CN115441925B - Wireless computing system based on air-ground communication network - Google Patents
Wireless computing system based on air-ground communication network Download PDFInfo
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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
- H04B7/15—Active relay systems
- H04B7/185—Space-based or airborne stations; Stations for satellite systems
- H04B7/1851—Systems using a satellite or space-based relay
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- G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
- G08G5/0043—Traffic management of multiple aircrafts from the ground
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
- G08G5/06—Traffic control systems for aircraft, e.g. air-traffic control [ATC] for control when on the ground
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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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 provides a wireless computing system based on an air-ground communication network, which considers an air-ground communication network fused with a low-altitude aircraft under multiple time slots, considers a flexible aircraft and an air data fusion computing system jointly, divides ground equipment into a plurality of cluster groups, divides total time length into a plurality of time slots, takes the aircraft as a data summarizing computing platform, plans an aircraft track, and collects and computes ground equipment sensing data in each cluster group in each time slot. The invention aims to minimize the mean square error problem of air data fusion calculation by combining and optimizing the design of the aircraft track, the transmitting coefficient of a transmitting end and the denoising coefficient of a receiving end, and provides performance indexes such as communication speed, communication quality and the like to optimally balance the error between signals and noise.
Description
Technical Field
The present invention relates to the field of wireless communication technology, and more particularly, to a wireless computing system based on an air-to-ground communication network.
Background
In recent years, with the development of wireless communication network technology, low-altitude aircrafts represented by small unmanned aerial vehicles and balloons are playing a great role in the fields of emergency communication, security search and rescue, environmental exploration and the like. For example, in the case of a damaged ground communication infrastructure, the low-altitude aircraft, as a mobile base station, can provide a timely communication network and computing services for ground users due to the characteristics of convenience in deployment, economy and the like.
The wireless calculation is a novel technology integrating wireless data communication and function calculation, and can realize communication and calculation functions at the same time. Specifically, in the wireless computing system, on the premise that individual data of each node does not need to be recovered, by means of concurrent transmission of all nodes, objective function operation is achieved by means of superposition characteristics of wireless channels, and huge transmission delay caused by transmission before computation can be reduced in a computing scene with limited communication capacity. However, under the condition of channel fading, receiver noise and limited transmission power of ground equipment, the performance of the air data fusion calculation cannot be guaranteed by only relying on the design of a transceiver.
In an air-ground communication network integrating low-altitude aircrafts, the aircrafts are deployed as motor base stations, and data are collected from mobile channel ground equipment through air data integration calculation in the Internet of things. Based on flexible mobility, the aircraft can establish a line-of-sight communication link channel with ground equipment, and wireless computing performance loss caused by non-line-of-sight wireless channel fading can be effectively avoided. In addition, the dynamic path planning of the low-altitude aircraft adjusts the deployment position of the aircraft according to the space-time distribution characteristics of the ground user equipment, and is hopeful to further improve the performance of the wireless computing system.
Most ground deployment base stations today are inflexible when facing complex and changeable environments, and require high operation expenditure, in some special communication scenarios, ground communication infrastructures are damaged or difficult to deploy, so that communication quality cannot be guaranteed, in addition, the traditional communication technology is to perform communication first and then calculation, in the scenario of limited channel capacity, huge transmission delay is caused, and communication expenditure is increased. Current schemes for guaranteeing wireless communication quality in special scenarios are not necessarily optimal in terms of communication rate and network performance.
Disclosure of Invention
The present invention provides a wireless computing system based on an air-to-ground communication network that can achieve an error between the balanced signal and noise.
In order to achieve the technical effects, the technical scheme of the invention is as follows:
a wireless computing system based on an air-ground communication network, comprising 1 low-altitude aircraft and K ground user devices; the low-altitude aircraft is deployed as an air base station, K ground user equipment adopts a K-means algorithm to carry out multi-region division, and the K ground user equipment is divided into N cluster groups, which are recorded asAnd N is more than or equal to 1 and K is more than or equal to K; dividing the finite time T into N time slots with the same length, wherein the length of each time slot is +.>In each time slot, the aircraft can only communicate with one cluster group at most, and wireless data aggregation calculation of the ground equipment of the corresponding cluster group is completed.
