TECHNICAL FIELD

The present invention relates to the technical field of autonomous coordination of unmanned aerial vehicle (UAV) swarms, and in particular to a wireless perception system energy and information transmission method of a UAV swarm.

BACKGROUND

In recent years, with the vigorous development of the wireless communication technology, the Internet of Things technology has been rapidly popularized and widely used. At present, intelligent perception networks based on low power sensors have been widely used in various fields such as environmental monitoring, medical care and smart cities. However, in order to satisfy, the miniaturization and light-weight requirements of wireless sensors, the battery capacity is generally limited. The problem of sustainable energy supply becomes a development bottleneck of wireless perception networks, and limits the large-scale deployment. Wireless power transmission (WPT) is regarded as an ideal solution to solve the long-term stable power supply of wireless sensor networks. Compared with the traditional energy acquisition modes of solar energy conversion and mechanical energy conversion, WPT has the characteristic of high controllability in power transmission, signal waveform and time-frequency resource allocation, and has obvious advantages. Based on WPT technology, domestic and foreign scholars have conducted a lot of, related research.

At present, the WPT technology is mainly based on two frames: in 2013, R. Zhang et, al. proposed a simultaneous wireless information and power transfer (SWIPT) method, simultaneously designed a receiver structure based on time switching and power splitting protocols and proved that the SWIPT was feasible in theory and in practice. After 2014, the wireless powered communication, network (WPCN) was also proposed. This technology does not require a complicated receiver structure and has good practicability. So far, based on the above two frames, the relay technology, the multi-antenna technology, the sensor coordination technology and the like have achieved good integration with the WPT technology, making it possible to use wireless powered sensors for perception and data collection.

However, the WPT technology faces serious double near-far effects in practical applications: because the WPT technology uses a radio frequency signal as a carrier for energy transmission, propagation loss becomes more serious with the increase of distance. Therefore, farther sensors often obtain less energy, but need to consume more energy in the process of transmitting perception, data back. The double far-near effects seriously affect the coverage range of the WPT technology, making it inapplicable to large-scale wireless perception systems. An effective method to solve the double near-far effects is to deploy multiple access points (AP). In a multi-AP scenario, the sensor can obtain energy from multiple APs at the same time, and by reasonably designing the transmission power of each AP, the coverage range of each AP can be effectively adjusted, and the energy harvested by each sensor can be balanced, to improve the overall performance of the system.

At present, there is no research in China and abroad to jointly design and improve the coverage range of the wireless perception system in the autonomous collaborative energy supply scenario of a UAV swarm. Considering that the practical use scenario of the wireless powered perception system generally has the characteristic of difficulty in arrival or high danger, an urgent problem to be solved by those skilled in the art is to provide an efficient and reliable wireless perception system energy and information transmission method of a UAV swarm.

SUMMARY

In view of this, the present invention provides a wireless perception system energy and information transmission method of a UAV swarm.

To achieve the above purpose, the present invention adopts the following technical solution:

A wireless perception system energy and information transmission method of a UAV swarm comprises the following steps:

building a wireless perception system architecture based on autonomous and collaborative energy supply of a UAV swarm, wherein the wireless perception system architecture comprises K wireless powered sensors pre-arranged at perception positions and a freely movable UAV swarm, the UAV swarm comprises N UAVs, and each perception subnet comprises a UAV and M sensors managed by the UAV; in the wireless perception system architecture, the UAVs are all provided with A antennas, and the sensors are only provided with a single antenna due to limitations of size and cost;

in the wireless perception system architecture, when perception is required, the UAV swarm arrives at a designated region to perform wireless energy transmission and wireless information transmission on the sensors; each transmission process comprises two stages: a wireless energy harvesting (WEH) stage and a wireless information transmission (WIT) stage;

based on the wireless perception system architecture, establishing a joint optimization model by combining a power allocation weight W, a receiving beam forming weight B and a time division factor τ in the UAV swarm, and solving optimal system configuration to obtain an optimal transmission strategy; maximizing the total throughput of a sensor uplink under the constraints of data throughput in the UAV swarm;

the joint optimization model is:

(P⁢1):maxw,B,τ∑i=1Nξi⁢Ci(W,Bij,τ)⁢s.t.C⁢1:W±WlbC⁢2:WWH=1,C⁢3:Bijo⁢11×A,C⁢4:τl⁢b≤τ<1.

