CN110418286B - Communication method and device for information and energy cooperative transmission, unmanned aerial vehicle and system - Google Patents
Communication method and device for information and energy cooperative transmission, unmanned aerial vehicle and system Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/14—Relay systems
- H04B7/15—Active relay systems
- H04B7/185—Space-based or airborne stations; Stations for satellite systems
- H04B7/18502—Airborne stations
- H04B7/18504—Aircraft used as relay or high altitude atmospheric platform
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
- H04W4/42—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for mass transport vehicles, e.g. buses, trains or aircraft
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC
- H04W52/18—TPC being performed according to specific parameters
- H04W52/26—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service]
- H04W52/265—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service] taking into account the quality of service QoS
Abstract
The invention provides a communication method and device for information and energy cooperative transmission, an unmanned aerial vehicle and a system. The communication method for information and energy cooperative transmission provided by the invention comprises the following steps: initializing power distribution, flight trajectory and first cooperation throughput between a source point transmitter and a destination point receiver of the unmanned aerial vehicle, then optimally calculating optimal decision distribution, optimal power distribution and optimal flight trajectory of the unmanned aerial vehicle according to the power distribution, flight trajectory and the first cooperation throughput, and finally carrying out cooperation communication according to the optimal decision distribution, optimal power distribution and optimal flight trajectory. According to the communication method for information and energy cooperative transmission, wireless energy carrying communication is introduced into auxiliary communication of the unmanned aerial vehicle, so that the energy limit of the unmanned aerial vehicle can be effectively relieved by acquiring wireless energy from a radio signal transmitted by a source point, and the cooperative throughput between the source point transmitter and a destination point receiver is maximized.
Description
Technical Field
The invention relates to the technical field of communication, in particular to a communication method, a communication device, an unmanned aerial vehicle and a communication system for information and energy cooperative transmission.
Background
To enhance network coverage and meet the increasing demand for data traffic, the deployment of wireless networks has shifted from the traditional 2D plane to the 3D space. In light of this trend, Unmanned Aerial Vehicle (UAV) assisted wireless communication has attracted great attention in the industry and academia. Unmanned aerial vehicles hovering in the air are more likely to establish wireless links (e.g., line of sight) with better channel conditions than traditional ground infrastructure due to their low cost, high mobility and high deployment flexibility characteristics, and thus unmanned aerial vehicle-assisted communication is believed to better support wireless communications in many practical applications, such as public safety and disaster management.
Among them, an important application of the drone-assisted wireless communication is as a relay in a cooperative communication system, which is an effective technology for improving communication performance and expanding a coverage area under a weak channel condition (a long distance or a serious obstacle existing between a source point and a terminal point). As an airborne mobile relay, drones can flexibly adjust positions to obtain more favorable channel conditions. Cooperative communication performance can be improved even if a direct link between a source point and a destination point is severely blocked.
However, the existing auxiliary wireless communication of the unmanned aerial vehicle still has some defects, and one practical problem is the problem of limited capacity of an onboard battery of the unmanned aerial vehicle due to the characteristic of small size and light weight of the unmanned aerial vehicle. Most of the existing work has focused on energy efficiency improvement of the drone-assisted wireless communication system in response to this practical problem. The exploration of renewable energy sources is also another important option with respect to reducing energy consumption. However, non-autonomous energy harvesting devices (e.g., solar panels) that are much larger than the drone itself may significantly increase the load, making the energy consumption higher.
Disclosure of Invention
The invention provides a communication method, a communication device, an unmanned aerial vehicle and a communication system for information and energy cooperative transmission, and aims to solve the problem.
In a first aspect, the present invention provides a communication method for information and energy cooperative transmission, which is applied to a cooperative communication system for information and energy cooperative transmission, and the system includes: a source transmitter, an endpoint receiver, and a drone as a communication relay between the source transmitter and the endpoint receiver, the drone being further configured to collect energy from a source signal sent by the source transmitter; the method comprises the following steps:
initializing a power profile of the drone, a flight trajectory, and a first coordinated throughput between the source transmitter and the destination receiver, wherein the power profile includes an output power value of the drone at each time node, and the flight trajectory is position information of the drone including at each time node;
calculating an optimal decision distribution, an optimal power distribution and an optimal flight trajectory of the drone according to the power distribution, the flight trajectory and the first cooperative throughput optimization, wherein the optimal decision distribution comprises an operating state of the drone at each time node, the operating state being a drone serving as a communication relay between the source transmitter and the destination receiver or energy collection from a source signal transmitted by the source transmitter;
performing cooperative communication according to the optimal decision profile, the optimal power profile, and the optimal flight trajectory to maximize cooperative throughput between the source transmitter and the destination receiver.
In one possible design, the calculating an optimal decision distribution, an optimal power distribution, and an optimal flight trajectory for the drone from the power distribution, the flight trajectory, and the first cooperative throughput optimization includes:
calculating decision distribution of the unmanned aerial vehicle according to the power distribution and the flight trajectory optimization;
updating the power distribution according to the decision distribution and the flight trajectory;
updating the flight trajectory according to the decision distribution and the power distribution;
calculating a second cooperative throughput between the source transmitter and the destination receiver from the decision profile, the power profile, and the flight trajectory;
judging whether the difference between the second cooperation throughput and the first cooperation throughput is smaller than a preset tolerance value or not;
if the decision distribution, the power distribution and the flight trajectory are output as the optimal decision distribution, the optimal power distribution and the optimal flight trajectory of the unmanned aerial vehicle respectively.
