CN113727371B - IRS (inter-Range instrumentation) assisted MEC (Multi-media communication) network wireless and computing resource allocation method and device - Google Patents

IRS (inter-Range instrumentation) assisted MEC (Multi-media communication) network wireless and computing resource allocation method and device Download PDF

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CN113727371B
CN113727371B CN202110904216.0A CN202110904216A CN113727371B CN 113727371 B CN113727371 B CN 113727371B CN 202110904216 A CN202110904216 A CN 202110904216A CN 113727371 B CN113727371 B CN 113727371B
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CN113727371A (en
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陈月云
陈广
邓韬玉
潘聪晗
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University of Science and Technology Beijing USTB
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
    • 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
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/06—Optimizing the usage of the radio link, e.g. header compression, information sizing, discarding information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/53—Allocation or scheduling criteria for wireless resources based on regulatory allocation policies
    • 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

Abstract

The invention discloses an IRS-assisted MEC network wireless and computing resource allocation method and a device, wherein the method comprises the following steps: establishing a wireless and computing resource allocation optimization model by taking the minimum user time delay and the energy consumption weighted sum as targets so as to optimize a signal decoding matrix, user transmitting power, an IRS phase shift coefficient matrix and MEC computing resources; decoupling is carried out on the optimization model by using a BCD algorithm, the optimization problem is divided into a plurality of different sub-problems, and the optimal values of a signal decoding matrix, user transmitting power, an IRS phase shift coefficient matrix and MEC computing resources are obtained through iterative optimization by using a continuous convex approximation technology in the sub-problem optimization process. The invention can solve the problem of the combined optimization of unloading delay and energy consumption in the MEC wireless network assisted by the IRS.

Description

IRS (inter-Range instrumentation) assisted MEC (Multi-media communication) network wireless and computing resource allocation method and device
Technical Field
The invention relates to the technical field of wireless communication, in particular to an IRS (inter-reference System) -assisted MEC (media independent component) network wireless and computing resource allocation method and device.
Background
Mobile Edge Computing (MEC) can effectively improve the performance of users handling compute-intensive traffic. However, when the communication link condition for task offloading is poor, the advantage of the MEC cannot be fully exploited. An Intelligent Reflective Surface (IRS) can enhance the direct signal of the receiver by adjusting the phase and amplitude of the reflected signal. Although the IRS can improve the quality of a communication link and reduce the calculation unloading transmission delay and energy consumption, the communication system model is more complex due to the addition of the IRS, the variable coupling is further enhanced, and the traditional resource allocation algorithm is not suitable for an IRS-assisted MEC network any more.
In IRS assisted MEC networks, existing work only optimizes latency or energy alone, while optimizing alone on the one hand does not guarantee performance on the other hand. In addition, the existing research work only optimizes one part of the uplink transmission power of the wireless terminal, the IRS phase coefficient matrix, the base station signal detection matrix and the edge server calculation resource allocation, and does not jointly optimize all the variables.
Disclosure of Invention
The invention provides an IRS-assisted MEC network wireless and computing resource allocation method and device, and aims to solve the technical problem that an existing resource allocation algorithm is not suitable for an IRS-assisted MEC network.
In order to solve the technical problems, the invention provides the following technical scheme:
in one aspect, the present invention provides an IRS-assisted MEC network radio and computing resource allocation method, including:
establishing a wireless and computing resource allocation optimization model by taking the minimum user time delay and the energy consumption weighted sum as targets; optimizing a signal decoding matrix, user transmitting power, an IRS phase shift coefficient matrix and MEC computing resources;
and decoupling the optimization model, splitting the optimization problem into a plurality of different sub-problems, and obtaining optimal values of a signal decoding matrix, user transmitting power, an IRS phase shift coefficient matrix and MEC calculation resources by iterative optimization by using a continuous convex approximation technology in the optimization process of the sub-problems.
Further, the expression of the optimization model is as follows:
Figure BDA0003200917710000021
s.t.0＜p i ≤P max,i ,i∈N
f si ＞0,i∈N
Figure BDA0003200917710000022
θ i ∈[0,2π],i∈M
Figure BDA0003200917710000023
/>
wherein s.t. represents a limiting condition;
Figure BDA0003200917710000024
n = {1, 2.., N } represents a user set; chi shape i A weight factor representing user i; beta is a ti ,β ei A preference factor, beta, representing user i for latency and energy consumption ti ,β ei Also has a unity normalizing effect, beta ti +β ei ＝1；t i Indicates an execution delay, and>
Figure BDA0003200917710000025
d i for the task size of user i, c i Computing resources, R, required for task execution by user i i (p,w i Θ) is the transmission rate at which user i offloads the task,
Figure BDA0003200917710000026
p is the user transmit power set, p = { p = { (p) 1 ,p 2 ,...,p n }，p i The transmit power for user i; p is max,i Represents the maximum transmission power of the user i, W is the set of signal detection vectors, W = [ W = [ [ W ] 1 ,w 2 ,...w n ]，w i Detects a vector for the signal of user i, and->
Figure BDA0003200917710000027
Denotes w i Is transposed and is present>
Figure BDA0003200917710000028
Is a diagonal matrix of IRS phase shift coefficients, theta i Is the phase of the ith reflection unit>
Figure BDA0003200917710000029
For a channel matrix of user i to the base station, <' >>
Figure BDA00032009177100000210
For the channel matrix of users i to IRS, <' >>
Figure BDA00032009177100000211
For the channel matrix from IRS to base station, <' >>
Figure BDA00032009177100000212
Representing a matrix of i x j, n 0 Is additive white gaussian noise; f. of s For the edge computing resource set allocated to the user, f s ＝{f s1 ,f s2 ,...,f sn }，f si Representing edge computing resources allocated to user i, F max Calculating the total amount of resources for the MEC server; e.g. of the type i Indicates execution energy consumption, based on the status of the system>
Figure BDA00032009177100000213
B denotes an uplink transmission bandwidth.
Further, decoupling the optimization model comprises:
and decoupling the optimization model by using a block coordinate descent BCD algorithm.
Further, the splitting the optimization problem into several different sub-problems and obtaining the optimal values of the signal decoding matrix, the user transmission power, the IRS phase shift coefficient matrix and the MEC calculation resources through iterative optimization by using the successive convex approximation technique in the sub-problem optimization process includes:
decomposing the optimization model into a computing resource allocation sub-problem and a communication resource allocation sub-problem; wherein, the first and the second end of the pipe are connected with each other,
the computing resource allocation sub-problem is:
Figure BDA0003200917710000031
s.t.f si ＞0,i∈N
Figure BDA0003200917710000032
the communication resource allocation sub-problem is:
Figure BDA0003200917710000033
s.t.0＜p i ≤P max,i ,i∈N
θ i ∈[0,2π],i∈M
Figure BDA0003200917710000034
aiming at the problem of the computing resource allocation sub-block, optimal computing resource allocation is obtained by utilizing a convex optimization theory
Figure BDA0003200917710000038
/>
Further, aiming at the communication resource allocation sub-problem, the BCD algorithm is continuously utilized to decompose the communication resource allocation sub-problem into a signal detection vector optimization sub-problem and a power-IRS phase optimization sub-problem; wherein, the first and the second end of the pipe are connected with each other,
the signal detection vector optimization sub-problem is as follows:
Figure BDA0003200917710000035
Figure BDA0003200917710000036
reducing the signal detection vector optimization subproblem into a characteristic value problem, and solving the characteristic value problem to obtain an optimal signal detection vector;
the power-IRS phase optimization sub-problem is as follows:
Figure BDA0003200917710000037
s.t.0＜p i ≤P max,i ,i∈N
θ i ∈[0,2π],i∈M
and continuously splitting the power-IRS phase optimization sub-problem into different sub-problems, converting the split sub-problems into convex problems by using a continuous convex approximation technology, and performing iterative optimization until convergence.
Further, the power-IRS phase optimization sub-problem is continuously divided into different sub-problems, the divided sub-problems are converted into convex problems by using a continuous convex approximation technology, and iterative optimization is carried out until convergence, wherein the method comprises the following steps:
transforming the power-IRS phase optimization sub-problem to a dual domain, transforming the power-IRS phase optimization sub-problem to a power-IRS phase optimization dual sub-problem, the power-IRS phase optimization dual sub-problem being:
Figure BDA0003200917710000041
s.t.0＜p i ≤P max,i ,i∈N
θ i ∈[0,2π],i∈M
wherein the content of the first and second substances,
Figure BDA0003200917710000042
and/or>
Figure BDA0003200917710000043
Lagrange dual variables and relaxation variables respectively;
transforming the power-IRS phase optimization dual sub-problem into a power-IRS phase optimization dual sub-problem by using a Lagrange dual reconstruction method, wherein the power-IRS phase optimization dual sub-problem is as follows:
Figure BDA0003200917710000044
s.t.0＜p i ≤P max,i ,i∈N
θ i ∈[0,2π],i∈M
where μ is the set of introduced auxiliary variables, μ = { μ = { [ μ ] 1 ,μ 2 ,...,μ n }；
Figure BDA0003200917710000045
Obtaining mu based on the power-IRS phase optimization dual reconstruction sub-problem i The optimum value of (c).
Further, continuously splitting the power-IRS phase optimization sub-problem into different sub-problems, converting the split sub-problems into convex problems by using a continuous convex approximation technology, and performing iterative optimization until convergence, and the method further comprises the following steps:
decomposing the power-IRS phase optimization dual reconstruction sub-problem into a power optimization sub-problem and an IRS optimization sub-problem by using a BCD algorithm and a quadratic transformation method; wherein the content of the first and second substances,
the power optimization sub-problem is:
Figure BDA0003200917710000046
s.t.0＜p i ≤P max,i ,i∈N
wherein, v is an introduced auxiliary variable set, v = { v 1 ,ν 2 ,...,ν n }；
Figure BDA0003200917710000047
Solving the power optimization sub-problem to obtain the optimal v, p;
the IRS optimization sub-problem is as follows:
Figure BDA0003200917710000048
s.t.θ i ∈[0,2π],i∈M
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA00032009177100000410
for the introduced auxiliary variable set, be>
Figure BDA0003200917710000049
Figure BDA0003200917710000051
Figure BDA0003200917710000052
Is->
Figure BDA00032009177100000511
Iteratively optimizes ≥ the conjugate value>
Figure BDA00032009177100000512
And theta, the optimum->
Figure BDA00032009177100000513
；
Fixing the device
Figure BDA00032009177100000514
And (3) optimizing theta, and converting the IRS optimization subproblem into an IRS phase iterative optimization subproblem by using a method for optimizing a minimum value, wherein the IRS phase iterative optimization subproblem is as follows:
Figure BDA0003200917710000053
s.t.|φ i |＝1,i∈N
wherein phi is an introduced substitute variable, phi = [ phi ] 1 ,...,φ m ]，
Figure BDA0003200917710000054
g(Φ|Φ t ) As a substitute function, phi t Is the value of the tth iteration;
Figure BDA0003200917710000055
Figure BDA0003200917710000056
Figure BDA0003200917710000057
Figure BDA0003200917710000058
Figure BDA0003200917710000059
is w i Solving the IRS phase iterative optimization sub-problem to obtain phi with t +1 th iterative optimization;
obtaining the t +1 th optimal theta based on the t +1 th optimal phi in an iterative manner;
for the optimization variables p, W, theta, f s Continuously iterating until convergence; wherein the convergence condition is as follows: delta is less than or equal to epsilon;
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA00032009177100000510
Y (t) is the tth iteration value of Y; ε is the threshold value at which iteration stops.
In another aspect, the present invention further provides an IRS-assisted MEC network wireless and computing resource allocation apparatus, including:
the wireless and computing resource allocation optimization model building module is used for building a wireless and computing resource allocation optimization model by taking the minimum user time delay and the weighted sum of energy consumption as targets; optimizing a signal decoding matrix, user transmitting power, an IRS phase shift coefficient matrix and MEC computing resources;
and the optimization solving module is used for decoupling the optimization model, splitting the optimization problem into a plurality of different sub-problems, and obtaining the optimal values of a signal decoding matrix, user transmitting power, an IRS phase shift coefficient matrix and MEC computing resources by iterative optimization by using a continuous convex approximation technology in the sub-problem optimization process.
In yet another aspect, the present invention also provides an electronic device comprising a processor and a memory; wherein the memory has stored therein at least one instruction that is loaded and executed by the processor to implement the above-described method.
In yet another aspect, the present invention also provides a computer-readable storage medium having at least one instruction stored therein, the instruction being loaded and executed by a processor to implement the above method.
The technical scheme provided by the invention has the beneficial effects that at least:
the wireless and computing resource allocation method provided by the invention combines the IRS technology, optimizes time delay and energy; the optimization variables are comprehensive and comprise uplink transmission power of the wireless terminal, an IRS phase coefficient matrix, a base station signal detection matrix and edge server calculation resource allocation; in the aspect of optimization algorithm, the problem is split into several different sub-problems by using the BCD algorithm, the non-convex problem is converted into the convex problem by using the continuous convex approximation technology in the sub-problem optimization process, and the closed solution of the optimization variable is obtained, so that the algorithm has high convergence speed and low complexity.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flowchart of an IRS assisted MEC network wireless and computing resource allocation method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an IRS assisted MEC network;
FIG. 3 is a diagram of a simulation scenario provided by an embodiment of the present invention;
FIG. 4 shows a task size d provided by an embodiment of the present invention i Impact on offload overhead is illustrated.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
First embodiment
The embodiment provides an IRS assisted MEC network wireless and computing resource allocation method, which may be implemented by an electronic device, where the electronic device may be a terminal or a server. The execution flow of the radio and computing resource allocation method of the IRS-assisted MEC network is shown in fig. 1, and includes the following steps:
s1, establishing a wireless and computing resource allocation optimization model by taking the minimized user time delay and energy consumption weighted sum as targets; optimizing a signal decoding matrix, user transmitting power, an IRS phase shift coefficient matrix and MEC computing resources;
and S2, decoupling the optimization model by using a BCD algorithm, splitting the optimization problem into a plurality of different sub-problems, and obtaining optimal values of a signal decoding matrix, user transmitting power, an IRS phase shift coefficient matrix and MEC computing resources by iterative optimization by using a continuous convex approximation technology in the sub-problem optimization process.
The method of the present embodiment will be described in detail with reference to fig. 2 to 4.
(1) Network model
As shown in fig. 2, the IRS assisted MEC network includes 1 k antenna cells equipped with MEC servers; 1 IRS with M reflection units, the set of reflection units being M = {1,2, ·, M }; n users, the set of users being N = {1,2,.., N }. The MEC server collects the mobile user task information requesting to be unloaded, channel information between the user and the base station, channel information between the user and the IRS, channel information between the IRS and the base station and the like.
(2) Communication model
Special symbol definition:
Figure BDA0003200917710000071
representing an i x j matrix, diag {. Denotes a diagonal matrix, [. Cndot.)] H Representation matrix [ ·]Is transposed and is present>
Figure BDA0003200917710000072
Represents the conjugation of (. Cndot.) (t) Represents (·) the value of the t-th iteration.
h d,i : the direct radiation of the channel is used,
Figure BDA0003200917710000073
h r,i : the user-to-IRS channel is set up,
Figure BDA0003200917710000074
g: the IRS-to-base station channel is,
Figure BDA0003200917710000075
θ: IRS angle of reflection, θ = [ ] 1 ,θ 2 ,...,θ m ]，
Figure BDA0003200917710000076
W: set of signal detection vectors, W = { W = { (W) 1 ,w 2 ,...w n }，
Figure BDA0003200917710000077
It is assumed that in the MEC system, each user has a task y i User i's task needs to be offloaded to edge execution with a binary < c i ,d i Is > where c i Computing resources required for task execution for user i, d i The task size of user i.
All users use the same frequency resource at the same time, and the transmission rate of the user i is as follows:
Figure BDA0003200917710000078
where p is the user transmit power set, p = { p = { (p) } 1 ,p 2 ,...,p n }，p i For the transmit power of user i, W is the set of signal detection vectors, W = { W = { (W) 1 ,w 2 ,...w n }，w i A vector is detected for the signal of user i,
Figure BDA00032009177100000712
denotes w i The conjugate transpose of (a) is performed,
Figure BDA0003200917710000079
is a diagonal matrix of IRS phase shift coefficients, theta i Is the phase of the ith reflection unit>
Figure BDA00032009177100000710
For a channel matrix of a subscriber i to a base station, based on a channel number in a subscriber number field>
Figure BDA00032009177100000711
For the channel matrix of users i to IRS, <' >>
Figure BDA0003200917710000081
Is the channel matrix from IRS to the base station, n 0 Is additive white gaussian noise. B denotes an uplink transmission bandwidth.
(3) System model
The unloading expense of the user i is defined as the weighted sum of the time delay and the energy consumption of the user i in the process of calculating unloading:
Y i ＝β ti t i +β ei e i
wherein, beta ti ,β ei A preference factor, beta, representing user i for latency and energy consumption ti ,β ei Also has a unity normalizing effect, beta ti +β ei ＝1；t i Indicating that the user i has performed a time delay,
Figure BDA0003200917710000082
e i indicates execution energy consumption, based on the status of the system>
Figure BDA0003200917710000083
The system aims at minimizing the unloading overhead of n users, and the system model is as follows:
Figure BDA0003200917710000084
s.t.0＜p i ≤P max,i ,i∈N
f si ＞0,i∈N
Figure BDA0003200917710000085
θ i ∈[0,2π],i∈M
Figure BDA0003200917710000086
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003200917710000087
χ i a weight factor representing user i; p max,i Represents the maximum transmission power of user i; f. of s ＝{f s1 ,f s2 ,...,f sn }，f si Representing edge computing resources allocated to user i, F max The total amount of resources is calculated for the MEC server.
(4) Algorithm
The system model is non-convex, and a Block Coordinate Descent (BCD) algorithm is used for decomposing the problem into a computing resource allocation sub-problem and a communication resource allocation sub-problem.
The computational resource allocation sub-problem is:
Figure BDA0003200917710000088
s.t.f si ＞0,i∈N
Figure BDA0003200917710000089
optimal computing resource allocation using convex optimization theory
Figure BDA00032009177100000810
The communication resource allocation sub-problem is:
Figure BDA0003200917710000091
s.t.0＜p i ≤P max,i ,i∈N
θ i ∈[0,2π],i∈M
Figure BDA0003200917710000092
because the communication resource allocation sub-problem is still not convex, the BCD algorithm is continuously utilized to decompose the communication resource allocation sub-problem into a signal detection vector optimization sub-problem and a power-IRS phase optimization sub-problem.
The signal detection vector optimization sub-problem is:
Figure BDA0003200917710000093
Figure BDA0003200917710000094
the signal detection direction quantum problem can be reduced into a characteristic value problem, the solving process of the problem is simple, and the optimal signal detection vector is easy to obtain.
The power-IRS phase optimization sub-problem is:
Figure BDA0003200917710000095
s.t.0＜p i ≤P max,i ,i∈N
θ i ∈[0,2π],i∈M
the sub-problem of power-IRS phase optimization is still a non-convex problem, the problem is solved by transforming to a dual domain, the sub-problem of power-IRS phase optimization is transformed to a sub-problem of power-IRS phase optimization dual, and the sub-problem of power-IRS phase optimization dual is as follows:
Figure BDA0003200917710000096
s.t.0＜p i ≤P max,i ,i∈N
θ i ∈[0,2π],i∈M
wherein the content of the first and second substances,
Figure BDA0003200917710000097
and/or>
Figure BDA0003200917710000098
Lagrange dual variables and relaxation variables, respectively.
The power-IRS phase optimization dual sub-problem is still a non-convex problem, and the power-IRS phase optimization dual sub-problem is converted into the power-IRS phase optimization dual reconstruction sub-problem by using a Lagrange dual reconstruction method. The power-IRS phase optimization dual reconstruction sub-problem is as follows:
Figure BDA0003200917710000101
s.t.0＜p i ≤P max,i ,i∈N
θ i ∈[0,2π],i∈M
where μ is the set of introduced auxiliary variables, μ = { μ = { [ μ ] 1 ,μ 2 ,...,μ n }；
Figure BDA0003200917710000102
Power-IRS phase optimization dual reconstruction sub-problem for relaxation variable μ i To solve the problem, μ can be obtained i The optimum value of (c).
The power-IRS phase optimization dual reconstruction sub-problem is still a non-convex problem about p and theta, and is decomposed into a power optimization sub-problem and an IRS optimization sub-problem by using a BCD algorithm and a quadratic transformation method.
The power optimization sub-problem is:
Figure BDA0003200917710000103
s.t.0＜p i ≤P max,i ,i∈N
wherein, v is an introduced auxiliary variable set, v = { v 1 ,ν 2 ,...,ν n }；
Figure BDA0003200917710000104
The power optimization subproblems are respectively for v and p as convex problems, so that the optimal v and p are easy to obtain.
The IRS phase optimization sub-problem is:
Figure BDA0003200917710000105
s.t.θ i ∈[0,2π],i∈M
wherein the content of the first and second substances,
Figure BDA0003200917710000106
for the introduced auxiliary variable set, be>
Figure BDA0003200917710000107
Figure BDA0003200917710000108
Figure BDA0003200917710000109
Is->
Figure BDA00032009177100001010
Iteratively optimizes ≥ the conjugate value>
Figure BDA00032009177100001014
And theta can be matched to obtain the optimal->
Figure BDA00032009177100001011
Fixing
Figure BDA00032009177100001015
And (3) optimizing theta, wherein the IRS phase optimization sub-problem is a non-convex problem, the IRS phase optimization sub-problem is converted into an IRS phase iterative optimization sub-problem by using a method for optimizing a minimum value, and the IRS phase iterative optimization sub-problem is as follows:
Figure BDA00032009177100001012
s.t.|φ i |＝1,i∈N
where phi is an introduced surrogate variable, phi = [ phi ] 1 ,...,φ m ]，
Figure BDA00032009177100001013
g(Φ|Φ t ) As a substitute function, phi t Is the value of the tth iteration;
g(Φ|Φ t )＝Φξ max I M Φ H -2Re{Φ(ξ max I M -Α)(Φ t ) H }+Φ t (ξ max I M -Α)(Φ t ) H +2Re{ΦΒ}-X；
Figure BDA0003200917710000111
Figure BDA0003200917710000112
Figure BDA0003200917710000113
Figure BDA0003200917710000114
is w i The sub-problem of IRS phase iterative optimization is solved to obtain the optimal phi of the t +1 iteration
Based on the t +1 th iteration optimal phi, the t +1 th optimal theta can be obtained
The above-mentioned optimized variables p, W, theta, f s Iterations are required until convergence.
The convergence conditions are as follows: delta is less than or equal to epsilon
Wherein the content of the first and second substances,
Figure BDA0003200917710000115
Y (t) is the tth iteration value of Y; ε is the threshold value at which iteration stops.
And the IRS and the mobile user obtain the optimization result of the MEC server through the base station, and accordingly execute corresponding unloading operation.
(5) Simulation (Emulation)
Simulation scenario As shown in FIG. 3, task size d i The impact on offload overhead is shown in fig. 4.
The coordinates of the base station are (0, 0), and the coverage area r of the base station 1 =100m, the number of base station antennas is 4;
IRS coordinate is (x) 1 ，0)，x 1 =150m, irs has 20 reflection units;
users are arbitrarily distributed in (x) 2 ，y 2 ) As a center, x 2 ＝200m，y 2 =150m, in r 2 =50m is the circle of radius, there are 4 users;
the path loss model is: l is a radical of an alcohol p ＝L 0 +10αlog 2 (d [m] ) Wherein L is 0 =30dB as reference distance of path loss, d [m] Alpha is the path loss factor, which is the distance between different devices. The path loss from the terminal to the base station, from the terminal to the IRS and from the IRS to the base station is set to be 3.5,2.1 and 2.1 respectively.
And (3) setting other parameters: c. C i ＝0.8Mcycles,i∈N；d i =500kb, i ∈ N; system bandwidth B =20MHz; noise n 0 ＝-100dBm；β ti ＝0.8,β ei =0.2,i ∈ N; MEC Server computing resource Total F max =700 Gcycles/s; maximum transmitting power P of user max,i ＝2W,i∈N。
In summary, the resource allocation method of the present embodiment combines the IRS technology, and optimizes the time delay and energy; the optimization variables are comprehensive and comprise uplink transmission power of the wireless terminal, an IRS phase coefficient matrix, a base station signal detection matrix and edge server calculation resource allocation; in the aspect of optimization algorithm, the problem is split into several different sub-problems by using the BCD algorithm, the non-convex problem is converted into the convex problem by using the continuous convex approximation technology in the sub-problem optimization process, and the closed solution of the optimization variable is obtained, so that the algorithm has high convergence speed and low complexity.
Second embodiment
The embodiment provides an IRS assisted MEC network wireless and computing resource allocation apparatus, including:
the wireless and computing resource allocation optimization model building module is used for building a wireless and computing resource allocation optimization model by taking the minimum user time delay and the weighted sum of energy consumption as targets; optimizing a signal decoding matrix, user transmitting power, an IRS phase shift coefficient matrix and MEC computing resources;
and the optimization solving module is used for decoupling the optimization model, splitting the optimization problem into a plurality of different sub-problems, and obtaining the optimal values of a signal decoding matrix, user transmitting power, an IRS phase shift coefficient matrix and MEC computing resources by iterative optimization by using a continuous convex approximation technology in the sub-problem optimization process.
The IRS-assisted MEC network radio and computing resource allocation apparatus of this embodiment corresponds to the IRS-assisted MEC network radio and computing resource allocation method of the first embodiment described above; the functions implemented by the functional modules in the IRS-assisted MEC network radio and computing resource allocation apparatus of this embodiment correspond to the flow steps in the IRS-assisted MEC network radio and computing resource allocation method of the first embodiment one to one; therefore, it is not described herein.
Third embodiment
The present embodiment provides an electronic device, which includes a processor and a memory; wherein the memory has stored therein at least one instruction that is loaded and executed by the processor to implement the method of the first embodiment.
The electronic device may have a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) and one or more memories, where at least one instruction is stored in the memory, and the instruction is loaded by the processor and executes the method.
Fourth embodiment
The present embodiment provides a computer-readable storage medium, in which at least one instruction is stored, and the instruction is loaded and executed by a processor to implement the method of the first embodiment. The computer readable storage medium may be, among others, ROM, random access memory, CD-ROM, magnetic tape, floppy disk, optical data storage device, and the like. The instructions stored therein may be loaded by a processor in the terminal and perform the above-described method.
Furthermore, it should be noted that the present invention may be provided as a method, apparatus or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media having computer-usable program code embodied in the medium.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It is further noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrases "comprising one of \ ...does not exclude the presence of additional like elements in a process, method, article, or terminal device that comprises the element.
Finally, it should be noted that while the above describes a preferred embodiment of the invention, it will be appreciated by those skilled in the art that, once having the benefit of the teaching of the present invention, numerous modifications and adaptations may be made without departing from the principles of the invention and are intended to be within the scope of the invention. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.

Claims (7)

1. An IRS-assisted MEC network wireless and computing resource allocation method, the IRS-assisted MEC network wireless and computing resource allocation method comprising:
establishing a wireless and computing resource allocation optimization model by taking the minimum user time delay and the energy consumption weighted sum as targets; optimizing the signal decoding matrix, user transmitting power, IRS phase shift coefficient matrix and MEC computing resource of the IRS-assisted MEC network;
decoupling the optimization model, splitting the optimization problem into several different sub-problems, and obtaining optimal values of a signal decoding matrix, user transmitting power, an IRS phase shift coefficient matrix and MEC calculation resources through iterative optimization by using a continuous convex approximation technology in the sub-problem optimization process;
the expression of the optimization model is as follows:
Figure FDA0004057596360000011
s.t.0＜p i ≤P max,i ,i∈N
f si ＞0,i∈N
Figure FDA0004057596360000012
θ i ∈[0,2π],i∈M
Figure FDA0004057596360000013
wherein s.t. represents a limiting condition;
Figure FDA0004057596360000014
representing a set of users; chi shape i A weight factor representing user i; beta is a ti ,β ei A preference factor, beta, representing user i for latency and energy consumption ti +β ei ＝1；t i Indicates an execution delay, and>
Figure FDA0004057596360000015
d i for the task size of user i, c i Computing resources, R, required for task execution by user i i (p,w i Θ) is the transmission rate at which user i offloads the task,
Figure FDA0004057596360000016
p is the user transmit power set, p = { p = { (p) 1 ,p 2 ,...,p n }，p i The transmit power for user i; p max,i Represents the maximum transmission power of the user i, W is the set of signal detection vectors, W = [ W = [ [ W ] 1 ,w 2 ,...w n ]，w i Detects a vector for the signal of user i, and->
Figure FDA0004057596360000017
Denotes w i In conjunction with the device, in conjunction with the device>
Figure FDA0004057596360000018
For the IRS phase shift coefficient diagonal matrix, θ i For the phase of the i-th reflection unit>
Figure FDA0004057596360000019
For a channel matrix of user i to the base station, <' >>
Figure FDA00040575963600000110
Channel matrices for users i to IRS, in combination>
Figure FDA0004057596360000021
For the channel matrix from IRS to base station, <' >>
Figure FDA0004057596360000022
Representing a matrix of i x j, n 0 Is additive white gaussian noise; f. of s For the edge computing resource set allocated to the user, f s ＝{f s1 ,f s2 ,...,f sn }，f si Representing edge computing resources allocated to user i, F max Calculating the total amount of resources for the MEC server; e.g. of the type i Which represents the energy consumption for the execution,
Figure FDA0004057596360000023
b denotes an uplink transmission bandwidth.
2. The IRS-assisted MEC network wireless and computing resource allocation method of claim 1, wherein decoupling the optimization model comprises:
and decoupling the optimized model by using a Block Coordinate Descent (BCD) algorithm.
3. The method of claim 2, wherein the splitting the optimization problem into several different sub-problems and obtaining the optimal values of the signal decoding matrix, the user transmission power, the IRS phase shift coefficient matrix, and the MEC computation resources through iterative optimization by using a successive convex approximation technique in the sub-problem optimization process comprises:
decomposing the optimization model into a computing resource allocation sub-problem and a communication resource allocation sub-problem; wherein the content of the first and second substances,
the computing resource allocation sub-problem is:
Figure FDA0004057596360000024
s.t.f si ＞0,i∈N
Figure FDA0004057596360000025
the communication resource allocation sub-problem is:
Figure FDA0004057596360000026
s.t.0＜p i ≤P max,i ,i∈N
θ i ∈[0,2π],i∈M
Figure FDA0004057596360000027
aiming at the problem of the computing resource allocation sub-block, optimal computing resource allocation is obtained by utilizing a convex optimization theory
Figure FDA0004057596360000028
4. The IRS assisted MEC network radio and computing resources allocation method of claim 3, wherein for the communication resource allocation sub-problem, the BCD algorithm continues to be utilized to decompose the communication resource allocation sub-problem into a signal detection vector optimization sub-problem and a power-IRS phase optimization sub-problem; wherein the content of the first and second substances,
the signal detection vector optimization sub-problem is as follows:
Figure FDA0004057596360000031
Figure FDA0004057596360000032
reducing the signal detection vector optimization subproblem into a characteristic value problem, and solving the characteristic value problem to obtain an optimal signal detection vector;
the power-IRS phase optimization sub-problem is as follows:
Figure FDA0004057596360000033
s.t.0＜p i ≤P max,i ,i∈N
θ i ∈[0,2π],i∈M
and continuously splitting the power-IRS phase optimization sub-problem into different sub-problems, converting the split sub-problems into convex problems by using a continuous convex approximation technology, and performing iterative optimization until convergence.
5. The method of claim 4, wherein the power-IRS phase optimization sub-problem is further broken into different sub-problems, and the broken sub-problems are converted into convex problems by successive convex approximation, and the iterative optimization until convergence comprises:
transforming the power-IRS phase optimization sub-problem to a dual domain, transforming the power-IRS phase optimization sub-problem to a power-IRS phase optimization dual sub-problem, the power-IRS phase optimization dual sub-problem being:
Figure FDA0004057596360000034
s.t.0＜p i ≤P max,i ,i∈N
θ i ∈[0,2π],i∈M
wherein the content of the first and second substances,
Figure FDA0004057596360000035
and/or>
Figure FDA0004057596360000036
Lagrange dual variables and relaxation variables respectively;
transforming the power-IRS phase optimization dual sub-problem into a power-IRS phase optimization dual sub-problem by using a Lagrangian dual reconstruction method, wherein the power-IRS phase optimization dual sub-problem is as follows:
Figure FDA0004057596360000037
s.t.0＜p i ≤P max,i ,i∈N
θ i ∈[0,2π],i∈M
where μ is the set of introduced auxiliary variables, μ = { μ = { [ μ ] 1 ,μ 2 ,...,μ n }；
Figure FDA0004057596360000038
Obtaining mu based on the power-IRS phase optimization dual reconstruction sub-problem i The optimum value of (c).
6. The method of IRS-assisted MEC network wireless and computing resource allocation of claim 5 wherein the power-IRS phase optimization sub-problem is continuously split into different sub-problems and the split sub-problems are transformed into convex problems using successive convex approximation techniques, iteratively optimizing until convergence, further comprising:
decomposing the power-IRS phase optimization dual reconstruction sub-problem into a power optimization sub-problem and an IRS optimization sub-problem by using a BCD algorithm and a quadratic transformation method; wherein the content of the first and second substances,
the power optimization sub-problem is:
Figure FDA0004057596360000041
s.t.0＜p i ≤P max,i ,i∈N
wherein, v is an introduced auxiliary variable set, v = { v 1 ,ν 2 ,...,ν n }；
Figure FDA0004057596360000042
Solving the power optimization sub-problem to obtain the optimal v, p;
the IRS optimization sub-problem is as follows:
Figure FDA0004057596360000043
s.t.θ i ∈[0,2π],i∈M
wherein the content of the first and second substances,
Figure FDA0004057596360000044
for an introduced auxiliary set of variables>
Figure FDA0004057596360000045
Figure FDA0004057596360000046
/>
Figure FDA0004057596360000047
Is->
Figure FDA0004057596360000048
Iteratively optimizes ≥ the conjugate value>
Figure FDA0004057596360000049
And theta, the optimum->
Figure FDA00040575963600000410
Fixing
Figure FDA00040575963600000411
And (3) optimizing theta, and converting the IRS optimization subproblem into an IRS phase iterative optimization subproblem by using a method of optimizing a minimum value, wherein the IRS phase iterative optimization subproblem is as follows:
Figure FDA00040575963600000412
s.t.|φ i |＝1,i∈N
wherein phi is an introduced substitute variable, phi = [ phi ] 1 ,...,φ m ]，
Figure FDA00040575963600000413
g(Φ|Φ t ) As a substitute function, phi t Is the value of the tth iteration;
g(Φ|Φ t )＝Φξ max I M Φ H -2Re{Φ(ξ max I M -Α)(Φ t ) H }+Φ t (ξ max I M -Α)(Φ t ) H +2Re{ΦΒ}-X；
Figure FDA00040575963600000414
Figure FDA0004057596360000051
Figure FDA0004057596360000052
Figure FDA0004057596360000053
is w i Solving the IRS phase iterative optimization sub-problem to obtain the optimal phi of the t +1 th iteration;
obtaining the t +1 th optimal theta based on the t +1 th optimal phi in an iterative manner;
for the optimization variables p, W, theta, f s Continuously iterating until convergence; wherein the convergence condition is as follows: delta is less than or equal to epsilon;
wherein the content of the first and second substances,
Figure FDA0004057596360000054
Y (t) is the tth iteration value of Y; ε is the threshold value to stop the iteration.
7. An IRS assisted MEC network wireless and computing resource allocation apparatus, wherein the IRS assisted MEC network wireless and computing resource allocation apparatus comprises:
the wireless and computing resource allocation optimization model building module is used for building a wireless and computing resource allocation optimization model by taking the minimum user time delay and the weighted sum of energy consumption as targets; optimizing the signal decoding matrix, user transmitting power, IRS phase shift coefficient matrix and MEC computing resource of the IRS-assisted MEC network;
the optimization solving module is used for decoupling the optimization model, splitting the optimization problem into a plurality of different sub-problems, and obtaining optimal values of a signal decoding matrix, user transmitting power, an IRS phase shift coefficient matrix and MEC computing resources through iterative optimization by utilizing a continuous convex approximation technology in the sub-problem optimization process;
the expression of the optimization model is as follows:
Figure FDA0004057596360000055
s.t.0＜p i ≤P max,i ,i∈N
f si ＞0,i∈N
Figure FDA0004057596360000056
/>
θ i ∈[0,2π],i∈M
Figure FDA0004057596360000057
wherein s.t. represents a limiting condition;
Figure FDA0004057596360000058
representing a set of users; chi shape i A weight factor representing user i; beta is a ti ,β ei A preference factor, beta, representing user i for latency and energy consumption ti +β ei ＝1；t i Indicates an execution delay, and>
Figure FDA0004057596360000059
d i for the task size of user i, c i Computing resources, R, required for task execution by user i i (p,w i Θ) is the transmission rate at which user i offloads the task,
Figure FDA0004057596360000061
p is the user transmit power set, p = { p = { (p) 1 ,p 2 ,...,p n }，p i The transmit power for user i; p is max,i Represents the maximum transmission power of the user i, W is the set of signal detection vectors, W = [ W = [ [ W ] 1 ,w 2 ,...w n ]，w i Detecting a vector for a signal of user i>
Figure FDA0004057596360000062
Denotes w i In conjunction with the device, in conjunction with the device>
Figure FDA0004057596360000063
Is a diagonal matrix of IRS phase shift coefficients, theta i Is the phase of the ith reflection unit>
Figure FDA0004057596360000064
For a channel matrix of user i to the base station, <' >>
Figure FDA0004057596360000065
Channel matrices for users i to IRS, in combination>
Figure FDA0004057596360000066
For the channel matrix of IRS to base station>
Figure FDA0004057596360000067
Representing the matrix i x j, n 0 Is additive white gaussian noise; f. of s For the edge computing resource set allocated to the user, f s ＝{f s1 ,f s2 ,...,f sn }，f si Representing edge computing resources allocated to user i, F max Calculating the total amount of resources for the MEC server; e.g. of a cylinder i Indicates execution energy consumption, based on the status of the system>
Figure FDA0004057596360000068
B denotes an uplink transmission bandwidth. />
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