CN113194446B - Unmanned aerial vehicle auxiliary machine communication method - Google Patents

Unmanned aerial vehicle auxiliary machine communication method Download PDF

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CN113194446B
CN113194446B CN202110432123.2A CN202110432123A CN113194446B CN 113194446 B CN113194446 B CN 113194446B CN 202110432123 A CN202110432123 A CN 202110432123A CN 113194446 B CN113194446 B CN 113194446B
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aerial vehicle
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白琳
张学军
王佳星
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Beihang University
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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
    • H04W4/70—Services for machine-to-machine communication [M2M] or machine type communication [MTC]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22—Traffic simulation tools or models
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W74/00—Wireless channel access
    • H04W74/08—Non-scheduled access, e.g. ALOHA
    • H04W74/0808—Non-scheduled access, e.g. ALOHA using carrier sensing, e.g. carrier sense multiple access [CSMA]
    • H04W74/0825—Non-scheduled access, e.g. ALOHA using carrier sensing, e.g. carrier sense multiple access [CSMA] with collision detection
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention discloses an unmanned aerial vehicle auxiliary machine communication method, belonging to the field of unmanned aerial vehicle communication; the method specifically comprises the following steps: firstly, a communication scene that a central base station and N unmanned aerial vehicles cover the M machine devices is established; each machine device simultaneously sends respective information to all unmanned aerial vehicles, respectively calculates and stores signals received by each unmanned aerial vehicle, and all unmanned aerial vehicles return all collected signals to the central base station; the central base station obtains an unmanned aerial vehicle channel matrix H through l according to the received signal Y and the pre-estimationF1‑lFAnd constructing a compressed sensing model by the norm difference, solving by an alternative direction multiplier method to obtain data S sent by the user, and realizing communication of the auxiliary machines. The invention effectively reduces the energy loss of the machine type communication device and improves the service time of the machine type communication device.

Description

Unmanned aerial vehicle auxiliary machine communication method
Technical Field
The invention belongs to the field of unmanned aerial vehicle communication, and particularly relates to an unmanned aerial vehicle auxiliary machine communication method.
Background
Currently, most research efforts in the field of wireless communication networks are mainly focused on land mobile communication systems. The ground communication base station is fixed in position, so that the ground communication base station is severely restricted in a scene needing flexible deployment or an application scene built by temporary communication nodes. Conventional cellular wireless networks do provide reliable and stable communication quality to some extent, but cannot meet some emergency communication requirements requiring fast mobility. For example, in the face of communication scenes such as forest patrol, temporary battlefield and the like, the traditional base station cellular communication network is limited by the coverage range, and therefore the problem of network coverage range change caused by the requirement of fast movement cannot be solved well. In addition, the ground station arrangement and operation cost is high, and the ground station arrangement and operation cost is also an important factor for restricting the traditional ground station-based communication. Therefore, a fast, flexible and low-cost wireless communication method is needed to solve the problems of temporary establishment of a wireless network or movement of a coverage area.
Under this background, unmanned aerial vehicle communication arises as it is. Unmanned aerial vehicle communication because its mobility is stronger, can dispose according to indexes such as communication service demand, communication quality and communication efficiency in a flexible way, and unmanned aerial vehicle basic station operation cost is low, can regard as the good replenishment of ground station communication. Aiming at the high-quality communication demand in the urban complex environment and the establishment of emergency communication links in battlefields and disaster areas, unmanned aerial vehicle communication plays a crucial role. By using the unmanned aerial vehicle platform as a temporary mobile base station and assisting with the existing network architecture for wireless information transmission, the wireless network coverage and the fast movement of the coverage area can be realized. The unmanned aerial vehicle can also form a virtual multi-antenna array in a mutual cooperation mode so as to further improve the throughput of the system and ensure the reliability of emergency communication.
At present, unmanned aircraft systems have played an increasingly important role in national defense and economic construction, and application requirements are continuously increasing. With the vigorous development of the unmanned aerial vehicle industry and the continuous expansion of the application range thereof, a communication scheme using rapid maneuverability, flexible deployment and low operation cost of the unmanned aerial vehicle becomes a research hotspot at present.
