CN111031600A - OFDMA network distributed power robustness control algorithm for cluster flight spacecraft - Google Patents

OFDMA network distributed power robustness control algorithm for cluster flight spacecraft Download PDF

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CN111031600A
CN111031600A CN201911332738.7A CN201911332738A CN111031600A CN 111031600 A CN111031600 A CN 111031600A CN 201911332738 A CN201911332738 A CN 201911332738A CN 111031600 A CN111031600 A CN 111031600A
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node
subcarrier
mth
spacecraft
power
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胡圣波
施燕峰
舒恒
宋小伟
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Guizhou Education University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
    • H04W52/241—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters taking into account channel quality metrics, e.g. SIR, SNR, CIR, Eb/lo
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/26—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service]
    • H04W52/265—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service] taking into account the quality of service QoS
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/30—TPC using constraints in the total amount of available transmission power
    • H04W52/34—TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading
    • H04W52/346—TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading distributing total power among users or channels
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/38—TPC being performed in particular situations

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  • Engineering & Computer Science (AREA)
  • Quality & Reliability (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)

Abstract

The invention discloses a cluster flight spacecraft OFDMA network distributed power robustness control algorithm, which comprises initialization parameters, estimation of inter-node channel gain, measurement of signal interference noise ratio received by a receiving module node, calculation of Lagrange multiplier variable, and control of cluster flight spacecraft OFDMA network distributed power robustness
Figure DDA0002330099940000011
And
Figure DDA0002330099940000012
feedback to receiving node, receiving node receiving through control channel
Figure DDA0002330099940000013
And
Figure DDA0002330099940000014
update αmAnd transmitting power; the invention provides a system robustness power control model under channel uncertainty through an OFDMA network system model based on the cluster flight spacecraft, provides a robustness power control algorithm and ensures the network performance and the space exploration capability of the cluster flight spacecraft.

Description

OFDMA network distributed power robustness control algorithm for cluster flight spacecraft
Technical Field
The invention relates to a cluster flight spacecraft network distributed power robustness control algorithm, in particular to an OFDMA-based cluster flight spacecraft network distributed power robustness control algorithm, and belongs to the field of aerospace technologies and electronic information.
Background
With the emergence of large-scale spacecrafts such as space stations, spacecrafts, space shuttles and the like, the space detection capability of human beings is remarkably enhanced. The large-scale spacecraft with a single structure integrated by functional modules at present has the defects of high launching cost, load carrying capacity, poor self-adaptability, low robustness and the like, is difficult to adjust and adapt to a new task, and is difficult to complete even a preset task once a fault occurs.
The cluster flight spacecraft network is used as an innovative structure of a distributed space system, has the advantages of quick response, strong robustness, flexibility, low cost, long service life and the like, and is considered to be a next-generation distributed space system. However, like other distributed space systems, clustered flying spacecraft networks are also a resource-shared, energy-limited system. Particularly, because the spacecraft flies at a high speed, the channel between the network nodes of the cluster flying spacecraft has obvious uncertainty, so how to realize the power robustness control of the network system of the cluster flying spacecraft is still one of the important problems to be faced on the basis of ensuring the network QoS.
Disclosure of Invention
The invention aims to provide an OFDMA network distributed power robustness control algorithm of the cluster flight spacecraft based on OFDMA, realize the power robustness control of the cluster flight spacecraft network, and ensure the network performance and the space exploration capability of the cluster flight spacecraft.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
the cluster flight spacecraft OFDMA network distributed power robustness control algorithm comprises the following steps:
step one, initializing parameters: i is equal to 0, and i is equal to 0,
Figure BDA0002330099920000021
αm(0)＞0，
Figure BDA0002330099920000022
wherein i is the number of iterations; m is 1,2, …, m, which represents the mth module node; n is 1,2, …, n, which represents the nth orthogonal subcarrier;
Figure BDA0002330099920000023
the transmitting power of the nth orthogonal subcarrier adopted by the mth module node is shown;
Figure BDA0002330099920000024
is a continuous variable and represents the subcarrier allocation condition when
Figure BDA0002330099920000025
Then, it means that the mth node is allocated the nth subcarrier; when in use
Figure BDA0002330099920000026
When, it means that the mth node is not allocated to the subcarrier;
Figure BDA0002330099920000027
αm(0) is the corresponding lagrange multiplier variable;
step two, estimating the channel gain between nodes
Figure BDA0002330099920000028
Measuring signal to interference plus noise ratio (SINR) values received by a receiving module node
Figure BDA0002330099920000029
Figure BDA00023300999200000210
In the formula, two variables
Figure BDA00023300999200000211
Indicating a condition reflecting subcarrier allocation, i.e. when
Figure BDA00023300999200000212
Then, it means that the mth node is allocated the nth subcarrier; on the contrary, when
Figure BDA00023300999200000213
The time indicates that the mth node is not allocated to a subcarrier.
Figure BDA00023300999200000214
Is the noise power and can assume that at each moment, every node is the same and is σ2；
Step three, calculating a Lagrange multiplier variable αm(i+1)、
Figure BDA00023300999200000215
Will be provided with
Figure BDA00023300999200000216
And
Figure BDA00023300999200000217
feeding back to the receiving node;
step four, the receiving node receives through the control channel
Figure BDA0002330099920000031
And
Figure BDA0002330099920000032
update αmAnd transmit power
Figure BDA0002330099920000033
In the formula
Figure BDA0002330099920000034
Figure BDA0002330099920000035
And step five, returning to the step two and the step three.
In the OFDMA network distributed power robustness control algorithm for cluster flight spacecraft, specifically, in step three, the algorithm
Figure BDA0002330099920000036
Figure BDA0002330099920000037
Wherein i is the number of iterations, b1And b2Is a small positive step size.
The invention has the beneficial effects that: the cluster flight spacecraft network is a form of a distributed spacecraft network, and has the characteristics that nodes are separated in space and are mutually independent, the nodes are bounded relatively, and the control is relatively easy. The invention provides a system robustness power control model under channel uncertainty through an OFDMA network system model based on the cluster flight spacecraft, provides a robustness power control algorithm and ensures the network performance and the space exploration capability of the cluster flight spacecraft.
The robustness in the present invention is the transliteration of Robust, which is the key to system survival in abnormal and dangerous situations. For example, whether computer software is halted or crashed in the case of input error, disk failure, network overload, or intentional attack is the robustness of the software. By "robustness", it is meant that the control system maintains certain other performance characteristics under certain (structural, size) parameter perturbations. OFDMA (Orthogonal Frequency Division multiple access) is a multiple access technique in a wireless communication system, and is a transmission technique in which after a channel is sub-carrier-converted by OFDM, transmission data is loaded on a part of sub-carriers. QoS (Quality of service) refers to a network that can provide better service capability for specified network communication by using various basic technologies, and is a security mechanism of the network, which is a technology for solving problems such as network delay and congestion.
Drawings
FIG. 1 is a diagram of a network model of the present invention.
The invention is further described with reference to the following figures and detailed description.
Detailed Description
First, system model
(1) Network model
Consider an OFDMA cluster flying spacecraft network of a star network topology consisting of M nodes, and the M high-speed flight module nodes share N orthogonal subcarriers. Suppose that
Figure BDA0002330099920000044
Representing the channel gain between the transmitting and receiving nodes. Where M is 1,2, …, M denotes an mth module node, N is 1,2, …, and N denotes an nth orthogonal subcarrier. While
Figure BDA0002330099920000045
It means that the mth module node uses the transmission power of the nth orthogonal subcarrier, as shown in fig. 1.
(2) Transmission model
In the OFDMA cluster space vehicle network, when the nth subcarrier is used to transmit data according to the free space electromagnetic wave propagation model and the kth node at time t, the signal power received by the receiving module node is:
Figure BDA0002330099920000041
in the formula (I), the compound is shown in the specification,
Figure BDA0002330099920000042
is the gain of the link of the channel,
Figure BDA0002330099920000043
is the distance between the transmitting and receiving nodes at time t, and C is a constant related to the antenna gain and operating frequency between the transmitting and receiving nodes.
It should be noted that, according to the motion model between the nodes of the cluster flight spacecraft network, the distance distribution between the nodes can be approximated by a normal distribution:
Figure BDA0002330099920000051
in the formula, the upper and lower bounds of the distance between the nodes of the cluster flight spacecraft module are M and M respectively, D is (M + M)/4, and the parameters a, b and c to be estimated can be determined according to the correlation theory of the empirical cumulative distribution function.
From equation (1), the signal to interference plus noise ratio of the receiving module node can be obtained as:
Figure BDA0002330099920000052
in the formula, two variables
Figure BDA0002330099920000053
Indicating a condition reflecting subcarrier allocation, i.e. when
Figure BDA0002330099920000054
Then, it means that the mth node is allocated the nth subcarrier; on the contrary, when
Figure BDA0002330099920000055
The time indicates that the mth node is not allocated to a subcarrier.
Figure BDA0002330099920000056
Is the noise power and can assume that at each moment, every node is the same and is σ2。
To guarantee QoS, it is necessary to meet minimum SINR requirements, i.e. if minimum SINR requirements are given
Figure BDA0002330099920000057
Then the following constraints should apply:
Figure BDA0002330099920000058
thus, the OFDMA cluster space vehicle network distributed power control problem can be expressed as an optimization problem as follows:
Figure BDA0002330099920000061
in the formula, the module node has limited energy and is restricted by C1Reflects this point, constrains C3It indicates that the network can only use each subcarrier at each time.
(3) Robust optimization model
In fact, in a dynamic environment of a cluster flight spacecraft network, modules move at high speed, the distance between nodes is randomly distributed, channel gain is uncertain, and the system performance is directly influenced, so that the robustness optimization problem needs to be researched.
From an analytical standpoint, the uncertainty of the channel can be described by an ellipsoid uncertainty set, while for a normal random variable u ∈ RnIf its mean value is
Figure BDA0002330099920000069
The covariance matrix is ∑ ═ ΣT> 0, then the ellipsoid uncertainty set for u can be defined as:
Figure BDA0002330099920000062
in general, inter-node channel gain
Figure BDA0002330099920000063
Can be expressed as an estimated value
Figure BDA0002330099920000064
Deviation from the estimated value
Figure BDA0002330099920000065
And (c) the sum, i.e.:
Figure BDA0002330099920000066
therefore, the temperature of the molten metal is controlled,like notes
Figure BDA0002330099920000067
Then under the condition of the ellipsoid uncertainty set, the inter-node channel gain uncertainty set is:
Figure BDA0002330099920000068
the constraint C2 in equation (5) is transformed as follows:
Figure BDA0002330099920000071
therefore, when considering channel uncertainty, the power allocation problem of equation (5) can be rewritten as:
Figure BDA0002330099920000072
the power allocation problem in equation (10) is a semi-positive deterministic problem that can be solved by the schwarz inequality as a worst-case deterministic problem, i.e.:
Figure BDA0002330099920000073
therefore, the problem of equation (11) can be changed to the worst case optimization problem as follows:
Figure BDA0002330099920000074
robust power control algorithm
In the optimization problem described above, if constraint C is present2The channel gain in (1) is well known and the above optimization problem is a convex optimization problem, the so-called nominal optimization problem, and can be solved using existing methods. However, in the dynamic environment of the cluster spacecraft network, the modules move at high speed, and the distances among the nodes are randomly distributed, so that the channel gain has uncertainty, and SINR (signal to interference plus noise ratio) is restricted by C2Is greatly affected. Therefore, it is necessary to study the robust powerAnd (5) controlling.
(1) Relaxed Lagrangian function of optimization problem
The formula (5) is a binary variable
Figure BDA0002330099920000081
The mixed integer non-convex optimization problem of (2) is also an NP-hard problem. For this kind of problem, relaxation binary variables are mainly used
Figure BDA0002330099920000082
The method of (3) changing such non-convex optimization problem into convex optimization problem solution. Here, continuous variables are defined
Figure BDA0002330099920000083
Relaxation binary variable
Figure BDA0002330099920000084
Thus, for example, remember
Figure BDA0002330099920000085
Equation (12) may be changed to:
Figure BDA0002330099920000086
the power distribution problem is a convex optimization problem and can be effectively solved by adopting a Lagrange dual method. For this purpose, the following lagrangian function is defined:
Figure BDA0002330099920000087
in the formula (I), the compound is shown in the specification,
Figure BDA0002330099920000091
αmis the corresponding lagrange multiplier variable.
Order:
Figure BDA0002330099920000092
then the sub-gradient update method is utilizedThe solution of the Lagrange multiplier variable can be obtained by the method:
Figure BDA0002330099920000093
Figure BDA0002330099920000094
wherein i is the number of iterations, b1And b2Is a small positive step size.
The optimal transmit power can be obtained by solving the following equation:
Figure BDA0002330099920000095
like notes
Figure BDA0002330099920000096
The optimal solution is then:
Figure BDA0002330099920000097
thus, the proposed robust power control algorithm can be described as follows:
first, initializing parameters: i is equal to 0, and i is equal to 0,
Figure BDA0002330099920000098
αm(0)＞0，
Figure BDA0002330099920000099
second, estimating the inter-node channel gain
Figure BDA00023300999200000910
Measuring a received SINR (signal to interference plus noise ratio) value;
thirdly, calculating Lagrange multiplier variable, and calculating
Figure BDA00023300999200000911
And
Figure BDA00023300999200000912
fed back to the receiving node.
Step four, the receiving node receives through the control channel
Figure BDA00023300999200000913
And
Figure BDA00023300999200000914
update αmAnd transmit power
Figure BDA00023300999200000915
pnew(i +1) is calculated from formula (18);
and step five, returning to the step two and the step three.
The foregoing are only some embodiments of the invention. Several variations and modifications can be made without departing from the inventive concept as defined by the appended claims.

Claims (2)

1. The cluster flight spacecraft OFDMA network distributed power robustness control algorithm is characterized in that: comprises the following steps of (a) carrying out,
step one, initializing parameters: i is equal to 0, and i is equal to 0,
Figure FDA0002330099910000011
αm(0)＞0，
Figure FDA0002330099910000012
wherein i is the number of iterations; m is 1,2, …, m, which represents the mth module node; n is 1,2, …, n, which represents the nth orthogonal subcarrier;
Figure FDA0002330099910000013
the transmitting power of the nth orthogonal subcarrier adopted by the mth module node is shown;
Figure FDA0002330099910000014
is a continuous variable and represents the subcarrier allocation condition when
Figure FDA0002330099910000015
Then, it means that the mth node is allocated the nth subcarrier; when in use
Figure FDA0002330099910000016
When, it means that the mth node is not allocated to the subcarrier;
Figure FDA0002330099910000017
αm(0) is the corresponding lagrange multiplier variable;
step two, estimating the channel gain between nodes
Figure FDA0002330099910000018
Measuring signal interference noise ratio received by receiving module node
Figure FDA0002330099910000019
Figure FDA00023300999100000110
In the formula, two variables
Figure FDA00023300999100000111
Indicating a condition reflecting subcarrier allocation, i.e. when
Figure FDA00023300999100000112
Then, it means that the mth node is allocated the nth subcarrier; on the contrary, when
Figure FDA00023300999100000113
The time indicates that the mth node is not allocated to a subcarrier.
Figure FDA00023300999100000114
Is the noise power and can assume each time instant, eachThe nodes are all the same and are sigma2；
Step three, calculating a Lagrange multiplier variable αm(i+1)、
Figure FDA00023300999100000115
Will be provided with
Figure FDA00023300999100000116
And
Figure FDA00023300999100000117
feeding back to the receiving node;
step four, the receiving node receives through the control channel
Figure FDA00023300999100000118
And
Figure FDA00023300999100000119
update αmAnd transmit power
Figure FDA0002330099910000021
In the formula
Figure FDA0002330099910000022
Figure FDA0002330099910000023
And step five, returning to the step two and the step three.
2. The cluster-flight spacecraft OFDMA network distributed power robustness control algorithm of claim 1, characterized by: in step three, the
Figure FDA0002330099910000024
Wherein i is the number of iterations, b1And b2Is a small positive step size.
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CN107276704A (en) * 2017-05-10 2017-10-20 重庆邮电大学 The maximized optimal robustness Poewr control method of efficiency is based in two layers of Femtocell network
CN107613554A (en) * 2017-09-29 2018-01-19 西安电子科技大学 One kind disturbs aware distributed robust method for controlling downlink power
CN108650705A (en) * 2018-03-30 2018-10-12 重庆邮电大学 A kind of maximized heterogeneous wireless network robust power control method of capacity usage ratio
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Application publication date: 20200417