where ξiis an importance factor and ξi>0; Ci(W, Bij, τ) is the total throughput in the ithsubnet; Bijis a receiving beam forming weight vector; wilbis a lower bound of the ithelement of a power allocation weight vector W; WHrepresents the conjugate transpose of the vector W; and τlbis a lower bound of the time division factor τ;

C1, C2 and C3 are constraints; C1 is a data throughput constraint in the UAV swarm; Wlb∈└1×Nis a vector with wilbas an element, and represents the lower bound of W; C2 constrains the total power of the wireless energy harvesting (WEH) stage to be constant P; C3 means that the receiving beam forming weight cannot exceed 1 because there is no extra energy for signal amplification, where 11×Arepresents an all-1 vector of size 1×A; C4 limits the upper and lower bounds of the time division factor;

the step of solving optimal system configuration comprises:

S1. setting the maximum number of optimizations and a convergence threshold, setting initial values of W, B and τ, and calculating a common parameter;

S2. conducting optimal value search in a feasible region [τlb,1) through a golden section search algorithm to obtain τ*, and updating the common parameter, optimizing W under a new common parameter to obtain W*, and updating the common parameter; optimizing B under a new common parameter to obtain B*, and updating the common parameter; obtaining a new τ* again through the golden section search algorithm under a new common parameter, and updating the common parameter;

S3. repeating S2 until a difference between two adjacent optimizations is less than the convergence threshold or reaches the maximum number of optimizations, to obtain final W*, B* and τ*.

It should be noted that:

In the practical application process, the optimization order of W, B and τ can be changed. Experiments show that the difference of the optimization orders of W, B and τ has a certain influence on the convergence speed of the algorithm. Firstly, optimization of τ has higher convergence speed in most cases.

The optimal transmission strategy is obtained on the basis of mathematical representation of wireless energy transmission and wireless data communication transmitting-receiving process, and analysis of energy harvesting performance of each sensor node and information transmitting-receiving performance of each UAV node, including:

(1) Channel Model

The channel parameters of radio frequency signals are mainly affected by propagation loss, shadow effect and multipath reflection. A path loss model adopted by the present invention is
PL(d,f)=PL′(d,f)·X

where PL′ is spatial propagation loss; d represents a spatial distance between a transmitter and a receiver, f represents signal transmission frequency; and X is a fading factor caused by the shadow effect and multipath reflection.

2) Analysis of Information Transmission Throughput in the Swarm

Assuming that the total transmission power of the UAV swarm in the WEH stage is P and W=[w1, . . . , wN] is the power allocation weight (WWH=1) of each UAV, then the total transmission power of the UAV i is:
PUi=wi2P

Because the transmission power of each UAV is evenly distributed on

A2
transmitting antennas, the transmission power of a single antenna of the UAV i can be expressed as

2⁢wi2⁢PA
(i=1, . . . , N), and a transmitting signal is:

x^Ui=2⁢PA⁢wi⁢xUi

where xUiis a unit power signal carrying the state information of the UAV i, i.e., E[|xUi|2]=1. A channel between the UAV i and k (k=1, . . . , N) is represented as Jik∈|A/2×A/2, and a maximum ratio combination method is adopted for multi-antenna reception. Therefore, the receiving weight can be represented as Vik=Ĵik/|Ĵik|, where the vector Ĵik∈└1×A/2is obtained by summing the channel matrix Jikby column. The signal-to-noise ratio of a signal received by the UAV k from the UAV i is:

Γik=2⁢P⁢wi2|Jˆik⁢VikH|2A⁢σU2|Vik|2

where σU2is noise power on a single antenna of the UAV. The length of each transmission time block is represented as T, and then the length in the WEH stage is τT, where τ is a time division factor and the throughput between the UAVs i to k can be represented as:
rik=τTlog2(1+Γik)

The minimum throughput requirement for the UAVs i to k (k≠i) is set as Yik, and rik≥Yikneeds to be satisfied. At this moment:

wi≥(2Yikτ⁢T-1)⁢A⁢σU2|Vik|22⁢P|Jˆik⁢VikH|2=(wii⁢b)k

Because wineeds to be greater than all (wilb)kat the same time, it can be obtained
wi≥Max[(wilb)k]=wilb

Similarly, there is a lower bound for τ

τ≥Max[γikT⁢log2(1+Γik)]=τlb

(3) Analysis of Energy Harvested by Sensor

The quantity of the sensors in each subnet is defined. Hkij∈└1×A/2is a channel vector from the UAV k to the kthsensor Sij(j=1, . . . , M) in the subnet i; and noise power on a single antenna of the sensor is σS2. An energy harvesting model can be expressed as a linear function Pharvest=ηPinput, where η(0<η<1) is conversion efficiency, and in the WEH stage, the total energy harvested by Sijis:

ESij=ητ⁢T⁡(2⁢P|W⁢HijH|2A+σS2)≈2⁢η⁢τ⁢T⁢P|W⁢HijH|2A

where Hij=[(H1ij)H, (H2ij)H, . . . , (HNij)H] represents a channel matrix from the UAV swarm to the sensor Sij.

