CN116545810B - Method, device and medium for maximizing throughput of multi-user wireless power communication network - Google Patents

Method, device and medium for maximizing throughput of multi-user wireless power communication network Download PDF

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CN116545810B
CN116545810B CN202310191118.6A CN202310191118A CN116545810B CN 116545810 B CN116545810 B CN 116545810B CN 202310191118 A CN202310191118 A CN 202310191118A CN 116545810 B CN116545810 B CN 116545810B
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matrix
energy
precoding
phase
precoding matrix
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CN116545810A (en
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吴春月
柯峰
秦梦娇
杨协宜
温淼文
李东
章秀银
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South China University of Technology SCUT
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03891—Spatial equalizers
    • H04L25/03961—Spatial equalizers design criteria
    • H04L25/03974—Spatial equalizers design criteria throughput maximization
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • H04B7/046—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting taking physical layer constraints into account
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03891—Spatial equalizers
    • H04L25/03898—Spatial equalizers codebook-based design
    • H04L25/0391—Spatial equalizers codebook-based design construction details of matrices
    • 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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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Power Engineering (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention discloses a method, a device and a medium for maximizing throughput of a multi-user wireless power communication network, wherein the method improves the throughput of the network by jointly optimizing the energy precoding of an energy collection link, the information precoding of an information transmission link and the phase of an intelligent super surface; and the problem is changed from non-convex to convex by combining alternate optimization, successive convex approximation and semi-definite relaxation: firstly, finding the optimal design of energy precoding; an alternate optimization scheme is used to solve this problem; each time alternating optimization is used, successive convex approximations and semi-positive relaxation are used. The invention greatly improves the throughput of the communication system, fully reduces the complexity of the communication system, saves hardware resources and has great application value.

Description

Method, device and medium for maximizing throughput of multi-user wireless power communication network
Technical Field
The invention relates to a method, a device, computer equipment and a storage medium for maximizing throughput of a multi-user wireless power communication network, and belongs to the technical field of wireless communication.
Background
With the development of semiconductor technology and integrated circuit industry, the power consumption of the wireless communication electronic circuit is greatly reduced, so that the power supply of radio frequency signals of the wireless sensor network is possible; besides providing energy through the environment radio frequency signals, the special power station can be actively deployed to realize wireless energy transmission of the sensor, so that the limitation of passive acquisition of other environment energy is broken, and the device has good controllability; the wireless transmission of information accounts for most of the energy consumption of the low-power consumption sensor node, and from the perspective of improving the energy efficiency of the wireless sensor node information transmission, the wireless power supply communication network has become a research hot spot in academia, however, due to the fading characteristic of the wireless link, the wireless power communication network still has difficulty in maintaining continuous information transmission.
With the recent development of metamaterials, intelligent super surface (RIS) technology can realize intelligent customization of wireless communication environment, effectively improve wireless link quality, RIS can program signal propagation of wireless channels through intelligent control of a large number of passive reflecting elements, and RIS can support low-cost, high-energy efficiency and high-information rate communication of a 6G communication system in auxiliary wireless communication.
Because the low efficiency of wireless information transmission and wireless power transmission leads to small power obtained by downlink information users, the low reachable rate of an uplink leads to the problem that the wireless power communication network has unreliable data transmission, and the introduction of RIS can alleviate the defects of the wireless power communication network; the non-orthogonal multiple access technique is able to serve more IUs in the same time/frequency/code resources and can use different power to distinguish between different users without adding additional resources than based on time division multiple access. RIS and non-orthogonal multiple access technology are integrated into a wireless power communication network at the same time, so that the performance of wireless information transmission and wireless power transmission can be improved at the same time.
Disclosure of Invention
In view of this, the present invention provides a method, apparatus, computer device and storage medium for maximizing throughput of a multi-user wireless power communication network, which combines alternate optimization, successive convex approximation and semi-definite relaxation to combine optimization, greatly improves throughput of a communication system, sufficiently reduces complexity of the communication system, saves hardware resources, and has great application value.
It is a first object of the present invention to provide a method for maximizing throughput for a multi-user wireless power communication network.
A second object of the present invention is to provide a multi-user wireless power communication network maximizing throughput device.
A third object of the present invention is to provide a computer device.
A fourth object of the present invention is to provide a storage medium.
The first object of the present invention can be achieved by adopting the following technical scheme:
a method of maximizing throughput for a multi-user wireless power communication network, the method comprising:
acquiring an energy link channel through an access point, and decomposing an energy link channel matrix by adopting a singular value decomposition technology to obtain a left unitary matrix, a right unitary matrix and a diagonal matrix;
setting energy precoding according to a first singular vector of a right unitary matrix;
and adopting an alternate optimization algorithm to iteratively solve an optimal information precoding matrix, a second phase matrix and an updated energy precoding matrix until the throughput convergence condition is met, so as to obtain a final energy precoding matrix, the optimal information precoding matrix and the second phase matrix, wherein the second phase matrix is a matrix obtained by solving the optimal information precoding matrix.
Further, the singular value decomposition technique is adopted to decompose the energy link channel matrix to obtain a left unitary matrix, a right unitary matrix and a diagonal matrix, and the following formula is shown in the specification:
H e ＝U e Λ e V e H
wherein matrix U e And V e A left unitary matrix and a right unitary matrix respectively; Λ type e Is a diagonal matrix, and the diagonal elements satisfy H e Singular value lambda 1 ≥λ 2 ≥…≥λ n ＞0。
Further, the energy precoding is set according to the first singular vector of the right unitary matrix, and the following formula is shown:
wherein G is energy precoding, P 0 Representing maximum transmit power of a power station, b 1 Is the first singular vector, i.e. (H) RA ΘH PR +H PA ) Maximum singular value lambda of (2) 1 Is used for the feature vector of (a),representation b 1 Is a conjugate transpose of (2); Θ represents the phase matrix of the reconfigurable intelligent subsurface; h e For energy link channel matrix, H e ＝H RA ΘH PR +H PA ，H PR 、H PA 、H RA The baseband equivalent channel from the power station to the intelligent super surface, the baseband equivalent channel from the power station to the access point and the baseband equivalent channel from the intelligent super surface to the access point are respectively represented.
Further, the method adopts an alternative optimization algorithm to iteratively solve the optimal information precoding matrix, the second phase matrix and update the energy precoding matrix until the throughput convergence condition is satisfied, and obtains a final energy precoding matrix, the optimal information precoding matrix and the second phase matrix, which specifically comprises the following steps:
starting an alternate optimization algorithm, and setting the iteration times to be 0;
solving an optimal information precoding matrix, a second phase matrix and an updated energy precoding matrix;
judging whether the throughput convergence condition is met;
if the throughput convergence condition is not met, adding 1 to the iteration times, and continuing to solve the optimal information precoding matrix, the second phase matrix and the updated energy precoding matrix;
and if the convergence condition is met, obtaining a final energy precoding matrix, an optimal information precoding matrix and a second phase matrix.
Further, the solving the optimal information precoding matrix and updating the energy precoding specifically includes:
solving an optimal information precoding matrix based on a first phase matrix obtained by the last iteration;
and solving a second phase matrix based on the optimal information precoding matrix, and enabling the second phase matrix to be used as a first phase matrix obtained in the iteration, and updating the energy precoding matrix.
Further, the solving the optimal information precoding matrix based on the first phase matrix obtained in the previous iteration specifically includes:
by using the semi-normal relaxation method, the optimization problem is written as:
C2：Tr(GG H )≤P 0
C3：θ i ∈[0，2π)
C4：SINR k→m ≥SINR k ，if x(m)≥x(k)
in the question ofWherein P1 in the C1 constraint represents a transmit power at the access point, the C1 constraint indicating that the transmit power P1 at the access point is less than or equal to the power collected from the power station; p in C2 constraint 0 Representing the maximum transmit power of the power station, the C2 constraint indicates that the energy of the energy precoding matrix G is less than or equal to the maximum transmit power P of the power station 0 The method comprises the steps of carrying out a first treatment on the surface of the C3 represents the phase shift constraint of the intelligent subsurface; c4 is the power constraint that SIC demodulation needs to meet; c5, C6 constraints ensure that there is a minimum signal to interference plus noise ratio threshold gamma min，k The quality of service requirement of the kth user; wherein w is k Is information pre-coding, < >> Is a variation of the phase matrix Θ; />Is the channel of the kth user; delta is the noise power;
for non-convex constraintsApplying successive convex approximation to solve, introducing an auxiliary variable a k And b k Equivalently rewrite->The following are provided:
according to a k And b k At the feasible pointIs considered as the lower bound, i.e. +.> And->Solving the lower bound solution to obtain:
s.t.C1，C3-C5，C7
problem-solvingConstructing a qualified matrix +.>As an initialization; according to the->Constructing a reasonable approach point +.>According to reasonable approach point->Solving the approximation problem by convex optimization method to obtain new +.>Use of a new->As an initial point for a new round, and updating the value of n until the difference between the two results is less than the threshold value l th Precoding matrix of optimal information obtained by convex optimization in last cycle +.>As an output.
