CN113708818A - Resource allocation method and device of FDMA communication system assisted by intelligent reflector - Google Patents

Resource allocation method and device of FDMA communication system assisted by intelligent reflector Download PDF

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CN113708818A
CN113708818A CN202110953454.0A CN202110953454A CN113708818A CN 113708818 A CN113708818 A CN 113708818A CN 202110953454 A CN202110953454 A CN 202110953454A CN 113708818 A CN113708818 A CN 113708818A
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transmission
fdma
internet
wireless
intelligent
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CN113708818B (en
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朱政宇
杨晨一
楚征
孙钢灿
候庚旺
马梦园
郝万明
刘沛佳
王宁
王忠勇
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Zhengzhou University
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    • 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
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y10/00—Economic sectors
    • G16Y10/75—Information technology; Communication
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y20/00—Information sensed or collected by the things
    • G16Y20/30—Information sensed or collected by the things relating to resources, e.g. consumed power
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0446—Resources in time domain, e.g. slots or frames
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0453—Resources in frequency domain, e.g. a carrier in FDMA
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
    • H04W72/542—Allocation or scheduling criteria for wireless resources based on quality criteria using measured or perceived quality

Abstract

The invention provides a resource allocation method and a device of an FDMA communication system assisted by an intelligent reflector, and the method comprises the following steps: establishing an intelligent reflector assisted wireless power supply Internet of things network system model; a transmission strategy of 'power splitting-collection first and then transmission' is provided; the problem of the total throughput of the network system of the wireless power supply Internet of things assisted by the intelligent reflector is maximized by jointly optimizing the reflection phase of the intelligent reflector, the transmission time slot of the Internet of things equipment in wireless energy transmission and wireless information transmission and FDMA bandwidth allocation; the target function contains a plurality of coupling variables, so that the problem is non-convex, and for the plurality of coupling variables in the target function, the Lagrange duality method and the Coulter conditions are utilized to derive the optimal closed solution of the bandwidth distribution of the transmission time slot and the FDMA of the equipment of the Internet of things in wireless energy transmission and wireless information transmission. Then, an AO algorithm is adopted to alternately design IRS phases in wireless energy transmission and wireless information transmission phases, and an optimal closed-form solution of the IRS phases is iteratively deduced by utilizing EBCD and CCM algorithms. And finally, jointly solving the optimal solution of the maximum throughput of the system. Compared with the traditional algorithm, the performance of the algorithm is greatly improved.

Description

Resource allocation method and device of FDMA communication system assisted by intelligent reflector
Technical Field
The invention belongs to the technical field of communication, and particularly relates to a resource allocation method and device of an intelligent reflector assisted FDMA communication system.
Background
An Intelligent Reflection Surface (IRS) is the most effective solution for solving the problem of communication link blockage in wireless communication scenarios and improving the performance of wireless communication systems, and is a planar version composed of a large number of low-cost electronic components and a central controller, and the performance of a wireless communication network is significantly improved by intelligently reconfiguring a wireless propagation environment by integrating a large number of low-cost passive reflection components on the planar version. And has the remarkable characteristics of low cost, low power consumption, easy deployment and the like.
Wireless Power Transfer (WPT) is regarded as one of key technologies of the next generation of Wireless communication systems, Wireless energy transmission is performed on Internet of things (IoT) devices through radio frequency link radiation energy, a Wireless charging effect is achieved, the current situation of the layout of the Internet of things devices is greatly improved, but at present, the Wireless energy transmission is greatly influenced by a transmission distance, so that the Internet of things devices suffer from dual attenuation in the transmission process of an uplink and a downlink, and the overall system performance is influenced. In practical application, the method for reducing signal attenuation by simply shortening the distance is difficult to realize, a relay transmission scheme is needed, the problem is well solved due to the intelligent reflecting surface, and the system performance of the whole wireless communication system is greatly improved.
Disclosure of Invention
The invention provides a resource allocation method and a resource allocation device of an FDMA communication system assisted by an intelligent reflecting surface. The intelligent reflecting surface is used for enhancing energy collection and information transmission capacity so as to improve the total throughput of the system to the maximum extent.
