CN104540232B - A kind of wireless cooperative network relay power optimization method - Google Patents
A kind of wireless cooperative network relay power optimization method Download PDFInfo
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- H—ELECTRICITY
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Abstract
The invention discloses a kind of wireless cooperative network relay power optimization method, the described method comprises the following steps：Large Copacity cooperation transmission model is built based on compressive sensing theory；Power optimization is carried out to the Large Copacity cooperation transmission model, the optimal power allocation coefficient expressions of via node is obtained, that is, obtains optimal relay power distribution method.This method is based on compressed sensing, ensureing that signal can be on the premise of accurate reconstruction, the power distribution of more relay node cooperation communication systems based on compressed sensing is discussed, the optimal power allocation coefficient expressions of via node are drawn, obtain optimal relay power distribution method, so as to improve the transmission rate of information, save energy consumption and extend network life, improve the performance of whole system.
Description
Technical Field
The invention relates to the field of wireless communication physical layers, in particular to a relay power optimization method for a wireless cooperative network.
Background
The research range and content related to cooperative communication technology are quite extensive, and relay power allocation is generally important research content due to the fact that wireless resources are quite scarce.
The power control problem first appears in the solution problem of the capacity of the parallel independent sub-channel system, and the algorithm proposed by Gallager, Cover and the like is a well-known water injection algorithm which can be used for calculating the channel capacity of a multiple-input multiple-output (MIMO) system, an Orthogonal Frequency Division Multiplexing (OFDM) system and the like. The power distribution in the cooperative communication aims at different system models, and the limited power is reasonably distributed on each communication link under different constraint conditions according to certain judgment criteria, so that the power resource can be more efficiently utilized, and the performance of the relay system is further improved. Many researches on the power distribution problem in the relay cooperative communication system are currently carried out, various system models are generated, and many results are obtained. The method is used for researching the power distribution problem of the multiple relay nodes in the cooperative compressed sensing network model.
Disclosure of Invention
The invention provides a relay power optimization method of a wireless cooperative network, which applies a multi-relay cooperative technology to a compressed sensing technology and adopts an optimal relay power distribution strategy in relay forwarding power distribution, thereby further improving the channel capacity of the system and improving the performance of the whole system, and is described in detail in the following description:
a method for relay power optimization in a wireless cooperative network, the method comprising the steps of:
constructing a high-capacity cooperative transmission model based on a compressive sensing theory;
and performing power optimization on the high-capacity cooperative transmission model to obtain an optimal power distribution coefficient expression of the relay node, namely obtaining an optimal relay power distribution method.
The high-capacity cooperative transmission model specifically comprises the following steps:
each source node, each relay node and each destination node are configured to be a single antenna; the total transmitting power of the source node is PSThe total forwarding power of the relay node is PR；
Assuming that the channel matrix between the source node and the relay node is a,α1representing the path loss, H, of the first slot signal transmissionSRRepresenting multipath fading between the source node and the relay node; the orthogonal channel matrix between the relay node and the destination node is H,α2representing the path loss, H, of the signal transmission in the second time slotRDRepresenting multipath fading between the relay node and the destination node.
The expression for acquiring the optimal power distribution coefficient of the relay node is specifically as follows:
wherein,andchannel noise power for a first time slot and a second time slot, respectively; n is the number of source nodes;Hiia channel fading coefficient from each relay node to a destination node; a. theijThe channel fading coefficient from the jth source node to the ith relay node; λ is the lagrange multiplier.
The technical scheme provided by the invention has the beneficial effects that: the method is based on compressed sensing, under the premise of ensuring accurate reconstruction of signals, the power distribution of the multi-relay-node cooperative communication system based on the compressed sensing is discussed, the optimal power distribution coefficient expression of the relay nodes is obtained, and the optimal relay power distribution method is obtained, so that the transmission rate of information is improved, the energy consumption is reduced, the service life of a network is prolonged, and the performance of the whole system is improved.
Drawings
FIG. 1 is a schematic diagram of constructing a high-capacity cooperative transmission model based on a compressive sensing theory;
FIG. 2 is a diagram illustrating a comparison of channel capacities under different allocation algorithms;
fig. 3 is a flowchart of a relay power optimization method in a wireless cooperative network.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention are described in further detail below.
The basic principle of cooperative communication is as follows: mobile terminals equipped with a single antenna transmit data by means of the antennas of other users by means of antenna cooperation with each other, so that a virtual multi-antenna array is formed in a certain form. According to different information processing modes of relay nodes in cooperative communication, the cooperative schemes can be mainly divided into three types: amplify-and-forward, decode-and-forward, and encode-and-forward cooperation.
Since radio resources are very scarce, one of the main applications in the physical layer is power allocation. In cooperative communication, the performance of the system can be improved by efficient relay power allocation. The optimal power allocation algorithm is generally a power allocation algorithm based on the optimization of a certain performance of the system.
The core idea of compressed sensing is that compression and sampling are combined, and the measured value is far smaller than the data volume of the traditional sampling method, so that the bottleneck of Shannon's sampling theorem is broken through, and high-resolution signal acquisition becomes possible. It is indicated that as long as the signal is compressible or sparse in a certain transform domain, the transformed high-dimensional signal can be projected onto a low-dimensional space using an observation matrix that is not related to the transform basis, and then the original signal can be reconstructed with high probability from these small number of projections by solving an optimization problem, which can prove that such projections contain sufficient information for reconstructing the signal. At present, research on positioning, detection, data collection and the like of Compressed Sensing (CS) in a wireless sensor network is carried out.
A relay power optimization method for a wireless cooperative network, referring to fig. 1 and 3, the method comprising the following steps:
101: constructing a high-capacity cooperative transmission model based on a compressive sensing theory;
referring to fig. 1, a CS-based cooperative communication system model of N source nodes S, M relay nodes R and 1 destination node D is presented. Each source node Si(i is more than or equal to 1 and less than or equal to N) and each relay node Ri(1 ≦ i ≦ M) and the destination node D are both configured as a single antenna. Wherein the total transmitting power of the source node S is PSThe total forwarding power of the relay node S is PR. All communication is cooperatively completed by forwarding through the relay node R, assuming that there is no direct link between the source node S and the destination node D.
Assuming that the channel matrix between S and R is a,α1representing the path loss of the first time slot signal transmission, i.e. α1＝dSR -γ，dSRIs the distance of S to R, γ is the path loss factor; hSRRepresenting the multipath fading between S and R, with each element independent and obeying a gaussian distribution with mean zero and variance of 1/M. The orthogonal channel matrix between R and D is H,α2representing the path loss of the signal transmission in the second time slot, i.e. α2＝dRD -γ，dRDIs the distance from the relay node R to the destination node D; hRDRepresenting the multipath fading between R and D, and each element is an independent identically distributed Gaussian random variable with the mean value of zero.
102: and performing power optimization on the high-capacity cooperative transmission model, and acquiring an optimal power distribution coefficient expression of the relay node, namely acquiring an optimal relay power distribution method.
Performing singular value decomposition on a channel matrix A between a source node S and a relay node R in the model to obtain
A＝U·Λ·V' (1)
Wherein U and V are unitary matrices, the matrix Λ is 0 for off-diagonal elements, and the diagonal elements are square roots of eigenvalues of the matrix AA'. Wherein A' is the conjugate transpose matrix of A. The channel capacity per unit bandwidth can be expressed as
In the above formula, the first and second carbon atoms are,ai(1. ltoreq. i. ltoreq.M) is a power distribution coefficient of each relay node, and0≤ai≤1；Λiis the ith element on the diagonal of the matrix Λ;andchannel noise power for a first time slot and a second time slot, respectively; hii(i is more than or equal to 1 and less than or equal to M) is the channel fading coefficient from each relay node to the destination node; a. theij(i is more than or equal to 1 and less than or equal to M, and j is more than or equal to 1 and less than or equal to N) is the channel fading coefficient between the jth source node and the ith relay node.
Using channel capacity as an optimization objective function, i.e.
And is
Using the lagrange multiplier method, the function can be obtained:
order toCan obtain aiExpression (2)
ByThe value of lambda can be obtained, and then the lambda value is substituted into the formula (5) to obtain each aiThe value of (c). If there is aiLarger than 1 or smaller than 0, M is selected from M relay nodes1The average power allocation is performed for each channel with better conditions (M channels that maximize the channel capacity).
