CN107172636B - WiFi rate control method based on environment noise and STA distance - Google Patents

WiFi rate control method based on environment noise and STA distance Download PDF

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CN107172636B
CN107172636B CN201710343143.6A CN201710343143A CN107172636B CN 107172636 B CN107172636 B CN 107172636B CN 201710343143 A CN201710343143 A CN 201710343143A CN 107172636 B CN107172636 B CN 107172636B
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陈云川
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Microgrid Union Technology Chengdu Co ltd
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
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Abstract

The invention provides a WiFi rate control method based on environment noise and STA distance, which comprises the following steps: randomly deploying a plurality of sensing nodes in the WIFI system, and cooperatively finishing sensing and uploading of the WIFI network; and performing channel optimization by adopting a particle swarm algorithm according to the sensing result. The invention provides a WiFi rate control method based on environment noise and STA distance, which can be used for more accurately acquiring the condition of a wireless electromagnetic environment in real time, so that a network transmission channel is optimized, crosstalk is avoided, and the system performance is improved.

Description

WiFi rate control method based on environment noise and STA distance
Technical Field
The invention relates to a wireless network, in particular to a WiFi rate control method based on environment noise and STA distance.
Background
The WIFI technology has rapidly developed to become the global most and universal network access technology, and basically all user mobile devices, including smart phones, tablet computers and notebook computers, have WIFI access capability. Meanwhile, the crosstalk problem of the WIFI system gradually becomes the most problem affecting actual deployment, including crosstalk of microwave, bluetooth, radar, and the like and intersystem crosstalk. In order to solve the crosstalk problem of the WIFI system, the scheme adopted in the prior art is mainly to expand new frequency band resources and reduce the probability of repetition of a working channel. However, as more users select WIFI to surf the internet, and the bandwidth demand of 802.11ac for reaching the peak of a single user is multiplied, the bandwidth expansion cannot meet the demand of network development all the time.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a WiFi rate control method based on environment noise and STA distance, which comprises the following steps:
randomly deploying a plurality of sensing nodes in the WIFI system, and cooperatively finishing sensing, acquisition and processing of the WIFI network;
and performing channel optimization by adopting a particle swarm algorithm according to the sensing result.
Preferably, in the WIFI system, the operating frequency band and the transmitting power of the access node are both controlled by the AC router; a user side communicates with an access node through an air interface to acquire signaling and data service; the sensing node senses the working frequency band of the WIFI and reports the sensing result to the adjacent sink node; the sink node selects the optimal working frequency band for communication according to the sensing result, and the frequency band sensing results obtained by the access node and the user side are integrated in the AC router.
Preferably, the sensing node employs the following sensing algorithm:
for a single sensing node, band sensing is represented as one detection event:
Figure BDA0001295746540000021
wherein x (t) represents the signal received by the sensing node, s (t) is the sending signal of the sensing object, n (t) is noise, H represents the channel gain, and the event H0Representing a band free, event H1Represents that the band is busy;
probability of band acquisition PdDetermined by the channel response h:
Figure BDA0001295746540000022
where λ is the decision threshold for energy detection, and the Qm function is defined as:
Figure BDA0001295746540000023
the channel response h is constantly changing due to fading, when the probability of acquisition P is presentdExpressed as:
Figure BDA0001295746540000024
where r is the attenuated signal-to-noise ratio, fr(x) As a function of the probability density reflecting the change in signal-to-noise ratio under attenuation:
if the frequency band sensing results of each node are independently and equally distributed, n nodesIn, if there are more than k nodes, the decision is H1Determining that the frequency band is occupied;
capture probability Q for joint band sensingdExpressed as:
Qd＝1-(1-Pd)n
n represents the number of nodes participating in the sensing of the frequency band.
Preferably, for the sampled signal, the signal is divided by using an orthogonal window, each part of the periodogram is calculated, and finally, the superposition is performed to obtain the frequency band estimation.
Preferably, the communication flow between the sensing node and the sink node is as follows:
step 1: the sensing node is in a dormant state by default, TwFor periodic awakening, detecting whether a search request exists;
step 2: the convergent node triggers a search request by an event or a period with a period of T0；
Step 3: if the sensing node detects the search request and does not start the frequency band sensing in a given period, immediately starting the working frequency band sensing;
step 4: after the frequency band sensing is finished, the sensing node is TrStarting sensing result reporting for a period, after reporting for 3 times, entering a dormant state again, and caching a frequency band sensing result;
step 5: the sending time of the search request reaches a preset interval T1Then, the sink node starts sensing result collection;
step 6: and reporting all the collected sensing reports to the AC router in a gathering way.
Preferably, the sensing request of the sensing node lasts for a long enough time to ensure that the sensing nodes near the aggregation node all listen to the search request; while the feedback of the sensed information is short enough to avoid collisions between nodes.
Preferably, the channel optimization by using a particle swarm algorithm according to the sensing result further includes:
after the sensing results are gathered, the AC router obtains the crosstalk condition of the network; and optimizing the network through channel selection, and calculating a solution which enables the overall uplink and downlink crosstalk level of the whole network to be the lowest by adopting a particle swarm algorithm under the constraint condition that each access node can only select one channel.
Compared with the prior art, the invention has the following advantages:
the invention provides a WiFi rate control method based on environment noise and STA distance, which can be used for more accurately acquiring the condition of a wireless electromagnetic environment in real time, so that a network transmission channel is optimized, crosstalk is avoided, and the system performance is improved.
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Fig. 1 is a flowchart of a WiFi rate control method based on ambient noise and STA distance according to an embodiment of the present invention.
Detailed Description
A detailed description of one or more embodiments of the invention is provided below along with accompanying figures that illustrate the principles of the invention. The invention is described in connection with such embodiments, but the invention is not limited to any embodiment. The scope of the invention is limited only by the claims and the invention encompasses numerous alternatives, modifications and equivalents. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. These details are provided for the purpose of example and the invention may be practiced according to the claims without some or all of these specific details.
One aspect of the invention provides a WiFi rate control method based on environmental noise and STA distance. Fig. 1 is a flowchart of a WiFi rate control method based on ambient noise and STA distance according to an embodiment of the present invention.
According to the invention, a large number of random nodes with sensing capability are cooperated to complete sensing, acquisition and processing of the WIFI system. In the WIFI system, the working frequency band and the transmitting power of the access node are controlled by the AC router; and the user side communicates with the access node through an air interface to acquire signaling and data services. In the coverage range of a WIFI system, a plurality of sensing nodes are randomly distributed, the working frequency band of the WIFI is sensed, and the sensing result is reported to the aggregation nodes nearby; and the sink node selects the optimal working frequency band for communication according to the sensing result. The frequency band sensing results obtained by the access node and the user terminal are integrated at the AC router.
The sensing node in the present invention employs the following sensing algorithm.
For a single sensing node, band sensing is represented as one detection event:
Figure BDA0001295746540000041
where x (t) represents the signal received by the sensing node, s (t) is the signal sent by the sensing object, n (t) is noise, and h represents the channel gain. Event H0Representing a band free, event H1Representing that the band is busy.
Probability of band acquisition PdDetermined by the channel response h:
Figure BDA0001295746540000051
where λ is the decision threshold for energy detection, and the Qm function is defined as:
Figure BDA0001295746540000052
the channel response h is constantly changing due to fading, when the probability of acquisition P is presentdIs shown as
Figure BDA0001295746540000053
Where r is the attenuated signal-to-noise ratio, fr(x) Is a probability density function reflecting the change of the signal-to-noise ratio under the attenuation condition.
If the frequency band sensing results of the nodes are independently and equally distributed, if more than k nodes exist in the n nodes, the node is judged to be H1Then it is determined that the frequency band is occupied. Capture probability Q for joint band sensingdExpressed as:
Qd＝1-(1-Pd)n
n represents the number of nodes participating in the sensing of the frequency band.
For a sampled signal, the invention uses an orthogonal window to divide the signal, calculates the periodogram of each part, and finally carries out superposition to obtain the frequency band estimation. In order to keep both the bias of the estimate and the mean square error small. The following signature sequences are defined:
Figure BDA0001295746540000054
where N is the length of the eigenvector and-W, W is the band in which the energy is concentrated.
Obtaining sampling data x (N) within a frequency band, N being 0, 1.. N-1; after the values of N and W are determined, the characteristic v is calculatedkSorting the characteristic values into the first 2 NxW to prepare the subsequent calculation, and then calculating the characteristic coefficient x of x (N)k(f)：
Figure BDA0001295746540000061
Averaging the K characteristic coefficients to obtain a complete spectrum estimation:
Figure BDA0001295746540000062
the communication scheme between the sensing nodes and the sink nodes takes the power saving requirements of the sensing nodes and the synergistic effect requirements of the sink nodes into account. The specific communication flow is as follows:
step 1: the sensing node is in a dormant state by default, awakens by taking Tw as a period, and detects whether a search request exists or not;
step 2: the convergent node triggers a search request by an event or a period with a period of T0；
Step 3: if the sensing node detects the search request and does not start the frequency band sensing in a given period, immediately starting the working frequency band sensing;
step 4: after the frequency band sensing is finished, the sensing node is TrPeriodically starting the sensing result reporting, and after reporting for 3 times, re-entering the dormancyStatus, and buffer the frequency band sensing result;
step 5: the sending time of the search request reaches a preset interval T1Then, the sink node starts sensing result collection;
step 6: and reporting all the collected sensing reports to the AC router in a gathering way.
The sensing request of the sensing node lasts for a long enough time to ensure that the sensing nodes near the aggregation node all monitor the search request; while the feedback of the sensed information is short enough to avoid collisions between nodes.
And after the sensing results are gathered, the AC router obtains the crosstalk condition of the network. The network is optimized by channel selection to achieve the best network performance.
Optimal channel selection algorithm
Definition i ═ 1, 2.... cndot.n; n is the number of access nodes in the network;
j 1, 2.... am; m is the number of available channels;
k is 0,1, 2,. said., K; k is the number of the user terminals under a single access node, and the access node is when K is 0;
1,2, … …, L; l is the number of sensing nodes under a single sink node;
when the sensing node reports the sensing result RS of the available channel to the sink node;
RSk，l＝{RS1，RS2，……RSM}
defining an N-M dimensional matrix D representing the downlink crosstalk level, and defining an element D in the matrixijThe downlink crosstalk level of the jth channel in the coverage area of the ith access node is expressed as:
Figure BDA0001295746540000071
the sensing reports collected by the access nodes are used for calculating the uplink crosstalk level, and an N-M dimensional matrix U is defined to represent the uplink crosstalk level and UijThe uplink crosstalk level of the jth channel in the coverage area of the ith access node is expressed as:
Figure BDA0001295746540000072
establishing an element X in an N-M access node channel allocation matrix X, XipThe value ranges are 0 and 1, 1 indicates that the ith access node selects the p channel, and 0 indicates that the channel is not selected. Then the channel selection optimization problem is converted into a model which enables the uplink and downlink overall crosstalk level Z of the whole network to be the lowest under the constraint condition that each access node can only select one channel:
Figure BDA0001295746540000073
α represents the balance weight of the up and down link, if the system is up priority, α >0.5, if the system is down priority, α <0.5, adopt the particle swarm algorithm to calculate the optimal solution or suboptimum solution.
In summary, the invention provides a WiFi rate control method based on environment noise and STA distance, which can more accurately obtain the condition of the wireless electromagnetic environment in real time, so as to optimize the network transmission channel, avoid crosstalk, and improve the system performance.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented in a general purpose computing system, centralized on a single computing system, or distributed across a network of computing systems, and optionally implemented in program code that is executable by the computing system, such that the program code is stored in a storage system and executed by the computing system. Thus, the present invention is not limited to any specific combination of hardware and software.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.

Claims (5)

1. A WiFi rate control method based on environment noise and STA distance is characterized by comprising the following steps:
randomly deploying a plurality of sensing nodes in the WIFI system, and cooperatively finishing sensing and uploading of the WIFI network;
performing channel optimization by adopting a particle swarm algorithm according to a sensing result;
in the WIFI system, the working frequency band and the transmitting power of an access node are controlled by an AC router; a user side communicates with an access node through an air interface to acquire signaling and data service; the sensing node senses the working frequency band of the WIFI and reports the sensing result to the adjacent sink node; the sink node selects the optimal working frequency band for communication according to the sensing result, and the frequency band sensing results obtained by the access node and the user side are integrated in the AC router; the AC router is a router supporting 802.11AC wireless standard;
the sensing node employs the following sensing algorithm:
for a single sensing node, band sensing is represented as one detection event:
Figure FDA0002363786710000011
wherein x (t) represents the signal received by the sensing node, s (t) is the sending signal of the sensing object, n (t) is noise, H represents the channel gain, and the event H0Representing a band free, event H1Represents that the band is busy;
probability of band acquisition PdDetermined by the channel response m:
Figure FDA0002363786710000012
wherein lambda is the decision threshold for energy detection, QmThe function is defined as:
Figure FDA0002363786710000013
at this time the capture probability PdExpressed as:
Figure FDA0002363786710000014
where r is the attenuated signal-to-noise ratio, fr(x) As a function of the probability density reflecting the change in signal-to-noise ratio under attenuation:
if the frequency band sensing results of the nodes are independently and equally distributed, if more than k nodes exist in the n nodes, the node is judged to be H1Determining that the frequency band is occupied;
capture probability Q for joint band sensingdExpressed as:
Qd＝1-(1-Pd)n
n represents the number of nodes participating in band sensing;
definition i ═ 1, 2.... cndot.n; n is the number of access nodes in the network;
j 1, 2.... am; m is the number of available channels;
k is 0,1, 2,. said., K; k is the number of the user terminals under a single access node, and the access node is when K is 0;
1,2, … …, L; l is the number of sensing nodes under a single sink node;
when the sensing node reports the sensing result RS of the available channel to the sink node;
RSk，l＝{RS1，RS2，……RSM}
defining an N-M dimensional matrix D representing the downlink crosstalk level, and defining an element D in the matrixijThe downlink crosstalk level of the jth channel in the coverage area of the ith access node is shown in the tableShown as follows:
Figure FDA0002363786710000021
the sensing reports collected by the access nodes are used for calculating the uplink crosstalk level, and an N-M dimensional matrix U is defined to represent the uplink crosstalk level and UijThe uplink crosstalk level of the jth channel in the coverage area of the ith access node is expressed as:
Figure FDA0002363786710000022
establishing an element X in an N-M access node channel allocation matrix X, XipThe value range is 0 and 1, 1 represents that the ith access node selects the p channel, and 0 represents that the channel is not selected; for the same reason, xjpThe value range is 0 and 1, 1 represents that the jth access node selects the pth channel, and 0 represents that the channel is not selected; then the channel selection optimization problem is converted into a model which enables the uplink and downlink overall crosstalk level Z of the whole network to be the lowest under the constraint condition that each access node can only select one channel:
Figure FDA0002363786710000031
α represents the balance weight of up and down link, if the system is up link priority, α >0.5, if the system is down link priority, α <0.5, the optimal solution or suboptimum solution is calculated by adopting particle swarm optimization, the new solution generated by particle swarm is that two columns in N x M matrix are exchanged randomly, the more disturbance times, the more variation of solution, the more possible to get the optimal solution, when the Z value is minimum, the optimal channel selection combination is obtained.
2. The method of claim 1, wherein for a sampled signal, the signal is divided using orthogonal windows, the periodograms of the portions are calculated, and finally the overlap is performed to obtain the band estimate.
3. The method of claim 2, wherein the communication flow between the sensing node and the sink node is:
step 1: the sensing node is in a dormant state by default, TwFor periodic awakening, detecting whether a search request exists;
step 2: the convergent node triggers a search request by an event or a period with a period of T0；
Step 3: if the sensing node detects the search request and does not start the frequency band sensing in a given period, immediately starting the working frequency band sensing;
step 4: after the frequency band sensing is finished, the sensing node is TrStarting sensing result reporting for a period, after reporting for 3 times, entering a dormant state again, and caching a frequency band sensing result;
step 5: the sending time of the search request reaches a preset interval T1Then, the sink node starts sensing result collection;
step 6: and reporting all the collected sensing reports to the AC router in a gathering way.
4. The method of claim 3, wherein the sensing requests of the sensing nodes last long enough to ensure that the sensing nodes near the sink node all listen to the search request; while the feedback of the sensed information is short enough to avoid collisions between nodes.
5. The method of claim 4, wherein channel optimization is performed by using a particle swarm optimization according to the sensing result, and further comprising:
after the sensing results are gathered, the AC router obtains the crosstalk condition of the network; and optimizing the network through channel selection, and calculating a solution which enables the overall uplink and downlink crosstalk level of the whole network to be the lowest by adopting a particle swarm algorithm under the constraint condition that each access node can only select one channel.
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