CN111615200B - Unmanned aerial vehicle auxiliary communication resource allocation method for Hybrid NOMA network - Google Patents
Unmanned aerial vehicle auxiliary communication resource allocation method for Hybrid NOMA network Download PDFInfo
- Publication number
- CN111615200B CN111615200B CN202010281341.6A CN202010281341A CN111615200B CN 111615200 B CN111615200 B CN 111615200B CN 202010281341 A CN202010281341 A CN 202010281341A CN 111615200 B CN111615200 B CN 111615200B
- Authority
- CN
- China
- Prior art keywords
- user
- channel
- base station
- aerial vehicle
- unmanned aerial
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000013468 resource allocation Methods 0.000 title claims abstract description 29
- 238000004891 communication Methods 0.000 title claims abstract description 26
- 206010042135 Stomatitis necrotising Diseases 0.000 title claims abstract description 15
- 201000008585 noma Diseases 0.000 title claims abstract description 15
- 230000001413 cellular effect Effects 0.000 claims abstract description 13
- 230000005540 biological transmission Effects 0.000 claims description 25
- 230000002776 aggregation Effects 0.000 claims description 7
- 238000004220 aggregation Methods 0.000 claims description 7
- 230000007613 environmental effect Effects 0.000 claims description 6
- 238000001228 spectrum Methods 0.000 claims description 6
- 238000012546 transfer Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 5
- 230000000694 effects Effects 0.000 claims description 4
- 238000005516 engineering process Methods 0.000 claims description 4
- 230000006872 improvement Effects 0.000 claims description 4
- 238000004458 analytical method Methods 0.000 claims description 3
- 230000001419 dependent effect Effects 0.000 claims description 3
- 238000005457 optimization Methods 0.000 abstract description 8
- 238000013461 design Methods 0.000 abstract description 4
- 238000000354 decomposition reaction Methods 0.000 abstract description 3
- 238000002347 injection Methods 0.000 abstract description 3
- 239000007924 injection Substances 0.000 abstract description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 abstract description 3
- 230000006978 adaptation Effects 0.000 abstract 1
- 239000000243 solution Substances 0.000 description 3
- 230000003595 spectral effect Effects 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000001351 cycling effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000002360 explosive Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- 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
- H04W72/0473—Wireless resource allocation based on the type of the allocated resource the resource being transmission power
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/50—Allocation or scheduling criteria for wireless resources
- H04W72/53—Allocation or scheduling criteria for wireless resources based on regulatory allocation policies
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
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 of user experience, and decomposes a joint resource allocation problem into 2 sub-problems of three-party matching and water injection type MOS power allocation, and comprises the following steps: 1. modeling a multi-carrier NOMA network system scene; 2. decomposition and mathematical description of the problem of multi-cellular multi-carrier non-orthogonal multiple access radio resource allocation; 3. base station selection, sub-channel matching, unmanned aerial vehicle high optimization and power distribution multi-objective optimization adaptation method design. After the hybrid NOMA multi-cellular unmanned aerial vehicle auxiliary communication system is established, a three-dimensional matching strategy of unmanned aerial vehicle/base station-user-subchannel based on user experience QoE and a unmanned aerial vehicle high-level optimization and power distribution mode which accord with the scene are innovatively provided, so that the maximum user experience is used 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, relates to a wireless resource allocation method of a NOMA and OMA Hybrid heterogeneous small cellular network based on user experience, and particularly relates to an unmanned aerial vehicle auxiliary communication resource allocation method of a Hybrid NOMA network.
Background
As is well known, future wireless networks need to satisfy communication connections whenever and wherever in a diverse communication mode. In order to increase the strain capacity of a communication network to sudden conditions such as faults, natural disasters, and unexpected traffic, unmanned Aerial Vehicle (UAV) -assisted wireless communication systems may provide a unique opportunity to meet these demands in time without relying on over-engineered cellular networks. The drone may act as a drone base station (UAV-bs), handling short-term unstable traffic demands in hot spots such as sporting events and concerts, or to provide support for terrestrial wireless networks by alleviating congestion through data offloading in the access network. The additional degrees of freedom of UAV-BS mobility are exploited to improve spectral and energy efficiency.
