CN104540232A - Method for optimizing relay power of wireless cooperative network - Google Patents

Abstract

The invention discloses a method for optimizing the relay power of a wireless cooperative network. The method comprises the following steps: constructing a high-capacity cooperative transmission model on the basis of a compressive sensing theory; and performing power optimization on the high-capacity cooperative transmission model to obtain an optimal power distribution coefficient expression of relay nodes, thereby obtaining an optimal relay power distribution method. The method is based on compressive sensing, the power distribution of a multi-relay-node cooperative communication system based on compressive sensing is discussed on the premise that signals are ensured to be accurately restructured, 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 saved, the network life is prolonged, and the performance of a whole system is improved.

Description

A kind of wireless cooperative network relay power optimization method

Technical field

The present invention relates to radio communication physical layer field, particularly relate to a kind of wireless cooperative network relay power optimization method.

Background technology

The research range that cooperative communication technology relates to and content very wide, and due to Radio Resource very rare, therefore relay power distributes normally wherein important research contents.

First Power Control Problem appears in the Solve problems of parallel independent subchannels power system capacity, the famous water-filling algorithm that Gallager and Cover etc. propose, this algorithm may be used for the channel capacity etc. calculating multiple-input, multiple-output (MIMO) system and OFDM (OFDM) system.And the power division in collaboration communication is for different system models, criterion is passed judgment on according to some, consider, under different constraints, limited power is carried out reasonable distribution on each communication link, more efficiently can utilize power resource, improve the performance of relay system further.The current research about power distribution problems in relay cooperative communication system is a lot, has occurred multiple systems model, and has drawn a lot of achievement.This method research be based on the many via nodes power distribution problems in cooperation compressed sensing network model.

Summary of the invention

The invention provides a kind of wireless cooperative network relay power optimization method, multi-relay cooperation technology is applied in compressed sensing technology by the present invention, optimum relay power allocation strategy is adopted in relay forwarding power division, thus the channel capacity of further raising system, promote the performance of whole system, described below:

A kind of wireless cooperative network relay power optimization method, said method comprising the steps of:

Large Copacity cooperation transmission model is built based on compressive sensing theory;

Power optimization is carried out to described Large Copacity cooperation transmission model, obtains the optimal power allocation coefficient expressions of via node, namely obtain optimum relay power distribution method.

Described Large Copacity cooperation transmission model is specially:

Each source node, each via node and destination node are all configured to single antenna; Total transmitting power of source node is P _s, total repeating power of via node is P _r;

Assuming that the channel matrix between source node and via node is A, α ₁represent the path loss of the first time slot signal transmission, H _sRrepresent the multipath fading between source node and via node; Orthogonal channel matrix between via node and destination node is H, α ₂represent the path loss of the second time slot signal transmission, H _rDrepresent the multipath fading between via node and destination node.

The optimal power allocation coefficient expressions of described acquisition via node is specially:

$\begin{array}{c}{a}_i=\frac{\sqrt{\frac{P_S}{N}}\·{\mathrm{\σ}}_{{\mathrm{\ω}}_0}{\mathrm{\Λ}}_i\sqrt{\frac{P_S}{N}\·{{\mathrm{\Λ}}_i}^2\·{\mathrm{\σ}}_{{\mathrm{\ω}}_0}^2-\frac{4c_i({\mathrm{\σ}}_{n_0}^2+\frac{P_S}{N}\·{{\mathrm{\Λ}}_i}^2)\·{\mathrm{\σ}}_{n_0}^2}{\mathrm{\λ}\ln 2}}}{2\left[c_i({\mathrm{\σ}}_{n_0}^2+\frac{P_S}{N}\·{{\mathrm{\Λ}}_i}^2){\mathrm{\σ}}_{n_0}^2\right]}\\ -\frac{[{\mathrm{\σ}}_{{\mathrm{\ω}}_0}^2{\mathrm{\σ}}_{n_0}^2+({\text{\σ}}_{n_0}^2+\frac{P_S}{N}\·{{\mathrm{\Λ}}_i}^2){\mathrm{\σ}}_{{\mathrm{\ω}}_0}^2]}{2\left[c_i({\mathrm{\σ}}_{n_0}^2+\frac{P_S}{N}\·{{\mathrm{\Λ}}_i}^2){\mathrm{\σ}}_{n_0}^2\right]}\end{array}$

Wherein, with be respectively the channel noise power of the first time slot and the second time slot; N is source node number; h _iifor each via node is to the channel fading coefficient of destination node; A _ijfor the channel fading coefficient between a jth source node to i-th via node; λ is Lagrange multiplier.

