CN115632687A - Resource allocation method of RIS-assisted MISO symbiotic radio system - Google Patents

Resource allocation method of RIS-assisted MISO symbiotic radio system Download PDF

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CN115632687A
CN115632687A CN202211295993.0A CN202211295993A CN115632687A CN 115632687 A CN115632687 A CN 115632687A CN 202211295993 A CN202211295993 A CN 202211295993A CN 115632687 A CN115632687 A CN 115632687A
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ris
resource allocation
secondary reflection
node
symbiotic
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徐勇军
周春宇
来容
陈量
周继华
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Chongqing University of Post and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/382—Monitoring; Testing of propagation channels for resource allocation, admission control or handover
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0473—Wireless resource allocation based on the type of the allocated resource the resource being transmission power

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Abstract

The invention belongs to the field of symbiotic radio network architecture and resource allocation, and particularly relates to a resource allocation method of a RIS (RIS assisted single input single output) MISO symbiotic radio system; the method comprises the following steps: introducing the RIS into the MISO symbiotic radio system, and constructing an RIS auxiliary MISO symbiotic radio system; establishing an energy efficiency maximization resource allocation model of the RIS auxiliary MISO symbiotic radio system; the energy efficiency maximization resource allocation is converted into 3 subproblems by adopting a block coordinate descent method based on the Buckbach; solving the 3 sub-problems to obtain a resource allocation scheme; the system allocates resources according to the resource allocation scheme; the invention ensures the total energy efficiency of the system to be maximized, and simultaneously obviously improves the performance compared with the traditional algorithm.

Description

Resource allocation method of RIS-assisted MISO symbiotic radio system
Technical Field
The invention belongs to the field of symbiotic radio network architecture and resource allocation, and particularly relates to a resource allocation method of a RIS (RIS assisted single input multiple output) MISO symbiotic radio system.
Background
With the rapid increase of the number of the devices of the internet of things, the problems of shortage of spectrum resources and sharp increase of energy consumption are increasingly highlighted. The cognitive radio can solve the problem of spectrum resource shortage through a spectrum sharing mechanism, but interference exists in primary and secondary systems in the cognitive radio. Thus, in order to fully utilize interference signals and realize resource sharing, symbiotic radio is provided, by the cooperative operation of primary and secondary systems, the sharing of frequency spectrum, power and infrastructure can be realized, and a primary user can regard signals from a secondary transmission system as multipath gain rather than interference, and the system capacity can be remarkably improved.
The intelligent reflecting surface (RIS) is a uniform array plane integrated by a large number of passive electromagnetic reflecting elements with low power consumption, low cost and sub-wavelength structures and independent controllability, and has the main function of adjusting the amplitude and the phase of a reflected signal in a software programming mode according to communication link information fed back by signal propagation so as to add the reflected signal and signals of other paths constructively, thereby enhancing the expected signal power of a receiving end and improving the communication quality.
Therefore, symbiotic radio and RIS can be used as two main technologies for improving spectrum efficiency and energy efficiency. Many documents research on the RIS-assisted symbiotic radio network, such as performance analysis, minimization of transmission power, and system capacity, but energy efficiency optimization and energy collection capability of the secondary reflection node are neglected, which is very critical for an energy-limited symbiotic radio system.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a resource allocation method of an RIS auxiliary MISO symbiotic radio system, which comprises the following steps:
s1: introducing the RIS into the MISO symbiotic radio system, and constructing an RIS auxiliary MISO symbiotic radio system;
s2: establishing an energy efficiency maximization resource allocation model of the RIS auxiliary MISO symbiotic radio system;
s3: the energy efficiency maximization resource allocation is converted into 3 subproblems by adopting a block coordinate descent method based on the Buckbach;
s4: solving the 3 sub-problems to obtain a resource allocation scheme; and the system allocates resources according to the resource allocation scheme.
Preferably, the RIS assisted MISO co-generation radio system includes: a RIS having N reflecting elements, a primary transmitter equipped with Q antennas, a single-antenna primary receiver and a plurality of single-antenna secondary reflecting nodes;
the main transmitter transmits signals to the main receiver;
each secondary reflection node modulates the self information onto an incident main signal from the main transmitter, and transmits the modulated signal to the main receiver by adjusting the reflection coefficient;
each secondary reflection node is equipped with an energy harvesting circuit and a passive backscatter circuit, and all signals from the main transmitter undergo reflection by the RIS for signal enhancement.
