CN112672361A - Large-scale MIMO capacity increasing method based on unmanned aerial vehicle cluster deployment - Google Patents

Large-scale MIMO capacity increasing method based on unmanned aerial vehicle cluster deployment Download PDF

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CN112672361A
CN112672361A CN202011492087.0A CN202011492087A CN112672361A CN 112672361 A CN112672361 A CN 112672361A CN 202011492087 A CN202011492087 A CN 202011492087A CN 112672361 A CN112672361 A CN 112672361A
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高宁
金石
李潇
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Southeast University
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Abstract

The invention discloses a large-scale MIMO capacity improving method based on unmanned aerial vehicle cluster deployment, which comprises the following steps that firstly, each single-antenna unmanned aerial vehicle is randomly deployed in an area above a multi-antenna ground base station, and each unmanned aerial vehicle assists in estimating channel state information through a geographic position system; then randomly selecting an unmanned aerial vehicle to communicate with a neighbor unmanned aerial vehicle, constructing local information and calculating the current profit; according to the income learning deployment behavior, other unmanned aerial vehicles keep the positions unchanged; and finally, determining the optimal deployment position of each unmanned aerial vehicle after a plurality of rounds of interaction. According to the invention, the deployment position of each unmanned aerial vehicle is adjusted, so that the channel capacity is improved; deployment is completed only by utilizing local information, an optimization control center is not provided, communication energy consumption is reduced, communication control delay is shortened, and endurance and real-time control capability of the unmanned aerial vehicle cluster are improved. Applicable wireless communication scenarios include, but are not limited to: high density place communications, battlefield communications, disaster relief, and rescue communications.

Description

Large-scale MIMO capacity increasing method based on unmanned aerial vehicle cluster deployment
Technical Field
The invention belongs to the field of unmanned aerial vehicle cluster communication, and particularly relates to a method for increasing the channel capacity of an uplink communication link based on a large-scale MIMO (multiple input multiple output) capacity increase method deployed by an unmanned aerial vehicle cluster.
Background
In the development process of mobile communication, the MIMO technology is a key technology for improving data rate and resisting interference in mobile communication, and in recent years, with the continuous increase of frequency bands and the explosive increase of the number of users, the number of antennas at a transmitting end and a receiving end is increased sharply, and the mobile communication technology based on large-scale MIMO becomes a hot topic in academia and industry. The unmanned aerial vehicle communication has the characteristics of high-speed mobility, deployment flexibility, strong line-of-sight communication and the like, can fully develop a large-scale MIMO technology in an air-ground three-dimensional space, and becomes a communication technology with great development potential in a future 6G communication network. In particular, the deployment technology of the unmanned aerial vehicle can maximize the communication performance of the large-scale MIMO network and optimize the network resource configuration by adjusting the deployment position of the unmanned aerial vehicle, and gradually draws wide attention.
In the beginning of research, the optimal deployment condition of the one-dimensional low-altitude flight platform in the altitude direction is researched by taking the maximized large-scale MIMO wireless coverage range as an optimization target. Then, researchers begin to pay attention to the situation of optimal deployment of the unmanned aerial vehicles in the two-dimensional space, optimization targets gradually tend to be diversified, and a combined optimization strategy considering unmanned aerial vehicle position deployment and network resource allocation becomes a relatively hot research direction. Recently, the problem of unmanned aerial vehicle deployment optimization facing three-dimensional space is gradually developed. It is worth pointing out that due to the performance advantages of the multi-unmanned aerial vehicle communication scene, the problem of optimized deployment of large-scale MIMO formed by unmanned aerial vehicle clusters in two-dimensional and three-dimensional spaces is very worthy of close attention. The current unmanned aerial vehicle and unmanned aerial vehicle cluster optimization deployment methods mostly adopt a central optimization control method, and face a lot of problems and challenges under the conditions that communication infrastructure is limited and unmanned aerial vehicle flight capacity is limited.
