WO2015184733A1 - 一种帧聚合方法及电子设备 - Google Patents

一种帧聚合方法及电子设备 Download PDF

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Publication number
WO2015184733A1
WO2015184733A1 PCT/CN2014/090728 CN2014090728W WO2015184733A1 WO 2015184733 A1 WO2015184733 A1 WO 2015184733A1 CN 2014090728 W CN2014090728 W CN 2014090728W WO 2015184733 A1 WO2015184733 A1 WO 2015184733A1
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Prior art keywords
operator
information
state
parameter
electronic device
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PCT/CN2014/090728
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English (en)
French (fr)
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支周
吕宁
禹忠
卢忱
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西安中兴新软件有限责任公司
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Priority to KR1020167033862A priority Critical patent/KR101962393B1/ko
Priority to US15/315,722 priority patent/US10178013B2/en
Publication of WO2015184733A1 publication Critical patent/WO2015184733A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/16—Threshold monitoring
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
    • H04L43/0823—Errors, e.g. transmission errors
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/003—Arrangements for allocating sub-channels of the transmission path
    • H04L5/0053—Allocation of signaling, i.e. of overhead other than pilot signals
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/08—Testing, supervising or monitoring using real traffic

Definitions

  • the present invention relates to control technologies for electronic devices in the field of mobile communications, and in particular, to a frame aggregation method and an electronic device.
  • a method of determining whether to use a frame aggregation technique or frame aggregation to determine a frame length is generally based on the quality of a wireless channel.
  • the frame aggregation technology can effectively improve the throughput of the LAN.
  • the frame aggregation technology is not used or only the short aggregation frame is used, which can reduce the impact of retransmission on the system throughput. .
  • the above discriminating method does not distinguish the characteristics of the device adopting the frame aggregation technology, that is, the device is a handheld wireless terminal and a wireless access device without user participation. In this way, the accuracy and effectiveness of the obtained channel state information cannot be guaranteed, which may affect the accuracy of determining whether to adopt the frame aggregation technique.
  • an object of the present invention is to provide a frame aggregation method and an electronic device.
  • An embodiment of the present invention provides a frame aggregation method, where the method includes:
  • the calculation result is obtained according to the state information of the wireless channel and the state information of the operator, and when the calculation result satisfies the first condition, frame aggregation is performed.
  • the status information of the wireless channel includes at least one of the following: a data error rate, a packet loss rate, a number of retransmissions, and a wireless signal strength.
  • the method further includes: setting a status parameter corresponding to the operator.
  • the state parameter corresponding to the operator includes: a model
  • the method for establishing the model includes: collecting sensing parameters in a specified duration, converting the sensing parameters into change information of N states corresponding to an operator, and training the change information of the N states as an input parameter.
  • the model is trained by taking the state corresponding to the operator as an output result.
  • the satisfying the first condition includes: when the calculation result reaches a preset threshold, the first condition is met.
  • An embodiment of the present invention further provides an electronic device, where the electronic device includes:
  • An information acquiring module configured to acquire state information of a wireless channel, and acquire state information of the operator according to the preset state parameter
  • a decision module configured to calculate, according to status information of the wireless channel and state information of the operator, a calculation result
  • the adjustment module is configured to perform frame aggregation when the calculation result satisfies the first condition.
  • the status information of the wireless channel includes at least one of the following: a data error rate, a packet loss rate, a number of retransmissions, and a wireless signal strength.
  • the information acquiring module is further configured to set a state parameter corresponding to the operator.
  • the information acquiring module is further configured to use the pre-stored model as a state parameter corresponding to the operator;
  • the method for establishing the model includes: converting the sensing parameter into the change information of the N states corresponding to the operator according to the sensor collecting the sensing parameter in the specified duration, and using the change information of the N state as the input parameter The training is performed, and the state corresponding to the operator is used as an output result to train the model.
  • the determining module is further configured to determine that the first condition is met when the calculation result reaches a preset threshold.
  • the frame aggregation method and the electronic device provided by the embodiments of the present invention can perform calculation according to the state of the wireless channel and the state of the operator, and determine whether to perform frame aggregation according to the calculation result, so that it is possible to accurately determine whether it is necessary to perform Frame aggregation, which can further ensure the frame transmission quality of electronic devices.
  • FIG. 1 is a schematic flowchart 1 of a frame aggregation method provided by the present invention.
  • FIG. 2 is a schematic diagram of various sensors provided in an electronic device
  • FIG. 3 is a schematic flowchart of a frame aggregation method provided by the present invention.
  • FIG. 4 is a schematic structural diagram of a frame structure in a frame aggregation technique
  • FIG. 5 is a schematic structural diagram of an electronic device according to the present invention.
  • the frame aggregation method provided by the embodiment of the present invention, as shown in FIG. 1 includes:
  • Step 101 Acquire status information of a wireless channel.
  • Step 102 Obtain state information of the operator according to the preset state parameter.
  • Step 103 Calculate the calculation result according to the state information of the wireless channel and the state information of the operator, and perform frame aggregation when the calculation result satisfies the first condition.
  • the sequence of the foregoing step 101 and the step 102 may be performed at the same time, or the step 101 may be performed first, and the step 101 is not performed.
  • the status information of the wireless channel includes at least one of the following: a data error rate, a packet loss rate, a number of retransmissions, and a wireless signal strength.
  • the threshold when the value is greater than the preset, it indicates that its channel state is poor; otherwise, it indicates that its channel state is good.
  • wireless signal strength when its value is greater than a preset threshold, it indicates that its channel state is good; conversely, it indicates that its channel state is poor.
  • the acquisition of the state of the wireless channel may be: for closed-loop transmission, the transmitting end may acquire wireless channel state information even if there is a delay, an error, or the like.
  • the sender cannot obtain information such as data error rate, packet loss rate, and wireless signal strength through feedback from the receiver.
  • the heuristic algorithm can be used to infer the channel state by information such as the number of retransmissions. When the channel cannot obtain any channel status information, the default channel status is "poor".
  • the state information of the operator may be the mood state of the operator, or may be the operating environment state of the operator, etc.; the same identifier may also be used for characterization, for example, when the state is divided into two levels, that is, when the difference is good, It can be characterized by "1", and the difference is represented by "0"; or when the state is divided into three grades, that is, good, medium, and poor, it can be characterized by "11", and "01” is characterized by "00". Poor representation.
  • the method may further include: setting a state parameter corresponding to the operator.
  • the state parameter corresponding to the operator may include: a model; the training method of the model may include: converting the sensing parameter into a change of the N states corresponding to the operator according to the sensor collecting the sensing parameter in the specified duration Information, training the change information of the N states as an input parameter, and using the state corresponding to the operator as an output result, training the model;
  • the senor may include a compass, a camera, a gyroscope and a light sensor, a Global Positioning System (GPS), and a pressure sensing device as shown in FIG. 2 . , temperature sensor, acceleration sensor, etc.
  • GPS Global Positioning System
  • the change information of the N states may be: change information of the emoticon such as facial expression emotion recognition; change information of the sound, such as speech emotion recognition, semantic emotion recognition, central nervous signal emotion recognition, autonomic signal signal emotion recognition, etc. ;, position change information, action change information, temperature change information, etc.
  • the change information of the emoji can be obtained by collecting the emoji input by the user when using various chat softwares; the acquired sound can be collected through the microphone, and then analyzing the change information such as the strength and frequency of the sound sent by the user; acquiring the position or action
  • the data may be passed through a gyroscope, or a GPS, or an acceleration sensor, etc.; the temperature of the operator may be obtained by a temperature sensor, and then the temperature change information may be analyzed.
  • the methods for obtaining corresponding information by using various sensors are all prior art, and are not described herein.
  • training the change information of the N states as an input parameter may be selecting only one state change information, for example, a typical parameter may be used to obtain a certain emotion using a single parameter, such as using facial expression parameters to determine emotions, When this parameter is not applicable, the multi-parameter method can be combined to judge the user's emotions.
  • the operator's mood state is described, and after collecting the mood related information of the user for a period of time by the sensor, it can be quantized into numerical information that can be processed by the program (such as 0, 1, or in a certain range of values). Within the discrete values), using this information as a sample, a classification model (ie, a user mood state model) is generated using a two-class classification method in machine learning (such as the Bayesian classification method). Through the model, mood state recognition and recognition can be performed on newly collected user mood information.
  • a classification model ie, a user mood state model
  • machine learning such as the Bayesian classification method
  • the voice, expression, and different characteristics may be expressed when the user changes in different emotions, and the machine learning may be through machine learning training by extracting user feature data in a certain state. Identify a certain percentage of user-character emotions. Such as a user's emotions, such as anger, the tone of the spectrum has its characteristics, expressions It has its characteristics, and its feature is used as a feature quantity input classifier to achieve a certain degree of emotion recognition.
  • the classifier is a machine learning program, which is essentially a mathematical model. According to the different models, there are many branches, including: Bayes classifier, BP neural network classifier, decision tree algorithm, SVM (support vector machine) algorithm, etc.
  • the calculation formula can be:
  • X1, X2...Xn respectively represent physiological characteristics of different users, and may represent multiple emotional feature parameters, including but not limited to parameters herein, and may also include other parameters, using different emotional feature parameters, and applying comprehensive judgment to obtain the final emotional information of the user. .
  • the satisfying the first condition includes: when the calculation result reaches a preset threshold, satisfying the first condition;
  • Ag_condition channel_stat ⁇ usr_mode; where the parameter ag_condition indicates an aggregation condition, which takes values of "1" and "0".
  • Step 301 The wireless mobile terminal collects state information of the wireless channel.
  • Step 302 The wireless mobile terminal collects various status information of the operator by using the sensor, and after the collected information is processed, uses the status parameter to calculate the status information of the operator.
  • Step 303 Calculate a frame aggregation condition according to the state information of the wireless channel and the state information of the operator.
  • Step 304 Determine whether the first condition is met. If the calculated result is "1", it indicates that the first condition is met, and frame aggregation is performed; otherwise, no operation is performed.
  • the frame aggregation includes: A-MSDU and A-MPDU.
  • the A-MSDU technology is on the top of the MAC layer, which aggregates multiple MSDUs into one MPDU.
  • the A-MPDU technology aggregates multiple MPDUs into one PSDU at the bottom of the MAC layer, and the MPDUs therein may include aggregated A-MSDUs.
  • the electronic device provided by the embodiment of the present invention, as shown in FIG. 5, includes:
  • the information acquiring module 51 is configured to acquire state information of the wireless channel, and acquire state information of the operator according to the preset state parameter;
  • the determining module 52 is configured to calculate, according to the state information of the wireless channel and the state information of the operator, a calculation result
  • the adjusting module 53 is configured to perform frame aggregation when the calculation result satisfies the first condition.
  • the status information of the wireless channel includes at least one of the following: a data error rate, a packet loss rate, a number of retransmissions, and a wireless signal strength.
  • the information acquiring module 51 can be used for closed loop transmission, and the transmitting end can acquire wireless channel state information even if there is a delay, an error, or the like.
  • the sender cannot obtain information such as data error rate, packet loss rate, and wireless signal strength through feedback from the receiver.
  • the heuristic algorithm can be used to infer the channel state by information such as the number of retransmissions. When the channel cannot obtain any channel status information, the default channel status is "poor".
  • the state information of the operator may be the mood state of the operator, or may be the operating environment state of the operator, etc.; the same identifier may also be used for characterization, for example, when the state is divided into two levels, that is, when the difference is good, It can be characterized by "1", and the difference is represented by "0"; or when the state is divided into three grades, that is, good, medium, and poor, it can be characterized by "11", and "01” is characterized by "00". Poor representation.
  • the information obtaining module 51 can be configured to set a state parameter corresponding to the operator.
  • the state parameter corresponding to the operator may include: a model; the training method of the model may include: converting the sensing parameter into a change of the N states corresponding to the operator according to the sensor collecting the sensing parameter in the specified duration Information, training the change information of the N states as an input parameter, and using the state corresponding to the operator as an output result, training the model;
  • the change information of the N states may be: change information of an emoticon, information of a sound, change information of a position, motion change information, and the like.
  • the change information of the emoji can be obtained by collecting the emoji input by the user when using various chat softwares; the acquired sound can be collected through the microphone, and then analyzing the strength, speed, and the like of the sound sent by the user; acquiring the position or the action, etc. Can be passed through the gyroscope.
  • a single parameter can be used to obtain a certain emotion, such as using facial expression parameters to judge emotions. When this parameter is not applicable, the multi-parameter method can be combined to determine the user's emotion.
  • the sensor After the sensor collects the mood related information of the user for a period of time, it is quantized into numerical information that can be processed by the program (such as 0, 1, or discrete values within a certain range of values), and the information is taken as a sample.
  • a two-class classification method in machine learning (such as the Bayesian classification method) generates a classification model (ie, a user mood state model). Through the model, The mood state recognition and recognition can be performed on the newly collected user mood information.
  • the operator's state information is to represent the operator's emotions
  • different characteristics can be expressed when the user's different emotions change, the voice and the expression, and the machine learning is performed by extracting the user characteristic data in a certain state and learning through the machine learning.
  • the classifier is a machine learning program, which is essentially a mathematical model. According to the different models, there are many branches, including: Bayes classifier, BP neural network classifier, decision tree algorithm, SVM (support vector machine) algorithm, etc.
  • the calculation formula can be:
  • X1, X2...Xn respectively represent physiological characteristics of different users, and may represent multiple emotional feature parameters, including but not limited to parameters herein, and may also include other parameters, using different emotional feature parameters, and applying comprehensive judgment to obtain the final emotional information of the user. .
  • the satisfying the first condition includes: when the calculation result reaches a preset threshold, satisfying the first condition;
  • Ag_condition channel_stat ⁇ usr_mode; where the parameter ag_condition indicates an aggregation condition, which takes values of "1" and "0".
  • embodiments of the present invention can be provided as a method, system, or computer program product. Accordingly, the present invention can take the form of a hardware embodiment, a software embodiment, or a combination of software and hardware. Moreover, the present invention may employ computer-usable storage media (including but not limited to disks) in one or more of the computer-usable program code embodied therein. A form of computer program product embodied on a memory and optical storage, etc.).
  • the computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device.
  • the apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.
  • These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device.
  • the instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.
  • the present invention discloses a frame aggregation method and an electronic device, which acquires state information of a wireless channel, acquires state information of the operator according to a preset state parameter, and obtains state information of the wireless channel and state information of the operator according to the state information of the wireless channel. , frame aggregation. In this way, it is possible to accurately determine whether or not frame aggregation is required, thereby further securing the frame of the electronic device. Transmission quality.

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Abstract

本发明公开了一种帧聚合方法及电子设备，其中所述方法包括：获取无线信道的状态信息；根据预设的状态参数，获取操作者的状态信息；根据所述无线信道的状态信息以及所述操作者的状态信息进行计算得到计算结果，当计算结果满足第一条件时，进行帧聚合。

Description

一种帧聚合方法及电子设备 技术领域
本发明涉及移动通信领域中的电子设备的控制技术，尤其涉及一种帧聚合方法及电子设备。
背景技术
目前，在判别是否采用帧聚合技术、帧聚合时确定帧长度的方法，通常是基于无线信道的质量。当信道质量好时，采用帧聚合技术，可以有效地提高局域网的吞吐量；当信道质量差时，不采用帧聚合技术或者只使用较短聚合帧，这样可以降低重传对系统吞吐量的影响。
但是，上述判别方法，不区分采用帧聚合技术的设备特点，即不区分设备是手持无线终端和无用户参与的无线接入设备。如此，就无法保证获取的信道状态信息的准确性和实效性，从而会影响到判断是否采用帧聚合技术的准确性。
发明内容
为解决上述技术问题，本发明的目的在于提供一种帧聚合方法及电子设备。
本发明实施例提供了一种帧聚合方法，所述方法包括：
获取无线信道的状态信息；
根据预设的状态参数，获取操作者的状态信息；
根据所述无线信道的状态信息以及所述操作者的状态信息进行计算得到计算结果，当计算结果满足第一条件时，进行帧聚合。
上述方案中，所述无线信道的状态信息包括以下至少一种：数据错误率、丢包率、重传次数、无线信号强度。
上述方案中，所述方法还包括：设置操作者对应的状态参数。
上述方案中，所述操作者对应的状态参数包括：模型；
所述模型的建立方法包括：收集指定时长中的传感参数，将所述传感参数转换为操作者对应的N种状态的变化信息，将所述N种状态的变化信息作为输入参数进行训练，将操作者对应的状态作为输出结果，对模型进行训练。
上述方案中，所述满足第一条件包括：当所述计算结果达到预设的阈值时，满足第一条件。
本发明实施例还提供了一种电子设备，所述电子设备包括：
信息获取模块，用于获取无线信道的状态信息；根据预设的状态参数，获取操作者的状态信息；
决策模块，用于根据所述无线信道的状态信息以及所述操作者的状态信息进行计算得到计算结果；
调整模块，用于当计算结果满足第一条件时，进行帧聚合。
上述方案中，所述无线信道的状态信息包括以下至少一种：数据错误率、丢包率、重传次数、无线信号强度。
上述方案中，所述信息获取模块，还用于设置操作者对应的状态参数。
上述方案中，所述信息获取模块，还用于将预存的模型作为所述操作者对应的状态参数；
所述模型的建立方法包括：根据传感器收集指定时长中的传感参数，将所述传感参数转换为操作者对应的N种状态的变化信息，将所述N种状态的变化信息作为输入参数进行训练，将操作者对应的状态作为输出结果，对模型进行训练。
上述方案中，所述决策模块，还用于当所述计算结果达到预设的阈值时，确定满足第一条件。
本发明实施例所提供的帧聚合方法及电子设备，能根据无线信道的状态以及操作者的状态进行计算，并根据计算结果来确定是否进行帧聚合，如此，就能够准确的判断是否需要能够进行帧聚合，从而能够进一步的保证电子设备的帧传输质量。
附图说明
图1为本发明提供的帧聚合方法流程示意图一；
图2为电子设备具备的多种传感器示意图；
图3为本发明提供的帧聚合方法流程示意图而；
图4为帧聚合技术中帧组成结构示意图；
图5为本发明提供的电子设备组成结构示意图。
具体实施方式
下面结合附图及具体实施例对本发明再作进一步详细的说明。
实施例一、
本发明实施例提供的帧聚合方法，如图1所示，包括：
步骤101：获取无线信道的状态信息；
步骤102：根据预设的状态参数，获取操作者的状态信息；
步骤103：根据所述无线信道的状态信息以及所述操作者的状态信息进行计算得到计算结果，当计算结果满足第一条件时，进行帧聚合。
优选地，上述步骤101以及步骤102不分执行的先后顺序，可以同时执行，也可以先执行步骤102再执行步骤101，本实施例不对其进行限定。
其中，所述无线信道的状态信息包括以下至少一种：数据错误率、丢包率、重传次数、无线信号强度。
对于数据错误率、丢包率和重传次数等信息，当其数值大于预设的 阈值时，表示其信道状态差；否则，表示其信道状态好。对于无线信号强度，当其值大于预设的阈值时，表示其信道状态好；相反地，表示其信道状态差。
比如当状态分为两个等级，即好跟差时，可以用“1”来表征好，用“0”表征差；或者当状态分为三个等级，即好、中、差时，可以使用“11”表征好，“01”表征中，“00”表征差。
无线信道的状态的获取可以为：对于闭环传输，即使存在延迟、误差等情况，发送端可以获取无线信道状态信息。对于开环传输，发送端不能通过接收端反馈而获得数据错误率、丢包率、和无线信号强度等信息。但是可以通过重传次数等信息，采用启发式算法推断出信道状态。在发送端无法获得任何信道状态信息时，默认信道状态为“差”状态。
所述操作者的状态信息可以为操作者的心情状态，或者可以为操作者的操作环境状态等；同样可以使用对应的标识来表征，比如当状态分为两个等级，即好跟差时，可以用“1”来表征好，用“0”表征差；或者当状态分为三个等级，即好、中、差时，可以使用“11”表征好，“01”表征中，“00”表征差。
优选地，执行步骤101之前，所述方法还可以包括：设置操作者对应的状态参数。
所述操作者对应的状态参数可以包括：模型；所述模型的训练方法可以包括：根据传感器收集指定时长中的传感参数，将所述传感参数转换为操作者对应的N种状态的变化信息，将所述N种状态的变化信息作为输入参数进行训练，将操作者对应的状态作为输出结果，对模型进行训练；
其中，所述传感器可以如图2所示包括罗盘、摄像头、陀螺仪和光传感器、全球定位系统(GPS，Global Positioning System)、压力传感 器、温度传感器、加速度传感器等。
所述N种状态的变化信息可以为：表情符号的变化信息如人脸表情情绪识别；声音的变化信息，比如语音情绪识别、语义情绪识别、中枢神经信号情绪识别、自主神经信号信号情绪识别等；、位置的变化信息、动作变化信息、温度变化信息等。
比如，获取表情符号的变化信息可以通过采集用户使用各种聊天软件时输入的表情符号；获取声音可以为通过麦克风采集，进而分析用户发出的声音的强弱、频率等变化信息；获取位置或者动作等可以为通过陀螺仪、或者GPS、或者加速度传感器等；获取操作者的温度可以通过温度传感器，进而分析得到温度变化信息。其中，通过各种传感器得到对应的信息的方法均为现有技术，这里不做赘述。
优选地，将所述N种状态的变化信息作为输入参数进行训练可以为仅选取一种状态的变化信息，比如对典型参数可以使用单一参数获取某一情绪，如使用面部表情参数判断情绪，在此参数不适用时，可以综合多参数方法判断用户情绪。
这里，以操作者的心情状态进行说明，可以为通过传感器收集用户在一段时间内的心情相关信息之后，将其作量化为程序可以处理的数值信息(如0，1，或者在一定取值范围内的离散数值)，将这些信息作为样本，采用机器学习中的二类分类方法(如贝叶斯分类方法)生成分类模型(即用户心情状态模型)。通过该模型，可以对新近收集到的用户心情信息进行心情状态识别识别。
当操作者的状态信息为表征操作者的情绪时，可以通过在用户不同情绪变化时，语音，表情，会表现不同特征，机器学习即通过提取一定状态下用户特征数据，通过机器学习训练，可以识别一定比例的用户特征情绪。如用户的一种情绪，如愤怒情绪，音调的频谱有其特征，表情 有其特征，将其特征作为特征量输入分类器实现一定程度的情绪识别。
所述分类器是一种机器学习程序，其实质为数学模型。针对模型的不同，目前有多种分支，包括：Bayes分类器，BP神经网络分类器，决策树算法，SVM(支持向量机)算法等，计算公式可以为：
Mood＝X(X1，X2…Xn)
X1,X2…Xn分别代表不同用户生理特征，可以表示多个情绪特征参数，此处包括但不限于此处参数，也可以包含其它参数，利用不同情绪特征参数，应用综合判断获得用户最终情绪信息。
所述满足第一条件包括：当所述计算结果达到预设的阈值时，满足第一条件；
其中，所述计算可以采用公式：
ag_condition＝channel_stat∪usr_mode；式中，参数ag_condition表示聚合条件，其取值为“1”和“0”。参数channel_stat表示无线信道的状态，参数usr_stat表示操作者的状态。当ag_condition＝1时，表示满足帧聚合条件；当ag_condition＝0时，表示不满足帧聚合条件。
实施例二、
下面对本发明一个实施方式进行说明，如图3所示，具体流程如下：
步骤301，无线移动终端收集无线信道的状态信息。
步骤302，无线移动终端使用传感器收集操作者的多种状态信息，将收集到的信息经过处理之后，使用状态参数，计算得到操作者的状态信息。
步骤303，根据无线信道的状态信息和操作者的状态信息，计算帧聚合条件。
步骤304，判断是否满足第一条件，如果计算出的结果为“1”，表示满足第一条件，进行帧聚合；否则，不做操作。
所述帧聚合，如图4所示，包括：A-MSDU和A-MPDU。A-MSDU技术在MAC层顶部，其聚合多个MSDU为一个MPDU；A-MPDU技术在MAC层底部，聚合多个MPDU为一个PSDU，其中的MPDU中可以包括聚合的A-MSDU。
实施例三、
本发明实施例提供的电子设备，如图5所示，包括：
信息获取模块51，用于获取无线信道的状态信息；根据预设的状态参数，获取操作者的状态信息；
决策模块52，用于根据所述无线信道的状态信息以及所述操作者的状态信息进行计算得到计算结果；
调整模块53，用于当计算结果满足第一条件时，进行帧聚合。
其中，所述无线信道的状态信息包括以下至少一种：数据错误率、丢包率、重传次数、无线信号强度。
对于数据错误率、丢包率和重传次数等信息，当其数值大于预设的阈值时，表示其信道状态差；否则，表示其信道状态好。对于无线信号强度，当其值大于预设的阈值时，表示其信道状态好；相反地，表示其信道状态差。
比如当状态分为两个等级，即好跟差时，可以用“1”来表征好，用“0”表征差；或者当状态分为三个等级，即好、中、差时，可以使用“11”表征好，“01”表征中，“00”表征差。
所述信息获取模块51，可以用于对于闭环传输，即使存在延迟、误差等情况，发送端可以获取无线信道状态信息。对于开环传输，发送端不能通过接收端反馈而获得数据错误率、丢包率、和无线信号强度等信息。但是可以通过重传次数等信息，采用启发式算法推断出信道状态。在发送端无法获得任何信道状态信息时，默认信道状态为“差”状态。
所述操作者的状态信息可以为操作者的心情状态，或者可以为操作者的操作环境状态等；同样可以使用对应的标识来表征，比如当状态分为两个等级，即好跟差时，可以用“1”来表征好，用“0”表征差；或者当状态分为三个等级，即好、中、差时，可以使用“11”表征好，“01”表征中，“00”表征差。
所述信息获取模块51，可以用于设置操作者对应的状态参数。
所述操作者对应的状态参数可以包括：模型；所述模型的训练方法可以包括：根据传感器收集指定时长中的传感参数，将所述传感参数转换为操作者对应的N种状态的变化信息，将所述N种状态的变化信息作为输入参数进行训练，将操作者对应的状态作为输出结果，对模型进行训练；
其中，所述传感器可以包括摄像头、陀螺仪和光传感器等，如图2所示。所述N种状态的变化信息可以为：表情符号的变化信息、声音的信息、位置的变化信息、动作变化信息等。比如，获取表情符号的变化信息可以通过采集用户使用各种聊天软件时输入的表情符号；获取声音可以为通过麦克风采集，进而分析用户发出的声音的强弱、速度等信息；获取位置或者动作等可以为通过陀螺仪。如1.人脸表情情绪识别2.语音情绪识别3.语义情绪识别4.中枢神经信号情绪识别5.自主神经信号信号情绪识别。对典型参数可以使用单一参数获取某一情绪，如使用面部表情参数判断情绪，在此参数不适用时，可以综合多参数方法判断用户情绪。
通过传感器收集用户在一段时间内的心情相关信息之后，将其作量化为程序可以处理的数值信息(如0，1，或者在一定取值范围内的离散数值)，将这些信息作为样本，采用机器学习中的二类分类方法(如贝叶斯分类方法)生成分类模型(即用户心情状态模型)。通过该模型， 可以对新近收集到的用户心情信息进行心情状态识别识别。
比如，当操作者的状态信息为表征操作者的情绪时，可以通过在用户不同情绪变化时，语音，表情，会表现不同特征，机器学习即通过提取一定状态下用户特征数据，通过机器学习训练，可以识别一定比例的用户特征情绪。如用户的一种情绪，如愤怒情绪，音调的频谱有其特征，表情有其特征，将其特征作为特征量输入分类器实现一定程度的情绪识别。
所述分类器是一种机器学习程序，其实质为数学模型。针对模型的不同，目前有多种分支，包括：Bayes分类器，BP神经网络分类器，决策树算法，SVM(支持向量机)算法等，计算公式可以为：
Mood＝X(X1，X2…Xn)
X1,X2…Xn分别代表不同用户生理特征，可以表示多个情绪特征参数，此处包括但不限于此处参数，也可以包含其它参数，利用不同情绪特征参数，应用综合判断获得用户最终情绪信息。
所述满足第一条件包括：当所述计算结果达到预设的阈值时，满足第一条件；
其中，所述计算可以采用公式：
ag_condition＝channel_stat∪usr_mode；式中，参数ag_condition表示聚合条件，其取值为“1”和“0”。参数channel_stat表示无线信道的状态，参数usr_stat表示操作者的状态。当ag_condition＝1时，表示满足帧聚合条件；当ag_condition＝0时，表示不满足帧聚合条件。
本领域内的技术人员应明白，本发明的实施例可提供为方法、系统、或计算机程序产品。因此，本发明可采用硬件实施例、软件实施例、或结合软件和硬件方面的实施例的形式。而且，本发明可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘 存储器和光学存储器等)上实施的计算机程序产品的形式。
本发明是参照根据本发明实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
以上所述，仅为本发明的较佳实施例而已，并非用于限定本发明的保护范围。
工业实用性
本发明公开了一种帧聚合方法及电子设备，获取无线信道的状态信息；根据预设的状态参数，获取操作者的状态信息；根据所述无线信道的状态信息以及所述操作者的状态信息，进行帧聚合。如此，就能够准确的判断是否需要能够进行帧聚合，从而能够进一步的保证电子设备的帧 传输质量。

Claims (10)

  1. 一种帧聚合方法，所述方法包括：
    获取无线信道的状态信息；
    根据预设的状态参数，获取操作者的状态信息；
    根据所述无线信道的状态信息以及所述操作者的状态信息进行计算得到计算结果，当计算结果满足第一条件时，进行帧聚合。
  2. 根据权利要求1所述的方法，其中，所述无线信道的状态信息包括以下至少一种：数据错误率、丢包率、重传次数、无线信号强度。
  3. 根据权利要求1或2所述的方法，其中，所述方法还包括：设置操作者对应的状态参数。
  4. 根据权利要求3所述的方法，其中，所述操作者对应的状态参数包括：模型；
    所述模型的建立方法包括：收集指定时长中的传感参数，将所述传感参数转换为操作者对应的N种状态的变化信息，将所述N种状态的变化信息作为输入参数进行训练，将操作者对应的状态作为输出结果，对模型进行训练。
  5. 根据权利要求3所述的方法，其中，所述满足第一条件包括：当所述计算结果达到预设的阈值时，满足第一条件。
  6. 一种电子设备，所述电子设备包括：
    信息获取模块，配置为获取无线信道的状态信息；根据预设的状态参数，获取操作者的状态信息；
    决策模块，配置为根据所述无线信道的状态信息以及所述操作者的状态信息进行计算得到计算结果；
    调整模块，配置为当计算结果满足第一条件时，进行帧聚合。
  7. 根据权利要求6所述的电子设备，其中，所述无线信道的状态信 息包括以下至少一种：数据错误率、丢包率、重传次数、无线信号强度。
  8. 根据权利要求7所述的电子设备，其中，所述信息获取模块，配置为设置操作者对应的状态参数。
  9. 根据权利要求8所述的电子设备，其中，所述信息获取模块，配置为将预存的模型作为所述操作者对应的状态参数；
    所述模型的建立方法包括：根据传感器收集指定时长中的传感参数，将所述传感参数转换为操作者对应的N种状态的变化信息，将所述N种状态的变化信息作为输入参数进行训练，将操作者对应的状态作为输出结果，对模型进行训练。
  10. 根据权利要求9所述的电子设备，其中，所述决策模块，配置为当所述计算结果达到预设的阈值时，确定满足第一条件。
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CN103348730A (zh) * 2011-02-10 2013-10-09 英派尔科技开发有限公司 语音服务的体验质量测量
CN102355324A (zh) * 2011-08-10 2012-02-15 中国联合网络通信集团有限公司 帧聚合控制方法、装置和系统

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