CN114630297A - Position optimization method for relay of unmanned aerial vehicle with intelligent reflecting surface - Google Patents
Position optimization method for relay of unmanned aerial vehicle with intelligent reflecting surface Download PDFInfo
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Abstract
The invention discloses a position optimization method of an unmanned aerial vehicle relay with an intelligent reflector, which comprises the steps of S1, establishing a multi-user mathematical model; s2, establishing a three-dimensional coordinate system, and calculating a corresponding distance according to the position in the coordinate system; s3, calculating each channel gain by using a 3GPP model of the urban micro-scene; and S4, optimizing the position of the intelligent reflecting surface based on the sum of the maximum user reachable speeds. The invention regards the signal transmission between the base station and the intelligent reflecting surface and the signal transmission between the intelligent reflecting surface and the user as line-of-sight transmission, and regards the signal transmission between the base station and the user as non-line-of-sight transmission. And successively iterating through calculation of the user reachable rate in each moving direction until the optimal position of the unmanned aerial vehicle is obtained. The invention is suitable for being used when the height of the barrier is lower and the coverage area is larger, and ensures that higher channel capacity is obtained under the condition of multiple users, thereby improving the performance of a mobile communication system.
Description
Technical Field
The invention belongs to the technical field of wireless relay communication, and particularly relates to a position optimization method for an unmanned aerial vehicle relay with an intelligent reflecting surface.
Background
Wireless relaying is not only an effective way to extend the transmission range, but also an important way to increase capacity. The deployment of relays can be achieved by using fixed relay nodes or mobile relay platforms. Since fixed relay nodes cannot provide flexible relay service, in recent years, researchers have shown great interest in mobile relays, such as relay systems based on unmanned intelligent reflectors.
As the drone is able to change its position quickly and dynamically, they can be provided with flexible and on-demand services according to their real-time location on the ground. In particular, using ad hoc, each drone may be dynamically and automatically assigned to an on-demand ground user and perform flight tracking to provide one-to-one service until the edge of the coverage area is reached. The coverage areas of deployed drones may also overlap to avoid service holes.
Endurance is an important problem in unmanned aerial vehicle relay, though intelligent plane of reflection is because its lightweight, and adopts the characteristics of electromagnetic passive reflection principle and the ultralow consumption of intelligent reflection phase place regulating circuit, can reduce unmanned aerial vehicle's consumption, improves unmanned aerial vehicle's endurance, but unmanned aerial vehicle self removes, and the acquisition of unmanned aerial vehicle position and the adjustment of intelligent plane of reflection phase place still need consume some energy. Therefore, the existing unmanned aerial vehicle relay position optimization method takes energy efficiency maximization as a basis, which is beneficial to improving endurance capacity, but the optimization mode often causes that an intelligent reflecting surface has a service blind area and cannot obtain the best communication quality.
Disclosure of Invention
The invention aims to provide a position optimization method for an unmanned aerial vehicle relay with an intelligent reflecting surface, and solves the technical problems that in the prior art, the unmanned aerial vehicle relay method takes the maximum energy efficiency as a position optimization basis, so that the intelligent reflecting surface has a service blind area and cannot obtain the best communication quality.
In order to solve the technical problems, the invention is realized by the following technical modes:
a position optimization method for an unmanned aerial vehicle relay with an intelligent reflecting surface comprises the following steps:
s1, establishing a mathematical model of the multi-user received signal, and analyzing the user channel capacity;
in the intelligent reflecting surface auxiliary multi-user communication system, a base station sends signals to K users which are obstructed through an intelligent reflecting surface, and N passive reflecting elements are installed on the intelligent reflecting surface;
the signal received at user k is represented as:
wherein k is [1, M ]]k. k are all positive integers, T denotes the matrix transposition, pkRepresenting the transmission power of the kth user, s representing a unity power information signal, wkThe additive white Gaussian noise represents that the mean value of a channel where a user k is located is 0 and the variance is sigma;
hsd,kindicating a deterministic channel, h, from the base station to user ksrRepresenting a deterministic channel, h, from the base station to the intelligent reflecting surfacerd,kRepresenting a deterministic channel from the intelligent reflecting surface to user k; Θ is a phase shift matrix of the intelligent reflective surface, expressed as:
wherein j is an imaginary symbol, alpha is a reflection coefficient with fixed amplitude and belongs to (0,1), and thetanFor the phase of the nth reflecting element in IRS, N ∈ [1, N]N and N are positive integers;
therefore, the channel capacity of the intelligent reflector auxiliary communication network is as follows:
in the formula, p is signal transmitting power, and sigma is Gaussian white noise variance;
for any given Θ, the achievable rate for the kth user is expressed as:
the channel from the base station to the user via the intelligent reflecting surface can be expressed as:
when the phase θ of the n-th reflecting elementn＝arg(hsd)-arg([hsr]n[hrd]n) When the utility model is used, the water is discharged,
each of (1) and hsdHave the same phase, can get the maximum channel capacity or can reach the speed at this moment;
s2, establishing a three-dimensional coordinate system used for determining the position and the distance, and calculating the corresponding distance according to the position in the coordinate system:
establishing a three-dimensional rectangular coordinate system OXYZ, wherein the center of a base station root is an origin O, and XOY is a reference horizontal plane; setting the height of the base station to HBSHeight of user is HuserMaximum height of obstacle is Hblock(ii) a The base station position coordinate is (0, 0, H)BS) And the position coordinates of the intelligent reflecting surface carried by the unmanned aerial vehicle are (x)IRS，yIRS,zIRS) The position coordinate of the user is (x)user,k,yuser,k,Huser)；
According to the position information, the distance d from the base station to the intelligent reflecting surfacesrDistance d from intelligent reflecting surface to userrd,kAnd base station to user distance dsd,kAre respectively provided withExpressed as:
s3, using the 3GPP model of the urban micro scenario, calculating a single channel gain:
the signal transmission between the base station and the intelligent reflection surface and between the intelligent reflection surface and the user is line of sight (LOS), and the path LOSs is respectively expressed as PLLOS(dsr) And PLLOS(drd,k)；
The transmission between the base station and the user is a non line of sight (NLOS) transmission, and the path loss is expressed as PLNLOS(dsd,k) (ii) a Base station antenna gain of GBSAfter the gain of the intelligent reflector antenna is optimized through phase, the gain is regarded as constant in movement and is GIRS(ii) a Antenna gain of user receiving end is GUE(ii) a Using the 3GPP model of the urban micro-scenario as the channel gain model, then
Deterministic channel gain from base station to user k:
|hsr|2[dB]＝GBS[dBi]+GIRS[dBi]+PLLOS(dsr)；
deterministic channel gain from base station to intelligent reflector:
|hrd,k|2[dB]＝GIRS[dBi]+GUE[dBi]+PLLOS(drd,k)；
deterministic channel gain from intelligent reflector to user k:
|hsd,k|2[dB]＝GBS[dBi]+GUE[dBi]+PLNLOS(dsd,k)；
the total gain of the channel from the base station to the user through the intelligent reflecting surface is as follows:
|hIRS,k|2[dB]＝|hsr|2[dB]+|hrd,k|2[dB]。
s4, optimizing the position of the intelligent reflecting surface based on the sum of the maximum user reachable speeds:
s41, obtaining the position (x) of each user based on the positioning deviceuser,k,yuser,k,Huser) Calculating and initializing the position (x) of the intelligent reflecting surfaceIRS，yIRS,zIRS) Calculating sum of reachable rates of each user
S42, initializing Data array Data, storing S in Data array Data, and obtaining Data ═ S;
s43, setting step length d, and respectively calculating xIRS+d,xIRS-d,yIRS+d,yIRSSum of achievable rates at-d Sx+d，Sx-d,Sy+d,Sy-dAnd storing in Data array Data to obtain Data [ S, S ═ Sx+d,Sx-d,Sy+d,Sy-d]；
S44, finding the maximum value in the Data array and setting the maximum value as S*And making the following judgments:
s441, if | S-S*|<E, ending the circulation, wherein the position of the intelligent reflecting surface is the optimal position of the relay of the unmanned aerial vehicle;
s442, otherwise, carrying out the following steps:
s4421: when S isx+d＝S*Let xIRS＝xIRS+d,S＝Sx+dRepeating steps S42-S44;
s4422: when S isx-d＝S*Let x beIRS＝xIRS-d,S＝Sx-dRepeating steps S42-S44;
s4423: when S isy+d＝S*Let yIRS＝yIRS+d,S＝Sy+dRepeating the steps S42-S44;
s4424: when S isy-d＝S*Let yIRS＝yIRS-d,S＝Sy-dRepeating steps S42-S44;
and S45, updating the position information of the user at intervals, repeating the steps S41-S44 when the position of the user is changed, recalculating the optimal position of the unmanned aerial vehicle relay at the moment, wherein the initial position of the intelligent reflecting surface in the calculation is the optimal position of the unmanned aerial vehicle relay after the last adjustment is finished.
In the invention, the unmanned aerial vehicle relay is adjusted by acquiring the user position at regular time, the sum of the reachable rates is recalculated, and the position of the unmanned aerial vehicle is updated again through iteration, so that the optimal position of the unmanned aerial vehicle aerial relay is obtained. When the coverage area of the barrier is larger and the height of the barrier is lower, the invention is obviously superior to the performance of the fixed position intelligent reflecting surface for assisting wireless communication.
Because intelligent reflector has the characteristics of lightweight and low-power consumption, can show the consumption that reduces unmanned aerial vehicle to the time that unmanned aerial vehicle provided relay service is prolonged greatly. In addition, because intelligent plane of reflection can be miniaturized, the aerial platform of minimum unmanned aerial vehicle even the large-scale miniature unmanned aerial vehicle carries intelligent plane of reflection and constitutes the cluster and also can be used to supplementary wireless communication. The performance of the invention is improved by the intelligent reflecting surface carried by the unmanned aerial vehicle, which is mainly because the height of the unmanned aerial vehicle is controlled to avoid a dead zone in the service area of the unmanned aerial vehicle, and the equivalent line-of-sight channel gain is optimized as much as possible by changing the position of the unmanned aerial vehicle, thereby greatly improving the communication service quality of ground users.
Further preferably, in step S2, the height of the obstacle is set to be close to the height of the base station without large fluctuation, and the minimum distance from the user activity area to the obstacle is ddb,minThe nearest distance from the user activity area to the intelligent reflecting surface is drd,min(ii) a In order to ensure that the sight distance transmission between the intelligent reflecting surface and the user is satisfied, the height z of the intelligent reflecting surface is setIRSThe distance between the user activity area and the obstacle is determined by the shortest distance between the user activity area and the obstacle, the maximum height of the obstacle and the shortest distance between the intelligent reflecting surface and the user area, and the distances are expressed as follows:
in order to ensure that the sight distance transmission is met between the intelligent reflecting surface and the base station and the unmanned aerial vehicle can normally fly, the height z of the intelligent reflecting surfaceIRS>Hblock。
Height z of intelligent reflecting surface in the inventionIRSThe calculation formula is obtained after the communication environment is simplified, and can be changed into other calculation methods according to different communication environments so as to ensure that the base station and the intelligent reflecting surface, and the intelligent reflecting surface and all users meet the sight distance propagation.
Further preferably, in step S1, the initial values are set as: carrier frequency fc3GHz, the number N of the intelligent reflecting surface reflecting elements is 250, the number K of the users is 50, and the base station transmits power p to each userk30dBm, a fixed amplitude reflection coefficient α of 1, and an additive white gaussian noise variance σ2＝-94dBm。
Further preferably, in step S2, the initial values are set as: height H of base stationBS10m, user height Huser1.5m, maximum height of obstacle Hblock10m, the initial coordinates of the intelligent reflecting surface are set as the geometric centers of each user and the base station, the user activity area is set as a square area with two points of (50, 50, 1.5) and (150, 150, 1.5) as diagonal vertexes, the point of the user activity area closest to the obstacle is (50, 50, 1.5), and the minimum distance d is set asdb,minThe user positions are set to be randomly distributed within the user activity area, 10 m.
Further optimization, in the step S2, the height z of the intelligent reflection surfaceIRSOther calculation methods may be modified depending on the communication environment.
Further preferably, the initial value in step S3 is set as: base station antenna gain GBS＝8dBi，
Intelligent reflector antenna gain GIRSUser antenna gain G of 8dBiUE＝0dBi。
Further preferably, in step S4, the step d is 0.5 m.
Compared with the prior art, the invention has the following advantages:
1. according to the invention, the height of the unmanned aerial vehicle is calculated according to the distance between the user and the obstacle, the horizontal distance between the user and the unmanned aerial vehicle and the maximum height of the obstacle, so that the sight distance transmission between the user and the intelligent reflecting surface is not influenced by too low height of the unmanned aerial vehicle or too large distance between the unmanned aerial vehicle and the user, and the optimal communication quality is ensured.
2. According to the invention, the positions of the unmanned aerial vehicle and each user are acquired by the timing device according to a certain time interval, so that energy consumption caused by the fact that the positioning device is always opened or position information is frequently acquired is avoided, and the problem that the unmanned aerial vehicle continuously moves according to the position information after the position is frequently acquired so as to consume a large amount of energy is also avoided.
3. The invention optimizes the unmanned aerial vehicle path based on the reachable rate, can obtain the optimal position of the unmanned aerial vehicle which enables the reachable rate to be maximum, and improves the communication quality. Compared with the traditional base station-terminal model and the base station-fixed position intelligent reflecting surface-terminal model, the intelligent reflecting surface reflecting model based on the unmanned aerial vehicle can change the relay position in time according to the positions of multiple users so as to achieve the optimal relay effect.
Drawings
Fig. 1 is a flowchart of a method for optimizing the position of an unmanned aerial vehicle relay with an intelligent reflector according to the present invention;
FIG. 2 is a model diagram of a system for an unmanned aerial vehicle carrying an intelligent reflector to assist communication, constructed according to the present invention;
FIG. 3 is a graph comparing the achievable rate of fixed position intelligent reflector assisted communication and no intelligent reflector assisted communication with the transmission power of the present invention.
Detailed Description
The following describes in detail a method for optimizing a path of an unmanned aerial vehicle based on an intelligent reflecting surface carried by the unmanned aerial vehicle, with reference to the accompanying drawings:
as shown in fig. 1, a method for optimizing the position of an unmanned aerial vehicle relay with an intelligent reflector includes the following steps:
step S1: establishing a mathematical model of a user receiving signal, analyzing the user channel reachable rate, and calculating the channel reachable rate through an unmanned aerial vehicle built-in program or hardware;
in an intelligent reflector assisted multi-user communication system, a base station transmits signals to K users that are blocked through an Intelligent Reflector (IRS). The intelligent reflecting surface is provided with N passive reflecting elements. The signals reflected by each element of the Intelligent Reflective Surface (IRS) and the signals transmitted by the source may be summed at the destination. Thus, the signal received at user k is represented as:
in the formula, T represents a matrix transposition, pkDenotes the transmission power of the kth user, s denotes a unity power information signal, wkAnd the additive white gaussian noise represents that the mean value of the channel where the user k is located is 0 and the variance is sigma.
Respectively, the deterministic channels from base station to user k, from base station to IRS, and from IRS to user k. Θ is the phase shift matrix for IRS, expressed as:
wherein j is an imaginary symbol, alpha is a reflection coefficient with fixed amplitude and belongs to (0,1), and thetanAn optimization can be made for the phase of the nth reflecting element in the IRS.
Thus, the channel capacity of the IRS auxiliary communication network is:
where p is the signal transmit power and σ is the gaussian white noise variance.
For any given Θ, the achievable rate for the kth user can be expressed as:
the channel from the base station to the user via the IRS can be represented as:
when the phase θ of the n-th reflecting elementn＝arg(hsd)-arg([hsr]n[hrd]n) When the temperature of the water is higher than the set temperature,
each term in (1) and hsdWith the same phase, the maximum channel capacity or achievable rate can be achieved.
When the phase meets the requirement of maximum channel capacity, and the signal reaches the channel gain | h of the user through the intelligent reflecting surfacesr||hrd,kWhen | is maximum, the channel capacity gets the maximum value;
step S2: and establishing a three-dimensional coordinate system of the communication system, and determining the positions of the user, the base station and the unmanned aerial vehicle, wherein the coordinate system is shown in figure 2. From the established coordinate system and the respective positions represented by the coordinate points, the distance can be calculated. Height of base station is HBSUser height of HuserMaximum height of obstacle is Hblock. The base station location is represented as (0, 0, H)BS) The position of the intelligent reflecting surface carried by the unmanned aerial vehicle is expressed as (x)IRS，yIRS,zIRS) The user position is represented as (x)user,k,yuser,k,Huser)。
In this embodiment, the height of the obstacle is set to be close to the height of the base station without large fluctuation, and the minimum distance from the user activity area to the obstacle is ddb,minThe nearest distance from the active area to the intelligent reflecting surface is drd,min. In order to ensure that the sight distance transmission between the intelligent reflecting surface and a user is satisfied, the height of the intelligent reflecting surface is set to be determined by the shortest distance between a user activity area and an obstacle, the maximum height of the obstacle and the shortest distance between the intelligent reflecting surface and the user area, and the height is expressed as follows:
in order to ensure that the sight distance transmission is met between the intelligent reflecting surface and the base station and the unmanned aerial vehicle can normally fly, the height z of the intelligent reflecting surfaceIRS>Hblock。
According to the position information, the distance from the base station to the intelligent reflecting surface, the distance from the intelligent reflecting surface to the user and the distance from the base station to the user are respectively expressed as:
step S3: selecting a 3GPP model of an Urban Micro (UMi) scene as a channel gain model, and calculating an initial position of the unmanned aerial vehicle and an initial channel reachable rate;
the signal transmission between the base station and the intelligent reflection surface and between the intelligent reflection surface and the user is line of sight (LOS), and the path LOSs is respectively expressed as PLLOS(dsr) And PLLOS(drd,k). The transmission between the base station and the user is a non line of sight (NLOS) transmission, and the path loss is expressed as PLNLOS(dsd,k). Base station antenna gain of GBSAfter phase optimization, the gain of the intelligent reflector antenna is regarded as constant in movement, namely GIRS. Antenna gain at the receiving end isGUE. Using the 3GPP model for the Urban Micro (UMi) scenario as a channel gain model, and the 3GPP model for the Urban Micro (UMi) scenario as a channel gain model the channel gain is defined as:
|hsr|2[dB]＝GBS[dBi]+GIRS[dBi]+PLLOS(dsr)；
|hrd,k|2[dB]＝GIRS[dBi]+GUE[dBi]+PLLOS(drd,k)；
|hsd,k|2[dB]＝GBS[dBi]+GUE[dBi]+PLNLOS(dsd,k)；
step S4: and optimizing the position of the intelligent reflecting surface according to the position information acquired at the timing by using the S4 based on the sum of the maximum user reachable rates and the sum of the maximum user reachable rates:
s41, obtaining the position (x) of each user based on the positioning deviceuser,k,yuser,k,Huser) Calculating and initializing the position (x) of the intelligent reflecting surfaceIRS，yIRS,zIRS) Calculating sum of reachable rates of each user
S42, initializing Data array Data, storing S in Data array Data, and obtaining Data ═ S;
s43, setting step length d, and respectively calculating xIRS+d,xIRS-d,yIRS+d,yIRSSum of achievable rates at-d Sx+d，Sx-d,Sy+d,Sy-dAnd storing the Data in Data array Data to obtain Data as [ S, Sx+d,Sx-d,Sy+d,Sy-d]；
S44, searching the maximum value in the Data array and setting the maximum value as S*And making the following judgments:
s441, if | S-S*|<E, ending the circulation, wherein the position of the intelligent reflecting surface is the optimal position of the relay of the unmanned aerial vehicle;
s442, otherwise, carrying out the following steps:
s4421: when S isx+d＝S*Let xIRS＝xIRS+d,S＝Sx+dRepeating steps S42-S44;
s4422: when S isx-d＝S*Let xIRS＝xIRS-d,S＝Sx-dRepeating steps S42-S44;
s4423: when S isy+d＝S*Let yIRS＝yIRS+d,S＝Sy+dRepeating steps S42-S44;
s4424: when S isy-d＝S*Let yIRS＝yIRS-d,S＝Sy-dRepeating steps S42-S44;
and S45, updating the position information of the user at intervals, repeating the steps S41-S44 when the position of the user is changed, recalculating the optimal position of the unmanned aerial vehicle relay at the moment, wherein the initial position of the intelligent reflecting surface in the calculation is the optimal position of the unmanned aerial vehicle relay after the last adjustment is finished.
In this embodiment, in step S1, the initial value is set as: carrier frequency fc3GHz, the number N of the intelligent reflecting surface reflecting elements is 250, the number K of the users is 50, and the base station transmits power p to each userk30dBm, a fixed amplitude reflection coefficient α of 1, and an additive white gaussian noise variance σ2＝-94dBm。
In this embodiment, in step S2, the initial value is set as: height H of base stationBS10m, user height Huser1.5m, maximum height of obstacle Hblock10m, the initial coordinates of the intelligent reflecting surface are set as the geometric centers of each user and the base station, the user activity area is set as a square area with two points of (50, 50, 1.5) and (150, 150, 1.5) as diagonal vertexes, the point of the user activity area closest to the obstacle is (50, 50, 1.5), and the minimum distance d is set asdb,minThe user positions are set to be randomly distributed within the user activity area, 10 m.
In other embodiments, in the step S2, the height z of the intelligent reflective surfaceIRSOther calculation methods may be modified depending on the communication environment.
In this implementationIn an example, the initial values in step S3 are set as: base station antenna gain GBS8dBi, gain G of the smart reflector antennaIRSUser antenna gain G of 8dBiUE＝0dBi。
In this embodiment, in step S4, the step d is 0.5 m.
The computer simulates the average reachable speed of 50 users under the transmitting power of-20 dBm-50dBm, the position of the intelligent reflecting surface of the IRS auxiliary communication at a fixed position is set to be (0, 100, 10), the initial position of the intelligent reflecting surface of the unmanned aerial vehicle carrying the intelligent reflecting surface auxiliary communication is set to be a random position above an obstacle, the optimization effects of the two schemes on the channel are compared, and the obtained result is shown in figure 3.
Fig. 3 shows that the unmanned aerial vehicle carries the intelligent reflector for auxiliary communication, and the comparison between the average reachable rate of the user based on the fixed-position intelligent reflector for auxiliary communication and the average reachable rate of the user without the intelligent reflector for auxiliary communication is shown, and as can be seen from the figure, the average reachable rate of the user carrying the IRS for auxiliary communication is obviously higher than the average reachable rate of the user without the IRS for auxiliary communication, and higher than the average reachable rate of the user carrying the IRS for auxiliary communication when the IRS position is (0, 100, 10), so that when the area of the obstacle is larger, that is, the fixed-position IRS is farther from the base station, the communication method for the unmanned aerial vehicle carrying the IRS is more effective. The unmanned aerial vehicle path optimization method considers the total reachable speed, and the quality of the communication system is improved more obviously.
The present invention and its embodiments have been described above schematically, and the description is not intended to be limiting, and what is shown in the drawings is only one of the embodiments of the present invention, and the actual structure is not limited thereto. Therefore, if a person skilled in the art should appreciate that the invention is not limited to the specific embodiments described above, other embodiments and configurations can be devised without departing from the spirit and scope of the invention.
Claims (7)
1. The position optimization method for the relay of the unmanned aerial vehicle with the intelligent reflecting surface is characterized by comprising the following steps of:
s1, establishing a mathematical model of the multi-user received signal, and analyzing the user channel capacity;
in the intelligent reflecting surface assisted multi-user communication system, a base station sends signals to K users which are blocked through an intelligent reflecting surface, and N passive reflecting elements are installed on the intelligent reflecting surface;
the signal received at user k is represented as:
wherein k is [1, M ]]k. k are all positive integers, T denotes the matrix transposition, pkRepresenting the transmission power of the kth user, s representing a unity power information signal, wkThe additive white Gaussian noise represents that the mean value of a channel where a user k is located is 0 and the variance is sigma;
hsd,kindicating a deterministic channel, h, from the base station to user ksrRepresenting a deterministic channel, h, from the base station to the intelligent reflecting surfacerd,kRepresenting a deterministic channel from the intelligent reflecting surface to user k; Θ is a phase shift matrix of the intelligent reflective surface, expressed as:
wherein j is an imaginary symbol, alpha is a reflection coefficient with fixed amplitude and belongs to (0,1), and thetanFor the phase of the nth reflecting element in IRS, N ∈ [1, N]N and N are positive integers;
therefore, the channel capacity of the intelligent reflector auxiliary communication network is as follows:
wherein, p is signal emission power, and sigma is Gaussian white noise variance;
for any given Θ, the achievable rate for the kth user is expressed as:
the channel from the base station to the user via the intelligent reflecting surface can be represented as:
when the phase θ of the n-th reflecting elementn＝arg(hsd)-arg([hsr]n[hrd]n) When the utility model is used, the water is discharged,
each term in (1) and hsdHave the same phase, can get the maximum channel capacity or can reach the speed at this moment;
s2, establishing a three-dimensional coordinate system used for determining the position and the distance, and calculating the corresponding distance according to the position in the coordinate system:
establishing a three-dimensional rectangular coordinate system OXYZ, wherein the center of a base station root is an origin O, and XOY is a reference horizontal plane; setting the height of the base station to HBSHeight of user is HuserMaximum height of obstacle is Hblock(ii) a The base station position coordinate is (0, 0, H)BS) And the position coordinate of the intelligent reflecting surface carried by the unmanned aerial vehicle is (x)IRS，yIRS,zIRS) The position coordinate of the user is (x)user,k,yuser,k,Huser)；
According to the position information, the distance d from the base station to the intelligent reflecting surfacesrDistance d from intelligent reflecting surface to userrd,kAnd base station to user distance dsd,kRespectively expressed as:
s3, using the 3GPP model of the urban micro-scenario, calculating individual channel gains:
the signal transmission between the base station and the intelligent reflection surface and between the intelligent reflection surface and the user is line of sight (LOS), and the path LOSs is respectively expressed as PLLOS(dsr) And PLLOS(drd,k)；
The transmission between the base station and the user is a non line of sight (NLOS) transmission, and the path loss is expressed as PLNLOS(dsd,k) (ii) a Base station antenna gain of GBSAfter phase optimization, the gain of the intelligent reflector antenna is regarded as constant in movement, namely GIRS(ii) a Antenna gain of user receiving end is GUE(ii) a Using the 3GPP model of the urban micro-scenario as the channel gain model, then
Deterministic channel gain from base station to user k:
|hsr|2[dB]＝GBS[dBi]+GIRS[dBi]+PLLOS(dsr)；
deterministic channel gain from base station to intelligent reflector:
|hrd,k|2[dB]＝GIRS[dBi]+GUE[dBi]+PLLOS(drd,k)；
deterministic channel gain from intelligent reflector to user k:
|hsd,k|2[dB]＝GBS[dBi]+GUE[dBi]+PLNLOS(dsd,k)
the total gain of the channel from the base station to the user through the intelligent reflecting surface is as follows:
|hIRS,k|2[dB]＝|hsr|2[dB]+|hrd,k|2[dB]；
s4, optimizing the position of the intelligent reflecting surface based on the sum of the maximum user reachable speeds:
s41, obtaining the position (x) of each user based on the positioning deviceuser,k,yuser,k,Huser) Calculating and initializing the position (x) of the intelligent reflecting surfaceIRS，yIRS,zIRS) Calculating sum of reachable rates of each user
S42, initializing Data array Data, storing S in Data array Data, and obtaining Data ═ S;
s43, setting step length d, and respectively calculating xIRS+d,xIRS-d,yIRS+d,yIRSSum of achievable rates at-d Sx+d，Sx-d,Sy+d,Sy-dAnd storing the Data in Data array Data to obtain Data as [ S, Sx+d,Sx-d,Sy+d,Sy-d]；
S44, finding the maximum value in the Data array and setting the maximum value as S*And making the following judgments:
s441, if | S-S*|<E, ending the circulation, wherein the position of the intelligent reflecting surface is the optimal position of the relay of the unmanned aerial vehicle;
s442, otherwise, carrying out the following steps:
s4421: when S isx+d＝S*Let xIRS＝xIRS+d,S＝Sx+dRepeating steps S42-S44;
s4422: when S isx-d＝S*Let xIRS＝xIRS-d,S＝Sx-dRepeating steps S42-S44;
s4423: when S isy+d＝S*Let yIRS＝yIRS+d,S＝Sy+dRepeating the steps S42-S44;
s4424: when S isy-d＝S*Let yIRS＝yIRS-d,S＝Sy-dRepeating steps S42-S44;
and S45, when the position of the user is changed, repeating the steps S41-S44, recalculating the optimal position of the unmanned aerial vehicle relay at the moment, wherein the initial position of the intelligent reflecting surface in the calculation is the optimal position of the unmanned aerial vehicle relay after the last adjustment is finished.
2. The method of claim 1, wherein the method comprises: in step S2, the height of the obstacle is set to be close to the height of the base station without large fluctuation, and the minimum distance from the user activity area to the obstacle is set to be ddb,minThe nearest distance from the user activity area to the intelligent reflecting surface is drd,min(ii) a In order to ensure that the sight distance transmission between the intelligent reflecting surface and the user is satisfied, the height z of the intelligent reflecting surface is setIRSThe distance between the user activity area and the obstacle is determined by the shortest distance between the user activity area and the obstacle, the maximum height of the obstacle and the shortest distance between the intelligent reflecting surface and the user area, and the distances are expressed as follows:
in order to ensure that the sight distance transmission is met between the intelligent reflecting surface and the base station and the unmanned aerial vehicle can normally fly, the height z of the intelligent reflecting surfaceIRS>Hblock。
3. The method of claim 1, wherein the method comprises: in step S2, the height z of the intelligent reflection surfaceIRSOther calculation methods may be modified depending on the communication environment.
4. The method of claim 1, wherein the method comprises the steps of: in step S1, the initial values are set as: carrier frequency fc3GHz, the number N of the intelligent reflecting surface reflecting elements is 250, the number K of the users is 50, and the base station transmits power p to each userk30dBm, a fixed amplitude reflection coefficient α of 1, and an additive white gaussian noise variance σ2＝-94dBm。
5. The method of claim 1, wherein the method comprises: in step S2, the initial values are set as: height H of base stationBS10m, user height Huser1.5m, maximum height of obstacle Hblock10m, the initial coordinates of the intelligent reflecting surface are set as the geometric centers of each user and the base station, the user activity area is set as a square area with two points of (50, 50, 1.5) and (150, 150, 1.5) as diagonal vertexes, the point of the user activity area closest to the obstacle is (50, 50, 1.5), and the minimum distance d is set asdb,minThe user positions are set to be randomly distributed within the user activity area, 10 m.
6. The method of claim 1, wherein the method comprises: the initial values in step S3 are set as: base station antenna gain GBS8dBi, intelligent reflector antenna gain GIRSUser antenna gain G of 8dBiUE＝0dBi。
7. The method of claim 1, wherein the method comprises: in step S4, the step d is 0.5 m.
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