Preferably, the energy efficiency maximization resource allocation model is expressed as:
Figure BDA0003902705770000021
Figure BDA0003902705770000022
Figure BDA0003902705770000023
C3:|φ n | 2 ＝1,
C4:0≤α m ≤1,
Figure BDA0003902705770000024
Figure BDA0003902705770000025
wherein R is sum To systems and rates, E total For the total power consumption of the system, Φ is the RIS phase shift matrix, R m Representing the velocity of the mth secondary reflection node,
Figure BDA0003902705770000026
a minimum rate representing the mth secondary reflection node requirement; r s Which is indicative of the primary user rate,
Figure BDA0003902705770000027
minimum rate, phi, indicative of primary user demand n An nth element of a diagonal vector representing the RIS phase shift matrix; alpha is alpha m Is the reflection coefficient of the mth secondary reflection node; e m Energy collected for the mth secondary reflection node;
Figure BDA0003902705770000031
circuit power consumption for the mth secondary reflection node; w is an active beamforming vector of the main transmitter; p max Is the maximum transmit power of the primary transmitter.
Preferably, the process of converting the resource optimization model into 3 sub-problems includes:
s31, fixing an RIS phase shift matrix and a secondary reflection node reflection coefficient, and establishing a resource distribution model with an optimized variable as an active beam forming vector;
s32, fixing the RIS phase shift matrix and the active beam forming vector, and establishing a resource distribution model with optimized variables as secondary reflection node reflection coefficients;
s33, fixing the active beam forming vector and the reflection coefficient of the secondary reflection node, and establishing a resource allocation model with an optimized variable being an RIS phase shift matrix.
Further, the resource allocation model with the optimized variables as the active beamforming vectors is expressed as:
Figure BDA0003902705770000032
Figure BDA0003902705770000033
Figure BDA0003902705770000034
Figure BDA0003902705770000035
Figure BDA0003902705770000036
Figure BDA0003902705770000037
Figure BDA0003902705770000038
wherein W = ww H W is an active beamforming vector of the main transmitter; eta is an energy efficiency auxiliary variable, P cir Is the sum of the power consumption of the system circuit,
Figure BDA00039027057700000314
representing a first equivalent rate for the mth secondary reflection node,
Figure BDA0003902705770000039
the first equivalent rate representing the primary user rate, M is the number of secondary reflection nodes in the system,
Figure BDA00039027057700000310
represents a first energy equivalent value collected by the mth secondary reflection node, tr () represents a trace of the matrix,
Figure BDA00039027057700000311
denotes the m-thThe minimum rate required by the individual secondary reflection nodes,
Figure BDA00039027057700000312
which represents the minimum rate required by the primary user,
Figure BDA00039027057700000313
for the circuit power consumption of the mth secondary reflection node, P max Rank () represents the rank of the matrix, which is the maximum transmit power of the main transmitter.
Further, the resource allocation model with the optimized variable as the reflection coefficient of the secondary reflection node is expressed as:
Figure BDA0003902705770000041
Figure BDA0003902705770000042
Figure BDA0003902705770000043
C4:0≤α m ≤1,
Figure BDA0003902705770000044
where eta is an energy efficiency auxiliary variable, P cir The sum of the power consumption of a system circuit is obtained, and M is the number of secondary reflection nodes in the system; w = ww H W is an active beam forming vector of the main transmitter, and Tr () represents a track of a matrix;
Figure BDA0003902705770000045
representing a first equivalent rate of the mth secondary reflection node,
Figure BDA0003902705770000046
a first equivalent rate representing a primary user rate,
Figure BDA0003902705770000047
representing the minimum rate required by the mth secondary reflection node,
Figure BDA0003902705770000048
minimum rate, alpha, indicating primary user demand m Is the reflection coefficient of the mth secondary reflection node,
Figure BDA0003902705770000049
representing a first energy equivalent collected by the mth secondary reflection node,
Figure BDA00039027057700000410
the circuit power consumption of the mth secondary reflection node.