In one possible design, determining whether a difference between the second cooperative throughput and the first cooperative throughput is smaller than a preset tolerance value, and determining whether the difference is negative;
updating the first cooperative throughput to the second cooperative throughput;
calculating decision distribution of the unmanned aerial vehicle according to the power distribution and the flight trajectory optimization;
updating the power distribution according to the decision distribution and the flight trajectory;
updating the flight trajectory according to the decision distribution and the power distribution;
updating a second cooperative throughput between the source transmitter and the destination receiver as a function of the decision profile, the power profile, and the flight trajectory;
judging whether the difference between the second cooperation throughput and the first cooperation throughput is smaller than the preset tolerance value or not;
if the decision distribution, the power distribution and the flight trajectory are output as the optimal decision distribution, the optimal power distribution and the optimal flight trajectory of the unmanned aerial vehicle respectively.
In one possible design, the calculating a decision distribution of the drone from the power distribution and the flight trajectory includes:
relaxing the decision variables of the decision distribution into continuous variables from 0 to 1 so as to convert the optimization calculation problem of the decision distribution into a linear programming problem;
solving the linear programming problem to obtain the decision distribution.
In one possible design, the updating the power profile based on the decision profile and the flight trajectory includes:
converting the optimization calculation problem of the power distribution into a Lagrange dual equation solving problem according to a Lagrange multiplier method;
solving the optimal solution to the Lagrangian dual equation to obtain the power distribution.
In one possible design, the updating the flight trajectory based on the decision profile and the power profile includes:
calculating the track increment of the unmanned aerial vehicle in unit time according to the decision distribution and the power distribution;
and updating the flight track according to the initial position parameters in the flight track and the track increment.
In one possible design, before outputting the decision distribution, the power distribution, and the flight trajectory as the optimal decision distribution, the optimal power distribution, and the optimal flight trajectory of the drone, respectively, further includes:
binarizing the optimal decision distribution such that the drone acts as a relay between the source transmitter and the destination receiver when the optimal decision distribution has a value of 1, and energy is collected from a source signal transmitted by the source transmitter when the drone has a value of 0 when the optimal decision distribution has a value of 0.
In a second aspect, the present invention further provides a communication device for cooperative transmission of information and energy, which is applied to a cooperative communication system for cooperative transmission of information and energy, and the system includes: a source transmitter, an endpoint receiver, and a drone as a communication relay between the source transmitter and the endpoint receiver, the drone being further configured to collect energy from a source signal sent by the source transmitter; the method comprises the following steps:
an initialization module configured to initialize a power distribution of the drone, a flight trajectory, and a first cooperative throughput between the source transmitter and the destination receiver, wherein the power distribution includes an output power value of the drone at each time node, and the flight trajectory is position information of the drone at each time node;
an optimization calculation module, configured to optimally calculate an optimal decision distribution, an optimal power distribution, and an optimal flight trajectory of the drone according to the power distribution, the flight trajectory, and the first cooperative throughput, where the optimal decision distribution includes an operating state of the drone at each time node, and the operating state is the drone serving as a communication relay between the source point transmitter and the destination point receiver or energy collection from a source signal transmitted by the source point transmitter;
and the cooperative communication module is used for performing cooperative communication according to the optimal decision distribution, the optimal power distribution and the optimal flight trajectory so as to maximize cooperative throughput between the source point transmitter and the destination point receiver.
In one possible design, the optimization calculation module is specifically configured to:
calculating decision distribution of the unmanned aerial vehicle according to the power distribution and the flight trajectory optimization;
updating the power distribution according to the decision distribution and the flight trajectory;
updating the flight trajectory according to the decision distribution and the power distribution;
calculating a second cooperative throughput between the source transmitter and the destination receiver from the decision profile, the power profile, and the flight trajectory;
judging whether the difference between the second cooperation throughput and the first cooperation throughput is smaller than a preset tolerance value or not;
if the decision distribution, the power distribution and the flight trajectory are output as the optimal decision distribution, the optimal power distribution and the optimal flight trajectory of the unmanned aerial vehicle respectively.
In one possible design, the optimization calculation module is further specifically configured to: when the difference between the second cooperative throughput and the first cooperative throughput is not less than a preset tolerance value;
updating the first cooperative throughput to the second cooperative throughput;
calculating decision distribution of the unmanned aerial vehicle according to the power distribution and the flight trajectory optimization;
updating the power distribution according to the decision distribution and the flight trajectory;
updating the flight trajectory according to the decision distribution and the power distribution;
updating a second cooperative throughput between the source transmitter and the destination receiver as a function of the decision profile, the power profile, and the flight trajectory;
judging whether the difference between the second cooperation throughput and the first cooperation throughput is smaller than the preset tolerance value or not;
if the decision distribution, the power distribution and the flight trajectory are output as the optimal decision distribution, the optimal power distribution and the optimal flight trajectory of the unmanned aerial vehicle respectively.
In one possible design, the optimization calculation module is further specifically configured to:
relaxing the decision variables of the decision distribution into continuous variables from 0 to 1 so as to convert the optimization calculation problem of the decision distribution into a linear programming problem;
solving the linear programming problem to obtain the decision distribution.
In one possible design, the optimization calculation module is further specifically configured to:
converting the optimization calculation problem of the power distribution into a Lagrange dual equation solving problem according to a Lagrange multiplier method;
solving the optimal solution to the Lagrangian dual equation to obtain the power distribution.
In one possible design, the optimization calculation module is further specifically configured to:
calculating the track increment of the unmanned aerial vehicle in unit time according to the decision distribution and the power distribution;
and updating the flight track according to the initial position parameters in the flight track and the track increment.
