A method for optimizing throughput of a radio frequency powered reflector and wireless relay system

Abstract

The present application relates to wireless communication technology and wireless energy transfer technology field, and disclose a kind of radio frequency energy reflection surface and the throughput optimization method of wireless relay system, comprising the following steps: establishing wireless relay system network model diagram;Determine relay and IRS battery energy state;Establish system throughput model;Formulate relay and IRS energy collection and consumption, relay forwarding information transmission power, mode selection constraint condition;According to all channels obtained by central controller, with system throughput maximization as optimization goal, it has solved that existing research has considered the IRS using fixed power supply mode and wireless relay assisted communication system, but is limited by battery capacity, in the situation that battery cannot be replaced or the cost of replacing battery is high, the working life of IRS and wireless relay is limited, using radio frequency energy supply IRS and wireless relay, can make system working life not be limited by the battery capacity used by wireless relay and IRS, while improving the throughput performance of system.

Description

Technical Field

[0001] This invention relates to the fields of wireless communication technology and wireless power transmission technology, specifically to a method for optimizing the throughput of a radio frequency power supply reflector and a wireless relay system. Background Technology

[0002] With the ever-increasing demand for data traffic on wireless networks, 5G mobile communication networks need to achieve 1000 times the network capacity for at least 100 billion devices. With the massive proliferation of smartphones, computers, smart home appliances, and sensors for autonomous vehicles, it is projected to connect at least 100 billion devices globally with approximately 7.6 billion mobile users, providing a personal user experience of up to 10Gb / s. With the surge in traffic and devices, 5G must also integrate human-machine interaction and machine-to-machine communication to facilitate more flexible information sharing in a networked society, aiming for one million connections per square kilometer. Therefore, sensors are expected to become wireless communication entities for exchanging information, leading to the well-known Internet of Things (IoT). However, the massive increase in the number of wireless devices presents serious sustainability challenges for future cellular networks. With economic and social development, coupled with the growing material and cultural demands for higher standards in wireless communication technology, Simultaneous Wireless Information and Power Transfer (SWIPT) technology has become a major research direction.

[0003] SWIPT technology is an extension of both Wireless Information Transmission (WIT) and Wireless Energy Transmission (WPT) technologies. This technology requires no additional power supply, signal, or spectrum, and the system maintains the same high spectral efficiency. The key technology for implementing SWIPT communication lies in the design of the hardware circuitry, especially at the relay nodes, which affects the signal-to-noise ratio (SNR) of the destination node. Current mainstream receiver architectures include Power Splitting (PS), Time Switching (TS), and Antenna Switching (AS). Currently, there is considerable research on system performance in academia and industry; metrics such as interrupt probability and throughput can be used to evaluate differences in system performance.

[0004] To improve the channel gain provided by wireless relays and enhance the quality of communication systems, this invention combines wireless power-carrying transmission (SWIPT) to broaden the applicability of communication protocols. This invention considers the circuit power consumption of relays and intelligent reflectors (IRS) and utilizes relays and intelligent reflectors to assist wireless communication systems in signal transmission. Through energy scheduling, it improves the throughput performance of wireless relay systems and proposes a throughput optimization method for radio frequency powered reflectors and wireless relay systems. This method solves the energy scheduling problem of relays and intelligent reflectors (IRS) to maximize the system's throughput performance. Summary of the Invention

[0005] The purpose of this invention is to provide a method for optimizing the throughput of a radio frequency power supply reflector and a wireless relay system, thereby solving the problems in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing the throughput of a radio frequency power supply reflector and a wireless relay system, comprising the following steps:

[0007] S1: Establish a network model diagram for the wireless relay system;

[0008] S2: Determine the energy state of the relay and IRS batteries;

[0009] S3: Establish a system throughput model;

[0010] S4: Define relay and IRS energy harvesting and consumption, relay forwarding information transmission power, and mode selection constraints;

[0011] S5: Based on all channels obtained by the central controller, formulate mode selection, time slot and battery energy scheduling and allocation strategies with the optimization goal of maximizing system throughput.

[0012] Preferably, an optimization method for energy scheduling in a wireless relay system is provided, comprising the following steps:

[0013] A1: Based on a new energy scheduling protocol, the wireless relay system can adaptively switch between (Relay-EH,IRS-EH) mode and (Relay-(EH,IR,IT),IRS-EH) mode according to channel conditions and the energy status of the IRS battery.

[0014] A2: Based on the developed protocol, model the system optimization problem to maximize the average throughput from the source to the destination node;

[0015] A3: Using the Lyapunov optimization method, the problem is decomposed into a time-slot-based deterministic optimization problem, and the corresponding deterministic optimization problem is solved using convex optimization theory.

[0016] Preferably, the optimization steps for A1 are as follows: establishing a channel model and designing a communication signal transmission protocol.

[0017] Preferably, the specific steps of A2 are as follows: energy model, system throughput model, and problem modeling.

[0018] This invention provides a method for optimizing the throughput of a radio frequency power supply reflector and a wireless relay system. This method for optimizing the throughput of a radio frequency power supply reflector and a wireless relay system has the following beneficial effects:

[0019] (1) In terms of system model, it solves the problem that existing studies have considered the use of fixed power supply (such as battery) to assist communication system with IRS and wireless relay. However, due to the limitation of battery capacity, the working life of IRS and wireless relay is limited when the battery cannot be replaced or the cost of replacing the battery is high. By adopting radio frequency powered IRS and wireless relay, the working life of the system can be freed from the limitation of battery capacity used by wireless relay and IRS, while improving the throughput performance of the system.

[0020] (2) In a system with relays, compared with a single relay, the presence of a smart reflector (IRS) makes wireless communication, time allocation and energy scheduling more flexible, which can further improve the throughput performance of the wireless relay system. Attached Figure Description

[0021] Figure 1 This is a network model diagram of the wireless relay system of the present invention;

[0022] Figure 2 This is a flowchart of the present invention;

[0023] Figure 3 This is a performance comparison chart of various schemes under different signal transmission powers at the source end of the present invention;

[0024] Figure 4 This is a performance comparison chart of various schemes under different numbers of IRS reflective elements according to the present invention.

[0025] Figure 5 This is a simulation result diagram of the distance between the source end and the IRS in this invention.

[0026] Figure 6 This is a system schematic diagram of the present invention.

[0027] In the diagram: 1. Box body 2. Side door 3. Pop-out mechanism 4. Fixing mechanism 5. Grip 6. Grinding wheel body 7. Operating door 8. Partition 301. Long rod 302. Pin 303. Pull plate one 304. Top rod 305. Spring one 306. Arc-shaped push plate 307. Fixing block one 308. Nut 401. Insert rod 402. Fixing block two 403. Pull plate two 404. Short pin 405. Spring two. Detailed Implementation

[0028] like Figure 1-4 As shown, the present invention provides a technical solution: a method for optimizing the throughput of a radio frequency power supply reflector and a wireless relay system, comprising the following steps:

[0029] S1: Establish a network model diagram for the wireless relay system;

[0030] S2: Determine the energy state of the relay and IRS batteries;

[0031] S3: Establish a system throughput model;

[0032] S4: Define relay and IRS energy harvesting and consumption, relay forwarding information transmission power, and mode selection constraints;

[0033] S5: Based on all channels obtained by the central controller, formulate mode selection, time slot and battery energy scheduling and allocation strategies with the optimization goal of maximizing system throughput.

[0034] An optimization method for energy scheduling in a wireless relay system is provided, comprising the following steps:

[0035] A1: Based on a new energy scheduling protocol, the wireless relay system can adaptively switch between (Relay-EH,IRS-EH) mode and (Relay-(EH,IR,IT),IRS-EH) mode according to channel conditions and the energy status of the IRS battery.

[0036] The optimization steps for A1 are as follows: establish a channel model and design a communication signal transmission protocol;

[0037] Establish a channel model:

[0038] Based on the time-varying nature of the channels, it is assumed that the channels in the wireless relay system are quasi-static. Each information transmission task from the source node to the destination node is performed on a separate time slot basis. That is, at the beginning of each time slot when the source node S transmits a signal, the IRS controller and the relay decide whether to collect enough energy to transmit the signal transmitted by the source node to the destination node (D). It is assumed that the channels in the IRS-assisted system under consideration remain constant within a time slot, but they can vary independently in different time slots;

[0039] As mentioned above, in the k-th time slot, the channel coefficients for the SR, SI, RI, ID, and RD links are expressed as h. l (k), l∈{SR,SI,RI,ID,RD}, are modeled as Rice distributions, as shown in the following formula: Where K m It is the Rice factor associated with small-scale fading, where It is the array response vector of ULA. φ represents the non-direct component in a Ricean fading channel. Each element represents a random scattering component, which is a stationary narrowband Gaussian process with a mean of 0 and a variance of σ². m (k) represents the arrival angle or departure angle of the corresponding signal, and then (·) T This is represented as a transpose operation. There is another link h from the source to the IRS to collect RF signal energy. SI-EH (k) represents the Rayleigh fading channel;

[0040] also, Let λ represent the equivalent reflection vector of the IRS in the k-th time slot, where the reflection coefficient λ I Let λ be a constant, satisfying the condition 0 ≤ λ I ≤1, let in It is the phase shift of the m-th reflecting unit of the IRS, θ m (k)∈[0,2π), let

[0041] To simplify the analysis, the channel coefficient of the SID link in the k-th time slot can be expressed as h. SID (k)=h SI (k)Θ M (k)h ID (k), using rate-free coding techniques, the channel coefficients of the RID link reflected by the relay via link RD and IRS can be expressed as: h RID (k)=h RD (k)+(h RI (k)Θ M (k)h ID (k)).

[0042] Communication signal transmission protocol design:

[0043] Consider a strategy for relay and IRS cooperative signal transmission. At the beginning of the k-th time slot, consider using the relay and IRS to collect RF signal energy or cooperate in signal transmission to improve system throughput. Therefore, the system operates in two modes, such as... Figure 6As shown: Mode a and Mode b are denoted as a subset N∈{a,b}. The system can only select one mode within a time slot. If mode n is selected, the constraint is S. n (k) = 1, other patterns S n (k) = 0. In mode a, the source node sends an RF signal to the destination node; the relay only collects energy and does not forward it. The IRS in t... I,a-EH The energy of the radio frequency signal is collected within time (k), and in time t I,a-IT (k) The reflected signal reaches the destination node within time (k); in mode b, the source node sends an RF signal to the destination node, and the relay and IRS are within time t. I,b-EH (k) collects energy from the radio frequency signal, and t I,b-IT1 During time (k), the relay receives the signal sent by the source, and the IRS reflects the signal from the source to the destination node and the relay. During time t... I,b-IT2 Within time (k), the relay encodes the signal, which is then reflected to the destination node via the RD link and IRS. The IRS reflects the signals from the source and relay to the destination node. Therefore, the time constraint is:

[0044] S a (k)t R-a-EH (k)+S b (k)(t R-b-EH (k)+t R-b-IR (k)+t R-b-IT (k))=T

[0045] S a (k)(t I-a-EH (k)+t I-a-IT (k))+S b (k)(t I-b-EH (k)+t I-b-IT1 (k)+t I-b-IT2 (k))=T

[0046] The mode selection constraint is: S n (k)∈{0,1},∑ n∈N S n (k) = 1.

[0047] A2: Based on the developed protocol, model the system optimization problem to maximize the average throughput from the source to the destination node;

[0048] The specific steps of A2 are as follows: energy model, system throughput model, and problem modeling;

[0049] Energy model:

[0050] Assume the source end is P S The signal is transmitted to the destination node at a constant power. Simultaneously, the energy harvesting efficiency of the IRS is η. I It is equipped with a rechargeable battery with a maximum capacity of B. I-max The energy required to adjust the phase shift for a single element reflecting the signal is μ. I The efficiency of energy harvesting by the relay is η. R It is equipped with a rechargeable battery, and the relay's internal circuitry consumes μg / m³ of power. R The power of the relay signal is P. R-b-IT (k), the maximum battery capacity is B R-max ;

[0051] This protocol adopts the TS protocol, and the energy collection formula for the IRS is: E I,n (k)=η I |h SI-EH (k)| 2 P S t I-n-EH (k), n∈{a,b}, the energy consumption formula is: C I-a (k)=Mμ I t I-a-IT (k), C I-b (k)=Mμ I (t I-b-IT1 (k)+t I,b-IT2 (k)); If mode a is selected, the Relay energy collection formula is: E R-a (k)=η R |h SR (k)| 2 P S T, the energy consumed is calculated using the formula: C R-a (k)=0, if mode b is selected, the formula for relay energy collection is: E R-b (k)=η R |h SR (k)| 2 P S t R-b-EH (k), the energy consumption formula for relay is: C R-b (k)=μ R (t R-b-IR (k)+t R-b-IT (k))+P R-b-IT (k)t R-b-IT (k);

[0052] Therefore, the formulas for energy collection and consumption by relays and IRS are as follows: E i (k)=∑n∈N S n (k)E i,n (k), i∈{R,I},C i (k)=∑ n∈N S n (k)C i-n (k), i∈{R,I}, define the battery energy state of the relay and IRS in the k-th time slot as b i (k), i∈{R,I}, the battery energy update status of the relay and IRS in the k-th and k+1-th time slots is: b R (k+1)=b R (k)+E R (k)-C R (k)(2),b I (k+1)=b I (k)+E I (k)-C I (k)(3).

[0053] System throughput model:

[0054] In the k-th time slot, the mode selection constraint is S. n (k)∈{0,1},∑ n∈ NS n (k) = 1 (1.1). If the system selects mode a, the throughput is If the system selects mode b, the throughput is: R b (k)=min{γ b1 (k),γ b2 (k)}, where The system throughput is:

[0055] Problem modeling:

[0056] Assume π = {x(0), x(1), ..., x(K)} ∈ ∏, which represents a deterministic decision made by the system to determine the battery state of the relay and IRS, as well as the system throughput. Here, x(k) is the decision made by the system in the k-th time slot, and ∏ is the set of all strategies adopted. If the source adopts strategy π, then the average throughput can be expressed as...

[0057] The aim is to find the optimal strategy π * To maximize the average maximum throughput of the destination node, we obtain the optimization problem (P1):

[0058] (P1)

[0059] st

[0060] (C1)S a (k)t R-a-EH (k)+S b (k)(t R-b-EH (k)+t R-b-IR (k)+t R-b-IT (k))=T.(1.1a)

[0061] (C2)S a (k)(t I-a-EH (k)+t I-a-IT (k))+S b (k)(t I-b-EH (k)+t I-b-IT1 (k)+t I-b-IT2 (k))=T.(1.1b)

[0062] (C3)

[0063] (C4)

[0064] (C5)

[0065] A3: Using the Lyapunov optimization method, the problem is decomposed into a time-slot-based deterministic optimization problem, and the corresponding deterministic optimization problem is solved using convex optimization theory.

[0066] The specific steps for A3 are as follows:

[0067] C1: Battery state of energy based on relay and IRS i (k), i∈{R,I} Introduce a virtual queue Where A i This refers to the perturbation value of energy accumulation, which can be described as X. i (k+1)=X i (k)+E i (k)-E i (k), i = R, I, and they are merged into a virtual queue.

[0068] C2: Define the quadratic Lyapunov function as

[0069] C3: Introducing the Lyapunov drift function From this, we can derive: L(X(k+1))-L(X(k))=(1 / 2)(E R (k)-C R (k)) 2 +XR (k)(E R (k)-C R (k))+(1 / 2)(E I (k)-C I (k)) 2 +X I (k)(E I (k)-C I (k) gives the upper bound of the expressions for Relay and IRS: 0 ≤ E R (k)≤η R PT||h SR (k)|| 2 0≤E I (k)≤η I PTg SI-EH (k) 0≤C I (k)=Mμ I t IT (k)≤Mμ I T, 0≤C R (k)≤(μ R +P R-n-IT (k))T,0≤(E I (k)-C I (k)) 2 ,(E I (k)-C I (k)) 2 ≤max{η I PT||g SI-EH (k)||,Mμ I T},

[0070] C4: Further define the Lyapunov drift penalty function, which is obtained by weighting the Lyapunov drift function and the objective function of the deterministic optimization problem: Where V is a non-negative weighting factor.

[0071] C5: Define an upper bound for the Lyapunov drift penalty function: Δ(X(k))≤B+∑ i＝R,I X i (k)E[(E i (k)-C i (k))|X i [(k)], where B is a finite constant of V,

[0072] C6: Order The formula in S5 can be rewritten as the following expression:

[0073] Δ(X(k))-VE[ψ(k)|X(k)]≤B+∑ i＝R,I X i (k)E[(E i (k)-C i (k))|X i (k)]-VE[ψ(k)|X(k)].

[0074] C7: Transform the objective function in the optimization problem into a Lyapunov drift penalty function, solve for the upper bound of the drift penalty function, and transform the original problem into a problem of finding the minimum value of this upper bound, as shown below:

[0075] (P3)

[0076] stS n (k)∈{0,1},∑ n∈N S n (k)=1

[0077] S a (k)t R-a-EH (k)+S b (k)(t R-b-EH (k)+t R-b-IR (k)+t R-b-IT (k))=T

[0078] S a (k)(t I-a-EH (k)+t I-a-IT (k))+S b (k)(t I-b-EH (k)+t I-b-IT1 (k)+t I-b-IT2 (k))=T

[0079]

[0080]

[0081]

[0082] C8: Solving for the optimal mode selection based on problem-solving and system optimization algorithms. This involves considering the system operating mode indicator S, which is the optimization variable. n (k) is a binary optimization variable. However, since there are only two system operating modes, the optimal system operating mode can be determined by solving the corresponding optimization problems under the two system operating modes respectively, thereby obtaining the optimal solution to the optimization problem.

[0083] 1)S a(k) = 1, the wireless relay system selects the RELAY-EH, IRS-(EH,IT) mode, and the optimization problem is rewritten as P2:

[0084] (P2)

[0085] st

[0086]

[0087] t I-a-EH (k)+t I-a-IT (k)=T,t I-a-EH (k)≥0,t I-a-IT (k)≥0

[0088] whereψ a (k)={t I-a-EH (k),t I-a-IT (k)}.

[0089] The phase shift Θ of the IRS is solved using a phase calibration method. a (k), to obtain Solving for the optimization variable ψ using convex optimization theory a (k) It can be proven that the above optimization problem is a convex problem. Therefore, the optimal solution to this problem can be obtained by the interior-point method, and the average throughput R can be calculated. a (k) and the optimal value of the objective function Opt a (k).

[0090] 2)S b (k) = 1, the wireless relay system selects the RELAY-(EH,IR,IT)IRS-(EH,IT) mode, and introduces auxiliary variables. ,make The optimization problem can be rewritten as (P3):

[0091] (P3)

[0092] st

[0093]

[0094] t R-b-EH (k)=t I-b-EH (k)≥0,t R-b-IR (k)=t I-b-IT1 (k)≥0,t R-b-IT (k)=t I-b-IT2 (k)≥0

[0095]

[0096]

[0097] whereψ b (k)={t R-b-EH (k),t R-b-IR (k),t R-b-IT (k),t I-b-EH (k),t I-b-IT1 (k),t I-b-IT2 (k),Θ b2 (k),Θ b3 (k),P R-b-IT (k)}.

[0098] Given Θ b2 (k),Θ b3 (k) Initial value. It can be proven that the conditions and constraints in the above optimization problem are convex functions. Solving for the average throughput R... b1 (k) and the optimal value of the objective function Opt a1 (k). Then keep the remainder Θ. b2 (k),Θ b3 The optimization variables other than (k) remain unchanged. First, optimize variable Θ. b3 (k), definition Rewrite about Θ b3 The optimization problem for (k) is B1.1:

[0099] B1.1:

[0100] whereΦ b,SID (k)=λ I diag(h SI (k))h ID (k)∈C M×1 ,Φ b,RID (k)=λ I diag(h RI (k))h ID (k)∈C 1×1 .

[0101] Then, the optimization problem B1.1 is transformed into B1.2:

[0102] B1.2

[0103] Introducing auxiliary variable t b3 (k) transforms optimization problem B1.2 into B1.3:

[0104] B1.3

[0105] where

[0106]

[0107] make Satisfy the constraint: V b3 (k)≥0,rank(V b3 (k))=1. This simplifies optimization problem B1.3 to B1.4:

[0108] B1.4

[0109] It can be proven that optimization problem B1.4 is a convex problem, and the solution can be obtained up to the IRS phase shift Θ. b3 (k) The optimal solution. And so on, with Θ fixed. b3 (k) value, solve for IRS phase shift Θ b2 (k) value.

[0110] Let v b2,m (k)=θ b2,m (k)and Introducing auxiliary variable δ b2 The original optimization problem is rewritten as B2.1:

[0111] B2.1

[0112] st

[0113] where

[0114] Φ b,SID (k)=λ I diag(h SI (k))h ID (k)∈C M×1 ,Φ b,SIR =λ I diag(h SI (k))h RI (k)∈C 1×1 ,

[0115]

[0116] Through formula transformation, the following formula can be obtained by clever conversion:

[0117]

[0118]

[0119] Its correlation matrix is ​​shown below:

[0120]

[0121] Introducing auxiliary variable t b2 (k), rewrite optimization problem B2.1 as B2.2:

[0122] B2.2

[0123]

[0124] where

[0125] definition make It satisfies V b2 (k)≥0,rank(V b2 (k))=1. Rewrite optimization problem B2.2 as B2.3:

[0126] B2.3

[0127]

[0128] It can be proven that this is a convex problem, and the optimal solution Θ of the IRS can be obtained. b2 (k), Θ b2 (k) and Θ b3 Substituting (k) and other optimization variables into (P3) yields R. b2 (k), we can know R b2 (k)≥R b1 (k). When |R b2 (k)-R b1 If (k)|≥γ (γ is a threshold), then substitute it into the updated Θ b2 (k) and Θ b3 Given the value of (k), continue iterating to find the optimal value. Otherwise, output the sum of the optimal value and R. b (k) and the objective function value Opt b (k). Obtain the optimal value R. * (k)=max{R a (k),R b (k)} and optimal mode selection n * (k)=argmax{S a (k),S b (k)}. Define R sum =0, let R sum =R sum +R * (k) and update the battery state of the relay and IRS according to equations (2) and (3). i(k+1), i∈{R,I}. At the last time frame, the battery state of the relay and IRS satisfies formula b. i (K)+E i (K)-C i (K)=0,i∈{R,I}, and finally the average throughput R is obtained. aver =R sum / K.

Claims

1. A method for optimizing throughput in a radio frequency power supply reflector and wireless relay system, characterized in that: Includes the following steps: S1: Establish a network model diagram for the wireless relay system; S2: Determine the energy state of the relay and IRS batteries; S3: Establish a system throughput model; S4: Define relay and IRS energy harvesting and consumption, relay forwarding information transmission power, and mode selection constraints; S5: Based on all channels obtained by the central controller, with the optimization goal of maximizing system throughput, formulate mode selection, time slot and battery energy scheduling and allocation strategies; S5 includes: A1: Based on a new energy scheduling protocol, the wireless relay system can adaptively switch between (Relay-EH,IRS-EH) mode and (Relay-(EH,IR,IT),IRS-EH) mode according to channel conditions and the energy status of the IRS battery. A2: Based on the developed protocol, model the system optimization problem to maximize the average throughput from the source to the destination node; A3: Using the Lyapunov optimization method, it is decomposed into a time-slot-based deterministic optimization problem, and the corresponding deterministic optimization problem is solved using convex optimization theory; The optimization steps for A1 are as follows: establish a channel model and design a communication signal transmission protocol; Establish a channel model: In the k-th time slot, the channel coefficients for the SR, SI, RI, ID, and RD links are expressed as follows: They can be modeled as Rice distributions, as shown in the following formula: ,in It is the Rice factor associated with small-scale fading, where It is the array response vector of ULA; , , is the non-direct component in the Ricean fading channel. Each element represents a random scattering component, which is a stationary narrowband Gaussian process with a mean of 0 and a variance of σ². This is expressed as the arrival angle or departure angle of the corresponding signal, then... This indicates a transpose operation, with an additional link from the source to the IRS to collect RF signal energy. This is a Rayleigh fading channel; also, Let represent the equivalent reflection vector of the IRS in the k-th time slot, where the reflection coefficient is denoted by . It is a constant that satisfies the condition ,make ,in It is the phase shift of the m-th reflecting unit in the IRS. ,make ; In the k-th time slot The channel coefficient of the link is expressed as Using rateless coding techniques, the channel coefficients of the RID link reflected by the relay via link RD and IRS are expressed as follows: ; Relay-EH is for relay energy collection, IRS-EH is for IRS energy collection, Relay-(EH,IR,IT) is for relay energy collection, information reception or information transmission, and IRS-EH is for IRS energy collection. SR is from source to relay, SI is from source to IRS, RI is from relay to IRS, ID is from IRS to destination, and RD is from relay to destination.

2. The throughput optimization method for a radio frequency power supply reflector and wireless relay system according to claim 1, characterized in that: The specific steps of A2 are as follows: energy model, system throughput model, and problem modeling.

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