In a first aspect, a method for resource allocation for intelligent reflector assisted FDMA communications systems, comprises:
s1: a system model of a base station, an IRS, Internet of things (IoT) equipment and a process node is established, and a transmission strategy of 'power distribution-collection first and then transmission' is provided.
S2: and (3) optimizing the intelligent reflecting surface reflection phase, the transmission time slot of the Internet of things equipment in wireless energy transmission and wireless information transmission and FDMA bandwidth allocation to obtain the optimization problem of the system throughput maximization.
S3: and for a plurality of coupling variables in the objective function, utilizing a Lagrange dual method and a KKT condition, and deducing and calculating an optimal closed-form solution of the maximum throughput of the system through an alternative optimization algorithm.
Specifically, the step S1 specifically includes:
the system comprises a single-antenna base station, a single-antenna process node, K pieces of Internet of things equipment and an IRS (intelligent resource system) provided with N reflection units.
Specifically, the step S2 specifically includes:
under the constraints of intelligent reflecting surface reflection phase, equipment transmission time slot and FDMA bandwidth allocation, the maximum throughput of the system is expressed as follows:
Figure BDA0003219428060000021
Figure BDA0003219428060000022
Figure BDA0003219428060000023
Figure BDA0003219428060000024
in problem (3), (3a) is the reflection phase constraint of the intelligent reflecting surface, (3b) is the device transmission slot constraint, and (3c) is the FDMA bandwidth allocation constraint.
Specifically, the step S3 specifically includes:
since the objective function of the problem (3) contains a plurality of coupling variables, the problem (3) is non-convex, and in order to overcome the non-convex problem, for the plurality of coupling variables in the objective function, an optimal closed-form solution of the transmission time slot and the FDMA bandwidth distribution is derived by using a Lagrange duality method and a KKT condition. Then, an AO algorithm is adopted to alternately design IRS phases in wireless energy transmission and wireless information transmission phases, and an optimal closed-form solution of the IRS phases is iteratively deduced by utilizing EBCD and CCM algorithms. And finally, jointly solving the optimal solution of the maximum throughput of the system.
An apparatus for resource allocation for intelligent reflector assisted FDMA telecommunications systems, comprising:
the model establishing module is used for establishing an IRS auxiliary IoT uplink and downlink system model;
the equation construction module is used for calculating the maximum throughput of the system by jointly optimizing the reflection phase of the intelligent reflecting surface, the transmission time slot of the Internet of things equipment in wireless energy transmission and wireless information transmission and FDMA bandwidth allocation;
and the iteration processing module is used for deducing and calculating the optimal closed-form solution of the maximum throughput of the system for a plurality of coupling variables in the objective function by using a Lagrange dual method and a KKT condition through an alternative optimization algorithm.
The modeling module includes:
the first modeling unit is used for allocating 1 antenna at a base station, and each Internet of things device is provided with 1 antenna, a single-antenna process node and an IRS provided with N reflecting units;
a second modeling unit that considers the energy harvested for the downstream WET order, as well as the FDMA bandwidth allocation and individual throughput at the process node in the upstream WIT phase.
The equation construction module includes:
an optimization equation for jointly optimizing the reflection phase of the intelligent reflecting surface, the transmission time slot of the Internet of things equipment in wireless energy transmission and wireless information transmission and FDMA bandwidth allocation to obtain the maximum throughput of the system is as follows:
Figure BDA0003219428060000031
Figure BDA0003219428060000032
Figure BDA0003219428060000041
Figure BDA0003219428060000042
the iteration module comprises:
firstly, deducing the optimal closed type solution of FDMA bandwidth distribution by utilizing a Lagrange duality method and a KKT condition as follows:
Figure BDA0003219428060000043
the optimal closed form solution for the transmission time slot is:
Figure BDA0003219428060000044
then, an AO algorithm is adopted to alternately design IRS phases at the wireless energy transmission stage and the wireless information transmission stage, and EBCD and CCM algorithms are used to iteratively deduce the optimal closed solution of the IRS phases as follows:
Figure BDA0003219428060000045
according to the technical scheme, the total throughput of the system is maximized by jointly optimizing the reflection phase of the IRS, the transmission time slot of the equipment and the FDMA bandwidth allocation, and a closed-form solution is deduced, so that the calculation complexity of the system is reduced, and the calculation time is greatly reduced.
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In order to more clearly illustrate the embodiments or technical solutions of the present invention in the prior art, the drawings used in the description of the embodiments or prior art are briefly introduced below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flowchart of a resource allocation method and apparatus for an intelligent reflector assisted FDMA communications system according to an embodiment of the present invention;
FIG. 2 is a diagram of a model of a system in which the present invention is implemented;
fig. 3 is a diagram of a transmission scheme proposed by the present invention;
FIG. 4 is a graph of total system throughput versus base station transmit power provided by an implementation of the present invention;
FIG. 5 is a diagram of the total system throughput versus the number of IRS reflection units provided by an embodiment of the present invention;
FIG. 6 is a graph of the system total throughput provided by the implementation of the present invention versus the x-axis coordinate of the IRS.
Fig. 7 is a diagram of the relationship between total system throughput and IRS-IoT device path loss provided by an implementation of the present invention.
FIG. 8 illustrates the system throughput and the discrete phase resolution B of the IRS provided by the present invention0A graph of the relationship (c).
Fig. 9 is a schematic structural diagram of a resource allocation method and apparatus of an FDMA communication system based on intelligent reflector assistance according to an embodiment of the present invention;
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
S1: establishing a system model of a base station, an IRS, Internet of things equipment and a process node;
s2: and jointly optimizing IRS reflection phase, transmission time slot of the Internet of things equipment and FDMA bandwidth allocation to obtain the optimization problem of maximizing system throughput.
S3: and for a plurality of coupling variables in the objective function, deriving an optimal closed-form solution of the transmission time slot and FDMA bandwidth distribution by utilizing a Lagrange duality method and a KKT condition. Then, an AO algorithm is adopted to alternately design IRS phases in wireless energy transmission and wireless information transmission phases, and an optimal closed-form solution of the IRS phases is iteratively deduced by utilizing EBCD and CCM algorithms. And finally, jointly solving the optimal solution of the maximum throughput of the system.
Specifically, step S1 includes:
the method of this embodiment is applied to an IRS-assisted wireless power supply internet of things network system model shown in fig. 2, where the system parameters include: base station equipped with single antenna, process node of single antenna, K ═ 6 single antennasAnd an IRS equipped with N-50 reflection units. The transmitted power of the base station is 30dBm, the noise sigma coming from the process node2The energy collection efficiency η is 0.8, the total transmission time T is 1 second, and the total bandwidth B is 1 dBm. Path loss ePS2IRS＝∈IRS2AP＝2，∈IRS2D2.5 and ePS2D＝∈D2AP＝3.5。
Specifically, step S2 includes:
fig. 3 is a transmission strategy provided in an embodiment of the present invention, which uses a protocol of collecting first and then transmitting, and sets T as a whole time period, including a time τ of a downlink WET phase0And time of uplink WIT phase1To ensure
Figure BDA0003219428060000061
And
Figure BDA0003219428060000062
in the uplink WIT stage, assuming that FDMA protocol is adopted, the total bandwidth is set as B, and each device allocates bandwidth Bk∈[0,B],
Figure BDA0003219428060000063
Satisfy the requirement of
Figure BDA0003219428060000064
At tau0The stage is a wireless energy transmission stage, the base station provides wireless energy transmission service for the IRS and the IoT equipment, and the IRS reflects collected energy signals to the IoT equipment.
In WET phase of downlink, in DkThe energy harvested was:
Ek＝ητ0P0|gd,k+g0Θ0gr,k|2 (8)
wherein eta and P0Respectively representing the energy conversion efficiency and the transmitted power at the Power Station (PS). The uplink Wireless information transfer phase (WIT) phase employs FDMA protocol, where DkThe personal throughput at the Access Point (AP) is:
Figure BDA0003219428060000071
wherein t is0,k＝|gd,k+g0Θ0gr,k|2，t1,k＝|hd,k+hkΘ1hr|2，
Figure BDA0003219428060000072
And σ2Is the noise power spectral density.
Jointly optimizing the reflection phase of the intelligent reflecting surface, the transmission time slot of the Internet of things equipment in wireless energy transmission and wireless information transmission and FDMA bandwidth allocation, and constructing an optimization problem of the maximum throughput of the system:
Figure BDA0003219428060000073
Figure BDA0003219428060000074
Figure BDA0003219428060000075
Figure BDA0003219428060000076
in the problem (10), (10a) is the reflection phase constraint of the intelligent reflecting surface, (10b) is the device transmission slot constraint, and (10c) is the FDMA bandwidth allocation constraint.
Specifically, step S3 includes:
firstly, considering the problem of establishment, firstly, a Lagrangian dual method is considered, and an optimal closed-form solution of transmission time scheduling and FDMA bandwidth allocation is deduced by adopting a KKT condition. Through observation of the problem (10), optimization of the objective function (10) after the phase shift term operation is equivalent to the optimization of the following parts:
Figure BDA0003219428060000077
Figure BDA0003219428060000078
Figure BDA0003219428060000079
Figure BDA00032194280600000710
where θ ═ θ0,θ1To solve (11), we first consider its lagrangian dual function to get:
Figure BDA0003219428060000081
wherein λ, μ ≧ 0 represent the lagrangian dual multipliers associated with the constraints in (10b) and (10c), respectively.
Thus, its dual problem can be expressed as:
Figure BDA0003219428060000082
wherein
Figure BDA0003219428060000083
Is composed of
Figure BDA0003219428060000084
And
Figure BDA0003219428060000085
is selected. It can be easily demonstrated that (11) can be rewritten to a satisfactory Slater's stripThe convex nature of the part. Because of the fact that
Figure BDA0003219428060000086
And bkIs greater than 0. For any of i and k, there are
Figure BDA0003219428060000087
And
Figure BDA0003219428060000088
therefore, there is a strong duality between problem (11) and its duality problem (13) to ensure that the optimal solution of (11) satisfies the following KKT condition:
Figure BDA0003219428060000089
Figure BDA00032194280600000810
Figure BDA00032194280600000811
for (14a) and (14b), there are λ > 0 and μ>0 to ensure that the equations exist in constraints (10b) and (10c), i.e.
Figure BDA00032194280600000812
And
Figure BDA00032194280600000813
from (14c), we compute (14a) vs. bkAnd make it zero, i.e. the first derivative of
Figure BDA00032194280600000814
Obtaining:
Figure BDA00032194280600000815
starting from (15), function
Figure BDA00032194280600000816
Is monotonically increasing with respect to x. To satisfy the K equation in (15), let
Figure BDA00032194280600000817
Then, define
Figure BDA00032194280600000818
Thus, it is possible to provide
Figure BDA0003219428060000091
Because of the fact that
Figure BDA0003219428060000092
Obtaining:
Figure BDA0003219428060000093
substituting expression of rho into bkIs expressed to obtain DkThe optimal solution for bandwidth allocation is:
Figure BDA0003219428060000094
substituting (19) into the question (11) we obtain:
Figure BDA0003219428060000095
s.t.(11b),τ0∈[0,T] (20b)
due to the objective function (20a) and the unit mode constraints (11b), (20) are still non-convex. To solve the problem (20), the transmission slot τ is first deduced for a given phase shift θ0The optimal solution of (1). Then, an alternative design of the optimal phase is proposedThe AO algorithm for the shift theta, each phase shift theta can be obtained by the EBCD and CCM algorithms.
In problem (20), given a phase shift θ, solve for τ0The question (20) can be rewritten as:
Figure BDA0003219428060000096
wherein the content of the first and second substances,
Figure BDA0003219428060000097
calculating the first derivative of the objective function in (21) and making it zero, i.e.
Figure BDA0003219428060000098
Obtaining:
Figure BDA0003219428060000101
wherein the content of the first and second substances,
Figure BDA0003219428060000102
then, utilize
Figure BDA0003219428060000103
In combination with the problem (22), a transmission slot τ is obtained0The optimal solution of (a) is:
Figure BDA0003219428060000104
solving the phase shift θ in the problem (10), the maximum value of (20a) expressed in θ is equivalent to the maximum value of the following problem:
Figure BDA0003219428060000105
due to coupling phase shift
Figure BDA0003219428060000106
Knowing that the problem (24) deploys a joint convex problem, the problem (24) is solved alternately by using the AO algorithm. First, give θ1Optimizing theta0Then for a given theta0And (5) carrying out optimized design. Rewrite the question (24) to:
Figure BDA0003219428060000107
Figure BDA0003219428060000108
to solve the problem (18), we propose the EBCD and CCM algorithms, iteratively deriving θ0Closed form optimal solution of. The problem (18) is expanded to:
Figure BDA0003219428060000109
wherein
Figure BDA00032194280600001010
And
Figure BDA00032194280600001011
definition of
Figure BDA00032194280600001012
Removing constant terms
Figure BDA00032194280600001013
Problem (25) can be equated with:
Figure BDA00032194280600001014
s.t.(25b).(27b)
1) an EBCD algorithm solution (27) is proposed, given
Figure BDA00032194280600001015
Iterative optimization
Figure BDA00032194280600001016
The objective function is expanded as:
Figure BDA0003219428060000111
s.t.|θ0(l)|＝1 (28b)
wherein
Figure BDA0003219428060000112
θ0(n)，γ0(n) and phi0(n, m) each represents θ0，γ0The nth term of (1), Φ0The (n, m) term of (1). Due to phi0Hermite property of,. phi.,0(n,m)＝conj(Φ0(m, n)). Because of | theta0(l)|2Problem (28) can be simplified to 1:
Figure BDA0003219428060000113
for problem (29), | θ0(l) The optimal solution of | is:
Figure BDA0003219428060000114
2) CCM algorithm: and (6) solving (28) by using a CCM algorithm to obtain the optimal phase of the WET stage. The main idea is to derive a gradient descent algorithm over the manifold space. To apply this method, the problem (29) is first restated as:
Figure BDA0003219428060000115
where κ > 0 is a constant that controls the convergence of the CCM algorithm. Due to the fact that
Figure BDA0003219428060000116
Problem (25) is equivalent to problem (31).
The CCM algorithm iteratively solves a problem (31), each iteration comprising the steps of:
(1) and (3) solving a search direction: setting the objective function at the i-th iteration (31) as
Figure BDA0003219428060000117
First, the search direction of the question (31) is set, which is equal to
Figure BDA0003219428060000118
The euclidian space gradient of (a) is reversed, i.e.:
Figure BDA0003219428060000119
(2) projection of the search direction on the tangent space: optimizing step length in manifold space to find out current point
Figure BDA00032194280600001110
To
Figure BDA00032194280600001111
Has a Riemann gradient, the point being in tangential space
Figure BDA00032194280600001112
Searching direction iota in Euclidean space(m)Is projected to
Figure BDA00032194280600001113
In the above, will
Figure BDA00032194280600001114
Riemann gradient projection to
Figure BDA00032194280600001115
In the above, will
Figure BDA00032194280600001116
In that
Figure BDA00032194280600001117
The Riemann gradient projection of (A) is:
Figure BDA0003219428060000121
(3) and (3) performing descending updating in a tangential space: updating the cutting space
Figure BDA0003219428060000122
On
Figure BDA0003219428060000123
Expressed as:
Figure BDA0003219428060000124
where ζ is the step size.
(4) Retraction operation: because of the fact that
Figure BDA0003219428060000125
Is out of SNIn the process of retraction operation
Figure BDA0003219428060000126
Mapping to manifold SNIn (1). By adopting a retraction operation
Figure BDA0003219428060000127
Is normalized to:
Figure BDA0003219428060000128
according to the technical scheme, the invention provides the resource allocation method of the FDMA communication system assisted by the intelligent reflecting surface, and the method for calculating the maximum throughput of the system is deduced by jointly optimizing the reflection phase of the intelligent reflecting surface, the transmission time slot of the Internet of things equipment in wireless energy transmission and wireless information transmission and FDMA bandwidth allocation.
The system configuration parameters of this implementation are as follows:
number of users K 6
Number N of IRS reflection units 50
Base station transmission power P0 30dBm
Noise sigma2 -100dBm
Energy efficiency eta 0.8
Total time of transmission T 1
Coordinates of base station (-10,0,0)
Coordinates of IRS (-2,6,0)
Coordinates of process nodes (10,0,0)
FIG. 4 depicts a system throughputVolume and base station transmitting power P0The relationship (2) of (c). When the transmission power of the base station is increased, the throughput of each scheme is increased, wherein the scheme provided with the IRS has better effect compared with the scheme without the IRS and the IRS in the random phase shift state. Meanwhile, the EBCD algorithm and the CCM algorithm reach the same performance, and the scheme is verified. FDMA bandwidth allocation and optimal design of transmission slots are also intensively studied to improve the overall throughput performance compared to the reference scheme.
Fig. 5 depicts the relationship between the total system throughput and the number of IRS reflection units (N). When the number of IRS reflection units is increased, stronger reflection signals are provided to enhance energy and information reflection, and the total throughput of the system is increased accordingly, so that the gain is improved greatly compared with other schemes.
FIG. 6 depicts system throughput versus the x-coordinate of the IRS. XIRSIn the range of [ -10,10 [)]Varies with X total throughputIRSThe change of (a) is in a trend of increasing first and then decreasing. The solution of providing IRS is superior to the other solutions. The IRS needs to be optimally deployed to achieve maximum throughput.
Fig. 7 depicts the relationship of the system overall throughput and IRS-IoT device path loss. The overall throughput decreases as the path loss exponent increases. This is because large scale fading will result in weaker energy reception, thereby reducing the gain from the IRS.
FIG. 8 depicts the relationship of discrete phase resolution and sum throughput of IRS. The value of the EBCD and CCM continuous phase is the upper limit of their discretely corresponding sum throughput, with the discrete phase resolution (B) of the IRS0) From 1 bit to 9 bits, the gap between the continuous phase shift and the discrete phase shift of the EBCD and CCM algorithms gradually decreases.
In an embodiment, the model establishment specifically includes:
the model establishing module is used for establishing an IRS-assisted wireless power supply Internet of things network system model; the system comprises a single-antenna base station, a single-antenna process node, an IRS provided with N reflection units and K users.
In this example, the equation constructing module specifically includes:
the equation construction module is used for constructing an optimization problem of the maximum throughput of the system by jointly optimizing the reflection phase of the intelligent reflecting surface, the transmission time slot of the Internet of things equipment in wireless energy transmission and wireless information transmission and FDMA bandwidth allocation;
Figure BDA0003219428060000131
Figure BDA0003219428060000141
Figure BDA0003219428060000142
Figure BDA0003219428060000143
in the problem (36), (36a) is the reflection phase constraint of the intelligent reflecting surface, (36b) is the device transmission time slot constraint, and (36c) is the FDMA bandwidth allocation constraint.
In this example, the iterative solution module specifically includes:
and the iteration solving module is used for deducing the optimal closed-form solution of the maximum throughput of the computing system by using a Lagrange multiplier method and a KKT condition and an alternative optimization algorithm for a plurality of coupling variables in the objective function.

Claims (8)

1. A method and apparatus for resource allocation in an intelligent reflector assisted FDMA communication system, the method comprising:
s1: establishing a system model of an intelligent reflector assisted wireless power supply Internet of things network system;
s2: the method provides a transmission strategy of 'power distribution-collection-transmission-first', and maximizes the total throughput of the system by jointly optimizing the reflection phase of an intelligent reflecting surface, the transmission time slot of the Internet of things equipment in wireless energy transmission and wireless information transmission and FDMA bandwidth distribution;
s3: and for a plurality of coupling variables in the objective function, utilizing a Lagrange multiplier method and a KKT condition, and deducing and calculating the optimal closed-form solution of the maximum throughput of the system through an alternative optimization algorithm.
2. The method according to claim 1, wherein the step S1 specifically includes:
the system comprises 1 single-antenna base station, 1 single-antenna process node, K pieces of Internet of things equipment and an intelligent reflecting surface provided with N reflecting units, and provides a transmission strategy of 'power distribution-collection first and then transmission'.
3. The method according to claim 1, wherein the step S2 specifically includes:
and planning an objective function, and solving the problem by jointly optimizing the reflection phase of the intelligent reflecting surface, the transmission time slot of the Internet of things equipment in wireless energy transmission and wireless information transmission and FDMA bandwidth allocation to obtain the maximum throughput of the system.
4. The method according to claim 1, wherein the step S3 specifically includes:
and for a plurality of coupling variables in the target problem, deriving an optimal closed-form solution of the transmission time slot and FDMA bandwidth distribution by utilizing a Lagrange duality method and a KKT condition. Then, an AO algorithm is adopted to alternately design IRS phases at wireless energy transmission and wireless information transmission stages, the EBCD and CCM algorithms are used for iteratively deducing the optimal closed solution of the IRS phases, and finally the closed solution calculation method of the maximum throughput of the system is deduced. The target question is then:
Figure FDA0003219428050000021
Figure FDA0003219428050000022
Figure FDA0003219428050000023
Figure FDA0003219428050000024
in the problem (1), (1a) is a reflection phase constraint of the intelligent reflection surface, (1b) is a transmission slot constraint, and (1c) is an FDMA bandwidth allocation constraint.
5. An apparatus for resource allocation for intelligent reflector assisted FDMA communications systems, comprising:
the model establishing module is used for establishing an uplink and downlink system model of the intelligent reflector assisted wireless power supply Internet of things network system;
the equation construction module is used for calculating the maximum throughput of the system by jointly optimizing the reflection phase of the intelligent reflecting surface, the transmission time slot of the Internet of things equipment in wireless energy transmission and wireless information transmission and FDMA bandwidth allocation;
and the iteration processing module is used for deducing and calculating the optimal closed-form solution of the maximum throughput of the system for a plurality of coupling variables in the objective function by using a Lagrange dual method and a KKT condition through an alternative optimization algorithm.
6. The apparatus of claim 5, comprising: the model building module comprises:
the first modeling unit is used for allocating 1 antenna at a base station, and each Internet of things device is provided with 1 antenna, a single-antenna process node and an intelligent reflecting surface provided with N reflecting units;
a second modeling unit that considers the energy harvested for the downstream WET order, as well as the FDMA bandwidth allocation and individual throughput at the process node in the upstream WIT phase.
7. The apparatus of claim 5, comprising:
an equation construction module, which jointly optimizes the reflection phase of the intelligent reflecting surface, the transmission time slot of the internet of things equipment in wireless energy transmission and wireless information transmission and FDMA bandwidth allocation to obtain the optimization equation of the maximum throughput of the system:
Figure FDA0003219428050000031
Figure FDA0003219428050000032
Figure FDA0003219428050000033
Figure FDA0003219428050000034
8. the apparatus of claim 5, comprising:
and the iteration processing module is used for deducing an optimal closed-form solution of the transmission time slot and the FDMA bandwidth distribution by utilizing a Lagrange duality method and a KKT condition aiming at a plurality of coupling variables in the target function. Then, an AO algorithm is adopted to alternately design IRS phases in wireless energy transmission and wireless information transmission phases, and an optimal closed-form solution of the IRS phases is iteratively deduced by utilizing EBCD and CCM algorithms. And finally, jointly solving the optimal solution of the maximum throughput of the system.
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