CN109195214A - A kind of NOMA power distribution method based on genetic algorithm - Google Patents
A kind of NOMA power distribution method based on genetic algorithm Download PDFInfo
- Publication number
- CN109195214A CN109195214A CN201811038383.6A CN201811038383A CN109195214A CN 109195214 A CN109195214 A CN 109195214A CN 201811038383 A CN201811038383 A CN 201811038383A CN 109195214 A CN109195214 A CN 109195214A
- Authority
- CN
- China
- Prior art keywords
- user
- subcarrier
- power
- signal
- users
- 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.)
- Granted
Links
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC
- H04W52/18—TPC being performed according to specific parameters
- H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
- H04W52/241—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters taking into account channel quality metrics, e.g. SIR, SNR, CIR, Eb/lo
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC
- H04W52/18—TPC being performed according to specific parameters
- H04W52/26—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service]
- H04W52/267—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service] taking into account the information rate
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC
- H04W52/30—TPC using constraints in the total amount of available transmission power
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/14—Spectrum sharing arrangements between different networks
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Quality & Reliability (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention discloses a kind of NOMA power distribution method based on genetic algorithm, by judging whether user distributes the power distribution matrix determined in system on sub-carriers, system goal function is obtained by the analysis to system model, the solution that genetic algorithm carries out the optimization problem of Prescribed Properties is introduced in system goal function.The present invention not only makes system overall transmission rate close to the overall transmission rate of total space searching algorithm, while can substantially reduce the computation complexity of system.
Description
Technical field
The present invention relates to a kind of NOMA power distribution method based on genetic algorithm, belongs to wireless communication technology field.
Background technique
With the development that mobile Internet is applied, mobile data flow and terminal connection number are also in explosive growth, future
The equipment total amount of global mobile communication network connection is up to hundred billion scales.The huge mobile data amount for the 5G system that faces the future and
Terminal connection number and the new opplication to emerge one after another, orthogonal access mode cannot more efficiently lifting system performances.
Therefore, the novel access technology of the following 5G system becomes research hotspot technology instantly.It is nonopiate as novel access way
Multiple access NOMA technology becomes 5G Core candidate technology.NOMA technology can be divided into power domain multiplexing NOMA, encoding domain multiplexing
NOMA and other NOMA, such as pattern segmentation multiple access access (Pattern Division Multiple Access, PDMA) are handed over and are divided
It is multiplexed multiple access access (Interleave Division Multiple Access, IDMA) etc..Power domain is multiplexed NOMA technology
Main thought is multiple users in power domain using different transmission powers, shares identical frequency domain resource, can be using scheduling
Multiple users pairing or clustering combination, each user is right/and the schemes such as group shared same resource block transmit；Receiving end is based on more
User detects (Multi-User Detection, MUD) thought, using serial interference elimination (Successive
Interference Cancellation, SIC) technology successively detects each subscriber signal.
The Performance Evaluating Indexes of NOMA system are not only considered to study power system capacity, transmission speed from Shannon information theory angle
Rate etc. can also consider BER etc. from link layer angle, be based on the above NOMA system performance evaluation correlative study, design transmitting terminal
Scheme, lifting system performance, the key for becoming NOMA system study a question.And power distribution gulps down the user under NOMA system
The amount of spitting performance can have a huge impact, and only affect overall system throughput, and also have to the handling capacity of each user very big
Influence, thus reasonable power distribution algorithm can be effectively reduced the multi-access inference between subscriber signal, improve system
Handling capacity plays important role in NOMA system.
In view of the above-mentioned problems, Y.Lan, A.Benjebbour et al. propose a kind of power and frequency spectrum distributing method, it is based on
User is divided into cell edge and Cell Center User, while distributed for Cell Edge User by the deployment scenario of intra-cell users
More power are that Cell Center User distributes more bandwidth resources to weaken inter-cell interference, so that it is flat to improve entire cell
Equal handling capacity and cell edge throughput, frequency spectrum resource distribution is according to fractional frequency reuse (Fractional Frequency
Reuse, FFR).N.Otao, Y.Kishiyama et al. propose full-search algorithm, and theoretic throughput-optimal may be implemented,
But computation complexity is high, is difficult to apply in actual system.SAITO Y and F.Liu et al. propose constant power respectively
Allocation algorithm and fractional order power distribution algorithm.Fix power allocation algorithm does not consider the current channel status of user, only presses
Carry out distribution power according to fixed Geometric Sequence, which is that the complexity of calculating is lower, the disadvantage is that the handling capacity of system
It can be bad.Fractional order power distribution algorithm considers the channel status of user, carrys out distribution power according to the path loss ratio of user,
Throughput performance is lost relative to full-search algorithm.
Summary of the invention
The object of the invention is that a kind of NOMA power distribution method based on genetic algorithm is provided, in the processing of objective function
The optimization problem under genetic algorithm solution constraint condition is introduced in the process, is greatly reduced the computation complexity of algorithm, is improved
The performance indicator of the overall transmission rate of system.
To achieve the above object, present invention employs following technical solutions:
A kind of NOMA power distribution method based on genetic algorithm, comprising the following steps:
1) base station end of NOMA system be based on power sharing, multi-user shared running time-frequency resource unit, multiple users with it is non-just
The form of friendship is superimposed upon respectively on each subcarrier, and obtaining the superposed signal on n-th of subcarrier is xn, user k is in n-th of son
Reception signal on carrier wave is yk,n；
2) signal x is sentnIt is ranked up decoding according to Signal to Interference plus Noise Ratio in receiving end, after SIC is detected, obtains user m
Signal to Interference plus Noise Ratio SINR on n-th of subcarrierm,n；
3) after SIC detection processing, transmission rate R of the user m on n-th of subcarrier is calculatedm,nAnd preceding n son
Overall transmission rate R on carrier waven；
4) according to number of users in NOMA system and in conjunction with sk,n, the maximum objective function of system overall transmission rate is established,
In, sk,n=1 indicates that user distributes the s on n-th of subcarrierk,n=0 indicates that user is unallocated on n-th of subcarrier；
5) objective function is solved using genetic algorithm, obtains optimal power contribution matrix.
In aforementioned step 1), the number of users being superimposed on each subcarrier is not less than 1.
In aforementioned step 1), if n-th of subcarrier is by knA userIt is shared, superposed signal table
It is shown as:
Wherein, xi,nIndicate the signal of user i on n-th of subcarrier, i=1,2 ..., kn, pi,nIndicate n-th of subcarrier
The power of upper user i.
In aforementioned step 1),
Receiving signal indicates are as follows: yk,n=hk,nxn+wk,n
Wherein, hk,nIt is the channel gain of user k on n-th of subcarrier, wk,nIt is the channel of user k on n-th of subcarrier
White Gaussian noise and area interference.
In aforementioned step 2), SIC detection process are as follows:
21) receiving end will first receive signal and be ranked up according to signal power size, obtain before first order detection
22) strongest to signal powerCarry out data decision, output
23) then to userWhen being detected, first subtractCaused by interfereAgain to user
Carry out data decision output；
24) identical operation is successively executed according to power sequence, is finally sequentially output x2,nAnd x1,n, complete to all use
Family signal detection.
In aforementioned step 2), Signal to Interference plus Noise Ratio SINRm,nIt calculates as follows:
Wherein, pm,nIndicate the power of user m on n-th of subcarrier,It indicates to use on n-th of subcarrier
The signal-to-noise ratio of family m, hm,nIt is the channel gain of user m on n-th of subcarrier,Indicate noise power.
In aforementioned step 3), Rm,nAnd RnIt calculates as follows:
Rm,n=log2(1+SINRm,n)
Wherein, total number of users before Kn is indicated on n subcarrier, Rk,jIndicate transmission of the user k on j-th of subcarrier
Rate.