Further, the horizontal coordinate of the aircraft at the nth slot is q (n) = (x (n), y (n)), the deployment altitude H is fixed, assuming the aircraft is at a fixed speed between the two cluster setsFlying at a constant speed;
the coordinates of the kth ground user equipment are recorded as w k (n)＝(x k (n),y k (n)) modeling a wireless channel model of the aircraft with an nth ground device at an nth time slot as follows:
wherein the method comprises the steps ofRepresenting channel gain, beta 0 Represents the channel power gain at a reference distance d=1 meter, c represents the path loss index, +.>Representing the effects of small scale channel fading.
Further, the aircraft uses the aerial data aggregation calculation to aggregate the distributed ground device transmission data into an alignment chart function, namely in N time slots, and the aircraft needs to obtain the following function calculation:
wherein x is k Is the perceived data of the ground user equipment k.
Further, in the nth time slot, K is used n Representing an aircraft service clusterGroup ofOf ground equipment, i.e. K n The ground equipment transmits the perceived data to an aerial base station of the aircraft through an aerial data fusion method;
representing the noise signal received by the aircraft with y (n), modeling the aircraft receiver signal:
wherein b k Signal transmission coefficient h representing ground equipment k k Channel coefficients for ground equipment to aircraftRepresenting the receiver additive white gaussian noise.
Further, modeling ground user equipment awareness data { x } k All are independent and equidistributed and obey a random variable with mean 0 and variance 1, modeling the transmit power constraint of the kth terrestrial user device:
wherein P is k Indicating the maximum transmit power of the kth terrestrial user equipment.
Further, after completing N time slots, the function calculation model of the aircraft receiver:
wherein eta n >0 represents an aircraft receiver denoising factor.
Further, a wireless calculation mean square error model is established:
further, joint optimization of ground equipment cluster group partitioningAircraft at N time slots->Signal transmission coefficients of K ground devices +.>Receiving denoising factor of aircraft>The method is characterized by taking the minimization of the mean square error of wireless calculation as a design criterion, modeling the joint optimization problem of wireless calculation and flight deployment based on an air-ground communication network, and comprising the following steps:
further, for all device cluster groupsNumbering and sorting from left to right to obtain cluster groupsNext, let device cluster group->As data collection hover position q (1), q (2), …, q (n) of the aircraft, the aircraft accesses q (1), q (2), …, q (n) in order from the origin to obtain the total flight trajectory.
Further, the variable { η } n Sum { b } k The two variables are mutually coupled, and are solved by an iterative algorithm of alternate optimization, namely, one variable is fixed while the other variable is optimized until the iteration converges to obtain the optimal signal emission coefficientAnd receive a denoising factor->
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
according to the invention, an air-ground communication network of a low-altitude aircraft is considered under the condition of multiple time slots, a flexible aircraft and an air data fusion computing system are considered jointly, ground equipment is divided into a plurality of cluster groups, the total time length is divided into a plurality of time slots, the aircraft is used as a data summarizing computing platform, the aircraft track is planned, and the collection and computation of ground equipment sensing data in each cluster group are realized in each time slot. The invention aims to minimize the mean square error problem of air data fusion calculation by combining and optimizing the design of the aircraft track, the transmitting coefficient of a transmitting end and the denoising coefficient of a receiving end, and provides performance indexes such as communication speed, communication quality and the like to optimally balance the error between signals and noise.
Drawings
FIG. 1 is a block diagram of a system according to the present invention.
FIG. 2 is a flow chart of a method of the present invention for system application.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the present patent;
for the purpose of better illustrating the embodiments, certain elements of the drawings may be omitted, enlarged or reduced and do not represent the actual product dimensions;
it will be appreciated by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical scheme of the invention is further described below with reference to the accompanying drawings and examples.
Example 1
As shown in fig. 1, a wireless computing system based on an air-to-ground communication network includes 1 low-altitude aircraft and K ground user devices; the low-altitude aircraft is deployed as an air base station, K ground user equipment adopts a K-means algorithm to carry out multi-region division, and the K ground user equipment is divided into N cluster groups, which are recorded asAnd N is more than or equal to 1 and K is more than or equal to K; dividing the finite time T into N time slots with the same length, wherein the length of each time slot is +.>In each time slot, the aircraft can only communicate with one cluster group at most, and wireless data aggregation calculation of the ground equipment of the corresponding cluster group is completed.
The horizontal coordinate of the aircraft at the nth slot is q (n) = (x (n), y (n)), the deployment altitude H is fixed, assuming the aircraft is at a fixed speed between the two cluster setsFlying at a constant speed;
the coordinates of the kth ground user equipment are recorded as w k (n)＝(x k (n),y k (n)) modeling a wireless channel model of the aircraft with an nth ground device at an nth time slot as follows:
wherein the method comprises the steps ofRepresenting channel gain, beta 0 Represents the channel power gain at a reference distance d=1 meter, c represents the path loss index, +.>Representing the effects of small scale channel fading.
The aircraft uses the aerial data aggregation calculation to aggregate distributed ground equipment transmission data into an alignment chart function, namely in N time slots, and the aircraft needs to obtain the following function calculation:
wherein x is k Is the perceived data of the ground user equipment k.
In the nth time slot, use K n Representing a group of aircraft service clustersOf ground equipment, i.e. K n The ground equipment transmits the perceived data to an aerial base station of the aircraft through an aerial data fusion method;
representing the noise signal received by the aircraft with y (n), modeling the aircraft receiver signal:
wherein b k Signal transmission coefficient h representing ground equipment k k Channel coefficients for ground equipment to aircraftRepresentation ofThe receiver adds white gaussian noise.
Modeling ground user equipment awareness data { x k All are independent and equidistributed and obey a random variable with mean 0 and variance 1, modeling the transmit power constraint of the kth terrestrial user device:
wherein P is k Indicating the maximum transmit power of the kth terrestrial user equipment.
After completing N time slots, the function calculation model of the aircraft receiver:
wherein eta n >0 represents an aircraft receiver denoising factor.
Establishing a wireless calculation mean square error model:
joint optimization of ground device cluster group partitioningAircraft at N time slots->Signal transmission coefficients of K ground devices +.>Receiving denoising factor of aircraft>Modeling a combination of wireless computation and flight deployment based on an air-to-ground communication network by taking wireless computation mean square error minimization as a design criterionThe optimization problem is as follows:
example 2
As shown in fig. 2, the present invention proposes an efficient two-stage design scheme for wireless computing of the mean square error minimization design problem, which solves by dividing the objective problem into two independent sub-problems. The concrete description is as follows:
the first is the trajectory optimization problem of the aircraft.
Firstly, dividing all ground equipment into N cluster groups by adopting a K-means clustering algorithm: randomly selecting N coordinate points from a ground equipment distribution area as initial class cluster center points, calculating distances from each ground equipment to the N class cluster center points, dividing each ground equipment into class clusters represented by the nearest class cluster center points, and marking the classes; and then, according to the clustering result, carrying out arithmetic average on the coordinate points of all the ground devices in each class cluster, recalculating the new center point of each class cluster, and reclassifying the marked classes of all the ground devices according to the new center point until the positions of the center points of the class clusters are not changed, thereby completing the clustering. Otherwise, repeating the above steps. Thus, all the ground devices can be divided into N cluster groupsAnd ensure that the ground equipment among each cluster group is not overlapped。
According to the coordinate position of the center point of the class cluster, all equipment clusters are clusteredNumbering and sorting from left to right to obtain cluster group +.>Next, let device cluster group->As data collection hover position q (1), q (2), …, q (n) of the aircraft, the aircraft accesses q (1), q (2), …, q (n) in order from the origin to obtain the total flight trajectory.
And secondly, the joint optimization design problem of the signal transmission coefficient and the receiving denoising factor.
Due to optimizing the variable { eta ] in the objective function n Sum { b } k The original problem is a non-convex optimization problem, and can be solved by an iterative algorithm of alternate optimization, namely, one variable is fixed while the other variable is optimized until the iteration converges to obtain an optimal signal emission coefficientAnd receive a denoising factor->
Example 3
A wireless computing system based on an air-ground communication network, comprising 1 low-altitude aircraft and K ground user devices; the low-altitude aircraft is deployed as an air base station, K ground user equipment adopts a K-means algorithm to carry out multi-region division, and the K ground user equipment is divided into N cluster groups, which are recorded asAnd N is more than or equal to 1 and K is more than or equal to K; dividing the finite time T into N time slots with the same length, wherein the length of each time slot is +.>In each time slot, the aircraft can only communicate with one cluster group at most, and wireless data aggregation calculation of the ground equipment of the corresponding cluster group is completed.
The horizontal coordinate of the aircraft at the nth slot is q (n) = (x (n), y (n)), the deployment altitude H is fixed, assuming the aircraft is at a fixed speed between the two cluster setsFlying at a constant speed;
the coordinates of the kth ground user equipment are recorded as w k (n)＝(x k (n),y k (n)) modeling a wireless channel model of the aircraft with an nth ground device at an nth time slot as follows:
wherein the method comprises the steps ofRepresenting channel gain, beta 0 Represents the channel power gain at a reference distance d=1 meter, c represents the path loss index, +.>Representing the effects of small scale channel fading.
The aircraft uses the aerial data aggregation calculation to aggregate distributed ground equipment transmission data into an alignment chart function, namely in N time slots, and the aircraft needs to obtain the following function calculation:
wherein x is k Is the perceived data of the ground user equipment k.
In the nth time slot, use K n Representing a group of aircraft service clustersOf ground equipment, i.e. K n The ground equipment transmits the perceived data to an aerial base station of the aircraft through an aerial data fusion method;
representing the noise signal received by the aircraft with y (n), modeling the aircraft receiver signal:
wherein b k Signal transmission coefficient h representing ground equipment k k Channel coefficients for ground equipment to aircraftRepresenting the receiver additive white gaussian noise.
Modeling ground user equipment awareness data { x k All are independent and equidistributed and obey a random variable with mean 0 and variance 1, modeling the transmit power constraint of the kth terrestrial user device:
wherein P is k Indicating the maximum transmit power of the kth terrestrial user equipment.
After completing N time slots, the function calculation model of the aircraft receiver:
wherein eta n >0 represents an aircraft receiver denoising factor.
Establishing a wireless calculation mean square error model:
joint optimization of ground device cluster group partitioningAircraft at N time slots->Signal transmission coefficients of K ground devices +.>Receiving denoising factor of aircraft>The method is characterized by taking the minimization of the mean square error of wireless calculation as a design criterion, modeling the joint optimization problem of wireless calculation and flight deployment based on an air-ground communication network, and comprising the following steps:
for all device cluster groupsNumbering and sorting from left to right to obtain cluster group +.>Next, let device cluster group->As data collection hover position q (1), q (2), …, q (n) of the aircraft, the aircraft accesses q (1), q (2), …, q (n) in order from the origin to obtain the total flight trajectory.
Variable { eta } n Sum { b } k The two variables are mutually coupled, and are solved by an iterative algorithm of alternate optimization, namely, one variable is fixed while the other variable is optimized until the iteration converges to obtain the optimal signal emission coefficientAnd receive a denoising factor->
The same or similar reference numerals correspond to the same or similar components;
the positional relationship depicted in the drawings is for illustrative purposes only and is not to be construed as limiting the present patent;
it is to be understood that the above examples of the present invention are provided by way of illustration only and not by way of limitation of the embodiments of the present invention. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are desired to be protected by the following claims.
