CN116056198A - Unmanned aerial vehicle-assisted 6G heterogeneous network green resource allocation method - Google Patents

Unmanned aerial vehicle-assisted 6G heterogeneous network green resource allocation method Download PDF

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CN116056198A
CN116056198A CN202211232122.4A CN202211232122A CN116056198A CN 116056198 A CN116056198 A CN 116056198A CN 202211232122 A CN202211232122 A CN 202211232122A CN 116056198 A CN116056198 A CN 116056198A
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unmanned aerial
aerial vehicle
resource allocation
matching
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秦鹏
武雪
伏阳
付民
和昊婷
王淼
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North China Electric Power University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/06—TPC algorithms
    • H04W52/14—Separate analysis of uplink or downlink
    • 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
    • H04B7/18502—Airborne stations
    • H04B7/18506—Communications with or from aircraft, i.e. aeronautical mobile service
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/30—TPC using constraints in the total amount of available transmission power
    • H04W52/34—TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0453—Resources in frequency domain, e.g. a carrier in FDMA
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • 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
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
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Abstract

The invention discloses an unmanned aerial vehicle-assisted green resource allocation method for a 6G heterogeneous network. The scheme comprises the following steps: firstly, the unmanned aerial vehicle is set to fly according to a preset track, and is used as a relay to connect the airship with a user. Secondly, setting the unmanned aerial vehicle to fly according to a preset track, realizing channel multiplexing by adopting C-NOMA, introducing a propulsion power consumption model and a time slot model of the unmanned aerial vehicle, converting channel selection into a matching problem by designing some reasonable limits, and optimizing the transmitting power based on the obtained channel selection. And finally, adopting a matching and Lagrangian dual decomposition method to solve the problem of resource allocation. The system improves the energy efficiency and the frequency spectrum efficiency of the system, and effectively improves the low-carbon green performance of the system.

Description

Unmanned aerial vehicle-assisted 6G heterogeneous network green resource allocation method
Technical Field
The invention relates to the field of 6G heterogeneous networks, in particular to an unmanned aerial vehicle-assisted green resource allocation method for a 6G heterogeneous network.
Technical Field
Although fifth generation (5G) mobile communications can meet the demands of internet of things applications in hot spots, there is little 5G coverage in some remote areas, and the ground network has poor flexibility in resource deployment and weak emergency capability, so that more economic and efficient network coverage is still urgently needed in remote areas. The sixth generation (6G) mobile communication technology can realize seamless coverage of a network and ubiquitous access of mass Internet of things equipment by constructing an air-ground integrated network based on a non-ground network. Unmanned aerial vehicles are of great interest compared to space-based satellite communications with their lower cost, lower path loss, higher line-of-sight connectivity and dynamic deployment characteristics. Meanwhile, the airship has the advantages of low cost, large capacity and the like, and can realize wide-area coverage. Therefore, unmanned aerial vehicles are used as relays, airships are used as backhaul base stations, and the unmanned aerial vehicles are an important method for solving the problem of insufficient communication coverage in remote areas, disaster areas and hot spot areas.
The matching game theory is used as a mathematical tool and is derived from the research of the mating problems of men and women and the admission problems of universities. Each male can only be matched with one female at most in the mating problem, and each female can only be matched with one male at most, and the mating problem is expressed as one-to-one bilateral matching; multiple students can be recorded in each school in university recording, and each student can only enter one school, which is shown as one-to-many matching. The matched game is based on 2 basic assumptions, namely that the matched party participant sets are mutually disjoint and can not be interchanged; and secondly, matching can be formed by the mutual agreement of the two parties. The user-channel selection problem is abstracted into a many-to-one matching game, and the energy efficiency of the system is maximized while the QoS of the user is ensured.
The invention multiplexes the channels by adopting the C-NOMA technology, considers the thrust power consumption influence of the unmanned aerial vehicle, solves the problems of channel selection and power optimization based on a matching and Lagrange dual decomposition method, effectively reduces the complexity of the system while guaranteeing the service quality, and improves the overall performance of the system.
Disclosure of Invention
In order to solve the problems, the invention discloses an unmanned aerial vehicle-assisted green resource allocation method for a 6G heterogeneous network. The scheme comprises the following steps: firstly, the unmanned aerial vehicle is set to fly according to a preset track, and is used as a relay to connect the airship with a user. And secondly, realizing channel multiplexing by adopting C-NOMA, introducing a propulsion power consumption model and a time slot model of the unmanned aerial vehicle, converting channel selection into a matching problem by designing some reasonable limits, and optimizing the transmitting power based on the obtained channel selection. And finally, adopting a matching and Lagrangian dual decomposition method to solve the problem of resource allocation. The system improves the energy efficiency and the frequency spectrum efficiency of the system, and effectively improves the low-carbon green performance of the system.
The air-ground integrated heterogeneous network model based on the C-NOMA comprises a frame with the height g A Is shared by the airships of the channel with bandwidth W
Figure SMS_3
Unmanned aerial vehicle frame and->
Figure SMS_5
Figure SMS_6
And a ground terminal. The frequency band resources occupied by the unmanned aerial vehicle are uniformly divided into M sections, so that each unmanned aerial vehicle works in different frequency bands, and no interference exists between the unmanned aerial vehicle and the frequency band resources. Dividing the frequency band resources occupied by each unmanned aerial vehicle into +.>
Figure SMS_2
And a bar sub-channel. The drone serves users through the C-NOMA cluster. The flight time slot of the unmanned aerial vehicle comprises->
Figure SMS_4
Figure SMS_7
Each time slot is deltat. The unmanned plane flies periodically within the time T & gt0, returns to the starting point after one week of flying, and is always kept at a fixed height & lt & gt>
Figure SMS_8
Let r m (t)＝(x m (t)，y m (t))，/>
Figure SMS_1
Respectively representing the track, the speed and the acceleration of the mth unmanned aerial vehicle. By S n Representing the position of the nth terminal, whereS n ＝(x n ，y n ,0). The communication between the terminal and the drone, and between the drone and the airship is considered line of sight transmission, and the channel fading is rice fading.
The distance from the unmanned aerial vehicle to the ground intelligent equipment is
Figure SMS_9
Channel power gain between the nth terminal of the first sub-channel and the mth drone
Figure SMS_10
Can be expressed as
Figure SMS_11
The channel gain of the mth drone to the airship may be expressed as:
Figure SMS_12
wherein G is 2 Gain for the directional antenna of the airship.
To trade-off complexity and spectral efficiency, a two-decision clustering algorithm based on channel condition differences and variances is employed herein. The greater the channel gain difference between terminals within a cluster sharing the same channel, the greater the NOMA performance gain. It is assumed that this method is performed with the channel conditions between the drone and the terminal known. Thereby defining the channel power gain difference between the terminals in the first sub-channel of the mth unmanned aerial vehicle as
Figure SMS_13
I m，l Is the number of devices in the first sub-channel of the mth unmanned aerial vehicle.
The sum of the channel power gain differences of L channels of the mth unmanned aerial vehicle is
Figure SMS_14
And selecting a connection scheme of the unmanned aerial vehicle with the maximum s and the terminal as a candidate scheme. If there are multiple schemes to reach s maximum, Δg needs to be calculated m,l (t) variance. The variance Δg should be chosen in consideration of fairness among clusters m,l (t) the smallest scheme is the best connection scheme.
Assume that in the t time slot, the transmission power from the terminal in the nth ground to the mth unmanned aerial vehicle is P n,m,l (t)∈P D →u The transmission power from the mth unmanned aerial vehicle to the airship is P m (t)∈P U→H . Channel selection between a ground terminal and a drone is represented as
Figure SMS_15
Figure SMS_16
If in time slot t terminal n and drone m are connected by channel l, c n,m,l (t) =1; otherwise c n,m,l (t)＝0。
In the t-th time slot, for the m-th unmanned network, the unmanned reception signal-to-interference-and-noise ratio of the n-th smart device occupying the l-th sub-channel can be expressed as:
Figure SMS_17
/>
N 0 in order for the noise power to be high,
Figure SMS_18
representing interference from other users sharing the same channel. The SIC technique can be used to decode signals from terminal devices occupying the same sub-channel.
Similarly, the received signal-to-noise ratio of the mth drone in the nth time slot may be expressed as:
Figure SMS_19
in the t-th time slot, the uplink accessibility of the nth terminal to the airship is expressed as
Figure SMS_20
The system capacity is:
Figure SMS_21
the drone power consumption consists of two parts. One is communication-related power, including radiation, signal processing, and power generated by other circuitry. The other is propulsion power consumption to maintain flight and maneuverability. The propulsion energy required by the mth unmanned aerial vehicle is:
Figure SMS_22
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure SMS_23
represents the force required to overcome the resistance, g represents gravity,
Figure SMS_24
Figure SMS_25
the kinetic energy of flight in the cycle is defined. m is m m ' is the weight of unmanned plane m, c 1 ，c 2 Is two parameters related to aircraft weight, air density, etc.
The total power consumption of the system is as follows:
Figure SMS_26
the system energy efficiency can be expressed as
Figure SMS_27
The aim is to maximize the energy efficiency of the system by designing the trajectory and speed of the unmanned aerial vehicle, and the problem is expressed as follows:
Figure SMS_28
s.t.C1：P n，m，l (t)≥0
C2：
Figure SMS_29
C3：P m (t)≥0
C4：
Figure SMS_30
C5：
Figure SMS_31
C6：c n，m，l (t)∈{0，1}
C7：
Figure SMS_32
c1 and C2 are transmit power constraints of the terminal,
Figure SMS_33
representing the maximum transmit power of the terminal, C3 and C4 being the transmit power constraints of the unmanned aerial vehicle,/->
Figure SMS_34
Indicating the maximum transmit power of the drone. C5 is rate constraint for each user, R min Indicating the minimum transmission data rate requirements that each terminal must meet. C6 represents a channel selection constraint. C7 represents the capacity constraint of the system, +.>
Figure SMS_35
Representing the maximum capacity of the system.
For the channel problem, the channel selection problem is converted into a many-to-one matching game by using the matching game theory, and the channel and the user are two groups of participants. The user clusters are matched to the subchannels, targeting uplink reachability and rate maximization, to find a solution.
Defining it as a many-to-one matching mapping from user set n to channel k set, under which model each user cluster only care about the uplink reachable rate of its cluster terminals, thereby obtaining a utility function
Figure SMS_36
Each user can only match one channel in one time slot. When the terminal and/of the cluster 1 The uplink accessibility is higher than that of the sub-channel matching 2 When the sub-channels are matched, the cluster user preferentially selects l 1 Sub-channels other than l 2 A sub-channel. In exchange matching, two user clusters exchange the sub-channels that they match while the other matches remain unchanged. If the utility of one or more participants increases while the utility of the other participants does not decrease, then this exchange match is referred to as a blocking pair. For blocking pairs, each user cluster would like to match with other participants as a pair, rather than remaining matched with the currently matching participant. If there is no blocking pair in the match, it is said to be stable.
According to the definition, a many-to-one matching algorithm of resource allocation is proposed to find a stable result. First, an initial match is given, where the user and the subchannel are randomly matched. Next, two different channels are randomly selected for exchange matching. Utility is then calculated and if a blocking pair, then a swap operation is performed. The exchange matching process continues until there is no blocking pair. Finally, stable matching is realized.
The system uplink and rate increase after each switching operation. In limited switching operations, a stable match can be found. R (t) is the uplink achievable sum rate of the match in each slot, and increases after each switching operation.
For the power optimization problem, based on given channel selection and unmanned aerial vehicle flight trajectory, changing an objective function into a subtraction form R-gamma P, and introducing an auxiliary variable ρ n，m，l (t)＝c n，m，l (t)P n，m，l (t) ∈ρ. The problem of the possible dual gap is solved by utilizing the Lagrangian dual decomposition method. It has been demonstrated that the dual gap of the non-convex optimization problem can be zero if the number of subcarriers is large enough. Thus, the Lagrangian function of this problem is
Figure SMS_37
Figure SMS_38
Where μ, v, ω, η are Lagrangian multipliers, the Lagrangian dual problem can be expressed as
Figure SMS_39
s.t.μ，v，ω，η≥0
Re-representing Lagrangian function
Figure SMS_40
Figure SMS_41
Figure SMS_42
Because ψ is a constant, it has no effect on the final result and can be ignored. Although Φ is non-convex for both variables, if one is retained and the other is fixed, the objective function is concave.
The problem is addressed using the KKT condition, wherein Φ is applied to P m (t)，ρ n,m,l The partial derivative of (t) is set to zero, expressed as
Figure SMS_43
Figure SMS_44
Thus, the following results were obtained.
Figure SMS_45
Wherein, kappa 1 ＝W/MK ln 2，
Figure SMS_46
Figure SMS_47
According to C4, the optimal solution of the unmanned plane power is
Figure SMS_48
Wherein p is 1 ＝(α 1 +β 1 )β 1 (γ+v tm )，q 1 ＝(α 1 +2β 1 )Δ 1 (γ+v tm )，
Figure SMS_49
Figure SMS_50
In the same way, can obtain
Figure SMS_51
Wherein, kappa 2 ＝κ 1 ，
Figure SMS_52
Figure SMS_53
According to C2, the optimal transmit power of the user is
Figure SMS_54
Wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure SMS_55
p 2 ＝(α 2 +β 2 )β 2 (γ+μ tn )，q 2 ＝(α 2 +2β 2 )Δ 2 (γ+μ tn )，/>
Figure SMS_56
finally, the lagrangian multiplier is updated using a sub-gradient approach:
Figure SMS_57
Figure SMS_58
Figure SMS_59
Figure SMS_60
where θ is the update step size, [] + ＝max{0,·}.
The technical method of the invention has the following advantages:
the invention discloses an unmanned aerial vehicle-assisted green resource allocation method for a 6G heterogeneous network. The scheme comprises the following steps: firstly, the unmanned aerial vehicle is set to fly according to a preset track, and is used as a relay to connect the airship with a user. Secondly, setting the unmanned aerial vehicle to fly according to a preset track, realizing channel multiplexing by adopting C-NOMA, introducing a propulsion power consumption model and a time slot model of the unmanned aerial vehicle, converting channel selection into a matching problem by designing some reasonable limits, and optimizing the transmitting power based on the obtained channel selection. And finally, adopting a matching and Lagrangian dual decomposition method to solve the problem of resource allocation. The system improves the energy efficiency and the frequency spectrum efficiency of the system, and effectively improves the low-carbon green performance of the system.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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In order to more clearly illustrate the embodiments of the present invention or the technical methods in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained according to the drawings without inventive effort for a person having ordinary skill in the art.
FIG. 1 is a graph of system energy efficiency versus number of terminals for different algorithms.
Fig. 2 is a diagram of a user cluster in one slot.
Fig. 3 is a graph of spectral efficiency versus the number of terminals for different algorithms.
Detailed Description
The invention provides an unmanned aerial vehicle-assisted 6G heterogeneous network green resource allocation method, and an embodiment is described in detail below with reference to the accompanying drawings.
The specific implementation scene of the invention is an area with the size of 200m multiplied by 120m, wherein the area comprises 3 unmanned aerial vehicles and intelligent devices with different numbers distributed randomly, and each cluster comprises 2 terminals. The simulation parameters were selected as follows: the airship has a height of 15km, the unmanned aerial vehicle has a flying height of 160m, a flying period of 20s, the maximum transmission power of the unmanned aerial vehicle is 3W, the maximum transmission power of a user is 0.1W, the channel bandwidth is 3MHz, the channel number is 5, and the noise power is-105 dBm.
The implementation mode of the invention is divided into two steps, wherein the first step is to build a system model, and the second step is to implement an algorithm.
The present invention uses MATLAB for simulation.
FIG. 1 is a diagram of system energy efficiency under different reference algorithms. Studies have shown that as the number of users increases, the energy efficiency of the system increases. It can be seen intuitively that the proposed method is advantageous in terms of system energy efficiency. In particular, the performance of the random algorithm is worst, indicating a significant impact on the energy efficiency of resource allocation. Furthermore, a comparison between OMA and C-NOMA demonstrates that channel multiplexing results in improved spectral efficiency, thereby optimizing system energy efficiency.
Fig. 2 shows the connection state of the users and the unmanned aerial vehicles in one time slot, and it can be seen from the figure that each unmanned aerial vehicle serves its associated user, and two users in the cluster multiplex sub-channels. The C-NOMA is an effective solution capable of improving the throughput of the system and considering the spectrum efficiency and complexity of the receiving end, the terminal is divided into a plurality of clusters, and the NOMA is used by users in the clusters, so that the system performance can be obviously improved.
Fig. 3 shows the spectral efficiency versus the number of users for the comparison of the algorithm herein and the OMA algorithm. It can be seen that the spectral efficiency is positively correlated with the number of users. This is because as the number of users increases, the utilization of channel resources increases, and the overall throughput of the system increases, resulting in higher spectral efficiency. It can also be seen that the proposed method based on C-NOMA is better in spectral efficiency than OMA because of the channel multiplexing of NOMA.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by those skilled in the art that the scope of the invention referred to in this disclosure is not limited to the specific combination of features described above, but encompasses other embodiments in which any combination of features described above or their equivalents is contemplated without departing from the inventive concepts described. Such as those described above, are mutually substituted with the technical features having similar functions disclosed in the present disclosure (but not limited thereto).

Claims (4)

1. The invention discloses an unmanned aerial vehicle-assisted green resource allocation method for a 6G heterogeneous network. The scheme comprises the following steps: firstly, the unmanned aerial vehicle is set to fly according to a preset track, and is used as a relay to connect the airship with a user. And secondly, realizing channel multiplexing by adopting C-NOMA, introducing a propulsion power consumption model and a time slot model of the unmanned aerial vehicle, converting channel selection into a matching problem by designing some reasonable limits, and optimizing the transmitting power based on the obtained channel selection. And finally, adopting a matching and Lagrangian dual decomposition method to solve the problem of resource allocation. The system improves the energy efficiency and the frequency spectrum efficiency of the system, and effectively improves the low-carbon green performance of the system.
2. The method for introducing the propulsion power consumption and the time slot model of the unmanned aerial vehicle according to claim 1, and solving the problem of resource allocation by adopting a matching and Lagrangian dual decomposition method.
3. The unmanned aerial vehicle assisted 6G heterogeneous network green resource allocation method according to claim 1 is characterized by comprising the following steps: the C-NOMA technology is adopted to multiplex channels, so that the complexity of the system is reduced, the matching and Lagrange dual decomposition method is used for carrying out resource allocation, the problem that the resource allocation is not efficient enough in the system is solved, and the energy efficiency of the system is maximized; the energy efficiency of the system is defined as the ratio of the uplink reachable sum rate of the ground terminal to the system power consumption, and is expressed as:
Figure QLYQS_1
wherein R is the uplink reachable sum rate of the ground terminal, P is the system power consumption, R sum (t) is the terrestrial terminal uplink achievable rate in one slot,
Figure QLYQS_2
representing the propulsive power consumption of the unmanned aerial vehicle in each time slot, P n,m,l (t) is the transmitting power of the ground terminal, P m (t) is the transmitting power of the unmanned aerial vehicle, c n,m,l And (t) represents the channel selection of the ground terminal. The most important factor used to measure system performance is system energy efficiency, so the objective function is expressed as: max gamma.
4. The problem solving according to claim 1 can be divided into the following steps: firstly, the unmanned aerial vehicle is set to fly according to a preset track, and is used as a relay to connect the airship with a user. Secondly, setting the unmanned aerial vehicle to fly according to a preset track, realizing channel multiplexing by adopting C-NOMA, introducing a propulsion power consumption model and a time slot model of the unmanned aerial vehicle, converting channel selection into a matching problem by designing some reasonable limits, and optimizing the transmitting power based on the obtained channel selection. And finally, adopting a matching and Lagrangian dual decomposition method to solve the problem of resource allocation.
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