In aforementioned step 5), objective function solves as follows:
51) initialising subscriber number, sub-carrier number and radius of society, the radius of society are the models for referring to reliable communication
It encloses；
52) general power is transmitted from the relational angle of overall transmission rate and users multiplexing number setting system, or transmits speed from total
Number of users is arranged in the relational angle of rate and system transmission general power；
53) limitation of general power and number of users is transmitted according to system, mean allocation user's power determines initial user power
Allocation matrix P；
54) determine that objective function is as follows:
The constraint condition that determination need to meet is as follows:
s.t.
Wherein, ptotGeneral power is transmitted for system, N indicates that system subcarrier number, K indicate total number of users；
55) theoretical, generation globally optimal solution is assumed using the building block of genetic algorithm；By being imitated in matlab
Very, optimal power contribution matrix is finally obtained；
56) according to optimal power contribution matrix, the system found out under the number of users or system transmission general power always transmits speed
Rate；
56) return step 52), it is once again set up number of users or system transmission general power, computing system overall transmission rate passes through
It repeatedly calculates, obtains the system overall transmission rate under different user number or not homologous ray transmission general power restrictive condition.
The beneficial effects of the present invention are: carrying out Prescribed Properties most by introducing genetic algorithm in system goal function
The solution of optimization problem not only makes system overall transmission rate close to the overall transmission rate of total space searching algorithm, while can be with
Substantially reduce the computation complexity of system.
Detailed description of the invention
Fig. 1 is NOMA system model figure of the invention.
Fig. 2 is the NOMA power distribution method flow chart of the invention based on genetic algorithm.
Specific embodiment
The invention will be further described below.Following embodiment is only used for clearly illustrating technical side of the invention
Case, and not intended to limit the protection scope of the present invention.
As shown in Figure 1, NOMA system includes base station end and receiving end, in base station end, subscriber signal passes through non-orthogonal shape
Formula is superimposed upon on different subcarriers, and the transmission of signal is carried out by wireless channel.In receiving end, user receives the letter of superposition
The deletion for carrying out interference signal is detected after number by SIC, to obtain signal needed for user.
As shown in Fig. 2, the NOMA power distribution method of the invention based on genetic algorithm, comprising the following steps:
Step (1): the base station end of NOMA system uses power sharing technology, and multi-user shared running time-frequency resource unit is multiple
User is superimposed upon on each subcarrier respectively in the form of non-orthogonal, and the number of users being superimposed on each subcarrier is not less than 1.?
On n-th of subcarrier, the superposed signal that base station end is sent on subcarrier n is xn, reception of the user k on n-th of subcarrier
Signal is yk,n；
Step (2): signal x is sentnIt is ranked up decoding according to Signal to Interference plus Noise Ratio in receiving end, the user m after SIC is detected
Signal to Interference plus Noise Ratio on n-th of subcarrier is SINRm,n；
The decoding process that sorts is as follows:
If n-th of subcarrier is by knA userIt is shared, superimposed signal are as follows:
Wherein, xi,nIndicate the signal of user i on n-th of subcarrier, i=1,2 ..., kn, pi,nIndicate n-th of subcarrier
The power of upper user i.
In receiving end, user's k received signal are as follows:
yk,n=hk,nxn+wk,n
Wherein, hk,nIt is the channel gain of user k on n-th of subcarrier, wk,nIt is the channel of user k on n-th of subcarrier
White Gaussian noise and area interference.
This knThe power of a signal meetsFirst will before the first order detection receiving end
It receives signal to be ranked up according to signal power size, the easier capture of the stronger user of signal power, here due to xk,nSignal
Power is most strong, first rightCarry out data decision (adjudicating according to the relationship of input signal and output signal), output
To userWhen being detected, first subtractCaused by interfereIt is defeated that data decision is carried out to user again
Out.Identical operation is successively executed according to power sequence, is finally sequentially output x2,nAnd x1,n, complete to examine all subscriber signals
It surveys.
After the detection of SIC serial interference deleting technique, Signal to Interference plus Noise Ratio of the user m on n-th of subcarrier is SINRm,n,
It calculates as follows:
Wherein, pm,nIndicate the power of user m on n-th of subcarrier,It indicates to use on n-th of subcarrier
The signal-to-noise ratio of family m, wherein hm,nIt is the channel gain of user m on n-th of subcarrier,Indicate noise power.
