CN111615200A - UAV-assisted communication resource allocation method for hybrid Hybrid NOMA network - Google Patents

Abstract

The UAV-assisted communication resource allocation method of the hybrid Hybrid NOMA network, the multi-cell multi-channel hybrid NOMA/OMA small cell network based on user experience, decomposes the joint resource allocation problem into two sub-problems: tripartite matching and water-flooding-like MOS power allocation. Including: 1. Multi-carrier NOMA network system scenario modeling; 2. Decomposition and mathematical description of the wireless resource allocation problem of multi-cellular and multi-carrier non-orthogonal multiple access; 3. Base station selection, sub-channel matching, unmanned Design of multi-objective optimization and adaptation method for machine height optimization and power distribution. After the establishment of a hybrid NOMA-oriented multi-cellular UAV-assisted communication system, a three-dimensional matching strategy of UAV/base station-user-sub-channel based on user experience QoE, as well as the UAV height optimization and power allocation method, are innovatively proposed in this scenario. , with the ultimate goal of maximizing user experience, avoiding the problem of unreasonable resource allocation caused by blindly pursuing the maximum rate, and improving the wireless resource allocation efficiency based on NOMA scenarios.

Description

UAV-assisted communication resource allocation method for hybrid Hybrid NOMA network

technical field

The invention belongs to the technical field of wireless communication, and relates to a wireless resource allocation method of NOMA and OMA hybrid heterogeneous small cell networks based on user experience, in particular to an unmanned aerial vehicle-assisted communication resource allocation method of the hybrid Hybrid NOMA network.

Background technique

As we all know, the future wireless network needs to meet the communication connection anytime and anywhere with diversified communication modes. To improve the resilience of communication networks to emergencies such as failures, natural disasters, and unexpected traffic, unmanned aerial vehicle (UAV)-assisted wireless communication systems can provide a unique opportunity to meet these needs in a timely manner without relying on over-engineering cellular network. Drones can serve as unmanned aerial vehicle base stations (UAV-bs) to handle short-term erratic traffic demands in hotspots such as sporting events and concerts, or to relieve congestion through data offloading in the access network, thus providing the foundation for terrestrial wireless networks. provide support. Take advantage of the additional degrees of freedom of UAV-BS maneuverability to improve spectral efficiency and energy efficiency.

In existing wireless communication systems, Orthogonal Frequency Division Multiple Access (OFDMA) technology and Time Division Multiple Access (TDMA) technology are widely used for user scheduling and data transmission in the orthogonal domain. Due to the explosive growth in demand for wireless communications, future fifth-generation 5G and beyond wireless systems will face greater challenges, requiring higher spectral efficiency, larger-scale connections, and lower latency. When the number of access devices is large, the traditional Orthogonal Multiple Access (OMA) scheme will have serious congestion problems. Non-Orthogonal Multiple Access (NOMA) technology allows users to access channels in a non-orthogonal manner through power domain multiplexing or code domain multiplexing, which can greatly improve spectral efficiency and user access capabilities.

In the existing NOMA cellular network wireless resource allocation technology, the user access rate and the maximum capacity are taken as the access targets, and the service differences of different terminal users are not considered. Broadband background tasks such as low-speed, low-latency transmission of sensor data using small packets and movie downloads cannot be considered the same quality of service (QoS) requirements for users. Additionally, NOMA encourages multiple users to share the same channel at the same time based on their channel conditions. Therefore, the performance gain of NOMA over OMA may be reduced under similar user channel conditions. Note also that NOMA is more complex to implement compared to OMA, requiring the use of multi-user detection (MUD) techniques at the receiving end, such as sequential interference cancellation (SIC), to decode the received signal at the expense of increased computational complexity. Therefore, according to the channel conditions, it is very necessary to design the resource allocation mode of the mixed NOMA and OMA multiple access modes.

To sum up, the present invention mainly aims at the problems of terminal user access, channel allocation, UAV height optimization and power optimization of the UAV auxiliary communication system, and provides the capacity, Improvements in coverage, energy efficiency and spectral efficiency.

SUMMARY OF THE INVENTION

In view of this, in order to solve the above-mentioned deficiencies of the prior art, the purpose of the present invention is to provide a method for allocating UAV-assisted communication resources in a hybrid Hybrid NOMA network, a multi-cell multi-channel hybrid NOMA/OMA small cell network based on user experience, involving The matching problem of users, base stations/UAVs, and sub-channels, as well as the power allocation problem in sub-channels in the highly optimized UAV and NOMA mode, improves the overall service efficiency of the system while ensuring the diverse QoE requirements of users.

For achieving the above object, the technical scheme adopted in the present invention is:

The UAV-assisted communication resource allocation method of the hybrid Hybrid NOMA network includes the following steps:

S1: The base station matches the user;

S11: Initialize the user selection and service information calculation stage: the transmit power of each base station is p _n , and the access rate and QoE score of the corresponding user are calculated;

S111: The user finds all available base stations; the position of the drone is calculated according to the projected position;

S112: The user randomly accesses a base station or accesses the nearest base station, and then reports the location information and service type to all available base stations;

S113: All base stations calculate the transmission rate and user experience score of users in the base station according to the actual user access situation, create a service user list, and calculate their own service utility according to the user experience score and the service user list;

S12: User transfer matching stage:

S121: The base station, for the purpose of increasing its own service utility, polls the remaining available base stations according to the user's location information and sends out a user transfer matching application or a user exchange matching application;

S122: The base station being applied for chooses to accept or reject the application according to whether its own service utility is improved. If the base station's own service utility is improved, it accepts the application and updates its service user list, and if its own service utility is reduced or unchanged, the application is rejected;

S123: All polling is completed, and the matching ends;

S2: (base station, user) two-dimensional unit and sub-channel matching: using iterative matching algorithm, two-dimensional preference list;

S21: initialization (base station, user)-subchannel matching, randomly selecting subchannels for access according to services;

S22: Calculate the sum of user MOS scores of adjacent base stations and a channel access list according to the initial random access situation;

S23: Information exchanged between base stations, if the MOS score between neighboring base stations increases, accept the application for channel exchange, update the MOS score, and the channel access list; otherwise, reject the MOS score, and the channel access list remains unchanged ;

S3: Power allocation: Based on the first and second parts, the matching results of users and sub-channels are obtained, and the third part realizes the optimal position adjustment of the UAV and the power allocation of users on the sub-channels of each base station;

S31: Assuming that the UAV adopts a fixed transmission power and obtains the users who need to be served through the S2 step, since the channel capacity is a function of the channel gain, the optimal UAV altitude for a given user distribution can be obtained; the UAV and the user The average path loss of the terminal can be expressed in probabilistic form:

可求得无人机接入的最优高度；According to the terminal location that needs to be served, the maximum service radius can be obtained, and the parameters of the road loss formula can be set according to the specific environment (such as suburbs, cities, dense urban areas and CB high-rise building gathering areas);

The optimal altitude for UAV access can be obtained;

S32: if only one user accesses the same channel of the same base station, directly assign p _n /m to the user;

S33: Otherwise, if there are 2 or more users accessing the same channel of the same base station, the power is allocated according to the ratio of (QoE _n = 5 - MOS score obtained); assuming that there are 3 users in one channel, the third user The power is:

其中η(0≤η≤1)为衰退因子；

where η (0≤η≤1) is the decay factor;

S4: User receiving and decoding: the user receives their respective signals, decodes the signals on multiple (≥1) channels accessed, and finally synthesizes the transmission information according to the spectrum aggregation technology;

由小到大依次解码，及距离基站最远的用户先解码；S41: In each sub-channel, according to the arrangement of the NOMA protocol, the users of each base station decode once in order, and the decoding order is based on the channel conditions

Decode in order from small to large, and the user farthest from the base station decodes first;

S42: Finally, each user aggregates all the information of the access sub-channel to obtain the final information.

Further, before the step S1, the user's UAV access channel model is established, which specifically includes the following steps:

A1: Assuming a downlink UAV-assisted cellular network, set the small cell base station as SBS={SBS ₁ , SBS ₂ ,...,SBS _n }, the set of UAVs UAV={UAV ₁ , UAV ₂ , ..., UAV _l }, the user set is represented as UE={UE ₁ ,UE ₂ ,...,UE _k }, and the sub-channel is represented as SC={SC ₁ ,SC ₂ ,...,SC _m };

A2: Establish a quasi-stationary low-altitude rotary-wing UAV-assisted base station UAV-BS to provide a disc-shaped wireless coverage with a radius of Rc meters, the ground height is H, and the vertical projection is Q point;

k∈{用户集}，UAV-BS相对于每个用户的仰角为θ_k＝arctan(H/D_k),k∈{用户集}；A3: Assuming that 2 users are connected, the distance from Q point is D _j , then the distance from the drone is

k∈{user set}, the elevation angle of UAV-BS relative to each user is θ _k =arctan(H/D _k ), k∈{user set};

A4: The user's UAV access channel model depends on the density and height of the buildings in the coverage area and the position of the environmental profile defined by the relative distance between the user and the building, that is, the elevation angle. Based on the probability model, it can be divided into visual Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) transmission; the probability that the user experiences the line-of-sight link is:

Line-of-sight access probability:

Non-line-of-sight access probability: Pr _k (NLOS)=1-Pr _k (LOS).

Further, in the step A4, α and β are constant values related to the characteristics of the coverage area, and the line-of-sight access probability is an increasing function proportional to the elevation angle.

Further, the transmission power of the user accessing the UAV is: p _rx,k (dB)=p _tx (dB)-L _k (dB),

in,

Further, where L _k is the path loss from the UAV to the user, η is the free space path loss index, ψ _LOS and ψ _NLOS are the excessive loss caused by the shadowing effect caused by the occlusion of the object, both of which obey the normal distribution, and their mean and variance depends on the elevation angle and environment-dependent constant values.

Further, combining LOS and NLOS link analysis, the average path loss of UAV and user terminal can be expressed in probabilistic form:

Further, in the step S2, the following steps are specifically included:

基站n在子信道m的叠加编码符号可表示为：A1: Set the base station user matching relationship χ _n,k , the subchannel and user matching relationship

The superimposed coded symbols of base station n in subchannel m can be expressed as:

表示基站n在子信道m给用户k的传输符号；

表示基站n在子信道m给用户k分配的传输功率；in,

represents the transmission symbol from base station n to user k on subchannel m;

represents the transmission power allocated by base station n to user k on subchannel m;

A2: The signal received by user k can be expressed as a combination of three parts: the transmission signal of base station n in subchannel m, and the transmission signal of other base stations in subchannel m, that is, the accumulated interference to user k, white noise.

The beneficial effects of the present invention are:

The UAV-assisted communication resource allocation method of the hybrid Hybrid NOMA network of the present invention, the multi-cell multi-channel hybrid NOMA/OMA small cell network based on user experience, involves user base station/UAV matching, sub-channel selection and power optimization. The matching problem and the power allocation problem in the sub-channel in NOMA mode improve the overall service efficiency of the system while ensuring the diversified QoE requirements of users;

In the present invention, the joint resource allocation problem is decomposed into two sub-problems: tripartite matching and quasi-water-filling MOS power allocation, including: 1. Multi-carrier NOMA network system scenario modeling; 2. Multi-cellular and multi-carrier non-orthogonal multiple access The decomposition and mathematical description of the incoming wireless resource allocation problem; 3. The matching method design of the base station selection, sub-channel matching, and power allocation sub-problems; The three-dimensional matching strategy and power allocation method of base station-user-subchannel for user experience QoE, with the ultimate goal of maximizing user experience, avoiding the problem of unreasonable resource allocation caused by blindly pursuing the maximum rate, and effectively improving the NOMA-based scenario. Radio resource allocation performance.

Description of drawings

In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings without creative efforts.

FIG. 1 is a schematic diagram of the principle of the present invention.

Detailed ways

Specific embodiments are given below to further illustrate the technical solutions of the present invention in a clear, complete and detailed manner. This embodiment is the best embodiment based on the technical solution of the present invention, but the protection scope of the present invention is not limited to the following embodiments.

The UAV-assisted communication resource allocation method of the hybrid Hybrid NOMA network includes the following steps:

S1: The base station matches the user;

S11: Initialize the user selection and service information calculation stage: the transmit power of each base station is p _n , and the access rate and QoE score of the corresponding user are calculated;

S111: The user finds all available base stations; the initial position of the drone is calculated according to the projected position;

S112: The user randomly accesses a base station or accesses the nearest base station, and then reports the location information and service type to all available base stations;

S113: All base stations calculate the transmission rate and user experience score of users in the base station according to the actual user access situation, create a service user list, and calculate their own service utility according to the user experience score and the service user list;

S12: User transfer matching stage:

S121: The base station, for the purpose of increasing its own service utility, polls the remaining available base stations according to the user's location information and sends out a user transfer matching application or a user exchange matching application;

S122: The base station being applied for chooses to accept or reject the application according to whether its own service utility is improved. If the base station's own service utility is improved, it accepts the application and updates its service user list, and if its own service utility is reduced or unchanged, the application is rejected;

S123: All polling is completed, and the matching ends;

S2: (base station, user) two-dimensional unit and sub-channel matching: using an iterative matching algorithm, two-dimensional preference list; in this embodiment, the sub-channel power is obtained by dividing the power of each base station equally, wherein the sub-channel bandwidth is W/n, The subchannel power is p _n /n. According to the results of user allocation of base stations, determine the actual access power of users in each base station, users can access multiple channels at the same time, calculate their MOS score and count the number of occupied channels according to each access method, and according to the two-dimensional index, and MOS score and number of channels build a preference list. The establishment of the list is divided into two steps. First, according to the MOS score, the item with the least number of occupied channels is selected from the items with the highest MOS score. If there are multiple cases that satisfy the two conditions at the same time, the most optimal option is randomly selected. Each user applies the optimal option to the channel as a strategy. The base station calculates the corresponding QoE score of each user's achievable rate after the spectrum aggregation of each channel. The user applies for a channel to the base station, and the base station determines the user QoE score sum of the channel. After matching and iterative operation, the optimal allocation of the user in the respective channel is finally obtained;

Further, taking three sub-channels as an example, there are 7 types of access to sub-channels in total, with 3 bits in each row representing 3 sub-channels, "1" for access, and "0" for no access. [100;010;001;110;011;101;111];

S21: Initialization (base station, user)-sub-channel matching, randomly select sub-channel access according to the service; for example, low-rate services are randomly selected in 1-3 cases, and only one channel is occupied initially; high-rate services are in 4-7 cases Random selection, initial access to more than 2 channels; S22: Calculate the user MOS score sum of adjacent base stations and the channel access list according to the initial random access situation; wherein, loop for t=1: the maximum set In the rotation training method for the number of iterations, each user calculates the achievable rate of 7 access situations and the MOS score of the user experience, and applies to the base station for the access situation with the highest MOS score; go to step S23;

S23: Information exchanged between base stations, if the MOS score between neighboring base stations increases, accept the application for channel exchange, update the MOS score, and the channel access list; otherwise, reject the MOS score, and the channel access list remains unchanged ;

S3: Power allocation: Based on the first and second parts, the matching results of users and sub-channels are obtained, and the third part realizes the optimal position adjustment of the UAV and the allocation of power to users on the sub-channels of each base station; in this embodiment, each sub-channel is set The channel power is the same, the sub-channel has only one user, and the OMA mode is used for access. If there are multiple users on the sub-channel and access is made in NOMA mode, the base station allocates the power of each user on the channel according to the principle of proportional fairness. Power is allocated in 2 cases:

S31: Assuming that the UAV adopts a fixed transmission power and obtains the users who need to be served through the S2 step, since the channel capacity is a function of the channel gain, the optimal UAV altitude for a given user distribution can be obtained; the UAV and the user The average path loss of the terminal can be expressed in probabilistic form:

可求得无人机接入的最优高度；According to the terminal location that needs to be served, the maximum service radius can be obtained, and the parameters of the road loss formula can be set according to the specific environment (such as suburbs, cities, dense urban areas and CB high-rise building gathering areas);

The optimal altitude for drone access can be obtained;

S32: if only one user accesses the same channel of the same base station, directly assign p _n /m to the user;

S33: Otherwise, if there are 2 or more users accessing the same channel of the same base station, the power is allocated according to the ratio of (QoE _n = 5 - MOS score obtained); assuming that there are 3 users in one channel, the third user The power is:

其中η(0≤η≤1)为衰退因子；

where η (0≤η≤1) is the decay factor;

S4: User receiving and decoding: the user receives their respective signals, decodes the signals on multiple (≥1) channels accessed, and finally synthesizes the transmission information according to the spectrum aggregation technology;

由小到大依次解码，及距离基站最远的用户先解码；S41: In each sub-channel, according to the arrangement of the NOMA protocol, the users of each base station decode once in order, and the decoding order is based on the channel conditions

Decode in order from small to large, and the user farthest from the base station decodes first;

S42: Finally, each user aggregates all the information of the access sub-channel to obtain the final information.

Further, before the step S1, the user's UAV access channel model is established, which specifically includes the following steps:

A1: Assuming a downlink UAV-assisted cellular network, set the small cell base station as SBS={SBS ₁ , SBS ₂ ,...,SBS _n }, the set of UAVs UAV={UAV ₁ , UAV ₂ , ..., UAV _l }, the user set is represented as UE={UE ₁ ,UE ₂ ,...,UE _k }, and the sub-channel is represented as SC={SC ₁ ,SC ₂ ,...,SC _m };

A2: Establish a quasi-stationary low-altitude rotary-wing UAV-assisted base station UAV-BS to provide a disc-shaped wireless coverage with a radius of Rc meters, the ground height is H, and the vertical projection is Q point;

k∈{用户集}，UAV-BS相对于每个用户的仰角为θ_k＝arctan(H/D_k),k∈{用户集}；A3: Assuming that 2 users are connected, the distance from Q point is D _j , then the distance from the drone is

k∈{user set}, the elevation angle of UAV-BS relative to each user is θ _k =arctan(H/D _k ), k∈{user set};

A4: The user's UAV access channel model depends on the density and height of the buildings in the coverage area and the position of the environmental profile defined by the relative distance between the user and the building, that is, the elevation angle. Based on the probability model, it can be divided into visual Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) transmission; the probability that the user experiences the line-of-sight link is:

Line-of-sight access probability:

Non-line-of-sight access probability: Pr _k (NLOS)=1-Pr _k (LOS).

Further, in the step A4, α and β are constant values related to the characteristics of the coverage area, and the line-of-sight access probability is an increasing function proportional to the elevation angle.

Further, the transmission power of the user accessing the UAV is: p _rx,k (dB)=p _tx (dB)-L _k (dB),

in,

Further, where L _k is the path loss from the UAV to the user, η is the free space path loss index, ψ _LOS and ψ _NLOS are the excessive loss caused by the shadowing effect caused by the occlusion of the object, both of which obey the normal distribution, and their mean and variance depends on the elevation angle and environment-dependent constant values. In the present invention, due to the reflection and shadow effects of obstacles, the closer the building is to the user, the greater the scattering and the greater the loss of non-line-of-sight distance.

Further, combining LOS and NLOS link analysis, the average path loss of UAV and user terminal can be expressed in probabilistic form:

根据需要服务的终端位置，可求得无人机最大的服务半径(及服务最远终端的距离)。根据具体的环境设置路损公式参数(如η＝(η_视距,η_非视距)在郊区，城市，密集城区和CB高层建筑聚集区分别设置为(0.1,21),(1.0,20),(1.6,23),(2.3,34)。由于无人机和用户终端的平均路径损耗是凸函数且内含高度参数，根据

可求得无人机接入的最优高度。

According to the position of the terminal that needs to be served, the maximum service radius of the drone (and the distance to the farthest terminal) can be obtained. Set the parameters of the road loss formula according to the specific environment (such as η = (η line of _sight , η _{non-line of sight} ) in suburbs, cities, dense urban areas and CB high-rise building clusters are set to (0.1, 21), (1.0, 20) , (1.6, 23), (2.3, 34). Since the average path loss of the UAV and the user terminal is a convex function and contains a height parameter, according to

The optimal altitude for UAV access can be obtained.

In the present invention, because setting parameters does not affect the framework flow of the present invention, to simplify the expression, it is assumed that the transmit power, bandwidth and number of channels of the UAV-assisted hotspot and the common small and micro base station are the same. In actual implementation, the settings of the transmit power, bandwidth and number of channels of the UAV-assisted base station can be different. Let the transmit power of each base station be p _n , which is allocated between sub-channels; the total bandwidth of the access of each small and micro base station/UAV-assisted base station is W, and the entire bandwidth W is divided into m sub-channels. The bandwidth is W/m.

Further, each user can access multiple channels of adjacent base stations. According to the NOMA protocol, each sub-channel can access multiple users, and each user can access only one base station. 2 arrangement matrices 0-1 elements; in the step S2, the following steps are specifically included:

基站n在子信道m的叠加编码符号可表示为：A1: Set the base station user matching relationship χ _n,k , the subchannel and user matching relationship

The superimposed coded symbols of base station n in subchannel m can be expressed as:

表示基站n在子信道m给用户k的传输符号；

表示基站n在子信道m给用户k分配的传输功率；in,

represents the transmission symbol from base station n to user k on subchannel m;

represents the transmission power allocated by base station n to user k on subchannel m;

A2: The signal received by user k can be expressed as a combination of three parts: the transmission signal of base station n in subchannel m, and the transmission signal of other base stations in subchannel m, that is, the accumulated interference to user k, white noise.

其中，

表示在子信道m基站n到用户k的信道参数；in,

in,

Represents the channel parameters from base station n to user k in subchannel m;

利用频谱聚合技术，用户UE_n,k表示接入基站n的用户k，它的速率等于所有接入信道的速率和；Define the equivalent channel gain:

Using spectrum aggregation technology, user UE _n,k represents user k accessing base station n, and its rate is equal to the sum of the rates of all access channels;

此时获得的速率为：

User k accesses base station n, and the signal-to-interference-noise ratio on channel m can be expressed as:

The rate obtained at this point is:

The reachable rate of user k accessing base station n is:

We convert the reachable rate of the user terminal into the user experience index and the MOS score. The specific calculation method is as follows:

由用户业务类型通过用户体验的打分数据统计得到，分别对应该业务类型所需的用户平均吞吐速率的下限值和满足流畅传输需要的推荐值，a与b是2个计算参数且随业务类型同步改变；where θ represents the average user throughput rate,

The user service type is obtained through the user experience scoring data statistics, which correspond to the lower limit of the average user throughput rate required by the service type and the recommended value to meet the needs of smooth transmission. a and b are two calculation parameters and vary with the service type. synchronous change;

Specifically, different definition methods can be used for the MOS conversion form of the end user. The mainstream is to use the logarithmic function form for conversion. The present invention mainly innovates the distribution framework, and different MOS conversion forms can be described in the present invention. The framework is implemented; the final optimization goal is:

The description of the four constraints is as follows: (1) the elements of the access state matrix; (2) a user can only access one base station at most; (3) the constraints on the number of access channels for each base station and user; (4) each power constraints of each base station.

To solve the above problem, it is decomposed into three sub-problems, namely base station and user matching, user and sub-channel matching, and base station assigning power to users on sub-channels.

To sum up, the UAV-assisted communication resource allocation method of the hybrid Hybrid NOMA network of the present invention, the multi-cell multi-channel hybrid NOMA/OMA small cell network based on user experience, involves user base station/UAV matching and sub-channel selection. The three-party matching problem with power optimization and the power allocation problem in sub-channels in NOMA mode can improve the overall service efficiency of the system while ensuring the diversified QoE requirements of users;

In the present invention, the joint resource allocation problem is decomposed into two sub-problems: tripartite matching and quasi-water-filling MOS power allocation, including: 1. Multi-carrier NOMA network system scenario modeling; 2. Multi-cellular and multi-carrier non-orthogonal multiple access The decomposition and mathematical description of the incoming wireless resource allocation problem; 3. The matching method design of the base station selection, sub-channel matching, and power allocation sub-problems; The three-dimensional matching strategy and power allocation method of base station-user-subchannel for user experience QoE, with the ultimate goal of maximizing user experience, avoiding the problem of unreasonable resource allocation caused by blindly pursuing the maximum rate, and effectively improving the NOMA-based scenario. Radio resource allocation performance.

The foregoing has shown and described the main features, basic principles, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above-mentioned embodiments. The above-mentioned embodiments and descriptions only illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will also Various changes and modifications are possible, which fall within the scope of the claimed invention. The claimed scope of the present invention is defined by the appended claims and their equivalents.

Claims (7)

1. the unmanned aerial vehicle auxiliary communication resource allocation method of hybrid Hybrid NOMA network is characterized in that: may further comprise the steps: S1: The base station matches the user; S11: Initialize the user selection and service information calculation stage: the transmit power of each base station is p _n , and the access rate and QoE score of the corresponding user are calculated; S111: The user finds all available base stations; the initial position of the drone is calculated according to the projected position; S112: The user randomly accesses a base station or accesses the nearest base station, and then reports the location information and service type to all available base stations; S113: All base stations calculate the transmission rate and user experience score of users in the base station according to the actual user access situation, create a service user list, and calculate their own service utility according to the user experience score and the service user list; S12: User transfer matching stage: S121: The base station, for the purpose of increasing its own service utility, polls the remaining available base stations according to the user's location information and sends out a user transfer matching application or a user exchange matching application; S122: The base station being applied for chooses to accept or reject the application according to whether its own service utility is improved. If the base station's own service utility is improved, it accepts the application and updates its service user list, and if its own service utility is reduced or unchanged, the application is rejected; S123: All polling is completed, and the matching ends; S2: (base station, user) two-dimensional unit and sub-channel matching: using iterative matching algorithm, two-dimensional preference list; S21: initialization (base station, user)-subchannel matching, randomly selecting subchannels for access according to services; S22: Calculate the sum of user MOS scores of adjacent base stations and a channel access list according to the initial random access situation; S23: Information exchanged between base stations, if the MOS score between neighboring base stations increases, accept the application for channel exchange, update the MOS score, and the channel access list; otherwise, reject the MOS score, and the channel access list remains unchanged ; S3: Power allocation: Based on the first and second parts, the matching results of users and sub-channels are obtained, and the third part realizes the optimal position adjustment of the UAV and the power allocation of users on the sub-channels of each base station; S31: Assuming that the UAV adopts a fixed transmission power and obtains the users who need to be served through the S2 step, since the channel capacity is a function of the channel gain, the optimal UAV altitude for a given user distribution can be obtained; the UAV and the user The average path loss of the terminal can be expressed in probabilistic form:

According to the terminal location that needs to be served, the maximum service radius can be obtained, and the parameters of the road loss formula can be set according to the specific environment (such as suburbs, cities, dense urban areas and CB high-rise building gathering areas);

The optimal altitude for UAV access can be obtained; S32: if only one user accesses the same channel of the same base station, directly assign p _n /m to the user; S33: Otherwise, if there are 2 or more users accessing the same channel of the same base station, the power is allocated according to the ratio of (QoE _n = 5 - MOS score obtained); assuming that there are 3 users in one channel, the third user The power is:

where η (0≤η≤1) is the decay factor; S4: User receiving and decoding: the user receives their respective signals, decodes the signals on multiple (≥1) channels accessed, and finally synthesizes the transmission information according to the spectrum aggregation technology; S41: In each sub-channel, according to the arrangement of the NOMA protocol, the users of each base station decode once in order, and the decoding order is based on the channel conditions

Decode in order from small to large, and the user farthest from the base station decodes first; S42: Finally, each user aggregates all the information of the access sub-channel to obtain the final information. 2. the unmanned aerial vehicle auxiliary communication resource allocation method of hybrid Hybrid NOMA network according to claim 1, is characterized in that: before described step S1, establish the unmanned aerial vehicle access channel model of user, specifically comprises the following steps: A1: Assuming a downlink UAV-assisted cellular network, set the small cell base station as SBS={SBS ₁ , SBS ₂ ,...,SBS _n }, the set of UAVs UAV={UAV ₁ , UAV ₂ , ..., UAV _l }, the user set is represented as UE={UE ₁ ,UE ₂ ,...,UE _k }, and the sub-channel is represented as SC={SC ₁ ,SC ₂ ,...,SC _m }; A2: Establish a quasi-stationary low-altitude rotary-wing UAV-assisted base station UAV-BS to provide a disc-shaped wireless coverage with a radius of Rc meters, the ground height is H, and the vertical projection is Q point; A3: Assuming that 2 users are connected, the distance from Q point is D _j , then the distance from the drone is

k∈{user set}, the elevation angle of UAV-BS relative to each user is θ _k =arctan(H/D _k ), k∈{user set}; A4: The user's UAV access channel model depends on the density and height of the buildings in the coverage area and the position of the environmental profile defined by the relative distance between the user and the building, that is, the elevation angle. Based on the probability model, it can be divided into visual Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) transmission; the probability that the user experiences the line-of-sight link is: Line-of-sight access probability:

Non-line-of-sight access probability: Pr _k (NLOS)=1-Pr _k (LOS). 3. the unmanned aerial vehicle-aided communication resource allocation method of hybrid Hybrid NOMA network according to claim 2, is characterized in that: in described step A4, α and β are constant values related to coverage area characteristics, line-of-sight access The probability is an increasing function proportional to the elevation angle. 4. the unmanned aerial vehicle auxiliary communication resource allocation method of hybrid Hybrid NOMA network according to claim 2 is characterized in that: the transmission power of user access unmanned aerial vehicle is: p _{rx, k} (dB)=p _tx (dB )-L _k (dB), in,

5. the unmanned aerial vehicle auxiliary communication resource allocation method of hybrid HybridNOMA network according to claim 4, is characterized in that: wherein, L _k is the path loss of unmanned aerial vehicle to user, η is free space path loss index, ψ _LOS and ψ _NLOS is the excessive loss caused by the shadow effect caused by the occlusion of the object. Both of them obey the normal distribution, and their mean and variance depend on the constant value of the elevation angle and the environment. 6. the unmanned aerial vehicle auxiliary communication resource allocation method of hybrid HybridNOMA network according to claim 4 is characterized in that: comprehensive LOS and NLOS link analysis, then the average path loss of unmanned aerial vehicle and user terminal can be expressed as probability form :

7. The unmanned aerial vehicle auxiliary communication resource allocation method of the hybrid HybridNOMA network according to claim 1, is characterized in that: in described step S2, specifically comprises the following steps: A1: Set the base station user matching relationship χ _n,k , the subchannel and user matching relationship

The superimposed coded symbols of base station n in subchannel m can be expressed as:

in,

represents the transmission symbol from base station n to user k on subchannel m;

represents the transmission power allocated by base station n to user k on subchannel m; A2: The signal received by user k can be expressed as a combination of three parts: the transmission signal of base station n in subchannel m, and the transmission signal of other base stations in subchannel m, that is, the accumulated interference to user k, white noise.

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