Disclosure of Invention
In order to realize large-scale machine type communication and make up the defects that a fixed far-end radio frequency device cannot meet the requirements of deployment as required and flexible distribution, the invention provides an unmanned aerial vehicle auxiliary machine type communication method.
The unmanned aerial vehicle auxiliary machinery communication method specifically comprises the following steps:
step one, building a communication scene of a central base station, N unmanned aerial vehicles and M machine devices;
n < < M; the M machine type devices are randomly distributed, the N unmanned aerial vehicles cover the upper parts of all the machine type devices according to the distribution quantity and range of the machine type devices, and the N unmanned aerial vehicles are all connected with the central base station and transmit the collected radio signals of all the machine type devices to the central base station;
the number of the antennas of the N unmanned aerial vehicles and the M machine devices is K respectively.
Step two, taking each machine device as a user, simultaneously sending information of all the users to N unmanned aerial vehicles through wireless channels, and calculating signals received by each unmanned aerial vehicle;
the number of the activated M machine devices is Q, and Q is less than M;
the signal that nth unmanned aerial vehicle received is:
Figure GDA0003469196390000021
wherein the content of the first and second substances,
Figure GDA0003469196390000022
for the wireless channel from the mth user to the nth drone, N is 1 … … N;
Figure GDA0003469196390000023
for the signal transmitted by the m-th user,
Figure GDA0003469196390000024
smkta signal sent by an antenna k at the t moment for the mth user; t is the total length of the activation time period; if the mth user is an inactivated user, the signals sent by the mth user in the time period T are all 0; namely Sm0, otherwise, Sm≠0；
Figure GDA0003469196390000025
For additive noise, wnktThe signal received at time t for the kth antenna of the nth drone.
Thirdly, each unmanned aerial vehicle respectively temporarily stores the received signals in a memory until all unmanned aerial vehicles are completely collected, all the signals are transmitted back to the central base station, and a signal Y received by the central base station is calculated;
the signal received by the central base station is calculated by the formula:
Figure GDA0003469196390000031
s is a matrix with the dimensionality of KM multiplied by T; the set of Q active users is: { a1,a2,…aQE.g. {1,2, … M }; then a corresponding signaling is sent for each active user
Figure GDA0003469196390000032
All the signals are not 0, and the corresponding values of the signals sent by other users are 0.
Step four, the central base station passes through l according to the received signal Y and the unmanned aerial vehicle channel matrix H obtained by pre-estimationF1-lFThe norm difference is used for estimating a compressed sensing model constructed by the transmitted signal S;
the compressed sensing model is as follows:
Figure GDA0003469196390000033
Figure GDA0003469196390000034
the target function represents a finally obtained sending signal matrix S, the F-1 norm minus the F norm is satisfied to obtain the minimum value, and the sparsity of the sending signal matrix S is reflected;
the constraint condition represents that the product of the solved sending signal matrix S and the channel matrix H is subtracted from the received signal Y, and the product is as close as possible to additive white Gaussian noise with power of epsilon;
wherein the content of the first and second substances,
Figure GDA0003469196390000035
is the signal S transmitted by the mth usermF norm of (d); | S | non-woven phosphorFIs the F norm of all users' transmitted signals S;
Figure GDA0003469196390000036
is the square of the F-norm of the matrix.
And step five, solving the compressed sensing model by an alternative direction multiplier method to obtain data S sent by the user, so as to realize the communication of the auxiliary machines.
Compared with the prior art, the invention has the following advantages:
(1) the unmanned aerial vehicle-assisted machine communication method provided by the invention has the advantages that the unmanned aerial vehicle is used for collecting radio signals of users as machine communication devices, so that the requirements of communication network deployment as required and flexible distribution can be met;
(2) according to the unmanned aerial vehicle auxiliary machine communication method, the machine communication device can be activated when information is transmitted, and is in a standby state when no information is transmitted, so that the energy loss of the machine communication device is effectively reduced, and the service life of the machine communication device is prolonged;
(3) the communication method for the auxiliary machines of the unmanned aerial vehicle can allow a plurality of communication devices to be randomly activated at the same time, and signal detection failure caused by information transmission conflict can be avoided;
(4) according to the unmanned aerial vehicle auxiliary machine communication method, the wireless communication device adopts a random access strategy, handshaking is not needed, and the complexity of the machine communication device is further reduced;
(5) the invention relates to an unmanned aerial vehicle auxiliary machine communication method, which is characterized in that a compressed sensing algorithm is utilized to detect radio signals of a machine communication device, the calculation complexity is low, and information sent by the machine communication device can be accurately detected.
