CN103580806A - Method for energy-efficient transmission of robustness in cognitive network - Google Patents

Abstract

The invention discloses a method for energy-efficient transmission of robustness in a cognitive network. According to the method for energy-efficient transmission of the robustness in the cognitive network, channel state information of relevant channels is measured through the receiving end of a secondary user and is fed back to the sending end of the secondary user; the sending end of the secondary user carries out measurement multiple times to determine an uncertain set of the gain of each channel; the secondary user establishes a model according to a robustness optimizing method to maximize secondary user energy efficiency; the poorest channel gain is solved, and a service quality requirement constraint of a main user is converted into a convex constraint; the optimal sending power is solved according to a power distribution algorithm, transmission is carried out on each channel according to the optimal sending power, and the optimal transmission power of the robustness is solved when the channel state information is uncertain. The method for energy-efficient transmission of the robustness in the cognitive network can be used for cognitive network communication and has the advantages that the maximization of the energy efficiency of the secondary user is guaranteed under the circumstance that the channel state information is uncertain, interference power at the receiving end of the main user is strictly controlled, the service quality of the main user is guaranteed, and negative influence of the uncertainty of the channel state information on the performance of the cognitive network is effectively eliminated.

Description

A kind of method of robustness efficiency transmission in cognition network

Technical field

The invention belongs to wireless communication technology field, relate in particular to the method for robustness efficiency transmission in a kind of cognition network.

Background technology

Cognitive radio networks allows secondary user's under the prerequisite of service quality QoS that does not reduce primary user, to utilize primary user's mandate frequency spectrum to carry out frequency spectrum share, improves the utilance of whole frequency spectrum.On the other hand, the Radio Transmission Technology of maximization efficiency becomes the hot issue of current green communications.Especially, in cognitive radio networks, how secondary user's maximizes self efficiency when improving the availability of frequency spectrum is the hot issue of current research.

Existing most of document is when maximizing secondary user's efficiency, and the state information CSI that mostly supposes channel is accurately.Yet, due to the time-varying characteristics of wireless transmission channel, the impact of the finiteness of training sequence and the factors such as time delay of feedback channel, the channel condition information CSI that transmitting terminal obtains is inaccurate often.And inaccurate channel condition information can affect the energetic efficiency characteristic of secondary user's and primary user's service quality.

Because the existing maximization efficiency method for cognitive radio networks designs in the accurate channel information situation of hypothesis.These transmission methods are applied in practical wireless systems, tend to cause the deterioration of secondary user's efficiency, also can reduce primary user's service quality.

Summary of the invention

The object of the embodiment of the present invention is to provide the method for robustness efficiency transmission in a kind of cognition network, be intended to solve existing cognitive radio networks and maximize efficiency method because not considering the uncertain deterioration that causes secondary user's efficiency of channel information, reduce the problem of primary user's service quality.

The embodiment of the present invention is achieved in that the method for robustness efficiency transmission in a kind of cognition network, and in this cognition network, the method for robustness efficiency transmission comprises the following steps:

Step 1, secondary user's receiving terminal SR measures the interference signal receiving when not receiving useful data or before receiving, and estimates that the primary user of transmitting on channel k is to the estimated value of disturbing ; When primary user mourns in silence, secondary user's receiving terminal SR measures the white noise power σ on channel k ², when receiving data, according to training sequence, estimate the channel gain of secondary user's transmitting terminal ST

, secondary user's receiving terminal SR also sends to secondary user's transmitting terminal ST by numerical value by feedback channel;

Step 2, secondary user's transmitting terminal ST when not having data to send, demodulation primary user's public transmitted signal, as confirmed to reply ACK, negative response NAK etc., estimate the channel gain of primary user's receiving terminal PR accordingly

Step 3, before communication starts, by repeating step one to three repeatedly, determines the uncertain set of relevant parameter;

Step 4, according to the parameter of step 1 to three acquisition, can be expressed as a plurality of secondary user's at the descending efficiency f of a plurality of channel (P)

$f\left(P\right)=\frac{\underset{k=1}{\overset{3}{\mathrm{\Σ}}}{B\log }_2(1+\frac{{\mathrm{\Γ}}_k{P}_k{H}_s^k}{{\mathrm{\σ}}^2+I_P^k})}{P_C+5\underset{k=1}{\overset{3}{\mathrm{\Σ}}}{p}_k}$

Wherein, B is the bandwidth of channel, while adopting quadrature amplitude modulation MQAM when error rate BER=0.001 of secondary user's, and the now channel speed of actual transmissions system and the coefficient of variation Γ between channel capacity _k=0.1258, P _cthe secondary user's transmitting terminal ST that cognitive base station CBS(above mentions) constant power expense, efficiency 1/ η of the power amplifier of cognitive base station CBS ₀=0.20, P=[P ₁, P ₂, P ₃] represent the transmitted power of cognitive base station CBS on all channels vector, here P _k, { 1,2,3} is illustrated in the transmitted power on channel k to k ∈;

Now, according to robustness optimization method, the problem that maximizes secondary user's efficiency is modeled as

$P1:\underset{P_x}{\max }\·\underset{G_{\mathrm{Sj}},H_S^k,I_P^k}{\min }\·f\left(P\right)$

$s.t.C1:\underset{k=1}{\overset{3}{\mathrm{\Σ}}}{P}_k\≤P_{\max },$

$C2:P\·G_{\mathrm{Sj}}^T\≤{\mathrm{IT}}_j,\∀j\∈\left\{\mathrm{1,2}\right\},$

$C3:G_{\mathrm{Sj}}\∈Q_j,\∀j\∈\left\{\mathrm{1,2}\right\},$

$C4:H_S^k\∈F_k,\∀k\∈\left\{\mathrm{1,2,3}\right\},$

$C5:I_P^k\∈I_k,\∀k\∈\left\{\mathrm{1,2,3}\right\}\·$

Wherein, P _maxthe maximum transmit power that represents cognitive base station CBS, IT _jj the interference threshold value that primary user's receiving terminal PR can tolerate;

Step 5, according to ${\left(H_S^k\right)}^{*}=\arg \underset{(H_S^k\∈F_k)}{\min }\·(1+\frac{{\mathrm{\Γ}}_k{P}_k{H}_S^k}{{\mathrm{BN}}_0+I_P^k})=\arg \underset{(H_S^k\∈F_k)}{\min }\·\left(H_S^k\right),\∀k\∈\left\{\mathrm{1,2,3}\right\},$ Solve the channel gain of secondary user's on channel k

worst-case value

wherein, worst-case value refers at a given feasible transmitted power vector

make target function in problem P1

minimum value;

Step 6, then solve the synthetic interference that primary user causes secondary user's on channel k

worst-case value

wherein, worst-case value refers at a given feasible transmitted power vector make target function in problem P1

minimum

value; ;

Step 7, according to the correlation criterion of robustness optimization, can will retrain C2 and C3 and be converted into the protruding constraint C6 of following equivalence in P1:

$C6:P\·G_{\mathrm{Sj}}^T+{\mathrm{\ϵ}}_0{\left|\right|P\left|\right|}_2\≤{\mathrm{IT}}_j,\∀j\∈\left\{\mathrm{1,2}\right\}$

Wherein, weight matrix M _j=I _{3 * 3}, inverse matrix in addition l, ₂norm || || ₂dual norm be still l ₂norm || || ₂,

Step 8, by the result substitution problem P1 in step 5 to seven, can be converted into

$\begin{array}{c}P2:\underset{P_k}{\max }\·f_1\left(P\right)=\frac{U_R\left(P\right)}{U_{\mathrm{TP}}\left(P\right)}=\frac{\underset{k=1}{\overset{3}{\mathrm{\Σ}}}{B\log }_2(1+\frac{{\mathrm{\Γ}}_k{P}_k{\left(H_S^k\right)}^{*}}{{\mathrm{\σ}}^2+{\left(I_P^k\right)}^{*}})}{P_C+5\underset{k=1}{\overset{3}{\mathrm{\Σ}}}{P}_k}\\ s.t.C1,C6.\end{array};$

Step 9, adopts following power distribution algorithm, Solve problems P2:

The first step, given maximum iteration time L _max=50 and error tolerance threshold value κ=0.0001;

Second step, the current efficiency value of initialization q=0 gives current iteration index assignment n=0 simultaneously;

The 3rd step, as convergence identifier F==0 and current iteration index n≤L _maxtime, order is carried out statement below; Otherwise, jump to the 7th step;

The 4th step, when maximum efficiency q gives regularly, solves following protruding optimization problem P3, obtains power allocation vector P ' now;

$\begin{array}{c}P3:\underset{P}{\max }.U_R\left(P\right)-q{U}_{\mathrm{TP}}\left(P\right)\\ s.t.C1,C6.\end{array}$

The 5th step, if inequality U _r(P ')-qU _tP(P ') < κ sets up, so assignment P*=P ' and convergence sign assignment F=1; Otherwise, assignment

and n=n+1, convergence sign assignment F=0;

The 6th step, turns back to the 3rd step, and correlative is sequentially carried out in continuation;

The 7th step, returns to optimum efficiency value q ^*with optimal power allocation vector P ^*=[P ₁ ^*, P ₂ ^*, P ₃ ^*];

Step 10, cognitive base station CBS adopts given vector power P ^*=[P ₁ ^*, P ₂ ^*, P ₃ ^*], on channel 1 with power P ₁ ^*to secondary user's S1, send data, on channel 2 and 3, adopt respectively P ₂ ^*and P ₃ ^*to secondary user's S2, send data.

Further, in step 3, determine that the uncertain set of relevant parameter is as follows:

Make ε ₀=ε _j=τ _k=δ _k, { 1,2}, { 1,2,3} represents the border of unified uncertain set to k ∈ to j ∈, and generalized norm adopts l in addition ₂norm || || ₂, for vectorial X ∈ R ^{1 * n}, l ₂norm || || ₂can be expressed as ${\left|\right|X\left|\right|}_2={({\left|x_1\right|}^2+...+{\left|x_n\right|}^2)}^{\frac{1}{2}}:$

The first step, for parameter G _sj, for the receiving terminal of primary user P1, set up following uncertain set

$Q_1=\left\{G_{S1}\right|{\left|\right|{(G_{S1}-{\hat{G}}_{S1})}^T\left|\right|}_2\&le;{\mathrm{\&epsiv;}}_0\}$

Wherein,

when primary user P1 does not transmit on channel 2, now

making weight matrix value is unit matrix M ₁=I _{3 * 3}∈ R ^{3 * 3}, g _s1current estimated value;

For the receiving terminal of primary user P2, can obtain similar uncertain set

${\mathrm{<figure-callout\; id="2"\; label="Q"\; filenames="HDA0000405747240000041.png"\; state="\{\{state\}\}">Q</figure-callout>}}_2=\left\{{\mathrm{<figure-callout\; id="2"\; label="G\; S"\; filenames="HDA0000405747240000041.png"\; state="\{\{state\}\}">G</figure-callout>}}_S\right|{\left|\right|{(G_{S2}-{\hat{G}}_{S2})}^T\left|\right|}_2\&le;{\mathrm{\&epsiv;}}_0\}$

Wherein,

when primary user P1 does not transmit on channel 1 and 3, now

making weight matrix value is unit matrix M ₂=I _{3 * 3}∈ R ^{3 * 3}, g _s2current estimated value;

Second step, about parameter for secondary user's S1, set up following uncertain set

$F_1=\left\{H_S^1\right|{\left|\right|H_S^1-{\hat{H}}_S^1\left|\right|}_2\&le;{\mathrm{\&epsiv;}}_0\}$

About secondary user's S2, set up following uncertain set

${\mathrm{<figure-callout\; id="2"\; label="F"\; filenames="HDA0000405747240000041.png"\; state="\{\{state\}\}">F</figure-callout>}}_2=\left\{H_{\mathrm{<figure-callout\; id="2"\; label="S"\; filenames="HDA0000405747240000041.png"\; state="\{\{state\}\}">S</figure-callout>}}^2\right|{\left|\right|H_S^2-{\hat{H}}_{\mathrm{<figure-callout\; id="2"\; label="S"\; filenames="HDA0000405747240000041.png"\; state="\{\{state\}\}">S</figure-callout>}}^2\left|\right|}_2\&le;{\mathrm{\&epsiv;}}_0\}$

$F_3=\left\{H_S^3\right|{\left|\right|H_S^3-{\hat{H}}_S^3\left|\right|}_2\&le;{\mathrm{\&epsiv;}}_0\}$

Wherein, F ₁the channel gain of secondary user's S1 on channel 1 uncertain set, F ₂and F ₃respectively the channel gain of secondary user's S2 on channel 2 and 3

with

uncertain set, now weight coefficient assignment is

be secondary user's in secondary user's the channel gain on channel k

estimated value;

The 3rd step, for parameter

can obtain following uncertain set

$L_k=\left\{I_P^k\right|{\left|\right|I_P^k-{\hat{I}}_P^k\left|\right|}_2\&le;{\mathrm{\&epsiv;}}_0\},\&ForAll;k\&Element;\{\mathrm{1,2,3}\}$

Wherein, L _kit is the synthetic interference that primary user produces secondary user's on channel k

uncertain set, Z _kthat weight coefficient assignment is

it is the synthetic interference that primary user produces secondary user's on channel k

estimated value.

Further, in step 5, worst-case value expression formula be:

${\left(H_S^k\right)}^{*}=\arg \underset{(H_S^k\&Element;F_k)}{\min }\&CenterDot;(1+\frac{{\mathrm{\&Gamma;}}_k{\stackrel{\&OverBar;}{P}}_k{H}_S^k}{{\mathrm{BN}}_0+I_P^k})=\arg \underset{(H_S^k\&Element;F_k)}{\min }\&CenterDot;\left(H_S^k\right),\&ForAll;k\&Element;\left\{\mathrm{1,2,3}\right\}.$

Further, in step 6, worst-case value expression formula be:

${\left(I_S^k\right)}^{*}=\arg \underset{(I_S^k\&Element;L_k)}{\min }\&CenterDot;(1+\frac{{\mathrm{\&Gamma;}}_k{\stackrel{\&OverBar;}{P}}_k{H}_S^k}{{\mathrm{BN}}_0+I_P^k})=\arg \underset{(I_S^k\&Element;L_k)}{\min }\&CenterDot;\left(I_S^k\right),\&ForAll;k\&Element;\left\{\mathrm{1,2,3}\right\}.$

In cognition network provided by the invention, the method for robustness efficiency transmission, measures the channel condition information of correlated channels, and feeds back to secondary user's transmitting terminal by secondary user's receiving terminal; Secondary user's transmitting terminal is by repeatedly measuring the uncertain set of determining each channel gain; Secondary user's maximizes the problem of secondary user's efficiency according to robustness optimization method modeling; Solve difference channel gain and the constraint of primary user's QoS requirement is converted into protruding constraint; According to power distribution algorithm, solve optimum transmitted power, and according to this power, transmit on each channel, when channel condition information uncertain, solve the optimal transmission power of robust, strict guarantee primary user's service quality maximized the efficiency of secondary user's simultaneously, can be used for cognition network communication;

Tool of the present invention has the following advantages:

1. in the present invention, secondary user's determines by measurement the uncertain set that its channel information and primary user arrive secondary user's interfere information, optimizes accordingly the power of self, has guaranteed the maximization of secondary user's efficiency in the uncertain situation of channel information;

2. in the present invention, secondary user's utilizes the uncertain set of primary user's channel information to determine transmitted power, strictly control primary user's receiving terminal interference power, and then in the uncertain situation of channel information, strictly guaranteed primary user's service quality (QoS);

3. the present invention, by considering the uncertainty of channel information and interference power, adopts the power transmission method of robustness, has effectively eliminated the deterioration of channel information uncertainty to cognition network performance.