Claims (7)
1. A wireless computing system based on an air-to-ground communication network, comprising 1 low-altitude aircraft and K ground user devices; the low-altitude aircraft is deployed as an air base station, K ground user equipment adopts a K-means algorithm to carry out multi-region division, and the K ground user equipment is divided into N cluster groups, which are recorded asAnd N is more than or equal to 1 and less than or equal toK, performing K; dividing the finite time T into N time slots with the same length, wherein the length of each time slot is +.>In each time slot, the aircraft can only communicate with one cluster group at most, and the wireless data aggregation calculation of the ground equipment of the corresponding cluster group is completed, wherein the process of the wireless data aggregation calculation comprises the following steps:
the horizontal coordinate of the aircraft at the nth slot is q (n) = (x (n), y (n)), the deployment altitude H is fixed, assuming the aircraft is at a fixed speed between the two cluster setsFlying at a constant speed;
the coordinates of the kth ground user equipment are recorded as w k (n)＝(x k (n),y k (n)) modeling a wireless channel model of the aircraft with an nth ground device at an nth time slot as follows:
wherein the method comprises the steps ofRepresenting channel gain, beta 0 Represents the channel power gain at a reference distance d=1 meter, c represents the path loss index, +.>Representing small-scale channel fading effects;
the aircraft uses the aerial data aggregation calculation to aggregate distributed ground equipment transmission data into an alignment chart function, namely in N time slots, and the aircraft needs to obtain the following function calculation:
wherein x is k The perceived data of the ground user equipment k;
in the nth time slot, use K n Representing a group of aircraft service clustersOf ground equipment, i.e. K n The ground equipment transmits the perceived data to an aerial base station of the aircraft through an aerial data fusion method;
representing the noise signal received by the aircraft with y (n), modeling the aircraft receiver signal:
wherein b k Signal transmission coefficient h representing ground equipment k k Channel coefficients for ground equipment to aircraftRepresenting receiver additive white gaussian noise;
the method comprises the steps of taking the minimum mean square error of wireless calculation as a design criterion, modeling a joint optimization problem of wireless calculation and flight deployment based on an air-ground communication network, aiming at the minimum mean square error of the wireless calculation, solving the design problem by dividing a target problem into two independent sub-problems.
2. A wireless computing system based on a space-to-ground communication network according to claim 1, wherein modeling ground user equipment awareness data { x } k All are independent and equidistributed and obey a random variable with mean 0 and variance 1, modeling the transmit power constraint of the kth terrestrial user device:
wherein P is k Indicating the maximum transmit power of the kth terrestrial user equipment.
3. The air-to-ground communication network-based wireless computing system of claim 2, wherein after completing N time slots, the function calculation model of the aircraft receiver:
wherein eta n >0 represents an aircraft receiver denoising factor.
4. A wireless computing system based on a space-time communication network according to claim 3, wherein a wireless computing mean square error model is established:
5. a wireless computing system based on a space-to-ground communication network as recited in claim 4, wherein the ground equipment cluster group partitioning is jointly optimizedAircraft at N time slots->Signal transmission coefficients of K ground devicesReceiving denoising factor of aircraft>Modeling air-to-ground communication network-based wireless with wireless calculation mean square error minimization as design criterionThe joint optimization problem of computation and flight deployment is as follows:
6. a wireless computing system based on a space-to-ground communication network as recited in claim 5, wherein for all device cluster groupsNumbering and sorting from left to right to obtain cluster group +.>Then, the cluster group of the equipment is madeAs data collection hover position q (1), q (2), …, q (n) of the aircraft, the aircraft accesses q (1), q (2), …, q (n) in order from the origin to obtain the total flight trajectory.
7. A wireless computing system based on a space-to-ground communication network according to claim 6, wherein the variable { η } n Sum { b } k The solutions being mutually coupled, i.e. a variant being fixed, by an iterative algorithm of alternating optimisationOptimizing another variable while measuring until iteration converges to obtain optimal signal emission coefficientAnd receive a denoising factor->
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