CN114666833A - Uplink distributed receiving method for unmanned aerial vehicle cluster - Google Patents

Uplink distributed receiving method for unmanned aerial vehicle cluster Download PDF

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CN114666833A
CN114666833A CN202210477427.5A CN202210477427A CN114666833A CN 114666833 A CN114666833 A CN 114666833A CN 202210477427 A CN202210477427 A CN 202210477427A CN 114666833 A CN114666833 A CN 114666833A
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unmanned aerial
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CN114666833B (en
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郑重
潘学松
周胜平
费泽松
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Beijing Institute of Technology BIT
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/0231—Traffic management, e.g. flow control or congestion control based on communication conditions
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/08—Load balancing or load distribution
    • H04W28/0858—Load balancing or load distribution among entities in the uplink
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/12—Wireless traffic scheduling
    • H04W72/1263—Mapping of traffic onto schedule, e.g. scheduled allocation or multiplexing of flows
    • H04W72/1268—Mapping of traffic onto schedule, e.g. scheduled allocation or multiplexing of flows of uplink data flows
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/20—Control channels or signalling for resource management
    • H04W72/21—Control channels or signalling for resource management in the uplink direction of a wireless link, i.e. towards the network

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Abstract

The invention provides an uplink distributed receiving method facing an unmanned aerial vehicle cluster, which comprises the steps of constructing a communication system model consisting of the unmanned aerial vehicle cluster, obtaining channel statistical information of the communication system model by each distributed unmanned aerial vehicle and sending the channel statistical information to a central unmanned aerial vehicle, receiving user signals by each distributed unmanned aerial vehicle to carry out local signal estimation and sending an estimation result to the central unmanned aerial vehicle, calculating the weight of each distributed unmanned aerial vehicle by the central unmanned aerial vehicle according to the channel statistical information corresponding to each distributed unmanned aerial vehicle on the basis of a deterministic equivalence principle, and carrying out weighted combination on the estimation results of the distributed unmanned aerial vehicles by using the weight to obtain a final estimation signal. The invention can obviously reduce the interaction amount of information, reduce the interaction time delay and realize weak interaction.

Description

Uplink distributed receiving method for unmanned aerial vehicle cluster
Technical Field
The invention relates to an uplink distributed receiving method for an unmanned aerial vehicle cluster, and belongs to the technical field of multi-unmanned aerial vehicle cooperative receiving under an unmanned aerial vehicle cluster communication network architecture.
Background
In the future, the B5G/6G network has the characteristics of service diversification and network form diversification, and besides the traditional cellular network, novel network forms such as the Internet of things, the Internet of vehicles, the self-organizing network in flight and the like are developed and used as the supplement and extension of the cellular network. The unmanned aerial vehicle is widely set as a wireless network node of the novel networks due to the characteristics of easy deployment, low cost, high flexibility and the like of the unmanned aerial vehicle so as to provide temporary and sudden mobile network service coverage. Compared with a cellular network wireless access point, the communication, calculation capacity and energy consumption of the unmanned aerial vehicle air node are limited by the load capacity of the unmanned aerial vehicle air node, and the networking and transmission technology of the traditional cellular network is difficult to directly apply. In order to solve the problem of weak bearing capacity of a single unmanned aerial vehicle, a mode of cooperative communication of a cluster of multiple unmanned aerial vehicles is considered to achieve the extreme performances of high speed, high capacity, low time delay and the like similar to those of a foundation network. Ngo, Hien Quoc and the like propose a centralized MMSE (minimum mean square error) receiving algorithm aiming at an uplink distributed network architecture, and compared with single unmanned aerial vehicle uplink receiving, the algorithm greatly improves the system throughput; however, in the algorithm, the distributed unmanned aerial vehicle needs to feed back the received signal and the instantaneous channel state information in real time, and interaction delay caused by huge signaling interaction overhead makes the system difficult to be practically deployed. Therefore, how to realize a simple distributed cooperative receiving scheme with high speed and weak interaction under a multi-unmanned aerial vehicle cluster communication network framework is still a difficult problem to be solved urgently.
Disclosure of Invention
The invention provides an uplink distributed receiving method for an unmanned aerial vehicle cluster, which can obviously reduce the interaction amount of information, reduce the interaction time delay and realize weak interaction.
The invention is realized by the following technical scheme:
an uplink distributed receiving method for an unmanned aerial vehicle cluster comprises the following steps:
step S1, constructing a communication system model composed of an unmanned aerial vehicle cluster, which comprises a central unmanned aerial vehicle, P distributed unmanned aerial vehicles equipped with N receiving antennas, K users with single antennas, signal-to-noise ratio at a reference distance, transmitting signals of the users, receiving signals of the distributed unmanned aerial vehicles and receiving noise at the unmanned aerial vehicles;
step S2, each distributed unmanned aerial vehicle obtains the channel statistical information of the communication system model and sends the channel statistical information to the central unmanned aerial vehicle, wherein the channel statistical information comprises a channel fixed part
Figure BDA0003626406450000024
Normalized equivalent path loss matrix
Figure BDA0003626406450000025
And antenna correlation matrix CpThe channel statistics of (2);
step S3, each distributed unmanned aerial vehicle receives the user signal to carry out local signal estimation, and the estimation result is obtained
Figure BDA0003626406450000026
Sending the data to a central unmanned aerial vehicle;
and step S4, the central unmanned aerial vehicle calculates the weight of each distributed unmanned aerial vehicle based on the deterministic equivalence principle according to the channel statistical information corresponding to each distributed unmanned aerial vehicle, and the weight is used for weighting and combining the estimation results of the distributed unmanned aerial vehicles to obtain the final estimation signal.
Further, step S4 specifically includes the following steps:
step S41, setting weight vector ω ═ ω1,...,ωP]T，ωPFor the weight of the pth distributed unmanned aerial vehicle, based on the deterministic equivalence principle, the calculation of ω is represented as ω →a.s.W＝(A+R)-1V，
The expression for the pth element of vector V is:
Figure BDA0003626406450000021
the expression for the (p, q) th element of matrix a is:
Figure BDA0003626406450000022
of the p-th diagonal element of the matrix RThe expression is as follows:
Figure BDA0003626406450000023
wherein ξpFor distributed unmanned plane p at reference distance dpReceived signal-to-noise ratio, ξqRepresenting distributed drones q at a reference distance dqThe received signal-to-noise ratio of (c);
Tpthe expression of (z) is:
Figure BDA0003626406450000031
in the formula, the compound is shown in the specification,
Figure BDA0003626406450000032
Dpis a matrix
Figure BDA0003626406450000033
A matrix of eigenvalues of, i.e.
Figure BDA0003626406450000034
ηp(z) and
Figure BDA0003626406450000035
is an auxiliary variable with only one positive solution, and κ is a rice factor;
T′pthe expression of (z) is:
Figure BDA0003626406450000036
Figure BDA0003626406450000037
of formula eta'p(z) and
Figure BDA0003626406450000038
are each ηp(z) and
Figure BDA0003626406450000039
a derivative of (a);
step S42, central unmanned aerial vehicleWeighting and combining the estimation structures of the distributed unmanned aerial vehicles according to the asymptotic form W of the weight vector omega calculated in the step S41 to obtain a final estimation signal
Figure BDA00036264064500000310
Further, said etap(z) and
Figure BDA00036264064500000311
respectively obtained by the following motionless point equation:
Figure BDA00036264064500000312
Figure BDA00036264064500000313
eta 'of'p(z) and
Figure BDA00036264064500000314
respectively obtained by the following motionless point equation:
Figure BDA00036264064500000315
Figure BDA00036264064500000316
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003626406450000041
Figure BDA0003626406450000042
i denotes an identity matrix.
Further, in the step S2, the channel fixing part
Figure BDA0003626406450000043
Calculated from the following formula:
Figure BDA0003626406450000044
wherein the content of the first and second substances,
Figure BDA0003626406450000045
θiis the angle of arrival of the transmitted signal for user i to the distributed user drone p.
Further, the normalized equivalent path loss matrix
Figure BDA0003626406450000046
Calculated from the following formula:
Figure BDA0003626406450000047
where α is the path loss factor, dpIs the reference distance, dp,kIs the distance of the distributed drone p to the user k.
Further, the antenna correlation matrix CpCalculated from the following formula:
Figure BDA0003626406450000048
where φ is a random angle, and d is the antenna spacing of the p-th distributed wireless subset.
Further, in step S3, the p-th distributed drone estimates the user signal according to the following formula:
Figure BDA0003626406450000049
wherein, BpFor MMSE receive filter matrix, yp＝ξpHpx+npFor the received signal of the pth distributed drone, x ═ x1,x2,…,xK]TTransmitting signal vectors for K users; hpThe instantaneous channel matrix from the user to the p distributed unmanned aerial vehicle is calculated by the formula
Figure BDA00036264064500000410
Kappa is the Rice factor, WpTo represent the random part of the channel for non-line-of-sight propagation, npIs the received noise at the p-th distributed drone.
Further, in step S2 and step S3, the distributed drone sends the channel statistics information and the signal estimation result to the central drone through the wireless backhaul link.
Further, in step S2, after the distributed drone sends the channel statistics information to the central drone, retransmission is not performed again within a fixed time.
The invention has the following beneficial effects:
1. the invention is based on a communication system model of a central unmanned aerial vehicle and P distributed unmanned aerial vehicles, each distributed unmanned aerial vehicle obtains channel statistical information of the communication system model and sends the channel statistical information to the central unmanned aerial vehicle, each distributed unmanned aerial vehicle receives a user signal to carry out local signal estimation and sends an estimation result to the unmanned aerial vehicles, the central unmanned aerial vehicle calculates the weight of each distributed unmanned aerial vehicle according to the channel statistical information corresponding to each distributed unmanned aerial vehicle based on the determinacy equal principle, the estimation results of the distributed unmanned aerial vehicles are weighted and combined by utilizing the weight to obtain a final estimation signal, in the process, based on the determinacy equal principle, the central unmanned aerial vehicle calculates the weight of each distributed unmanned aerial vehicle only depends on slowly-varying statistical channel information without carrying out instantaneous channel information interaction with the distributed unmanned aerial vehicles, and the distributed unmanned aerial vehicles can realize cooperative receiving only by interacting a vector with the central unmanned aerial vehicles, the dimension of which is equal to the number of users, the interaction amount of information is remarkably reduced, the interaction test is reduced, and the weak interaction cooperative receiving network is realized; compared with a single unmanned aerial vehicle receiving network, the invention can improve the system access capacity, improve the spectrum efficiency and enlarge the service range, and through intensive distributed unmanned aerial vehicle deployment, the invention can also improve the spatial freedom degree and diversity multiplexing gain of large-scale signal processing, and can obtain higher system spectrum frequency compared with the centralized antenna sending/receiving in the prior art by utilizing the system macro diversity gain, thereby reducing the communication overhead while ensuring the system capacity and the spectrum efficiency.
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The present invention will be described in further detail with reference to the accompanying drawings.
FIG. 1 is a flow chart of the present invention.
Fig. 2 is a graph showing the comparison result between the centralized MMSE receiving algorithm and the data transmission amount of the present invention in the prior art.
Fig. 3 is a comparison graph of average user, rate and MSE for 3000 groups of channels for the present invention, a centralized MMSE reception algorithm, and a single drone reception algorithm.
Detailed Description
As shown in fig. 1, the uplink distributed receiving method for the unmanned aerial vehicle cluster includes the following steps:
step S1, constructing a communication system model composed of a cluster of drones, where the communication system model includes a central drone, where P is 8 distributed drones perform cooperative reception, and each distributed drone is equipped with N is 64 receiving antennas, and receives the data at 2 × 2km2Is randomly set up as 128 users of a single antenna.
The received signal of the p distributed unmanned aerial vehicle is yp＝ξpHpx+npThe signal is an N × 1 complex vector, where x ═ x1,x2,…,xK]TA vector of transmitted signals for K users, the vector being a K × 1 complex vector; hpAn instantaneous channel matrix from the user to the p distributed unmanned aerial vehicle is an N multiplied by K complex matrix; n ispIs the received noise at the pth distributed drone, which is a complex gaussian distribution with mean 0 and variance 1; xipTo be at a reference distance dpThe received signal-to-noise ratio.
Step S2, each distributed drone obtains the channel statistical information of the communication system model and sends the channel statistical information to the central drone through the wireless backhaul link, and does not retransmit within a fixed time, where the fixed time is determined according to the change speed of the channel statistical information and the change speed of the small-scale fadingThe speed is 40 times slower; the channel statistics include a channel fixed part
Figure BDA0003626406450000061
Normalized equivalent path loss matrix
Figure BDA0003626406450000062
And antenna correlation matrix CpThe channel statistics of (2);
in this embodiment, the transmission power P of the user is set010 mw; channel fixed part representing line-of-sight propagation
Figure BDA0003626406450000063
Is an N × K complex matrix calculated by:
Figure BDA0003626406450000064
wherein the content of the first and second substances,
Figure BDA0003626406450000065
θithe arrival angle signal from the transmission signal of the user i to the distributed unmanned aerial vehicle p to the arrival angle of the user unmanned aerial vehicle.
Normalized equivalent path loss matrix
Figure BDA0003626406450000071
Is a K complex matrix, calculated by:
Figure BDA0003626406450000072
where α is the path loss factor, dpIs the reference distance, dp,kIs the distance of the distributed drone p to the user k.
Antenna correlation matrix CpIs an N × N complex matrix, calculated by:
Figure BDA0003626406450000073
where φ is a random angle, and d is the antenna spacing of the p-th distributed wireless subset.
Step (ii) ofS3, each distributed unmanned aerial vehicle receives the user signal to carry out local signal estimation, and the estimation result is obtained
Figure BDA0003626406450000074
Sending the information to a central unmanned aerial vehicle through a wireless backhaul link;
the pth distributed drone estimates the transmitted signals of all users by:
Figure BDA0003626406450000075
wherein, BpIs MMSE receiving filter matrix; instantaneous channel matrix HpThe calculation formula of (2) is as follows:
Figure BDA0003626406450000076
κ is a rice factor, and in the present embodiment, κ is set to 10; wpThe random part of the channel representing non-line-of-sight propagation is an N multiplied by K complex matrix, and elements of the matrix are subjected to complex Gaussian distribution with the mean value of 0 and the variance of 1; i represents an identity matrix [ ·]HRepresentation matrix [ ·]The conjugate transpose of (c).
Step S4, the central unmanned aerial vehicle calculates the weight of each distributed unmanned aerial vehicle based on the deterministic equivalence principle according to the channel statistical information corresponding to each distributed unmanned aerial vehicle, and the weight is used for weighting and combining the estimation results of the distributed unmanned aerial vehicles to obtain the final estimation signal;
the method specifically comprises the following steps:
step S41, setting weight vector ω ═ ω1,...,ωP]T，ωPFor the weight of the pth distributed unmanned aerial vehicle, based on the deterministic equivalence principle, the calculation of ω is represented as ω →a.s.W＝(A+R)-1V, represents that the weight vector ω approaches infinity with K, N and approaches W gradually. A is a P complex matrix, R is a P complex diagonal matrix, and V is a P1 complex vector.
The expression for the pth element of vector V is:
Figure BDA0003626406450000081
the expression for the (p, q) th element of matrix a is:
Figure BDA0003626406450000082
the expression for the pth diagonal element of matrix R is:
Figure BDA0003626406450000083
wherein, (.)[pq]The (p, q) th element of the representation matrix (. (-)[p]The p-th element representing the vector (·); wherein ξpFor distributed unmanned plane p at reference distance dpReceived signal-to-noise ratio, ξqRepresenting distributed drones q at a reference distance dqThe received signal-to-noise ratio and the calculation of the reference distance corresponding to each distributed unmanned aerial vehicle are respectively related to the channel statistical information of each distributed unmanned aerial vehicle, specifically, the equivalent path loss matrix is obtained by normalizing, and the calculation process is the prior art;
Tpthe expression of (z) is:
Figure BDA0003626406450000084
in the formula, the compound is shown in the specification,
Figure BDA0003626406450000085
Dpis a matrix
Figure BDA0003626406450000086
A matrix of eigenvalues of, i.e.
Figure BDA0003626406450000087
ηp(z) and
Figure BDA0003626406450000088
is an auxiliary variable with only one positive solution, which can be solved by the following equation of the dead point:
Figure BDA0003626406450000089
Figure BDA00036264064500000810
in the expression of matrix A, R, T'pThe expression of (z) is:
Figure BDA00036264064500000811
Figure BDA0003626406450000091
of formula eta'p(z) and
Figure BDA0003626406450000092
are each ηp(z) and
Figure BDA0003626406450000093
can be obtained by solving the following stationary point equation:
Figure BDA0003626406450000094
Figure BDA0003626406450000095
wherein the content of the first and second substances,
Figure BDA0003626406450000096
Figure BDA0003626406450000097
step S42, estimating each distributed unmanned aerial vehicle by the central unmanned aerial vehicle according to the asymptotic form W of the weight vector omega calculated in the step S41The structure is counted and one-shot weighted combination is carried out to obtain a final estimation signal
Figure BDA0003626406450000098
Wherein, W[p]The weight vector of the p-th distributed unmanned aerial vehicle.
Calculating a final estimated signal by the following formula
Figure BDA0003626406450000099
Mean square error M from the original transmitted signal xdist：
Figure BDA00036264064500000910
Fig. 2 is a graph showing the comparison result between the data transmission amount of the present invention and the centralized MMSE receiving algorithm in the prior art. As can be seen from the figure, if a centralized MMSE reception algorithm is adopted, in a primary user signal estimation process, each distributed drone needs to send an instantaneous channel matrix H with dimension N × K to a central drone through a wireless backhaul linkpAnd a received signal vector y of dimension N × 1pThe total information interaction amount in the network is PN (K +1) complex numbers. If the method is adopted, in the process of one-time user signal estimation, each distributed unmanned aerial vehicle only needs to send a local estimation signal with dimension Kx 1 to the central unmanned aerial vehicle through the wireless backhaul link
Figure BDA0003626406450000101
The total information interaction amount in the network is P (K +1) complex numbers. Therefore, the receiving method provided by the invention can reduce the data interaction amount by N times, can obviously reduce the total interaction amount of the system and can reduce the interaction time delay of the system.
Fig. 3 shows a comparison graph of average user, rate and MSE (mean square error) of 3000 groups of channels for the present invention, the centralized MMSE reception algorithm, and the single-drone reception algorithm. The centralized MMSE receiving algorithm is that all distributed unmanned aerial vehicles are used as relay nodes, and received signals and instantaneous channel information are forwarded to a central unmanned aerial vehicle in real time so as to realize distributed cooperative receiving; the single unmanned aerial vehicle receiving algorithm is that an unmanned aerial vehicle with the number of antennas equal to the total number of antennas of the distributed unmanned aerial vehicles is arranged in a network center, and the unmanned aerial vehicle only utilizes local channel information to carry out local signal estimation on user signals and does not need to carry out signal interaction. It can be seen from the figure that under the two standards of MSE and rate, the performance of the invention is far superior to that of a single-drone receiving algorithm, and the invention can achieve the performance similar to that of a centralized MMSE receiving algorithm. Therefore, the invention can reduce the interaction amount, reduce the interaction time delay and simultaneously improve the system performance and the access capacity.
The above description is only a preferred embodiment of the present invention, and therefore should not be taken as limiting the scope of the invention, which is defined by the appended claims and their equivalents and modifications within the scope of the description.

Claims (9)

1. An uplink distributed receiving method for an unmanned aerial vehicle cluster is characterized in that: the method comprises the following steps:
step S1, constructing a communication system model composed of an unmanned aerial vehicle cluster, which comprises a central unmanned aerial vehicle, P distributed unmanned aerial vehicles equipped with N receiving antennas, K users with single antennas, signal-to-noise ratio at a reference distance, transmitting signals of the users, receiving signals of the distributed unmanned aerial vehicles and receiving noise at the unmanned aerial vehicles;
step S2, each distributed unmanned aerial vehicle obtains the channel statistical information of the communication system model and sends the channel statistical information to the central unmanned aerial vehicle, wherein the channel statistical information comprises a channel fixed part
Figure FDA0003626406440000011
Normalized equivalent path loss matrix
Figure FDA0003626406440000012
And antenna correlation matrix CpThe channel statistics of (2);
step S3, each distributed unmanned aerial vehicle receives the user signal to carry out local signal estimation, and the estimation result is obtained
Figure FDA0003626406440000013
Sending to a central unmanned aerial vehicle;
and step S4, the central unmanned aerial vehicle calculates the weight of each distributed unmanned aerial vehicle based on the deterministic equivalence principle according to the channel statistical information corresponding to each distributed unmanned aerial vehicle, and the weight is used for weighting and combining the estimation results of the distributed unmanned aerial vehicles to obtain the final estimation signal.
2. The uplink distributed receiving method for the unmanned aerial vehicle cluster as claimed in claim 1, wherein: step S4 specifically includes the following steps:
step S41, let weight vector ω ═ ω1,...,ωP]T，ωPFor the weight of the pth distributed unmanned aerial vehicle, based on the deterministic equivalence principle, the calculation of ω is represented as ω →a.s.W＝(A+R)-1V，
The expression for the pth element of vector V is:
Figure FDA0003626406440000014
the expression for the (p, q) th element of matrix a is:
Figure FDA0003626406440000015
the expression for the pth diagonal element of matrix R is:
Figure FDA0003626406440000021
wherein ξpFor distributed unmanned plane p at reference distance dpReceived signal-to-noise ratio, ξqRepresenting distributed drones q at a reference distance dqThe received signal-to-noise ratio of (c);
Tpthe expression of (z) is:
Figure FDA0003626406440000022
in the formula, the raw materials are shown in the specification,
Figure FDA0003626406440000023
Dpis a matrix
Figure FDA0003626406440000024
A matrix of eigenvalues of, i.e.
Figure FDA0003626406440000025
ηp(z) and
Figure FDA0003626406440000026
is an auxiliary variable with only one positive solution, and κ is a rice factor;
T′pthe expression of (z) is:
Figure FDA0003626406440000027
Figure FDA00036264064400000217
of formula eta'p(z) and
Figure FDA0003626406440000028
are each ηp(z) and
Figure FDA0003626406440000029
a derivative of (d);
step S42, the central unmanned aerial vehicle performs weighting combination on the estimation structures of the distributed unmanned aerial vehicles according to the asymptotic form W of the weight vector omega calculated in the step S41 to obtain a final estimation signal
Figure FDA00036264064400000210
3. The uplink distributed receiving method for the unmanned aerial vehicle cluster as claimed in claim 2, wherein: eta ofp(z) and
Figure FDA00036264064400000211
respectively obtained by the following motionless point equation:
Figure FDA00036264064400000212
Figure FDA00036264064400000213
eta'p(z) and
Figure FDA00036264064400000214
respectively obtained by the following motionless point equation:
Figure FDA00036264064400000215
Figure FDA00036264064400000216
wherein the content of the first and second substances,
Figure FDA0003626406440000031
Figure FDA0003626406440000032
i denotes an identity matrix.
4. The uplink distributed receiving method for the unmanned aerial vehicle cluster according to claim 1, 2 or 3, wherein: in the step S2, in the above step,the channel fixed part
Figure FDA0003626406440000033
Calculated from the following formula:
Figure FDA0003626406440000034
wherein the content of the first and second substances,
Figure FDA0003626406440000035
θiis the angle of arrival of the transmitted signal for user i to the distributed user drone p.
5. The uplink distributed receiving method for the unmanned aerial vehicle cluster according to claim 1, 2 or 3, wherein: the normalized equivalent path loss matrix
Figure FDA0003626406440000036
Calculated from the following formula:
Figure FDA0003626406440000037
where α is the path loss factor, dpIs the reference distance, dp,kIs the distance of the distributed drone p to the user k.
6. The uplink distributed receiving method for the unmanned aerial vehicle cluster according to claim 1, 2 or 3, wherein: the antenna correlation matrix CpCalculated from the following formula:
Figure FDA0003626406440000038
where φ is a random angle, and d is the antenna spacing of the p-th distributed wireless subset.
7. The uplink distributed receiving method for the unmanned aerial vehicle cluster according to claim 1, 2 or 3, wherein: in step S3, the pth distributed drone signals the user according to the following formulaLine estimation:
Figure FDA0003626406440000039
wherein, BpFor MMSE receive filter matrix, yp＝ξpHpx+npFor the received signal of the pth distributed drone, x ═ x1,x2,…,xK]TTransmitting signal vectors for K users; hpThe instantaneous channel matrix from the user to the p distributed unmanned aerial vehicle is calculated by the formula
Figure FDA0003626406440000041
Kappa is the Rice factor, WpTo represent the random part of the channel for non-line-of-sight propagation, npIs the received noise at the p-th distributed drone.
8. The uplink distributed receiving method for the unmanned aerial vehicle cluster according to claim 1, 2 or 3, wherein: in the step S2 and the step S3, the distributed drone transmits the channel statistic information and the signal estimation result to the central drone through the wireless backhaul link.
9. The uplink distributed receiving method for the unmanned aerial vehicle cluster according to claim 1, 2 or 3, wherein: in step S2, after the distributed drone sends the channel statistics information to the central drone, retransmission is not performed again within a fixed time.
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