The beneficial effect of technical scheme provided by the invention is: this method is based on compressed sensing, can under the prerequisite of accurate reconstruction at guarantee signal, discussion has been done to the power division of the many relay node cooperations communication system based on compressed sensing, draw the optimal power allocation coefficient expressions of via node, obtain optimum relay power distribution method, thus the transmission rate of the information of raising, save energy consumption prolong network lifetime, improve the performance of whole system.

Accompanying drawing explanation

Fig. 1 is the schematic diagram building Large Copacity cooperation transmission model based on compressive sensing theory;

Fig. 2 is that the channel capacity under different allocation algorithm compares schematic diagram;

Fig. 3 is a kind of flow chart of wireless cooperative network relay power optimization method.

Embodiment

For making the object, technical solutions and advantages of the present invention clearly, below embodiment of the present invention is described further in detail.

Collaboration communication general principle is: the mobile terminal of equipment individual antenna passes through the mode of antenna cooperative to each other, and transmit data with this antenna by other users, therefore virtual multi-antenna array just defines certain in form.According to the difference of via node process information mode in collaboration communication, cooperation scheme mainly can be divided into three kinds: amplification forwarding, decoding forward and coding cooperative.

Because Radio Resource is very rare, so be exactly power division in a main application of physical layer.In collaboration communication, the performance of system can be improved by effective relay power distribution.Optimal power allocation algorithm is normally based on the optimization of a certain performance of system and a kind of power distribution algorithm carried out.

The core concept of compressed sensing is that compression and sampling merging are carried out, and measured value is much smaller than the data volume of traditional sampling method, breaches the bottleneck of Shannon's sampling theorem, makes high-resolution signals collecting become possibility.As long as it points out that signal is compressible or is sparse at certain transform domain, so just with transform-based incoherent observing matrix, conversion gained height dimensional signal can be projected on a lower dimensional space with one, then just from these a small amount of projections, original signal can be reconstructed with high probability by solving an optimization problem, can prove that such projection contains the enough information of reconstruction signal.At present, compressed sensing (CS) location in wireless sensor network, detect, the research of the aspects such as Data Collection is carried out.

A kind of wireless cooperative network relay power optimization method, see Fig. 1 and Fig. 3, the method comprises the following steps:

101: build Large Copacity cooperation transmission model based on compressive sensing theory;

See Fig. 1, give the cooperation communication system model based on CS of N number of source node S, a M via node R and 1 destination node D.Each source node S _i(1≤i≤N), each via node R _i(1≤i≤M) and destination node D are all configured to single antenna.Wherein, total transmitting power of source node S is P _s, total repeating power of via node S is P _r.Suppose not direct transfer between source node S and destination node D link, all communication all will cooperate through the forwarding of via node R.

Assuming that the channel matrix between S and R is A, α ₁represent the path loss of the first time slot signal transmission, i.e. α ₁=d _sR ^-γ, d _sRbe the distance of S to R, γ is path-loss factor; H _sRrepresent the multipath fading between S and R, its each element is independent and obedience average is zero, and variance is the Gaussian Profile of 1/M.Orthogonal channel matrix between R and D is H, α ₂represent the path loss of the second time slot signal transmission, i.e. α ₂=d _rD ^-γ, d _rDit is the distance of via node R to destination node D; H _rDrepresent the multipath fading between R and D, its each element to be average be zero independent same distribution Gaussian random variable.

102: power optimization is carried out to Large Copacity cooperation transmission model, obtain the optimal power allocation coefficient expressions of via node, namely obtain optimum relay power distribution method.

Singular value decomposition is carried out to the channel matrix A in above-mentioned model between source node S and via node R,

A＝U·Λ·V' (1)

Wherein, U, V are unitary matrice, and matrix Λ off diagonal element is 0, and diagonal entry is the square root of the characteristic value of matrix A A'.Wherein, A' is the associate matrix of A.Channel capacity then under unit bandwidth can be expressed as

$C=\underset{i=1}{\overset{M}{\mathrm{\Σ}}}{\log }_2(1+\frac{P_S}{N}\·\frac{c_i\·{{\mathrm{\Λ}}_i}^2\·a_i}{{\mathrm{\σ}}_{{\mathrm{\ω}}_0}^2+{\mathrm{\σ}}_{n_0}^2\·c_i\·a_i})---\left(2\right)$

In above formula, a _i(1≤i≤M) is the power partition coefficient of each via node, and 0≤a _i≤ 1; Λ _ii-th element on matrix Λ diagonal; with be respectively the channel noise power of the first time slot and the second time slot; H _ii(1≤i≤M) is for each via node is to the channel fading coefficient of destination node; A _ij(1≤i≤M, 1≤j≤N) is the channel fading coefficient between a jth source node to i-th via node.

Take channel capacity as optimization object function, namely

$\max \left(C\right)=\max \left\{\underset{i=1}{\overset{M}{\mathrm{\Σ}}}{\log }_2(1+\frac{P_S}{N}\·\frac{c_i\·{{\mathrm{\Λ}}_i}^2\·a_i}{{\mathrm{\σ}}_{{\mathrm{\ω}}_0}^2+{\mathrm{\σ}}_{n_0}^2\·c_i\·a_i})\right\}---\left(3\right)$

And $\underset{i=1}{\overset{M}{\mathrm{\Σ}}}{a}_i=\mathrm{1,0}\≤a_i\≤1$

Utilize method of Lagrange multipliers, can function be obtained:

$\begin{array}{c}F(a_1,a_2,...,a_M,\mathrm{\λ})=\underset{i=1}{\overset{M}{\mathrm{\Σ}}}{\log }_2(1+\frac{P_S}{N}\·\frac{c_i\·{{\mathrm{\Λ}}_i}^2\·a_i}{{\mathrm{\σ}}_{{\mathrm{\ω}}_0}^2+{\mathrm{\σ}}_{n_0}^2\·c_i\·a_i})\\ +\mathrm{\λ}\·(a_1+a_2+...+a_M)\end{array}---\left(4\right)$

Order a can be obtained _iexpression formula

$\begin{array}{c}{a}_i=\frac{\sqrt{\frac{P_S}{N}}\·{\mathrm{\σ}}_{{\mathrm{\ω}}_0}{\mathrm{\Λ}}_i\sqrt{\frac{P_S}{N}\·{{\mathrm{\Λ}}_i}^2\·{\mathrm{\σ}}_{{\mathrm{\ω}}_0}^2-\frac{4c_i({\mathrm{\σ}}_{n_0}^2+\frac{P_S}{N}\·{{\mathrm{\Λ}}_i}^2)\·{\mathrm{\σ}}_{n_0}^2}{\mathrm{\λ}\ln 2}}}{2\left[c_i({\mathrm{\σ}}_{n_0}^2+\frac{P_S}{N}\·{{\mathrm{\Λ}}_i}^2){\mathrm{\σ}}_{n_0}^2\right]}\\ -\frac{[{\mathrm{\σ}}_{{\mathrm{\ω}}_0}^2{\mathrm{\σ}}_{n_0}^2+({\text{\σ}}_{n_0}^2+\frac{P_S}{N}\·{{\mathrm{\Λ}}_i}^2){\mathrm{\σ}}_{{\mathrm{\ω}}_0}^2]}{2\left[c_i({\mathrm{\σ}}_{n_0}^2+\frac{P_S}{N}\·{{\mathrm{\Λ}}_i}^2){\mathrm{\σ}}_{n_0}^2\right]}\end{array}---\left(5\right)$

By the value of λ can be tried to achieve, then λ value is updated in (5) formula, each a can be obtained _ivalue.If there is a _ilarger or less than 0 than 1, then from M via node, select M ₁be averaged individual channel condition reasonable (making M the channel that channel capacity reaches maximum) power division.

