CN108270496B - Bayesian criterion and channel correlation-based rapid spectrum detection method - Google Patents

Bayesian criterion and channel correlation-based rapid spectrum detection method Download PDF

Info

Publication number
CN108270496B
CN108270496B CN201711318856.3A CN201711318856A CN108270496B CN 108270496 B CN108270496 B CN 108270496B CN 201711318856 A CN201711318856 A CN 201711318856A CN 108270496 B CN108270496 B CN 108270496B
Authority
CN
China
Prior art keywords
channel
state
correlation
probability
channels
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.)
Active
Application number
CN201711318856.3A
Other languages
Chinese (zh)
Other versions
CN108270496A (en
Inventor
汪莹
李伟坚
林斌
吴赞红
亢中苗
刘紫健
陈辉煌
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Electric Power Dispatch Control Center of Guangdong Power Grid Co Ltd
Original Assignee
Electric Power Dispatch Control Center of Guangdong Power Grid Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Electric Power Dispatch Control Center of Guangdong Power Grid Co Ltd filed Critical Electric Power Dispatch Control Center of Guangdong Power Grid Co Ltd
Priority to CN201711318856.3A priority Critical patent/CN108270496B/en
Publication of CN108270496A publication Critical patent/CN108270496A/en
Application granted granted Critical
Publication of CN108270496B publication Critical patent/CN108270496B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/382—Monitoring; Testing of propagation channels for resource allocation, admission control or handover
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H04B17/3913—Predictive models, e.g. based on neural network models

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention relates to the technical field of intelligent home communication, in particular to a fast spectrum detection method based on Bayesian criterion and channel correlation, which groups channels through channel estimation based on channel correlation and channel prediction based on Bayesian criterion in intelligent home, finds a detection channel in each channel group and directly detects the detection channel, and estimates the states of other channels in the same group through the relation between the historical state and the detection channel; modeling the relation between the historical state of the channel and the current state of the channel based on Bayesian criterion, and predicting the probability of the next channel state; the contradiction generated by the two methods of channel estimation based on the channel correlation and channel prediction based on the Bayesian criterion is solved by adopting a minimum entropy algorithm; on the basis of ensuring certain accuracy, the detection time is shortened, and the frequency spectrum detection efficiency is improved.

Description

Bayesian criterion and channel correlation-based rapid spectrum detection method
Technical Field
The invention relates to the technical field of intelligent home communication, in particular to a fast spectrum detection method based on Bayesian criterion and channel correlation.
Background
Compared with the common home, the intelligent home has the traditional living function and can provide the information interaction function, so that people can look over home information and control related equipment of the home externally, the people can arrange time effectively, and the home life is safer and more comfortable. The system comprises the Internet, intelligent household appliances, a controller, a home network and a gateway. The network and the gateway of the intelligent home are key links for information interaction among intelligent household appliances, the internet and users, and are important contents and difficulties in the development and design stages. The final goal of smart home is to make the home environment more comfortable, safer, more environmentally friendly, and more convenient.
The appearance of the internet of things enables the functions of the existing intelligent home system to be richer, more diversified and personalized, and the system functions are mainly focused on intelligent lighting control, intelligent household appliance control, video chat, intelligent security and the like. The increasing demand for spectrum from communication systems due to the wide bandwidth and diversity of networks has also driven the spectrum resources to become more and more valuable. However, at present, the spectrum holes cannot be effectively utilized, and the accuracy cannot be guaranteed, and meanwhile, the spectrum detection event can be shortened and the detection efficiency cannot be improved.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a wireless communication rapid spectrum detection method based on Bayesian criterion and channel correlation.
In order to solve the technical problems, the invention adopts the technical scheme that:
a fast spectrum detection method based on Bayesian criterion and channel correlation is provided, which comprises the following steps:
s10, dividing the related channels into a group according to the channel correlation, selecting one channel in each group of related channels as a detection channel, and directly detecting the detection channel; the states of other channels in the same group are obtained by estimating the relation between the historical state of the channel and the state of the detection channel;
s20, calculating the entropy H1 of the channel according to the detected channel state, the historical channel state and the channel state estimated based on the channel correlation in the step S10;
s30, calculating by a cognitive user to obtain a probability function of the parameter according to the historical state of the spectrum sensing channel; modeling the relation between the historical state of the channel and the current state of the channel based on Bayesian criterion, and predicting the probability of the next channel state;
s40, calculating the entropy H2 of the channel according to the detected channel state, the historical channel state and the channel state predicted based on the Bayesian criterion in the step S30;
s50, comparing the sizes of the entropy H1 and the entropy H2: if H1< H2, outputting a channel state obtained based on channel correlation estimation as a detection result; and if H1> H2, outputting the channel state predicted based on the Bayesian criterion as a detection result.
The invention relates to a fast spectrum detection method based on Bayesian criterion and channel correlation, which groups channels through the correlation between the channels, finds a detection channel in each channel group, and directly detects the detection channel, and the states of other channels in the same group are jointly estimated through the relation between the historical state and the detection channel; modeling the relation between the historical state of the channel and the current state of the channel based on Bayesian criterion, and predicting the probability of the next channel state; the contradiction between the channel estimation based on the channel correlation and the channel prediction based on the Bayesian criterion needs a minimum entropy algorithm to be solved; on the basis of ensuring certain accuracy, the detection time is shortened, and the frequency spectrum detection efficiency is improved.
Preferably, the determination of the related channel in step S10 is performed as follows:
s11, representing the state of the channel in the determined time slot by binary information 0 or 1, wherein 0 represents that the channel is in an idle state, and 1 represents the state occupied by a main user in the channel; and by means of channel state vectors
Figure GDA0002287211080000021
To indicate the state of all channels, wherein
Figure GDA0002287211080000022
Represents the state of channel n in time slot M, M being the total number of time slots;
s12, calculating a correlation factor between the channel i and the channel j according to the formula (1); the correlation factor represents the probability that the two channels have the same state in a certain time slot;
wherein, CijIs the correlation factor between channel i and channel j, Θ is the exclusive nor operator;
s13, the correlation factor among the channels in the same service frequency domain is represented by a channel correlation matrix A, and the correlation matrix A is represented by the following formula (2):
Figure GDA0002287211080000031
wherein, CijIs the correlation factor between the channel i and the channel j, and N is the number of the channels in the service domain;
s14, setting a threshold value CthWhen the correlation factor C between channel i and channel jijGreater than a threshold value CthIf yes, judging that the channel i and the channel j are related channels; otherwise, the channel i and the channel j are judged to be non-correlated channels.
For accurately estimating the channel state, the operation of estimating the state of other channels in the same group is divided into two aspects, on one hand, in the same service network, the channels with large correlation are divided into one group, and one channel (i.e. a detection channel) in each group is selected to be directly detected, and the state of other channels (i.e. estimation channels) in the same group can be estimated according to the state of the detection channel. On the other hand, the channel state is very time-dependent, in other words, the current state of the channel can be predicted from its historical state.
Preferably, the method for determining the detection channel is as follows: obtaining a correlated channel group S from each row of the channel correlation matrix Ai，Si＝{cj|Cij≥CthIn which c isjRepresents the jth channel, cjThe correlation between the channel and other channels in the same group is large, and the channel group S is judgediThe detection channel in (c).
Preferably, the method for predicting the probability of the next channel state based on the bayesian criterion in step S30 includes the following steps:
s31, acquiring historical states of n channels through spectrum sensing, wherein X is ═ X1,x2,，......xn](ii) a Wherein x is1、x2……xnRespectively representing the states of channel 1, channel 2 … …, channel n; calculating a probability function of the parameter by the cognitive user;
s32, the cognitive user can calculate a probability function of a parameter theta to be P (theta/X), and can summarize the probability function into P (theta/X) × P (theta)/P (X) according to a Bayesian criterion;
s33, assuming that A represents the sum of the number of busy states in the history information, B represents the sum of the number of idle states in the history information, C represents the number of busy states before the current state is the busy state, and D represents the number of idle states before the current state is the idle state; defining P (A) as busy probability, idle probability as P (B), probability that the current state is busy and the previous state is busy as P (C/A), probability that the current state is idle and the previous state is idle as P (D/B); and under the condition that P (A), P (B), P (C/A) and P (D/B) are known, the probability that the current state is still busy is the probability of the next channel state under the condition that the previous state is busy and the previous state is idle.
Compared with the prior art, the invention has the beneficial effects that:
the invention relates to a fast spectrum detection method based on Bayesian criterion and channel correlation, which groups channels through the correlation between the channels, finds a detection channel in each channel group, and directly detects the detection channel, and the states of other channels in the same group are jointly estimated through the relation between the historical state and the detection channel; modeling the relation between the historical state of the channel and the current state of the channel based on Bayesian criterion, and predicting the probability of the next channel state; the contradiction generated by the two methods of channel estimation based on the channel correlation and channel prediction based on the Bayesian criterion is solved by adopting a minimum entropy algorithm; on the basis of ensuring certain accuracy, the detection time is shortened, and the frequency spectrum detection efficiency is improved.
Drawings
Fig. 1 is a flow chart of a fast spectrum detection method based on bayesian criterion and channel correlation.
Detailed Description
The present invention will be further described with reference to the following embodiments. Wherein the showings are for the purpose of illustration only and are shown by way of illustration only and not in actual form, and are not to be construed as limiting the present patent; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", etc. based on the orientation or positional relationship shown in the drawings, it is only for convenience of describing the present invention and simplifying the description, but it is not intended to indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and are not to be construed as limiting the present patent, and the specific meaning of the terms may be understood by those skilled in the art according to specific circumstances.
Example 1
Fig. 1 shows a first embodiment of the fast spectrum detection method based on bayesian criterion and channel correlation according to the present invention, which includes the following steps:
s10, dividing the related channels into a group according to the channel correlation, selecting one channel in each group of related channels as a detection channel, and directly detecting the detection channel; the states of other channels in the same group are obtained by estimating the relation between the historical state of the channel and the state of the detection channel;
s20, calculating the entropy H1 of the channel according to the detected channel state, the historical channel state and the channel state estimated based on the channel correlation in the step S10;
s30, calculating by a cognitive user to obtain a probability function of the parameter according to the historical state of the spectrum sensing channel; modeling the relation between the historical state of the channel and the current state of the channel based on Bayesian criterion, and predicting the probability of the next channel state;
s40, calculating the entropy H2 of the channel according to the detected channel state, the historical channel state and the channel state predicted based on the Bayesian criterion in the step S30;
s50, comparing the sizes of the entropy H1 and the entropy H2: if H1< H2, outputting a channel state obtained based on channel correlation estimation as a detection result; and if H1> H2, outputting the channel state predicted based on the Bayesian criterion as a detection result.
In step S10, the determination of the relevant channel is performed according to the following steps:
s11, representing the state of the channel in the determined time slot by binary information 0 or 1, wherein 0 represents that the channel is in an idle state, and 1 represents the state occupied by a main user in the channel; and by means of channel state vectors
Figure GDA0002287211080000051
To indicate the state of all channels, wherein
Figure GDA0002287211080000052
Represents the state of channel n in time slot M, M being the total number of time slots;
s12, calculating a correlation factor between the channel i and the channel j according to the formula (1); the correlation factor represents the probability that the two channels have the same state in a certain time slot;
wherein, CijIs the correlation factor between channel i and channel j, Θ is the exclusive nor operator;
s13, the correlation factor among the channels in the same service frequency domain is represented by a channel correlation matrix A, and the correlation matrix A is represented by the following formula (2):
Figure GDA0002287211080000054
wherein, CijIs the correlation factor between the channel i and the channel j, and N is the number of the channels in the service domain;
s14, setting a threshold value CthWhen the correlation factor C between channel i and channel jijGreater than a threshold value CthIf yes, judging that the channel i and the channel j are related channels; otherwise, the channel i and the channel j are judged to be non-correlated channels.
The judgment method for detecting the channel comprises the following steps: obtaining a correlated channel group S from each row of the channel correlation matrix Ai，Si＝{cj|Cij≥CthIn which c isjRepresents the jth channel, cjThe correlation between the channel and other channels in the same group is large, and the channel group S is judgediThe detection channel in (c).
Specifically, the method for predicting the probability of the next channel state based on the bayesian criterion in step S30 includes the following steps:
s31, acquiring historical states of n channels through spectrum sensing, wherein X is ═ X1,x2,，......xn](ii) a Wherein x is1、x2……xnRespectively representing the states of channel 1, channel 2 … …, channel n; calculating a probability function of the parameter by the cognitive user;
s32, the cognitive user can calculate a probability function of a parameter theta to be P (theta/X), and can summarize the probability function into P (theta/X) × P (theta)/P (X) according to a Bayesian criterion;
s33, assuming that A represents the sum of the number of busy states in the history information, B represents the sum of the number of idle states in the history information, C represents the number of busy states before the current state is the busy state, and D represents the number of idle states before the current state is the idle state; defining P (A) as busy probability, idle probability as P (B), probability that the current state is busy and the previous state is busy as P (C/A), probability that the current state is idle and the previous state is idle as P (D/B); and under the condition that P (A), P (B), P (C/A) and P (D/B) are known, the probability that the current state is still busy is the probability of the next channel state under the condition that the previous state is busy and the previous state is idle.
The contradiction between the channel estimation based on the channel correlation and the channel prediction based on the Bayesian criterion needs a minimum entropy algorithm to be solved.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (2)

1. A fast spectrum detection method based on Bayesian criterion and channel correlation is characterized by comprising the following steps:
s10, dividing the related channels into a group according to the channel correlation, selecting one channel in each group of related channels as a detection channel, and directly detecting the detection channel; obtaining the states of other channels in the same group through the relationship between the historical state of the channel and the state of the detection channel;
s20, calculating the entropy H1 of the channel according to the detected channel state, the historical channel state and the channel state estimated based on the channel correlation in the step S10;
s30, calculating by a cognitive user to obtain a probability function of the parameter according to the historical state of the spectrum sensing channel; modeling the relation between the historical state of the channel and the current state of the channel based on Bayesian criterion, and predicting the probability of the next channel state;
s40, calculating the entropy H2 of the channel according to the detected channel state, the historical channel state and the channel state predicted based on the Bayesian criterion in the step S30;
s50, comparing the sizes of the entropy H1 and the entropy H2: if H1< H2, outputting a channel state obtained based on channel correlation estimation as a detection result; if H1> H2, outputting a channel state predicted based on a Bayesian criterion as a detection result;
the determination of the relevant channel in step S10 is performed as follows:
s11, representing the state of the channel in the determined time slot by binary information 0 or 1, wherein 0 represents that the channel is in an idle state, and 1 represents the state occupied by a main user in the channel; and by means of channel state vectors
Figure FDA0002287211070000011
To indicate the state of all channels, wherein
Figure FDA0002287211070000012
Represents the state of channel n in time slot M, M being the total number of time slots;
s12, calculating a correlation factor between the channel i and the channel j according to the formula (1); the correlation factor represents the probability that the two channels have the same state in a certain time slot;
Figure FDA0002287211070000013
wherein, CijIs the correlation factor between channel i and channel j, Θ is the exclusive nor operator;
s13, the correlation factor among the channels in the same service frequency domain is represented by a channel correlation matrix A, and the correlation matrix A is represented by the following formula (2):
Figure FDA0002287211070000014
wherein, CijIs the correlation factor between the channel i and the channel j, and N is the number of the channels in the service domain;
s14, setting a threshold value CthWhen the correlation factor C between channel i and channel jijGreater than a threshold value CthIf yes, judging that the channel i and the channel j are related channels; otherwise, judging that the channel i and the channel j are non-related channels;
the detection messageThe method for determining the lane comprises the following steps: obtaining a correlated channel group S from each row of the channel correlation matrix Ai，Si＝{cj|Cij≥CthIn which c isjRepresents the jth channel, cjThe correlation between the channel and other channels in the same group is large, and the channel group S is judgediThe detection channel in (c).
2. The fast spectrum detecting method based on bayesian criterion and channel correlation according to claim 1, wherein the method for predicting the probability of the next channel state based on bayesian criterion in step S30 comprises the following steps:
s31, acquiring historical states of n channels through spectrum sensing, wherein X is ═ X1,x2,，......xn](ii) a Wherein x is1、x2……xnRespectively representing the states of channel 1, channel 2 … …, channel n; calculating a probability function of the parameter by the cognitive user;
s32, the cognitive user can calculate a probability function of a parameter theta to be P (theta/X), and can summarize the probability function into P (theta/X) × P (theta)/P (X) according to a Bayesian criterion;
s33, assuming that A represents the sum of the number of busy states in the history information, B represents the sum of the number of idle states in the history information, C represents the number of busy states before the current state is the busy state, and D represents the number of idle states before the current state is the idle state; defining P (A) as busy probability, idle probability as P (B), probability that the current state is busy and the previous state is busy as P (C/A), probability that the current state is idle and the previous state is idle as P (D/B); and under the condition that P (A), P (B), P (C/A) and P (D/B) are known, the probability that the current state is still busy is the probability of the next channel state under the condition that the previous state is busy and the previous state is idle.
CN201711318856.3A 2017-12-12 2017-12-12 Bayesian criterion and channel correlation-based rapid spectrum detection method Active CN108270496B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201711318856.3A CN108270496B (en) 2017-12-12 2017-12-12 Bayesian criterion and channel correlation-based rapid spectrum detection method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201711318856.3A CN108270496B (en) 2017-12-12 2017-12-12 Bayesian criterion and channel correlation-based rapid spectrum detection method

Publications (2)

Publication Number Publication Date
CN108270496A CN108270496A (en) 2018-07-10
CN108270496B true CN108270496B (en) 2020-02-04

Family

ID=62771869

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201711318856.3A Active CN108270496B (en) 2017-12-12 2017-12-12 Bayesian criterion and channel correlation-based rapid spectrum detection method

Country Status (1)

Country Link
CN (1) CN108270496B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110572181B (en) * 2019-08-12 2022-04-29 广东电网有限责任公司 Channel detection method, device and equipment of power line

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090238371A1 (en) * 2008-03-20 2009-09-24 Francis Rumsey System, devices and methods for predicting the perceived spatial quality of sound processing and reproducing equipment
CN102833020B (en) * 2012-09-10 2015-01-28 杭州电子科技大学 Bayes compression broadband frequency spectrum detection method in cognitive radio network based on self-adaptive measurement
CN103957537B (en) * 2014-03-26 2017-11-14 北京邮电大学 Frequency spectrum sensing method and device based on channel cluster
CN106231603B (en) * 2016-07-27 2019-05-10 北京邮电大学 A kind of spectrum prediction method and device

Also Published As

Publication number Publication date
CN108270496A (en) 2018-07-10

Similar Documents

Publication Publication Date Title
El Helou et al. A network-assisted approach for RAT selection in heterogeneous cellular networks
CN107452386B (en) Voice data processing method and system
KR100957264B1 (en) Systems and methods for selecting a transport mechanism for communication in a network
Ejaz et al. Distributed cooperative spectrum sensing in cognitive radio for ad hoc networks
KR101953144B1 (en) ACCESS POINT SUPPORTING 2.4GHz/5GHz DUAL BAND IN WIRELESS LAN AND ROAD BALANCING METHOD THEREOF
CN107070569A (en) Multipoint cooperative frequency spectrum sensing method based on HMM model
CN110855564B (en) Intelligent routing path selection method, device and equipment and readable storage medium
CN104703147B (en) Method for sending information and device
CN103428704A (en) Method and device for sensing frequency spectra
CN103971428A (en) Door control monitoring method and door control monitoring system
Noh et al. Reporting order control for fast primary detection in cooperative spectrum sensing
CN109039505B (en) Channel state transition probability prediction method in cognitive radio network
CN108270496B (en) Bayesian criterion and channel correlation-based rapid spectrum detection method
CN112804697A (en) Network distribution method and device for equipment to be networked, electronic equipment and computer readable medium
CN103781122A (en) Flow management device and method
Liu et al. Robust cooperative spectrum sensing scheduling optimization in multi-channel dynamic spectrum access networks
WO2022242207A1 (en) Voice information processing method and system, and apparatus for processing voice information
Iqbal et al. Machine learning and artificial intelligence in next-generation wireless network
CN106936527B (en) Multi-user cooperative spectrum sensing method based on double thresholds
CN103888423A (en) Information processing method and information processing device
WO2010116036A1 (en) Method and device for identifying applications which generate data traffic flows
CN109561482B (en) Data acquisition method, data acquisition device and intelligent terminal
CN106255121B (en) Channel allocation method and device
CN107241174B (en) Network switching method and network equipment
Long et al. An estimation algorithm of channel state transition probabilities for cognitive radio systems

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