CN116390124A - Resource optimization method for honeycomb-removing large-scale MIMO system based on unmanned aerial vehicle assistance - Google Patents

Resource optimization method for honeycomb-removing large-scale MIMO system based on unmanned aerial vehicle assistance Download PDF

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CN116390124A
CN116390124A CN202310340504.7A CN202310340504A CN116390124A CN 116390124 A CN116390124 A CN 116390124A CN 202310340504 A CN202310340504 A CN 202310340504A CN 116390124 A CN116390124 A CN 116390124A
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赵海涛
刘颖
王琴
倪艺洋
夏文超
孙金龙
刘淼
刘鹏飞
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Nanjing University of Posts and Telecommunications
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    • H—ELECTRICITY
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    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
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    • H04W—WIRELESS COMMUNICATION NETWORKS
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Abstract

The invention discloses a resource optimization method for a large-scale MIMO system for removing honeycomb based on unmanned aerial vehicle assistance. In a large-scale MIMO system scene of the auxiliary honeycomb removal of the unmanned aerial vehicle, the unmanned aerial vehicle is used as an air access point and a ground AP point to jointly provide services for ground users, and the method specifically designs a user scheduling rule based on the air-ground AP joint services; according to the scheduling rule, designing a communication model based on the space-ground joint service; and designing a fair resource allocation scheme, wherein the fair resource allocation scheme comprises a user scheduling scheme, a unmanned plane position deployment scheme and a user power allocation scheme, the minimum downlink rate of the users of the communication model is maximized, and finally the feasibility of the proposed utility model is verified through optimization solving methods such as block coordinate reduction and continuous convex optimization technology. According to the invention, the optimal resource allocation strategy including unmanned aerial vehicles can be provided according to the current user and ground resource distribution condition, and blind spot-free coverage of ground user service is realized.

Description

Resource optimization method for honeycomb-removing large-scale MIMO system based on unmanned aerial vehicle assistance
Technical Field
The invention belongs to the field of communication resource management, and particularly relates to a resource optimization method for a large-scale MIMO system for removing honeycomb based on unmanned aerial vehicle assistance.
Background
De-cellular massive MIMO (massive multiple input multiple output) is expected to outperform 5G (B5G) wireless technology because it provides higher spectral efficiency, higher energy efficiency, and better spatial diversity. However, the large number of long cables (front end transport requirements) between each Access Point (AP) and the Central Processing Unit (CPU) has hampered its practical application. In recent years, UAVs have been considered as air communication aids for rapid service restoration after natural disasters cause partial or complete infrastructure damage and in areas of communication congestion, by virtue of their absolute advantages in terms of rapid deployment, controllable maneuverability, low cost, and high probability of air-to-ground link line of sight. Therefore, the unmanned aerial vehicle can effectively improve challenges brought by a large number of long cables in a cellular large-scale MIMO system by being added as an air AP. Although UAVs can break through the limitations of traditional de-cellular massive MIMO, there are still some remaining problems to be solved.
One of the most critical issues with the joint UAV's de-cellular massive MIMO system is resource allocation. First, the ground AP is already able to provide high quality service to the neighboring users, and if UAV misscheduling causes communication handover for the neighboring users, the system burden will be increased and the quality of service for the users will be reduced. In addition, the performance and runtime of the UAV system is fundamentally limited by the limited energy on board, which can result in significant maneuver energy consumption of the UAV and reduced communication benefits if no better docking points are designed for the UAV. In addition, when radio resources are insufficient, deployment of UAVs in coverage of multiple furthest users cannot meet quality of service requirements (QoE) of certain users, it is easy to design multiple drones to meet users' QoE, but the overall system may incur significant resource consumption. Finally, the QoE requirements of users are diverse and randomly distributed, improving user experience and achieving fair performance among users, and making reasonable scheduling and allocation of resources is challenging for the CPU.
Disclosure of Invention
The invention aims to: the invention aims to provide a resource optimization method for a large-scale MIMO system for removing honeycomb based on unmanned aerial vehicle assistance, which meets the system equipment limit and QoE requirements of all users, namely, a CPU (Central processing Unit) can realize optimal deployment of a UAV (unmanned aerial vehicle) and user scheduling according to user requests, and meanwhile, the resource consumption of the system is considered, and the resources are reasonably distributed.
The technical scheme is as follows: according to the resource optimization method for the large-scale MIMO system for removing the honeycomb based on the unmanned aerial vehicle, the unmanned aerial vehicle is deployed in a target area to serve as an air AP, a joint service communication model is constructed, and the configuration of communication resources is optimized based on the model;
step 1: deploying unmanned aerial vehicle as air AP to provide service for users of signal coverage blind area in target area, designing user scheduling scheme of joint service of ground AP and air AP,
step 2: constructing a joint service communication model based on a user scheduling scheme of joint service of a ground AP and an air AP;
step 3: designing a resource allocation scheme comprising user scheduling, unmanned plane position deployment and user power allocation, and maximizing the minimum downlink rate of the users of the joint service communication model;
step 4: and verifying the feasibility of the joint service communication model through an optimization solving method.
Further, the step 1 specifically includes:
the AP selection method is to connect a plurality of APs in a service range at a time for each user by using a binary variable { χ } mk 、χ uk The scheduling conditions of the ground AP and the air AP of the user are respectively represented, and a user scheduling matrix is defined
Figure BDA0004157997900000021
Binary variable χ if user K is served by a ground AP point mk The coefficient is 1, otherwise 0, binary variable χ if the user is served by an over-the-air AP point uk =1, otherwise 0; according to the limitation of resource isomerism and unmanned aerial vehicle deployment, consider realizing the collaborative access rule of air and ground AP point: during each resource allocation, each user communicates with a single service point in the air or on the ground; the ground AP provides services for all users in the target area; the air AP provides service for users in the wireless coverage range of the target area, and the service is expressed as a binary constraint condition:
Figure BDA0004157997900000022
Figure BDA0004157997900000023
Figure BDA0004157997900000024
wherein the user and the set of ground APs are each denoted K, M,
Figure BDA0004157997900000025
represents the horizontal coordinates, w, of the kth user a Represents the horizontal coordinates of the ground AP, < >>
Figure BDA0004157997900000026
Represents the horizontal position of the unmanned plane u, R a 、R u Representing service ranges of ground AP and air AP respectively, R a Affected by the length of the cable, R u Is determined by the flying height electric quantity of the unmanned aerial vehicle.
Further, the step 2 specifically includes:
step 2-1, designing a channel model combining the effects of small-scale fading and large-scale fading, and setting g mk E ∈E ∈k represents the channel gain between the mth AP and the kth user, and the channel transmission model is
Figure BDA0004157997900000031
Wherein beta is mk 、h mk Representing the large-scale fading coefficient and the small-scale fading factor between the mth AP and the kth user, respectively, wherein the small-scale fading is rayleigh fading, i.e., h mk Large scale fading coefficient beta of ground AP point (CN (0, 1)) mk Modeling as the product of path loss and shadowing fading, i.e. beta mk ＝pl mk ·s mk Wherein s is mk Representing log normal shadows, pl mk Expressed in terms of three-stage path loss:
Figure BDA0004157997900000032
where L is a constant that depends on the carrier frequency, the user and the AP height, d mk Is the horizontal distance between AP m and user k, d 0 、d 1 Is the reference value distance;
step 2-2, for the communication link between the over-the-air AP point and the user, is expressed as:
Figure BDA0004157997900000033
wherein a is 1 、a 2 Path loss indices for line of sight LoS and non-line of sight NLoS respectively,
Figure BDA0004157997900000034
probability of LoS link and NLoS link, respectively,/-for each link>
Figure BDA0004157997900000035
The Euclidean distance between the ith unmanned aerial vehicle and the kth user is the fixed flying height of the unmanned aerial vehicle; when designing a communication system based on an air AP point, considering randomness of LoS and NLoS links, the LoS transmission probability between a u-th air AP and a k-th user is as follows:
Figure BDA0004157997900000036
where a, b are environmental parameters, U is the set of over-the-air APs,
Figure BDA0004157997900000037
is the elevation angle between the kth user and the (u) th aerial AP, and the LoS probability model is increased along with the increase of the AP height and the elevation angle; the average large-scale fading between the kth user and the ith over-the-air AP is denoted +.>
Figure BDA0004157997900000038
Deriving the channel model in large scale with E { g } uk }＝β uk ；
Step 2-3, in the situation that the channel conditions are known, the downlink adopts conjugate beam forming to transmit signals to the user; combining user k received signals under user scheduling as
Figure BDA0004157997900000039
Wherein x is ik Signals sent by the AP are represented; if the user is served by a terrestrial AP, then there are:
Figure BDA0004157997900000041
