CN114900266A - OAM-MIMO system energy efficiency optimization method based on OPGW joint box - Google Patents

OAM-MIMO system energy efficiency optimization method based on OPGW joint box Download PDF

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CN114900266A
CN114900266A CN202210355804.8A CN202210355804A CN114900266A CN 114900266 A CN114900266 A CN 114900266A CN 202210355804 A CN202210355804 A CN 202210355804A CN 114900266 A CN114900266 A CN 114900266A
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energy efficiency
oam
total
ucas
power
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林楚婷
唐杰
陈真
黄嘉毅
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South China University of Technology SCUT
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04J—MULTIPLEX COMMUNICATION
    • H04J14/00—Optical multiplex systems
    • H04J14/02—Wavelength-division multiplex systems
    • H04J14/0227—Operation, administration, maintenance or provisioning [OAMP] of WDM networks, e.g. media access, routing or wavelength allocation
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention discloses an OAM-MIMO system energy efficiency optimization method based on an OPGW joint box, which comprises the following steps: an OAM-MIMO communication system energy efficiency model based on UCAs is established and an optimization problem is proposed, wherein the optimization problem is non-convex and difficult to directly solve due to mutual interference of different UCAs in the same mode. Based on a double-layer iterative algorithm, the inner layer cyclic utilization first-order Taylor approximation converts the non-convex power distribution problem into a convex problem, and the outer layer cyclic utilization dichotomy updates the energy efficiency of the outer layer, so that the suboptimal solution of the optimization problem is solved and sought. The invention combines the advantages of OAM-MIMO technology based on UCAs and double-layer iterative algorithm, and ensures that each mode is distributed to maximize the energy efficiency of the system on the basis of power by reasonably setting the minimum capacity and the transmitting power of each OAM mode, thereby optimizing the overall performance of the system.

Description

OAM-MIMO system energy efficiency optimization method based on OPGW joint box
Technical Field
The invention relates to the technical field of wireless communication, in particular to an OAM-MIMO system energy efficiency optimization method based on an OPGW joint box.
Background
Because most of high-voltage lines of a power grid are provided with OPGW optical cables, an OPGW connector box capable of realizing flexible service access is very critical, and influences the intellectualization of a power transmission line. The OPGW splice closure capable of realizing flexible service access can realize flexible access of the service of the power transmission line, and provides a basic channel with large bandwidth, high safety and flexible access for the power transmission service. In addition, in order to meet the requirements of technical development while implementing a digital communication system, how to alleviate the increase of the energy consumption of the communication network is also an urgent problem to be solved. Due to limited spectrum resources, new theories and techniques are needed to improve the energy efficiency of communication systems.
Based on the advantages of orthogonality of Orbital Angular Momentum (OAM) modes, OAM mode multiplexing provides a new view for meeting the requirements of explosive data traffic. Because the wave front of the OAM beam is a spiral structure, the OAM beam is a non-planar wave, which is also referred to as a vortex electromagnetic wave. In addition, the orthogonality of different modes of OAM can guarantee the independence of each channel, thereby increasing the degree of freedom of the system, which is also called OAM modal multiplexing. Because the OAM mode is theoretically unlimited, OAM has a great application potential in future communication systems, and research on wireless communication systems based on OAM is continuously developing, and breakthrough progress is made in recent years. Some scholars such as tambourini have conducted experiments on an OAM multiplex wireless communication system in venetian, in which two signals are simultaneously transmitted using a spiral parabolic antenna. The results show that OAM multiplexing systems improve system capacity. In addition, in order to realize more OAM multiplexing, Y.Yan et al build a 32Gbit/s millimeter wave wireless communication link, and realize 8-path multiplexing data transmission of OAM in a transmission distance of 2.5 meters. Researchers of NTT (network technology transport) companies in Japan research an OAM-MIMO (operation administration and maintenance-multiple input and output) system based on dual polarization, realize data transmission of 21 channels within 10 meters, and realize polarization multiplexing, OAM multiplexing and MIMO multiplexing. It is seen that OAM technology has received a great deal of attention in the field of communications.
There are various antenna structures for generating OAM, including a spiral reflection structure, a transmission spiral structure, a transmission grating structure, and a Uniform Circular Array (UCA), etc. However, except for UCA, the mode values of OAM beams generated by the first three structures are difficult to change. Therefore, researchers have gradually conducted system research on the UCA-based OAM beamforming method. The Butler phase shift matrix is combined with the UCA, each antenna array in the UCA is fed simultaneously, and multiplexing of OAM wave beams can be achieved. Wangjoo-Lee et al studied the use of Butler phase shifters and UCAs to achieve multiplexing of OAM beams in three modes and studied their power and isolation characteristics in microwaves. Simulation results show that the maximum achievable modal value depends on the number of antenna arrays. Due to divergence, OAM beams are difficult to transmit over long distances. The higher the divergence of the higher mode beam, the more detrimental the reception of the OAM beam. As the transmission distance increases, the beam radius of the OAM beam gradually increases, eventually resulting in an energy void in the center of the beam, limiting the transmission distance.
Since OAM and MIMO are two potential technologies for next generation wireless communication, much research has been focused on the combination of OAM and MIMO. The prior literature focuses mainly on studying the maximum capacity of OAM-MIMO systems, with relatively few concerns regarding energy efficiency optimization.
Disclosure of Invention
In order to overcome the defects and shortcomings of the prior art, the invention provides an OAM-MIMO system energy efficiency optimization method based on an OPGW (optical fiber composite overhead ground wire) joint box.
The invention also aims to provide an OAM-MIMO system energy efficiency optimization system based on the OPGW joint box.
A third object of the present invention is to provide a storage medium.
It is a fourth object of the invention to provide a computing device.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides an OAM-MIMO system energy efficiency optimization method based on an OPGW joint box, which comprises the following steps:
the OAM-MIMO system is provided with a plurality of UCAs, the number of the UCAs of a transmitting end is the same as that of a receiving end, and an OAM-MIMO system capacity model, a total power loss model and an energy efficiency model of the transmitting and receiving end are constructed based on the UCAs and the Butler matrix;
obtaining channel matrixes corresponding to all UCAs under the same mode by adopting a ZF-SIC technology, calculating the total capacity of the system according to the channel matrixes corresponding to all modes, calculating the total power loss of the system according to the static power consumption of the UCAs and the emission power weight of the modes, and calculating the ratio of the total capacity of the system to the total power loss to obtain the energy efficiency;
the energy efficiency maximization is realized on the basis of meeting the optimal power distribution, and an energy efficiency maximization mathematical model of an OAM-MIMO system based on UCAs is established according to the total system capacity and the total system power loss;
taking the minimum capacity requirement and the minimum transmitting power requirement which are required to be met by each OAM wave beam as constraint conditions, and constructing an energy efficiency optimization problem according to the obtained total system capacity and the constraint conditions;
establishing a double-layer iterative algorithm for maximizing energy efficiency based on a power distribution scheme for optimizing a plurality of OAM mode wave beams on a plurality of UCAs, converting a non-convex power distribution problem into a convex problem by utilizing first-order Taylor approximation of an inner layer, updating the energy efficiency of an outer layer by utilizing a dichotomy, and solving a suboptimal solution of an optimization problem.
As a preferred technical scheme, the method for constructing the OAM-MIMO system capacity model, the total power loss model and the energy efficiency model of the transmitting and receiving end based on the UCAs and the Butler matrix comprises the following specific steps:
regarding each OAM wave beam on each UCA as a link, sorting OAM wave beams of the same mode on different UCAs, where the total transmission power and the total power consumed by system hardware are respectively expressed as:
Figure BDA0003582654640000041
Figure BDA0003582654640000042
total power loss P of system tot (P) representsComprises the following steps:
Figure BDA0003582654640000043
the SINR of the ith link is defined as:
Figure BDA0003582654640000044
wherein i 'and j' represent the ith link in the channel matrix G l Relative position of (a);
total capacity C of the system tot (P) is represented by:
Figure BDA0003582654640000045
energy efficiency lambda of OAM-MIMO system based on UCAs EE Expressed as:
Figure BDA0003582654640000046
wherein, N represents the number of UCAs at the sending end, N L Indicates the total number of links, C i Representing the current capacity, σ, of each link 2 Represents the noise power, and α is the power amplification factor.
As an optimal technical scheme, an energy efficiency maximization mathematical model of the OAM-MIMO system based on UCAs is established according to the obtained total system capacity and constraint conditions, and is specifically expressed as:
Figure BDA0003582654640000051
Figure BDA0003582654640000052
Figure BDA0003582654640000053
Figure BDA0003582654640000054
where C1 represents the minimum capacity constraint for each link, C2 guarantees the effectiveness of each link, C3 represents the total transmit power constraint, R req To be the minimum capacity that each link needs to achieve, P max Representing the total maximum transmit power.
As a preferred technical solution, the method for establishing an energy efficiency maximization mathematical model of an OAM-MIMO system based on UCAs according to total system capacity and total system power loss comprises the following specific steps:
energy efficiency for a given z-th iteration update
Figure BDA0003582654640000055
The corresponding optimal power allocation is obtained by solving the following problem:
Figure BDA0003582654640000056
Figure BDA0003582654640000057
Figure BDA0003582654640000058
Figure BDA0003582654640000059
wherein, C tot (P) represents the total capacity of the system, P tot (P) represents the total power loss of the system, C1 represents the minimum capacity constraint of each link, C2 guarantees the effectiveness of each link, C3 represents the total transmit power constraint, R req Indicating the minimum capacity, P, that each link needs to achieve max Representing the total maximum transmit power, N L Representing the total number of links;
the objective function of the optimization problem P2 is converted into:
Figure BDA0003582654640000061
Figure BDA0003582654640000062
Figure BDA0003582654640000063
the constraint C1 of the optimization problem P2 is transformed into the equivalent convex linear form:
Figure BDA0003582654640000064
converting the optimization problem P2 into an optimization problem P3, which is specifically represented as:
Figure BDA0003582654640000065
s.t.C1′,C2,C3
definition P q For the power allocation scheme of the q-th iteration of the inner loop, v (p) corresponds to a first order taylor approximation:
Figure BDA0003582654640000066
wherein
Figure BDA0003582654640000067
Gradient representing v (p):
Figure BDA0003582654640000068
wherein e represents a column vector;
converting the optimization problem P3 into an optimization problem P4, which is specifically represented as:
Figure BDA0003582654640000071
s.t.C1′,C2,C3
the constraint set of the optimization problem P4 is convex and the objective function is a concave function.
As an optimal technical scheme, the optimization problem P2 is solved to obtain fixed energy efficiency
Figure BDA0003582654640000072
The following corresponding optimal power allocation scheme specifically comprises the following steps:
initialization: setting iteration number and accuracy, setting transmission power P 0 Calculating I 0 ＝U(P 0 )-V(P 0 )；
Repeating the iterative calculation until reaching a convergence condition, wherein the convergence condition is I k -I k-1 |≤ε；
Solving the optimization problem P4 to obtain the corresponding power distribution scheme P * Updating the iteration number k as k +1, P k ＝P * Calculating I k ＝U(P k )-V(P k )，I k Represents the convergence of the power allocation scheme;
judgment of | I k -I k-1 If | ≦ epsilon, if not, P 0 ＝P k Returning to solve the optimization problem P4;
if I k -I k-1 If | ≦ epsilon, P k ＝P * To find out the fixed energy efficiency
Figure BDA0003582654640000073
Corresponding optimal power allocation scheme P z 。
As a preferred technical solution, the updating the energy efficiency of the outer layer by using the dichotomy specifically includes:
initialization: the iteration times and the accuracy are set, the energy efficiency boundary value is set,
Figure BDA0003582654640000074
repeating the iterative calculation until reaching a convergence condition of
Figure BDA0003582654640000075
Energy efficiency calculation: energy efficiency at z-th iteration
Figure BDA0003582654640000076
At a determined
Figure BDA0003582654640000077
Next, the corresponding optimal power allocation scheme P is derived based on the optimization problem P2 k ；
Updating: power allocation scheme P z ＝ k ；
Judgment of
Figure BDA0003582654640000081
If the result is true, judging whether the result is true or not, and if not, judging
Figure BDA0003582654640000082
Whether or not it is true, if
Figure BDA0003582654640000083
Is established to
Figure BDA0003582654640000084
If not, let
Figure BDA0003582654640000085
Returning to energy efficiency calculation;
if it is
Figure BDA0003582654640000086
Is established, P opt ＝P z To find out the optimal energy efficiency
Figure BDA0003582654640000087
And updating the iteration number z to be z + 1.
In order to achieve the second object, the invention adopts the following technical scheme:
the invention provides an OAM-MIMO system energy efficiency optimization system based on an OPGW joint box, which comprises: the system comprises an OAM-MIMO system model construction module, an energy efficiency maximization mathematical model construction module, an energy efficiency optimization problem construction module and an optimization problem solving module;
the OAM-MIMO system model building module is used for building an OAM-MIMO system capacity model, a total power loss model and an energy efficiency model of a transmitting and receiving end based on UCAs and Butler matrices, the OAM-MIMO system is provided with a plurality of UCAs, and the number of the UCAs of the transmitting end is the same as that of the receiving end;
obtaining channel matrixes corresponding to all UCAs under the same mode by adopting a ZF-SIC technology, calculating the total capacity of the system according to the channel matrixes corresponding to all modes, calculating the total power loss of the system according to the static power consumption of the UCAs and the emission power of the modes, and calculating the ratio of the total capacity of the system to the total power loss to obtain the energy efficiency;
the energy efficiency maximization mathematical model construction module is used for realizing maximization of energy efficiency on the basis of meeting optimal power distribution, and establishing an energy efficiency maximization mathematical model of an OAM-MIMO system based on UCAs according to the total system capacity and the total system power loss;
the energy efficiency optimization problem construction module is used for constructing an energy efficiency optimization problem by taking the minimum capacity requirement and the minimum transmitting power requirement which are required to be met by each OAM wave beam as constraint conditions according to the obtained total system capacity and the constraint conditions;
the optimization problem solving module is used for establishing a double-layer iterative algorithm for maximizing the energy efficiency based on a power distribution scheme for optimizing a plurality of OAM modal wave beams on a plurality of UCAs, converting a non-convex power distribution problem into a convex problem by utilizing first-order Taylor approximation of an inner layer, updating the energy efficiency of an outer layer by utilizing a dichotomy, and solving a suboptimal solution of the optimization problem.
In order to achieve the third object, the invention adopts the following technical scheme:
a computer readable storage medium stores a program which, when executed by a processor, implements the above OPGW splice box based OAM-MIMO system energy efficiency optimization method.
In order to achieve the fourth object, the invention adopts the following technical scheme:
a computing device comprises a processor and a memory for storing processor executable programs, and when the processor executes the programs stored in the memory, the OAM-MIMO system energy efficiency optimization method based on the OPGW connector box is realized.
Compared with the prior art, the invention has the following advantages and beneficial effects:
the invention adopts a double-layer iterative energy efficiency maximization algorithm, the inner layer adopts a first-order Taylor and Lagrange dual to convert the non-convex power distribution problem into the convex power distribution problem, the outer layer adopts a dichotomy to update the energy efficiency, and the technical problem that the energy efficiency optimization problem of the OAM-MIMO system based on UCA is non-convex and difficult to solve is solved, so that the optimal power distribution scheme is obtained.
Drawings
Fig. 1 is a flow chart of the method for optimizing the energy efficiency of the OAM-MIMO system based on the OPGW splice closure of the present invention;
FIG. 2 illustrates OAM-MIMO systems and the energy efficiency of MIMO systems according to the present invention
Figure BDA0003582654640000091
And total transmission power P max A schematic diagram of the relationship of (1);
FIG. 3 illustrates OAM-MIMO systems and the energy efficiency of MIMO systems according to the present invention
Figure BDA0003582654640000092
And the minimum capacity R that each modality needs to reach req A schematic diagram of the relationship of (1);
FIG. 4 illustrates the energy efficiency of OAM-MIMO system with different multiplexing mode numbers
Figure BDA0003582654640000101
And total transmission power P max A schematic diagram of the relationship of (1);
FIG. 5 illustrates the energy efficiency of OAM-MIMO system with different multiplexing mode numbers
Figure BDA0003582654640000102
And the minimum capacity R that each modality needs to reach req Schematic diagram of the relationship of (1).
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
As shown in fig. 1, this embodiment provides an OAM-MIMO system energy efficiency optimization method based on an OPGW splice closure, including the following steps:
the method comprises the following steps: the system comprises an OAM-MIMO system with a plurality of UCAs, where the transmitter and receiver are assumed to be aligned. The UCAs number of the sending end and the receiving end is the same, the number of the transmitting antennas and the number of the receiving antennas on each UCA are also set to be the same, and an OAM-MIMO system capacity model, a total power loss model and an energy efficiency model of the sending end and the receiving end are constructed based on the UCAs and the Butler matrix;
in step one, the number of UCAs at the transmitting end and the receiving end is the same, and is respectively represented by N and M. In addition, the number of transmit antennas and receive antennas on each UCA is also set to be the same, denoted by T and R, respectively. Wherein, the number of multiplexed OAM channels on each UCA is the same, and the data carried by each OAM channel is different. Since the transmitting end simultaneously transmits the multiplexed signals, the receiving end needs to perform demultiplexing first. And for the mutual interference of different UCAs in the same mode, acquiring the corresponding channel matrixes of all the UCAs in the same mode by adopting a ZF-SIC technology so as to recover the multiplexing signals. Obtaining a channel matrix G corresponding to the I-th mode l The total capacity of the overall system is then expressed as:
Figure BDA0003582654640000111
wherein σ 2 Representing the noise power, p n,l And the transmitting power of the l mode of the UCA transmitted by the nth circle is shown.
The total power consumption model of the system is represented as:
Figure BDA0003582654640000112
wherein the PC n The static power consumption (circuit power) of the nth UCA in the system hardware is shown, and alpha is a power amplification coefficient. Energy efficiency is the ratio of the total system capacity to the total power consumption, so the energy efficiency of the OAM-MIMO system based on UCAs is expressed as:
Figure BDA0003582654640000113
to achieve optimization, each OAM beam on each UCA is treated as a link, so the system exists N L N × L links. The OAM beams of the same mode on different UCAs are ordered, for example, if the OAM beam of the first mode of the first UCA in the innermost layer is link i ═ 1, then the OAM beam of the second mode of the first UCA in the inner layer is link i ═ N + 1. The total transmit power and the total power consumed by the system hardware is then re-expressed as:
Figure BDA0003582654640000121
Figure BDA0003582654640000122
according to equations (4) and (5), the total power loss of the system can be re-expressed as:
Figure BDA0003582654640000123
based on the above conversion, the SINR of the ith link is defined as:
Figure BDA0003582654640000124
wherein i 'and j' represent the ith link in the channel matrix G l The relative positions in (a) and (b) are i- ((i-1)/N) N and j ═ j ((j-1)/N) N. In addition, l ═ 1/N +1 and Q ═ ((j-1)/N) N.
C i (P)＝log 2 (1+γ i (P)) (8)
The total capacity of the system can be expressed as:
Figure BDA0003582654640000125
according to the above formula of total power loss of the system and the formula of total system capacity, the system energy efficiency can be expressed as:
Figure BDA0003582654640000126
Figure BDA0003582654640000131
step two: the aim is to maximize the energy efficiency on the basis of satisfying the optimal power allocation, the system capacity is monotonously increased with respect to the base station transmission power, but the energy efficiency is not necessarily higher the larger the transmission power is. The energy efficiency of the system depends on the system capacity and the total system power consumption, and an energy efficiency maximization mathematical model of the OAM-MIMO system based on the UCAs is established on the basis;
in the second step, the energy efficiency maximization mathematical model of the UCAs-based OAM-MIMO system is as follows:
Figure BDA0003582654640000132
Figure BDA0003582654640000133
Figure BDA0003582654640000134
Figure BDA0003582654640000135
where C1 represents the minimum capacity constraint for each link, C2 guarantees the effectiveness of each link, and C3 represents the total transmit power constraint. C i For the current capacity of each link, R req To be the minimum capacity that each link needs to achieve, P max Representing the total maximum transmit power and N L Representing the total number of links. Since the same modes on different UCAs interfere with each other, the objective function of the optimization problem is not a concave function with respect to the power vector P. The objective function is the ratio of two real-valued functions, so the proposed optimization problem is a generalized fractional order programming problem. However, the existing fractional planning cannot directly obtain the optimal transmission power allocation scheme in the global scope. To solve the optimization problem, the objective function must be converted into a concave function.
