CN114615759B - Unmanned aerial vehicle auxiliary communication method in non-orthogonal multiple access network - Google Patents
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Abstract
The invention belongs to the field of unmanned aerial vehicle auxiliary communication, and provides a method for unmanned aerial vehicle auxiliary communication in a non-orthogonal multiple access network, wherein the network comprises unmanned aerial vehicle and ground Internet of things nodes; the unmanned aerial vehicle serves as an air base station to provide service for the ground node; the ground target area is divided into an inner circle area and a circular ring area, and the ground nodes are uniformly distributed in the target area. By reasonably grouping the nodes in the two areas, the user scheduling of the unmanned aerial vehicle is jointly optimized, the transmitting power and the flight parameters are optimized, and the overall energy efficiency of the unmanned aerial vehicle is maximized. Compared with an unmanned aerial vehicle assisted orthogonal multiple access scheme, the method can obviously improve the energy efficiency of the system and has higher application value.
Description
Technical Field
The invention relates to the field of unmanned aerial vehicle auxiliary communication, in particular to a unmanned aerial vehicle auxiliary communication method in a non-orthogonal multiple access network.
Background
In recent years, with the rapid development of wireless communication technology, conventional cellular communication networks have faced significant challenges in terms of improving network capacity, expanding network coverage, and improving quality of service. In practical communication applications, the ground base station commonly used at present faces pain points to be solved urgently. For example, deployment of ground base stations in remote or severe terrain areas has the problem of high capital construction difficulty and high cost; the channel link between the base station and the user has the problem of serious signal attenuation of communication quality; and the ground base station cannot normally operate after being damaged due to natural disasters and the like. In order to enhance network coverage, network deployment is performed at any time according to requirements, and unmanned aerial vehicle (unmanned aerial vehicle, UAV) -assisted wireless communication is brought into the field of view of people, and is considered as an effective method for solving the problems faced by the ground mobile communication system.
The Non-orthogonal multiple access technology (Non-Orthogonal Multiple Access, NOMA) is proposed, and originates from the requirement that the fifth generation mobile communication system (5th Generation,5G) supports more user connections at the same time, the conventional orthogonal multiple access (Orthogonal Multiple Access, OMA) manner cannot meet the explosive growth of user access requests in 5G, and the NOMA technology can not only accommodate more users, but also can improve the utilization rate of the spectrum. The NOMA of the power domain sends signals with different powers on the same time-frequency resource to multiplex the resources, and the user terminal obtains the respective information of the users by a continuous interference cancellation (Successive Interference Cancellation, SIC) technology. It should be noted that the NOMA scheme can obtain better spectral efficiency gain than the conventional OMA scheme only when the channel difference between each user and the transmitting end is large. Because unmanned aerial vehicle can the dynamic adjustment self flight orbit, optimize the channel gain of each user dynamically, consequently unmanned aerial vehicle and the combination of non-orthogonal multiple access can better exert NOMA mode's advantage.
Disclosure of Invention
Aiming at the problems of user capacity and unmanned aerial vehicle power consumption in an unmanned aerial vehicle auxiliary communication network, the invention provides that NOMA technology is introduced into the unmanned aerial vehicle auxiliary communication network, and the user scheduling, communication power and flight parameters of the unmanned aerial vehicle are combined to effectively improve the user capacity of the system and the energy efficiency of the unmanned aerial vehicle.
The technical scheme of the invention is as follows: a method for unmanned aerial vehicle auxiliary communication in a non-orthogonal multiple access network comprises unmanned aerial vehicle and ground Internet of things nodes.
Two unmanned aerial vehicles are used as aerial base stations and fly above corresponding ground target areas with fixed heights and flight periods, so as to provide services for ground Internet of things nodes, and the flight periods are decomposed into N time slots; each time slot should be small enough to ensure that the drone can be considered relatively stationary in each time slot.
The ground target area is divided into an inner circle area and an annular area, and the areas of the inner circle area and the annular area are equal; the ground internet of things nodes are uniformly distributed in a ground target area, and the nodes of the inner circle area and the nodes of the circular ring area are paired in pairs to form an I group of non-orthogonal multiple access group; through reasonable grouping of the nodes in the inner circle and the circular ring area, the user scheduling of the unmanned aerial vehicle is jointly optimized, the transmitting power and the flight parameters are optimized, and the overall energy efficiency of the unmanned aerial vehicle is maximized.
The method specifically comprises the following steps:
step one: constructing a non-orthogonal multiple access network system model assisted by the unmanned aerial vehicle according to actual conditions, wherein the non-orthogonal multiple access network system model comprises a network channel model, a non-orthogonal multiple access grouping model, an unmanned aerial vehicle motion model and the like; under the condition of a network channel model, non-orthogonal multiple access grouping is carried out on the nodes, the dynamic track of the unmanned aerial vehicle is determined according to the unmanned aerial vehicle motion model, and communication service is provided for the ground non-orthogonal multiple access group;
step two: the method comprises the steps of designing and optimizing the problem, maximizing the overall energy efficiency of the unmanned aerial vehicle, and meeting the kinematic constraint of the unmanned aerial vehicle, the relative position constraint between the unmanned aerial vehicle and each node, the constraint of the unmanned aerial vehicle flight area and the constraint of the minimum communication rate of each node;
step three: and decomposing the optimization problem into three non-convex sub-problems, converting the non-convex sub-problems into convex problems by using a Lagrange multiplier method and a continuous convex approximation method, and then placing the convex problems into a CVX tool box in MATLAB for solving.
The network channel model in the first step specifically comprises the following steps: the channel gain from the mth unmanned aerial vehicle to the jth ground Internet of things node of the ith group of non-orthogonal multiple access groups is expressed as h m,i,j (N), wherein i=1, 2, … I, n=1, 2, … N, m=1 represents the unmanned aerial vehicle flying above the annular region, m=2 represents the unmanned aerial vehicle flying above the inner circular region, j=1 represents the node in the annular region, and j=2 represents the node in the inner circular region; considering that the channel between the drone and the node is a line-of-sight channel, a free space fading model is used to simulate the channel, i.e
Wherein beta is 0 Represents the channel gain at a unit distance of 1 meter, d m,i,j (n) represents the distance from the mth unmanned aerial vehicle to the jth ground Internet of things node of the ith group of non-orthogonal multiple access groups, q m (n) represents the position of the mth unmanned plane in the nth time slot, q i,j And the position of the j ground Internet of things node of the i group of non-orthogonal multiple access groups is shown.
The non-orthogonal multiple access packet model in the first step specifically includes:
dividing a ground target area into four quadrants, calculating the distance between the node in the circular area and the node in the inner circular area in each quadrant, storing the distance information into a matrix d, wherein the a-th row and the b-th column of the matrix d represent the distance between the a-th node of the inner circular area and the b-th node of the circular area along the anticlockwise direction;
[d ord ,L]＝sort(d) (2)
wherein, the sort (& gt) is to arrange each column of elements in the matrix in a descending order, and store the ordering result into d ord And will be ordered d ord The inner circle node labels corresponding to the elements are stored in the corresponding positions in L;
traversing from the first row of the matrix L, pairing the ith node of the circular ring area along the anticlockwise direction with the L (1, i) th node of the inner ring area when the inner ring node marks in all columns of the first row do not appear repeatedly;
recording unrepeated inner circle node marks when repeated inner circle node marks appearA position, pairing a w-th node of the circular ring area with an L (1, w) th node of the inner circular area along the anticlockwise direction, wherein w represents a column without repeated marks; recording the positions of the repeated internal circle node marks, and comparing d ord Selecting the element with the largest corresponding distance value, recording the column number e of the element, and pairing the e node of the circular ring area along the anticlockwise direction with the first node of the inner circular area; after pairing is completed, removing elements in the w column and the e column in L, continuing to traverse from the next row of L, and following the rule of the first traversal process until all node pairing is completed.
The unmanned aerial vehicle motion model comprises three parts, namely, the unmanned aerial vehicle flight kinematic constraint, the unmanned aerial vehicle flight area constraint and unmanned aerial vehicle energy consumption; the position coordinates and speed of each time slot of the unmanned aerial vehicle satisfy the following kinematic constraints:
wherein q m (n) represents the position coordinates of the mth time slot and the mth unmanned plane, a m (n) represents acceleration of the mth time slot mth unmanned aerial vehicle, V m (n) represents the flight speed, delta of the mth unmanned aerial vehicle in the nth time slot t Indicating the length of each time slot;
two unmanned aerial vehicles fly above the inner circle and the circular ring area respectively, and the flight range of the unmanned aerial vehicle is constrained as follows
r in ≤d m,z (n)≤r out ,m＝1 (5)
d m,z (n)≤r in ,m＝2 (6)
Wherein d m,z (n) represents the distance of the mth unmanned aerial vehicle from the region central axis z-axis in the case of height H; r is (r) in Represents the radius of the inner circle; r is (r) out Representation ofRadius of the whole ground target area;
the flying period of the unmanned aerial vehicles is fixed, the initial position and the end position of one period of the unmanned aerial vehicles are the same, meanwhile, collision between the unmanned aerial vehicles is avoided, and anti-collision conditions are introduced, wherein the two conditions are expressed as
Wherein d min Representing a minimum safe distance for maintaining safe flight of the unmanned aerial vehicle; if the distance between the unmanned aerial vehicles is smaller than d min A collision may occur, resulting in a system that does not operate properly.
The unmanned aerial vehicle energy consumption comprises two parts, namely, unmanned aerial vehicle communication power consumption and unmanned aerial vehicle propulsion power consumption; in the nth time slot, the communication power paid by the mth unmanned aerial vehicle for the jth node in the ith non-orthogonal multiple access group is P m,i,j (n); in the case of unit bandwidth, the communication rate of each non-orthogonal multiple access group is
Wherein sigma 2 Representing channel noise power;
the total throughput of the mth unmanned plane in one flight period and the total throughput of the system are respectively
R total ＝R 1 +R 2 (12)
Wherein u is m,i (n) is a binary discrete variable representing a user schedule that, when equal to 0, represents that the mth drone is not communicating with the ith non-orthogonal multiple access group at this time, and when equal to 1, represents that the mth drone is communicating with the ith non-orthogonal multiple access group at this time;
the propulsion power of the unmanned plane in one time slot is
Wherein c 1 And c 2 Two constant coefficients, related to air density and the hardware of the unmanned aerial vehicle itself; g represents gravitational acceleration, V m (n) represents the flight speed of the mth unmanned plane in the nth time slot, a m (n) represents the acceleration of the mth unmanned aerial vehicle in the nth time slot;
the energy consumption of the mth unmanned plane in one flight cycle and the total energy consumption of the system are respectively
E total ＝E 1 +E 2 (15)
In order to maximize the energy efficiency of the unmanned aerial vehicle as a whole, the optimization problem is that
||a m (n)||≤a max (15j)
const.(3),(4),(5),(6),(7),(8) (15l)
Wherein (15 b) and (15 c) indicate scheduling constraints, each NOMA group can only communicate with one drone in one time slot, and one drone can only serve one NOMA group in one time slot. (15e) And (15 f) ensuring that the distances from the unmanned aerial vehicle to the nodes in the inner circle and the circular ring area are different; so that the information transmission can be carried out by multiplexing the transmission powers with different magnitudes in a NOMA mode. (15g) And (15 h) a certain limit is made on the transmitting power of the unmanned aerial vehicle, and (15 i) and (15 j) limit the speed and the acceleration of the unmanned aerial vehicle, so that the unmanned aerial vehicle can reasonably flyThe row, (15 k) ensures that the communication rate of each node is not below the set minimum threshold. P (P) max Representing the maximum transmission power available for transmission by the unmanned aerial vehicle, V min And V max Representing minimum and maximum flight speeds of an unmanned aerial vehicle, a max Represents the maximum flying acceleration of the unmanned plane, R min Representing the lowest communication rate threshold for each node.
Further, the optimization problem proposed in the second step is a mixed integer partial non-convex optimization problem, which is difficult to directly solve. Therefore, in step three, the original problem is first decomposed into three sub-problems, namely, the user schedule of the unmanned aerial vehicle, the communication power of the unmanned aerial vehicle and the flight parameters of the unmanned aerial vehicle are optimized individually. And then, three sub-problems are converted into convex optimization problems which can be directly solved by introducing auxiliary variables and applying a Lagrange multiplier method and a continuous convex approximation (Successive Convex Approximation, SCA) method, an iterative algorithm of alternate optimization is designed, and a CVX tool box is used for gradually approaching the solution of the optimization problems.
The invention has the beneficial effects that: the invention provides a method for unmanned aerial vehicle auxiliary communication in a non-orthogonal multiple access network, which can improve the energy efficiency of a system under the condition of ensuring the throughput of each node by jointly optimizing user scheduling, unmanned aerial vehicle transmitting power and unmanned aerial vehicle flight parameters. Compared with the unmanned aerial vehicle auxiliary communication method in the orthogonal multiple access network, the method effectively improves the overall energy efficiency of the unmanned aerial vehicle, enables the system to operate more efficiently, and has strong application value.
Drawings
Fig. 1 is a diagram of the network model structure of the present invention.
Fig. 2 is a motion trajectory of the unmanned aerial vehicle in the present invention.
Fig. 3 is a graph of the achievable rates for each node in the present invention.
Fig. 4 is a comparison of the present invention with a minimum energy consumption scheme at a change in height H with respect to energy efficiency, maximizing throughput.
Fig. 5 is a comparison of the energy efficiency as a function of transmit power for the present invention with an unmanned aerial vehicle assisted OMA scheme if the trajectory is optimized.
Detailed Description
The invention is further illustrated below with reference to specific examples.
In the network model shown in fig. 1, β is set 0 ＝-50dB，σ 2 ＝-110dBm，r out ＝500m，K＝20，H＝100m，T＝80s，δ t ＝0.5s，P max ＝1w，V max ＝40m/s，V min ＝3m/s，a max ＝5m/s 2 ，R min ＝6bps/Hz。
Taking the center of a ground target area as the center of a circular initial track of two unmanned aerial vehicles, wherein the radius of the initial track of the two unmanned aerial vehicles is r respectively 1 ＝(r in +r out )/2,r 2 ＝r in /2. Firstly, fixing the transmitting power and initial flight parameters of the unmanned aerial vehicle, and obtaining user scheduling under the current condition; fixing the initial flight parameters and the obtained user schedule to optimize the transmitting power of the unmanned aerial vehicle; and finally, optimizing the flight parameters of the unmanned aerial vehicle by fixedly solving the user scheduling and the unmanned aerial vehicle transmitting power. The above process is cycled until the objective function value converges.
Fig. 2 shows the optimized trajectories of two unmanned aerial vehicles, the motion trajectories of the two unmanned aerial vehicles are respectively close to the ground internet of things nodes in the corresponding areas, and because certain distance difference exists between the unmanned aerial vehicle and each node in the NOMA mode, the transmitting power of different sizes is determined according to the distance between the unmanned aerial vehicle and each node, and then the multiplexing of the power is performed. The ground internet of things nodes in the respective flight areas are used as the near users of the two unmanned aerial vehicles, the ground internet of things nodes in the adjacent areas are used as the far users, and the unmanned aerial vehicles are closer to the near users of the unmanned aerial vehicles through optimization in order to better exert the advantages of NOMA. The communication rate of each ground internet of things node is shown in fig. 3, where the communication rate of each node is above a set minimum threshold.
Fig. 4 shows the energy efficiency as a function of height H for an optimization objective. The energy efficiency of each scheme gradually decreases along with the increase of the height, and under the condition of limited transmission power, the channel gain is reduced due to the increase of the height, so that the throughput is reduced, and the propulsion energy of the unmanned aerial vehicle is not changed excessively, so that the overall energy efficiency is reduced. It is also known that the overall energy efficiency is the lowest at the maximum throughput goal, since the drone needs to stay around the node for a longer time in order to achieve the maximum throughput, which also results in the drone constantly changing its own flight speed during the flight, which results in a dramatic increase in propulsion consumption and thus the lowest overall energy efficiency. While the speed of flight of the drone is not substantially changed with the aim of minimizing energy consumption, this results in throughput being affected, resulting in overall energy efficiency lower than that of the present invention.
To further embody the energy efficiency superiority of the present invention, an unmanned aerial vehicle orthogonal multiple access (UAV-OMA) scheme was introduced for comparison. To illustrate the improvement in energy efficiency of the system by trajectory optimization, the results of not optimizing the trajectory are taken into comparison, and it can be seen in fig. 5 that trajectory optimization is apparent for the improvement in overall energy efficiency. Meanwhile, compared with the UAV-OMA scheme, the invention has higher energy efficiency and higher application value.
The examples described above represent only embodiments of the invention and are not to be understood as limiting the scope of the patent of the invention, it being pointed out that several variants and modifications may be made by those skilled in the art without departing from the concept of the invention, which fall within the scope of protection of the invention.
Claims (5)
1. The unmanned aerial vehicle auxiliary communication method in the non-orthogonal multiple access network is characterized in that the non-orthogonal multiple access network comprises unmanned aerial vehicle and ground Internet of things nodes;
two unmanned aerial vehicles are used as aerial base stations and fly above corresponding ground target areas with fixed heights and flight periods, so as to provide services for ground Internet of things nodes, and the flight periods are decomposed into N time slots;
the ground target area is divided into an inner circle area and an annular area, and the areas of the inner circle area and the annular area are equal; the ground internet of things nodes are uniformly distributed in a ground target area, and the nodes of the inner circle area and the nodes of the circular ring area are paired in pairs to form an I group of non-orthogonal multiple access group;
through grouping the nodes in the inner circle area and the annular area, the user scheduling, the transmitting power and the flight parameters of the unmanned aerial vehicle are jointly optimized, the overall energy efficiency of the unmanned aerial vehicle is maximized, and the method specifically comprises the following steps:
step one: constructing an unmanned aerial vehicle-assisted non-orthogonal multiple access network system model, wherein the unmanned aerial vehicle-assisted non-orthogonal multiple access network system model comprises a network channel model, a non-orthogonal multiple access grouping model and an unmanned aerial vehicle motion model, performing non-orthogonal multiple access grouping on nodes under the condition of the network channel model, determining a dynamic track of the unmanned aerial vehicle according to the unmanned aerial vehicle motion model, and providing communication service for a ground non-orthogonal multiple access group;
step two: the method comprises the steps of designing and optimizing the problem, maximizing the overall energy efficiency of the unmanned aerial vehicle, and meeting the kinematic constraint of the unmanned aerial vehicle, the relative position constraint between the unmanned aerial vehicle and each node, the constraint of the unmanned aerial vehicle flight area and the constraint of the minimum communication rate of each node;
step three: and decomposing the optimization problem into three non-convex sub-problems, converting the non-convex sub-problems into convex problems by using a Lagrange multiplier method and a continuous convex approximation method, and then solving.
2. The method of unmanned aerial vehicle assisted communication in a non-orthogonal multiple access network of claim 1, wherein the network channel model in step one is specifically: the channel gain from the mth unmanned aerial vehicle to the jth ground Internet of things node of the ith group of non-orthogonal multiple access groups is expressed as h m,i,j (N) wherein i=1, 2,..i, n=1, 2,..n, m=1 represents the unmanned aerial vehicle flying above the torus region, m=2 represents the unmanned aerial vehicle flying above the inner torus region, j=1 represents the node in the torus region, j=2 represents the node in the inner torus region;
wherein beta is 0 Represents the channel gain at a unit distance of 1 meter, d m,i,j (n) represents the distance from the mth unmanned aerial vehicle to the jth ground Internet of things node of the ith group of non-orthogonal multiple access groups, q m (n) represents the position of the mth unmanned plane in the nth time slot, q i,j And the position of the j ground Internet of things node of the i group of non-orthogonal multiple access groups is shown.
3. The method for unmanned aerial vehicle assisted communication in a non-orthogonal multiple access network according to claim 1, wherein the non-orthogonal multiple access packet model in the step one is specifically:
dividing a ground target area into four quadrants, calculating the distance between the node in the circular area and the node in the inner circular area in each quadrant, storing the distance information into a matrix d, wherein the a-th row and the b-th column of the matrix d represent the distance between the a-th node of the inner circular area and the b-th node of the circular area along the anticlockwise direction;
[d ord ,L]＝sort(d) (2)
wherein, the sort (& gt) is to arrange each column of elements in the matrix in a descending order, and store the ordering result into d ord And will be ordered d ord The inner circle node labels corresponding to the elements are stored in the corresponding positions in L;
traversing from the first row of the matrix L, pairing the ith node of the circular ring area along the anticlockwise direction with the L (1, i) th node of the inner ring area when the inner ring node marks in all columns of the first row do not appear repeatedly;
when repeated inner circle node marks appear, recording positions of unrepeated inner circle node marks, and pairing a w node of the circular ring area with an L (1, w) node of the inner circle area along the anticlockwise direction, wherein w represents a column without repeated marks; recording the positions of the repeated internal circle node marks, and comparing d ord The corresponding position distance value is selected, wherein the corresponding distance value is the largestRecording the column number e of the element, and pairing the e-th node of the circular ring area along the anticlockwise direction with the first node of the inner circular area; after pairing is completed, removing elements in the w column and the e column in L, continuing to traverse from the next row of L, and following the rule of the first traversal process until all node pairing is completed.
4. The method for unmanned aerial vehicle assisted communication in a non-orthogonal multiple access network according to claim 1, wherein the unmanned aerial vehicle motion model in the first step specifically comprises three parts, namely, a kinematic constraint of unmanned aerial vehicle flight, a constraint of unmanned aerial vehicle flight area and unmanned aerial vehicle energy consumption; the position coordinates and speed of each time slot of the unmanned aerial vehicle satisfy the following kinematic constraints:
wherein q m (n) represents the position coordinates of the mth time slot and the mth unmanned plane, a m (n) represents acceleration of the mth time slot mth unmanned aerial vehicle, V m (n) represents the flight speed, delta of the mth unmanned aerial vehicle in the nth time slot t Indicating the length of each time slot;
two unmanned aerial vehicles fly above the inner circle and the circular ring area respectively, and the flight range of the unmanned aerial vehicle is constrained as follows
r in ≤d m,z (n)≤r out ,m＝1 (5)
d m,z (n)≤r in ,m＝2 (6)
Wherein d m,z (n) represents the distance of the mth unmanned aerial vehicle from the region central axis z-axis in the case of height H; r is (r) in Represents the radius of the inner circle; r is (r) out Representing the radius of the entire ground target area;
the initial position and the end position of one period of the unmanned aerial vehicle are the same, and collision between the unmanned aerial vehicles is avoided;
wherein d min Representing a minimum safe distance for maintaining safe flight of the unmanned aerial vehicle;
the unmanned aerial vehicle energy consumption comprises two parts, namely, unmanned aerial vehicle communication power consumption and unmanned aerial vehicle propulsion power consumption; in the nth time slot, the communication power paid by the mth unmanned aerial vehicle for the jth node in the ith non-orthogonal multiple access group is P m,i,j (n); in the case of unit bandwidth, the communication rate of each non-orthogonal multiple access group is
Wherein sigma 2 Representing channel noise power;
the total throughput of the mth unmanned plane in one flight period and the total throughput of the system are respectively
R total ＝R 1 +R 2 (12)
Wherein u is m,i (n) is a binary discrete variable representing the user schedule, which is equal to 0, instead ofThe mth unmanned aerial vehicle does not communicate with the ith non-orthogonal multiple access group at the moment, and when the mth unmanned aerial vehicle is equal to 1, the mth unmanned aerial vehicle is communicated with the ith non-orthogonal multiple access group at the moment;
the propulsion power of the unmanned plane in one time slot is
Wherein c 1 And c 2 Is two constant coefficients, g represents the gravitational acceleration, V m (n) represents the flight speed of the mth unmanned plane in the nth time slot, a m (n) represents the acceleration of the mth unmanned aerial vehicle in the nth time slot;
the energy consumption of the mth unmanned plane in one flight cycle and the total energy consumption of the system are respectively
E total ＝E 1 +E 2 (15)。
5. The method of unmanned aerial vehicle assisted communication in a non-orthogonal multiple access network of claim 1, wherein the optimization problem is
||a m (n)||≤a max (15j)
const.(3),(4),(5),(6),(7),(8) (15l)
Wherein, (15 b) and (15 c) indicate that a non-orthogonal multiple access group is defined for each time slot to communicate with a drone; (15e) And (15 f) indicating that the unmanned aerial vehicle has different node distances from the inside circle to the inside circle and from the inside circle area; p (P) max Representing the maximum transmission power available for transmission by the unmanned aerial vehicle, V min And V max Representing minimum and maximum flight speeds of an unmanned aerial vehicle, a max Represents the maximum flying acceleration of the unmanned plane, R min Minimum communication rate gate representing each nodeAnd (5) limiting.
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