CN104065403B - Design method of key parameter of downlink chain in high-energy-efficiency large-scale antenna system - Google Patents
Design method of key parameter of downlink chain in high-energy-efficiency large-scale antenna system Download PDFInfo
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Abstract
The invention discloses a design method of a key parameter of a downlink chain in a high-energy-efficiency large-scale antenna system, belongs to the communication field, and aims at acquiring the system parameters that enables the maximum energy efficiency of the downlink chain of the large-scale antenna system, such as the amount M of antennas of a base station, the amount K of cell users, and the emitting power PT of the base station. The parameter design method includes two steps, namely, the step of calculating user distribution model parameters, and the step of analyzing and optimizing and calculating the system energy efficiency, wherein the step of calculating user distribution model parameters is that calculating the information of the distance from the user within the coverage range of the base station to the base station, establishing an actual user distribution model, and calculating the key parameter of the corresponding user distribution model; the step of analyzing and optimizing and calculating the system energy efficiency is that deducing the energy efficiency expression EE as shown in EE=f (M, K, PT) of the downlink chain of the high-energy-efficiency large-scale antenna system, analyzing according to the resource configuration condition of the system to obtain different conditions, and then further obtaining the optimal parameter under each condition. The formula is described in the specification.
Description
Technical field
The present invention relates to the communications field, downlink key parameter in the extensive antenna system of more particularly to a kind of high energy efficiency
Method for designing.
Background technology
Extensive antenna (Massive MIMO) technology is the extension of traditional MIMO technology, by configuring up to a hundred in base station
Antenna can be substantially improved in theory the energy efficiency of system servicing dozens of user.According to existing document, antenna is sent out
Power is penetrated with the linear downward trend of the growth of number of antennas.But, the conclusion is built upon not considering base station circuitry power
On the basis of loss.In real system, except antenna transmission power, the hardware support kit such as digital to analog converter of every antenna, work(
The equipment such as rate amplifier, wave filter are required for consumed energy.When number of antennas increases, the power consumption of circuit part will significantly increase
Plus, therefore in the communication system of one high energy efficiency of design, how to seek optimal balance and seem most important.
The method that extensive antenna system downlink energy efficiency is improved in existing document, mainly has：(1) wave beam is optimized
Former, the direction of the launch of control base station end wave beam；(2) for the needs of user in cell, in base station end Power Control is carried out；
(3) for the environment of low-load, base station adopts the data is activation mode of discontinuous (DTX).
After being analyzed to existing method, inventor has found：The method being previously mentioned in document be all system set up after,
Energy efficiency is improved by optimizing power, wave beam etc., or it is excessively complicated, feasibility aspect has been short of, or applicable model
Enclose narrow, with certain limitation.Method (1) needs to obtain whole channel informations, carries out the solution of optimal beam former,
It is relatively complicated, it is difficult to realize.The scope of Power Control is limited by transmitting-receiving hardware dynamic scope in method (2), with one
Fixed limitation.Method (3) is only applicable to the environment of low-load, and for the cell of high capacity, the method can not be used.
User distribution is another crucial problem in extensive antenna system.Network analysis and optimization in existing document
On the one hand assume that user obeys greatly to be uniformly distributed, but in fact, the distribution situation of user is restricted by physical environmental factors,
Such as road, river, building, park etc.；On the one hand affected by crowd massing, such as shopping center, the height such as airport
The hot spot region of concentration；Non-uniform Distribution is often presented on geographical space, therefore for more practical non-uniform Distribution mould
Type is optimized with important value.
Inventor from pervasive non-homogeneous user distribution model, using system average area compose the enclosed expression of effect as
Point of penetration, sets up system energy efficiency function, and design antenna for base station number M, community user number K are proposed on this basis, and
Base station transmitting power PTThe short-cut method of these three key parameters, lifts the energy efficiency of extensive antenna system downlink.
The content of the invention
The purpose of the present invention is, system energy efficiency relatively low problem not actual enough for user distribution model in present situation,
The method for designing of downlink key parameter in a kind of extensive antenna system of high energy efficiency is provided.
The method for designing of downlink key parameter in the extensive antenna system of high energy efficiency：Use in accounting base-station coverage
Family and the range information of base station, obtain actual user distribution model；Calculate the key parameter of relative users distributed model；Derive
Energy efficiency expression formula EE=f (M, K, the P of extensive antenna system downlinkT)；According to the resource distribution condition of system, point
Analysis obtains several different situations, and further obtain it is every kind of in the case of optimal parameter
Described actual user distribution model includes：
(1) if the user while center of housing estate integrated distribution, the user distribution model concentrated using center, user distribution it is general
Rate density function adopts equation below
Wherein, R represents radius of society, r0The minimum range of user distance base station is represented, a and b is and user distribution model
Relevant key parameter；
(2) if the user while cell edge integrated distribution, the user distribution model concentrated using edge, user distribution it is general
Rate density function adopts equation below
Wherein, R represents radius of society, r0The minimum range of user distance base station is represented, a and b is and user distribution model
Relevant key parameter.
The calculating of described key parameter a and b includes：
(1) if the user distribution model of center concentration, a controls the degree of user's convergence center of housing estate, and a is bigger, table
, in the central area of cell, vice versa for bright more user distributions, and during a=0, user obeys and is uniformly distributed, and b is probability density
The normaliztion constant of function, by can be calculated
The user distribution model that the center is concentrated is megastore, street；
(2) if the user distribution model of edge concentration, a controls the degree of user's convergence cell edge, and a is bigger, table
, in the fringe region of cell, vice versa for bright more user distributions, and during a=0, user obeys and is uniformly distributed, and b is probability density
The normaliztion constant of function, by can be calculated
The resource distribution condition of described system, including given (M, K, PT) any two parameter, any one parameter
With three kinds of situations of zero parameter, thus, three kinds of different optimization methods are generated：Including single argument optimization method, bivariate connection
Close optimization method and ternary combined optimization method.
Described single argument optimization method is：Given (M, K, PT) any two in these three parameters, optimize another
Parameter, for single optimization antenna for base station number M and Base Transmitter general power PTBoth of these case, by closed solutions MoptWith
Directly obtain, and during for single optimization number of users K, need by the energy efficiency that system is made in the confined space most
Height, obtains optimal value Kopt。
Described bivariate combined optimization method is：Given (M, K, PT) any one in these three parameters, optimization is another
Outer two parameters, including：
(1) number of users K, combined optimization antenna for base station number M and base station transmitting power P are givenT；
When S100, algorithm bring into operation, initial base station transmission power P is setT＞ 0；
S101, renewal antenna for base station number M are Mopt；
S102, renewal transmission power PTFor
S103, circulation execution step S101 and S102, until algorithmic statement, obtain optimum antenna for base station number MoptWith
Base station transmitting power
Wherein the Rule of judgment of algorithmic statement no longer changes for integer M；
(2) antenna for base station number M, combined optimization number of users K and base station transmitting power P are givenT；
When S110, algorithm bring into operation, initial base station transmission power P is setT＞ 0；
S111, renewal transmission power PTFor
S112, optimization number of users K are Kopt；
S113, circulation execution step S111 and S112, until algorithmic statement, obtain optimum number of users KoptAnd base station
Transmission power
Wherein the Rule of judgment of algorithmic statement no longer changes for integer K；
(3) antenna for base station number P is givenT, combined optimization number of users K and base station transmitting power M；
When S120, algorithm bring into operation, initial user number K=1 is set；
S121, renewal antenna for base station number M are Mopt；
S122, to MoptTake and round up, optimization number of users K is Kopt；
S123, circulation execution step S121 and S122, until algorithmic statement, obtain optimum number of users KoptAnd base station
Transmission power Mopt；
Wherein the Rule of judgment of algorithmic statement is that integer K and M no longer change.
Described ternary combined optimization method is：To (M, K, PT) these three parameters carry out combined optimization；
When S20, algorithm bring into operation, initial base station transmission power P is setT＞ 0, number of users K=1, antenna for base station number
Mesh M=3；
S21, renewal number of users K are Kopt；
S22, renewal antenna for base station number M are Mopt；
S23, renewal transmission power PTFor
S24, circulation execution step S21 to S23, until algorithmic statement, obtain three optimum important system parameters
Wherein the Rule of judgment of algorithmic statement is that integer K and M no longer change.
Beneficial effects of the present invention are：
(1) present invention in view of user distribution by physical environment restriction and crowd massing affected, set up one kind more
Actual user distribution model, including center collection neutralization edge concentration both of which, can adjust Parameters in Mathematical Model to adapt to
Different user distribution scenes.Avoid the impracticableness of conventional uniform distributed model.
