CN113114395B - Channel determination method and device - Google Patents
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- H04B17/309—Measuring or estimating channel quality parameters
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
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- H04W36/0011—Control or signalling for completing the hand-off for data sessions of end-to-end connection
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- 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
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Abstract
The embodiment of the application provides a channel determining method and device, wherein the method comprises the following steps: firstly, acquiring first channel state data of each available channel in a monitoring time period which is the last time period of a target monitoring time period, wherein the first channel state data is used for representing the data quantity transmitted on each available channel in the monitored last time period, then selecting an observation channel from each available channel according to the first channel state data, monitoring each observation channel to obtain second channel state data of each observation channel in a set time period before the arrival of the target monitoring time period, the second channel state data is used for representing the data quantity transmitted on each observation channel in the monitored set time period, and finally, determining a target monitoring channel in each available channel according to the first channel state data and the second channel state data, wherein the target monitoring channel is the channel monitored in the target monitoring time period.
Description
The present patent application is a divisional application of Chinese patent application with application date 2019-04-29, application number 2019103541242 and named "channel determination method and device".
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method and apparatus for determining a channel.
Background
To provide better internet services to users, it is often necessary to monitor the communication channel of the user terminal to collect specific data. Wherein the communication channel may be a Wi-Fi (a wireless local area network technology) channel or a bluetooth channel, etc. In one scenario, the collected specific data includes user location data, and by determining the location of each user, real-time road conditions can be determined and road condition navigation can be provided to the user.
Considering that a user terminal can select a certain channel from a plurality of communication channels to communicate according to a communication scene or other factors, for example, select a certain Wi-Fi channel to perform Wi-Fi communication, it is necessary to provide a technical scheme to accurately determine a channel to be monitored, so as to improve data collection efficiency.
Disclosure of Invention
The embodiment of the application aims to provide a channel determining method and device so as to accurately determine a channel to be monitored and improve data collection efficiency.
In order to achieve the technical effects, the embodiment of the application is realized in the following way:
the embodiment of the application provides a channel determining method, which comprises the following steps:
Acquiring first channel state data of each available channel in a monitoring time period which is the last time period of a target monitoring time period; the first channel state data is used for representing the data volume transmitted on the available channel in the monitored last monitoring time period;
selecting an observation channel from the available channels according to the first channel state data;
before the target monitoring time period arrives, monitoring the observation channel to obtain second channel state data of the observation channel within a set time length; the second channel state data is used for representing the size of the data quantity transmitted on the observation channel within the monitored set duration;
determining a target monitoring channel in each available channel according to the first channel state data and the second channel state data; the target monitoring channel is a channel monitored in the target monitoring time period.
The embodiment of the application provides a channel determining device, which comprises:
a first obtaining module, configured to obtain first channel state data of each available channel in a previous listening period of the target listening period; the first channel state data is used for representing the data volume transmitted on the available channel in the monitored last monitoring time period;
The channel selection module is used for selecting an observation channel from the available channels according to the first channel state data;
the second acquisition module is used for monitoring the observation channel before the target monitoring time period arrives so as to obtain second channel state data of the observation channel within a set duration; the second channel state data is used for representing the size of the data quantity transmitted on the observation channel within the monitored set duration;
a channel determining module, configured to determine a target listening channel from the available channels according to the first channel state data and the second channel state data; the target monitoring channel is a channel monitored in the target monitoring time period.
The embodiment of the application provides a channel determining device, which comprises: a processor; and a memory arranged to store computer executable instructions which, when executed, cause the processor to implement the steps of the channel determination method of the first aspect described above.
Embodiments of the present application provide a storage medium storing computer executable instructions that when executed implement the steps of the channel determination method described in the first aspect.
According to the method and the device for monitoring the data, the first channel state data and the second channel state data can be obtained, the first channel state data are used for representing the data size transmitted on the available channel in the last monitoring time period of the monitored target monitoring time period, the second channel state data are used for representing the data size transmitted on the observed channel in the monitored set duration, and the target monitoring channel is determined in each available channel according to the first channel state data and the second channel state data, so that the monitored channel in the target monitoring time period is accurately determined by combining the historical condition of the monitored channel transmission data and the observed channel transmission data, and further the data collection efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present application, and that other drawings may be obtained according to these drawings without inventive effort to a person skilled in the art.
Fig. 1 is an application scenario schematic diagram of a channel determining method according to an embodiment of the present application;
fig. 2 is a flow chart of a channel determining method according to an embodiment of the present application;
FIG. 3 is a schematic diagram of channel observation according to an embodiment of the present application;
fig. 4 is a schematic block diagram of a channel determining apparatus according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a channel determining apparatus according to an embodiment of the present application.
Detailed Description
In order to better understand the technical solutions in the present application, the following description will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.
The embodiment of the application aims to provide a channel determining method and device so as to accurately determine a channel to be monitored and improve data collection efficiency. The Wi-Fi mentioned below in this embodiment refers to a wireless local area network technology created in the IEEE 802.11 standard. Wi-Fi channels refer to wireless channels that an IEEE 802.11 network should use. IEEE 802.11 refers to a standard common to wireless local area networks, which is a standard for wireless network communications defined by the Institute of Electrical and Electronics Engineers (IEEE).
Fig. 1 is a schematic diagram of an application scenario of a channel determining method according to an embodiment of the present application, and as shown in fig. 1, the scenario includes a user terminal 100 and a Wi-Fi probe device 200. The user terminal 100 may select one Wi-Fi channel from a plurality of Wi-Fi channels to communicate according to a specific scenario, and transmit data on the Wi-Fi channel. The Wi-Fi probe device 200 may perform the method in the embodiments of the present application, and determine the Wi-Fi channel that needs to be monitored, thereby improving the data collection efficiency. In this embodiment, the number of the user terminals 100 may be one or more, and the user terminals 100 may be mobile phones, computers, tablet computers, etc., and fig. 1 schematically illustrates the user terminals 100 as mobile phones.
In a specific embodiment, the Wi-Fi probe apparatus 200 collects probe request frames broadcast by the user terminal 100 on the Wi-Fi channel by passive data collection. The passive data collection mode refers to that when the Wi-Fi probe equipment collects data, detection signals are not actively sent to the user terminal, and only data broadcasted by the user terminal on a Wi-Fi channel are monitored and collected passively. The Probe Request frame, also called Probe Request frame, is a management frame actively broadcasted by the user terminal on the Wi-Fi channel for searching for available Wi-Fi services. In this embodiment, the Wi-Fi Probe device 200 can determine the channel most likely to be used by the user terminal 100 for transmitting the Probe Request frame and listen through executing the method in the embodiment of the present application, so as to improve efficiency of collecting Probe Request frames. In this embodiment, the Wi-Fi probe device 200 passively collects the probe request frames broadcast by the user terminal 100, so as to achieve the effect of sensing the number of customer flows of the user in various scenes, such as off-line retail stores, restaurants, bus subways, and the like, and further providing relevant services to the user.
It should be noted that, the method in the embodiment of the present application may be used not only in the scenario shown in fig. 1 to determine the Wi-Fi channel to be monitored, but also in other scenarios to determine other communication channels to be monitored, such as bluetooth channels, etc., which are not illustrated here.
Fig. 2 is a schematic flow chart of a channel determining method according to an embodiment of the present application, as shown in fig. 2, the flow chart includes the following steps:
step S202, obtaining first channel state data of each available channel in a monitoring time period which is the last time period of a target monitoring time period; the first channel state data is used for indicating the monitored data size transmitted on the available channel in the last monitoring time period;
step S204, according to the first channel state data, selecting an observation channel from all available channels;
step S206, before the target monitoring time period comes, monitoring the observation channel to obtain second channel state data of the observation channel in a set time length; the second channel state data is used for representing the data quantity transmitted on the observation channel within the monitored set time length;
step S208, determining a target monitoring channel in each available channel according to the first channel state data and the second channel state data; the target listening channel is a channel that is listening during a target listening period.
According to the method and the device for monitoring the data, the first channel state data and the second channel state data can be obtained, the first channel state data are used for representing the data size transmitted on the available channel in the last monitoring time period of the monitored target monitoring time period, the second channel state data are used for representing the data size transmitted on the observed channel in the monitored set duration, and the target monitoring channel is determined in each available channel according to the first channel state data and the second channel state data, so that the monitored channel in the target monitoring time period is accurately determined by combining the historical condition of the monitored channel transmission data and the observed channel transmission data, and further the data collection efficiency is improved.
In step S202, first channel state data of each available channel in a previous listening period of the target listening period is obtained, where the first channel state data is used to represent the monitored data size transmitted on each available channel in the previous listening period.
In this embodiment, the available channel may be a Wi-Fi channel, and of course, the available channel may also be other types of communication channels.
In this embodiment, a plurality of listening periods are set, and the duration of the listening period may be set according to requirements, for example, set to 30 seconds or other durations. The plurality of listening periods may be time-sequential periods, such as two listening periods, for example, a first listening period of 1 second to 30 seconds and a second listening period of 31 seconds to 60 seconds. Of course, the plurality of listening periods may be time-discontinuous periods, such as two listening periods, for example, a first listening period of 1 second to 20 seconds and a second listening period of 31 seconds to 50 seconds, with a 10 second interval therebetween.
In this embodiment, the first channel state data may be obtained by (1) monitoring data transmitted on each available channel in a previous monitoring period of the target monitoring period, determining, according to a monitoring result, a size of data transmitted on each available channel in the previous monitoring period, and generating the first channel state data according to the size of data transmitted on each available channel in the previous monitoring period; (2) The amount of data transmitted on each of the available channels in the last listening period is predicted by a specific prediction means (a prediction means as mentioned below) and the first channel state data is generated based on the amount of data transmitted on each of the available channels in the last listening period.
In this embodiment, the first channel state data may be represented in the form of a vector, for example, taking m available channels as an example, a vector S (N) = [ c1, c2, … cm ] is defined to represent the size of the data amount transmitted on each available channel in the nth listening period, where c1 represents the size of the data amount transmitted on the 1 st available channel, c2 represents the size of the data amount transmitted on the 2 nd available channel, and so on. All of c1, c2, … and cm were normalized values. When the available channel is a Wi-Fi channel, the value of m is equal to 13.
In the step S204, according to the first channel state data, an observation channel is selected from the available channels, specifically:
(a1) Selecting a first channel with the largest transmitted data quantity represented by the first channel state data, a second channel with the second largest transmitted data quantity, a third channel which is not overlapped with the first channel in a frequency domain, and any fourth channel except the first channel, the second channel and the third channel from all available channels;
(a2) Any one or more of the first channel, the second channel, the third channel and the fourth channel is used as an observation channel.
Specifically, the first channel state data is used to represent the size of the data amount transmitted on each available channel in the last listening period of the target listening period. In the above-described operation (a 1), the channel with the largest amount of data to be transmitted, which is represented by the first channel state data, is selected as the first channel from among the available channels, and the channel with the second largest amount of data to be transmitted, which is represented by the first channel state data, is selected as the second channel from among the available channels, and the channel that does not overlap with the first channel in the frequency domain is selected as the third channel from among the available channels, and any one of the channels other than the first channel, the second channel, and the third channel is selected randomly from among the available channels as the fourth channel. In the above-mentioned operation (a 2), any one or more selected first channel, second channel, third channel and fourth channel is used as an observation channel, for example, the observation channel may be the first channel, the second channel, the first channel and the second channel, or the like.
It can be understood that if the available channel is a Wi-Fi channel, the observation channel selected from each available channel is also a Wi-Fi channel. In a specific embodiment, if the Wi-Fi probe device executes the method in this embodiment, determines a Wi-Fi channel to be monitored, and acquires a probe request frame actively broadcasted by the ue by monitoring the Wi-Fi channel, the first channel state data may be used to indicate the monitored data size of the probe request frame transmitted on each available channel in the last monitoring period, and in step S204, an observation channel is selected from the available channels according to the monitored data size of the probe request frame transmitted on each available channel in the last monitoring period.
In the step S206, before the target monitoring period arrives, each observation channel is monitored to obtain second channel state data of each observation channel within a set period, where the second channel state data is used to represent the size of the data amount transmitted on each observation channel within the monitored set period.
In this embodiment, the set duration may be set as required, for example, 10 seconds or 5 seconds. The second channel state data is used to represent the size of the data amount transmitted on each observation channel within the monitored set time period. In this embodiment, the second channel state data may be represented in the form of a vector, for example, the number of observation channels is 4, and the defined vector Z (N) = [ a1, a2, a3, a4] represents the data size transmitted on each observation channel in a set duration obtained by listening before the nth listening period arrives, where a1 to a4 represent the data size transmitted on each observation channel respectively. All of a1 to a4 are values after normalization processing.
In a specific embodiment, if the Wi-Fi probe device executes the method in this embodiment, determines a Wi-Fi channel to be monitored, and acquires a probe request frame actively broadcasted by the user terminal by monitoring the Wi-Fi channel, the second channel state data may be used to indicate the data size of the probe request frame transmitted on each observation channel within the monitored set duration.
In the step S208, a target listening channel is determined in each available channel according to the first channel state data and the second channel state data, specifically:
(b1) Predicting third channel state data of each available channel in the target monitoring time period according to the first channel state data and the second channel state data, wherein the third channel state data is used for representing the size of data quantity transmitted on the available channel in the predicted target monitoring time period;
(b2) And selecting the channel with the largest data quantity of transmission represented by the third channel state data from all available channels as a target monitoring channel.
Specifically, the first channel state data is data representing the amount of data transmitted on each available channel in the last listening period of the monitored target listening period, and the second channel state data is data representing the amount of data transmitted on each observation channel in the monitored set period, which can be understood as the observation channel being the channel most likely to be the target listening channel selected from the available channels. In the above-mentioned action (b 1), third channel state data of each available channel in the target listening period is predicted according to the first channel state data and the second channel state data, and the third channel state data is used for indicating the data size transmitted on each available channel in the predicted target listening period. In the above-described operation (b 2), among the available channels, a channel having the largest amount of data to be transmitted, which is indicated by the third channel state data, is selected as the target listening channel. It can be understood that if the Wi-Fi channel is an available channel, the target listening channel selected from the available channels is also a Wi-Fi channel.
The step (b 1) predicts third channel state data of each available channel in the target listening period according to the first channel state data and the second channel state data, which may specifically be:
(b11) Acquiring a state transition matrix and a process noise matrix corresponding to the first channel state data, and acquiring an observation noise matrix corresponding to the second channel state data;
(b12) The first channel state data, the second channel state data, the state transition matrix, the process noise matrix and the observation noise matrix are input into an autoregressive filter, and the third channel state data is predicted through the autoregressive filter.
In this embodiment, an autoregressive filter is provided, and the autoregressive filter may be a kalman filter, such as a standard kalman filter, an extended kalman filter, a lossless kalman filter, or the like. In this embodiment, a state transition matrix and a process noise matrix corresponding to the first channel state data are calculated in advance, an observation noise matrix corresponding to the second channel state data is calculated, the first channel state data, the second channel state data, the state transition matrix, the process noise matrix and the observation noise matrix are input into an autoregressive filter to perform operation, and an operation result output by the autoregressive filter is third channel state data.
Wherein the state transition matrix is used to represent a state transition relationship between the first channel state data and the third channel state data. The process noise matrix is used to represent noise of the state transition process between the first channel state data and the third channel state data. The observation noise matrix is used for representing the observation noise corresponding to the second channel state data.
In this embodiment, after the third channel state data is predicted by the autoregressive filter, the values of the state transition matrix, the process noise matrix, and the observation noise matrix may also be updated based on the predicted third channel state data.
Further, when the target listening channel includes a Wi-Fi channel, the method in this embodiment may further include: and in the target monitoring time period, monitoring the target monitoring channel to acquire a Wi-Fi detection request frame transmitted by the user terminal on the target monitoring channel. By acquiring the Wi-Fi detection request frame of the user terminal, the effect of sensing the passenger flow and providing further Internet service for the user can be achieved.
Fig. 3 is a schematic diagram of channel observation provided in an embodiment of the present application, where, as shown in fig. 3, a time dimension is divided into a plurality of listening periods, and a preset duration, for example, 30 ms, is provided between each listening period for executing the method in the embodiment of the present application to determine a target listening channel and switch the channel to the target listening channel. In the scenario shown in fig. 3, the method in this embodiment can dynamically adjust the channel to be monitored, thereby improving the data acquisition efficiency.
In a specific embodiment, the Wi-Fi probe device executes the method in the embodiment, determines a Wi-Fi channel to be monitored, and acquires a probe request frame actively broadcasted by the user terminal by monitoring the Wi-Fi channel. Firstly, before the nth listening period arrives, channel state data corresponding to the last listening period is acquired, the channel state data can be expressed as S (N-1) = [ c1, c2, … c13], c1 to c13 respectively represent the data size of the probe request frame transmitted on each Wi-Fi channel in the monitored 13 Wi-Fi channels, and c1, c2, … and c13 are all normalized values. Before the first listening period has come, since there is no time S0, vector S (0) can be formed here, wherein relatively large values of c1, c6, c11 can be set.
Then, in S (N-1), a channel ck with the largest data amount of the transmitted probe request frame and a channel cs with the second largest data amount of the transmitted probe request frame are selected, and the channel cl which is not overlapped with ck in the frequency domain is selected, any one channel cr except for ck, cs and cl is used as an observation channel, and before the nth monitoring period comes, the observation channel is observed for a set period of time, such as 30 milliseconds, so as to obtain channel state data Z (N) = [ cl, cs, cr, ck ] Z (N) can also be called an observation vector.
Then, a state transition matrix F is obtained, where the state transition matrix F is used to represent a state transition relationship (or referred to as a mapping relationship) between S (N-1) and S (N), and F may be a matrix of 13×4. The state transition matrix F is updated after the prediction of S (N). At the initial time of the system, F may be constructed and the 1,6,9, 11 channels given relatively large weights based on experience in F. In this embodiment, when constructing the state transition matrix F, a plurality of channels may be selected by other selection methods, such as a random selection method following uniform distribution, and the channels may be given higher weights.
A process noise matrix W is obtained, the process noise matrix W being used to represent the transition process noise when transitioning from S (N-1) to S (N). The process noise matrix W may conform to a high and new distribution or a continuous distribution or a discontinuous distribution, etc. The process noise matrix W is updated after the prediction S (N). At the initial time of the system, a process noise matrix W may be constructed according to the requirements.
And acquiring an observation noise matrix V, wherein the observation noise matrix V is used for representing the observation noise corresponding to the observation vector. The observation noise matrix V may conform to a high-new distribution or a continuous distribution or a discontinuous distribution, etc. The observation noise matrix V is updated after the prediction S (N). At the initial time of the system, the observation noise matrix V may be constructed according to the requirement. In the case of predicting S (N), the state transition matrix F, the process noise matrix W, and the observation noise matrix V obtained by updating after using the prediction S (N-1) are used.
Finally, Z (N), F, V, W and S (N-1) are input to the kalman filter, and the channel state data S (N) of the predicted nth listening period and the updated F, V, W are obtained by the operation of the kalman filter. And selecting a channel with the largest data volume of the detection request frame transmitted in the S (N) as a channel monitored in the N monitoring time period, and switching the channel to the channel when the N monitoring time period arrives. By repeating the method, iterative selection and monitoring of Wi-Fi channels can be realized, so that a detection request frame of the user terminal is obtained.
The operation principle of the kalman filter in the above-described embodiment is described in detail below.
S (N) represents channel state data corresponding to the nth listening period, S (N) = [ c1, c2, c2 … c13], so S (N) is a 13-dimensional vector.
F represents a state transition matrix, and represents the transition relation from S (N-1) to S (N), and has the following relation: s (N) =f S (N-1) +w (N). Where W (N) represents the process noise matrix. And, define Q (N) to be the covariance matrix of W (N). In constructing F, the weights of the 1, 6, 11 channels may be empirically increased, i.e., c1, c6, c11 are artificially amplified by F in estimating S (N) from S (N-1).
Z (N) is a vector obtained by indirect or partial observation of the system, and has the following relation: z (N) =h (S (N)) +v (N), H expressing the effect of observation noise V (N) on the system before the nth listening period arrives, to obtain Z (N).
When predicting S (N), the prior estimation is firstly performed according to the channel state data S (N-1) corresponding to the previous listening period, that is, the prior estimation is performed according to the state transition equation S (N) =f×s (N-1). And, the prior error covariance matrix P' (N) =f×p (N-1) ×ft+q (N-1) of the previous listening period is obtained.
Then, before the nth listening period comes, observation is performed to obtain Z (N). Measurement margin Y (N) =z (N) -H (S '(N)), measurement margin covariance C (N) =jh (S' (N)) P '(N) JHT (S' (N)) +r (N), where R (N) is a covariance matrix of measurement noise V (N), is calculated.
The measurement margin described above represents, to some extent, the difference between the a priori estimated value and the observed value and the probability distribution, and from this difference and the probability distribution, the kalman gain K (N) =p '(N) JHT (S' (N)) inv (C (N)) is calculated. The kalman gain is estimated based on the minimum mean square error, and can be considered as a regulator: whether the estimation based on the last state is more believed or the observation is more believed. The kalman filter in this embodiment gives a combination ratio of the two, for example, when the observed noise is stronger, the kalman gain becomes smaller, i.e., the a priori estimate is more believed; conversely, observations are more believed.
Finally, a corrected estimate is derived, i.e. a posterior estimate S (N) =s' (N) +k (N) y (N). The system parameters F, V, W are updated simultaneously, here represented using an error covariance matrix: p (N) =p ' (N) -K (N) JH (S ' (N)) P ' (N).
By the method in the embodiment of the application, the prediction and the observation can be combined, the data quantity transmitted on the channel is predicted, and the dynamic selection and the switching of the channel are realized by combining the observation result, so that the aim of better comprehensive performance of channel monitoring in an actual scene is fulfilled. In addition, the method in the embodiment of the application is a continuous iterative optimization process, so that the relation among the observation result of the whole channel, the process noise and the observation noise is considered, and the optimization effect is achieved.
Corresponding to the above-mentioned channel determining method, the embodiment of the present application further provides a channel determining apparatus, and fig. 4 is a schematic block diagram of the channel determining apparatus provided in an embodiment of the present application, as shown in fig. 4, where the apparatus includes:
a first obtaining module 41, configured to obtain first channel state data of each available channel in a previous listening period of the target listening period; the first channel state data is used for representing the data volume transmitted on the available channel in the monitored last monitoring time period;
A channel selection module 42, configured to select an observation channel from the available channels according to the first channel state data;
a second obtaining module 43, configured to monitor the observation channel to obtain second channel state data of the observation channel within a set duration before the target monitoring period arrives; the second channel state data is used for representing the size of the data quantity transmitted on the observation channel within the monitored set duration;
a channel determining module 44, configured to determine a target listening channel from the available channels according to the first channel state data and the second channel state data; the target monitoring channel is a channel monitored in the target monitoring time period.
Optionally, the channel selection module 42 is specifically configured to: selecting a first channel with the largest data quantity of transmission, a second channel with the second largest data quantity of transmission, a third channel which is not overlapped with the first channel in a frequency domain and any fourth channel except the first channel, the second channel and the third channel from the available channels; any one or more of the first channel, the second channel, the third channel and the fourth channel is used as an observation channel.
Optionally, the channel determining module 44 is specifically configured to: predicting third channel state data of each available channel in the target monitoring time period according to the first channel state data and the second channel state data; the third channel state data is used for representing the predicted data size transmitted on the available channel in the target monitoring time period; and selecting a channel with the largest data volume of transmission represented by the third channel state data from the available channels as the target monitoring channel.
Optionally, the channel determining module 44 is further specifically configured to: acquiring a state transition matrix and a process noise matrix corresponding to the first channel state data, and acquiring an observation noise matrix corresponding to the second channel state data; inputting the first channel state data, the second channel state data, the state transition matrix, the process noise matrix and the observed noise matrix into an autoregressive filter, and predicting the third channel state data by the autoregressive filter; wherein the state transition matrix is used for representing a state transition relationship between the first channel state data and the third channel state data; the process noise matrix is used for representing noise of a state transition process between the first channel state data and the third channel state data; the observation noise matrix is used for representing observation noise corresponding to the second channel state data.
Optionally, the apparatus further comprises an update module for: after predicting the third channel state data by the autoregressive filter, updating values of the state transition matrix, the process noise matrix, and the observation noise matrix based on the predicted third channel state data.
Optionally, the target listening channel comprises a Wi-Fi channel; the device also comprises a monitoring module for: and in the target monitoring time period, monitoring the target monitoring channel to acquire a Wi-Fi detection request frame transmitted by the user terminal on the target monitoring channel.
According to the method and the device for monitoring the data, the first channel state data and the second channel state data can be obtained, the first channel state data are used for representing the data size transmitted on the available channel in the last monitoring time period of the monitored target monitoring time period, the second channel state data are used for representing the data size transmitted on the observed channel in the monitored set duration, and the target monitoring channel is determined in each available channel according to the first channel state data and the second channel state data, so that the monitored channel in the target monitoring time period is accurately determined by combining the historical condition of the monitored channel transmission data and the observed channel transmission data, and further the data collection efficiency is improved.
The channel determining device in the embodiment of the present application can implement each process of the foregoing channel determining method embodiment, and achieve the same effects and functions, which are not repeated here.
The embodiment of the present application further provides a channel determining device, and fig. 5 is a schematic structural diagram of the channel determining device provided in an embodiment of the present application, as shown in fig. 5, the channel determining device may generate relatively large differences due to different configurations or performances, and may include one or more processors 901 and a memory 902, where one or more storage applications or data may be stored in the memory 902. Wherein the memory 902 may be transient storage or persistent storage. The application program stored in the memory 902 may include one or more modules (not shown in the figures), each of which may include a series of computer executable instructions for use in a channel determination device. Still further, the processor 901 may be arranged to communicate with the memory 902 and execute a series of computer executable instructions in the memory 902 on the channel determination device. The channel-determining device may also include one or more power supplies 903, one or more wired or wireless network interfaces 904, one or more input-output interfaces 905, one or more keyboards 906, and the like.
In a particular embodiment, the channel-determining device includes a memory, and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for the channel-determining device, and configured to be executed by the one or more processors, the one or more programs comprising computer-executable instructions for:
acquiring first channel state data of each available channel in a monitoring time period which is the last time period of a target monitoring time period; the first channel state data is used for representing the data volume transmitted on the available channel in the monitored last monitoring time period;
selecting an observation channel from the available channels according to the first channel state data;
before the target monitoring time period arrives, monitoring the observation channel to obtain second channel state data of the observation channel within a set time length; the second channel state data is used for representing the size of the data quantity transmitted on the observation channel within the monitored set duration;
Determining a target monitoring channel in each available channel according to the first channel state data and the second channel state data; the target monitoring channel is a channel monitored in the target monitoring time period.
Optionally, the computer executable instructions, when executed, select an observation channel from the available channels according to the first channel state data, including: selecting a first channel with the largest data quantity of transmission, a second channel with the second largest data quantity of transmission, a third channel which is not overlapped with the first channel in a frequency domain and any fourth channel except the first channel, the second channel and the third channel from the available channels; any one or more of the first channel, the second channel, the third channel and the fourth channel is used as an observation channel.
Optionally, the computer executable instructions, when executed, determine a target listening channel from the respective available channels based on the first channel state data and the second channel state data, comprising: predicting third channel state data of each available channel in the target monitoring time period according to the first channel state data and the second channel state data; the third channel state data is used for representing the predicted data size transmitted on the available channel in the target monitoring time period; and selecting a channel with the largest data volume of transmission represented by the third channel state data from the available channels as the target monitoring channel.
Optionally, the computer executable instructions, when executed, predict third channel state data for the respective available channels within the target listening period based on the first channel state data and the second channel state data, comprising: acquiring a state transition matrix and a process noise matrix corresponding to the first channel state data, and acquiring an observation noise matrix corresponding to the second channel state data; inputting the first channel state data, the second channel state data, the state transition matrix, the process noise matrix and the observed noise matrix into an autoregressive filter, and predicting the third channel state data by the autoregressive filter; wherein the state transition matrix is used for representing a state transition relationship between the first channel state data and the third channel state data; the process noise matrix is used for representing noise of a state transition process between the first channel state data and the third channel state data; the observation noise matrix is used for representing observation noise corresponding to the second channel state data.
Optionally, the computer executable instructions, when executed, further comprise: after predicting the third channel state data by the autoregressive filter, updating values of the state transition matrix, the process noise matrix, and the observation noise matrix based on the predicted third channel state data.
Optionally, the computer-executable instructions, when executed, the target listening channel comprises a Wi-Fi channel; further comprises: and in the target monitoring time period, monitoring the target monitoring channel to acquire a Wi-Fi detection request frame transmitted by the user terminal on the target monitoring channel.
According to the method and the device for monitoring the data, the first channel state data and the second channel state data can be obtained, the first channel state data are used for representing the data size transmitted on the available channel in the last monitoring time period of the monitored target monitoring time period, the second channel state data are used for representing the data size transmitted on the observed channel in the monitored set duration, and the target monitoring channel is determined in each available channel according to the first channel state data and the second channel state data, so that the monitored channel in the target monitoring time period is accurately determined by combining the historical condition of the monitored channel transmission data and the observed channel transmission data, and further the data collection efficiency is improved.
The channel determining device in the embodiment of the present application can implement each process of the foregoing channel determining method embodiment, and achieve the same effects and functions, which are not repeated here.
Further, the embodiment of the present application further provides a storage medium, which is configured to store computer executable instructions, and in a specific embodiment, the storage medium may be a usb disk, an optical disc, a hard disk, etc., where the computer executable instructions stored in the storage medium can implement the following flow when executed by a processor:
acquiring first channel state data of each available channel in a monitoring time period which is the last time period of a target monitoring time period; the first channel state data is used for representing the data volume transmitted on the available channel in the monitored last monitoring time period;
selecting an observation channel from the available channels according to the first channel state data;
before the target monitoring time period arrives, monitoring the observation channel to obtain second channel state data of the observation channel within a set time length; the second channel state data is used for representing the size of the data quantity transmitted on the observation channel within the monitored set duration;
determining a target monitoring channel in each available channel according to the first channel state data and the second channel state data; the target monitoring channel is a channel monitored in the target monitoring time period.
Optionally, the computer executable instructions stored on the storage medium, when executed by the processor, select an observation channel from the available channels according to the first channel state data, including: selecting a first channel with the largest data quantity of transmission, a second channel with the second largest data quantity of transmission, a third channel which is not overlapped with the first channel in a frequency domain and any fourth channel except the first channel, the second channel and the third channel from the available channels; any one or more of the first channel, the second channel, the third channel and the fourth channel is used as an observation channel.
Optionally, the computer executable instructions stored on the storage medium, when executed by the processor, determine a target listening channel from the first channel state data and the second channel state data, including: predicting third channel state data of each available channel in the target monitoring time period according to the first channel state data and the second channel state data; the third channel state data is used for representing the predicted data size transmitted on the available channel in the target monitoring time period; and selecting a channel with the largest data volume of transmission represented by the third channel state data from the available channels as the target monitoring channel.
Optionally, the computer executable instructions stored on the storage medium, when executed by the processor, predict third channel state data for the respective available channels within the target listening period based on the first channel state data and the second channel state data, comprising: acquiring a state transition matrix and a process noise matrix corresponding to the first channel state data, and acquiring an observation noise matrix corresponding to the second channel state data; inputting the first channel state data, the second channel state data, the state transition matrix, the process noise matrix and the observed noise matrix into an autoregressive filter, and predicting the third channel state data by the autoregressive filter; wherein the state transition matrix is used for representing a state transition relationship between the first channel state data and the third channel state data; the process noise matrix is used for representing noise of a state transition process between the first channel state data and the third channel state data; the observation noise matrix is used for representing observation noise corresponding to the second channel state data.
Optionally, the storage medium stores computer executable instructions that when executed by the processor further comprise: after predicting the third channel state data by the autoregressive filter, updating values of the state transition matrix, the process noise matrix, and the observation noise matrix based on the predicted third channel state data.
Optionally, the storage medium stores computer executable instructions that, when executed by the processor, the target listening channel comprises a Wi-Fi channel; further comprises: and in the target monitoring time period, monitoring the target monitoring channel to acquire a Wi-Fi detection request frame transmitted by the user terminal on the target monitoring channel.
According to the method and the device for monitoring the data, the first channel state data and the second channel state data can be obtained, the first channel state data are used for representing the data size transmitted on the available channel in the last monitoring time period of the monitored target monitoring time period, the second channel state data are used for representing the data size transmitted on the observed channel in the monitored set duration, and the target monitoring channel is determined in each available channel according to the first channel state data and the second channel state data, so that the monitored channel in the target monitoring time period is accurately determined by combining the historical condition of the monitored channel transmission data and the observed channel transmission data, and further the data collection efficiency is improved.
The storage medium in the embodiments of the present application can implement the respective processes of the foregoing channel determining method embodiments, and achieve the same effects and functions, which are not repeated here.
In the 90 s of the 20 th century, improvements to one technology could clearly be distinguished as improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (e.g., field programmable gate array (Field Programmable Gate Array, FPGA)) is an integrated circuit whose logic function is determined by the programming of the device by a user. A designer programs to "integrate" a digital system onto a PLD without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented by using "logic compiler" software, which is similar to the software compiler used in program development and writing, and the original code before the compiling is also written in a specific programming language, which is called hardware description language (Hardware Description Language, HDL), but not just one of the hdds, but a plurality of kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), lava, lola, myHDL, PALASM, RHDL (Ruby Hardware Description Language), etc., VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable logic controllers, and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in one or more software and/or hardware elements when implemented in the present application.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus. 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 apparatus that comprises the element.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (10)
1. A channel determination method, comprising:
acquiring first channel state data of each available channel in a monitoring time period which is the last time period of a target monitoring time period; the first channel state data is used for representing the data volume transmitted on the available channel in the monitored last monitoring time period;
selecting an observation channel from the available channels according to the first channel state data;
Before the target monitoring time period arrives, monitoring the observation channel to obtain second channel state data of the observation channel within a set time length; the second channel state data is used for representing the size of the data quantity transmitted on the observation channel within the monitored set duration;
acquiring a state transition matrix and a process noise matrix corresponding to the first channel state data, acquiring an observation noise matrix corresponding to the second channel state data, inputting the first channel state data, the second channel state data, the state transition matrix, the process noise matrix and the observation noise matrix into an autoregressive filter, and predicting third channel state data through the autoregressive filter;
the third channel state data is used for representing the predicted data size transmitted on the available channel in the target monitoring time period; the state transition matrix is used for representing a state transition relation between the first channel state data and the third channel state data; the process noise matrix is used for representing noise of a state transition process between the first channel state data and the third channel state data; the observation noise matrix is used for representing observation noise corresponding to the second channel state data;
Determining a target monitoring channel in each available channel according to the third channel state data; the target monitoring channel is a channel monitored in the target monitoring time period; the target listening channel comprises a Wi-Fi channel or a bluetooth channel.
2. The method of claim 1, selecting an observation channel from the respective available channels based on the first channel state data, comprising:
selecting a first channel with the largest data quantity of transmission, a second channel with the second largest data quantity of transmission, a third channel which is not overlapped with the first channel in a frequency domain and any fourth channel except the first channel, the second channel and the third channel from the available channels;
any one or more of the first channel, the second channel, the third channel and the fourth channel is used as an observation channel.
3. The method of claim 1, determining a target listening channel among the respective available channels based on the third channel state data, comprising:
and selecting a channel with the largest data volume of transmission represented by the third channel state data from the available channels as the target monitoring channel.
4. The method of claim 1, the method further comprising:
after predicting the third channel state data by the autoregressive filter, updating values of the state transition matrix, the process noise matrix, and the observation noise matrix based on the predicted third channel state data.
5. A channel determining apparatus comprising:
the first acquisition module acquires first channel state data of each available channel in a monitoring time period which is the last time period of the target monitoring time period; the first channel state data is used for representing the data volume transmitted on the available channel in the monitored last monitoring time period;
the channel selection module is used for selecting an observation channel from the available channels according to the first channel state data;
the second acquisition module monitors the observation channel before the target monitoring time period arrives so as to obtain second channel state data of the observation channel within a set duration; the second channel state data is used for representing the size of the data quantity transmitted on the observation channel within the monitored set duration;
the channel determining module is used for acquiring a state transition matrix and a process noise matrix corresponding to the first channel state data, acquiring an observation noise matrix corresponding to the second channel state data, inputting the first channel state data, the second channel state data, the state transition matrix, the process noise matrix and the observation noise matrix into an autoregressive filter, and predicting third channel state data through the autoregressive filter; the third channel state data is used for representing the predicted data size transmitted on the available channel in the target monitoring time period; the state transition matrix is used for representing a state transition relation between the first channel state data and the third channel state data; the process noise matrix is used for representing noise of a state transition process between the first channel state data and the third channel state data; the observation noise matrix is used for representing observation noise corresponding to the second channel state data; determining a target monitoring channel in each available channel according to the third channel state data; the target monitoring channel is a channel monitored in the target monitoring time period; the target listening channel comprises a Wi-Fi channel or a bluetooth channel.
6. The apparatus of claim 5, the channel selection module to:
selecting a first channel with the largest data quantity of transmission, a second channel with the second largest data quantity of transmission, a third channel which is not overlapped with the first channel in a frequency domain and any fourth channel except the first channel, the second channel and the third channel from the available channels;
any one or more of the first channel, the second channel, the third channel and the fourth channel is used as an observation channel.
7. The apparatus of claim 5, the channel determination module to:
and selecting a channel with the largest data volume of transmission represented by the third channel state data from the available channels as the target monitoring channel.
8. The apparatus of claim 5, further comprising an update module,
after predicting the third channel state data by the autoregressive filter, updating values of the state transition matrix, the process noise matrix, and the observation noise matrix based on the predicted third channel state data.
9. A channel determining device, comprising: a processor; and a memory arranged to store computer executable instructions which, when executed, cause the processor to implement the steps of the channel determination method of any of the preceding claims 1 to 4.
10. A storage medium storing computer executable instructions which when executed implement the steps of the channel determination method of any one of the preceding claims 1 to 4.
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| CN105049136A (en) * | 2015-08-07 | 2015-11-11 | 宇龙计算机通信科技(深圳)有限公司 | Method and device for detecting unauthorized frequency spectrum channel |
| CN105338551B (en) * | 2015-09-25 | 2020-03-24 | 联想(北京)有限公司 | Channel monitoring method and device and electronic equipment |
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