In existing wireless communication systems, orthogonal Frequency Division Multiple Access (OFDMA) techniques and time division multiple access techniques (Time division multiple access, TDMA) are widely used for user scheduling and data transmission in the orthogonal domain. Due to the explosive growth in 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. Conventional Orthogonal Multiple Access (OMA) schemes can present serious congestion problems 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 taken as access targets, and service differences of different terminal users are not considered. The broadband background tasks such as sensor data and movie downloads using small data packets for low-speed low-latency transmission cannot be seen as the same quality of service (QoS) requirements for the user. In addition, NOMA encourages multiple users to share the same channel at the same time, depending on their channel conditions. Thus, the performance gain of NOMA over OMA may be reduced with similar user channel conditions. 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 of hybrid NOMA and OMA multiple access modes is necessary according to channel conditions.
In summary, the invention mainly aims at the problems of terminal user access, channel allocation, unmanned aerial vehicle high optimization and power optimization of an unmanned aerial vehicle auxiliary communication system, and provides a whole set of improvement schemes of capacity, coverage, energy efficiency and spectrum efficiency of a hybrid NOMA-OMA network unmanned aerial vehicle auxiliary communication system.
Disclosure of Invention
In view of the above, in order to solve the above-mentioned shortcomings in the prior art, the present invention aims to provide an unmanned aerial vehicle auxiliary communication resource allocation method for a Hybrid NOMA network, which is based on a multi-cell multi-channel Hybrid NOMA/OMA small cellular network experienced by users, and relates to the matching problem of users, base stations/unmanned aerial vehicles and sub-channels, and the power allocation problem in the sub-channels in the unmanned aerial vehicle highly optimizing and NOMA mode, so that the overall service efficiency of the system is improved under the condition of guaranteeing the diversified QoE requirements of users.
In order to achieve the above 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: the base station is matched with the user;
s11: initializing a user selection and service information calculation phase: the transmitting power of each base station is p n Calculating 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: the user randomly accesses a base station or accesses the nearest base station, and then reports the position information and the service type to all available base stations;
s113: all base stations calculate the transmission rate and user experience score of users in the base stations according to the actual user access condition, create a service user list, and calculate the service utility of the base stations according to the user experience score and the service user list;
s12: user transfer matching phase:
s121: the base station polls other available base stations according to the position information of the user to send out a user transfer matching application or a user exchange matching application;
s122: the applied base station selects to accept or reject the application according to whether the self service utility is improved, if the self service utility of the base station is improved, the application is accepted and the self service user list is updated, and if the self service utility is reduced or unchanged, the application is rejected;
s123: finishing all polling and finishing matching;
s2: the (base station, user) two-dimensional element matches with the sub-channel: using an iterative matching algorithm, a two-dimensional list of preferences;
s21: initializing (base station, user) -sub-channel matching, and randomly selecting sub-channel access according to service;
s22: calculating a user MOS score sum of adjacent base stations and a channel access list according to the initial random access condition;
s23: the base stations exchange information, if MOS score and improvement between adjacent base stations, the application of channel exchange, updating and MOS score and channel access list are accepted; otherwise, reject and MOS score, and channel access list remain unchanged;
s3: and (3) power distribution: based on the first and second parts, obtaining the matching result of the user and the sub-channel, and the third part realizes the optimal position adjustment of the unmanned aerial vehicle and the power distribution of the user on the sub-channel of each base station;
s31: assuming that the unmanned aerial vehicle adopts fixed transmitting power, and obtaining a user needing service through the step S2, because the channel capacity is a function of the channel gain, the optimal unmanned aerial vehicle elevation of given user distribution can be obtained; the average path loss of the drone and the user terminal may be expressed in the form of probabilities:
the maximum service radius can be obtained according to the terminal position needing service, and road loss formula parameters are set according to specific environments (such as suburbs, cities, dense urban areas and CB high-rise building aggregation areas); by passing through
The optimal height of unmanned aerial vehicle access can be obtained;
s32: if only one user accesses the same channel of the same base station, p is directly allocated to the user n /m；
S33: otherwise, if the same channel of the same base station has 2 or more user accesses, the method is carried out according to (QoE n =5-derived MOS score); assuming that there are 3 users for a channel, the power of the third user is:
s4: user reception decoding: the user receives the signals, decodes the signals respectively on a plurality of accessed channels (more than or equal to 1), and finally synthesizes transmission information according to the spectrum aggregation technology;
s41: in each sub-channel, according to NOMA protocol arrangement, users of each base station decode once according to sequence, and the decoding sequence is according to channel condition
Sequentially decoding from small to large, and decoding firstly by a user farthest from the base station;
s42: and finally, each user aggregates all information of the access sub-channels to obtain the final information.
