CN113939040A - State updating method based on state prediction in cognitive Internet of things - Google Patents

State updating method based on state prediction in cognitive Internet of things Download PDF

Info

Publication number
CN113939040A
CN113939040A CN202111173385.8A CN202111173385A CN113939040A CN 113939040 A CN113939040 A CN 113939040A CN 202111173385 A CN202111173385 A CN 202111173385A CN 113939040 A CN113939040 A CN 113939040A
Authority
CN
China
Prior art keywords
state
prediction
data packet
secondary user
information
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202111173385.8A
Other languages
Chinese (zh)
Other versions
CN113939040B (en
Inventor
于宝泉
蔡跃明
陈泳
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Army Engineering University of PLA
Original Assignee
Army Engineering University of PLA
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Army Engineering University of PLA filed Critical Army Engineering University of PLA
Priority to CN202111173385.8A priority Critical patent/CN113939040B/en
Publication of CN113939040A publication Critical patent/CN113939040A/en
Application granted granted Critical
Publication of CN113939040B publication Critical patent/CN113939040B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W74/00—Wireless channel access
    • H04W74/08—Non-scheduled access, e.g. ALOHA
    • H04W74/0808—Non-scheduled access, e.g. ALOHA using carrier sensing, e.g. carrier sense multiple access [CSMA]
    • H04W74/0816—Non-scheduled access, e.g. ALOHA using carrier sensing, e.g. carrier sense multiple access [CSMA] with collision avoidance
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W74/00—Wireless channel access
    • H04W74/08—Non-scheduled access, e.g. ALOHA
    • H04W74/0833—Random access procedures, e.g. with 4-step access
    • H04W74/0841—Random access procedures, e.g. with 4-step access with collision treatment
    • H04W74/085—Random access procedures, e.g. with 4-step access with collision treatment collision avoidance
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Quality & Reliability (AREA)
  • Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Mobile Radio Communication Systems (AREA)
  • Data Exchanges In Wide-Area Networks (AREA)

Abstract

The invention discloses a state updating method based on state prediction in a cognitive wireless network. The sending end of the secondary user firstly monitors the channel, when the primary user does not send information, the secondary user accesses the channel, predicts the state information of the target at a certain time in the future according to the current state information of the target, and adjusts the transmission power and the length of the prediction interval to improve the state updating performance. And the secondary user sending end sends the predicted state information to the receiving end. The receiving end judges whether the prediction fails according to the length of the received data packet, if the transmitting end is successful in prediction, the receiving end decodes the data packet and updates the state information of the receiving end about the target. The invention greatly improves the state updating performance. Meanwhile, the receiving end judges whether the prediction fails according to the length of the received data packet, so that negative effects on the system caused by the fact that the receiving end uses wrong prediction information are avoided.

Description

State updating method based on state prediction in cognitive Internet of things
Technical Field
The invention relates to the field of wireless communication state updating, in particular to a state updating method based on state prediction in a cognitive Internet of things.
Technical Field
The coming of the internet of things era promotes the development of emerging applications and brings great convenience to the production and life of people. Some internet of things applications require real-time status updates, such as: roadside units in the Internet of vehicles need to acquire real-time position information of vehicles to ensure traffic safety; in intelligent medicine, sensors within the body can send real-time health information of a patient to a doctor for immediate treatment. Traditional performance indexes such as throughput, time delay, reliability and the like cannot accurately depict the performance of state updating. In contrast, the present invention uses the information age (s.k.kaul, r.d.yates, and m.gruteser, "Real-time status: How much shoulded one update. However, most of the available spectrum resources are allocated, and the internet of things node as an unauthorized user cannot obtain a single spectrum resource for communication. In contrast, the cognitive internet of things technology is considered to be an effective means for improving the utilization rate of spectrum resources and providing the spectrum resources for unauthorized users. In view of the above, the invention aims to design a novel and effective state updating method in the cognitive internet of things.
However, due to the time delay of wireless transmission, there is a certain information age when the data packet arrives at the receiving end, and the receiving end of the secondary user cannot obtain the latest status information of the monitored target. In order to improve the performance of status update, there are many documents that improve the freshness of status information acquired by the receiving end from the viewpoint of reducing the delay, such as: and the queuing delay of the latest status data packet is reduced by adopting a later-first-service mode at the transmitting end, and the access delay of the data packet is reduced by adopting an authorization-free access mode. However, with the development of new applications, the requirement for timeliness of state update is increasing, especially in applications such as car networking, which are concerned with personal safety. How to further improve the performance of state updating without changing the network architecture becomes a key problem for promoting the practical development of emerging applications. The state prediction technology enables the node to predict and send state information of a target at a certain future time, so that the actual time delay of the state information is greatly reduced, and the state prediction technology becomes a key technology for supporting high-reliability low-time-delay communication.
At present, a small amount of literature is available to study the application of state estimation techniques in state update scenarios. Documents (m.klgel, m.h.mamduhi, s.hirche, et., "AoI-dependency minimization for network control systems with packet loss," in Proc IEEE INFOCOM,2019, pp.189-196.) propose a state estimation problem for a state update system, and a destination can estimate the current state of the destination according to a received state packet and propose a threshold policy for a minimum age penalty function. The literature (L.Lyu.Y.Dai, N.Cheng, et., "AoI-aware co-design of cooperative transmission and state estimation for marine IoT systems," IEEE Internet thingsJ., vol.8, No.10, pp.7889-7901, May,2021.) discusses the mean square error of state estimation and proposes a cooperative transmission scheme for minimizing the mean square error. However, the existing documents all research the state updating performance of the system when the receiving end has the state estimation technology. It should be noted that, when the receiving end performs state estimation, the receiving end estimates the current state information of the target according to outdated information sent by the sending end, which is called "state estimation" in the existing literature. However, in the existing document, the transmission structure based on "state estimation" enables the receiving end to estimate the current state information of the target by means of the received state information, and the receiving end cannot judge whether the estimated state information is accurate. In a state updating system, the consequences of using incorrect state information by a receiving end are more serious than those of not obtaining timely state updating.
Disclosure of Invention
The invention aims to provide a state updating method based on state prediction in a cognitive Internet of things, which solves the problem that a receiving end cannot judge whether prediction information is accurate or not and avoids negative effects on a state updating system caused by the fact that the receiving end uses wrong prediction information.
The technical scheme adopted by the invention is as follows: the scheme is characterized in that:
step 1: channel monitoring: the secondary user sending end monitors the channel before sending the data packet each time, and can access the channel only when the channel is idle, namely the primary user communication pair does not send information, and sends a state updating data packet to the secondary user receiving end;
step 2: state prediction and packet transmission: the secondary user sending end continuously samples the real-time state information of the target, predicts the state information of the target at a certain future moment according to the current state information and sends the predicted information to the secondary user receiving end, wherein the length of the prediction interval is smaller than the length of a state updating data packet sent by the secondary user;
and step 3: setting transmission parameters: the secondary user sending end adjusts and optimizes the sending power and the length of the prediction interval under the constraints of the average transmission power and the collision degree of the primary user, and the performance of state updating is improved;
and 4, step 4: and (3) comparing the prediction information: in the process of sending a state updating data packet, when the time reaches the position of a prediction point, the secondary user sending end compares the prediction information with the actual information of a target at the prediction point, if the prediction is accurate, the secondary user sending end continues to send the data packet, otherwise, the secondary user sending end immediately stops sending the data packet and restarts a new round of state prediction and data packet sending;
and 5: and (3) updating the state: the secondary user receiving end decodes the state updating data packet sent by the secondary user sending end, and when the secondary user sending end successfully predicts the state information and the secondary user receiving end successfully decodes the data packet, the secondary user receiving end updates the state information of the target according to the newly arrived data packet;
compared with the existing state updating method, the method has the following advantages and remarkable effects:
the invention provides a state updating method based on state prediction in a cognitive Internet of things, which applies a state prediction technology to a sending end and realizes the improvement of state updating performance on the premise of not changing a network architecture. At a sending end, when a channel is idle, a secondary user sending end predicts and sends target state information to a secondary user receiving end, when the time reaches a prediction point, the sending end compares the predicted information with target actual information, if prediction failure is found, the current sending process is stopped, and a new round of state prediction and data packet sending are carried out; the receiving end can judge whether the state prediction fails according to the length of the received data packet, and if the transmitting end is successful in prediction, the receiving end selects to decode the data packet and updates the state information of the receiving end.
The invention provides a state updating method based on state prediction in a cognitive Internet of things, which reduces the transmission delay of state information by using a state prediction technology and improves the state updating performance; the method comprises the steps that a sending end predicts state information of a target at a certain future moment and sends the state information to a receiving end, the sending end compares the predicted information with actual state information of the target when the time reaches a predicted point due to the fact that prediction may fail, if prediction failure is found, sending of a next predicted data packet is carried out immediately, and therefore state updating efficiency is improved, and meanwhile the sending end adjusts sending power of the sending end and the length of a prediction interval to improve state updating performance; because the length of the data packet which fails to be predicted is short, the receiving end can judge whether the prediction is accurate according to the length of the data packet, and therefore negative effects of the receiving end on a state updating system caused by using wrong prediction information are avoided.
The invention applies the prediction technology to the sending end, greatly improves the state updating performance and does not need to change the existing network architecture. In the invention, when the time reaches the prediction point, the sending end can compare the prediction information with the actual state information of the target, and if the prediction fails, the sending of the current data packet is immediately stopped, thereby improving the efficiency of state updating. In addition, the receiving end judges whether the prediction fails according to the length of the received data packet, so that the negative influence of the wrong prediction information used by the receiving end on the system is avoided.
Drawings
FIG. 1 is a system model diagram of the present invention.
FIG. 2 is a diagram illustrating the data packet transmission process and information age variation according to the present invention.
Fig. 3 is a schematic diagram of the variation of the average peak information age with the length of the prediction interval in accordance with the present invention.
Fig. 4 is a diagram illustrating the variation of the average peak information age with the transmission power of the transmitting end of the secondary user according to the present invention.
FIG. 5 is a diagram comparing the performance of the present invention with that of the conventional no-prediction state update method.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the drawings in the specification.
As shown in fig. 1, the system of the present invention has a primary user communication pair and a secondary user communication pair, and all nodes are equipped with a single antenna. And when the primary user is sensed not to send information, the secondary user sending end selects an access channel. Because the secondary user cannot sense the channel in the sending process and the primary user can access the channel at any time, when the primary user accesses the channel in the sending process of the secondary user, the secondary user and the primary user collide with each other to cause the receiving end to be incapable of decoding information in the data packet. And when the secondary user sending end detects that the channel is idle, the secondary user sending end predicts future state information according to the target current state information. And the secondary user sending end adjusts and optimizes the sending power and the prediction interval length of the secondary user sending end under the constraint of meeting the average transmission power and the collision probability with the primary user. And then, the sending end of the secondary user sends the predicted state information to the receiving end. And when the time reaches the prediction point, the sending end compares the prediction information with the target actual state information, if the prediction fails, the sending of the current data packet is immediately stopped, and a new round of data packet is sent, otherwise, the data packet is sent completely. The receiving end judges whether the prediction fails according to the length of the received data packet, if the transmitting end is successful in prediction, the receiving end decodes the data packet and updates the state information of the receiving end about the target.
The specific transmission method of the invention is realized by the following steps:
step 1: channel monitoring
The secondary user sending end monitors the channel before sending the data packet each time, and when the channel is idle, namely the primary user communication pair does not send information, the secondary user sending end can access the channel and send a state updating data packet to the secondary user receiving end. The transmitting end of the master user can access a channel at any time, the idle time and the time for transmitting the data packet are respectively subjected to exponential distribution with coefficients of alpha and beta, and the secondary user can transmit the data packet by utilizing blank spectrum resources because the master user occupies sparsely the spectrum, namely beta is larger than alpha. In addition, when beta tends to infinity and alpha tends to 0, the master user can be regarded as not sending a data packet all the time, namely, the channel is always idle, and the active probability of the master user has no influence on the design of the invention, so the invention is also suitable for a non-cognitive network, namely, a scene of state updating for a user who monopolizes the channel;
step 2: state prediction and packet delivery
Step 2.1, predicting failure probability: at time i, the sending end of the secondary user samples the real-time state information of the target
Figure BDA0003294195670000041
Then n ispThe state of the target after the use of each channel can be expressed as
Figure BDA0003294195670000051
Wherein
Figure BDA0003294195670000052
In order to be a state transition matrix,
Figure BDA0003294195670000053
for noise of the target state transition, the elements in W are obedient mean 0, variance Σ1,...,ΣLL is the number of states of the monitoring target at the sending end of the secondary user,
Figure BDA0003294195670000054
is a real number set.
To represent the state of the monitored object at a future time, n is introducedpThis quantity, representing the nth future starting point from the ith channel usagepStatus of target when a channel is used, where the channel is used as a time-frequency resource block, when bandwidth is fixedThe timing may also be understood as a time slot. m has no meaning, and in this formula, m is taken from 1 to npRepresenting the equation for a total of n after the right plus signpAnd (4) items.
The target state estimated by the user
Figure BDA0003294195670000055
Is composed of
Figure BDA0003294195670000056
Thus, the error of the state estimation is
Figure BDA0003294195670000057
The estimated error of the l-th state is
Figure BDA0003294195670000058
Due to epsilonl(np) Is the sum of several Gaussian variables with mean value 0, epsilonl(np) Is also a Gaussian variable with a mean value of 0 and a variance of
Figure BDA0003294195670000059
The state updating system has certain tolerance to prediction errors, for example, in the internet of vehicles, the prediction can be considered to be accurate when the vehicle position deviation predicted by the sensor is smaller than a certain threshold value, and the actual road traffic cannot be influenced. Assume that the prediction error tolerance of the l-th state is δlThen the prediction failure probability can be expressed as
Figure BDA00032941956700000510
Where ψ () is the cumulative distribution function of a standard normal distribution.
Only when the L states are accurately predicted, the secondary user sending end can send the complete data packet to the receiving end. Thus, the total prediction failure probability is
Figure BDA0003294195670000061
Step 2.2: probability of decoding failure: when prediction fails, the node sends a complete data packet to the receiving end, the receiving end decodes the data packet, the sending end of a secondary user is a sensor, the sent information amount is small, the length of the data packet is short, the decoding performance of the receiving end is described by a short packet communication theory, and the packet error rate of the decoding data packet of the receiving end can be expressed as
Figure BDA0003294195670000062
Wherein
Figure BDA0003294195670000063
Is the signal-to-noise ratio of the secondary user receiving end, P is the transmitting power of the secondary user transmitting end, χ0Is a shadow fading coefficient, h is a small-scale Rayleigh fading coefficient, d is the distance of a receiving end and a transmitting end of a secondary user,
Figure BDA0003294195670000064
is a path fading factor, σ2Is the noise power, ndThe length of the coded data packet sent by the sending end of the secondary user is D, and the D is the size of the target state information quantity.
Thus, the average packet error rate can be expressed as
Figure BDA0003294195670000065
Step 2.3: and (3) state updating process:
in the prediction-based state updating system, at time t, the information age Δ (t) may be expressed as
Δ(t)＝t-U(t) (10)
Wherein U (t) is the time corresponding to the state information contained in the latest state data packet received by the receiving end, and the average peak information age is
Figure BDA0003294195670000066
Wherein t iskThe average peak information age can be calculated to be
Figure BDA0003294195670000071
And step 3: setting transmission parameters: the secondary user sending end adjusts and optimizes the sending power and the length of the prediction interval under the constraint of the average transmission power and the collision degree of the primary user so as to reduce the average peak information age of the system, and the optimization problem can be expressed as
Figure BDA0003294195670000072
Figure BDA0003294195670000073
Figure BDA0003294195670000074
0≤np≤nd(13d)
Figure BDA0003294195670000075
Where phi is the transmission power of the secondary user's originating side,
Figure BDA0003294195670000076
for transmit power constraints, θ is the proportion of primary user traffic that is collided by secondary users,
Figure BDA0003294195670000077
in order to restrain the collision,
Figure BDA0003294195670000078
is a natural number set, (13d) ensures that the length of the prediction interval is smaller than the length of the data packet, and (13e) indicates that the length of the prediction interval can only take an integer.
The average transmission power of the transmitting end of the secondary user is
Figure BDA0003294195670000079
As can be seen from the packet error rate expression (8), the packet error rate decreases with the increase of the transmission power, and the decrease of the packet error rate increases the state updating performance, so the transmission power P should be the maximum value that satisfies the transmission power constraint. The optimum transmission power is
Figure BDA0003294195670000081
The average proportion of the primary users collided by the secondary users is
Figure BDA0003294195670000082
Therefore, the optimal prediction interval length
Figure BDA0003294195670000083
Should satisfy
Figure BDA0003294195670000084
Because the length of the prediction interval is a natural number, the optimal length value of the prediction interval can be quickly obtained by a one-dimensional search method.
The specific solution implementation of the transmission power and the prediction interval length can be expressed as follows:
Figure BDA0003294195670000085
and 4, step 4: comparison of predictive information
And in the process of sending the state updating data packet, when the time reaches the position of the prediction point, the secondary user sending end compares the prediction information with the actual information of the target at the prediction point, if the prediction is accurate, the secondary user sending end continues to send the data packet, otherwise, the secondary user sending end immediately stops sending the data packet and restarts a new round of state prediction and data packet sending. The receiving end judges whether the prediction fails according to the length of the received data packet, and when the length of the data packet is npWhen the data packet length is n, the receiving end discards the data packetdWhen the prediction is successful, the receiving end decodes the data packet;
and 5: status update
The secondary user receiving end decodes the state updating data packet sent by the secondary user sending end, and when the secondary user sending end successfully predicts the state information and the secondary user receiving end successfully decodes the data packet, the secondary user receiving end updates the state information of the target according to the newly arrived data packet;
example (b):
setting simulation parameters: the transmitting power of the transmitting end of the secondary user is 0.2W, the distance between the transmitting end and the receiving end of the secondary user is 200m, and the small-scale fading factor
Figure BDA0003294195670000091
Shadow fading χ0The power spectral density of noise is-174 dBm/Hz, the average idle time of a primary user and the time for sending the data packet are 2000 channels and 500 channels, the length of a prediction interval is 50 channels, the length of the data packet is 150 channels, and the information quantity of state information is 100 nats. Considering a narrow-band communication scene, a secondary user sending end detects two states of a target, a certain correlation exists between the two states, such as the speed and the position of a vehicle in the internet of vehicles, the system bandwidth is B-180 kHz, and a state transition matrix is
Figure BDA0003294195670000092
State transition noise is W ═ 0; 0.01]The tolerance of the system to the estimation errors of the two states is 0.05, wherein the Monte Carlo simulation times are 106Next, the process is carried out.
FIG. 3 is a graph illustrating the variation of the average peak information age with the length of the prediction interval according to the present invention. It can be found from the figure that the simulation points and the theoretical curve are well fitted, and the correctness of theoretical analysis is proved. As the prediction interval increases, the average peak information age decreases and then increases. This is because the increase in the prediction interval makes the packet arriving at the receiving end refreshed, resulting in a decrease in the average peak information age. However, the prediction error probability increases with an increase in the prediction interval, which has a negative effect on the state update. Therefore, there is an optimal prediction interval length such that the system state update performance is best.
Fig. 4 is a schematic diagram of the variation of the average peak information age with the transmission power of the transmitting end of the secondary user according to the present invention. As can be seen from the figure, the average peak information age decreases with the increase of the transmission power, because the increase of the transmission power decreases the probability of decoding failure at the receiving end, which improves the efficiency of state updating. Therefore, the larger the transmission power, the better the state update performance of the system. Therefore, the secondary user transmitting end needs to select the maximum transmission power under the constraint of satisfying the average transmission power.
FIG. 5 is a diagram comparing the performance of the present invention with that of the conventional no-prediction state update method. As can be seen from the figure, the proposed prediction-based state update scheme is superior to the conventional non-prediction scheme, illustrating the positive effect of the prediction technique on state update. Meanwhile, as the tolerance of the system to the prediction error increases, the performance gain obtained by the prediction mechanism also increases. This is because the prediction failure probability decreases with the increase of the tolerance, and at this time, the secondary user transmitting end can select a longer prediction interval without seriously deteriorating the prediction performance, thereby significantly improving the state update performance.
The invention introduces the prediction technology into the state updating system innovatively, the sending end predicts the state information of a certain time in the future according to the current state information of the monitored target and sends the predicted state information to the receiving end, so that the transmission delay of the state information is reduced, and the information freshness of the data packet reaching the receiving end is greatly improved. More importantly, the sending end can acquire the real-time state information of the target, and when the sending end finds that the prediction information is inaccurate, the sending end can stop the current sending process, so that the negative influence of the receiving end on the system caused by using the wrong prediction information is avoided. The state updating method can be widely applied to the point-to-point state updating scene in the cognitive network, but is not limited to the range listed above.
The above embodiments are described in some detail and detail, but only represent one possible embodiment of the present invention, and are not intended to limit the scope of the invention. It should be noted that, in the framework of the present invention, scientific research personnel and engineering personnel can add several modifications or improvements on the embodiment, but these are all within the protection scope of the present patent, and the protection scope of the present patent is subject to the appended claims.

Claims (6)

1. A state updating method based on state prediction in a cognitive Internet of things is characterized by comprising a master user communication pair and a secondary user communication pair, wherein nodes are provided with single antennas, the master user communication is the communication of a person, the sent information amount is large, the length of a data packet is long, the secondary user communication is machine communication, and the length of the sent data packet is short; the method comprises the following steps:
step 1: channel monitoring: the secondary user sending end monitors the channel before sending the data packet each time, and can access the channel only when the channel is idle, namely the primary user communication pair does not send information, and sends a state updating data packet to the secondary user receiving end;
step 2: state prediction and packet transmission: the secondary user sending end continuously samples the real-time state information of the target, predicts the state information of the target at a certain future moment according to the current state information and sends the predicted information to the secondary user receiving end, wherein the length of the prediction interval is smaller than the length of a state updating data packet sent by the secondary user;
and step 3: setting transmission parameters: the secondary user sending end adjusts and optimizes the sending power and the length of the prediction interval under the constraints of the average transmission power and the collision degree of the primary user, and the performance of state updating is improved;
and 4, step 4: and (3) comparing the prediction information: in the process of sending a state updating data packet, when the time reaches the position of a prediction point, the secondary user sending end compares the prediction information with the actual information of a target at the prediction point, if the prediction is accurate, the secondary user sending end continues to send the data packet, otherwise, the secondary user sending end immediately stops sending the data packet and restarts a new round of state prediction and data packet sending;
and 5: and (3) updating the state: and the secondary user receiving end decodes the state updating data packet sent by the secondary user sending end, and updates the state information of the target according to the newly arrived data packet when the secondary user sending end successfully predicts the state information and the secondary user receiving end successfully decodes the data packet.
2. The state updating method based on state prediction in the cognitive internet of things according to claim 1, wherein the channel monitoring in the step 1 comprises the following specific contents:
the secondary user sending end monitors the channel before sending the data packet each time, and can access the channel only when the channel is idle, namely the primary user communication pair does not send information, and sends a state updating data packet to the secondary user receiving end; a master user sending end can access a channel at any time, the idle time of the master user sending end and the time of sending a data packet are respectively subjected to exponential distribution with coefficients of alpha and beta, and a secondary user can send the data packet by utilizing blank spectrum resources because the master user occupies sparsely frequency spectrum, namely beta is larger than alpha; in addition, when β tends to infinity and α tends to 0, the primary user is regarded as not sending data packets all the time, i.e., the channel is idle all the time.
3. The state updating method based on state prediction in the cognitive internet of things according to claim 2, wherein the state prediction and data packet transmission in the step 2 are as follows:
step 2.1, predicting failure probability: at time i, the sending end of the secondary user samples the real-time state information of the target
Figure FDA0003294195660000021
Then n ispThe state of the target after the use of each channel is expressed as
Figure FDA0003294195660000022
Wherein
Figure FDA0003294195660000023
In order to be a state transition matrix,
Figure FDA0003294195660000024
for noise of the target state transition, the elements in W are obedient mean 0, variance Σ1,...,ΣLL is the number of states of the monitoring target at the sending end of the secondary user,
Figure FDA0003294195660000025
is a real number set;
the target state estimated by the user
Figure FDA0003294195660000026
Comprises the following steps:
Figure FDA0003294195660000027
thus, the error of the state estimate is:
Figure FDA0003294195660000028
the estimation error for the l-th state is:
Figure FDA0003294195660000029
wherein
Figure FDA00032941956600000210
Is a matrix
Figure FDA00032941956600000211
Element of j row and l column, wl(i+np-1) indicates that the l-th state is at the i + n-th statep1 state transition noise at channel usage, wj(i + m-1) is the state transition noise for the jth state at the use of the (i + m-1) th channel, wjIs a vector
Figure FDA00032941956600000212
The jth element;
due to epsilonl(np) Is the sum of several Gaussian variables with mean value 0, epsilonl(np) Is likewise a Gaussian variable with a mean value of 0, εl(np) The variance of (c) is:
Figure FDA00032941956600000213
the state updating system has certain tolerance to the prediction error; assume that the prediction error tolerance of the l-th state is δlThen the prediction failure probability is expressed as:
Figure FDA00032941956600000214
where ψ () is a cumulative distribution function of a standard normal distribution;
only when the L states are accurately predicted, the secondary user sending end can send a complete data packet to the receiving end; thus, the total prediction failure probability is:
Figure FDA0003294195660000031
step 2.2: probability of decoding failure: when prediction fails, the node sends a complete data packet to the receiving end, the receiving end decodes the data packet, the sending end of the secondary user is a sensor, the sent information amount is small, the length of the data packet is short, the decoding performance of the receiving end is described by using a short packet communication theory, and the packet error rate of the decoding data packet of the receiving end is expressed as:
Figure FDA0003294195660000032
wherein
Figure FDA0003294195660000033
Is the signal-to-noise ratio of the secondary user receiving end, P is the transmitting power of the secondary user transmitting end, χ0Is a shadow fading coefficient, h is a small-scale Rayleigh fading coefficient, d is the distance of a receiving end and a transmitting end of a secondary user,
Figure FDA0003294195660000034
is a path fading factor, σ2Is the noise power, ndThe length of the coded data packet sent by the sending end of the secondary user is D, and the D is the size of the target state information quantity;
thus, the average packet error rate can be expressed as
Figure FDA0003294195660000035
Step 2.3: and (3) state updating process:
in this prediction-based state updating system, at time t, the information age Δ (t) is represented as
Δ(t)＝t-U(t) (10)
Wherein U (t) is the time corresponding to the state information contained in the latest state data packet received by the receiving end, and the average peak information age is
Figure FDA0003294195660000036
Wherein t iskCalculating the average peak information age of the k state update data packet at the moment when the receiving end successfully decodes the k state update data packet
Figure FDA0003294195660000041
4. The state updating method based on state prediction in the cognitive internet of things according to claim 3, wherein the transmission parameter setting in the step 3 specifically comprises the following contents:
the secondary user sending end adjusts and optimizes the sending power and the length of the prediction interval under the constraint of the average transmission power and the collision degree of the primary user so as to reduce the average peak information age of the system, and the optimization problem can be expressed as
Figure FDA0003294195660000042
Figure FDA0003294195660000043
Figure FDA0003294195660000044
0≤np≤nd (13d)
Figure FDA0003294195660000045
Where phi is the transmission power of the secondary user's originating side,
Figure FDA0003294195660000046
in order to transmit the power constraint,
Figure FDA0003294195660000047
as is the proportion of primary user communications that are collided by secondary users,
Figure FDA0003294195660000048
in order to restrain the collision,
Figure FDA0003294195660000049
for the natural number set, equation 13d can ensure that the prediction interval length is smaller than the data packet length, and equation 13e indicates that the prediction interval length can only take integers.
The average transmission power of the transmitting end of the secondary user is
Figure FDA00032941956600000410
As can be seen from the packet error rate expression (8), the packet error rate decreases with the increase of the transmission power, and the decrease of the packet error rate will bring about the improvement of the state updating performance, so the transmission power P should be the maximum value under the condition of satisfying the transmission power constraint; the optimum transmission power is
Figure FDA0003294195660000051
The average proportion of the primary users collided by the secondary users is
Figure FDA0003294195660000052
Therefore, the optimal prediction interval length
Figure FDA0003294195660000053
Should satisfy
Figure FDA0003294195660000054
Because the length of the prediction interval is a natural number, the optimal length value of the prediction interval is quickly obtained by a one-dimensional search method.
5. The state updating method based on state prediction in the cognitive internet of things according to claim 4, wherein the predicted information comparison in the step 4 specifically comprises the following steps:
and in the process of sending the state updating data packet, when the time reaches the position of the prediction point, the secondary user sending end compares the prediction information with the actual information of the target at the prediction point, if the prediction is accurate, the secondary user sending end continues to send the data packet, otherwise, the secondary user sending end immediately stops sending the data packet and restarts a new round of state prediction and data packet sending. The receiving end judges whether the prediction fails according to the length of the received data packet, and when the length of the data packet is npWhen the data packet length is n, the receiving end discards the data packetdWhen the prediction is successful, the receiving end decodes the data packet.
6. The state updating method based on state prediction in the cognitive internet of things according to claim 5, wherein the state updating in the step 5 is specifically as follows:
and the secondary user receiving end decodes the state updating data packet sent by the secondary user sending end, and updates the state information of the target according to the newly arrived data packet when the secondary user sending end successfully predicts the state information and the secondary user receiving end successfully decodes the data packet.
CN202111173385.8A 2021-10-08 2021-10-08 State updating method based on state prediction in cognitive Internet of things Active CN113939040B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111173385.8A CN113939040B (en) 2021-10-08 2021-10-08 State updating method based on state prediction in cognitive Internet of things

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111173385.8A CN113939040B (en) 2021-10-08 2021-10-08 State updating method based on state prediction in cognitive Internet of things

Publications (2)

Publication Number Publication Date
CN113939040A true CN113939040A (en) 2022-01-14
CN113939040B CN113939040B (en) 2023-04-28

Family

ID=79278269

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111173385.8A Active CN113939040B (en) 2021-10-08 2021-10-08 State updating method based on state prediction in cognitive Internet of things

Country Status (1)

Country Link
CN (1) CN113939040B (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114615684A (en) * 2022-02-25 2022-06-10 哈尔滨工业大学（深圳） Information age optimization method and device of closed-loop system and storage medium
CN115460654A (en) * 2022-08-29 2022-12-09 中国人民解放军陆军工程大学 Real-time adjustment method for packet length aiming at information age minimization in Internet of things
CN115484611A (en) * 2022-09-30 2022-12-16 中国人民解放军陆军工程大学 State updating method based on truncation automatic retransmission in cognitive relay Internet of things

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107769823A (en) * 2016-08-18 2018-03-06 中山大学 A kind of information energy cooperative beam manufacturing process based on cognition bilateral relay network
CN111654342A (en) * 2020-06-03 2020-09-11 中国人民解放军国防科技大学 Dynamic spectrum access method based on reinforcement learning with priori knowledge
US20210044985A1 (en) * 2018-02-14 2021-02-11 Industry-Acdemic Cooperation Foundation, Yonsei University Cognitive radio communication control device for determining cognitive radio terminal and cognitive radio terminal determining method

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107769823A (en) * 2016-08-18 2018-03-06 中山大学 A kind of information energy cooperative beam manufacturing process based on cognition bilateral relay network
US20210044985A1 (en) * 2018-02-14 2021-02-11 Industry-Acdemic Cooperation Foundation, Yonsei University Cognitive radio communication control device for determining cognitive radio terminal and cognitive radio terminal determining method
CN111654342A (en) * 2020-06-03 2020-09-11 中国人民解放军国防科技大学 Dynamic spectrum access method based on reinforcement learning with priori knowledge

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
R. YU, ETC.: "QoS differencial scheduling in cognitive-radio-based smart grid networks:An adaptive dynamic programming approach" *
于宝泉等: "认知无线电非正交多址接入随机网络物理层安全性能分析" *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114615684A (en) * 2022-02-25 2022-06-10 哈尔滨工业大学（深圳） Information age optimization method and device of closed-loop system and storage medium
CN114615684B (en) * 2022-02-25 2023-07-25 哈尔滨工业大学（深圳） Information age optimization method and device of closed-loop system and storage medium
CN115460654A (en) * 2022-08-29 2022-12-09 中国人民解放军陆军工程大学 Real-time adjustment method for packet length aiming at information age minimization in Internet of things
CN115484611A (en) * 2022-09-30 2022-12-16 中国人民解放军陆军工程大学 State updating method based on truncation automatic retransmission in cognitive relay Internet of things

Also Published As

Publication number Publication date
CN113939040B (en) 2023-04-28

Similar Documents

Publication Publication Date Title
CN113939040A (en) State updating method based on state prediction in cognitive Internet of things
Alsenwi et al. Intelligent resource slicing for eMBB and URLLC coexistence in 5G and beyond: A deep reinforcement learning based approach
Bruno et al. Optimization of efficiency and energy consumption in p-persistent CSMA-based wireless LANs
Zhao et al. Deep reinforcement learning aided intelligent access control in energy harvesting based WLAN
Zhang et al. Endogenous security-aware resource management for digital twin and 6G edge intelligence integrated smart park
Ullah et al. Performance analysis of priority-based ieee 802.15. 6 protocol in saturated traffic conditions
Pandey et al. An energy efficient distributed queuing random access (EE-DQRA) MAC protocol for wireless body sensor networks
Saha et al. On the relationship between mean absolute error and age of incorrect information in the estimation of a piecewise linear signal over noisy channels
Yu et al. Can we improve the information freshness with prediction for cognitive IoT?
Alawadhi et al. IEEE 802.15. 4 MAC protocol optimisation in body sensor networks: a survey, outlook and open issues
Zhou et al. Age of information aware scheduling for dynamic wireless body area networks
CN118019033A (en) Method for improving 5G slice and WiFi6 mixed networking delay performance in transformer substation scene
Fiems et al. Mean Value Analysis of the Age of Information in Slotted ALOHA
Li et al. Reliability-Latency-Rate Tradeoff in Low-Latency Communications with Finite-Blocklength Coding
Kwon et al. Reinforcement learning-based contention window adjustment for wireless body area networks
Deng PSSB: priority enforced slow-start backoff algorithm for multimedia transmission in wireless ad-hoc networks
CN115315020A (en) Intelligent CSMA/CA (Carrier sense multiple Access/Carrier aggregation) backoff method based on IEEE (institute of Electrical and electronics Engineers) 802.15.4 protocol of differentiated services
Patel et al. A simple but effective collision and error aware adaptive back-off mechanism to improve the performance of IEEE 802.11 DCF in error-prone environment
Liu et al. Pushback: A hidden Markov model based scheme for energy efficient data transmission in sensor networks
Zhao et al. Backoff entropy: Predicting presaturation peak for IEEE 802.11 DCF networks
Kim et al. Performance Improvement of Random Access by Prioritizing Collisions
CN106921413B (en) Low-voltage power line communication local area network performance optimization method based on dynamic game
Liu et al. A traffic modeling based power saving mechanism for mobile devices in wireless systems
Kim et al. Multi-level service differentiation scheme for the IEEE 802.15. 4 sensor networks
Feukeu et al. Machine Learning Algorithm for a Link Adaptation strategy in a Vehicular Ad hoc Network

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant