AU2021101111A4 - Multivariate Resource Allocation Method for Heterogeneous Massive MIMO System Based on Network Slicing - Google Patents
Multivariate Resource Allocation Method for Heterogeneous Massive MIMO System Based on Network Slicing Download PDFInfo
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Abstract
The present invention discloses a multivariate resource allocation method for
heterogeneous Massive MIMO system based on network slicing, comprising the
following specific steps: S1, building a heterogeneous Massive MIMO system model;
S2, initializing a quantum artificial algal community and system parameters, and
obtaining the measurement state of quantum artificial algal population based on the
measurement rule; S3, calculating the fitness of all quantum artificial algal populations,
recording the measurement state of the quantum artificial algal population with the
largest fitness as a global optimal solution; S4, updating the quantum artificial algal
population according to different evolution rules of spiral motion, evolution process and
adaptive process; S5, obtaining the measurement state of updated quantum artificial
algal population based on the measurement rule, calculating the fitness of updated
quantum artificial algal population, and updating the global optimal solution; and S6,
returning to S4 if the iterations are less than the maximum iterations; otherwise
outputting a global optimal solution, and further obtaining a corresponding multivariate
resource allocation scheme of heterogeneous Massive MIMO system.
FIGURES OF THE SPECIFICATION
1/3
Building a heterogeneous Massive MIMO system model;
Initializing a quantum artificial algal community and system
parameters, and obtaining the measurement state of quantum
artificial algal population based on the measurement rule;
Calculating the fitness of all quantum artificial algal populations,
recording the measurement state of the quantum artificial algal
population with the largest fitness as a global optimal solution;
Updating the quantum artificial algal population according to
different evolution rules of spiral motion, evolution process and
adaptive process;
Obtaining the measurement state of updated quantum artificial
algal population based on the measurement rule, calculating
the fitness of updated quantum artificial algal population, and
updating the global optimal solution;
rations < maximum Yes
iterations
No
Outputting a global optimal solution, and further obtaining a
corresponding multivariate resource allocation scheme of
heterogeneous Massive MIMO system.
Fig. 1
Description
1/3
Building a heterogeneous Massive MIMO system model;
Initializing a quantum artificial algal community and system parameters, and obtaining the measurement state of quantum artificial algal population based on the measurement rule;
Calculating the fitness of all quantum artificial algal populations, recording the measurement state of the quantum artificial algal population with the largest fitness as a global optimal solution;
Updating the quantum artificial algal population according to different evolution rules of spiral motion, evolution process and adaptive process;
Obtaining the measurement state of updated quantum artificial algal population based on the measurement rule, calculating the fitness of updated quantum artificial algal population, and updating the global optimal solution;
rations < maximum Yes iterations
No
Outputting a global optimal solution, and further obtaining a corresponding multivariate resource allocation scheme of heterogeneous Massive MIMO system.
Fig. 1
Multivariate Resource Allocation Method for Heterogeneous Massive
MIMO System Based on Network Slicing
The present invention relates to the key technical field of 6G
communication, in particular to a multivariate resource allocation method for
heterogeneous Massive MIMO system based on network slicing.
With the vigorous development of information technology, the rapid
popularization and wide application of intelligent devices set a higher demand
on data transmission rate, energy consumption and local service quality of
existing communication networks. Based on the base station with large-scale
antennas, Massive MIMO can obtain a high spatial diversity gain and provide
services for multiple users with less energy consumption, which effectively
improve system capacity, spectral efficiency and energy efficiency. However,
with the rapid development of wireless communication, the demand for data
calculation and information processing is increasing with the access of a large
number of intelligent devices; as a result, the business forms carried by wireless
networks tend to be diversified and the network system is more complex.
Facing the challenge of massive data, an urgent problem in 6G communication
is how to design an effective resource allocation scheme for Massive MIMO
system meeting the requirements for high-speed, high-quality and low-latency communication.
The network slicing technology enables operators to flexibly provide
personalized services for users at a lower cost by dividing a physical network
into multiple mutually independent virtual networks, each of which corresponds
to different communication requirements. In addition, the network slicing
technology can achieve the rational allocation of limited resources of the system,
and thus has important research significance for the improvement of network
service quality and user service experience. Through searching the existing
literature, it was found that, according to "Virtual Resource Allocation Algorithm
for Network Utility Maximization Based on Network Slicing" published in Journal
of Electronics and Information Technology (2017, vol. 39, no. 8, pp. 1812-1818)
by Tang Lun et al., a virtual resource allocation algorithm for network utility
maximization based on Lagrange duality decomposition method was provided
by combining the different demands of different user services on computing
resources and spectrum resources, instead of considering the slice resource
allocation in Massive MIMO communication. According to "Dynamic Resource
Allocation for Virtualized Wireless Networks in Massive-MIMO-Aided and
Fronthaul-Limited C-RAN" published in IEEE Transactions on Vehicular
Technology (2017, vol. 66, no. 10, pp. 9512-9520) by Saeedeh Parsaeefard et
al., the resource allocation scheme with the maximum total transmission rate
was obtained by continuous convex approximation and distributed iteration of
geometric programming, without giving any consideration to the co-channel
interference among users and the division of network slices. According to
"Intelligent Resource Scheduling for 5G Radio Access Network Slicing"
published in IEEE Transactions on Vehicular Technology (2019, vol. 68, no. 8, pp. 7691-7703) by Mu Yan et al., a resource allocation method based on deep learning and reinforcement learning was proposed to meet the specific service requirements for radio access network slicing, which required historical traffic information and thus made it difficult to obtain an optimal resource allocation scheme in real communication systems. Literature has shown that existing studies on a Massive MIMO system based on network slicing are rarely seen and the multivariate resource allocation of heterogeneous Massive MIMO communication system for large-scale users is not discussed.
For the shortcomings of existing resource allocation mechanism based on
network slicing, the present invention provides a multivariate resource
allocation method for heterogeneous Massive MIMO system based on network
slicing. The method effectively solves the division of network slices, user
scheduling and spectrum resource allocation in the heterogeneous Massive
MIMO system, and greatly improves the resource utilization rate and capacity
of the Massive MIMO system on the premise of meeting the business needs of
different users.
In order to achieve the purpose, the technical solutions used in the present
invention are as follows:
The present invention provides a multivariate resource allocation method
for heterogeneous Massive MIMO system based on network slicing, comprising
the following specific steps:
S1, building a heterogeneous Massive MIMO system model comprising a
heterogeneous Massive MIMO system capacity, and obtaining a resource allocation scheme for heterogeneous Massive MIMO system based on the heterogeneous Massive MIMO system capacity;
S2, setting a plurality of quantum artificial algal populations corresponding
to the heterogeneous Massive MIMO system, initializing scale and hunger
value of each quantum artificial algal population, and obtaining the
measurement state of each quantum artificial algal population based on the
measurement rule;
S3, calculating the fitness of all quantum artificial algal populations based
on the measurement state, wherein the heterogeneous Massive MIMO system
capacity increases with the increasing fitness; recording the measurement state
of the quantum artificial algal population with the largest fitness as a global
optimal solution, setting the maximum iterations, and starting iteration from S4;
S4, enabling the quantum artificial algal population to complete the spiral
motion, and judging the fitness of the quantum artificial algal population:
maintaining the hunger value unchanged if the fitness is optimum; otherwise,
increasing the hunger value, and recording the scale of quantum artificial algal
population;
S5, executing an evolution process and an adaptive process, and updating
the quantum artificial algal population after completing the spiral motion, the
evolution process and the adaptive process;
S6, obtaining the measurement state of updated quantum artificial algal
population based on the measurement rule, calculating the fitness of updated
quantum artificial algal population, and updating the global optimal solution of
quantum artificial algal community; and
S7, judging whether the iterations are less than the maximum iterations; if
so, returning to S4; otherwise, terminating the iteration, outputting the global
optimal solution of quantum artificial algal population, and further obtaining a
corresponding multivariate resource allocation scheme of heterogeneous
Massive MIMO system.
Furthermore, the model further comprises a base station and a plurality of
users, and the base station comprises a plurality of antennas;
the network of the system is divided into a plurality of mutually independent
slices, and each of the slices occupies a different spectrum resource block and
is assigned at least one antenna;
signal-to-noise ratio, channel gain and throughput the users to the base
station in the uplink transmission process is calculated, throughput of the slice
is calculated according to the throughput from the users to the base station,
and a user scheduling and spectrum resource allocation formula is obtained
based on the heterogeneous Massive MIMO system capacity and constraint to
maximize the heterogeneous Massive MIMO system capacity.
Furthermore, the constraint comprises a user scheduling constraint, a
spectrum resource allocation constraint and a throughput constraint;
wherein the user scheduling constraint is 1, i.e. a user can be only
assigned one slice;
the spectrum resource allocation constraint is 1; i.e. the users included in
each slice only occupy one spectrum resource block and each user is assigned
at least one spectrum resource block; the throughput constraint means that each slice shall be greater than or equal to the minimum throughput requirement threshold, and the minimum throughput threshold of each slice is obtained according to actual communication requirements.
Furthermore, the measurement state of each quantum artificial algal
population corresponds to a spectrum resource allocation scheme of the
heterogeneous Massive MIMO system.
Furthermore, the quantum artificial algal population evolves towards rich
nutrients and large fitness in the spiral motion.
Furthermore, in the evolution process, the largest quantum artificial algal
population is proliferated, the smallest quantum artificial algal population begins
to die out, and one quantum artificial algae in the smallest quantum artificial
algal population is replaced by one quantum artificial algae in the largest
quantum artificial algal population;
wherein the quantum artificial algal population with the largest hunger
value evolves towards the largest quantum artificial algal population in the
adaptive process.
Furthermore, the measurement rule is
X- 1,Aid(x )2
where, Ad is a uniform random number ranging [0, 1], and 4 d is the dth
quantum artificial algae in the th quantum artificial algal population, 0 < X4dl
1.
The present invention discloses the following technical effects:
(1) In view of the fact that existing resource allocation schemes for Massive
MIMO systems are not suitable for large-scale heterogeneous communication
scenarios, the present invention provides a multivariate resource allocation
method for heterogeneous Massive MIMO system based on network slicing.
The quantum artificial algal population mechanism is adopted to achieve
flexible allocation of network resources, which effectively reduces the co
channel interference among different users, and significantly improves the
resource utilization rate and capacity of the Massive MIMO system on the
premise of meeting the business needs of different users.
(2) The quantum artificial algal population mechanism designed by the
present invention effectively solves the complex high-dimensional optimization
problems including the division of network slices, user scheduling and spectrum
resource allocation in the heterogeneous Massive MIMO system. Compared
with existing resource allocation methods of Massive MIMO system, the
designed quantum artificial algal population mechanism effectively reduces the
computational complexity, presents stable performance, and provides the
optimum resource allocation scheme in a short time, which can meet the
communication requirements of actual Massive MIMO system.
(3) Different from traditional artificial algal population mechanism, the
quantum artificial algal population mechanism designed by the present
invention controls the evolution mode depending on different quantum state
evolution rules based on the quantum programming idea, and has the
characteristics of fast convergence speed, high convergence precision and strong global optimization ability. It overcomes the defects of traditional artificial algal population mechanism, which is easy to fall into local convergence and difficult to solve high-dimensional optimization problems. Moreover, it provides a new idea for solving complex engineering problems, and can be transplanted to other engineering problems. Thus, it is suitable for being widely popularized.
In order to explain more clearly the embodiments in the present invention
or the technical solutions in the prior art, the following will briefly introduce the
figures needed in the description of the embodiments. Obviously, figures in the
following description are only some embodiments of the present invention, and
for a person skilled in the art, other figures may also be obtained based on
these figures without paying any creative effort.
Fig. 1 is a schematic diagram of the multivariate resource allocation
method for heterogeneous Massive MIMO system based on network slicing,
which combines the quantum artificial algal population mechanism.
Fig. 2 is a graph showing the change of system capacity with iterations in
the multivariate resource allocation method for heterogeneous Massive MIMO
system based on network slicing, which combines the quantum artificial algal
population mechanism and the artificial algal population mechanism.
Fig. 3 is a graph showing the change of system capacity with user
transmission power in the multivariate resource allocation method for
heterogeneous Massive MIMO system based on network slicing, which
combines the quantum artificial algal population mechanism and the artificial
algal population mechanism.
Fig. 4 is a graph showing the change of system capacity with different
number of antennas at the base station and different number of users in the
multivariate resource allocation method for heterogeneous Massive MIMO
system based on network slicing, which combines the quantum artificial algal
population mechanism and the artificial algal population mechanism.
Various exemplary embodiments of the present invention will now be
described in detail, which should not be construed as being limited thereto, but
should be understood as a more detailed description of certain aspects,
features and embodiments thereof.
It should be understood that the terms described herein are only intended
to describe specific embodiments, and are not intended to limit the present
invention. Furthermore, the range of values in the present invention should be
such understood that each intermediate value between the upper and lower
limits of the range is also specifically disclosed. Each smaller range between
any stated value or intermediate value within a stated range and any other
stated value or intermediate value within a stated range is also included in the
present invention. The upper and lower limits of these smaller ranges can be
independently included in or excluded from the scope.
Unless otherwise indicated, all technical and scientific terms used herein
have the same meaning as commonly understood to one of ordinary skill in the
art to which this present invention belongs. Although the present invention
describes only preferred methods and materials, any methods and materials
similar or equivalent to those described herein can be used in the practice or testing of the present invention. All literatures mentioned herein are incorporated herein by reference for the purpose of disclosing and describing the methods and/or materials associated with the literatures. In the event of a conflict with any incorporated literature, the contents of this specification shall prevail.
It will be readily apparent to those skilled in the art that various
modifications and changes can be made to the specific embodiments of the
specification of the present invention without departing from the scope or spirit
of the present invention. Upon reading this disclosure, many alternative
embodiments of the present invention will be apparent to persons of ordinary
skill in the art. The specification and examples of this application are only
exemplary.
As used herein, the terms "including", "comprising", "having" and
"containing" are all open terms, which means including but not limited to.
Unless otherwise specified, "parts" mentioned in the present invention are
calculated by parts by mass.
Example 1
step 1, building a heterogeneous Massive MIMO system model;
In a heterogeneous Massive MIMO communication system consisting of a
base station with PM antennas and K different single-antenna users, the
network is divided into S mutually independent slices to meet various service
requirements of users. Each user is assigned a corresponding slice for
information transmission. The user scheduling scheme is represented by ak, E
{0,1}, where, k = 1,2,...,X and s 1,2,...,S. If the kth userisassignedtheSth
slice, then ak,s = 1; otherwise, aks = 0. If a user can be only assigned one
slice, then the user scheduling constraint is Ej akS, - 1.
To ensure that different slices do not interfere with each other, each slice
occupies a different spectrum resource block, the number of spectrum resource
block occupied by the th slice is recorded as s and Ls <1, s
where, L is the total number of spectrum resource blocks in the
heterogeneous Massive MIMO system. bks,,I E f0,1} represents the
spectrum resource allocation scheme for the kth user in the Sth slice, wherein
k = 1,2,...,K, is = 1,2,...,LS . If the kth user occupies the Is th spectrum
resource block in the sth slice,thenbk,s,ls = 1; otherwise, bk,s,ls = 0. It is
assumed that the users included in each slice only occupy one spectrum
resource block and each user is assigned at least one spectrum resource block,
then the spectrum allocation constraint is _, 1 bk,s,s- 1
All slices are assigned antennas. Ms represents the number of base
station antennas assigned to the sthslice,andeachsliceisassignedatleast
one antenna, i.e. 1 Ms < M, and Z 1 Ms 5 M. In the uplink transmission
process, the signal-to-noise-plus-interference ratio from the kthusertothebase
station is S--1 , s1 bkP Gk,s,s s-j=1je kbj,s,lsPjGjslsO~ 2 , where, k =k1,2,...,X, P
represents the transmission power of the kth user, Gksls represents the
channel gain from the kth usertothebasestationinthesthspectrumresource
block of the Sth slice, Pj represents the transmission power of thefh user, G
represents the channel gain from the fh usertothebasestationinthesth spectrum resource block of the sth slice, 1jkbj,sPjGj,s, 1 represents total interference from other users occupying the same spectrum resource block as the kth user on the th slice,and 2 represents Gaussian white noise power.