The unmanned aerial vehicle cluster deployment method based on the center optimization control requires real-time communication between the unmanned aerial vehicle and the optimization control center, which consumes a large amount of communication energy, and on the other hand, wireless communication uncertainty caused by physical phenomena such as wireless channel fading and the like seriously reduces communication quality and does not meet the performance requirements of unmanned aerial vehicle communication control on high-reliability low-delay communication. In addition, in an actual situation, it is difficult for the drone to acquire global information of the network, so that there are some defects in the central deployment method of the drone cluster by using the global information.
Disclosure of Invention
The purpose of the invention is as follows: in order to solve the defects in the background art, the invention provides a large-scale MIMO capacity improving method based on unmanned aerial vehicle cluster deployment, and aims to solve the problems that when a line-of-sight link of an air-ground wireless channel has strong correlation, the deployment position of each unmanned aerial vehicle in a cluster is adjusted in a distributed mode by using local information to increase the rank of a channel matrix, and the channel capacity of a large-scale MIMO uplink communication link formed by an unmanned aerial vehicle cluster and a ground base station is improved.
The technical scheme is as follows: in order to achieve the purpose, the technical scheme of the invention is as follows:
a large-scale MIMO capacity improving method based on unmanned aerial vehicle cluster deployment comprises the following steps:
step 1, clustering all unmanned aerial vehicles to be deployed in an area above a ground base station, and randomly selecting an aerial position as an initial position; initializing the interaction times T of the unmanned aerial vehicle to be 0, and setting the maximum interaction times to be T;
step 2, each unmanned aerial vehicle of the cluster estimates the state of a channel in an auxiliary way through a geographic position system carried by the unmanned aerial vehicle;
step 3, if T is 0, 1, …, T is less than T and the algorithm is not converged, randomly selecting an unmanned aerial vehicle m in the cluster with equal probability; otherwise, the method is terminated, the unmanned aerial vehicle cluster finishes deploying to the optimal position, and the channel capacity is improved;
step 4, the selected unmanned aerial vehicle m communicates with the neighboring unmanned aerial vehicle m, local information is constructed, and the current profit R is calculatedm；
Step 5, the selected unmanned aerial vehicle m selects an exploration behavior from the restricted behavior set according to the agreed behavior exploration probability;
step 6, if the current behavior is a new exploration behavior, the selected unmanned aerial vehicle m calculates income according to the new exploration behavior, updates a behavior selection strategy and returns to the step 3, and other unmanned aerial vehicles in the cluster keep the previous behavior unchanged; otherwise, directly returning to the step 3 for the next iteration interaction.
Further, the channel state in step 2 is estimated by the following formula:
Figure BDA0002841019970000031
wherein h ismnRepresenting the channel between drone m and ground base station nth antenna, dmnThe distance between the unmanned aerial vehicle m and the nth antenna of the ground base station is shown, lambda is the wavelength of the signal, and H is an uplink communication link MIMO channel matrix.
Further, the channel capacity is estimated by the following formula:
C＝log2(det[IN+ρHHH/N])
wherein INIs an N-order identity matrix, and ρ is the signal-to-noise ratio of each receiving antenna.
Further, the channel capacity boost establishes the following optimization objectives:
P:max rank(H)。
further, the profit R described in step 4mSelf-income r by drone mmAnd its neighbor unmanned aerial vehicle profit riSum composition, calculated using the formula:
Figure BDA0002841019970000032
wherein
Figure BDA0002841019970000033
Set of neighbor drones representing drone m, amRepresent unmanned aerial vehicle m
Figure BDA0002841019970000034
Distance between root antenna and first unmanned aerial vehicle, dnkThe distance between the nth antenna of the ground base station and the kth unmanned aerial vehicle is obtained.
Further, the behavior exploration probability of step 5 is:
Figure BDA0002841019970000035
wherein
Figure BDA0002841019970000036
For the exploration behavior currently selected by drone m,
Figure BDA0002841019970000037
in order to explore the new behavior,
Figure BDA0002841019970000038
for previous behavior, emIn order to achieve the purpose of the exploration rate,
Figure BDA0002841019970000039
is the restricted behavior set for drone m.
Further, the update behavior selection policy in step 6 is:
Figure BDA0002841019970000041
Figure BDA0002841019970000042
Figure BDA0002841019970000043
further, unmanned aerial vehicle is rotor unmanned aerial vehicle, but not limited to the rotor unmanned aerial vehicle of characteristic model.
Further, the cluster unmanned aerial vehicle of step 1 is respectively configured with a single antenna, and the ground base station is configured with a plurality of antennas.
Further, the antenna is an omni-directional antenna, but is not limited to an omni-directional antenna.
Has the advantages that: compared with the prior art, the invention has the beneficial effects that:
1) in the large-scale MIMO capacity increasing method based on unmanned aerial vehicle cluster deployment, each unmanned aerial vehicle carries out distributed optimized deployment on an unmanned aerial vehicle cluster based on local information, and maximization of channel capacity of a large-scale MIMO uplink communication link is realized;
2) the method belongs to a distributed deployment method, a network optimization control center is not needed, deployment is not limited to the limitation of a specific network topology framework on the cluster optimization deployment of the unmanned aerial vehicle, and the method can be better suitable for network areas with poor communication infrastructure, such as suburbs or disaster areas;
3) the method only needs communication and local information interaction between neighboring unmanned aerial vehicles, and does not need real-time complex communication and network optimization parameter interaction with an optimization control center; compared with the existing center optimization control method, the method reduces communication energy consumption, shortens communication control time delay, and improves the cruising ability and the real-time control ability of the unmanned aerial vehicle cluster.
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FIG. 1 is a network architecture diagram of a large-scale MIMO capacity boosting method based on unmanned aerial vehicle cluster deployment according to the present invention;
fig. 2 is a flowchart of a large-scale MIMO capacity increasing method based on unmanned aerial vehicle cluster deployment according to the present invention.
Detailed Description
The invention is further illustrated by the following figures and examples.
In the embodiment of the invention, as shown in fig. 1, a large-scale MIMO capacity increasing method network architecture is deployed based on an unmanned aerial vehicle cluster, the unmanned aerial vehicle cluster is located in an area above a ground base station, M cluster unmanned aerial vehicles are respectively provided with a single antenna, each unmanned aerial vehicle is a rotor unmanned aerial vehicle, and the ground base station is provided with N antennas. For simplifying the analysis, if the number M of the cluster unmanned aerial vehicles is equal to the number N of the ground base station antennas, that is, M is equal to N, then the receiving signal of the ground base station can be represented as:
Figure BDA0002841019970000051
wherein E issFor transmit power, s is the transmit signal of the drone cluster, n0Is zero mean, unit variance N0H is the uplink communication link MIMO channel matrix. Neglecting the distance difference between each unmanned aerial vehicle and the ground base station
Figure BDA0002841019970000052
Representing the channel between the mth drone and the nth antenna of the ground base station, dmnThe distance between the mth unmanned aerial vehicle and the nth antenna of the ground base station is defined, and lambda is the wavelength of the signal.
The instantaneous capacity of the MIMO channel can be expressed as:
C＝log2(det[IN+ρHHH/N])
wherein, INIs an N-order identity matrix, rho ═ Es/N0For the signal-to-noise ratio of each receive antenna.
Considering that strong correlation exists in the air-ground wireless channel line-of-sight link, the uplink communication link channel is a rank degraded channel, and in order to maximize the channel capacity, the following optimization targets are established:
P:max rank(H)
however, considering the case that each drone of the cluster can only obtain the local information of the neighboring drones, the above optimization problem is decomposed into M optimization sub-problems, and the mth optimization sub-problem can be expressed as:
P-m:max rank(Hm)，
Figure BDA0002841019970000053
wherein HmThe channel matrix formed by the unmanned aerial vehicle m and the neighboring unmanned aerial vehicles.