(2) present invention is directed to different system requirements, optimizes (M, K, PT) these three important system parameters so that system energy
Amount efficiency highest, compared to conventional power control or the method for beam forming, has the advantages that algorithm complex is low.By optimization
Design, saves part resource and expense, and system energy efficiency obtains maximized lifting, meets the theory of green communications.
Description of the drawings
Fig. 1 is the important parameter method for designing flow chart of extensive antenna system；
Fig. 2 (a) is the user profile that the center of the present invention is concentrated；
Fig. 2 (b) is the user profile that the edge of the present invention is concentrated；
Fig. 3 be the present invention different user distribution and number of antennas in the case of, energy efficiency with number of users change；
Fig. 4 is of the invention in the case of given number of users, and combined optimization antenna for base station number and transmit power are obtained
Curve.
Specific embodiment
It is as follows the step of the method for designing of downlink key parameter in the extensive antenna system of high energy efficiency：
User and the range information of base station, obtain actual user distribution model in accounting base-station coverage；Calculate phase
Answer the key parameter of user distribution model；Derive extensive antenna system downlink energy efficiency expression EE=f (M, K,
PT)；According to the resource distribution condition of system, analysis obtains several different situations, and further obtain it is every kind of in the case of it is optimal
Parameter
Described actual user distribution model includes：
(1) if the user while center of housing estate integrated distribution, the user distribution model concentrated using center, user distribution it is general
Rate density function adopts equation below
Wherein, R represents radius of society, r0The minimum range of user distance base station is represented, a and b is and user distribution model
Relevant key parameter；
(2) if the user while cell edge integrated distribution, the user distribution model concentrated using edge, user distribution it is general
Rate density function adopts equation below
Wherein, R represents radius of society, r0The minimum range of user distance base station is represented, a and b is and user distribution model
Relevant key parameter.
The calculating of described key parameter a and b includes：
(1) if the user distribution model of center concentration, a controls the degree of user's convergence center of housing estate, and a is bigger, table
, in the central area of cell, vice versa for bright more user distributions, and during a=0, user obeys and is uniformly distributed, and b is probability density
The normaliztion constant of function, by can be calculated
The user distribution model that the center is concentrated is megastore, street；
(2) if the user distribution model of edge concentration, a controls the degree of user's convergence cell edge, and a is bigger, table
, in the fringe region of cell, vice versa for bright more user distributions, and during a=0, user obeys and is uniformly distributed, and b is probability density
The normaliztion constant of function, by can be calculated
The resource distribution condition of described system, including given (M, K, PT) any two parameter, any one parameter
With three kinds of situations of zero parameter, thus, three kinds of different optimization methods are generated：Including single argument optimization method, bivariate connection
Close optimization method and ternary combined optimization method.
Described single argument optimization method is：Given (M, K, PT) any two in these three parameters, optimize another
Parameter, for single optimization antenna for base station number M and Base Transmitter general power PTBoth of these case, by closed solutions MoptWith
Directly obtain, and during for single optimization number of users K, need by the energy efficiency that system is made in the confined space most
Height, obtains optimal value Kopt。
Described bivariate combined optimization method is：Given (M, K, PT) any one in these three parameters, optimization is another
Outer two parameters, including：
(1) number of users K, combined optimization antenna for base station number M and base station transmitting power P are givenT；
When S100, algorithm bring into operation, initial base station transmission power P is setT＞ 0；
S101, renewal antenna for base station number M are Mopt；
S102, renewal transmission power PTFor
S103, circulation execution step S101 and S102, until algorithmic statement, obtain optimum antenna for base station number MoptWith
Base station transmitting power
Wherein the Rule of judgment of algorithmic statement no longer changes for integer M；
(2) antenna for base station number M, combined optimization number of users K and base station transmitting power P are givenT；
When S110, algorithm bring into operation, initial base station transmission power P is setT＞ 0；
S111, renewal transmission power PTFor
S112, optimization number of users K are Kopt；
S113, circulation execution step S111 and S112, until algorithmic statement, obtain optimum number of users KoptAnd base station
Transmission power
Wherein the Rule of judgment of algorithmic statement no longer changes for integer K；
(3) antenna for base station number P is givenT, combined optimization number of users K and base station transmitting power M；
When S120, algorithm bring into operation, initial user number K=1 is set；
S121, renewal antenna for base station number M are Mopt；
S122, to MoptTake and round up, optimization number of users K is Kopt；
S123, circulation execution step S121 and S122, until algorithmic statement, obtain optimum number of users KoptAnd base station
Transmission power Mopt；
Wherein the Rule of judgment of algorithmic statement is that integer K and M no longer change.
Described ternary combined optimization method is：To (M, K, PT) these three parameters carry out combined optimization；
When S20, algorithm bring into operation, initial base station transmission power P is setT＞ 0, number of users K=1, antenna for base station number
Mesh M=3；
S21, renewal number of users K are Kopt；
S22, renewal antenna for base station number M are Mopt；
S23, renewal transmission power PTFor
S24, circulation execution step S21 to S23, until algorithmic statement, obtain three optimum important system parameters
Wherein the Rule of judgment of algorithmic statement is that integer K and M no longer change.
Below in conjunction with the accompanying drawings the present invention will be further described with specific embodiment.
The method for designing of downlink key parameter is fitted in the extensive antenna system of high energy efficiency that the inventive method is designed
For multi-user multi-antenna (Multi-user MIMO) system of single cell.Specifically include：
One base station is located at the center of circular cell, and base station configuration M root antennas, the user of K single antenna is randomly dispersed in
In cell, noise power is -114dbm, and shadow fading obeys logarithm normal distribution, and average is 0, and variance is 8dB.Definition system
Energy efficiency isWherein RdlFor in system all users and speed (bits/channel use), PtotalRepresent
Whole energy loss (Joule/channel use) of system.
The energy loss of single cell system includes three parts：
(1) energy loss P of power amplifierPA。
Wherein, PTThe total transmit power in base station is represented, 0 ＜ η≤1 is the energy efficiency of amplifier, by the performance of amplifier
Determine.
(2) circuit energy loss PC。
Circuit energy loss is the important component part of system total energy consumption, and the hardware facility of every antenna of base station is comprising number
Word analog converter, frequency mixer, wave filter etc., its energy loss is PBS, the hardware facility of each user is including low noise amplification
Device, direct frequency amplifier, wave filter and analog to digital converter etc., its energy loss is PUE。PsynFor the energy of frequency synthesizer
Amount loss, P0Energy loss including refrigeration system etc..So the energy loss of this part can be expressed as：
PC=MPBS+2Psyn+KPUE+P0
(3) signal transacting energy consumption PSP。
With increasing for number of antennas, the energy consumption of signal transacting can not be ignored, before signal Analysis process energy consumption, definition
One characterizes amount L (flops/Watt) of calculating signal transacting efficiency, and thinks that channel status is constant in T time section.According to
The order of signal transacting, first, base station is encoded and is modulated to K information sequence for being sent to user, every time using channel,
Expend KPcodThe energy of joule.Secondly, in each coherence time T, base station all carries out first-time precoding, select to have herein compared with
ZF (zero forcing) method for precoding of low complex degree, so utilizing channel every time, expends
The energy of joule.Then, in signal transmission phase, pre-coding matrix needs to be multiplied with information sequence, expendsThe energy of joule
Amount.Finally, user side to receive signal is decoded, expend KPdecThe energy of joule.So PSPIt is represented by：
Because using ZF precoding algorithms, K ＜ M in base station end in present example.Flow chart is as shown in Figure 1.
S30, in the coverage of base station, the range information of user and base station in accounting base-station coverage obtains real
The user distribution model on border；
S31, calculating key parameter a and b；
S32, the energy efficiency expression formula EE=f (M, K, the P that are calculated extensive antenna systemT)
Wherein,Represent signal to noise ratio, PTFor the total transmission power of base station, according to user distribution situation, β is obtained,
For the user distribution model that center is concentrated,For edge concentration
User distribution model,α is large scale fading factor.
S33, resource requirement analysis and respective algorithms.Including：
S330, the demand according to system and given key parameter (M, K, PT) number, be divided into three kinds of situations.
Situation 1：Given (M, K, PT) any two parameter, the remaining parameter of optimization；
Situation 2：Given (M, K, PT) any one parameter, combined optimization residue two parameters；
Situation 3：Given (M, K, PT) zero parameter, combined optimization these three parameters；
The algorithm of S331, the 1st kind of situation.
If situation 1, single argument optimization method is performed, including：
(1) (M, K, P are givenT) (M, K) in these three parameters, optimize PT；
In this case,Directly can be obtained by closed solutions
(2) (M, K, P are givenT) (M, P in these three parametersT), optimize K；
In this case, need by the energy efficiency highest that system is made in the confined space, obtain Kopt。
(3) (M, K, P are givenT) (K, P in these three parametersT), optimize M；
In this case, MoptDirectly can be obtained by closed solutions
The algorithm of S332, the 2nd kind of situation.
If situation 2, bivariate combined optimization method is performed, including：
(1) number of users K, combined optimization antenna for base station number M and base station transmitting power P are givenT；
Execution step S100-S103；
(2) antenna for base station number M, combined optimization number of users K and base station transmitting power P are givenT；
Execution step S110-S113；
(3) antenna for base station number P is givenT, combined optimization number of users K and base station transmitting power M；
Execution step S120-S123；
The algorithm of S333, the 3rd kind of situation.
If situation 3, ternary combined optimization method, i.e. execution step S20-S24 are performed.
The principle of said method realization and the technique effect for reaching can be：
The present invention provides a kind of with finger for the design of downlink key parameter in the extensive antenna system of high energy efficiency
The method for leading meaning, i.e., it is actual simultaneously accurately to obtain user distribution model, with antenna for base station number M, community user number K and send out
Penetrate power PTThese three key parameters, for different system requirements, optimize (M, K, P as point of penetrationT) these three critical systems
Parameter so that system energy efficiency highest.Compared to the method for traditional Power Control or beam forming, with algorithm complex
Low advantage, and part resource and expense are saved, the energy efficiency of maximized lift system meets the reason of green communications
Read.
Fig. 2 (a) is the user distribution model that center is concentrated in the present embodiment, arranges a=1, randomly generates 1000 users
In (R, r0)=(600,50) in the cell of m, wherein blue round dot represents user.
Fig. 2 (b) is the user distribution model that edge is concentrated in the present embodiment, arranges a=1, randomly generates 1000 users
In (R, r0)=(600,50) in the cell of m, wherein blue round dot represents user.
Fig. 3 is that different user models is directed in the present embodiment, arranges base station total transmission power PTDuring=0.001W, not
With antenna for base station number in the case of, system energy efficiency with intra-cell users number situation of change.Can see, tradition
Antenna for base station number M it is bigger, system energy efficiency is more high here and not to be applied to, but there is the optimum of antenna for base station number
Value.In addition, by Fig. 3 it is also seen that impact of the user distribution to system energy efficiency.
Fig. 4 is that radius of society is R=600m in the present embodiment, in the case of giving different number of users K, performs double change
The optimum antenna for base station number M that amount combined optimization method is obtainedoptAnd base station transmitting powerSubgraph above is described not
The corresponding optimum antenna for base station number M of same number of users Kopt, it is corresponding that following subgraph describes different number of users K
Optimum base station transmitting powerIt is convenient herein for expression, make ordinate be expressed as the base station transmitting power of optimum
It is corresponding most to have signal to noise ratio ρopt。
The foregoing is only presently preferred embodiments of the present invention, not to limit the present invention, all spirit in the present invention and
Within principle, any modification, equivalent substitution and improvements made etc. all should be included within the scope of the present invention.
Claims (5)
1. in a kind of extensive antenna system of high energy efficiency downlink key parameter method for designing, it is characterised in that statistics base
User and the range information of base station, obtain actual user distribution model in coverage of standing；Calculate relative users distributed model
Key parameter；Derive energy efficiency expression EE=f (M, K, the P of extensive antenna system downlinkT), wherein M is base station
Number of antennas；K is number of users；PTFor base station transmitting power；According to the resource distribution condition of system, including given (M, K, PT)
Any two parameter, three kinds of situations of any one parameter and zero parameter, thus produce three kinds of different optimization methods：Including
Single argument optimization method, bivariate combined optimization method and ternary combined optimization method；And further obtain it is every kind of in the case of
Optimal parameterWherein MoptFor the antenna for base station number of optimization；KoptFor the number of users of optimization；
For the base station transmitting power of optimization；
Described actual user distribution model includes：
(1) if the user while center of housing estate integrated distribution, using the user distribution model of center concentration, the probability of user distribution is close
Degree function adopts equation below
Wherein, R represents radius of society, r0The minimum range of user distance base station is represented, a and b is relevant with user distribution model
Key parameter；
(2) if the user while cell edge integrated distribution, using the user distribution model of edge concentration, the probability of user distribution is close
Degree function adopts equation below
Wherein, R represents radius of society, r0The minimum range of user distance base station is represented, a and b is relevant with user distribution model
Key parameter.
2. in the extensive antenna system of high energy efficiency according to claim 1 downlink key parameter method for designing, its
It is characterised by, the calculating of described key parameter a and b includes：
(1) if the user distribution model of center concentration, a controls the degree of user's convergence center of housing estate, and a is bigger, shows more
, in the central area of cell, vice versa for many user distributions, and during a=0, user obeys and is uniformly distributed, and b is probability density function
Normaliztion constant, by calculate
The user distribution model that the center is concentrated is megastore, street；
(2) if the user distribution model of edge concentration, a controls the degree of user's convergence cell edge, and a is bigger, shows more
, in the fringe region of cell, vice versa for many user distributions, and during a=0, user obeys and is uniformly distributed, and b is probability density function
Normaliztion constant, by calculate
。
3. in the extensive antenna system of high energy efficiency according to claim 1 downlink key parameter method for designing, its
It is characterised by, described single argument optimization method is：Given (M, K, PT) any two in these three parameters, optimize another
Parameter, for single optimization antenna for base station number M and Base Transmitter general power PTBoth of these case, by closed solutions MoptWith
Directly obtain, and during for single optimization number of users K, need by the energy efficiency that system is made in the confined space most
Height, obtains optimal value Kopt。
4. in the extensive antenna system of high energy efficiency according to claim 1 downlink key parameter method for designing, its
It is characterised by, described bivariate combined optimization method is：Given (M, K, PT) any one in these three parameters, optimization is another
Outer two parameters, including：
(1) number of users K, combined optimization antenna for base station number M and base station transmitting power P are givenT；
When S100, algorithm bring into operation, initial base station transmission power P is setT>0；
S101, renewal antenna for base station number M are Mopt；
S102, renewal transmission power PTFor
S103, circulation execution step S101 and S102, until algorithmic statement, obtain optimum antenna for base station number MoptAnd base station
Transmission power
Wherein the Rule of judgment of algorithmic statement no longer changes for integer M；
(2) antenna for base station number M, combined optimization number of users K and base station transmitting power P are givenT；
When S110, algorithm bring into operation, initial base station transmission power P is setT>0；
S111, renewal transmission power PTFor
S112, optimization number of users K are Kopt；
S113, circulation execution step S111 and S112, until algorithmic statement, obtain optimum number of users KoptAnd Base Transmitter
Power
Wherein the Rule of judgment of algorithmic statement no longer changes for integer K；
(3) antenna for base station number P is givenT, combined optimization number of users K and base station transmitting power M；
When S120, algorithm bring into operation, initial user number K=1 is set；
S121, renewal antenna for base station number M are Mopt；
S122, to MoptTake and round up, optimization number of users K is Kopt；
S123, circulation execution step S121 and S122, until algorithmic statement, obtain optimum number of users KoptAnd Base Transmitter
Power Mopt；
Wherein the Rule of judgment of algorithmic statement is that integer K and M no longer change.
5. in the extensive antenna system of high energy efficiency according to claim 1 downlink key parameter method for designing, its
It is characterised by, described ternary combined optimization method is：To (M, K, PT) these three parameters carry out combined optimization；
When S20, algorithm bring into operation, initial base station transmission power P is setT>0, number of users K=1, antenna for base station number M=3；
S21, renewal number of users K are Kopt；
S22, renewal antenna for base station number M are Mopt；
S23, renewal transmission power PTFor
S24, circulation execution step S21 to S23, until algorithmic statement, obtain three optimum important system parameters
Wherein the Rule of judgment of algorithmic statement is that integer K and M no longer change.
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| CN105991168A (en) * | 2015-01-30 | 2016-10-05 | 中国移动通信集团公司 | Antenna selection method and antenna selection device |
| CN105827294B (en) * | 2016-04-27 | 2019-05-21 | 东南大学 | A kind of method of uplink extensive MIMO combined optimization antenna for base station number and user emission power |
| CN105933971B (en) * | 2016-05-16 | 2019-12-31 | 重庆邮电大学 | Energy efficiency optimization method suitable for large-scale multi-input multi-output system |
| CN106879016B (en) * | 2017-03-28 | 2019-11-29 | 湘潭大学 | A kind of base station electromagnetic radiation prediction technique based on user distribution |
| CN107689932B (en) * | 2017-08-01 | 2019-10-25 | 浙江大学 | A method of reducing bit error rate in coarse quantization relay system |
| CN109168194B (en) * | 2018-11-08 | 2021-06-11 | 南京邮电大学 | Method for determining transmitting power in uplink NOMA system based on dynamic user |
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