Step (3): after SIC detection processing, transmission rate of the user m on n-th of subcarrier is Rm,n, then first n
Overall transmission rate on subcarrier is Rn；It calculates as follows:
Rm,n=log2(1+SINRm,n)
Wherein, total number of users before Kn is indicated on n subcarrier, Rk,jIndicate transmission of the user k on j-th of subcarrier
Rate.
Step (4): according to number of users in NOMA system and s is combinedk,nDetermine whether user distributes on n-th of subcarrier,
sk,n=1 indicates that user distributes the s on n-th of subcarrierk,n=0 indicates that user is unallocated on n-th of subcarrier, establishes system
The objective function of system:Rk,nIndicate transmission rate of the user k on n-th of subcarrier.
Step (5): drawn according to the constraint condition that the power that user in objective function and power distribution algorithm distributes is met
Enter genetic algorithm, the maximization of the power that user distributes in system and function to achieve the objective is obtained according to genetic algorithm.It is main
Algorithmic procedure is as follows:
Input: input signal xn, sub-carrier number N, number of users K；
Output: system overall transmission rate；
51) initialising subscriber number and sub-carrier number and radius of society, radius of society are the ranges for referring to reliable communication；
52) from the relational angle setting system of overall transmission rate and users multiplexing number general power is transmitted or from overall transmission rate
Number of users is set with the relational angle of system transmission general power；
53) limitation of general power and system user number is transmitted according to system, mean allocation user's power determines initial user
Power distribution matrix P；
54) pass through sk,nJudge whether user distributes on sub-carriers, sk,n=1 indicates user's distribution on sub-carriers, sk,n
=0 indicates that user is unallocated on sub-carriers, using genetic algorithm, and combines the limitation item of system transmission general power and number of users
Part carries out the processing of objective function optimization, and optimal objective function is as follows:
Constraint condition need to be met:
Wherein, ptotGeneral power is transmitted for system.
55) optimal objective function is solved by genetic algorithm, low order, short distance, height are average to fit using having in genetic algorithm
The mode (building block) of response selects in genetic operator, intersect and variation effect under, can generate high-order, it is long away from, high average adapt to
The mode of degree, ultimately generates globally optimal solution, is emulated in matlab, finally obtains optimal power contribution matrix, and ask
The number of users or system transmit the system overall transmission rate under general power out.
56) return step 52), it is once again set up number of users or system transmission general power, computing system overall transmission rate passes through
It repeatedly calculates, obtains the system overall transmission rate under different user number or not homologous ray transmission general power restrictive condition.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, without departing from the technical principles of the invention, several improvement and deformations can also be made, these improvement and deformations
Also it should be regarded as protection scope of the present invention.
Claims (8)
1. a kind of NOMA power distribution method based on genetic algorithm, which comprises the following steps:
1) base station end of NOMA system is based on power sharing, and multi-user shared running time-frequency resource unit, multiple users are with non-orthogonal
Form is superimposed upon respectively on each subcarrier, and obtaining the superposed signal on n-th of subcarrier is xn, user k is in n-th of subcarrier
On reception signal be yk,n；
2) signal x is sentnIt is ranked up decoding according to Signal to Interference plus Noise Ratio in receiving end, after SIC is detected, obtains user m n-th
Signal to Interference plus Noise Ratio SINR on a subcarrierm,n；
3) after SIC detection processing, transmission rate R of the user m on n-th of subcarrier is calculatedm,nAnd preceding n subcarrier
On overall transmission rate Rn；
4) according to number of users in NOMA system and in conjunction with sk,n, establish the maximum objective function of system overall transmission rate, wherein sk,n
=1 indicates that user distributes the s on n-th of subcarrierk,n=0 indicates that user is unallocated on n-th of subcarrier；
5) objective function is solved using genetic algorithm, obtains optimal power contribution matrix.
