Optimization method for combining intelligent reflector shifting and time scheduling based on wireless energy supply Internet of Things network

Abstract

The invention relates to an optimization method for combining intelligent reflector shifting and time scheduling based on a wireless energy supply internet of things network, and belongs to the technical field of wireless communication. The problem of poor network self-maintenance capability caused by poor edge user communication quality and low throughput of a traditional WPCN is solved. The method comprises the following steps: firstly, constructing an optimization problem for maximizing the total throughput of a system in order to optimize phase shift and time scheduling of WET and WIT stages; then, respectively deducing the optimal closed-form solution forms of time distribution in the WET stage and the WIT stage, separating the RIS phase shift in the WIT stage from the optimization problem, and deriving the optimal solution expression of the RIS phase shift; and finally, under the condition that closed-form solutions of other optimization variables are known, an EBCD algorithm is adopted to obtain an RIS phase shift in a WET stage.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications and relates to an optimization method for joint intelligent reflector phase shift and time scheduling based on a wireless power supply Internet of Things network. Background Art

[0002] With the widespread application of the Internet of Things (IoT) in various fields such as smart healthcare, environmental monitoring, and emergency service event detection, more and more energy-constrained IoT devices are being deployed in various extreme and challenging environments. Traditional methods of extending device life, such as regular maintenance or battery replacement, are becoming increasingly unfeasible, and microwave wireless power transfer (WPT) technology has attracted much attention in recent years as a potential solution. This technology continuously provides radio frequency (RF) energy to devices through dedicated power stations, thereby supporting wireless powered communication networks (WPCN). Specifically, the device uses the energy collected from the power station in the downlink wireless energy transfer (WET) phase to transmit data to the access point in the uplink wireless information transfer (WIT) phase. The self-maintenance characteristics of WPCN have promoted the trend of IoT networks towards self-sufficiency.

[0003] Existing WPT technologies can only transmit tens of microwatts of RF power within a range of about 10 meters, and it is still challenging to support devices with a few milliwatts of power consumption in larger-scale WPCNs. In addition, the randomness of wireless channels causes devices to be affected by power and distance attenuation, which severely limits the coverage and performance of the network. Existing research mainly focuses on coping with signal fading and attenuation in wireless channels, and introduces a large number of active RF chains to ensure the quality of service (QoS), but this often leads to high power consumption and high costs. In contrast, reconfigurable intelligent surfaces (RIS) provide a smarter solution that reconstructs the radio environment in a low-power and low-cost manner, thereby improving the quality of wireless links. The RIS controller can adjust the phase shift and amplitude of the reflected signal without consuming additional energy, creating favorable conditions for energy and information transmission.

[0004] In recent years, the integration of RIS and WPCN has received extensive attention and is regarded as a new paradigm to improve the robustness of Wireless Powered IoT (WP-IoT) networks. However, existing research works rarely explore RIS-assisted multi-cluster WP-IoT. In addition, previous studies mainly focus on optimization strategies to improve the overall performance of the system, such as throughput, energy efficiency, and confidentiality, which obviously ignores the impact of the survivability of individual devices. With the expansion of the network and the increase in the number of devices accessing the network, the farther the device is from the power station, the less energy it obtains due to the path loss attenuation of the RF signal, which leads to serious survivability issues for edge devices in WP-IoT networks. In WP-IoT networks, the survivability of individual devices is a key consideration for achieving self-sustainability even in the presence of instantaneous performance loss. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide an optimization method for joint intelligent reflector phase shift and time scheduling based on wireless power supply Internet of Things network. Based on the RIS-assisted multi-cluster WP-IoT system, in order to optimize the phase shift and time scheduling of the WET and WIT stages, an optimization problem to maximize the total system throughput is constructed; then, the closed-form solutions of the best time allocation in the WET and WIT stages are derived respectively, and then the RIS phase shift in the WIT stage is separated from the optimization problem and its optimal solution expression is derived; finally, when the closed-form solutions of other optimization variables are known, the EBCD algorithm is used to obtain the RIS phase shift in the WET stage. Under the condition of ensuring the signal-to-noise ratio requirements of a single device, the total system throughput is maximized, which is suitable for RIS-assisted multi-cluster WP-IoT networks and effectively improves the self-sustaining ability of the network.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] An optimization method for joint intelligent reflector phase shift and time scheduling based on a wireless power supply Internet of Things network, the method comprising the following steps:

[0008] S1: Considering the imperfect channel state information and the phase shift error caused by the passive Reconfigurable Intelligent Surface (RIS) hardware itself, an optimization problem is constructed with the goal of maximizing the total system throughput. The RIS phase shift in the two stages of downlink wireless energy transmission and uplink information transmission, as well as the energy collection time of each device in each cluster and the dedicated information transmission time slot allocation are optimized.

[0009] S2: Derivation of τ0 and t by using the dual method and KKT conditions k,mThe closed expression of is used to obtain the optimal time scheduling scheme for downlink wireless energy transfer (Wireless Energy Transfer, WET) and uplink wireless information transfer (Wireless Information Transfer, WIT);

[0010] S3: The S3 and Substitute the expression into the S1 optimization problem to obtain the objective function, simplify the optimization problem in S1, and separate the RIS phase shift θ in the WIT stage 0,k,m , solve its optimal solution expression;

[0011] S4: The fractional objective function is converted into a tractable form using the QT method, and the RIS phase shift in the WET stage is obtained by iterative optimization using the EBCD algorithm.

[0012] Further, the S1 comprises the following steps:

[0013] S1-1: Consider a RIS-assisted multi-cluster wireless powered IoT (WP-IoT) system, which consists of a power station, an access point, a RIS, and M energy-constrained devices. Assume that the number of RIS reflection units is N, and other nodes are equipped with only a single antenna; define the set of clusters to which the devices belong as The set of devices within a particular cluster is The reflection unit set of RIS is Among them, M devices are divided into K clusters, each cluster contains equipment, i.e. The mth device in the kth cluster is represented by Without loss of generality, the power station, access point, and all devices operate at the same frequency, and the total transmission time is denoted as T = 1;

[0014] S1-2: From power station to equipment And from the device The cascade links to the access point are denoted as g c,k,m =diag(g 0,k,m ) r,k,m ,h c,k,m =diag(h 0,k,m )h r,k,m ; The channel model is modeled as in, and represents the estimated CSI, and Represents the uncertainty of the channel, and defines ψ0 and ψ1 as error factors that satisfy

[0015] S1-3: Consider the PSE caused by the non-ideal characteristics of RIS and evaluate its impact on network performance; the actual phase shift is expressed as in, are amplitude and phase respectively; in order to maximize the received energy or information signal strength, the amplitude is set to 1; α E,k,m,n represents additive PSE, which obeys uniform distribution, that is, definition

[0016] S1-4: Construct an optimization problem to maximize the total throughput of the system and express it as:

[0017] (P1):

[0018]

[0019] ξ k,m ≥ξ0

[0020]

[0021] in, P0 is the available power of the power station, σ 2 is the noise power at the access point, ξ0 is the signal-to-noise ratio of a single device, ι k,m =2.463,κ k,m =1.635,

[0022]

[0023] d 0,k,m =Θ E,0,m diag(g 0,k,m ) r,k,m =Θ E,0,m g c,k,m ,,d 1,k,m =Θ E,k,m diag(h 0,k,m )h r,k,m =Θ E,k,m h c,k,m ,

[0024]

[0025] Further, the S2 comprises the following steps:

[0026] definition By introducing a dual variable, the Lagrangian function of the S1 optimization problem is obtained, and the expression of the general signal-to-noise ratio is obtained by using the KKT condition: To avoid conflicts with the signal-to-noise ratio constraint, the optimal signal-to-noise ratio is expressed as: Substitute it into Derived t k,m and closed-form expressions for τ0.

[0027] Further, the S3 comprises the following steps:

[0028] The optimization problem in S1 is transformed into:

[0029] (P2):

[0030]

[0031] In addition to the constraint of unity modulus phase shift, θ L,k,m It depends only on the objective function, and the objective function is about h d,k,m +θ L,k,m d 1,k,m | 2 Monotonically increasing; transform (P2) into the following sub-problem to obtain θ L,k,m The optimal solution is:

[0032]

[0033] Further, the S4 comprises the following steps:

[0034] remember Substituting the optimal phase shift solution of RIS in the WIT stage obtained by S3 into (P2) we get:

[0035] (P4):

[0036]

[0037] To solve (P4), we first use the QT method to convert the fractional objective function into a form that is easy to handle, and convert the problem of solving θ0 into:

[0038] (P5):

[0039]

[0040] The beneficial effect of the present invention is that a more practical research framework is proposed, taking into account the imperfect CSI of statistical PSE and RIS-assisted cascade channels caused by the non-ideal characteristics of RIS. In order to evaluate the impact of these factors on system performance, the present invention proposes a total throughput maximization problem, jointly designs passive beamforming and time allocation, and focuses on the survivability of a single device. The optimization problem is limited by PSE, RIS phase shift, allocation time and received SNR of a single device. The results verify the effectiveness of the proposed scheme and highlight the advantages of RIS.

[0041] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0043] Figure 1 Schematic diagram of a multi-cluster WP-IoT network that powers RIS;

[0044] Figure 2 It is a diagram of TDMA-TDMA transmission scheme;

[0045] Figure 3 This is a CSI error factor performance comparison chart;

[0046] Figure 4 Schematic diagram of power transmission for the power station;

[0047] Figure 5 This is a performance comparison chart of the number of RIS reflection units;

[0048] Figure 6 This is a performance comparison chart of the number of clusters. DETAILED DESCRIPTION

[0049] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0050] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0051] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "front", "rear", etc. indicate the orientation or position relationship, they are based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0052] See also Figure 1 to Figure 6 The invention provides a method for jointly optimizing phase shift and time scheduling of intelligent reflector surfaces in a wireless power supply Internet of Things network. The invention is further described in detail below in conjunction with an embodiment.

[0053] The RIS-assisted multi-cluster WP-IoT network considered in this paper consists of a power station, an access point, and multiple clusters of wireless devices, with the total transmission time denoted as T = 1. In this system, a general “collect first-send later” protocol is adopted in this paper. Figure 2 As shown, the transmission process is divided into two stages:

[0054] Phase 1: The duration of the downlink WET process is τ0∈[0,1]. During this period, the power station transmits RF signals to provide energy for wireless devices and RIS. Part of the energy collected by RIS is used to power its controller, and the rest is used to provide energy for the device through reflected signals.

[0055] Phase 2: In the uplink WIT / RIS reflection process, the TDMA-TDMA scheme is adopted. Specifically, the device uses the collected energy to transmit data to the access point with the assistance of RIS. Each cluster is assigned a dedicated TDMA time slot, and within the same cluster, each device occupies a different TDMA transmission time slot, denoted as t k,m , and satisfies

[0056] Since the cascade channel CSI is difficult to obtain, this paper studies a robust scheme based on imperfect CSI to convert the CSI from power station to device. And from the device The cascade links to the access point are denoted as g c,k,m =diag(g 0,k,m ) r,k,m ,h c,k,m =diag(h 0,k,m )h r,k,m Similar to the literature

[41] , the channel model can be expressed as:

[0057]

[0058] in, and represents the estimated CSI, and Represents the uncertainty of the channel, and defines ψ0 and ψ1 as error factors that satisfy

[0059] This paper considers the PSE caused by the non-ideal characteristics of RIS and evaluates its impact on network performance. For further study, the actual phase shift is expressed as in, are the amplitude and phase respectively. To maximize the received energy or information signal strength, the amplitude is set to 1. E,k,m,n represents additive PSE, which obeys uniform distribution, that is, definition in

[0060] During the WET period, this paper adopts the fractional NLEH model. The energy harvested at can be expressed as:

[0061]

[0062] The SNR at is expressed as:

[0063]

[0064] The optimization goal of this example is to maximize the total throughput of the system. The achievable throughput of the kth cluster is expressed as:

[0065]

[0066] The optimization problem constructed in this example can be expressed as:

[0067] (P1):

[0068]

[0069] The optimization parameters are the RIS phase shift and time scheduling in the WET and WIT phases.

[0070] To satisfy the SNR constraints of individual devices, the overall throughput must be maximized without considering the constrained SNR conditions. Introducing a dual variable μ ≥ 0, the general signal-to-noise ratio can be expressed as:

[0071]

[0072] However, The value of may violate the signal-to-noise ratio constraint. To avoid this contradiction, the optimal signal-to-noise ratio can be expressed as

[0073]

[0074] Substituting the above formula into Then we can deduce that t k,m The closed-form expressions for and τ0 are

[0075]

[0076] Substituting the above formula into the objective function, it can be expressed as

[0077]

[0078] The above function can be viewed as a class of functions defined by the following equation: Among them, Δ represents a constant. Its first-order derivative is derived as

[0079]

[0080] Obviously, f1 is about the variable ξ k ' ,m A monotonically increasing function. Therefore, maximizing f1 is equivalent to maximizing (P1) can be simplified to

[0081] (P2):

[0082]

[0083] To solve this problem, we first derive the closed-form solution of the phase shift during WIT, namely: It can be observed that, in addition to the constraint of unity modulus phase shift, θ L,k,m It depends only on the objective function, which is related to h d,k,m +θ L,k,m d 1,k,m | 2 Monotonically increasing. Therefore, (P2) can be transformed into the following sub-problems to obtain the optimal solution:

[0084] (P3):

[0085]

[0086] When the RIS reflected signal is aligned with the direct signal, the effective power gain of the uplink channel is maximum, and the optimal solution expression is:

[0087]

[0088] Where arg(·) represents the phase operator, d 1,k,m,n Indicates d 1,k,m The nth element of . Since θ L,k,m,n =1, optimal phase shift Can be To recover.

[0089] Note θ 0,k,m is θ0. Substituting into (P2) we get

[0090] (P4):

[0091]

[0092] in, In order to solve (P4), we first use the QT method to convert the fractional objective function into a form that is easy to handle. In order to make the above optimization problem easier to handle, we convert the problem of solving θ0 into

[0093] (P5):

[0094]

[0095] Objective Function in

[0096]

[0097] After removing the irrelevant constant term, we get:

[0098] (P6):

[0099]

[0100] To solve the above problems, an iterative optimization method based on EBCD is proposed to obtain the local optimal solution of RIS phase shift in the WET stage.

[0101] In the simulation experiment of the present invention, a WP-IoT network system was built using Matlab software. The symbols of the channel coefficients are summarized in Table 1, and the specific system parameters are shown in Table 2.

[0102] Table 1

[0103]

[0104] Table 2

[0105] Channel parameters value Channel parameters value <![CDATA[L0]]> -20dB <![CDATA[λ PD ]]> 3.5 <![CDATA[σ 2 ]]> -100dBm <![CDATA[λ PR ]]> 2 <![CDATA[P0]]> 30dBm <![CDATA[λ DR ]]> 2.5 K 3 <![CDATA[λ DA ]]> 3.5 N 40 <![CDATA[λ RD ]]> 2.5 <![CDATA[M k ]]> 10 <![CDATA[λ RA ]]> 2 η 0.8 <![CDATA[ψ0]]> 0.1 <![CDATA[ξ0]]> 20dB <![CDATA[ψ1]]> 0.1

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. An optimization method for joint intelligent reflector phase shift and time scheduling based on a wireless power supply Internet of Things network, characterized by: The method comprises the following steps: S1: Considering the imperfect channel state information and the phase shift error caused by the passive reconfigurable intelligent surface (RIS) hardware itself, an optimization problem with the goal of maximizing the total system throughput is constructed to optimize the RIS phase shift in the two stages of downlink wireless energy transmission and uplink information transmission, as well as the energy collection time and information transmission time slot allocation of each device in each cluster; S2: τ0 and t are derived by using the dual method and the Karush-Kuhn-Tucker (KKT) condition. k,m The closed expression of is used to obtain the optimal time scheduling scheme for downlink wireless energy transfer (Wireless Energy Transfer, WET) and uplink wireless information transfer (Wireless Information Transfer, WIT); S3: The S3 and Substitute the expression into the S1 optimization problem to obtain the objective function, simplify the optimization problem in S1, and separate the RIS phase shift θ in the WIT stage 0,k,m , solve its optimal solution expression; S4: The fractional objective function is converted into a tractable form using the QT method, and the RIS phase shift in the WET stage is iteratively optimized using the Element-wise Block Coordinate Descent (EBCD) algorithm.

2. The optimization method of phase shift and time scheduling of a joint intelligent reflector surface based on a wireless power supply Internet of Things network according to claim 1 is characterized in that: The S1 comprises the following steps: S1-1: Consider a RIS-assisted multi-cluster wireless powered IoT (WP-IoT) system, which consists of a power station, an access point, a RIS, and M energy-constrained devices. Assume that the number of RIS reflection units is N, and other nodes are equipped with only a single antenna; define the set of clusters to which the devices belong as The set of devices within a particular cluster is The reflection unit set of RIS is Among them, M devices are divided into K clusters, each cluster contains M k , equipment, i.e. The mth device in the kth cluster is represented by Without loss of generality, the power station, access point, and all devices operate at the same frequency, and the total transmission time is denoted as T = 1; S1-2: From power station to equipment And from the device The cascade links to the access point are denoted as g c,k,m =diag(g 0,k,m ) r,k,m ,h c,k,m =diag(h 0,k,m )h r,k,m ; The channel model is modeled as in, and represents the estimated channel state information (Channel State Information, CSI), and Represents the uncertainty of the channel, and defines ψ0 and ψ1 as error factors that satisfy S1-3: Consider the phase-shift error (PSE) caused by the non-ideal characteristics of RIS and evaluate its impact on network performance; the actual phase shift is expressed as in, are amplitude and phase respectively; in order to maximize the received energy or information signal strength, the amplitude is set to 1; α E,k,m,n represents additive PSE, which obeys uniform distribution, that is, definition S1-4: Construct an optimization problem to maximize the total throughput of the system and express it as: in, P0 is the available power of the power station, σ 2 is the noise power at the access point, ξ0 is the signal-to-noise ratio of a single device, ι k,m =2.463,κ k,m =1.635, d 0,k,m Θ E,0,m diag(g 0,k,m )g r,k,m Θ E,0,m g c,k,m ,,d 1,k,m Θ E,k,m diag(h 0,k,m )h r,k,m Θ E,k, m h c,k,m , 3. The optimization method of phase shift and time scheduling of a joint intelligent reflector surface based on a wireless power supply Internet of Things network according to claim 1 is characterized in that: The S2 comprises the following steps: definition By introducing a dual variable, the Lagrangian function of the S1 optimization problem is obtained, and the expression of the general signal-to-noise ratio is obtained by using the KKT condition: To avoid conflicts with the signal-to-noise ratio constraint, the optimal signal-to-noise ratio is expressed as: Substitute it into Derived t k,m and closed-form expressions for τ0.

4. The optimization method of phase shift and time scheduling of a joint intelligent reflector surface based on a wireless power supply Internet of Things network according to claim 1 is characterized in that: The S3 comprises the following steps: The optimization problem in S1 is transformed into: In addition to the constraint of unity modulus phase shift, θ L,k,m It depends only on the objective function, and the objective function is about |h d,k,m +θ L,k, m d 1,k,m | 2 Monotonically increasing; transform (P2) into the following sub-problem to obtain θ L,k,m The optimal solution is:

5. The optimization method of phase shift and time scheduling of a joint intelligent reflector surface based on a wireless power supply Internet of Things network according to claim 4 is characterized in that: The S4 comprises the following steps: remember Substituting the optimal phase shift solution of RIS in the WIT stage obtained by S3 into (P2) we get: To solve (P4), we first use the QT method to convert the fractional objective function into a form that is easy to handle, and convert the problem of solving θ0 into:

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