Wireless communication system throughput online optimization method based on intelligent reflecting surface

Abstract

The invention provides a wireless communication system throughput online optimization method based on an intelligent reflecting surface. The method comprises the following steps: constructing an intelligent reflecting surface auxiliary wireless communication system with a battery; dividing the intelligent reflector-assisted wireless communication system into an energy acquisition stage and a signal reflection stage based on an online time switching strategy, and establishing a long-term average throughput maximization model by taking the long-term average throughput maximization of the intelligent reflector-assisted wireless communication system and the realization of battery energy neutralization as targets; and the intelligent reflector-assisted wireless communication system executes data transmission according to the optimal time switching strategy, and updates the state of remaining power of a battery in the intelligent reflector-assisted wireless communication system in real time. Compared with the prior art, the method has the advantages that after the actual channel environment arrives, according to the decision made by the system, the battery remaining capacity state is executed and updated, the continuity and stability of communication in the dynamically changing wireless network environment are guaranteed, and battery energy neutralization is achieved.

Description

Technical Field

[0001] The present invention is applicable to the technical field of wireless communication, and in particular relates to an online optimization method for throughput of a wireless communication system based on an intelligent reflecting surface. Background Art

[0002] With the rapid development of wireless networks, intelligent reflecting surfaces (IRS) have been widely studied in improving the signal coverage and communication efficiency of wireless networks. IRS has flexible deployment characteristics and can be easily installed on building exterior walls, billboards or other structures, enabling it to be seamlessly integrated into existing wireless communication systems. However, most existing IRS optimization strategies focus on improving signal processing or propagation models, while ignoring the energy consumption of the IRS itself. In wireless communication networks assisted by battery IRS, the energy consumption of the IRS is provided by a small battery, but the battery has limited energy reserves and needs to be replaced or recharged regularly as the energy is consumed, which is extremely inconvenient in environments with limited resources or difficult human intervention. Although energy harvesting technology (such as radio frequency (RF) energy harvesting) is a feasible solution to provide continuous power for IRS, due to the dynamic nature of the channel in the wireless environment, traditional channel estimation and energy management strategies often cannot adapt to environmental changes in real time, reducing energy utilization efficiency and affecting the stability of signal quality. In particular, the states of the incident channel and reflected channel on both sides of the IRS are in dynamic change, and it is difficult for the system to obtain channel state information in a timely and accurate manner, which increases the decision-making complexity in achieving energy-neutral operation of the IRS and maximizing long-term average throughput.

[0003] Therefore, there is an urgent need for a new online optimization method for the throughput of a wireless communication system based on a smart reflective surface to solve the above technical problems. Summary of the invention

[0004] The present invention proposes an online optimization method for the throughput of a wireless communication system based on an intelligent reflective surface, aiming to adaptively maintain a certain residual power of the intelligent reflective surface in different dynamic environments through an optimal time switching strategy, thereby ensuring the continuity and stability of communication in a dynamically changing wireless network environment.

[0005] The present invention proposes an online optimization method for throughput of a wireless communication system based on a smart reflective surface, comprising the following steps:

[0006] S1. Construct a smart reflective surface-assisted wireless communication system with a battery;

[0007] S2. Based on the online time switching strategy, the intelligent reflective surface assisted wireless communication system is divided into an energy collection stage and a signal reflection stage, and a long-term average throughput maximization model is established with the goal of maximizing the long-term average throughput of the intelligent reflective surface assisted wireless communication system and achieving battery energy neutrality;

[0008] S3, solving the long-term average throughput maximization model based on the Markov decision process, the strategy iteration algorithm and the target reward function to obtain the optimal time switching strategy;

[0009] S4. The intelligent reflecting surface assisted wireless communication system performs data transmission according to the optimal time switching strategy, and updates the remaining power status of the battery in the intelligent reflecting surface assisted wireless communication system in real time.

[0010] Preferably, the smart reflective surface assisted wireless communication system comprises a base station, a smart reflective surface equipped with a rechargeable battery, and a plurality of single-antenna sensor nodes.

[0011] Preferably, the long-term average throughput maximization model satisfies the following relationship:

[0012]

[0013] st0≤E c (n)≤E a (n+τ(n))

[0014] 0≤E a (n+1)≤E max

[0015] 0≤τ(n)≤1

[0016] E c (n)≤N r P u ;

[0017] Among them, P u represents the power consumption of a single reflective element in the smart reflective surface, N r represents the number of reflective elements in the smart reflective surface, E c (n) represents the energy consumption of the smart reflective surface at time slot n, τ(n) represents the time allocation of the energy collection phase of the smart reflective surface at time slot n, E a (n+τ(n)) represents the remaining battery energy of the smart reflective surface after the energy collection phase at time slot n, E max represents the maximum battery capacity of the smart reflective surface, E a (n) represents the battery energy state at time slot n, Jπ represents the long-term average throughput, C(n) represents the throughput under the execution strategy at time slot n, and Ea (n+1) represents the battery energy status at time slot n+1.

[0018] Preferably, step S3 includes the following sub-steps:

[0019] S31, establishing a system state model based on the Markov decision process, initializing the channel state transfer matrix and the battery power state;

[0020] S32, predicting the future channel state according to the system state model, and determining the battery energy neutral boundary according to the sliding window historical data;

[0021] S33, iteratively optimizing the system state model according to the strategy iteration algorithm;

[0022] S34, determine whether the current solution is the optimal solution; if so, output the current solution as the optimal time switching strategy; if not, return to step S33.

[0023] Preferably, step S31 includes the following sub-steps:

[0024] S311, establishing a state space based on the channel state of the first transmission channel, the channel state of the second transmission channel, and the remaining battery power state of the smart reflective surface;

[0025] S312, establishing an action space based on the time proportion of the battery of the smart reflective surface in the absorption state and the reflection state;

[0026] S313, establishing a state transfer function;

[0027] S314: Establish the target reward function to obtain the system state model.

[0028] Preferably, the target reward function satisfies the following relationship:

[0029]

[0030] Among them, R e (t) represents the target reward function, E a (t) represents the battery energy state at time slot t, E L (t) and E U (t) are the lower limit and upper limit of battery energy neutrality, respectively, and π represents a constant.

[0031] Preferably, the optimal time switching strategy satisfies the following relationship:

[0032]

[0033] Among them, τ *(t) represents the optimal time switching strategy, represents the average return of the s′th state of the system state model under strategy τ, γ represents the discount factor, a(t)∈A represents the action strategy selected in the action space A in time slot t, R e (t) represents the target reward function, T represents the state transfer function, and a represents the action taken.

[0034] Compared with the prior art, the present invention uses a sliding window to update historical data in real time in each time slot. The historical data includes the incident channel state and the reflection channel state of the past time slot, the difference between the energy collection and the reflection energy consumption of the intelligent reflection surface, and the battery energy state. Through the energy difference data and the battery energy state, the system obtains the initial energy boundary. According to the channel state of the past time slot, the energy boundary adjustment factor is calculated, and the initial boundary is adjusted to obtain the energy-neutral target boundary. The energy optimization method based on the Markov process and the online time switching strategy is adopted. According to the designed target reward function, when the channel conditions on both sides of the comprehensive intelligent reflection surface are not good, the system stores energy through the online time switching strategy. When the comprehensive channel conditions of the intelligent reflection surface are good, efficient data transmission is implemented. An optimal time switching strategy based on the predicted state is obtained through the algorithm optimization strategy of strategy iteration. After the actual channel environment arrives, it is executed according to the decision made by the system, and the battery remaining power state is updated. The intelligent reflection surface can adaptively maintain a certain remaining power in different dynamic environments, greatly enhancing the reliability and efficiency of the communication system, while ensuring the continuity and stability of communication in a dynamically changing wireless network environment. This enables energy-neutral operation of the smart reflector and maximizes the long-term average throughput of the system, greatly improving the overall performance of the wireless communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The present invention will be described in detail below in conjunction with the accompanying drawings. The above and other aspects of the present invention will become clearer and easier to understand through the detailed description made in conjunction with the following drawings. In the accompanying drawings:

[0036] Figure 1 It is a schematic structural diagram of a smart reflective surface-assisted wireless communication system with a battery according to an online optimization method for wireless communication system throughput based on a smart reflective surface provided in an embodiment of the present invention.

[0037] Figure 2 It is a flowchart of a method for online optimization of throughput of a wireless communication system based on a smart reflective surface provided in an embodiment of the present invention;

[0038] Figure 3 It is a logical schematic diagram of an online time switching strategy of an online optimization method for throughput of a wireless communication system based on an intelligent reflective surface provided in an embodiment of the present invention;

[0039] Figure 4 It is a schematic diagram of the target reward function result when the power constraint of the online optimization method for throughput of a wireless communication system based on a smart reflective surface provided by an embodiment of the present invention is [0.3, 0.7];

[0040] Figure 5 It is a schematic diagram of changes in channel environment gains on both sides of a smart reflecting surface in an online optimization method for throughput of a wireless communication system based on a smart reflecting surface provided by an embodiment of the present invention;

[0041] Figure 6 It is a schematic diagram comparing the long-term average throughput of the smart reflective surface assisted wireless communication system with the online energy management optimization method applicable to the smart reflective surface provided by an embodiment of the present invention and the offline known channel state;

[0042] Figure 7 It is a schematic diagram of a neutral state of the remaining battery power of an intelligent reflecting surface in the online optimization method for throughput of a wireless communication system based on an intelligent reflecting surface provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0044] Please refer to Figure 1-Figure 7 The present invention proposes an online optimization method for throughput of a wireless communication system based on a smart reflective surface, comprising the following steps:

[0045] S1. Construct a smart reflective surface-assisted wireless communication system with a battery;

[0046] In an embodiment of the present invention, the smart reflective surface assisted wireless communication system includes a base station, a smart reflective surface equipped with a rechargeable battery, and a plurality of single-antenna sensor nodes.

[0047] Specifically, the intelligent reflective surface assisted wireless communication system with a battery includes N t Base station (BS) with 1 transmitting antenna, N r It consists of an IRS with fixed positions of passive reflective elements and K single-antenna sensor nodes. Figure 1 shown.

[0048] The wireless transmission channel from BS to IRS is defined as And the wireless transmission channel from IRS to end user is denoted as Channel gains G and F on both sides of the IRS k They are given by the following formulas respectively

[0049]

[0050] in and They represent the large-scale path loss from BS to IRS plane and from IRS plane to sensor nodes respectively. g and ε f They represent the small-scale attenuation of the channel gain between BS and IRS and IRS and sensor nodes, respectively, reflecting the randomness and instability of the channel. Assume that the wireless channel transmission is block fading, that is, the channel fading remains unchanged within a time slot t, but the channel gain is time-varying in the entire time slot sequence.

[0051] The wireless channel gain on both sides of the IRS is modeled as a stationary, time-homogeneous and irreducible Markov channel model, whose state space is C = {C1, C2, …, C M}, where each state C i represents different channel fading gains. Then the state transition of the Markov process can be expressed as: The channel gain state at time t+1 depends only on the state at time t, and has nothing to do with the previous state. In the present invention, it is assumed that the historical channel state information (HCSI) on both sides of the IRS is known. In a time slot, the signal received by the system relay IRS can be represented by the following vector form:

[0052]

[0053] Where P t is the base station transmission power, w∈C M×1 is the energy carrier signal vector transmitted from BS, n irs represents the noise vector at IRS. The energy received by IRS is expressed as

[0054] P irs (t) = ηP t ||G(t)w|| 2 (4)

[0055] Where η (0 < η < 1) represents the energy conversion efficiency, which depends on the rectification process and the energy collection circuit. The effect of noise power on the received energy is ignored here.

[0056] S2. Based on the online time switching strategy, the intelligent reflective surface assisted wireless communication system is divided into an energy collection phase and a signal reflection phase, and a long-term average throughput maximization model is established with the goal of maximizing the long-term average throughput of the intelligent reflective surface assisted wireless communication system and achieving battery energy neutrality;

[0057] In the embodiment of the present invention, after the signal energy is transmitted to the IRS, the IRS divides the time slot t into two stages: energy harvesting (EH) and signal reflection (SR) through a time factor τ∈[0,1].

[0058] A time slot t is divided into two phases: energy harvesting (EH) and signal reflection (SR). The battery capacity (E max ), the remaining battery power in time slot t is E a (t), is known only at the end of time slot t-1. The IRS adjusts the duration of these two phases by a time factor τ∈[0,1] within a complete time slot t. During the time τ, the IRS adjusts the amplitude β of the reflector n is 0, so that the IRS collects energy and stores it in the battery. In the optimal beam ||w|| 2 = 1, in the EH stage, the energy collection of IRS is

[0059] e(t)=τ(t)ηP t L0MNε g 2 (t) (5)

[0060] At this time, the energy of the IRS battery is E a (t+τ(t))=E a (t)+e(t). Within (1-τ) time, the IRS sets the reflection amplitude to 1, completely reflecting the energy signal from the BS to the user, and in this process, the IRS no longer absorbs energy, and its power consumption is provided by the energy stored in the battery. Therefore, the signal received at the kth sensor user is expressed as

[0061] y k (t) = F k (t)Φy irs (t)+n k (6)

[0062] where Φ = diag(vec(θ))∈C Nr×Nr .n k represents the Gaussian white noise at the sensor node. In the signal reflection SR stage, the energy that the sensor node can capture is given by

[0063] E k (t) = (1-τ)ηP t L0L k MN 2 ε g (t) 2 ε f (t) 2 (7)

[0064] According to the reflection scheme of time switching within a time slot, assuming that the available energy at the beginning of time slot t is E a (t), the kinetic equation for updating the battery state is obtained as follows:

[0065] E a (t+1)=min{E max , E a (t)+e(t)-P c (t)} (8)

[0066] P c (t)=(1-τ)NP u (9)

[0067] Where e(t) is the energy absorbed by the sub-time slot τ, P c (t) is the energy consumed by the IRS reflection operation in this time slot, P u represents the power consumption of each passive component. And the remaining battery capacity is updated and constrained to

[0068] E max ≥E a (t+1)≥0 (10)

[0069] The channel capacity of the system is expressed as:

[0070]

[0071] in is the average noise power of the system. To this end, the main objective of the present invention is to achieve battery energy neutral operation of the IRS and maximize the long-term average throughput of the system by effectively managing the remaining power of the battery by adjusting the time factor τ.

[0072] The long-term average throughput maximization model satisfies the following relationship:

[0073]

[0074] st0≤E c (n)≤E a (n+τ(n))

[0075] 0≤E a (n+1)≤E max

[0076] 0≤τ(n)≤1

[0077] E c (n)≤N r P u ;

[0078] Among them, Pu represents the power consumption of a single reflective element in the smart reflective surface, N r represents the number of reflective elements in the smart reflective surface, E c (n) represents the energy consumption of the smart reflective surface at time slot n, τ(n) represents the time allocation of the energy collection phase of the smart reflective surface at time slot n, E a (n+τ(n)) represents the remaining battery energy of the smart reflective surface after the energy collection phase at time slot n, E max represents the maximum battery capacity of the smart reflective surface, E a (n) represents the battery energy state at time slot n, Jπ represents the long-term average throughput, C(n) represents the throughput under the execution strategy at time slot n, and E a (n+1) represents the battery energy status at time slot n+1.

[0079] By adjusting the time factor τ, the remaining battery power can be effectively managed to maximize the long-term average throughput of the system.

[0080] S3, solving the long-term average throughput maximization model based on the Markov decision process, the strategy iteration algorithm and the target reward function to obtain the optimal time switching strategy;

[0081] In the embodiment of the present invention, step S3 includes the following sub-steps:

[0082] S31, establishing a system state model based on the Markov decision process, initializing the channel state transfer matrix and the battery power state;

[0083] S32, predicting the future channel state according to the system state model, and determining the neutral boundary of the electric quantity and energy according to the sliding window historical data;

[0084] S33, iteratively optimizing the system state model according to the strategy iteration algorithm;

[0085] S34, determine whether the current solution is the optimal solution; if so, output the current solution as the optimal time switching strategy; if not, return to step S33.

[0086] In the embodiment of the present invention, step S31 includes the following sub-steps:

[0087] S311, establishing a state space based on the channel state of the first transmission channel, the channel state of the second transmission channel, and the remaining battery power state of the smart reflective surface;

[0088] S312, establishing an action space based on the time proportion of the battery of the smart reflective surface in the absorption state and the reflection state;

[0089] S313, establishing a state transfer function;

[0090] S314: Establish the target reward function to obtain the system state model.

[0091] In this embodiment of the present invention, the target reward function satisfies the following relationship:

[0092]

[0093] Among them, R e (t) represents the target reward function, E a (t) represents the battery energy state at time slot t, E L (t) and E U (t) are the lower limit and upper limit of battery energy neutrality, respectively, and π represents a constant.

[0094] In an embodiment of the present invention, the optimal time switching strategy satisfies the following relationship:

[0095]

[0096] Among them, τ * (n) represents the optimal time switching strategy, represents the average return of the s′th state of the system state model under strategy τ, γ represents the discount factor, a(t)∈A represents the action strategy selected in the action space A in time slot t, R e (t) represents the target reward function, T represents the state transfer function, and a represents the action taken. S4. The intelligent reflective surface assisted wireless communication system performs data transmission according to the optimal time switching strategy, and updates the remaining power status of the battery in the intelligent reflective surface assisted wireless communication system in real time.

[0097] Specifically, a system state model is established based on the Markov decision process, and a reward function R that is adaptive to environmental changes is designed. The Markov decision process is represented by a four-tuple (S, A, R, T), which are: state space S, action space A, reward function R and state transfer function T.

[0098] The state of the system state model consists of three parts: the channel state C from BS to IRS g and the channel state C from IRS to sensor user Uk f and the remaining battery charge state E of the IRS a The state space of the entire system S = C g ×C f ×E a Then in a certain time slot t, the system state is expressed as: s t={C g (t),C f (t),E a (t)}, where s∈S.

[0099] The action space of the system state model mainly considers the time proportion τ of absorption and reflection in a time slot, so the value range is τ∈[0,1]. t Expressed as: a t =τ.

[0100] The state transition probability function T of the system state model is used to better simulate the transition process of the channel gain state. Four channel gain states C = {C L ,C ML ,C MH ,C H}, respectively refer to low, medium-low, medium-high, and high gain. According to the known HCSI, the channel state transfer matrix on both sides of the IRS is constructed and The state transition function of the system T(s t+1 ∣s t ,a t ) describes that the system is in state s at time slot t t And perform action a t After that, it transfers to the next time slot t+1 state s t+1 The probability distribution of . Therefore, the state transition function T of the system is:

[0101]

[0102] For the update of the remaining battery capacity, at the beginning of time slot t, the remaining available capacity E in the battery a (t), and the working energy consumption of IRS in this time slot t is P c (t), the energy state E of the next time slot a (t+1) is calculated by formula (8).

[0103] Design the objective reward function R for optimizing the online energy management of IRS adaptive environmental changes. The purpose is to take actions that are conducive to battery energy neutrality management and energy signal transmission through positive rewards. Specifically: when the future incident and reflection channel state environment is good, the rechargeable battery state maintains a low power level, then the system will adopt a more aggressive strategy to transmit signal energy to the sensor node as much as possible. On the contrary, when the future reflection channel state environment is not good, the rechargeable battery remaining power level maintains a high power level, then the system will adopt a more conservative strategy to store signal energy as much as possible. The online dynamic management of IRS battery energy is realized to ensure that the system achieves IRS battery energy neutrality under channel uncertainty conditions and maximizes the long-term average throughput of the system.

[0104] In IRS battery power management, the system uses a rolling window to collect status information over a period of time, analyze the current channel status on both sides of the IRS, and predict the channel gain. The system calculates and compares the mean and variance of the error data Γ between the actual and predicted energy consumption to ensure that the error is controlled within 95%, so that even if there is an operational error, the IRS can still work normally using battery energy. At the same time, since the environmental models on both sides of the IRS change independently, the system adaptively adjusts the battery limit by analyzing historical channel data, optimizing the strategy while ensuring system operation. Therefore, the battery power has a suitable constraint range under each environmental model [E L (t),E U (t)]. Therefore, a reward for battery power management is given.

[0105]

[0106] Among them, R e (t) represents the target reward function, E a (t) represents the battery energy state at time slot t, E L (t) and E U (t) are the lower limit and upper limit of battery energy neutrality, respectively, and π represents a constant.

[0107] In MDP, the goal is to find a strategy π that maximizes the long-term expected reward, which expresses the average return that can be obtained in the long run by starting from the system state S and acting according to the strategy π. It can be expressed as:

[0108]

[0109] Here, R(t) is the reward received at time t. γ is a discount factor that represents the present value of future rewards, satisfying the boundary 0<γ<1.

[0110] Through the policy iteration algorithm, the optimal decision τ is obtained in each time block t based on the predicted expected gain value combined with the remaining energy of the rechargeable battery configured by the current IRS. * value.

[0111]

[0112] Among them, τ * (n) represents the optimal time switching strategy, represents the average return of the s′th state of the system state model under strategy τ, γ represents the discount factor, a(t)∈A represents the action strategy selected in the action space A in time slot t, R e (t) represents the target reward function, T represents the state transfer function, and a represents the action taken. S4. The intelligent reflective surface assisted wireless communication system performs data transmission according to the optimal time switching strategy, and updates the remaining power status of the battery in the intelligent reflective surface assisted wireless communication system in real time.

[0113] In an embodiment of the present invention, the intelligent reflecting surface assisted wireless communication system makes a good decision τ* before the arrival of the t time slot. After the actual t time slot channel state arrives, the intelligent reflecting surface assisted wireless communication system will execute data transmission according to the optimal time switching strategy and update the remaining battery power status in real time to ensure the battery energy neutral operation of the system and improve system performance.

[0114] Compared with the prior art, the present invention uses a sliding window to update historical data in real time in each time slot. The historical data includes the incident channel state and the reflection channel state of the past time slot, the difference between the energy collection and the reflection energy consumption of the intelligent reflection surface, and the battery energy state. Through the energy difference data and the battery energy state, the system obtains the initial energy boundary. The energy boundary adjustment factor is calculated according to the channel state of the past time slot, and the initial boundary is adjusted to obtain the energy-neutral target boundary. The energy optimization method based on the Markov process and the online time switching strategy is adopted. According to the designed target reward function, when the channel conditions on both sides of the comprehensive intelligent reflection surface are not good, the system stores energy through the online time switching strategy. When the comprehensive channel conditions of the intelligent reflection surface are good, efficient data transmission is implemented. An optimal time switching strategy based on the energy neutral boundary is obtained through the algorithm optimization strategy of strategy iteration. After the actual channel environment arrives, it is executed according to the decision made by the system, and the battery remaining power state is updated. The intelligent reflection surface can adaptively maintain a certain remaining power in different dynamic environments, greatly enhancing the reliability and efficiency of the communication system, while ensuring the continuity and stability of communication in a dynamically changing wireless network environment. This enables battery energy-neutral operation of the smart reflector and maximizes the long-term average throughput of the system, greatly improving the overall performance of the wireless communication system.

[0115] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0116] The embodiments of the present invention are described above in conjunction with the accompanying drawings. What is disclosed is only the preferred embodiment of the present invention. However, the present invention is not limited to the above-mentioned specific implementation manner. The above-mentioned specific implementation manner is only illustrative rather than restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms and equivalent changes without departing from the scope of protection of the purpose of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A method for online optimization of wireless communication system throughput based on smart reflective surface, characterized in that: The following steps are involved: S1. Construct a smart reflective surface-assisted wireless communication system with a battery; S2. Based on the online time switching strategy, the intelligent reflective surface assisted wireless communication system is divided into an energy collection stage and a signal reflection stage, and a long-term average throughput maximization model is established with the goal of maximizing the long-term average throughput of the intelligent reflective surface assisted wireless communication system and achieving battery energy neutrality; S3, solving the long-term average throughput maximization model based on the Markov decision process, the strategy iteration algorithm and the target reward function to obtain the optimal time switching strategy; S4. The intelligent reflecting surface assisted wireless communication system performs data transmission according to the optimal time switching strategy, and updates the remaining power status of the battery in the intelligent reflecting surface assisted wireless communication system in real time.

2. The online optimization method for throughput of a wireless communication system based on a smart reflective surface according to claim 1, characterized in that: The intelligent reflective surface assisted wireless communication system comprises a base station, an intelligent reflective surface equipped with a rechargeable battery, and a plurality of single-antenna sensor nodes.

3. The online optimization method for throughput of a wireless communication system based on a smart reflective surface according to claim 1, characterized in that: The long-term average throughput maximization model satisfies the following relationship: Among them, P u represents the power consumption of a single reflective element in the smart reflective surface, N r represents the number of reflective elements in the smart reflective surface, E c (n) represents the energy consumption of the smart reflective surface at time slot n, τ(n) represents the time allocation of the energy collection phase of the smart reflective surface at time slot n, E a (n+τ(n)) represents the remaining battery energy of the smart reflective surface after the energy collection phase at time slot n, E max Indicates the maximum battery capacity of the smart reflective surface, E a (n) represents the battery energy state at time slot n, Jπ represents the long-term average throughput, C(n) represents the throughput under the execution strategy at time slot n, and E a (n+1) represents the battery energy status at time slot n+1.

4. The method for online optimization of wireless communication system throughput based on smart reflective surface according to claim 1, characterized in that: Step S3 includes the following sub-steps: S31, establishing a system state model based on the Markov decision process, initializing the channel state transfer matrix and the battery power state; S32, predicting the future channel state according to the system state model, and determining the battery energy neutral boundary according to the sliding window historical data; S33, iteratively optimizing the system state model according to the strategy iteration algorithm; S34, determine whether the current solution is the optimal solution; if so, output the current solution as the optimal time switching strategy; if not, return to step S33.

5. The online optimization method for throughput of a wireless communication system based on a smart reflective surface according to claim 1, characterized in that: Step S31 includes the following sub-steps: S311, establishing a state space based on the channel state of the first transmission channel, the channel state of the second transmission channel, and the remaining battery power state of the smart reflective surface; S312, establishing an action space based on the time proportion of the battery of the smart reflective surface in the absorption state and the reflection state; S313, establishing a state transfer function; S314: Establish the target reward function to obtain the system state model.

6. The online energy management optimization method applicable to the smart reflective surface according to claim 5, characterized in that: The target reward function satisfies the following relationship: Among them, R e (t) represents the target reward function, E a (t) represents the battery energy state at time slot t, E L (t) and E U (t) are the lower limit and upper limit of battery energy neutrality, respectively, and π represents a constant.

7. The online optimization method for throughput of a wireless communication system based on a smart reflective surface according to claim 1, characterized in that: The optimal time switching strategy satisfies the following relationship: Among them, τ * (n) represents the optimal time switching strategy, represents the average return of the s′th state of the system state model under strategy τ, γ represents the discount factor, a=τ∈A represents the action strategy selected in the action space A in time slot t, R e (t) represents the target reward function, T represents the state transfer function, and a represents the action taken.

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