Accompanying drawing explanation

Fig. 1 is the method flow diagram of robustness efficiency transmission in the cognition network that provides of the embodiment of the present invention;

Fig. 2 is the use cognition network downlink transfer scene graph that the embodiment of the present invention provides;

Fig. 3 is the method realization flow figure of robustness efficiency transmission in the cognition network that provides of the embodiment of the present invention;

Fig. 4 is the sub-process figure of the power distribution algorithm that provides of the embodiment of the present invention.

Embodiment

In order to make object of the present invention, technical scheme and advantage clearer, below in conjunction with embodiment, the present invention is further elaborated.Should be appreciated that specific embodiment described herein, only in order to explain the present invention, is not intended to limit the present invention.

Below in conjunction with drawings and the specific embodiments, application principle of the present invention is further described.

As shown in Figure 1, in the cognition network of the embodiment of the present invention, the method for robustness efficiency transmission comprises the following steps:

S101: secondary user's receiving terminal is measured the channel condition information of correlated channels, and feeds back to secondary user's transmitting terminal;

S102: secondary user's transmitting terminal is by repeatedly measuring the uncertain set of determining each channel gain;

S103: secondary user's maximizes the problem of secondary user's efficiency according to robustness optimization method modeling;

S104: solve difference channel gain and the constraint of primary user's QoS requirement is converted into protruding constraint;

S105: solve optimum transmitted power according to power distribution algorithm, and transmit according to this power on each channel.

The present invention is described further in conjunction with specific embodiments:

As shown in Figure 2, in simulating scenes of the present invention, comprising two pairs of primary user's transmission nodes is P=2 to PT and PR, primary user is numbered to P1 and P2, a cognitive base station CBS and two secondary user's SR, be numbered respectively S1 and S2, suppose always to have three channels and get K=3, wherein secondary user's receiving terminal S1 busy channel 1, secondary user's receiving terminal S1 busy channel 2 and 3, primary user P1 busy channel 1 and channel 3, how busy channel does not affect use of the present invention for primary user P2 busy channel 2(secondary user's and primary user, here only provide the example of a concrete channel occupancy),

As shown in Figure 3, in the present invention, maximize the method for the robustness transmission of efficiency, specifically comprise that step is as follows:

Step 1, secondary user's receiving terminal S1 measures the interference signal receiving when not receiving useful data or before receiving, the estimated value of the primary user of estimation transmission on channel 1 to its interference secondary user's receiving terminal S2 estimates the estimated value to its interference at channel 2 and 3 primary users of transmitting

with

when primary user mourns in silence, secondary user's receiving terminal S1 and S2 measure the white noise power σ on three channels ²(without loss of generality, supposing that on three channels, white noise power is identical here), secondary user's receiving terminal S1 arrives its channel gain according to training sequence cognitive base station CBS on channel 1

secondary user's receiving terminal S2 estimates on channel 2 and 3

with secondary user's receiving terminal also sends to cognitive base station CBS by the numerical value of measurement by feedback channel;

Step 2, cognitive base station CBS estimates on three channels that according to primary user's public information it arrives the channel gain between primary user's receiving terminal PR

with

Step 3, before communication starts, by repeating step (to three) repeatedly, determines that the uncertain set of relevant parameter is following (for convenient statement, to make ε here ₀=ε _j=τ _k=δ _k, { 1,2}, { 1,2,3} represents the border of unified uncertain set to k ∈ to j ∈, and generalized norm adopts l in addition ₂norm || || ₂, for vectorial X ∈ R ^{1 * n}, l ₂norm || || ₂can be expressed as ${\left|\right|X\left|\right|}_2={({\left|x_1\right|}^2+...+{\left|x_n\right|}^2)}^{\frac{1}{2}}:$

The first step, for parameter G _sj, for the receiving terminal of primary user P1, set up following uncertain set

$Q_1=\left\{G_{S1}\right|{\left|\right|{(G_{S1}-{\hat{G}}_{S1})}^T\left|\right|}_2\&le;{\mathrm{\&epsiv;}}_0\}$

Wherein,

when primary user P1 does not transmit on channel 2, now

making weight matrix value is unit matrix M ₁=I _{3 * 3}∈ R ^{3 * 3},

g _s1current estimated value;

For the receiving terminal of primary user P2, can obtain similar uncertain set

${\mathrm{<figure-callout\; id="2"\; label="Q"\; filenames="HDA0000405747240000041.png"\; state="\{\{state\}\}">Q</figure-callout>}}_2=\left\{{\mathrm{<figure-callout\; id="2"\; label="G\; S"\; filenames="HDA0000405747240000041.png"\; state="\{\{state\}\}">G</figure-callout>}}_S\right|{\left|\right|{(G_{S2}-{\hat{G}}_{S2})}^T\left|\right|}_2\&le;{\mathrm{\&epsiv;}}_0\}$

Wherein,

when primary user P1 does not transmit on channel 1 and 3, now

making weight matrix value is unit matrix M ₂=I _{3 * 3}∈ R ^{3 * 3},

g _s2current estimated value;

Second step, about parameter

for secondary user's S1, set up following uncertain set

$F_1=\left\{H_S^1\right|{\left|\right|H_S^1-{\hat{H}}_S^1\left|\right|}_2\&le;{\mathrm{\&epsiv;}}_0\}$

About secondary user's S2, set up following uncertain set

${\mathrm{<figure-callout\; id="2"\; label="F"\; filenames="HDA0000405747240000041.png"\; state="\{\{state\}\}">F</figure-callout>}}_2=\left\{{\mathrm{<figure-callout\; id="2"\; label="H\; S"\; filenames="HDA0000405747240000041.png"\; state="\{\{state\}\}">H</figure-callout>}}_S^{}\right|{\left|\right|H_S^2-{\hat{H}}_{\mathrm{<figure-callout\; id="2"\; label="S"\; filenames="HDA0000405747240000041.png"\; state="\{\{state\}\}">S</figure-callout>}}^2\left|\right|}_2\&le;{\mathrm{\&epsiv;}}_0\}$

$F_3=\left\{H_S^3\right|{\left|\right|H_S^3-{\hat{H}}_S^3\left|\right|}_2\&le;{\mathrm{\&epsiv;}}_0\}$

Wherein, F ₁the channel gain of secondary user's S1 on channel 1 uncertain set, F ₂and F ₃respectively the channel gain of secondary user's S2 on channel 2 and 3

with

uncertain set, now weight coefficient assignment is

be secondary user's in secondary user's the channel gain on channel k

estimated value;

The 3rd step, for parameter

can obtain following uncertain set

$L_k=\left\{I_P^k\right|{\left|\right|I_P^k-{\hat{I}}_P^k\left|\right|}_2\&le;{\mathrm{\&epsiv;}}_0\},\&ForAll;k\&Element;\{\mathrm{1,2,3}\}$

Wherein, L _kit is the synthetic interference that primary user produces secondary user's on channel k

uncertain set, Z _kthat weight coefficient assignment is

it is the synthetic interference that primary user produces secondary user's on channel k

estimated value;

Step 4, according to the parameter of step 1 to three acquisition, can be expressed as a plurality of secondary user's at the descending efficiency f of a plurality of channel (P)

$f\left(P\right)=\frac{\underset{k=1}{\overset{3}{\mathrm{\&Sigma;}}}{B\log }_2(1+\frac{{\mathrm{\&Gamma;}}_k{P}_k{H}_s^k}{{\mathrm{\&sigma;}}^2+I_P^k})}{P_C+5\underset{k=1}{\overset{3}{\mathrm{\&Sigma;}}}{p}_k}$

Wherein, B is the bandwidth of channel, while adopting quadrature amplitude modulation MQAM when error rate BER=0.001 of secondary user's, and the now channel speed of actual transmissions system and the coefficient of variation Γ between channel capacity _k=0.1258, P _cthe constant power expense of cognitive base station CBS, efficiency 1/ η of the power amplifier of cognitive base station CBS ₀=0.20, P=[P ₁, P ₂, P ₃] represent the transmitted power of cognitive base station CBS on all channels vector, here P _k, { 1,2,3} represents its transmitted power on channel k to k ∈;

Now, according to robustness optimization method, the problem that maximizes secondary user's efficiency is modeled as

$P1:\underset{P_x}{\max }\&CenterDot;\underset{G_{\mathrm{Sj}},H_S^k,I_P^k}{\min }\&CenterDot;f\left(P\right)$

$s.t.C1:\underset{k=1}{\overset{3}{\mathrm{\&Sigma;}}}{P}_k\&le;P_{\max },$ $C2:P\&CenterDot;G_{\mathrm{Sj}}^T\&le;{\mathrm{IT}}_j,\&ForAll;j\&Element;\left\{\mathrm{1,2}\right\},$

$C3:G_{\mathrm{Sj}}\&Element;Q_j,\&ForAll;j\&Element;\left\{\mathrm{1,2}\right\},$

$C4:H_S^k\&Element;F_k,\&ForAll;k\&Element;\left\{\mathrm{1,2,3}\right\},$

$C5:I_P^k\&Element;I_k,\&ForAll;k\&Element;\left\{\mathrm{1,2,3}\right\}\&CenterDot;$

Wherein, P _maxthe maximum transmit power that represents cognitive base station CBS, IT _jj the interference threshold value that primary user's receiving terminal PR can tolerate;

Step 5, according to following formula, solves the channel gain of secondary user's on channel k

worst-case value

${\left(H_S^k\right)}^{*}=\arg \underset{(H_S^k\&Element;F_k)}{\min }\&CenterDot;(1+\frac{{\mathrm{\&Gamma;}}_k{P}_k{H}_S^k}{{\mathrm{BN}}_0+I_P^k})=\arg \underset{(H_S^k\&Element;F_k)}{\min }\&CenterDot;\left(H_S^k\right),\&ForAll;k\&Element;\left\{\mathrm{1,2,3}\right\}$

Wherein, worst-case value refers at a given feasible transmitted power vector

make target function in problem P1

minimum

Step 6, after this, then solves the synthetic interference that primary user causes secondary user's on channel k worst-case value

${\left(I_S^k\right)}^{*}=\arg \underset{(I_S^k\&Element;L_k)}{\min }\&CenterDot;(1+\frac{{\mathrm{\&Gamma;}}_k{\stackrel{\&OverBar;}{P}}_k{H}_S^k}{{\mathrm{BN}}_0+I_P^k})=\arg \underset{(I_S^k\&Element;L_k)}{\min }\&CenterDot;\left(I_S^k\right),\&ForAll;k\&Element;\left\{\mathrm{1,2,3}\right\}$

Wherein, worst-case value refers to when a given feasible transmitted power is vectorial, makes target function in problem P1

minimum

value;

Step 7, according to the correlation criterion of robustness optimization, can will retrain C2 and C3 and be converted into the protruding constraint C6 of following equivalence in P1:

$C6:P\&CenterDot;G_{\mathrm{Sj}}^T+{\mathrm{\&epsiv;}}_0{\left|\right|P\left|\right|}_2\&le;{\mathrm{IT}}_j,\&ForAll;j\&Element;\left\{\mathrm{1,2}\right\}$

Wherein, weight matrix M _j=I _{3 * 3}, its inverse matrix

in addition l, ₂norm || || ₂dual norm be still l ₂norm || || ₂, step 8, by the result substitution problem P1 in step 5 to seven, can be translated into

$\begin{array}{c}P2:\underset{P_k}{\max }\&CenterDot;f_1\left(P\right)=\frac{U_R\left(P\right)}{U_{\mathrm{TP}}\left(P\right)}=\frac{\underset{k=1}{\overset{3}{\mathrm{\&Sigma;}}}{B\log }_2(1+\frac{{\mathrm{\&Gamma;}}_k{P}_k{\left(H_S^k\right)}^{*}}{{\mathrm{\&sigma;}}^2+{\left(I_P^k\right)}^{*}})}{P_C+5\underset{k=1}{\overset{3}{\mathrm{\&Sigma;}}}{P}_k}\\ s.t.C1,C6.\end{array}$

Step 9, as shown in Figure 4, adopts following power distribution algorithm, Solve problems P2:

The first step, given maximum iteration time L _max=50 and error tolerance threshold value κ=0.0001;

Second step, the current efficiency value of initialization q=0 gives current iteration index assignment n=0 simultaneously;

The 3rd step, as convergence identifier F==0 and current iteration index n≤L _maxtime, order is carried out statement below; Otherwise, jump to the 7th step;

The 4th step, when maximum efficiency q gives regularly, solves following protruding optimization problem P3, obtains power allocation vector P ' now;

$\begin{array}{c}P3:\underset{P}{\max }.U_R\left(P\right)-q{U}_{\mathrm{TP}}\left(P\right)\\ s.t.C1,C6.\end{array}$

The 5th step, if inequality U _r(P ')-qU _tP(P ') < κ sets up, so assignment P ^*=P ' and

convergence sign assignment F=1; Otherwise, assignment

and n=n+1, convergence sign assignment F=0;

The 6th step, turns back to the 3rd step, and correlative is sequentially carried out in continuation;

The 7th step, returns to optimum efficiency value q ^*with optimal power allocation vector P ^*=[P ₁ ^*, P ₂ ^*, P ₃ ^*];

Step 10, cognitive base station CBS adopts given vector power P ^*=[P ₁ ^*, P ₂ ^*, P ₃ ^*], on channel 1 with power P ₁ ^*to secondary user's S1, send data, on channel 2 and 3, adopt respectively P ₂ ^*and P ₃ ^*to secondary user's S2, send data.

Secondary user's in the present invention determines by measurement the uncertain set that its channel information and primary user arrive secondary user's interfere information, has optimized the power of self, has guaranteed the maximization of secondary user's efficiency in the uncertain situation of channel information; Secondary user's utilizes the uncertain set of primary user's channel information to determine transmitted power, has strictly controlled primary user's receiving terminal interference power, has guaranteed primary user's service quality QoS; By considering the uncertainty of channel information and interference power, adopt the power transmission method of robustness, effectively eliminated the deterioration of information uncertainty to cognition network performance.

Robustness of the present invention maximizes efficiency transmission method and can be widely applied in various cognition wireless networks, for example, the uplink of the downlink transfer of cognitive base station (described in application example) and single secondary user's in centralized cognition wireless network, point-to-multipoint transmission between the transmission of point-to-point or secondary user's between distributed cognition network secondary user's.

The present invention has considered the negative effect of channel information uncertainty to cognition network performance, with existing hypothesis channel information accurately conventional method compare and there is following advantage: the present invention can strictly guarantee that primary user's service quality is (as cognitive base station CBS in application example will strictly be less than given interference threshold to the interference power of primary user's receiving terminal PR, with this, guarantee primary user's transmission quality), and conventional method is when channel information has uncertainty, it probably surpasses given interference threshold to primary user's interference, thereby seriously worsen primary user's transmission quality, the present invention can promote the efficiency (in application example, the descending efficiency of cognitive base station CBS can access maximization) of secondary user's transmitting terminal, and conventional method is when channel information has uncertainty, and the efficiency of secondary user's can significantly decrease.

In sum, compare with conventional method, the present invention is the in the situation that of strict guarantee primary user service quality, and the efficiency of maximizing secondary user's transmitting terminal, is the effective means that cognition wireless network is realized green communications.

The foregoing is only preferred embodiment of the present invention, not in order to limit the present invention, all any modifications of doing within the spirit and principles in the present invention, be equal to and replace and improvement etc., within all should being included in protection scope of the present invention.

Claims (4)

1. a method for robustness efficiency transmission in cognition network, is characterized in that, in this cognition network, the method for robustness efficiency transmission comprises the following steps:

Step 1, secondary user's receiving terminal SR measures the interference signal receiving when not receiving useful data or before receiving, and estimates that the primary user of transmitting on channel k is to the estimated value of disturbing

; When primary user mourns in silence, secondary user's receiving terminal SR measures the white noise power σ on channel k ², when receiving data, according to training sequence, estimate the channel gain of secondary user's transmitting terminal ST

, secondary user's receiving terminal SR also sends to secondary user's transmitting terminal ST by numerical value by feedback channel;

Step 2, secondary user's transmitting terminal ST when not having data to send, demodulation primary user's public transmitted signal, as confirmed to reply ACK, negative response NAK etc., estimate the channel gain of primary user's receiving terminal PR accordingly

Step 3, before communication starts, by repeating step one to three repeatedly, determines the uncertain set of relevant parameter;

Step 4, according to the parameter of step 1 to three acquisition, can be expressed as a plurality of secondary user's at the descending efficiency f of a plurality of channel (P)

$f\left(P\right)=\frac{\underset{k=1}{\overset{3}{\mathrm{\&Sigma;}}}{B\log }_2(1+\frac{{\mathrm{\&Gamma;}}_k{P}_k{H}_s^k}{{\mathrm{\&sigma;}}^2+I_P^k})}{P_C+5\underset{k=1}{\overset{3}{\mathrm{\&Sigma;}}}{p}_k}$

Wherein, B is the bandwidth of channel, while adopting quadrature amplitude modulation MQAM when error rate BER=0.001 of secondary user's, and the now channel speed of actual transmissions system and the coefficient of variation Γ between channel capacity _k=0.1258, P _cthe secondary user's transmitting terminal ST that cognitive base station CBS(above mentions) constant power expense, efficiency 1/ η of the power amplifier of cognitive base station CBS ₀=0.20, P=[P ₁, P ₂, P ₃] represent the transmitted power of cognitive base station CBS on all channels vector, here P _k, { 1,2,3} is illustrated in the transmitted power on channel k to k ∈;

Now, according to robustness optimization method, the problem that maximizes secondary user's efficiency is modeled as

$P1:\underset{P_x}{\max }\&CenterDot;\underset{G_{\mathrm{Sj}},H_S^k,I_P^k}{\min }\&CenterDot;f\left(P\right)$

$s.t.C1:\underset{k=1}{\overset{3}{\mathrm{\&Sigma;}}}{P}_k\&le;P_{\max },$

$C2:P\&CenterDot;G_{\mathrm{Sj}}^T\&le;{\mathrm{IT}}_j,\&ForAll;j\&Element;\left\{\mathrm{1,2}\right\},$

$C3:G_{\mathrm{Sj}}\&Element;Q_j,\&ForAll;j\&Element;\left\{\mathrm{1,2}\right\},$

$C4:H_S^k\&Element;F_k,\&ForAll;k\&Element;\left\{\mathrm{1,2,3}\right\},$

$C5:I_P^k\&Element;I_k,\&ForAll;k\&Element;\left\{\mathrm{1,2,3}\right\}\&CenterDot;$

Wherein, P _maxthe maximum transmit power that represents cognitive base station CBS, IT _jj the interference threshold value that primary user's receiving terminal PR can tolerate;

Step 5, first solves the channel gain of secondary user's on channel k

worst-case value

wherein, worst-case value refers at a given feasible transmitted power vector

make target function in problem P1

minimum

value;

Step 6, then solve the synthetic interference that primary user causes secondary user's on channel k worst-case value

wherein, worst-case value refers at a given feasible transmitted power vector

make target function in problem P1

minimum

value;

Step 7, according to the correlation criterion of robustness optimization, can will retrain C2 and C3 and be converted into the protruding constraint C6 of following equivalence in P1:

$C6:P\&CenterDot;G_{\mathrm{Sj}}^T+{\mathrm{\&epsiv;}}_0{\left|\right|P\left|\right|}_2\&le;{\mathrm{IT}}_j,\&ForAll;j\&Element;\left\{\mathrm{1,2}\right\}$

Wherein, weight matrix M _j=I _{3 * 3}, inverse matrix

in addition l, ₂norm || || ₂dual norm be still l ₂norm || || ₂,

Step 8, by the result substitution problem P1 in step 5 to seven, can be converted into

$\begin{array}{c}P2:\underset{P_k}{\max }\&CenterDot;f_1\left(P\right)=\frac{U_R\left(P\right)}{U_{\mathrm{TP}}\left(P\right)}=\frac{\underset{k=1}{\overset{3}{\mathrm{\&Sigma;}}}{B\log }_2(1+\frac{{\mathrm{\&Gamma;}}_k{P}_k{\left(H_S^k\right)}^{*}}{{\mathrm{\&sigma;}}^2+{\left(I_P^k\right)}^{*}})}{P_C+5\underset{k=1}{\overset{3}{\mathrm{\&Sigma;}}}{P}_k}\\ s.t.C1,C6.\end{array};$

Step 9, adopts following power distribution algorithm, Solve problems P2:

The first step, given maximum iteration time L _max=50 and error tolerance threshold value κ=0.0001;

Second step, the current efficiency value of initialization q=0 gives current iteration index assignment n=0 simultaneously;

The 3rd step, as convergence identifier F==0 and current iteration index n≤L _maxtime, order is carried out statement below; Otherwise, jump to the 7th step;

The 4th step, when maximum efficiency q gives regularly, solves following protruding optimization problem P3, obtains power allocation vector P ' now;

$\begin{array}{c}P3:\underset{P}{\max }.U_R\left(P\right)-q{U}_{\mathrm{TP}}\left(P\right)\\ s.t.C1,C6.\end{array}$

The 5th step, if inequality U _r(P ')-qU _tP(P ') < κ sets up, so assignment P ^*=P ' and

convergence sign assignment F=1; Otherwise, assignment

and n=n+1, convergence sign assignment F=0;

The 6th step, turns back to the 3rd step, and correlative is sequentially carried out in continuation;

The 7th step, returns to optimum efficiency value q ^*with optimal power allocation vector P ^*=[P ₁ ^*, P ₂ ^*, P ₃ ^*];

Step 10, cognitive base station CBS adopts given vector power P ^*=[P ₁ ^*, P ₂ ^*, P ₃ ^*], on channel 1 with power P ₁ ^*to secondary user's S1, send data, on channel 2 and 3, adopt respectively P ₂ ^*and P ₃ ^*to secondary user's S2, send data.

2. the method for robustness efficiency transmission in cognition network as claimed in claim 1, is characterized in that, in step 3, determines that the uncertain set of relevant parameter is as follows:

Make ε ₀=ε _j=τ _k=δ _k, { 1,2}, { 1,2,3} represents the border of unified uncertain set to k ∈ to j ∈, and generalized norm adopts l in addition ₂norm || || ₂, for vectorial X ∈ R ^{1 * n}, l ₂norm || || ₂can be expressed as ${\left|\right|X\left|\right|}_2={({\left|x_1\right|}^2+...+{\left|x_n\right|}^2)}^{\frac{1}{2}}:$

The first step, for parameter G _sj, for the receiving terminal of primary user P1, set up following uncertain set

Q ₁＝{G _S1|||(G _S1-G _S1) ^T|| ₂≤ε ₀}

Wherein, when primary user P1 does not transmit on channel 2, now

making weight matrix value is unit matrix M ₁=I _{3 * 3}∈ R ^{3 * 3}, G _s1g _s1current estimated value;

For the receiving terminal of primary user P2, can obtain similar uncertain set

Q ₂＝{G _S2|||(G _S2-G _S2) ^T|| ₂≤ε ₀]

Wherein,

when primary user P1 does not transmit on channel 1 and 3, now

making weight matrix value is unit matrix M ₂=I _{3 * 3}∈ R ^{3 * 3}, G _s2g _s2current estimated value;

Second step, about parameter

for secondary user's S1, set up following uncertain set

$F_1=\left\{H_S^1\right|{\left|\right|H_S^1-{\hat{H}}_S^1\left|\right|}_2\&le;{\mathrm{\&epsiv;}}_0\}$

About secondary user's S2, set up following uncertain set

$F_2=\left\{H_S^2\right|{\left|\right|H_S^2-{\hat{H}}_S^2\left|\right|}_2\&le;{\mathrm{\&epsiv;}}_0\}$

$F_3=\left\{H_S^3\right|{\left|\right|H_S^3-{\hat{H}}_S^3\left|\right|}_2\&le;{\mathrm{\&epsiv;}}_0\}$

Wherein, F ₁the channel gain of secondary user's S1 on channel 1

uncertain set, F ₂and F ₃respectively the channel gain of secondary user's S2 on channel 2 and 3

with uncertain set, now weight coefficient assignment is

be secondary user's in secondary user's the channel gain on channel k

estimated value;

The 3rd step, for parameter

can obtain following uncertain set

$L_k=\left\{I_P^k\right|{\left|\right|I_P^k-{\hat{I}}_P^k\left|\right|}_2\&le;{\mathrm{\&epsiv;}}_0\},\&ForAll;k\&Element;\{\mathrm{1,2,3}\}$

Wherein, L _kit is the synthetic interference that primary user produces secondary user's on channel k

uncertain set, Z _kthat weight coefficient assignment is

it is the synthetic interference that primary user produces secondary user's on channel k estimated value.

3. the method for robustness efficiency transmission in cognition network as claimed in claim 1, is characterized in that, in step 5, and worst-case value expression formula be:

${\left(H_S^k\right)}^{*}=\arg \underset{(H_S^k\&Element;F_k)}{\min }\&CenterDot;(1+\frac{{\mathrm{\&Gamma;}}_k{\stackrel{\&OverBar;}{P}}_k{H}_S^k}{{\mathrm{BN}}_0+I_P^k})=\arg \underset{(H_S^k\&Element;F_k)}{\min }\&CenterDot;\left(H_S^k\right),\&ForAll;k\&Element;\left\{\mathrm{1,2,3}\right\}.$

4. the method for robustness efficiency transmission in cognition network as claimed in claim 1, is characterized in that, in step 6, and worst-case value

expression formula be:

${\left(I_S^k\right)}^{*}=\arg \underset{(I_S^k\&Element;L_k)}{\min }\&CenterDot;(1+\frac{{\mathrm{\&Gamma;}}_k{\stackrel{\&OverBar;}{P}}_k{H}_S^k}{{\mathrm{BN}}_0+I_P^k})=\arg \underset{(I_S^k\&Element;L_k)}{\min }\&CenterDot;\left(I_S^k\right),\&ForAll;k\&Element;\left\{\mathrm{1,2,3}\right\}.$

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