In order to assess the performance of optimal power allocation in cooperation compressed sensing network model, this method emulates, described below:

In simulation model, assuming that source node, via node and destination node are positioned on straight line, source node is 1, d to the range normalization of destination node _sRfor source is to distance.Source node and destination node lay respectively at (0,0) and (1,0), and the y coordinate of via node is fixed as 0, and abscissa changes between [0,1].Assuming that selected cooperating relay has identical path loss to destination node, path loss coefficient is 4.Total transmitting power of source node and via node is all 10 ^-2w, noise is 10 ^-8w.

Fig. 2 simulates under average power allocation and optimal power allocation, and system channel capacity is with the variation relation of intermediate position.Can see in Fig. 2, when via node is near source node, the channel capacity under optimal power allocation is obviously greater than under average power allocation, along with via node is near destination node, gap is more and more less, and the channel capacity under two kinds of power allocation schemes reaches unanimity.

It will be appreciated by those skilled in the art that accompanying drawing is the schematic diagram of a preferred embodiment, the invention described above embodiment sequence number, just to describing, does not represent the quality of embodiment.

The foregoing is only preferred embodiment of the present invention, not in order to limit the present invention, within the spirit and principles in the present invention all, any amendment done, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.

Claims (3)

1. a wireless cooperative network relay power optimization method, is characterized in that, said method comprising the steps of:

Large Copacity cooperation transmission model is built based on compressive sensing theory;

Power optimization is carried out to described Large Copacity cooperation transmission model, obtains the optimal power allocation coefficient expressions of via node, namely obtain optimum relay power distribution method.

2. a kind of wireless cooperative network relay power optimization method according to claim 1, is characterized in that, described Large Copacity cooperation transmission model is specially:

Each source node, each via node and destination node are all configured to single antenna; Total transmitting power of source node is P _s, total repeating power of via node is P _r;

Assuming that the channel matrix between source node and via node is A, α ₁represent the path loss of the first time slot signal transmission, H _sRrepresent the multipath fading between source node and via node; Orthogonal channel matrix between via node and destination node is H, α ₂represent the path loss of the second time slot signal transmission, H _rDrepresent the multipath fading between via node and destination node.

3. a kind of wireless cooperative network relay power optimization method according to claim 2, it is characterized in that, the optimal power allocation coefficient expressions of described acquisition via node is specially:

$\begin{array}{c}{a}_i=\frac{\sqrt{\frac{P_S}{N}}\·{\mathrm{\σ}}_{{\mathrm{\ω}}_0}{\mathrm{\Λ}}_i\sqrt{\frac{P_S}{N}\·{{\mathrm{\Λ}}_i}^2\·{\mathrm{\σ}}_{{\mathrm{\ω}}_0}^2-\frac{4c_i({\mathrm{\σ}}_{n_0}^2+\frac{P_S}{N}\·{{\mathrm{\Λ}}_i}^2)\·{\mathrm{\σ}}_{n_0}^2}{\mathrm{\λ}\ln 2}}}{2\left[c_i({\mathrm{\σ}}_{n_0}^2+\frac{P_S}{N}\·{{\mathrm{\Λ}}_i}^2){\mathrm{\σ}}_{n_0}^2\right]}\\ -\frac{[{\mathrm{\σ}}_{{\mathrm{\ω}}_0}^2{\mathrm{\σ}}_{n_0}^2+({\mathrm{\σ}}_{n_0}^2+\frac{P_S}{N}\·{{\mathrm{\Λ}}_i}^2){\mathrm{\σ}}_{{\mathrm{\ω}}_0}^2]}{2\left[c_i({\mathrm{\σ}}_{n_0}^2+\frac{P_S}{N}\·{{\mathrm{\Λ}}_i}^2){\mathrm{\σ}}_{n_0}^2\right]}\end{array}$

Wherein, with be respectively the channel noise power of the first time slot and the second time slot; N is source node number;

h _iifor each via node is to the channel fading coefficient of destination node; A _ijfor the channel fading coefficient between a jth source node to i-th via node; λ is Lagrange multiplier.