(4) Analysis of Sensor Uplink Throughput

In the WIT stage, the transmission time of each sensor is

(1-τ)⁢TM,
and then the transmission power of the sensor Sijcan be expressed as:

PSij=ESij(1-τ)⁢T/M=2⁢M⁢η⁢τ⁢P|WHijH|2(1-τ)⁢A

Because the UAV does not transmit signals in the WIT stage, A antennas can be used for data reception. Gkji∈└A×1is a channel vector from the sensor Skjto the UAV i, xSkjis a unit power signal carrying the perception information of the sensor Skj, i.e., E[|xSkj|2]=1. To suppress the interference from the sensors in adjacent subnets, multi-antenna weighted reception is conducted at the UAV to improve the signal-to-noise ratio. For the UAV i, when the data of the jthsensor in the subnets is received, the receiving beam forming weight vector is expressed as Bij∈└1×A, and a received signal is:

yUij=Bij(∑k=1NGkji⁢PSkj⁢xSkj+nU)

where nU=[nU1, nU2, . . . , nUA]His the antenna noise of the UAV, and nUaCN (0, σnU2). Therefore, the signal-to-noise ratio of the received signal of the UAV i in time slot j is:

γij=PSij|Bij⁢Giji|2∑k=1,k≠iNPSkj|Bij⁢Gkji|2+|Bij|2σU2

The total throughput in the ithsubnet is:

Ci(W,Bij,τ)=(1-τ)⁢TM⁢log2(1+γij)=(1-τ)⁢TM⁢∑j=1Mlog2(1+2⁢MP⁢ητ⁢❘"\[LeftBracketingBar]"WHijH❘"\[RightBracketingBar]"2⁢❘"\[LeftBracketingBar]"Bij⁢Giji❘"\[RightBracketingBar]"2∑k=1,k≠iN2⁢MP⁢ητ⁢❘"\[LeftBracketingBar]"WHkjH❘"\[RightBracketingBar]"2⁢❘"\[LeftBracketingBar]"Bij⁢Gkji❘"\[RightBracketingBar]"2+A⁡(1-τ)⁢❘"\[LeftBracketingBar]"Bij❘"\[RightBracketingBar]"2⁢σU2).

Preferably, the sensors are awakened after receiving an energy signal, and broadcast an access request to the UAV swarm after obtaining sufficient energy, each UAV selects M=K/N sensors with the best channel state within a range, and broadcasts indexes of selected sensors in the swarm; for unselected sensors, the UAV with the best channel state is designated to be responsible for information receiving; then, the remaining sensors are distributed according to channel states to form perception subnets; each of the perception subnets comprises a UAV and M sensors managed by the UAV;

each UAV in the swarm shares the own state through a broadcast channel to realize joint control of swarm transmission strategies;

the sensors are operated in a half-duplex mode in fSband and the UAV is operated in a full-duplex mode in fUband.

Preferably, in the WEH stage, sensor energy harvesting and broadcast communication in the swarms are conducted; during the period, all UAVs adopt an orthogonal frequency division multiplexing (OFDM) technology, and broadcast own state information to other UAV nodes in the swarm at the same time; A antennas are equally divided into transmitting antennas and receiving antennas to realize full-duplex communication; meanwhile, the sensors use radio frequency signals radiated during communication in the UAV swarm for conducting energy harvesting for data transmission in the WIT stage; the WIT stage is distributed to the sensors for uploading perception data, and the transmission time of the WIT stage is equally divided into M time slots; and in a jthtime slot, jthsensors in all the subnets simultaneously use the harvested energy to send perception information to corresponding UAVs, and realize point-to-point communication through fixed channels.

Preferably, the specific contents of conducting optimal value search in a feasible region [τlb,1) through a golden section search algorithm to obtain an optimal solution τ* of the time division factor in S2 comprise:

fixing B and W as constants, and making χij=Σk=1,k≠iN2MPη|WHkjH|2|BijGkji|2

ψij=2MPη|WHijH|2|BijGiji|2, then τ* is:

maxτ∑i=1N∑j=1Mξi⁢(1-τ)⁢TM⁢log2(1+τ⁢ψijχij⁢τ+A⁡(1-τ)⁢❘"\[LeftBracketingBar]"Bij❘"\[RightBracketingBar]"2⁢σU2)⁢s.t.τl⁢b≤τ<1.

where χijand ψijare intermediate variables used to simplify mathematical description; P is the total transmission power of the UAV swarm in the WEH stage; η is conversion efficiency and 0<η<1; Hkij∈└1×A/2is a channel vector from the UAV k to the jthsensor Sijin the perception subnet i; and Hij=[(H1ij)H, (H2ij)H, . . . , (HNij)H] represents a channel matrix from the UAV swarm to the sensor Sij.

It should be noted that:

χijand ψijare intermediate variables introduced to simplify mathematical description, and have no practical physical significance. Without simplifying the description, the expression of an optimization problem may become complex and obscure.

Preferably, the specific contents of optimizing W under a new common parameter to obtain an optimal solution W* of the power allocation weight in S2 comprise:

fixing B and τ as constants, and defining Qijn(Bij,τ)=Σk=1N2MPητ|BijGkji|2, |Hkj|2+A(1−τ)σU2|Bij|2INand Qijd(Bij,τ)=Σk=1,k≠iN2MPητ|BijGkji|2|Hkj|2+A(1−τ)σU2|Bij|2IN,

then W* is:

(P⁢3):maxW⁢W⁢Qijn(Bij,τ)⁢WHW⁢Qijd(Bij,τ)⁢WH⁢s.t.C⁢1:W±Wl⁢b,C⁢2:WWH=1.

where Gkji∈└A×1is a channel vector from the sensor Skjto the UAV i; Hkij∈└1×A/2is a channel vector from the UAV k to the jthsensor Sijin the perception subnet {dot over (r)}; Hij=[(H1ij)H, (H2ij)H, . . . , (HNij)H] represents a channel matrix from the UAV swarm to the sensor Sij; σU2is the noise power on the single antenna of the UAV; and INis an N-order unit matrix.

It should be further noted that:

The objective function in the above formula can be solved by solving the eigenvector of the matrix Qij=[Qijd(Bij,τ)]−1Qijn(Bij,τ). Firstly, the above formula is divided into M×N separate optimization problems, and Wij=[wij1, wij2, . . . , wijN] is used to represent a copy of W in the separate optimization problems. To transform into a standard Rayleigh quotient, a new variable Kij=Wij[Qijd(Bij,τ)]1/2is introduced, and then Wij=Kij[Qijd(Bij,τ)]−1/2exists.

The solving process of (P3) is:

Firstly, (P3) is divided into M×N separate optimization problems, and the eigenvector of the matrix Qij=[Qijd(Bij,τ)]−1Qijn(Bij, τ) is solved to solve Wij*.

Secondly, a new variable Kij=Wij[Qijd(Bij,τ)]1/2is introduced to transform an optimization objective into a standard Rayleigh quotient form, and a transformational relation Wij=Kij[Qijd(Bij,τ)]−1/2is obtained.

Next, in order to satisfy C1, wijkwhich does not reach the lower bound is forcibly set as the lower bound (wijk)lb, and the corrected allocation weight vector is defined as Ŵij, and {circumflex over (K)}ij=Ŵij[Qijd(Bij,τ)]1/2.

Moreover, the matrix Qijis corrected and recorded as {circumflex over (Q)}ijso that the matrix still satisfies the eigenvalue definition {circumflex over (K)}ij{circumflex over (Q)}ij=β{circumflex over (K)}ij, where βijis a maximum eigenvalue of Qij.

Then, Eigmaxv({circumflex over (Q)}ij) is recalculated; and the element forcibly set as the lower bound is fixed, normalized and recorded as Kij* to obtain Wij*=Kij*[Qijd(Bij,τ)]−1/2.

Finally, based on weighted approximation, Wij* is transformed into W*, and the weighting coefficient is set as ρij, =ξiαij.

Preferably, the specific contents of optimizing B under a new common parameter to obtain an optimal solution B* of the receiving beam forming weight in S2 comprise:

fixing W and τ as constants, defining DUn*(W,τ)=Σk=1N2MPητ|WHkjH|2|Gkji|2+A (1−τ)σU2IAand Dijd(W,τ)=Σk=1,k≠iN2MPητ|WHkjH|2|Gkji|2+A (1−τ)σ2UIA, and then solving a maximum value of throughput C(B) to optimize receiving beam forming:

C⁡(B)=(1-τ)⁢TM⁢∑i=1N∑j=1Mξi⁢log2(Bij⁢Dijn(W,τ)⁢BijHBij⁢Dijd,(W,τ)⁢BijH)

where every logarithmic term is a generalized Rayleigh quotient; for the UAV i, when the data from the jthsensor in the subnets is received, the receiving beam forming weight vector is expressed as Bij∈└1×A; Dijnand Dijdare intermediate variables used to simplify the mathematical description.

It should be noted that Bijis independent. Therefore, in order to obtain the maximum value of C(B), the optimal solution of the vector Bijis only required to be found. Considering the monotonicity of logarithmic functions, the maximum value of C(B) is solved by solving the optimal solution of the vector Bijrespectively, namely:

(P⁢2):maxBijBij⁢Dijn(W,τ)⁢BijHBij⁢Dijd(W,τ)⁢BijH⁢s.t.C⁢1:Bij⁢°⁢11×A.

According to a Rayleigh quotient theory, the optimal solution of Bijis obtained by solving an eigenvector corresponding to a maximum eigenvalue of the matrix Dij=[Dijd(W,τ)]−1Dijn(W,τ), and the calculation of solving the optimal solution of Bijis defined as Eigmaxv(⋅), then
Bij*=Eigmaxv(Dij)

Bij* is calculated separately to obtain each column in the matrix B*, to obtain B*.

It should be further noted that:

There are two main differences between (P3) and (P2).

The first difference is that in (P2), due to the existence of the lower bound, the obtained eigenvector needs to be corrected: firstly, in order to satisfy C1, wijkwhich does not reach the lower bound needs to be forcibly set as the lower bound (wijk)lb, and the corrected allocation weight vector is defined as Ŵijand {circumflex over (K)}ij=Ŵij[Qijd(Bij,τ)]1/2. Then, on this basis, the matrix Qijis corrected and recorded as {circumflex over (Q)}ijso that the matrix still satisfies the eigenvalue definition {circumflex over (K)}ij{circumflex over (Q)}ij=β{circumflex over (K)}ij, where βijis a maximum eigenvalue of Qij. Finally, Eigmaxv({circumflex over (Q)}ij) is recalculated; and the element forcibly set as the lower bound is fixed, normalized and recorded as Kij* to obtain Wij*=Kij*[Qijd(Bij,τ)]−1/2.

The second difference is that, because W is shared by M×N individual optimization problems, Wij* also needs to be converted to W*. A weighted approximation method is adopted in the present invention. A weighting coefficient is set as ρij=ξiαij, where ξiis the importance factor of each subnet, and αijis the maximum throughput of the sensor Sijunder Wij*. The method can obtain a suboptimal W*, but can ensure the monitoring effect of key subnets and the normal operation of the sensors far away from the UAV.

According to the above detailed discussion, the receiving beam forming optimization can be solved by the Rayleigh quotient theory; the power allocation weight can be solved by Rayleigh quotient and weighted approximation; and the time division factor can be determined based on the golden section search algorithm. Based on the above conclusion, each optimization variable can be optimized and updated alternately. The main idea is: firstly, the remaining optimization variables are regarded as fixed values; the optimal value of a variable is solved and the common parameter is updated; then the remaining variables are optimized in sequence according to the method; at this moment, an alternate optimization is completed; and the above process is repeated until the preset optimization frequency or algorithm convergence is reached, to obtain the final parameters of the solved joint optimization.

It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses and provides a wireless perception system energy and information transmission method for a UAV swarm. With respect to the defects of small coverage range and serious double near-far effects of a wireless power supply network, the present invention proposes to expand the coverage range of a data acquisition system through network division and multi-UAV collaboration, so as to suppress the double near-far effects. The present invention fully considers extra energy and interference in a multi-network environment, and the constructed system model is more accurate. In addition, the traditional infinite energy transmission solution cannot make full use of the energy of radio frequency signals. However, the present invention recovers the signals radiated during information interaction in the UAV swarm to charge the sensors, to maximize the network energy efficiency. The technology of the present invention can be widely used in various fields of regular bridge detection, farmland monitoring and the like, and has strong practical application value.