In one possible design, the optimization calculation module is further specifically configured to:
binarizing the optimal decision distribution such that the drone acts as a relay between the source transmitter and the destination receiver when the optimal decision distribution has a value of 1, and energy is collected from a source signal transmitted by the source transmitter when the drone has a value of 0 when the optimal decision distribution has a value of 0.
In a third aspect, the present invention further provides an unmanned aerial vehicle, including: an on-board battery and a cooperative communication device as in any of the second aspects;
the vehicle-mounted battery is used for providing power for the maneuvering flight of the unmanned aerial vehicle;
the cooperative communication device is used for providing a relay between the source point transmitter and the destination point receiver or collecting energy from a source signal sent by the source point transmitter;
wherein the energy collected by the cooperative communication device from the source point transmitter is used to power communication transmissions of the drone.
In a fourth aspect, the present invention further provides a communication system for cooperative transmission of information and energy, including: a source transmitter, an end point receiver, and a drone as provided by the third aspect, the drone being for use as a communication relay between the source transmitter and the end point receiver or to collect energy from a source signal sent by the source transmitter.
According to the communication method, the device, the unmanned aerial vehicle and the system for information and energy collaborative transmission, wireless energy carrying communication is introduced into auxiliary communication of the unmanned aerial vehicle, so that the energy limitation of the unmanned aerial vehicle can be effectively relieved by acquiring wireless energy from a radio signal transmitted by a source point, and the optimal decision distribution, the optimal power distribution and the optimal flight trajectory of the unmanned aerial vehicle are calculated by initializing the power distribution and the flight trajectory of the unmanned aerial vehicle and optimizing the first collaborative throughput between the source point transmitter and a destination point receiver, so that the collaborative throughput between the source point transmitter and the destination point receiver is maximized.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a diagram illustrating an application scenario of a communication method for cooperative transmission of information and energy according to an exemplary embodiment;
FIG. 2 is a flow diagram illustrating a communication method for coordinated transmission of information and energy, according to an example embodiment;
FIG. 3 is a flow diagram illustrating a communication method for coordinated transmission of information and energy, according to another exemplary embodiment;
FIG. 4 is a comparison graph of the effect of the method provided by the present embodiment and the prior art method;
fig. 5 is a schematic diagram illustrating a structure of a communication device for cooperative transmission of information and energy according to an exemplary embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a diagram illustrating an application scenario of a communication method for cooperative transmission of information and energy according to an exemplary embodiment. As shown in fig. 1, the communication method for information and energy cooperative transmission provided by this embodiment is applied to a cooperative communication system for information and energy cooperative transmission, and specifically, the system includes: the unmanned aerial vehicle is also used for collecting energy from a source signal sent by the source point transmitter.
The Simultaneous signal and energy transmission is realized through Wireless energy-carrying communication (SWIPT), namely, the Wireless device is provided with energy while Information interaction is carried out with the Wireless device. The system not only can make full use of information, but also can make full use of energy carried by radio signals. Therefore, wireless energy carrying communication becomes an economical and effective way to supplement the energy of the drone, since no additional energy harvesting devices need to be installed.
Fig. 2 is a flow diagram illustrating a communication method for cooperative transmission of information and energy according to an example embodiment. As shown in fig. 2, the communication method for information and energy cooperative transmission provided by this embodiment includes:
Specifically, the cooperative communication system for cooperative transmission of information and energy is a classic point-to-point wireless communication system assisted by an unmanned aerial vehicle with an Amplify-and-Forward (AF) protocol as an air mobile relay, in which a source point transmitter and a destination point receiver are fixed at two different positions on the ground. In order to effectively utilize the energy of the surrounding environment, wireless energy-carrying communication can be introduced into the unmanned aerial vehicle cooperative communication system, namely the transmission capability of the unmanned aerial vehicle is completely supplied by the radio signal of the source point transmitter, and the vehicle-mounted battery only supplies power for mobility. Energy harvesting and information transmission of a wireless communication system is optimized over a period of time without the need for additional energy harvesting equipment.
For the energy-carrying wireless communication of the unmanned aerial vehicle, a time division mechanism is mainly considered, and in the mechanism, the energy collection and relay transmission of the unmanned aerial vehicle are not overlapped in the same time period.
The positions of the source transmitter and destination receiver may be considered to be represented by S and D, respectively, within a three-dimensional cartesian coordinate system. At this time, assuming that the drone flies in the air at a fixed height H within a limited time range T from a start position to an end position as a mobile relay, and the time range T is discretized into N time slots with equal gaps, the position coordinate of the drone within the time slot N may be represented by a two-dimensional coordinate [ x, y ], where the mobility constraint of the drone may be derived from the maximum flying distance of each time slot, i.e. the displacement between adjacent time slots is less than or equal to the maximum flying distance of the drone within each time slot, i.e.:
wherein x isnFor the position abscissa, y, of the drone within the time slot nnAnd V is a preset maximum flight distance value for the position ordinate of the unmanned aerial vehicle in the time slot n.
Assuming that the source transmitter to drone and drone to destination receiver channels are dominated by line-of-sight links, the source to drone and drone to destination channel power increase in time slot n follows a free space path loss model, in inverse relation to the distance between them, namely:
wherein the content of the first and second substances,
in order to increase the power of the channel,
for the distance of the source transmitter from the drone in time slot n, the reference channel power gain is η units of distance (e.g., 1 meter).
This embodiment uses a time-sharing receiver structure with SWIPT on the drone, i.e. in each time slot, the drone must decide to collect energy from the signal sent by the signal source or to forward the data to the destination as a relay. The decision distribution of the drone is represented by a binary indicator, where 1 represents acting as a relay and 0 represents the energy taken from the source signal; of course, it is also possible that 1 represents energy extraction from the source signal, and 0 represents relay, which is not particularly limited in this embodiment.
