Wireless energy supply backscattering communication method and system based on IRS
阅读说明：本技术 一种基于irs的无线供能反向散射通信方法及系统 (Wireless energy supply backscattering communication method and system based on IRS ) 是由 徐赛 肖素杰 刘家佳 于 2021-08-13 设计创作，主要内容包括：一种基于IRS的无线供能反向散射通信方法及系统,包括以下步骤：1)构建基于IRS的无线供能反向散射通信网络模型；2)表述反向散射通信吞吐量优化问题；3)求解反向散射通信吞吐量优化问题和算法复杂性分析：通过将优化问题分解成两个易于解决的优化问题,进而问题通过调用SDR、AO和高斯随机方法完成求解,对求解方案进行了复杂度分析；4)仿真验证。(An IRS-based wireless energy supply backscatter communication method and system comprises the following steps: 1) constructing an IRS-based wireless energy supply backscatter communication network model; 2) expressing a backscattering communication throughput optimization problem; 3) solving a backscattering communication throughput optimization problem and algorithm complexity analysis: the optimization problem is decomposed into two optimization problems which are easy to solve, and then the problems are solved by calling SDR, AO and Gaussian random methods, so that the complexity analysis is carried out on the solving scheme; 4) and (5) simulation verification.)
1. An IRS-based wirelessly powered backscatter communication method, comprising the steps of:
1) constructing an IRS-based wireless energy supply backscatter communication network model: when an antenna AP sends a signal carrying information to a single-antenna main user PU, a part of wireless signal energy is received by a reflection unit IRS, the energy can be stored in an energy storage or used for backscattering communication, and the operation of the IRS in a time block is modeled into a simple two-stage process;
2) expressing the backscattering communication throughput optimization problem: the method comprises the steps of taking the maximization of the backscattering communication throughput of unit bandwidth as a target, taking beam forming at an AP and an IRS and time allocation of a two-stage process as optimization variables, and expressing an optimization problem;
3) solving a backscattering communication throughput optimization problem and algorithm complexity analysis: the optimization problem is decomposed into two optimization problems which are easy to solve, the problems are solved by calling SDR, AO and Gaussian random methods, and then complexity analysis is carried out on the solving scheme;
4) simulation verification: the proposed simulation was verified using numerical simulation, and the feasibility and communication performance gain of the proposed scheme was verified by comparing the maximum ratio transmission, random phase, random time, and active antenna schemes.
2. An IRS-based wirelessly powered backscatter communications method according to claim 1, wherein the wirelessly powered backscatter communications network model comprises an N-antenna AP, a single-antenna primary user PU, a single-antenna secondary user SU, and an IRS having L reflection units.
3. An IRS-based wirelessly powered backscatter communications method according to claim 2, wherein operation of the IRS in the time block is modelled as a simple two-stage process dividing the time block into two preceding and succeeding time segments 1-t and t; during a first time period 1-t, the IRS switches to an energy harvesting mode, wherein the energy of the wireless signals received by the IRS is harvested and stored in an energy storage connected with the IRS; during a second time period t, the previously stored energy is used to maintain the reflective elements of the IRS in operation.
4. The method of claim 2, wherein during the first phase, the SINR at the PU and the SU is represented by
Where P is the transmit power of the AP,is the channel gain of the AP to the PU,is the channel gain, w, of the AP to SU1Is a signal for the first stageThe beam-forming of (a) is performed,andvariance of white gaussian noise at PU and SU, respectively; assuming that the sum of the energy collected at the IRS is proportional to the power of the received signal, as shown in the following relation
E＝η(1-t)P||Hw1||2，
WhereinIs the AP to IRS channel gain, η is the IRS energy harvesting efficiency;
in the second phase, the ambient radio signal s is modulated into a new signal when it reaches the IRSAnd is backscattered, the expressions of the signals s received at the PU and SU in this phase are represented by
Wherein w2Is the second stage of beamforming of the AP,andgaussian white noise at PU and SU respectively,andchannel gains from AP to PU and AP to SU, respectively, Ψ Θ xi determined by the reflection coefficient matrix Θ and the modulation matrix xi; order toWhereinL ∈ L ═ {1,2l＝[0,2π]Respectively indicating the amplitude and the phase of the first reflection unit of the IRS; the following expression was derived
SINRs at PU and SU at this stage are represented by the following formula
The energy collected in the second stage and the first stage satisfies the energy constraintη(1-t)P||Hw1||2More than or equal to tL mu, wherein mu refers to the power consumption of the reflection element unit;
jointly optimizing beamforming at the AP and IRS and time allocation for the two-stage process; the optimization problem can be expressed as
s.t.η(1-t)P||Hw1||2≥tLμ， (1)
Wherein gamma ispRefers to the minimum SINR required at PU, Θl,lRefers to the ith diagonal element of Θ; jointly optimizing the parameter w assuming that the information exchange between the AP and the IRS is sufficiently smooth1、w2Θ and t maximize the backscatter communications throughput per unit bandwidth.
