JP2019176465A - Coexistence interference analytical method, traffic type training group determination method, and apparatus thereof - Google Patents

Abstract

To provide a coexistence interference analytical method, a traffic type training group determination method, and an apparatus thereof.SOLUTION: A coexistence interference analytical method includes steps of: acquiring a first data packet transmitted by using a first communication technique on a first channel in a 1st sub-window within a first prescribed time; acquiring a second data packet transmitted by using a second communication technique on a second channel in at least one sub-window within the first prescribed time; determining whether an interference due to the second communication technique exists in the first channel on the basis of a statistical first performance index based on the first data packet in regards to a communication channel characteristic; and determining a traffic type using the second communication technique on the basis of a first feature extracted on the basis of the second data packet acquired in each sub-window within the first prescribed time when the interference exists.SELECTED DRAWING: Figure 2

Description

  The present invention relates to the field of communication technology, and in particular, to a coexistence interference analysis method, a traffic type training collection determination method, and an apparatus thereof.

  In conventional wireless communication technologies, many technologies can use the same frequency range. For example, in the frequency range of 2.4 GHz, a LAN based on IEEE (Institute of Electrical and Electronics Engineers, IEEE) 802.11b standard, for example, Wi-Fi (Wireless Fidelity, Wi-Fi); Bluetooth (registered trademark); MWO (Microwave Oven, MWO); and LANs based on the IEEE 802.15.4 standard, such as ZigBee networks, can use this frequency range.

  1A to 1D are frequency spectrum diagrams in which Wi-Fi, Bluetooth, MWO, and ZigBee work in the 2.4 GHz frequency range, respectively. As shown in FIG. 1A, the Wi-Fi network is a broadband system, has 14 channels, and its channel bandwidth is 22 MHz, of which channels 1, 6, and 11 are well The channel used. As shown in FIG. 1B, the Bluetooth network is a frequency hopping narrowband system, which has 79 channels and each channel bandwidth is 1 MHz. The MWO network has different models, all of which have a narrowband characteristic with a period of 60 Hz, and FIG. 1C shows one of them. As shown in FIG. 1D, the ZigBee network has 16 channels and each channel bandwidth is 2 MHz.

  Since Wi-Fi, Bluetooth, MWO, and ZigBee all work in the same frequency range, there is interference between Wi-Fi, Bluetooth, MWO, and ZigBee networks. For example, when a ZigBee network is working using channel 20, a Wi-Fi network working using channels 7-10 may interfere with the ZigBee network. However, at present, there is interference in the current network, and a specific traffic type of interference cannot be detected at the same time.

  Embodiments of the present invention provide a coexistence interference analysis method and apparatus, which can detect not only the presence of coexistence interference but also the traffic type of interference, and overhead. Is relatively small and the implementation complexity is low.

  Embodiments of the present invention provide a method and apparatus for determining a traffic type training set, whereby a training set for identifying a traffic type of interference can be formed without the need for human interference.

According to a first aspect of an embodiment of the present invention, there is provided a coexistence interference analyzer,
A first acquisition unit for acquiring a first data packet transmitted by a first communication technique on a first channel within a first sub-window within a first predetermined time;
A second acquisition unit for acquiring a second data packet transmitted by a second communication technology on a second channel within at least one sub-window within a first predetermined time, the second channel comprising the first channel A second acquisition unit that is the channel closest to the channel and / or is within a predetermined range of the channel closest to the first channel;
A first determination unit for determining whether there is interference due to a second communication technology in the first channel based on a first performance indicator relating to communication channel characteristics, statistically based on the first data packet; and Based on a first feature extracted based on the second data packet acquired by the second acquisition unit within each sub-window within a first predetermined time when it is determined by one determination unit that interference exists. A second determination unit for determining a traffic type using the second communication technology.

According to a second aspect of an embodiment of the present invention, there is provided a coexistence interference analysis method, which comprises:
Obtaining a first data packet transmitted by a first communication technique on a first channel within a first sub-window within a first predetermined time;
Obtaining a second data packet transmitted by a second communication technology on a second channel within at least one sub-window within a first predetermined time, wherein the second channel is the channel closest to the first channel And / or a channel within a predetermined range of channels closest to the first channel;
Determining whether there is interference due to a second communication technology in the first channel based on a first performance indicator relating to communication channel characteristics, which is statistically based on the first data packet; and determining that interference exists Determining a traffic type using the second communication technique based on the first feature extracted based on the second data packet acquired in each sub-window within the first predetermined time.

According to a third aspect of an embodiment of the present invention, a traffic type training collection determination device is provided, the device comprising:
A third acquisition unit for acquiring a third data packet transmitted by the second communication technology, the third data packet including data packets within a second predetermined number of periods, each period having one traffic Third acquisition unit corresponding to the type;
A first statistical unit for statistically analyzing a first feature for distinguishing traffic types based on the third data packet; and clustering the first feature after normalization to determine the training collection Includes a third deterministic unit.

According to a fourth aspect of an embodiment of the present invention, there is provided a traffic type training collection determination method, the method comprising:
Obtaining a third data packet transmitted by the second communication technology, wherein the third data packet includes data packets within a second predetermined number of periods, each period corresponding to one traffic type;
Statistics a first feature for distinguishing traffic types based on the third data packet; and clustering the first feature after normalization to determine the training collection.

  The beneficial effects of the present invention are as follows: a first data packet transmitted by a first communication technique on a first channel, acquired within a first subwindow of a plurality of subwindows. Based on the second data packet transmitted by the second communication technology on the second channel, determined within a plurality of sub-windows, determining whether there is interference by the second communication technology on the first channel By determining the traffic type using the second communication technology, it is possible not only to detect the presence of coexistence interference, but also to detect the traffic type of interference, and the overhead is relatively small. And the implementation complexity is low.

  Further, the beneficial effect of the present invention is as follows: based on the acquired second data packet transmitted by the second communication technology, the first characteristic for distinguishing the traffic type is statistical. Then, by performing the clustering process on the first feature after normalization, it is possible to form a training collection for identifying the traffic type of the interference without the need for human interference.

It is a working frequency spectrum figure of Wi-Fi in the frequency range of 2.4 GHz. It is the working frequency spectrum figure of Bluetooth in the frequency range of 2.4GHz. It is a working frequency spectrum figure of MWO in the frequency range of 2.4 GHz. It is a working frequency spectrum figure of ZigBee in the frequency range of 2.4 GHz. 3 is a flowchart of a coexistence interference analysis method in Embodiment 1. 3 is a diagram illustrating a format of a MAC frame of a first data packet in Embodiment 1. FIG. 10 is a flowchart of one implementation method of step 203 in the first embodiment. 3 is a flowchart of a coexistence interference analysis method in Embodiment 1. 6 is a diagram illustrating acquisition of a first data packet and a second data packet in Embodiment 1. FIG. 10 is a flowchart of a traffic type training collection determination method in Embodiment 2. 6 is a diagram illustrating a third data packet in Embodiment 2. FIG. FIG. 5 is a configuration diagram of a coexistence interference analyzer in Example 3. FIG. 10 is a configuration diagram of an implementation system of a second confirmation unit 904 in Embodiment 3. FIG. 6 is a configuration diagram of a terminal device according to a fourth embodiment. FIG. 10 is a configuration diagram of a traffic type training collection confirmation device in Embodiment 5. FIG. 10 is a configuration diagram of a terminal device according to a sixth embodiment.

  Hereinafter, preferred embodiments for carrying out the present invention will be described in detail with reference to the accompanying drawings.

  The embodiments of the present invention will be described by taking a network working within the frequency range of 2.4 GHz as an example. However, it should be understood that the embodiments of the present invention are not limited to the network. The method and apparatus provided by way of example can also be applied to networks in other frequency ranges.

  The first embodiment provides a coexistence interference analysis method. FIG. 2 is a flowchart of the coexistence interference analysis method according to the first embodiment. As shown in FIG. 2, the method includes the following steps.

Step 201: Acquire a first data packet transmitted by a first communication technology on a first channel within a first subwindow within a first predetermined time, and within at least one subwindow within a first predetermined time A second data packet transmitted by a second communication technology on a second channel; wherein the second channel is the channel closest to the first channel and / or the first channel A channel within a predetermined range of channels closest to
Step 202: Determine whether there is interference due to the second communication technology on the first channel based on a first performance indicator regarding communication channel characteristics, which is statistically based on the first data packet;
Step 203: Based on the first feature extracted based on the second data packet acquired in each sub-window within the first predetermined time when it is determined that interference exists, the second communication technique Determine the traffic type using.

  Thereby, based on the first data packet transmitted by the first communication technology on the first channel acquired in the first sub-window of the plurality of sub-windows, the second communication is made to the first channel. Determine if there is interference due to the technology and based on the second data packet transmitted by the second communication technology on the second channel, acquired in multiple sub-windows, the traffic type using the second communication technology By determining, it is possible not only to detect the presence of coexistence interference, but also to detect the traffic type of the interference, the overhead is relatively small, and the implementation complexity is low.

  In this embodiment, the first communication technology and the second communication technology are communication technologies working within the same frequency range, and the channel working using the first communication technology is a first communication channel, The channel working using the second communication technology is the second communication channel. The first channel is a channel in the first communication channel, and the second channel is a channel in the second communication channel. For example, the first communication technology and the second communication technology may be communication technologies that work within a frequency range of 2.4 GHz. The first communication technology and the second communication technology are different and may be one of Wi-Fi, Bluetooth, MWO, and ZigBee, respectively, but the present embodiment is not limited to this. For example, in the present embodiment, the first communication technology and the second communication technology may be determined based on the channel bandwidth. For example, a channel with a small bandwidth is likely to receive interference from a channel with a large bandwidth, and in the present embodiment, interference caused by the second communication technology occurs in the first channel that transmits data using the first communication technology. The first channel bandwidth of the first communication technology may be smaller than the second channel bandwidth of the second communication technology, for example, the first communication technology. The technology is ZigBee, and the second communication technology is Wi-Fi. As shown in FIG. 1A and FIG. 1D, the ZigBee network has 16 channels (first communication channels), each channel bandwidth is 2 MHz, and Wi-Fi has 14 channels (second Communication channel), and its channel bandwidth is 22 MHz. Although the case where the first communication technology is ZigBee and the second communication technology is Wi-Fi has been described as an example, the present embodiment is not limited to this.

  In this embodiment, the second communication channel using the second communication technology that is close to the first channel can interfere with the first channel that transmits data using the first communication technology. The two channels may be channels in the second communication channel closest to the first channel and / or channels within a predetermined range of channels closest to the first channel. Of these channels, the closest channel refers to the channel whose band center frequency is the shortest, and the channel within the predetermined range of the closest channel has a distance from the center frequency of the closest channel bandwidth. The channel is within a predetermined bandwidth range and has an overlap with the frequency range of the first channel. For example, the channel has a difference in channel order number within a predetermined value. For example, the first communication technology is ZigBee, and the second communication technology is Wi-Fi. As shown in FIGS. 1A and 1D, when the first channel is 11/12, the second channel may be the second communication channel 1 closest to the first channel, or the It may be a channel whose difference from the sequence number of the second communication channel 1 is within a predetermined value and overlapped with the frequency range of the first channel. For example, the second channel further includes channel 2 It may be. When the first channel is 24/25/26, the second channel may be the second communication channel 13 closest to the first channel, or the sequence number of the second communication channel 13 and May be a channel that is within a predetermined value and overlapped with the frequency range of the first channel. For example, the second channel may be a channel 12. When the first channel is 23, the second channel may be the second communication channel 12 closest to the first channel, or the difference from the sequence number of the second communication channel 12 is The channel may be within a predetermined value and overlap with the frequency range of the first channel. For example, the second channel may be channels 10, 11, and 13. In the above, the case where the first communication technology is ZigBee and the second communication technology is Wi-Fi has been described as an example. However, the present embodiment is not limited to this.

  In the present embodiment, the first predetermined time may be determined according to the current environment, and one coexistence interference analysis result may be output within each first predetermined time. Among them, the second channel using the second communication technology that interferes with the first channel is a channel within a predetermined range of the channel closest to the first channel and / or the channel closest to the first channel. The first predetermined time can be divided into a first predetermined number of subwindows. The first predetermined number is determined based on the number of channels within the predetermined range. For example, when the number of channels in the predetermined range is M, the first predetermined number is M + 1, so that one sub-window is transmitted by the second communication technology in the second channel within each sub-window. A second data packet can be obtained. For example, when the first channel is 11/12, the distance from the second communication channel 1 closest to the first channel is within a predetermined bandwidth range, and the overlap with the frequency range of the first channel is not One channel is channel 2 and the number is 1, in which case the first predetermined number is 2. Note that the present embodiment is not limited to this, and the first predetermined number may be M or less, and at least one second transmitted in the second channel in the second channel within at least one subwindow. Data packets may be acquired.

  In this embodiment, in step 201, the first data packet may be acquired by a first sniffer, and the second data packet may be acquired by a second sniffer. Among these, the first sniffer and the second sniffer may refer to the conventional sniffer implementation method. For example, the first sniffer may be a TI ZigBee sniffer. By arranging the channel z, a ZigBee data packet (first data packet) in the channel z is obtained, and the ZigBee data packet is analyzed. Can generate a ZigBee traffic log, and the second sniffer can be a Wi-Fi dongle, set it to monitor mode and channel w By arranging, a Wi-Fi data packet (second data packet) in the channel w is obtained, and a Wi-Fi traffic log is generated by analyzing the Wi-Fi data packet. be able to.

  In this embodiment, it is possible to determine whether interference exists using the acquired first data packet. When interference exists, the acquired second data packet is used to generate interference traffic. The type can be determined. Hereinafter, how the interference exists and how the traffic type is determined will be described in detail.

  Hereinafter, how to determine whether interference exists within the first predetermined time will be described based on Step 201 and Step 202.

  In the present embodiment, in step 201, a first data packet transmitted by the first communication technique on the first channel is acquired within the first subwindow within the first predetermined time, and in step 202, the first data packet is acquired. Based on a first performance indicator relating to communication channel characteristics, which is statistically based on the first data packet, it is determined whether there is interference due to the second communication technology in the first channel. Among them, after acquiring the first data packet using a sniffer, a traffic log of the first data packet can be obtained, and from the traffic log, a first performance index relating to communication channel characteristics can be obtained by statistics. it can. For example, the first performance indicator for communication channel characteristics may include one or more of the following: link quality indication (LQI) information, response time information, packet loss information, retransmission information. , Packet error information, and received signal strength indication information (RSSI).

  In this embodiment, LQI information, response time information, and RSSI information may be LQI, response time, and RSSI for one packet; LQI average value, response time average value, and RSSI within a unit time. It may be an average value; may be an LQI standard deviation, a response time standard deviation, and an RSSI standard deviation; may be an LQI variance, a response time variance, and an RSSI variance. It is not limited to. Among them, LQI and RSSI information can be obtained directly from the traffic log of the first data packet taken by the sniffer, and the response time is the reception time of the request data packet in the first data packet and the request data. The difference with the reception time of the response data packet corresponding to the packet can be calculated and acquired.

  In this embodiment, the packet loss information, the retransmission information, and the packet error information may be the number of lost packets, the number of retransmissions, and the number of packet errors obtained by statistics in the first subwindow; The packet loss rate, the retransmission rate, and the packet error rate obtained by statistics may be used, but the present embodiment is not limited to this. Of these, the packet loss information and the retransmission information may be statistics using the frame format in the header of the first data packet. FIG. 3 is a diagram showing a frame format of the first data packet. As shown in Figure 3, retransmission information may be statistically based on the number of occurrences of duplicate sequence numbers, packet loss information may be statistically based on sequence number continuity, and cyclic redundancy check (CRC) results. The packet error information may be statistics using

  In the present embodiment, it is possible to determine whether there is interference due to the second communication technology based on the first performance index. For example, when there is no interference, it indicates that the channel condition is very stable, and in this state, the number of lost packets or lost packet rate, the number of retransmitted packets or the retransmitted rate, and the number of packet errors or the packet error rate are relatively small. RSSI and LQI are relatively large and response time is relatively small. Also, if there is interference, the number of lost packets or lost packet rate, the number of retransmitted or retransmitted packets, the number of packet errors or the packet error rate is relatively large, the RSSI and LQI are relatively small, and the response time is compared. Big. Among them, it is possible to determine whether there is interference due to the second communication technology by the machine learning algorithm. Specifically, coexistence interference analysis may be performed using a K-nearest neighbor method (K Nearest Neighbor, KNN). For example, using pre-stored training data, optimizing KNN parameters and weight values, and calculating the distance between the first performance indicator and all pre-stored training data, for example, Euclidean distance, Neighboring K training data, each of which has been previously labeled as interference or no interference, can be found. Among these K training data, if it is determined that the number of training data belonging to a certain type is the largest, the first performance index includes the type including the training data with the largest number (with or without interference). (No interference). Among them, the value of K is given by optimization of the algorithm, and it belongs to the category of conventional machine learning, and therefore detailed description thereof is omitted here.

  In this embodiment, the first data packet may be acquired in the first subwindow, and the acquisition of the first data packet may be stopped in other subwindows other than the first subwindow, thereby reducing overhead. Savings and reducing complexity.

  In the present embodiment, a second data packet transmitted by the second communication technique on the second channel is acquired within at least one subwindow within the first predetermined time. Among them, the second data packet transmitted by the second communication technique on the second channel is acquired within the first sub-window within the first predetermined time. When it is determined in step 202 that there is no interference, it means that traffic type analysis is not required, and thus it is not necessary to acquire the second data packet in another subwindow other than the first subwindow. . When it is determined in step 202 that there is interference, it means that further analysis of the traffic type of the interference is necessary, and thus the second data continues in other subwindows other than the first subwindow. Need to get a packet.

  In the present embodiment, one second data packet on the second channel may be acquired in each sub-window. For example, the second communication technique may be acquired on the second channel acquired in the first sub-window. A second data packet to be transmitted may be obtained, in which case the second channel may be the channel closest to the first channel and in other subwindows other than the first subwindow. The acquired second data packet transmitted by the second communication technology on the second channel may be acquired. In this case, the second channel is a channel within a predetermined range of the channel closest to the first channel. And the second channel corresponding to the second data packet acquired in each sub-window is different. For example, when the first channel is 11/12, the distance from the second communication channel 1 closest to the first channel is within a predetermined bandwidth range, and the overlap with the frequency range of the first channel is not One channel is channel 2 and the number of subwindows is 2. In this case, the second data packet transmitted by the second communication technology in the second channel 1 is acquired in the first subwindow, The second data packet transmitted by the second communication technique in the second channel 2 is acquired in the second subwindow. Note that the above is merely an illustrative description, and the present embodiment is not limited to this. For example, for a second data packet transmitted by the second communication technology on the second channel acquired in the first sub-window, the second channel has a distance from the channel closest to the first channel. It may be one channel that is within a predetermined bandwidth range and overlaps with the frequency range of the first channel, and is obtained on a second channel acquired in another subwindow other than the first subwindow. For the second data packet transmitted by the second communication technique, the distance between the second channel and the channel closest to the first channel is within a predetermined bandwidth range, and the frequency range of the first channel And other channels other than the one channel and the nearest channel. For example, when the first channel is 23, the channel closest to the first channel is the channel 12, the difference from the sequence number of the channel 12 is within a predetermined value, and the first channel The channels with overlap with the frequency range are channels 10, 11, and 13, and the number of subwindows is 4, in this case, the second transmission technology transmitted on the second channel 12 within the first subwindow by the second communication technology. Two data packets may be acquired, and second data packets transmitted by the second communication technology may be acquired on the second channels 10, 11, 13 on the other three subwindows, respectively (here, the second channel). The order of acquisition of the second data packet on 10, 11, 13 is not limited); or alternatively, the second data packet transmitted by the second communication technology on the second channel 10 in the first subwindow is acquired, The second data packet transmitted by the second communication technology on the second channels 11, 12, and 13 may be acquired on the three sub-windows (in this case, on the second channels 11, 12, and 13). The order of acquisition of the second data packet is not limited); or is not limited to acquiring the second data packet on only one second channel within each subwindow, eg, the channel within the first subwindow The second data packet transmitted by the second communication technology on 10 and 11 may be acquired, but an exhaustive list is omitted here.

  Hereinafter, based on Step 201 and Step 203, how the traffic type is determined by the second data packet when there is interference will be described.

  In the present embodiment, in step 201, a second data packet transmitted by the second communication technique on the second channel is acquired within each subwindow within the first predetermined time. In step 203, the second data packet is acquired. The traffic type using the second communication technology is determined based on the first feature extracted based on the first feature. Among them, after obtaining the second data packet using the second sniffer, the traffic log of the second data packet can be obtained, and the first feature can be obtained from the traffic log by statistics. For example, the first feature may include one or more of the following: data packet length, data packet interval, and data packet reception time. Among them, the data packet length represents the length of the data packet acquired within 1 byte unit, the data packet interval is the time interval between adjacent data packets, and the data packet reception time is the data from the start of data acquisition. Represents the time until acquisition ends. Among them, the data packet length, the data packet interval, and the data packet reception time may be calculated based on the prior art. For example, the traffic log includes a data packet length field, a data packet A reception start time field and a reception time field are included. By extracting these three fields from the traffic log, the data packet length and the data packet reception time can be determined. The calculation result of the difference between the reception start times of the data packets to be performed can be used as the data packet interval. The traffic types may include idle traffic, file download traffic, video traffic, web page browsing traffic, audio traffic, mixed traffic, and the like. Different traffic types can be distinguished based on at least one of data packet length, data packet interval, and data packet reception time. For example, different traffic types can be distinguished on condition that the data packet length of video traffic exceeds the first threshold and the data packet length of web page browsing traffic is smaller than the second threshold. Exhaustive enumeration is omitted.

  In the present embodiment, in step 203, after obtaining the first feature, the traffic type can be determined based on the training collection obtained in advance. In the present embodiment, the training collection can be obtained in the following manner, that is, the first feature under different traffic types is acquired in advance, and the specific training method is similar to the prior art. For example, in order to avoid interference due to the external environment, first, scenes of different traffic types of interest are artificially selected or generated in a darkroom where external interference is shielded, and collected in these scenes. A clustering process is performed on the features, a clustering result is labeled based on the traffic identification information, a training book is formed, and a classifier is configured based on the training book. Thereafter, the first feature is compared with a pre-stored training collection, that is, the first feature is input to the classifier, thereby determining the traffic type. The present embodiment is not limited to this, and the training collection can be obtained by another method. Specifically, the second embodiment is referred to.

  In this embodiment, in step 203, in order to improve the accuracy rate of determining the traffic type, the number of APs and terminals (STAs) in the network is first determined, and then the traffic for each pair (AP-STA) is determined. A type analysis may be performed and finally the analysis results of all pairs may be integrated; alternatively, a traffic type analysis may be performed for each subwindow and the analysis results of all subwindows may be integrated. Thereby, the traffic type of the final second communication technology can be obtained. This will be specifically described below.

  FIG. 4 is a flowchart of one implementation method of step 203 in the present embodiment.

Step 401: Statistics the number of access points (AP) and the number of terminals (STAs) communicating with each AP based on the acquired second data packet;
Step 402: Perform a classification process on each pair of APs and the first feature of the second data packet of a terminal communicating with the AP based on a traffic type training collection of a predetermined second communication technology, Determining the traffic type of the second communication technology used for communication between each pair of APs and terminals communicating with the APs;
Step 403: Determine the traffic type of the final second communication technology based on the traffic type of the second communication technology used for communication between each pair of APs and terminals communicating with the AP.

  In this embodiment, in step 401, the number of APs in the network can be statistically determined based on a beacon frame in the second data packet. For example, the MAC address field (BSSID) indicating the AP is included in the beacon frame, and the BSSIDs corresponding to different APs are different, so the number of APs can be statistically calculated based on the number of BSSIDs. Then, for each AP, the number of STAs communicating with the AP is statistics. The specific statistical method can refer to the prior art. For example, the number of STAs can be determined based on the MAC address field indicating the STAs in the second data packet.

  In this embodiment, in step 402, all AP-STA pairs in the network are determined based on the statistical result of step 401. Among them, the STA is a terminal that communicates with the AP, and the first feature of the second data packet between each pair of AP-STAs is input to the classifier formed by the training collection, so that each pair The traffic type of the second communication technology used for communication between AP-STAs can be determined. The training collection acquisition method can refer to the above-described conventional technique, or can also refer to the second embodiment, but the present embodiment is not limited to this. Since the header of each second data packet includes the BSSID field of the MAC address indicating the AP and the MAC address field of the STA, based on these two fields, which pair of AP- Whether the second data packet belongs between STAs can be determined. In step 402, in order to save overhead and reduce complexity, parameters for communication traffic quality of the second data packet of each pair of APs and terminals communicating with the APs may be further determined, and Based on the traffic type training collection of the predetermined second communication technology, the classification process may be performed on the first feature of the second data packet in which the parameter regarding the communication traffic quality satisfies the predetermined condition. Parameters related to communication traffic quality include one or more of the following: received signal strength indication (RSSI) information and each pair of APs and second data packets of terminals communicating with the APs Number information. For example, when the RSSI obtained by statistics based on the second data packet is smaller than a threshold (for example, -70 dBm), it corresponds to an RSSI smaller than the threshold (for example, -70 dBm) when determining the traffic type. The first characteristic of all second data packets between AP-STAs is not considered, i.e., when the traffic type is determined, the second characteristics of all second data packets between AP-STAs corresponding to RSSI above the threshold are determined. Consider only one feature. For example, when the number of second data packets obtained by statistics based on the second data packet is smaller than a threshold (the threshold may be determined according to the length of the subwindow), the traffic type is determined. Sometimes the first characteristic of these second data packets is not considered, i.e., the first characteristic of these second data packets is determined only when the number of second data packets is equal to or greater than the threshold when determining the traffic type. Statistics may be used, but an exhaustive list is omitted here. Among them, the parameter acquisition method regarding the communication traffic quality can refer to the first performance index, but the present embodiment is not limited to this.

  In this embodiment, in step 403, the traffic type of the second communication technology used for communication between each pair of AP-STAs is integrated, and the final traffic type of the second communication technology is determined. For example, the traffic type with the maximum number of appearances may be determined as the traffic type of the final second communication technology among the traffic types statistically calculated for all AP-STA pairs.

  In the present embodiment, the traffic of the second communication technology in each subwindow is further based on the traffic type of the second communication technology used for communication between each pair of APs and terminals communicating with the AP in each subwindow. The type may be determined, and the traffic type of the second communication technology within the final first predetermined time may be determined based on the traffic type of the second communication technology in the respective sub-windows. Among them, the method for determining the traffic type in each sub-window is the same, and the above-described steps 402 to 403 can be referred to. Based on the traffic type of the second communication technology in each sub-window, when determining the traffic type of the final second communication technology, first determine the traffic amount of the traffic type of the second communication technology in each sub-window, The traffic type with the highest traffic volume may be determined as the final traffic type using the second communication technology within the first predetermined time. If the difference between the traffic amounts of the plurality of traffic types does not exceed the threshold value, the final traffic type using the second communication technology within the first predetermined time is determined as the mixed traffic. In this embodiment, the length of each second data packet includes the length of the data packet header and the length of the data packet payload, and the traffic volume is the same for all second data packets of the same traffic type. The total packet length may be used.

  FIG. 5 is a flowchart of the coexistence interference analysis method in the present embodiment. As shown in FIG. 5, the method includes the following steps.

Step 501: Setting a first predetermined time (IDW) and dividing the first predetermined time into a first predetermined number of subwindows (IDSW);
Step 502: Acquire a first data packet and a second data packet in the first subwindow.

  For example, the first data packet is acquired on the first channel z, the second data packet is acquired on the second channel w, and w is the second communication channel closest to z. However, the present embodiment is not limited to this, and w may be a second communication channel within a predetermined range of a channel closest to z.

Step 503: Based on the first performance index statistically based on the first data packet, it is determined whether there is interference due to the second communication technology in the first channel, and when the determination result is “No” Return to step 501 to perform the next one coexistence interference analysis within the first predetermined time, and when the confirmed result is “yes”, execute step 504;
Step 504: Obtain a second data packet in the remaining subwindow.

  For example, the second data packet may be acquired on a channel within a predetermined range of the channel closest to the first channel. For example, when the predetermined range is N = 1, The data packet is acquired on the second channels w + 1 and w-1, and the acquisition of the first data packet is stopped in the remaining subwindow.

  Step 505: Based on the first feature extracted based on the second data packet acquired in step 502 and step 504, the traffic type using the second communication technology is determined. The specific determination method is as follows: Since 203 can be referred to, the detailed explanation is omitted here.

  FIG. 6 is a diagram illustrating acquisition of the first data packet and the second data packet. As shown in FIG. 6, the first predetermined time is divided into three sub-windows, and the first data packet on the first channel z and the second data packet on the second channel w are divided in the first sub-window. Based on the first performance indicator obtained and statistically based on the first data packet on the first channel z, determine whether there is interference due to the second communication technology in the first channel, and there is interference The second data packets on the second channels w + 1 and w-1 are acquired in the second subwindow and the third subwindow, respectively, and the acquired second channels w and w are acquired. Based on the first feature extracted based on the second data packet on +1, w−1, the traffic type using the second communication technology is determined.

  In this embodiment, the second channel is a channel closest to the first channel and / or a channel within a predetermined range of a channel closest to the first channel, and the second channel is the first channel. And a frequency range superimposed on each other. In the above example, the case where the second channel is w, w + 1, and w−1 has been described as an example. However, the present embodiment is not limited to this. For example, when z belongs to [13,23], the second channel may be w, w + 1, w-1; when z belongs to [11,12], the second channel is , W, w + 1; when z belongs to [24, 26], the second channel may be w, w-1, but an exhaustive list is omitted here. .

  In the present embodiment, within the first predetermined time, the interference analysis is performed by the method in steps 501 to 505 described above, the interference analysis result is acquired, the interference traffic type is determined when interference exists, and the first After the predetermined time, interference analysis can be performed again by the method in steps 501 to 505 within the next first predetermined time. However, the present embodiment is not limited to this.

  As a result, the first data packet transmitted by the first communication technology on the first channel is acquired within the first subwindow of the plurality of subwindows, and interference due to the second communication technology exists in the first channel. Coexistence interference by acquiring the second data packet transmitted by the second communication technology on the second channel within a plurality of sub-windows and determining the traffic type using the second communication technology within a plurality of sub-windows As well as the type of interference traffic, the overhead is relatively small, and the implementation complexity is low.

  The second embodiment provides a traffic type training collection determination method. FIG. 7 is a flowchart of the traffic type training collection determination method according to the second embodiment. As shown in FIG. 7, the method includes the following steps.

Step 701: Acquiring a third data packet transmitted by the second communication technology; wherein the third data packet includes data packets within a second predetermined number of periods, each period being one traffic type Correspondingly;
Step 702: Statistics a first feature for distinguishing traffic types based on the third data packet;
Step 703: A clustering process is performed on the first feature after normalization to determine the training collection.

  In this embodiment, in step 701, a third data packet can be obtained using a sniffer, of which the sniffer can refer to a sniffer implementation method in the prior art. For example, the sniffer may be a Wi-Fi sniffer, obtains an airborne Wi-Fi data packet (third data packet), parses the Wi-Fi data packet, and a Wi-Fi traffic log (Traffic log) can be generated. Since a specific implementation method of the sniffer and the second communication technique can refer to the first embodiment, a detailed description thereof is omitted here.

  In this embodiment, one traffic type corresponding to each period includes idle traffic (representing no traffic using the second communication technology), file download traffic, video traffic, web page browsing traffic, audio traffic, and the like. The second predetermined number may be determined according to the number of traffic types, for example, the second predetermined number may be equal to the number of traffic types, or an integer number of traffic types. It may be doubled. Thereby, since the same traffic type can be acquired within at least two periods of the second predetermined number of periods, the detection accuracy can be improved. Further, in order to easily distinguish traffic types such as file download traffic, video traffic, web page browsing traffic, and audio traffic, idle traffic may be inserted between the above traffic types. FIG. 8 is a diagram illustrating the third data packet. As illustrated in FIG. 8, the third data packet includes eight periods, and traffic types corresponding to the periods are idle traffic, File download traffic, idle traffic, video traffic, idle traffic, web page browsing traffic, idle traffic, audio traffic. Note that the above is merely an example, and the present embodiment is not limited to this. For example, in order to improve detection accuracy, it further includes a period of an integer X times 8 and the traffic types in FIG. It may be repeated X times.

  In this embodiment, the first feature in step 702 may include one or more of the following: data packet length, data packet interval, and data packet reception time. is there. The specific statistical method can refer to Example 1, and detailed description thereof is omitted here.

  In this embodiment, in Step 703, normalization is performed on the first feature acquired based on the third data packet, and clustering processing is performed on the first feature after normalization by a conventional machine learning method. I do. For example, the first feature is expressed as a three-dimensional vector [data packet length, data packet interval, data packet reception time], and normalization processing is performed, and then clustered into clusters using the K-means algorithm Although it is good, a present Example is not limited to this.

  In this embodiment, the number of clusters is determined based on the number of traffic types, for example, the number of clusters may be equal to the number of traffic types, or a distinction between different traffic types in some scenes. In this case, the number of clusters may be smaller or larger than the number of traffic types. For example, when the number of traffic types is 5, the number of clusters is 4, 5, and 6. Clustering may be performed using the first feature after setting and normalization.

  In this embodiment, the identification information of the data packet within the second predetermined number of periods is determined. In step 703, when performing clustering multiple times with different numbers of clusters, it is necessary to first determine the best clustering result based on the clustering evaluation index. Specifically, the clustering quality can be evaluated based on a silhouette coefficient, and the prior art can be referred to for details. Thereafter, based on the identification information, each type of labeled traffic type in the clustering result is determined, and the labeled clustering result is used as a training collection. Among them, the identification information is a time stamp of the reception start of the third data packet, and each type of labeled traffic type includes idle traffic, file download traffic, video traffic, web page browsing traffic, audio traffic, and mixed traffic. including. For example, for each type of first feature (for example, a three-dimensional vector) after the clustering process, the traffic type corresponding to each three-dimensional vector is determined based on the time stamp of the data packet corresponding to each three-dimensional vector, If all three-dimensional vectors that exceed the threshold ratio belong to the same traffic type A, label the type as belonging to traffic type A, and if the three-dimensional vector exceeds the threshold ratio, If there are at least two traffic types to which the vector belongs, label the types to belong to mixed traffic.

  In this example, prior to step 701, the method may further include placing a test environment (not shown). Among them, in order to obtain a more accurate detection result, one anechoic chamber is created in advance, AP and STA that communicate with each other using the second communication technology are installed in the anechoic chamber, and a sniffer (for example, Wi- To obtain a third data packet transmitted by the second communication technology between the AP and STA using a Fi dongle (a sniffer may be connected to the AP and STA by a coaxial cable in order to avoid signal leakage). It is possible to obtain pure third data packet data. The present embodiment is not limited to this, and the third data packet can be acquired not only in the anechoic chamber but also in an actual environment, or acquired in an actual environment and a test environment. The third data packet may be mixed and used to determine the training collection.

  As a result, the second data packet transmitted by the second communication technology is acquired, the first feature for distinguishing the traffic type is statistics, and the clustering process is performed on the first feature after normalization. Thus, it is possible to form a training collection for identifying traffic types of interference without requiring human interference.

  The third embodiment provides a coexistence interference analyzer. Since the principle that the apparatus solves the problem is similar to the method of the first embodiment, the specific implementation can refer to the implementation of the method of the first embodiment, and the duplicate description is omitted.

  FIG. 9 is a configuration diagram of a coexistence interference analyzer in Embodiment 3 of the present invention. The apparatus 900 includes the following.

First acquisition unit 901: acquiring a first data packet transmitted by a first communication technique on a first channel within a first sub-window within a first predetermined time;
Second acquisition unit 902: acquires a second data packet transmitted by a second communication technology on a second channel within at least one sub-window within a first predetermined time; A channel closest to one channel and / or a channel within a predetermined range of channels closest to the first channel;
First determining unit 903: determining whether there is interference due to the second communication technology in the first channel based on a first performance indicator relating to the communication channel characteristics, which is statistically based on the first data packet;
Second determinating unit 904: extracted based on the second data packet acquired in each subwindow within the first predetermined time when the first deterministic unit determines that interference exists. Based on the first feature, the traffic type using the second communication technology is determined.

  In the present embodiment, for specific implementation methods of the first acquisition unit 901, the second acquisition unit 902, the first determination unit 903, and the second determination unit 904, refer to steps 201 to 203 in the first embodiment. Yes, duplicate explanation is omitted.

  In the present embodiment, the first acquisition unit 901 acquires the first data packet in the first subwindow, stops acquiring the first data packet in other subwindows other than the first subwindow, and performs interference. When it is determined that there is none, the second acquisition unit 902 acquires the second data packet only in the first subwindow, stops the acquisition of the second data packet in the other remaining subwindows, and has interference. Since the second acquisition unit 902 acquires the second data packet in each sub-window and the specific acquisition method can refer to the first embodiment, I will omit the detailed explanation.

  In the present embodiment, the first predetermined time includes a first predetermined number of sub-windows, and the first predetermined number is determined according to the number of channels within the predetermined range.

  In this embodiment, in order to improve the accuracy rate of traffic type determination, the second determination unit 904 first determines the number of APs and terminals (STAs) in the network, and then determines the pair (AP-STA ) Traffic type analysis may be performed every time, and finally the analysis results of all pairs may be integrated; alternatively, the traffic type analysis may be performed for each subwindow and the analysis results of all subwindows may be integrated. Thereby, the traffic type of the second communication technology within the final first predetermined time can be obtained.

  FIG. 10 is a diagram showing an implementation method of the second confirmation unit 904. As shown in FIG. As shown in FIG. 10, the second determination unit 904 includes the following.

A first statistics module 1001: statistics the number of access points (AP) and the number of terminals communicating with each AP based on the acquired second data packet;
First processing module 1002: Based on a traffic type training collection of a predetermined second communication technology, performs a classification process on the first feature of each pair of APs and the second data packet of the terminal communicating with the AP, Determining the traffic type of the second communication technology used for communication between each pair of APs and the terminals communicating with the APs;
Second processing module 1003: Based on the traffic type of the second communication technology used for communication between each pair of APs and the terminals communicating with the AP, the traffic type of the second communication technology within the final first predetermined time is determined. Determine.

  In the present embodiment, the second processing module 1003 is based on the traffic type of the second communication technology used for communication between each pair of APs and the terminals that communicate with the APs in each subwindow. A traffic type of the second communication technology is determined, and a traffic type of the second communication technology within the final first predetermined time is determined based on the traffic type of the second communication technology in each sub-window.

  In the present embodiment, the second confirmation unit 904 may further include the following.

A first determination module 1004: determines a parameter relating to communication traffic quality of a second data packet of each pair of APs and a terminal communicating with the AP;
In addition, the first processing module 1002 further performs classification processing on the first feature of the second data packet in which the parameters related to communication traffic quality satisfy the predetermined condition based on the traffic type training collection of the predetermined second communication technology. I do.

  In the present embodiment, the specific implementation methods of the first statistical module 1001, the first processing module 1002, the second processing module 1003, and the first determination module 1004 can refer to steps 401 to 403 in the first embodiment. Because of this, duplicate explanation is omitted.

  In this embodiment, the first performance indicator may include one or more of the following: link quality indication (LQI) information, response time information, packet loss information, retransmission information. , Packet error information, and received signal strength indication information (RSSI); parameters related to communication traffic quality may include one or more of the following: received signal strength indication (RSSI) ) Information and number information of each pair of APs and second data packets of terminals communicating with the AP; the first feature may include one or more of the following: , Data packet length, data packet interval, and data packet reception time. Since a specific statistical method can refer to Example 1, detailed description thereof is omitted here.

  As a result, the first data packet transmitted by the first communication technology on the first channel in the first subwindow of the plurality of subwindows is acquired, and interference by the second communication technology is received in the first channel. Coexistence interference by determining whether it exists, obtaining a second data packet transmitted by the second communication technology on the second channel of the plurality of sub-windows, and determining the traffic type using the second communication technology As well as the type of interference traffic, the overhead is relatively small, and the implementation complexity is low.

  The fourth embodiment provides a terminal device. Since the principle by which the apparatus solves the problem is similar to the method of Example 1, the specific implementation can refer to the implementation of the method of Example 1, and the duplicate description with the same content is , Omitted.

  In the present embodiment, a terminal device (not shown) is further provided. The terminal device includes the coexistence interference analyzer 900 described above.

  The fourth embodiment further provides a terminal device. FIG. 11 is a configuration diagram of a terminal device according to the embodiment of the present invention. As shown in FIG. 11, the device 1100 may include an interface (not shown), a central processing unit (CPU) 1120, and a storage 1110, which is connected to the central processing unit 1120. . Among them, the storage unit 1110 can store various types of data, and further stores a program for coexistence interference analysis, and can execute the program under the control of the central processing unit 1120.

  In one implementation, the functionality of the device 900 can be integrated into the central processing unit 1120. Of these, the central processing unit 1120 may be configured to realize the coexistence interference analysis method described in the first embodiment.

  For example, the central processing unit 1120 may be configured as follows: a first sniffer module on a first channel according to a first communication technology within a first subwindow within a first predetermined time. Control to obtain a first data packet to be transmitted; a second data packet transmitted by the second sniffer module over the second channel over a second channel within at least one sub-window within a first predetermined time period; Wherein the second channel is a channel closest to the first channel and / or a channel within a predetermined range of channels closest to the first channel; Based on the first performance indicator of communication channel characteristics, which is statistically based on one data packet, the first channel has interference from the second communication technology. Based on the first feature extracted based on the second data packet acquired in each subwindow within the first predetermined time when it is determined that there is interference. Determine the traffic type using the second communication technology.

  In one implementation, the central processing unit 1120 may be further configured as follows: a first sniffer module obtains the first data packet within the first subwindow, and the first subwindow. Control to stop the acquisition of the first data packet in other sub-windows, wherein the first predetermined time includes a first predetermined number of sub-windows, and the first predetermined number is within the predetermined range It is determined according to the number of channels within.

  In one implementation, the central processing unit 1120 may be further configured as follows: based on the acquired second data packet, the number of access points (APs) and communication with each AP. And classifying the first feature of each pair of APs and the second data packet of the terminals communicating with the AP based on a traffic type training collection of a predetermined second communication technology. To determine a traffic type of the second communication technology used for communication between each pair of APs and a terminal communicating with the AP; and a second type used for communication between each pair of APs and a terminal communicating with the AP. Based on the traffic type of the second communication technology, the final traffic type of the second communication technology is determined.

  In one implementation, the central processing unit 1120 may be further configured as follows: a second used for communication between each pair of APs and terminals communicating with the APs within each sub-window. Determine the traffic type of the second communication technology in each sub-window based on the traffic type of the communication technology; determine the traffic type of the final second communication technology based on the traffic type of the second communication technology in each sub-window. Determine.

  In one implementation, the central processing unit 1120 may be further configured as follows: parameters for communication traffic quality of each pair of APs and second data packets of terminals communicating with the APs. Based on the traffic type training collection of a predetermined second communication technology, the classification process is performed on the first feature of the second data packet in which the parameter relating to the communication traffic quality satisfies the predetermined condition.

  In one implementation, the first performance indicator may include one or more of the following: link quality indication (LQI) information, response time information, packet loss information, retransmission Information, packet error information, and received signal strength indication information (RSSI): parameters related to communication traffic quality may include one or more of the following: received signal strength indication ( RSSI) information and the number of second data packets of each pair of APs and terminals communicating with the AP; the first feature includes one or more of the following: Data packet length, data packet interval, and data packet reception time.

  In another implementation, the apparatus 900 described above may be arranged independently of the central processing unit 1120, for example, the apparatus 900 is configured as a chip connected to the central processing unit 1120, and the central processing unit 1120 The function of the apparatus 900 may be realized by controlling the above.

  In the present embodiment, the terminal device 1100 may further include a first sniffer module 11041, a second sniffer module 11402, a power supply module 1105, etc., among which the functions of these components are similar to those of the prior art. . Note that the apparatus 1100 does not necessarily include all the parts in FIG. 11, and the apparatus 1100 may further include parts not shown in FIG. 11. For this, reference can be made to the prior art. .

  In the present embodiment, the terminal device may be a portable device, for example, a Raspberry Pi, but the present embodiment is not limited to this.

  As a result, the first data packet transmitted by the first communication technology on the first channel in the first subwindow of the plurality of subwindows is acquired, and interference by the second communication technology is received in the first channel. Coexistence by determining whether it exists, acquiring a second data packet transmitted by the second communication technology on the second channel within a plurality of sub-windows, and determining the traffic type using the second communication technology Not only can it detect whether there is interference, it can also detect the traffic type of the interference, it has a relatively low overhead and low implementation complexity.

  The fifth embodiment provides a traffic type training collection determination device. Since the principle by which the apparatus solves the problem is similar to the method of Example 2, the specific implementation can refer to the implementation of the method of Example 2, and duplicate descriptions are omitted.

  FIG. 12 is a configuration diagram of a traffic type training collection confirmation apparatus in Embodiment 5 of the present invention. Apparatus 1200 includes the following.

Third acquisition unit 1201: acquires a third data packet transmitted by the second communication technology; of which the third data packet includes data packets within a second predetermined number of periods, each period having one Corresponding to traffic types;
A first statistical unit 1202: statistics a first characteristic for distinguishing traffic types based on the third data packet;
Third determination unit 1203: Clustering processing is performed on the first feature after normalization to determine the training collection.

  In the present embodiment, the specific implementation method of the third acquisition unit 1201, the first statistical unit 1202, and the third determination unit 1203 can refer to steps 701 to 703 in the second embodiment, where Detailed description thereof is omitted.

  In the present embodiment, the number of clusters is determined according to the number of traffic types, and the third determination unit 1203 further determines the identification information of the data packet within the second predetermined number of periods, and the identification information Then, each type of labeled traffic type in the clustering result is determined, and the labeled clustering result is used as a training collection. For example, the identification information may be a time stamp for starting reception of the third data packet.

  In the present embodiment, since the specific implementation method of the first feature, the traffic type, and the labeled traffic type can refer to the second embodiment, the duplicate description is omitted.

  As a result, the second data packet transmitted by the second communication technology is acquired, the first feature for distinguishing the traffic type is statistically analyzed, and the clustering process is performed on the first feature after normalization. A collection of training to identify the type of interference traffic can be formed without human intervention.

  The sixth embodiment provides a terminal device. Since the principle by which the apparatus solves the problem is similar to the method of Example 2, the specific implementation can refer to the implementation of the method of Example 2, and the duplicate explanation is the same in content. , Omitted.

  In the present embodiment, a terminal device (not shown) is further provided, and the above-described traffic type training collection determination device 1200 is configured in the terminal device.

  The sixth embodiment further provides a terminal device. FIG. 13 is a configuration diagram of the terminal device in the embodiment of the present invention. As shown in FIG. 13, the device 1300 may include one interface (not shown), a central processing unit (CPU) 1320, and a storage 1310, which is connected to the central processing unit 1320. . Among them, the storage device 1310 can store various data, and can further store a traffic type training collection determination program and execute the program under the control of the central processing unit 1320.

  In one implementation, the functionality of the device 1200 can be integrated into the central processing unit 1320. Among them, the central processing unit 1320 may be configured to realize the traffic type training collection determination method described in the second embodiment.

  For example, the central processing unit 1320 may be configured as follows, that is, the sniffer module controls to acquire a third data packet transmitted by the second communication technology, of which the third data packet Includes data packets within a second predetermined number of periods, each period corresponding to one traffic type; statistics a first feature for distinguishing traffic types based on the third data packet; A clustering process is performed on the first feature after conversion to determine the training collection.

  In one implementation, the central processing unit 1320 may be further configured as follows: determine identification information of data packets within a second predetermined number of periods; clustering based on the identification information Each type of labeled traffic type in the result is determined, and the labeled clustering result is used as a training collection. For example, the identification information may be a time stamp for starting reception of the third data packet.

  In one implementation, the number of clusters is determined according to the number of traffic types. The first feature includes one or more of the following: data packet length, data packet interval, and data packet reception time.

  In another implementation, the apparatus 1200 described above may be arranged independently of the central processing unit 1320, for example, the apparatus 1200 is configured as a chip connected to the central processing unit 1320, and the central processing unit 1320 The function of the device 1200 may be realized by controlling the above.

  In the present embodiment, the terminal device 1300 may further include a sniffer module 1304, a power supply module 1305, and the like, and the functions of these components are similar to those of the prior art. Note that the apparatus 1300 does not necessarily include all the parts in FIG. 13, and the apparatus 1300 may further include parts not shown in FIG. 13. For this, reference can be made to the prior art. .

  In the present embodiment, the terminal device may be a portable device, for example, a Raspberry Pi, but the present embodiment is not limited to this.

  As a result, the second data packet transmitted by the second communication technology is acquired, the first feature for distinguishing the traffic type is statistically analyzed, and the clustering process is performed on the first feature after normalization. Thus, it is possible to form a training collection for identifying the type of interference traffic without requiring human interference.

  Embodiments of the present invention further provide a computer readable program, of which, when executing the program in the coexistence interference analyzer, the program is stored in the computer in the coexistence interference analyzer as in the first embodiment. The described coexistence interference analysis method is executed.

  The embodiment of the present invention further provides a storage medium storing a computer-readable program, wherein the computer-readable program is stored in the computer in a coexistence interference analyzer. Is executed.

  Embodiments of the present invention further provide a computer readable program, of which, when executing the program in a traffic type training collection determiner, the program is transmitted to a computer in the traffic type training collection determination apparatus. The traffic type training collection determination method described in the second embodiment is executed.

  Embodiments of the present invention further provide a storage medium storing a computer readable program, wherein the computer readable program is provided to a computer in the traffic type training collection determination device in the traffic type described in Example 2. The training collection confirmation method is executed.

  Further, the apparatus, method, and the like according to the embodiments of the present invention may be realized by software, may be realized by hardware, or may be realized by a combination of hardware and software. The present invention also relates to such a computer-readable program, that is, when the program is executed by a logic component, the logic component can realize the above-described apparatus or component, or the logic The component can implement the above-described method or its steps. The present invention further relates to a storage medium storing the above-described program, for example, a hard disk, a magnetic disk, an optical disk, a DVD, a fresh memory, and the like.

  Further, the following supplementary notes are disclosed with respect to the above-described embodiments and the like.

(Appendix 1)
A coexistence interference analyzer,
A first acquisition unit for acquiring a first data packet transmitted by a first communication technique on a first channel within a first sub-window within a first predetermined time;
A second acquisition unit for acquiring a second data packet transmitted by a second communication technology on a second channel within at least one sub-window within a first predetermined time, wherein the second channel is A channel within a predetermined range of the channel closest to the first channel and / or the channel closest to the first channel;
A first determination unit for determining whether interference due to a second communication technology exists in the first channel based on a first performance indicator relating to communication channel characteristics, which is statistically based on the first data packet; And a second deterministic unit, wherein when the first deterministic unit determines that there is interference, the second acquisition unit acquires the second data packet acquired in each sub-window within a first predetermined time. An apparatus comprising: determining a traffic type using a second communication technology based on a first feature extracted based on the first feature.

(Appendix 2)
The apparatus according to appendix 1, wherein
The apparatus, wherein the first acquisition unit acquires the first data packet in the first subwindow and stops acquiring the first data packet in a subwindow other than the first subwindow.

(Appendix 3)
The apparatus according to appendix 1, wherein
The first predetermined time includes a first predetermined number of sub-windows, and the first predetermined number is determined according to the number of channels within the predetermined range.

(Appendix 4)
The apparatus according to appendix 1 or 2,
The second confirmation unit is
A first statistics module for statistics on the number of access points (AP) and the number of terminals communicating with each AP based on the acquired second data packet;
A first processing module that performs a classification process on the first feature of each pair of APs and a second data packet of a terminal communicating with the APs based on a traffic type training collection of a predetermined second communication technology; And determining a traffic type of the second communication technology used for communication between each pair of APs and a terminal communicating with the AP; and a second processing module, which communicates with each pair of APs and the AP. An apparatus including one that determines a traffic type of a final second communication technology based on a traffic type of a second communication technology used for communication between terminals.

(Appendix 5)
The apparatus according to appendix 4, wherein
In the second processing module, the traffic of the second communication technology in each subwindow is determined based on the traffic type of the second communication technology used for communication between each pair of APs and terminals communicating with the AP in each subwindow. An apparatus for determining a type and determining a final second communication technology traffic type based on the second communication technology traffic type in each of the sub-windows.

(Appendix 6)
The apparatus according to appendix 4, wherein
The second confirmation unit further includes:
A first determination module comprising determining parameters relating to communication traffic quality of each pair of APs and a second data packet of a terminal communicating with the AP;
The first processing module further performs a classification process on the first feature of the second data packet in which a parameter related to communication traffic quality satisfies a predetermined condition based on a traffic type training collection of a predetermined second communication technology. Do the equipment.

(Appendix 7)
The apparatus according to appendix 1 or 6,
The first performance indicator for the communication channel characteristics includes one or more of the following:
Link quality indication (LQI) information, response time information, packet loss information, retransmission information, packet error information, and received signal strength indication information (RSSI),
Parameters related to communication traffic quality include one or more of the following:
Received signal strength indication (RSSI) information and information on the number of second data packets of each pair of APs and terminals communicating with the APs,
Said first feature comprises one or more of the following:
A device that is a data packet length, a data packet interval, and a data packet reception time.

(Appendix 8)
The apparatus according to appendix 1, wherein
The second deterministic unit is extracted based on the second data packet acquired in each subwindow within the first predetermined time by the second acquisition unit based on a traffic type training collection of a predetermined second communication technology. A device that performs classification processing on the first feature and determines a traffic type using the second communication technology.

(Appendix 9)
The apparatus according to appendix 8, further comprising:
A third acquisition unit for acquiring a third data packet transmitted by the second communication technology, wherein the third data packet includes data packets within a second predetermined number of periods, each period being: Corresponding to one traffic type;
A first statistical unit for statistically determining a first feature for distinguishing traffic types based on the third data packet; and a third deterministic unit comprising the first feature after normalization. An apparatus comprising: clustering into clusters and establishing the training collection.

(Appendix 10)
The apparatus according to appendix 9, wherein
The number of clusters is determined according to the number of traffic types.

(Appendix 11)
The apparatus according to appendix 9, wherein
The third determination unit further determines identification information of the data packet within the second predetermined number of periods, determines each type of labeled traffic type in the clustering result based on the identification information, and performs labeling A device that uses the obtained clustering results as a training collection.

(Appendix 12)
A traffic type training collection confirmation device,
A third acquisition unit for acquiring a third data packet transmitted by the second communication technology, wherein the third data packet includes data packets within a second predetermined number of periods, and each period includes one data packet; Corresponding to traffic types;
A first statistical unit for statistically determining a first feature for distinguishing traffic types based on the third data packet; and a third deterministic unit comprising the first feature after normalization. An apparatus comprising: clustering into clusters and establishing the training collection.

(Appendix 13)
The apparatus according to appendix 12, wherein
The number of clusters is determined according to the number of traffic types.

(Appendix 14)
The apparatus according to appendix 12, wherein
The third determination unit further determines identification information of the data packet within the second predetermined number of periods, determines each type of labeled traffic type in the clustering result based on the identification information, and performs labeling A device that uses the obtained clustering results as a training collection.

(Appendix 15)
A coexistence interference analysis method,
Obtaining a first data packet transmitted by a first communication technique on a first channel within a first sub-window within a first predetermined time;
Obtaining a second data packet transmitted by a second communication technology on a second channel within at least one sub-window within a first predetermined time, wherein the second channel is the channel closest to the first channel; and Or a channel within a predetermined range of channels closest to the first channel;
Based on a first performance indicator relating to communication channel characteristics, statistically based on the first data packet, to determine whether there is interference due to the second communication technology in the first channel; and determined to be interference A method comprising: determining a traffic type using a second communication technology based on a first feature extracted based on the second data packet acquired in each sub-window within a first predetermined time .

(Appendix 16)
The method according to appendix 15, wherein
The method of obtaining the first data packet in the first subwindow and not obtaining the first data packet in a subwindow other than the first subwindow.

(Appendix 17)
The method according to appendix 15, wherein
The first predetermined time includes a first predetermined number of sub-windows, and the first predetermined number is determined according to the number of channels within the predetermined range.

(Appendix 18)
The method according to appendix 15 or 16, comprising:
Based on the first feature extracted based on the second data packet acquired in each sub-window within the first predetermined time, determining the traffic type using the second communication technology,
Determining the number of access points (AP) and the number of terminals communicating with each AP based on the acquired second data packet;
Based on the traffic type training collection of a predetermined second communication technology, classification processing is performed on the first feature of each pair of APs and the second data packet of the terminal that communicates with the APs, Determine the traffic type of the second communication technology used for communication between the terminals communicating with the AP; and based on the traffic type of the second communication technology used for communication between each pair of APs and the terminal communicating with the AP. Determining a traffic type of the final second communication technology.

(Appendix 19)
The method according to appendix 18, wherein
Based on the traffic type of the second communication technology used for communication between each pair of APs and the terminals communicating with the AP, determining the traffic type of the final second communication technology is as follows:
Within each sub-window, based on the traffic type of the second communication technology used for communication between each pair of APs and terminals communicating with the AP, the traffic type of the second communication technology in each sub-window is determined, Determining a final second communication technology traffic type based on the second communication technology traffic type in the sub-window.

(Appendix 20)
The method according to appendix 18, wherein
Further determining parameters relating to communication traffic quality of each pair of APs and second data packets of terminals communicating with the APs;
A method of performing a classification process on the first feature of a second data packet in which a parameter related to communication traffic quality satisfies a predetermined condition based on a traffic type training collection of a predetermined second communication technology.

  The preferred embodiment of the present invention has been described above, but the present invention is not limited to this embodiment, and all modifications to the present invention belong to the technical scope of the present invention unless departing from the spirit of the present invention.

Claims (10)

An apparatus for analyzing coexistence interference,
A first acquisition unit for acquiring a first data packet transmitted using a first communication technology on a first channel within a first sub-window within a first predetermined time;
A second acquisition unit for acquiring a second data packet transmitted using a second communication technology on a second channel within at least one sub-window within the first predetermined time, wherein the second channel comprises: A second acquisition unit that is a channel closest to the first channel and / or a channel within a predetermined range of channels closest to the first channel;
A first determination unit for determining whether interference due to the second communication technology is present in the first channel based on a first performance indicator relating to communication channel characteristics, which is statistically based on the first data packet; and The first characteristic extracted by the first determination unit based on the second data packet acquired by the second acquisition unit within each sub-window within the first predetermined time when it is determined that interference exists. An apparatus comprising a second determination unit for determining a traffic type using the second communication technology based on the second communication unit. The apparatus of claim 1, wherein
The apparatus, wherein the first acquisition unit acquires the first data packet in the first subwindow and does not acquire the first data packet in a subwindow other than the first subwindow. The apparatus of claim 1, wherein
The first predetermined time includes a first predetermined number of sub-windows, and the first predetermined number is determined according to the number of channels within the predetermined range. The apparatus according to claim 1 or 2,
The second confirmation unit is
A first statistics module that statistics the number of access points (AP) and the number of terminals communicating with each AP based on the acquired second data packet;
Based on the traffic type training collection of a predetermined second communication technology, classification processing is performed on each first AP of each pair of APs and the second data packet of a terminal communicating with the AP, and each pair of APs and A first processing module for determining a traffic type of the second communication technology used for communication between the terminals communicating with the AP; and the second communication used for communication between each pair of APs and the terminal communicating with the AP. An apparatus comprising a second processing module for determining a traffic type of the final second communication technology based on a technology traffic type. The apparatus according to claim 4, wherein
The second processing module includes the second communication technology in each sub-window based on the traffic type of the second communication technology used for communication between each pair of APs and terminals communicating with the AP in each sub-window. And determining the final traffic type of the second communication technology based on the traffic type of the second communication technology in each of the sub-windows. The apparatus according to claim 4, wherein
The second confirmation unit is
A first determination module for determining a parameter relating to communication traffic quality of each pair of APs and the second data packet of a terminal communicating with the AP;
The first processing module is further configured for the first feature of the second data packet in which the parameter relating to communication traffic quality satisfies a predetermined condition based on a traffic type training collection of the predetermined second communication technology. A device that performs classification processing. The apparatus according to claim 1 or 6,
The first performance indicator relating to communication channel characteristics is one or one of link quality indication (LQI) information, response time information, packet loss information, retransmission information, packet error information, and received signal strength indication information (RSSI). Including more than one,
The parameters relating to communication traffic quality include one or more of received signal strength indication (RSSI) information and information on the number of second data packets of each pair of APs and terminals communicating with the APs,
The apparatus includes the first feature including one or more of a data packet length, a data packet interval, and a data packet reception time. A device for determining a traffic type training collection,
A third acquisition unit for acquiring a third data packet transmitted using the second communication technology, wherein the third data packet includes data packets within a second predetermined number of periods, each period having one traffic Third acquisition unit corresponding to the type;
A first statistical unit for statistics of a first feature for distinguishing traffic types based on the third data packet; and a cluster for clustering the first features after normalization to determine a traffic type training set A device containing three deterministic units. The apparatus according to claim 8, wherein
The number of clusters is determined according to the number of traffic types. The apparatus according to claim 8, wherein
The third determination unit further determines identification information of the data packet within the second predetermined number of periods, and determines each type of labeled traffic type in the clustering result based on the identification information, and performs labeling The clustering result is used as the traffic type training collection.

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