CN114629539A - RIS-based high-energy-efficiency resource allocation method in multi-user millimeter wave non-orthogonal multiple access system - Google Patents

RIS-based high-energy-efficiency resource allocation method in multi-user millimeter wave non-orthogonal multiple access system Download PDF

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CN114629539A
CN114629539A CN202210262485.6A CN202210262485A CN114629539A CN 114629539 A CN114629539 A CN 114629539A CN 202210262485 A CN202210262485 A CN 202210262485A CN 114629539 A CN114629539 A CN 114629539A
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ris
optimization
user
energy efficiency
mmwave
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虞湘宾
沈珂宇
王光英
蔡嘉丽
黄旭
黎宁
党小宇
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Nanjing University of Aeronautics and Astronautics
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Nanjing University of Aeronautics and Astronautics
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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
    • 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
    • H04B7/04013—Intelligent reflective surfaces
    • 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
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • 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
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
    • H04B7/0837—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using pre-detection combining
    • H04B7/0842—Weighted combining
    • H04B7/086—Weighted combining using weights depending on external parameters, e.g. direction of arrival [DOA], predetermined weights or beamforming

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  • Signal Processing (AREA)
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Abstract

The invention discloses a high energy efficiency resource allocation method in a multi-user millimeter wave non-orthogonal multiple access system based on RIS, which performs joint optimization on user power allocation, mixed beam forming of a base station and passive beam forming of a reconfigurable intelligent reflecting surface, maximizes system energy efficiency under the condition of meeting minimum rate constraint, maximum power constraint and constant modulus constraint, and provides a joint optimization algorithm based on alternative optimization to obtain high energy efficiency resource allocation; the joint resource allocation method provided by the invention is effective.