Further, the resource allocation model with the optimization variables being the RIS phase shift matrix is represented as:
Figure BDA00039027057700000411
Figure BDA00039027057700000412
Figure BDA00039027057700000413
Figure BDA00039027057700000414
Figure BDA00039027057700000415
Figure BDA00039027057700000416
C10:rank(Ψ)＝1.
where η is an energy efficiency auxiliary variable, P cir Is the sum of the power consumption of the system circuit,
Figure BDA00039027057700000417
representing a second equivalent rate of the mth secondary reflection node,
Figure BDA00039027057700000418
a second equivalent rate representing the primary user rate, M being the number of secondary reflection nodes in the system,
Figure BDA00039027057700000419
representing a second energy equivalent collected by the mth secondary reflection node; w = ww H W is an active beam forming vector of the main transmitter, and Tr () represents a track of a matrix;
Figure BDA00039027057700000420
representing the minimum rate required by the mth secondary reflection node,
Figure BDA00039027057700000421
which represents the minimum rate required by the primary user,
Figure BDA00039027057700000422
for the circuit power consumption of the mth secondary reflection node, N represents the total number of RIS reflection units; psi = [ phi, beta ]] T [φ，β]Phi denotes the diagonal vector of the RIS phase shift matrix and beta denotes the phase shift auxiliary variable.
Preferably, the process of solving the 3 sub-problems includes:
converting a resource allocation model with optimized variables as active beamforming vectors into a first convex optimization problem by adopting semi-positive definite relaxation and continuous convex approximation;
converting a resource distribution model with optimized variables of secondary reflection node reflection coefficients into a second convex optimization problem by adopting a quadratic transformation method;
converting a resource allocation model with an optimization variable of an RIS phase shift matrix into a third convex optimization problem by adopting semi-positive definite relaxation and continuous convex approximation;
and solving the first convex optimization problem, the second convex optimization problem and the third convex optimization problem by adopting a convex optimization theory to obtain an active beam forming vector of the main transmitter, a secondary reflection node reflection coefficient and an RIS phase shift matrix, namely a resource allocation scheme.
The invention has the beneficial effects that: aiming at the problem of poor signal reception caused by double fading channels and obstacle blocking of a traditional symbiotic radio system, the RIS is introduced into the symbiotic radio system, and an energy efficiency maximization resource allocation model is established by considering primary and secondary user minimum rate constraint, base station maximum transmitting power constraint and each secondary reflection node minimum collected energy constraint. Converting the original problem into an equivalent convex optimization form by using a Buckbach method, continuous convex approximation, semi-positive definite relaxation and secondary transformation, and solving the convex optimization problem to obtain a resource allocation scheme; compared with the traditional method only ensuring the transmitting power or the speed, the method disclosed by the invention has the advantages that the speed and the power are balanced, the consideration is more comprehensive, the energy collecting capacity of the secondary reflection node is fully considered, and the energy efficiency of the system can be effectively improved. In addition, compared with the existing RIS-free auxiliary system, the system energy efficiency is obviously improved.
Drawings
FIG. 1 is a flow chart of a resource allocation method of an RIS assisted MISO symbiotic radio system according to the present invention;
FIG. 2 is a graph of system energy efficiency for the present invention and comparison method at different primary user rates;
FIG. 3 is a graph of system energy efficiency for different energy conversion efficiencies for the present and comparative methods.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a resource allocation method of an RIS (RIS assisted MISO) symbiotic radio system, which comprises the following steps as shown in figure 1:
s1: and introducing the RIS into the MISO symbiotic radio system, and constructing the RIS auxiliary MISO symbiotic radio system.
