CN104168572A - Artificial physics optimization cognitive radio network spectrum distribution method - Google Patents
Artificial physics optimization cognitive radio network spectrum distribution method Download PDFInfo
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- CN104168572A CN104168572A CN201410323450.4A CN201410323450A CN104168572A CN 104168572 A CN104168572 A CN 104168572A CN 201410323450 A CN201410323450 A CN 201410323450A CN 104168572 A CN104168572 A CN 104168572A
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Abstract
The invention relates to an artificial physics optimization cognitive radio network spectrum distribution method for solving the problem that the spectrum distribution efficiency is low in the prior art. The realization steps are: (1) obtaining an idle matrix, an income matrix, and a disturbance matrix of the spectrum, (2) mapping a spectrum distribution matrix encoding as a particle in the artificial physic optimization and determining a fitness evaluation function, (3) initializing population and setting related parameters, (4) correcting particles and calculating fitness value, (5) determining whether latest evolution generations is achieved, if yes, mapping the optimal particle encoding as a spectrum distribution matrix type to obtain the optimal spectrum distribution matrix, if not, switching to the step (6) to calculate the composite force of each particle, (7) calculating the particle motion and updating, and (8) switching to the step (4) if the evolution generations is increased. The artificial physic method adopted by the invention has advantages of less in parameters and quick in rate of convergence, and can better realize the maximum of the network income.
Description
Technical field
The invention belongs to wireless communication field, the cognitive radio network spectrum allocation method that particularly a kind of artificial physics is optimized, can be used for the frequency spectrum resource of cognition wireless network to distribute.
Background technology
Wireless frequency spectrum is non-renewable scarce resource.At present, communication service rapid growth and extensive use based on wireless, cause frequency spectrum resource imbalance between supply and demand to become increasingly conspicuous.In cognitive radio networks, the frequency spectrum hole that cognitive user (inferior user) can chance insertion authority user (primary user), and then improve the utilance of radio spectrum resources.Spectrum allocation may is the eternal focus in cognitive radio networks research, mainly focuses on and perceives after idle frequency spectrum, how to meet under certain distribution target efficient allocation usable spectrum resource.
Spectrum allocation may has different mode classifications.According to frequency spectrum access way, can be divided into complete limited frequency spectrum distribution and part limited frequency spectrum and distribute; By network configuration, can be divided into centralized spectrum allocation may and distributed frequency spectrum and distribute; By approach to cooperation, can be divided into cooperative frequency spectrum distribution and non-cooperative frequency spectrum and distribute.In actual use, several distribution mechanisms often need to be joined together to consider, be proposed concrete solution for specific application scenarios.Existing frequency spectrum distributing method mainly contains: frequency spectrum transaction and auction, game theory, graph coloring theory etc.The centralized complete limited frequency spectrum that the present invention pays close attention to based on cooperation is distributed, mainly based on graph coloring model realization.
Spectrum allocation may based on graph coloring has proved a np hard problem.Therefore, intelligent optimization method is the efficient algorithm that solves this problem.The intelligent optimizations such as genetic algorithm, particle cluster algorithm, immune clone, artificial bee colony and improvement algorithm thereof are used to solve spectrum allocation may problem.Wherein, most typical is spectrum allocation may based on particle cluster algorithm.Due to the defect of particle cluster algorithm itself, also there is the problems such as solving precision is not high in prior art, affected the utilance of radio spectrum resources.In addition, artificial physics optimization is a kind of novel intelligent optimization algorithm, but there is not yet the report for spectrum allocation may Optimization Solution by artificial physics optimized algorithm.
Summary of the invention
Problem to be solved by this invention is, overcomes the deficiencies in the prior art, and the cognitive radio network spectrum allocation method that provides a kind of artificial physics to optimize, has solved the not high problem of spectrum allocation may benefit.
The present invention solves its technical problem and takes following technical scheme to realize:
The cognitive radio network spectrum allocation method of optimizing according to a kind of artificial physics provided by the invention, it comprises the following steps:
(1), according to frequency spectrum perception result, obtain the idle matrix of frequency spectrum
, gain matrix
, interference matrix
;
(2) the spectrum allocation may matrix that will solve
be mapped as the particulate in artificial physics, determine fitness function;
(3) initialization of population, sets relevant parameter: establish evolutionary generation
be 0, random initializtion scale is
population
, to each length be
particulate
, be expressed as
, random initializtion each
be 0 or 1; If the initial velocity of particulate
, the quality of optimum particulate is made as
; Universal gravitational constant is set
, maximum evolutionary generation is
;
(4) particulate correction and calculating fitness value: the each particulate in population is carried out and revised particle manipulating, be met the population of constraints; According to fitness function, calculate particulate adaptive value
, and select the optimum particulate of adaptive value maximum
;
(5) end condition judgement: if algorithm reaches maximum evolution number of times
, algorithm stops, by optimum particulate
encode and be mapped as spectrum allocation may matrix
form, obtained best spectrum allocation may; Otherwise, go to step (6);
(6) calculating particulate is suffered makes a concerted effort:
1) according to formula
calculate each particulate
quality:
Wherein,
,
represent particulate
quality, preferably the quality of particulate is made as
,
for being greater than 1 normal number;
2) according to following formula
calculate the active force of suffered other particulate of particulate:
Wherein,
for particulate
to particulate
active force,
3) according to following formula
calculate particulate suffered with joint efforts:
(7) calculate particle movement and upgrade:
Wherein:
for inertia weight (
), set
,
it is the stochastic variable of an obedience (0,1) normal distribution;
Wherein:
it is the random number of any [0,1] producing.
(8) evolutionary generation
, go to step (4).
It is to take following technical scheme further to realize that the present invention solves its technical problem:
In aforesaid step (1), cognitive user number is
, usable spectrum number is
, idle matrix
, gain matrix
, interference matrix
be expressed as follows respectively:
Idle matrix
be a two values matrix, be expressed as
.
represent frequency spectrum
can supply cognitive user
use, otherwise, can not use;
Gain matrix
represent cognitive user
use idle frequency spectrum
the income of rear acquisition;
Interference matrix
.
a two values matrix, wherein,
represent cognitive user
with
use frequency spectrum
can produce and disturb, otherwise, represent can not produce interference.
In aforesaid step (2), the noiseless allocation matrix that solve
, wherein
represent idle frequency spectrum
distribute to cognitive user
;
Coding mapping mode is: binary coding is carried out in the position that is not only 0 to element in matrix, and the length of each particulate is
,
for matrix
the number that middle element value is non-zero;
Fitness function is:
.
In aforesaid step (4), the correction of particulate is operating as: to any frequency spectrum
if,
, check matrix
in
the of row
row and the
whether row is 1.If so, become 0 by one at random, another remains unchanged.
The present invention compared with prior art has significant advantage and beneficial effect:
(1) due to artificial physics optimization of the present invention, to have parameter less, and convergence rate is advantage faster, and the present invention, in the time carrying out spectrum allocation may, has less running time, can realize better network profit and maximize.
(2) the present invention, according to the binary coding feature of spectrum allocation may problem, has improved the position renewal equation of particulate, is more suitable for problem solving.
(3) the present invention, in the time calculating the suffered active force of particulate, has adopted linear force rule, with the linear relation with increase of particulate spacing, has overcome the weak deficiency of negative exponent active force and unimodal active force ability of searching optimum.
The specific embodiment of the present invention is provided in detail by following examples and accompanying drawing thereof.
Brief description of the drawings
Fig. 1 is FB(flow block) of the present invention.
Embodiment
Below in conjunction with accompanying drawing and preferred embodiment, to according to embodiment provided by the invention, structure, feature and effect thereof, be described in detail as follows.
The cognitive radio network spectrum allocation method that a kind of artificial physics is as shown in Figure 1 optimized, it comprises the following steps:
(1), according to frequency spectrum perception result, obtain the idle matrix of frequency spectrum
, gain matrix
, interference matrix
;
In embodiments of the present invention, idle matrix
for what generate at random
0,1 binary matrix, and ensure that the rarest element of every 1 row is that 1(has at least the frequency spectrum can be with); Gain matrix
for
matrix, the reference value of income is network throughput; Interference matrix
for random 0, the 1 binary symmetric matrix generating, meanwhile, must meet corresponding constraints simultaneously.Cognitive user
span 1-20, usable spectrum
span 1-30.
，
，
。
(2) the spectrum allocation may matrix that will solve
be mapped as the particulate in artificial physics optimization, determine fitness function;
In an embodiment of the present invention, frequency spectrum
, by not being that binary coding is carried out in 0 position to element in matrix, in artificial physics optimization, the length of each particulate is
(when only having frequency spectrum idle, just may have allocation matrix), therefore, each particulate has represented a kind of possible spectrum allocation schemes.Fitness function is
.Spectrum allocation may problem is converted to: at known idle frequency spectrum matrix
, gain matrix
, interference matrix
situation under, seek to make the allocation matrix of network profit maximum
.
(3) initialization of population, sets relevant parameter.If evolutionary generation
be 0, random initializtion population scale
population
, to each length be
particulate
, be expressed as
, random initializtion each
be 0 or 1; If the initial velocity of particulate
, the quality of optimum particulate
=2; Universal gravitational constant is set
=1, maximum evolutionary generation is
=200;
(4) particulate correction and calculating fitness value.Each particulate in population is carried out and revised particle manipulating, be met the population of constraints; According to fitness function, calculate particulate adaptive value
, and select the optimum particulate of adaptive value maximum
; The correction of particulate is operating as: to any frequency spectrum
if,
, check matrix
in
the of row
row and the
whether row is 1.If so, become 0 by one at random, another remains unchanged.
(5) end condition judgement: if algorithm reaches maximum evolution number of times
=200, algorithm stops, by optimum particulate
encode and be mapped as spectrum allocation may matrix
form, obtained best spectrum allocation may; Otherwise, go to step (6);
(6) calculating particulate is suffered makes a concerted effort:
1) according to formula
calculate each particulate
quality:
Wherein,
,
represent particulate
quality.
In an embodiment of the present invention, select exponential function that the fine-grained quality beyond best particulate is limited between (0,1).
2) according to following formula
calculate the active force of suffered other particulate of particulate:
Wherein,
for particulate
to particulate
active force,
.
Employing linear force rule, with the linear relation with increase of particulate spacing, thereby has strengthened the robustness of algorithm.
3) according to following formula
calculate particulate suffered with joint efforts:
(7) calculate particle movement and upgrade:
Wherein:
for inertia weight (
), set
,
it is the stochastic variable of an obedience (0,1) normal distribution;
Because spectrum allocation may is binary coding, so, the position renewal equation of change particle
Wherein:
it is the random number of any [0,1] producing;
(8) evolutionary generation
, go to step (4).
Effect of the present invention can further illustrate by following experiment:
1. simulated conditions:
Be to use Matlab2008 to carry out emulation in the system of core 22.4GHZ, internal memory 4G, WINDOWS XP at CPU.2. emulation content:
In emulation experiment, by above algorithm operation 50 times, be averaged result, verified under different usable spectrums and cognitive user quantity the maximization network income summation that spectrum allocation may obtains
, and contrast with prior art.
Table 1 and table 2 are the network profit summations that obtain under different iterationses, are respectively
with
.
Table 1 network profit summation (M=N=5)
Table 2 network profit summation (M=N=20)
As can be seen from Table 1 and Table 2, the present invention is better than particle group optimizing method in network profit summation., also can find out, along with the increase of iterations, convergence rate of the present invention is faster than particle group optimizing method meanwhile.Experimental result has illustrated that the present invention has convergence rate and optimizing ability faster.
Claims (4)
1. the cognitive radio network spectrum allocation method that artificial physics is optimized, is characterized in that: it comprises the following steps:
(1), according to frequency spectrum perception result, obtain the idle matrix of frequency spectrum
, gain matrix
, interference matrix
;
(2) the spectrum allocation may matrix that will solve
be mapped as the particulate in artificial physics, determine fitness function;
(3) initialization of population, sets relevant parameter: establish evolutionary generation
be 0, random initializtion scale is
population
, to each length be
particulate
, be expressed as
, random initializtion each
be 0 or 1; If the initial velocity of particulate
, the quality of optimum particulate is made as
; Universal gravitational constant is set
, maximum evolutionary generation is
;
(4) particulate correction and calculating fitness value: the each particulate in population is carried out and revised particle manipulating, be met the population of constraints; According to fitness function, calculate particulate adaptive value
, and select the optimum particulate of adaptive value maximum
;
(5) end condition judgement: if algorithm reaches maximum evolution number of times
, algorithm stops, by optimum particulate
encode and be mapped as spectrum allocation may matrix
form, obtained best spectrum allocation may; Otherwise, go to step (6);
(6) calculating particulate is suffered makes a concerted effort:
1) according to formula
calculate each particulate
quality:
Wherein,
,
represent particulate
quality, preferably the quality of particulate is made as
,
for being greater than 1 normal number;
2) according to following formula
calculate the active force of suffered other particulate of particulate:
Wherein,
for particulate
to particulate
active force,
3) according to following formula
calculate particulate suffered with joint efforts:
(7) calculate particle movement and upgrade:
Wherein:
for inertia weight (
), set
,
it is the stochastic variable of an obedience (0,1) normal distribution;
Wherein:
it is the random number of any [0,1] producing; (8) evolutionary generation
, go to step (4).
2. the cognitive radio network spectrum allocation method that artificial physics according to claim 1 is optimized, is characterized in that: in described step (1), setting cognitive user number is
, usable spectrum number is
, idle matrix
, gain matrix
, interference matrix
be expressed as follows respectively:
Idle matrix
be a two values matrix, be expressed as
.
represent frequency spectrum
can supply cognitive user
use, otherwise, can not use;
Gain matrix
represent cognitive user
use idle frequency spectrum
the income of rear acquisition;
Interference matrix
;
a two values matrix, wherein,
represent cognitive user
with
use frequency spectrum
can produce and disturb, otherwise, represent can not produce interference.
3. the cognitive radio network spectrum allocation method that artificial physics according to claim 1 is optimized, is characterized in that: in described step (2), and the noiseless allocation matrix that solve
, wherein
represent idle frequency spectrum
distribute to cognitive user
;
Coding mapping mode is: binary coding is carried out in the position that is not only 0 to element in matrix, and the length of each particulate is
,
for matrix
the number that middle element value is non-zero;
Fitness function is:
.
4. the cognitive radio network spectrum allocation method that artificial physics according to claim 1 is optimized, is characterized in that:
In described step (4), the correction of particulate is operating as: to any frequency spectrum
if,
, check matrix
in
the of row
row and the
whether row is 1; If so, become 0 by one at random, another remains unchanged.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106230528A (en) * | 2016-07-27 | 2016-12-14 | 广东工业大学 | A kind of cognition wireless network frequency spectrum distributing method and system |
| CN107634811A (en) * | 2017-09-27 | 2018-01-26 | 天津工业大学 | A kind of cognition Internet of Things frequency spectrum detecting method based on mimicry physics multiple-objection optimization |
| CN108551676A (en) * | 2018-05-10 | 2018-09-18 | 河南工业大学 | A kind of cognition car networking frequency spectrum distributing method based on immune optimization |
-
2014
- 2014-07-09 CN CN201410323450.4A patent/CN104168572A/en active Pending
Non-Patent Citations (5)
| Title |
|---|
| JAMES KENNEDY: ""A Discrete Binary Version of the Particle Swarm Algorithm"", 《IEEE》 * |
| YAN WANG: ""A multi-objective artificial physics optimization algorithm based on ranks of individuals"", 《SOFT COMPUT》 * |
| 柴争义: ""基于免疫优化的认知无线网络频谱决策与资源分配"", 《中国博士学位论文全文数据库》 * |
| 谢丽萍: ""A Hybrid Vector Artificial Physics Optimization with Multi-Dimensional Search Method"", 《IEEE》 * |
| 谢丽萍: ""基于拟态物理学方法的全局优化算法"", 《计算机研究与发展》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106230528A (en) * | 2016-07-27 | 2016-12-14 | 广东工业大学 | A kind of cognition wireless network frequency spectrum distributing method and system |
| CN106230528B (en) * | 2016-07-27 | 2019-04-09 | 广东工业大学 | A kind of cognition wireless network frequency spectrum distributing method and system |
| CN107634811A (en) * | 2017-09-27 | 2018-01-26 | 天津工业大学 | A kind of cognition Internet of Things frequency spectrum detecting method based on mimicry physics multiple-objection optimization |
| CN107634811B (en) * | 2017-09-27 | 2021-03-09 | 天津工业大学 | Simulated physical multi-objective optimization-based cognitive Internet of things spectrum detection method |
| CN108551676A (en) * | 2018-05-10 | 2018-09-18 | 河南工业大学 | A kind of cognition car networking frequency spectrum distributing method based on immune optimization |
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Application publication date: 20141126 |