CN102324992A - Threshold adaptation energy detection method - Google Patents

Abstract

The invention discloses a threshold adaptation energy detection method. In the energy detection method, a parameter computing scheme for an offline energy judgment threshold decision function and an online energy judgment threshold judgment scheme are sequentially realized. In the threshold adaptation energy detection method provided by the invention, the energy judgment threshold decision function is offline established, a value of an energy judgment threshold is dynamically regulated according to different signal to noise ratios, and online judgment is performed, so the intellectualization level of a cognitive radio user is improved, false alarm probability Pf(xi) is decreased to maximize the average data transmission rate of a secondary user (SU), energy overhead required in a running process is decreased, and an average value of false dismissal probability Pm(xi) is limited within a preset probability threshold range to avoid excessive interference in a primary user (PU).

Description

A kind of energy detection method of adaptive threshold

Technical field

The present invention relates to field of wireless communication, particularly a kind of energy detection method of adaptive threshold.

Background technology

Along with wireless communication technology and rapid development of network, wireless user's quantity sharply increases, and the form of service that inserts through wireless network becomes more diverse, and system has been proposed increasingly high bandwidth requirement, makes frequency spectrum resource become at full stretch.In addition, broadband and data, business, network IPization and incorporate progressively evolution along with wireless network, people have proposed increasingly high requirement to the utilance of bandwidth and frequency spectrum.And cognitive radio, be used to exactly solve this frequency spectrum use unbalanced, the technological means of the very potentialization of problem such as the availability of frequency spectrum is not high.At present; A typical application of cognitive radio is exactly that opportunistic spectrum inserts; Its core concept is through the cognition of Wireless Telecom Equipment to communication environment, and then according to certain mode of " waiting for an opportunity ", in spatial domain, time domain and frequency domain, goes to find to authorize the spectrum opportunities that can be utilized that occurs on the frequency range in real time; I.e. " frequency spectrum hole ", and reasonable use.As unauthorized user (Secondary User; SU) use authorized user (Primary User through the mode of this " using "; During PU) frequency spectrum resource, must guarantee that its communication process can not have influence on the communication of PU, or the interference of PU is controlled within certain specific scope.

Frequency spectrum detection refers to cognitive radios perception and analyze the ability of the frequency range operating position in the wireless communications environment on specific time, specific zone; To its objective is " the frequency spectrum hole " that be fit to communication in order finding out, to make cognitive radio system under the prerequisite that does not influence existing communication system, to carry out work effectively.Existing frequency spectrum detecting method have energy measuring (H.Urkowitz, " Energy detection of unknown deterministic signals, " Proceedings of IEEE, vol.55, pp.523-531, April 1967; F.F.Digham, M.-S.Alouini and M.K.Simon, " On the energy detection of unknow signals over fading channels; " IEEE Trans.Commun, vol.55, no.1; Pp.3575-3579, Jan.2007), matched filter detects (S.M.Kay, Fundamental of Statistical Signal Processing:Dection Theory.New Jersey:Prentice Hall; 1998) and cyclostationary characteristic detect (S.Enserink and D.Cochran, " A cycolstationary feature detector, " in Proc.28thAsilomar Conference on Signals; Systems, and Computers, Monterey; CA, Oct.1994, pp.806-810) etc.The two kinds of method detection methods in back need be known the priori of current frequency range PU, and its implementation complexity and cost are higher simultaneously, and detection time is also longer.Therefore, frequency spectrum detection generally is to accomplish through energy measuring and judgement at the transmitting terminal of SU.Through the wireless signal in the observed frequency range being measured and energy calculates, and compare, thereby whether judgement " the frequency spectrum hole " of communicating requirement occurred meeting with specific threshold value.Can the performance of frequency spectrum detection have determined SU catch spectrum opportunities to realize transfer of data efficiently effectively; And can judge the radiofrequency signal collision accident more exactly; SU can initiatively be kept out of the way under the situation that PU occurs as early as possible, avoid too much influencing the communication of PU, being noted that of value; The frequency spectrum detection technology not only plays a key effect in the search in " frequency spectrum hole " with in judging, also brings into play crucial effects at aspects such as the monitoring of frequency spectrum user mode and statistics.Through the monitoring of wireless frequency spectrum, can collect the statistics of wireless environment, for the spectrum management of high level provides auxiliary, and the necessary parameter support is provided for the communication of SU.

What adaptive spectrum detect to be stressed is the adaptive ability that improves the intelligent of frequency spectrum detection mechanism and wireless communications environment is changed.Existing frequency spectrum detecting method (W-Y.Lee and I.F.Akyildiz based on the energy judgement; " Optimal spectrum sensing framework for cognitive radio networks; " IEEE Transactions on Wireless Communications, 2008, vol.7; No.10, pp.3845-3857; H.Su and X.Zhang; " Energy-efficient spectrum sensing for cognitive radio networks; " IEEE ICC 2010.) adopts fixing energy decision threshold (wherein usually; At H.Su and X.Zhang, " Energy-efficient spectrum sensing for cognitive radio networks, " IEEE ICC 2010 mentions a kind of method that adopts double threshold.)。Choosing of threshold value generally is under the condition that satisfies the interference-limited of PU, and spectrum utilization chance through optimizing SU or transmission capacity etc. draw.In the cognitive radio system running, the energy judging threshold is difficult to carry out online adjustment according to the real-time change of wireless channel parameter adaptively.

Some researchers are thought of as the means that SU provides certain statistical quality of service; Wherein, Document Q.Du and X.Zhang; " Queue-aware spectrum sensing for interference-constrained transmissions in cognitive radio networks, " in Proc.IEEE ICC 2010 hypothesis SU transmitting terminals have a data buffer queue (Queue), and statistical quality of service can guarantee through the occupancy of control formation.Its implementation is the value of adjusting dynamically, in real time the energy decision threshold according to the occupancy of formation, makes that SU can carry out more positive transfer of data (to reduce queue occupancy) when queue occupancy was higher.This method has broken through and has adopted the framework of fixed threshold in the past, but has ignored the influence that the real-time change of unlimited channel parameter is caused the SU transfer of data.In fact, because the SU internal system exists certain agreement and feedback mechanism between transmitting-receiving two-end, the real-time detected SNIR of SU receiving terminal (signal to noise ratio) can feed back to transmitting terminal.The value of present energy decision threshold can not dynamically be adjusted according to SNIR, has had a strong impact on the accuracy of signal sensing and detection.

Because energy detector has advantages such as realizing simple, as to need not to know in advance PU signal characteristic and modulation system information, the frequency spectrum detection in the present cognitive radio adopts the energy measuring decision mechanism mostly.Signal calculates gross energy Y through the quadratic sum integrator then at first through a band pass filter filtering out-of-band noise, and last and energy judging threshold ξ compares, to judge two kinds of possible situation: H ₀And H ₁, H wherein ₀Represent the vacant frequency spectrum of PU, and on behalf of PU, H1 using frequency spectrum.If Y＜ξ, the result of energy measuring are H ₀Otherwise, be H ₁Under spectrum interlace mode (SU the idle periods work at PU), SU can only be H at court verdict usually ₀The time could transmit data.At document F.F.Digham, M.-S.Alouini and M.K.Simon, " On the energy detection of unknow signals over fading channels; " IEEE Trans.Commun, vol.55, no.1; Pp.3575-3579 mentions among the Jan.2007, and Y is at H ₀And H ₁All obeying card side under two hypothesis distributes.Because it is that random card side distributes that SU receives energy, when PU in fact when using frequency spectrum, the detected energy of SU might Y＜ξ, and draws H by error ₀Court verdict; So SU brings into use frequency spectrum, and the communication of PU caused interference.This probability

is defined as false dismissal probability.Corresponding, on behalf of frequency spectrum, detection probability

can correctly be detected by the SU energy detector by the state that PU takies.In addition, under the situation of the vacant frequency spectrum resource of PU, it is alert that the SU energy detector also possibly produce mistake, and corresponding definition of probability is the mistake alarm probability $P_f\left(\mathrm{\ξ}\right)\stackrel{\mathrm{\Δ}}{=}\mathrm{Pr}\{Y\≥\mathrm{\ξ}|H_0\}.$

Summary of the invention

The objective of the invention is to overcome existing above-mentioned deficiency in the prior art; A kind of energy detection method of adaptive threshold is provided; Dynamically the value of adjustment energy decision threshold to improve the intelligent level of cognitive radio users, makes the average data transfer rate of SU reach maximum.

Another object of the present invention is with false dismissal probability P _mMean value (ξ) is limited to a preset probability threshold scope

Within, reduce mistake alarm probability P _f(ξ), and guarantee PU not to be caused excessive interference.

In order to realize the foregoing invention purpose, the invention provides following technical scheme:

A kind of energy detection method of adaptive threshold, this energy detection method are realized the calculation of parameter scheme of the decision function of off-line energy decision threshold earlier, are implemented in heat input decision threshold arbitration schemes again;

The calculation of parameter scheme of the decision function of said off-line energy decision threshold may further comprise the steps:

Step 1: the decision function of setting up the energy decision threshold;

Step 2: the average data transfer rate

of calculating the SU transmitting terminal

Step 3:

is optimized satisfying under the restrictive condition;

Step 4: the parameter that calculates decision function through optimization tool;

Saidly may further comprise the steps in heat input decision threshold arbitration schemes:

Step 5:SU transmitting terminal obtains the signal to noise ratio γ feedback of SU receiving terminal in real time;

Step 6:SU transmitting terminal is confirmed current decision threshold according to the optimized parameter of the decision function that has calculated by decision function;

Step 7:SU transmitting terminal is made comparisons detected signal energy value Y and thresholding ξ, if Y＜ξ, judgement is H so ₀, promptly adjudicate not work of PU; Otherwise judgement is promptly adjudicated PU and is worked for H1.

According to embodiments of the invention; The decision function of energy decision threshold described in the above-mentioned steps 1 for

wherein γ feed back to the SNIR (signal to noise ratio) of SU transmitting terminal for the SU receiving terminal; ξ is the energy decision threshold; K, b are the parameter of the decision function of energy decision threshold to be determined, are unknown numbers.

According to embodiments of the invention, the average data transfer rate of SU transmitting terminal described in the above-mentioned steps 2

does $\stackrel{\&OverBar;}{R}={\stackrel{\&OverBar;}{R}}_{{\mathrm{<figure-callout\; id="0"\; label="H"\; filenames="HDA0000104014250000012.png"\; state="\{\{state\}\}">H</figure-callout>}}_0}+{\stackrel{\&OverBar;}{R}}_{{\mathrm{<figure-callout\; id="1"\; label="H"\; filenames="HDA0000104014250000013.png"\; state="\{\{state\}\}">H</figure-callout>}}_1}=B\&times;E\left[{\log }_2(1+{\mathrm{\&gamma;}}_{H_0})\mathrm{Pr}\right\{H_0\left\}(1-P_f\left(\mathrm{\&xi;}\right))\right]+B\&times;E\left[{\log }_2(1+{\mathrm{\&gamma;}}_{H_1})\mathrm{Pr}\right\{H_1\left\}{P}_m\left(\mathrm{\&xi;}\right)\right],$ H wherein ₀For PU (main user) does not transmit the court verdict of data, H ₁Be the court verdict of PU transmission data, Be H ₀The average transmission rate of SU under the condition,

Be H ₁Average transmission rate under the condition,

Be H ₁The SU receiving terminal feeds back to the signal to noise ratio of SU transmitting terminal under the condition,

Be H ₀The SU receiving terminal feeds back to the signal to noise ratio of SU transmitting terminal, Pr{H under the condition ₁Take the probability of frequency spectrum, Pr{H for PU ₀Be the probability that PU does not take frequency spectrum, Pr{H ₁}+Pr{H ₀}=1, P _m(ξ) be false dismissal probability, Pf (ξ) is the mistake alarm probability, and B is a channel width.

According to embodiments of the invention; The method of the average data transfer rate

of said calculating SU transmitting terminal is: the bandwidth of supposing the system is B; According to shannon formula, the message transmission rate R of SU can be expressed as:

Because the SU transmitting terminal only just transmits data when detecting gross energy Y＜ξ, be so obtain the average data transfer rate of SU transmitting terminal:

$\stackrel{\&OverBar;}{R}={\stackrel{\&OverBar;}{R}}_{{\mathrm{<figure-callout\; id="0"\; label="H"\; filenames="HDA0000104014250000012.png"\; state="\{\{state\}\}">H</figure-callout>}}_0}+{\stackrel{\&OverBar;}{R}}_{{\mathrm{<figure-callout\; id="1"\; label="H"\; filenames="HDA0000104014250000013.png"\; state="\{\{state\}\}">H</figure-callout>}}_1}=B\&times;E\left[{\log }_2(1+{\mathrm{\&gamma;}}_{H_0})\mathrm{Pr}\right\{H_0\left\}(1-P_f\left(\mathrm{\&xi;}\right))\right]+B\&times;E\left[{\log }_2(1+{\mathrm{\&gamma;}}_{H_1})\mathrm{Pr}\right\{H_1\left\}{P}_m\left(\mathrm{\&xi;}\right)\right]$

Wherein γ feeds back to the SNIR (signal to noise ratio) of SU transmitting terminal for the SU receiving terminal; The average that asked in

expression, the average that

asked in

expression.

According to embodiments of the invention, restrictive condition does described in the above-mentioned steps 3 $E\left[P_m\left(\mathrm{\&xi;}\left({\mathrm{\&gamma;}}_{H_1}\right)\right)\right]={\&Integral;}_0^{\&infin;}{P}_m\left(\mathrm{\&xi;}\left({\mathrm{\&gamma;}}_{H_1}\right)\right)f_{H_1}\left({\mathrm{\&gamma;}}_{H_1}\right){\mathrm{d\&gamma;}}_{{\mathrm{<figure-callout\; id="1"\; label="H"\; filenames="HDA0000104014250000013.png"\; state="\{\{state\}\}">H</figure-callout>}}_1}\&le;{\stackrel{\&OverBar;}{P}}_m,$ Wherein

Be H ₁The SU receiving terminal feeds back to the signal to noise ratio of SU transmitting terminal under the condition, For The time false dismissal probability,

Be preset false dismissal probability P _m(ξ) probability threshold of mean value,

Be

Probability density function,

Expression is asked

Average.

According to embodiments of the invention, above-mentioned steps 3 is said to be satisfied under the restrictive condition

be optimized and may further comprise the steps:

S31: with the average data transfer rate formula of SU transmitting terminal

$\stackrel{\&OverBar;}{R}={\stackrel{\&OverBar;}{R}}_{{\mathrm{<figure-callout\; id="0"\; label="H"\; filenames="HDA0000104014250000012.png"\; state="\{\{state\}\}">H</figure-callout>}}_0}+{\stackrel{\&OverBar;}{R}}_{{\mathrm{<figure-callout\; id="1"\; label="H"\; filenames="HDA0000104014250000013.png"\; state="\{\{state\}\}">H</figure-callout>}}_1}=B\&times;E\left[{\log }_2(1+{\mathrm{\&gamma;}}_{H_0})\mathrm{Pr}\right\{H_0\left\}(1-P_f\left(\mathrm{\&xi;}\right))\right]+B\&times;E\left[{\log }_2(1+{\mathrm{\&gamma;}}_{H_1})\mathrm{Pr}\right\{H_1\left\}{P}_m\left(\mathrm{\&xi;}\right)\right]$

Develop into $\left\{\mathrm{Pr}\right\{H_0\left\}E\right[{\log }_2(1+{\mathrm{\&gamma;}}_{H_0})(1-P_f\left(\mathrm{\&xi;}\left({\mathrm{\&gamma;}}_{H_0}\right)\right))]+\mathrm{Pr}\{H_1\left\}E\right[{\log }_2(1+{\mathrm{\&gamma;}}_{H_1})P_m\left(\mathrm{\&xi;}\left({\mathrm{\&gamma;}}_{H_1}\right)\right)\left]\right\}$ Get into step S32;

S32: combine the mistake alarm probability under the rayleigh fading channel $P_f\left(\mathrm{\&xi;}\right)=1-\frac{\mathrm{\&Gamma;}(M,\mathrm{\&xi;}/2)}{\mathrm{\&Gamma;}\left(M\right)}$ And detection probability $P_d\left(\mathrm{\&xi;}\right)e^{-\frac{\mathrm{\&xi;}}{2}}\underset{m=0}{\overset{M-2}{\mathrm{\&Sigma;}}}\frac{1}{m!}{\left(\frac{\mathrm{\&xi;}}{2}\right)}^m+{\left(\frac{1+{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Ps}}}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Ps}}}\right)}^{M-1}\&times;[e^{-\frac{\mathrm{\&xi;}}{2(1-{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Ps}})}}-e^{-\frac{\mathrm{\&xi;}}{2}}\underset{m=0}{\overset{M-2}{\mathrm{\&Sigma;}}}\frac{1}{m!}{\left(\frac{\mathrm{\&xi;}{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Ps}}}{2(1+{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Ps}})}\right)}^m],$ Probability density function $f_{H_0}\left({\mathrm{\&gamma;}}_{H_0}\right)=f_{\mathrm{Sr}}\left({\mathrm{\&gamma;}}_{\mathrm{Sr}}\right)=\frac{1}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}}}{e}^{-\frac{{\mathrm{\&gamma;}}_{{\mathrm{<figure-callout\; id="0"\; label="H"\; filenames="HDA0000104014250000012.png"\; state="\{\{state\}\}">H</figure-callout>}}_0}}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}}}},$ ${\mathrm{\&gamma;}}_{{\mathrm{<figure-callout\; id="0"\; label="H"\; filenames="HDA0000104014250000012.png"\; state="\{\{state\}\}">H</figure-callout>}}_0}\&GreaterEqual;0$

The probability density function of

$f_{H_1}\left({\mathrm{\&gamma;}}_{H_1}\right)={\&Integral;}_0^{\&infin;}{f}_{\mathrm{Sr}}\left({\mathrm{\&gamma;}}_{H_1}t\right)f_{\mathrm{Pr}}(t-1)\mathrm{Tdt}=\frac{e^{-\frac{{\mathrm{\&gamma;}}_{{\mathrm{<figure-callout\; id="1"\; label="H"\; filenames="HDA0000104014250000013.png"\; state="\{\{state\}\}">H</figure-callout>}}_1}}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}}}}}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}}{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Pr}}}{\&Integral;}_0^{\&infin;}{e}^{-(\frac{{\mathrm{\&gamma;}}_{{\mathrm{<figure-callout\; id="1"\; label="H"\; filenames="HDA0000104014250000013.png"\; state="\{\{state\}\}">H</figure-callout>}}_1}}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}}}+\frac{1}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Pr}}})t}(t+1)\mathrm{Dt}=[\frac{1}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Pr}}{\mathrm{\&gamma;}}_{{\mathrm{<figure-callout\; id="1"\; label="H"\; filenames="HDA0000104014250000013.png"\; state="\{\{state\}\}">H</figure-callout>}}_1}+{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}}}+\frac{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}}{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Pr}}}{{({\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Pr}}{\mathrm{\&gamma;}}_{{\mathrm{<figure-callout\; id="1"\; label="H"\; filenames="HDA0000104014250000013.png"\; state="\{\{state\}\}">H</figure-callout>}}_1}+{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}})}^2}]e^{-\frac{{\mathrm{\&gamma;}}_{{\mathrm{<figure-callout\; id="1"\; label="H"\; filenames="HDA0000104014250000013.png"\; state="\{\{state\}\}">H</figure-callout>}}_1}}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}}}},$ Optimization problem is converted into maximization $\frac{\mathrm{B\&beta;}}{\mathrm{\&Gamma;}\left(M\right){\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}}(\mathrm{\&alpha;}+\mathrm{\&beta;})}{\&Integral;}_0^{\&infin;}{\mathrm{Log}}_2(1+{\mathrm{\&gamma;}}_{H_0})\&times;\mathrm{\&Gamma;}(M,\frac{\mathrm{\&xi;}\left({\mathrm{\&gamma;}}_{H_0}\right)}{2})e^{-\frac{{\mathrm{\&gamma;}}_{{\mathrm{<figure-callout\; id="0"\; label="H"\; filenames="HDA0000104014250000012.png"\; state="\{\{state\}\}">H</figure-callout>}}_0}}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}}}}{\mathrm{D\&gamma;}}_{{\mathrm{<figure-callout\; id="0"\; label="H"\; filenames="HDA0000104014250000012.png"\; state="\{\{state\}\}">H</figure-callout>}}_0}+$ $\frac{\mathrm{B\&alpha;}}{\mathrm{\&alpha;}+\mathrm{\&beta;}}{\&Integral;}_0^{\&infin;}{\mathrm{Log}}_2(1+{\mathrm{\&gamma;}}_{H_1})(1-P_d\left(\mathrm{\&xi;}\left({\mathrm{\&gamma;}}_{H_1}\right)\right))[\frac{1}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Pr}}{\mathrm{\&gamma;}}_{{\mathrm{<figure-callout\; id="1"\; label="H"\; filenames="HDA0000104014250000013.png"\; state="\{\{state\}\}">H</figure-callout>}}_1}+{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}}}+\frac{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}}{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Pr}}}{{({\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Pr}}{\mathrm{\&gamma;}}_{{\mathrm{<figure-callout\; id="1"\; label="H"\; filenames="HDA0000104014250000013.png"\; state="\{\{state\}\}">H</figure-callout>}}_1}+{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}})}^2}]e^{-\frac{{\mathrm{\&gamma;}}_{{\mathrm{<figure-callout\; id="1"\; label="H"\; filenames="HDA0000104014250000013.png"\; state="\{\{state\}\}">H</figure-callout>}}_1}}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}}}}{\mathrm{D\&gamma;}}_{{\mathrm{<figure-callout\; id="1"\; label="H"\; filenames="HDA0000104014250000013.png"\; state="\{\{state\}\}">H</figure-callout>}}_1},$ Wherein,

For The time the energy decision threshold,

For

Mistake alarm probability under the condition,

For

False dismissal probability under the condition,

Be preset false dismissal probability P _m(ξ) probability threshold of mean value, Be

Probability density function, γ _PrRepresent main user's transmitting terminal to signal to noise ratio from user's receiving terminal,

Be γ _PrAverage, γ _SrExpression is from user's transmitting terminal and the signal to noise ratio between the receiving terminal, Be γ _SrAverage, f _Sr() expression γ _SrProbability density function, this moment f _Sr" " does in ()

Expression γ under the condition _SrProbability density function, f _Pr() expression γ _PrProbability density function, this moment f _Pr" " in () is " t-1 ", and Γ (M) is a gamma function, Γ (M; ξ/2) be incomplete gamma function, M is that sampled signal is counted, and m is that variable is used in summation; Represent m sampled point, suppose that the PU course of work is the life of the two condition markoff process that goes out, α is PU " birth probability "; β is " rate of death ", Pr{H ₁}=α/(alpha+beta), Pr{H ₀}=β/(alpha+beta).

According to embodiments of the invention, the SU transmitting terminal based on the flow process that the adaptive threshold energy detection method carries out frequency spectrum detection is:

A. band pass filter (BPF) filtering out-of-band noise;

B. squarer and integrator calculating sampling signal belong to the time and the gross energy Y in the spectral range (supposing that signal was just sampled before getting into energy detector) of detection;

C. detected signal energy value Y and thresholding ξ are made comparisons, if the gross energy Y of the signal that samples less than energy decision threshold ξ, i.e. Y＜ξ, then the energy detector of SU transmitting terminal draws H ₀Court verdict, promptly judge not work of PU, and utilize in the current time slots spectrum opportunities to send data; If Y>=ξ, then the energy detector of SU transmitting terminal draws H ₁Court verdict, promptly PU work, SU can not send data in current time slots.

Compared with prior art, beneficial effect of the present invention: the energy detection method of adaptive threshold of the present invention, come the dynamically value of adjustment energy decision threshold according to the variation of the SINR of SU internal system,

1, improved the intelligent level of cognitive radio users, greatly strengthened to the time wireless communications environment that becomes adaptive ability;

2, mode of operation is simple, can realize the quick response of environmental change is also greatly reduced energy needed expense in the running.

3, reduced the mistake alarm probability, made the average data transfer rate of SU reach maximum, SU user can send data more, thereby has increased throughput, has improved power system capacity.

4, the mean value with false dismissal probability has been limited within the preset probability threshold scope, PU is not caused excessive interference.

Description of drawings:

Fig. 1 is PU, SU communication scenes sketch map;

Fig. 2 is the ξ of decision function family (γ) sketch map;

Fig. 3 is at heat input decision threshold arbitration schemes flow chart;

Fig. 4 is the calculation of parameter scheme flow chart of off-line decision threshold decision function;

Fig. 5 is the energy detector block diagram of SU transmitting terminal based on the adaptive threshold energy detection method.

Embodiment

Below in conjunction with Test Example and embodiment the present invention is made further detailed description.But should this be interpreted as that the scope of the above-mentioned theme of the present invention only limits to following embodiment, allly all belong to scope of the present invention based on the technology that content of the present invention realized.

With reference to figure 1, wherein, PU (Primary User) representes main user, and SU_S (Secondary User ' s Sender) expression is from user's transmitting terminal, and SU_R (Secondary User ' s Reciever) expression is from user's receiving terminal, γ _PsRepresent main user and from the signal to noise ratio between user's transmitting terminal, γ _PrRepresent main user and from the signal to noise ratio between user's receiving terminal, γ _SrExpression is from user's transmitting terminal and the signal to noise ratio between the receiving terminal.

Consider that is pressed the cognition wireless network that time slot is divided transmission channel, also concentrate for the simplification problem frequency spectrum detection technology is analyzed, suppose to have in the network PU transmitting terminal, a SU transmitting terminal, a SU receiving terminal.H ₀Represent the vacant frequency spectrum of PU, H ₁Represent PU using frequency spectrum.The probability that PU takies frequency spectrum is Pr{H ₁, the probability that does not take frequency spectrum is Pr{H ₀, Pr{H ₀}+Pr{H ₁}=1, its average statistical can be through adding up known in advance.The SU receiving terminal can also can feed back received signal to noise ratio γ to the SU transmitting terminal aperiodically on each slot cycle property ground to SU transmitting terminal feedback received signal to noise ratio γ when signal to noise ratio γ changes.

The assumed wireless channel is the rayleigh fading channel with additive white Gaussian noise, uses γ _SrExpression SU transmitting terminal is to the real-time signal to noise ratio of SU receiving terminal, γ _PrExpression PU transmitting terminal is to the signal to noise ratio of SU receiving terminal, γ _PrExpression PU transmitting terminal is to the signal to noise ratio of SU transmitting terminal, γ _Sr, γ _Pr, γ _PsBe the independently stochastic variable of exponential distribution, their average

With Known in advance through long-time measuring mode.Taking with vacant frequency spectrum at PU and to send under two kinds of different conditions of data, the detected signal to noise ratio SINR of SU receiving terminal (Signal to Interference plus Noise Ratio) is expressed as

Because the SU transmitting-receiving two-end exists certain communication protocol and feedback mechanism, the SU receiving terminal feeds back to the SU transmitting terminal with detected γ value at first at each time slot.Because the randomness of the time-varying characteristics of wireless channel and PU transmission data, γ has different values at different time slots, but can suppose that the value of γ remains unchanged in a time slot.Because γ is a stochastic variable, the SU transmitting terminal can only be discerned PU through energy measuring and whether take frequency spectrum, and can't confirm the user mode of frequency spectrum through the γ value that the SU receiving terminal feeds back.

Usually, bigger γ value has the hint of two aspects: 1) transmission quality of wireless channel is good, transmission capacity is high, thereby can support bigger transmission rate; What 2) PU was taking frequency spectrum maybe be less relatively, therefore causes the possibility of interference also relatively low to PU.Therefore, when the γ of SU receiving terminal feedback was big, the SU transmitting terminal should improve energy decision threshold ξ, made the SU transmitting terminal send possibly increasing of data, improved throughput; Otherwise γ hour of SU receiving terminal feedback, the SU transmitting terminal should reduce energy decision threshold ξ, makes the SU transmitting terminal send possibly reducing of data, avoids PU is caused interference.So energy decision threshold ξ is the increasing function of γ, with the decision function of ξ (γ) expression energy decision threshold ξ.From the complexity that reduces real-time operation, enhance rapid response capacity and energy-conservation angle is considered, decision function is chosen linear increasing function family and is analyzed, as shown in Figure 2, its expression formula is following:

Wherein k, b are the parameter of the decision function of energy decision threshold, are unknown numbers to be determined, make the adjustment of energy decision threshold ξ can guarantee that throughput of transmissions is maximum, and the interference of PU is controlled in the scope of tolerance.

Need to prove that this method for designing is general, all be suitable for that not necessarily requiring is linear increasing function to any type of decision function increasing function.

The calculation of parameter scheme flow chart of the decision function of off-line as shown in Figure 3 (promptly being illustrated in before the work, just that calculation of parameter is good) judgement energy decision threshold, confirm that the method for the value of parameter k, b is:

Wherein set Pr{H ₁, Pr{H ₀,

With Method be: suppose that the PU course of work is the life of the two condition markoff process that goes out, wherein PU " birth probability " is α, and " rate of death " is β, like this, Pr{H ₁}=α/(alpha+beta), Pr{H ₀}=β/(alpha+beta); γ _SrExpression SU transmitting terminal is to the real-time signal to noise ratio of SU receiving terminal, γ _PrExpression PU transmitting terminal is to the signal to noise ratio of SU receiving terminal, their average

It is known in advance through long-time measuring mode,

Be the false dismissal probability P that presets _m(ξ) mean value threshold.

The bandwidth of supposing the system is B, and according to shannon formula, the message transmission rate R of SU can be expressed as:

The SU transmitting terminal only just transmits data when detecting Y＜ξ, the average data transfer rate that then obtains the SU transmitting terminal is:

$\stackrel{\&OverBar;}{R}={\stackrel{\&OverBar;}{R}}_{{\mathrm{<figure-callout\; id="0"\; label="H"\; filenames="HDA0000104014250000012.png"\; state="\{\{state\}\}">H</figure-callout>}}_0}+{\stackrel{\&OverBar;}{R}}_{{\mathrm{<figure-callout\; id="1"\; label="H"\; filenames="HDA0000104014250000013.png"\; state="\{\{state\}\}">H</figure-callout>}}_1}=B\&times;E\left[{\log }_2(1+{\mathrm{\&gamma;}}_{H_0})\mathrm{Pr}\right\{H_0\left\}(1-P_f\left(\mathrm{\&xi;}\right))\right]+B\&times;E\left[{\log }_2(1+{\mathrm{\&gamma;}}_{H_1})\mathrm{Pr}\right\{H_1\left\}{P}_m\left(\mathrm{\&xi;}\right)\right]---\left(3\right)$

Wherein,

Be the average transmission rate of SU under the H0 condition,

Be H ₁Average transmission rate under the condition, Pr{H ₁Take the probability of frequency spectrum, Pr{H for PU ₀Be the probability that PU does not take frequency spectrum, Pr{H ₁}+Pr{H ₀}=1, P _m(ξ) be false dismissal probability, Be H ₁Signal to noise ratio under the condition,

Be H ₀Signal to noise ratio under the condition, P _f(ξ) be the mistake alarm probability,

Expression is asked Average,

Expression is asked

Average.

The form of function ξ (γ) through being expressed as ξ γ in conjunction with formula (1), (3), is converted into following form with the average data transfer rate formula (3) of SU transmitting terminal:

$\left\{\mathrm{Pr}\right\{H_0\left\}E\right[{\log }_2(1+{\mathrm{\&gamma;}}_{H_0})(1-P_f\left(\mathrm{\&xi;}\left({\mathrm{\&gamma;}}_{H_0}\right)\right))]+\mathrm{Pr}\{H_1\left\}E\right[{\log }_2(1+{\mathrm{\&gamma;}}_{H_1})P_m\left(\mathrm{\&xi;}\left({\mathrm{\&gamma;}}_{H_1}\right)\right)\left]\right\}---\left(4\right)$

And the definition optimization problem is for will (4) maximizing, and satisfies restrictive condition

$E\left[P_m\left(\mathrm{\&xi;}\left({\mathrm{\&gamma;}}_{H_1}\right)\right)\right]={\&Integral;}_0^{\&infin;}{P}_m\left(\mathrm{\&xi;}\left({\mathrm{\&gamma;}}_{H_1}\right)\right)f_{H_1}\left({\mathrm{\&gamma;}}_{H_1}\right){\mathrm{d\&gamma;}}_{{\mathrm{<figure-callout\; id="1"\; label="H"\; filenames="HDA0000104014250000013.png"\; state="\{\{state\}\}">H</figure-callout>}}_1}\&le;{\stackrel{\&OverBar;}{P}}_m---\left(5\right)$

Wherein,

For Mistake alarm probability under the condition,

For

False dismissal probability under the condition,

Be preset false dismissal probability P _m(ξ) probability threshold of mean value,

Be

Probability density function,

Expression is asked

Average.

Under rayleigh fading channel, mistake alarm probability and detection probability can be expressed as respectively:

The mistake alarm probability: $P_f\left(\mathrm{\&xi;}\right)=1-\frac{\mathrm{\&Gamma;}(M,\mathrm{\&xi;}/2)}{\mathrm{\&Gamma;}\left(M\right)}$

Detection probability: $P_d\left(\mathrm{\&xi;}\right)e^{-\frac{\mathrm{\&xi;}}{2}}\underset{m=0}{\overset{M-2}{\mathrm{\&Sigma;}}}\frac{1}{m!}{\left(\frac{\mathrm{\&xi;}}{2}\right)}^m+{\left(\frac{1+{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{ps}}}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{ps}}}\right)}^{M-1}\&times;[e^{-\frac{\mathrm{\&xi;}}{2(1-{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{ps}})}}-e^{-\frac{\mathrm{\&xi;}}{2}}\underset{m=0}{\overset{M-2}{\mathrm{\&Sigma;}}}\frac{1}{m!}{\left(\frac{\mathrm{\&xi;}{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{ps}}}{2(1+{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{ps}})}\right)}^m]$

Wherein, Γ (M) is a gamma function, and Γ (M, ξ/2) is incomplete gamma function, and M is that sampled signal is counted, and m is the variable of summation usefulness, representes m sampled point.Because

and

satisfies the exponential distribution of average for

, so the probability density function of

does $f_{H_0}\left({\mathrm{\&gamma;}}_{H_0}\right)=f_{\mathrm{sr}}\left({\mathrm{\&gamma;}}_{\mathrm{sr}}\right)=\frac{1}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{sr}}}{e}^{-\frac{{\mathrm{\&gamma;}}_{{\mathrm{<figure-callout\; id="0"\; label="H"\; filenames="HDA0000104014250000012.png"\; state="\{\{state\}\}">H</figure-callout>}}_0}}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{sr}}}},$ ${\mathrm{\&gamma;}}_{{\mathrm{<figure-callout\; id="0"\; label="H"\; filenames="HDA0000104014250000012.png"\; state="\{\{state\}\}">H</figure-callout>}}_0}\&GreaterEqual;0,$

On the other hand,

Be two independently exponential random variable γ _SrAnd γ _PrFunction.Make f _Pr(γ _Pr) represent γ _PrProbability density function, according to the computing formula of stochastic variable, can obtain:

$f_{H_1}\left({\mathrm{\&gamma;}}_{H_1}\right)={\&Integral;}_0^{\&infin;}{f}_{\mathrm{sr}}\left({\mathrm{\&gamma;}}_{H_1}t\right)f_{\mathrm{pr}}(t-1)\mathrm{tdt}=\frac{e^{-\frac{{\mathrm{\&gamma;}}_{{\mathrm{<figure-callout\; id="1"\; label="H"\; filenames="HDA0000104014250000013.png"\; state="\{\{state\}\}">H</figure-callout>}}_1}}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{sr}}}}}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{sr}}{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{pr}}}{\&Integral;}_0^{\&infin;}{e}^{-(\frac{{\mathrm{\&gamma;}}_{{\mathrm{<figure-callout\; id="1"\; label="H"\; filenames="HDA0000104014250000013.png"\; state="\{\{state\}\}">H</figure-callout>}}_1}}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{sr}}}+\frac{1}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{pr}}})t}(t+1)\mathrm{dt}=[\frac{1}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{pr}}{\mathrm{\&gamma;}}_{{\mathrm{<figure-callout\; id="1"\; label="H"\; filenames="HDA0000104014250000013.png"\; state="\{\{state\}\}">H</figure-callout>}}_1}+{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{sr}}}+\frac{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{sr}}{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{pr}}}{{({\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{pr}}{\mathrm{\&gamma;}}_{{\mathrm{<figure-callout\; id="1"\; label="H"\; filenames="HDA0000104014250000013.png"\; state="\{\{state\}\}">H</figure-callout>}}_1}+{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{sr}})}^2}]e^{-\frac{{\mathrm{\&gamma;}}_{{\mathrm{<figure-callout\; id="1"\; label="H"\; filenames="HDA0000104014250000013.png"\; state="\{\{state\}\}">H</figure-callout>}}_1}}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{sr}}}}$ Thus, optimization problem is converted into: maximization

$\frac{\mathrm{B\&beta;}}{\mathrm{\&Gamma;}\left(M\right){\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{sr}}(\mathrm{\&alpha;}+\mathrm{\&beta;})}{\&Integral;}_0^{\&infin;}{\log }_2(1+{\mathrm{\&gamma;}}_{H_0})\&times;\mathrm{\&Gamma;}(M,\frac{\mathrm{\&xi;}\left({\mathrm{\&gamma;}}_{H_0}\right)}{2})e^{-\frac{{\mathrm{\&gamma;}}_{{\mathrm{<figure-callout\; id="0"\; label="H"\; filenames="HDA0000104014250000012.png"\; state="\{\{state\}\}">H</figure-callout>}}_0}}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{sr}}}}{\mathrm{d\&gamma;}}_{{\mathrm{<figure-callout\; id="0"\; label="H"\; filenames="HDA0000104014250000012.png"\; state="\{\{state\}\}">H</figure-callout>}}_0}+$

$\frac{\mathrm{B\&alpha;}}{\mathrm{\&alpha;}+\mathrm{\&beta;}}{\&Integral;}_0^{\&infin;}{\mathrm{Log}}_2(1+{\mathrm{\&gamma;}}_{H_1})(1-P_d\left(\mathrm{\&xi;}\left({\mathrm{\&gamma;}}_{H_1}\right)\right))[\frac{1}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Pr}}{\mathrm{\&gamma;}}_{{\mathrm{<figure-callout\; id="1"\; label="H"\; filenames="HDA0000104014250000013.png"\; state="\{\{state\}\}">H</figure-callout>}}_1}+{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}}}+\frac{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}}{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Pr}}}{{({\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Pr}}{\mathrm{\&gamma;}}_{{\mathrm{<figure-callout\; id="1"\; label="H"\; filenames="HDA0000104014250000013.png"\; state="\{\{state\}\}">H</figure-callout>}}_1}+{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}})}^2}]e^{-\frac{{\mathrm{\&gamma;}}_{{\mathrm{<figure-callout\; id="1"\; label="H"\; filenames="HDA0000104014250000013.png"\; state="\{\{state\}\}">H</figure-callout>}}_1}}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}}}}{\mathrm{D\&gamma;}}_{H_1}---\left(6\right)$ And satisfy restrictive condition $E\left[P_m\left(\mathrm{\&xi;}\left({\mathrm{\&gamma;}}_{H_1}\right)\right)\right]={\&Integral;}_0^{\&infin;}{P}_m\left(\mathrm{\&xi;}\left({\mathrm{\&gamma;}}_{H_1}\right)\right)f_{H_1}\left({\mathrm{\&gamma;}}_{H_1}\right){\mathrm{D\&gamma;}}_{{\mathrm{<figure-callout\; id="1"\; label="H"\; filenames="HDA0000104014250000013.png"\; state="\{\{state\}\}">H</figure-callout>}}_1}\&le;{\stackrel{\&OverBar;}{P}}_m---\left(5\right)$

Wherein, f _Sr() expression γ _SrProbability density function, f wherein _Sr" " in () does

Deng variable, Expression

The time γ _SrProbability density function in time, f _Pr() expression γ _PrProbability density function, this moment f _Pr" " in () is " t-1 ".

Use optimization tool under restrictive condition (5) with formula (6) maximization, obtain parameter optimum, that satisfy restrictive condition (k, b); Here be that example specifies and to find the solution parameter (wherein the matlab version is R2010b for k, process b): the m type file of hypothetical target (being formula (6)) is called ob.m with the matlab optimization toolbox; Restricted function (being formula (5)) m type file is called const.m; The parameter that need find the solution (k, b) element (x (1), x (2)) in the corresponding vector x of difference.Use " optimtool " order to access Optimization Tool, select among the solver " fmincon-Constrained nonlinear minimization ", select among the Algorithm " Interior point "; Input " x ob (x) " among the Objective function is selected among the Derivatives " Approximated by solver ", input " [0 0] " among the Start point; A input " [0 0] " among the Linear inequalities, input " 0 " among the b, Aeq input " [0 0] " among the Linear equalities; Input " 0 " among the beq, Lower input " [0 0] " among the Bounds, Upper imports " [inf inf] "; Input " x const (x) " among the Nonlinear constraint function; Other are constant, click " start ", can obtain the result.

As for example, set B=100KHz, Pr{H ₁}=0.42, Pr{H ₀}=0.58, ${\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{ps}}={\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{pr}}={\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{sr}}=10,$ ${\stackrel{\&OverBar;}{P}}_m=0.01,$ M=3 uses offline optimization can try to achieve that (k, b) value is (0.682 ,-1.318).

Online (promptly being illustrated in real-time calculating parameter in the user job process) dynamic power decision threshold arbitration schemes flow chart as shown in Figure 4 comprises the steps:

The S21:SU transmitting terminal obtains the signal to noise ratio γ feedback of SU receiving terminal in real time, gets into step S22.

(k b) calculates ξ (γ)=k γ+b to the S22:SU transmitting terminal, confirms current decision threshold ξ, gets into step S23 according to the optimum that has calculated.

S23: calculate detection signal energy value Y, get into step S2,4.

The S24:SU transmitting terminal is made comparisons Y and thresholding ξ, if Y＜ξ gets into S25, otherwise gets into S26.

S25: adjudicate and be H ₀, promptly adjudicate not work of PU.

S26: adjudicate and be H ₁, promptly adjudicate PU and work.

Suppose that (k, b) value is (0.682 ,-1.318).The SU receiving terminal feeds back to received signal to noise ratio γ=20 to the SU transmitting terminal, and the SU transmitting terminal is then set decision threshold ξ=12.32.

Energy measuring block diagram as shown in Figure 5, wherein, () ²The expression squarer,

The expression integrator,

Expression energy decision device, when each time slot was incipient, the SU transmitting terminal carried out frequency spectrum detection based on the decision mechanism of energy measuring.Band pass filter (BPF) filtering out-of-band noise, gross energy Y in time that squarer and integrator calculating sampling signal place are detected and the spectral range (supposing that signal is getting into energy detector before just by sampling).If the gross energy Y of the signal that samples is less than energy decision threshold ξ, i.e. Y＜ξ, then the energy detector of SU transmitting terminal draws the court verdict of H0, promptly judges not work of PU, and utilizes in the current time slots spectrum opportunities to send data; If Y>=ξ, then the energy detector of SU transmitting terminal draws H ₁Court verdict, promptly PU work, SU can not send data in current time slots.

Suppose the detected energy Y=9 of SU transmitting terminal, then Y＜ξ (ξ=12.32) adjudicates output H ₀, promptly adjudicate not work of PU, the SU transmitting terminal can be launched data.

The energy detection method of adaptive threshold of the present invention in theory with Project Realization on all feasible, not only improved the intelligent level of cognitive radio users, and greatly strengthened to the time wireless communications environment that becomes adaptive ability.Simultaneously, the pattern that works online of scheme is very simple, can realize the quick response of environmental change is also greatly reduced energy needed expense in the running.

Experiment shows; Used the energy detection method of adaptive threshold being significantly improved aspect the minimizing mistake alarm probability; The most directly result who reduces to bring of mistake alarm probability exactly can be so that SU user sends data more, thereby increases throughput, improves power system capacity.Aspect false dismissal probability, used the energy detection method of adaptive threshold also accurate, and reducing of false dismissal probability can guarantee as wide as possible that PU user's work is interference-free than the energy detection method of fixed threshold.When PU took frequency spectrum, the result was approaching for the transmission rate that the energy detection method of fixed threshold and the energy detection method of adaptive threshold of the present invention obtain

; But when PU did not take frequency spectrum resource, the transmission rate that the energy detection method of adaptive threshold of the present invention obtains

will exceed much than the transmission rate that the energy detection method of fixed threshold obtains.

Claims (7)

1. the energy detection method of an adaptive threshold is characterized in that, this energy detection method is realized the calculation of parameter scheme of the decision function of off-line energy decision threshold earlier, is implemented in heat input decision threshold arbitration schemes again;

The calculation of parameter scheme of the decision function of said off-line energy decision threshold may further comprise the steps:

Step 1: the decision function of setting up the energy decision threshold;

Step 2: the average data transfer rate of calculating SU (from the user) transmitting terminal

Step 3: is optimized satisfying under the restrictive condition;

Step 4: the parameter that calculates decision function through optimization tool;

Saidly may further comprise the steps in heat input decision threshold arbitration schemes:

Step 5:SU transmitting terminal obtains the signal to noise ratio γ feedback of SU receiving terminal in real time;

Step 6:SU transmitting terminal is confirmed current decision threshold according to the optimized parameter of the decision function that has calculated by decision function;

Step 7:SU transmitting terminal is made comparisons detected signal energy value Y and energy decision threshold ξ, if Y＜ξ, judgement is H so ₀, promptly adjudicate not work (promptly not transmitting data) of PU (main user); Otherwise judgement is H ₁, promptly adjudicate PU work (promptly transmitting data).

2. the energy detection method of adaptive threshold according to claim 1; It is characterized in that; The decision function of the decision threshold of energy described in the step 1 for wherein γ feed back to the SNIR (signal to noise ratio) of SU transmitting terminal for the SU receiving terminal; ξ is the energy decision threshold; K, b are the parameter of the decision function of energy decision threshold, are unknown numbers to be determined.

3. the energy detection method of adaptive threshold according to claim 1; It is characterized in that the average data transfer rate of the transmitting terminal of SU described in the step 2

does $\stackrel{\&OverBar;}{R}={\stackrel{\&OverBar;}{R}}_{H_0}+{\stackrel{\&OverBar;}{R}}_{H_1}=B\&times;E\left[{\log }_2(1+{\mathrm{\&gamma;}}_{H_0})\mathrm{Pr}\right\{H_0\left\}(1-P_f\left(\mathrm{\&xi;}\right))\right]+B\&times;E\left[{\log }_2(1+{\mathrm{\&gamma;}}_{H_1})\mathrm{Pr}\right\{H_1\left\}{P}_m\left(\mathrm{\&xi;}\right)\right],$ H wherein ₀For PU (main user) does not transmit the court verdict of data, H ₁Be the court verdict of PU transmission data,

Be H ₀The average transmission rate of SU under the condition,

Be H ₁The average transmission rate of SU under the condition,

Be H ₁The SU receiving terminal feeds back to the signal to noise ratio of SU transmitting terminal under the condition,

Be H ₀The SU receiving terminal feeds back to the signal to noise ratio of SU transmitting terminal under the condition,

Expression is asked Average,

Expression is asked

Average, Pr{H ₁Take the probability of frequency spectrum, Pr{H for PU ₀Be the probability that PU does not take frequency spectrum, Pr{H ₁}+Pr{H ₀}=1, P _m(ξ) be false dismissal probability, P _f(ξ) be the mistake alarm probability, B is a channel width.

4. the energy detection method of adaptive threshold according to claim 1; It is characterized in that; The method of the average data transfer rate

of said calculating SU transmitting terminal is: the bandwidth of supposing the system is B; According to shannon formula; The message transmission rate R of SU can be expressed as:

because the SU transmitting terminal only just transmits data when detecting gross energy Y＜ξ, so obtain the average data transfer rate

of SU transmitting terminal be:

$\stackrel{\&OverBar;}{R}={\stackrel{\&OverBar;}{R}}_{H_0}+{\stackrel{\&OverBar;}{R}}_{H_1}=B\&times;E\left[{\log }_2(1+{\mathrm{\&gamma;}}_{H_0})\mathrm{Pr}\right\{H_0\left\}(1-P_f\left(\mathrm{\&xi;}\right))\right]+B\&times;E\left[{\log }_2(1+{\mathrm{\&gamma;}}_{H_1})\mathrm{Pr}\right\{H_1\left\}{P}_m\left(\mathrm{\&xi;}\right)\right]$

Wherein γ feeds back to the SNIR (signal to noise ratio) of SU transmitting terminal for the SU receiving terminal.

5. the energy detection method of adaptive threshold according to claim 1 is characterized in that, restrictive condition described in the step 3 does $E\left[P_m\left(\mathrm{\&xi;}\left({\mathrm{\&gamma;}}_{H_1}\right)\right)\right]={\&Integral;}_0^{\&infin;}{P}_m\left(\mathrm{\&xi;}\left({\mathrm{\&gamma;}}_{H_1}\right)\right)f_{H_1}\left({\mathrm{\&gamma;}}_{H_1}\right){\mathrm{d\&gamma;}}_{H_1}\&le;{\stackrel{\&OverBar;}{P}}_m,$ Wherein

Be H ₁The SU receiving terminal feeds back to the signal to noise ratio of SU transmitting terminal under the condition,

For The time the energy decision threshold, For

False dismissal probability under the condition,

Be preset false dismissal probability P _m(ξ) probability threshold of mean value,

Be

Probability density function under the condition,

Expression is asked

Average.

6. the energy detection method of adaptive threshold according to claim 1; It is characterized in that step 3 is said to be satisfied under the restrictive condition

be optimized and may further comprise the steps:

S31: with the average data transfer rate formula of SU transmitting terminal

$\stackrel{\&OverBar;}{R}={\stackrel{\&OverBar;}{R}}_{H_0}+{\stackrel{\&OverBar;}{R}}_{H_1}=B\&times;E\left[{\log }_2(1+{\mathrm{\&gamma;}}_{H_0})\mathrm{Pr}\right\{H_0\left\}(1-P_f\left(\mathrm{\&xi;}\right))\right]+B\&times;E\left[{\log }_2(1+{\mathrm{\&gamma;}}_{H_1})\mathrm{Pr}\right\{H_1\left\}{P}_m\left(\mathrm{\&xi;}\right)\right]$

Develop into $\left\{\mathrm{Pr}\right\{H_0\left\}E\right[{\log }_2(1+{\mathrm{\&gamma;}}_{H_0})(1-P_f\left(\mathrm{\&xi;}\left({\mathrm{\&gamma;}}_{H_0}\right)\right))]+\mathrm{Pr}\{H_1\left\}E\right[{\log }_2(1+{\mathrm{\&gamma;}}_{H_1})P_m\left(\mathrm{\&xi;}\left({\mathrm{\&gamma;}}_{H_1}\right)\right)\left]\right\}$ Get into step S32;

S32: combine the mistake alarm probability under the rayleigh fading channel $P_f\left(\mathrm{\&xi;}\right)=1-\frac{\mathrm{\&Gamma;}(M,\mathrm{\&xi;}/2)}{\mathrm{\&Gamma;}\left(M\right)}$ And detection probability $P_d\left(\mathrm{\&xi;}\right)e^{-\frac{\mathrm{\&xi;}}{2}}\underset{m=0}{\overset{M-2}{\mathrm{\&Sigma;}}}\frac{1}{m!}{\left(\frac{\mathrm{\&xi;}}{2}\right)}^m+{\left(\frac{1+{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Ps}}}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Ps}}}\right)}^{M-1}\&times;[e^{-\frac{\mathrm{\&xi;}}{2(1-{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Ps}})}}-e^{-\frac{\mathrm{\&xi;}}{2}}\underset{m=0}{\overset{M-2}{\mathrm{\&Sigma;}}}\frac{1}{m!}{\left(\frac{\mathrm{\&xi;}{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Ps}}}{2(1+{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Ps}})}\right)}^m],$

Probability density function $f_{H_0}\left({\mathrm{\&gamma;}}_{H_0}\right)=f_{\mathrm{Sr}}\left({\mathrm{\&gamma;}}_{\mathrm{Sr}}\right)=\frac{1}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}}}{e}^{-\frac{{\mathrm{\&gamma;}}_{H_0}}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}}}},$ ${\mathrm{\&gamma;}}_{H_0}\&GreaterEqual;0,$ The probability density function of

$f_{H_1}\left({\mathrm{\&gamma;}}_{H_1}\right)={\&Integral;}_0^{\&infin;}{f}_{\mathrm{Sr}}\left({\mathrm{\&gamma;}}_{H_1}t\right)f_{\mathrm{Pr}}(t-1)\mathrm{Tdt}=\frac{e^{-\frac{{\mathrm{\&gamma;}}_{H_1}}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}}}}}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}}{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Pr}}}{\&Integral;}_0^{\&infin;}{e}^{-(\frac{{\mathrm{\&gamma;}}_{H_1}}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}}}+\frac{1}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Pr}}})t}(t+1)\mathrm{Dt}=[\frac{1}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Pr}}{\mathrm{\&gamma;}}_{H_1}+{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}}}+\frac{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}}{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Pr}}}{{({\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Pr}}{\mathrm{\&gamma;}}_{H_1}+{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}})}^2}]e^{-\frac{{\mathrm{\&gamma;}}_{H_1}}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}}}},$ Optimization problem is converted into maximization $\frac{\mathrm{B\&beta;}}{\mathrm{\&Gamma;}\left(M\right){\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}}(\mathrm{\&alpha;}+\mathrm{\&beta;})}{\&Integral;}_0^{\&infin;}{\mathrm{Log}}_2(1+{\mathrm{\&gamma;}}_{H_0})\&times;\mathrm{\&Gamma;}(M,\frac{\mathrm{\&xi;}\left({\mathrm{\&gamma;}}_{H_0}\right)}{2})e^{-\frac{{\mathrm{\&gamma;}}_{H_0}}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{Sr}}}}{\mathrm{D\&gamma;}}_{H_0}+$

$\frac{\mathrm{B\&alpha;}}{\mathrm{\&alpha;}+\mathrm{\&beta;}}{\&Integral;}_0^{\&infin;}{\log }_2(1+{\mathrm{\&gamma;}}_{H_1})(1-P_d\left(\mathrm{\&xi;}\left({\mathrm{\&gamma;}}_{H_1}\right)\right))[\frac{1}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{pr}}{\mathrm{\&gamma;}}_{H_1}+{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{sr}}}+\frac{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{sr}}{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{pr}}}{{({\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{pr}}{\mathrm{\&gamma;}}_{H_1}+{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{sr}})}^2}]e^{-\frac{{\mathrm{\&gamma;}}_{H_1}}{{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}_{\mathrm{sr}}}}{\mathrm{d\&gamma;}}_{H_1}$

Wherein,

For The time the energy decision threshold,

For

Mistake alarm probability under the condition,

For False dismissal probability under the condition,

Be preset false dismissal probability P _m(ξ) probability threshold of mean value,

Be

Probability density function, γ _PrRepresent main user's transmitting terminal to signal to noise ratio from user's receiving terminal,

Be γ _PrAverage, γ _SrExpression is from user's transmitting terminal and the signal to noise ratio between the receiving terminal, Be γ _SrAverage, f _Sr() expression γ _SrProbability density function, this moment f _Sr" " in () does

Expression

γ under the condition _SrProbability density function, f _Pr() expression γ _PrProbability density function, this moment f _Pr" " in () is " t-1 ", and Γ (M) is a gamma function, Γ (M; ξ/2) be incomplete gamma function, M is that sampled signal is counted, and m is that variable is used in summation; Represent m sampled point, suppose that the PU course of work is the life of the two condition markoff process that goes out, α is PU " birth probability "; β is " rate of death ", Pr{H ₁}=α/(alpha+beta), Pr{H ₀}=β/(alpha+beta).

7. according to the energy detection method of one of claim 1-6 described adaptive threshold, it is characterized in that the SU transmitting terminal based on the flow process that the adaptive threshold energy detection method carries out frequency spectrum detection is:

A. band pass filter (BPF) filtering out-of-band noise;

B. squarer and integrator calculating sampling signal belong to the time and the gross energy Y in the spectral range (supposing that signal was just sampled before getting into energy detector) of detection;

C. detected signal energy value Y and energy decision threshold ξ are made comparisons; If the gross energy Y of the signal that samples is less than energy decision threshold ξ; Be Y＜ξ; Then the energy detector of SU transmitting terminal draws the court verdict of H0, promptly judges not work of PU, and utilizes spectrum opportunities transmission data in the current time slots; If Y >=ξ, then the energy detector of SU transmitting terminal draws the court verdict of H1, and promptly PU is in work, and SU can not send data in current time slots.

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