And the objective function for maximizing the end-to-end cooperative throughput according to the decision distribution, power distribution and flight trajectory of the drone is as follows:
where n is the nth time slot, pnFor unmanned aerial vehicle power at nth time slot, βnIs the binary decision of the drone at the nth time slot, wherein βn1 indicates that the drone serves as a relay at the nth slot, βnWith 0 meaning that the drone derives energy from the source signal at n time slots, rnFor cooperative communication rate in the nth slot, rdThe direct connection rate of the source transmitter to the destination receiver, [ x ]n,yn]The position of the drone is the nth slot.
The following constraints are specifically required to be satisfied for solving the problem of maximizing end-to-end cooperative throughput:
(x1-xs)2+(y1-ys)2≤V2
(xe-xN)2+(ye-yN)2≤V2
wherein p isiFor unmanned aerial vehicle power at the ith time slot, βiFor the binary decision of the drone at the ith time slot, PsTransmitting power, gamma, for a source point transmitter0For the reference signal-to-noise ratio of the cooperative channel,
distance, x, from the source point to the drone at the ith time slotsIs the abscissa of the initial position of the unmanned aerial vehicle, ysIs the vertical coordinate, x, of the initial position of the unmanned aerial vehicleeIs the abscissa of the terminal position of the unmanned aerial vehicle, yeIs the vertical coordinate of the terminal position of the unmanned aerial vehicle.
However, since the above problem solution is not a convex optimization problem, an optimal solution cannot be obtained by a standard solution. Therefore, we break the above equation into three sub-problems for round robin optimization (also called block descent) by a method that solves each set of variables given two variables: decision distribution, power distribution and unmanned aerial vehicle flight trajectory. The two other variables are fixed during sub-problem optimization and optimized so that end-to-end cooperative throughput can be iteratively increased until convergence.
In this embodiment, a power profile of the drone, including an output power value of the drone at each time node, a flight trajectory, which is position information of the drone included at each time node, and a decision distribution variable β may be given, and a first cooperative throughput between the source point transmitter and the destination point receiver initialized, and the decision distribution variable β may be determinednThe continuous variable of 0 to 1 is relaxed to transform the above-mentioned non-convex optimization problem into a typical linear programming problem, which can be solved efficiently by existing optimization techniques.
And 102, calculating the optimal decision distribution, the optimal power distribution and the optimal flight trajectory of the unmanned aerial vehicle according to the power distribution, the flight trajectory and the first cooperation throughput optimization.
The problem of maximizing end-to-end cooperative throughput is reduced by the steps as follows:
where n is the nth time slot, βnIs the binary decision of the drone at the nth time slot, wherein βn1 indicates that the drone serves as a relay at the nth slot, βnWith 0 meaning that the drone derives energy from the source signal at n time slots, rnFor cooperative communication rate in the nth slot, rdThe direct connection rate from the source transmitter to the destination receiver.
The following constraints are specifically required to be satisfied for solving the problem of maximizing end-to-end cooperative throughput:
wherein p isiFor unmanned aerial vehicle power at the ith time slot, βiFor the binary decision of the drone at the ith time slot, PsTransmitting power, gamma, for a source point transmitter0For the reference signal-to-noise ratio of the cooperative channel,
is the distance of the source point from the drone at the ith time slot.
Since the optimal solution of the above equation is usually non-binary, the feasible decision distribution variables obtained after the iterative optimization of the total problem need to be binarized. Therefore, an algorithm for binarizing the decision distribution can be proposed in this subproblem: if the constraint conditions of the subproblems are all satisfied, rounding the optimal decision distribution as a feasible solution; otherwise, the partial integer in the whole N time slot must be inverted to a negative value. As a greedy algorithm, a roll-over process is run in each positive decision distribution to minimize the decrement in end-to-end cooperative throughput caused by the roll-over decision distribution under all constraints that satisfy the subproblems.
Decision distribution β from optimizationnAnd the flight path [ x ] given in the above stepn,yn]The problem can be reduced to a power distribution pnThe solution of (1) reduces the problem of maximizing end-to-end cooperative throughput to:
where n is the nth time slot, pnFor unmanned aerial vehicle power at nth time slot, βnIs the binary decision of the drone at the nth time slot, wherein βn1 indicates that the drone serves as a relay at the nth slot, βnWith 0 meaning that the drone derives energy from the source signal at n time slots, rnIs the cooperative communication rate at the nth slot.
The following constraints are specifically required to be satisfied for solving the problem of maximizing end-to-end cooperative throughput:
wherein p isiFor unmanned aerial vehicle power at the ith time slot, βiFor the binary decision of the drone at the ith time slot, PsTransmitting power, gamma, for a source point transmitter0For the reference signal-to-noise ratio of the cooperative channel,
is the distance of the source point from the drone at the ith time slot.
The subproblem is a standard convex optimization problem and meets Slaters conditions, so that strong dual is established, a Lagrangian dual equation of the above formula can be obtained according to a Lagrangian multiplier method, and then an optimal solution is obtained by solving the dual equation of the Lagrangian dual equation.
Decision distribution β from optimizationnAnd power distribution pnThe problem can be reduced to a flight trajectory [ x ]n,yn]The solution of (1) reduces the problem of maximizing end-to-end cooperative throughput to:
where n is the nth time slot, βnIs the binary decision of the drone at the nth time slot, wherein βn1 indicates that the drone serves as a relay at the nth slot, βnWith 0 meaning that the drone derives energy from the source signal at n time slots, rnFor cooperative communication rate in the nth slot, [ x ]n,yn]The position of the drone is the nth slot.
The following constraints are specifically required to be satisfied for solving the problem of maximizing end-to-end cooperative throughput:
(x1-xs)2+(y1-ys)2≤V2
(xe-xN)2+(ye-yN)2≤V2
wherein p isiFor unmanned aerial vehicle power at the ith time slot, βiFor the binary decision of the drone at the ith time slot, PsTransmitting power, gamma, for a source point transmitter0For the reference signal-to-noise ratio of the cooperative channel,
distance, x, from the source point to the drone at the ith time slotsIs the abscissa of the initial position of the unmanned aerial vehicle, ysIs the vertical coordinate, x, of the initial position of the unmanned aerial vehicleeIs the abscissa of the terminal position of the unmanned aerial vehicle, yeIs the vertical coordinate of the terminal position of the unmanned aerial vehicle.
Due to the above-mentioned objective function in the flight path variable [ xn,yn]The upper surface is non-convex, so the sub-problem is non-convex. Thus, a continuous convex optimization technique may be employed to maximize the lower bound of the trajectory by iteratively optimizing the trajectory increments. The approximation of the first order Taylor expansion due to the convex function is the global lower bound. Therefore, the initial track of the unmanned aerial vehicle is given, and in a predefined tolerance range, the optimal track increment is solved and the track of the unmanned aerial vehicle is updated through an interior point method to obtain the optimal track of the unmanned aerial vehicle.
And calculating the optimal decision distribution, the optimal power distribution and the optimal flight trajectory of the unmanned aerial vehicle according to the power distribution, the flight trajectory and the first cooperative throughput optimization, wherein the optimal decision distribution comprises the working state of the unmanned aerial vehicle at each time node, and the working state is the unmanned aerial vehicle serving as a communication relay between the source point transmitter and the destination point receiver or energy collection from a source signal transmitted by the source point transmitter.
And 103, performing cooperative communication according to the optimal decision distribution, the optimal power distribution and the optimal flight trajectory.
Specifically, the unmanned aerial vehicle performs cooperative communication according to the optimal decision distribution, the optimal power distribution and the optimal flight trajectory obtained by the optimization calculation in the above steps, so as to maximize cooperative throughput between the source point transmitter and the destination point receiver.
In the embodiment, by introducing the wireless energy-carrying communication into the auxiliary communication of the unmanned aerial vehicle, the energy limit of the unmanned aerial vehicle can be effectively relieved by acquiring wireless energy from a radio signal transmitted by a source point, and the optimal decision distribution, the optimal power distribution and the optimal flight trajectory of the unmanned aerial vehicle are calculated by initializing the power distribution, the flight trajectory and the first cooperative throughput optimization between the source point transmitter and the destination point receiver of the unmanned aerial vehicle, so that the cooperative throughput between the source point transmitter and the destination point receiver is maximized.
Fig. 3 is a flowchart illustrating a communication method for cooperative transmission of information and energy according to another exemplary embodiment. As shown in fig. 3, the communication method for information and energy cooperative transmission provided by this embodiment includes:
It should be noted that, the specific implementation manner of step 201 refers to the description of step 101 in the embodiment shown in fig. 2, and is not described herein again.
And 202, calculating decision distribution of the unmanned aerial vehicle according to the power distribution and flight trajectory optimization.
Specifically, the problem of maximizing end-to-end cooperative throughput is reduced by the above steps to:
where n is the nth time slot, βnIs the binary decision of the drone at the nth time slot, wherein βn1 indicates that the drone serves as a relay at the nth slot, βnWith 0 meaning that the drone derives energy from the source signal at n time slots, rnFor cooperative communication rate in the nth slot, rdThe direct connection rate from the source transmitter to the destination receiver.
The following constraints are specifically required to be satisfied for solving the problem of maximizing end-to-end cooperative throughput:
wherein p isiFor unmanned aerial vehicle power at the ith time slot, βiFor the binary decision of the drone at the ith time slot, PsTransmitting power, gamma, for a source point transmitter0For the reference signal-to-noise ratio of the cooperative channel,
is at the same timeThe distance between the source point and the unmanned aerial vehicle at the ith time slot.
Since the optimal solution of the above equation is usually non-binary, the feasible decision distribution variables obtained after the iterative optimization of the total problem need to be binarized. Therefore, an algorithm for binarizing the decision distribution can be proposed in this subproblem: if the constraint conditions of the subproblems are all satisfied, rounding the optimal decision distribution as a feasible solution; otherwise, the partial integer in the whole N time slot must be inverted to a negative value. As a greedy algorithm, a roll-over process is run in each positive decision distribution to minimize the decrement in end-to-end cooperative throughput caused by the roll-over decision distribution under all constraints that satisfy the subproblems.
And step 203, updating the power distribution according to the decision distribution and the flight trajectory.
Specifically, decision distribution β is derived from the optimizationnAnd the flight path [ x ] given in the above stepn,yn]The problem can be reduced to a power distribution pnThe solution of (1) reduces the problem of maximizing end-to-end cooperative throughput to:
where n is the nth time slot, pnFor unmanned aerial vehicle power at nth time slot, βnIs the binary decision of the drone at the nth time slot, wherein βn1 indicates that the drone serves as a relay at the nth slot, βnWith 0 meaning that the drone derives energy from the source signal at n time slots, rnIs the cooperative communication rate at the nth slot.
The following constraints are specifically required to be satisfied for solving the problem of maximizing end-to-end cooperative throughput:
wherein p isiFor unmanned aerial vehicle power at the ith time slot, βiFor the binary decision of the drone at the ith time slot, PsTransmitting power, gamma, for a source point transmitter0For the reference signal-to-noise ratio of the cooperative channel,
is the distance of the source point from the drone at the ith time slot.
The subproblem is a standard convex optimization problem and meets Slaters conditions, so that strong dual is established, a Lagrangian dual equation of the above formula can be obtained according to a Lagrangian multiplier method, and then an optimal solution is obtained by solving the dual equation of the Lagrangian dual equation.
And step 204, updating the flight trajectory according to the decision distribution and the power distribution.
Specifically, decision distribution β is derived from the optimizationnAnd power distribution pnThe problem can be reduced to a flight trajectory [ x ]n,yn]The solution of (1) reduces the problem of maximizing end-to-end cooperative throughput to:
where n is the nth time slot, βnIs the binary decision of the drone at the nth time slot, wherein βn1 indicates that the drone serves as a relay at the nth slot, βnWith 0 meaning that the drone derives energy from the source signal at n time slots, rnFor cooperative communication rate in the nth slot, [ x ]n,yn]The position of the drone is the nth slot.
The following constraints are specifically required to be satisfied for solving the problem of maximizing end-to-end cooperative throughput:
(x1-xs)2+(y1-ys)2≤V2
(xe-xN)2+(ye-yN)2≤V2
wherein p isiFor unmanned aerial vehicle power at the ith time slot, βiFor the binary decision of the drone at the ith time slot, PsTransmitting power, gamma, for a source point transmitter0For the reference signal-to-noise ratio of the cooperative channel,
distance, x, from the source point to the drone at the ith time slotsIs the abscissa of the initial position of the unmanned aerial vehicle, ysIs the vertical coordinate, x, of the initial position of the unmanned aerial vehicleeIs the abscissa of the terminal position of the unmanned aerial vehicle, yeIs the vertical coordinate of the terminal position of the unmanned aerial vehicle.
Due to the above-mentioned objective function in the flight path variable [ xn,yn]The upper surface is non-convex, so the sub-problem is non-convex. Thus, a continuous convex optimization technique may be employed to maximize the lower bound of the trajectory by iteratively optimizing the trajectory increments. The approximation of the first order Taylor expansion due to the convex function is the global lower bound. Therefore, the initial track of the unmanned aerial vehicle is given, and in a predefined tolerance range, the optimal track increment is solved and the track of the unmanned aerial vehicle is updated through an interior point method to obtain the optimal track of the unmanned aerial vehicle.
Specifically, a second cooperative throughput between the source point transmitter and the destination point receiver is calculated according to the optimally calculated decision distribution, power distribution, flight trajectory and the objective function.
Specifically, it is determined whether a difference between the second cooperative throughput and the first cooperative throughput is smaller than a preset tolerance value. If yes, step 207 is executed, and if no, step 209 is executed.
And step 207, outputting the decision distribution, the power distribution and the flight trajectory as the optimal decision distribution, the optimal power distribution and the optimal flight trajectory of the unmanned aerial vehicle respectively.
And if the difference between the second cooperation throughput and the first cooperation throughput between the source point transmitter and the destination point receiver is less than a preset tolerance value, calculating according to the optimally calculated decision distribution, power distribution, flight trajectory and the objective function. It is indicated that the optimized computation end-to-end cooperative throughput has been iterated to converge.
And step 208, performing cooperative communication according to the optimal decision distribution, the optimal power distribution and the optimal flight trajectory.
It should be noted that, the specific implementation of step 208 refers to the description of step 103 in the embodiment shown in fig. 2, and is not described herein again.
Step 209 updates the first cooperative throughput to the second cooperative throughput.
Specifically, a specific value of the second cooperation throughput calculated through the steps is assigned to the first cooperation throughput, and then the steps 201 to 206 are repeated according to the calculated power distribution and flight trajectory of the unmanned aerial vehicle until whether the difference between the second cooperation throughput and the first cooperation throughput is smaller than a preset tolerance value, that is, the optimal calculation end-to-end cooperation throughput is iterated until convergence.
In the following, in combination with the communication method of information and energy cooperative transmission provided by the present embodiment and the comparison of end-to-end cooperative throughput and source point power setting of two baseline policies, fig. 4 is a comparison graph of the effect of using the method provided by the present embodiment and the prior art method. As shown in fig. 4, specifically, for the static policy, the unmanned aerial vehicle continuously spirals at the midpoint between the source point and the destination point, energy is collected in a manner of energy storage and energy transmission, the time can be divided into 50 time slots, that is, the unmanned aerial vehicle collects energy from the source point in the first m time slots and transmits information as a relay in the remaining 50-m time slots, and the harvested energy is evenly distributed in the 50-m time slots, and the optimal number of energy collection time slots is determined by enumerating m, and finally the optimal energy is selected; the other strategy is a linear movement strategy, which means that the unmanned aerial vehicle flies along a linear track from a starting position to an end point, and energy collection and relay transmission are respectively carried out in front and back 25 time slots; finally, the strategy provided by the communication method for the cooperative transmission of the information and the energy provided by the embodiment is provided. It can be seen from the figure that, because the unmanned aerial vehicle decision distribution of this embodiment, power curve and unmanned aerial vehicle flight trajectory are reasonable in design, along with the increase of source point power, the performance gap also increases significantly.
Fig. 5 is a schematic diagram illustrating a structure of a communication device for cooperative transmission of information and energy according to an exemplary embodiment. As shown in fig. 5, the communication apparatus for information and energy cooperative transmission provided by this embodiment is applied to a cooperative communication system for information and energy cooperative transmission, and the system includes: a source transmitter, an endpoint receiver, and a drone as a communication relay between the source transmitter and the endpoint receiver, the drone being further configured to collect energy from a source signal sent by the source transmitter; the method comprises the following steps:
an initialization module 301, configured to initialize a power distribution of the drone, a flight trajectory, and a first cooperative throughput between the source transmitter and the destination receiver, where the power distribution includes an output power value of the drone at each time node, and the flight trajectory is position information of the drone at each time node;
an optimization calculation module 302, configured to calculate an optimal decision distribution, an optimal power distribution, and an optimal flight trajectory of the drone according to the power distribution, the flight trajectory, and the first cooperative throughput optimization, where the optimal decision distribution includes an operating state of the drone at each time node, and the operating state is a drone serving as a communication relay between the source transmitter and the destination receiver or energy collection from a source signal transmitted by the source transmitter;
a cooperative communication module 303, configured to perform cooperative communication according to the optimal decision distribution, the optimal power distribution, and the optimal flight trajectory, so as to maximize cooperative throughput between the source point transmitter and the destination point receiver.
In one possible design, the optimization calculation module 302 is specifically configured to:
calculating decision distribution of the unmanned aerial vehicle according to the power distribution and the flight trajectory optimization;
updating the power distribution according to the decision distribution and the flight trajectory;
updating the flight trajectory according to the decision distribution and the power distribution;
calculating a second cooperative throughput between the source transmitter and the destination receiver from the decision profile, the power profile, and the flight trajectory;
judging whether the difference between the second cooperation throughput and the first cooperation throughput is smaller than a preset tolerance value or not;
if the decision distribution, the power distribution and the flight trajectory are output as the optimal decision distribution, the optimal power distribution and the optimal flight trajectory of the unmanned aerial vehicle respectively.
In a possible design, the optimization calculation module 302 is further specifically configured to: when the difference between the second cooperative throughput and the first cooperative throughput is not less than a preset tolerance value;
updating the first cooperative throughput to the second cooperative throughput;
calculating decision distribution of the unmanned aerial vehicle according to the power distribution and the flight trajectory optimization;
updating the power distribution according to the decision distribution and the flight trajectory;
updating the flight trajectory according to the decision distribution and the power distribution;
updating a second cooperative throughput between the source transmitter and the destination receiver as a function of the decision profile, the power profile, and the flight trajectory;
judging whether the difference between the second cooperation throughput and the first cooperation throughput is smaller than the preset tolerance value or not;
if the decision distribution, the power distribution and the flight trajectory are output as the optimal decision distribution, the optimal power distribution and the optimal flight trajectory of the unmanned aerial vehicle respectively.
In a possible design, the optimization calculation module 302 is further specifically configured to:
relaxing the decision variables of the decision distribution into continuous variables from 0 to 1 so as to convert the optimization calculation problem of the decision distribution into a linear programming problem;
solving the linear programming problem to obtain the decision distribution.
In a possible design, the optimization calculation module 302 is further specifically configured to:
converting the optimization calculation problem of the power distribution into a Lagrange dual equation solving problem according to a Lagrange multiplier method;
solving the optimal solution to the Lagrangian dual equation to obtain the power distribution.
In a possible design, the optimization calculation module 302 is further specifically configured to:
calculating the track increment of the unmanned aerial vehicle in unit time according to the decision distribution and the power distribution;
and updating the flight track according to the initial position parameters in the flight track and the track increment.
In a possible design, the optimization calculation module 302 is further specifically configured to:
binarizing the optimal decision distribution such that the drone acts as a relay between the source transmitter and the destination receiver when the optimal decision distribution has a value of 1, and energy is collected from a source signal transmitted by the source transmitter when the drone has a value of 0 when the optimal decision distribution has a value of 0.
The communication device for cooperative transmission of information and energy provided in the embodiment shown in fig. 5 may be used to execute the method provided in the embodiments shown in fig. 2 to fig. 3, and the specific implementation manner and technical effect are similar and will not be described herein again.
In addition, the present invention also provides an unmanned aerial vehicle, comprising: the vehicle-mounted battery and the communication device for the cooperative transmission of the information and the energy provided by the embodiment shown in FIG. 4;
the vehicle-mounted battery is used for providing power for the maneuvering flight of the unmanned aerial vehicle;
the cooperative communication device is used for providing a relay between the source point transmitter and the destination point receiver or collecting energy from a source signal sent by the source point transmitter;
wherein the energy collected by the cooperative communication device from the source point transmitter is used to power communication transmissions of the drone.
In addition, the invention also provides a communication system for information and energy cooperative transmission, which comprises: the source point transmitter, the destination point receiver and the unmanned aerial vehicle provided by the above embodiments are used as a communication relay between the source point transmitter and the destination point receiver or collect energy from a source signal sent by the source point transmitter.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. A communication method for cooperative transmission of information and energy is applied to a cooperative communication system for cooperative transmission of information and energy, and the system comprises: a source transmitter, an endpoint receiver, and a drone as a communication relay between the source transmitter and the endpoint receiver, the drone being further configured to collect energy from a source signal sent by the source transmitter; the method comprises the following steps:
initializing a power profile of the drone, a flight trajectory, and a first coordinated throughput between the source transmitter and the destination receiver, wherein the power profile includes an output power value of the drone at each time node, and the flight trajectory is position information of the drone including at each time node;
calculating an optimal decision distribution, an optimal power distribution and an optimal flight trajectory of the drone according to the power distribution, the flight trajectory and the first cooperative throughput optimization, wherein the optimal decision distribution comprises an operating state of the drone at each time node, the operating state being a drone serving as a communication relay between the source transmitter and the destination receiver or energy collection from a source signal transmitted by the source transmitter;
performing cooperative communication according to the optimal decision profile, the optimal power profile, and the optimal flight trajectory to maximize cooperative throughput between the source transmitter and the destination receiver;
the optimally calculating an optimal decision distribution, an optimal power distribution and an optimal flight trajectory of the drone according to the power distribution, the flight trajectory and the first cooperative throughput optimization comprises:
calculating decision distribution of the unmanned aerial vehicle according to the power distribution and the flight trajectory optimization;
updating the power distribution according to the decision distribution and the flight trajectory;
updating the flight trajectory according to the decision distribution and the power distribution;
calculating a second cooperative throughput between the source transmitter and the destination receiver from the decision profile, the power profile, and the flight trajectory;
judging whether the difference between the second cooperation throughput and the first cooperation throughput is smaller than a preset tolerance value or not;
if the decision distribution, the power distribution and the flight trajectory are output as the optimal decision distribution, the optimal power distribution and the optimal flight trajectory of the unmanned aerial vehicle respectively.
2. The method of claim 1, wherein determining whether a difference between the second cooperative throughput and the first cooperative throughput is less than a preset tolerance value is performed, and determining no;
updating the first cooperative throughput to the second cooperative throughput;
calculating decision distribution of the unmanned aerial vehicle according to the power distribution and the flight trajectory optimization;
updating the power distribution according to the decision distribution and the flight trajectory;
updating the flight trajectory according to the decision distribution and the power distribution;
updating a second cooperative throughput between the source transmitter and the destination receiver as a function of the decision profile, the power profile, and the flight trajectory;
judging whether the difference between the second cooperation throughput and the first cooperation throughput is smaller than the preset tolerance value or not;
if the decision distribution, the power distribution and the flight trajectory are output as the optimal decision distribution, the optimal power distribution and the optimal flight trajectory of the unmanned aerial vehicle respectively.
3. The method of claim 1, wherein said calculating a decision profile for the drone from the power profile and the flight trajectory comprises:
relaxing the decision variables of the decision distribution into continuous variables from 0 to 1 so as to convert the optimization calculation problem of the decision distribution into a linear programming problem;
solving the linear programming problem to obtain the decision distribution.
4. The method of claim 1, wherein updating the power profile based on the decision profile and the flight trajectory comprises:
converting the optimization calculation problem of the power distribution into a Lagrange dual equation solving problem according to a Lagrange multiplier method;
solving the optimal solution to the Lagrangian dual equation to obtain the power distribution.
5. The method of claim 1, wherein updating the flight trajectory based on the decision profile and the power profile comprises:
calculating the track increment of the unmanned aerial vehicle in unit time according to the decision distribution and the power distribution;
and updating the flight track according to the initial position parameters in the flight track and the track increment.
6. The method of any of claims 1-5, further comprising, prior to outputting the decision profile, the power profile, and the flight trajectory as the optimal decision profile, the optimal power profile, and the optimal flight trajectory, respectively, for the drone:
binarizing the optimal decision distribution such that the drone acts as a relay between the source transmitter and the destination receiver when the optimal decision distribution has a value of 1, and energy is collected from a source signal transmitted by the source transmitter when the drone has a value of 0 when the optimal decision distribution has a value of 0.
7. A communication device for cooperative transmission of information and energy, which is applied to a cooperative communication system for cooperative transmission of information and energy, the system comprising: a source transmitter, an endpoint receiver, and a drone as a communication relay between the source transmitter and the endpoint receiver, the drone being further configured to collect energy from a source signal sent by the source transmitter; the method comprises the following steps:
an initialization module configured to initialize a power distribution of the drone, a flight trajectory, and a first cooperative throughput between the source transmitter and the destination receiver, wherein the power distribution includes an output power value of the drone at each time node, and the flight trajectory is position information of the drone at each time node;
an optimization calculation module, configured to optimally calculate an optimal decision distribution, an optimal power distribution, and an optimal flight trajectory of the drone according to the power distribution, the flight trajectory, and the first cooperative throughput, where the optimal decision distribution includes an operating state of the drone at each time node, and the operating state is the drone serving as a communication relay between the source point transmitter and the destination point receiver or energy collection from a source signal transmitted by the source point transmitter;
a cooperative communication module configured to perform cooperative communication according to the optimal decision distribution, the optimal power distribution, and the optimal flight trajectory to maximize cooperative throughput between the source transmitter and the destination receiver;
the optimization calculation module is specifically configured to:
calculating decision distribution of the unmanned aerial vehicle according to the power distribution and the flight trajectory optimization;
updating the power distribution according to the decision distribution and the flight trajectory;
updating the flight trajectory according to the decision distribution and the power distribution;
calculating a second cooperative throughput between the source transmitter and the destination receiver from the decision profile, the power profile, and the flight trajectory;
judging whether the difference between the second cooperation throughput and the first cooperation throughput is smaller than a preset tolerance value or not;
if the decision distribution, the power distribution and the flight trajectory are output as the optimal decision distribution, the optimal power distribution and the optimal flight trajectory of the unmanned aerial vehicle respectively.
8. An unmanned aerial vehicle, comprising: a vehicle-mounted battery and a communication device for the cooperative transmission of information and energy according to claim 7;
the vehicle-mounted battery is used for providing power for the maneuvering flight of the unmanned aerial vehicle;
the cooperative communication device is used for providing a relay between the source point transmitter and the destination point receiver or collecting energy from a source signal sent by the source point transmitter;
wherein the energy collected by the cooperative communication device from the source point transmitter is used to power communication transmissions of the drone.
9. A communication system for cooperative transmission of information and energy, comprising: a source transmitter, an end point receiver, and a drone as claimed in claim 8, the drone being for relaying communication between the source transmitter and the end point receiver or harvesting energy from a source signal sent by the source transmitter.
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