Information age optimization method for auxiliary communication of unmanned aerial vehicle carrying intelligent reflecting surface under non-ideal condition

Abstract

The invention discloses an information age optimization method for auxiliary communication of an unmanned aerial vehicle carrying an intelligent reflecting surface under a non-ideal condition, and the method comprises the steps: constructing an average information age optimization model in a communication scene through joint optimization of a pilot frequency length and the position of the unmanned aerial vehicle carrying the intelligent reflecting surface, carrying out the solving, and obtaining an optimal transmission parameter; and then setting the position of the unmanned aerial vehicle based on the determined optimal transmission parameter, controlling to transmit a pilot signal, estimating a channel, regulating and controlling the phase of an intelligent reflecting surface, and sending data information. The scheme of the invention focuses on improving the information freshness of the system and determining the position of the unmanned aerial vehicle and the pilot signal length of the sensor through joint optimization by taking the average information age as an index, and the information age optimization method can be applied to a monitoring Internet of Things scene facing a high timeliness requirement.

Description

Technical Field

[0001] The present invention belongs to the field of perception information update in monitoring the Internet of Things, and specifically relates to an information age optimization method for unmanned aerial vehicles equipped with intelligent reflective surfaces to assist in communication under non-ideal conditions. Background Art

[0002] During emergencies such as earthquakes, military conflicts, and forest fires, it is crucial to quickly establish a monitoring network and ensure reliable regional surveillance. Drones, with their flexible deployment capabilities and high line-of-sight probability, have become an indispensable and key component in emergency wireless monitoring systems. To further improve the reliability and coverage of wireless communication systems, drone systems equipped with smart reflectors have shown broad application potential, and communication systems assisted by drones equipped with smart reflectors have therefore been widely researched. Specifically, the paper (L. Wei, K. Wang, C. Pan, M. Elkashlan, “Average error probability for UAV-RIS enabled short packet communications,” IEEE Trans. Veh. Technol., vol. 73, no. 2, pp. 2912-2917, 2023) analyzed the average packet error rate of this system. However, because it did not consider timeliness, its research cannot be directly applied to scenarios with high timeliness requirements. The paper (W. Jiang, B. Ai, M. Li, W. Wu, and X. Shen, “Average age-of-information minimization in aerial IRS-assisted data delivery,” IEEE Internet of Things J., vol. 10, no. 17, pp. 15133–15146, 2023) investigated the deployment of UAVs, aiming to achieve timely and reliable data transmission. However, its assumption of long wireless packet lengths limits its direct applicability in short packet communication scenarios. To fill this research gap, the paper (Y. Zhang, X. Guan, Q. Wu, and Y. Cai, “Optimizing age of information in UAV-mounted IRS-assisted short packet systems,” IEEE Trans. Veh. Technol., vol. 6, no. 21, pp. 231–235, 2024) studied the average information age of a UAV-mounted IRS-assisted short packet communication system. However, all the above studies assume that the smart reflective surface adopts continuous phase control. In practice, due to hardware limitations, it is very challenging to achieve continuous phase control.In contrast, the paper (Q. Wu, R. Zhang, “Beamforming optimization for wireless network aided by intelligent reflecting surface with discrete phase shifts,” IEEE Trans. Commun., vol. 68, no. 3, pp. 1838-1851, 2019) considers both continuous and discrete phase control under ideal channel conditions and compares their performance through simulation. However, the performance comparison between continuous and discrete phase control under non-ideal channel conditions remains understudied, which is the core motivation of this invention.

[0003] The present invention studies a short packet communication system assisted by an intelligent reflector mounted on a drone under non-ideal conditions. Since it is difficult for the intelligent reflector to obtain completely accurate link channel information, there is a deviation between its estimated channel and the actual channel, which makes it impossible for the intelligent reflector to achieve perfect coherent superposition of all reflected link signals by adjusting the phase of the reflector unit. In addition, the discretization control characteristic of the reflection phase of the intelligent reflector further widens the gap between the actual beamforming gain and the ideal value. Since there are channel estimation errors and phase quantization errors when the intelligent reflector adjusts the phase of the reflector unit, and the impact of the phase error on the beamforming performance of the intelligent reflector is difficult to quantify, the system performance analysis becomes particularly complicated. To address this problem, the present invention uses an approximate method to derive the probability distribution of the channel gain under non-ideal conditions, and based on this distribution, further derives an expression for the average packet error rate, thereby obtaining a closed-form expression for the average information age. On this basis, a joint optimization scheme for pilot length and drone position is proposed with the goal of minimizing the average information age. Summary of the Invention

[0004] In response to the above problems, the purpose of the present invention is to provide an information age optimization method for drones equipped with intelligent reflective surfaces to assist in communication under non-ideal conditions for monitoring the Internet of Things, and to obtain better information age performance.

[0005] The specific technical solutions for achieving the purpose of the present invention are as follows:

[0006] A method for optimizing information age of a UAV equipped with an intelligent reflective surface for assisted communication under non-ideal conditions comprises the following steps:

[0007] Step 1: In the communication scenario, the average information age optimization model is constructed by jointly optimizing the pilot length and the position of the UAV equipped with the smart reflector;

[0008] Step 2: Solve the average information age optimization model constructed in step 1 to obtain the optimal transmission parameters;

[0009] Step 3: Set the UAV position based on the determined optimal transmission parameters and control the transmission of the pilot signal;

[0010] Step 4: Estimate the channel based on the pilot signal, adjust the phase of the smart reflector, and send data information.

[0011] Compared with the prior art, the present invention has the following beneficial effects:

[0012] (1) The solution of the present invention determines the position of the drone and the length of the sensor's pilot signal through joint optimization. The base station estimates the channel information based on the pilot signal and then feeds the optimized reflection phase back to the smart reflective surface. The sensor then sends the data signal to the base station. This information age optimization method can be applied to IoT monitoring scenarios with high timeliness requirements, but is not limited to such scenarios.

[0013] (2) The method of the present invention uses smart reflective surface technology, which has the advantages of low cost and low energy consumption compared with traditional relay technology. In addition, the smart reflective surface only passively reflects the signal without requiring complex signal processing capabilities, so it does not add additional noise to the reflected signal.

[0014] (3) The method of the present invention uses the average information age as an indicator and focuses on improving the information freshness of the system. Many existing technologies focus on traditional performance indicators such as reliability, latency, and throughput. However, these indicators cannot measure the freshness of information well, so these technologies cannot be directly used to guide monitoring scenarios that have relatively high requirements for information freshness.

[0015] The present invention will be further described below with reference to specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of the information age optimization method for UAV equipped with intelligent reflective surface assisted communication under non-ideal conditions of the present invention.

[0017] Figure 2 Schematic diagram of a communication scenario in an embodiment of the present invention.

[0018] Figure 3 Graph showing the relationship between average information age and data packet information volume in an embodiment of the present invention.

[0019] Figure 4 FIG. 4 is a graph showing the relationship between the average information age and the pilot length in an embodiment of the present invention.

[0020] Figure 5FIG. 4 is a graph showing the relationship between the average information age and the channel estimation error coefficient in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] Example

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. The described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts are within the scope of protection of the present invention.

[0023] As used in this application and the claims, the words "a," "an," "an," and / or "the" are not intended to refer to the singular but may include the plural unless the context clearly indicates otherwise. Generally speaking, the terms "comprises" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0024] Unless otherwise specifically stated, the relative arrangement of the parts and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present application. At the same time, it should be understood that, for ease of description, the sizes of the various parts shown in the drawings are not drawn according to actual proportional relationships. The techniques, methods and equipment known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the techniques, methods and equipment should be considered as part of the authorization specification. In all examples shown and discussed here, any specific values should be interpreted as being merely exemplary and not as limitations. Therefore, other examples of the exemplary embodiments may have different values. It should be noted that similar numbers and letters represent similar items in the following figures, and therefore, once an item is defined in one figure, it does not need to be further discussed in subsequent figures.

[0025] Combine Figure 1 ,A method for optimizing information age of UAV equipped with intelligent reflective surface assisted communication under non-ideal conditions, comprising the following steps:

[0026] Step 1: In the communication scenario, the average information age optimization model is constructed by jointly optimizing the pilot length and the position of the UAV equipped with the smart reflector;

[0027] In the communication scenario:

[0028] A drone equipped with a smart reflective surface hovers at a height h and assists in short packet transmission. The smart reflective surface includes M reflective units.

[0029] K sensors are deployed in the communication scenario, and the sensors send n p pilot symbols for channel estimation, and then encode the sensing information into short data packets and transmit them to the base station, including n d Data symbols are used for information transmission;

[0030] In the communication scenario, the available bandwidth is B, the channel coherence time is T, and each channel coherence block is divided into J equal resource blocks and allocated to K sensors for data transmission.

[0031] Combine Figure 2 In this embodiment, K sensors are deployed in the disaster-stricken area to monitor earthquake activity in real time, and the perception information is encoded into short data packets and transmitted to the base station. In order to improve network coverage and data transmission efficiency, the system deploys a drone equipped with an intelligent reflective surface to hover in the air and assist in short packet transmission, where the IRS is equipped with M reflection units. Assume that all communication links experience quasi-static fading, the channel coherence time is T, and the system available bandwidth is B. To optimize the utilization of time-frequency resources, each channel coherence block is divided into J equal resource blocks and appropriately allocated to K sensors for data transmission. In order to avoid conflicts caused by multiple sensors accessing the same resource block at the same time, the present invention considers giving priority to allocating resource blocks to sensors with higher information age. According to statistical analysis, all sensors have similar communication performance, so it is assumed that each sensor has the same probability of being allocated a resource block within a coherent block, that is, J / K.

[0032] Assume that the coordinates of the base station are q a , and the UAV equipped with the intelligent reflective surface hovers at a fixed height h, and its position coordinates are q r =[x r ,y r ,h], sensor nodes are randomly distributed in the s The coordinates of sensor k are q k , k=1,...,K. The baseband equivalent channels of the link between sensor k and the smart reflective surface and the link between the smart reflective surface and the base station are expressed as and in m=1,...,M, L0 represents the path loss at the reference distance, d kr =||q k -q r ||2 and d ra =||q r -q a ||2 represents the distance between the two links, η represents the path loss exponent, g kr,m and g ra,mrepresents the Rayleigh fading coefficient with a mean of zero and a variance of 1. The diagonal phase matrix of the smart reflector is expressed as Φ = diag(v1,v2,…,v M ),in θ m ∈[0,2π) represents the phase of the mth reflection unit. Therefore, the composite channel of sensor k-intelligent reflection surface-base station can be modeled as By defining v H =[v1,…,v M ]and You can also use h k Convert to h k =v H h kra .

[0033] It is usually difficult for the base station to obtain ideal channel information. The present invention adopts a pilot-based channel estimation model. Assuming that the sensor is allocated to a resource block in a certain channel coherent time block, the sensor will first send n p pilot symbols for channel estimation, and then send n d Data symbols are used for information transmission (n p +n d =BT / J). The pilot symbol and data symbol sent by sensor k are expressed as and where i1=1,…,n p , i2=1,…,n d , then the pilot signal received by the base station can be expressed as

[0034]

[0035] Among them, P k represents the power of the pilot symbol sent by sensor k, represents Gaussian white noise.

[0036] By simply setting the pilot symbols to and will receive the signal Stacked as Can get

[0037]

[0038] Where V H and w are and

[0039] By properly designing V H , so that rank(V H)=M, then the pilot signal y received by the base station kp The channel vector h can be derived kra The least squares estimate of

[0040]

[0041] where Δh kra ~CN(0,σ 2 (P k n p ) -1 I M ) represents the channel estimation error.

[0042] Based on estimated channel The phase of the smart reflector can be optimized to in, express The mth element of m represents the phase quantization error, φ m Obeying uniform distribution, for the smart reflector with phase quantization bit number Q, φ m ~U(-2 -Q π,2 -Q π). Therefore, the data signal received by the base station can be expressed as

[0043]

[0044] h kra,m The phase estimation error is expressed as Right now So, Can be converted to From this we can deduce that the expression of signal-to-noise ratio is

[0045]

[0046] In order to quantify the impact of discretized phase and non-ideal channel information on system performance, the variable And decompose it into g according to its real and imaginary parts k =U+jV. According to the central limit theorem, when M is large, It can be approximated as a Gaussian random variable, whose mean and variance are calculated as

[0047]

[0048]

[0049] In order to further simplify μ(U) and σ 2 (U), needs to be calculated and The expression of . The probability distribution of |Δh is more complicated. To simplify the analysis, assume that |Δh kra,m |much smaller than|h kra,m |, then we can get the following approximation: as well as From this we can calculate and And φ m Obey uniform distributionφ m ~U(-2 -Q π,2 -Q π), so we have Therefore, μ(U) and σ 2 The expression of (U) is simplified to:

[0050]

[0051]

[0052] in, represents the channel estimation error coefficient,

[0053]

[0054] Similarly, It can be approximated as a function with a mean of 0 and a variance of Gaussian random variable. After obtaining the probability distribution of U and V, the moment matching approximation method can be used to obtain |h k | 2 The distribution of . Note that |g k | 2 =U 2 +V 2 is a non-negative random variable with mean and variance μ 2 (U)+σ 2 (U)+σ 2 (V) and 2σ 4 (U)+4μ 2 (U)σ 2 (U)+σ 4 (V). According to the moment matching approximation theory, |g k | 2 It is approximately a Gamma variable, and its probability distribution function is

[0055]

[0056] Where α = μ 2 (|g k | 2 ) / σ 2 (|gk | 2 ), β=μ(|g k | 2 ) / σ 2 (|g k | 2 ).

[0057] because Then |h k | 2 The probability distribution function of can be calculated as

[0058]

[0059] Sensor k sends a data packet to the base station, and there is a non-negligible packet error rate in the process of decoding the short packet by the base station. Its expression is

[0060]

[0061] Where D represents the amount of information carried by the data packet. Due to the complex structure of Q(x), it is difficult to directly solve the expression of the average packet error rate. Therefore, the present invention uses a linear function to approximate the packet error rate of the data packet transmitted by sensor k, and its expression is

[0062]

[0063] in, Therefore, the average packet error rate of sensor k's transmitted data packets can be calculated as

[0064]

[0065] The average information age is the long-term average of the information age. It is derived from the literature (B. Yu, Y. Cai and D. Wu, “Joint access control and resource allocation for short-packet-based mMTC in status update systems,” IEEE Journal on Selected Areas in Communications, vol. 39, no. 3, pp. 851-865, March 2021.) and is expressed as

[0066]

[0067] This scheme jointly optimizes the pilot length and the UAV position to minimize the average information age of all perception information and constructs an average information age optimization model. Therefore, the optimization problem can be expressed as:

[0068]

[0069] Among them, q r =[x r ,y r ,h] represents the coordinates of the drone, n p Indicates the number of pilot symbols sent by the sensor to the base station for channel estimation after allocating resource blocks. represents the average information age of the information sent by the k-th sensor, represents the average packet error rate of the kth sensor, t d Indicates the information transmission time,

[0070] is an intermediate variable.

[0071] Specifically in this embodiment, the simulation parameters are set as follows: the coordinates of the base station are q a = [0,0,0]m, the drone hovering height is 60m, the number of reflective units equipped on the intelligent reflective surface is M = 100, the number of phase quantization bits is Q = 4. The number of sensor devices is K = 12, which are distributed in a q s =[800,0,0]m is the center of the circular area with a radius of R = 50m. The channel coherence time is T = 1ms, the total available bandwidth of the system is B = 2.8MHz, the number of equal resource blocks divided into each channel coherence block is J = 8, and the transmission power is P k =7dBm, the amount of information carried by the short packet is D = 300 bits, the power spectral density of the noise is -174dBm / Hz, the path loss at the reference distance is L0 = -30dB, and the path loss coefficient is η = 2. In addition, the parameters of the Neldermead simplex algorithm are set as δ = 1, λ = 2, ω = 0.5, and ξ = 0.5.

[0072] To simplify the description, in this embodiment, DPS represents the discrete phase control strategy, CPS represents the continuous phase control strategy, and Q represents the number of phase quantization bits in the discrete phase control strategy.

[0073] Step 2: Solve the average information age optimization model constructed in step 1 to obtain the optimal transmission parameters;

[0074] The average information age optimization model is divided into two sub-problems. Among them, the UAV position is fixed and the pilot length is optimized. The first sub-problem is:

[0075]

[0076] Considering n pThe value range of is one-dimensional and limited in size, so the exhaustive search method can solve this subproblem simply and effectively;

[0077] Fixing the pilot length and optimizing the UAV position, the second sub-problem is:

[0078]

[0079] This subproblem is an optimization problem with a complex objective function and no constraints. It can be solved by the typical algorithm in the pattern search method - the Neldermead simplex algorithm. The Neldermead simplex algorithm consists of four basic operations: reflection, expansion, contraction, and compression. In the process of algorithm implementation, we first define Then set the reflection coefficient, expansion coefficient, contraction coefficient and compression coefficient to δ, λ, ω and ξ respectively. The specific steps of solving the subproblem by Neldermead simplex algorithm are as follows:

[0080] First define Then set the reflection coefficient, expansion coefficient, contraction coefficient and compression coefficient to δ, λ, ω and ξ respectively;

[0081] The specific steps of solving the subproblem using the Neldermead simplex algorithm are:

[0082] (1) Input the initial vertex of the simplex:

[0083] (2) Sort the vertices of the simplex as follows:

[0084] (3) Excluding vertices Then calculate the centroid of all remaining vertices

[0085] (4) Calculate vertices The reflection point is calculated as Where δ>0;

[0086] (5) Extension step: If the objective function value of the reflection point The objective function value that is better than the current best point Calculating extension points Where λ>1, then, if the objective function value of the expansion point Better than the function value of the reflection point Use extension points Replace the worst point Otherwise, use the reflection point Replace the worst point

[0087] (6) Reflection step: If the objective function value of the reflection point Between the current optimal point function value and sub-difference function value Between, use the reflection point Replace the worst point

[0088] (7) Contraction step: If the objective function value of the reflection point Worse than the second worst function value Calculate the shrinkage point Where ω>0; if the objective function value of the contraction point Better than the current worst point function value Use the contraction point Replace the worst point

[0089] (8) Compression step: maintain the current optimal point No change, perform compression transformation on all other points, that is, calculate the new point position Where ξ>0;

[0090] (9) Calculate the covariance of all vertices of the simplex. If it exceeds the set threshold, go to step (2). Otherwise, end the search and output the optimal solution.

[0091] By using the alternating optimization algorithm to iteratively optimize the two sub-problems in turn, when the decrease value of the average information age is less than the set threshold, the iterative optimization is stopped and the optimal solution of the average information age optimization model is obtained.

[0092] Step 3: Set the UAV position based on the determined optimal transmission parameters and control the transmission of the pilot signal;

[0093] According to the optimal transmission parameters in step 2, the UAV is deployed at the optimized position, and the sensor is controlled to send a data packet of length n to the base station according to the optimized parameters. p pilot signal;

[0094] Step 4: Estimate the channel based on the pilot signal, adjust the phase of the smart reflector, and send data information.

[0095] The pilot symbol sent by sensor k is expressed as where i1=1,…,n p , then the pilot signal received by the base station can be expressed as

[0096]

[0097] Among them, v H =[v1,…,v M ], θ m ∈[0,2π) represents the reflection phase of the mth unit of the smart reflection surface, and denote the baseband equivalent channels of the link between sensor k and IRS and the link between IRS and base station, respectively, P k represents the power of the pilot symbol sent by sensor k, represents Gaussian white noise;

[0098] By simply setting the pilot symbols to and will receive the signal Stacked as Can get

[0099]

[0100] Where V H and w are and Indicates pilot symbols Reflected beam during transmission.

[0101] By properly designing V H , so that rank(V H )=M, then the pilot signal y received by the base station kp The channel vector h can be derived kra The least squares estimate of

[0102]

[0103] where Δh kra ~CN(0,σ 2 (P k n p ) -1 I M ) represents the channel estimation error;

[0104] Based on the estimated channel, the base station optimizes the reflection phase of the mth unit of the smart reflection surface to in, Represents the estimated channel The mth element in φ m represents the phase quantization error, φ m Obeying uniform distribution, for the smart reflector with phase quantization bit number Q, φ m ~U(-2 -Q π,2 -Q π);

[0105] The intelligent reflective surface is adjusted based on the optimized reflection phase;

[0106] The sensor then encodes the sensing information into short data packets and sends them to the base station;

[0107] The data symbols sent by sensor k are expressed as where i2=1,…,n d , n d represents the data symbol length, then the data signal received by the base station can be expressed as

[0108]

[0109] in, represents Gaussian white noise.

[0110] This solution flexibly deploys drones equipped with smart reflective surfaces and appropriately sets the pilot signal length. The base station estimates the channel information based on the pilot signal and then feeds the optimized reflection phase back to the smart reflective surface. The sensor then sends the data signal to the base station. This information age optimization method can be applied to IoT monitoring scenarios with high timeliness requirements, but is not limited to such scenarios.

[0111] Figure 3 The relationship between the average information age and the amount of data packet information is shown, with theoretically derived values compared with simulation results. As shown in the figure, the theoretical curves closely match the simulation results, validating the accuracy of the theoretical derivation. It can also be observed that the average information age increases with increasing information volume. This is because an increase in information volume leads to an increase in the packet error rate, which in turn increases the average information age. Furthermore, when the amount of information is small and the number of resource blocks used in the coherent block is , the timeliness performance is better than that of and , indicating that the relationship between timeliness performance and the number of resource blocks is not a simple monotonic one. This phenomenon can be explained by two mutually restrictive factors: first, an increase in the number of resource blocks increases the probability of sensor access to a resource block, which helps reduce the average information age; second, an increase in the number of resource blocks reduces the size of each resource block, which increases the packet error rate and, consequently, the average information age. Therefore, to minimize the average information age, it is necessary to optimize the number of resource blocks to find the optimal balance between access probability and packet error rate.

[0112] Figure 4The relationship between average information age and pilot length is shown. As can be observed from the figure, the average information age first decreases and then increases with increasing pilot length. This indicates that increasing pilot length has a trade-off effect on timing performance. When the pilot length is short, increasing the pilot length can effectively reduce channel estimation error, thereby improving the signal-to-noise ratio (SNR) of the received signal and improving timing performance. However, as the pilot length continues to increase, its marginal benefit gradually decreases. That is, the improvement in SNR for increasing the pilot length by the same amount gradually weakens. At the same time, the resulting reduction in packet length increases the packet error rate (PER), ultimately leading to deterioration in timing performance. Notably, simulation results show that the DPS scheme using 4-bit phase quantization can achieve timing performance similar to that of the CPS scheme. This indicates that in actual system deployments, 4-bit phase quantization is sufficient to ensure near-optimal performance. This finding differs from the conclusions of a previous study (Q. Wu, R. Zhang, “Beamforming optimization for wireless networks aided by intelligent reflecting surface with discrete phase shifts,” IEEE Trans. Commun., vol. 68, no. 3, pp. 1838-1851, 2019). In that study, 3-bit phase quantization was considered sufficient to achieve performance similar to that of the CPS scheme. This discrepancy stems from the differing research objectives. The reference study focused primarily on transmit power consumption, where 3-bit phase quantization resulted in a relatively small loss in channel gain, sufficient to meet that objective. This study, however, focused on timing performance, where 3-bit phase quantization increases the packet error rate, significantly increasing the information age. Therefore, to achieve optimal timing performance, a higher phase quantization resolution (4 bits) is required.

[0113] Figure 5The figure shows how the average information age varies with the channel estimation error coefficient. As can be seen from the figure, the timeliness performance shows a monotonically decreasing trend as the channel estimation error coefficient increases. This is because higher channel estimation errors lead to inaccurate smart reflector phase control, which reduces beamforming gain and ultimately degrades timeliness performance. Furthermore, it is important to note that the performance difference between the DPS scheme using 4-bit phase quantization and the CPS scheme increases as the channel estimation error coefficient increases. This is because the DPS scheme inherently has a large phase quantization error. As the channel estimation error increases, the DPS scheme's smart reflector beamforming gain is more significantly affected, leading to a more pronounced degradation in timeliness performance. Therefore, in scenarios with high channel estimation error, the 4-bit quantization DPS scheme struggles to maintain a similar level of timeliness performance as the CPS scheme.

[0114] The present invention also provides an information age optimization system for UAV equipped with intelligent reflective surface assisted communication under non-ideal conditions, comprising the following modules:

[0115] Average Information Age Optimization Model Construction Module: This module is used to construct an average information age optimization model in communication scenarios by jointly optimizing the pilot length and the position of drones equipped with smart reflective surfaces.

[0116] Optimal transmission parameter acquisition module: used to construct the average information age optimization model and obtain the optimal transmission parameters;

[0117] Channel estimation module: used to set the UAV position based on the determined optimal transmission parameters and control the transmission of pilot signals;

[0118] Adjustment and data transmission module: used to estimate the channel and adjust the phase of the smart reflector according to the pilot signal, and send data information.

[0119] The present invention further provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following steps are implemented:

[0120] Step 1: In the communication scenario, the average information age optimization model is constructed by jointly optimizing the pilot length and the position of the UAV equipped with the smart reflector;

[0121] Step 2: Solve the average information age optimization model constructed in step 1 to obtain the optimal transmission parameters;

[0122] Step 3: Set the UAV position based on the determined optimal transmission parameters and control the transmission of the pilot signal;

[0123] Step 4: Estimate the channel based on the pilot signal, adjust the phase of the smart reflector, and send data information.

[0124] The present invention further provides a computer storable medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0125] Step 1: In the communication scenario, the average information age optimization model is constructed by jointly optimizing the pilot length and the position of the UAV equipped with the smart reflector;

[0126] Step 2: Solve the average information age optimization model constructed in step 1 to obtain the optimal transmission parameters;

[0127] Step 3: Set the UAV position based on the determined optimal transmission parameters and control the transmission of the pilot signal;

[0128] Step 4: Estimate the channel based on the pilot signal, adjust the phase of the smart reflector, and send data information.

[0129] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for optimizing information age for UAV-mounted intelligent reflective surface-assisted communication under non-ideal conditions, characterized in that: The following steps are involved: Step 1: In the communication scenario, the average information age optimization model is constructed by jointly optimizing the pilot length and the position of the UAV equipped with the smart reflector; Step 2: Solve the average information age optimization model constructed in step 1 to obtain the optimal transmission parameters; Step 3: Set the UAV position based on the determined optimal transmission parameters and control the transmission of the pilot signal; Step 4: Estimate the channel based on the pilot signal, adjust the phase of the smart reflector, and send data information.

2. The information age optimization method for UAV-mounted intelligent reflective surface-assisted communication under non-ideal conditions according to claim 1 is characterized in that: In the communication scenario: A drone equipped with a smart reflective surface hovers at a height h and assists in short packet transmission. The smart reflective surface includes M reflective units. K sensors are deployed in the communication scenario, and the sensors send n p pilot symbols for channel estimation, and then encode the sensing information into short data packets and transmit them to the base station, including n d Data symbols are used for information transmission; In the communication scenario, the available bandwidth is B, the channel coherence time is T, and each channel coherence block is divided into J equal resource blocks and allocated to K sensors for data transmission.

3. The information age optimization method for UAV-mounted intelligent reflective surface-assisted communication under non-ideal conditions according to claim 2 is characterized in that: In step 1, the pilot length and the drone position are jointly optimized to minimize the average information age of all perception information and construct an average information age optimization model: s.t.n p ≤BT / J n p ∈N t d =n d / B n p +n d =BT / J Among them, q r =[x r ,y r ,h] represents the coordinates of the drone, n p Indicates the number of pilot symbols sent by the sensor to the base station for channel estimation after allocating resource blocks. represents the average information age of the information sent by the k-th sensor, represents the average packet error rate of the kth sensor, t d Indicates the information transmission time, is an intermediate variable.

4. The information age optimization method for UAV-mounted intelligent reflective surface-assisted communication under non-ideal conditions according to claim 3 is characterized in that: The solution to the average information age optimization model in step 2 is specifically as follows: The average information age optimization model is divided into two sub-problems. Among them, the UAV position is fixed and the pilot length is optimized. The first sub-problem is: s.t.n p ≤BT / J n p ∈N Fixing the pilot length and optimizing the UAV position, the second sub-problem is: By using the alternating optimization algorithm to iteratively optimize the two sub-problems in turn, when the decrease value of the average information age is less than the set threshold, the iterative optimization is stopped and the optimal solution of the average information age optimization model is obtained.

5. The information age optimization method for UAV-mounted intelligent reflective surface-assisted communication under non-ideal conditions according to claim 4 is characterized in that: The sub-problem of optimizing the position of the UAV with a fixed pilot length is solved by the Nelder-Mead simplex algorithm, a typical algorithm in the pattern search method; First define Then set the reflection coefficient, expansion coefficient, contraction coefficient and compression coefficient to δ, λ, ω and ξ respectively; The specific steps of solving the subproblem using the Neldermead simplex algorithm are: (1) Input the initial vertex of the simplex: (2) Sort the vertices of the simplex as follows: (3) Excluding vertices Then calculate the centroid of all remaining vertices (4) Calculate vertices The reflection point is calculated as Where δ>0; (5) Extension step: If the objective function value of the reflection point The objective function value that is better than the current best point Calculating extension points Where λ>1, then, if the objective function value of the expansion point Better than the function value of the reflection point Use extension points Replace the worst point Otherwise, use the reflection point Replace the worst point (6) Reflection step: If the objective function value of the reflection point Between the current optimal point function value and sub-difference function value Between, use the reflection point Replace the worst point (7) Contraction step: If the objective function value of the reflection point Worse than the second worst function value Calculate the shrinkage point Where ω>0; if the objective function value of the contraction point Better than the current worst point function value Use the contraction point Replace the worst point (8) Compression step: maintain the current optimal point No change, perform compression transformation on all other points, that is, calculate the new point position Where ξ>0; (9) Calculate the covariance of all vertices of the simplex. If it exceeds the set threshold, go to step (2). Otherwise, end the search and output the optimal solution.

6. The information age optimization method for UAV-mounted intelligent reflective surface-assisted communication under non-ideal conditions according to claim 2 is characterized in that: In step 3, the UAV is deployed at the optimized position according to the optimal transmission parameters of step 2, and the sensor is controlled to send a data packet of length n to the base station according to the optimized parameters. p pilot signal.

7. The information age optimization method for UAV-mounted intelligent reflective surface-assisted communication under non-ideal conditions according to claim 2 is characterized in that: The step 4 of estimating the channel according to the pilot signal and adjusting the phase of the smart reflector to send data is specifically as follows: Based on the estimated channel, the base station optimizes the reflection phase of the mth unit of the smart reflection surface to in, Represents the estimated channel The mth element in φ m represents the phase quantization error; The intelligent reflective surface is adjusted based on the optimized reflection phase; The sensor then encodes the sensing information into short data packets and sends them to the base station.

8. An information age optimization system for drone-mounted intelligent reflective surface-assisted communication under non-ideal conditions, characterized in that: Includes the following modules: Average Information Age Optimization Model Construction Module: This module is used to construct an average information age optimization model in communication scenarios by jointly optimizing the pilot length and the position of drones equipped with smart reflective surfaces. Optimal transmission parameter acquisition module: used to construct the average information age optimization model and obtain the optimal transmission parameters; Channel estimation module: used to set the UAV position based on the determined optimal transmission parameters and control the transmission of pilot signals; Adjustment and data transmission module: used to estimate the channel and adjust the phase of the smart reflector according to the pilot signal, and send data information.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer storable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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