An Optimization Method for Joint Fluid Antenna Port Selection and Time Slot Scheduling Based on Wireless Power Supply Communication Networks

Abstract

This invention relates to an optimization method for port selection and time slot scheduling of a joint fluid antenna based on a wireless powered communication network (WPCN), belonging to the field of wireless communication technology. It solves the problems of poor communication quality and low throughput of traditional WPCNs, leading to poor network self-sustainability. First, to optimize port selection and time scheduling in the WET and WIT phases, an optimization problem to maximize the total system throughput is constructed. Then, closed-form solutions for the optimal time allocation in the WET and WIT phases are derived, and the optimal positions of the port selection parameters in the WIT phase are also derived. Finally, the optimal port index in the WET phase is derived, and an efficient algorithm based on the alternating optimization (AO) method is proposed to obtain the optimal port activation state iteratively.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology and relates to an optimization method for joint fluid antenna port selection and time slot scheduling based on a wireless power supply communication network. Background Technology

[0002] With the ubiquitous presence of Internet of Things (IoT) applications such as smart healthcare, industrial automation, and environmental monitoring, large-scale IoT devices are expected to be deployed in a variety of complex and extreme environments. Traditionally, energy-constrained devices often require regular maintenance or battery replacements to extend their lifespan, which seems increasingly impractical. To alleviate this problem, Wireless Powered Communication Networks (WPCNs) are considered a promising solution, in which devices can obtain power from a dedicated power station via Microwave Wireless Power Transfer (WPT) technology. Specifically, the power station provides radio frequency (RF) energy to the device during downlink Wireless Energy Transfer (WET), and the device then uses this energy to transmit data to an access point during uplink Wireless Information Transfer (WIT).

[0003] Existing work primarily focuses on various access schemes for downlink WET in WPCN, utilizing energy-constrained devices to transmit data to the access point via Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), or Non-Orthogonal Multiple Access (NOMA). In reality, due to the extremely limited space available to wireless devices, most are equipped with only a single antenna, severely limiting their energy harvesting and data transmission capabilities. Fluid antennas (FAs) have been proposed as a promising solution, providing spatial diversity through a dynamic radiation structure, thereby achieving extremely low energy transmission. An FA consists of a radio frequency chain and multiple physical locations (i.e., ports). Generally, an FA can switch to the desired port by selecting from multiple ports, depending on the specific requirements of the device. Especially when the number of ports is sufficient, systems deploying FAs have the potential to significantly improve diversity and multiplexing.

[0004] Preliminary studies indicate that deploying FA (Automatic Facilitator) in a system can yield many potential advantages. However, existing research primarily focuses on downlink communication between power plants and devices. In a WPCN (Power Plant Networking Center), optimizing ports for downlink communication in a single phase may be insufficient to meet current network performance requirements. In other words, the potential of applying FA to IoT networks has not been fully realized in previous research. For a two-phase WPCN, investigating the integration of FA with the WPCN to fully unleash its greater potential is a promising research direction. Considering the above background, this paper explores the impact of deploying FA on devices within a WPCN on system performance. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide an optimization method for joint fluid antenna port selection and time slot scheduling based on a wireless powered communication network, and to obtain the optimal resource allocation scheme for the optimization problem with the help of a proposed alternating optimization algorithm. This method maximizes the total system throughput while ensuring the signal-to-noise ratio requirements of individual devices, and is applicable to FA-assisted multi-cluster WPCNs, effectively improving the network's self-sustaining capability and overall performance. The method is applicable to FA-assisted WPCN systems and includes the following steps: First, to optimize port selection and time scheduling in the WET and WIT phases, an optimization problem to maximize the total system throughput is constructed; then, closed-form solutions for the optimal time allocation in the WET and WIT phases are derived separately, and the port selection parameters in the WIT phase are separated from the optimization problem and their optimal solution expression is derived; finally, given the closed-form solutions of other optimization variables, the optimal port selection expression for the WET phase is derived.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] An optimization method for joint fluid antenna port selection and time slot scheduling based on wireless power supply communication networks, the method comprising the following steps:

[0008] S1: Under the premise of ensuring the received signal-to-noise ratio of a single device, construct an optimization problem with the goal of maximizing the overall throughput. Improve the overall throughput of the WPCN under consideration by jointly optimizing time scheduling and two-stage port selection, that is, optimize the optimal port location selection and energy harvesting time and dedicated information transmission time slot allocation for each device in the downlink wireless power transmission and uplink information transmission stages.

