CN111405667B - Node and method for TDMA dynamic time slot allocation based on linear prediction - Google Patents

Abstract

The invention provides a TDMA dynamic time slot allocation method based on linear prediction, which belongs to the technical field of wireless Wi-Fi communication and comprises the steps of dividing time into periodic superframes; the equipment node sends a first message to the access node in a feedback frame; the access node performs linear regression prediction based on the first message; the access node transmits the predicted slot allocation information to the device node via a beacon frame. The invention aims to solve the problem that the existing time slot allocation method of the Wi-Fi technology in the industrial Internet of things cannot adapt to business diversity, and can improve the overall performance of the network.

Description

Node and method for TDMA dynamic time slot allocation based on linear prediction

technical field

The invention belongs to the technical field of wireless Wi-Fi communication, and in particular relates to a node and a method for TDMA dynamic time slot allocation based on linear prediction.

Background technique

In recent years, with the innovation of industrial technology, the application scope of the Industrial Internet of Things has become wider and wider. The traditional industrial IoT wireless protocol mainly adopts the IEEE 802.15.4 protocol, but now, Wi-Fi technology has gradually become the mainstream technology of the industrial IoT. With the advantages of high speed, low price, and easy development, Wi-Fi has been rapidly popularized in the industrial Internet of Things industry. Many industrial equipment manufacturers have launched industrial wireless Ethernet products for the IEEE 802.11 protocol family.

In order to meet the real-time requirements of communication in the Industrial Internet of Things, the existing technology transforms the IEEE 802.11 protocol from the original Carrier Sense Multiple Access (CSMA) to Time Division Multiple Access (Time Division Multiple Access, TDMA) method. At present, the industrial wireless technology based on the TDMA protocol mainly adopts a relatively fixed time slot allocation method. Each device node needs to be manually configured or reconnected to update the number of allocated time slot resources, which is suitable for a single type of traditional sensing equipment. As more and more audio and video devices are continuously deployed in factories as media control applications, it brings diversified features to data services, and also causes diverse demands on latency and bandwidth for transmission services. The existing time slot allocation algorithm cannot adjust the allocation scheme according to the changes of the business, and cannot meet the already diversified data services of the Industrial Internet of Things. Therefore, the industry urgently needs a real-time dynamic time slot for the diversity of transmission services in the industrial environment. allocation method.

SUMMARY OF THE INVENTION

In view of this, the purpose of the present invention is to provide a time slot allocation method suitable for industrial Internet of Things business diversity, in particular to a node and method for TDMA dynamic time slot allocation based on linear prediction.

To achieve the above object, the present invention provides the following technical solutions:

The TDMA dynamic time slot allocation method based on linear prediction is characterized in that, comprises the following steps:

S1, dividing time into periodic superframes, wherein the superframes at least include a beacon frame, a feedback frame and a data transmission frame;

S2. The device node sends a first message to the access node in the feedback frame, where the first message includes a current data queue length message and/or an update flag message of the device node;

S3. The access node performs linear regression prediction based on the first message;

S4. The access node sends the predicted time slot allocation information to the device node via the beacon frame.

Optionally, the beacon frame includes an added part of a reserved field, wherein the added part carries at least a scheduling field for carrying predicted time slot allocation information.

Optionally, before the access node performs the linear regression prediction based on the first message, the method further includes performing parameter update according to the update flag message in the first message.

Optionally, the access node performing linear regression prediction based on the first message further includes, using the first parameter ω and the second parameter b, by formula f(T _n )=ωT _n +b, n∈( 1, 2, . . . , K) calculates the predicted time slot allocation number f(T _n ) of the device node n among the K device nodes when the queue length is T _n .

The method in the present disclosure effectively solves the problem of time slot allocation by using a linear prediction algorithm, and satisfactorily solves the technical problem of resource allocation caused by business diversity in the existing Industrial Internet of Things.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure.

Description of drawings

In order to make the purpose, technical solutions and beneficial effects of the present invention clearer, the present invention provides the following drawings for description:

Fig. 1 is the superframe structure schematic diagram that the present invention adopts;

2 is a schematic diagram of a feedback frame structure adopted by the present invention;

Fig. 3 is the dynamic time slot allocation flow diagram that the present invention adopts;

Fig. 4 is the time slot prediction algorithm flow diagram based on linear regression that the present invention adopts;

FIG. 5 is a schematic diagram of a Beacon frame structure adopted in the present invention.

Detailed ways

The present invention will be further described below with reference to the accompanying drawings.

As shown in FIG. 1 , it is a schematic diagram of a superframe structure adopted in the present invention. The superframe T is divided into a beacon area (Beacon), a feedback area (CSMA) and a data transfer area (TDMA) within its period range. In any area, at least one time slot (Time Slot) is included. Preferably, the time slot of the beacon area may be defined as 1 time slot, the feedback area may be defined as 3 time slots, and the data transmission area may be positioned as 30 time slots in length. Preferably, an access node (Access Point, AP) sends a beacon signal in the form of broadcasting in the beacon area, and the beacon signal includes a lot of system information, synchronization information, and various resource allocation information, such as a time slot allocation table. The device node sending multiple services sends various feedback information, or request information, etc. to the AP in the feedback area, or the AP sends ACK response information to the device node. Finally, in the data transmission area, uplink and downlink communication between AP and device node.

Referring to FIG. 2, it is a schematic diagram of the frame structure of the feedback area of the present invention. Among them, the header information of 24 bytes, the frame information of 7 bytes, and the FCS field are exemplarily included. In the 7-byte frame information, the frame identification code (Element ID), the frame length information (Length), the association identification code (Association ID, AID) of the device node, and the data queue length information (Dataqueuelength) are included. The management frame sent in the feedback area is also called an INFO_FEEDBACK frame. Generally, if the device node successfully sends an INFO_FEEDBACK frame, the AP will return an ACK response frame; if the transmission fails or does not receive any response or receives a NACK response frame, the device node will resend the INFO_FEEDBACK frame in the next feedback phase.

Referring to FIG. 3 , it is a flow chart of dynamic time slot allocation according to a preferred embodiment of the present invention. In the figure, step 301 is the overall division scheme of the current channel resources. The whole system divides time resources into periodic superframes, that is, superframes as shown in Figure 1, and further divides them into multiple equal or unequal time slots, and divides equal or unequal number of adjacent time slots. The time slots make up three regions. The first area is used for the AP to send broadcast information including beacons, synchronization information, device node-specific information, etc., and is called a beacon area. Part of the adjacent time slots form a feedback area (CSMA) for the device node to send feedback information to the AP or from the AP to the device node. The composition of the feedback information is complex and diverse. For example, the device node can feed back channel information to the AP, feed back any information requested by the AP, or feed back buffer queue length information. Similarly, the AP can also feed back information to the device node in this area, mainly including ACK/NACK response information.

In step 302, the device node sends the feedback first information to the AP. Wherein, the first information is carried on the management frame structure as shown in FIG. 2 . The first information at least includes data queue length information and an update flag, which is sent by the device node to the AP to inform the AP how much data at the device node needs to allocate a time slot at the moment. On the other hand, when the device node generates a data queue to be sent, it is also necessary to compare and judge whether the previous time slot allocation meets the requirements according to the historical time slot allocation information and the current data queue length to be sent. If the requirement is not met, the update flag in the first information is set to 1, indicating that the parameter needs to be updated. If the requirement has been met, the update flag position in the first information is set to 0, indicating that the parameter does not need to be updated.

Step 303, the AP performs linear regression prediction according to at least the data queue length information and the update flag bit in the received first information, and obtains the predicted time slot allocation result. If the update flag is 0, the original parameters are used. If the update flag is 1, the update parameters need to be recalculated. Both the original parameter and the updated parameter include the first parameter ω and the second parameter b. For any calculation parameter, a data model is established based on the least square method, and the values of the first parameter ω and the second parameter b are solved by minimizing the mean square error, as shown below:

对ω和b求偏导数计算出ω和b的特定值：by function

Taking the partial derivatives with respect to ω and b computes specific values of ω and b:

表示设备节点前N次预测分配时隙的平均数目，

表示设备节点前N 次数据队列平均长度，f(T_i)表示设备节点第i次预测分配的时隙数，N表示设备节点分配时隙的历史总次数，S_i表示设备节点的第i次分配的时隙数，T_i表示设备节点历史第i次数据队列长度。in,

Represents the average number of time slots allocated for the first N predictions of the device node,

Represents the average length of the first N data queues of the device node, f(T _i ) represents the number of time slots allocated by the device node for the i-th prediction, N represents the historical total number of timeslots allocated by the device node, and S _i represents the i-th time of the device node. The number of time slots allocated, T _i represents the length of the i-th data queue in the history of the device node.

That is, when the device node thinks it needs to be updated and requests the AP to update, the AP uses the historical data queue length information T _i (i=1,2,...,N) to calculate the corresponding first parameter ω and the first parameter according to this algorithm. The second parameter b. This calculation can be skipped when no update is required.

Using the first parameter and the second parameter, the AP builds the slot prediction model by the formula f(T _n )=ωT _n +b, n∈(1,2,...,K). Among them, T _n represents the current data queue length of the device node n, f(T _n ) represents the number of time slots predicted under T _n , ω and b are the parameters of the prediction function, that is, the first parameter and the first parameter in the preceding steps Two parameters. The purpose of the prediction is to make the value of f(T _n ) close to the number of time slots S _n actually required by the device node n, that is

As shown in the calculation process shown in FIG. 4 , the AP stores the historical queue length information sent by the device node. Based on this information, the AP can or has calculated the first parameter and the second parameter suitable for the historical queue information. The AP receives the first information from the device node, and further includes identifying whether the update flag needs to be updated. If it needs to be updated, perform a parameter calculation according to the scheme described in step 303, and input the queue line length information sent by the device node this time into the prediction model after updating the parameters; The sent queue length information is input into the prediction model of the original parameters. The AP calculates the predicted time slot allocation result of the device node in both options, and updates the result to the historical queue length information.

Returning to FIG. 3, in step 304, the AP predicts the number of time slot allocations for each device node calculated, lists the time slot table, and sends it to each device node via the beacon area, and each device node according to the time slot table and AP to communicate.

Referring to FIG. 5 , it is a schematic diagram of the structure of the Beacon frame adopted by the present invention. In order to adapt to the solution in the present invention, in the original Beacon frame structure, part or all of the reserved fields are modified into the added partial frame of the present invention, which is used to carry the time slot allocation information, such as a time slot table. Among them, the time slot allocation information is stored in the Schedule field, and other contents may also include the frame identification code (Element ID) field, the frame length information field (Length), the absolute superframe number (ASN) field, the current superframe length (sLen) field and the Size field indicating the size of the Schedule field. The time slot allocation information of each device node is added to the Schedule field by bit operation. The device node parses out the dedicated time slot allocation information through bit operation according to the AID, and updates its own time slot table.

The invention also relates to an access node AP, which includes a receiving module, a prediction module, a central processing unit, a transmitting module and the like. The periodic superframe that divides the time into a beacon frame, a feedback frame and a data transmission frame, during the communication process, the receiving module receives the first message from the device node in the feedback frame, wherein the first message The message includes the current data queue length message and/or the update flag message of the device node; the prediction module performs linear regression prediction based on the first message; the transmission module sends the predicted time slot allocation information to the device node via the beacon frame.

The invention also relates to a device node, including a receiving module, a transmitting module, a central processing unit, a buffer memory module and the like. The periodic superframes divided into time include beacon frames, feedback frames and data transmission frames. During the communication process, the transmitting module sends a first message to the access node in the feedback frame, wherein the first message is sent to the access node. The message includes the data queue length message and/or the update flag message stored in the current buffer memory of the device node; the receiving module receives the predicted time slot allocation information sent by the access node AP in the beacon frame.

Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium can include: ROM, RAM, magnetic disk or optical disk, etc.

The above-mentioned embodiments further describe the purpose, technical solutions and advantages of the present invention in detail. It should be understood that the above-mentioned embodiments are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made to the present invention within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims (4)

1. TDMA dynamic time slot allocation method based on linear prediction, is characterized in that, comprises the following steps: S1. Divide the time into periodic superframes, wherein the superframe includes at least a beacon area, a feedback area and a data transmission area; S2, the device node sends a first message to the access node in the feedback area, wherein all the The first message includes a current data queue length message and an update flag message of the device node; S3, the access node performs linear regression prediction based on the first message; S4, the access node sends the predicted time slot allocation information via the beacon area Sent to the device node; wherein the performing linear regression prediction includes updating parameters according to the update flag message in the first message, specifically establishing a data model based on the least squares method, and solving the first parameter ω by minimizing the mean square error and the second parameter b, where the least squares method establishes a data model as:

by function

Taking the partial derivatives with respect to ω and b computes specific values of ω and b:

in,

Represents the average number of time slots allocated for the first N predictions of the device node,

Represents the average length of the first N data queues of the device node, f(T _i ) represents the number of time slots allocated by the device node for the i-th prediction, N represents the total number of timeslots allocated by the device node, and S _i represents the i-th allocation of the device node. The number of time slots of , T _i represents the i-th data queue length in the history of the device node; using the first parameter ω and the second parameter b, through the formula f(T _n )=ωT _n +b, n∈(1,2,… ,K) Calculate the number of time slots f(T _n ) that the device node n in the K device nodes predicts and allocates when the queue length is T _n . 2 . The method according to claim 1 , wherein the beacon area includes an added part of a reserved field, wherein the added part carries at least a scheduling field for carrying predicted time slot allocation information. 3 . 3. the access node of the TDMA dynamic time slot allocation based on linear prediction, at least including receiving module, predicting module and transmitting module, it is characterized in that, in the superframe that is divided into periodicity by time, at least including beacon area, feedback area and the data transmission area, the receiving module receives the first message from the device node in the feedback area, wherein the first message includes the device node current data queue length message and the update flag message; the prediction module is based on the The first message is to perform linear regression prediction; wherein the performing linear regression prediction includes updating parameters according to the update flag message in the first message, specifically establishing a data model based on the least squares method, and solving by minimizing the mean square error The first parameter ω and the second parameter b, wherein the data model established by the least squares method is:

by function

Taking the partial derivatives with respect to ω and b computes specific values of ω and b:

in,

Represents the average number of time slots allocated for the first N predictions of the device node,

Represents the average length of the first N data queues of the device node, f(T _i ) represents the number of time slots allocated by the device node for the i-th prediction, N represents the total number of timeslots allocated by the device node, and S _i represents the i-th allocation of the device node. The number of time slots of , T _i represents the i-th data queue length in the history of the device node; using the first parameter ω and the second parameter b, through the formula f(T _n )=ωT _n +b, n∈(1,2,… , K) calculate the number of time slots f(T _n ) that the device node n in the K device nodes predicts and allocates when the queue length is T _n ; The transmit module transmits the predicted time slot allocation information to the device node via the beacon region. 4. the equipment node of the TDMA dynamic time slot allocation based on linear prediction, at least comprise receiving module and transmitting module, it is characterized in that, at least comprise beacon area, feedback area and data transmission area in the superframe that is divided into periodicity by time , the transmitting module sends a first message to the access node in the feedback area, wherein the first message includes the current data queue length message and the update flag message of the device node; the receiving module receives the receiving node in the beacon area The predicted time slot allocation information sent by the access node; wherein, the predicted time slot allocation information sent by the access node is the information that the access node performs linear regression prediction based on the first message, and the performing The linear regression prediction includes updating the parameters according to the update flag message in the first message, specifically establishing a data model based on the least squares method, and solving the first parameter ω and the second parameter b by minimizing the mean square error, wherein the The least squares method establishes the data model as:

by function

Taking the partial derivatives with respect to ω and b computes specific values of ω and b:

in,

Represents the average number of time slots allocated for the first N predictions of the device node,

Represents the average length of the first N data queues of the device node, f(T _i ) represents the number of time slots allocated by the device node for the i-th prediction, N represents the total number of timeslots allocated by the device node, and S _i represents the i-th allocation of the device node. The number of time slots of , T _i represents the i-th data queue length in the history of the device node; using the first parameter ω and the second parameter b, through the formula f(T _n )=ωT _n +b, n∈(1,2,… ,K) Calculate the number of time slots f(T _n ) that the device node n in the K device nodes predicts and allocates when the queue length is T _n .

Images