Further, before the step S1, an unmanned aerial vehicle access channel model of the user is established, which specifically includes the following steps:
a1: assuming a downlink unmanned aerial vehicle assisted cellular network, a small cellular base station is set to represent sbs= { SBS 1 ,SBS 2 ,...,SBS n Unmanned aerial vehicle aggregate uav= { UAV } 1 ,UAV 2 ,...,UAV l User set denoted ue= { UE } 1 ,UE 2 ,...,UE k The sub-channel is denoted sc= { SC } 1 ,SC 2 ,...,SC m }；
A2: establishing a quasi-stationary low-altitude rotary wing unmanned aerial vehicle auxiliary base station UAV-BS to provide disc-shaped wireless coverage with a radius Rc meters, a ground height H and a vertical projection Q point;
a3: suppose that 2 users are accessed, whose distance from the Q point is D j The distance from the unmanned aerial vehicle is
k.epsilon { user set }, the elevation angle of UAV-BS with respect to each user is θ k ＝arctan(H/D k ) K e { user set };
a4: the unmanned access channel model Of the user can be classified into Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) based on a probability model depending on the density, altitude, and position Of the environmental profile defined by the relative distance between the user and the building within the coverage area, i.e., elevation angle; the probability that the user experiences a line-of-sight link is:
line-of-sight access probability:
non line-of-sight access probability: pr (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 unmanned aerial vehicle is as follows: p is p rx,k (dB)＝p tx (dB)-L k (dB),
wherein ,
further, wherein L k Is the path loss from the unmanned aerial vehicle to the user, eta is the free space path loss index, psi LOS and ψNLOS The excessive loss caused by shadow effect for object occlusion, both subject to a positive too-distribution, the mean and variance of which depend on elevation and environmental dependent constant values.
Further, combining LOS and NLOS link analysis, the average path LOSs of the drone and user terminal can be expressed as a probability form:
further, in the step S2, the method specifically includes the following steps:
a1: setting the matching relationship χ of base station users n,k Sub-channel and user matching relationship
The superposition coded symbols of base station n on subchannel m may be expressed as:
wherein ,
representing the transmission symbols of base station n to user k in subchannel m; />
Representing the transmission power allocated by base station n to user k in subchannel m;
a2: the signal received by user k may be expressed as a combination of three parts: the transmission signals of the base station n in the sub-channel m, and the transmission signals of other base stations in the sub-channel m are accumulated interference and white noise to the user k.
The beneficial effects of the invention are as follows:
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 of user experience, and relates to the matching problem of user base station/unmanned aerial vehicle matching, sub-channel selection and power optimization and the power allocation problem in sub-channels in the NOMA mode, so that the overall service efficiency of the system is improved under the condition of guaranteeing the diversified QoE requirements of users;
in the invention, the joint resource allocation problem is decomposed into 2 sub-problems of three-party matching and water injection type MOS power allocation, which comprises the following steps: 1. modeling a multi-carrier NOMA network system scene; 2. decomposition and mathematical description of the problem of multi-cellular multi-carrier non-orthogonal multiple access radio resource allocation; 3. base station selection, sub-channel matching and matching method design of power distribution sub-problems; after the hybrid NOMA multi-cellular system is established, the three-dimensional matching strategy and the power allocation mode of the base station-user-subchannel based on the user experience QoE according to the scene are innovatively provided, the maximized user experience is taken as a final target, the problem of unreasonable resource allocation caused by the maximum blind pursuit rate is avoided, and the wireless resource allocation efficiency based on the NOMA scene is effectively improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of the present invention.