[0009] S2: τ0 and τ are derived by using the dual method and KKT conditions. kThe closed expression is used to obtain the optimal time scheduling scheme for the two stages of downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT).

[0010] S3: The result of S2 and Substituting the expression into the optimization problem in S1 yields the objective function, which simplifies the optimization problem in S1 and separates the port selection parameter r for the WIT stage. 1,k Solve for its optimal solution expression; derive the port selection parameter r for the WET phase. 0,k .

[0011] Furthermore, S1 includes the following steps:

[0012] S1-1: Consider a Wireless Powered Communication Network (WPCN) system deploying fluid antennas (FAs), consisting of a power station, an access point, a passive RIS (Reflector Array) and M energy-constrained devices equipped with a single fluid antenna. Assume the RIS has N reflector elements, and other nodes are equipped with only a single fixed antenna. Each FA has a linear structure of dimension WΛ and N pre-defined uniformly distributed ports, where Λ represents the wavelength. Assume the RIS acts as an artificial scatterer, and the k-th device is represented as... And define the set of devices as The set of reflection units in RIS is The port set of the fluid antenna is Without loss of generality, the power station, access point and all equipment operate at the same frequency, and the total transmission time is expressed as T=1;

[0013] S1-2: Model the correlated channel considering the spatial correlation between ports; given a sufficiently large number of ports, N→∞, the spatial correlation parameter is expressed as μ. k , is represented as:

[0014]

[0015] S1-3: Construct an optimization problem to maximize the total throughput of the system, which can be expressed as:

[0016]

[0017] in, P0 is the available power of the power plant, σ 2 ξ0 is the noise power at the access point, ξ0 is the signal-to-noise ratio of a single device, and ι is the noise power at the access point.k =2.463, κ k =1.635, Θ 0,k and Θ 1,k These are the RIS phase shifts for the WET and WIT stages, respectively, where each reflection unit in the RIS is a random phase shift;

[0018] p 0,k =|(g d,k +g0Θ 0,k g r,k )r 0,k | 2 =|(g d,k +θ 0,k d 0,k )r 0,k | 2 d 0,k =g0g r,k ,

[0019]

[0020] Furthermore, S2 includes the following steps:

[0021] First, we introduce a dual variable to obtain the Lagrangian function of the S1 optimization problem. Then, using the KKT conditions, we obtain the expression for the general signal-to-noise ratio: To avoid conflicting with the signal-to-noise ratio (SNR) constraint, the optimal SNR is expressed as: Substitute it Derive τ k The closed expression for τ0.

[0022] Furthermore, S3 includes the following steps:

[0023] The optimization problem in S1 is transformed into:

[0024]

[0025] definition G k =(g d,k +θ 0,k d 0,k (g) d,k +θ 0,k d 0,k ) H H k =(g d,k +θ 1,k d 1,k (g) d,k +θ 1,k d 1,k ) HTransform (P2) into the following subproblem to obtain the optimal port index:

[0026]

[0027] Since the two phases of port selection are independent, the optimization objective is to determine the optimal port index for each phase; therefore, the optimal expression for uplink WIT is obtained by solving:

[0028]

[0029] The optimal index for the second stage is: Similarly, the subproblem of determining the optimal index port in the first stage is constructed as follows:

[0030]

[0031] The optimal expression for downlink WET is:

[0032] The beneficial effect of this invention lies in providing a more practical research framework. To evaluate the impact of these factors on system performance, this invention proposes a total throughput maximization problem, jointly designing port optimization and time allocation for a FA (Automatic Facilitator), focusing on the survivability of individual devices. This optimization problem is constrained by port activation state, allocation time, and the received SNR of individual devices. The results verify the effectiveness of the proposed scheme and highlight the advantages of FA.

[0033] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0034] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0035] Figure 1 A schematic diagram of the WPCN for deploying FA;

[0036] Figure 2 A schematic diagram of the power transmission capacity of the power plant;

[0037] Figure 3 A performance comparison chart under different signal-to-noise ratio constraints;

[0038] Figure 4 A chart comparing the performance of different cluster sizes;

[0039] Figure 5This is a comparison chart of port quantity and performance. Detailed Implementation

[0040] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed 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 are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0041] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0042] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0043] Please see Figures 1-5 This invention provides an optimization method for joint fluid antenna port selection and time slot scheduling based on a wireless power supply communication network. The invention will be further described in detail below with reference to an embodiment.

[0044] The FA-assisted multi-cluster WPCN considered in this paper consists of a power station, an access point, a passive RIS (Resonance Array), and multiple wireless devices, with a total transmission time denoted as T=1. In this system, a general "collect-then-transmit" protocol is adopted. Devices use the collected energy from the RIS to transmit data to the access point. Each wireless device occupies a dedicated TDMA (Transmission-Time DMA) transmission slot, denoted as τ. k And satisfy

[0045] The optimization problem of maximizing the total throughput of the system is formulated as follows:

[0046]

[0047] in, P0 is the available power of the power plant, σ 2 ξ0 is the noise power at the access point, ξ0 is the signal-to-noise ratio of a single device, and ι is the noise power at the access point. k =2.463, κ k =1.635, Θ 0,k and Θ 1,k These are the RIS phase shifts for the WET and WIT stages, respectively, where each reflection unit in the RIS undergoes a random phase shift.

[0048] p 0,k =|(g d,k +g0Θ 0,k g r,k )r 0,k | 2 =|(g d,k +θ 0,k d 0,k )r 0,k | 2 d 0,k =g0g r,k ,

[0049]

[0050] S2 includes the following steps:

[0051] Introducing a dual variable, we obtain the Lagrangian function for the S1 optimization problem. Using the KKT conditions, we can derive the expression for the general signal-to-noise ratio: To avoid conflicting with the signal-to-noise ratio (SNR) constraint, the optimal SNR can be expressed as: Substitute it τ can be derived k The closed expression for τ0.

[0052] S3 includes the following steps:

[0053] The optimization problem in S1 is transformed into:

[0054]

[0055] For the sake of brevity, define G k =(g d,k +θ 0,k d 0,k (g) d,k +θ 0,k d 0,k ) H Hk =(g d,k +θ 1,k d 1,k (g) d,k +θ 1,k d 1,k ) H Therefore, (P2) is transformed into the following subproblem: obtaining the optimal port index:

[0056]

[0057] Since the two phases of port selection are independent, the optimization objective is to determine the optimal port index for each phase. Therefore, the optimal expression for uplink WIT can be obtained by solving:

[0058]

[0059] The optimal index for the second stage is: Similarly, the subproblem of determining the optimal index port in the first stage can be constructed as follows:

[0060]

[0061] The optimal expression for downlink WET is:

[0062] In the simulation experiment of this invention, a WPCN system was built using Matlab software. The signs of the channel coefficients are summarized in Table 1, and the specific system parameters are shown in Table 2.

[0063] Table 1

[0064]

[0065] Table 2

[0066] Channel parameters value Channel parameters value <![CDATA[L0]]> -30dB <![CDATA[λ PD ]]> 3.5 <![CDATA[σ 2 ]]> -60dBm <![CDATA[λ PR ]]> 2 <![CDATA[P0]]> 30dBm <![CDATA[λ DR ]]> 2.5 K 3 <![CDATA[λ DA ]]> 3.5 N 80 <![CDATA[λ RD ]]> 2.5 <![CDATA[ξ0]]> 20dB <![CDATA[λ RA ]]> 2 M 60

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An optimization method for joint fluid antenna port selection and time slot scheduling based on wireless powered communication networks, characterized in that: The method comprises the following steps: S1: Under the premise of ensuring the received signal-to-noise ratio of a single device, an optimization problem is constructed with the optimization goal of maximizing the overall throughput, and the overall throughput of the considered WPCN is improved through joint optimization of time scheduling and two-stage port selection, that is, the optimal port position selection and the energy collection time τ0 and the dedicated information transmission time slot allocation τ of each device in the two stages of downlink wireless energy transmission and uplink information transmission are optimized k ; S2: Obtain the optimal time scheduling scheme of downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT) by deriving closed-form expressions of τ0 and τ k using the dual method and KKT conditions; first introduce a dual variable to obtain the Lagrangian function of the S1 optimization problem, and then use the KKT conditions to obtain the general signal-to-noise ratio expression as follows: To avoid conflict with the signal-to-noise ratio constraint, the optimal signal-to-noise ratio is expressed as: Substitute it into to derive the closed-form expressions of τ k and τ0, τ k and τ0 are respectively and S3: Substitute the result of S2 into the optimization problem in S1 and The objective function of the optimization problem in S1 is obtained by substituting the expression into the optimization problem in S1, the optimization problem in S1 is simplified, and the port selection parameter r of the WIT stage is separated 1,k , the optimal solution expression is solved; the port selection parameter r of the WET stage is derived 0,k ; The S3 specifically comprises the following steps: The optimization problem in S1 is converted into: where N is the number of RIS reflecting elements; the kth device is denoted as and define the set of devices as The set of reflecting elements of the RIS is The set of ports of the fluid antenna is P0is the available power of the power plant, σ 2 is the noise power at the access point, ξ0is the signal-to-noise ratio of a single device, ι k = 2.463, κ k = 1.635, g d,k denotes the channel coefficient from the power plant to d 0,k = g0g r,k , g0denotes the channel coefficient from the power plant to the RIS, g r,k denotes the channel coefficient from the RIS to d 1,k = h 0,k h r,k , h 0,k denotes the channel coefficient from to the RIS, h r,k denotes the channel coefficient from the RIS to the access point; Definition G k = (g d,k + θ 0,k d 0,k )(g d,k + θ 0,k d 0,k ) H , H k = (g d,k + θ 1,k d 1,k )(g d,k + θ 1,k d 1,k ) H ; converting (P2) to the following subproblem to obtain the optimal index of the port: In view of the fact that the two stages of port selection are independent of each other, the optimization objective is to determine the optimal port index of each stage; therefore, the optimal expression of the uplink WIT is obtained by solving: The optimal index of the second stage is: Similarly, the subproblem of determining the optimal index port of the first stage is constructed as: The optimal expression of downlink WET is:

2. The method for optimization of joint fluid antenna port selection and time slot scheduling over a wireless powered communication network according to claim 1, wherein: The S1 comprises the following steps: S1-1: Consider a wireless power communication network (WPCN) system with fluid antennas (FA) deployed, consisting of a power station, an access point, and a passive reconfigurable intelligent surface (RIS) and M energy-limited devices equipped with single fluid antennas, assuming that the number of RIS reflecting elements is N, and other nodes are equipped with only a single fixed antenna; wherein each FA has a linear structure with dimension WΛ, with N preset uniformly distributed ports, where Λ represents the wavelength; assume that the RIS acts as an artificial scatterer, and the kth device is represented as and define the set of devices as the set of reflecting elements of the RIS is the set of ports of the fluid antennas is For the sake of generality, the power station, the access point, and all devices operate at the same frequency, and the total transmission time is represented as T = 1; S1-2: Model the correlated channel taking into account the spatial correlation between ports; considering a sufficient number of ports, N→∞, the spatial correlation parameter is denoted as μ k denoted as: S1-3: An optimization problem of maximizing the system total throughput is constructed, which is expressed as: s.t.ξ k ≥ξ0 wherein, P0is the available power of the power plant, σ 2 is the noise power at the access point, ξ0is the signal-to-noise ratio of a single device, ι k = 2.463, κ k = 1.635, g d,k denotes the channel coefficient from the power plant to d 0,k = g0g r,k , g0denotes the channel coefficient from the power plant to the RIS, g r,k denotes the channel coefficient from the RIS to h 0,k denotes the channel coefficient from to the RIS, h r,k denotes the channel coefficient from the RIS to the access point; Θ 0,k and Θ 1,k are the RIS phase shifts for the WET and WIT phases, respectively, where the RIS is randomly phase shifted for each reflection unit. p 0,k = | (g d,k + g0Θ 0,k g r,k ) r 0,k | 2 = | (g d,k + θ 0,k d 0,k ) r 0,k | 2 , d 0,k = g0g r,k , d 1,k = h 0,k h r,k ,

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