CN114554527B - Internet of Things link optimization method and system combining IRS technology and SR technology - Google Patents

Internet of Things link optimization method and system combining IRS technology and SR technology Download PDF

Info

Publication number
CN114554527B
CN114554527B CN202210198813.0A CN202210198813A CN114554527B CN 114554527 B CN114554527 B CN 114554527B CN 202210198813 A CN202210198813 A CN 202210198813A CN 114554527 B CN114554527 B CN 114554527B
Authority
CN
China
Prior art keywords
internet
things
receiver
base station
information
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202210198813.0A
Other languages
Chinese (zh)
Other versions
CN114554527A (en
Inventor
宋清洋
豆舒楠
亓伟敬
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Chongqing University of Post and Telecommunications
Original Assignee
Chongqing University of Post and Telecommunications
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Chongqing University of Post and Telecommunications filed Critical Chongqing University of Post and Telecommunications
Priority to CN202210198813.0A priority Critical patent/CN114554527B/en
Publication of CN114554527A publication Critical patent/CN114554527A/en
Application granted granted Critical
Publication of CN114554527B publication Critical patent/CN114554527B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/14—Spectrum sharing arrangements between different networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/06—Testing, supervising or monitoring using simulated traffic
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • Computing Systems (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The application discloses an internet of things link optimization method and system combining an IRS technology and an SR technology, which intelligently reconstruct a wireless communication environment by introducing an intelligent reflecting surface, and enhance the information transmission of an internet of things link by optimizing the reflecting coefficients of each reflecting unit under the condition of guaranteeing the minimum rate requirement of a main link cellular user; meanwhile, a multi-user scene is considered, the idea of user matching is introduced, and users with optimal reachable rate are matched for each IoT device through optimizing a matching strategy matrix, so that the total rate of the links of the Internet of things of the system is optimal. The method and the system effectively solve the problems that massive IoT devices in the symbiotic communication system in a multi-user scene occupy a large amount of frequency spectrum resources, weaken information transmission rate and influence cellular user communication, do not generate extra energy resource consumption, and the total rate of information transmission of the internet of things reaches the maximum on the premise of meeting the minimum transmission rate requirement of a main link.

