CN108055665B - Target channel access method based on improved multi-target PSO optimization - Google Patents

Target channel access method based on improved multi-target PSO optimization Download PDF

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CN108055665B
CN108055665B CN201711027043.9A CN201711027043A CN108055665B CN 108055665 B CN108055665 B CN 108055665B CN 201711027043 A CN201711027043 A CN 201711027043A CN 108055665 B CN108055665 B CN 108055665B
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张煜培
赵知劲
杨安锋
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Hangzhou Dianzi University
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Abstract

The invention discloses a target channel access method based on improved multi-target PSO optimization. First, the channel access sequence is represented by the particle position, so to encode the channel number into a discrete binary [0,1] sequence, the encoding needs to be corrected in order to ensure that the encoded sequence is not repeated. And then introducing a V-shaped function to update the particle position, taking the accumulated time delay and the channel capacity as two fitness functions, determining non-dominated solutions and adding the non-dominated solutions into an external archive set, outputting the solutions in the external archive set when the maximum iteration number is reached, wherein each solution corresponds to a target channel access sequence, and the solutions are Pareto optimal solutions. Simulation results show that the optimal channel access solution set obtained by the proposed spectrum switching algorithm can give consideration to both the real-time performance and the high throughput rate of the network, and the algorithm complexity is low.

Description

Target channel access method based on improved multi-target PSO optimization
Technical Field
The invention belongs to the field of cognitive radio in wireless communication, and particularly relates to a target channel access method in target channel access by utilizing cumulative delay and channel capacity joint optimization of multi-target PSO (power system over optical) optimization.
Background
The rapid growth of wireless devices has resulted in a dramatic increase in band access requirements, and investigations by the Federal Communications Commission (FC-C) have shown that up to 85% of the licensed spectrum is underutilized. Cognitive Radio (CR) is a technology that solves the contradiction between low spectrum utilization and the growing spectrum access demand, and a Cognitive User (SU) searches for a spectrum hole in a space-time varying Radio environment, adaptively adjusts its own parameters, and opportunistically accesses an authorized frequency band that is not used by a master User. In order to not affect Primary User (PU) communication, when a PU with a high priority suddenly appears in a channel used by a cognitive User, the cognitive User must leave a current channel to search a new idle channel to ensure continuity of communication, and this process is frequency spectrum switching. The target channel is critical to spectrum switching because the target channel is a hope that communication will be maintained. Currently, the selection of the target channel sequence is based on the following: the rest idle time is longest, the idle probability is largest, the switching time delay is shortest, the energy consumption is reduced, and the like.
However, in the existing methods, a target channel is selected from a single angle, however, in the actual communication process, the channel capacity is also an important reference factor, and even if the number of times of switching is small, if the channel capacity is small, the communication requirement of the cognitive user cannot be met.
Disclosure of Invention
Aiming at the limitation of the existing target channel sequence design method, the invention comprehensively considers the switching accumulated time delay and the channel capacity, which is a multi-target problem, so that a target channel access method based on the improved multi-target PSO optimization is provided, and a target channel access method based on the combined optimization of the accumulated time delay and the channel capacity optimized by the multi-target PSO is provided, so as to give consideration to the requirements of network real-time performance and high throughput rate.
The technical scheme adopted by the invention for solving the technical problem comprises the following steps:
step 1, establishing a frequency spectrum switching optimization model;
step 2, establishing a target channel access mechanism in spectrum switching, obtaining a switching time delay and channel capacity function formula under the mechanism, and designing a target function;
step 3, encoding and initializing, namely encoding the particle position x by using a target channel access sequence and initializing various parameters;
step 4, updating the global optimal value g, calculating objective function values E [ D ] and-E [ C ] of each particle, determining that a non-dominated solution is added into an external archive set NP, and selecting the particle with the minimum density as g by using a self-adaptive grid;
step 5, updating the individual speed v and the position x, and calculating the objective function value of each particle, namely switching delay and channel capacity;
and 6, updating the individual optimal value p.
And 7, repeating the steps 4 to 6, and outputting a non-dominated solution set (PF leading edge) in the NP set as a result set when the maximum iteration number is reached.
The invention has the beneficial effects that:
1. a target channel access method is provided, which comprehensively considers two targets of accumulated switching delay and effective channel capacity. The method can give consideration to both network real-time performance and high throughput rate, and has effectiveness and practicability.
2. An improved multi-target particle swarm optimization algorithm is provided, the time complexity is reduced, and the requirement on real-time performance is guaranteed.
3. And redesigning a coding mode, wherein the coding method can correct repeated codes.
4. And a position updating formula is redesigned, and the formula can increase the position change probability and enhance the optimization capability of the algorithm.
Drawings
Fig. 1 is a schematic of spectrum switching.
Detailed Description
The following describes the implementation steps of the present invention in further detail with reference to the attached drawings.
As shown in fig. 1, a target channel access method based on improved multi-target PSO optimization specifically includes the following steps:
step 1, establishing a frequency spectrum switching optimization model, which specifically comprises the following steps:
considering that in a Cognitive Radio Network (CRN), there are N independent channels, SU1 and SU2 are communicating on one idle channel, a master user suddenly accesses the channel at a certain time, in order to maintain communication, SU1 and SU2 need to perform spectrum switching, and according to a predetermined target channel sequence, the sequence is subjected to a period T
Figure BDA0001448612760000031
M ≦ N is accessed in turn, so that the full permutation of the M channels constitutes the solution space Ω of the search, whose dimensions M! And (4) x M. Fig. 1 shows a schematic diagram of cognitive user switching according to a target channel sequence. Cognitive user first access ck1Channel, shaded, indicates that cognitive users SU1 and SU2 are in handshake to adjust communication parameters for a time ThSince the establishment of the M target channels is the result of periodic sensing, whether a channel is truly idle is a probabilistic event. If at ck1During handshake or communication, if a master user suddenly appears, the cognitive user needs to stop current communication and access c in sequencek2,…,cki… until finding the free channel, representing the success of the switching;if M channels are visited and no idle channel is found yet, the switching is failed.
Step 2, establishing a target channel access mechanism in spectrum switching, obtaining a switching time delay and channel capacity function formula under the mechanism, and designing a target function, which is specifically as follows:
2-1, assuming that the channel idle time follows an exponential distribution, the channel idle time probability density function is:
Figure BDA0001448612760000032
in the formula
Figure BDA0001448612760000033
Denotes ciAverage idle time of the channel. Assume that the target channel sequence is [ c ]1,c2,…,ci,…,cM]Making cognitive users access ith channel ciThe probability of failure is
Figure BDA0001448612760000034
I.e. suddenly a primary user accesses the channel during the current handshake or has been engaged and occupied before, then at ciThe probability of upper handshake failure is:
Figure BDA0001448612760000035
then at ciThe probability of success of the upper handshake is:
Figure BDA0001448612760000041
the probability of the failure of the spectrum switching at this time is as follows:
Figure BDA0001448612760000042
the probability of successful switching after i (i is more than or equal to 1 and less than or equal to M) handshake is as follows:
Figure BDA0001448612760000043
2-2, as can be seen from 2-1 analysis, the handover delay is composed of two parts, one part is delay caused by handover success, and the other part is delay caused by handover failure, so that the expectation of the handover delay can be obtained as follows:
Figure BDA0001448612760000044
2-3, assuming that the total bandwidth of CRN is B, dividing into N channels, each channel bandwidth is B/N, ciThe signal-to-noise ratio of the channel is
Figure BDA0001448612760000045
The available average effective channel capacity is:
Figure BDA0001448612760000046
2-4, establishing the following objective function:
Figure BDA0001448612760000047
wherein c is*＝[c1,c2,…,cM]E Ω denotes the optimal target channel access order.
Step 3, encoding and initializing, namely encoding the particle position x by using a target channel access sequence, and initializing various parameters, wherein the method specifically comprises the following steps:
3-1, assuming there are M channels in the target channel access sequence, the set of channel numbers Θ is [0,1,2, …, M-1 ═ l](ii) a Each channel is numbered with L ═ ceil (log)2M) bits, ceil indicates rounding up, and the dimension of each particle code is d ═ LM. Since the initialization channel sequence in the algorithm is random, so that repeated channels may occur, the coding needs to be corrected, and the specific steps are as follows:
3-1-1. decoding: positioning the ith particle at xi＝(xi1,xi2,…,xid) After decimal decoding, the sequence is zi＝(zi1,zi2,…,ziM). Wherein z isijFrom xiIn (x)i,(j-1)L+1,…,xi,jL) Decoding to obtain;
3-1-2. mapping: by zi＝(zi1modM,zi2modM,…,ziMmodM) maps it into Θ;
3-1-3. mixing ziPutting different elements in a set P, executing steps 3-1-4 and 3-1-5 when the number num (P) of the elements in the set P is less than M, or stopping;
3-1-4.ziwhere 2 identical elements are present, an element λ is randomly selected from the set Ψ Θ -PiInstead of;
3-1-5. update P ═ P λi]Ψ — P, if
Figure BDA0001448612760000051
Stopping the operation; otherwise, turning to 3-1-4.
3-2. initialize the particle velocity v, subject to a uniform distribution over [ Varmin, Varmax ], usually let Varmax-Varmin-4. The initial position of the default particle is the individual optimum p, the support set NP [ ], the maximum number of iterations I is set, and t is 1.
Step 4, updating the global optimal value g, calculating objective function values E [ D ] and-E [ C ] of each particle, determining that a non-dominated solution is added into an external archive set NP, and selecting the particle with the minimum density as g by using a self-adaptive grid, wherein the method specifically comprises the following steps:
4-1, decoding to obtain a target channel sequence access sequence according to the position of each particle, respectively calculating an objective function value E [ D ] and-E [ C ] of each particle by using 2-2 and 2-3, and determining that a non-dominant solution is added into the NP, wherein the definition of the non-dominant solution is as follows:
pareto Dominance (Pareto Dominance) one channel solution set
Figure BDA0001448612760000052
If and only if minE [ D ]]≤minE'[D]∧-E[C]≤-E'[C]Otherwise, the two solutions are said to be mutually non-dominant.
4-2. calculate the boundary [ min (E) of the grid in the t-th iterationt(D)),max(Et(D))]、 [min(-Et(C)),max(-Et(C))]It is divided into M multiplied by M small grids, and the length and the width of each small grid are respectively
Figure BDA0001448612760000061
And
Figure BDA0001448612760000062
the grid number of the ith particle is
Figure BDA0001448612760000063
Wherein
Figure BDA0001448612760000069
Indicating rounding up. Then, the number of particles in each grid is counted, and one particle with the minimum density is selected as g.
Step 5, updating the individual velocity v and the position x, and calculating the objective function value of each particle, namely the switching delay and the channel capacity, specifically as follows:
5-1. update the individual velocity v according to equation (9):
Figure BDA0001448612760000064
wherein the content of the first and second substances,
Figure BDA0001448612760000065
the speed of the jth dimensional subspace of the ith particle in the tth iteration is referred to; d is the particle dimension; w is a weight value, and is updated in a linearly decreasing manner according to equation (10), wmaxAnd wminIs a constant, representing the weight maximum and minimum values, respectively; i is the total number of iterations; r is1And r2Is two obeys [0,1]]Uniformly distributed random variables;
Figure BDA0001448612760000066
and gjRespectively, of the local optimal solution p searched by the ith particle so far and the global optimal solution g searched by all the particles so farThe jth element.
5-2. update the individual position x according to equation (10) and equation (11):
Figure BDA0001448612760000067
Figure BDA0001448612760000068
and 5-3, after updating the individual position x, correcting the code according to 3-1, then decoding to obtain a target channel access sequence, and finally calculating the switching delay E [ D ] and the channel capacity-E [ C ] in the sequence.
And 6, updating the individual optimal value p, which is specifically as follows:
and comparing the current solution obtained in the particle flight process with the last p, if the current solution dominates p, making the current solution p, and otherwise, keeping p unchanged. If the two are not mutually independent, one is randomly selected as p.
And 7, repeating the steps 4 to 6, and outputting a non-dominated solution set in the NP set as a result set when the maximum iteration number is reached, wherein the result set is as follows:
and judging whether the I is equal to or less than t, if so, executing the step 4 to the step 6, guiding the particle flight by using the switching delay ED and the channel capacity EC as fitness functions, searching an optimal target channel access sequence and putting the optimal target channel access sequence into an external archive set NP, and outputting an NP concentrated non-dominated solution when the maximum iteration number is reached.

Claims (5)

1. A target channel access method based on improved multi-target PSO optimization is characterized by comprising the following steps:
step 1, establishing a frequency spectrum switching optimization model;
step 2, establishing a target channel access mechanism in spectrum switching, obtaining a switching time delay and channel capacity function formula under the mechanism, and designing a target function;
step 3, encoding and initializing, namely encoding the particle position x by using a target channel access sequence and initializing various parameters;
step 4, updating the global optimal value g, calculating objective function values E [ D ] and-E [ C ] of each particle, determining that a non-dominated solution is added into an external archive set NP, and selecting the particle with the minimum density as g by using a self-adaptive grid;
step 5, updating the individual speed v and the position x, and calculating the objective function value of each particle, namely switching delay and channel capacity;
step 6, updating the individual optimal value p;
step 7, repeating the step 4 to the step 6, and outputting a non-dominated solution set in the NP set as a result set when the maximum iteration times are reached;
the establishment of the spectrum switching optimization model in the step 1 specifically comprises the following steps:
considering that in a cognitive radio network, N independent channels exist, SU1 and SU2 are communicating on one idle channel, a primary user suddenly accesses the channel at a certain moment, in order to maintain communication, SU1 and SU2 need to perform spectrum switching, and according to a target channel sequence which is determined in advance, the sequence is subjected to a period T
Figure FDA0002357159490000011
M is less than or equal to N, and access is carried out in sequence, so that the full arrangement of the M channels forms a searched solution space omega, and the dimension of the solution space omega is M multiplied by M; cognitive user first access ck1Channel to adjust communication parameters for a time ThSince the establishment of the M target channels is the result of periodic sensing, whether a channel is truly idle is a probabilistic event; if at ck1During handshake or communication, if a master user suddenly appears, the cognitive user needs to stop current communication and access c in sequencek2,…,cki… until finding the free channel, representing the success of the switching; if M channels are visited and no idle channel is found yet, the switching is failed;
step 2, establishing a target channel access mechanism in spectrum switching, obtaining a switching delay and channel capacity function formula under the mechanism, and designing a target function, specifically as follows:
2-1, assuming that the channel idle time follows an exponential distribution, the channel idle time probability density function is:
Figure FDA0002357159490000021
in the formula
Figure FDA0002357159490000022
Denotes ciAverage idle time of the channel; assume that the target channel sequence is [ c ]1,c2,…,ci,…,cM]Making cognitive users access ith channel ciThe probability of failure is
Figure FDA0002357159490000023
I.e. suddenly a primary user accesses the channel during the current handshake or has been engaged and occupied before, then at ciThe probability of upper handshake failure is:
Figure FDA0002357159490000024
then at ciThe probability of success of the upper handshake is:
Figure FDA0002357159490000025
the probability of the failure of the spectrum switching at this time is as follows:
Figure FDA0002357159490000026
the probability of successful switching after i (i is more than or equal to 1 and less than or equal to M) handshake is as follows:
Figure FDA0002357159490000027
2-2, as can be seen from 2-1 analysis, the handover delay is composed of two parts, one part is delay caused by handover success, and the other part is delay caused by handover failure, so that the expectation of obtaining the handover delay is as follows:
Figure FDA0002357159490000028
2-3, assuming that the total bandwidth of CRN is B, dividing into N channels, each channel bandwidth is B/N, ciThe signal-to-noise ratio of the channel is
Figure FDA0002357159490000034
The available average effective channel capacity is:
Figure FDA0002357159490000031
2-4, establishing the following objective function:
Figure FDA0002357159490000032
wherein c is*＝[c1,c2,…,cM]E.g. omega represents the optimal target channel access order;
the encoding and initialization described in step 3, encode the particle position x by using the target channel access order, and initialize various parameters, as follows:
3-1, assuming there are M channels in the target channel access sequence, the set of channel numbers Θ is [0,1,2, …, M-1 ═ l](ii) a Each channel is numbered with L ═ ceil (log)2M) bits, ceil indicates rounding up, and the dimension of each particle code is d ═ LM; since the initialization channel sequence is random, so that repeated channels may occur, the coding needs to be corrected, and the specific steps are as follows:
3-1-1. decoding: positioning the ith particle at xi＝(xi1,xi2,…,xid) After decimal decoding, the sequence is zi＝(zi1,zi2,…,ziM) (ii) a Wherein z isijFrom xiIn (x)i,(j-1)L+1,…,xi,jL) Decoding to obtain;
3-1-2. mapping: by zi＝(zi1modM,zi2modM,…,ziMmodM) maps it into Θ;
3-1-3. mixing ziDifferent elements are placed in the set P, steps 3-1-4 and 3-1-5 are performed when the number of elements in P num (P) < M, otherwise stop;
3-1-4.ziwhere 2 identical elements occur, one element λ is randomly selected in the set Ψ Θ -piInstead of;
3-1-5. update P ═ P λi]Ψ Θ - Ρ, if
Figure FDA0002357159490000033
Stopping the operation; otherwise, turning to 3-1-4;
initializing the particle velocity v, following a uniform distribution over [ Varmin, Varmax ], typically letting Varmax-Varmin-4; the initial position of the default particle is the individual optimum p, the support set NP [ ], the maximum number of iterations I is set, and t is 1.
2. The method of claim 1, wherein the global optimal value g is updated in step 4, objective function values E [ D ] and-E [ C ] of each particle are calculated, non-dominant solution is determined to be added to the external archive set NP, and the particle with the minimum density is selected as g by using an adaptive mesh, specifically as follows:
4-1, decoding to obtain a target channel sequence access order according to the position of each particle, calculating an objective function value E [ D ] and-E [ C ] of each particle by using the step 2-2 and the step 2-3 respectively, and determining that a non-dominant solution is added into the NP, wherein the definition of the non-dominant solution is as follows:
pareto governs one channel solution set
Figure FDA0002357159490000047
(
Figure FDA0002357159490000048
Indicating dominance) if and only if minE [ D ]]≤minE'[D]^-E[C]≤-E'[C]Whether or notThen, the two solutions are said to be mutually independent;
4-2. calculate the boundary [ min (E) of the grid in the t-th iterationt(D)),max(Et(D))]、[min(-Et(C)),max(-Et(C))]It is divided into M multiplied by M small grids, and the length and the width of each small grid are respectively
Figure FDA0002357159490000041
And
Figure FDA0002357159490000042
the grid number of the ith particle is
Figure FDA0002357159490000043
Wherein
Figure FDA0002357159490000044
The expression is rounded up, then the number of particles in each grid is counted, and the particle with the smallest density is selected as g.
3. The method of claim 2, wherein the individual velocity v and the position x are updated in step 5, and the objective function value, i.e. the switching delay and the channel capacity, of each particle are calculated as follows:
5-1. update the individual velocity v according to equation (9):
Figure FDA0002357159490000045
wherein the content of the first and second substances,
Figure FDA0002357159490000046
the speed of the jth dimensional subspace of the ith particle in the tth iteration is referred to; d is the particle dimension; w is a weight value, and is updated in a linearly decreasing manner according to equation (10), wmaxAnd wminIs a constant, representing the weight maximum and minimum values, respectively; i is the total number of iterations; r is1And r2Is twoObey 0,1]Uniformly distributed random variables;
Figure FDA0002357159490000051
and gjThe local optimal solution p searched by the ith particle so far and the jth element of the global optimal solution g searched by all the particles so far are respectively elements;
5-2. update the individual position x according to equation (10) and equation (11):
Figure FDA0002357159490000052
Figure FDA0002357159490000053
and 5-3, after updating the individual position x, correcting the codes according to the step 3-1, then decoding to obtain a target channel access sequence, and finally calculating the switching delay E [ D ] and the channel capacity-E [ C ] in the sequence.
4. The method of claim 3, wherein the individual optimal value p is updated in step 6 as follows:
comparing the current solution obtained in the particle flight process with the last p, if the current solution dominates p, making the current solution p, and otherwise, keeping p unchanged; if the two are not mutually independent, one is randomly selected as p.
5. The method of claim 4, wherein the non-dominated solution set in the NP set is output as a result set in step 7, and the method comprises the following steps:
and judging whether the I is equal to or less than t, if so, executing the step 4 to the step 6, guiding the particle flight by using the switching time delay ED and the channel capacity EC as fitness functions, searching an optimal target channel access sequence and putting the optimal target channel access sequence into an external archive set NP, and outputting an NP concentrated non-dominated solution when the maximum iteration number is reached.
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