2. a kind of NOMA power distribution method based on genetic algorithm according to claim 1, which is characterized in that the step
It is rapid 1) in, the number of users that is superimposed on each subcarrier is not less than 1.
3. a kind of NOMA power distribution method based on genetic algorithm according to claim 1, which is characterized in that the step
It is rapid 1) in, if n-th of subcarrier is by knA userShared, superposed signal indicates are as follows:
Wherein, xi,nIndicate the signal of user i on n-th of subcarrier, i=1,2 ..., kn, pi,nIndicate user on n-th of subcarrier
The power of i.
4. a kind of NOMA power distribution method based on genetic algorithm according to claim 3, which is characterized in that the step
It is rapid 1) in,
Receiving signal indicates are as follows: yk,n=hk,nxn+wk,n
Wherein, hk,nIt is the channel gain of user k on n-th of subcarrier, wk,nIt is the channel Gauss of user k on n-th of subcarrier
White noise and area interference.
5. a kind of NOMA power distribution method based on genetic algorithm according to claim 4, which is characterized in that the step
It is rapid 2) in, SIC detection process are as follows:
21) receiving end will first receive signal and be ranked up according to signal power size, obtain before first order detection
22) strongest to signal powerCarry out data decision, output
23) then to userWhen being detected, first subtractCaused by interfereUser is carried out again
Data decision output；
24) identical operation is successively executed according to power sequence, is finally sequentially output x2,nAnd x1,n, complete to believe all users
Number detection.
6. a kind of NOMA power distribution method based on genetic algorithm according to claim 4, which is characterized in that the step
It is rapid 2) in, Signal to Interference plus Noise Ratio SINRm,nIt calculates as follows:
Wherein, pm,nIndicate the power of user m on n-th of subcarrier,Indicate user m on n-th of subcarrier
Signal-to-noise ratio, hm,nIt is the channel gain of user m on n-th of subcarrier,Indicate noise power.
7. a kind of NOMA power distribution method based on genetic algorithm according to claim 6, which is characterized in that the step
It is rapid 3) in, Rm,nAnd RnIt calculates as follows:
Rm,n=log2(1+SINRm,n)
Wherein, total number of users before Kn is indicated on n subcarrier, Rk,jIndicate transmission rate of the user k on j-th of subcarrier.
8. a kind of NOMA power distribution method based on genetic algorithm according to claim 7, which is characterized in that the step
It is rapid 5) in, objective function solve it is as follows:
51) initialising subscriber number, sub-carrier number and radius of society, the radius of society are the ranges for referring to reliable communication；
52) transmit general power from the relational angle setting system of overall transmission rate and users multiplexing number, or from overall transmission rate with
Number of users is arranged in the relational angle that system transmits general power；
53) limitation of general power and number of users is transmitted according to system, mean allocation user's power determines initial user power distribution
Matrix P；
54) determine that objective function is as follows:
The constraint condition that determination need to meet is as follows:
s.t.
Wherein, ptotGeneral power is transmitted for system, N indicates that system subcarrier number, K indicate total number of users；
55) theoretical, generation globally optimal solution is assumed using the building block of genetic algorithm；By being emulated in matlab, most
Optimal power contribution matrix is obtained eventually；
56) according to optimal power contribution matrix, the system overall transmission rate under the number of users or system transmission general power is found out；
56) return step 52), it is once again set up number of users or system transmission general power, computing system overall transmission rate, by multiple
It calculates, obtains the system overall transmission rate under different user number or not homologous ray transmission general power restrictive condition.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811038383.6A CN109195214B (en) | 2018-09-06 | 2018-09-06 | Genetic algorithm-based NOMA power allocation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811038383.6A CN109195214B (en) | 2018-09-06 | 2018-09-06 | Genetic algorithm-based NOMA power allocation method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109195214A true CN109195214A (en) | 2019-01-11 |
| CN109195214B CN109195214B (en) | 2021-02-12 |
Family
ID=64914917
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811038383.6A Active CN109195214B (en) | 2018-09-06 | 2018-09-06 | Genetic algorithm-based NOMA power allocation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109195214B (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109714085A (en) * | 2019-01-23 | 2019-05-03 | 哈尔滨工业大学（深圳） | A kind of downlink NOMA transmission method based on dual polarization MIMO |
| CN109861866A (en) * | 2019-02-22 | 2019-06-07 | 华南理工大学 | Take the resource allocation methods minimized in energy multicarrier NOMA system based on transmission power |
| CN110086515A (en) * | 2019-04-25 | 2019-08-02 | 南京邮电大学 | A kind of MIMO-NOMA system uplink Precoding Design method |
| CN110505681A (en) * | 2019-08-13 | 2019-11-26 | 东南大学 | Non-orthogonal multiple based on genetic method accesses scene user matching method |
| CN111182511A (en) * | 2020-02-21 | 2020-05-19 | 重庆邮电大学 | AGA-based NOMA resource allocation method in mMTC scene |
| CN111328146A (en) * | 2020-03-10 | 2020-06-23 | 西安电子科技大学 | Service scheduling method for optimizing transmission rate weight based on genetic algorithm |
| CN112469113A (en) * | 2020-10-30 | 2021-03-09 | 南京邮电大学 | Resource allocation method and device of multi-carrier NOMA system |
| CN113056015A (en) * | 2021-03-16 | 2021-06-29 | 西安电子科技大学 | Power distribution method of NOMA downlink system under non-ideal channel state information |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104158631A (en) * | 2014-08-27 | 2014-11-19 | 北京邮电大学 | Data stream transmitting method and device |
| CN104640220A (en) * | 2015-03-12 | 2015-05-20 | 重庆邮电大学 | Frequency and power distributing method based on NOMA (non-orthogonal multiple access) system |
| CN106162846A (en) * | 2016-06-21 | 2016-11-23 | 华中科技大学 | A kind of two users NOMA descending efficiency optimization method considering SIC energy consumption |
| CN106385300A (en) * | 2016-08-31 | 2017-02-08 | 上海交通大学 | Uplink NOMA power distribution method based on dynamic decoding SIC receiver |
| EP3273736A1 (en) * | 2016-07-19 | 2018-01-24 | Institut Mines Telecom / Telecom Bretagne | Method and apparatus for power and user distribution to sub-bands in noma systems |
-
2018
- 2018-09-06 CN CN201811038383.6A patent/CN109195214B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104158631A (en) * | 2014-08-27 | 2014-11-19 | 北京邮电大学 | Data stream transmitting method and device |
| CN104640220A (en) * | 2015-03-12 | 2015-05-20 | 重庆邮电大学 | Frequency and power distributing method based on NOMA (non-orthogonal multiple access) system |
| CN106162846A (en) * | 2016-06-21 | 2016-11-23 | 华中科技大学 | A kind of two users NOMA descending efficiency optimization method considering SIC energy consumption |
| EP3273736A1 (en) * | 2016-07-19 | 2018-01-24 | Institut Mines Telecom / Telecom Bretagne | Method and apparatus for power and user distribution to sub-bands in noma systems |
| CN106385300A (en) * | 2016-08-31 | 2017-02-08 | 上海交通大学 | Uplink NOMA power distribution method based on dynamic decoding SIC receiver |
Non-Patent Citations (1)
| Title |
|---|
| 汪成功: "《NOMA系统联合资源分配方案研究》", 15 February 2020 * |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109714085A (en) * | 2019-01-23 | 2019-05-03 | 哈尔滨工业大学（深圳） | A kind of downlink NOMA transmission method based on dual polarization MIMO |
