CN114286312A - Method for enhancing unmanned aerial vehicle communication based on reconfigurable intelligent surface - Google Patents

Method for enhancing unmanned aerial vehicle communication based on reconfigurable intelligent surface Download PDF

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CN114286312A
CN114286312A CN202111479838.XA CN202111479838A CN114286312A CN 114286312 A CN114286312 A CN 114286312A CN 202111479838 A CN202111479838 A CN 202111479838A CN 114286312 A CN114286312 A CN 114286312A
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unmanned aerial
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戴亿平
徐汝昊
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University of Science and Technology Beijing USTB
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Abstract

The embodiment of the invention discloses a method for enhancing unmanned aerial vehicle communication based on a reconfigurable intelligent surface, which comprises the following steps: the method comprises the following steps: a plurality of legal users working on the ground are used as transceiving nodes, a reconfigurable intelligent surface is deployed on the outer vertical surface of a surrounding building to construct a communication system model, and the instantaneous signal-to-noise ratio and the throughput of the communication system model are determined; step two: constructing an optimization problem with the aim of maximizing the minimum throughput of a user according to the instantaneous signal-to-noise ratio and the transmission rate; step three: according to the deployment of the fixed RIS, solving the target problem of the minimum throughput of the maximized user by adopting alternate optimization to obtain an RIS beam forming local optimal strategy; step four: and (4) obtaining a local optimal strategy of the Q of the unmanned aerial vehicle track by a continuous convex approximation optimization method based on the UAV-RIS beam forming local optimal strategy obtained in the step three. The invention utilizes the reconfigurable intelligent surface to enhance the unmanned aerial vehicle communication and realizes higher transmission capacity than the traditional uplink multi-user communication system.

Description

Method for enhancing unmanned aerial vehicle communication based on reconfigurable intelligent surface
Technical Field
The invention relates to the technical field of wireless communication, in particular to a reconfigurable intelligent surface-based method for enhancing unmanned aerial vehicle communication.
Background
During the past decade, Unmanned Aerial Vehicles (UAVs), also known as aerial Base Stations (BSs), have attracted significant attention in a variety of applications. Unmanned aerial vehicles equipped with advanced transceivers and batteries are becoming increasingly popular for relaying, data collection, secure transmission, and information distribution. In particular, due to the high mobility and flexibility of on-demand deployment, the likelihood of having a line-of-sight (LoS) communication link with a drone is high, the drone being able to be deployed flexibly as an aerial base station to provide uninterrupted and ubiquitous connectivity at temporary remote traffic hotspots formed after meetings, sporting events or natural disasters. However, drone communication faces many challenges, particularly in urban areas. One prominent challenge is blockage by common objects (e.g., buildings, trees, and people), which obstacles may exacerbate coverage and connectivity issues. On the other hand, excessive spatiotemporal variations of non-stationary channels are caused by the mobility of the drones and ground operators, which can cause severe non-stationarity of the drone system. In addition, the large path loss associated with long distances in the 3D plane is a problem that cannot be ignored in drone systems.
In recent years, Reconfigurable Intelligent Surface (RIS) has raised a wave in the field of wireless communication as an emerging wireless technology. The RIS is a reflecting surface formed by a large number of low-cost reflecting elements, which are controllable and intelligent, and can change the electromagnetic characteristics of an incident signal according to needs to improve the signal reception, so that the RIS has various applications in the wireless field. It can take advantage of the specific features and characteristics of the metasurfaces to recover existing radio waves and facilitate seamless integration of communication with sensing, storage and computation. From an operational point of view, the RIS can be integrated into existing basic wireless infrastructure and buildings, seen as a complement to existing wireless communication networks, interfering signals can be mitigated after reflections, while legitimate signals can be enhanced by appropriate adjustment of reflections.
Although IRS-assisted communication techniques may dynamically configure the reflected signal phase offset, they exhibit several advantages over conventional communication techniques. However, if the IRS is introduced into a conventional wireless mobile network, joint resource optimization design must be performed on the network transmitting end and the IRS to find out its potential performance gain, for example, if the joint design is not performed or the IRS is left to randomly configure the phase offset of the reflected signal, not only effective performance gain cannot be obtained, but also the network performance may be seriously deteriorated.
