CN114339858A - Terminal packet sending parameter adjusting method and device and related equipment - Google Patents
Terminal packet sending parameter adjusting method and device and related equipment Download PDFInfo
- Publication number
- CN114339858A CN114339858A CN202111654336.6A CN202111654336A CN114339858A CN 114339858 A CN114339858 A CN 114339858A CN 202111654336 A CN202111654336 A CN 202111654336A CN 114339858 A CN114339858 A CN 114339858A
- Authority
- CN
- China
- Prior art keywords
- value
- congestion
- packet sending
- parameter value
- parameter
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 35
- 230000006870 function Effects 0.000 claims description 23
- 230000005540 biological transmission Effects 0.000 claims description 11
- 238000012549 training Methods 0.000 claims description 10
- 238000004590 computer program Methods 0.000 claims description 8
- 230000008569 process Effects 0.000 claims description 5
- 238000005457 optimization Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 2
- 238000004891 communication Methods 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- ORILYTVJVMAKLC-UHFFFAOYSA-N Adamantane Natural products C1C(C2)CC3CC1CC2C3 ORILYTVJVMAKLC-UHFFFAOYSA-N 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 210000002569 neuron Anatomy 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 1
- QVFWZNCVPCJQOP-UHFFFAOYSA-N chloralodol Chemical compound CC(O)(C)CC(C)OC(O)C(Cl)(Cl)Cl QVFWZNCVPCJQOP-UHFFFAOYSA-N 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Data Exchanges In Wide-Area Networks (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention discloses a method, a device and a related component for adjusting a terminal package sending parameter, wherein the method comprises the following steps: continuously acquiring a congestion parameter value of a current network according to a fixed frequency, and combining the congestion parameter value with a current packet sending parameter value to obtain an input vector; inputting the input vector into a pre-trained LSTM model, and predicting a congestion parameter value of a network congestion state at a target moment to obtain a congestion predicted value at the target moment; calculating the difference value between the congestion parameter value of the current network and the congestion predicted value, calculating the adjustment value of the packet sending parameter value according to the difference value and a preset adjustment function, and adjusting the packet sending parameter value according to the adjustment value. The invention enables the terminal to independently adjust the packet sending parameter value according to the congestion predicted value and send the packet according to the packet sending parameter value, thereby reducing the possibility that the network congestion is easy to occur due to the fixed packet sending parameter value distributed by the base station.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for adjusting a terminal package sending parameter and a related component.
Background
The internet of things is based on the idea that everything can be connected to the internet, and the purpose of realizing interconnection of everything is taken as a final goal. With this goal, with the rapid development of networking intelligence, we are in the stage of exponential development of the internet of things (IoT). As the number of connectable internet of things devices increases, the internet of things will continue to evolve by providing connections and interactions between the physical and networked worlds. Unlike conventional networked devices such as smartphones and personal computers, however, such internet of things devices typically transmit data that has been collected in bursts or communicate with a base station at a steady rate.
However, as the number of terminal devices changes, a single communication base station is likely to cause network congestion due to its limited capacity. In addition, most of the existing communication modes adopt a preemption mode or a fixed time-sharing mode, and the terminal neglects the network requirements of other terminal equipment in the network for the efficiency and smoothness of self data transmission. The existing network conditions may gradually deteriorate.
Disclosure of Invention
The embodiment of the invention provides a method, a device and a related component for adjusting a terminal packet sending parameter, and aims to solve the problem of network congestion caused by data reporting in a terminal equipment contention mode in the prior art.
In a first aspect, an embodiment of the present invention provides a method for adjusting a terminal packet sending parameter, including:
continuously acquiring a congestion parameter value of a current network according to a fixed frequency, and combining the congestion parameter value with a current packet sending parameter value to obtain an input vector;
inputting the input vector into a pre-trained LSTM model, and predicting a congestion parameter value of a network congestion state at a target moment to obtain a congestion predicted value at the target moment;
calculating the difference value between the congestion parameter value of the current network and the congestion predicted value, calculating the adjustment value of the packet sending parameter value according to the difference value and a preset adjustment function, and adjusting the packet sending parameter value according to the adjustment value.
In a second aspect, an embodiment of the present invention provides a device for adjusting a terminal packet sending parameter, including:
the combination module is used for continuously acquiring the congestion parameter value of the current network according to the fixed frequency, and combining the congestion parameter value with the current packet sending parameter value to obtain an input vector;
the prediction module is used for inputting the input vector into a pre-trained LSTM model to predict a congestion parameter value of the network congestion state at the target moment to obtain a congestion prediction value at the target moment;
and the adjusting module is used for calculating the difference value between the congestion parameter value of the current network and the congestion predicted value, calculating the adjusting value of the packet sending parameter value according to the difference value and a preset adjusting function, and adjusting the packet sending parameter value according to the adjusting value.
In a third aspect, an embodiment of the present invention provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the method for adjusting the terminal package sending parameter according to the first aspect when executing the computer program.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when executed by a processor, the computer program implements the method for adjusting the terminal package sending parameter according to the first aspect.
The embodiment of the invention provides a method, a device and a related component for adjusting a terminal package sending parameter, wherein the method comprises the following steps: continuously acquiring a congestion parameter value of a current network according to a fixed frequency, and combining the congestion parameter value with a current packet sending parameter value to obtain an input vector; inputting the input vector into a pre-trained LSTM model, and predicting a congestion parameter value of a network congestion state at a target moment to obtain a congestion predicted value at the target moment; calculating the difference value between the congestion parameter value of the current network and the congestion predicted value, calculating the adjustment value of the packet sending parameter value according to the difference value and a preset adjustment function, and adjusting the packet sending parameter value according to the adjustment value. The embodiment of the invention predicts the congestion parameter value of the target moment by configuring the pre-trained LSTM model, takes the congestion parameter value of the current network and the packet sending parameter value of the terminal as the input vector of the LSTM model, adjusts the packet sending parameter value according to the congestion predicted value of the network and the difference value of the congestion predicted value, enables the terminal to independently adjust the packet sending parameter value according to the congestion predicted value, and sends packets according to the packet sending parameter value, thereby reducing the possibility that the network congestion is easy to occur due to the fixed packet sending parameter value distributed by the base station of the terminal.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart illustrating an embodiment of a method for adjusting a terminal packet sending parameter according to the present invention;
fig. 2 is a schematic flow chart of a pre-training process of an LSTM model in a method for adjusting a terminal package delivery parameter according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a sub-example of a method for adjusting a terminal packet sending parameter according to an embodiment of the present invention;
fig. 4 is a schematic block diagram of a device for adjusting a terminal packet sending parameter according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for adjusting a terminal packet sending parameter according to an embodiment of the present invention, which specifically includes: steps S101 to S105.
Step S101, continuously acquiring a congestion parameter value of a current network according to a fixed frequency, and combining the congestion parameter value with a current packet sending parameter value to obtain an input vector;
in this embodiment, since the terminal does not have any prior information, in an initialization state, the terminal initiates a congestion parameter value acquisition request to the base station according to an initialization fixed frequency to obtain a congestion parameter value of the current network, including: link establishment times/failure times, average time delay, retransmission times, sector utilization rate and the like, and simple parameter statistics can not occupy the load of base station hardware. And taking the congestion parameter value obtained from the base station and parameters such as a packet sending time slot, a packet sending frequency, a packet sending time delay random value and the like of the current terminal as an input vector. It should be noted that the packet sending delay random value is a strategy for preventing multiple terminals from sending packets at the same time in the same time slot to cause channel congestion, so that the multiple terminals do not contend for the channel in the initialization stage.
Step S102, inputting the input vector into a pre-trained LSTM model, and predicting a congestion parameter value of a network congestion state at a target moment to obtain a congestion predicted value at the target moment;
in this embodiment, a pre-trained LSTM model is used to predict the congestion parameter value of the network congestion state at the target time. Because the performance of the bearing environment terminal of the model is insufficient, the lstm model with a simpler structure and fewer parameters is adopted for prediction, and the online learning capability is provided. Thereby achieving the effect of reducing the load of the base station. An LSTM model is defined with 50 neurons in its first hidden layer and 1 neuron in the output layer for predicting the congestion parameter values of the network. The dimensions of the input data will be 1 time instant (the duration being defined by the network administrator), the congestion parameter value and the packet delivery parameter value. Training is carried out by using a real congestion index at the next moment, and optimization is carried out by using a Mean Absolute Error (MAE) loss function and an Adam optimizer. And inputting the input vector into a pre-trained LSTM model to obtain a network predicted value of the network congestion state at the target moment.
As shown in FIG. 2, in one embodiment, the pre-training process of the LSTM model comprises:
step S201, obtaining a historical parameter value of a historical network and a historical packet sending value of a corresponding time terminal, and forming a training vector by the historical parameter value and the historical packet sending value;
step S202, inputting the training vector into an initial LSTM model to predict a congestion parameter value of a historical network to obtain a historical predicted value of the historical network;
and S203, calculating a parameter loss value by adopting a preset loss function based on the historical parameter value and the historical predicted value, and optimizing the initial LSTM model according to the parameter loss value to obtain the LSTM model.
In the embodiment, historical parameter values of the network congestion state at the historical time and historical packet sending values corresponding to the historical time terminals are obtained, and the historical parameter values and the historical packet sending values form a training vector; inputting the training vector into an initial LSTM model to predict the congestion parameter value of the historical network to obtain a historical predicted value of the historical network; based on the historical parameter values and the historical predicted values, calculating parameter loss values by adopting a Mean Absolute Error (MAE) loss function, and optimizing model parameters of the initial LSTM model according to the parameter loss values and an Adam optimizer to obtain the LSTM model. In addition, the prediction performance of the LSTM model is judged by Root Mean Square Error (RMSE), when the root mean square error is less than 10%, the self-adaptive data transmission frequency adjustment can be started, and the LSTM model is updated on line.
Step S103, calculating the difference value between the congestion parameter value of the current network and the congestion predicted value, calculating the adjustment value of the packet sending parameter value according to the difference value and a preset adjustment function, and adjusting the packet sending parameter value according to the adjustment value.
In this embodiment, a difference between a congestion parameter value of a current network and a congestion prediction value is calculated, an adjustment value of a packet sending parameter value is calculated according to the difference and a preset adjustment function, and the packet sending parameter value is adjusted according to the adjustment value, where the adjustment function is as follows:
f1＝(fmax-f0)·Δ+f0，
rand((1/f1)/ts)，
dt1＝(ts-dt0)·Δ+dt0，
wherein f is1A packet transmission frequency representing a target time; f. ofmaxThe maximum frequency of single terminal packet sending of the initial setting is represented; delta represents the difference value of the congestion parameter value of the current network and the congestion predicted value; rand denotes a random function; ts represents the time duration of a single packet-sending time slot, from different transmissionsDetermining a protocol; dt1A random value of packet sending delay representing a target moment; dt0And the random value of the packet sending delay of the target time is represented.
As shown in fig. 3, in an embodiment, after step S103, the method further includes:
step S301, acquiring a congestion actual value of a network at a target moment, and calculating a congestion predicted value and a loss of the congestion actual value according to a preset loss function to obtain a predicted loss;
and S302, performing back propagation on the model parameters of the LSTM model according to the predicted loss, and optimizing the LSTM model.
In this embodiment, after the terminal performs packet sending according to the adjusted packet sending parameter value, the terminal continues to obtain the congestion parameter value of the network from the base station according to the fixed frequency, calculates the prediction loss by using an average absolute error (MAE) loss function based on the actual congestion value and the predicted congestion value of the network at the target time, and performs back propagation on the model of the LSTM model according to the prediction loss to update the LSTM model so as to realize online updating of the LSTM model parameter. In addition, in order to increase the efficiency of network data transmission, when an analysis end needs to extract and analyze terminal data in the network, the analysis section may adopt a compressed sensing technology to perform high-frequency recovery on the non-uniformly sampled terminal data. The method and the device have good effect on the multi-terminal network environment in the NB-IoT scene and the LoRa scene.
The embodiment of the invention predicts the congestion parameter value of the target moment by configuring the pre-trained LSTM model, takes the congestion parameter value of the current network and the packet sending parameter value of the terminal as the input vector of the LSTM model, adjusts the packet sending parameter value according to the congestion predicted value of the network and the difference value of the congestion predicted value, enables the terminal to independently adjust the packet sending parameter value according to the congestion predicted value, and sends packets according to the packet sending parameter value, thereby reducing the possibility that the network congestion is easy to occur due to the fixed packet sending parameter value distributed by the base station of the terminal.
The embodiment of the invention also provides a terminal packet sending parameter adjusting device, which is used for executing any embodiment of the terminal packet sending parameter adjusting device method. Specifically, referring to fig. 4, fig. 4 is a schematic block diagram of a terminal packet sending parameter adjusting device according to an embodiment of the present invention. The terminal packet parameter adjusting apparatus 100 may be configured in the service end node.
As shown in fig. 4, the terminal packet parameter adjusting apparatus 100 includes a combination module 110, a prediction module 120, and an adjustment module 130.
The combination module 110 is configured to continuously obtain a congestion parameter value of a current network according to a fixed frequency, and combine the congestion parameter value with a current packet sending parameter value to obtain an input vector;
the prediction module 120 is configured to input the input vector into a pre-trained LSTM model to predict a congestion parameter value of a network congestion state at a target time, so as to obtain a congestion prediction value at the target time;
the adjusting module 130 is configured to calculate a difference between a congestion parameter value of the current network and the congestion prediction value, calculate an adjustment value of the packet sending parameter value according to the difference and a preset adjusting function, and adjust the packet sending parameter value according to the adjustment value.
In an embodiment, the apparatus 100 for adjusting terminal packet sending parameters further includes:
the loss calculation module is used for acquiring a congestion actual value of the network at a target moment, and calculating the congestion predicted value and the loss of the congestion actual value according to a preset loss function to obtain predicted loss;
and the optimization module is used for performing back propagation on the model parameters of the LSTM model according to the predicted loss and optimizing the LSTM model.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses, devices and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided by the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only a logical division, and there may be other divisions when the actual implementation is performed, or units having the same function may be grouped into one unit, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electric, mechanical or other form of connection.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a storage medium. Based on such understanding, the technical solution of the present invention essentially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a service end node, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a magnetic disk, or an optical disk.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A method for adjusting terminal packet sending parameters is characterized by comprising the following steps:
continuously acquiring a congestion parameter value of a current network according to a fixed frequency, and combining the congestion parameter value with a current packet sending parameter value to obtain an input vector;
inputting the input vector into a pre-trained LSTM model, and predicting a congestion parameter value of a network congestion state at a target moment to obtain a congestion predicted value at the target moment;
calculating the difference value between the congestion parameter value of the current network and the congestion predicted value, calculating the adjustment value of the packet sending parameter value according to the difference value and a preset adjustment function, and adjusting the packet sending parameter value according to the adjustment value.
2. The method of claim 1, wherein the pre-training process of the LSTM model comprises:
acquiring historical parameter values of a historical network and historical packet sending values of corresponding time terminals, and forming a training vector by the historical parameter values and the historical packet sending values;
inputting the training vector into an initial LSTM model to predict congestion parameter values of the historical network to obtain historical predicted values of the historical network;
and calculating a parameter loss value by adopting a preset loss function based on the historical parameter value and the historical predicted value, and optimizing the initial LSTM model according to the parameter loss value to obtain the LSTM model.
3. The method of claim 1, wherein the congestion parameter values include link establishment/failure times, average delay, retransmission times, and sector utilization of the network.
4. The method according to claim 1, wherein the packet transmission parameter values include a packet transmission time slot, a packet transmission frequency, and a packet transmission delay random value of the terminal.
5. The method for adjusting the terminal packet sending parameter according to claim 1, wherein after calculating the difference between the congestion parameter value of the current network and the congestion prediction value, calculating the adjustment value of the packet sending parameter value according to the difference and a preset adjustment function, and adjusting the packet sending parameter value according to the adjustment value, the method further comprises:
acquiring a congestion actual value of a network at a target moment, and calculating the congestion predicted value and the loss of the congestion actual value according to a preset loss function to obtain predicted loss;
and carrying out back propagation on the model parameters of the LSTM model according to the predicted loss, and optimizing the LSTM model.
6. The method according to claim 1, wherein the adjusting function comprises:
f1＝(fmax-f0)·Δ+f0，
rand((1/f1)/ts)，
dt1＝(ts-dt0)·Δ+dt0，
wherein f is1A packet transmission frequency representing a target time; f. ofmaxThe maximum frequency of single terminal packet sending of the initial setting is represented; delta represents the difference value of the congestion parameter value of the current network and the congestion predicted value; rand denotes a random function; ts represents the time length of a single packet sending time slot and is determined by different transmission protocols; dt1A random value of packet sending delay representing a target moment; dt0And the random value of the packet sending delay of the target time is represented.
7. A terminal package sending parameter adjusting device is characterized by comprising:
the combination module is used for continuously acquiring the congestion parameter value of the current network according to the fixed frequency, and combining the congestion parameter value with the current packet sending parameter value to obtain an input vector;
the prediction module is used for inputting the input vector into a pre-trained LSTM model to predict a congestion parameter value of the network congestion state at the target moment to obtain a congestion prediction value at the target moment;
and the adjusting module is used for calculating the difference value between the congestion parameter value of the current network and the congestion predicted value, calculating the adjusting value of the packet sending parameter value according to the difference value and a preset adjusting function, and adjusting the packet sending parameter value according to the adjusting value.
8. The apparatus for adjusting the terminal packet sending parameter according to claim 7, wherein the terminal data transmission apparatus further comprises:
the loss calculation module is used for acquiring a congestion actual value of the network at a target moment, and calculating the congestion predicted value and the loss of the congestion actual value according to a preset loss function to obtain predicted loss;
and the optimization module is used for performing back propagation on the model parameters of the LSTM model according to the predicted loss and optimizing the LSTM model.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method for adjusting the terminal package parameter according to any one of claims 1 to 6 when executing the computer program.
10. A computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when executed by a processor, the computer program implements the terminal package parameter adjusting method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111654336.6A CN114339858B (en) | 2021-12-30 | 2021-12-30 | Terminal packet sending parameter adjusting method and device and related equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111654336.6A CN114339858B (en) | 2021-12-30 | 2021-12-30 | Terminal packet sending parameter adjusting method and device and related equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114339858A true CN114339858A (en) | 2022-04-12 |
| CN114339858B CN114339858B (en) | 2023-12-05 |
Family
ID=81019650
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111654336.6A Active CN114339858B (en) | 2021-12-30 | 2021-12-30 | Terminal packet sending parameter adjusting method and device and related equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114339858B (en) |
Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080298240A1 (en) * | 2007-06-01 | 2008-12-04 | Industry Collaboration Foundation Of Inha University | Node availability prediction-based grid network congestion control device and method therefor |
| CN104320809A (en) * | 2014-11-05 | 2015-01-28 | 四川九洲电器集团有限责任公司 | Wireless multi-hop network congestion control method and system based on RTT |
| CN106713055A (en) * | 2017-02-27 | 2017-05-24 | 电子科技大学 | Energy-saving deployment method of virtual CDN |
| US20180137412A1 (en) * | 2016-11-16 | 2018-05-17 | Cisco Technology, Inc. | Network traffic prediction using long short term memory neural networks |
| CN108900546A (en) * | 2018-08-13 | 2018-11-27 | 杭州安恒信息技术股份有限公司 | The method and apparatus of time series Network anomaly detection based on LSTM |
| US20200014628A1 (en) * | 2017-03-22 | 2020-01-09 | Huawei Technologies Co., Ltd. | Method And Node For Traffic Control |
| CN111132190A (en) * | 2019-11-28 | 2020-05-08 | 中通服建设有限公司 | Base station load early warning method and device |
| WO2020221224A1 (en) * | 2019-04-30 | 2020-11-05 | 华为技术有限公司 | Congestion information acquisition method and system, related device and computer storage medium |
| CN111970163A (en) * | 2020-06-30 | 2020-11-20 | 网络通信与安全紫金山实验室 | Network flow prediction method of LSTM model based on attention mechanism |
| WO2021026509A1 (en) * | 2019-08-08 | 2021-02-11 | Intel Corporation | Link performance prediction and media streaming technologies |
| CN112532409A (en) * | 2019-09-17 | 2021-03-19 | 华为技术有限公司 | Network parameter configuration method and device, computer equipment and storage medium |
| CN113079104A (en) * | 2021-03-22 | 2021-07-06 | 新华三技术有限公司 | Network congestion control method, device and equipment |
| CN113141318A (en) * | 2021-03-29 | 2021-07-20 | 重庆邮电大学 | Improved TFRC congestion control system and method based on video traffic prediction model |
| CN113259255A (en) * | 2021-06-03 | 2021-08-13 | 鹏城实验室 | Network congestion control method, device, terminal and storage medium |
| CN113283179A (en) * | 2021-06-17 | 2021-08-20 | 湘潭大学 | Short-term load prediction method based on multi-target LSTM integrated network |
| CN113825171A (en) * | 2021-09-30 | 2021-12-21 | 新华三技术有限公司 | Network congestion control method, device, equipment and medium |
-
2021
- 2021-12-30 CN CN202111654336.6A patent/CN114339858B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080298240A1 (en) * | 2007-06-01 | 2008-12-04 | Industry Collaboration Foundation Of Inha University | Node availability prediction-based grid network congestion control device and method therefor |
| CN104320809A (en) * | 2014-11-05 | 2015-01-28 | 四川九洲电器集团有限责任公司 | Wireless multi-hop network congestion control method and system based on RTT |
| US20180137412A1 (en) * | 2016-11-16 | 2018-05-17 | Cisco Technology, Inc. | Network traffic prediction using long short term memory neural networks |
| CN106713055A (en) * | 2017-02-27 | 2017-05-24 | 电子科技大学 | Energy-saving deployment method of virtual CDN |
| US20200014628A1 (en) * | 2017-03-22 | 2020-01-09 | Huawei Technologies Co., Ltd. | Method And Node For Traffic Control |
| CN108900546A (en) * | 2018-08-13 | 2018-11-27 | 杭州安恒信息技术股份有限公司 | The method and apparatus of time series Network anomaly detection based on LSTM |
| WO2020221224A1 (en) * | 2019-04-30 | 2020-11-05 | 华为技术有限公司 | Congestion information acquisition method and system, related device and computer storage medium |
| WO2021026509A1 (en) * | 2019-08-08 | 2021-02-11 | Intel Corporation | Link performance prediction and media streaming technologies |
| CN112532409A (en) * | 2019-09-17 | 2021-03-19 | 华为技术有限公司 | Network parameter configuration method and device, computer equipment and storage medium |
| CN111132190A (en) * | 2019-11-28 | 2020-05-08 | 中通服建设有限公司 | Base station load early warning method and device |
| CN111970163A (en) * | 2020-06-30 | 2020-11-20 | 网络通信与安全紫金山实验室 | Network flow prediction method of LSTM model based on attention mechanism |
| CN113079104A (en) * | 2021-03-22 | 2021-07-06 | 新华三技术有限公司 | Network congestion control method, device and equipment |
| CN113141318A (en) * | 2021-03-29 | 2021-07-20 | 重庆邮电大学 | Improved TFRC congestion control system and method based on video traffic prediction model |
| CN113259255A (en) * | 2021-06-03 | 2021-08-13 | 鹏城实验室 | Network congestion control method, device, terminal and storage medium |
| CN113283179A (en) * | 2021-06-17 | 2021-08-20 | 湘潭大学 | Short-term load prediction method based on multi-target LSTM integrated network |
| CN113825171A (en) * | 2021-09-30 | 2021-12-21 | 新华三技术有限公司 | Network congestion control method, device, equipment and medium |
Non-Patent Citations (3)
| Title |
|---|
| SANCHITA BASAK ET AL.: "Analyzing the Cascading Effect of Traffic Congestion Using LSTM Networks", 《2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)》 * |
| 周鹏: "视频传输QoS优化方案研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 邱泽敏 等: "复杂网络下具有预测与自我调节能力的拥塞控制算法", 《计算机工程》, no. 12 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114339858B (en) | 2023-12-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP7333449B2 (en) | Flow Control for Probabilistic Relays in Blockchain Networks | |
| CN110049357B (en) | Bandwidth estimation method, device, equipment and storage medium | |
| US7096274B1 (en) | Optimum frame size predictor for wireless Local Area Network | |
| WO2021227508A1 (en) | Deep reinforcement learning-based industrial 5g dynamic multi-priority multi-access method | |
| CN110809306A (en) | Terminal access selection method based on deep reinforcement learning | |
| CN113315716A (en) | Method and equipment for training congestion control model and method and equipment for congestion control | |
| CN109890069A (en) | Method for connecting network, terminal, base station and computer storage medium | |
| CN113595923A (en) | Network congestion control method and device | |
| US8730875B2 (en) | Wireless communication terminal, wireless communication forwarding control system, wireless communication forwarding control method, and wireless communication forwarding control program | |
| CN110875915A (en) | Data transmission method and device | |
| CN115277689B (en) | Cloud edge network communication optimization method and system based on distributed federal learning | |
| CN110519175A (en) | A kind of information processing method, equipment and computer readable storage medium | |
| CN113438315B (en) | Internet of things information freshness optimization method based on double-network deep reinforcement learning | |
| CN112887217A (en) | Control data packet sending method, model training method, device and system | |
| CN113507328B (en) | Time slot MAC protocol method, system, device and medium for underwater acoustic network | |
| CN113114581A (en) | TCP congestion control method and device based on multi-agent deep reinforcement learning | |
| CN113132490A (en) | MQTT protocol QoS mechanism selection scheme based on reinforcement learning | |
| CN108449408B (en) | Game acceleration system based on multipath | |
| CN113890564A (en) | Special ad hoc network frequency hopping anti-interference method and device for unmanned aerial vehicle based on federal learning | |
| CN114726799A (en) | Training method of congestion control agent, congestion control method and congestion control device | |
| CN114339858A (en) | Terminal packet sending parameter adjusting method and device and related equipment | |
| CN109150743B (en) | Network congestion control strategy switching method and system | |
| CN110581810A (en) | Data communication method, device, equipment and medium based on SPMA protocol | |
| CN110192378B (en) | Apparatus and method for controlling use of non-optimal path | |
| WO2023142351A1 (en) | Weight adjustment method and apparatus, and storage medium and electronic apparatus |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |