CN111615200A - Unmanned aerial vehicle auxiliary communication resource allocation method of Hybrid NOMA network - Google Patents
Unmanned aerial vehicle auxiliary communication resource allocation method of Hybrid NOMA network Download PDFInfo
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Abstract
An unmanned aerial vehicle auxiliary communication resource allocation method of a Hybrid NOMA network is based on a multi-cell multi-channel Hybrid NOMA/OMA small cellular network experienced by a user, and decomposes a joint resource allocation problem into 2 sub-problems of three-party matching and water injection type-like MOS power allocation, and comprises the following steps: 1. modeling a multi-carrier NOMA network system scene; 2. decomposition of radio resource allocation problem of non-orthogonal multiple access of multi-cellular multi-carrier and mathematical description thereof; 3. base station selection, sub-channel matching, unmanned aerial vehicle height optimization and power distribution. After the hybrid NOMA multi-cellular unmanned aerial vehicle auxiliary communication system is built, a three-dimensional matching strategy of the unmanned aerial vehicle/base station-user-sub-channel based on QoE of user experience and a high optimization and power distribution mode of the unmanned aerial vehicle are innovatively provided, the maximum user experience is taken as a final target, the problem of unreasonable resource distribution caused by the maximum blind pursuit rate is avoided, and the wireless resource distribution efficiency based on the NOMA scene is improved.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and relates to a wireless resource allocation method of a NOMA (non-orthogonal multiple access) and OMA (open mobile alliance) mixed heterogeneous small cellular network based on user experience, in particular to an unmanned aerial vehicle auxiliary communication resource allocation method of a mixed Hybrid NOMA network.
Background
It is well known that future wireless networks need to satisfy communication connections regardless of time and place in a diversified communication pattern. In order to improve the communications network's ability to respond to sudden conditions such as failures, natural disasters, and unexpected traffic, Unmanned Aerial Vehicle (UAV) assisted wireless communications systems can provide a unique opportunity to meet these needs in a timely manner, without relying on an over-engineered cellular network. Drones may serve as unmanned aerial vehicle base stations (UAV-bs) to handle short term, unstable traffic demands in hot spots such as sporting events and concerts, or to alleviate congestion by offloading data in the access network, thereby providing support for ground based wireless networks. Additional degrees of freedom of UAV-BS mobility are utilized to improve spectral and energy efficiency.
In the existing wireless communication system, 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 of wireless communication demand, future fifth generation 5G and beyond wireless systems will face greater challenges, requiring higher spectral efficiency, larger scale connections, and lower latency. The conventional Orthogonal Multiple Access (OMA) scheme may have a serious congestion problem when the number of access devices is large. Non-orthogonal multiple access (NOMA) techniques allow 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 capability.
In the existing NOMA cellular network wireless resource allocation technology, the user access rate and the maximum capacity are used as access targets, and the service difference of different terminal users is not considered. Broadband background tasks such as sensor data and movie downloads using small packets for low-speed low-latency transmission cannot be seen as the same user quality of service (QoS) requirements. In addition, NOMA encourages multiple users to share the same channel at the same time according to their channel conditions. Therefore, the performance gain of NOMA over OMA may be reduced in cases where the user channel conditions are similar. Note also that NOMA is more complex to implement than OMA, requiring the use of multi-user detection (MUD) techniques, such as Successive Interference Cancellation (SIC), at the receiving end to decode the received signal at the expense of increased computational complexity. Therefore, a resource allocation scheme design that mixes NOMA and OMA multiple access modes is necessary according to channel conditions.
In conclusion, the invention mainly aims at the problems of terminal user access, channel allocation, unmanned aerial vehicle height optimization and power optimization of the unmanned aerial vehicle auxiliary communication system, and provides a scheme for improving the capacity, the coverage area, the energy efficiency and the spectrum efficiency of a whole set of hybrid NOMA-OMA network unmanned aerial vehicle auxiliary communication system.
Disclosure of Invention
In view of the above, to solve the above-mentioned deficiencies of the prior art, an object of the present invention is to provide an auxiliary communication resource allocation method for an unmanned aerial vehicle in a Hybrid NOMA network, which is a multi-cell multi-channel Hybrid NOMA/OMA small cell network based on user experience, and relates to a matching problem among a user, a base station/unmanned aerial vehicle and a sub-channel and a power allocation problem in the sub-channel in an unmanned aerial vehicle high-degree optimization and NOMA mode, and improves the overall service efficiency of a system under the condition of ensuring diversified QoE requirements of the user.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the unmanned aerial vehicle auxiliary communication resource allocation method of the Hybrid NOMA network comprises the following steps:
s1: matching the base station with the user;
s11: initializing user selection and service information calculation stage: each base station transmitting power is pnCalculating the access rate and QoE score of the corresponding user;
s111: the user discovers all available base stations; calculating the position of the unmanned aerial vehicle according to the projection position;
s112: a user randomly accesses one base station or accesses the nearest base station, and then reports position information and service types to all available base stations;
s113: all base stations calculate the transmission rate and user experience scores of users in the base stations according to the actual user access conditions, create a service user list, and calculate the service utility of the base stations according to the user experience scores and the service user list;
s12: user transfer matching stage:
s121: the base station polls and sends out a user transfer matching application or a user exchange matching application to other available base stations according to the position information of the user for the purpose of increasing the service utility of the base station;
s122: the applied base station selects to accept or reject the application according to whether the service utility of the applied base station is improved, if the service utility of the applied base station is improved, the applied base station accepts the application and updates a service user list of the applied base station, and if the service utility of the applied base station is reduced or unchanged, the applied base station rejects the application;
s123: finishing all polling and finishing matching;
s2: the (base station, user) two-dimensional cell is matched with the sub-channel: using an iterative matching algorithm, a two-dimensional preference list;
s21: initializing (base station, user) -sub-channel matching, and randomly selecting a sub-channel to access according to services;
s22: according to the initial random access condition, calculating the user MOS score sum of the adjacent base stations and a channel access list;
s23: the mutual information between the base stations, if the MOS score between the adjacent base stations is increased, the application of channel exchange is received, and the MOS score and the channel access list are updated; otherwise, the rejection and MOS score and the channel access list are kept unchanged;
s3: power distribution: the matching result of the user and the sub-channel is obtained based on the first part and the second part, and the third part realizes the optimal position adjustment of the unmanned aerial vehicle and the user distribution power on the sub-channel of each base station;
s31: assuming that the unmanned aerial vehicle adopts fixed transmitting power, and users needing service are obtained through the step of S2, the optimal unmanned aerial vehicle altitude distributed by given users can be obtained as the channel capacity is a function of the channel gain; the average path loss for the drone and the user terminal may be expressed in the form of probability:
depending on the location of the terminal that needs to be serviced,the maximum service radius can be obtained, and road loss formula parameters are set according to specific environments (such as suburbs, cities, dense urban areas and CB high-rise building gathering areas); by passing
The optimal height of the unmanned aerial vehicle access can be obtained;
s32: if only one user accesses the same channel of the same base station, directly allocating p to the usern/m；
S33: otherwise, if the same channel of the same base station has 2 or more users to access, the (QoE) is determinedn5-obtained MOS score) is allocated; assuming that there are 3 users in a channel, the power of the third user is:
s4: and (3) receiving and decoding by a user: users receive respective signals, respectively decode the signals in a plurality of (more than or equal to 1) accessed channels, and finally synthesize transmission information according to a frequency spectrum aggregation technology;
s41: in each sub-channel, the users of the base stations decode once in sequence according to the NOMA protocol, and the decoding sequence is according to the channel condition
Decoding is carried out in sequence from small to large, and a user farthest from the base station decodes the signals first;
s42: finally, each user aggregates all the information of the access sub-channels to obtain the final information.
