CN108683442B - Energy efficiency optimization method of unmanned aerial vehicle communication system based on interference alignment - Google Patents

Energy efficiency optimization method of unmanned aerial vehicle communication system based on interference alignment Download PDF

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CN108683442B
CN108683442B CN201810497153.XA CN201810497153A CN108683442B CN 108683442 B CN108683442 B CN 108683442B CN 201810497153 A CN201810497153 A CN 201810497153A CN 108683442 B CN108683442 B CN 108683442B
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CN108683442A (en
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陈炳才
杨曼柔
陈宇
赵楠
宁芊
余超
潘伟民
年梅
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Dalian University of Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04B17/00—Monitoring; Testing
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
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    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
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    • H04L25/0242—Channel estimation channel estimation algorithms using matrix methods
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    • 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
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Abstract

The invention belongs to the technical field of mobile communication, and discloses an energy efficiency optimization method of an unmanned aerial vehicle communication system based on interference alignment. Firstly, a system model is determined and feasibility of interference alignment in the system model is analyzed according to the theory of Bezout. Secondly, after the feasible conditions are determined to be met, a precoding matrix is initialized, and the precoding matrix and the decoding matrix solution of interference alignment can be iteratively solved by utilizing network reciprocity. And finally, considering the collected energy, providing a fusion method, on the basis of antenna selection, applying a power segmentation technology to an antenna for information decoding to further collect redundant energy, calculating the rate and the collected energy aiming at each scheme, and selecting an antenna selection and power segmentation method which enables the comprehensive performance of the rate and the energy efficiency to be optimal as an energy efficiency optimization solution in the unmanned aerial vehicle communication system based on interference alignment.

Description

Energy efficiency optimization method of unmanned aerial vehicle communication system based on interference alignment
Technical Field
The invention belongs to the technical field of mobile communication, and designs an energy efficiency optimization method of an unmanned aerial vehicle communication system based on interference alignment. The method is different from the existing method in that in the all-unmanned aerial vehicle communication system, antenna selection and energy segmentation technologies are combined and applied to the relay unmanned aerial vehicle. The method can effectively improve the speed and the energy efficiency of the communication system, so that the performance of the unmanned aerial vehicle communication system is better.
Background
Unmanned aerial vehicle communication systems are an emerging mode of communication. With the continuous update and upgrade of mobile networks, drones are widely used in computer networks due to their mobility and flexibility. In the unmanned aerial vehicle communication system, each node represents an unmanned aerial vehicle, can send and receive information, and also has the capability of processing information. Its communication range is limited due to its energy limitation. Therefore, in the drone group flight network, three types of drones are generally used, namely a transmitter, a relay and a receiver. Wherein the relay operates in full duplex.
After the sending end of the unmanned aerial vehicle collects the information, the information is sent to the relay, and the relay forwards the information to the receiving end. In the communication process, the relay terminal is interfered by the sending signals of other unexpected sending terminals and also interfered by other relays when forwarding information; and the receiving end is interfered by other relay end signals. Therefore, it is necessary to perform interference cancellation by using interference alignment technology to ensure that the receiving end can receive the desired signal without interference. In addition, because the power supply mode of unmanned aerial vehicle is battery powered, there is very big restriction in the aspect of the energy. There is therefore a need for a method of optimizing it to achieve better performance of the communication system. The existing energy collection technology comprises an antenna selection method and a power division method, and the two methods are different in target, wherein the former method aims at improving the transmission rate of a system, and the latter method aims at collecting energy to improve energy efficiency. The advantages of the two are combined to make up for the defects of the unmanned aerial vehicle communication system on the other hand, and an energy efficiency optimization method of the unmanned aerial vehicle communication system based on interference alignment is provided, so that the speed and the energy performance are comprehensively improved.
Disclosure of Invention
The invention provides an Interference Alignment (IA) based energy efficiency optimization method for an unmanned aerial vehicle communication system, aiming at optimizing the energy efficiency performance of the system and ensuring the transmission rate of information.
The technical method comprises the following steps:
an energy efficiency optimization method of an unmanned aerial vehicle communication system based on interference alignment comprises the following steps:
first, a system model is determined and the feasibility of interference alignment therein is analyzed
Establishing a model of an unmanned aerial vehicle communication system;
unmanned aerial vehicle group flight ad hoc network middle packetThe system comprises K sending-end unmanned aerial vehicles, K relay unmanned aerial vehicles and K receiving-end unmanned aerial vehicles; the relay-end unmanned aerial vehicle is in a full-duplex working mode; suppose that each sending-end unmanned aerial vehicle is provided with M antennas, each receiving-end unmanned aerial vehicle is provided with N antennas, and each relay unmanned aerial vehicle is provided with N antennastA transmitting antenna and NrA receiving antenna; the information transmission process is divided into two stages, wherein in the first stage, the unmanned aerial vehicle at the sending end sends information to the relay unmanned aerial vehicle; the second stage is that the relay unmanned aerial vehicle sends information to the receiving end unmanned aerial vehicle; in the communication process, the relay unmanned aerial vehicle is interfered by the sending signals of other unexpected sending ends and is also interfered by other relays when forwarding information; and the receiving-end unmanned aerial vehicle can also be interfered by signals of an unexpected relay end.
The signal received by the ith relay drone is represented as:
Figure BDA0001669491080000021
wherein: i is 1, …, K; u. of[i]Represents NrX d decoding matrix on relay drone, and u[i]u[i]H1 is ═ 1; h represents a conjugate transpose; h is[ii]Represented is N from the ith sending terminal unmanned aerial vehicle to the ith relay unmanned aerial vehiclerA xm channel matrix, which is a channel matrix of the desired signal; h is[il](l ═ 1, …, K, l ≠ i) represents N from the l-th transmitting-end drone to the i-th relaying dronerA xm channel matrix in which each element is independently distributed; g[ik](K ≠ 1, …, K ≠ i) represents the channel matrix from the kth relay drone to the ith relay drone, which is Nr×NtA matrix of (a); v. of[i]Is an Mxd matrix, which represents the precoding matrix of the ith sending-end UAV, where v is[i]v[i]H＝Id；v[l](l ═ 1, …, K, l ≠ i) is an mxd matrix, representing the precoding matrix of the l-th transmitting-end drone, where v ≠ i[l]v[l]H＝Id；V[k](K ≠ 1, …, K ≠ i) is NtX d matrix, representing the coding matrix of the kth relay drone, where V[k]V[k]H＝Id；x[i]It shows that d independent data streams with power P are sent by the ith sending end unmanned aerial vehicle1 [i]The signal of (a); x is the number of[l](l ═ 1, …, K, l ≠ i) indicates that the l-th transmitting-end drone transmits d independent data streams with power P1 [l]The signal of (a); x is the number of1 [k](K is 1, …, K) indicates that the kth relay drone transmits d independent data streams with power P2 [k]The transmission signal of (1); z is a radical of[i]Is N of the ith relay dronerA gaussian white noise vector of x 1; the first item on the right side of the equation of formula (1) represents an expected received signal of the ith relay-end unmanned aerial vehicle, the second item represents interference of an unexpected sending-end unmanned aerial vehicle on the ith relay-end unmanned aerial vehicle, the third item represents interference of other relay-end unmanned aerial vehicles on the ith relay-end unmanned aerial vehicle, and the last item represents Gaussian white noise interference on the ith relay-end unmanned aerial vehicle;
the signal received by the ith receiving end unmanned aerial vehicle is represented as:
Figure BDA0001669491080000031
wherein i is 1, …, K; u shape[i]Representing the Nxd decoding matrix on the ith receiving-end UAV, while U[j]U[j]H＝Id；H[ii]Represented is NxN from the ith relay-end unmanned aerial vehicle to the ith receiving-end unmanned aerial vehicletA channel matrix, which is a channel matrix of the desired signal; h[ik](K ═ 1, …, K ≠ i) means nxn of the kth relay-side drone to the ith receiving-side dronetA channel matrix; v[i]Is a number NtX d matrix, representing the coding matrix of the ith relay drone, where V[i]V[i]H＝Id；z1 [i]Is the ith receiving terminal unmanned aerial vehiclerX 1 Gaussian white noise vector(ii) a The first item on the right side of the equation in the formula (2) represents an expected receiving signal of the ith receiving-end unmanned aerial vehicle, the second item represents interference of an unexpected relay-end unmanned aerial vehicle on the ith receiving-end unmanned aerial vehicle, and the third item represents Gaussian white noise interference on the ith receiving-end unmanned aerial vehicle;
since the influence of interference on information transmission is very serious in this information transmission system, we consider applying interference alignment technology to perform interference removal. In order to effectively reduce the interference between users, we should coordinate the design of the precoding matrix and the decoding matrix. At the same time, to ensure that interference alignment can achieve its best performance, system feasibility conditions need to be met.
According to the theory of Bezout, a general polynomial system can be solved if and only if the number of equations does not exceed the number of variables. The IA network can therefore be classified as viable and non-viable according to the comparison of the number of equations and the number of variables.
Second, iterative solution of precoding and decoding matrices using distributed interference alignment
After the feasibility condition is satisfied, applying interference alignment technology to the unmanned aerial vehicle communication network.
The interference alignment technology is an emerging interference management technology, and the main idea thereof is to effectively remove communication interference in a system through the design of an encoding matrix and a decoding matrix. By utilizing network reciprocity, a coding matrix and a decoding matrix which are designed by the user are solved iteratively by adopting distributed interference alignment. The specific steps can be subdivided into the following algorithm steps.
Step 1, initializing a channel matrix h from the ith sending-end unmanned aerial vehicle to the ith relay unmanned aerial vehicle by a Gaussian channel model[il](i 1, …, K, l 1, …, K), and channel matrix H relaying drones to ith receiving drone[ik](i-1, …, K-1, …, K); initializing a channel matrix G from a kth relay unmanned aerial vehicle to an ith relay unmanned aerial vehicle by using a Gaussian channel model[ik](i ≠ 1, …, K ≠ 1, …, K ≠ i); initializing the ith transmission with a normally distributed random number matrix having a mean of 0 and a variance of 1End precoding matrix v[i](i-1, …, K) and the K-th relay precoding matrix V[k](k＝1,…,K)；
Step 2, designing a decoding matrix based on an iterative algorithm of Min _ LI: the standard is to minimize the residual interference signal power after the receiving end passes through the interference suppression filter, namely the 'interference leakage energy'; complete interference alignment is achieved by gradually iteratively reducing the leaked interference signal until the interference leakage energy is zero.
At the ith relay terminal unmanned aerial vehicle, the interference leakage energy is:
Figure BDA0001669491080000051
wherein i is 1,2, 3; tr [ A ]]，Qb [i]Respectively representing the trace of the matrix A and the interference covariance matrix of the ith relay unmanned aerial vehicle; qb [i]Is the formula (4), using the values initialized in step 1, the communication system degree of freedom d and the transmission signal power P1 [l](l＝1,…,K,l≠i)，
Figure BDA0001669491080000052
Calculate Qb [i]；
Figure BDA0001669491080000053
At the ith receiving end unmanned aerial vehicle, the interference leakage energy is:
I[i]＝Tr[U[i]HQ[i]U[i]](5)
wherein Q[i]Representing an interference covariance matrix of the ith receiving end unmanned aerial vehicle;
Figure BDA0001669491080000054
step 3, the ith relay terminal unmanned aerial vehicle selects a decoding matrix u[i]To minimize interference leakage, an interference covariance matrix Q may be selected accordinglyb [i]The matrix expanded by the eigenvector corresponding to the minimum d eigenvalues is used as a decoding matrix, and the ith relay terminal unmanned aerial vehicle corresponds to Qb [i]The feature vector for each feature value of (a) is represented as:
Figure BDA0001669491080000056
wherein v iss(A) The function represents the eigenvector corresponding to the s-th eigenvalue of the matrix a. D eigenvectors are combined together to obtain a decoding matrix u of the ith relay-end unmanned aerial vehicle[i]。
Similarly, the ith receiving end unmanned aerial vehicle decodes the matrix U through selection[i]To minimize interference leakage. Corresponding Q in ith receiving end unmanned aerial vehicle[i]The feature vector for each feature value of (a) may be expressed as:
Figure BDA0001669491080000055
d eigenvectors are combined together to obtain a decoding matrix U of the ith receiving-end unmanned aerial vehicle[i]；
And 4, turning over the communication direction and exchanging the roles of the transceiving end by utilizing the network reciprocity according to the iteration algorithm of Min _ LI. In the reverse network, we add ← representation above the encoding matrix and decoding matrix. At this moment, the original receiving end unmanned aerial vehicle is used as a sending end, the original sending end unmanned aerial vehicle is used as a receiving end, and the relay end is still the relay end, but the information transmission direction is opposite to that before.