Drawings
Fig. 1 is a flow chart of a communication method of an auxiliary machine type of unmanned aerial vehicle according to the present invention;
fig. 2 is a scene diagram constructed by the unmanned aerial vehicle and the auxiliary machinery communication device constructed by the invention.
Detailed Description
The present invention will be described in further detail and with reference to the accompanying drawings so that those skilled in the art can understand and practice the invention.
The invention relates to an unmanned aerial vehicle auxiliary machine communication method, which is characterized in that an unmanned aerial vehicle is used for collecting radio information of a machine communication device and returning the collected radio information to a central base station, and the central base station detects signals of the machine communication device through a compressed sensing algorithm, so that machine communication which is deployed according to needs and flexibly distributed is realized.
The communication method for the auxiliary machines of the unmanned aerial vehicle comprises the following specific steps as shown in fig. 1:
step one, a central base station, N unmanned aerial vehicles with K antennas for communication and M machine communication devices with K antennas are set up, wherein N is less than M.
As shown in fig. 2, M machine type communication devices equipped with K antennas are randomly distributed, N unmanned planes cover the space above all the machine type communication devices, and radio signals of the machine type communication devices are collected; n unmanned aerial vehicle links to each other with central basic station simultaneously.
Step two, M machine type communication devices simultaneously send own information to N unmanned aerial vehicles through wireless channels, and respectively calculate signals received by each unmanned aerial vehicle;
according to actual requirements, only a few parts of the M machine type communication devices are in an active state at the same time in the same time slot, and the active number is assumed to be Q, Q < < M. Q machine type communication device that is in activated state simultaneously at same time slot sends the information of oneself to N unmanned aerial vehicle simultaneously through wireless channel, and the signal that nth unmanned aerial vehicle received is:
Figure GDA0003469196390000041
wherein the content of the first and second substances,
Figure GDA0003469196390000042
for the wireless channel from the mth user to the nth drone, N is 1 … … N;
Figure GDA0003469196390000043
for the signal sent by the mth user, M is 1, … … M;
Figure GDA0003469196390000051
smkta signal sent by an antenna k at the t moment for the mth user; t isTotal length of activation period; if the mth user is an inactive user, Sm0; otherwise, Sm≠0；
Figure GDA0003469196390000052
For additive noise, wnktThe signal received at time t for the kth antenna of the nth drone.
Thirdly, the N unmanned aerial vehicles respectively temporarily store the signals received by the various machine communication devices into a memory until all the unmanned aerial vehicles are completely collected, and transmit all the signals back to the central base station to calculate a signal Y received by the central base station;
the signal received by the central base station is calculated by the formula:
Figure GDA0003469196390000053
wherein S is a matrix with the dimensionality of KM multiplied by T; the set of Q active users is: { a1,a2,…aQE.g. {1,2, … M }; then a corresponding signaling is sent for each active user
Figure GDA0003469196390000054
All the signals are not 0, and the corresponding values of the signals sent by other users are 0.
Step four, the central base station passes through l according to the received signal Y and the unmanned aerial vehicle channel matrix H obtained by pre-estimationF1-lFA norm algorithm is used for constructing a compressed sensing model for the transmitted signal S to estimate;
after the central base station obtains the signals returned by each unmanned aerial vehicle, because the number Q of the activated users is far less than the total number M of the users, and the unmanned aerial vehicle channels can be obtained by pilot frequency sequence estimation in advance, the signals are obtained through lF1-lFA norm algorithm, which estimates a sending signal S of a user according to a receiving signal Y and a known channel matrix H;
the compressed sensing model is as follows:
Figure GDA0003469196390000061
Figure GDA0003469196390000062
the objective function represents that a sending signal matrix S is required to be found, the F-1 norm is subtracted from the F norm to obtain the minimum value, and the sparsity of the sending signal matrix S is reflected;
the constraint represents that the product of the received signal Y minus the sought transmitted signal matrix S and the channel matrix H is as close as possible to additive white gaussian noise with power epsilon.
Wherein the content of the first and second substances,
Figure GDA0003469196390000063
is the signal S transmitted by the mth usermF norm of, smktIs the element sent by the kth antenna of the mth user at the tth moment; | S | non-woven phosphorFIs the F norm of all users' transmitted signals S;
Figure GDA0003469196390000064
is the square of the F norm of the matrix; ε is the noise power.
And step five, solving the compressed sensing model through an Alternative Direction Multiplier Method (ADMM) to obtain data S sent by the user, so as to realize communication of the auxiliary machinery devices.

Claims (4)

1. An unmanned aerial vehicle auxiliary machinery communication method is characterized by comprising the following specific steps:
firstly, a communication scene that a central base station and N unmanned aerial vehicles cover the M machine devices is established; each machine device simultaneously sends respective information to all unmanned aerial vehicles, and respectively calculates signals received by each unmanned aerial vehicle;
then, each unmanned aerial vehicle respectively temporarily stores the received signals in a memory until all unmanned aerial vehicles are completely collected, all the signals are transmitted back to the central base station, and a signal Y received by the central base station is calculated;
the signal received by the central base station is calculated by the formula:
Figure FDA0003469196380000011
Figure FDA0003469196380000012
a wireless channel for the mth robotic device to the nth drone; the number of the antennae of the N unmanned aerial vehicles and the M machine devices is respectively K;
Figure FDA0003469196380000013
a signal transmitted for the mth machine type device; t is the total time period for activating the machinery device; s is a matrix with the dimensionality of KM multiplied by T; wnAdditive noise for the nth drone;
the number of activations in the M machine devices is Q, and the set is as follows: { a1,a2,…aQE.g. {1,2, … M }; then a corresponding signaling is sent for each active machine type device
Figure FDA0003469196380000014
All the signals are not 0, and the corresponding values of the signals sent by the other machine devices are all 0;
and finally, the central base station passes through l according to the received signal Y and the unmanned aerial vehicle channel matrix H obtained by pre-estimationF1-lFAnd constructing a compressed sensing model by the norm difference, and solving by an alternative direction multiplier method to obtain data S sent by the machine device, thereby realizing the communication of the auxiliary machines.
2. An unmanned aerial vehicle assisted machine type communication method as claimed in claim 1, wherein in the communication scenario, N < < M; m machine type device distributes at random, and N unmanned aerial vehicle covers the top at all machine type devices according to the distribution quantity and the scope of machine type device, and N unmanned aerial vehicle all is connected with central basic station, transmits the radio signal of all machine type devices of collecting for central basic station.
3. The drone-assisted machine type communication method of claim 1, wherein the signal received by the nth drone is:
Figure FDA0003469196380000015
wherein the content of the first and second substances,
Figure FDA0003469196380000021
smktthe signal sent by an antenna k at the t moment for the mth machine type device;
if the mth machine type device is an inactive machine type device, the sending signals of the mth machine type device in the time period T are all 0; namely Sm0, otherwise, Sm≠0；
Figure FDA0003469196380000022
wnktThe signal received at time t for the kth antenna of the nth drone.
4. The drone-assisted machine type communication method of claim 1, wherein the signal received by the nth drone is: the compressed sensing model is as follows:
Figure FDA0003469196380000023
Figure FDA0003469196380000024
the target function represents a finally obtained sending signal matrix S, the F-1 norm minus the F norm is satisfied to obtain the minimum value, and the sparsity of the sending signal matrix S is reflected;
the constraint condition represents that the product of the solved sending signal matrix S and the channel matrix H is subtracted from the received signal Y, and the product is as close as possible to additive white Gaussian noise with power of epsilon;
wherein the content of the first and second substances,
Figure FDA0003469196380000025
is the signal S transmitted by the mth machinemF norm of (d); | S | non-woven phosphorFIs the F norm of the signal S transmitted by all the machine type devices;
Figure FDA0003469196380000026
is the square of the F-norm of the matrix.
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