The invention concerns a RIS (intelligent reflector) assisted MISO (multiple input single output) symbiotic radio network. A main transmitter serving a single-antenna main receiver is provided in a network, which is provided with Q antennas, M single-antenna secondary reflection nodes, an RIS having N reflection units, and a main receiver. Wherein each secondary reflection node can modulate its own information onto an incident primary signal from a primary transmitter and then transmit the modulated signal to a primary receiver by adjusting a reflection coefficient, provided that each secondary reflection node is equipped with an energy harvesting circuit and a passive backscatter circuit, and all signals from the primary transmitter undergo reflection by an RIS for signal enhancement. Definition of T s ，T c Signal periods of the primary and secondary signals, respectively, and having a value of T c ＝LT s (L > 1). That is, one sub signal period covers L main signal periods. Defining a set of secondary reflection nodes
Figure BDA0003902705770000061
RIS phase shift matrix Φ = diag { Φ 1 ,φ 2 ,...,φ N }, number of signal cycles
Figure BDA0003902705770000062
S2: and establishing an energy efficiency maximization resource allocation model of the RIS auxiliary MISO symbiotic radio system.
And establishing an energy efficiency maximization resource allocation model by combining the user quality constraint of each user, the phase shift constraint of the RIS, the maximum transmitting power constraint at the main transmitter and the minimum collected energy constraint of each secondary reflection node, wherein the energy efficiency maximization resource allocation model is expressed as:
Figure BDA0003902705770000071
Figure BDA0003902705770000072
Figure BDA0003902705770000073
C3:|φ n | 2 ＝1,
C4:0≤α m ≤1,
Figure BDA0003902705770000074
Figure BDA0003902705770000075
the constraints in the above equations include: c1 is the minimum secondary signal rate constraint of each secondary reflection node, C2 is the minimum main signal rate constraint, C3 is the RIS phase shift constraint, C4 is the reflection coefficient constraint of each secondary reflection node, C5 is the energy constraint collected by each secondary reflection node, and C6 is the maximum emission power constraint of the main transmitter; wherein R is m Representing the velocity of the mth secondary reflection node,
Figure BDA0003902705770000076
and
Figure BDA0003902705770000077
minimum rates required by primary and secondary users respectively;
Figure BDA0003902705770000078
represents the power consumption, R, of the secondary reflection node m s Indicating primary user rate, P max Is the maximum transmission power of the main transmitter, phi n An nth element of a diagonal vector representing the RIS phase shift matrix;
Figure BDA0003902705770000079
a beamforming vector for the primary transmitter; alpha is alpha m Is the reflection coefficient from the mth secondary reflection node; Φ represents the RIS phase shift matrix;
Figure BDA00039027057700000710
m is the number of secondary reflection nodes in the system;
Figure BDA00039027057700000711
as a result of the total power consumption of the system,
Figure BDA00039027057700000712
respectively representing the circuit power consumption of the main transmitter, the main receiver, the RIS and the secondary reflection node,
Figure BDA00039027057700000713
the energy collected for the mth secondary reflection node, ρ is the energy conversion efficiency of the secondary reflection node,
Figure BDA00039027057700000714
for the channel coefficients from the primary receiver to the mth secondary reflection node,
Figure BDA00039027057700000715
is the channel coefficient from the RIS to the mth secondary reflection node,
Figure BDA00039027057700000716
is the channel coefficient from the master transmitter to the RIS;
Figure BDA00039027057700000717
for the mth secondary reflection node velocity,
Figure BDA0003902705770000081
for decoding the sub-signal c m Signal to interference plus noise ratio, sigma, of time 2 In order to be able to measure the power of the noise,
Figure BDA0003902705770000082
is the channel coefficient from the m-th secondary reflection node to the primary receiver;
Figure BDA0003902705770000083
represents the main signal rate, c = [ c ] 1 ,c 2 ,...,c M ] T Symbol vector of secondary reflection node, c M For all possible sets of values of c,
Figure BDA0003902705770000084
for decoding the signal-to-noise ratio of the main signal s (l), wherein
Figure BDA0003902705770000085
Channel coefficients from the master transmitter to the master receiver and RIS to the master receiver, respectively.
S3: and the energy efficiency maximization resource allocation is converted into 3 subproblems by adopting a block coordinate descent method based on the Buckbach.
P1 was written as the following optimization problem using the tyckbach method:
Figure BDA0003902705770000086
s.t.C1-C6.
wherein eta is an energy efficiency auxiliary variable;
Figure BDA0003902705770000087
represents the m-th secondary reflection node equivalent velocity, wherein
Figure BDA0003902705770000088
U i Represents a matrix with channel coefficients from RIS to the ith secondary reflection node diagonalized, phi = [ phi ] 1 ,φ 2 ,...φ N ] T ；
Figure BDA0003902705770000089
For primary user equivalent rate, V = diag { V } H }；
Figure BDA00039027057700000810
And collecting the equivalent value of energy for the mth secondary reflection node, and the rho is the energy conversion efficiency of the secondary reflection node.
S31, fixing the RIS phase shift matrix and the reflection coefficient of the secondary reflection node, and establishing a resource distribution model with optimized variables as active beam forming vectors.
Fix phi and alpha in P2 m P2 can be converted into a resource allocation model with optimized variables as active beamforming vectors, in particular, defining
Figure BDA0003902705770000091
Figure BDA0003902705770000092
Figure BDA0003902705770000093
W＝ww H P2 can be translated into a sub-problem that optimizes w:
Figure BDA0003902705770000094
Figure BDA0003902705770000095
Figure BDA0003902705770000096
Figure BDA0003902705770000097
Figure BDA0003902705770000098
Figure BDA0003902705770000099
Figure BDA00039027057700000910
wherein the content of the first and second substances,
Figure BDA00039027057700000911
the first equivalent rate for the secondary reflection node m,
Figure BDA00039027057700000912
is the first equivalent rate of the primary user,
Figure BDA00039027057700000913
the first energy equivalent value collected for the mth secondary reflection node, tr () represents the trace of the matrix, and rank () represents the rank of the matrix.
S32, fixing the RIS phase shift matrix and the active beam forming vector, and establishing a resource distribution model with optimized variables as secondary reflection node reflection coefficients.
Fixing phi and w in P2, P2 can be converted into a resource allocation model with optimized variables being active beamforming vectors, i.e. into optimized alpha m The sub-problems of (1):
Figure BDA0003902705770000101
Figure BDA0003902705770000102
and S33, fixing the active beam forming vector and the reflection coefficient of the secondary reflection node, and establishing a resource allocation model with an optimized variable of an RIS phase shift matrix.
Fixing w and alpha in P2 m P1 can be converted into a resource allocation model with the optimization variable being the RIS phase shift matrix, i.e. into a subproblem of optimizing phi. Concretely, it isBy definition psi = [ phi, beta ]] T ，Ψ＝ψψ H Beta is a phase shift auxiliary variable;
Figure BDA0003902705770000103
Figure BDA0003902705770000104
H 6,m ＝α m |c m b m | 2 H 7,m ，H 8,m ＝|b m | 2 H 7,m . P2 can be converted to:
Figure BDA0003902705770000105
Figure BDA0003902705770000106
Figure BDA0003902705770000107
Figure BDA0003902705770000108
Figure BDA0003902705770000109
Figure BDA00039027057700001010
C10:rank(Ψ)＝1.
wherein the content of the first and second substances,
Figure BDA00039027057700001011
the second equivalent rate for the secondary reflection node m,
Figure BDA00039027057700001012
a second equivalent rate for the primary user,
Figure BDA00039027057700001013
second energy equivalent value [ psi ] collected for secondary reflection node m] n,n Represents the elements at the transformed RIS phase shift matrix (n, n).
S4: solving the 3 sub-problems to obtain a resource allocation scheme; and the system allocates resources according to the resource allocation scheme.
The process of solving the 3 sub-problems includes:
converting a resource allocation model with optimized variables as active beamforming vectors into a first convex optimization problem by adopting semi-positive definite relaxation and continuous convex approximation; specifically, the method comprises the following steps:
to solve the problem of
Figure BDA0003902705770000111
The non-convexity brought, using successive convex approximations and taylor expansion, P3 can be transformed into a first convex optimization problem:
Figure BDA0003902705770000112
Figure BDA0003902705770000113
Figure BDA0003902705770000114
wherein the content of the first and second substances,
Figure BDA00039027057700001114
r m 、λ m 、
Figure BDA0003902705770000115
in order to be a function of the relaxation variable,
Figure BDA0003902705770000116
and
Figure BDA0003902705770000117
are respectively as
Figure BDA0003902705770000118
And
Figure BDA0003902705770000119
the last iteration value of (c). It can be seen that P4 is a standard semi-definite programming problem, and P4 can be solved by using a convex optimization theory to obtain an analytic solution of W, so as to obtain an analytic solution of the active beamforming vector W of the main transmitter.