In order to ensure that the sub-problem P-m of channel capacity maximization of each unmanned aerial vehicle is consistent with the result of the global channel capacity maximization problem P, the optimization problem needs to be modeled as a potential game problem:
Figure BDA0002841019970000054
wherein V is 1, 2, …, M is a set of drones,
Figure BDA0002841019970000061
behavior aggregation for mth drone1Respectively representing up, down, left, right, front, back, hovering behavior, RmFor the m' th unmanned aerial vehicle, including its own profit rmAnd the profit r of the neighboring unmanned aerial vehicleiTwo parts, in particular, the yield function of the mth drone is:
Figure BDA0002841019970000062
wherein
Figure BDA0002841019970000063
dnl is the distance between the nth antenna of the ground base station and the first unmanned aerial vehicle, dnkThe distance between the nth antenna of the ground base station and the kth unmanned aerial vehicle,
Figure BDA0002841019970000064
set of neighbor drones representing drone m, amRepresenting the behaviour of the drone m,
Figure BDA0002841019970000065
the behavior of neighbor drones representing drone m.
The corresponding potential function is:
Figure BDA0002841019970000066
according to the gain function and the potential function, the following unmanned aerial vehicle cluster distributed optimal deployment method is constructed, and the channel capacity of a large-scale MIMO uplink communication link is maximized.
The specific steps of the large-scale MIMO capacity increasing method shown in fig. 2 are as follows:
step 1, initializing the number of interaction times T to be 0, and setting the maximum number of interaction times to be T; randomly selecting an aerial position as an initial deployment position by each unmanned aerial vehicle in the cluster;
step 2, randomly selecting an unmanned aerial vehicle m in the cluster with equal probability, wherein T is 0, 1, …, and T is less than T;
step 3, the selected unmanned aerial vehicle m communicates with the neighboring unmanned aerial vehicle m, local information is constructed, and the current profit R is calculatedm；
Step 4, the selected unmanned aerial vehicle m limits the behavior set according to the agreed behavior exploration probability
Figure BDA0002841019970000067
Select an exploration behavior
Figure BDA0002841019970000068
Is provided with
Figure BDA0002841019970000069
In order to explore the new behavior,
Figure BDA00028410199700000610
for previous behavior, emFor the exploration rate, the agreed behavior exploration probability is:
Figure BDA00028410199700000611
Figure BDA0002841019970000071
step 5, if
Figure BDA0002841019970000072
The selected unmanned aerial vehicle m calculates income according to the new exploration behaviors, updates the behavior selection strategy and returns to the step 2, meanwhile, other unmanned aerial vehicles in the cluster keep the previous behaviors unchanged, and the behavior selection strategy updating rule is expressed as:
Figure BDA0002841019970000073
Figure BDA0002841019970000074
Figure BDA0002841019970000075
otherwise, directly returning to the step 2 to perform the next round of interaction until the interaction time T is T or the algorithm is converged, and terminating.
Through the interaction of the steps 1-5, the unmanned aerial vehicle cluster can be deployed to an optimal position in a distributed mode, the correlation of the air-ground wireless channel line-of-sight link is reduced, and the maximization of the large-scale MIMO uplink communication link channel capacity is realized by improving the rank of the channel matrix.
The above embodiments are merely illustrative of the technical ideas of the present invention, and the scope of the present invention should not be limited thereto; meanwhile, for a person skilled in the art, any modifications made in the specific embodiments and the application scope according to the idea of the present invention are within the protection scope of the present invention.

Claims (10)

1. A large-scale MIMO capacity improving method based on unmanned aerial vehicle cluster deployment is characterized by comprising the following steps:
step 1, clustering all unmanned aerial vehicles to be deployed in an area above a ground base station, and randomly selecting an aerial position as an initial position; initializing the interaction times T of the unmanned aerial vehicle to be 0, and setting the maximum interaction times to be T;
step 2, each unmanned aerial vehicle of the cluster estimates the state of a channel in an auxiliary way through a geographic position system carried by the unmanned aerial vehicle;
step 3, if T is 0, 1, …, T is less than T and the algorithm is not converged, randomly selecting an unmanned aerial vehicle m in the cluster with equal probability; otherwise, the method is terminated, the unmanned aerial vehicle cluster finishes deploying to the optimal position, and the channel capacity is improved;
step 4, the selected unmanned aerial vehicle m communicates with the neighboring unmanned aerial vehicle m, local information is constructed, and the current profit R is calculatedm；
Step 5, the selected unmanned aerial vehicle m selects an exploration behavior from the restricted behavior set according to the agreed behavior exploration probability;
step 6, if the current behavior is a new exploration behavior, the selected unmanned aerial vehicle m calculates income according to the new exploration behavior, updates a behavior selection strategy and returns to the step 3, and other unmanned aerial vehicles in the cluster keep the previous behavior unchanged; otherwise, directly returning to the step 3 for the next iteration interaction.
2. The massive MIMO capacity increasing method based on unmanned aerial vehicle cluster deployment of claim 1, wherein the channel state of step 2 is estimated by the following formula:
Figure FDA0002841019960000011
wherein h ismnRepresenting the channel between drone m and ground base station nth antenna, dmnThe distance between the unmanned aerial vehicle m and the nth antenna of the ground base station is shown, lambda is the wavelength of the signal, and H is an uplink communication link MIMO channel matrix.
3. The massive MIMO capacity boosting method based on unmanned aerial vehicle cluster deployment of claim 1, wherein the channel capacity is estimated by the following formula:
C＝log2(det[IN+ρHHH/N])
wherein INIs an N-order identity matrix, and ρ is the signal-to-noise ratio of each receiving antenna.
4. The massive MIMO capacity boosting method based on unmanned aerial vehicle cluster deployment of claim 1, wherein the channel capacity boosting establishes the following optimization objectives:
P:max rank(H)。
5. the massive MIMO capacity increasing method based on unmanned aerial vehicle cluster deployment as claimed in claim 1, wherein the method comprisesCharacterized in that the profit R of step 4mSelf-income r by drone mmAnd its neighbor unmanned aerial vehicle profit riSum composition, calculated using the formula:
Figure FDA0002841019960000021
wherein
Figure FDA0002841019960000022
Set of neighbor drones representing drone m, amRepresent unmanned aerial vehicle m
Figure FDA0002841019960000023
Distance between root antenna and first unmanned aerial vehicle, dnkThe distance between the nth antenna of the ground base station and the kth unmanned aerial vehicle is obtained.
6. The massive MIMO capacity increasing method based on unmanned aerial vehicle cluster deployment of claim 1, wherein the behavior exploration probability of step 5 is:
Figure FDA0002841019960000024
wherein
Figure FDA0002841019960000025
For the exploration behavior currently selected by drone m,
Figure FDA0002841019960000026
in order to explore the new behavior,
Figure FDA0002841019960000027
for previous behavior, emIn order to achieve the purpose of the exploration rate,
Figure FDA0002841019960000028
is the restricted behavior set for drone m.
7. The massive MIMO capacity increasing method based on unmanned aerial vehicle cluster deployment of claim 1, wherein the update behavior selection policy of step 6 is:
Figure FDA0002841019960000029
Figure FDA00028410199600000210
Figure FDA00028410199600000211
8. the massive MIMO capacity increasing method for unmanned aerial vehicle cluster deployment of claim 1, wherein the unmanned aerial vehicle is a rotary-wing unmanned aerial vehicle, but not limited to a specific model of rotary-wing unmanned aerial vehicle.
9. The method of claim 1, wherein the clustered drones of step 1 are respectively configured with a single antenna, and the ground base station is configured with a plurality of antennas.
10. The massive MIMO capacity increasing method for unmanned aerial vehicle cluster deployment of claim 9, wherein the antenna is an omni-directional antenna, but is not limited to an omni-directional antenna.
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