| CN109861866A (en) * | 2019-02-22 | 2019-06-07 | 华南理工大学 | Take the resource allocation methods minimized in energy multicarrier NOMA system based on transmission power |
| CN110086515B (en) * | 2019-04-25 | 2021-09-28 | 南京邮电大学 | Uplink precoding design method of MIMO-NOMA system |
| CN110086515A (en) * | 2019-04-25 | 2019-08-02 | 南京邮电大学 | A kind of MIMO-NOMA system uplink Precoding Design method |
| CN110505681B (en) * | 2019-08-13 | 2022-02-22 | 东南大学 | Non-orthogonal multiple access scene user pairing method based on genetic method |
| CN110505681A (en) * | 2019-08-13 | 2019-11-26 | 东南大学 | Non-orthogonal multiple based on genetic method accesses scene user matching method |
| CN111182511A (en) * | 2020-02-21 | 2020-05-19 | 重庆邮电大学 | AGA-based NOMA resource allocation method in mMTC scene |
| CN111182511B (en) * | 2020-02-21 | 2022-05-03 | 重庆邮电大学 | AGA-based NOMA resource allocation method in mMTC scene |
| CN111328146A (en) * | 2020-03-10 | 2020-06-23 | 西安电子科技大学 | Service scheduling method for optimizing transmission rate weight based on genetic algorithm |
| CN111328146B (en) * | 2020-03-10 | 2022-04-05 | 西安电子科技大学 | Service scheduling method for optimizing transmission rate weight based on genetic algorithm |
| CN112469113A (en) * | 2020-10-30 | 2021-03-09 | 南京邮电大学 | Resource allocation method and device of multi-carrier NOMA system |
| CN112469113B (en) * | 2020-10-30 | 2023-02-14 | 南京邮电大学 | Resource allocation method and device of multi-carrier NOMA system |
| CN113056015A (en) * | 2021-03-16 | 2021-06-29 | 西安电子科技大学 | Power distribution method of NOMA downlink system under non-ideal channel state information |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109195214B (en) | 2021-02-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109195214A (en) | A kind of NOMA power distribution method based on genetic algorithm | |
| Mounchili et al. | Pairing distance resolution and power control for massive connectivity improvement in NOMA systems | |
| CN108462950B (en) | NOMA-based D2D communication combined sub-channel and power distribution method | |
| CN102017763B (en) | Null pilots for interference estimation in a wireless communication network | |
| CN110086555B (en) | Grouping pilot frequency distribution method and device in large-scale MIMO system | |
| CN105049098B (en) | The portion of pilot multiplexing method of pilot pollution is overcome in extensive mimo system | |
| CN102055563B (en) | Adaptive joint linear precoding method applicable to multi-base station coordination | |
| Lu et al. | Dynamic user grouping and joint resource allocation with multi-cell cooperation for uplink virtual MIMO systems | |
| CN110365463B (en) | Pilot frequency distribution method for twice clustering allocation of large-scale MIMO system | |
| CN113411105B (en) | AP selection method of non-cell large-scale antenna system | |
| CN112911711A (en) | Resource allocation method of multi-carrier NOMA system | |
| CN110225578A (en) | A method of the D2D resource allocation based on graph coloring | |
| CN108924934B (en) | Heterogeneous network interference management method based on multi-dimensional resource allocation | |
| Ammar et al. | Power delay profile in coordinated distributed networks: User-centric v/s disjoint clustering | |
| CN108260215A (en) | The resource allocation methods that channel conditions optimize in a kind of NOMA of low-density code | |
| CN108521668A (en) | Water injection power suitable for 5G network N OMA systems distributes optimization method | |
| Murti et al. | User pairing schemes based on channel quality indicator for uplink non-orthogonal multiple access | |
| CN112543056B (en) | Power and grouping combined optimization method for PD-NOMA-VLC system | |
| He et al. | 5G‐oriented non‐orthogonal multiple access technology | |
| CN112469113B (en) | Resource allocation method and device of multi-carrier NOMA system | |
| Wu et al. | On energy efficiency optimization in downlink MIMO-NOMA | |
| Gkonis et al. | A new subcarrier allocation strategy for MIMO-OFDMA multicellular networks based on cooperative interference mitigation | |
| Mounchili et al. | Efficient pairing distance for better radio capacity in noma systems | |
| Chikezie et al. | Power allocation optimization in NOMA system for user fairness in 5G networks | |
| Rathod et al. | Fusion of artificial intelligence and game theory for resource allocation in non‐orthogonal multiple access‐assisted device‐to‐device cooperative communication |
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 |