Converting a resource distribution model with optimized variables of secondary reflection node reflection coefficients into a second convex optimization problem by adopting a quadratic transformation method; specifically, the method comprises the following steps:
to solve the problem
Figure BDA00039027057700001110
The non-convexity brought about, with a quadratic transformation, P5 can be converted into:
Figure BDA00039027057700001111
Figure BDA00039027057700001112
wherein the content of the first and second substances,
Figure BDA00039027057700001113
which is the equivalent velocity, y, of the secondary reflection node m m For quadratic transformation of the auxiliary variable, the optimal value is
Figure BDA0003902705770000121
P6 can be transformed into a first convex optimization problem:
Figure BDA0003902705770000122
Figure BDA0003902705770000123
solving the second convex optimization problem by adopting a convex optimization theory to obtain the reflection coefficient alpha of the secondary reflection node m The analytical solution of (2).
Adopting semi-positive definite relaxation and continuous convex approximation to convert a resource allocation model with an optimization variable being an RIS phase shift matrix into a third convex optimization problem; specifically, the method comprises the following steps:
with successive convex approximations and taylor expansions, P8 can be converted to:
Figure BDA0003902705770000124
Figure BDA0003902705770000125
Figure BDA0003902705770000126
wherein the content of the first and second substances,
Figure BDA0003902705770000127
τ m 、ω m 、
Figure BDA0003902705770000128
in order to be a function of the relaxation variable,
Figure BDA0003902705770000129
and
Figure BDA00039027057700001210
are respectively as
Figure BDA00039027057700001211
And
Figure BDA00039027057700001212
the last iteration value of (a). It can be seen that P9 is a standard semi-definite programming problem, P9 can be solved by using a convex optimization theory to obtain an analytic solution of Ψ, and then the RIS phase shift matrix Φ is obtained by using a gaussian randomization method.
Through the process, an active beam forming vector of the main transmitter, a secondary reflection node reflection coefficient and an RIS phase shift matrix, namely a resource allocation scheme, are obtained; the system may perform resource allocation according to a resource allocation scheme.
The present invention was evaluated:
the application effect of the invention is described in detail by combining simulation:
1) Simulation conditions
Assuming that there are two secondary reflection nodes in the system, the coordinates of the main transmitter, RIS, main receiver, secondary reflection node 1 and secondary reflection node 2 in the system are respectively: (0,0), (25,37), (23,34), (28,35), (27,36). The path loss model is
Figure BDA0003902705770000131
Where ξ denotes the path loss exponent, l 0 Is the transmission distance as the reference distance d 0 The time path loss is as follows: l 0 ＝-20dB，d 0 =1m. Other simulation parameters are given in table 1:
TABLE 1 simulation parameters Table
Figure BDA0003902705770000132
2) Simulation result
Fig. 2 shows the relationship between the system energy efficiency and the master user rate of the present invention, and it can be seen that as the master user rate increases, the system energy efficiency decreases. Fig. 3 shows a relationship between the system energy efficiency and the energy conversion efficiency of the present invention, it can be seen that the system energy efficiency increases with the increase of the energy conversion efficiency, and fig. 2 and fig. 3 show that the energy efficiency of the algorithm proposed by the present invention is significantly improved compared to the conventional RIS-free algorithm and the random phase algorithm, and in summary, the present invention has better performance compared to the conventional method.
The above-mentioned embodiments, which are further detailed for the purpose of illustrating the invention, technical solutions and advantages, should be understood that the above-mentioned embodiments are only preferred embodiments of the present invention, and should not be construed as limiting the present invention, and any modifications, equivalents, improvements, etc. made to the present invention within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (8)

1. A resource allocation method of an RIS assisted MISO symbiotic radio system, comprising:
s1: introducing the RIS into the MISO symbiotic radio system, and constructing an RIS auxiliary MISO symbiotic radio system;
s2: establishing an energy efficiency maximization resource allocation model of the RIS auxiliary MISO symbiotic radio system;
s3: the energy efficiency maximization resource allocation is converted into 3 subproblems by adopting a block coordinate descent method based on the Buckbach;
s4: solving the 3 sub-problems to obtain a resource allocation scheme; and the system performs resource allocation according to the resource allocation scheme.
2. The resource allocation method for the RIS assisted MISO symbiotic radio system according to claim 1, wherein the RIS assisted MISO symbiotic radio system comprises: a RIS having N reflecting elements, a primary transmitter equipped with Q antennas, a single-antenna primary receiver and a plurality of single-antenna secondary reflecting nodes;
the main transmitter transmits signals to the main receiver;
each secondary reflection node modulates the self information onto an incident main signal from the main transmitter, and transmits the modulated signal to the main receiver by adjusting the reflection coefficient;
each secondary reflection node is equipped with an energy harvesting circuit and a passive backscatter circuit, and all signals from the primary transmitter undergo reflection from the RIS for signal enhancement.
3. The resource allocation method for the RIS assisted MISO symbiotic radio system according to claim 1, wherein the energy efficiency maximization resource allocation model is expressed as:
Figure FDA0003902705760000021
Figure FDA0003902705760000022
Figure FDA0003902705760000023
C3:|φ n | 2 ＝1,
C4:0≤α m ≤1,
Figure FDA0003902705760000024
Figure FDA0003902705760000025
wherein R is sum To systems and rates, E total For the total power consumption of the system, Φ is the RIS phase shift matrix, R m Representing the velocity of the mth secondary reflection node,
Figure FDA0003902705760000026
a minimum rate representing the mth secondary reflection node requirement; r s Which is indicative of the primary user rate,
Figure FDA0003902705760000027
minimum rate, phi, indicative of primary user demand n Representing RIS phase shiftThe nth element of the diagonal vector of the matrix; alpha is alpha m Is the reflection coefficient of the mth secondary reflection node; e m Energy collected for the mth secondary reflection node;
Figure FDA0003902705760000028
circuit power consumption for the mth secondary reflection node; w is an active beamforming vector of the main transmitter; p max The maximum transmission power of the main transmitter.
4. The resource allocation method of RIS assisted MISO symbiotic radio system according to claim 1, wherein the process of transforming the resource optimization model into 3 sub-problems comprises:
s31, fixing an RIS phase shift matrix and a secondary reflection node reflection coefficient, and establishing a resource distribution model with an optimized variable as an active beam forming vector;
s32, fixing an RIS phase shift matrix and an active beam forming vector, and establishing a resource distribution model with optimized variables as secondary reflection node reflection coefficients;
and S33, fixing the active beam forming vector and the reflection coefficient of the secondary reflection node, and establishing a resource allocation model with an optimized variable of an RIS phase shift matrix.
5. The resource allocation method of the RIS assisted MISO coexisting radio system as claimed in claim 4, wherein the resource allocation model whose optimized variables are active beamforming vectors is represented as:
Figure FDA0003902705760000031
Figure FDA0003902705760000032
Figure FDA0003902705760000033
Figure FDA0003902705760000034
Figure FDA0003902705760000035
Figure FDA0003902705760000036
Figure FDA0003902705760000037
wherein W = ww H W is an active beamforming vector of the main transmitter; eta is an energy efficiency auxiliary variable, P cir Is the sum of the power consumption of the system circuit,
Figure FDA0003902705760000038
representing a first equivalent rate for the mth secondary reflection node,
Figure FDA0003902705760000039
the first equivalent rate representing the primary user rate, M is the number of secondary reflection nodes in the system,
Figure FDA00039027057600000310
represents a first energy equivalent value collected by the mth secondary reflection node, tr () represents a trace of the matrix,
Figure FDA00039027057600000311
representing the minimum rate required by the mth secondary reflection node,
Figure FDA00039027057600000312
a minimum rate indicative of the primary user's demand,
Figure FDA00039027057600000313
for the circuit power consumption of the mth secondary reflection node, P max Rank () represents the rank of the matrix, which is the maximum transmit power of the main transmitter.
6. The resource allocation method of the RIS assisted MISO symbiotic radio system according to claim 4, wherein the resource allocation model with optimized variables as the reflection coefficients of the secondary reflection nodes is represented as:
Figure FDA00039027057600000314
Figure FDA00039027057600000315
Figure FDA00039027057600000316
C4:0≤α m ≤1,
Figure FDA00039027057600000317
where η is an energy efficiency auxiliary variable, P cir The sum of the power consumption of a system circuit is obtained, and M is the number of secondary reflection nodes in the system; w = ww H W is an active beam forming vector of the main transmitter, and Tr () represents a track of a matrix;
Figure FDA00039027057600000318
representing a first equivalent rate for the mth secondary reflection node,
Figure FDA00039027057600000319
a first equivalent rate representing a primary user rate,
Figure FDA00039027057600000320
representing the minimum rate required by the mth secondary reflection node,
Figure FDA00039027057600000321
minimum rate, α, representing primary user demand m Is the reflection coefficient of the mth secondary reflection node,
Figure FDA00039027057600000322
representing a first energy equivalent collected by the mth secondary reflective node,
Figure FDA0003902705760000041
the circuit power consumption of the mth secondary reflection node.
7. The resource allocation method of the RIS assisted MISO symbiotic radio system according to claim 4, wherein the resource allocation model with optimized variables being RIS phase shift matrix is represented as:
Figure FDA0003902705760000042
Figure FDA0003902705760000043
Figure FDA0003902705760000044
Figure FDA0003902705760000045
Figure FDA0003902705760000046
Figure FDA0003902705760000047
C10:rank(Ψ)＝1.
where η is an energy efficiency auxiliary variable, P cir Is the sum of the power consumption of the system circuit,
Figure FDA0003902705760000048
representing a second equivalent rate of the mth secondary reflection node,
Figure FDA0003902705760000049
a second equivalent rate representing the primary user rate, M being the number of secondary reflection nodes in the system,
Figure FDA00039027057600000410
representing a second energy equivalent collected by the mth secondary reflection node; w = ww H W is an active beam forming vector of the main transmitter, and Tr () represents a track of a matrix;
Figure FDA00039027057600000411
representing the minimum rate required by the mth secondary reflection node,
Figure FDA00039027057600000412
which represents the minimum rate required by the primary user,
Figure FDA00039027057600000413
for the circuit power consumption of the mth secondary reflection node, N represents the total number of RIS reflection units; Ψ = [ φ, β ]] T [φ，β]Phi denotes the diagonal vector of the RIS phase shift matrix and beta denotes the phase shift assist variable.
8. The resource allocation method for RIS assisted MISO symbiotic radio system according to claim 1, wherein the process of solving 3 sub-problems comprises:
converting a resource allocation model with optimized variables as active beamforming vectors into a first convex optimization problem by adopting semi-positive definite relaxation and continuous convex approximation;
converting a resource distribution model with optimized variables as secondary reflection node reflection coefficients into a second convex optimization problem by adopting a quadratic transformation method;
converting a resource allocation model with an optimization variable of an RIS phase shift matrix into a third convex optimization problem by adopting semi-positive definite relaxation and continuous convex approximation;
and solving the first convex optimization problem, the second convex optimization problem and the third convex optimization problem by adopting a convex optimization theory to obtain an active beam forming vector of the main transmitter, a secondary reflection node reflection coefficient and an RIS phase shift matrix, namely a resource allocation scheme.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116546507A (en) * 2023-04-11 2023-08-04 南京邮电大学 Multi-IRS auxiliary broadband CR system resource optimization method based on deep reinforcement learning
CN117135641A (en) * 2023-10-26 2023-11-28 国网冀北电力有限公司 Resource allocation method and device of RIS-based power fusion communication network

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116546507A (en) * 2023-04-11 2023-08-04 南京邮电大学 Multi-IRS auxiliary broadband CR system resource optimization method based on deep reinforcement learning
CN117135641A (en) * 2023-10-26 2023-11-28 国网冀北电力有限公司 Resource allocation method and device of RIS-based power fusion communication network
CN117135641B (en) * 2023-10-26 2024-01-30 国网冀北电力有限公司 Resource allocation method and device of RIS-based power fusion communication network

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