wherein p is mk Parameters s are allocated for de-cellular massive MIMO downlink ground AP power k Satisfying e { |s for the signal transmitted to the user k | 2 }＝1，ω k Is Additive White Gaussian Noise (AWGN);
in the scenario that the air AP only provides service for users in a fixed range, when the air AP hovers at a fixed position, signals received by the users in a communication range are as follows:
Figure BDA0004157997900000042
wherein ρ is uk Allocating parameters for power;
the entire received signal of the user is represented as
Figure BDA0004157997900000043
Further, the step 3 specifically includes:
step 3-1, a user scheduling scheme based on joint service of a ground AP and an air AP, wherein in the situations of a ground AP shared channel and an air AP shared channel, a ground user is served by one AP at a time, and user scheduling constraint meets the requirement
Figure BDA0004157997900000044
Step 3-2, the system resource is limited, and the transmitting power of the AP cannot exceed the maximum transmitting power limit of the AP, and the constraint is as follows:
Figure BDA0004157997900000045
wherein P is max 、ρ max Maximum transmitting power of the AP and the unmanned plane respectively;
step 3-3, maximizing the minimum downlink communication rate of the user under the joint service communication model, and constructing a resource allocation optimization problem of a traditional cellular MIMO system based on unmanned aerial vehicle assistance by combining user scheduling constraint, unmanned aerial vehicle deployment constraint and power allocation constraint, so as to realize blind spot-free coverage of the user in the system, wherein the optimization problem is as follows:
Figure BDA0004157997900000051
Figure BDA0004157997900000052
wherein (1) (2) in equation (8) is a constraint on user scheduling, constraint (3) is a constraint on the deployment location of the unmanned aerial vehicle, (4) (5) is a constraint on power allocation, S af Is the maximum safe distance between a plurality of unmanned aerial vehicles.
Further, the step 4 specifically includes:
step 4-1, the optimization problem (8) is a mixed integer non-convex problem, and the non-convex problem is converted into a convex problem to be solved in an iteration way by adopting a block coordinate descent and continuous convex approximation technology;
step 4-2, firstly, adjusting binary variables of user scheduling to be continuous variables of 0-1, and decomposing a new non-convex problem into three sub-problems of user scheduling (χ), air AP deployment (q) and power distribution (P, P)) by adopting a block coordinate descent method according to optimization variables χ, q and P (P, P) contained in the problem; optimizing the air AP deployment for the given user scheduling and power allocation by optimizing the user scheduling based on a continuous convex optimization technology, and optimizing the complex power allocation for the given user scheduling and air AP deployment by combining variable fast decomposition;
step 4-3, recording the maximized user minimum rate problem as a function
Figure BDA0004157997900000053
Given the unmanned aerial vehicle deployment location and power allocation, the user scheduling convex optimization problem is:
Figure BDA0004157997900000061
Figure BDA0004157997900000062
wherein,,
Figure BDA0004157997900000063
is->
Figure BDA0004157997900000064
The first-order taylor expansion upper bound of the expansion interference term, λ, is approximated by a more tractable function at a given local point in each iteration r For the target result in the r-th iteration, χ r Defined as the user result obtained in the r-th iteration, then
Figure BDA0004157997900000065
Given q, p, ρ, the problem is a convex problem, solved by a solver;
step 4-4, for a given χ, p, ρ and any given local point q r The overhead AP deployment convex optimization problem is:
Figure BDA0004157997900000066
Figure BDA0004157997900000067
wherein,,
Figure BDA0004157997900000068
Figure BDA0004157997900000069
is R u Is a scaled value of +.>
Figure BDA00041579979000000610
Figure BDA00041579979000000611
Respectively->
Figure BDA00041579979000000612
First order coefficients and constants of +.>
Figure BDA00041579979000000613
The method comprises the steps of obtaining an aerial AP deployment result in the r-th iteration;
step 4-5 introducing a variable block p representing the transmission power at AP point m m M=1..m, p -m Representing a set of variable blocks other than m variable blocks; for a given χ, q, the power allocation convex optimization problem is:
Figure BDA0004157997900000071
Figure BDA0004157997900000072
wherein,,
Figure BDA0004157997900000073
respectively->
Figure BDA0004157997900000074
First order taylor expansion upper bound;
step 4-6, if the feasible set of each sub-problem is a subset of the feasible set
λ(χ r ,q r ，{p r ，ρ r })<λ(χ r+1 ,q r+1 ，{p r+1 ，ρ r+1 })
The solution process of the problem is therefore convergent. Therefore, the resource optimization method for the large-scale MIMO system for removing the honeycomb based on the unmanned aerial vehicle assistance, provided by the invention, realizes the improvement of the system coverage performance and the user communication performance through schemes such as user scheduling, unmanned aerial vehicle deployment, power distribution and the like.
The beneficial effects are that: compared with the prior art, the invention has the following remarkable advantages:
1. according to the method, an air AP point UAV is added in the traditional honeycomb-removed large-scale MIMO, so that the wireless coverage performance of the system is improved;
2. the method can reasonably arrange the deployment of the UAV, and prevent random switching of user connection and waste of system resources;
3. the method designs a fair resource allocation scheme, including a user scheduling scheme, a unmanned plane position deployment scheme and a user power allocation scheme, and the schemes are solved in an iterative manner, so that the rationality and stability of the system can be ensured.
Drawings
Fig. 1 is a flowchart of a method for resource optimization of a large-scale MIMO system based on unmanned aerial vehicle assisted cellular removal.
Fig. 2 is a system model diagram of a resource optimization method of a large-scale MIMO system for cell removal based on unmanned aerial vehicle assistance.
Detailed Description
The technical scheme of the invention is further described below with reference to the accompanying drawings.
A resource optimization method for a large-scale MIMO system for removing honeycomb based on unmanned aerial vehicle assistance, as shown in figure 1, comprises the following steps:
because each AP on the ground and the CPU are deployed by using a long cable, the long-distance user cannot access the AP due to the limitation of the cable length, and the UAV easy to deploy can serve as an aerial AP for the user in a coverage blind area, but the addition of the UAV increases the system resource burden to a certain extent, and in addition, if the UAV is improperly deployed, other users on the ground can be interfered, and the overall stability of the system is affected. Therefore, in the following part, a user scheduling scheme and a joint communication model are designed from the standpoint of efficient and fair resource allocation, so that the edge users can normally connect communication.
Step 101: user scheduling rules based on joint services of ground APs and air APs are designed.
Step 101-1. Suppose that the AP selection method connects multiple APs within service range for each user at a time, using binary variable { χ } mk 、χ uk Respectively representing the scheduling conditions of users, defining a user scheduling matrix
Figure BDA0004157997900000081
Binary variable χ if user K is served by a ground AP point mk The coefficient is 1, otherwise 0, i.e
Figure BDA0004157997900000082
Binary variable χ if a user is served by an over-the-air AP point uk =1, otherwise 0, i.e
Figure BDA0004157997900000083
Step 101-2, to realize the synergistic effect of the air and ground AP points, further consider the limitation of resource isomerism and unmanned aerial vehicle deployment, consider the following access rule: during each resource allocation, 1) each user can only communicate with a single service point in the air or on the ground at a time; 2) The ground AP can provide service for all users in a limited range; 3) The over-the-air AP serves users within its wireless coverage, these principles are expressed in terms of binary constraints:
Figure BDA0004157997900000084
Figure BDA0004157997900000085
Figure BDA0004157997900000086
wherein the user and the ground AP are respectively collected by K,M represents a group represented by the formula,
Figure BDA0004157997900000087
represents the horizontal coordinates, w, of the kth user a Represents the horizontal coordinates of the ground AP, < >>
Figure BDA0004157997900000088
Represents the horizontal position of the unmanned plane u, R a 、R u Representing service ranges of ground AP and air AP respectively, R a Affected by the length of the cable, R u Determined by the flying height electric quantity of the unmanned aerial vehicle and the like.
Step 102: ground communications large scale fading coefficients are mainly affected by path loss and shadow fading, and UAV and ground user communications are typically line of sight propagation, large scale fading and UAV height and angle to ground user. According to the above-described user scheduling rule, the user equipment receives only one service at a time, and thus, a communication model of a joint service influenced by the user scheduling rule is designed as follows.
Step 102-1. The channel model combines the effects of small-scale fading and large-scale fading, let g be mk E ∈E ∈k represents the channel gain between the mth AP and the kth user, and the channel transmission model is
Figure BDA0004157997900000091
Wherein beta is mk 、h mk Representing the large-scale fading coefficient and the small-scale fading factor between the mth AP and the kth user, respectively, wherein the small-scale fading is rayleigh fading, i.e., h mk Large scale fading coefficient beta of ground AP point (CN (0, 1)) mk Modeling as the product of path loss and shadowing fading, i.e. beta mk ＝pl mk ·s mk Wherein s is mk Representing log normal shadows, pl mk Expressed in terms of three-stage path loss:
Figure BDA0004157997900000092
where L is a function of carrier frequency, user and AP heightConstant d of (d) mk Is the horizontal distance between AP m and user k, d 0 、d 1 Is the reference value distance; .
Step 102-2 channel gain may be obtained in a similar manner for the communication link between the over-the-air AP point and the user, i.e.
Figure BDA0004157997900000093
Wherein a is 1 、a 2 Path loss indices for line of sight (LoS) and non-line of sight (NLoS) respectively,
Figure BDA0004157997900000094
probability of LoS link and NLoS link, respectively,/-for each link>
Figure BDA0004157997900000095
And H is the fixed flying height of the unmanned aerial vehicle, and is the Euclidean distance between the u unmanned aerial vehicle and the kth user. In designing an over-the-air AP point-based communication system, randomness of LoS and NLoS links should be considered, and LoS transmission probability between a ith over-the-air AP and a kth user is as follows
Figure BDA0004157997900000101
Wherein a, b are environmental parameters,
Figure BDA0004157997900000102
is the elevation angle between the kth user and the ith airborne AP, so the LoS probability model will become larger as the drone height and elevation angle increase. Further, the average large-scale fading between the kth user and the ith over-the-air AP can be expressed as +.>
Figure BDA0004157997900000103
Since the time scale of over-the-air AP deployment is much larger than the channel coherence time, leakage interference, network metrics and practical constraints should be large-scaleForm derived, i.e. E { g } uk }＝β uk 。
Step 102-3. Assuming that the channel conditions are known, the downlink transmits signals to the user using conjugate beam forming. Thus, the received signal is received in connection with user k under user scheduling as
Figure BDA0004157997900000104
Wherein x is ik Representing the signal sent by the AP. Specifically, if the user is served by a terrestrial AP, then
Figure BDA0004157997900000105
Wherein p is mk Parameters s are allocated for de-cellular massive MIMO downlink ground AP power k Satisfying e { |s for the signal transmitted to the user k | 2 }＝1，ω k Is Additive White Gaussian Noise (AWGN).
It is assumed that the over-the-air AP provides traffic services only to users within the communication coverage area without interfering with other user signals. When the over-the-air AP hovers at a fixed location, the signals received by users within communication range are
Figure BDA0004157997900000106
Wherein ρ is uk Parameters are allocated for power.
The entire received signal of the user is represented as
Figure BDA0004157997900000107
Step 103: the scheduling of the air AP mainly provides service for remote users, and the deployment of the unmanned aerial vehicle is required among a plurality of remote users, and meanwhile, service resources which cannot be preempted by the close-range users are considered. In addition, the unmanned aerial vehicles should keep a safe distance, so that collision is avoided. In this case, it is necessary to design a fair resource allocation scheme, maximizing user coverage with limited resources.
Step 103-1. Assuming that the channel is shared between ground APs, the channel is shared between air APs, and there is no interference between them, the ground user is served by only one AP at a time, and therefore, the user schedule should satisfy
Figure BDA0004157997900000111
Step 103-2. Deployment of airborne APs is limited to a certain extent by geographical environment, and secure physical distance q is maintained between multiple airborne APs j -q u ||≤S af U+.j. The resources of the system are limited and,
the transmit power of the AP cannot exceed its own maximum transmit power limit, satisfying the following constraints:
Figure BDA0004157997900000112
wherein P is max 、ρ max Maximum transmitting power of the AP and the unmanned plane respectively;
step 103-3, maximizing the downlink communication rate of the user under the communication model, and constructing the resource allocation optimization problem of the traditional cellular MIMO system based on unmanned aerial vehicle assistance by combining the user scheduling constraint, unmanned aerial vehicle deployment constraint and power allocation constraint, so as to realize blind spot-free coverage of the user in the system, wherein the optimization problem is as follows:
Figure BDA0004157997900000113
Figure BDA0004157997900000114
where (1) (2) is a constraint on user scheduling, constraint (3) is a constraint on the deployment location of the drone, and (4) (5) is a constraint on power allocation.
Step 104: and verifying the feasibility of the proposed unmanned aerial vehicle auxiliary model through an optimization solving method.
Step 104-1. Because the user schedule and associated optimization variables are binary and the unmanned deployment variable q and the transmit power variable are non-convex constraints, the optimization problem is a mixed integer non-convex problem, and the non-convex problem is converted into a convex problem iteration solution by adopting a block coordinate descent and continuous convex approximation technique. Specifically, based on a continuous convex optimization technique, the over-the-air AP deployment is optimized for a given over-the-air AP deployment and power allocation by optimizing the user schedule, and the complex power allocation is optimized for a given user schedule and over-the-air AP deployment in combination with a variable fast decomposition.
Step 104-2, in order to make the problem easier to process, firstly, the binary variable of user scheduling is relaxed to be a continuous variable of 0-1, and a block coordinate descent method is further adopted to decompose the new non-convex problem into three sub-problems of user scheduling, air AP deployment and power distribution.
Step 104-3. Record maximizing user minimum Rate problem as a function
Figure BDA0004157997900000121
Under the given deployment position of the unmanned aerial vehicle and power distribution, the convex optimization problem obtained by the user scheduling optimization process is as follows:
Figure BDA0004157997900000122
Figure BDA0004157997900000123
wherein,,
Figure BDA0004157997900000124
is->
Figure BDA0004157997900000125
The first-order taylor expansion upper bound of expansion interference term, in each iteration, the original function is approximated by a more tractable function at a given local point, χ r Definition of the definitionFor the user result obtained in the r-th iteration, < >>
Figure BDA0004157997900000127
For the target value in the r-th solution, then
Figure BDA0004157997900000126
Given q, p, ρ, the problem is a convex problem, which can be solved by a solver.
Step 104-4. For a given χ, p, ρ and any given local point q r The original over-the-air AP deployment problem is approximated by the following:
Figure BDA0004157997900000131
Figure BDA0004157997900000132
wherein,,
Figure BDA0004157997900000133
Figure BDA0004157997900000134
is R u Is a scaled value of +.>
Figure BDA0004157997900000135
Respectively->
Figure BDA0004157997900000136
First order coefficients and constants of +.>
Figure BDA0004157997900000137
And (5) obtaining an aerial AP deployment result in the r-th iteration.
Step 104-5. The power optimization problem at the ground AP is more complex, and therefore, a transmission is introduced to represent the AP point mVariable block p of power m M=1..m, p -m Representing a set of variable blocks other than m variable blocks. For a given χ, q, the approximate convex problem of the original power optimization problem is:
Figure BDA0004157997900000138
Figure BDA0004157997900000139
wherein,,
Figure BDA00041579979000001310
respectively->
Figure BDA00041579979000001311
The taylor expansion upper bound.
Step 104-5. In the classical block-coordinate descent method, the sub-problem of updating each variable block needs to be solved accurately with optimality in each iteration, and user scheduling, deployment optimization, and transmit power optimization are all lower bounds of the overall constraint function, i.e., the following three formulas satisfy:
λ(χ r ,q r ，{p r ，ρ r })<λ(χ r+1 ,q r ，{p r ，ρ r })
＜λ(χ r+1 ,q r+1 ，{p r ，ρ r })
＜λ(χ r+1 ,q r+1 ，{p r+1 ，ρ r+1 })
therefore, the solving process of the problem is convergent, and the resource optimization method for the large-scale MIMO system for removing the honeycomb based on the unmanned aerial vehicle assistance, provided by the invention, realizes the improvement of the system coverage performance and the user communication performance through schemes such as user scheduling, unmanned aerial vehicle deployment, power distribution and the like.

Claims (5)

1. A resource optimization method for a honeycomb-removing large-scale MIMO system based on unmanned aerial vehicle assistance is characterized in that unmanned aerial vehicles are deployed in a target area to serve as an air AP, a joint service communication model is constructed, and the configuration of communication resources is optimized based on the model;
step 1: deploying an unmanned aerial vehicle in a target area as an air AP to provide service for users in a signal coverage blind area, and designing a user scheduling scheme of ground AP and air AP combined service;
step 2: constructing a joint service communication model based on a user scheduling scheme of joint service of a ground AP and an air AP;
step 3: designing a resource allocation scheme comprising user scheduling, unmanned plane position deployment and user power allocation, and maximizing the minimum downlink rate of the users of the joint service communication model;
step 4: and verifying the feasibility of the joint service communication model through an optimization solving method.
2. The method for optimizing resources of a large-scale MIMO system for cellular removal based on unmanned aerial vehicle assistance according to claim 1, wherein the step 1 specifically comprises:
the AP selection method is that a user connects a plurality of APs in a service range each time by using a binary variable { χ } mk 、χ uk The scheduling conditions of the ground AP and the air AP of the user k are respectively represented, and a user scheduling matrix χ E is defined K×(U+1) Binary variable χ if user K is served by a ground AP point mk The coefficient is 1, otherwise 0, binary variable χ if the user is served by an over-the-air AP point uk =1, otherwise 0; according to the limitation of resource isomerism and unmanned aerial vehicle deployment, consider realizing the collaborative access rule of air and ground AP point: during each resource allocation, each user communicates with a single service point in the air or on the ground; the ground AP provides services for all users in the target area; the air AP provides service for users in the wireless coverage range of the target area, and the service is expressed as a binary constraint condition:
Figure FDA0004157997890000011
Figure FDA0004157997890000012
Figure FDA0004157997890000013
wherein,,
Figure FDA0004157997890000014
represents the horizontal coordinates, w, of the kth user a Representing the horizontal coordinates of the ground AP,
Figure FDA0004157997890000015
represents the horizontal position of the unmanned plane u, R a 、R u Representing service ranges of ground AP and air AP respectively, R a Affected by the length of the cable, R u Is determined by the flying height electric quantity of the unmanned aerial vehicle.
3. The method for optimizing resources of a large-scale MIMO system for cellular removal based on unmanned aerial vehicle assistance according to claim 1, wherein the step 2 specifically comprises:
step 2-1, designing a channel model combining the effects of small-scale fading and large-scale fading, and setting g mk E ∈E ∈k represents the channel gain between the mth AP and the kth user, and the channel transmission model is
Figure FDA0004157997890000021
Wherein beta is mk 、h mk Representing the large-scale fading coefficient and the small-scale fading factor between the mth AP and the kth user, respectively, wherein the small-scale fading is rayleigh fading, i.e., h mk Large scale fading coefficient beta of ground AP point (CN (0, 1)) mk Modeling as the product of path loss and shadowing fading, i.e. beta mk ＝pl mk ·s mk Wherein s is mk Representing log normal shadows, pl mk Expressed in terms of three-stage path loss:
Figure FDA0004157997890000022
where L is a constant that depends on the carrier frequency, the user and the AP height, d mk Is the horizontal distance between APm and user k, d 0 、d 1 Is the reference value distance;
step 2-2, for the communication link between the over-the-air AP point and the user, is expressed as:
Figure FDA0004157997890000023
wherein a is 1 、a 2 Path loss indices for line of sight LoS and non-line of sight NLoS respectively,
Figure FDA0004157997890000024
probability of LoS link and NLoS link, respectively,/-for each link>
Figure FDA0004157997890000025
The Euclidean distance between the ith unmanned aerial vehicle and the kth user is the fixed flying height of the unmanned aerial vehicle; when designing a communication system based on an air AP point, considering randomness of LoS and NLoS links, the LoS transmission probability between a u-th air AP and a k-th user is as follows:
Figure FDA0004157997890000026
where a, b are environmental parameters, U is the set of over-the-air APs,
Figure FDA0004157997890000027
is the elevation angle between the kth user and the ith air AP, and the LoS probability model will followBecome larger with increasing AP height and elevation; the average large-scale fading between the kth user and the ith over-the-air AP is denoted +.>
Figure FDA0004157997890000028
Deriving the channel model in large scale with E { g } uk }＝β uk ；
Step 2-3, in the situation that the channel conditions are known, the downlink adopts conjugate beam forming to transmit signals to the user; combining user k received signals under user scheduling as
Figure FDA0004157997890000031
Wherein x is ik Signals sent by the AP are represented; if the user is served by a terrestrial AP, then there are:
Figure FDA0004157997890000032
wherein p is mk Parameters s are allocated for de-cellular massive MIMO downlink ground AP power k Satisfying e { |s for the signal transmitted to the user k | 2 }＝1，ω k Is additive white gaussian noise;
in the scenario that the air AP only provides service for users in a fixed range, when the air AP hovers in a fixed position, signals received by users in a communication range are:
Figure FDA0004157997890000033
wherein ρ is uk Allocating parameters for power;
the entire received signal of the user is represented as
Figure FDA0004157997890000034
4. The method for optimizing resources of a large-scale MIMO system for cellular removal based on unmanned aerial vehicle assistance according to claim 1, wherein the step 3 specifically comprises:
step 3-1, a user scheduling scheme based on joint service of a ground AP and an air AP, wherein in the situations of a ground AP shared channel and an air AP shared channel, a ground user is served by one AP each time, and user scheduling constraint meets the requirement
Figure FDA0004157997890000035
Step 3-2, restraining the transmitting power of the AP as follows:
Figure FDA0004157997890000036
wherein P is max 、ρ max Maximum transmitting power of the AP and the unmanned plane respectively;
step 3-3, maximizing the minimum downlink communication rate of the user under the joint service communication model, and constructing a resource allocation optimization problem of a traditional cellular MIMO system based on unmanned aerial vehicle assistance by combining user scheduling constraint, unmanned aerial vehicle deployment constraint and power allocation constraint, so as to realize blind spot-free coverage of the user in the system, wherein the optimization problem is as follows:
Figure FDA0004157997890000041
Figure FDA0004157997890000042
χ mk 、χ uk ∈(0，1} (2)
Figure FDA0004157997890000043
Figure FDA0004157997890000044
wherein (1) (2) in equation (8) is a constraint on user scheduling, constraint (3) is a constraint on the deployment location of the unmanned aerial vehicle, (4) (5) is a constraint on power allocation, S af Is the maximum safe distance between a plurality of unmanned aerial vehicles.
5. The method for optimizing resources of a large-scale MIMO system for cellular removal based on unmanned aerial vehicle assistance according to claim 4, wherein the step 4 specifically comprises:
step 4-1, the optimization problem (8) is a mixed integer non-convex problem, and the non-convex problem is converted into a convex problem to be solved in an iteration way by adopting a block coordinate descent and continuous convex approximation technology;
step 4-2, firstly, adjusting binary variables of user scheduling to be continuous variables of 0-1, and decomposing a new non-convex problem into three sub-problems of user scheduling (χ), air AP deployment (q) and power distribution P (P, P) by adopting a block coordinate descent method according to optimization variables χ, q and P (P, P) contained in the problem; optimizing the air AP deployment for the given user scheduling and power allocation by optimizing the user scheduling based on a continuous convex optimization technology, and optimizing the complex power allocation for the given user scheduling and air AP deployment by combining variable fast decomposition;
step 4-3, recording the maximized user minimum rate problem as a function
Figure FDA0004157997890000045
Given the unmanned aerial vehicle deployment location and power allocation, the user scheduling convex optimization problem is:
Figure FDA0004157997890000051
Figure FDA0004157997890000052
Figure FDA0004157997890000053
Figure FDA0004157997890000054
Figure FDA0004157997890000055
wherein,,
Figure FDA0004157997890000056
is->
Figure FDA0004157997890000057
The first-order taylor expansion upper bound of the expansion interference term, λ, is approximated by a more tractable function at a given local point in each iteration r For the target result in the r-th iteration, χ r Defined as the user result obtained in the r-th iteration, then
Figure FDA0004157997890000058
Given q, p, ρ, the problem is a convex problem, solved by a solver;
step 4-4, for a given χ, p, ρ and any given local point q r The overhead AP deployment convex optimization problem is:
Figure FDA0004157997890000059
Figure FDA00041579978900000510
wherein,,
Figure FDA00041579978900000511
Figure FDA00041579978900000512
is R u Is a scaled value of +.>
Figure FDA00041579978900000513
Figure FDA00041579978900000514
Respectively->
Figure FDA00041579978900000515
First order coefficients and constants of +.>
Figure FDA00041579978900000516
The method comprises the steps of obtaining an aerial AP deployment result in the r-th iteration;
step 4-5 introducing a variable block p representing the transmission power at AP point m m M=1..m, p -m Representing a set of variable blocks other than m variable blocks; for a given χ, q, the power allocation convex optimization problem is:
Figure FDA0004157997890000061
Figure FDA0004157997890000062
Figure FDA0004157997890000063
wherein,,
Figure FDA0004157997890000064
respectively->
Figure FDA0004157997890000065
First order taylor expansion upper bound;
step 4-6, if the feasible set of each sub-problem is a subset of the feasible set
λ(χ r ,q r ，{p r ，ρ r })<λ(χ r+1 ,q r+1 ，{p r+1 ，ρ r+1 })
The solving process is convergent, and the system coverage performance and the user communication performance are improved through user scheduling, unmanned aerial vehicle deployment and a power distribution scheme.
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