At this time, the interference covariance matrix of the unmanned aerial vehicle at the ith sending end of the reciprocal network can be expressed as:
Figure BDA0001669491080000061
wherein, i is 1, …, K;
Figure BDA0001669491080000062
represents the channel matrix from the ith relay terminal unmanned aerial vehicle to the ith sending terminal unmanned aerial vehicle, and
Figure BDA0001669491080000063
Figure BDA0001669491080000064
the coding matrix of the representative first relay-end unmanned aerial vehicle in the reciprocal network is given by the decoding matrix of the relay-end unmanned aerial vehicle obtained in the step 3,
Figure BDA0001669491080000065
the interference covariance matrix of the ith relay-side drone may be expressed as:
Figure BDA0001669491080000066
wherein, i is 1, …, K;
Figure BDA0001669491080000067
represents the channel matrix from the l receiving end unmanned aerial vehicle to the i relay end unmanned aerial vehicle, and
Figure BDA0001669491080000068
Figure BDA0001669491080000069
representing the coding matrix of the first receiving-end unmanned aerial vehicle in the reciprocal network, given by the decoding matrix of the receiving-end unmanned aerial vehicle obtained in the step 3,
Figure BDA00016694910800000610
Figure BDA00016694910800000611
represents a channel matrix from the kth relay-side unmanned aerial vehicle to the ith relay-side unmanned aerial vehicle, and
Figure BDA00016694910800000612
Figure BDA00016694910800000613
the coding matrix representing the kth relay-end unmanned aerial vehicle in the reciprocal network is given by the decoding matrix of the relay-end unmanned aerial vehicle obtained in the step 3,
Figure BDA00016694910800000614
similar to the step 3, solving the corresponding interference covariance matrix in the ith relay-end unmanned aerial vehicle in the reciprocal network
Figure BDA00016694910800000615
Feature vector for each feature value of (1):
Figure BDA00016694910800000616
d eigenvectors are combined together to obtain a decoding matrix of the ith relay-end unmanned aerial vehicle in the reciprocal network
Figure BDA00016694910800000617
Solving corresponding interference covariance matrix in ith sending end unmanned aerial vehicle in reciprocal network
Figure BDA00016694910800000618
Feature vector for each feature value of (1):
Figure BDA0001669491080000071
d eigenvectors are combined together to obtain a decoding matrix of the ith sending end unmanned aerial vehicle in the reciprocal network
Figure BDA0001669491080000072
Step 5, turning over againThe signal direction, at this time, the coding matrix v of the i (i ═ 1, …, K) th transmitting-end drone[i]Is that
Figure BDA0001669491080000073
Coding matrix V of ith (i-1, …, K) relay terminal unmanned aerial vehicle[i]Is that
Figure BDA0001669491080000074
Returning to the step 2, and performing a new iteration until interference leakage is converged;
step 6, coding matrix v of sending end in interference leakage matrix convergence[i](i ═ 1, …, K), relay side coding matrix V[i](i-1, …, K) and decoding matrix u[i](i ═ 1, …, K), and decoding matrix U at the receiving end[i](i-1, …, K) is solved;
and thirdly, obtaining an energy efficiency optimization method of the unmanned aerial vehicle communication system based on the antenna selection and power division technology.
In the drone communication system, since the energy supply of the drone is limited, higher requirements are required in terms of energy efficiency. The existing main energy harvesting methods are an antenna selection method and an energy splitting method. In the antenna selection method, only a part of antennas are selected for information decoding, and the rest antennas are used for energy collection to improve energy efficiency. In the energy division method, part of energy is collected, and the rest of energy performs the task of information decoding. The two methods aim at different targets, the former aims at improving the transmission rate of the system, and the latter aims at collecting energy to improve energy efficiency.
The advantages of the unmanned aerial vehicle communication system and the communication method are combined to make up for the defects of the unmanned aerial vehicle communication system in other aspects, an energy efficiency optimization method of the unmanned aerial vehicle communication system based on interference alignment is provided, and the rate and the energy performance are comprehensively improved. Namely, on the basis of antenna selection, energy division is applied, energy division is carried out on the antenna selected for decoding, energy is further collected, and the energy efficiency performance of the antenna is improved. The specific steps can be subdivided into the following algorithm steps.
Step 1, in the initialization stage, in the ith (i ═ i-1, …, K) N relay dronesrSelecting S from one receiving antenna[i]∈(0,Nr) And decoding information by each antenna, and listing different antenna selection methods. The antenna selection method of the ith relay terminal unmanned aerial vehicle can be expressed as formula (13), which has
Figure BDA0001669491080000081
And (4) possibility.
Figure BDA0001669491080000082
On the ith relay terminal unmanned aerial vehicle, S is selected[i]The antennas are used for information decoding, and the decoding matrix of the ith relay terminal can be represented as S[i]X d of
Figure BDA0001669491080000083
S from jth sending terminal unmanned aerial vehicle to ith relay terminal unmanned aerial vehicle[i]The decoded channel matrix of xm can be expressed as
Figure BDA0001669491080000084
Initializing the channel by using a Gaussian channel model; (N) remainingr-S[i]) Each antenna is used for collecting energy from the jth sending terminal unmanned aerial vehicle to the ith relay terminal unmanned aerial vehicle (N)r-S[i]) The xM energy harvesting channel matrix may be expressed as
Figure BDA0001669491080000085
Initializing the channel by using a Gaussian channel model;
step 2, S selected from the ith relay terminal unmanned aerial vehicle[i]A power splitting scheme is applied to (i ═ 1, …, K) antennas. The power division matrix of the ith relay-side drone can be expressed as equation (14).
Figure BDA0001669491080000086
Wherein i is 1, …, K;
Figure BDA0001669491080000087
denotes S[i]The power division factor for each of the antennas satisfies ρ e (0,1) for each ρ. At S for information decoding[i]On each antenna, the received signal energy is divided again, and for the signal energy received by each antenna,
Figure BDA0001669491080000088
part for decoding information, and collecting
Figure BDA0001669491080000089
Partial energy to improve the energy efficiency of the network as much as possible.
Step 3, in each antenna selection scheme on the ith relay unmanned aerial vehicle, adopting various different power division schemes W[i]. Decoding channel matrix from jth sending-end unmanned aerial vehicle to ith relay-end unmanned aerial vehicle after initialization
Figure BDA00016694910800000810
Substitute for h[ij](i 1, …, K, j 1, …, K), and the power division matrix W[i]The decoded channel matrix is further constrained. Obtaining a coding matrix v of a transmitting end when interference leakage convergence by applying an interference alignment technology in the second step[i](i ═ 1, …, K), relay side coding matrix V[i](i-1, …, K) and decoding matrix
Figure BDA00016694910800000811
And decoding matrix U at receiving end[i](i is 1, …, K), then the scheme information transmission rate can be obtained according to the formula (15); the energy collected by the scheme can be found according to equation (16). Here we target the rate of the first stage as the optimal rate.
The target optimization rate of the ith relay-side drone can be expressed as equation (15).
Figure BDA0001669491080000091
Wherein, i is 1, …, K; i isdAn identity matrix representing d × d;
Figure BDA0001669491080000092
an information decoding matrix representing the ith relay terminal unmanned aerial vehicle is selected as S[i]One antenna is used for information decoding, so it is one S[i]A matrix of x d; w[i]Representative application is at ith relay end unmanned aerial vehicle S[i]S on a selected antenna[i]×S[i]A power division matrix; i isSDenotes S[i]×S[i]The identity matrix of (1); sigma2ISRepresenting white gaussian noise at the relay-side drone.
The energy collected by the ith relay-end drone may be represented as equation (16).
Figure BDA0001669491080000093
Wherein, i is 1, …, K; η and ζ represent energy conversion efficiencies.
Figure BDA0001669491080000094
Power splitting matrices for energy harvesting on behalf of the ith relay-end drone, i.e.
Figure BDA0001669491080000095
Figure BDA0001669491080000096
Representing a decoding channel matrix from the kth sending-end unmanned aerial vehicle to the ith relay-end unmanned aerial vehicle;
Figure BDA0001669491080000097
representing an energy collection channel matrix from the kth sending terminal unmanned aerial vehicle to the ith relay terminal unmanned aerial vehicle; equation (16) the first term to the right of the equation represents S for information decoding in the ith relay drone[i]An antennaEnergy further collected above; the second term represents the remainder of (N)r-S[i]) Energy collected at each antenna;
step 4, calculating target optimization rates corresponding to all schemes and energy collected by the system through the step 3, and selecting an antenna selection scheme g which enables the overall performance of the system rate and the collected energy to be optimal, namely, meets the requirement of a target function[i](i ═ 1, …, K) and power splitting scheme W[i](i ═ 1, …, K) as the optimization solution. The objective function can be expressed as equation (17).
Figure BDA0001669491080000101
Wherein 0 is not less than alpha[i]≦ 1, representing the weight of the rate performance.
The invention provides an unmanned aerial vehicle communication system energy efficiency optimization method based on interference alignment. First, the system model and its feasible conditions are analyzed. Then, an interference alignment algorithm is used for solving an encoding matrix and a decoding matrix. And then, the energy problem in the unmanned aerial vehicle communication system is optimized and solved by utilizing a fusion algorithm of antenna selection and power division, so that the aim of optimizing the performance of the unmanned aerial vehicle communication system is fulfilled.
Detailed Description
The following further describes the specific embodiments of the present invention in combination with the technical solutions.
In a first step, a system model is determined and the feasibility of interference alignment therein is analyzed.
Firstly, a model of an unmanned aerial vehicle communication system is established, and the problems to be solved are clarified.
We consider a 3-user drone group-fly ad hoc network. Wherein contain 3 sending end unmanned aerial vehicle, 3 relay unmanned aerial vehicle and 3 receiving terminals. Wherein the relay drone is in full duplex mode of operation. Suppose that install 3 antennas on every sending end unmanned aerial vehicle, install 3 antennas on the receiving end unmanned aerial vehicle, and install 3 sending antenna and 3 receiving antenna on the relay end unmanned aerial vehicle, d is 1 simultaneously. The whole information transmission process can be divided into two stages, wherein the first stage is that the sending end unmanned aerial vehicle sends information to the relay unmanned aerial vehicle, and the second stage is that the relay unmanned aerial vehicle sends information to the receiving end unmanned aerial vehicle. In the communication process, the relay terminal is interfered by the sending signals of other unexpected sending terminals and is also interfered by other relays when forwarding information; and the receiving end is also interfered by signals of other relay ends.
The signal received by the ith relay drone may be represented as:
Figure BDA0001669491080000102
wherein i is 1,2, 3. u. of[i]Represented is a 3 × 1 decoding matrix on the relay drone, and u[i]u[i]H＝1。h[ii]Representing a 3 multiplied by 3 channel matrix from the ith sending-end unmanned aerial vehicle to the ith relay unmanned aerial vehicle, namely the channel matrix of the expected signal; h is[il](l ≠ i) represents a 3 × 3 channel matrix from the l-th transmitting-end drone to the i-th relaying drone, with each element being independently distributed; g[ik](k ≠ i) is a channel matrix from the kth relay drone to the ith relay drone, which is a 3 × 3 matrix; v. of[i]Is a 3 x 1 matrix, representing the precoding matrix of the ith transmitting-end drone, where v is[i]v[i]H＝Id；V[k](k ≠ 1,2,3, k ≠ i) is a 3 × 1 matrix, representing the coding matrix of the kth relay drone, where V ≠ i[k]V[k]H＝Id；x[i]It shows that the ith sending end unmanned aerial vehicle sends 1 independent data stream with power P1 [i]The signal of (a); x is the number of1 [k](k is 1,2,3) indicates that the kth relay drone transmits 1 independent data stream with power P2 [k]The transmission signal of (1); z is a radical of[i]Is the 3 × 1 gaussian white noise vector for the ith relay drone.
The signal received by the ith receiving-end drone may be expressed as:
Figure BDA0001669491080000111
wherein i is 1,2, 3. U shape[i]Representing a 3 x 1 decoding matrix at the ith user, while U[i]U[i]H＝Id。H[ii]Representing a 3 multiplied by 3 channel matrix from the ith relay terminal unmanned aerial vehicle to the ith receiving terminal unmanned aerial vehicle, namely the channel matrix of the expected signal; h[ik](k ≠ i) represents a 3 × 3 channel matrix from the kth relay-end drone to the ith receiving-end drone; v[i]Is a 3 x 1 matrix, representing the coding matrix of the ith relay drone, where V[i]V[i]H＝Id；z1 [i]Is the 3 x 1 gaussian white noise vector of the ith receiving-end drone.
Since the influence of interference on information transmission is very serious in this information transmission system, we consider applying interference alignment technology to perform interference removal. In order to effectively reduce the interference between users, we should coordinate the design of the precoding matrix and the decoding matrix. At the same time, to ensure that interference alignment can achieve its best performance, system feasibility conditions need to be met.
Figure BDA0001669491080000112
Figure BDA0001669491080000113
Figure BDA0001669491080000114
rank(u[i]Hh[ii]v[i])＝d(23)
rank(U[j]HH[jj]V[j])＝d (24)
When (20) to (22) are satisfied, (23) and (24) are also almost necessarily satisfied. Therefore we need to solve (10) - (12) synergistically to ensure our approach is viable. According to the theory of Bezout, a general polynomial system can be solved if and only if the number of equations does not exceed the number of variables. IA networks can therefore be classified as viable and non-viable networks based on a comparison of the number of equations and the number of variables.
From the above equation, the number of equations can be expressed as:
N＝3×K×(K-1)×d2 (25)
the number of variables can be expressed as:
Nv＝K×d×(M+N+Nr+Nt-4×d) (26)
when the feasible condition of IA is satisfied, i.e. Nv≥NInterference in the system can be perfectly removed. In the case we consider, N＝18，Nv24 satisfies Nv≥NThus satisfying the feasible conditions.
And secondly, carrying out iterative solution on the precoding matrix and the decoding matrix by utilizing distributed interference alignment.
After the feasibility condition is satisfied, we can apply the interference alignment technology to the unmanned aerial vehicle communication system.
The interference alignment technology is an emerging interference management technology, and the main idea thereof is to effectively remove communication interference in a system through the design of an encoding matrix and a decoding matrix. By utilizing network reciprocity, the distributed interference alignment is adopted to carry out iterative solution on the coding and decoding matrix to be designed. The specific steps can be subdivided into the following algorithm steps.