In order to evaluate the performance of optimal power allocation in the cooperative compressive sensing network model, the method performs simulation, which is described in detail in the following description:
in the simulation model, assuming that a source node, a relay node and a destination node are positioned on a straight line, the distance from the source node to the destination node is normalized to 1, dSRIs the source-to-distance. The source node and the destination node are respectively positioned at (0, 0) and (1, 0), the y coordinate of the relay node is fixed to be 0, and the abscissa is [0, 1 ]]To change between. Assuming that the selected cooperative relay has the same path loss to the destination node, the path loss coefficient is 4. The total transmission power of the source node and the relay node is 10-2w, noise 10-8w。
Fig. 2 simulates the variation of system channel capacity with relay location under average power allocation and optimal power allocation. As can be seen from fig. 2, when the relay node is close to the source node, the channel capacity under the optimal power allocation is significantly greater than that under the average power allocation, and as the relay node is close to the destination node, the difference is smaller and smaller, and the channel capacities under the two power allocation schemes tend to be consistent.
Those skilled in the art will appreciate that the drawings are only schematic illustrations of preferred embodiments, and the above-described embodiments of the present invention are merely provided for description and do not represent the merits of the embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (2)
1. A relay power optimization method for a wireless cooperative network is characterized by comprising the following steps:
constructing a high-capacity cooperative transmission model based on a compressive sensing theory;
performing power optimization on the high-capacity cooperative transmission model to obtain an optimal power distribution coefficient expression of the relay node, namely obtaining an optimal relay power distribution method;
the high-capacity cooperative transmission model specifically comprises:
each source node and each relay nodeAnd the destination node are both configured as a single antenna; the total transmitting power of the source node is PSThe total forwarding power of the relay node is PR；
Assuming that the channel matrix between the source node and the relay node is a,α1representing the path loss, H, of the first slot signal transmissionSRRepresenting multipath fading between the source node and the relay node; the orthogonal channel matrix between the relay node and the destination node is H,α2representing the path loss, H, of the signal transmission in the second time slotRDRepresenting multipath fading between the relay node and the destination node.
2. The method for optimizing relay power in a wireless cooperative network according to claim 1, wherein the expression for obtaining the optimal power allocation coefficient of the relay node is specifically:
<mfenced open = "" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>a</mi> <mi>i</mi> </msub> <mo>=</mo> <mfrac> <mrow> <msqrt> <mfrac> <msub> <mi>P</mi> <mi>S</mi> </msub> <mi>N</mi> </mfrac> </msqrt> <mo>&CenterDot;</mo> <msub> <mi>&sigma;</mi> <msub> <mi>&omega;</mi> <mn>0</mn> </msub> </msub> <msub> <mi>&Lambda;</mi> <mi>i</mi> </msub> <msqrt> <mrow> <mfrac> <msub> <mi>P</mi> <mi>S</mi> </msub> <mi>N</mi> </mfrac> <mo>&CenterDot;</mo> <msup> <msub> <mi>&Lambda;</mi> <mi>i</mi> </msub> <mn>2</mn> </msup> <mo>&CenterDot;</mo> <msubsup> <mi>&sigma;</mi> <msub> <mi>&omega;</mi> <mn>0</mn> </msub> <mn>2</mn> </msubsup> <mo>-</mo> <mfrac> <mrow> <mn>4</mn> <msub> <mi>c</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <msubsup> <mi>&sigma;</mi> <msub> <mi>n</mi> <mn>0</mn> </msub> <mn>2</mn> </msubsup> <mo>+</mo> <mfrac> <msub> <mi>P</mi> <mi>S</mi> </msub> <mi>N</mi> </mfrac> <mo>&CenterDot;</mo> <msup> <msub> <mi>&Lambda;</mi> <mi>i</mi> </msub> <mn>2</mn> </msup> <mo>)</mo> </mrow> <mo>&CenterDot;</mo> <msubsup> <mi>&sigma;</mi> <msub> <mi>n</mi> <mn>0</mn> </msub> <mn>2</mn> </msubsup> </mrow> <mrow> <mi>&lambda;</mi> <mi>ln</mi> <mn>2</mn> </mrow> </mfrac> </mrow> </msqrt> </mrow> <mrow> <mn>2</mn> <mo>&lsqb;</mo> <msub> <mi>c</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <msubsup> <mi>&sigma;</mi> <msub> <mi>n</mi> <mn>0</mn> </msub> <mn>2</mn> </msubsup> <mo>+</mo> <mfrac> <msub> <mi>P</mi> <mi>S</mi> </msub> <mi>N</mi> </mfrac> <mo>&CenterDot;</mo> <msup> <msub> <mi>&Lambda;</mi> <mi>i</mi> </msub> <mn>2</mn> </msup> <mo>)</mo> </mrow> <msubsup> <mi>&sigma;</mi> <msub> <mi>n</mi> <mn>0</mn> </msub> <mn>2</mn> </msubsup> <mo>&rsqb;</mo> </mrow> </mfrac> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>-</mo> <mfrac> <mrow> <mo>&lsqb;</mo> <msubsup> <mi>&sigma;</mi> <msub> <mi>&omega;</mi> <mn>0</mn> </msub> <mn>2</mn> </msubsup> <msubsup> <mi>&sigma;</mi> <msub> <mi>n</mi> <mn>0</mn> </msub> <mn>2</mn> </msubsup> <mo>+</mo> <mrow> <mo>(</mo> <msubsup> <mi>&sigma;</mi> <msub> <mi>n</mi> <mn>0</mn> </msub> <mn>2</mn> </msubsup> <mo>+</mo> <mfrac> <msub> <mi>P</mi> <mi>S</mi> </msub> <mi>N</mi> </mfrac> <mo>&CenterDot;</mo> <msup> <msub> <mi>&Lambda;</mi> <mi>i</mi> </msub> <mn>2</mn> </msup> <mo>)</mo> </mrow> <msubsup> <mi>&sigma;</mi> <msub> <mi>&omega;</mi> <mn>0</mn> </msub> <mn>2</mn> </msubsup> <mo>&rsqb;</mo> </mrow> <mrow> <mn>2</mn> <mo>&lsqb;</mo> <msub> <mi>c</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <msubsup> <mi>&sigma;</mi> <msub> <mi>n</mi> <mn>0</mn> </msub> <mn>2</mn> </msubsup> <mo>+</mo> <mfrac> <msub> <mi>P</mi> <mi>S</mi> </msub> <mi>N</mi> </mfrac> <mo>&CenterDot;</mo> <msup> <msub> <mi>&Lambda;</mi> <mi>i</mi> </msub> <mn>2</mn> </msup> <mo>)</mo> </mrow> <msubsup> <mi>&sigma;</mi> <msub> <mi>n</mi> <mn>0</mn> </msub> <mn>2</mn> </msubsup> <mo>&rsqb;</mo> </mrow> </mfrac> </mrow> </mtd> </mtr> </mtable> </mfenced>
wherein,andchannel noise power for a first time slot and a second time slot, respectively; n is the number of source nodes;Hiia channel fading coefficient from each relay node to a destination node; a. theijThe channel fading coefficient from the jth source node to the ith relay node; λ is lagrange multiplier; lambdaiIs the ith element on the diagonal of the matrix Λ; the non-diagonal elements of matrix Λ are 0 and the diagonal elements are the square root of the eigenvalues of matrix AA ', where a' is aThe conjugate transpose matrix of (2).
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| CN106162797B (en) * | 2016-08-14 | 2019-08-06 | 深圳市凯贝罗科技有限公司 | A kind of multi-relay cooperation resource assignment method of communication system based on fractional programming |
| CN107612605A (en) * | 2017-09-20 | 2018-01-19 | 天津大学 | A kind of data transmission method based on compressed sensing and decoding forwarding |
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| CN104184554A (en) * | 2014-09-03 | 2014-12-03 | 北京邮电大学 | Undersampling quantification and forwarding method for relay network |
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| CN104184554A (en) * | 2014-09-03 | 2014-12-03 | 北京邮电大学 | Undersampling quantification and forwarding method for relay network |
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