CN110113115B - Channel determination method and device - Google Patents
Channel determination method and device Download PDFInfo
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- CN110113115B CN110113115B CN201910354124.2A CN201910354124A CN110113115B CN 110113115 B CN110113115 B CN 110113115B CN 201910354124 A CN201910354124 A CN 201910354124A CN 110113115 B CN110113115 B CN 110113115B
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- 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
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/382—Monitoring; Testing of propagation channels for resource allocation, admission control or handover
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W36/00—Hand-off or reselection arrangements
- H04W36/0005—Control or signalling for completing the hand-off
- H04W36/0011—Control or signalling for completing the hand-off for data sessions of end-to-end connection
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W36/00—Hand-off or reselection arrangements
- H04W36/0005—Control or signalling for completing the hand-off
- H04W36/0083—Determination of parameters used for hand-off, e.g. generation or modification of neighbour cell lists
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- 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
Abstract
The embodiment of the application provides a channel determination method and a device, wherein the method comprises the following steps: first, first channel state data of each available channel in the last listening time period of the target listening time period is obtained, the first channel state data being used for representing the size of the data volume transmitted on each available channel in the last listening time period to be listened to, and then, according to the first channel state data, selecting an observation channel from the available channels, monitoring each observation channel before a target monitoring time period comes to obtain second channel state data of each observation channel within a set time length, wherein the second channel state data is used for representing the monitored data volume transmitted on each observation channel within the set time length, and finally, according to the first channel state data and the second channel state data, and determining a target monitoring channel in each available channel, wherein the target monitoring channel is a channel monitored in the target monitoring time period.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a channel determination method and apparatus.
Background
In order to provide better internet services to users, it is now often necessary to listen to the communication channel of the user terminal to collect certain 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 location data of users, and by determining the location of each user, real-time traffic can be determined and traffic navigation can be provided to the user.
Considering that the user terminal may select a certain channel from the multiple communication channels for communication according to a communication scenario or other factors, for example, select a certain Wi-Fi channel for Wi-Fi communication, it is necessary to provide a technical solution 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 determination method and a channel determination device, so as to accurately determine a channel to be monitored and improve data collection efficiency.
In order to achieve the above technical effects, the embodiment of the present application is implemented as follows:
the embodiment of the application provides a channel determination method, which comprises the following steps:
acquiring first channel state data of each available channel in the last monitoring time period of the target monitoring time period; the first channel state data is used for representing the size of the data transmitted on the available channel in the last listening time period for listening;
selecting an observation channel from the available channels according to the first channel state data;
monitoring the observation channel before the target monitoring time period comes 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 volume transmitted on the observation channel in the monitored set time length;
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.
An embodiment of the present application provides a channel determining apparatus, including:
the first acquisition module is used for acquiring first channel state data of each available channel in the last monitoring time period of the target monitoring time period; the first channel state data is used for representing the size of the data transmitted on the available channel in the last listening time period for listening;
a channel selection module, configured to select an observation channel from the available channels according to the first channel state data;
a second obtaining module, configured to monitor the observation channel before the target monitoring time period comes, to obtain second channel state data of the observation channel within a set time period; the second channel state data is used for representing the size of the data volume transmitted on the observation channel in the monitored set time length;
a channel determining module, configured to determine a target listening 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.
An embodiment of the present application provides a channel determination device, including: a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to carry out the steps of the channel determination method of the first aspect described above.
An embodiment of the present application provides a storage medium for storing computer-executable instructions, which, when executed, implement the steps of the channel determination method according to the first aspect.
According to the embodiment of the application, the first channel state data and the second channel state data can be obtained, the first channel state data is used for representing the size of the data volume transmitted on the available channel in the last monitoring time period of the monitored target monitoring time period, the second channel state data is used for representing the size of the data volume transmitted on the observation channel in the set monitoring time period, 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 channel monitored in the target monitoring time period is accurately determined according to the historical condition of the monitored channel transmission data and the condition of channel transmission data obtained by observation, and 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 needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort.
Fig. 1 is a schematic view of an application scenario of a channel determination method according to an embodiment of the present application;
fig. 2 is a schematic flowchart of a channel determination method according to an embodiment of the present application;
FIG. 3 is a schematic diagram of channel observation provided by 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 determination device according to an embodiment of the present application.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The embodiment of the application aims to provide a channel determination method and a channel determination 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 lan technology created in the IEEE 802.11 standard. Wi-Fi channels refer to radio 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 communication defined by the international Institute of Electrical and Electronics Engineering (IEEE).
Fig. 1 is a schematic view of an application scenario of a channel determination method according to an embodiment of the present application, 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 of the Wi-Fi channels for communication according to a specific scenario, and transmit data on the Wi-Fi channel. The Wi-Fi probe device 200 may perform the methods of embodiments of the present application to determine the Wi-Fi channels that need to be monitored, thereby improving data collection efficiency. In this embodiment, the number of the user terminals 100 may be one or more, the user terminals 100 may be devices such as a mobile phone, a computer, and a tablet computer, and the user terminal 100 is schematically illustrated as a mobile phone in fig. 1.
In one embodiment, the Wi-Fi probe device 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 device collects data, the Wi-Fi probe device does not actively send a detection signal to the user terminal, and only passively monitors and collects the data broadcasted by the user terminal on a Wi-Fi channel. The Probe Request frame is also called a Probe Request frame, and is a management frame actively broadcasted on a Wi-Fi channel by a user terminal for searching available Wi-Fi services. In this embodiment, the Wi-Fi Probe device 200 can determine and listen to the channel used by the ue 100 for transmitting the Probe Request frame, by performing the method in this embodiment, so as to improve the efficiency of collecting the Probe Request frame. In this embodiment, the Wi-Fi probe device 200 passively collects the probe request frame broadcast by the user terminal 100, so that the effects of sensing the number of the user passenger flows and further providing relevant services to the user in various scenes, such as off-line retail stores, restaurants, buses, subways, and the like, can be achieved.
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 a Wi-Fi channel that needs to be monitored, but also in other scenarios to determine other communication channels that need to be monitored, such as a bluetooth channel, and the like, which is not illustrated here.
Fig. 2 is a schematic flowchart of a channel determination method according to an embodiment of the present application, and as shown in fig. 2, the flowchart includes the following steps:
step S202, acquiring first channel state data of each available channel in the last monitoring time period of the target monitoring time period; the first channel state data is used for representing the size of the data transmitted on the available channel in the last listening time period for listening;
step S204, selecting an observation channel from each available channel according to the first channel state data;
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 size of data quantity transmitted on the observation channel in the set time length monitored;
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 to be listened to in the target listening time period.
According to the embodiment of the application, the first channel state data and the second channel state data can be obtained, the first channel state data is used for representing the size of the data volume transmitted on the available channel in the last monitoring time period of the monitored target monitoring time period, the second channel state data is used for representing the size of the data volume transmitted on the observation channel in the set monitoring time period, 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 channel monitored in the target monitoring time period is accurately determined according to the historical condition of the monitored channel transmission data and the condition of channel transmission data obtained by observation, and the data collection efficiency is improved.
In step S202, first channel state data of each available channel in a last listening time period of the target listening time period is obtained, where the first channel state data is used to indicate an amount of data transmitted on each available channel in the last listening time period to be listened to.
In this embodiment, the available channel may be a Wi-Fi channel, and of course, the available channel may also be another type of communication channel.
In this embodiment, a plurality of monitoring time periods are set, and the duration of the monitoring time periods may be set according to requirements, for example, set to 30 seconds or other durations. The plurality of listening periods may be consecutive periods in time, 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 multiple listening periods may be discontinuous in time, such as two listening periods, for example, a first listening period is 1 second to 20 seconds, a second listening period is 31 seconds to 50 seconds, and there is a 10 second interval between the two listening periods.
In this embodiment, the first channel state data may be obtained in two ways, that is, (1) data transmitted on each available channel is monitored in a last monitoring time period of the target monitoring time period, a size of a data amount transmitted on each available channel in the last monitoring time period is determined according to a monitoring result, and the first channel state data is generated according to the size of the data amount transmitted on each available channel in the last monitoring time period; (2) the amount of data transmitted on each available channel in the last listening period is predicted by a specific prediction method (such as the prediction method mentioned below), and the first channel state data is generated according to the amount of data transmitted on each available channel in the last listening period.
In this embodiment, the first channel state data may be represented in a vector form, for example, taking m available channels as an example, a definition vector s (N) [ c1, c2, … cm ] represents 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 maximum transmitted data quantity represented by the first channel state data, a second channel with the second maximum transmitted data quantity, a third channel which is not overlapped with the first channel on a frequency domain, and any fourth channel except the first channel, the second channel and the third channel from all available channels;
(a2) and taking any one or more of the first channel, the second channel, the third channel and the fourth channel as an observation channel.
Specifically, the first channel state data is used to indicate 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 (a1), a channel, in which the amount of data of the transmission represented by the first channel state data is the largest, is selected as the first channel from among the available channels, a channel, in which the amount of data of the transmission represented by the first channel state data is the second largest, is selected as the second channel from among the available channels, a channel, which 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 randomly selected as the fourth channel from among the available channels. In the above operation (a2), one or more of the selected first channel, second channel, third channel, and fourth channel are used as observation channels, and for example, the observation channels may be the first channel, the second channel, or the like.
It can be understood that if the available channels are Wi-Fi channels, the observation channel selected from among the available channels 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 broadcast 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 previous monitoring time period, and in step S204, an observation channel is selected from the available channels according to the data size of the probe request frame transmitted on each available channel in the previous monitoring time period.
In step S206, before the target monitoring time period comes, each observation channel is monitored to obtain second channel state data of each observation channel in the set time period, where the second channel state data is used to indicate the size of the data volume transmitted on each observation channel in the set time period.
In this embodiment, the set time period may be set as required, for example, 10 seconds or 5 seconds. The second channel state data is used for representing the size of data transmitted on each observation channel in the set time length monitored. 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 definition vector z (N) ([ a1, a2, a3, a4] represents the size of the data amount transmitted on each observation channel in the set time duration obtained by listening before the nth listening time period comes, where a1 to a4 represent the size of the data amount transmitted on each observation channel respectively. Note that a1 to a4 are normalized values.
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 broadcast by the user terminal by monitoring the Wi-Fi channel, the second channel state data may be used to indicate a data size of the probe request frame transmitted on each observation channel within a set monitored duration.
In the step S208, according to the first channel state data and the second channel state data, a target listening channel is determined in each available channel, specifically:
(b1) according to the first channel state data and the second channel state data, third channel state data of each available channel in the target monitoring time period are predicted, and the third channel state data are used for representing the size of data volume transmitted on the available channel in the predicted target monitoring time period;
(b2) and selecting the channel with the maximum transmitted data quantity represented by the third channel state data from all the available channels as a target listening channel.
Specifically, the first channel state data is data indicating the size of the data amount transmitted on each available channel in the last listening period of the monitored target listening period, and the second channel state data is data indicating the size of the data amount transmitted on each observed channel in the set duration of the monitoring, which can be understood as that the observed channel is the channel most likely to be the target listening channel selected from the available channels. In the above-mentioned act (b1), 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 to indicate the size of the data amount transmitted on each available channel in the predicted target listening period. In the above-described operation (b2), among the available channels, the channel indicated by the third channel state data and having the largest amount of transmitted data is selected as the target listening channel. It can be understood that if the channels are available Wi-Fi channels, the target listening channel selected from the available channels is also a Wi-Fi channel.
The step (b1) of predicting the third channel state data of each available channel in the target listening time period according to the first channel state data and the second channel state data 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 to an autoregressive filter, and the third channel state data is predicted by 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, and the like. In this embodiment, a state transition matrix and a process noise matrix corresponding to the first channel state data are pre-calculated, 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 to the autoregressive filter for operation, and an operation result output by the autoregressive filter is the third channel state data.
Wherein the state transition matrix is used for representing the state transition relation between the first channel state data and the third channel state data. The process noise matrix is used to represent 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.
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 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: and in the target monitoring time period, monitoring the target monitoring channel to acquire a Wi-Fi detection request frame transmitted on the target monitoring channel by the user terminal. By acquiring the Wi-Fi detection request frame of the user terminal, the effect of sensing passenger flow and providing further internet service for the user can be achieved.
Fig. 3 is a schematic diagram of channel observation according to an embodiment of the present application, and as shown in fig. 3, a time dimension is divided into a plurality of listening time periods, and a preset time duration, for example, 30 ms, is provided between each listening time period for determining a target listening channel and switching a channel to the target listening channel according to the method in the embodiment of the present application. As can be seen, 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 this embodiment, determines a Wi-Fi channel to be monitored, and acquires a probe request frame actively broadcast by the user terminal by monitoring the Wi-Fi channel. First, before the nth listening period comes, channel state data corresponding to the last listening period is obtained, where the channel state data may be represented as S (N-1) [ c1, c2, … c13], c1 to c13 respectively represent the data amount of probe request frames transmitted on each of 13 monitored Wi-Fi channels, and c1, c2, …, and c13 are normalized values. It should be noted that, before the first listening period comes, since there is no time of S0, a vector S (0) may be constructed here, wherein relatively large values of c1, c6 and c11 may be set.
Then, in S (N-1), a channel ck indicating the maximum amount of data of the transmitted probe request frame, a channel cs indicating the second maximum amount of data of the transmitted probe request frame, a channel cl that does not overlap with ck in the frequency domain, and any channel cr other than ck, cs, and cl are selected, and ck, cs, cl, and cr are used as observation channels, and before the nth listening period, a set time duration, for example, 30 milliseconds, is observed on the observation channels to obtain channel state data z (N) ([ cl, cs, cr, ck ], z (N)) which may 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 S (N) is predicted. At the initial moment of the system, F can be constructed and given relatively large weights for 1, 6, 9, 11 channels based on experience in F. In this embodiment, when the state transition matrix F is constructed, a plurality of channels may be selected by other selection methods, such as a random selection method following uniform distribution, and a higher weight may be given to the channels.
A process noise matrix W is obtained, which is used to represent the transition process noise when S (N-1) is transitioned to S (N). The process noise matrix W may conform to a high-new distribution or a continuous distribution or a non-continuous distribution, etc. The process noise matrix W is updated after s (n) is predicted. At the initial moment of the system, a process noise matrix W can be constructed as required.
And acquiring an observation noise matrix V, wherein the observation noise matrix V is used for representing 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 s (n) is predicted. At the initial moment of the system, an observation noise matrix V can be constructed according to requirements. In the case of predicting S (N), the state transition matrix F, the process noise matrix W, and the observation noise matrix V, which are updated after predicting S (N-1), are used.
Finally, inputting Z (N), F, V, W and S (N-1) into a Kalman filter, and obtaining predicted channel state data S (N) of the Nth monitoring time period and updated F, V, W through the operation of the Kalman filter. And selecting the channel with the maximum data volume of the detection request frame transmitted in the step (S) and the step (N) as the monitored channel in the Nth monitoring time period, and switching the channel to the channel when the Nth monitoring time period arrives. By repeating the method, the iterative selection and monitoring of the Wi-Fi channel can be realized, so that the detection request frame of the user terminal is obtained.
The operation principle of the kalman filter in the above embodiment is described in detail below.
S (N) represents channel state data corresponding to the nth listening period, and s (N) [ c1, c2, c2 … c13], so s (N) is a 13-dimensional vector.
F represents a state transition matrix, representing the transition relationship from S (N-1) to S (N), and having the following relationship: s (N) ═ F (N-1) + w (N). Where W (N) represents the process noise matrix. And, q (n) is defined as the covariance matrix of w (n). In the process of constructing F, the weights of the 1, 6 and 11 channels can be empirically increased, that is, in the process of estimating S (N) from S (N-1), c1, c6 and c11 are artificially amplified by F.
Z (N) is a vector obtained by indirect or partial observation of the system, and has the following relationship: z (N) ═ H (s (N)) + v (N), H expresses the observation of the system before the nth listening period comes, plus the effect of the observation noise v (N), yielding z (N).
When predicting S (N), a priori estimation is performed according to the channel state data S (N-1) corresponding to the previous listening time period, that is, a priori estimation is performed according to a state transition equation S (N) ═ F × S (N-1). And obtaining the prior error covariance matrix P' (N) ═ F × P (N-1) × FT + Q (N-1) in the last monitoring time period.
Then, before the Nth monitoring time period, observation is carried out, and Z (N) is obtained. The measurement margin y (N) ═ z (N) — H (S '(N)), and the measurement margin covariance c (N) ═ JH (S' (N)) P '(N) JHT (S' (N)) + r (N), where r (N) is the covariance matrix of the measurement noise v (N).
The measurement margin represents a difference and a probability distribution between the prior estimation value and the observation value to some extent, and the kalman gain k (N) ═ P '(N) JHT (S' (N)) inv (c (N)) is calculated from the difference and the probability distribution. The kalman gain is estimated based on the minimum mean square error, which can be considered as a regulator: whether the estimation is based on the last state or the observation is more trusted. The kalman filter in this embodiment gives a combined ratio of the two, for example, when the observation noise is stronger, the kalman gain becomes smaller, i.e., the prior estimation is more believed; instead, the observation is more confident.
Finally, a corrected estimate is obtained, i.e. a posteriori estimate S (N) ═ S' (N) + k (N) y (N). The system parameters F, V, W are updated simultaneously, here using an error covariance matrix to represent: 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 can be predicted, the dynamic selection and switching of the channel can be realized by combining the observation result, and the aim of better comprehensive performance of channel monitoring in an actual scene is fulfilled. In addition, because the method in the embodiment of the application is a continuous iterative optimization process, the relationship among the observation result of the full channel, the process noise and the observation noise is considered, and the optimization effect is achieved.
Corresponding to the above channel determining method, an embodiment of the present application further provides a channel determining apparatus, fig. 4 is a schematic diagram of module compositions of the channel determining apparatus provided in the embodiment of the present application, and as shown in fig. 4, the apparatus includes:
a first obtaining module 41, configured to obtain first channel state data of each available channel in a last listening time period of a target listening time period; the first channel state data is used for representing the size of the data transmitted on the available channel in the last listening time period for listening;
a channel selecting 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 before the target monitoring time period comes, so as to obtain second channel state data of the observation channel within a set time period; the second channel state data is used for representing the size of the data volume transmitted on the observation channel in the monitored set time length;
a channel determining module 44, configured to determine a target listening 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 channel selecting module 42 is specifically configured to: selecting a first channel with the maximum transmitted data volume, a second channel with the second maximum transmitted data volume, a third channel which is not overlapped with the first channel on a frequency domain and any fourth channel except the first channel, the second channel and the third channel, wherein the first channel is represented by the first channel state data, from the available channels; taking any one or more of the first channel, the second channel, the third channel and the fourth channel 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 volume size transmitted on the available channel in the target listening time period; and selecting the channel with the maximum transmission data quantity represented by the third channel state data from the available channels as the target listening 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 observation noise matrix into an autoregressive filter, predicting the third channel state data with 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 to represent 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 configured to: 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 apparatus further comprises a listening module configured to: and monitoring the target monitoring channel in the target monitoring time period to acquire a Wi-Fi detection request frame transmitted by the user terminal on the target monitoring channel.
According to the embodiment of the application, the first channel state data and the second channel state data can be obtained, the first channel state data is used for representing the size of the data volume transmitted on the available channel in the last monitoring time period of the monitored target monitoring time period, the second channel state data is used for representing the size of the data volume transmitted on the observation channel in the set monitoring time period, 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 channel monitored in the target monitoring time period is accurately determined according to the historical condition of the monitored channel transmission data and the condition of channel transmission data obtained by observation, and the data collection efficiency is improved.
The channel determination device in the embodiment of the present application can implement each process of the foregoing channel determination method embodiment, and achieve the same effect and function, which are not repeated here.
Fig. 5 is a schematic structural diagram of the channel determination device provided in an embodiment of the present application, and as shown in fig. 5, the channel determination device may generate a relatively large difference due to different configurations or performances, and may include one or more processors 901 and a memory 902, and the memory 902 may store one or more stored applications or data. Memory 902 may be, among other things, transient storage or persistent storage. The application program stored in memory 902 may include one or more modules (not shown), each of which may include a series of computer-executable instructions for the channel determination device. Still further, the processor 901 may be configured to communicate with the memory 902 to execute a series of computer-executable instructions in the memory 902 on the channel determination device. The channel determination apparatus 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 determination apparatus 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 determination apparatus, and the one or more programs configured to be executed by the one or more processors include computer-executable instructions for:
acquiring first channel state data of each available channel in the last monitoring time period of the target monitoring time period; the first channel state data is used for representing the size of the data transmitted on the available channel in the last listening time period for listening;
selecting an observation channel from the available channels according to the first channel state data;
monitoring the observation channel before the target monitoring time period comes 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 volume transmitted on the observation channel in the monitored set time length;
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 observed channel among the available channels based on the first channel state data, comprising: selecting a first channel with the maximum transmitted data volume, a second channel with the second maximum transmitted data volume, a third channel which is not overlapped with the first channel on a frequency domain and any fourth channel except the first channel, the second channel and the third channel, wherein the first channel is represented by the first channel state data, from the available channels; taking any one or more of the first channel, the second channel, the third channel and the fourth channel as an observation channel.
Optionally, when executed, the computer-executable instructions determine a target listening channel among the available channels according to 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 volume size transmitted on the available channel in the target listening time period; and selecting the channel with the maximum transmission data quantity represented by the third channel state data from the available channels as the target listening channel.
Optionally, when executed, the computer-executable instructions predict third channel state data of the respective available channels in the target listening period according to the first channel state data and the second channel state data, including: 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 observation noise matrix into an autoregressive filter, predicting the third channel state data with 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 to represent 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 target listening channel comprises a Wi-Fi channel when the computer executable instructions are executed; further comprising: and monitoring the target monitoring channel in the target monitoring time period to acquire a Wi-Fi detection request frame transmitted by the user terminal on the target monitoring channel.
According to the embodiment of the application, the first channel state data and the second channel state data can be obtained, the first channel state data is used for representing the size of the data volume transmitted on the available channel in the last monitoring time period of the monitored target monitoring time period, the second channel state data is used for representing the size of the data volume transmitted on the observation channel in the set monitoring time period, 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 channel monitored in the target monitoring time period is accurately determined according to the historical condition of the monitored channel transmission data and the condition of channel transmission data obtained by observation, and the data collection efficiency is improved.
The channel determination device in the embodiment of the present application can implement each process of the foregoing channel determination method embodiment, and achieve the same effect and function, which is not repeated here.
Further, embodiments of the present application also provide a storage medium for storing computer-executable instructions, in a specific embodiment, the storage medium may be a usb disk, an optical disk, a hard disk, and the like, and the storage medium stores computer-executable instructions that, when executed by a processor, implement the following processes:
acquiring first channel state data of each available channel in the last monitoring time period of the target monitoring time period; the first channel state data is used for representing the size of the data transmitted on the available channel in the last listening time period for listening;
selecting an observation channel from the available channels according to the first channel state data;
monitoring the observation channel before the target monitoring time period comes 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 volume transmitted on the observation channel in the monitored set time length;
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 storage medium stores computer-executable instructions that, when executed by the processor, select an observed channel among the available channels based on the first channel state data, comprising: selecting a first channel with the maximum transmitted data volume, a second channel with the second maximum transmitted data volume, a third channel which is not overlapped with the first channel on a frequency domain and any fourth channel except the first channel, the second channel and the third channel, wherein the first channel is represented by the first channel state data, from the available channels; taking any one or more of the first channel, the second channel, the third channel and the fourth channel as an observation channel.
Optionally, the storage medium stores computer-executable instructions that, when executed by the processor, determine a target listening channel among the respective available channels based on 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 volume size transmitted on the available channel in the target listening time period; and selecting the channel with the maximum transmission data quantity represented by the third channel state data from the available channels as the target listening channel.
Optionally, the storage medium stores computer-executable instructions that, when executed by the processor, predict third channel state data of the respective available channels within the target listening period according to the first channel state data and the second channel state data, including: 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 observation noise matrix into an autoregressive filter, predicting the third channel state data with 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 to represent 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 comprising: and monitoring the target monitoring channel in the target monitoring time period to acquire a Wi-Fi detection request frame transmitted by the user terminal on the target monitoring channel.
According to the embodiment of the application, the first channel state data and the second channel state data can be obtained, the first channel state data is used for representing the size of the data volume transmitted on the available channel in the last monitoring time period of the monitored target monitoring time period, the second channel state data is used for representing the size of the data volume transmitted on the observation channel in the set monitoring time period, 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 channel monitored in the target monitoring time period is accurately determined according to the historical condition of the monitored channel transmission data and the condition of channel transmission data obtained by observation, and the data collection efficiency is improved.
The storage medium in the embodiment of the present application can implement the processes in the foregoing embodiment of the channel determination method, and achieve the same effects and functions, which are not repeated here.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using 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, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, 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 for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, 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 divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.
As will be appreciated by one skilled in the art, 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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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 a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
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 computer storage media 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 that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
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 an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, 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.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (12)
1. A method of channel determination, comprising:
acquiring first channel state data of each available channel in the last monitoring time period of the target monitoring time period; the first channel state data is used for representing the size of the data transmitted on the available channel in the last listening time period for listening;
selecting an observation channel from the available channels according to the first channel state data;
monitoring the observation channel before the target monitoring time period comes 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 volume transmitted on the observation channel in the monitored set time length;
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;
wherein the third channel state data is used to represent the predicted data size transmitted on the available channel in the target listening time period; the state transition matrix is used for representing the state transition relation between the first channel state data and the third channel state data; the process noise matrix is used to represent 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.
2. The method of claim 1, selecting an observed channel among the respective available channels based on the first channel state data, comprising:
selecting a first channel with the maximum transmitted data volume, a second channel with the second maximum transmitted data volume, a third channel which is not overlapped with the first channel on a frequency domain and any fourth channel except the first channel, the second channel and the third channel, wherein the first channel is represented by the first channel state data, from the available channels;
taking any one or more of the first channel, the second channel, the third channel and the fourth channel 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 the channel with the maximum transmission data quantity represented by the third channel state data from the available channels as the target listening channel.
4. The method of claim 1, 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. The method of any of claims 1 to 4, the target listening channel comprising a Wi-Fi channel; the method further comprises the following steps:
and monitoring the target monitoring channel in the target monitoring time period to acquire a Wi-Fi detection request frame transmitted by the user terminal on the target monitoring channel.
6. A channel determination apparatus, comprising:
the first acquisition module is used for acquiring first channel state data of each available channel in the last monitoring time period of the target monitoring time period; the first channel state data is used for representing the size of the data transmitted on the available channel in the last listening time period for listening;
a channel selection module, configured to select an observation channel from the available channels according to the first channel state data;
a second obtaining module, configured to monitor the observation channel before the target monitoring time period comes, to obtain second channel state data of the observation channel within a set time period; the second channel state data is used for representing the size of the data volume transmitted on the observation channel in the monitored set time length;
a channel determination module, configured to obtain a state transition matrix and a process noise matrix corresponding to the first channel state data, obtain an observation noise matrix corresponding to the second channel state data, input 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 auto-regression filter, and predict third channel state data through the auto-regression filter; wherein the third channel state data is used to represent the predicted data size transmitted on the available channel in the target listening time period; the state transition matrix is used for representing the state transition relation between the first channel state data and the third channel state data; the process noise matrix is used to represent 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.
7. The apparatus of claim 6, wherein the channel selection module is specifically configured to:
selecting a first channel with the maximum transmitted data volume, a second channel with the second maximum transmitted data volume, a third channel which is not overlapped with the first channel on a frequency domain and any fourth channel except the first channel, the second channel and the third channel, wherein the first channel is represented by the first channel state data, from the available channels;
taking any one or more of the first channel, the second channel, the third channel and the fourth channel as an observation channel.
8. The apparatus of claim 6, the channel determination module being specifically configured to:
and selecting the channel with the maximum transmission data quantity represented by the third channel state data from the available channels as the target listening channel.
9. The apparatus of claim 6, the apparatus further comprising an update module to:
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.
10. The apparatus of any of claims 6 to 9, the target listening channel comprising a Wi-Fi channel; the apparatus further comprises a listening module configured to:
and monitoring the target monitoring channel in the target monitoring time period to acquire a Wi-Fi detection request frame transmitted by the user terminal on the target monitoring channel.
11. A channel determination device, comprising: a processor; and a memory arranged to store computer executable instructions which, when executed, cause the processor to carry out the steps of the channel determination method of any of the preceding claims 1 to 5.
12. A storage medium storing computer-executable instructions that, when executed, implement the steps of the channel determination method of any of the preceding claims 1 to 5.
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| CN113114395B (en) | 2023-05-30 |
| CN110113115A (en) | 2019-08-09 |
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