Detailed Description
Specific examples are given below to further clarify, complete and detailed description of the technical scheme of the 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: the base station is matched with the user;
s11: initializing a user selection and service information calculation phase: the transmitting power of each base station is p n Calculating 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: the user randomly accesses a base station or accesses the nearest base station, and then reports the position information and the service type to all available base stations;
s113: all base stations calculate the transmission rate and user experience score of users in the base stations according to the actual user access condition, create a service user list, and calculate the service utility of the base stations according to the user experience score and the service user list;
s12: user transfer matching phase:
s121: the base station polls other available base stations according to the position information of the user to send out a user transfer matching application or a user exchange matching application;
s122: the applied base station selects to accept or reject the application according to whether the self service utility is improved, if the self service utility of the base station is improved, the application is accepted and the self service user list is updated, and if the self service utility is reduced or unchanged, the application is rejected;
s123: finishing all polling and finishing matching;
s2: the (base station, user) two-dimensional element matches with the sub-channel: using an iterative matching algorithm, a two-dimensional list of preferences; in this embodiment, the sub-channel power is obtained by equally dividing the power of each base station, wherein the sub-channel bandwidth is W/n, and the sub-channel power is p n And/n. According to the result of base station user distribution, determining the actual access power of user in each base station, the user can access several channels at the same time, according to each access mode, calculating MOS score and counting occupied channel number, according to two-dimensional index, MOS score and channel number building preference list. The list is established in 2 steps, firstly, the items with the least occupied channels are selected from the items with the highest MOS score according to the MOS score sequence, and if a plurality of items simultaneously meet 2 conditions, one most optimal item is selected randomly. Each user applies for the best option as a policy to the channel. The base station calculates the corresponding QoE score of the reachable rate of each user after spectrum aggregation of each channel. The user applies for the channel to the base station, the base station judges the user QoE score sum of the channel, and the optimal allocation of the user in each channel is finally obtained through matching iterative operation;
further, taking three subchannels as an example, the case of accessing the subchannels is divided into 7 types in total, 3 bits in each row indicate 3 subchannels, "1" indicates access, and "0" indicates no access. [100;010;001;110;011;101;111];
s21: initializing (base station, user) -sub-channel matching, and randomly selecting sub-channel access according to service; 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 in 4-7 cases, and is initially accessed to more than 2 channels; s22: calculating a user MOS score sum of adjacent base stations and a channel access list according to the initial random access condition; the method comprises the steps of (1) cycling for t=1, wherein in the set maximum iteration number training method, each user respectively calculates the reachable rates of 7 access conditions and MOS scores of user experience, and applies for the access condition with the highest MOS score to a base station; turning to step S23;
s23: the base stations exchange information, if MOS score and improvement between adjacent base stations, the application of channel exchange, updating and MOS score and channel access list are accepted; otherwise, reject and MOS score, and channel access list remain unchanged;
s3: and (3) power distribution: based on the first and second parts, obtaining the matching result of the user and the sub-channel, and the third part realizes the optimal position adjustment of the unmanned aerial vehicle and the power distribution of the user on the sub-channel of each base station; in this embodiment, the power of each sub-channel is set to be the same, only one user is used for the sub-channel, and the OMA mode is accessed. 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 allocated in 2 cases:
s31: assuming that the unmanned aerial vehicle adopts fixed transmitting power, and obtaining a user needing service through the step S2, because the channel capacity is a function of the channel gain, the optimal unmanned aerial vehicle elevation of given user distribution can be obtained; the average path loss of the drone and the user terminal may be expressed in the form of probabilities:
the maximum service radius can be obtained according to the terminal position needing service, and road loss formula parameters are set according to specific environments (such as suburbs, cities, dense urban areas and CB high-rise building aggregation areas); by passing through
The optimal height of unmanned aerial vehicle access can be obtained;
s32: if only one user accesses the same channel of the same base station, giving the same channel to the base stationUser direct allocation p n /m；
S33: otherwise, if the same channel of the same base station has 2 or more user accesses, the method is carried out according to (QoE n =5-derived MOS score); assuming that there are 3 users for a channel, the power of the third user is:
s4: user reception decoding: the user receives the signals, decodes the signals respectively on a plurality of accessed channels (more than or equal to 1), and finally synthesizes transmission information according to the spectrum aggregation technology;
s41: in each sub-channel, according to NOMA protocol arrangement, users of each base station decode once according to sequence, and the decoding sequence is according to channel condition
Sequentially decoding from small to large, and decoding firstly by a user farthest from the base station;
s42: and finally, each user aggregates all information of the access sub-channels to obtain the final information.
Further, before the step S1, an unmanned aerial vehicle access channel model of the user is established, which specifically includes the following steps:
a1: assuming a downlink unmanned aerial vehicle assisted cellular network, a small cellular base station is set to represent sbs= { SBS 1 ,SBS 2 ,...,SBS n Unmanned aerial vehicle aggregate uav= { UAV } 1 ,UAV 2 ,...,UAV l User set denoted ue= { UE } 1 ,UE 2 ,...,UE k The sub-channel is denoted sc= { SC } 1 ,SC 2 ,...,SC m }；
A2: establishing a quasi-stationary low-altitude rotary wing unmanned aerial vehicle auxiliary base station UAV-BS to provide disc-shaped wireless coverage with a radius Rc meters, a ground height H and a vertical projection Q point;
a3: suppose that 2 users are accessed, whose distance from the Q point is D j The distance from the unmanned aerial vehicle is
k.epsilon { user set }, the elevation angle of UAV-BS with respect to each user is θ k ＝arctan(H/D k ) K e { user set };
a4: the unmanned access channel model Of the user can be classified into Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) based on a probability model depending on the density, altitude, and position Of the environmental profile defined by the relative distance between the user and the building within the coverage area, i.e., elevation angle; the probability that the user experiences a line-of-sight link is:
line-of-sight access probability:
non line-of-sight access probability: pr (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 unmanned aerial vehicle is as follows: p is p rx,k (dB)＝p tx (dB)-L k (dB),
wherein ,
further, wherein L k Is the path loss from the unmanned aerial vehicle to the user, eta is the free space path loss index, psi LOS and ψNLOS The excessive loss caused by shadow effect for object occlusion, both subject to a positive too-distribution, the mean and variance of which depend on elevation and environmental dependent constant values. In the invention, due to the reflection and shadow effect of the obstacle, the closer the building is to the user, the larger the scattering and the larger the loss of non-line-of-sight.
Further, combining LOS and NLOS link analysis, the average path LOSs of the drone and user terminal can be expressed as a probability form:
In the invention, because the setting parameters do not affect the frame flow of the invention, for simplifying the expression, the transmission power, the bandwidth and the channel number of the unmanned aerial vehicle auxiliary hot spot and the common micro base station are assumed to be the same. In actual implementation, the settings of the transmit power, bandwidth and channel number of the drone-assisted base station may be different. Let the transmitting power of each base station be p n Allocating among all sub-channels; the total bandwidth of the access of each 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 having access to only one base station, according to the NOMA protocol. 2 arrangement matrix 0-1 elements; the step S2 specifically includes the following steps:
a1: setting the matching relationship χ of base station users n,k Sub-channel and user matching relationship
The superposition coded symbols of base station n on subchannel m may be expressed as:
wherein ,
representing the transmission symbols of base station n to user k in subchannel m; />
Representing the transmission power allocated by base station n to user k in subchannel m;
a2: the signal received by user k may be expressed as a combination of three parts: the transmission signals of the base station n in the sub-channel m, and the transmission signals of other base stations in the sub-channel m are accumulated interference and white noise to the user k.
wherein ,
wherein ,/>
Channel parameters representing base station n to user k at subchannel m;
defining equivalent channel gain:
user UE using spectrum aggregation techniques n,k Representing user k accessing base station n, whose rate is equal to the sum of the rates of all access channels;
the signal-to-interference-and-noise ratio of user k, access base station n, on channel m can be expressed as:
the rates obtained at this time were:
the achievable rate of user k accessing base station n is:
the reachable rate of the user terminal is converted into user experience indexes and MOS scores, and the specific calculation method is as follows:
where θ represents the average user throughput rate,
the user service type is obtained through scoring data statistics of user experience, and the lower limit value of the average throughput rate of the user required by the service type and the recommended value meeting the requirement of smooth transmission are respectively corresponding, wherein a and b are 2 calculation parameters and synchronously change along with the service type;
specifically, different definition methods can be used for the MOS conversion form of the end user, the main stream is converted by adopting a log-like function form, 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:
the description for the 4 constraints is as follows: (1) accessing elements of a state matrix; (2) a user can access only one base station at most; (3) a constraint on the number of access channels per base station and user; (4) power constraints for each base station.
And solving the problems, namely decomposing the problems into 3 sub-problems, namely matching the base station with the user, matching the user with the sub-channel, and distributing power to the user on the sub-channel by the base station, so as to realize suboptimal solution of the original problems and realize MOS and maximum QoE of the user.
In summary, the unmanned aerial vehicle auxiliary communication resource allocation method of the Hybrid NOMA network provided by the invention is based on the multi-cell multi-channel Hybrid NOMA/OMA small cellular network of user experience, and relates to the matching problem of three parties of user base station/unmanned aerial vehicle matching, subchannel selection and power optimization and the power allocation problem in subchannels in the NOMA mode, so that the overall service efficiency of the system is improved under the condition of guaranteeing the diversified QoE requirements of users;
in the invention, the joint resource allocation problem is decomposed into 2 sub-problems of three-party matching and water injection type MOS power allocation, which comprises the following steps: 1. modeling a multi-carrier NOMA network system scene; 2. decomposition and mathematical description of the problem of multi-cellular multi-carrier non-orthogonal multiple access radio resource allocation; 3. base station selection, sub-channel matching and matching method design of power distribution sub-problems; after the hybrid NOMA multi-cellular system is established, the three-dimensional matching strategy and the power allocation mode of the base station-user-subchannel based on the user experience QoE according to the scene are innovatively provided, the maximized user experience is taken as a final target, the problem of unreasonable resource allocation caused by the maximum blind pursuit rate is avoided, and the wireless resource allocation efficiency based on the NOMA scene is effectively improved.
The foregoing has outlined and described the features, principles, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are merely illustrative of the principles of the present invention, and that various changes and modifications may be made in the invention without departing from the spirit and scope of the invention, which is defined by the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. The unmanned aerial vehicle auxiliary communication resource allocation method of the hybrid NOMA network is characterized by comprising the following steps of: the method comprises the following steps:
s1: the base station is matched with the user;
s11: initializing a user selection and service information calculation phase: the transmitting power of each base station is p n Calculating 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: the user randomly accesses a base station or accesses the nearest base station, and then reports the position information and the service type to all available base stations;
s113: all base stations calculate the transmission rate and user experience score of users in the base stations according to the actual user access condition, create a service user list, and calculate the service utility of the base stations according to the user experience score and the service user list;
s12: user transfer matching phase:
s121: the base station polls other available base stations according to the position information of the user to send out a user transfer matching application or a user exchange matching application;
s122: the applied base station selects to accept or reject the application according to whether the self service utility is improved, if the self service utility of the base station is improved, the application is accepted and the self service user list is updated, and if the self service utility is reduced or unchanged, the application is rejected;
s123: finishing all polling and finishing matching;
s2: the (base station, user) two-dimensional element matches with the sub-channel: using an iterative matching algorithm, a two-dimensional list of preferences;
s21: initializing (base station, user) -sub-channel matching, and randomly selecting sub-channel access according to service;
s22: calculating a user MOS score sum of adjacent base stations and a channel access list according to the initial random access condition;
s23: the base stations exchange information, if MOS score and improvement between adjacent base stations, the application of channel exchange, updating and MOS score and channel access list are accepted; otherwise, reject and MOS score, and channel access list remain unchanged;
s3: and (3) power distribution: based on the first and second parts, obtaining the matching result of the user and the sub-channel, and the third part realizes the optimal position adjustment of the unmanned aerial vehicle and the power distribution of the user on the sub-channel of each base station;
s31: assuming that the unmanned aerial vehicle adopts fixed transmitting power, and obtaining a user needing service through the step S2, because the channel capacity is a function of the channel gain, the optimal unmanned aerial vehicle elevation of given user distribution can be obtained; the average path loss of the drone and the user terminal may be expressed in the form of probabilities:
according to the terminal position needing service, the maximum service radius can be obtained, and the path loss formula parameters are set according to the specific environment; by passing through
The optimal height of unmanned aerial vehicle access can be obtained;
s32: if only one user accesses the same channel of the same base station, p is directly allocated to the user n /m；
S33: otherwise, if the same channel of the same base station has 2 or more user accesses, distributing power according to the proportion, wherein the proportion is as follows:
QoE n =5-obtained MOS score;
assuming that there are 3 users for a channel, the power of the third user is:
wherein eta (0.ltoreq.eta.ltoreq.1) is a decay factor;
s4: user reception decoding: the user receives the signals, decodes the signals respectively on more than one accessed channel, and finally synthesizes transmission information according to the spectrum aggregation technology;
s41: in each sub-channel, according to NOMA protocol arrangement, users of each base station decode once according to sequence, and the decoding sequence is according to channel condition
Sequentially decoding from small to large, and decoding firstly by a user farthest from the base station;
s42: and finally, each user aggregates all information of the access sub-channels to obtain the final information.
2. The unmanned aerial vehicle auxiliary communication resource allocation method of the hybrid noma network according to claim 1, wherein: before the step S1, an unmanned aerial vehicle access channel model of a user is established, and the method specifically comprises the following steps:
a1: assuming a downlink unmanned aerial vehicle assisted cellular network, a small cellular base station is set to represent sbs= { SBS 1 ,SBS 2 ,...,SBS n Unmanned aerial vehicle aggregate uav= { UAV } 1 ,UAV 2 ,...,UAV l User set denoted ue= { UE } 1 ,UE 2 ,...,UE k The sub-channel is denoted sc= { SC } 1 ,SC 2 ,...,SC m }；
A2: establishing a quasi-stationary low-altitude rotary wing unmanned aerial vehicle auxiliary base station UAV-BS to provide disc-shaped wireless coverage with a radius Rc meters, a ground height H and a vertical projection Q point;
a3: suppose that 2 users are accessed, whose distance from the Q point is D j The distance from the unmanned aerial vehicle is
k.epsilon { user set }, elevation angle of UAV-BS with respect to each userFor theta k ＝arctan(H/D k ) K e { user set };
a4: the unmanned access channel model Of the user can be classified into Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) based on a probability model depending on the density, altitude, and position Of the environmental profile defined by the relative distance between the user and the building within the coverage area, i.e., elevation angle; the probability that the user experiences a line-of-sight link is:
line-of-sight access probability:
non line-of-sight access probability: pr (Pr) k (NLOS)＝1-Pr k (LOS)。
3. The unmanned aerial vehicle auxiliary communication resource allocation method of the hybrid noma network according to claim 2, wherein: 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.
4. The unmanned aerial vehicle auxiliary communication resource allocation method of the hybrid noma network according to claim 2, wherein: the transmission power of the user accessing the unmanned aerial vehicle is as follows: p is p rx,k (dB)＝p tx (dB)-L k (dB),
wherein ,
5. the unmanned aerial vehicle-assisted communication resource allocation method of the hybrid noma network according to claim 4, wherein: wherein L is k Is the path loss from the unmanned aerial vehicle to the user, eta is the free space path loss index, psi LOS and ψNLOS The excessive loss caused by shadow effect for object occlusion, both subject to a positive too-distribution, the mean and variance of which depend on elevation and environmental dependent constant values.
6. The unmanned aerial vehicle-assisted communication resource allocation method of the hybrid noma network according to claim 4, wherein: combining LOS and NLOS link analysis, the average path LOSs of the drone and user terminal can be expressed in the form of probabilities:
7. the unmanned aerial vehicle auxiliary communication resource allocation method of the hybrid noma network according to claim 1, wherein: the step S2 specifically includes the following steps:
a1: setting the matching relationship χ of base station users n,k Sub-channel and user matching relationship
The superposition coded symbols of base station n on subchannel m may be expressed as:
wherein ,
representing the transmission symbols of base station n to user k in subchannel m; />
Representing the transmission power allocated by base station n to user k in subchannel m;
a2: the signal received by user k may be expressed as a combination of three parts: the transmission signals of the base station n in the sub-channel m, and the transmission signals of other base stations in the sub-channel m are accumulated interference and white noise to the user k.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010281341.6A CN111615200B (en) | 2020-04-10 | 2020-04-10 | Unmanned aerial vehicle auxiliary communication resource allocation method for Hybrid NOMA network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010281341.6A CN111615200B (en) | 2020-04-10 | 2020-04-10 | Unmanned aerial vehicle auxiliary communication resource allocation method for Hybrid NOMA network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111615200A CN111615200A (en) | 2020-09-01 |
| CN111615200B true CN111615200B (en) | 2023-04-28 |
Family
ID=72201815
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010281341.6A Active CN111615200B (en) | 2020-04-10 | 2020-04-10 | Unmanned aerial vehicle auxiliary communication resource allocation method for Hybrid NOMA network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111615200B (en) |
Families Citing this family (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112333767B (en) * | 2020-11-16 | 2022-08-26 | 南京邮电大学 | Method for deploying unmanned aerial vehicle emergency communication system in post-disaster area |
| CN112583460B (en) * | 2020-12-08 | 2023-02-03 | 重庆邮电大学 | QoE-based MIMO-NOMA system power distribution method |
| CN112737837B (en) * | 2020-12-28 | 2021-09-14 | 北京邮电大学 | Method for allocating bandwidth resources of unmanned aerial vehicle cluster under high dynamic network topology |
| CN113329428B (en) * | 2021-06-07 | 2022-07-12 | 大连理工大学 | Request scheduling method of unmanned aerial vehicle-assisted mobile edge network |
| CN113395756B (en) * | 2021-06-22 | 2023-06-02 | 中山大学 | Air base station random access method based on preamble sequence grouping optimal power |
| CN114071432B (en) * | 2021-11-18 | 2024-04-26 | 中国矿业大学 | Unmanned aerial vehicle relay selection method for underground space post-disaster emergency scene |
| CN114268391B (en) * | 2021-11-26 | 2023-07-25 | 重庆邮电大学 | NOMA enhanced unmanned aerial vehicle auxiliary modeling analysis method |
| CN114448490B (en) * | 2021-12-22 | 2024-04-26 | 天翼云科技有限公司 | Path planning and spectrum resource allocation method and system for multiple unmanned aerial vehicles |
| CN114257299B (en) * | 2022-01-16 | 2023-06-30 | 中国人民解放军空军工程大学 | Unmanned aerial vehicle non-orthogonal multiple access network reliable and safe transmission method |
| CN114554450B (en) * | 2022-02-21 | 2024-04-02 | 华北电力大学 | Unmanned aerial vehicle auxiliary 6G network resource allocation method based on trilateral matching |
| CN114828244A (en) * | 2022-03-03 | 2022-07-29 | 华北电力大学 | Sky-ground 6G network resource allocation method facing content service trilateral matching |
| CN114499650B (en) * | 2022-04-06 | 2022-09-16 | 国网江苏省电力有限公司信息通信分公司 | Unmanned aerial vehicle multi-user downlink wireless transmission method, unmanned aerial vehicle and device |
| CN114915998B (en) * | 2022-05-31 | 2023-05-05 | 电子科技大学 | Channel capacity calculation method for unmanned aerial vehicle auxiliary ad hoc network communication system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106559123A (en) * | 2015-09-28 | 2017-04-05 | 中兴通讯股份有限公司 | A kind of weights acquisition methods and device |
| CN106658606A (en) * | 2016-11-01 | 2017-05-10 | 洛阳理工学院 | QoE based user base station matching method of distributed hierarchical heterogeneous network |
| KR102038605B1 (en) * | 2018-12-31 | 2019-10-30 | 세종대학교 산학협력단 | Noma system using user grouping and receiver diversity and signal transmission method thereof |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7782829B2 (en) * | 2008-06-22 | 2010-08-24 | Intel Corporation | Energy-efficient link adaptation and resource allocation for wireless OFDMA systems |
-
2020
- 2020-04-10 CN CN202010281341.6A patent/CN111615200B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106559123A (en) * | 2015-09-28 | 2017-04-05 | 中兴通讯股份有限公司 | A kind of weights acquisition methods and device |
| CN106658606A (en) * | 2016-11-01 | 2017-05-10 | 洛阳理工学院 | QoE based user base station matching method of distributed hierarchical heterogeneous network |
| KR102038605B1 (en) * | 2018-12-31 | 2019-10-30 | 세종대학교 산학협력단 | Noma system using user grouping and receiver diversity and signal transmission method thereof |
Non-Patent Citations (1)
| Title |
|---|
| 基于干扰管理的异构VLC/WiFi网络子信道分配;刘焕淋等;《中国激光》;20190827(第12期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111615200A (en) | 2020-09-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111615200B (en) | Unmanned aerial vehicle auxiliary communication resource allocation method for Hybrid NOMA network | |
| Duan et al. | SDN enabled 5G-VANET: Adaptive vehicle clustering and beamformed transmission for aggregated traffic | |
| Mounchili et al. | Pairing distance resolution and power control for massive connectivity improvement in NOMA systems | |
| CN110380772B (en) | Resource allocation and flight route optimization method for unmanned aerial vehicle relay system | |
| CN111586646B (en) | Resource allocation method for D2D communication combining uplink and downlink channels in cellular network | |
| CN115441939B (en) | MADDPG algorithm-based multi-beam satellite communication system resource allocation method | |
| CN111629352B (en) | V2X resource allocation method based on Underlay mode in 5G cellular network | |
| CN111885732B (en) | Dynamic resource allocation method for enhancing NOMA visible light communication network security | |
| CN110062359A (en) | Based on the highly reliable low delay radio resource allocation optimization method of the short coded block transmission of NOMA in MTC | |
| Cai et al. | Topology-transparent time division multiple access broadcast scheduling in multihop packet radio networks | |
| CN111465054A (en) | D2D communication resource allocation method based on utility fairness | |
| CN111083708B (en) | V2V communication heterogeneous frequency spectrum allocation method based on interference perception multiple graphs | |
| CN115173922A (en) | CMADDQN network-based multi-beam satellite communication system resource allocation method | |
| CN113507716A (en) | SWIPT-based CR-NOMA network interruption and energy efficiency optimization method | |
| CN110034856B (en) | Design method for non-orthogonal multiple access beam width of unmanned aerial vehicle | |
| Fan et al. | Channel assignment and power allocation utilizing noma in long-distance uav wireless communication | |
| CN108347761A (en) | Power distribution method in CRN and its application in wisdom traffic network | |
| Xu et al. | Energy efficiency optimization of NOMA IoT communication for 5G | |
| CN108540246B (en) | Resource allocation method based on cognitive radio | |
| CN114615745B (en) | Wireless self-organizing network channel access and transmission power joint scheduling method | |
| CN107249212B (en) | The maximized power distribution method of efficiency in a kind of D2D communication | |
| Fu et al. | Power allocation intelligent optimization for mobile NOMA communication system | |
| CN115767703A (en) | Long-term power control method for SWIPT-assisted de-cellular large-scale MIMO network | |
| CN114615296A (en) | Joint optimization method for access control and transmission scheduling of nodes in Internet of vehicles | |
| Chen et al. | Deployment for NOMA-UAV base stations based on hybrid sparrow search algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |