CN112672361B - 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 PDFInfo
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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; only local information is utilized to complete deployment, an optimization control center is not provided, communication energy consumption is reduced, communication control time delay is shortened, and the cruising and real-time control capabilities 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
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 for unmanned aerial vehicle cluster deployment.
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 (multiple input multiple output) 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 optimizing deployment of the unmanned aerial vehicle in the two-dimensional space, optimization targets gradually tend to diversify, and a combined optimization strategy considering unmanned aerial vehicle position deployment and network resource allocation becomes a more fire-burning 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 plurality of problems and challenges under the conditions of limited communication infrastructure and limited unmanned aerial vehicle flight capacity.
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 will seriously reduce 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 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 =0,1, \ 8230;, T < 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 calculated m ；
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:
wherein h is mn Representing the channel between drone m and ground base station nth antenna, d mn The distance between the unmanned aerial vehicle m and the nth antenna of the ground base station is defined as lambda, which is the wavelength of the signal, and H is the uplink communication link MIMO channel matrix.
Further, the channel capacity is estimated by the following equation:
C＝log 2 (det[I N +ρHH H /N])
wherein I N Is an N-order identity matrix, and rho 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 of step 4 m Self-income r by drone m m And its neighbor unmanned aerial vehicle profit r i Sum composition, calculated using the formula:
wherein
Set of neighbor drones representing drone m, a m Represent unmanned aerial vehicle m
Distance between root antenna and first unmanned aerial vehicle, d nk The distance between the nth antenna of the ground base station and the kth unmanned aerial vehicle.
Further, the behavior exploration probability of step 5 is:
wherein
For the exploration behavior currently selected by drone m,
in order to explore the new behavior,
for previous behavior, e m In order to achieve the rate of exploration,
is the restricted behavior set for drone m.
Further, the update behavior selection policy in step 6 is:
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 vehicles in step 1 are 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 beneficial effects 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.
Drawings
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 simplicity of analysis, if the number M of the cluster drones is equal to the number N of the ground base station antennas, that is, M = N, then the received signal of the ground base station can be represented as:
wherein E is s For transmit power, s is the transmit signal of the drone cluster, n 0 Is zero mean, unit variance N 0 H is the uplink communication link MIMO channel matrix. Neglecting the distance difference between each unmanned aerial vehicle and the ground base station
Represents the channel between the mth unmanned aerial vehicle and the nth antenna of the ground base station, d mn The 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＝log 2 (det[I N +ρHH H /N])
wherein, I N Is an N-order identity matrix, rho = E s /N 0 For 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 where each drone of the cluster can only obtain local information of neighboring drones, the above optimization problem is decomposed into M optimization sub-problems, where the mth optimization sub-problem can be expressed as:
P-m:max rank(H m )，
wherein H m The 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:
wherein V =1,2, \ 8230, M is a set of drones,
wherein
d n l is the distance between the nth antenna of the ground base station and the first unmanned aerial vehicle, d nk The distance between the nth antenna of the ground base station and the kth unmanned aerial vehicle,
set of neighbor drones representing drone m, a m Representing the behaviour of the drone m,
the behavior of a neighbor drone on behalf of drone m.
The corresponding potential function is:
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:
2.t =0,1, \8230, wherein T < T, and one unmanned aerial vehicle m in the cluster is randomly selected according to equal probability;
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 calculated m ；
Step 4, the selected unmanned aerial vehicle m limits the behavior set according to the appointed behavior exploration probability
In which an exploration behavior is selected
Is provided with
In order to explore the new behavior, the method comprises the following steps of,
for previous behavior, e m For the exploration rate, the agreed behavior exploration probability is:
step 5, if
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:
otherwise, directly returning to the step 2 to perform the next round of interaction until the number of interactions T = 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 idea of the present invention, and the scope of the present invention should not be limited thereby; 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 fall within the protection scope of the present invention.
Claims (3)
1. A large-scale MIMO capacity improving method based on unmanned aerial vehicle cluster deployment is characterized by comprising the following steps:
step 1, deploying all unmanned aerial vehicles in an area above a ground base station, and randomly selecting an aerial position as an initial position; initializing the interaction times T =0 of the unmanned aerial vehicle, and setting the maximum interaction times as 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 =0,1, \8230, T < T and the algorithm is not converged, randomly selecting an unmanned aerial vehicle m in the cluster according to 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 calculated m ；
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 to carry out the next iteration interaction;
wherein the content of the first and second substances,
the channel state in step 2 is estimated by the following formula:
wherein h is mn Representing the channel between the drone m and the nth antenna of the ground base station, d mn The distance between an unmanned aerial vehicle m and an nth antenna of a ground base station is defined, lambda is the wavelength of a signal, and H is an uplink communication link MIMO channel matrix;
the channel capacity is estimated using the following equation:
C＝log 2 (det[I N +ρHH H /N])
in which I N Is an N-order unit matrix, and rho is the signal-to-noise ratio of each receiving antenna;
the channel capacity boost establishes the following optimization objectives:
P:max rank(H)；
profit R described in step 4 m Self-income r by drone m m And neighbor unmanned aerial vehicle profit r i Sum composition, calculated using the formula:
wherein
Set of neighbor drones representing drone m, a m Representative of nobody
Distance between nth antenna and l unmanned aerial vehicle, d nk For ground basic station nth antenna and kth unmanned aerial vehicleThe distance therebetween;
the behavior exploration probability of the step 5 is as follows:
wherein
For the exploration behavior currently selected by drone m,
in order to explore the new behavior,
for previous behavior, e m In order to achieve the purpose of the exploration rate,
is the restricted behavior set of drone m;
the update behavior selection policy in step 6 is:
the cluster unmanned aerial vehicle is respectively provided with a single antenna, and the ground base station is provided with N antennas.
2. The massive MIMO capacity boosting method based on unmanned aerial vehicle cluster deployment of claim 1, wherein the unmanned aerial vehicle is a rotary-wing unmanned aerial vehicle or a specific type of rotary-wing unmanned aerial vehicle.
3. The massive MIMO capacity boosting method based on drone swarm deployment of claim 1, wherein the antennas are omni-directional antennas.
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| CN113406974B (en) * | 2021-08-19 | 2021-11-02 | 南京航空航天大学 | Learning and resource joint optimization method for unmanned aerial vehicle cluster federal learning |
| CN114553290A (en) * | 2022-01-07 | 2022-05-27 | 西安理工大学 | Wireless ultraviolet light communication tracking and maintaining method based on MIMO structure |
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