Step three: considering that each OAM wave beam is successfully transmitted by the system, taking the minimum capacity requirement and the minimum transmission power requirement which are required to be met by each OAM wave beam as constraint conditions, and based on the obtained total capacity C of the system tot And constraining conditions to construct an energy efficiency optimization problem;
in the third step:
first, define
Figure BDA0003582654640000141
Represents the optimum energy efficiency:
Figure BDA0003582654640000142
according to equation (12), define
Figure BDA0003582654640000143
As can be seen from equation (13), let the optimal power allocation be P opt When P is equal to P opt Time, energy efficiency λ EE Reach the optimal value and can obtain
Figure BDA0003582654640000144
Suppose that
Figure BDA0003582654640000145
Definition P 1 And P 2 Is the corresponding optimal power distribution scheme, and can obtain
Figure BDA0003582654640000146
Thus F (λ) EE ) Is about EE Is a monotonically decreasing function of (a). Therefore, the temperature of the molten metal is controlled,
Figure BDA0003582654640000147
when the temperature of the water is higher than the set temperature,
Figure BDA0003582654640000148
Figure BDA0003582654640000149
when the temperature of the water is higher than the set temperature,
Figure BDA00035826546400001410
updating an approximate optimal energy efficiency using a dichotomy
Figure BDA00035826546400001411
Energy efficiency for a given z-th iteration update
Figure BDA00035826546400001412
The corresponding optimal power allocation is obtained by solving the following problem:
Figure BDA00035826546400001413
s.t.C1，C2，C3 (15b)
it is clear that equation (9) can be translated into a difference of two concave functions with respect to the transmission power. Thus, the objective function of the optimization problem P2 is transformed into:
Figure BDA00035826546400001414
Figure BDA0003582654640000151
Figure BDA0003582654640000152
c1 in problem P2 is a non-convex constraint, so C1 needs to be converted to the equivalent convex linear form:
Figure BDA0003582654640000153
from the above transformation, the optimization problem can be re-expressed as:
Figure BDA0003582654640000154
s.t.C1′,C2,C3 (20b)
the specific solving steps for the optimization problem P3 are as follows:
the feasible set of constraints C1', C2, C3 in problem P3 is convex. However, the objective function of the optimization problem P3 is the difference between two concave functions, and it cannot be determined whether the objective function is concave or convex. Therefore, the optimization problem P3 is difficult to prove as a convex optimization problem. Since the problem P3 is non-convex and NP-hard, the objective function (20a) is transformed using a first order Taylor approximation, i.e., V (P) is converted to an affine function. Definition P q For the power allocation scheme of the q-th iteration of the inner loop, then v (p) corresponds to a first order taylor approximation:
Figure BDA0003582654640000155
wherein
Figure BDA0003582654640000156
Gradient representing V (P):
Figure BDA0003582654640000157
wherein e ∈ C N×1 Is a column vector. When j 'is not less than i',
Figure BDA0003582654640000161
otherwise e (j') is 0. By substituting equation (21) into the problem P3, the optimization problem can be converted into:
Figure BDA0003582654640000162
s.t.C1′,C2,C3 (23b)
since the objective function is converted to a concave function minus an affine function, it approximates a concave function. The constraint set of optimization problem P4 is convex and the objective function is a concave function, so problem P4 is a convex optimization problem that can be solved.
Solving problem P2 to obtain fixed energy efficiency
Figure BDA0003582654640000163
The following corresponding optimal power allocation scheme:
initialization: the iteration number k is 0 and e is 10 -3 Setting the transmit power P for accuracy 0 Calculating I 0 ＝U(P 0 )-V(P 0 )；
The following process is repeated until a convergence condition is reached, the convergence condition being | I k -I k-1 |≤ε；
Solving the optimization problem P4 can obtain the corresponding power distribution scheme P * Updating the iteration number k as k +1, P k ＝P * Calculating I k ＝U(P k )-V(P k )，I k Represents the convergence of the power allocation scheme;
judgment of | I k -I k-1 If | ≦ epsilon, if not, P 0 ＝P k Returning to solve the optimization problem P4;
if I k -I k-1 If | ≦ ε, P k ＝P * To find out the fixed energy efficiency
Figure BDA0003582654640000164
Corresponding optimal power allocation scheme P z ；
Step four: according to the proposed energy efficiency optimization problem, due to the fact that different UCAs interfere with each other in the same mode, the optimization problem is non-convex and difficult to directly solve, and therefore the optimization problem is split into two sub-problems. The method comprises the steps of establishing a double-layer iterative algorithm for maximizing energy efficiency based on a power distribution scheme for optimizing a plurality of OAM mode wave beams on a plurality of UCAs, converting a non-convex power distribution problem into a convex problem by utilizing first-order Taylor approximation of an inner layer, and updating the energy efficiency of an outer layer by utilizing a dichotomy, so as to solve and seek a suboptimal solution of an optimization problem.
In this embodiment, the specific steps of updating and approximating the optimal energy efficiency by using the bisection method are as follows:
initialization: the number of iterations z is 0 and epsilon 10 -3 For the sake of accuracy, energy efficiency boundary values are set,
Figure BDA0003582654640000171
repeating the following process until reaching a convergence condition of
Figure BDA0003582654640000172
Energy efficiency calculation: energy efficiency at z-th iteration
Figure BDA0003582654640000173
At a determined
Figure BDA0003582654640000174
Next, a corresponding optimal power allocation scheme P is derived based on the optimization problem P2 k ；
Updating: power allocation scheme P z ＝P k ；
Judgment of
Figure BDA0003582654640000175
If the result is true, judging whether the result is true or not, and if not, judging
Figure BDA0003582654640000176
Whether or not it is true, if
Figure BDA0003582654640000177
Is established to
Figure BDA0003582654640000178
If not, let
Figure BDA0003582654640000179
Returning to energy efficiency calculation;
if it is
Figure BDA00035826546400001710
Is established, P opt ＝P z To find out the optimal energy efficiency
Figure BDA00035826546400001711
Updating the iteration times z to z + 1;
as shown in fig. 2 to fig. 5, the present embodiment provides simulation effect diagrams of an OPGW splice closure-based OAM-MIMO system energy efficiency optimization method.
As shown in fig. 2 and 3, the performance of the proposed EE maximization algorithm on OAM-MIMO systems and conventional point-to-point MIMO systems is compared. The multiplexing modality value on each UCA is [ -6, 6 ]. For fair comparison, antennas of the MIMO system are set to be the same as the OAM-MIMO system. In addition, the area covered by all antennas of the MIMO system is the same as the area covered by the UCA of the largest radius in the OAM-MIMO system. In order to ensure that the channel can meet the minimum capacity requirement, after singular value decomposition is carried out on a channel matrix in the MIMO system, a channel with a larger characteristic value, namely a channel with relatively better performance, is selected.
As shown in FIG. 2, set R req Total transmission power varies by P0.5 max ∈[0.1W,80W]. As can be seen from FIG. 2, when P is max At the lower time, the temperature of the alloy is lower,
Figure BDA0003582654640000181
with P max Increases sharply up to more than 20W. The results show that the extra power budget is no longer given
Figure BDA0003582654640000182
To balance the total capacity and the total power consumption.
As shown in FIG. 3, setting P max The minimum capacity constraint varies by R, 0.1W req E [0.5, 2. As can be seen from FIG. 3, with R req In the case of the increase in the number of,
Figure BDA0003582654640000183
are continuously decreasing. As can be seen from fig. 2 and 3, the OAM-MIMO system has a higher stability value than the MIMO system. This is because different OAM of the OAM-MIMO system is mutually orthogonal, resulting in less interference of the channel than the MIMO system.
To analyze multiplexing modes and P max For OAM-MIMO system
Figure BDA0003582654640000184
The 4-UCA structure is adopted as the transmitting and receiving end of the OAM-MIMO system. The number of multiplexing modes on each UCA is 1, 2 and 3 respectively.
As shown in FIG. 4, set R req The total transmitting power varies in the range of P1 max ∈[0.1W,40W]. From FIG. 4, it can be seen that
Figure BDA0003582654640000185
The number of the multiplexing modes is gradually increased and gradually converged to a stable value. Under the same UCA number, the increase of the number of multiplexing modes is beneficial to increasing the degree of freedom of a channel, thereby improving the capacity. Therefore, the energy efficiency is in the same R req 、P max And under the static circuit power, along with the increase of the capacity, the energy efficiency is obviously improved.
As shown in FIG. 5, setting P max The minimum capacity constraint varies by R, 5W req ∈[0.2,1]From FIG. 5, it can be seen that with R req In the case of the increase in the number of,
Figure BDA0003582654640000186
at a gradual decline. At the same R teq Under the condition, along with the increase of the number of multiplexing modes,
Figure BDA0003582654640000187
the reason for this improvement is the same as in fig. 4.
Example 2
The embodiment provides an OAM-MIMO system energy efficiency optimization system based on OPGW splice closure, including: the system comprises an OAM-MIMO system model construction module, an energy efficiency maximization mathematical model construction module, an energy efficiency optimization problem construction module and an optimization problem solving module;
in this embodiment, the OAM-MIMO system model building module is configured to build an OAM-MIMO system capacity model, a total power loss model, and an energy efficiency model of a transmitting and receiving end based on UCAs and butler matrices, the OAM-MIMO system has a plurality of UCAs, and the number of UCAs is the same for the transmitting end and the receiving end;
obtaining channel matrixes corresponding to all UCAs under the same mode by adopting a ZF-SIC technology, calculating the total capacity of the system according to the channel matrixes corresponding to all modes, calculating the total power loss of the system according to the static power consumption of the UCAs and the emission power weight of the modes, and calculating the ratio of the total capacity of the system to the total power loss to obtain the energy efficiency;
in this embodiment, the energy efficiency maximization mathematical model construction module is configured to maximize energy efficiency on the basis of satisfying optimal power allocation, and establish an energy efficiency maximization mathematical model of an OAM-MIMO system based on UCAs according to a total system capacity and a total system power loss;
in this embodiment, the energy efficiency optimization problem construction module is configured to construct an energy efficiency optimization problem according to the obtained total system capacity and the constraint condition, where the minimum capacity requirement and the minimum transmission power requirement that each OAM beam needs to meet are used as the constraint condition;
in this embodiment, the optimization problem solving module is configured to establish a two-layer iterative algorithm that maximizes energy efficiency based on a power distribution scheme that optimizes a plurality of OAM modal beams on a plurality of UCAs, convert a non-convex power distribution problem into a convex problem by using first-order Taylor approximation of an inner layer, update energy efficiency of an outer layer by using a bisection method, and solve a suboptimal solution of an optimization problem.
Example 3
The present embodiment provides a storage medium, which may be a storage medium such as a ROM, a RAM, a magnetic disk, or an optical disk, and the storage medium stores one or more programs, and when the programs are executed by a processor, the method for optimizing the energy efficiency of the OAM-MIMO system based on the OPGW splice enclosure according to embodiment 1 is implemented.
Example 4
The embodiment provides a computing device, which may be a desktop computer, a notebook computer, a smart phone, a PDA handheld terminal, a tablet computer, or other terminal devices with a display function, and the computing device includes a processor and a memory, where the memory stores one or more programs, and when the processor executes the programs stored in the memory, the method for optimizing the energy efficiency of the OAM-MIMO system based on the OPGW connector box in embodiment 1 is implemented.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.

Claims (9)

1. An OAM-MIMO system energy efficiency optimization method based on an OPGW splice box is characterized by comprising the following steps:
the OAM-MIMO system is provided with a plurality of UCAs, the number of the UCAs of a transmitting end is the same as that of a receiving end, and an OAM-MIMO system capacity model, a total power loss model and an energy efficiency model of the transmitting and receiving end are constructed based on the UCAs and the Butler matrix;
obtaining channel matrixes corresponding to all UCAs under the same mode by adopting a ZF-SIC technology, calculating the total capacity of the system according to the channel matrixes corresponding to all modes, calculating the total power loss of the system according to the static power consumption of the UCAs and the emission power weight of the modes, and calculating the ratio of the total capacity of the system to the total power loss to obtain the energy efficiency;
the energy efficiency maximization is realized on the basis of meeting the optimal power distribution, and an energy efficiency maximization mathematical model of an OAM-MIMO system based on UCAs is established according to the total system capacity and the total system power loss;
taking the minimum capacity requirement and the minimum transmitting power requirement which are required to be met by each OAM wave beam as constraint conditions, and constructing an energy efficiency optimization problem according to the obtained total system capacity and the constraint conditions;
establishing a double-layer iterative algorithm for maximizing energy efficiency based on a power distribution scheme for optimizing a plurality of OAM mode wave beams on a plurality of UCAs, converting a non-convex power distribution problem into a convex problem by utilizing first-order Taylor approximation of an inner layer, updating the energy efficiency of an outer layer by utilizing a dichotomy, and solving a suboptimal solution of an optimization problem.
2. The OAM-MIMO system energy efficiency optimization method based on the OPGW connector box as claimed in claim 1, wherein the OAM-MIMO system capacity model, the total power loss model and the energy efficiency model of the transmitting and receiving end are constructed based on UCAs and Butler matrices, and the specific steps include:
regarding each OAM wave beam on each UCA as a link, sorting OAM wave beams of the same mode on different UCAs, where the total transmission power and the total power consumed by system hardware are respectively expressed as:
Figure FDA0003582654630000011
Figure FDA0003582654630000012
total power loss P of system tot (P) is represented by:
Figure FDA0003582654630000021
the SINR of the ith link is defined as:
Figure FDA0003582654630000022
wherein i 'and j' represent the ith link in the channel matrix G i Relative position of (a);
total capacity C of the system tot (P) is represented by:
Figure FDA0003582654630000023
energy efficiency lambda of OAM-MIMO system based on UCAs EE Expressed as:
Figure FDA0003582654630000024
wherein, N represents the number of UCAs at the sending end, N L Indicates the total number of links, C i Representing the current capacity, σ, of each link 2 Representing the noise power, and α is the power amplification factor.
3. The energy efficiency optimization method of the OAM-MIMO system based on the OPGW splice closure of claim 2, wherein the energy efficiency maximization mathematical model of the OAM-MIMO system based on UCAs is established according to the obtained total system capacity and constraint conditions, which is specifically expressed as:
P1:
Figure FDA0003582654630000025
s.t.C1:
Figure FDA0003582654630000026
C2:
Figure FDA0003582654630000027
C3:
Figure FDA0003582654630000028
where C1 represents the minimum capacity constraint for each link, C2 guarantees the effectiveness of each link, C3 represents the total transmit power constraint, R req To be the minimum capacity that each link needs to achieve, P max Representing the total maximum transmit power.
4. The energy efficiency optimization method of the OAM-MIMO system based on the OPGW splice closure of claim 1, wherein the step of establishing an energy efficiency maximization mathematical model of the OAM-MIMO system based on UCAs according to the total system capacity and the total system power loss comprises the specific steps of:
energy efficiency for a given z-th iteration update
Figure FDA0003582654630000031
The corresponding optimal power allocation is obtained by solving the following problem:
P2:
Figure FDA0003582654630000032
s.t.C1:
Figure FDA0003582654630000033
C2:
Figure FDA0003582654630000034
C3:
Figure FDA0003582654630000035
wherein, C tot (P) represents the total capacity of the system, P tot (P) represents the total power loss of the system, C1 represents the minimum capacity constraint of each link, C2 guarantees the effectiveness of each link, C3 represents the total transmit power constraint, R represents req Indicating the minimum capacity, P, that each link needs to achieve max Representing the total maximum transmit power, N L Representing the total number of links;
the objective function of the optimization problem P2 is converted into:
Figure FDA0003582654630000036
Figure FDA0003582654630000037
Figure FDA0003582654630000038
the constraint C1 of the optimization problem P2 is transformed into the equivalent convex linear form:
C1′:
Figure FDA0003582654630000041
converting the optimization problem P2 into an optimization problem P3, which is specifically represented as:
P3:
Figure FDA0003582654630000042
s.t.C1′,C2,C3
definition P q For the power allocation scheme of the q-th iteration of the inner loop, v (p) corresponds to a first order taylor approximation:
Figure FDA0003582654630000043
wherein
Figure FDA0003582654630000044
Gradient representing V (P):
Figure FDA0003582654630000045
wherein e represents a column vector;
converting the optimization problem P3 into an optimization problem P4, which is specifically represented as:
P4:
Figure FDA0003582654630000046
s.t.C1′,C2,C3
the constraint set of the optimization problem P4 is convex and the objective function is a concave function.