The algorithm comprises the following steps:
step 1, initializing a channel matrix h from the ith sending-end unmanned aerial vehicle to the ith relay unmanned aerial vehicle by using a Gaussian channel model[il](i ═ 1,2,3, l ═ 1,2,3), and channel matrix H for the k-th relay drone to the i-th receiving drone[ik](i ═ 1,2,3, k ═ 1,2, 3); initializing a channel matrix G from a kth relay unmanned aerial vehicle to an ith relay unmanned aerial vehicle by using a Gaussian channel model[ik](i ≠ 1,2,3, k ≠ i); initializing the ith sending end precoding matrix v by using normally distributed random number matrix with mean value of 0 and variance of 1[i](i ═ 1,2,3) and the kth relay precoding matrix V[k](k＝1,2,3)；
And 2, designing a decoding matrix based on an iterative algorithm of Min _ LI. The criterion is to minimize the interference signal power remaining after the receiving end passes through the interference suppression filter, i.e., the "interference leakage energy". The leaked interference signal is gradually reduced through gradual iteration until the interference leakage energy is zero after the complete interference alignment is realized.
At the ith relay terminal unmanned aerial vehicle, the interference leakage energy is:
Figure BDA0001669491080000131
wherein i is 1,2, 3; tr [ A ]]，Qb [i]Respectively representing the trace of the matrix A and the interference covariance matrix of the ith relay drone. Q can be calculated by using the value initialized in step 1b [i]. In this invention is provided with
Figure BDA0001669491080000132
Figure BDA0001669491080000133
At the ith receiving end unmanned aerial vehicle, the interference leakage energy is:
I[i]＝Tr[U[i]HQ[i]U[i]] (29)
wherein Q[i]Representing the interference covariance matrix of the ith receiving-end drone.
Figure BDA0001669491080000134
Step 3, the ith relay end unmanned aerial vehicle passes through selectionSelecting a decoding matrix u[i]To minimize interference leakage, an interference covariance matrix Q may be selected accordinglyb [i]The matrix expanded by the eigenvector corresponding to the minimum d eigenvalues is used as a decoding matrix, and the ith relay terminal unmanned aerial vehicle corresponds to Qb [i]The feature vector for each feature value of (a) may be expressed as:
Figure BDA0001669491080000135
wherein v iss(A) The function represents the eigenvector corresponding to the s-th eigenvalue of the matrix a. D eigenvectors are combined together to obtain a decoding matrix u of the ith relay-end unmanned aerial vehicle[i]. Similarly, the ith receiving end unmanned aerial vehicle decodes the matrix U through selection[i]To minimize interference leakage. Corresponding Q in ith receiving end unmanned aerial vehicle[i]The feature vector for each feature value of (a) may be expressed as:
Figure BDA0001669491080000141
d eigenvectors are combined together to obtain a decoding matrix U of the ith receiving-end unmanned aerial vehicle[i]；
In the invention, d is 1;
and 4, turning over the communication direction and exchanging the roles of the transceiving end by utilizing the network reciprocity according to the iteration algorithm of Min _ LI. In the reverse network, we add ← representation above the encoding matrix and decoding matrix. At this moment, the original receiving end unmanned aerial vehicle is used as a sending end, the original sending end unmanned aerial vehicle is used as a receiving end, and the relay end is still the relay end, but the information transmission direction is opposite to that before.
At this time, the interference covariance matrix of the unmanned aerial vehicle at the ith sending end of the reciprocal network can be expressed as:
Figure BDA0001669491080000142
wherein i is 1,2, 3;
Figure BDA0001669491080000143
represents the channel matrix from the ith relay terminal unmanned aerial vehicle to the ith sending terminal unmanned aerial vehicle, and
Figure BDA0001669491080000144
Figure BDA0001669491080000145
the coding matrix of the representative first relay-end unmanned aerial vehicle in the reciprocal network is given by the decoding matrix of the relay-end unmanned aerial vehicle obtained in the step 3,
Figure BDA0001669491080000146
the interference covariance matrix of the ith relay-side drone may be expressed as:
Figure BDA0001669491080000147
wherein i is 1,2, 3;
Figure BDA0001669491080000148
represents the channel matrix from the l receiving end unmanned aerial vehicle to the i relay end unmanned aerial vehicle, and
Figure BDA0001669491080000149
Figure BDA00016694910800001410
representing the coding matrix of the first receiving-end unmanned aerial vehicle in the reciprocal network, given by the decoding matrix of the receiving-end unmanned aerial vehicle obtained in the step 3,
Figure BDA00016694910800001411
Figure BDA00016694910800001412
representing the k relay terminal unmanned aerial vehicle to the ith relay terminal unmanned aerial vehicleChannel matrix of relay-side unmanned aerial vehicle, and
Figure BDA00016694910800001413
Figure BDA00016694910800001414
the coding matrix representing the kth relay-end unmanned aerial vehicle in the reciprocal network is given by the decoding matrix of the relay-end unmanned aerial vehicle obtained in the step 3,
Figure BDA0001669491080000151
similar to the step 3, solving the corresponding interference covariance matrix in the ith relay-end unmanned aerial vehicle in the reciprocal network
Figure BDA0001669491080000152
Feature vector for each feature value of (1):
Figure BDA0001669491080000153
d eigenvectors are combined together to obtain a decoding matrix of the ith relay-end unmanned aerial vehicle
Figure BDA0001669491080000154
Solving corresponding interference covariance matrix in ith sending end unmanned aerial vehicle in reciprocal network
Figure BDA0001669491080000155
Feature vector for each feature value of (1):
Figure BDA0001669491080000156
d eigenvectors are combined together to obtain a decoding matrix of the ith sending end unmanned aerial vehicle
Figure BDA0001669491080000157
In this invention, d is 1;
and 5, reversing the communication direction again, wherein at the moment, the coding matrix v of the ith (i is 1,2,3) sending-end unmanned aerial vehicle[i]Is that
Figure BDA0001669491080000158
Coding matrix V of ith (i ═ 1,2,3) relay-end unmanned aerial vehicle[i]Is that
Figure BDA0001669491080000159
Returning to the step 2, and performing a new iteration until interference leakage is converged;
step 6, coding matrix v of sending end in interference leakage matrix convergence[i](i ═ 1,2,3), coding matrix V of relay end[i](i ═ 1,2,3) and decoding matrix u[i](i ═ 1,2,3), and decoding matrix U at the receiving end[i](i ═ 1,2,3) is solved;
and thirdly, obtaining an energy efficiency optimization method of the unmanned aerial vehicle communication system based on the antenna selection and power division technology.
The advantages of the unmanned aerial vehicle communication system and the communication method are combined to make up for the defects of the unmanned aerial vehicle communication system in other aspects, an energy efficiency optimization method of the unmanned aerial vehicle communication system based on interference alignment is provided, and the rate and the energy performance are comprehensively improved. Namely, on the basis of antenna selection, energy division is applied, energy division is carried out on the antenna selected for decoding, energy is further collected, and the energy efficiency performance of the antenna is improved. The specific steps can be subdivided into the following algorithm steps.
Step 1, in an initialization stage, selecting S from 3 receiving antennas of the ith relay unmanned aerial vehicle[i]E (0,3) antennas are subjected to information decoding, and different antenna selection methods are listed. The antenna selection method of the ith relay terminal drone can be expressed as formula (37), which has
Figure BDA0001669491080000161
And (4) possibility.
Figure BDA0001669491080000162
On the ith relay terminal unmanned aerial vehicle, S is selected[i]The antennas are used for information decoding, and the decoding matrix of the ith relay terminal can be represented as S[i]X 1 of
Figure BDA0001669491080000163
S from jth sending terminal unmanned aerial vehicle to ith relay terminal unmanned aerial vehicle[i]The x 3 decoded channel matrix may be expressed as
Figure BDA0001669491080000164
Initializing the channel by using a Gaussian channel model; the remaining (3-S)[i]) Each antenna is used for collecting energy, and the (3-S) from the jth sending terminal unmanned aerial vehicle to the ith relay terminal unmanned aerial vehicle[i]) The x 3 energy harvesting channel matrix may be represented as
Figure BDA0001669491080000165
Initializing the channel by using a Gaussian channel model;
step 2, S selected from the ith relay terminal unmanned aerial vehicle[i]Power splitting is applied on (i ═ 1,2,3) antennas. The power division matrix of the ith relay-side drone can be expressed as equation (38).
Figure BDA0001669491080000166
Wherein i is 1,2, 3;
Figure BDA0001669491080000167
denotes S[i]The power division factor for each of the antennas satisfies ρ e (0,1) for each ρ. At S for information decoding[i]On each antenna, the received signal energy is divided again, and for the signal energy received by each antenna,
Figure BDA0001669491080000168
part for decoding information, and collecting
Figure BDA0001669491080000169
Partial energy to improve the energy efficiency of the network as much as possible. In this invention, it is assumed that, on the ith relay drone,
Figure BDA00016694910800001610
and the value of rho is increased from 0 to 1 by taking 0.1 as a step length. Therefore, for each relay drone, there are 11 power splitting schemes.
Step 3, in each antenna selection scheme on the ith relay unmanned aerial vehicle, 11 different power division schemes W are adopted[i]. Decoding channel matrix from jth sending terminal unmanned aerial vehicle to ith relay terminal unmanned aerial vehicle after initialization
Figure BDA00016694910800001611
Substitute for h[ij](i 1,2,3, j 1,2,3), the power division matrix W is reused[i]The decoded channel matrix is further constrained. Obtaining the coding matrix v of the transmitting end by applying the interference alignment technology in the second step[i](i ═ 1,2,3), coding matrix V of relay end[i](i ═ 1,2,3) and decoding matrix
Figure BDA0001669491080000171
And decoding matrix U at receiving end[i]After (i ═ 1,2,3), the scheme information transmission rate can be found according to equation (39); the energy collected by the scheme can be found according to equation (40). Here we target the rate of the first stage as the optimal rate.
The target optimization rate of the ith relay-side drone can be expressed as formula (39).
Figure BDA0001669491080000172
Wherein i is 1,2, 3; i isdAn identity matrix representing d × d;
Figure BDA0001669491080000178
an information decoding matrix representing the ith relay terminal unmanned aerial vehicle is selected as S[i]One antenna is used for information decoding, so it is one S[i]A matrix of x 1; w[i]Representative application is at ith relay end unmanned aerial vehicle S[i]S on a selected antenna[i]×S[i]A power division matrix; i isSDenotes S[i]×S[i]The identity matrix of (1); sigma2ISRepresenting white gaussian noise at the relay-side drone.
The energy collected by the ith relay-end drone can be expressed as equation (40).
Figure BDA0001669491080000173
Wherein i is 1,2, 3; η and ζ represent energy conversion efficiency, and η is set to 0.7 and ζ is set to 0.8 in the present invention.
Figure BDA0001669491080000174
Power splitting matrices for energy harvesting on behalf of the ith relay-end drone, i.e.
Figure BDA0001669491080000175
Figure BDA0001669491080000176
Representing a decoding channel matrix from the kth sending-end unmanned aerial vehicle to the ith relay-end unmanned aerial vehicle;
Figure BDA0001669491080000177
representing an energy collection channel matrix from the kth sending terminal unmanned aerial vehicle to the ith relay terminal unmanned aerial vehicle; equation (40) the first term to the right of the equation represents S for information decoding in the ith relay drone[i]Energy further collected on each antenna; the second term represents the remainder of (N)r-S[i]) Energy collected at each antenna;
step 4, calculating target optimization rates corresponding to all schemes and energy collected by the system through the step 3, and selecting the systemThe overall performance of the speed and the collected energy is optimal, namely the antenna selection scheme g meeting the requirement of an objective function[i](i ═ 1,2,3) and power splitting scheme W[i](i ═ 1,2,3) as an optimization solution. The objective function can be expressed as formula (41).
Figure BDA0001669491080000181
Wherein 0 is not less than alpha[i]≦ 1, representing the weight of the rate performance. In this invention alpha[i]＝0.5(i＝1,2,3)。

Claims (1)

1. An energy efficiency optimization method of an unmanned aerial vehicle communication system based on interference alignment is characterized by comprising the following steps:
first, a system model is determined and the feasibility of interference alignment therein is analyzed
Establishing a model of an unmanned aerial vehicle communication system;
the unmanned aerial vehicle group flight self-organizing network comprises K sending end unmanned aerial vehicles, K relay unmanned aerial vehicles and K receiving end unmanned aerial vehicles; the relay-end unmanned aerial vehicle is in a full-duplex working mode; suppose that each sending-end unmanned aerial vehicle is provided with M antennas, each receiving-end unmanned aerial vehicle is provided with N antennas, and each relay unmanned aerial vehicle is provided with N antennastA transmitting antenna and NrA receiving antenna; the information transmission process is divided into two stages, wherein in the first stage, the unmanned aerial vehicle at the sending end sends information to the relay unmanned aerial vehicle; the second stage is that the relay unmanned aerial vehicle sends information to the receiving end unmanned aerial vehicle; in the communication process, the relay unmanned aerial vehicle is interfered by the sending signals of other unexpected sending ends and is also interfered by other relays when forwarding information; the receiving-end unmanned aerial vehicle is interfered by signals of an unexpected relay end;
the signal received by the ith relay drone is represented as:
Figure FDA0002676862400000011
wherein: 1, ·, K; u. of[i]Represents NrX d decoding matrix on relay drone, and u[i]u[i]H1 is ═ 1; h represents a conjugate transpose; h is[ii]Represented is N from the ith sending terminal unmanned aerial vehicle to the ith relay unmanned aerial vehiclerA xm channel matrix, which is a channel matrix of the desired signal; h is[il](l ═ 1., K, l ≠ i) represents N from the l-th transmitting-end drone to the i-th relay dronerA xm channel matrix in which each element is independently distributed; g[ik](K ═ 1.,. K, K ≠ i) represents the channel matrix from the kth relay drone to the ith relay drone, which is an Nr×NtA matrix of (a); v. of[i]Is an Mxd matrix, which represents the precoding matrix of the ith sending-end UAV, where v is[i]v[i]H＝Id；v[l](l ═ 1.,. K, l ≠ i) is an mxd matrix, which represents the precoding matrix of the l-th transmitting-end drone, where v ≠ is[l]v[l]H＝Id；V[k](K ≠ 1., K ≠ i) is NtX d matrix, representing the coding matrix of the kth relay drone, where V[k]V[k]H＝Id；x[i]It shows that d independent data streams with power P are sent by the ith sending end unmanned aerial vehicle1 [i]The signal of (a); x is the number of[l]That is, the sending-end drone sends d independent data streams with power P ≠ i ═ 11 [l]The signal of (a); x is the number of1 [k]The (K ═ 1.. multidata., K) indicates that the K-th relay drone has d independent data streams and the power is P2 [k]The transmission signal of (1); z is a radical of[i]Is N of the ith relay dronerA gaussian white noise vector of x 1; the first term on the right of equation of formula (1) represents an expected received signal of the ith relay-end unmanned aerial vehicle, the second term represents interference of an unexpected sending-end unmanned aerial vehicle on the ith relay-end unmanned aerial vehicle, the third term represents interference of other relay-end unmanned aerial vehicles on the ith relay-end unmanned aerial vehicle, and the last term represents interference on the ith relay-end unmanned aerial vehicleWhite gaussian noise interference;
the signal received by the ith receiving end unmanned aerial vehicle is represented as:
Figure FDA0002676862400000021
wherein i 1.., K; u shape[i]Representing the Nxd decoding matrix on the ith receiving-end UAV, while U[j]U[j]H＝Id；H[ii]Represented is NxN from the ith relay-end unmanned aerial vehicle to the ith receiving-end unmanned aerial vehicletA channel matrix, which is a channel matrix of the desired signal; h[ik](K ═ 1., K ≠ i) represents nxn of the kth relay-end drone to the ith receiving-end dronetA channel matrix; v[i]Is a number NtX d matrix, representing the coding matrix of the ith relay drone, where V[i]V[i]H＝Id；z1 [i]Is the ith receiving terminal unmanned aerial vehiclerA gaussian white noise vector of x 1; the first item on the right side of the equation in the formula (2) represents an expected receiving signal of the ith receiving-end unmanned aerial vehicle, the second item represents interference of an unexpected relay-end unmanned aerial vehicle on the ith receiving-end unmanned aerial vehicle, and the third item represents Gaussian white noise interference on the ith receiving-end unmanned aerial vehicle;
second, iterative solution of precoding and decoding matrices using distributed interference alignment
2.1, initializing a channel matrix h from the ith sending-end unmanned aerial vehicle to the ith relay unmanned aerial vehicle by a Gaussian channel model[il](i 1. -, K, l 1. -, K), and a channel matrix H that the kth relay drone to the ith receiving drone[ik](i 1., K ═ 1., K.; initializing a channel matrix G from a kth relay unmanned aerial vehicle to an ith relay unmanned aerial vehicle by using a Gaussian channel model[ik](i ≠ 1., K ≠ 1., K ≠ i); initializing the ith sending end precoding matrix v by using normally distributed random number matrix with mean value of 0 and variance of 1[i](i ═ 1.. multidata., K) and the kth relay precoding matrix V[k](k＝1,...,K)；
2.2, designing a decoding matrix based on an iterative algorithm of Min _ LI: the standard is to minimize the residual interference signal power after the receiving end passes through the interference suppression filter, namely the 'interference leakage energy'; the complete interference alignment is realized by gradually iterating and gradually reducing the leaked interference signals until the interference leakage energy is zero;
at the ith relay terminal unmanned aerial vehicle, the interference leakage energy is:
Figure FDA0002676862400000031
wherein i is 1,2, 3; tr [ A ]]，Qb [i]Respectively representing the trace of the matrix A and the interference covariance matrix of the ith relay unmanned aerial vehicle; qb [i]Is the formula (4), using the initialized values in 2.1 and the communication system degree of freedom d and the transmission signal power P1 [l](l＝1,...,K,l≠i)，
Figure FDA0002676862400000032
Calculate Qb [i]；
Figure FDA0002676862400000033
At the ith receiving end unmanned aerial vehicle, the interference leakage energy is:
I[i]＝Tr[U[i]HQ[i]U[i]] (5)
wherein Q[i]Representing an interference covariance matrix of the ith receiving end unmanned aerial vehicle;
Figure FDA0002676862400000034
2.3 the ith relay terminal unmanned aerial vehicle decodes the matrix u through selection[i]To minimize interference leakage, andselecting an interference covariance matrix Qb [i]The matrix expanded by the eigenvector corresponding to the minimum d eigenvalues is used as a decoding matrix, and the ith relay terminal unmanned aerial vehicle corresponds to Qb [i]The feature vector for each feature value of (a) is represented as:
u*s [i]＝vs(Qb [i]),s＝1,...d (7)
wherein v iss(A) The function represents the eigenvector corresponding to the s-th eigenvalue of the matrix A; combining the d eigenvectors to obtain a decoding matrix u of the ith relay-end unmanned aerial vehicle[i]；
Similarly, the ith receiving end unmanned aerial vehicle decodes the matrix U through selection[i]To minimize interference leakage; corresponding Q in ith receiving end unmanned aerial vehicle[i]The feature vector for each feature value of (a) is represented as:
Figure FDA0002676862400000041
d eigenvectors are combined together to obtain a decoding matrix U of the ith receiving-end unmanned aerial vehicle[i]；
2.4, according to the iteration algorithm of Min _ LI, the communication direction is turned over by utilizing the network reciprocity, and the roles of the transmitting and receiving ends are interchanged; in the reverse network, adding ← representation above an encoding matrix and a decoding matrix; at this time, the original receiving-end unmanned aerial vehicle serves as a sending end, the original sending-end unmanned aerial vehicle serves as a receiving end, the relay end is still the relay end, but the information transmission direction is opposite to that of the original sending-end unmanned aerial vehicle;
at this time, the interference covariance matrix of the unmanned aerial vehicle at the ith sending end of the reciprocal network is represented as:
Figure FDA0002676862400000042
wherein, i is 1. -, K;
Figure FDA0002676862400000043
represents the channel matrix from the ith relay terminal unmanned aerial vehicle to the ith sending terminal unmanned aerial vehicle, and
Figure FDA0002676862400000044
Figure FDA0002676862400000045
the coding matrix of the representative first relay-end unmanned aerial vehicle in the reciprocal network is given by the decoding matrix of the relay-end unmanned aerial vehicle obtained in the step 3,
Figure FDA0002676862400000046
the interference covariance matrix of the ith relay-end unmanned aerial vehicle is expressed as:
Figure FDA0002676862400000047
wherein, i is 1. -, K;
Figure FDA0002676862400000048
represents the channel matrix from the l receiving end unmanned aerial vehicle to the i relay end unmanned aerial vehicle, and
Figure FDA0002676862400000049
Figure FDA00026768624000000410
representing the coding matrix of the first receiving-end unmanned aerial vehicle in the reciprocal network, given by the decoding matrix of the receiving-end unmanned aerial vehicle obtained in the step 3,
Figure FDA00026768624000000411
Figure FDA00026768624000000412
representing the communication from the kth relay terminal unmanned aerial vehicle to the ith relay terminal unmanned aerial vehicleA channel matrix, and
Figure FDA00026768624000000413
Figure FDA00026768624000000414
the coding matrix representing the kth relay-side unmanned aerial vehicle in the reciprocal network is given by the decoding matrix of the relay-side unmanned aerial vehicle calculated in step 2.3,
Figure FDA00026768624000000415
same as the step 2.3, solving a corresponding interference covariance matrix in the ith relay-end unmanned aerial vehicle in the reciprocal network
Figure FDA00026768624000000416
Feature vector for each feature value of (1):
Figure FDA0002676862400000051
d eigenvectors are combined together to obtain a decoding matrix of the ith relay-end unmanned aerial vehicle in the reciprocal network
Figure FDA0002676862400000052
Solving corresponding interference covariance matrix in ith sending end unmanned aerial vehicle in reciprocal network
Figure FDA0002676862400000053
Feature vector for each feature value of (1):
Figure FDA0002676862400000054
d eigenvectors are combined together to obtain a decoding matrix of the ith sending end unmanned aerial vehicle in the reciprocal network
Figure FDA0002676862400000055
2.5, the communication direction is turned over again, and at this time, the coding matrix v of the i (i ═ 1., K) th sending-end unmanned aerial vehicle[i]Is that
Figure FDA0002676862400000056
Coding matrix V of i (i ═ 1.,. K.) th relay-end unmanned aerial vehicle[i]Is that
Figure FDA0002676862400000057
Returning to the step 2, and performing a new iteration until interference leakage is converged;
2.6 coding matrix v of transmitting end in interference leakage matrix convergence[i](i ═ 1.. times, K), the coding matrix V of the relay end[i](i ═ 1.. times, K) and decoding matrix u[i](i ═ 1.. multidata., K), and a decoding matrix U at the receiving end[i](i 1.., K) is solved;
thirdly, obtaining an energy efficiency optimization method of the unmanned aerial vehicle communication system based on the antenna selection and power division technology
3.1, in an initialization phase, relay N of drone at i (i ═ 1.., K) thrSelecting S from one receiving antenna[i]∈(0,Nr) Decoding information by each antenna, and listing different antenna selection methods; the antenna selection method of the ith relay terminal unmanned aerial vehicle is expressed as a formula (13), including
Figure FDA0002676862400000058
A seed probability;
Figure FDA0002676862400000059
on the ith relay terminal unmanned aerial vehicle, S is selected[i]Each antenna is used for information decoding, and a decoding matrix of the ith relay terminal is represented as S[i]X d of
Figure FDA0002676862400000061
S from jth sending terminal unmanned aerial vehicle to ith relay terminal unmanned aerial vehicle[i]The decoded channel matrix of x M is represented as
Figure FDA0002676862400000062
Initializing the channel by using a Gaussian channel model; (N) remainingr-S[i]) Each antenna is used for collecting energy from the jth sending terminal unmanned aerial vehicle to the ith relay terminal unmanned aerial vehicle (N)r-S[i]) The xM energy harvesting channel matrix is represented as
Figure FDA0002676862400000063
Initializing the channel by using a Gaussian channel model;
3.2S selected in the ith relay terminal unmanned aerial vehicle[i]Applying a power division scheme to (i ═ 1.,. K) antennas, and expressing a power division matrix of the i-th relay-end drone as formula (14):
Figure FDA0002676862400000064
wherein i 1.., K;
Figure FDA0002676862400000065
denotes S[i]Power division factor of each of the antennas, for each
Figure FDA0002676862400000066
Satisfy the requirement of
Figure FDA0002676862400000067
At S for information decoding[i]On each antenna, the received signal energy is divided again, and for the signal energy received by each antenna,
Figure FDA0002676862400000068
part for decoding information, and part for receivingCollection
Figure FDA0002676862400000069
Partial energy to improve the energy efficiency of the network as much as possible;
3.3 in each antenna selection scheme on the ith relay unmanned aerial vehicle, adopting various different power division schemes W[i](ii) a Decoding channel matrix from jth sending-end unmanned aerial vehicle to ith relay-end unmanned aerial vehicle after initialization
Figure FDA00026768624000000610
Substitute for h[ij](i 1.. K, j 1.. K), and a power division matrix W[i]Further constraining the decoded channel matrix; obtaining a coding matrix v of a transmitting end when interference leakage convergence by applying an interference alignment technology in the second step[i](i ═ 1.. times, K), the coding matrix V of the relay end[i](i ═ 1.. multidot.k) and decoding matrix
Figure FDA00026768624000000611
And decoding matrix U at receiving end[i]After (i ═ 1.., K), the scheme information transmission rate is calculated according to formula (15); solving the energy collected by the scheme according to the formula (16); taking the rate of the first stage as a target optimizing rate;
the target optimization rate of the ith relay terminal unmanned aerial vehicle is expressed as formula (15):
Figure FDA00026768624000000612
Figure FDA0002676862400000071
wherein, i is 1. -, K; i isdAn identity matrix representing d × d;
Figure FDA0002676862400000072
indicates that the ith relay terminal is unmannedInformation decoding matrix of machine, due to selection of S[i]One antenna is used for information decoding, so it is one S[i]A matrix of x d; w[i]Representative application is at ith relay end unmanned aerial vehicle S[i]S on a selected antenna[i]×S[i]A power division matrix; i isSDenotes S[i]×S[i]The identity matrix of (1); sigma2ISWhite gaussian noise at the relay-side drone;
the energy collected by the ith relay-end drone is expressed as formula (16):
Figure FDA0002676862400000073
wherein, i is 1. -, K; η and ζ represent energy conversion efficiency;
Figure FDA0002676862400000074
power splitting matrices for energy harvesting on behalf of the ith relay-end drone, i.e.
Figure FDA0002676862400000075
Figure FDA0002676862400000076
Representing a decoding channel matrix from the kth sending-end unmanned aerial vehicle to the ith relay-end unmanned aerial vehicle;
Figure FDA0002676862400000077
representing an energy collection channel matrix from the kth sending terminal unmanned aerial vehicle to the ith relay terminal unmanned aerial vehicle; equation (16) the first term to the right of the equation represents S for information decoding in the ith relay drone[i]Energy further collected on each antenna; the second term represents the remainder of (N)r-S[i]) Energy collected at each antenna;
3.4, calculating target optimization rates corresponding to all schemes and energy collected by the system according to the 3.3, and selecting the target optimization rates and the energy collected by the system to enable the system rate and the collected energy to be integratedOptimal performance, i.e. antenna selection scheme g meeting objective function requirements[i](i 1.., K) and a power splitting scheme W[i](i ═ 1.., K) as an optimization solution: the objective function is expressed as formula (17):
Figure FDA0002676862400000078
wherein 0 is not less than alpha[i]≦ 1, representing the weight of the rate performance.
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