CN115065391A - Resource allocation method for assisting multi-user transmission by multiple intelligent reflecting surfaces under URLLC - Google Patents

Resource allocation method for assisting multi-user transmission by multiple intelligent reflecting surfaces under URLLC Download PDF

Info

Publication number
CN115065391A
CN115065391A CN202210391602.9A CN202210391602A CN115065391A CN 115065391 A CN115065391 A CN 115065391A CN 202210391602 A CN202210391602 A CN 202210391602A CN 115065391 A CN115065391 A CN 115065391A
Authority
CN
China
Prior art keywords
intelligent reflecting
reflecting surface
user
optimal
base station
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.)
Pending
Application number
CN202210391602.9A
Other languages
Chinese (zh)
Inventor
张华�
吴洲
陈端云
王俊波
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.)
Southeast University
State Grid Fujian Electric Power Co Ltd
Original Assignee
Southeast University
State Grid Fujian Electric Power Co Ltd
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 Southeast University, State Grid Fujian Electric Power Co Ltd filed Critical Southeast University
Priority to CN202210391602.9A priority Critical patent/CN115065391A/en
Publication of CN115065391A publication Critical patent/CN115065391A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/04013—Intelligent reflective surfaces
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0452—Multi-user MIMO systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
    • H04B7/0837—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using pre-detection combining
    • H04B7/0842—Weighted combining
    • H04B7/086—Weighted combining using weights depending on external parameters, e.g. direction of arrival [DOA], predetermined weights or beamforming
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • H04B7/145—Passive relay systems

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention provides a resource allocation method for assisting multi-user transmission by a plurality of intelligent reflecting surfaces under URLLC, which is suitable for an electric power regulation and control scene. Aiming at the correlation between the base station transmitting power and the intelligent reflecting surface and the user, the method for jointly optimizing the beam forming vector and the intelligent reflecting surface phase shift vector is provided, and specifically comprises the following steps: aiming at a user, establishing a system model of a base station and an intelligent reflecting surface; aiming at a plurality of users and a plurality of intelligent reflecting surfaces, the optimal association between the users and the intelligent reflecting surfaces is solved; according to the optimal correlation, the optimal beam forming vector and the optimal intelligent reflecting surface phase shift vector are obtained by taking the rate sum of the users as an optimization target; judging whether the iteration completion condition is met; if yes, obtaining the optimal beamforming vector according to singular value decomposition. The method optimizes the association between the intelligent reflecting surfaces and the users, and performs the optimal resource allocation of the system on the basis, thereby improving the total rate performance of the users and saving the frequency spectrum resources.

Description

Resource allocation method for assisting multi-user transmission by multiple intelligent reflecting surfaces under URLLC
Technical Field
The invention relates to a resource allocation method, in particular to a resource allocation method for assisting multi-user transmission by a plurality of intelligent reflecting surfaces under URLLC, which is suitable for the technical field of communication.
Background
The intelligent reflecting surface is a brand new revolutionary technology, and can intelligently reconfigure a wireless propagation environment by integrating a large number of low-cost passive reflecting elements on a plane, so that the performance of a wireless communication network is remarkably improved. The numerical calculation results show that compared with the traditional network only consisting of active devices, the application of the intelligent reflecting surface in a typical wireless network greatly improves the performance of the system.
The intelligent reflecting surface has great advantages compared with other relays. First, it does not use any active transmit module (e.g., power amplifier) and only reflects the received signal as a passive array, as compared to active wireless relaying, which assists source-destination communication through signal regeneration and retransmission. Furthermore, active relays are typically operated in half-duplex mode, and therefore their spectral efficiency is lower than intelligent reflective surfaces operated in full-duplex mode. Second, unlike conventional backscatter communications, such as Radio Frequency Identification (RFID) tags that communicate with a reader by identifying reflected signals transmitted from the reader, smart reflective surfaces are used to facilitate existing communication links, rather than transmitting any information of their own. Therefore, a reader in backscatter communication needs to implement self-interference cancellation at its receiver to decode the tag's message. In contrast, in IRS-assisted communication, both the direct-path signal and the reflected-path signal can carry the same useful information, and thus can be coherently added at the receiver to improve the decoded signal strength. Third, since the array architecture (passive versus active) and the operation mechanism (reflection versus transmission) of the intelligent reflective surface are different, it is also different from massive MIMO based on active surface.
The existing intelligent reflecting surface research is rarely combined with the URLLC, the improvement of the system performance of a single intelligent reflecting surface is basically considered, and the influence of a plurality of intelligent reflecting surfaces on the high reliability of the system is considered, so that the maximum user rate sum of the URLLC is improved. Due to the existence of a plurality of intelligent reflecting surfaces, the association of each user to the intelligent reflecting surface in the multi-user system is a problem to be solved.
Disclosure of Invention
The purpose of the invention is as follows: in view of the above technical problems, the present invention provides a method for associating a plurality of intelligent reflective surfaces with URLLC users and optimizing beamforming vectors.
The technical scheme adopted for realizing the technical purpose is as follows: a resource allocation method for assisting multi-user transmission by a plurality of intelligent reflecting surfaces under URLLC comprises the following specific steps:
step 1: aiming at URLLC users, a single base station and a plurality of intelligent reflecting surface communication system models are established, wherein a direct link between the base station and the URLLC is shielded by a barrier without consideration; the base station transmits the transmitted signals to the intelligent reflecting surface, an incidence matrix exists between the intelligent reflecting surface and the URLLC users, one user is connected with the associated intelligent reflecting surface, the signals transmitted by the base station are received through the gain of the intelligent reflecting surface, and the frequency spectrum utilization rate is improved;
step 2: jointly designing a beam forming vector of a base station and a phase shift vector of an intelligent reflecting surface according to the single base station and a plurality of intelligent reflecting surface communication system models in the step 1, optimizing the sum of all user rates on the ground according to optimal resource allocation, and simultaneously optimizing an optimal intelligent reflecting surface and a URLLC user incidence matrix, namely, one user is connected with one intelligent reflecting surface at most, and the intelligent reflecting surface can be connected with a plurality of users;
and step 3: the method comprises the steps of optimizing the sum of all URLLC user rates, simultaneously performing incidence matrix optimization and resource allocation to convert the sum into a convex problem easy to solve, specifically, optimizing an incidence matrix between an intelligent reflecting surface and URLLC users, optimizing a beam forming vector of a base station and a phase shift vector of the intelligent reflecting surface according to optimal incidence, and judging whether an iteration completion condition is met or not;
and 4, step 4: and if the iteration condition of the step 3 is met, converting the beam forming matrix and the phase shift vector matrix into the optimal beam forming vector and phase shift vector according to singular value decomposition, thereby completing the optimal resource allocation.
Preferably, the model of the communication system with the single base station and the plurality of intelligent reflection surfaces in step 1 is specifically:
let the system have an N T The base station with the number of the antennas comprises K single-antenna users under URLLC service and L intelligent reflecting surfaces, wherein M is arranged on the first intelligent reflecting surface l A reflective element. It is assumed that the direct link between the base station and the user is blocked by obstacles, which need not be taken into account.
Order to
Figure BDA0003595813240000021
Representing the channel between user k and IRSl, F l Indicating the channel between the base station and IRSl, phi l ＝diag(v l ) A reflection phase shift matrix representing the intelligent reflection surface, wherein
Figure BDA0003595813240000022
Then user k received signal may be represented as
Figure BDA0003595813240000023
Where w represents the beamforming vector of the base station and n k ～CN(0,σ 2 ) Is white Gaussian noise at the user, a kl It means that there is a connection between the ith IRS and the kth user, and one IRS can connect multiple users, and each user can be connected by only one IRS.
Preferably, the step 2 optimizes the sum of all user rates, and the specific steps are as follows:
step 2.1: according to the system model established in the step 1, the signal-to-interference-and-noise ratio of the user is
Figure BDA0003595813240000024
According to a short packet formula in a URLLC scene, the transmission rate of a user k is as follows:
Figure BDA0003595813240000025
step 2.2: the sum of the information rates of all URLLC users is taken as an optimization target, an optimization problem is established, the maximum power constraint of a base station is met, the rate of each user is greater than a certain rate threshold, the association constraint between the intelligent reflecting surface and the URLLC users and the phase shift vector constraint of the intelligent reflecting surface are carried out, namely:
Figure BDA0003595813240000026
wherein, B k Is the minimum transmission capacity, P, required by user k max Is the base station maximum power.
Preferably, the specific method for obtaining optimal resource allocation in step 3 includes:
step 3.1: since the optimization problem of step 2.2 is non-convex, the correlation matrix is first optimized, assuming that the beamforming vector and the phase shift vector are known, for the correlation matrix a, there will be a corresponding rate matrix u, which is:
Figure BDA0003595813240000031
the maximum value exists in each row of the calculated u, and the ith row is set as u ij Indicating that the ith user has the maximum rate when connecting with the jth IRS. Then take a in a ij The remaining elements in row i are 0. I.e. with an optimum a.
Step 3.2: on the basis of solving the correlation problem, the optimal selection mode a is assumed to be obtained, and the beam forming vector and the phase shift vector of the intelligent reflecting surface are continuously optimized. I amThey introduce an auxiliary variable χ k As the lower bound of the signal to interference plus noise ratio:
Figure BDA0003595813240000032
order to
Figure BDA0003595813240000033
Figure BDA0003595813240000034
Since V (χ) monotonically increases with respect to χ, V (χ) + t is equal to V (χ) max ) Similarly, has V k (χ k )+ζ k ＝V k (χ max,k ) Then the problem can be restated as:
Figure BDA0003595813240000035
step 3.3: for the molecular terms in the signal to interference and noise ratio, there are
Figure BDA0003595813240000036
Wherein the content of the first and second substances,
Figure BDA0003595813240000037
Figure BDA0003595813240000038
defining a relaxation optimization variable I k To constrain the upper bound of the sir denominator, then the problem is restated as:
Figure BDA0003595813240000041
step 3.4: DC planning and Taylor approximation of the non-convex constraint
Figure BDA0003595813240000042
Wherein f is 1 (χ k ,I k )＝0.5(χ k +I k ) 2 ，f 2 (χ k ,I k )＝0.5(χ k ) 2 +0.5(I k ) 2
Then to f 2 (χ k ,I k ) Performing taylor expansion, one can obtain:
Figure BDA0003595813240000043
for the same reason, have
Figure BDA0003595813240000044
We then perform an alternate iterative optimization of the two variables W and V, and when one of the variables is fixed,
Figure BDA0003595813240000045
the term is already convex for another variable, so in each iteration, we do a convex optimization on W and then on V.
Assuming that the variable V is fixed,
Figure BDA0003595813240000046
and fixing the optimized W, and solving the following problems:
Figure BDA0003595813240000051
to this end, the above two problems have been convex in the case of semi-positive relaxation, solved with the interior point method, and the optimal solutions W and V are obtained.
Preferably, the method for obtaining the optimal beamforming vector and the optimal phase shift vector by decomposing the optimal beamforming matrix and the optimal phase shift matrix by using the singular values in step 4 is as follows: using the formula
Figure BDA0003595813240000052
Wherein the content of the first and second substances,
Figure BDA0003595813240000053
τ * and
Figure BDA0003595813240000054
the eigenvalues of the optimal matrices V and W, respectively.
Has the advantages that:
1) the method combines the advantages of the intelligent reflecting surfaces with the advantages of the URLLC, improves the total rate performance of URLLC users, and provides an optimization scheme for the association of all the URLLC users and the intelligent reflecting surfaces;
2) the resource allocation method designed by the method of the invention maximizes the utilization efficiency of resources and improves the overall performance of the system;
3) the method of the invention provides a high-reliability performance technology of a communication system.
Drawings
FIG. 1 is a diagram of a user communication system with multiple intelligent reflective surfaces and URLLC constructed by the invention;
fig. 2 is a flowchart of a resource allocation method for multi-user transmission assisted by multiple intelligent reflection planes under URLLC.
Detailed Description
The following further describes embodiments of the present invention with reference to the accompanying drawings:
as shown in fig. 1 and 2, a resource allocation method for multiple intelligent reflection planes to assist multi-user transmission under URLLC of the present invention includes the following steps:
step 1: aiming at URLLC users, a single base station and a plurality of intelligent reflecting surface communication system models are established, wherein a direct link between the base station and the URLLC is shielded by a barrier without consideration; the base station transmits the transmitted signals to the intelligent reflecting surface, an incidence matrix exists between the intelligent reflecting surface and the URLLC users, one user is connected with the associated intelligent reflecting surface, the signals transmitted by the base station are received through the gain of the intelligent reflecting surface, and the frequency spectrum utilization rate is improved;
the single base station and a plurality of intelligent reflecting surface communication system models are specifically as follows:
let the system have an N T The base station with the number of the antennas comprises K single-antenna users under URLLC service and L intelligent reflecting surfaces, wherein M is arranged on the first intelligent reflecting surface l A reflective element. It is assumed that the direct link between the base station and the user is blocked by obstacles, which need not be taken into account.
Order to
Figure BDA0003595813240000061
Representing the channel between user k and IRSl, F l Indicating the channel between the base station and IRSl, phi l ＝diag(v l ) A reflection phase shift matrix representing the intelligent reflection surface, wherein
Figure BDA0003595813240000062
Then user k received signal may be represented as
Figure BDA0003595813240000063
Where w represents the beamforming vector of the base station and n k ～CN(0,σ 2 ) Is white Gaussian noise at the user, a kl It means that there is a connection between the ith IRS and the kth user, and one IRS can connect multiple users, and each user can be connected by only one IRS.
And 2, step: jointly designing a beam forming vector of a base station and a phase shift vector of an intelligent reflecting surface according to the single base station and a plurality of intelligent reflecting surface communication system models in the step 1, optimizing the sum of all user rates on the ground according to optimal resource allocation, and simultaneously optimizing an optimal intelligent reflecting surface and a URLLC user incidence matrix, namely, one user is connected with one intelligent reflecting surface at most, and the intelligent reflecting surface can be connected with a plurality of users;
optimizing the sum of all user rates, and specifically comprising the following steps:
step 2.1: according to the system model established in the step 1, the signal-to-interference-and-noise ratio of the user is
Figure BDA0003595813240000064
According to a short packet formula in a URLLC scene, the transmission rate of a user k is as follows:
Figure BDA0003595813240000065
step 2.2: the sum of the information rates of all URLLC users is taken as an optimization target, an optimization problem is established, the maximum power constraint of a base station is met, the rate of each user is greater than a certain rate threshold, and the association constraint and the intelligent reflecting surface phase shift vector constraint between the intelligent reflecting surface and the URLLC users are as follows:
Figure BDA0003595813240000066
wherein, B k Is the minimum transmission capacity, P, required by user k max Is the base station maximum power.
And step 3: the method comprises the steps of optimizing the sum of all URLLC user rates, simultaneously performing incidence matrix optimization and resource allocation to convert the sum into a convex problem easy to solve, specifically, optimizing an incidence matrix between an intelligent reflecting surface and URLLC users, optimizing a beam forming vector of a base station and a phase shift vector of the intelligent reflecting surface according to optimal incidence, and judging whether an iteration completion condition is met or not;
the specific method for obtaining the optimal resource allocation comprises the following steps:
step 3.1: since the optimization problem of step 2.2 is non-convex, the correlation matrix is first optimized, assuming that the beamforming vector and the phase shift vector are known, for the correlation matrix a, there will be a corresponding rate matrix u, which is:
Figure BDA0003595813240000071
the maximum value exists in each row of the calculated u, and the ith row is set as u ij Indicating that the ith user has the maximum rate when connecting with the jth IRS. Then take a in a ij 1, and the remaining elements in row i all take 0. I.e. with an optimum a.
Step 3.2: on the basis of solving the correlation problem, the optimal selection mode a is assumed to be obtained, and the beam forming vector and the phase shift vector of the intelligent reflecting surface are continuously optimized. We introduce the auxiliary variable χ k As the lower bound of the signal to interference plus noise ratio:
Figure BDA0003595813240000072
order to
Figure BDA0003595813240000073
Figure BDA0003595813240000074
Since V (χ) monotonically increases with respect to χ, V (χ) + t ═ V (χ) max ) Similarly, has V k (χ k )+ζ k ＝V k (χ max,k ) Then the problem can be restated as:
Figure BDA0003595813240000075
step 3.3: for the molecular terms in the signal to interference and noise ratio, there are
Figure BDA0003595813240000076
Wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003595813240000077
Figure BDA0003595813240000078
defining a relaxation optimization variable I k To constrain the upper bound of the sir denominator, then the problem is restated as:
Figure BDA0003595813240000081
step 3.4: DC planning and Taylor approximation of the non-convex constraint
Figure BDA0003595813240000082
Wherein f is 1 (χ k ,I k )＝0.5(χ k +I k ) 2 ，f 2 (χ k ,I k )＝0.5(χ k ) 2 +0.5(I k ) 2
Then to f 2 (χ k ,I k ) Performing taylor expansion, one can obtain:
Figure BDA0003595813240000083
in the same way, there are
Figure BDA0003595813240000084
We then perform an alternate iterative optimization of the two variables W and V, and when one of the variables is fixed,
Figure BDA0003595813240000085
the term is already convex for another variable, so in each iteration, we do a convex optimization on W and then on V.
Assuming that the variable V is fixed,
Figure BDA0003595813240000086
fixing the optimized W, and solving the following problems:
Figure BDA0003595813240000091
to this end, the above two problems have been convex in the case of semi-positive relaxation, solved with the interior point method, and the optimal solutions W and V are obtained.
And 4, step 4: and if the iteration condition of the step 3 is met, converting the beam forming matrix and the phase shift vector matrix into the optimal beam forming vector and phase shift vector according to singular value decomposition, thereby completing the optimal resource allocation.
The method for obtaining the optimal beamforming vector and the phase shift vector by decomposing the optimal beamforming matrix and the phase shift matrix by the singular value comprises the following steps: using the formula
Figure BDA0003595813240000092
Wherein the content of the first and second substances,
Figure BDA0003595813240000093
τ * and
Figure BDA0003595813240000094
representing the eigenvalues of the optimal matrices V and W, respectively.
In the experiment, in order to verify the performance of the calculation method, a scene is assumed to have 4 URLLC users and 2 intelligent reflecting surfaces, and the base station is located at the origin. And evaluating the performance of the algorithm of different base station transmitting powers and different intelligent reflecting surface reflection element numbers. And finally, comparing the algorithm with other reference algorithms, wherein the algorithm comprises a Shannon performance curve, an algorithm when the intelligent reflecting surface is in a random phase and an algorithm of an unoptimized incidence matrix.
It should be noted that, for those skilled in the art, without departing from the principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be construed as the protection scope of the present invention. All the components not specified in the present embodiment can be realized by the prior art.

Claims (5)

1. A resource allocation method for multi-intelligent reflecting surface assisted multi-user transmission under URLLC is characterized by comprising the following specific steps:
step 1: aiming at URLLC users, a single base station and a plurality of intelligent reflecting surface communication system models are established, wherein a direct link between the base station and the URLLC is shielded by a barrier without consideration; the base station transmits the transmitted signals to the intelligent reflecting surface, an incidence matrix exists between the intelligent reflecting surface and the URLLC users, one user is connected with the associated intelligent reflecting surface, the signals transmitted by the base station are received through the gain of the intelligent reflecting surface, and the frequency spectrum utilization rate is improved;
and 2, step: jointly designing a beam forming vector of a base station and a phase shift vector of an intelligent reflecting surface according to the single base station and a plurality of intelligent reflecting surface communication system models in the step 1, optimizing the sum of all user rates on the ground according to optimal resource allocation, and simultaneously optimizing an optimal intelligent reflecting surface and a URLLC user incidence matrix, namely, one user is connected with one intelligent reflecting surface at most, and the intelligent reflecting surface can be connected with a plurality of users;
and step 3: the method comprises the steps of optimizing the sum of all URLLC user rates, simultaneously performing incidence matrix optimization and resource allocation to convert the sum into a convex problem easy to solve, specifically, optimizing an incidence matrix between an intelligent reflecting surface and URLLC users, optimizing a beam forming vector of a base station and a phase shift vector of the intelligent reflecting surface according to optimal incidence, and judging whether an iteration completion condition is met or not;
and 4, step 4: and if the iteration condition of the step 3 is met, converting the beam forming matrix and the phase shift vector matrix into the optimal beam forming vector and phase shift vector according to singular value decomposition, thereby completing the optimal resource allocation.
2. The method according to claim 1, wherein the single base station and the plurality of intelligent reflective surfaces in step 1 are based on a communication system model including:
let the system have an N T The base station with the number of the antennas comprises K single-antenna users under URLLC service and L intelligent reflecting surfaces, wherein M is arranged on the first intelligent reflecting surface l A reflective element; it is assumed that the direct link between the base station and the user is blocked by an obstacle, without consideration;
order to
Figure FDA0003595813230000011
Representing the channel between user k and IRSl, F l Indicating the channel between the base station and IRSl, phi l ＝diag(v l ) A reflection phase shift matrix representing the intelligent reflection surface, wherein
Figure FDA0003595813230000012
Then user k receives a signal denoted as
Figure FDA0003595813230000013
Where w represents the beamforming vector of the base station and n k ～CN(0,σ 2 ) Is white Gaussian noise at the user, a kl It means that there is a connection between the ith IRS and the kth user, and one IRS can connect multiple users, and each user can be connected by only one IRS.
3. The method according to claim 2, wherein the step 2 of optimizing the sum of the rates of all users comprises the following specific steps:
step 2.1: according to the system model established in the step 1, the signal-to-interference-and-noise ratio of the user is
Figure FDA0003595813230000014
According to a short packet formula in a URLLC scene, the transmission rate of a user k is as follows:
Figure FDA0003595813230000021
step 2.2: the sum of the information rates of all URLLC users is taken as an optimization target, an optimization problem is established, the maximum power constraint of a base station is met, the rate of each user is greater than a certain rate threshold, the association constraint between the intelligent reflecting surface and the URLLC users and the phase shift vector constraint of the intelligent reflecting surface are carried out, namely:
Figure FDA0003595813230000022
wherein, B k Is the minimum transmission capacity, P, required by user k max Is the base station maximum power.
4. The method according to claim 3, wherein the method for obtaining optimal resource allocation in step 3 comprises:
step 3.1: since the optimization problem of step 2.2 is non-convex, the correlation matrix is first optimized, assuming that the beamforming vector and the phase shift vector are known, for the correlation matrix a, there will be a corresponding rate matrix u, which is:
Figure FDA0003595813230000023
in calculated uThere is a maximum value for each row, let i be u ij Indicating that the ith user has the maximum rate when connecting with the jth IRS; then take a in a ij 1, and the rest elements in the ith row are 0; namely, the optimal a;
step 3.2: on the basis of solving the correlation problem, assuming that an optimal selection mode a is obtained, and continuously optimizing a beam forming vector and a phase shift vector of the intelligent reflecting surface; introducing an auxiliary variable chi k As the lower bound of the signal to interference plus noise ratio:
Figure FDA0003595813230000024
order to
Figure FDA0003595813230000025
C k (χ k )＝log 2 (1+χ k ),
Figure FDA0003595813230000026
Since V (χ) monotonically increases with respect to χ, V (χ) + t ═ V (χ) max ) Similarly, has V k (χ k )+ζ k ＝V k (χ max,k ) Then, the problem is restated as:
Figure FDA0003595813230000031
step 3.3: for the molecular terms in the SINR, there are
Figure FDA0003595813230000032
Wherein the content of the first and second substances,
Figure FDA0003595813230000033
Figure FDA0003595813230000034
defining a relaxation optimization variable I k To constrain the upper bound of the signal to interference plus noise ratio denominator, the problem is then restated as:
Figure FDA0003595813230000035
step 3.4: DC planning and Taylor approximation of the non-convex constraint
Figure FDA0003595813230000036
Wherein, f 1 (χ k ,I k )＝0.5(χ k +I k ) 2 ，f 2 (χ k ,I k )＝0.5(χ k ) 2 +0.5(I k ) 2
Then to f 2 (χ k ,I k ) Taylor expansion was performed to obtain:
Figure FDA0003595813230000041
for the same reason, have
Figure FDA0003595813230000042
And then carrying out alternate iterative optimization on the two variables of W and V, when one variable is fixed,
Figure FDA0003595813230000043
the term is already convex for another variable, so in each iteration, convex optimization is performed on W first, and then convex optimization is performed on V;
assuming that the variable V is fixed,
Figure FDA0003595813230000044
and fixing the optimized W, and solving the following problems:
Figure FDA0003595813230000045
to this end, the above two problems have been convex problems in the case of semi-positive relaxation, solved with the interior point method, and the optimal solutions W and V are obtained.
5. The method according to claim 4, wherein the method for obtaining the optimal beamforming vector and the phase shift vector by singular value decomposition of the optimal beamforming matrix and the phase shift matrix in step 4 comprises: using the formula
Figure FDA0003595813230000046
Wherein the content of the first and second substances,
Figure FDA0003595813230000051
τ * and
Figure FDA0003595813230000052
representing the eigenvalues of the optimal matrices V and W, respectively.
CN202210391602.9A 2022-04-14 2022-04-14 Resource allocation method for assisting multi-user transmission by multiple intelligent reflecting surfaces under URLLC Pending CN115065391A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210391602.9A CN115065391A (en) 2022-04-14 2022-04-14 Resource allocation method for assisting multi-user transmission by multiple intelligent reflecting surfaces under URLLC

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210391602.9A CN115065391A (en) 2022-04-14 2022-04-14 Resource allocation method for assisting multi-user transmission by multiple intelligent reflecting surfaces under URLLC

Publications (1)

Publication Number Publication Date
CN115065391A true CN115065391A (en) 2022-09-16

Family

ID=83196941

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210391602.9A Pending CN115065391A (en) 2022-04-14 2022-04-14 Resource allocation method for assisting multi-user transmission by multiple intelligent reflecting surfaces under URLLC

Country Status (1)

Country Link
CN (1) CN115065391A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117135641A (en) * 2023-10-26 2023-11-28 国网冀北电力有限公司 Resource allocation method and device of RIS-based power fusion communication network

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117135641A (en) * 2023-10-26 2023-11-28 国网冀北电力有限公司 Resource allocation method and device of RIS-based power fusion communication network
CN117135641B (en) * 2023-10-26 2024-01-30 国网冀北电力有限公司 Resource allocation method and device of RIS-based power fusion communication network

Similar Documents

Publication Publication Date Title
CN111313951B (en) IRS (inter-Range instrumentation Standard) auxiliary secure communication wireless transmission method based on non-ideal CSI (channel State information)
CN113411115B (en) Intelligent reflection surface assisted millimeter wave physical layer security communication joint optimization method
CN113726383B (en) Intelligent reflection surface-assisted wireless communication system
US8059058B2 (en) Antenna system and method for operating an antenna system
US6917820B2 (en) Method and apparatus for selection and use of optimal antennas in wireless systems
CN113163325B (en) Wireless communication method and system assisted by multiple intelligent reflecting surfaces
Yatawatta et al. Energy efficient channel estimation in MIMO systems
US20080089267A1 (en) Multi-antenna upgrade for a transceiver
EP2710747A1 (en) Improved amplify-and-forward relaying in communication systems
CN102571179B (en) Based on the cross-layer optimizing method for designing of incomplete channel condition information in mimo system
Nguyen et al. Hybrid active-passive reconfigurable intelligent surface-assisted multi-user MISO systems
CN111277311B (en) Active and passive combined beam forming design method for millimeter wave symbiotic communication system
CN113490223A (en) Wireless energy supply backscattering communication method and system based on IRS
CN101505171A (en) Communication method based on bi-directional relay network coding system
CN115065391A (en) Resource allocation method for assisting multi-user transmission by multiple intelligent reflecting surfaces under URLLC
CN116033461B (en) Symbiotic radio transmission method based on STAR-RIS assistance
Fernández et al. Wireless Information and power transfer assisted by reconfigurable intelligent surfaces
CN106851771B (en) Energy efficiency optimization method of full-duplex D2D combining relay selection and power allocation
CN115734238A (en) Intelligent reflecting surface assisted communication network coverage optimization method
CN115037337A (en) Intelligent reflecting surface driven multi-user cooperative transmission method
Loskot et al. A unified approach to computing error probabilities of diversity combining schemes over correlated fading channels
CN115696437A (en) IRS-based user total rate maximization method of wireless energy transmission network
Singh et al. A NOMA-enabled hybrid RIS-UAV-aided full-duplex communication system
AV Performance analysis of power splitting SWIPT‐enabled full duplex cooperative NOMA system with direct link.
Samy et al. Multiple RIS-Assisted Cooperative NOMA with User Selection

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