5. An IRS-based wirelessly powered backscatter communications method according to claim 4, characterised in that the problem P1 is decomposed into two more easily solved optimisation problems, namely backscatter communications rate maximization and parameters t and w1The optimal solution of (1).
6. The IRS-based wirelessly powered backscatter communication method of claim 5,
backscatter communication rate maximization:
examination of the following absence of w prior to solving the question (P1)1And t problem
s.t.(3)(4).
Definition ofThen Θ is diag { θ ═ diagH}; then obtainAndwhereinAndthus, the problem (P2) is expressed as
The problem (P3) is solved by invoking the semi-positive definite relaxation SDR, alternating optimization AO and gaussian random methods; the problem is solved by first decomposing the problem into the following two sub-problems; definition ofWhen θ is constant, the problem (P3) is reduced to
Tr(W2)≤1，rank(W2)＝1.
Derived from matrix theory
Let Q be W2ξ (ξ > 0), then the subproblem (P3.1) is equivalent to
Tr(Q)≤ξ，rank(Q)＝1，
Wherein
Removing the constraint rank (w) 1, the subproblem (P3.2) is a convex problem that is easy to solve; when w is2For fixed values, the problem (P3) is reduced to
Sub-problem (P3.3) is equivalent to
Removing constraintsThe subproblem (P3.4) is a convex problem that is easy to solve; once the sub-problems (P3.2) and (P3.4) are optimized in turn using semi-positive definite programming, the solution to the problem (P3) can be recovered using gaussian random methods.
7. The IRS-based wirelessly powered backscatter communication method of claim 5,
parameters t and w1Is optimized
The increase in t in the problem (P1) causes the corresponding one in (P1)Is pushed down under constraint (1) to derive the optimum t*Analysis formula (II)
Combining w obtained from the problem (P2)2And Θ, the problem (P1) reduces to
In accordance with SDR theory and function t*(w1) Is represented as a problem (P5)
Tr(W1)≤1，rank(W1)＝1.
Remove constraint rank (W)1) The subproblem (P4) is a convex problem that is easy to solve; the rank-1 solution can then be recovered using a gaussian random approachAnd t*The problem (P1) is solved.
8. The IRS-based wirelessly powered backscatter communication method of claim 5,
computational complexity analysis
After alternately optimizing (P3) the two sub-problems (P3.2) and (P3.4) of the problem (P3) a set of solutions to the problem (P1) is obtained, and then (P3) (P5) a solution to the problem (P1) is obtained; the complexity of deriving the subproblem (P3.2) from the interior Point method IPM is
And the complexity of the sub-problem (P3.4) is
Wherein n is1＝Ο{4N2}、n2＝Ο{4L2The } and ε refer to the optimization accuracy; the complexity of the problem (P3) is then C3＝K(C1+C2)，
K represents the number of iterations; the complexity of the problem (P5) is expressed as
In summary, the complexity of the entire solution process can be represented as Ctotal＝C3+C4(ii) a When the matrix solution is obtained, the original problem (P1) recovers its set of safe approximation vector solutions using Gaussian random methods.
9. An IRS-based wirelessly powered backscatter communications system according to any one of claims 1 to 8, comprising:
the network model building module is used for building an IRS-based wireless energy supply backscatter communication network model: when an antenna AP sends a signal carrying information to a single-antenna main user PU, a part of wireless signal energy is received by a reflection unit IRS, the energy can be stored in an energy storage or used for backscattering communication, and the operation of the IRS in a time block is modeled into a simple two-stage process;
a backscatter communications throughput optimization module configured to formulate a backscatter communications throughput optimization problem: the method comprises the steps of taking the maximization of the backscattering communication throughput of unit bandwidth as a target, taking beam forming at an AP and an IRS and time allocation of a two-stage process as optimization variables, and expressing an optimization problem;
and the solving and analyzing module is used for solving the backscattering communication throughput optimization problem and algorithm complexity analysis: the optimization problem is decomposed into two optimization problems which are easy to solve, the problems are solved by calling SDR, AO and Gaussian random methods, and then complexity analysis is carried out on the solving scheme;
the simulation verification module is used for simulation verification: the proposed simulation was verified using numerical simulation, and the feasibility and communication performance gain of the proposed scheme was verified by comparing the maximum ratio transmission, random phase, random time, and active antenna schemes.
Technical Field
The invention belongs to the technical field of wireless energy supply backscattering communication, and particularly relates to an IRS-based wireless energy supply backscattering communication method and system.
Background
One of the important issues in low power wireless network communication technology is the relationship between energy limitation and throughput maximization.
The wireless energy collection technology enables a wireless system to collect and store signal energy in a wireless environment, and passive wireless communication is achieved. On the other hand, backscatter communication can transmit its own data by modulating a radio signal in an environment. Recently, emerging IRS technologies inject new energy into the two low power communication technologies mentioned above. The IRS is a two-dimensional super-surface, and can dynamically control the characteristics of incident electromagnetic waves in real time through a reflecting unit of the IRS, wherein the characteristics comprise reflection, transmission/refraction, focusing/beam forming, polarization, collimation, separation, analog processing and the like. The IRS is not provided with an active radio frequency chain, and can achieve the aims of enhancing target signals and suppressing interference through passive operation. Currently, there is a lot of research that has been done to use IRS in wireless energy harvesting technology or in backscatter communications. However, there is no relevant research on the organic combination of these three technologies. Additionally, because IRS has spatial modulation capability, it is also used to combine backscatter communications with IRS.
Disclosure of Invention
The invention aims to provide an IRS-based wireless energy supply backscatter communication method and system to solve the problems.
In order to achieve the purpose, the invention adopts the following technical scheme:
an IRS-based wirelessly powered backscatter communication method, comprising the steps of:
1) constructing an IRS-based wireless energy supply backscatter communication network model: when an antenna AP sends a signal carrying information to a single-antenna main user PU, a part of wireless signal energy is received by a reflection unit IRS, the energy can be stored in an energy storage or used for backscattering communication, and the operation of the IRS in a time block is modeled into a simple two-stage process;
2) expressing the backscattering communication throughput optimization problem: the method comprises the steps of taking the maximization of the backscattering communication throughput of unit bandwidth as a target, taking beam forming at an AP and an IRS and time allocation of a two-stage process as optimization variables, and expressing an optimization problem;
3) solving a backscattering communication throughput optimization problem and algorithm complexity analysis: the optimization problem is decomposed into two optimization problems which are easy to solve, the problems are solved by calling SDR, AO and Gaussian random methods, and then complexity analysis is carried out on the solving scheme;
4) simulation verification: the proposed simulation was verified using numerical simulation, and the feasibility and communication performance gain of the proposed scheme was verified by comparing the maximum ratio transmission, random phase, random time, and active antenna schemes.
Further, the wirelessly powered backscatter communications network model includes an N-antenna AP, a single-antenna primary user PU, a single-antenna secondary user SU, and an IRS having L reflecting elements.
Furthermore, the operation of the IRS in the time block can be modeled as a simple two-stage process, and the time block is divided into a front time period 1-t and a rear time period t; during a first time period 1-t, the IRS switches to an energy harvesting mode, wherein the energy of the wireless signals received by the IRS is harvested and stored in an energy storage connected with the IRS; during a second time period t, the previously stored energy is used to maintain the reflective elements of the IRS in operation.
Further, in the first stage, the SINR at the PU and SU is expressed by
Where P is the transmit power of the AP,is the channel gain of the AP to the PU,is the channel gain, w, of the AP to SU1Is a signal for the first stageThe beam-forming of (a) is performed,andvariance of white gaussian noise at PU and SU, respectively; assuming that the sum of the energy collected at the IRS is proportional to the power of the received signal, as shown in the following relation
E＝η(1-t)P||Hw1||2，
WhereinIs the AP to IRS channel gain, η is the IRS energy harvesting efficiency;
in the second phase, the ambient radio signal s is modulated into a new signal when it reaches the IRSAnd is backscattered, the expressions of the signals s received at the PU and SU in this phase are represented by
Wherein w2Is the second stage of beamforming of the AP,andgaussian white noise at PU and SU respectively,andchannels from AP to PU and AP to SU, respectivelyThe gain, Ψ Θ xi is determined by the reflection coefficient matrix Θ and the modulation matrix xi; order toWhereinL ∈ L ═ {1,2l＝[0,2π]Respectively indicating the amplitude and the phase of the first reflection unit of the IRS; the following expression was derived
SINRs at PU and SU at this stage are represented by the following formula
The energy collected in the second stage and the first stage meets the energy constraint eta (1-t) P | | | Hw1||2More than or equal to tL mu, wherein mu refers to the power consumption of the reflection element unit;
jointly optimizing beamforming at the AP and IRS and time allocation for the two-stage process; the optimization problem can be expressed as
s.t.η(1-t)P||Hw1||2≥tLμ， (1)
Wherein gamma ispRefers to the minimum SINR required at PU, Θl,lRefers to the ith diagonal element of Θ; jointly optimizing the parameter w assuming that the information exchange between the AP and the IRS is sufficiently smooth1、w2Θ and t maximize the backscatter communications throughput per unit bandwidth.
Further, the problem P1 is decomposed into two more easily solved optimization problems, namely, the backscattering communication rate maximization and the parameters t and w1The optimal solution of (1).
Further, the backscatter communication rate is maximized:
examination of the following absence of w prior to solving the question (P1)1And t problem
s.t. (3)(4).
Definition ofThen Θ is diag { θ ═ diagH}; then obtainAndwhereinAndthus, the question (P2) representsIs composed of
The problem (P3) is solved by invoking the semi-positive definite relaxation SDR, alternating optimization AO and gaussian random methods; the problem is solved by first decomposing the problem into the following two sub-problems; definition ofWhen θ is constant, the problem (P3) is reduced to
W2≥0，Tr(W2)≤1，rank(W2)＝1.
Derived from matrix theory
Let Q be W2ξ (ξ > 0), then the subproblem (P3.1) is equivalent to
Q≥0，Tr(Q)≤ξ，rank(Q)＝1，
Wherein
Removing the constraint rank (w) 1, the subproblem (P3.2) is a convex problem that is easy to solve; when w is2For fixed values, the problem (P3) is reduced to
Sub-problem (P3.3) is equivalent to
Removing constraintsThe subproblem (P3.4) is a convex problem that is easy to solve; once the sub-problems (P3.2) and (P3.4) are optimized in turn using semi-positive definite programming, the solution to the problem (P3) can be recovered using gaussian random methods.
Further, the parameters t and w1Is optimized
The increase in t in the problem (P1) causes the corresponding one in (P1)Is pushed down under constraint (1) to derive the optimum t*Analysis formula (II)
Combining w obtained from the problem (P2)2And Θ, the problem (P1) reduces to
In accordance with SDR theory and function t*(w1) Is represented as a problem (P5)
W1≥0，Tr(W1)≤1，rank(W1)＝1.
Remove constraint rank (W)1) The subproblem (P4) is a convex problem that is easy to solve; the rank-1 solution can then be recovered using a gaussian random approachAnd t*The problem (P1) is solved.
Further, computational complexity analysis
After alternately optimizing (P3) the two sub-problems (P3.2) and (P3.4) of the problem (P3) a set of solutions to the problem (P1) is obtained, and then (P3) (P5) a solution to the problem (P1) is obtained; the complexity of deriving the subproblem (P3.2) from the interior Point method IPM is
And the complexity of the sub-problem (P3.4) is
Wherein n is1＝Ο{4N2}、n2＝Ο{4L2The } and ε refer to the optimization accuracy; the complexity of the problem (P3) is then C3＝K(C1+C2)，
K represents the number of iterations; the complexity of the problem (P5) is expressed as
In summary, the complexity of the entire solution process can be represented as Ctotal＝C3+C4(ii) a When the matrix solution is obtained, the original problem (P1) recovers its set of safe approximation vector solutions using Gaussian random methods.
Further, an IRS-based wirelessly powered backscatter communications system, comprising:
the network model building module is used for building an IRS-based wireless energy supply backscatter communication network model: when an antenna AP sends a signal carrying information to a single-antenna main user PU, a part of wireless signal energy is received by a reflection unit IRS, the energy can be stored in an energy storage or used for backscattering communication, and the operation of the IRS in a time block is modeled into a simple two-stage process;
a backscatter communications throughput optimization module configured to formulate a backscatter communications throughput optimization problem: the method comprises the steps of taking the maximization of the backscattering communication throughput of unit bandwidth as a target, taking beam forming at an AP and an IRS and time allocation of a two-stage process as optimization variables, and expressing an optimization problem;
and the solving and analyzing module is used for solving the backscattering communication throughput optimization problem and algorithm complexity analysis: the optimization problem is decomposed into two optimization problems which are easy to solve, the problems are solved by calling SDR, AO and Gaussian random methods, and then complexity analysis is carried out on the solving scheme;
the simulation verification module is used for simulation verification: the proposed simulation was verified using numerical simulation, and the feasibility and communication performance gain of the proposed scheme was verified by comparing the maximum ratio transmission, random phase, random time, and active antenna schemes.
Compared with the prior art, the invention has the following technical effects:
the present invention organically combines wireless energy harvesting technology with IRS-based backscatter communications technology. Compared with the stability and safety requirements of an active relay communication system on external energy supply, the IRS in the system collects energy in the environment without using any active transmitting module, namely, the system does not depend on external energy supply, and the energy consumption of the system is reduced; for the problem of maximizing the backscattering throughput in wireless energy carrying transmission, the system optimizes beam forming at the AP and the IRS and time allocation in a two-stage process, and achieves satisfactory communication performance.
Drawings
FIG. 1 is a model of an IRS-based wirelessly powered backscatter communications system;
FIG. 2. unit bandwidth throughput at SU and transmit power at AP;
FIG. 3 is a relationship of throughput per bandwidth at SU to IRS reflection units;
FIG. 4. throughput per bandwidth at SU versus distance of IRS to SU;
FIG. 5 shows an embodiment of the process.
Detailed Description
The invention is further described below with reference to the accompanying drawings:
referring to fig. 1 to 5 of the drawings,
fig. 1 shows a model of an IRS-based wireless-powered backscatter communications system, comprising an AP with N antennas, a single-antenna Primary User (PU), a single-antenna Secondary User (SU), and an IRS with L reflection units. When the AP sends an information-bearing signal to the PU, a portion of the wireless signal energy may be received by the IRS, which can be stored in an energy store or used for backscatter communications. Consider a block of slow fading time (assumed to be a unit of time duration) during which all channels remain unchanged. The operation of the IRS in this time block can be modeled as a simple two-stage process, i.e. the time block is divided into two preceding and succeeding time segments 1-t and t. During a first time period 1-t, the IRS switches to an energy harvesting mode, where the wireless signal energy it receives is harvested and stored in its attached energy storage. During a second time period t, the previously stored energy is used to maintain the reflective elements of the IRS in operation.
Specifically, in the first stage, the Signal-to-Interference-plus-Noise-Ratio (SINR) at the PU and the SU is expressed by
Where P is the transmit power of the AP,is the channel gain of the AP to the PU,is the channel gain, w, of the AP to SU1Is a signal for the first stageThe beam-forming of (a) is performed,andthe variance of white gaussian noise at PU and SU, respectively. On the other hand, assume that the sum of the energy collected at the IRS is proportional to the power of the received signal, i.e., as shown in the following relation
E＝η(1-t)P||Hw1||2，
WhereinIs the AP to IRS channel gain, and η is the IRS energy harvesting efficiency.
In the second phase, the ambient radio signal s is modulated into a new signal when it reaches the IRSAnd is backscattered, the expressions of the signals s received at the PU and SU in this phase are represented by
Wherein w2Is the second stage of beamforming of the AP,andgaussian white noise at PU and SU respectively,andchannel gains from AP to PU and AP to SU, respectively, Ψ Θ xi is determined by the reflection coefficient matrix Θ and the modulation matrix xi. Order to
WhereinL ∈ L ═ {1,2l＝[0,2π]Respectively refer to the amplitude and phase of the first reflection element of the IRS. Therefore, the following expression can be derived
SINRs at PU and SU at this stage are represented by the following formula
Generally, the power consumption of an IRS is related to its number of reflective elements and phase resolution, with the larger the size and phase resolution of the IRS the higher the power consumption. Since the operation of the IRS is powered by ambient wireless energy, the power consumption of the second stage must be less than the energy collected in the first stage, and in particular the energy constraint η (1-t) P | | | Hw must be satisfied1||2And ≧ tL μ, where μ refers to power consumption of the reflective element unit.
To maximize the throughput of backscatter communications per bandwidth, it is necessary to jointly optimize the beamforming at the AP and IRS and the time allocation of the two-phase process. The optimization problem can be expressed as
s.t.η(1-t)P||Hw1||2≥tLμ， (1)
Wherein gamma ispRefers to the minimum SINR required at PU, Θl,lRefers to the ith diagonal element of Θ. FalseThe information exchange between the AP and the IRS is smooth enough, so that the parameter w can be optimized jointly1、w2Θ and t maximize the backscatter communications throughput per unit bandwidth.
Due to the coupling variable w in the objective function and constraints1、w2Θ and t, so (P1) is a non-convex problem. Next we will present a two-step solution process to decompose the problem (P1) into two more easily solved optimization problems, namely backscatter communication rate maximization and parameters t and w1The optimal solution of (1).
1) Backscatter communications rate maximization
For a given t, the objective function of the well-known problem (P1) has a direct relationship only with constraints (3) and (4), since constraints (1) and (2) do not contain Θ and w2. Therefore, before solving the problem (P1), the following test can be considered without w1And t problem
s.t.(3)(4).
Definition ofThen Θ is diag { θ ═ diagH}. Then we obtain AndwhereinAndthus, the problem (P2) can be expressed as
The problem (P3) can be solved by invoking Semi-Definite Relaxation (SDR), Alternate Optimization (AO), and gaussian random methods. The problem is first resolved into the following two sub-problems. Definition ofWhen θ is constant, the problem (P3) is reduced to
W2≥0，Tr(W2)≤1，rank(W2)＝1.
Is not difficult to obtain from matrix theory
Let Q be W2ξ (ξ > 0), then the subproblem (P3.1) is equivalent to
Q≥0，Tr(Q)≤ξ，rank(Q)＝1，
Wherein
Removing the constraint rank (w) of 1, the subproblem (P3.2) is a convex problem that is easy to solve. When w is2For fixed values, the problem (P3) is reduced to
Sub-problem (P3.3) is equivalent to
Removing constraintsThe sub-question (P3.4) is then a convex question that is easy to solve. Once the sub-problems (P3.2) and (P3.4) are optimized in turn using semi-positive definite programming, the solution to the problem (P3) can be recovered using gaussian random methods. Note that the AO process is convergent because both sub-problems (P3.2) and (P3.4) are non-decreasing in iteration.
2) Parameters t and w1Is optimized
The time allocation parameter t has a significant impact on the backscatter communications throughput. From the problem (P1) we can see that an increase in t necessarily causes a corresponding increase in (P1)Can derive the optimal t under the constraint condition (1)*Analysis formula (II)
Combining w obtained from the problem (P2)2And Θ, the problem (P1) can be reduced to
In accordance with SDR theory and function t*(w1) Can be expressed as a problem (P5)
W1≥0，Tr(W1)≤1，rank(W1)＝1.
Remove constraint rank (W)1) The sub-problem (P4) is then a convex problem that is easy to solve. The rank-1 solution can then be recovered using a Gaussian random methodAnd t*. To this point, the problem (P1) is solved.
3) Computational complexity analysis
After alternately optimizing (P3) the two sub-problems (P3.2) and (P3.4) a set of solutions to the problem (P3) is obtained, and then through (P3) (P5) a solution to the problem (P1) is obtained. The complexity of the subproblem (P3.2) obtainable by the Interior Point Method (IPM) is
And the complexity of the sub-problem (P3.4) is
Wherein n is1＝Ο{4N2}、n2＝Ο{4L2And ε denote the optimization accuracy. The complexity of the problem (P3) is then C3＝K(C1+C2)，
K denotes the number of iterations. The complexity of the problem (P5) can be expressed as
In summary, the complexity of the entire solution process can be represented as Ctotal＝C3+C4. When a matrix solution is obtained, the original problem (P1) can recover its set of safe approximation vector solutions using gaussian random methods.
This section will evaluate the communication performance of the proposed IRS-based wireless-powered backscatter communication system by numerical simulation. The comparative scheme is as follows:
1) maximum ratio transmission: the beamformer at the AP is co-directional with the channel from the AP to the PU;
2) random phase: the amplitude and the phase of the IRS reflection unit are respectively set to a unit value and a random value;
3) random time: the time distribution parameter t is randomly generated from t to U (0, 1);
4) an active antenna: using a transmission power of PaInstead of a co-located IRS, four active antennas.
In the simulation, assuming that all channels are slow fading rice channels, the rice factor from AP to PU, SU and IRS is set to κa2, the rice factor from IRS to SU and PU is set to κi3. The path loss of all channels is expressed as PL ═ PL0-20log(d/d0) dB, where PL0-20dB is expressed in d-d0Where path loss, d denotes transmission distance, d01m refers to the reference distance. Since the IRS is plane scattering, its element unit has a gain of 3 dBi. Other parameter settings are as follows: transmitting power P at AP is 5W; distances from AP to PU, SU and IRS are dp＝50m、ds55m and di48 m; distance of IRS from PU and SU is dip12m and dis＝10m，The number of antennas at the AP is N-4, the number of IRS element units is L-80, and the noise variance isElement unit power consumption mu is 1.5 × 10-7(ii) a The energy collection efficiency η of IRS is 0.8. In the active antenna contrast scheme, the power of the active antenna is set to Pa0.1W or Pa0.05W. Note that: when P is presentaL and disAs a simulation variable, the above assignments are no longer used.
FIGS. 2, 3 and 4 show the throughput per bandwidth tlog (1+ γ) at SU, respectivelys,2) The number of units L of transmit power P, IRS elements at the AP and the IRS to SU spacing disThe relationship (2) of (c). As can be seen from fig. 2, 3 and 4, an increase in P and L favors an increase in throughput, whereas throughput decreases as d increases. The reason for this is that larger P and L means more energy consumption can be used for backscattering, but longer distances result in higher path loss. We have also found that when P, L is large enough and d is small enough, our proposed optimization scheme can take a certain P with the active antennaaThe same or even higher throughput is achieved with the value.
The implementation scheme is as shown in fig. 5, the establishment of the wireless energy supply backscatter communication system based on the IRS is divided into four steps, namely the establishment of a wireless energy supply backscatter communication network model based on the IRS, the expression of a backscatter communication throughput optimization problem, the solution of the backscatter communication throughput optimization problem and algorithm complexity analysis, and simulation verification.