Further, the method for solving the second phase matrix based on the optimal information precoding matrix, and using the second phase matrix as the first phase matrix obtained in the iteration, updates the energy precoding matrix specifically includes:
the optimization function after introducing the semi-normal relaxation and successive convex approximation can be expressed as
s.t.C1，C3-C5
C8：|V m，m |＝1，m＝1，2，…，M+1
Wherein, is obtained by theta conversion; problem->The specific process of solving is as follows: constructing a qualified matrix V (0) As an initialization; according to initialized V (0) Constructing a reasonable approximation point +.>According to reasonable approach pointsSolving the approximation problem by adopting a convex optimization method to obtain a new +.>Use of a new->As an initial point for a new round, and updating the value of n until the difference between the two results is less than the threshold value l th The method comprises the steps of carrying out a first treatment on the surface of the The second phase matrix V obtained by convex optimization in the last cycle * As an output, let the second phase matrix V * The first phase matrix V obtained as the current iteration (t) At the same time, the energy precoding matrix G is updated.
The second object of the invention can be achieved by adopting the following technical scheme:
a multi-user wireless power communication network maximizing throughput apparatus, the apparatus comprising:
the singular value decomposition module is used for acquiring an energy link channel through an access point, and decomposing an energy link channel matrix by adopting a singular value decomposition technology to obtain a left unitary matrix, a right unitary matrix and a diagonal matrix;
the energy precoding setting module is used for setting energy precoding according to the first singular vector of the right unitary matrix;
the iteration module is used for adopting an alternative optimization algorithm to iteratively solve the optimal information precoding matrix, the second phase matrix and the updated energy precoding matrix until the throughput convergence condition is met, so as to obtain a final energy precoding matrix, the optimal information precoding matrix and the second phase matrix, wherein the second phase matrix is a matrix obtained by solving the optimal information precoding matrix.
The third object of the present invention can be achieved by adopting the following technical scheme:
a computer device comprising a processor and a memory for storing a program executable by the processor, wherein the processor, when executing the program stored in the memory, implements the multi-user wireless power communication network throughput maximization method described above.
The fourth object of the present invention can be achieved by adopting the following technical scheme:
a storage medium storing a program which, when executed by a processor, implements the multi-user wireless power communication network maximization throughput method described above.
Compared with the prior art, the invention has the following beneficial effects:
the invention greatly improves the channel capacity of the communication system by jointly optimizing the energy precoding of the energy collecting link, the information precoding of the information transmission link and the phase of the intelligent super surface; the maximum channel capacity is a non-convex optimization problem due to the tight coupling between information precoding, energy precoding and intelligent super-surface phase; the invention provides a combined optimization method combining alternating optimization, successive convex approximation and semi-definite relaxation, which is called ASS method for short; numerical results show that compared with a communication system without intelligent super-surface assistance, the throughput of the multi-user communication system can be improved by 504.14% after the throughput is optimized; an intelligent super surface is added in the communication system, two links of an energy acquisition link and an information transmission link are assisted, and optimization is carried out through the same phase matrix; the invention greatly improves the throughput of the communication system, fully reduces the complexity of the communication system, saves hardware resources and has great application value.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the structures shown in these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a communication system model diagram of embodiment 1 of the present invention.
Fig. 2 is a flowchart of a method for maximizing throughput in a multi-user wireless power communication network in accordance with embodiment 1 of the present invention.
Fig. 3 is a flowchart of an alternate optimization algorithm of embodiment 1 of the present invention.
Fig. 4 a-4 c are graphs of average throughput versus power station transmit power for embodiment 1 of the present invention.
Fig. 5 is a graph showing the relationship between the average throughput and the distances between the receiving unit and the access point in embodiment 1 of the present invention.
Fig. 6 is a block diagram showing the structure of a multi-user wireless power communication network throughput maximizing apparatus according to embodiment 2 of the present invention.
Fig. 7 is a block diagram showing the structure of a computer device according to embodiment 3 of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by persons of ordinary skill in the art without making any inventive effort based on the embodiments of the present invention are within the scope of protection of the present invention.