Description

Internet of things link optimization method and system combining IRS technology and SR technology
Technical Field
The application relates to the technical field of wireless communication networks, in particular to an Internet of things link optimization method and system combining an IRS technology and an SR technology.
Background
With the development of wireless communication technology, high broadband and low time delay provide a foundation for future internet of things, but the arrival of the internet of things age also brings a series of problems, such as huge occupation of spectrum resources caused by massive IoT devices accessing into a wireless communication network, on one hand, the huge amount of spectrum resources are wasted, and on the other hand, normal communication among cellular users is affected to a certain extent. Also, the large number of IoT nodes operating simultaneously can also present significant energy consumption issues.
For the above problems, the solution to the problems described above may be effectively achieved with symbiotic radio technology, which is considered to be one of the most effective solutions for accessing wireless communication networks by mass internet of things devices in the future. Symbiotic radio (SymbioticRadio, SR) is a cooperative environmental backscatter communication with the advantages of high spectral efficiency, high energy efficiency and low cost. The system consists of two main communication links: one is a main link, mainly serving users, including cellular links, wiFi, etc.; the other is a secondary link, which is mainly used for transmitting the internet of things information collected by the internet of things node, such as an IoT link. The secondary link modulates own IoT information onto the received main link signal, and reflects the modulated information to the receiver (user) through the passive IoT device (the backscatter device BackscatterDevice, BD) so as to achieve the purpose of internet of things information communication. In this process, the secondary link shares not only the spectrum of the primary link but also the transmitter (a series of infrastructures such as base station, BS and receiver) of the primary link, and the backscatter devices are passive, do not generate additional energy resource consumption, and are low cost.
In addition, the smart reflective surface (IntelligentReflectingSurface, IRS) is a wireless communication assistance technology with high spectral efficiency, high energy efficiency, and low cost, and is considered as one of the key technologies for next-generation wireless communication. The intelligent reflecting surface is composed of a large number of low-cost passive reflecting units, the reflecting coefficients (including amplitude and phase) of each unit can be independently adjusted, and through optimizing mathematical theory, the wireless channel environment is intelligently reconstructed according to the requirements of an actual communication system, so that the purposes of enhancing useful signals, suppressing interference signals, protecting safety privacy and the like are achieved.
However, since IoT devices of the internet of things link are composed of passive reflective elements; in addition, in order to accurately decode the internet of things information and the cellular information at the receiver, the internet of things information symbol period is much larger than the main link information symbol period; furthermore, ioT devices are far from the base station, with some occlusion between each other. All the above factors will lead to a reduced transmission rate of the internet of things link.
In view of this, the present application has been made.
Disclosure of Invention
The technical problems to be solved by the application are as follows: how to enhance the transmission rate of the internet of things link in the multi-user symbiotic communication system. The aim is to provide an internet of things link optimization method and system combining an IRS technology and an SR technology, an intelligent reflection surface is introduced to carry out intelligent reconstruction on a wireless communication environment, and under the condition that the minimum speed requirement of a main link cellular user is ensured, the information transmission of the internet of things link is enhanced by optimizing the reflection coefficients of all reflection units of the intelligent reflection surface.
The application is realized by the following technical scheme:
in one aspect, the application provides a link optimization method of the internet of things combining an IRS technology and an SR technology, which comprises the following steps:
the base station transmitting the primary link signal to the plurality of receivers, the intelligent reflective surface, and the plurality of IoT devices;
the intelligent reflection surface receives a main link signal sent by the base station, and the intelligent reflection surface reconstructs the channel environment from the base station to the plurality of receivers, and after the reconstruction is completed, the received main link signal is sent to the plurality of receivers; the intelligent reflection surface reconstructs channel environments from the base station to the plurality of IoT devices, and after the reconstruction is completed, the received main link signals are sent to the receiver and the plurality of IoT devices;
the plurality of IoT devices receive internet of things information, and main link information sent by the base station and the intelligent reflective surface; the plurality of IoT devices modulate the internet of things information onto the received primary link information and transmit the modulated information to the plurality of receivers.
As a further description of the present application,
the information after the receiver receives the modulation is:
where p represents the base station transmit power,represents in order the channel between the base station to the mth user, the base station to the nth IoT device, and the mth user to the nth IoT device, Φ=diag { Φ } 1 ,φ 2 ,…,φ Q -representing a matrix of reflection coefficients of the smart reflective surface, < ->Sequentially representing the channel between the smart reflective surface to the mth user and the smart reflective surface to the nth IoT device,/for>Representing the channel from the base station to the smart reflective surface x m Information symbol, alpha, representing the transmission of the base station to the mth receiver n Representing the reflection coefficient of the nth IoT device and taking a value between 0-1 c n Information symbol, μ representing the nth IoT device sent to the mth receiver m Representing power sigma 2 Additive white gaussian noise of zero mean value of (c).
The main link information received by the receiver is:
the transmission rate of the main link information received by the receiver is expected to be:
the internet of things information received by the receiver is:
or (b)
The transmission rate of the internet of things information received by the receiver is as follows:
wherein a is m,n Representing a matching relationship between the mth user and the nth IoT device (0 representing no match, 1 representing a match), K representing a symbol period of the internet of things information being K times a symbol period of the main link information.
As a further description of the present application, the method for optimizing the link of the internet of things further includes the following steps:
s1: establishing a receiver matching relation matrix A;
s2: establishing a total rate maximization model of an Internet of things link of the symbiotic communication system according to the receiver matching relation matrix A and the reflection coefficient matrix phi, wherein the Internet of things link of the symbiotic communication system consists of a plurality of receivers;
s3: receivers that can reach an optimal rate are matched for each IoT device, optimizing the target value of the overall rate maximization model.
As a further description of the application, the overall rate maximization model is:
P:
s.t.
m∈1,2,…,M,n∈1,2,…,N，
wherein M represents a user machine in a symbiotic communication systemNumber, N, represents the number of IoT devices in the symbiotic communication system, and the receiver matching relationship matrix, a= { a mn |a mn ∈{0,1},m∈M,n∈N}，Representing a primary link minimum communication rate requirement; />M is larger than or equal to N, and the number of users is larger than the number of the IoT devices, so that a one-to-one matching relationship between the IoT devices and the receiver is ensured, and the information of each IoT device can be received; />The model characteristic constraints representing the reflection coefficients of the smart reflective surfaces.
As a further description of the present application, the step S3 includes the steps of:
s3.1: converting the total rate maximization model into an internal model and an external model, wherein the internal model aims at acquiring the optimal information transmission rate between the IoT device and the corresponding user machine, and the external model aims at maximizing the total rate of the internet of things link of the symbiotic communication system;
s3.2: solving the internal model by adopting a semi-definite relaxation algorithm to obtain an optimal reflection coefficient matrix phi * ；
S3.3: according to the reflection coefficient matrix phi * Acquiring optimal information transmission rate between each IoT device and corresponding user machine
S3.4: solving the external model by adopting a Hungary algorithm to obtain an optimal user machine matching relation matrix A * The optimal user machine matching relation matrix A * And maximizing the total rate of the internet of things links of the symbiotic communication system.
As a further description of the present application, the internal model is:
P1:
wherein phi is H The IRS phase shift matrix is transposed to the conjugate of the vectorized representation.
As a further description of the present application, the step S3.2 includes the steps of:
converting the internal model by adopting a semi-definite relaxation algorithm to obtain an equivalent model;
converting the equivalent model into a semi-definite programming model;
obtaining an approximate solution psi of the semi-definite programming model by a Gaussian randomization method * ；
Using the approximation solution ψ * Establishing a reflection coefficient matrix phi * 。
As a further description of the present application, the external model is:
P2:
wherein H is x Representing an intermediate variable generated during a mathematical transformation.
On the other hand, the application provides an internet of things link optimization system combining an IRS technology and an SR technology, which comprises the following components: a base station, a smart reflective surface having a plurality of transmitting units, a plurality of receivers, and a plurality of IoT devices; the intelligent reflecting surface is connected with an intelligent controller; between the base station and the receiver via channel h Bm A communication connection between the base station and the IoT device via a channel h Bn A communication connection between the receiver and the IoT device via a channel h mn Communication connection, the intelligent reflecting surface and the base station pass through a channel h Rm A communication connection, the smart reflective surface and the IoT device being connected by a channel h Rn And a communication connection.
Compared with the prior art, the application has the following advantages and beneficial effects: the Internet of things link optimization method and system combining the IRS technology and the SR technology provided by the embodiment of the application utilize the advantages of high spectrum efficiency, high energy efficiency and low cost of the symbiotic radio and intelligent reflecting surface, so that the secondary links share not only the spectrum of the main link, but also a series of infrastructures such as a transmitter, a receiver and the like of the main link; and the channels among the base station, the receiver and the IoT device are dynamically adjusted through the introduced intelligent reflecting surface, so that useful signals are enhanced, interference signals are suppressed, and the two are matched with each other, thereby effectively solving the problems that massive IoT devices occupy a large amount of frequency spectrum resources in a symbiotic communication system in a multi-user scene, weakening the information transmission rate and influencing the communication of cellular users. Also, ioT devices are passive, generating no additional energy resource consumption. In addition, the method for optimizing the Internet of things link introduces a strategy and a model resolving method for user matching, and a receiver which can reach the optimal rate is matched for each internet of things device in a multi-user scene, so that the total rate of information transmission of the Internet of things reaches the maximum on the premise of meeting the minimum transmission rate requirement of a main link.
Drawings
In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present application, the drawings that are needed in the examples will be briefly described below, it being understood that the following drawings only illustrate some examples of the present application and therefore should not be considered as limiting the scope, and that other related drawings may be obtained from these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of a structure and components of an internet of things link optimization system model provided by an embodiment of the application;
fig. 2 is a schematic structural diagram of a relationship diagram between an internet of things link and a rate and a base station transmitting power and a minimum rate threshold of a main link, which are provided by an embodiment of the present application;
fig. 3 is a diagram of a change relationship between an internet of things link and a rate, a base station transmitting power and a main link minimum rate threshold, which are provided by an embodiment of the present application;
fig. 4 is a change relation between the link and the rate of the internet of things and the transmitting power of the base station and the number of reflecting units on the intelligent reflecting surface, which are provided by the embodiment of the application;
fig. 5 is a change relation between an internet of things link and a rate, a base station transmitting power and a user matching algorithm provided by the embodiment of the application;
fig. 6 is a change relation between an internet of things link and a rate and a base station transmitting power and an intelligent reflection surface phase shift discrete quantization bit number, which are provided by the embodiment of the application.
Detailed Description
For the purpose of making apparent the objects, technical solutions and advantages of the present application, the present application will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present application and the descriptions thereof are for illustrating the present application only and are not to be construed as limiting the present application.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one of ordinary skill in the art that: no such specific details are necessary to practice the application.
Throughout the specification, references to "one embodiment," "an embodiment," "one example," or "an example" mean: a particular feature, structure, or characteristic described in connection with the embodiment or example is included within at least one embodiment of the application. Thus, the appearances of the phrases "in one embodiment," "in an example," or "in an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combination and/or sub-combination in one or more embodiments or examples. Moreover, those of ordinary skill in the art will appreciate that the illustrations provided herein are for illustrative purposes and that the illustrations are not necessarily drawn to scale. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
Examples
The embodiment provides a link aiming at the problem of how to enhance the transmission rate of the Internet of things link in the multiuser symbiotic communication systemThe method for optimizing the link of the internet of things combining the IRS technology and the SR technology is implemented in a system shown in the following figure 1, wherein the system consists of a base station BS provided with single antennas, an intelligent reflecting surface with the number of transmitting units of Q, M users with single antennas and N passive IoT devices with single antennas. Wherein the intelligent reflecting surface is connected with an intelligent controller, and the reflecting coefficients (including amplitude and phase shift) of each unit of the intelligent reflecting surface can be dynamically adjusted to intelligently reconstruct the wireless communication channel environment; between the base station and the receiver via channel h Bm A communication connection between the base station and the IoT device via a channel h Bn A communication connection between the receiver and the IoT device via a channel h mn Communication connection, the intelligent reflecting surface and the base station pass through a channel h Rm A communication connection, the smart reflective surface and the IoT device being connected by a channel h Rn And a communication connection.
The link optimization method of the Internet of things comprises the following steps:
step 1: the base station transmitting the primary link signal to the plurality of receivers, the intelligent reflective surface, and the plurality of IoT devices;
step 2: the intelligent reflection surface receives a main link signal sent by the base station, and the intelligent reflection surface reconstructs the channel environment from the base station to the plurality of receivers, and after the reconstruction is completed, the received main link signal is sent to the plurality of receivers; the intelligent reflection surface reconstructs channel environments from the base station to the plurality of IoT devices, and after the reconstruction is completed, the received main link signals are sent to the receiver and the plurality of IoT devices;
step 3: the plurality of IoT devices receive internet of things information, and main link information sent by the base station and the intelligent reflective surface; the plurality of IoT devices modulate the internet of things information onto the received primary link information and transmit the modulated information to the plurality of receivers.
According to the Internet of things link optimization method combining the IRS technology and the SR technology, firstly, aiming at the problems of large amount of spectrum resource waste caused by accessing a wireless communication network by a large amount of IoT devices and spectrum resource occupation affecting normal communication between cellular users, the problem of large amount of spectrum resource occupation by the large amount of IoT devices is optimized by utilizing the symbiotic radio technology: the base station transmits main link information to a plurality of receivers (users) in a time division multiple access mode; distributing a plurality of IoT devices (internet of things devices) in each time slot to work cooperatively with corresponding receivers, and sending main link information to the IoT devices by the base station; the method comprises the steps that an internet of things (IoT) device receives main link information and internet of things information sent by a base station, modulates the received internet of things information onto the main link information by adopting binary phase shift keying, and then reflects the modulated information to a receiver; the modulated information is decoded by adopting a continuous interference elimination technology at the receiver to obtain the information of the Internet of things (all from the IoT device) and the main link information (the information directly sent to the receiver by the base station BS, the information reflected by the intelligent reflection surface and the main link information decoded from the information of the internet of things reflected to the receiver by the IoT device), thereby achieving the purpose of information communication of the internet of things. On this basis, the problems that the distance between the base station and the receiver and the IoT device is far and a certain obstacle shielding exists are considered, so that a mode of combining the intelligent reflection surface technology with the symbiotic radio technology is adopted, and the intelligent reflection surface is utilized to reconstruct the channel environment between the base station and the receiver and between the base station and the IoT device: the main link information sent by the base station reconstructs the channel environment through the intelligent reflection surface in a mode of dynamically regulating and controlling the reflection coefficient of each unit, and then the intelligent reflection surface sends the main link information to the receiver and the IoT device through the reconstructed channel, so that the purposes of enhancing useful signals, suppressing interference signals, protecting safety privacy and the like are achieved.
The advantages of high spectrum efficiency, high energy efficiency and low cost of symbiotic radio and intelligent reflecting surfaces are utilized by combining the IRS technology and the SR technology, so that secondary links share not only the spectrum of a main link, but also a series of infrastructures such as a transmitter, a receiver and the like of the main link; and the channels among the base station, the receiver and the IoT device are dynamically adjusted through the introduced intelligent reflecting surface, so that useful signals are enhanced, interference signals are suppressed, and the two are matched with each other, thereby effectively solving the problems that massive IoT devices occupy a large amount of frequency spectrum resources in a symbiotic communication system in a multi-user scene, weakening the information transmission rate and influencing the communication of cellular users. Also, ioT devices are passive, generating no additional energy resource consumption.
In the above step 3, the information received by the mth receiver (hereinafter referred to as "user") after modulation may be expressed as:
where p represents the base station transmit power,represents in order the channel between the base station to the mth user, the base station to the nth IoT device, and the mth user to the nth IoT device, Φ=diag { Φ } 1 ,φ 2 ,…,φ Q -representing a matrix of reflection coefficients of the smart reflective surface, < ->Sequentially representing the channel between the smart reflective surface to the mth user and the smart reflective surface to the nth IoT device,/for>Representing the channel from the base station to the smart reflective surface x m Information symbol, alpha, representing the transmission of the base station to the mth receiver n Representing the reflection coefficient of the nth IoT device and taking a value between 0-1 c n Information symbol, μ representing the nth IoT device sent to the mth receiver m Representing power sigma 2 Additive white gaussian noise of zero mean value of (c).
As can be seen from equation (1), the signal received at the mth user is divided into two parts: the method comprises the steps of main link information and Internet of things link information. In order to ensure that a user can accurately decode main link information and Internet of things information, it is assumed that an information symbol c transmitted by an Internet of things link n Is much longer than the symbol period of the main link information symbol x m Symbol period, thusThe Internet of things signal can be used as one multipath component of the main link signal to decode the main link information, so that the characteristics of the reciprocal symbiosis of the main link and the secondary link of the SR system are reflected.
It is thus possible to obtain a product,
the main link information received at the user is:
on the one hand, since information reflected by IoT devices to users employs BPSK binary phase shift keying, c n Only 2 possible values of 0 or 1; on the other hand, information c sent by the nth IoT device n = {0,1} is unknown to the user, and the system allows only one user to receive information from one IoT device, which is available according to shannon's formula,
the transmission rate of the main link information received by the user is expected to be:
in this embodiment, the user decodes the internet of things information reflected by the IoT device in a continuous interference cancellation manner, and because the symbol period of the internet of things information is far greater than the symbol period of the main link information, it is assumed that the symbol period of the internet of things information is K times the symbol period of the main link information, therefore, considering that the user can completely decode the internet of things information,
the internet of things information transmitted by the nth IoT device and received by the user is:
due to the main link signal x m Usually obeys complex gaussian distribution, so equation (4) can also be expressed as:
from the shannon formula and the characteristics of the symbiotic radio system, one can obtain
The transmission rate of the internet of things information between the mth user and the nth IoT device is:
in the above formulas (2) to (6), a m,n Representing a matching relationship between the mth user and the nth IoT device (0 representing no match, 1 representing a match), K representing a symbol period of the internet of things information being K times a symbol period of the main link information.
Further, the method comprises the steps of,
the method and the device for transmitting the link information of the Internet of things consider the problem of optimizing the transmission rate of the link information of the Internet of things on the basis of considering how to enhance the transmission of the link information of the Internet of things under a multi-user symbiotic system. As the receivers matched by the same IoT device are different, different channel environments and internet of things link transmission information can be generated. Therefore, the method for optimizing the link of the Internet of things also introduces a strategy of user matching, and matches a receiver which can reach the optimal rate for each internet of things (IoT) device in a multi-user scene, so that the total rate of information transmission of the Internet of things is maximized.
The link optimization method of the Internet of things further comprises the following steps:
s1: establishing a receiver matching relation matrix A;
s2: establishing a total rate maximization model of an Internet of things link of the symbiotic communication system according to the receiver matching relation matrix A and the reflection coefficient matrix phi, wherein the Internet of things link of the symbiotic communication system consists of a plurality of receivers;
s3: the target value of the total rate maximization model is optimized by jointly optimizing the smart reflection surface reflection coefficient (amplitude and phase) matrix Φ, and the user matching policy matrix a, to match the receiver that can reach the optimal rate for each IoT device.
In S2 above, the total rate maximization model is:
P:
s.t.
m∈1,2,…,M,n∈1,2,…,N， (7)
where M represents the number of clients in the symbiotic communication system, N represents the number of IoT devices in the symbiotic communication system, and the receiver matching relationship matrix a= { a mn |a mn ∈{0,1},m∈M,n∈N}，Representing a primary link minimum communication rate requirement; />M is larger than or equal to N, and the number of users is larger than the number of the IoT devices, so that a one-to-one matching relationship between the IoT devices and the receiver is ensured, and the information of each IoT device can be received; />The model characteristic constraints representing the reflection coefficients of the smart reflective surfaces.
Since the two matrices to be optimized, the intelligent reflecting surface reflection coefficient (amplitude and phase) matrix Φ and the user matching relation matrix Α, are coupled in the objective function and constraint, and the objective function has a logarithmic summation term, the problem P is a non-convex optimization problem. Therefore, in order to solve the non-convex optimization problem, two coupling variables are considered to be decoupled into two sub-problems, and a semi-definite relaxation algorithm and a Hungary algorithm are adopted to solve the problem respectively;
specifically, the step S3 may be implemented by:
s3.1: and converting the total rate maximization model into an internal model and an external model, wherein the internal model aims at acquiring the optimal information transmission rate between the IoT device and the corresponding user machine, and the external model aims at maximizing the total rate of the internet of things link of the symbiotic communication system.
S3.2: solving the internal model by adopting a semi-definite relaxation algorithm to obtain an optimal reflection coefficient matrix phi * ，Φ * And maximizing the speed between the mth user and the nth Internet of things device. Comprising the following steps:
(1) Converting the internal model by adopting a semi-definite relaxation algorithm to obtain an equivalent model;
(2) Converting the equivalent model into a semi-definite programming model;
(3) Obtaining an approximate solution psi of the semi-definite programming model by a Gaussian randomization method * ；
(4) Using the approximation solution ψ * Establishing a reflection coefficient matrix phi * 。
S3.3: according to the reflection coefficient matrix phi * Solving the optimal information transmission rate between each IoT device and the corresponding user machineWill->As a weighted value between the mth user and the nth internet of things device.
S3.4: and converting the solving problem of the external model into a weighted bipartite matching problem. As shown in fig. 2, the external model is solved by using a hungarian algorithm to obtain an optimal user machine matching relation matrix a * The optimal user machine matching relation matrix A * And maximizing the total rate of the internet of things links of the symbiotic communication system. And then obtaining the link joint optimization scheme of the Internet of things of the symbiotic communication system by the intelligent reflection surface reflection coefficient matrix phi and the user matching strategy matrix alpha.
The specific optimization process is as follows:
aiming at the problem of optimizing the reflection coefficient matrix phi of the intelligent reflecting surface internally, the objective function of (7) is converted, namely, H is led to Ri ＝diag{h Ri },i＝n,m；φ＝[φ 1 ,...,φ Q ] T The objective function P of equation (7) can be converted into:
wherein phi is H The IRS phase shift matrix is transposed to the conjugate of the vectorized representation.
The following transformations were then performed on equation (7-1) using the semi-definite relaxation algorithm:
let h Bmn ＝h Bn h mn ,H BRn ＝H Rn h BR h mn ；
h 1 ＝h Bm +α n c n h Bn h mn ；
H 2 ＝H Rm h BR +α n c n H Rn H BR h mn ；
Then formula (7-1) can be further simplified to:
introducing an auxiliary variable t, and enabling:
then formula (7-1-1) can be further converted into:
next define ψ=ψ ψ H And obeys ψ > =0, rank (ψ) =1,
then formula (7-1-2) may be further converted into:
after conversion by the semi-definite relaxation algorithm, the constraint with rank 1 of formula (7-1-3) is abandoned, and then a typical semi-definite programming SDP problem is converted, the problem can be effectively solved by a CVX toolkit, but the solved rank may not be 1, so that the approximate solution ψ of formula (7-1-3) is obtained by Gaussian randomization technology GRT * Thereby obtaining the maximum transmission rate between the mth user and the nth IoT device
In addition, in the case of the optical fiber,
the external model is:
wherein H is x Representing an intermediate variable generated during a mathematical transformation.
The problem can be effectively solved by adopting the Hungary algorithm, so that the optimal user matching strategy A is obtained * 。
Further, the method comprises the steps of,
in order to verify the superiority of the multiuser symbiotic communication system in the link and the rate performance of the internet of things, another 2 reference systems are designed as comparison, and the reference systems are respectively: irs enhancements but not optimized user matching policies; 2. no IRS enhances but optimizes the user matching policy.
The system performance superiority of the application is verified by the simulation experiment. The system simulation parameters were set as follows: user number m=6, iot device number n=3, intelligent reflective surface reflection unitThe number is between 25 and 100, the base station BS transmitting power p=0 to 30dBm, and the additive Gaussian white noise power is sigma 2 = -114dbm, iot device reflection coefficients are all set to 0.5, a multiple k=128 between the main link information symbol period and the internet of things link information symbol period; smart reflective surface-related channel: base station BS to IRS, IRS to user, and IRS to Internet of things device channel h BR ,h Rm ,h Rn Modeling is carried out as a rice fading channel, the rice factor is 10, and the large-scale path loss is respectively set to 10 -3 d -2.2 ，10 -3 d -2.1 ，10 -3 d -2.3 (d is the distance between two nodes, the unit is meter), and the distances are respectively 50m, 0-10 m and 0-10 m;
base station BS to user, BS to IoT device, and channel h between IoT device to user Bm ,h Bn ,h mn Modeled as Rayleigh fading channels, the large-scale path loss is respectively set to 10 -3 d -3.7 ，10 -3 d -3.6 ，10 -3 d -2 The distances are 40-60 m, 40-60 m and 0-20 m respectively.
Fig. 3 shows a variation relationship between the link rate of the internet of things of the SR system and the BS transmit power and the minimum rate threshold of the main link. Firstly, it can be seen that, as the BS base station transmitting power p increases, the internet of things link rate of the multi-user symbiotic communication system gradually increases; when the main link rate constraint threshold is higher, the link rate of the Internet of things of the system is lower; secondly, when the main link rate threshold value is uniformly increased, the system performance is not uniformly reduced, and when the base station transmitting power is about 20dBm, the main link rate threshold value is between 6 and 10bps/Hz, and the influence on the system performance is larger; finally, by comparing the symbiotic communication system without IRS assistance and with the optimized user matching strategy, the IRS assistance scheme can be proved to be capable of improving the link rate of the Internet of things of the SR system by nearly one time.
FIG. 4 shows the variation relationship between the Internet of things link and rate of the system and the BS transmit power and the number of reflective units of the intelligent reflective surface when the main link rate threshold is 3 bps/Hz. It can be seen that when the number of IRS units is only 25, the internet of things link and rate of the system are still doubled compared with those of a multi-user SR system without IRS enhancement but with optimized user matching policy, and the system performance is gradually increased as the number of IRS units is increased.
Fig. 5 shows the variation relationship between the internet of things link and rate of the system and BS transmit power and user matching algorithm when the main link rate threshold is 3 bps/Hz. It can be seen that under three conditions of no IRS enhancement, IRS enhancement with IRS unit number of 25, and IRS enhancement with IRS unit number of 50, the optimization scheme of the user matching strategy by adopting the hungarian algorithm is better than the fixed matching scheme by more than 50%; and the more IRS units, the better the system performance.
FIG. 6 shows the variation relationship between the Internet of things link and rate of the system and the BS transmit power and the intelligent reflective surface phase shift discrete quantization bit number when the main link rate threshold is 3 bps/Hz. Since in practical applications the phase of the intelligent reflecting surface cannot be continuously adjusted, continuous phase values are quantized to a given discrete phase according to a fixed range for contrasting performance in a real scene. As can be seen from the graph, even if the quantization bit is 1, that is, the phase value of each unit of the IRS is adjusted only in two states, the performance of the system can be improved by more than one time compared with the scheme of optimizing the user matching strategy without the IRS assistance, and when the number of the quantization bits is 3, the performance of the system approaches to the performance of the continuous phase system.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the application, and is not meant to limit the scope of the application, but to limit the application to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the application are intended to be included within the scope of the application.

Claims (2)

1. The Internet of things link optimization method combining the IRS technology and the SR technology is characterized by comprising the following steps:
the base station transmitting the primary link signal to the plurality of receivers, the intelligent reflective surface, and the plurality of IoT devices;
the intelligent reflection surface receives a main link signal sent by the base station, and the intelligent reflection surface reconstructs the channel environment from the base station to the plurality of receivers, and after the reconstruction is completed, the received main link signal is sent to the plurality of receivers; the intelligent reflection surface reconstructs channel environments from the base station to the plurality of IoT devices, and after the reconstruction is completed, the received main link signals are sent to the receiver and the plurality of IoT devices;
the plurality of IoT devices receive internet of things information, and main link information sent by the base station and the intelligent reflective surface; the plurality of IoT devices modulate the internet of things information onto the received primary link information and send the modulated information to the plurality of receivers;
the modulated information received by the receiver is:
where p represents the base station transmit power,represents in order the channel between the base station to the mth user, the base station to the nth IoT device, and the mth user to the nth IoT device, Φ=diag { Φ } 1 ,φ 2 ,…,φ Q -representing a matrix of reflection coefficients of the smart reflective surface, < ->Sequentially representing the channel between the smart reflective surface to the mth user and the smart reflective surface to the nth IoT device,/for>Representing the channel from the base station to the smart reflective surface x m Information symbol, alpha, representing the transmission of the base station to the mth receiver n Representing the reflection coefficient of the nth IoT device and taking a value between 0-1 c n Information symbol, μ representing the nth IoT device sent to the mth receiver m Representing power sigma 2 Is a zero-mean additive white gaussian noise;
the main link information received by the receiver is:
the transmission rate of the main link information received by the receiver is expected to be:
the internet of things information received by the receiver is:
or->
The transmission rate of the internet of things information received by the receiver is as follows:
wherein a is m,n Representing a matching relationship between an mth user and an nth IoT device, wherein K represents that a symbol period of the internet of things information is K times a symbol period of the main link information;
the method also comprises the following steps:
s1: establishing a receiver matching relation matrix A;
s2: establishing a total rate maximization model of an Internet of things link of the symbiotic communication system according to the receiver matching relation matrix A and the reflection coefficient matrix phi, wherein the Internet of things link of the symbiotic communication system consists of a plurality of receivers;
s3: matching a receiver capable of achieving an optimal rate for each IoT device to optimize a target value of a total rate maximization model;
s3.1: converting the total rate maximization model into an internal model and an external model, wherein the internal model aims at acquiring the optimal information transmission rate between the IoT device and the corresponding user machine, and the external model aims at maximizing the total rate of the internet of things link of the symbiotic communication system;
s3.2: solving the internal model by adopting a semi-definite relaxation algorithm to obtain an optimal reflection coefficient matrix phi * ；
The method comprises the following steps:
converting the internal model by adopting a semi-definite relaxation algorithm to obtain an equivalent model;
converting the equivalent model into a semi-definite programming model;
obtaining an approximate solution psi of the semi-definite programming model by a Gaussian randomization method * ；
Using the approximation solution ψ * Establishing a reflection coefficient matrix phi * ；
S3.3: according to the reflection coefficient matrix phi * Acquiring optimal information transmission rate between each IoT device and corresponding user machine
S3.4: solving the external model by adopting a Hungary algorithm to obtain an optimal user machine matching relation matrix A * The optimal user machine matching relation matrix A * Maximizing total rate of internet of things links for symbiotic communication system
The total rate maximization model is:
P:
m∈1,2,…,M,n∈1,2,…,N，
where M represents the number of clients in the symbiotic communication system, N represents the number of IoT devices in the symbiotic communication system, and the receiver matching relationship matrix a= { a mn |a mn ∈{0,1},m∈M,n∈N}，Representing a primary link minimum communication rate requirement; />Representing that one receiver only receives information from one IoT device, and the number of users is greater than the number of IoT devices, for ensuring a one-to-one matching relationship between the IoT devices and the receiver, and ensuring that the information of each IoT device can be received; />A model characteristic constraint representing a reflection coefficient of the intelligent reflective surface;
the internal model is:
wherein phi is H A conjugate transpose of the vectorized representation of the IRS phase shift matrix;
the external model is as follows:
wherein H is x Representing intermediate variables generated during mathematical transformations.
2. An optimization system for an internet of things link optimization method based on the joint IRS technology and SR technology of claim 1, comprising: a base station, a smart reflective surface having a plurality of transmitting units, a plurality of receivers, and a plurality of IoT devices; the intelligent reflecting surface is connected with an intelligent controller; between the base station and the receiver via channel h Bm A communication connection between the base station and the IoT device via a channel h Bn A communication connection between the receiver and the IoT device via a channel h mn A communication connection is formed between the intelligent reflecting surface and the base station through a channel h Rm A communication connection between the smart reflective surface and the IoT device via a channel h Rn And a communication connection.
CN202210198813.0A 2022-03-01 2022-03-01 Internet of Things link optimization method and system combining IRS technology and SR technology Active CN114554527B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210198813.0A CN114554527B (en) 2022-03-01 2022-03-01 Internet of Things link optimization method and system combining IRS technology and SR technology

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210198813.0A CN114554527B (en) 2022-03-01 2022-03-01 Internet of Things link optimization method and system combining IRS technology and SR technology

Publications (2)

Publication Number Publication Date
CN114554527A CN114554527A (en) 2022-05-27
CN114554527B true CN114554527B (en) 2023-09-12

Family

ID=81661280

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210198813.0A Active CN114554527B (en) 2022-03-01 2022-03-01 Internet of Things link optimization method and system combining IRS technology and SR technology

Country Status (1)

Country Link
CN (1) CN114554527B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20230066273A1 (en) * 2021-08-30 2023-03-02 Qualcomm Incorporated Methods for measuring and controlling utility of an intelligent reflective surface

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116390056B (en) * 2023-04-10 2024-05-24 重庆邮电大学 STAR-RIS-assisted vehicle networking SR system link optimization method

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR102192234B1 (en) * 2019-10-28 2020-12-17 성균관대학교 산학협력단 Communication method of wireless communication system including intelligent reflecting surface and an apparatus for the communication method
CN112532289A (en) * 2020-11-20 2021-03-19 电子科技大学 Multi-antenna multicast transmission method of symbiotic communication system based on intelligent reflection surface
WO2021109345A1 (en) * 2020-03-03 2021-06-10 Zte Corporation Method to modulate signals by reflecting surfaces
CN113055065A (en) * 2021-03-10 2021-06-29 中国矿业大学 Internet of things hidden beam forming design method based on intelligent reflector
CN113709855A (en) * 2021-08-26 2021-11-26 南京邮电大学 Bidirectional intelligent reflection unit selection method based on wireless communication and wireless charging of Internet of things
CN113723036A (en) * 2021-08-27 2021-11-30 杭州腓腓科技有限公司 Environment sensing positioning system and method based on reconfigurable intelligent reflecting surface
CN113825159A (en) * 2021-09-03 2021-12-21 重庆邮电大学 Wireless energy-carrying communication system robust resource allocation method based on intelligent reflector

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10355728B2 (en) * 2017-06-01 2019-07-16 Qualcomm Incorporated Cross-operator cross-link adjacent channel interference with dynamic TDD
CN111355520B (en) * 2020-03-10 2022-03-08 电子科技大学 Design method of intelligent reflection surface assisted terahertz safety communication system

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR102192234B1 (en) * 2019-10-28 2020-12-17 성균관대학교 산학협력단 Communication method of wireless communication system including intelligent reflecting surface and an apparatus for the communication method
WO2021109345A1 (en) * 2020-03-03 2021-06-10 Zte Corporation Method to modulate signals by reflecting surfaces
CN112532289A (en) * 2020-11-20 2021-03-19 电子科技大学 Multi-antenna multicast transmission method of symbiotic communication system based on intelligent reflection surface
CN113055065A (en) * 2021-03-10 2021-06-29 中国矿业大学 Internet of things hidden beam forming design method based on intelligent reflector
CN113709855A (en) * 2021-08-26 2021-11-26 南京邮电大学 Bidirectional intelligent reflection unit selection method based on wireless communication and wireless charging of Internet of things
CN113723036A (en) * 2021-08-27 2021-11-30 杭州腓腓科技有限公司 Environment sensing positioning system and method based on reconfigurable intelligent reflecting surface
CN113825159A (en) * 2021-09-03 2021-12-21 重庆邮电大学 Wireless energy-carrying communication system robust resource allocation method based on intelligent reflector

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
Secrecy Rate Maximization for Intelligent Reflecting Surface Aided SWIPT Systems;Wei Sun et al;《2020 IEEE/CIC International Conference on Communications in China (ICCC)》;全文 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20230066273A1 (en) * 2021-08-30 2023-03-02 Qualcomm Incorporated Methods for measuring and controlling utility of an intelligent reflective surface

Also Published As

Publication number Publication date
CN114554527A (en) 2022-05-27

Similar Documents

Publication Publication Date Title
CN114554527B (en) Internet of Things link optimization method and system combining IRS technology and SR technology
CN113098652B (en) Design method of intelligent reflection-surface-assisted power domain NOMA (non-operational amplifier) safety communication system
CN113949427B (en) Multi-user wireless network security energy efficiency optimization design method and system
CN113727405B (en) Method for improving safety rate of wireless communication system based on intelligent reflecting surface
CN109068382B (en) NOMA cross-layer power distribution method based on time delay QoS
Majidzadeh et al. Partially connected hybrid beamforming for large antenna arrays in multi-user MISO systems
CN113644933B (en) Adaptive direct sequence spread spectrum communication method, system and medium
CN116390056B (en) STAR-RIS-assisted vehicle networking SR system link optimization method
Raviteja et al. FD-NOMA enabled multiuser transmission with backscatter communication
Vu et al. STAR-RIS-enabled short-packet NOMA systems
Van et al. Intelligence reflecting surface-aided integrated data and energy networking coexisting D2D communications
Qian et al. Optimization design of RIS-assisted high-capacity visible light communications based on HDMA
Fang et al. Improper Gaussian signaling for IRS assisted multiuser SWIPT systems with hardware impairments
CN117202171A (en) Minimum safe rate resource allocation method for RIS (radio resource allocation system) auxiliary RSMA (RSMA) system
CN115038099B (en) RIS-NOMA uplink transmission method and device under non-ideal SIC
Yeganeh et al. Multi-BD symbiotic radio-aided 6G IoT network: Energy consumption optimization with QoS constraint approach
CN108337023B (en) Secure communication method based on multi-user multi-antenna communication system and secret beam forming design
CN114157392B (en) Optimization method for secure transmission of distributed IRS auxiliary communication system
CN113891286A (en) Secret communication performance optimization device and method for unmanned aerial vehicle auxiliary communication system
Wang et al. Multi-Functional RIS for Sum-Rate Maximization in Rate-Splitting Multiple Access Networks
CN115225164B (en) Non-orthogonal terahertz communication method and system based on intelligent reflecting surface
CN111446993B (en) Beam forming method for safety-oriented communication in cognitive radio network
CN117241260A (en) Multicast secret rate maximization method of intelligent reflection surface backscatter communication system
Kim Link selection based on switching between full‐duplex and half‐duplex modes
CN117650819A (en) Transmission control method and system based on intelligent reflecting surface

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant