Optimization method for combining fluid antenna port selection and time slot scheduling based on wireless energy supply communication network

Abstract

The invention relates to an optimization method for combining fluid antenna port selection and time slot scheduling based on a wireless energy supply communication 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 for optimizing port selection and time scheduling in WET and WIT stages; then, respectively deriving closed solution forms of optimal time distribution in the WET stage and the WIT stage, and deriving optimal positions of port selection parameters in the WIT stage; and finally, deducing an optimal port index in a WET stage, providing an efficient algorithm based on an alternating optimization (AO) method, and obtaining an optimal port activation state in an iteration mode.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications, 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 Art

[0002] As Internet of Things (IoT) applications such as smart healthcare, industrial automation, and environmental monitoring become ubiquitous, large-scale IoT devices are expected to be deployed in a variety of complex and extreme environments. Traditionally, energy-constrained devices usually require regular maintenance or battery replacement to extend their service life, which seems to be becoming increasingly impractical. To alleviate this problem, Wireless Powered Communication Network (WPCN) is considered a promising solution, in which devices are able to obtain energy from a dedicated power station through microwave wireless power transfer (WPT) technology. In particular, the power station provides radio frequency (RF) energy to the device during the downlink wireless energy transfer (WET), and the device then uses this energy to transmit data to the access point during the uplink wireless information transfer (WIT).

[0003] Existing works mainly focus on multiple access schemes for downlink WET in WPCN, using energy-constrained devices to transmit data to access points through time division multiple access (TDMA), frequency division multiple access (FDMA), or non-orthogonal multiple access (NOMA). In practice, due to the extremely limited space available for wireless devices, most devices are equipped with only a single antenna, which severely limits their ability to collect energy and transmit data. Fluid Antenna (FA) has been proposed as a promising solution, which provides spatial diversity by using a dynamic radiating structure to achieve extremely low energy transmission. FA consists of an RF chain and multiple physical locations (i.e., ports). In general, FA can switch to the required port by selecting from multiple ports according to the specific requirements of the device. Especially when the number of ports is large enough, systems deploying FA have the potential to achieve significantly improved diversity and multiplexing.

[0004] Preliminary studies have shown that many potential advantages can be obtained by deploying FA in the system. However, existing research mainly focuses on the downlink communication between power stations and devices. In WPCN, optimizing ports for downlink communication in a single stage may not be sufficient to meet current network performance requirements. In other words, the potential of applying FA to IoT networks has not been fully explored in previous studies. For the two-stage WPCN, studying the integration of FA with 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 in WPCN on system performance. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide an optimization method for joint fluid antenna port selection and time slot scheduling based on a wireless power supply communication network, and obtain the best resource allocation solution for the optimization problem with the help of the proposed alternating optimization algorithm. Under the condition of ensuring the signal-to-noise ratio requirement of a single device, the total system throughput is maximized, which is suitable for FA-assisted multi-cluster WPCN, and effectively improves the self-sustaining ability and overall performance of the network. It is suitable for a fluid antenna (FA)-assisted WPCN system, including the following steps: first, in order to optimize the port selection 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 of the WET and WIT stages are derived respectively, and then the port selection parameters of the WIT stage are separated from the optimization problem and the optimal solution expression is derived; finally, when the closed-form solutions of other optimization variables are known, the optimal port selection expression of the WET stage is derived.

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

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

[0008] S1: Under the premise of ensuring the receiving signal-to-noise ratio of a single device, an optimization problem with the optimization goal of maximizing the overall throughput is constructed. The overall throughput of the considered WPCN is improved by jointly optimizing time scheduling and two-stage port selection, that is, optimizing the optimal port location selection of the two stages of downlink wireless energy transmission and uplink information transmission, and the energy collection time of each device and the dedicated information transmission time slot allocation;

[0009] S2: Derivation of τ0 and τ by using the dual method and KKT conditions kThe closed expression of is used to obtain the optimal time scheduling scheme for the two stages of 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 optimization problem in S1 to obtain the objective function, simplify the optimization problem in S1, and separate the port selection parameter r in the WIT stage 1,k , solve its optimal solution expression; derive the port selection parameter r in the WIT stage 0,k .

[0011] Further, the S1 comprises the following steps:

[0012] S1-1: Consider a wireless powered communication network (WPCN) system with a fluid antenna (FA), which consists of a power station, an access point, a passive RIS, and M energy-constrained devices equipped with a single fluid antenna. Assume that the number of RIS reflection units is N, and other nodes are only equipped with a single fixed antenna; each FA has a linear structure with dimension WΛ and N preset uniformly distributed ports, where Λ represents the wavelength; RIS is assumed to be an artificial scatterer, and the kth device is represented as And define the collection of devices as The reflection unit set of RIS is The port set of the fluid antenna is 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;

[0013] S1-2: Modeling the correlated channels by considering the spatial correlation between ports; considering a sufficiently large number of ports, N→∞, the spatial correlation parameter is expressed as μ k , expressed as:

[0014]

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

[0016] (P1):

[0017] stξ k ≥ξ0

[0018]

[0019] 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 =2.463,κ k =1.635, Θ 0,k and θ 1,k are the RIS phase shifts in the WET and WIT stages, respectively, where each reflection unit in the RIS is a random phase shift;

[0020] 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 ,

[0021] d 1,k,m =h 0,k h r,k ,

[0022]

[0023] Further, the S2 comprises the following steps:

[0024] First, a dual variable is introduced to obtain the Lagrangian function of the S1 optimization problem. 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 Derivation of τ k and closed-form expressions for τ0.

[0025] Further, the S3 comprises the following steps:

[0026] The optimization problem in S1 is transformed into:

[0027] (P2):

[0028]

[0029] definition G k =(g d,k +θ0,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 ; Convert (P2) into the following sub-problems to obtain the optimal port index:

[0030] (P3):

[0031]

[0032] Given that the two stages of port selection are independent of each other, the optimization goal is to determine the best port index for each stage; therefore, the optimal expression for upstream WIT is obtained by solving:

[0033] (P4):

[0034] Then the best index for the second stage is: Similarly, the subproblem of determining the optimal index port in the first phase is constructed as:

[0035] (P5):

[0036] The optimal expression of downstream WET is:

[0037] The beneficial effect of the present invention is that a more practical research framework is proposed. In order to evaluate the impact of these factors on system performance, the present invention proposes a total throughput maximization problem, jointly designs the port optimization and time allocation of FA, and focuses on the survivability of a single device. The optimization problem is limited by the port activation state, the allocation time, and the received SNR of a single device. The results verify the effectiveness of the proposed scheme and highlight the advantages of FA.

[0038] 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

[0039] 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:

[0040] Figure 1 Schematic diagram of WPCN with FA deployment;

[0041] Figure 2 Schematic diagram of power transmission for the power station;

[0042] Figure 3 This is a performance comparison chart under different signal-to-noise ratio constraints;

[0043] Figure 4 This is a performance comparison chart of the number of clusters;

[0044] Figure 5 This is a performance comparison chart based on the number of ports. DETAILED DESCRIPTION

[0045] 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.

[0046] 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.

[0047] 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.

[0048] See also Figure 1 to Figure 5The 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 is further described in detail below in conjunction with an embodiment.

[0049] The FA-assisted multi-cluster WPCN considered in this paper consists of a power station, an access point, a passive RIS and multiple wireless devices, and the total transmission time is denoted as T = 1. In this system, this paper adopts a general "collect first-then send" protocol. The device uses the collected energy under the reflection of RIS to transmit data to the access point. Each wireless device occupies a dedicated TDMA transmission time slot, denoted as τ k , and satisfies

[0050] Construct an optimization problem to maximize the total throughput of the system and express it as

[0051] (P1):

[0052] stξ k ≥ξ0

[0053]

[0054] 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 =2.463,κ k =1.635, Θ 0,k and θ 1,k They are the RIS phase shifts in the WET and WIT stages, respectively, where each reflection unit in the RIS has a random phase shift.

[0055] 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 ,

[0056] d 1,k,m =h 0,k h r,k ,

[0057]

[0058] S2 includes the following steps:

[0059] By introducing a dual variable, we get the Lagrangian function of the S1 optimization problem. Using the KKT condition, we can get the expression of the general signal-to-noise ratio: To avoid conflicts with the signal-to-noise ratio constraint, the optimal signal-to-noise ratio can be expressed as: Substitute it into It can be deduced that τ k and closed-form expressions for τ0.

[0060] S3 includes the following steps:

[0061] The optimization problem in S1 is transformed into:

[0062] (P2):

[0063]

[0064] For simplicity, define 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 Therefore, (P2) is transformed into the following sub-problem to obtain the optimal port index:

[0065] (P3):

[0066]

[0067] Given that the two stages of port selection are independent of each other, the optimization goal is to determine the best port index for each stage. Therefore, the optimal expression for upstream WIT can be obtained by solving:

[0068] (P4): Then the best index for the second stage is: Similarly, the subproblem of determining the optimal index port in the first phase can be formulated as:

[0069] (P5): The optimal expression of downstream WET is:

[0070] In the simulation experiment of the present invention, a WPCN 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.

[0071] Table 1

[0072]

[0073] Table 2

[0074] 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

[0075] 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 fluid antenna port selection and time slot scheduling based on a wireless power supply communication network, characterized in that: The method comprises the following steps: S1: Under the premise of ensuring the receiving signal-to-noise ratio of a single device, an optimization problem with the optimization goal of maximizing the overall throughput is constructed. The overall throughput of the considered WPCN is improved by jointly optimizing time scheduling and two-stage port selection, that is, optimizing the optimal port location selection of the two stages of downlink wireless energy transmission and uplink information transmission, and the energy collection time of each device and the dedicated information transmission time slot allocation; S2: Derivation of τ0 and τ by using the dual method and KKT conditions k The closed expression of is used to obtain the optimal time scheduling scheme for the two stages of 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 optimization problem in S1 to obtain the objective function, simplify the optimization problem in S1, and separate the port selection parameter r in the WIT stage 1,k , solve its optimal solution expression; derive the port selection parameter r in the WIT stage 0,k .

2. The optimization method for joint fluid antenna port selection and time slot scheduling based on a wireless power supply communication network according to claim 1 is characterized in that: The S1 comprises the following steps: S1-1: Consider a wireless powered communication network (WPCN) system with a fluid antenna (FA), which consists of a power station, an access point, a passive reconfigurable intelligent surface (RIS) and M energy-constrained devices equipped with a single fluid antenna. Assume that the number of RIS reflection units is N, and other nodes are only equipped with a single fixed antenna; each FA has a linear structure with a dimension of WΛ and N preset uniformly distributed ports, where Λ represents the wavelength; RIS is assumed to be an artificial scatterer, and the kth device is represented as And define the collection of devices as The reflection unit set of RIS is The port set of the fluid antenna is 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: Modeling the correlated channels by considering the spatial correlation between ports; considering a sufficiently large number of ports, N→∞, the spatial correlation parameter is expressed as μ k , expressed as: S1-3: Construct an optimization problem to maximize the total throughput of the system, which can be expressed 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 =2.463,κ k =1.635, Θ 0,k and θ 1,k are the RIS phase shifts in the WET and WIT stages, respectively, where each reflection unit in the RIS is a random phase shift; 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 , 3. The optimization method for joint fluid antenna port selection and time slot scheduling based on a wireless power supply communication network according to claim 2 is characterized in that: The S2 comprises the following steps: First, a dual variable is introduced to obtain the Lagrangian function of the S1 optimization problem, and the Carlo-Kuhn-Tucker The expression of the universal signal-to-noise ratio obtained by the Karush-Kuhn-Tucker (KKT) condition is: To avoid conflicts with the signal-to-noise ratio constraint, the optimal signal-to-noise ratio is expressed as: Substitute it into Derivation of τ k and closed-form expressions for τ0.

4. The optimization method for joint fluid antenna port selection and time slot scheduling based on a wireless power supply communication network according to claim 3 is characterized in that: The S3 comprises the following steps: The optimization problem in S1 is transformed into: 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 ; Convert (P2) into the following sub-problems to obtain the optimal port index: Given that the two stages of port selection are independent of each other, the optimization goal is to determine the best port index for each stage; therefore, the optimal expression for the upstream WIT is obtained by solving: Then the best index for the second stage is: Similarly, the subproblem of determining the optimal index port in the first phase is constructed as: The optimal expression of downstream WET is:

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