5. The OAM-MIMO system energy efficiency optimization method based on the OPGW splice closure of claim 4, wherein the optimization problem P2 is solved to obtain a fixed energy efficiency
Figure FDA0003582654630000047
The following corresponding optimal power allocation scheme specifically comprises the following steps:
initialization: setting iteration number and accuracy, setting transmission power P 0 Calculating I 0 ＝U(P 0 )-V(P 0 )；
Repeating the iterative calculation until reaching a convergence condition, wherein the convergence condition is I k -I k-1 |≤ε；
Solving the optimization problem P4 to obtain the corresponding power distribution scheme P * Updating the iteration number k as k +1, P k ＝P * Calculating I k ＝U(P k )-V(P k )，I k Represents the convergence of the power allocation scheme;
judgment of | I k -I k-1 If | ≦ epsilon, if not, P 0 ＝P k Returning to solve the optimization problem P4;
if I k -I k-1 If | ≦ ε, P k ＝P * To find out the fixed energy efficiency
Figure FDA0003582654630000051
Corresponding optimal power allocation scheme P z 。
6. The energy efficiency optimization method for the OAM-MIMO system based on the OPGW connector box according to claim 4, wherein the step of updating the energy efficiency of the outer layer by using the dichotomy comprises the following specific steps:
initialization: the iteration times and the accuracy are set, the energy efficiency boundary value is set,
Figure FDA0003582654630000052
repeating the iterative calculation until reaching a convergence condition of
Figure FDA0003582654630000053
Energy efficiency calculation: energy efficiency at z-th iteration
Figure FDA0003582654630000054
At a determined
Figure FDA0003582654630000055
Next, the corresponding optimal power allocation scheme P is derived based on the optimization problem P2 k ；
Updating: power allocation scheme P z ＝P k ；
Judgment of
Figure FDA0003582654630000056
If the result is true, judging whether the result is true or not, and if not, judging
Figure FDA0003582654630000057
Whether or not it is true, if
Figure FDA0003582654630000058
Is established to
Figure FDA0003582654630000059
If not, let
Figure FDA00035826546300000510
Returning to energy efficiency calculation;
if it is
Figure FDA00035826546300000511
Is established, P opt ＝P z To find out the optimal energy efficiency
Figure FDA00035826546300000512
And updating the iteration number z to be z + 1.
7. An OAM-MIMO system energy efficiency optimization system based on an OPGW splice closure, comprising: the system comprises an OAM-MIMO system model construction module, an energy efficiency maximization mathematical model construction module, an energy efficiency optimization problem construction module and an optimization problem solving module;
the OAM-MIMO system model building module is used for building an OAM-MIMO system capacity model, a total power loss model and an energy efficiency model of a transmitting and receiving end based on UCAs and Butler matrices, the OAM-MIMO system is provided with a plurality of UCAs, and the number of the UCAs of the transmitting end is the same as that of the receiving end;
obtaining channel matrixes corresponding to all UCAs under the same mode by adopting a ZF-SIC technology, calculating the total capacity of the system according to the channel matrixes corresponding to all modes, calculating the total power loss of the system according to the static power consumption of the UCAs and the emission power of the modes, and calculating the ratio of the total capacity of the system to the total power loss to obtain the energy efficiency;
the energy efficiency maximization mathematical model construction module is used for realizing maximization of energy efficiency on the basis of meeting optimal power distribution, and establishing an energy efficiency maximization mathematical model of an OAM-MIMO system based on UCAs according to the total system capacity and the total system power loss;
the energy efficiency optimization problem construction module is used for constructing an energy efficiency optimization problem by taking the minimum capacity requirement and the minimum transmitting power requirement which are required to be met by each OAM wave beam as constraint conditions according to the obtained total system capacity and the constraint conditions;
the optimization problem solving module is used for establishing a double-layer iterative algorithm for maximizing the energy efficiency based on a power distribution scheme for optimizing a plurality of OAM modal wave beams on a plurality of UCAs, converting a non-convex power distribution problem into a convex problem by utilizing first-order Taylor approximation of an inner layer, updating the energy efficiency of an outer layer by utilizing a dichotomy, and solving a suboptimal solution of the optimization problem.
8. A computer-readable storage medium storing a program, wherein the program, when executed by a processor, implements the OPGW splice box based OAM-MIMO system energy efficiency optimization method of any one of claims 1-6.
9. A computing device comprising a processor and a memory for storing processor-executable programs, wherein the processor, when executing the programs stored in the memory, implements the OPGW splice box based OAM-MIMO system energy efficiency optimization method of any one of claims 1-6.
CN202210355804.8A 2022-04-06 2022-04-06 OAM-MIMO system energy efficiency optimization method based on OPGW joint box Pending CN114900266A (en)

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