Example 1:
the embodiment provides a method for maximizing throughput of a multi-user wireless power communication network, which aims at an intelligent super-surface (Reconfigurable intelligent surface, RIS) assisted multi-user wireless power communication network (multi-user communication system) based on non-orthogonal multiple access, and an energy precoding matrix G is obtained by an alternate iteration method to serve as a precoding scheme of an energy link, and optimal information precoding matrix is obtainedAs a precoding scheme for information transmission links, a second phase matrix V * As a phase matrix for intelligent supersurfaces.
As shown in fig. 1, an Access Point (AP) obtains energy from a power station; the access point is fully operating in full duplex mode; in addition, by changing the phase of the incident signal, an intelligent super-system is designedThe surface system enables energy and information to be more concentrated, and improves transmission efficiency; the intelligent super-surface is provided with M uniform plane array reflecting units; is provided withRespectively representing a baseband equivalent channel from a Power Station (PS) to an intelligent super surface, a baseband equivalent channel from the Power Station to an access point and a baseband equivalent channel from the intelligent super surface to the access point; at the same time->Respectively representing a baseband equivalent channel from an intelligent super surface to a User (IU) and a baseband equivalent channel from an access point to the User; the present embodiment assumes that the channel state information of all channels is completely known, so that the channels can be directly modeled.
As shown in fig. 2, the method for maximizing throughput of the multi-user wireless power communication network of the present embodiment includes the following steps:
s201, an energy link channel is acquired through an access point, and an energy link channel matrix is decomposed by adopting a singular value decomposition technology, so that a left unitary matrix, a right unitary matrix and a diagonal matrix are obtained.
In this embodiment, a singular value decomposition technique is used to decompose the energy link channel matrix to obtain a left unitary matrix, a right unitary matrix and a diagonal matrix, where the following formula is:
H e ＝U e Λ e V e H
wherein matrix U e And V e A left unitary matrix and a right unitary matrix respectively; Λ type e Is a diagonal matrix, and the diagonal elements satisfy H e Singular value lambda 1 ≥λ 2 ≥…≥λ n ＞0。
S202, setting energy precoding according to a first singular vector of a right unitary matrix.
In this embodiment, energy precoding is set according to a first singular vector of a right unitary matrix, as follows:
wherein G is energy precoding, P 0 Representing maximum transmit power of a power station, b 1 Is the first singular vector, i.e. (H) RA ΘH PR +H PA ) Maximum singular value lambda of (2) 1 Is used for the feature vector of (a),representation b 1 Is a conjugate transpose of (2); Θ represents the phase matrix of the reconfigurable intelligent subsurface; h e For energy link channel matrix, H e ＝H RA ΘH pR +H PA ，H PR 、H PA 、H RA The baseband equivalent channel from the power station to the intelligent super surface, the baseband equivalent channel from the power station to the access point and the baseband equivalent channel from the intelligent super surface to the access point are respectively represented.
And S203, adopting an alternative optimization algorithm to iteratively solve the optimal information precoding matrix, the second phase matrix and the updated energy precoding matrix until the throughput convergence condition is met, and obtaining a final energy precoding matrix, the optimal information precoding matrix and the second phase matrix.
As shown in fig. 3, this step S203 includes the steps of:
s2031, starting an alternate optimization algorithm, and setting the iteration number to 0.
S2032, solving an optimal information precoding matrix, a second phase matrix, and an updated energy precoding matrix.
Further, this step S2032 is divided into two steps:
A. and solving an optimal information precoding matrix based on the first phase matrix obtained by the last iteration.
By using the semi-normal relaxation method, the optimization problem is rewritten as:
C2：Tr(GG H )≤P 0
C3：θ i ∈[0，2π)
C4：SINR k→m ≥SINR k ，if x(m)≥x(k)
in the question ofP in C1 constraint 1 Representing transmit power at an access point, the C1 constraint indicates transmit power P at the access point 1 Less than or equal to the power collected from the power station; p in C2 constraint 0 Representing the maximum transmit power of the power station, the C2 constraint indicates that the energy of the energy precoding matrix G is less than or equal to the maximum transmit power P of the power station 0 The method comprises the steps of carrying out a first treatment on the surface of the C3 represents the phase shift constraint of the intelligent subsurface; c4 is the power constraint that SIC demodulation needs to meet; the C5, C6 constraint guarantees a threshold γ with the lowest signal to interference plus noise ratio (Signal to Interference plus Noise Ratio, SINR for short) min，k Quality of service requirements (QoS) for the kth user; wherein w is k Is information pre-coding, < >> Is a variation of the phase matrix Θ; />Is the channel of the kth user; delta is the work of noiseThe rate.
For non-convex constraintsApplying successive convex approximation to solve, introducing an auxiliary variable a k And b k Equivalently rewrite->The following are provided:
according to a k And b k At the feasible pointIs considered as the lower bound, i.e. +.> And->Solving the lower bound solution to obtain:
s.t.C1，C3-C5，C7
is a convex problem, aiming at the problem +.>Constructing a qualified matrix +.>As an initialization; according to the->Constructing a reasonable approach point +.>According to reasonable approach point->Solving the approximation problem by convex optimization method to obtain new +.>Use of a new->As an initial point for a new round, and updating the value of n until the difference between the two results is less than the threshold value l th Precoding matrix of optimal information obtained by convex optimization in last cycle +.>As an output.
B. And solving a second phase matrix based on the optimal information precoding matrix, and enabling the second phase matrix to be used as a first phase matrix obtained in the iteration, and updating the energy precoding matrix.
The optimization function after introducing the semi-normal relaxation and successive convex approximation can be expressed as
s.t.C1，C3-C5
C8：|V m，m |＝1，m＝1，2，…，M+1
Wherein, is obtained by theta conversion; problem->The specific process of solving is as follows: constructing a qualified matrix V (0) As an initialization; according to initialized V (0) Constructing a reasonable approximation point +.>According to reasonable approach pointsSolving by adopting a convex optimization methodThe approximation problem, a new +.>Use of a new->As an initial point for a new round, and updating the value of n until the difference between the two results is less than the threshold value l th The method comprises the steps of carrying out a first treatment on the surface of the The second phase matrix V obtained by convex optimization in the last cycle * As an output, let the second phase matrix V * The first phase matrix V obtained as the current iteration (t) At the same time, the energy precoding matrix G is updated.
S2033, judging whether the throughput convergence condition is met, if so, obtaining a final energy precoding matrix, an optimal information precoding matrix and a second phase matrix, and if not, adding 1 to the iteration number, and returning to the step S2032.
As shown in fig. 4 a-4 c, comparing the variation of average throughput in the power station with increasing transmit power when m=10, 30, and 50, it can be seen that the average throughput per algorithm with Power Station (PS) transmit power P 0 Is gradually increased by the increase of (a). Compared with other algorithms, the maximum throughput method (ASS algorithm) of the present embodiment can obtain the best performance; in the figure, when the PS transmit power is 20dB, the throughput of the ASS algorithm is improved by nearly 504.14%, 502.51% and 272.70% compared with the non-RIS algorithm, the ASS-FP algorithm and the ASS-FI algorithm; as PS transmit power increases, the distance between the ASS algorithm and other algorithms will become greater; this is because the ASS algorithm optimizes energy precoding and information precoding by adjusting the intelligent subsurface (RIS) phase shift and making trade-offs between them to achieve maximum system throughput; for ASS-FI and ASS-FP algorithms, one only optimizes the phase matrix and the other only precodes the information, so that the maximum throughput cannot be obtained naturally; for non-RIS algorithms, the performance is the worst.
As shown in fig. 5, which shows the effect of the distance between IUs and AP on average throughput, it can be seen that the throughput of the four algorithms decreases with increasing distance; furthermore, it can be seen that the ASS algorithm has good performance gains over all distances compared to the three algorithms. This is because the ASS algorithm comprehensively considers the links of energy acquisition and information transmission, and optimizes the precoding information precoding and the phase matrix of the energy precoding. The ASS algorithm can make the distance between IUs and AP in the system larger under the same throughput requirements.
It should be noted that while the method operations of the above embodiments are described in a particular order, this does not require or imply that the operations must be performed in that particular order or that all of the illustrated operations be performed in order to achieve desirable results. Rather, the depicted steps may change the order of execution. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform.
Example 2:
as shown in fig. 6, the present embodiment provides a multi-user wireless power communication network throughput maximizing apparatus, which includes a singular value decomposition module 601, an energy precoding setting module 602, and an iteration module 603, where specific functions of the modules are as follows:
the singular value decomposition module 601 is configured to obtain an energy link channel through an access point, and decompose an energy link channel matrix by using a singular value decomposition technology to obtain a left unitary matrix, a right unitary matrix and a diagonal matrix;
an energy precoding setting module 602, configured to set energy precoding according to a first singular vector of a right unitary matrix;
and the iteration module 603 is configured to iteratively solve the optimal information precoding matrix, the second phase matrix, and the updated energy precoding matrix by using an alternative optimization algorithm until the throughput convergence condition is satisfied, so as to obtain a final energy precoding matrix, the optimal information precoding matrix, and the second phase matrix, where the second phase matrix is a matrix obtained by solving based on the optimal information precoding matrix.
Specific implementation of each module in this embodiment may be referred to embodiment 1 above, and will not be described in detail; it should be noted that, the apparatus provided in this embodiment is only exemplified by the division of the above functional modules, and in practical application, the above functional allocation may be performed by different functional modules according to needs, that is, the internal structure is divided into different functional modules, so as to perform all or part of the functions described above.
Example 3:
the present embodiment provides a computer apparatus, which may be a computer, as shown in fig. 7, including a processor 702, a memory, an input device 703, a display 704 and a network interface 705 connected through a device bus 701, where the processor is configured to provide computing and control capabilities, the memory includes a nonvolatile storage medium 706 and an internal memory 707, where the nonvolatile storage medium 706 stores an operating device, a computer program and a database, and where the internal memory 707 provides an environment for the operation of the operating device and the computer program in the nonvolatile storage medium, and where the processor 702 executes the computer program stored in the memory, to implement the multi-user wireless power communication network throughput maximizing method of the above embodiment 1, as follows:
acquiring an energy link channel through an access point, and decomposing an energy link channel matrix by adopting a singular value decomposition technology to obtain a left unitary matrix, a right unitary matrix and a diagonal matrix;
setting energy precoding according to a first singular vector of a right unitary matrix;
and adopting an alternate optimization algorithm to iteratively solve an optimal information precoding matrix, a second phase matrix and an updated energy precoding matrix until the throughput convergence condition is met, so as to obtain a final energy precoding matrix, the optimal information precoding matrix and the second phase matrix, wherein the second phase matrix is a matrix obtained by solving the optimal information precoding matrix.
Example 4:
the present embodiment provides a storage medium, which is a computer readable storage medium storing a computer program, where the computer program when executed by a processor implements the multi-user wireless power communication network throughput maximizing method of the foregoing embodiment 1, as follows:
acquiring an energy link channel through an access point, and decomposing an energy link channel matrix by adopting a singular value decomposition technology to obtain a left unitary matrix, a right unitary matrix and a diagonal matrix;
setting energy precoding according to a first singular vector of a right unitary matrix;
and adopting an alternate optimization algorithm to iteratively solve an optimal information precoding matrix, a second phase matrix and an updated energy precoding matrix until the throughput convergence condition is met, so as to obtain a final energy precoding matrix, the optimal information precoding matrix and the second phase matrix, wherein the second phase matrix is a matrix obtained by solving the optimal information precoding matrix.
The computer readable storage medium of the present embodiment may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an apparatus, device, or means of electronic, magnetic, optical, electromagnetic, infrared, or semiconductor, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
In this embodiment, the computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution apparatus, device, or apparatus. In the present embodiment, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with a computer-readable program embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable storage medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution apparatus, device, or apparatus. A computer program embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
The computer readable storage medium may be written in one or more programming languages, including an object oriented programming language such as Java, python, C ++ and conventional procedural programming languages, such as the C-language or similar programming languages, or combinations thereof for performing the present embodiments. The program may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
In summary, the channel capacity of the communication system is greatly improved by jointly optimizing the energy precoding of the energy collection link, the information precoding of the information transmission link and the phase of the intelligent super surface; the maximum channel capacity is a non-convex optimization problem due to the tight coupling between information precoding, energy precoding and intelligent super-surface phase; the invention provides a combined optimization method combining alternating optimization, successive convex approximation and semi-definite relaxation, which is called ASS method for short; numerical results show that compared with a communication system without intelligent super-surface assistance, the throughput of the multi-user communication system can be improved by 504.14% after the throughput is optimized; an intelligent super surface is added in the communication system, two links of an energy acquisition link and an information transmission link are assisted, and optimization is carried out through the same phase matrix; the invention greatly improves the throughput of the communication system, fully reduces the complexity of the communication system, saves hardware resources and has great application value.
The above description is only of the preferred embodiments of the present invention, but the protection scope of the present invention is not limited thereto, and any person skilled in the art can substitute or change the technical solution and the inventive conception of the present invention equally within the scope of the disclosure of the present invention.

Claims (8)

1. A method for maximizing throughput in a multi-user wireless power communication network, the method comprising:
acquiring an energy link channel through an access point, and decomposing an energy link channel matrix by adopting a singular value decomposition technology to obtain a left unitary matrix, a right unitary matrix and a diagonal matrix;
setting energy precoding according to a first singular vector of a right unitary matrix;
adopting an alternating optimization algorithm to iteratively solve an optimal information precoding matrix, a second phase matrix and an updated energy precoding matrix until a throughput convergence condition is met, and obtaining a final energy precoding matrix, the optimal information precoding matrix and the second phase matrix, wherein the second phase matrix is a matrix obtained by solving based on the optimal information precoding matrix;
decomposing the energy link channel matrix by adopting a singular value decomposition technology to obtain a left unitary matrix, a right unitary matrix and a diagonal matrix, wherein the following formula is as follows:
H e ＝U e Λ e V e H
wherein matrix U e And V e A left unitary matrix and a right unitary matrix respectively; Λ type e Is a diagonal matrix, and the diagonal elements satisfy H e Singular value lambda 1 ≥λ 2 ≥…≥λ n >0；
And setting energy precoding according to the first singular vector of the right unitary matrix, wherein the energy precoding comprises the following formula:
wherein G is energy precoding, P 0 Representing maximum transmit power of a power station, b 1 Is the first singular vector, i.e. (H) RA ΘH PR +H PA ) Maximum singular value lambda of (2) 1 Is used for the feature vector of (a),representation b 1 Is a conjugate transpose of (2); Θ represents the phase matrix of the reconfigurable intelligent subsurface; h e For energy link channel matrix, H e ＝H RA ΘH PR +H PA ，H PR 、H PA 、H RA The baseband equivalent channel from the power station to the intelligent super surface, the baseband equivalent channel from the power station to the access point and the baseband equivalent channel from the intelligent super surface to the access point are respectively represented.
2. The method for maximizing throughput in a multi-user wireless power communication network according to claim 1, wherein the iterative solution of the optimal information precoding matrix, the second phase matrix and the updated energy precoding matrix by using an alternative optimization algorithm until the throughput convergence condition is satisfied, and obtaining a final energy precoding matrix, the optimal information precoding matrix and the second phase matrix specifically comprises:
starting an alternate optimization algorithm, and setting the iteration times to be 0;
solving an optimal information precoding matrix, a second phase matrix and an updated energy precoding matrix;
judging whether the throughput convergence condition is met;
if the throughput convergence condition is not met, adding 1 to the iteration times, and continuing to solve the optimal information precoding matrix, the second phase matrix and the updated energy precoding matrix;
and if the convergence condition is met, obtaining a final energy precoding matrix, an optimal information precoding matrix and a second phase matrix.
3. The method for maximizing throughput in a multi-user wireless power communication network as claimed in claim 2, wherein said solving the optimal information precoding matrix and updating the energy precoding comprises:
solving an optimal information precoding matrix based on a first phase matrix obtained by the last iteration;
and solving a second phase matrix based on the optimal information precoding matrix, and enabling the second phase matrix to be used as a first phase matrix obtained in the iteration, and updating the energy precoding matrix.
4. The method for maximizing throughput of multi-user wireless power communication network as claimed in claim 3, wherein said solving the optimal information precoding matrix based on the first phase matrix obtained in the last iteration specifically comprises:
by using the semi-normal relaxation method, the optimization problem is rewritten as:
c2:Tr(GG H )≤P 0
C3:θ i ∈[0,2π)
C4:SINR k→m ≥SINR k ,if x(m)≥x(k)
in the question ofP in C1 constraint 1 Representing transmit power at an access point, the C1 constraint indicates transmit power P at the access point 1 Less than or equal to the power collected from the power station; p in C2 constraint 0 Representing the maximum transmit power of the power station, the C2 constraint indicates that the energy of the energy precoding matrix G is less than or equal to the maximum transmit power P of the power station 0 The method comprises the steps of carrying out a first treatment on the surface of the C3 represents the phase shift constraint of the intelligent subsurface; c4 is the power constraint that SIC demodulation needs to meet; c5, C6 constraints ensure that there is a minimum signal to interference plus noise ratio threshold gamma min,k The quality of service requirement of the kth user; wherein w is k Is information pre-coding, < >>Is a variation of the phase matrix Θ; />Is the channel of the kth user; delta is the noise power;
for non-convex constraintsApplying successive convex approximation to solve, introducing an auxiliary variable a k And b k Equivalently rewrite->The following are provided:
according to a k And b k At the feasible pointIs considered as the lower bound, i.e. +.> And->Solving the lower bound solution to obtain:
s.t.C1,C3-C5,C7
problem-solvingConstructing a qualified matrix +.>As an initialization; according to the initialized->Constructing a reasonable approach point +.>According to reasonable approach point->Solving the approximation problem by convex optimization method to obtain new +.>Use of a new->As an initial point for a new round, and updating the value of n until the difference between the two results is less than the threshold value l th Precoding matrix of optimal information obtained by convex optimization in last cycle +.>As an output.
5. The method for maximizing throughput of a multi-user wireless power communication network as claimed in claim 4, wherein said optimizing information precoding matrix, solving a second phase matrix, and using the second phase matrix as a first phase matrix obtained in the iteration, updating an energy precoding matrix, specifically comprises:
the optimization function after introducing the semi-normal relaxation and successive convex approximation can be expressed as
s.t.C1,C3-C5
C8:|V m,m |＝1,m＝1,2,…,M+1
Wherein,is obtained by theta conversion; problem->The specific process of solving is as follows: constructing a qualified matrix V (0) As an initialization; according to initialized V (0) Constructing a reasonable approximation point +.>According to reasonable approach pointsSolving the approximation problem by adopting a convex optimization method to obtain a new +.>Use of a new->As an initial point for a new round, and updating the value of n until the difference between the two results is less than the threshold value l th The method comprises the steps of carrying out a first treatment on the surface of the The second phase matrix V obtained by convex optimization in the last cycle * As an output, let the second phase matrix V * The first phase matrix V obtained as the current iteration (t) At the same time, the energy precoding matrix G is updated.
6. An apparatus for maximizing throughput in a multi-user wireless power communication network, the apparatus comprising:
the singular value decomposition module is used for acquiring an energy link channel through an access point, and decomposing an energy link channel matrix by adopting a singular value decomposition technology to obtain a left unitary matrix, a right unitary matrix and a diagonal matrix;
the energy precoding setting module is used for setting energy precoding according to the first singular vector of the right unitary matrix;
the iteration module is used for adopting an alternative optimization algorithm to iteratively solve the optimal information precoding matrix, the second phase matrix and the updated energy precoding matrix until the throughput convergence condition is met, so as to obtain a final energy precoding matrix, the optimal information precoding matrix and the second phase matrix, wherein the second phase matrix is a matrix obtained by solving the optimal information precoding matrix;
decomposing the energy link channel matrix by adopting a singular value decomposition technology to obtain a left unitary matrix, a right unitary matrix and a diagonal matrix, wherein the following formula is as follows:
wherein matrix U e And V e A left unitary matrix and a right unitary matrix respectively; Λ type e Is a diagonal matrix, and the diagonal elements satisfy H e Singular value lambda 1 ≥λ 2 ≥…≥λ n >0；
And setting energy precoding according to the first singular vector of the right unitary matrix, wherein the energy precoding comprises the following formula:
wherein G is energy precoding, P 0 Representing maximum transmit power of a power station, b 1 Is the first singular vector, i.e. (H) RA ΘH PR +H PA ) Maximum singular value lambda of (2) 1 Is used for the feature vector of (a),representation b 1 Is a conjugate transpose of (2); Θ represents the phase matrix of the reconfigurable intelligent subsurface; h e For energy link channel matrix, H e ＝H RA ΘH PR +H PA ，H PR 、H PA 、H RA The baseband equivalent channel from the power station to the intelligent super surface, the baseband equivalent channel from the power station to the access point and the baseband equivalent channel from the intelligent super surface to the access point are respectively represented.
7. A computer device comprising a processor and a memory for storing a processor executable program, wherein the processor, when executing the program stored in the memory, implements the multi-user wireless power communication network maximization throughput method of any one of claims 1-5.
8. A storage medium storing a program which, when executed by a processor, implements the multi-user wireless power communication network maximization throughput method of any one of claims 1-5.
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