Then, the throughput from the kthusertothe base stationisr B log2 (1 + Yk),
where, B is the bandwidth of spectrum resource block. According to the
throughput of users, the throughput of the Sth slice can be calculated as Rs
E=1ak,srk
To ensure the user service quality of heterogeneous Massive MIMO
system, each slice shall satisfy the corresponding minimum throughput
requirement. In this case, the throughput constraint is Rs > Rm" , where,
Rmi" represents the minimum throughput requirement threshold of the sth
slice, wherein the minimum throughput threshold of each slice is obtained
according to actual communication requirements.
The system capacity of heterogeneous Massive MIMO communication
network is:
K S Ls
+ ak,sb,s,i5 PkG,s,i 2 C = B 1og2 1 k=1 s=1S=1 j1,jk sjS +
The user scheduling and spectrum resource allocation problem with the
objective of maximizing the capacity of the heterogeneous Massive MIMO
system based on network slicing can be expressed as:
K S Ls
max C (a, b) = B 1og2 1+ aY,sbk,s,i PkGk,s,i2 k=1 s=1 Ls=1 j=1,j, k 1 5 1G +o2 j s~ls P constraint: ak,S Ef{0,1}, bk,s, 5 E {O,1}
S S Ls
a k,s 1, bk,s,ls 1 s=1 s=1 ls=1
Rs >RRmin RSm
where, a is a K x S-dimensional user scheduling scheme matrix, and
each element in the matrix is represented by aks ; b is a K x S x Ls
dimensional spectrum resource allocation scheme matrix, and each element in
the matrix is represented by b,s,ls.
step 2, initializing a quantum artificial algal community and system
parameters;
The scale and hunger value of each quantum artificial algal population is
initialized by setting the number of quantum artificial algal populations in the
quantum artificial algal community as H, the dimension of each quantum
artificial algal population as D. Thejhquantumartificialalgalpopulationofthe
t-generation quantum artificial algal community isx = [where,
x represents the dth quantumartificialhalgaeinthehquantumartificialalgal
population, 0 x4!< 4 1, i = 1,2,..., H, d = 1,2,...,D. The measurement state
ll = [,2 ,---,]of theth quantumartificialalgalcommunityisobtained
based on the measurement rule, and the measurement rule is .4 d =
1, Ad > ,1II' 4 :! ((x d) 2 d )2 ' where, Aid is a uniform random number ranging [0, 1]. The ,
measurement state of each quantum artificial algal population corresponds to
a resource allocation scheme of heterogeneous Massive MIMO system.
step 3, calculating the fitness of all quantum artificial algal populations;
The fitness of all quantum artificial algal populations in the quantum
artificial algal community is obtained by substituting the measurement state of
thejth quantum artificial algal population in the t-generation quantum artificial
algal community into the fitness function f() =theconstraint
The heterogeneous Massive MIMO system capacity increases with the
increasing fitness. The measurement state of the quantum artificial algal
population with the largest fitness is recorded as a global optimal
solutionpbest= [Pbest,1, -best,2, - - - best,D]
step 4, updating the quantum artificial algal population after completing the
spiral motion;
The quantum artificial algal population evolves towards rich nutrients and
large fitness in the spiral motion, and each quantum artificial algal population is
updated according to the following rules: 6 +=
c1(5 a - i id)|(# - Tr)p|, +1< 11
C2(X' h,- i,)( I - Tf) cosaf |,1 1 ( +l < r2 , uff
C3(X ht- id)I(P - Tf)sinf#+, I other
1-(xd4)2 , = 0 and 65t+1 < E ,where, O +represents the d 14x ' cos8 idl -1 - (xt )2 - sin8f+1, other
dimensional quantum rotation angle of the thquantumartificialalgalpopulation,
ud represents the quantum transition state of the dthquantumartificialalgae
in the thquantumartificialalgalpopulationaftercompletingthespiralmotion,
d = 1,2,..., D I = measurement state number of quantum artificial algal population corresponding to the global optimal solution, if x Pest measurement state number of quantum artificial algal population with the largest fitness except global optimal solution, if i = Pest where, ci , c2 and c3 are the influencing factors, < is the viscous resistance, Tr is the frictional surface area of theth quantumartificialalgal
/ 2 population, Tr = 2(T ,v is the scale of the quantumartificialalgal
population, p is a uniform random number ranging [-1,1], af' and
are the random angles ranging [0,27], 1.1 indicates taking absolute values,
is a uniform random number ranging [0,1], rl1 and r/2 are fixed
parameters, o5 *' is a uniform random number ranging [0,1], and E is the
mutation probability.
The measurement state i 1 = [i ,U, ... , Ut+] of the quantum
transition state of the hquantumartificialalgalpopulationisobtainedbasedon
the measurement rule. The fitness of quantum artificial algal population is
calculated as per the fitness function. If the hquantumartificialalgalpopulation
has an optimum fitness after completing the spiral movement, let x +' = u +',
i+ +1, and maintain the hunger value of theth quantumartificialalgal
population unchanged; otherwise, let x +' = x , :iV+ = A , and increase the
hunger value of the th quantum artificial algal population by AE, where, AE is
the hunger index. Upon the spiral movement, the scale of the h quantum
artificial algal population is vf+1 =+vi, where, pf+' is the growth rate,
t+1 _ 1 + (41 st 1+f (i+'ga
step 5, executing an evolution process and an adaptive process;
The evolution process and the adaptive process are executed after
completing the spiral motion. In the evolution process, the largest quantum
artificial algal population is proliferated, the smallest quantum artificial algal
population begins to die out, and one quantum artificial algae in the smallest
quantum artificial algal population is replaced by one quantum artificial algae in
the largest quantum artificial algal population.
In the adaptive process, the quantum artificial algal population with the
largest hunger value evolves towards the quantum artificial algal population
with the largest scale, with the probability of Ay, and the evolution rule is
w= c4(i- '9w~dl= simtdl z), C(- =Ixw- wtdl) -cowd - wd ~ d Ow1td 1-(xt±1)2 w,d - sinoi |, where,
oi~ is the d-dimensional quantum rotation angle of the quantum artificial algal
population with the largest hunger value, m is the serial number of the
quantum artificial algal population with the largest scale, w is the serial number
of the quantum artificial algal population with the largest hunger value, w E
{1,2,..., H}, c4 is the influencing factor. Then, let xt+l = gW+, and complete the
updating of the adaptive process.
step 6, obtaining theth quantumartificialalgalpopulationaccordingto
different evolution rules of spiral motion, evolution process and adaptive
process; and obtaining the measurement state ii+' = [o ]f. , of
updated quantum artificial algal population based on the measurement rule;
calculating the fitness of updated quantum artificial algal population; and
updating the global optimal solution pt of the quantum artificial algal
community.
step 7, if the iterations are less than the preset maximum iterations, letting
t = t + 1, and returning to step 4; otherwise, terminating the iteration, outputting
the global optimal solution Ptgl = [pbest,'P , Pbest,D] of the quantum
artificial algal population, and further obtaining a corresponding multivariate
resource allocation scheme of heterogeneous Massive MIMO system.
Example 2
For a heterogeneous Massive MIMO communication system, set M=
128, K = 20, L = 4 and S = 2, the bandwidth of each spectrum resource
block B = 180kHz, the base station located at (0,0) m, the coverage radius of
500m; all users are randomly distributed within the coverage area of the base
station, and the base station processes the received signals by the maximum
ratio combining (MRC) method. Then, the channel gain is , where, X is
the distance between two communication nodes, Xo is the reference distance,
vo is the path loss exponent. Based on the simulation results, Xo = 100m and
vo = 3.8, the system noise is Gaussian white noise with power spectral density
No, No = -174dB/Hz, and the transmission power of all users is 30dBm. Each
slice is assigned the same number of base station antennas and spectrum
resource blocks, and the minimum throughput requirement shall be O.5Mbit/s.
For the quantum artificial algal population mechanism, the number of quantum
artificial algal population is H = 20, the maximum iteration is 1000, ci = 0.03,
C2 = 0.03, c 3 = 0.03, c 4 = 0.03, irl = 1/3, r/2 = 2/3, the mutation probability
is E = 0.1/D, the initial scale of all quantum artificial algal populations is 1, the
hunger value is 0, AE = 1, A= 0.5. The experimental result is the mean value
of 100 experiments without exception. In order to conveniently compare the performance of the proposed quantum artificial algal population mechanism and the artificial algal population mechanism, the artificial algal population mechanism is applied to the multivariate resource allocation of heterogeneous
Massive MIMO system based on network slicing, and the number of algal
populations and the maximum iterations of the artificial algal population
mechanism are the same as those of the quantum artificial algal population
mechanism. The artificial algal population mechanism is coded in binary to
solve the complex, high-dimensional, discrete optimization problems such as
the division of network slices, user scheduling and spectrum resource allocation
in the heterogeneous Massive MIMO system.
Example 3
Fig. 2 is a graph showing the change of system capacity with iterations in
the multivariate resource allocation method for heterogeneous Massive MIMO
system based on network slicing, which combines the quantum artificial algal
population mechanism and the artificial algal population mechanism. The
simulation results clearly show that the quantum artificial algal population
mechanism is superior to the artificial algal population mechanism in terms of
convergence speed, convergence precision and optimization ability. Therefore,
the multivariate resource allocation method based on the quantum artificial
algal population mechanism is able to provide a larger system capacity and
improve the overall performance of the Massive MIMO system.
Fig. 3 is a graph showing the change of system capacity with user
transmission power in the multivariate resource allocation method for
heterogeneous Massive MIMO system based on network slicing, which combines the quantum artificial algal population mechanism and the artificial algal population mechanism. The simulation results clearly show that the multivariate resource allocation method based on the quantum artificial algal population mechanism is able to provide a larger system capacity compared with the artificial algal population mechanism.
Fig. 4 is a graph showing the change of system capacity with different
number of antennas at the base station and different number of users in the
multivariate resource allocation method for heterogeneous Massive MIMO
system based on network slicing, which combines the quantum artificial algal
population mechanism and the artificial algal population mechanism. Based on
the simulation results, the number of base station antennas is 32, 64, 128, 256,
512. The simulation results show that, when the multivariate resource allocation
method is based on the quantum artificial algal population mechanism and the
artificial algal population mechanism, the system capacity increases with the
increasing number of base station antennas, but the increase in the number of
users leads to an increase in co-channel interference among users, which limits
the space of improving the system capacity. In addition, the simulation results
also show that the multivariate resource allocation method based on the
quantum artificial algal population mechanism provides more stable
performance for different numbers of base station antennas and users, and the
system capacity obtained thereby is obviously superior to the resource
allocation method based on the artificial algal population mechanism. Therefore,
the designed method is proven to be effective.
The preferred embodiments described herein are only for illustration purpose, and are not intended to limit the present invention. Various modifications and improvements on the technical solution of the present invention made by those of ordinary skill in the art without departing from the design spirit of the present invention shall fall within the protection scope as claimed in claims of the present invention.
Claims (7)
1. A multivariate resource allocation method for heterogeneous Massive
MIMO system based on network slicing, characterized by comprising the
following specific steps:
S1, building a heterogeneous Massive MIMO system model comprising a
heterogeneous Massive MIMO system capacity, and obtaining a resource
allocation scheme for heterogeneous Massive MIMO system based on the
heterogeneous Massive MIMO system capacity;
S2, setting a plurality of quantum artificial algal populations corresponding
to the heterogeneous Massive MIMO system, initializing scale and hunger
value of each quantum artificial algal population, and obtaining the
measurement state of each quantum artificial algal population based on the
measurement rule;
S3, calculating the fitness of all quantum artificial algal populations based
on the measurement state, wherein the heterogeneous Massive MIMO system
capacity increases with the increasing fitness; recording the measurement state
of the quantum artificial algal population with the largest fitness as a global
optimal solution, setting the maximum iterations, and starting iteration from S4;
S4, enabling the quantum artificial algal population to complete the spiral
motion, and judging the fitness of the quantum artificial algal population:
maintaining the hunger value unchanged if the fitness is optimum; otherwise,
increasing the hunger value, and recording the scale of quantum artificial algal
population;
S5, executing an evolution process and an adaptive process, and updating
the quantum artificial algal population after completing the spiral motion, the
evolution process and the adaptive process;
S6, obtaining the measurement state of updated quantum artificial algal
population based on the measurement rule, calculating the fitness of updated
quantum artificial algal population, and updating the global optimal solution of
quantum artificial algal community; and
S7, judging whether the iterations are less than the maximum iterations; if
so, returning to S4; otherwise, terminating the iteration, outputting the global
optimal solution of quantum artificial algal population, and further obtaining a
corresponding multivariate resource allocation scheme of heterogeneous
Massive MIMO system.
2. The multivariate resource allocation method for heterogeneous Massive
MIMO system based on network slicing according to claim 1, characterized in
that the model further comprises a base station and a plurality of users, and the
base station comprises a plurality of antennas;
the network of the system is divided into a plurality of mutually independent
slices, and each of the slices occupies a different spectrum resource block and
is assigned at least one antenna;
signal-to-noise ratio, channel gain and throughput from the users to the
base station in the uplink transmission process is calculated, throughput of the
slice is calculated according to the throughput from the users to the base station,
and a user scheduling and spectrum resource allocation formula is obtained
based on the heterogeneous Massive MIMO system capacity and constraint to maximize the heterogeneous Massive MIMO system capacity.
3. The multivariate resource allocation method for heterogeneous Massive
MIMO system based on network slicing according to claim 2, characterized in
that the constraint comprises a user scheduling constraint, a spectrum resource
allocation constraint and a throughput constraint;
wherein the user scheduling constraint is 1, i.e. a user can be only
assigned one slice;
the spectrum resource allocation constraint is 1; i.e. the users included in
each slice only occupy one spectrum resource block and each user is assigned
at least one spectrum resource block;
the throughput constraint means that each slice shall be greater than or
equal to the minimum throughput requirement threshold.
4. The multivariate resource allocation method for heterogeneous Massive
MIMO system based on network slicing according to claim 1, characterized in
that the measurement state of each quantum artificial algal population
corresponds to a spectrum resource allocation scheme of the heterogeneous
Massive MIMO system.
5. The multivariate resource allocation method for heterogeneous Massive
MIMO system based on network slicing according to claim 1, characterized in
that the quantum artificial algal population evolves towards rich nutrients and
large fitness in the spiral motion.
6. The multivariate resource allocation method for heterogeneous Massive
MIMO system based on network slicing according to claim 1, characterized in that in the evolution process, the largest quantum artificial algal population is proliferated, the smallest quantum artificial algal population begins to die out, and one quantum artificial algae in the smallest quantum artificial algal population is replaced by one quantum artificial algae in the largest quantum artificial algal population; wherein the quantum artificial algal population with the largest hunger value evolves towards the largest quantum artificial algal population in the adaptive process.
7. The multivariate resource allocation method for heterogeneous Massive
MIMO system based on network slicing according to claim 1, characterized in
that the measurement rule is
(1,id>(x d) 2 X 0,1d :! (xd )2
4 where, Ad is a uniform random number ranging [0, 1], and d is the dth
quantum artificial algae in the th quantum artificial algal population, 0 < X4dl
1.
FIGURES OF THE SPECIFICATION
1/3
Building a heterogeneous Massive MIMO system model;
Initializing a quantum artificial algal community and system 2021101111
parameters, and obtaining the measurement state of quantum artificial algal population based on the measurement rule;
Calculating the fitness of all quantum artificial algal populations, recording the measurement state of the quantum artificial algal population with the largest fitness as a global optimal solution;
Updating the quantum artificial algal population according to different evolution rules of spiral motion, evolution process and adaptive process;
Obtaining the measurement state of updated quantum artificial algal population based on the measurement rule, calculating the fitness of updated quantum artificial algal population, and updating the global optimal solution;
Iterations ≤ maximum Yes iterations
No
Outputting a global optimal solution, and further obtaining a corresponding multivariate resource allocation scheme of heterogeneous Massive MIMO system.
Fig. 1
Fig. 3 Fig. 2 2/3
Fig. 4 3/3
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