Description

RIS-based high-energy-efficiency resource allocation method in multi-user millimeter wave non-orthogonal multiple access system
The technical field is as follows:
the invention belongs to the field of mobile communication, relates to a resource allocation method of a mobile communication system, and particularly relates to an energy efficiency optimization method in a multi-user mmWave-NOMA system based on RIS.
Background art:
mobile communication networks gradually step into the 5G era, 5G communication devices gradually come into the market, and in order to achieve exponential growth of system capacity, requirements for Spectrum Efficiency (SE), transmission rate, and time delay are gradually increased. However, if only SE is considered, the high density of mobile devices also puts a great pressure on energy consumption, which is not favorable for promoting the development of green communication. Therefore, in order to achieve both resource utilization and energy consumption reduction, taking EE into consideration, it is necessary to develop and utilize various innovative technologies, such as (mmWave) communication, or combination of NOMA technology, Reconfigurable Intelligent Surface (RIS), and the like.
With the progress and development of mobile communication technology, radio services on the traditional communication frequency band become more and more abundant and gradually saturate, and it is difficult to meet the increasing user requirements. The mmWave working in the high frequency band has a large amount of idle spectrum resources, and the development and utilization of the mmWave spectrum resources become the key point of the current mobile communication network. Compared with the traditional wave band, the mmWave frequency band is wide, and more devices can be supported to access a communication network, so that the transmission rate of the SE and the signal is improved.
The NOMA can provide service for a plurality of users at the same frequency band or the same time slot, thereby realizing the purpose of improving the performance of the communication system. By increasing the number of accessible users, a system employing the NOMA technology has higher resource utilization efficiency, and is considered as a multiple access technology having great potential in a future mobile communication system. Among them, one of the commonly used NOMA techniques is mainly power domain based NOMA. The NOMA technology allocates different powers to each user according to different channel qualities among the users, so that a plurality of data streams are transmitted simultaneously on the same time domain, frequency domain or code domain, in other words, the NOMA can realize simultaneous communication of a plurality of users on the same wireless resource, thereby improving the SE or EE of the system.
The RIS is an emerging technology at the time, and can greatly improve system performance. The RIS is made up of a large number of low cost passive reflective elements, each of which is capable of individually controlling the amplitude and/or phase changes of the incident signal, does not require any radio frequency link and increases SE, enabling dense deployment in wireless networks at low cost.
The invention content is as follows:
aiming at an RIS-assisted mmWave-NOMA system, in order to improve the energy efficiency of the system, the invention maximizes the energy efficiency of all users, jointly optimizes the power distribution of the users, the HBF of a base station and the PBF of the RIS, provides a multi-user energy efficiency optimization method in the RIS-based mmWave-NOMA system, and can obtain a better energy efficiency optimization scheme with polynomial time complexity.
The technical scheme adopted by the invention is as follows: a multi-user RIS-based mmWave-NOMA system energy efficiency optimization method comprises the following steps:
step S1: the multi-user mmWave-NOMA system based on RIS is established and consists of K single-antenna users and one mmWave Base Station (BS), wherein the BS adopts a Hybrid Beam Forming (HBF) framework and is provided with N antennas and NRFStrip radio frequency link, N low noise amplifiers and NNRFA phase shifter, each antenna passing through a low noise amplifier and NRFA phase shifter connected to the radio frequency link; dividing K single-antenna users into G groups, wherein NOMA is adopted in the groups; the HBF of the base station includes an Analog Beamforming (ABF) matrix
Figure BSA0000268915290000021
And a digital beamforming matrix
Figure BSA0000268915290000022
Definition set
Figure BSA0000268915290000023
The RIS consists of M reflecting elements, the reflecting phase vector of which
Figure BSA0000268915290000024
Wherein theta ism∈[0，2π]Definition set
Figure BSA0000268915290000025
Figure BSA0000268915290000026
It represents the group of the g-th group,
Figure BSA0000268915290000027
indicating the number of its users and,
Figure BSA0000268915290000028
representing channels between the kth user of the g-th group, BS-RIS
Figure BSA0000268915290000029
RIS and
Figure BSA00002689152900000210
mmWave channel between
Figure BSA00002689152900000211
And BS and
Figure BSA00002689152900000212
mmWave channel between
Figure BSA00002689152900000213
Is modeled as a millimeter-wave channel and,
Figure BSA00002689152900000214
indicating that the base station passes through RIS to
Figure BSA00002689152900000215
The cascade channel of (2);
step S2: after the grouping of the users is performed,
Figure BSA00002689152900000216
the received signal of (a) may be expressed as:
Figure BSA00002689152900000217
wherein xg，k，pg，kRespectively represent
Figure BSA00002689152900000218
The transmission signal, the power allocation of the network,
Figure BSA00002689152900000219
is BS and
Figure BSA00002689152900000220
the equivalent channel between the two channels,
Figure BSA00002689152900000221
representing complex white gaussian noise in the received signal, according to the NOMA protocol, in descending order of channel gain for example,
Figure BSA00002689152900000222
has an achievable rate of
Figure BSA0000268915290000031
Order to
Figure BSA0000268915290000032
The system energy efficiency optimization problem is modeled as
Figure BSA0000268915290000033
Where ζ represents the power amplifier coefficient, PC＝PBB+NRFPRF+NNRFPPS+NPLNARepresenting fixed circuit power consumption, PBB，PRF，PPS，PLNARespectively, baseband power consumption, radio frequency link power consumption, phase shifter power consumption and low noise amplifier power consumption, C1Represents a minimum rate constraint, rg，kDenotes the minimum rate, C2For maximum power constraint, PmaxDenotes maximum power consumption, C3、C4Is a normal mode constraint of ABF matrix and PBF vector, C5A decoding condition for successive interference cancellation;
step S3: the optimization problem in the step S2 belongs to a non-convex fractional programming problem, and auxiliary variables are introduced
Figure BSA0000268915290000034
wg＝AdgAnd
Figure BSA0000268915290000035
then
Figure BSA0000268915290000036
Has an achievable rate of
Figure BSA0000268915290000037
Introducing an auxiliary variable gammag，k，ug，k，
Figure BSA0000268915290000038
P and eta, by using penalty function method, the problem is converted into
Figure BSA0000268915290000041
Wherein
Figure BSA0000268915290000042
ρ(l)Is the coefficient of the penalty function method at the l-1 iteration;
step S4: aiming at the optimization problem in the step S3, the optimization problem is converted into four sub-problems according to an alternative optimization method: fixing { A, D, theta } solving chi subproblems and adopting SCA algorithm to approximate into
Figure BSA0000268915290000043
Solving by adopting a convex optimization tool to obtain chiopt(ii) a Fixing { χ, A, D, } to solve theta, and making u equal to theta*，
Figure BSA0000268915290000044
Problem is equivalent to
Figure BSA0000268915290000051
Wherein
Figure BSA0000268915290000052
Given u(s-1)The value of u at the s-th iteration of the MM algorithm is then the optimal solution
Figure BSA0000268915290000053
Wherein
Figure BSA0000268915290000054
λmax(V) represents the maximum eigenvalue of V,
Figure BSA0000268915290000055
to represent
Figure BSA0000268915290000056
Phase of (1) is
Figure BSA0000268915290000057
Fixing { χ, A, θ } the sub-problem of solving D is
Figure BSA0000268915290000058
Can obtain the product
Figure BSA0000268915290000059
Fix { χ, D, θ } to solve A, the subproblem is
Figure BSA00002689152900000510
Splitting it into g subproblems and expanding, similar to the PBF subproblem, we can solve:
Figure BSA00002689152900000511
wherein
Figure BSA00002689152900000512
The phase of (a) is determined,
Figure BSA00002689152900000513
the invention has the following beneficial effects: the energy efficiency optimization method in the multi-user RIS-based mmWave-NOMA system can effectively improve the energy efficiency of the system. The method fully considers the internal structure of the original optimization problem, firstly introduces auxiliary variables to obtain a punishment form of the original problem, equivalently converts the problem into a beam forming subproblem and a power distribution subproblem which are easier to solve by alternative optimization, provides an energy efficiency optimization algorithm of an AO algorithm, a penalty function method, an SCA, an MM and an RMO algorithm, can converge to a feasible suboptimal solution, and finally obtains an effective energy efficiency optimization scheme.
Description of the drawings:
FIG. 1 is a flow chart of a system in an embodiment of the invention.
FIG. 2 is a diagram of a system in an embodiment of the invention.
FIG. 3 is a simulation graph of the NOMA scheme proposed in the embodiment of the present invention and the conventional OMA.
Fig. 4 is a simulation graph of the proposed PBF scheme and two other optimization schemes in an embodiment of the present invention.
The specific implementation mode is as follows:
the invention will be further described with reference to the accompanying drawings.
First, system model
The model of the system involved in the multi-user RIS-based mmWave-NOMA system of the present invention is shown in FIG. 2, and the system consists of K single-antenna users and onemmWave Base Station (BS) with Hybrid Beamforming (HBF) architecture, N antennas, and N antennasRFStrip radio frequency link, N low noise amplifiers and NNRFA phase shifter, each antenna passing through a low noise amplifier and NRFA phase shifter connected to the radio frequency link; dividing K single-antenna users into G groups, wherein NOMA is adopted in the groups; the HBF of the base station includes an Analog Beamforming (ABF) matrix
Figure BSA0000268915290000061
And a digital beamforming matrix
Figure BSA0000268915290000062
Definition set
Figure BSA0000268915290000063
The RIS consists of M reflecting elements, the reflecting phase vector of which
Figure BSA0000268915290000064
Wherein theta ism∈[0，2π]Definition set
Figure BSA0000268915290000065
It represents the group of the g-th group,
Figure BSA0000268915290000066
indicating the number of its users and,
Figure BSA0000268915290000067
representing channels between the kth user of the g-th group, BS-RIS
Figure BSA0000268915290000068
RIS and
Figure BSA0000268915290000069
mmWave channel between
Figure BSA00002689152900000610
And BS and
Figure BSA00002689152900000611
mmWave channel between
Figure BSA00002689152900000612
Is modeled as a millimeter-wave channel and,
Figure BSA00002689152900000613
indicating that the base station passes through RIS to
Figure BSA00002689152900000614
The cascade channel of (2);
after the grouping of the users is performed,
Figure BSA00002689152900000615
the received signal of (a) may be expressed as:
Figure BSA00002689152900000616
wherein xg，k，pg，kRespectively represent
Figure BSA00002689152900000617
The transmission signal, the power allocation of the network,
Figure BSA00002689152900000618
is BS and
Figure BSA00002689152900000619
the equivalent channel between the two channels,
Figure BSA00002689152900000620
representing complex white gaussian noise in the received signal, according to the NOMA protocol, in descending order of channel gain for example,
Figure BSA00002689152900000621
has an achievable rate of
Figure BSA0000268915290000071
Second, energy efficiency optimization problem modeling and solving process
In order to improve the energy efficiency of the system, a corresponding maximum energy efficiency optimization problem is established, and the optimization aim is to maximize the energy efficiency of all users
Figure BSA0000268915290000072
The system energy efficiency optimization problem is modeled as
Figure BSA0000268915290000073
Where ζ represents the power amplifier coefficient, PC＝PBB+NRFPRF+NNRFPPS+NPLNARepresents the fixed circuit power consumption, PBB，PRF，PPS，PLNARespectively, baseband power consumption, radio frequency link power consumption, phase shifter power consumption and low noise amplifier power consumption, C1Represents a minimum rate constraint, rg，kDenotes the minimum rate, C2For maximum power constraint, PmaxDenotes maximum power consumption, C3、C4Is a normal mode constraint of ABF matrix and PBF vector, C5A decoding condition for successive interference cancellation;
introducing auxiliary variables
Figure BSA0000268915290000074
wg＝AdgAnd
Figure BSA0000268915290000075
then
Figure BSA0000268915290000076
Has an achievable rate of
Figure BSA0000268915290000077
Introducing auxiliary variablesγg，k，ug，k，
Figure BSA0000268915290000078
P and eta, by means of penalty functions, into which the problem is transformed
Figure BSA0000268915290000081
Wherein
Figure BSA0000268915290000082
ρ(l)Is the coefficient of the penalty function method at the l-1 iteration;
according to the alternative optimization method, the penalty problem is converted into four sub-problems: fixing { A, D, theta } solving chi subproblems and adopting SCA algorithm to approximate into
Figure BSA0000268915290000083
Solving by adopting a convex optimization tool to obtain chiopt(ii) a Fixing { χ, A, D, } to solve theta, and making u equal to theta*，
Figure BSA0000268915290000084
Problem is equivalent to
Figure BSA0000268915290000091
Wherein
Figure BSA0000268915290000092
Given u(s-1)The value of u at the s-th iteration of the MM algorithm is then the optimal solution
Figure BSA0000268915290000093
Wherein
Figure BSA0000268915290000094
λmax(V) represents the maximum eigenvalue of V,
Figure BSA0000268915290000095
to represent
Figure BSA0000268915290000096
Phase of (1) is
Figure BSA0000268915290000097
Fixing { χ, A, θ } the sub-problem of solving D is
Figure BSA0000268915290000098
Can obtain the product
Figure BSA0000268915290000099
Fix { χ, D, θ } solve A, a sub-problem of
Figure BSA00002689152900000910
Splitting the problem into g subproblems and expanding the problem, similar to the PBF subproblem, a solution can be obtained:
Figure BSA00002689152900000911
wherein
Figure BSA00002689152900000912
To represent
Figure BSA00002689152900000913
The phase of (a) is determined,
Figure BSA00002689152900000914
in summary, the invention provides an energy efficiency optimization algorithm using a penalty function method, an alternative optimization algorithm, a continuous convex approximation algorithm and an MM algorithm, and the energy efficiency of the algorithm provided by the invention is verified through Matlab simulation. Wherein the base station and the RIS are located at (0m, 0m) and (80m, 5m), respectively. All users are uniformly distributed in a range which takes (150m, 0m) as a center and 5m as a radius, the carrier frequency of the base station is 28GHz, and default parameter settings are listed in the following table:
Figure BSA00002689152900000915
Figure BSA0000268915290000101
FIG. 3 compares the energy efficiency performance of the NOMA scheme proposed by the present invention, which is exemplified by time division multiple access, with the conventional OMA scheme, in which "NOMA scheme" represents the energy efficiency optimization scheme proposed by the present invention; the 'TDMA scheme' means that a TDMA technology is adopted to maximize energy efficiency, the executed user grouping strategy is the same as the algorithm 'NOMA scheme', and users in each group are accessed in a time division multiple access mode with equal time slots. It can be seen from the figure that the NOMA-based algorithm achieves EE significantly better than the TDMA-based algorithm, thereby illustrating the advantages of the NOMA technique.
FIG. 4 is a graph comparing the RIS-mmWave-NOMA system energy efficiency performance under different PBF optimization algorithms proposed by the present invention. The PSO algorithm-based PSO PBF, Random PBF based on Random phase and the algorithm 'Designed PBF' proposed by the invention are included. As can be seen from the figure, the EE performance obtained by the PBF algorithm adopted by the invention is close to that of the PSO PBF, the complexity of the particle swarm optimization is higher, and the EE obtained by the Random PBF is lowest, because the PBF of the Random PBF is randomly generated and is not subjected to joint optimization, and the result shows the effectiveness of the proposed PBF algorithm.
In conclusion, the energy efficiency method provided by the invention can effectively improve the energy efficiency performance of the RIS-assisted mmWave-NOMA system, and meanwhile, the steps for realizing the method are simple, so that the effectiveness of the energy efficiency optimization method in the multi-user RIS-based mmWave-NOMA system provided by the invention is fully demonstrated.
The foregoing is only a preferred embodiment of this invention and it should be noted that modifications can be made by those skilled in the art without departing from the principle of the invention and these modifications should also be considered as the protection scope of the invention.

