed of the unmanned aerial vehicle and the power distribution scheme of the sensor nodes.

2. The method for optimizing the energy consumption of sensor transmission in the data acquisition system of a multi-antenna unmanned aerial vehicle according to claim 1, wherein the channel fading model in step 1 is:

wherein, β₀Representing the channel gain when the drone is 1m away from the sensor; d_kRepresenting the distance between the drone and the sensor node k;

adopting a uniform equidistant linear array to generate a guide vector for an unmanned aerial vehicle antenna array, wherein lambda is a wavelength corresponding to a signal center frequency f, and d is a distance between array vibration elements; j represents the jth road section on the unmanned aerial vehicle track, and the road section j is equally divided into N_jOne roadRadial clearance, n_jN-th indicating a link j_jA path gap; theta_jk[n_j]For unmanned aerial vehicle lie in n of highway section j_jWhen the path is separated, the included angle between the sensor node k and the array normal is formed; q. q.s_j[n_j]For the position of the drone, with a path gap n_jIs approximately represented by the midpoint of (a), w_kIs the horizontal coordinate position of the sensor node k; h is unmanned aerial vehicle's flying height.

3. The method for optimizing the energy consumption of sensor transmission of the data acquisition system of the multi-antenna unmanned aerial vehicle of claim 2, wherein the system model in the step 1 is:

in the flight process of the unmanned aerial vehicle, only the sensor nodes at two ends of all road sections send information to the unmanned aerial vehicle, and the nth road section j is 1_jAt each path gap, the uplink reachable rate of the sensor node j is:

n-th route in section j ═ K +1_jAnd when the path is in a gap, the uplink reachable rate of the sensor node j-1 is as follows:

when the road section j is 2, the signal reception is performed by the unmanned aerial vehicle by using the ZF scheme, and the nth road section j is_jAt each path gap, the uplink reachable rates of the sensor nodes j-1 and j are respectively as follows:

wherein w_1j[n_j]And w_2j[n_j]Is made withoutA man-machine reception matrix, w when j is 1_1j[n_j]Receiving a receive matrix for the drone receiving the 1 st sensor signal, when j ═ K +1, w_1j[n_j]A receive matrix for the drone to receive the kth sensor signal, when j 2_1j[n_j]And w_2j[n_j]Receiving matrices, p, for the unmanned aerial vehicle receiving the jth-1 and jth sensor signals, respectively_jj-1[n_j]For sensor node j-1 at nth section j_jThe signal power at the time of a path gap,

is the noise power spectral density.

4. The method for optimizing energy consumption of sensor transmission of data acquisition system of multi-antenna unmanned aerial vehicle of claim 3, wherein the optimization problem established in step 2 is:

P:

0≤0＜v_j[n_j]≤v_max,j＝1,...,K+1, (1.4)

0≤p_j+1j[n_j+1]≤p_max,j＝1,...,K, (1.5)

0≤p_jj[n_j]≤p_max,j＝1,...,K, (1.6)

where min represents the minimization operation, S_jRepresents the length of the jth path, v represents the speed of the drone, v_maxFor the maximum flight speed of the unmanned aerial vehicle, p represents the transmitting power of the sensor, and p_maxFor the maximum transmit power of the sensor, J represents the energy consumption of all sensors, T represents the flight time, s.t represents the constraint condition, (1.1) represents the upstream capacity constraint of the sensor node, (1.2) represents the energy consumption constraint of the sensor node, (1.3) limits the flight time of the drone, (1.4) represents the speed constraint of the drone, and (1.5-1.6) represents the power constraint of the sensor node.

5. The method for optimizing sensor emission energy consumption of a multi-antenna drone data acquisition system according to claim 4, characterized in that said step 3 comprises:

a) giving a set of feasible initial speed plans of the unmanned aerial vehicle and a power distribution scheme of the sensor nodes;

b) establishing a corresponding convex optimization problem aiming at the non-convex optimization problem in the step 2, updating the speed plan of the unmanned aerial vehicle and the power distribution scheme of the sensor node, and taking the scheme as a new initial value;

c) and c, iteratively executing the step b until the algorithm is converged, and using the output value as a power distribution scheme of the unmanned aerial vehicle speed plan and the sensor node.

Images