CN116232440B - Data acquisition method, system and storage medium - Google Patents
Data acquisition method, system and storage medium Download PDFInfo
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Abstract
The invention discloses a data acquisition method, a data acquisition system and a storage medium. The system comprises a base station, an unmanned aerial vehicle provided with an IRS and at least one data acquisition device corresponding to the unmanned aerial vehicle, wherein the unmanned aerial vehicle is used for transmitting environment information acquired by the data acquisition device to the base station; determining decoding error probability and signal-to-interference-plus-noise ratio of data acquisition equipment; determining the transmission rate of the data acquisition equipment according to the signal-to-interference-plus-noise ratio and the decoding error probability; determining AoI violation probability of data acquisition equipment according to the transmission rate and the data acquisition basic information, and taking AoI violation probability as a target constraint condition; and determining data acquisition parameters according to the target constraint conditions and a preset energy consumption minimum algorithm, and carrying out data acquisition according to the data acquisition parameters. The method and the device constraint a preset energy consumption minimum algorithm based on target constraint conditions to obtain the data acquisition parameters, and can reduce the energy consumption of data acquisition.
Description
Technical Field
The present invention relates to the field of data acquisition technologies, and in particular, to a data acquisition method, system, and storage medium.
Background
The non-orthogonal multiple access (non-orthogonal multiple access, noma) access technology is a key technology for the latter 5g communication as a multiple access technology with great potential for future mobile networks, and unlike the traditional orthogonal multiple access (orthogonal multiple access, oma) technology, noma allows multiple users to share the same time, frequency, space and other resources through superposition coding at a transmitting end and serial interference cancellation (successive interference cancellation, sic) technology at a receiving end, so that the spectrum efficiency of the system is significantly improved.
The smart reflective surface (IntelligentReflectingSurface, IRS) has excellent spectral efficiency and energy efficiency performance, and is also an emerging technology for the latter 5G communication system. The intelligent reflecting surface consists of a large number of passive reflecting elements, each reflecting element can independently change the reflection phase shift of an incident signal, and the intelligent reflecting surface can flexibly set the transmission of the reflected signal by adjusting the reflection phase shift so as to achieve the communication targets of improving the power of the received signal, reducing interference, safely transmitting and the like. In practical application, the intelligent reflecting surface is fundamentally different from the traditional relay, the intelligent reflecting surface is used as a reconfigurable diffuser, special energy is not required to be equipped for decoding, channel estimation and transmission, signals are reflected in a full duplex and noiseless mode without self-interference, and only a passive reflecting component with low energy consumption is used, so that compared with the traditional relay, the energy consumption and hardware deployment cost are greatly saved. Therefore, the adoption of intelligent reflective surfaces to assist in wireless communications is considered a promising, efficient, green solution for internet of things communications.
In the prior art, when an Unmanned plane (un-managed AERIAL VEHICLE, UAV) -intelligent reflection plane (INTELLIGENT REFLECTING Surface, IRS) assisted Non-orthogonal multiple access (Non-Orthogonal Multiple Access, NOMA) system collects environmental information, timeliness of the information and energy consumption of the whole system cannot be guaranteed.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
The invention mainly aims to provide a data acquisition method, a system and a storage medium, and aims to solve the technical problem of high energy consumption in the prior art when information is acquired through an unmanned plane, an intelligent reflection plane and an auxiliary non-orthogonal multiple access system.
In order to achieve the above object, the present invention provides a data acquisition method, which is applied to a non-orthogonal multiple access system, wherein the non-orthogonal multiple access system comprises a base station, an unmanned aerial vehicle with an IRS and at least one data acquisition device corresponding to the unmanned aerial vehicle, and the unmanned aerial vehicle is used for transmitting environmental information acquired by the data acquisition device to the base station;
The data acquisition method comprises the following steps:
determining decoding error probability and signal-to-interference-plus-noise ratio of the data acquisition equipment;
determining the transmission rate of the data acquisition equipment according to the signal-to-interference-plus-noise ratio and the decoding error probability;
determining AoI violation probability of the data acquisition equipment according to the transmission rate and the data acquisition basic information of the non-orthogonal multiple access system, and taking the AoI violation probability as a target constraint condition;
and determining data acquisition parameters according to the target constraint conditions and a preset energy consumption minimum algorithm, and carrying out data acquisition according to the data acquisition parameters.
Optionally, the step of determining a data acquisition parameter according to the target constraint condition and a preset energy consumption minimum algorithm and performing data acquisition according to the data acquisition parameter includes:
Determining first energy consumption of the unmanned aerial vehicle in a hovering state and second energy consumption of the unmanned aerial vehicle in a flying state according to the data acquisition basic information;
Determining a third energy consumption of the data acquisition device according to the data acquisition basic information;
Determining a preset energy consumption minimum algorithm according to the first energy consumption, the second energy consumption and the third energy consumption;
And determining data acquisition parameters according to the preset energy consumption minimum algorithm based on the target constraint condition, and carrying out data acquisition according to the data acquisition parameters.
Optionally, the step of determining the second energy consumption of the unmanned aerial vehicle in the flight state according to the data acquisition basic information includes:
Determining a second energy consumption of the unmanned aerial vehicle in a flight state according to the data acquisition basic information by the following formula:
Wherein, The method comprises the steps of representing second energy consumption of the unmanned aerial vehicle at a moment t under a flight state, representing flight distance of the unmanned aerial vehicle, representing speed of the unmanned aerial vehicle, representing blade profile power under a hovering state of the unmanned aerial vehicle by V (t), representing tail end speed of a rotor blade by P 0, representing blade induction power under the hovering state of the unmanned aerial vehicle by U tip, representing induction speed of the rotor under the hovering state of the unmanned aerial vehicle by P i, representing airframe resistance ratio by d 0, representing air density by ρ, representing fixity of the rotor by s, representing area of the rotor by Λ.
Optionally, the step of determining the first energy consumption of the unmanned aerial vehicle in the hovering state according to the data acquisition basic information includes:
determining a first energy consumption of the unmanned aerial vehicle in a hovering state according to the data acquisition basic information through the following formula:
Wherein, The method comprises the steps of representing first energy consumption of the unmanned aerial vehicle at t moment in a hovering state, wherein P h represents the energy consumption of the unmanned aerial vehicle in the hovering state, and tau represents the frame length.
Optionally, the step of determining the third energy consumption of the data acquisition device according to the data acquisition basic information includes:
Determining target energy consumption of each data acquisition device according to the data acquisition basic information through the following formula;
Wherein E k (t) is used to characterize the target energy consumption of the kth data acquisition device at time t, P k (t) is used to characterize the power of the kth data acquisition device at time t, and τ is used to characterize the frame length;
And determining a third energy consumption of the data acquisition device according to the target energy consumption.
Optionally, the data acquisition basic information of the non-orthogonal multiple access system includes a frame length and a size of a target data packet;
The step of determining AoI violation probability of the data acquisition device according to the transmission rate and the data acquisition basic information of the non-orthogonal multiple access system, and taking the AoI violation probability as a target constraint condition comprises the following steps:
Calculating the number of target data packets according to the transmission rate, the frame length and the size of the target data packets;
Determining AoI violation probabilities of the data acquisition equipment according to the number of the target data packets;
And taking the AoI violation probability as the target constraint condition.
Optionally, the step of determining the transmission rate of the data acquisition device according to the signal-to-interference-plus-noise ratio and the decoding error probability comprises
Determining a channel dispersion function according to the signal-to-interference-plus-noise ratio;
Calculating the transmission rate of the data acquisition equipment according to the channel dispersity function, the decoding error probability and the data acquisition basic information by the following formula:
Wherein r k (t) is used for representing the transmission rate of the data acquisition device, B is used for representing the bandwidth of a sub-channel, gamma k (t) is used for representing the signal to interference plus noise ratio, V k (t) is used for representing the channel dispersion function, L is used for representing the block length of a data packet, Q -1 () is used for representing the inverse function of the Q function, and epsilon k (t) is used for representing the decoding error probability.
Optionally, before the step of determining the data acquisition parameters according to the target constraint condition and the preset energy consumption minimum algorithm and performing data acquisition according to the data acquisition parameters, the method further includes:
Acquiring a motion boundary of the unmanned aerial vehicle;
the motion boundary is taken as the target constraint condition.
In addition, to achieve the above object, the present invention also provides a non-orthogonal multiple access system, the system comprising: the unmanned aerial vehicle is used for transmitting the environment information acquired by the data acquisition equipment to the base station; the system further comprises: a memory, a processor and a data acquisition program stored on the memory and executable on the processor, the data acquisition program configured to implement the steps of the data acquisition method as described above.
In addition, to achieve the above object, the present invention also proposes a data acquisition device, the device comprising: a memory, a processor and a data acquisition program stored on the memory and executable on the processor, the data acquisition program configured to implement the steps of the data acquisition method as described above.
In addition, in order to achieve the above object, the present invention also proposes a storage medium having stored thereon a data acquisition program which, when executed by a processor, implements the steps of the data acquisition method as described above.
The data acquisition method is applied to a non-orthogonal multiple access system, the non-orthogonal multiple access system comprises a base station, an unmanned aerial vehicle with an IRS and at least one data acquisition device corresponding to the unmanned aerial vehicle, and the unmanned aerial vehicle is used for transmitting environment information acquired by the data acquisition device to the base station; the method comprises the following steps: determining decoding error probability and signal-to-interference-plus-noise ratio of the data acquisition equipment; determining the transmission rate of the data acquisition equipment according to the signal-to-interference-plus-noise ratio and the decoding error probability; determining AoI violation probability of the data acquisition equipment according to the transmission rate and the data acquisition basic information of the non-orthogonal multiple access system, and taking the AoI violation probability as a target constraint condition; and determining data acquisition parameters according to the target constraint conditions and a preset energy consumption minimum algorithm, and carrying out data acquisition according to the data acquisition parameters. According to the method, the preset energy consumption minimum algorithm is constrained based on the target constraint condition, and the data acquisition parameters are determined according to the preset energy consumption minimum algorithm, so that the energy consumption of data acquisition can be reduced.
Drawings
FIG. 1 is a schematic diagram of a data acquisition device of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flowchart of a first embodiment of a data acquisition method according to the present invention;
Fig. 3 is a schematic diagram of a non-orthogonal multiple access system according to a first embodiment of the data acquisition method of the present invention;
FIG. 4 is a diagram showing a first embodiment AoI of a data acquisition method according to the present invention;
FIG. 5 is a flowchart of a second embodiment of the data acquisition method of the present invention;
fig. 6 is a block diagram of a first embodiment of a data acquisition device according to the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a data acquisition device in a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the data acquisition device may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (WI-FI) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
Those skilled in the art will appreciate that the structure shown in fig. 1 does not constitute a limitation of the data acquisition device and may include more or fewer components than shown, or may combine certain components, or may be arranged in different components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and a data collection program may be included in the memory 1005 as one type of storage medium.
In the data acquisition device shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the data acquisition device of the present invention may be disposed in the data acquisition device, where the data acquisition device invokes a data acquisition program stored in the memory 1005 through the processor 1001, and executes the data acquisition method provided by the embodiment of the present invention.
Based on the above data acquisition device, an embodiment of the present invention provides a data acquisition method, and referring to fig. 2, fig. 2 is a schematic flow chart of a first embodiment of the data acquisition method of the present invention.
In this embodiment, the data acquisition method is applied to a non-orthogonal multiple access system, where the non-orthogonal multiple access system includes a base station, an unmanned aerial vehicle with an IRS and at least one data acquisition device corresponding to the unmanned aerial vehicle, where the unmanned aerial vehicle is configured to transmit environmental information acquired by the data acquisition device to the base station; the data acquisition method comprises the following steps:
step S10: and determining the decoding error probability and the signal-to-interference-plus-noise ratio of the data acquisition device.
It should be noted that, the execution body of the embodiment may be a computing service device with functions of data processing, network communication and program running, such as a mobile phone, a tablet computer, a personal computer, or an electronic device or a non-orthogonal multiple access system capable of implementing the above functions. The present embodiment and the following embodiments will be described below by taking the non-orthogonal multiple access system as an example.
It should be noted that, the Non-orthogonal multiple access system in this embodiment is a Non-orthogonal multiple access (NOMA) system including Unmanned AERIAL VEHICLE, UAV (IRS) -intelligent reflection plane INTELLIGENT REFLECTING Surface, and reference may be made to fig. 3, and fig. 3 is a schematic diagram of the Non-orthogonal multiple access system in the first embodiment of the data acquisition method of the present invention; the center in fig. 3 is a base station, namely, an Access Point (AP), fig. 3 includes four data acquisition devices, which are respectively located at each vertex in fig. 3, the unmanned aerial vehicle can hover on the data acquisition devices, a line segment between each data acquisition device in fig. 3 is used for representing a track of the unmanned aerial vehicle, and the unmanned aerial vehicle with the IRS is hovered on the data acquisition device.
It should be noted that, the base station in this embodiment adopts a serial interference cancellation (SIC, successive Interference Cancellation) technique to cancel interference caused by the NOMA technique. Let |h AU(t)hU1(t)|≥L|hAU(t)hUk(t)|L≥|hAU(t)hUK (t) |. Because short packet communications have a non-negligible decoding error probability, the AP will not be able to implement perfect SIC techniques. At this time, at the AP, the decoding error probability of the device k may be expressed as:
Where h AU (t) ∈1×n denotes the channel state matrix between the AP and the UAV at t-frame, h Uk (t) denotes the channel state matrix between the UAV and the device k at t-frame, s k (t) denotes the signal of device k at t-frame, p k (t) denotes the power of device k at t-frame, N k denotes the noise of device k at AP-end, Φ (t) denotes the phase matrix of all subunits of the IRS at t-frame, Where F is used to characterize the number of subunits and j is used to characterize the index of the device.
Epsilon k (t) is used to characterize the decoding error probability of device k, epsilon j (t) is used to characterize the decoding error probability of device j,And/>For characterizing the decoding error probability of device k itself, which is a pre-known value, when the previous stronger k-1 devices can successfully decode, then the decoding error probability of device k itself is/>Otherwise, the decoding error probability of device k itself is/>At this time, the interference left by the previously decoded device is expressed as:
Where ρ i (t) represents the expected value of the difference square between the transmitted signal and the decoded signal of device k.
The signal to interference plus noise ratio (Signal to Interference plus Noise Ratio, SINR) of device k can be expressed as:
Where γ k (t) is used to characterize the signal to interference plus noise ratio, p j (t) represents the power of device j at t frames and σ 2 represents the noise power.
Step S20: and determining the transmission rate of the data acquisition equipment according to the signal-to-interference-plus-noise ratio and the decoding error probability.
It should be noted that, the determining the transmission rate of the data acquisition device according to the signal-to-interference-plus-noise ratio and the decoding error probability may be determining a channel dispersion function according to the signal-to-interference-plus-noise ratio, and specifically may be determining a channel dispersion function according to the signal-to-interference-plus-noise ratio by the following formula:
wherein V k (t) is used to characterize the channel dispersion function.
Calculating the transmission rate of the data acquisition equipment according to the channel dispersity function, the decoding error probability and the data acquisition basic information by the following formula:
Wherein r k (t) is used for representing the transmission rate of the data acquisition device, B is used for representing the bandwidth of a sub-channel, gamma k (t) is used for representing the signal to interference plus noise ratio, V k (t) is used for representing the channel dispersion function, L is used for representing the block length of a data packet, Q -1 () is used for representing the inverse function of the Q function, and epsilon k (t) is used for representing the decoding error probability.
Step S30: and determining AoI violation probability of the data acquisition equipment according to the transmission rate and the data acquisition basic information of the non-orthogonal multiple access system, and taking the AoI violation probability as a target constraint condition.
Note that AoI may be information ages (Age ofInformation, aoI), and in this embodiment, the information ages are used to quantify the timeliness of the information. Referring to fig. 4, fig. 4 is a schematic diagram AoI of a first embodiment of a data acquisition method according to the present invention; fig. 4 shows a diagram of a change AoI of three data acquisition devices, such as device 2, where the device side has 6 target data packets, i.e. status update data packets that need to be transmitted to the base station, and the number in each box indicates the generation time of the data packet, for example, the 4 th box is 2, and indicates that the data packet is generated at the 2 nd frame. In the 7 th frame, the device transmits three data packets, and the three data packets are transmitted according to the time sequence, wherein the three transmitted data packets are respectively generated in the 1 st frame and the 2 nd frame, and 3 data packets are generated in total in the 2 nd frame, and one data packet is not transmitted, so that AoI of the device 2 at the AP end is changed to 7-2=5, and the AoI threshold value of the device 2 is 4, which means that AoI of the device 2 does not meet the information timeliness requirement in the 7 th frame.
It should be noted that, the data acquisition basic information of the non-orthogonal multiple access system includes a frame length and a size of a target data packet;
The step of determining AoI violation probability of the data acquisition device according to the transmission rate and the data acquisition basic information of the non-orthogonal multiple access system, and taking the AoI violation probability as a target constraint condition comprises the following steps:
Calculating the number of target data packets according to the transmission rate, the frame length and the size of the target data packets;
Determining AoI violation probabilities of the data acquisition equipment according to the number of the target data packets;
And taking the AoI violation probability as the target constraint condition.
It should be noted that, the calculating the number of the target data packets according to the transmission rate, the frame length, and the size of the target data packets may be calculating the number of the target data packets according to the transmission rate, the frame length, and the size of the target data packets by the following formula:
Wherein a k (t) is used for representing the number of target data packets, r k (t) is used for representing the transmission rate of the data acquisition device, τ is used for representing the frame length, and Z is used for representing the size of the target data packets.
It should be noted that the number of the substrates,
AoI at t-frame for device k can be expressed as:
Wherein A k (t) represents AoI of device k at t frames, Indicating the time of arrival of the u-th packet at device k,/>Indicating the device k transmission of the u-th packet.
The AoI violation probability for each device k can be expressed as:
And, it may be equivalently: Wherein d k represents the threshold value of AoI of device k, pr { x } represents the probability that event x is true, delta k represents the maximum value of AoI violation probability of device k, which is a preset threshold value,/> Indicating that the number of packets reached at device k within time t-d k, t)/>Representing the number of transmission status update packets, Q k (t) represents the packet queue of device k at t frames.
Step S40: and determining data acquisition parameters according to the target constraint conditions and a preset energy consumption minimum algorithm, and carrying out data acquisition according to the data acquisition parameters.
It should be noted that, the preset energy consumption minimum algorithm may include calculating an energy consumption algorithm of the unmanned aerial vehicle in a hovering state, an energy consumption algorithm of the unmanned aerial vehicle in a flying state, and an energy consumption algorithm of the data acquisition device, and constraining the preset energy consumption minimum algorithm according to the target constraint condition to obtain a value of a data acquisition parameter in the preset energy consumption minimum algorithm when the energy consumption is minimum, and then performing data acquisition according to the data acquisition parameter.
The data acquisition method is applied to a non-orthogonal multiple access system, the non-orthogonal multiple access system comprises a base station, an unmanned aerial vehicle and at least one data acquisition device corresponding to the unmanned aerial vehicle, and the unmanned aerial vehicle is used for transmitting environment information acquired by the data acquisition device to the base station; the method comprises the following steps: determining decoding error probability and signal-to-interference-plus-noise ratio of the data acquisition equipment; determining the transmission rate of the data acquisition equipment according to the signal-to-interference-plus-noise ratio and the decoding error probability; determining AoI violation probability of the data acquisition equipment according to the transmission rate and the data acquisition basic information of the non-orthogonal multiple access system, and taking the AoI violation probability as a target constraint condition; and determining data acquisition parameters according to the target constraint conditions and a preset energy consumption minimum algorithm, and carrying out data acquisition according to the data acquisition parameters. According to the embodiment, the preset energy consumption minimum algorithm is constrained based on the target constraint condition, and the data acquisition parameters are determined according to the preset energy consumption minimum algorithm, so that the energy consumption of data acquisition can be reduced.
Referring to fig. 5, fig. 5 is a flowchart of a second embodiment of the data acquisition method according to the present invention.
Based on the first embodiment, in this embodiment, the step S40 includes:
Step S401: determining first energy consumption of the unmanned aerial vehicle in a hovering state and second energy consumption of the unmanned aerial vehicle in a flying state according to the data acquisition basic information
It should be noted that, the determining the second energy consumption of the unmanned aerial vehicle in the flight state according to the data acquisition basic information may be determining the second energy consumption of the unmanned aerial vehicle in the flight state according to the data acquisition basic information by the following formula:
Wherein, The method comprises the steps of representing second energy consumption of the unmanned aerial vehicle at a moment t under a flight state, representing flight distance of the unmanned aerial vehicle, representing speed of the unmanned aerial vehicle, representing blade profile power under a hovering state of the unmanned aerial vehicle by V (t), representing tail end speed of a rotor blade by P 0, representing blade induction power under the hovering state of the unmanned aerial vehicle by U tip, representing induction speed of the rotor under the hovering state of the unmanned aerial vehicle by P i, representing airframe resistance ratio by d 0, representing air density by ρ, representing fixity of the rotor by s, representing area of the rotor by Λ. Wherein/>
It should be noted that, the determining, according to the data acquisition basic information, the first energy consumption of the unmanned aerial vehicle in the hovering state may be determining, according to the data acquisition basic information, the first energy consumption of the unmanned aerial vehicle in the hovering state by the following formula:
Wherein, The method comprises the steps of representing first energy consumption of the unmanned aerial vehicle at t moment in a hovering state, wherein P h represents the energy consumption of the unmanned aerial vehicle in the hovering state, and tau represents the frame length.
From the first energy consumption of the unmanned aerial vehicle in the hovering state and the second energy consumption of the unmanned aerial vehicle in the flying state, the UAV energy consumption at t frames can be expressed as
Step S402: determining a third energy consumption of the data acquisition device based on the data acquisition basic information
It should be noted that, the determining the third energy consumption of the data acquisition device according to the data acquisition basic information may be determining the target energy consumption of each data acquisition device according to the data acquisition basic information by the following formula;
Wherein E k (t) is used to characterize the target energy consumption of the kth data acquisition device at time t, P k (t) is used to characterize the power of the kth data acquisition device at time t, and τ is used to characterize the frame length;
Determining the third energy consumption of the data acquisition device according to the target energy consumption may specifically be determining the third energy consumption of the data acquisition device according to the target energy consumption by the following formula:
wherein E dev (t) is used to characterize the third energy consumption of the data acquisition device at time t and K is used to characterize the total number of data acquisition devices.
Step S403: and determining a preset energy consumption minimum algorithm according to the first energy consumption, the second energy consumption and the third energy consumption.
It should be noted that, the preset energy consumption minimum algorithm determined according to the first energy consumption, the second energy consumption and the third energy consumption may be:
Wherein T is used to characterize the total number of frames, and may be a custom value, p= { p k (T), K e K, T e T } represents the transmission power of all devices, Φ represents the phase shift of all the subunits of IRS, Φ= { Φ 1(t),φ2(t)...φF (T), T e T }, α= { α (T), T e T } represents the angle of UAV flight, v= { V (T), T e T } represents the speed of UAV flight, l= { l (T), T e T } represents the distance of UAV flight.
Step S404: and determining data acquisition parameters according to the preset energy consumption minimum algorithm based on the target constraint condition, and carrying out data acquisition according to the data acquisition parameters.
It should be noted that the data acquisition parameters may be parameters such as transmission power of all devices, phase shift of all subunits of the IRS, angle of UAV flight, speed of UAV flight, and distance of UAV flight, which are obtained according to the preset energy consumption minimum algorithm.
It should be noted that, in consideration of the information such as the actual running state of the data acquisition device and the running track of the unmanned aerial vehicle, the target constraint condition may further include that the power of the data acquisition device is smaller than a preset power threshold, the running track of the unmanned aerial vehicle is required to be in a preset flight range, and the flight speed is smaller than or equal to the preset speed threshold.
Further, before the step S40, the method further includes: acquiring a motion boundary of the unmanned aerial vehicle; the motion boundary is taken as the target constraint condition.
In a specific implementation, a non-orthogonal multiple access system acquires a motion boundary x min,xmax,ymin,ymax of the unmanned aerial vehicle, and uses the motion boundary as the target constraint condition.
In an implementation, the target constraint may further include:
C1:V(t)≤vmax
C2：φf(t)∈[0,2π),f∈F
C4:α(t)∈[0,2π)
C5:xmin≤xUAV(t)≤xmax,ymin≤yUAV(t)≤ymax,
Wherein v max is used to characterize a preset speed threshold of the unmanned aerial vehicle, p k max is used to characterize a power threshold of the data acquisition device, and (x UAV(t),yUAV (t)) represents the position of the UAV at t frames.
Further, in order to ensure energy consumption minimization, the present embodiment uses an original-Dual (prime-Dual) method to convert the constrained markov decision process problem (Constrained Markov Decision Process, CMDP) into an unconstrained markov decision process problem (Markov Decision Process, MDP), which may specifically be:
A lagrangian function is constructed, which can be expressed as:
Where λ k represents the lagrangian multiplier corresponding to the AoI violation probability for device k, the lagrangian pair problem can be expressed as:
wherein the lagrangian multiplier is updated as follows:
Where [ x ] + = max { x,0}, β (t) represents the step size, pi represents the policy of CMDP, λ= { λ k, K e K } represents the lagrangian multiplier corresponding to the AoI violation probability constraint of all devices, and λ k represents the lagrangian multiplier corresponding to the AoI violation probability of device K.
Wherein, the action space, decision space and rewarding function of MDP after the conversion of the original-dual method are defined as follows:
System state space: Wherein R (t-1) represents a reward for a t-1 frame, Q k (t) represents a data packet queue for device k for a t frame,/> Representing the number of packets reached at device k within time t-d i, t-h AU (t) represents the channel state between the AP and the UAV at t-frame, h Uk (t) represents the channel state between the UAV and device k at t-frame, x UAV(t),yUAV (t) represents the position of the UAV at t-frame.
Decision space: p k (t) represents the transmission power of device k at t-frame, phi f (t) represents the phase shift of IRS subunit f at t-frame, alpha (t), l (t), V (t) represent the flight angle, flight distance and flight speed of the UAV at t-frame, respectively.
Bonus function:
E UAV(t),Edev (t) represents the energy consumption of the UAV and the device at t frames respectively, Indicating that the number of packets-1 is reached at device k within time t-d i, t), a k (t) indicates the number of transmission status update packets, Q k (t) indicates the packet queue of device k at t frames, d k indicates the threshold value of device k AoI, pr { x } indicates the probability that event x is true, and epsilon k indicates the maximum value of AoI violation probability for device k.
The determining the data acquisition parameters according to the preset energy consumption minimum algorithm based on the target constraint condition may be determining the data acquisition parameters according to the preset energy consumption minimum algorithm and a joint optimization mode based on the target constraint condition.
The combined optimization mode can be a UAV flight trajectory, equipment transmission power and IRS phase shift combined optimization mode based on double delay DDPG (TWIN DELAYED DEEP DETERMINISTIC Policy Gradient, TD-DDPG) +long Short-Term Memory (LSTM). The method is used for solving the problem of energy consumption minimization under the timeliness guarantee aiming at the condition of unpredictable hardware damage of the IRS. The specific steps can be as follows: the current system state s (t) is processed by an LSTM network to obtain a network output Y (t), a current decision a (t) is obtained according to a current strategy and the output Y (t) of the LSTM network, a reward r (t) and next state information s (t+1) are obtained according to current state information and actions, s (t), a (t), r (t), s (t+1)) are stored in experience playback, Q function values are learned by two Critics (critics) networks, wherein the two critics networks respectively learn to obtain the Q function values, the minimum value of the two Q function values is used as the Q function value updated by each Q function, and the parameter theta μ of the strategy network is updated according to strategy gradients. And the strategy update frequency is lower than the update frequency of the Q function, and the parameters of the target Q1 function and the target Q2 function are updated by using a soft update methodAnd/>According to the gradient descent method, the lagrangian multiplier λ k (t) is updated.
According to the embodiment, according to the data acquisition basic information, the first energy consumption of the unmanned aerial vehicle in a hovering state and the second energy consumption of the unmanned aerial vehicle in a flying state are determined; determining a third energy consumption of the data acquisition device according to the data acquisition basic information; determining a preset energy consumption minimum algorithm according to the first energy consumption, the second energy consumption and the third energy consumption; and determining data acquisition parameters according to the preset energy consumption minimum algorithm based on the target constraint condition, and carrying out data acquisition according to the data acquisition parameters. According to the embodiment, the preset energy consumption minimum algorithm is constrained according to the target constraint condition, so that the data acquisition parameters enabling the energy consumption to be minimum are obtained, and the energy consumption of data acquisition is further reduced.
Referring to fig. 6, fig. 6 is a block diagram illustrating a first embodiment of a data acquisition device according to the present invention.
As shown in fig. 6, a data acquisition device according to an embodiment of the present invention includes:
An acquisition module 10, configured to determine a decoding error probability and a signal-to-interference-plus-noise ratio of the data acquisition device;
A transmission rate calculation module 20, configured to determine a transmission rate of the data acquisition device according to the signal-to-interference plus noise ratio and the decoding error probability;
a target constraint condition determining module 30, configured to determine AoI violation probabilities of the data acquisition device according to the transmission rate and data acquisition basic information of the non-orthogonal multiple access system, and take the AoI violation probabilities as target constraint conditions;
The data acquisition module 40 is configured to determine data acquisition parameters according to the target constraint condition and a preset energy consumption minimization algorithm, and perform data acquisition according to the data acquisition parameters.
The data acquisition method is applied to a non-orthogonal multiple access system, the non-orthogonal multiple access system comprises a base station, an unmanned aerial vehicle and at least one data acquisition device corresponding to the unmanned aerial vehicle, and the unmanned aerial vehicle is used for transmitting environment information acquired by the data acquisition device to the base station; the method comprises the following steps: determining decoding error probability and signal-to-interference-plus-noise ratio of the data acquisition equipment; determining the transmission rate of the data acquisition equipment according to the signal-to-interference-plus-noise ratio and the decoding error probability; determining AoI violation probability of the data acquisition equipment according to the transmission rate and the data acquisition basic information of the non-orthogonal multiple access system, and taking the AoI violation probability as a target constraint condition; and determining data acquisition parameters according to the target constraint conditions and a preset energy consumption minimum algorithm, and carrying out data acquisition according to the data acquisition parameters. According to the embodiment, the preset energy consumption minimum algorithm is constrained based on the target constraint condition, and the data acquisition parameters are determined according to the preset energy consumption minimum algorithm, so that the energy consumption of data acquisition can be reduced.
It should be noted that the above-described working procedure is merely illustrative, and does not limit the scope of the present invention, and in practical application, a person skilled in the art may select part or all of them according to actual needs to achieve the purpose of the embodiment, which is not limited herein.
In addition, technical details not described in detail in this embodiment may refer to the data acquisition method provided in any embodiment of the present invention, and are not described herein.
In addition, the embodiment of the invention also provides a storage medium, wherein the storage medium is stored with a data acquisition program, and the data acquisition program realizes the steps of the data acquisition method when being executed by a processor.
In addition, the embodiment of the invention also provides a non-orthogonal multiple access system, which is characterized in that the system comprises: the unmanned aerial vehicle is used for transmitting the environment information acquired by the data acquisition equipment to the base station; the system further comprises: a memory, a processor and a data acquisition program stored on the memory and executable on the processor, the data acquisition program configured to implement the steps of the data acquisition method as described above.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. read-only memory/random-access memory, magnetic disk, optical disk), comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (8)
1. The data acquisition method is characterized by being applied to a non-orthogonal multiple access system, wherein the non-orthogonal multiple access system comprises a base station, an unmanned aerial vehicle provided with an IRS and at least one data acquisition device corresponding to the unmanned aerial vehicle, and the unmanned aerial vehicle is used for transmitting environment information acquired by the data acquisition device to the base station;
The data acquisition method comprises the following steps:
determining decoding error probability and signal-to-interference-plus-noise ratio of the data acquisition equipment;
determining the transmission rate of the data acquisition equipment according to the signal-to-interference-plus-noise ratio and the decoding error probability;
Determining information age AoI violation probability of the data acquisition equipment according to the transmission rate and the data acquisition basic information of the non-orthogonal multiple access system, and taking the information age AoI violation probability as a target constraint condition;
determining data acquisition parameters according to the target constraint conditions and a preset energy consumption minimum algorithm, and carrying out data acquisition according to the data acquisition parameters;
The data acquisition basic information of the non-orthogonal multiple access system comprises a frame length and a size of a target data packet;
The step of determining the information age AoI violation probability of the data acquisition device according to the transmission rate and the data acquisition basic information of the non-orthogonal multiple access system, and taking the information age AoI violation probability as a target constraint condition comprises the following steps:
Calculating the number of target data packets according to the transmission rate, the frame length and the size of the target data packets by the following formula:
Wherein, For characterising the number of target data packets,/>Transmission rate for characterizing a data acquisition device k,/>For characterizing a frame length, Z for characterizing a size of the target data packet;
determining the information age AoI violation probability of the data acquisition equipment according to the number of the target data packets;
taking the information age AoI violation probability as the target constraint condition;
the information age AoI of the data acquisition device k at t frames is expressed as:
Wherein, Information age AoI,/>, representing data acquisition device k at t framesRepresenting the time of arrival of the kth data packet of the data acquisition device k,/>Representing data acquisition device k transfer/>Time of each data packet;
the information age AoI violation probability of each data acquisition device k is expressed as:
Wherein, Threshold value of information age AoI representing data acquisition device k,/>Representing event/>The probability of the establishment of the two-dimensional model,The maximum value of the probability of violating the information age AoI representing the data acquisition device k.
2. The data acquisition method according to claim 1, wherein the step of determining data acquisition parameters according to the target constraint condition and a preset energy consumption minimization algorithm and performing data acquisition according to the data acquisition parameters comprises:
Determining first energy consumption of the unmanned aerial vehicle in a hovering state and second energy consumption of the unmanned aerial vehicle in a flying state according to the data acquisition basic information;
Determining a third energy consumption of the data acquisition device according to the data acquisition basic information;
Determining a preset energy consumption minimum algorithm according to the first energy consumption, the second energy consumption and the third energy consumption;
And determining data acquisition parameters according to the preset energy consumption minimum algorithm based on the target constraint condition, and carrying out data acquisition according to the data acquisition parameters.
3. The data acquisition method according to claim 2, wherein the step of determining the second energy consumption of the unmanned aerial vehicle in the flight state based on the data acquisition basic information comprises:
Determining a second energy consumption of the unmanned aerial vehicle in a flight state according to the data acquisition basic information by the following formula:
Wherein, Second energy consumption for characterizing the time t of the unmanned aerial vehicle in flight, v-A flight distance for representing unmanned aerial vehicle,/>Speed for characterizing unmanned aerial vehicle,/>Be used for representing blade profile power under unmanned aerial vehicle hovering state,/>For characterising the tip speed of a rotor blade,/>Blade induction power used for representing hovering state of unmanned aerial vehicle,/>Induction speed of rotating body used for representing hovering state of unmanned aerial vehicle,/>For characterization of the fuselage resistance ratio,/>For characterization of air Density,/>For characterising the fixability of a rotating body,/>For characterizing the area of the rotating body.
4. A data acquisition method according to claim 3, wherein the step of determining a first energy consumption of the drone in a hover state from the data acquisition basis information comprises:
determining a first energy consumption of the unmanned aerial vehicle in a hovering state according to the data acquisition basic information through the following formula:
Wherein, First energy consumption for characterizing t moment of the unmanned aerial vehicle in a hovering state,/>, andEnergy consumption,/>, for characterizing unmanned aerial vehicle hovering stateFor characterizing the frame length.
5. The data acquisition method of claim 2, wherein the step of determining a third energy consumption of the data acquisition device based on the data acquisition basis information comprises:
Determining target energy consumption of each data acquisition device according to the data acquisition basic information through the following formula;
Wherein, For characterizing the target energy consumption of a kth data acquisition device at time t,/>For characterizing the power of the kth data acquisition device at time t,/>For characterizing the frame length;
And determining a third energy consumption of the data acquisition device according to the target energy consumption.
6. The data acquisition method according to any one of claims 1 to 5, wherein the step of determining the transmission rate of the data acquisition device based on the signal-to-interference-plus-noise ratio and the decoding error probability comprises
Determining a channel dispersion function according to the signal-to-interference-plus-noise ratio;
Calculating the transmission rate of the data acquisition equipment according to the channel dispersity function, the decoding error probability and the data acquisition basic information by the following formula:
Wherein, Transmission rate for characterizing a data acquisition device,/>For characterizing sub-channel bandwidth,/>For characterising signal to interference plus noise ratio,/>For characterizing the channel dispersion function, L for characterizing the block length of the data packet,/>Inverse function for characterizing Q-function,/>For characterizing decoding error probabilities.
7. A non-orthogonal multiple access system, the system comprising: the unmanned aerial vehicle is used for transmitting the environment information acquired by the data acquisition equipment to the base station; the system further comprises: a memory, a processor and a data acquisition program stored on the memory and executable on the processor, the data acquisition program being configured to implement the steps of the data acquisition method of any one of claims 1 to 6.
8. A storage medium having stored thereon a data acquisition program which, when executed by a processor, implements the steps of the data acquisition method according to any one of claims 1 to 6.
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