Further, before step S1, establishing an unmanned aerial vehicle access channel model of the user specifically includes the following steps:
a1: assuming a downlink drone-assisted cellular network, the small cell base station is set to be SBS ═ SBS1,SBS2,...,SBSnThe unmanned aerial vehicle set UAV (UAV)1,UAV2,...,UAVlDenoted UE, user set as UE ═ UE1,UE2,...,UEkDenoted SC ═ SC1,SC2,...,SCm}；
A2: establishing a quasi-static low-altitude rotary wing unmanned aerial vehicle auxiliary base station UAV-BS to provide disc-shaped wireless coverage with the radius of Rc meters, wherein the ground height is H, and the vertical projection is a point Q;
a3: suppose 2 users are accessed and have a distance D from the point QjThen a distance from the unmanned aerial vehicle is
k ∈ { user set }, UAV-BS elevation angle θ relative to each userk＝arctan(H/Dk) K ∈ { user set };
a4: the drone access channel model Of the user can be divided into Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) based on probabilistic models, depending on the density and altitude Of the buildings within the coverage area and the location, i.e. elevation, Of the environmental profile defined by the relative distance between the user and the buildings; the probability that the user experiences the line-of-sight link is:
line-of-sight access probability:
non-line-of-sight access probability: pr (Pr) ofk(NLOS)＝1-Prk(LOS)。
Further, in 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 unmanned aerial vehicle is: p is a radical ofrx,k(dB)＝ptx(dB)-Lk(dB),
wherein ,
further, wherein LkIs the path loss from drone to user, η is the free space path loss exponent, ψLOS and ψNLOSThe excessive losses due to shadowing effects for object occlusions, both terms obey a positive distribution, the mean and variance of which depend on elevation and environment-dependent constant values.
Further, by combining LOS and NLOS link analysis, the average path LOSs of the drone and the user terminal can be expressed in the form of probability:
further, the step S2 specifically includes the following steps:
a1: setting base station user matching relation xn,kSub-channel and user matching relationships
The superposition coding symbol of the base station n on the subchannel m can be represented as:
wherein ,
a transmission symbol representing that the base station n gives the user k in the subchannel m;
represents the transmission power allocated to the user k by the base station n in the subchannel m;
a2: the signal received by user k can be represented as a combination of three parts: the transmission signal of the base station n on the sub-channel m, and the transmission signal of other base stations on the sub-channel m are the accumulated interference and white noise to the user k.
The invention has the beneficial effects that:
the unmanned aerial vehicle auxiliary communication resource allocation method of the Hybrid NOMA network is based on a multi-cell multi-channel Hybrid NOMA/OMA small cellular network experienced by a user, relates to the matching problem of three parties of user base station/unmanned aerial vehicle matching, sub-channel selection and power optimization and the power allocation problem in a sub-channel under an NOMA mode, and improves the overall service efficiency of the system under the condition of ensuring diversified QoE requirements of the user;
in the invention, the problem of joint resource allocation is decomposed into 2 subproblems of three-party matching and similar water injection type MOS power allocation, which comprises the following steps: 1. modeling a multi-carrier NOMA network system scene; 2. decomposition of radio resource allocation problem of non-orthogonal multiple access of multi-cellular multi-carrier and mathematical description thereof; 3. designing a matching method of base station selection, sub-channel matching and power distribution sub-problems; after the vertical hybrid NOMA multi-cellular system is established, the invention innovatively provides a base station-user-sub-channel three-dimensional matching strategy and a power allocation mode which accord with the scene and are based on the QoE of user experience, takes the maximized user experience as the final target, avoids the unreasonable resource allocation problem caused by the blind pursuit of the maximum rate, and effectively improves the wireless resource allocation efficiency based on the NOMA scene.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic diagram of the principle of the present invention.
Detailed Description
The following specific examples are given to further clarify, complete and detailed the technical solution of the present invention. The present embodiment is a preferred embodiment based on the technical solution of the present invention, but the scope of the present invention is not limited to the following embodiments.
The unmanned aerial vehicle auxiliary communication resource allocation method of the Hybrid NOMA network comprises the following steps:
s1: matching the base station with the user;
s11: initializing user selection and service information calculation stage: each base station transmitting power is pnCalculating the access rate and QoE score of the corresponding user;
s111: the user discovers all available base stations; calculating the initial position of the unmanned aerial vehicle according to the projection position;
s112: a user randomly accesses one base station or accesses the nearest base station, and then reports position information and service types to all available base stations;
s113: all base stations calculate the transmission rate and user experience scores of users in the base stations according to the actual user access conditions, create a service user list, and calculate the service utility of the base stations according to the user experience scores and the service user list;
s12: user transfer matching stage:
s121: the base station polls and sends out a user transfer matching application or a user exchange matching application to other available base stations according to the position information of the user for the purpose of increasing the service utility of the base station;
s122: the applied base station selects to accept or reject the application according to whether the service utility of the applied base station is improved, if the service utility of the applied base station is improved, the applied base station accepts the application and updates a service user list of the applied base station, and if the service utility of the applied base station is reduced or unchanged, the applied base station rejects the application;
s123: finishing all polling and finishing matching;
s2: the (base station, user) two-dimensional cell is matched with the sub-channel: using an iterative matching algorithm, a two-dimensional preference list; in this embodiment, the power of the sub-channel is obtained by equally dividing the power of each base station, where the bandwidth of the sub-channel is W/n and the power of the sub-channel is pnAnd/n. According to the result of base station user distribution, determining the actual access power of the user in each base station, the user can access a plurality of channels simultaneously, calculating MOS score and counting the number of occupied channels according to each access mode, and establishing a preference list according to the two-dimensional index, the MOS score and the number of channels. The list is established in 2 steps, firstly, the items with the least occupied channel number are selected from the items with the highest MOS score according to the ranking of the MOS scores, and if a plurality of the items meet 2 conditions at the same time, a most preferred item is randomly selected. Each user issues an application to the channel with the most preferred terms as a policy. Base station calculationEach user can reach the corresponding QoE score of the rate after spectrum aggregation of each channel. A user applies for a channel from a base station, the base station judges the sum of user QoE scores of the channels, and the best allocation of the user in each channel is finally obtained through matching iterative operation;
further, taking three sub-channels as an example, the number of sub-channel access cases is totally 7, each row has 3 bits, which indicates 3 sub-channels, "1" indicates access, "0" indicates no access. [ 100; 010; 001; 110; 011; 101, a first electrode and a second electrode; 111 ];
s21: initializing (base station, user) -sub-channel matching, and randomly selecting a sub-channel to access according to services; if the low-rate service is randomly selected in 1-3 cases, only one channel is initially occupied; the high-speed service is randomly selected under 4-7 conditions and is initially accessed to more than 2 channels; s22: according to the initial random access condition, calculating the user MOS score sum of the adjacent base stations and a channel access list; the method comprises the steps that a cycle for t is 1, a set maximum iteration number round training method is adopted, each user respectively calculates the reachable rate of 7 access conditions and the MOS score experienced by the user, and an application is made to a base station under the access condition with the highest MOS score; proceed to step S23;
s23: the mutual information between the base stations, if the MOS score between the adjacent base stations is increased, the application of channel exchange is received, and the MOS score and the channel access list are updated; otherwise, the rejection and MOS score and the channel access list are kept unchanged;
s3: power distribution: the matching result of the user and the sub-channel is obtained based on the first part and the second part, and the third part realizes the optimal position adjustment of the unmanned aerial vehicle and the user distribution power on the sub-channel of each base station; in this embodiment, the power of each sub-channel is set to be the same, the sub-channel has only one user, and the OMA mode is used for access. If the sub-channel has a plurality of users and the NOMA mode is accessed, the base station distributes the power of each user on the channel according to the proportional fairness principle. Power is distributed in 2 cases:
s31: assuming that the drones use a fixed transmission power and obtain the users to be served through step S2, the optimal drones for a given user distribution can be obtained since the channel capacity is a function of the channel gainPulling; the average path loss for the drone and the user terminal may be expressed in the form of probability:
according to the position of a terminal needing service, the maximum service radius can be obtained, and road loss formula parameters are set according to specific environments (such as suburbs, cities, dense urban areas and CB high-rise building gathering areas); by passing
The optimal height of the unmanned aerial vehicle access can be obtained;
s32: if only one user accesses the same channel of the same base station, directly allocating p to the usern/m；
S33: otherwise, if the same channel of the same base station has 2 or more users to access, the (QoE) is determinedn5-obtained MOS score) is allocated; assuming that there are 3 users in a channel, the power of the third user is:
s4: and (3) receiving and decoding by a user: users receive respective signals, respectively decode the signals in a plurality of (more than or equal to 1) accessed channels, and finally synthesize transmission information according to a frequency spectrum aggregation technology;
s41: in each sub-channel, the users of the base stations decode once in sequence according to the NOMA protocol, and the decoding sequence is according to the channel condition
Decoding is carried out in sequence from small to large, and a user farthest from the base station decodes the signals first;
s42: finally, each user aggregates all the information of the access sub-channels to obtain the final information.
Further, before step S1, establishing an unmanned aerial vehicle access channel model of the user specifically includes the following steps:
a1: assuming a downlink drone-assisted cellular network, the small cell base station is set to be SBS ═ SBS1,SBS2,...,SBSnThe unmanned aerial vehicle set UAV (UAV)1,UAV2,...,UAVlDenoted UE, user set as UE ═ UE1,UE2,...,UEkDenoted SC ═ SC1,SC2,...,SCm}；
A2: establishing a quasi-static low-altitude rotary wing unmanned aerial vehicle auxiliary base station UAV-BS to provide disc-shaped wireless coverage with the radius of Rc meters, wherein the ground height is H, and the vertical projection is a point Q;
a3: suppose 2 users are accessed and have a distance D from the point QjThen a distance from the unmanned aerial vehicle is
k ∈ { user set }, UAV-BS elevation angle θ relative to each userk＝arctan(H/Dk) K ∈ { user set };
a4: the drone access channel model Of the user can be divided into Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) based on probabilistic models, depending on the density and altitude Of the buildings within the coverage area and the location, i.e. elevation, Of the environmental profile defined by the relative distance between the user and the buildings; the probability that the user experiences the line-of-sight link is:
line-of-sight access probability:
non-line-of-sight access probability: pr (Pr) ofk(NLOS)＝1-Prk(LOS)。
Further, in 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 unmanned aerial vehicle is: p is a radical ofrx,k(dB)＝ptx(dB)-Lk(dB),
wherein ,
further, wherein LkIs the path loss from drone to user, η is the free space path loss exponent, ψLOS and ψNLOSThe excessive losses due to shadowing effects for object occlusions, both terms obey a positive distribution, the mean and variance of which depend on elevation and environment-dependent constant values. In the invention, due to the barrier reflection and shadow effect, the closer the building is to the user, the greater the scattering and the greater the loss of non-line-of-sight.
Further, by combining LOS and NLOS link analysis, the average path LOSs of the drone and the user terminal can be expressed in the form of probability:
In the invention, because the setting parameters do not influence the framework process of the invention, for simplifying the expression, the transmission power, the bandwidth and the channel number of the auxiliary hotspot of the unmanned aerial vehicle and the common small micro base station are assumed to be the same. In actual implementation, the settings of the transmission power, bandwidth and channel number of the drone assisting base station may be different. Let the transmit power of each base station be pnAllocating among each sub-channel; the total bandwidth of access of each small micro base station/unmanned aerial vehicle auxiliary base station is W, the whole bandwidth W is divided into m sub-channels, and the sub-channel bandwidth is W/m.
Further, each user may access multiple channels adjacent to the base station, and each sub-channel may access multiple users, each user may access only one base station, according to the NOMA protocol. 2 arrangement matrix 0-1 elements; in step S2, the method specifically includes the following steps:
a1: setting base station user matching relation xn,kSub-channel and user matching relationships
The superposition coding symbol of the base station n on the subchannel m can be represented as:
wherein ,
a transmission symbol representing that the base station n gives the user k in the subchannel m;
represents the transmission power allocated to the user k by the base station n in the subchannel m;
a2: the signal received by user k can be represented as a combination of three parts: the transmission signal of the base station n on the sub-channel m, and the transmission signal of other base stations on the sub-channel m are the accumulated interference and white noise to the user k.
wherein ,
wherein ,
representing the channel parameters from base station n to user k at subchannel m;
defining the equivalent channel gain:
user Equipment (UE) using spectrum aggregation technologyn,kRepresenting a user k accessing a base station n, whose rate is equal to the rate of all access channelsAnd;
user k, accessing base station n, the signal to interference plus noise ratio on channel m can be expressed as:
the rates obtained at this time are:
the achievable rate of user k accessing base station n is:
the reachable rate of a user terminal is converted into a user experience index and an MOS score, and the specific calculation method is as follows:
where theta represents the average user throughput rate,
the user service type is obtained through statistics of scoring data of user experience, the lower limit value of the user average throughput rate required by the service type and the recommended value meeting the requirement of smooth transmission are respectively corresponding, a and b are 2 calculation parameters and synchronously change along with the service type;
particularly, different definition methods can be used for the MOS conversion form of the terminal user, the mainstream adopts a logarithm-like function form for conversion, the invention mainly innovates the distribution framework, and different MOS conversion forms can be implemented in the framework of the invention; the final optimization objective is then:
the 4 constraints are explained as follows: (1) an element of an access state matrix; (2) one user can only access one base station at most; (3) the number of access channels of each base station and user is restricted; (4) power constraints for each base station.
Solving the problem, the problem is decomposed into 3 sub-problems, namely base station and user matching, user and sub-channel matching, and base station allocates power to users on sub-channels, so that suboptimal solution of the original problem is realized, and MOS and maximum of user QoE are realized.
In summary, the unmanned aerial vehicle-assisted communication resource allocation method of the Hybrid NOMA network of the present invention is based on a multi-cell multi-channel Hybrid NOMA/OMA small cell network experienced by a user, and relates to the matching problem of three parties of user base station/unmanned aerial vehicle matching, sub-channel selection and power optimization and the power allocation problem in the sub-channel in the NOMA mode, and improves the overall service efficiency of the system under the condition of ensuring the diversified QoE requirements of the user;
in the invention, the problem of joint resource allocation is decomposed into 2 subproblems of three-party matching and similar water injection type MOS power allocation, which comprises the following steps: 1. modeling a multi-carrier NOMA network system scene; 2. decomposition of radio resource allocation problem of non-orthogonal multiple access of multi-cellular multi-carrier and mathematical description thereof; 3. designing a matching method of base station selection, sub-channel matching and power distribution sub-problems; after the vertical hybrid NOMA multi-cellular system is established, the invention innovatively provides a base station-user-sub-channel three-dimensional matching strategy and a power allocation mode which accord with the scene and are based on the QoE of user experience, takes the maximized user experience as the final target, avoids the unreasonable resource allocation problem caused by the blind pursuit of the maximum rate, and effectively improves the wireless resource allocation efficiency based on the NOMA scene.
The principal features, principles and advantages of the invention have been shown and described above. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to explain the principles of the invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the invention as expressed in the following claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. An unmanned aerial vehicle auxiliary communication resource allocation method of a Hybrid NOMA network is characterized in that: the method comprises the following steps:
s1: matching the base station with the user;
s11: initializing user selection and service information calculation stage: each base station transmitting power is pnCalculating the access rate and QoE score of the corresponding user;
s111: the user discovers all available base stations; calculating the initial position of the unmanned aerial vehicle according to the projection position;
s112: a user randomly accesses one base station or accesses the nearest base station, and then reports position information and service types to all available base stations;
s113: all base stations calculate the transmission rate and user experience scores of users in the base stations according to the actual user access conditions, create a service user list, and calculate the service utility of the base stations according to the user experience scores and the service user list;
s12: user transfer matching stage:
s121: the base station polls and sends out a user transfer matching application or a user exchange matching application to other available base stations according to the position information of the user for the purpose of increasing the service utility of the base station;
s122: the applied base station selects to accept or reject the application according to whether the service utility of the applied base station is improved, if the service utility of the applied base station is improved, the applied base station accepts the application and updates a service user list of the applied base station, and if the service utility of the applied base station is reduced or unchanged, the applied base station rejects the application;
s123: finishing all polling and finishing matching;
s2: the (base station, user) two-dimensional cell is matched with the sub-channel: using an iterative matching algorithm, a two-dimensional preference list;
s21: initializing (base station, user) -sub-channel matching, and randomly selecting a sub-channel to access according to services;
s22: according to the initial random access condition, calculating the user MOS score sum of the adjacent base stations and a channel access list;
s23: the mutual information between the base stations, if the MOS score between the adjacent base stations is increased, the application of channel exchange is received, and the MOS score and the channel access list are updated; otherwise, the rejection and MOS score and the channel access list are kept unchanged;
s3: power distribution: the matching result of the user and the sub-channel is obtained based on the first part and the second part, and the third part realizes the optimal position adjustment of the unmanned aerial vehicle and the user distribution power on the sub-channel of each base station;
s31: assuming that the unmanned aerial vehicle adopts fixed transmitting power, and users needing service are obtained through the step of S2, the optimal unmanned aerial vehicle altitude distributed by given users can be obtained as the channel capacity is a function of the channel gain; the average path loss for the drone and the user terminal may be expressed in the form of probability:
according to the position of a terminal needing service, the maximum service radius can be obtained, and road loss formula parameters are set according to specific environments (such as suburbs, cities, dense urban areas and CB high-rise building gathering areas); by passing
The optimal height of the unmanned aerial vehicle access can be obtained;
s32: if only one user accesses the same channel of the same base station, directly allocating p to the usern/m；
S33: otherwise, if the same channel of the same base station has 2 or more users to access, the (QoE) is determinedn5-obtained MOS score) is allocated; assuming that there are 3 users in a channel, the power of the third user is:
wherein η (0 is equal to or more than η is equal to or less than 1) is a fading factor;
s4: and (3) receiving and decoding by a user: users receive respective signals, respectively decode the signals in a plurality of (more than or equal to 1) accessed channels, and finally synthesize transmission information according to a frequency spectrum aggregation technology;
s41: in each sub-channel, the users of the base stations decode once in sequence according to the NOMA protocol, and the decoding sequence is according to the channel condition
Decoding is carried out in sequence from small to large, and a user farthest from the base station decodes the signals first;
s42: finally, each user aggregates all the information of the access sub-channels to obtain the final information.
2. The drone-assisted communication resource allocation method of a Hybrid NOMA network according to claim 1, characterized in that: before step S1, establishing an unmanned aerial vehicle access channel model of the user, specifically including the following steps:
a1: assuming a downlink drone-assisted cellular network, the small cell base station is set to be SBS ═ SBS1,SBS2,...,SBSnThe unmanned aerial vehicle set UAV (UAV)1,UAV2,...,UAVlDenoted UE, user set as UE ═ UE1,UE2,...,UEkDenoted SC ═ SC1,SC2,...,SCm}；
A2: establishing a quasi-static low-altitude rotary wing unmanned aerial vehicle auxiliary base station UAV-BS to provide disc-shaped wireless coverage with the radius of Rc meters, wherein the ground height is H, and the vertical projection is a point Q;
a3: suppose 2 users are accessed and have a distance D from the point QjThen a distance from the unmanned aerial vehicle is
k ∈ { user set }, UAV-BS elevation angle θ relative to each userk＝arctan(H/Dk) K ∈ { user set };
a4: the drone access channel model Of the user can be divided into Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) based on probabilistic models, depending on the density and altitude Of the buildings within the coverage area and the location, i.e. elevation, Of the environmental profile defined by the relative distance between the user and the buildings; the probability that the user experiences the line-of-sight link is:
line-of-sight access probability:
non-line-of-sight access probability: pr (Pr) ofk(NLOS)＝1-Prk(LOS)。
3. The drone-assisted communication resource allocation method of a Hybrid NOMA network according to claim 2, characterized in that: in 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.
4. The drone-assisted communication resource allocation method of a Hybrid NOMA network according to claim 2, characterized in that: the transmission power of the user accessing the unmanned aerial vehicle is as follows: p is a radical ofrx,k(dB)＝ptx(dB)-Lk(dB),
wherein ,
5. the drone-assisted communication resource allocation method of a hybrid noma network according to claim 4, characterized in that: wherein L iskIs the path loss from drone to user, η is the free space path loss exponent, ψLOS and ψNLOSThe excessive losses due to shadowing effects for object occlusions, both terms obey a positive distribution, the mean and variance of which depend on elevation and environment-dependent constant values.
6. The drone-assisted communication resource allocation method of a hybrid noma network according to claim 4, characterized in that: by combining LOS and NLOS link analysis, the average path LOSs of the drone and the user terminal can be expressed in the form of probability:
7. the drone-assisted communication resource allocation method of a hybrid noma network according to claim 1, characterized in that: in step S2, the method specifically includes the following steps:
a1: setting base station user matching relation xn,kSub-channel and user matching relationships
The superposition coding symbol of the base station n on the subchannel m can be represented as:
wherein ,
a transmission symbol representing that the base station n gives the user k in the subchannel m;
represents the transmission power allocated to the user k by the base station n in the subchannel m;
a2: the signal received by user k can be represented as a combination of three parts: the transmission signal of the base station n on the sub-channel m, and the transmission signal of other base stations on the sub-channel m are the accumulated interference and white noise to the user k.
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