Most studies today use only a single RIS, which limits the improvement in system performance. Furthermore, the phase shift of the RIS and the number of UAV antennas also need to be considered. Also, the study is focused on the case of single antenna user service only, and the proposed results are not applicable to multi-user systems. In fact, for multiple RIS assisted multi-user multi-antenna drone communication systems, joint design of resource allocation, trajectory design, and phase shift control is important and challenging.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a reconfigurable intelligent surface-based method for enhancing unmanned aerial vehicle communication. By proposing a new multi-RIS assisted UAV communication system framework, the throughput of ground receiving users is improved by adjusting the trajectory, transmit power, and designing the beamforming matrix of the drone.
The invention is realized by the following technical scheme:
a method for enhancing unmanned aerial vehicle communication based on a reconfigurable intelligent surface comprises the following steps:
the method comprises the following steps: a plurality of legal users working on the ground are used as transceiving nodes, a reconfigurable intelligent surface is deployed on the outer vertical surface of a surrounding building to construct a communication system model, and the instantaneous signal-to-noise ratio and the throughput of the communication system model are determined;
step two: constructing an optimization problem with the aim of maximizing the minimum throughput of a user according to the instantaneous signal-to-noise ratio and the transmission rate;
step three: according to the deployment of the fixed RIS, solving the target problem of the minimum throughput of the maximized user by adopting alternate optimization to obtain an RIS beam forming local optimal strategy;
step four: based on the UAV-RIS beam forming local optimal strategy obtained in the step three, obtaining a local optimal strategy of the Q of the unmanned aerial vehicle track through an optimization method of continuous convex approximation;
step five: solving a local optimal strategy of the transmitting power P through dual Lagrange based on the UAV-RIS beam forming local optimal strategy obtained in the third step and the local optimal strategy of the Q of the trajectory of the four unmanned aerial vehicles in the step;
step six: and repeatedly executing the third step to the fifth step through a repeated alternate optimization framework based on the third RIS beam forming local optimal strategy, the fourth unmanned aerial vehicle track Q local optimal strategy and the fifth transmitting power P local optimal strategy, and iteratively updating until the RIS beam forming optimization strategy, the unmanned aerial vehicle track Q local optimal strategy, the transmitting power P local optimal strategy and the system throughput are converged to obtain the optimal transmission strategy in the communication network model.
Firstly, considering the condition of multiple intelligent reflecting surfaces, establishing multiple sight line links from the unmanned aerial vehicle to the intelligent reflecting surfaces and from the intelligent reflecting surfaces to ground users, and designing a new system model. Specific considerations consider that the ground infrastructure of the area is damaged or not installed due to natural disasters, and the UAV is equipped with NAThe antennas provide wireless service for K single-antenna users in total, IRS is adopted on the front face of the high-rise building, and each IRS is formed by Ml×MlAnd a reflection unit. Consider the decomposition of a total time T into N discrete time slots, with a sufficiently small time step Δ T, the duration δ at each time slottHere, the positions of the UAV and all channels are approximately constant within each time slot. It is assumed that the channels remain constant for a reasonable time interval and that channel information (CSI) is available at both the base station and the intelligent reflecting surface for all channels involved.
Parameter y of the signal received at the kth user, taking into account the linear transmit precoding at the UAVk[n]Can be written as:
Figure BDA0003394533030000031
where the vector x is the transmitted signal of the base station and can be expressed as
Figure BDA0003394533030000032
Diagonal matrix thetalIs to have a reflection efficiency of [0,1 ]]Is used to generate the RIS phase shift matrix. The instantaneous data rate of user k is determined by the shannon capacity Rk＝log2(1+γk) And (6) modeling. The signal-to-interference-plus-noise ratio (SINR) received at user k is defined as:
Figure BDA0003394533030000033
step two, establishing an expression of an optimization problem with the aim of maximizing the minimum throughput of the user as follows:
Figure BDA0003394533030000034
s.t.C1：
Figure BDA0003394533030000035
C2：
Figure BDA0003394533030000036
C3:||q[N]-qF||2≤D2 max,q[1]＝q0
C4：
Figure BDA0003394533030000037
C5:tr((HH AU+GH RUΦHAR)+P(HH AU+GH RUΦHAR)+H)≤Pmax
C6:pk[n]≥0 (3)
wherein
Figure BDA0003394533030000041
Figure BDA0003394533030000042
Solving: the method comprises the following steps that three non-convex sub-problems of minimum throughput target problem conversion success rate distribution, unmanned aerial vehicle track design and RIS beam forming matrix of a user are maximized;
fixing P and Q, and solving phi to obtain an RIS beam forming local optimal strategy;
fixing P and phi, solving Q, and obtaining a local optimal strategy of the Q of the unmanned aerial vehicle track by a continuous convex approximation optimization method;
fixing Q and phi, solving P, and solving a local optimal strategy of the transmitting power P through dual Lagrange;
each sub-problem can be optimized iteratively separately. And finally, jointly optimizing the transmitting power, the flight path and the IRS phase shift matrix by adopting an alternate optimization technology, thereby realizing the near-optimal power distribution, the path route and the phase shift matrix.