Claims (1)

1. A multi-user millimeter wave mmWave non-orthogonal multiple access system NOMA high energy efficiency resource allocation method based on reconfigurable intelligent reflecting surface RIS is characterized in that: the method comprises the following steps:
step S1: the multi-user mmWave-NOMA system based on RIS is established, and consists of K single-antenna users and an mmWave base station BS, wherein the base station adopts a hybrid beam forming HBF framework and is provided with N antennas and NRFStrip radio frequency link, N low noise amplifiers and NNRFA phase shifter, each antenna passing through a low noise amplifier and NRFA phase shifter connected to the radio frequency link; dividing K single-antenna users into groups, wherein NOMA is adopted in each group; HBF of base station includes analog beamforming ABF matrix
Figure FSA0000268915280000011
And a digital beamforming matrix
Figure FSA0000268915280000012
Wherein N issRepresenting the number of data streams; definition set
Figure FSA0000268915280000013
Due to the analog beamformer, A satisfies a constant modulus constraint, i.e.
Figure FSA0000268915280000014
And, users are divided into G groups, and in order to guarantee spatial multiplexing gain, G ═ N is sets≤NRFWhen D is ═ D1，...，dG]Wherein
Figure FSA0000268915280000015
The RIS consists of M reflecting elements, the reflecting phase vector of which
Figure FSA0000268915280000016
Corresponding reflection phase matrix is
Figure FSA0000268915280000017
Wherein theta ism∈[0，2π]Definition set
Figure FSA0000268915280000018
Figure FSA0000268915280000019
It represents the group of the g-th group,
Figure FSA00002689152800000110
indicating the number of its users and,
Figure FSA00002689152800000111
representing channels between the kth user of the g-th group, BS-RIS
Figure FSA00002689152800000112
RIS and
Figure FSA00002689152800000113
mmWave channel between
Figure FSA00002689152800000114
And BS and
Figure FSA00002689152800000115
mmWave channel between
Figure FSA00002689152800000116
Is modeled as a millimeter-wave channel and,
Figure FSA00002689152800000117
indicating that the base station passes through RIS to
Figure FSA00002689152800000118
The cascade channel of (2);
step S2: establishing an energy efficiency optimization problem under given constraint conditions, wherein the optimization target is to maximize the system energy efficiency
Figure FSA00002689152800000119
Optimizing the transmission power p of a variable to a userg，kThe HBF of the base station and the passive beamforming PBF vector of the RIS are optimized and constrained to the minimum speed and the maximum power of a user, the normal mode constraint of an ABF matrix, the normal mode constraint of the PBF vector and the decoding condition of serial interference elimination; wherein R isg，kFor the user
Figure FSA00002689152800000120
Is the achievable rate, ξ represents the power amplifier coefficient, PCExpressed as fixed circuit power consumption;
step S3: the optimization problem in the step S2 belongs to a non-convex fractional programming problem, and auxiliary variables are introduced
Figure FSA0000268915280000021
And using a penalty function method to constrain the equation to wg＝AdgAnd
Figure FSA0000268915280000022
transferring to a target function to obtain a punishment form of the problem;
step S4: aiming at the optimization problem in the step S3, let
Figure FSA0000268915280000023
It is decomposed into a series of sub-problems using alternating optimization: fixing { A, D, theta } solves the sub-problem of χ, fixing { χ, A, D } solves the sub-problem of theta, fixing { χ, A,theta solves the subproblem of D and the subproblem of A by fixing x, D and theta, and the subproblems are respectively solved by using a continuous convex approximation algorithm, an optimization minimization algorithm, a derivation algorithm and an alternative optimization algorithm in sequence, and a corresponding optimization algorithm is given;
step S5: and based on the obtained solution of the optimization variable, providing a high-energy-efficiency joint resource allocation method.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115426024A (en) * 2022-11-03 2022-12-02 鹏城实验室 Phase adjusting method of intelligent reflecting surface capable of spatial multiplexing

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115426024A (en) * 2022-11-03 2022-12-02 鹏城实验室 Phase adjusting method of intelligent reflecting surface capable of spatial multiplexing
CN115426024B (en) * 2022-11-03 2023-01-10 鹏城实验室 Phase adjusting method of intelligent reflecting surface capable of spatial multiplexing

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