And a user k receives a signal y from the reconfigurable intelligent surface, calculates the signal to interference plus noise ratio (SINR) at the user k according to the formula (2), and calculates the transmission rate through a Shannon formula to feed back to the UAV. The UAV obtains the RIS beam forming local optimal result according to the solution obtained in the third step and substitutes the RIS beam forming local optimal result into the fourth step according to the information fed back by the user k, and then substitutes the Q local optimal result into the fifth step to obtain the P local optimal strategy;
and in the sixth step, the third step to the fifth step are repeated, and iterative updating is carried out until an RIS beam forming optimization strategy, a local optimal strategy of an unmanned aerial vehicle track Q, a local optimal strategy of transmitting power P and system throughput are converged, so that an optimal transmission strategy in the communication network model is obtained.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a method for enhancing unmanned aerial vehicle communication based on a reconfigurable intelligent surface, and relates to a novel RIS auxiliary unmanned aerial vehicle communication system framework. To overcome congestion in the drone assisted communication system, multiple line-of-sight links are established from the drone to the RIS and from the RIS to the ground users. Jointly optimizing the active beamforming vector, the passive beamforming matrix, and the drone trajectory over a limited time range to maximize received power while considering actual mobility, transmit power, and phase shift constraints of the reconfigurable smart surface. Through the combined RIS reflection coefficient matrix and the design of aerial base station receiving beam forming, the solution is carried out through an alternate optimization framework, the spectrum utilization rate and the energy efficiency are improved, and meanwhile, the realization of the application of the smart city is facilitated.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of a reconfigurable intelligent transmitting surface auxiliary unmanned aerial vehicle communication system in the embodiment of the invention;
FIG. 2 is a schematic diagram of a basic structure of a reconfigurable intelligent surface;
fig. 3 is a schematic flow chart of a method for enhancing unmanned aerial vehicle communication based on a reconfigurable intelligent surface in the embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a component of a reconfigurable intelligent launching surface auxiliary unmanned aerial vehicle communication system, and two parts of the system are explained in detail as follows: first, a plurality of reconfigurable smart surfaces are deployed on the facade of the surrounding building as passive reflectors to provide configurable reflective paths between the drone and the user. Second, multiple line-of-sight links are established from the drone to the RIS and from the RIS to the ground users.
Fig. 2 is a basic structure of a reconfigurable intelligent surface, which generates electromagnetic behavior required by each electromagnetic unit by controlling bias voltages of a varactor diode, a PIN switch, a MEMS switch, liquid crystal, graphene, and the like. The RIS can design and store different digital coding sequences in advance under the real-time control of software, and can complete the dynamic regulation and control of electromagnetic waves, such as single-beam reflection, multi-beam reflection, diffuse scattering, transmission and the like, by switching the coding sequences.
Fig. 3 is a schematic flow chart of a method for enhancing unmanned aerial vehicle communication based on a reconfigurable intelligent surface in an embodiment of the present invention, including the following steps:
the method comprises the following steps: a communication system model is constructed and the instantaneous signal-to-noise ratio and throughput of the communication system model are determined.
Parameter y of the signal received at the k-th userk[n]Can be written as:
Figure BDA0003394533030000061
the signal to interference plus noise ratio (SINR) received at user k can be written as:
Figure BDA0003394533030000062
step two: and constructing an optimization problem aiming at maximizing the minimum throughput of the user according to the instantaneous signal-to-noise ratio and the transmission rate.
The target problem can be expressed as follows: .
Figure BDA0003394533030000063
s.t.C1：
Figure BDA0003394533030000064
C2：
Figure BDA0003394533030000065
C3:||q[N]-qF||2≤D2 max,q[1]＝q0
C4：
Figure BDA0003394533030000066
C5:tr((HH AU+GH RUΦHAR)+P(HH AU+GH RUΦHAR)+H)≤Pmax
C6:pk[n]≥0
Step three: according to the deployment of the fixed RIS, the objective problem of the minimum throughput of the maximized user is solved by adopting alternate optimization, and the RIS beam forming local optimal strategy is obtained.
Solving the RIS beam forming local optimum, and converting into the following steps according to the constraint condition problem:
Figure BDA0003394533030000067
s.t.λ≥0
Figure BDA0003394533030000068
solving to obtain:
Figure BDA0003394533030000071
step four: and (4) obtaining a local optimal strategy of the Q of the unmanned aerial vehicle track by a continuous convex approximation optimization method based on the UAV-RIS beam forming local optimal strategy obtained in the step three.
The problem translates into the following according to constraints:
Figure BDA0003394533030000072
s.t.tr((HH AU+GH RUΦHAR)+P(HH AU+GH RUΦHAR)+H)≤Pmax
Figure BDA0003394533030000073
||q[N]-qF||2≤D2 max,q[1]＝q0
further conversion according to conditions is as follows:
Figure BDA0003394533030000074
Figure BDA0003394533030000075
||q[N]-qF||2≤D2 max,q[1]＝q0
step five: and solving the local optimal strategy of the transmitting power P through dual Lagrange based on the UAV-RIS beam forming local optimal strategy obtained in the third step and the local optimal strategy of the Q of the trajectory of the quadruped unmanned aerial vehicle in the step.
The problem translates into the following according to constraints:
Figure BDA0003394533030000076
μ is the set of lagrange multipliers μ1,μ2.....,μkIn which lagrange multiplier mukIs a non-negative number.
Step six: and repeatedly executing the third step to the fifth step through a repeated alternate optimization framework based on the third RIS beam forming local optimal strategy, the fourth unmanned aerial vehicle track Q local optimal strategy and the fifth transmitting power P local optimal strategy, and iteratively updating until the RIS beam forming optimization strategy, the unmanned aerial vehicle track Q local optimal strategy, the transmitting power P local optimal strategy and the system throughput are converged to obtain the optimal transmission strategy in the communication network model.
The method for enhancing the unmanned aerial vehicle communication based on the reconfigurable intelligent surface can provide reliable transmission guarantee for users in a wireless communication network. Aiming at the problem that the direct link between the existing unmanned aerial vehicle base station and the mobile user can suffer from shadow fading, the reconfigurable intelligent surface is deployed on the outer vertical surface of the surrounding building, the blockage in the unmanned aerial vehicle auxiliary communication system is overcome, and a plurality of line-of-sight links from the unmanned aerial vehicle to the RIS and from the RIS to the ground user are established. Through the design of combined RIS reflection coefficient matrix and aerial base station receiving beam forming, the method is beneficial to the realization of smart city application while improving the spectrum utilization rate and the transmission capacity.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (8)

1. Method for enhancing unmanned aerial vehicle communication based on reconfigurable intelligent surface, and method is used for reconfigurable intelligent surface to assist unmanned aerial vehicleThe multi-input single-output multi-user downlink communication system of the machine is provided with an unmanned aerial vehicle base station, L RISs and K users, and the unmanned aerial vehicle is provided with NAThe reconfigurable intelligent surface is deployed on the outer vertical surface of a surrounding building, and the intelligent reflecting surface is provided with MlA reflective element to help the base station overcome adverse propagation conditions from the base station to the user by providing a virtual link, the l-th RIS deploying Ml(ii) a Defining the channel from the unmanned plane to the user as hAU,kThe channel from the unmanned aerial vehicle to the first IRS is HAR,lThe channel from the first IRS to the user k is GRU,l,k(ii) a Characterized in that the method comprises the following steps:
the method comprises the steps that firstly, a plurality of legal users working on the ground are used as transceiving nodes, a reconfigurable intelligent surface is deployed on the outer vertical surfaces of surrounding buildings to construct a communication system model, and the instantaneous signal-to-noise ratio and the throughput of the communication system model are determined;
step two: constructing an optimization problem with the aim of maximizing the minimum throughput of a user according to the instantaneous signal-to-noise ratio and the transmission rate;
step three: according to the deployment of the fixed RIS, solving the target problem of the minimum throughput of the maximized user by adopting alternate optimization to obtain an RIS beam forming local optimal strategy;
step four: based on the UAV-RIS beam forming local optimal strategy obtained in the step three, obtaining a local optimal strategy of the Q of the unmanned aerial vehicle track through an optimization method of continuous convex approximation;
step five: solving a local optimal strategy of the transmitting power P through dual Lagrange based on the UAV-RIS beam forming local optimal strategy obtained in the third step and the local optimal strategy of the Q of the trajectory of the four unmanned aerial vehicles in the step;
step six: and repeatedly executing the third step to the fifth step through a repeated alternate optimization framework based on the third RIS beam forming local optimal strategy, the fourth unmanned aerial vehicle track Q local optimal strategy and the fifth transmitting power P local optimal strategy, and iteratively updating until the RIS beam forming optimization strategy, the unmanned aerial vehicle track Q local optimal strategy, the transmitting power P local optimal strategy and the system throughput are converged to obtain the optimal transmission strategy in the communication network model.
2. The reconfigurable intelligent surface-enhanced unmanned aerial vehicle communication-based method according to claim 1, wherein in step one, a channel gain model between nodes is firstly analyzed through a communication system model, an instantaneous signal-to-noise ratio of the communication system model is determined according to the channel gain model, and throughput is determined according to the instantaneous signal-to-noise ratio.
3. The method for enhancing unmanned aerial vehicle communication based on reconfigurable intelligent surface according to claim 2, wherein the expression of the instantaneous signal-to-noise ratio is as follows:
Figure FDA0003394533020000021
wherein p iskIs the transmission power, HAR,l，hH AU,k，GH RU,l,kRepresenting the corresponding channel state matrix, σ2 kIs the background noise power, thetalIs the reflection coefficient matrix of the RIS;
the expression of the throughput is as follows:
Ck＝log2(1+γk)。
4. the method for enhancing unmanned aerial vehicle communication based on reconfigurable intelligent surface as claimed in claim 3, wherein the expression of the optimization problem aimed at maximizing user minimum throughput in step two is as follows:
Figure FDA0003394533020000022
s.t.C1:
Figure FDA0003394533020000023
C2:
Figure FDA0003394533020000024
C3:
Figure FDA0003394533020000025
C4:
Figure FDA0003394533020000026
C5:tr((HH AU+GH RUΦHAR)+P(HH AU+GH RUΦHAR)+H)≤Pmax
C6:pk[n]≥0
wherein C is throughput, and the two-dimensional horizontal coordinate q [ n ] of the unmanned aerial vehicle]＝(x[n],y[n])T，θmIs the phase shift of the reflective element.
5. The method for reconfigurable intelligent surface enhanced unmanned aerial vehicle communication based on claim 1, wherein the optimization problem cannot be solved directly due to variable coupling, and the problem is decomposed into three sub-problems by an alternating optimization technique and matrix lifting, and the three sub-problems are solved and optimized alternately, and the third step is specifically as follows:
solving the RIS beam forming local optimum, and converting into the following steps according to the constraint condition problem:
Figure FDA0003394533020000027
s.t.
Figure FDA0003394533020000028
order to
Figure FDA0003394533020000031
y＝vec(Φ-1)，
Figure FDA0003394533020000032
The following sub-problems with transmit beamforming are solved using the dual problem:
Figure FDA0003394533020000033
s.t.λ≥0
Figure FDA0003394533020000034
6. the method for enhancing unmanned aerial vehicle communication based on reconfigurable intelligent surface according to claim 1, wherein the fourth step is specifically:
solving the following sub-problems:
Figure FDA0003394533020000035
s.t.
Figure FDA0003394533020000036
Figure FDA0003394533020000037
Figure FDA0003394533020000038
7. the method for enhancing unmanned aerial vehicle communication based on reconfigurable intelligent surface according to claim 1, wherein the step five is specifically as follows:
solving the following sub-problems:
Figure FDA0003394533020000039
μ is the set of lagrange multipliers μ1,μ2.....,μkIn which lagrange multiplier mukIs a non-negative number.
8. The reconfigurable intelligent surface-enhanced unmanned aerial vehicle communication method based on claim 1, wherein the steps three to five are repeatedly executed through a repeated alternating optimization framework, and the iterative updating is performed until a beamforming optimization strategy, a local optimal strategy of an unmanned aerial vehicle track, a local optimal strategy of transmission power and system throughput are converged to obtain an optimal transmission strategy in a communication network model.
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