CN109194371B - Low-complexity power distribution method of distributed MISO system based on beam forming - Google Patents
Low-complexity power distribution method of distributed MISO system based on beam forming Download PDFInfo
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Abstract
The invention discloses a low-complexity power distribution method based on beam forming in a distributed multi-input single-output (MISO) system. The method takes the energy efficiency in the distributed MISO system as an optimization target and the power distribution and beam forming vectors of each remote antenna unit as optimization parameters to construct a system optimization model. Combining the optimal beam forming scheme of maximum ratio transmission, and obtaining the form of the optimal solution of the optimization model according to the KKT condition; and obtaining the optimal power distribution by utilizing a Lambert function and a dichotomy, so that the energy efficiency of the distributed antenna system is maximized. The invention optimally designs the energy efficiency power distribution of the distributed MISO system based on beam forming, and considers the influence of the actual medium and large scale fading on the channel. The designed method is simple and effective, and energy efficiency performance consistent with that of the existing method can be obtained while the calculation complexity is reduced.
Description
Technical Field
The invention belongs to the field of mobile communication, relates to a resource allocation method of mobile communication, and particularly relates to a low-complexity energy efficiency power allocation algorithm based on beam forming in a distributed multi-antenna system.
Background
With the increase of traffic and the continuous expansion of networks, the huge energy consumption of wireless communication networks and the environmental problems generated by the huge energy consumption become the focus of communication, and today with the increasing shortage of resources, how to save energy, reduce emission and reduce energy consumption becomes a hot point of communication industry research. In order to integrate the green concepts such as environment symbiosis and sustainability development into communication, the concept of "green communication" comes along. The green communication aims to reduce the energy consumption of the system and more effectively utilize the system resources. The traditional communication technology mainly aims at improving the spectrum efficiency SE of the system, and has less consideration on the aspect of energy conservation, but the over consideration of the spectrum efficiency is usually at the cost of huge energy consumption. The fifth generation mobile communication 5G, in addition to providing requirements for traditional performance indexes such as transmission rate, spectrum efficiency, etc., first takes the reduction of energy consumption and the improvement of energy efficiency EE as a definite research and development target.
The DAS has become one of the technologies with great development prospects in future wireless communication due to its advantages of large coverage, less switching, reduced transmission power, and greater diversity gain. Unlike traditional centralized antenna systems, distributed antenna systems place several antennas in a distributed fashion at different geographical locations in a cell, with each antenna connected to the cell's central processor by fiber, coaxial cable, or wireless link. Because the system is provided with a plurality of antennas which are separated in space, the distributed antenna system can overcome the channel path loss caused by large-scale fading and shadow fading, improve the system capacity, improve the diversity degree, solve the communication dead angle in a cell and improve the communication service quality. Compared with the traditional distributed antenna system, each remote antenna unit of the distributed multi-input single-output MISO system is provided with a plurality of antennas, so that the system can obtain space diversity gain at the same time, and the performance of the distributed antenna system is further improved. The beam forming technology is that a transmitting end firstly weights data and then transmits the data to form a narrow transmitting beam, and energy is directed to a target user, so that the demodulation signal-to-noise ratio of the target user is improved, and the method is particularly effective for improving the throughput rate of cell edge users. Beamforming techniques may achieve array gain, diversity gain, and multiplexing gain.
The existing literature (h.kim, e. -s.park, h. -w.park.i.lee, "Beamforming and power allocation schemes for efficiency amplification in MISO distributed antenna systems," IEEE com.letters, vol.17, No.11, pp.2100-2103, november.2013.) studies the energy efficiency of distributed MISO systems based on Beamforming and presents a resource allocation algorithm that maximizes energy efficiency. However, shadow fading is not considered in the document, the algorithm needs to use a lambertian function for a large number of times, the calculation complexity is high, and the calculation formula given in the document is not accurate (which is only true when the number of remote antenna units is 2). At present, no low-complexity algorithm is available for solving the power distribution problem of the distributed MISO system based on the maximization of energy efficiency of beam forming.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the invention provides a low-complexity power allocation method of a distributed MISO system based on beam forming, which solves the problem of power allocation of the distributed MISO system based on beam forming while maximizing energy efficiency and reducing complexity of a calculation method.
The technical scheme is as follows: in order to achieve the purpose, the invention adopts the technical scheme that: a distributed MISO system is based on the power distribution method of beam forming, the said distributed MISO system includes N remote antenna units, each remote antenna unit has L antennas, each remote antenna unit interacts with central processing unit separately; the method comprises the following steps:
(1) power distribution p of remote antenna units in distributed MISO systemiAnd a beamforming vector wiConstructing an optimization model for the optimization variables, wherein the optimization problem in the optimization model is as follows:
the constraint conditions are as follows: p is more than or equal to 0i≤Pmax，||wi||2N,. 1, i ═ 1; wherein p isiAnd wiRespectively representing the ith remote antenna unit RAUiTransmit power and beamforming column vector of; pmaxRepresenting the maximum transmit power, η, of each remote antenna unitEE(pi，wi) Power distribution p at remote antenna units for distributed MISO systemsiAnd a beamforming vector wiEnergy efficiency.
(2) For distributed MISO systems, the optimal beamforming scheme to maximize EE is distributed maximal ratio transmission D-MRT, wiThat is, the optimization problem can be reconstructed:
the constraint conditions are as follows: p is more than or equal to 0i≤Pmax，i＝1，...，N。
(3) Without loss of generality, let γ be assumed1＞γ2＞…＞γNWherein
Siis logarithmic shadow fading;
is RAUiPath loss to the subscriber, where diIs RAUiDistance to user, α is path loss exponent; h isiIs a small-scale fading channel vector. Constructing a Lagrangian function for the optimization model converted in the step (2), wherein the Lagrangian function can be constructed according to the KKT conditionThe optimal power allocation solution is obtained in the following form:
wherein,
when express maximum EE
The transmit power of. It should be noted here that when N is0When the number is equal to 1, the alloy is put into a container,
when N is present0When the content of the organic acid is more than or equal to 2,
(4) according to the form of the optimal power distribution solution given in the step (3), constructing the following piecewise function:
wherein
Theoretical analysis shows that the piecewise function is a convex function and has only one optimal solution.
(5) According to V'j(pj) Is determined by the sign of0When N is present0When 1, it can be determined from a Lambert function
Wherein
When N is present0When the average particle size is more than or equal to 2, the dichotomy is utilized in the interval
Upper solution
Has the advantages that: compared with the prior art, the invention has the following advantages:
a mixed channel model which is more consistent with the actual model and contains path loss, shadow fading and small-scale Rayleigh fading is built, the form of an optimal power distribution solution is obtained through a KKT condition, and a piecewise function is reconstructed to obtain the optimal transmitting power of all remote antennas, so that the energy efficiency of the distributed MISO system is maximized. The method has simple calculation flow, only uses Lambert function or dichotomy once at most, greatly reduces the time and space complexity compared with the prior method, and can obtain the same EE performance.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 is a schematic diagram of a distributed MISO system in accordance with an embodiment of the present invention;
FIG. 3 is a comparison chart of the optimization results of the embodiment of the present invention and the optimization results before and after modification of the existing method.
Fig. 4 is a diagram comparing the optimization results of the embodiment of the present invention and the existing method under different numbers of transmitting antennas L.
Table 1 shows a comparison of the complexity of the present embodiment and the prior art method.
Detailed Description
The present invention will be further described with reference to the accompanying drawings.
First, distributed MISO system model:
FIG. 2 is a block diagram of a distributed MISO system including N number of distributed remote antenna units, denoted RAUs, distributed in a cell according to an embodiment of the present inventionn(n＝1，2，.., N), each remote antenna unit is equipped with L antennas and is connected to the central processor through a specific transmission channel. In view of the practical limited size of a mobile station, it is only considered to have a single antenna. Definition of
Is RAUiChannel vector to mobile station, where diIs RAUiDistance to mobile station, α is path loss exponent, SiIs RAUiShadow fading to mobile station, hiIs RAUiSmall scale fading vectors to the mobile station; definition of wiIs RAUiBeam-forming the column vector of, and | | wi||2＝1。
Energy efficiency of distributed MISO system:
the data transmission rate obtained by the mobile station after the distributed MISO system is added into the beam forming is as follows:
wherein p isiTo indicate RAUiThe transmission power of the transmitter,
denotes giThe conjugate transpose of (a) is performed,
is complex additive white gaussian noise power.
The energy efficiency of the distributed MISO system after joining beamforming can be calculated as follows:
wherein p iscConsuming power for the system circuitry.
Thirdly, the power distribution method based on the energy efficiency optimization of the beam forming of the distributed MISO system is provided
The distributed MISO system EE maximization optimization function is defined as:
wherein
Since the problem is a non-linear programming problem, direct solution is difficult. Thus without loss of generality, let γ be assumed1＞γ2＞…＞γNConstructing a Langerian day function for the reconstructed optimization function, and obtaining the form of an optimal power distribution solution by using a KKT condition:
wherein
When express maximum EE
The transmit power of. It is noted here that N0When the number is equal to 1, the alloy is put into a container,
when N is present0When the content of the organic acid is more than or equal to 2,
based on this, the optimization problem can be converted into an optimal solution for solving the following piecewise function:
wherein,
by pairs of Vj(pj) And (4) derivative analysis shows that the piecewise function is a convex function and has only one optimal solution. When V'j(Pmax) When the pressure is higher than 0, the pressure is higher,
when in
Thereon is provided with
When the water-soluble polymer is existed in the water,
unique solution
Thus can be according to V'j(pj) Is determined by the sign of0When N is present0When 1, a closed-form solution can be determined by the lambert function:
wherein
When N is present0When the average particle size is more than or equal to 2, the dichotomy is utilized in the interval
Upper solution
The transmission power of all RAUs at maximum EE can be obtained according to the form of the optimal power allocation solution.
Specific power allocation methods are given below:
(a) initializing n-1; will gammaiIn descending order, i.e. gamma1＞γ2＞…＞γN；
(b) Judging whether N satisfies N and is less than or equal to N, and if so, calculating V'n(Pmax) Entering step (c); otherwise, entering the step (e);
(c) judging V'n(Pmax) Whether or not to satisfy V'n(Pmax) Less than or equal to 0; if the judgment result is yes, then let N0Entering step (d); if not, then,
returning to the step (b) when n is n + 1;
(d) judgment of N0Whether the number is equal to 1 or not, if so, calculating by utilizing a Lambert function
Otherwise, using dichotomy in interval
Upper solution
Entering step (e);
(e) obtaining the transmit power and maximum energy efficiency η for all RAUsEE。
The Matlab simulation platform is used for comparing the maximum value of the energy efficiency of the distributed MISO system obtained by the technical scheme provided by the embodiment with the maximum value of the energy efficiency of the existing method so as to verify the effectiveness of the method.
FIG. 3 is a graph comparing the optimization results of the present invention with the prior art before and after modification. Wherein, the method 1 represents the experimental result of the technical proposal adopted in the embodiment, and the method 2 represents the experimental result of the method. Simulation results show that the formula given by the existing method is not very accurate (only is true when the remote antenna unit is 2), but the method provided by the invention can obtain completely consistent EE performance after the formula is modified, and simultaneously reduces the huge calculation amount of the existing method. Fig. 4 shows the evaluation of the system EE performance for different numbers of transmit antennas L after beamforming is added to the distributed MISO system. As can be seen from fig. 4, the system EE performance improves as the number of antennas L increases, because the increase of L brings more spatial diversity gain, thereby improving the system EE performance. The accompanying table 1 shows the comparison of the complexity of the present method and the technical solution in time and space, and it can be seen that the time required by the method of the present invention is only one tenth of the time required by the prior method, and only once lambertian function is used at most, while the prior method needs to use N times, so the complexity of the method of the present invention is far lower than that of the prior method.
TABLE 1
| |
|
|
| Run time/sec | 106.396820 | 1165.912855 |
| Number of times of Lambert function use | ≤1 | Nt |
In summary, the technical solution provided by the present invention can obtain the EE performance consistent with the existing method, and the method is simple, and greatly reduces the time and space complexity, which fully proves the effectiveness of the power allocation method based on beamforming for the distributed MISO system provided by the present invention.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.
Claims (1)
1. A low-complexity power distribution method of a distributed multi-input single-output MISO system based on beam forming is characterized in that the distributed MISO system comprises N remote antenna units, each remote antenna unit is provided with L antennas, and each remote antenna unit is respectively connected with a central processing unit; the method comprises the following steps:
step 1) with the transmission power p of each remote antenna unit in the distributed MISO systemiAnd a beamforming vector wiConstructing an optimization model for the optimization variables, wherein the optimization problem in the optimization model is
The constraint conditions are as follows: p is more than or equal to 0i≤Pmax，||wi||2N,. 1, i ═ 1; wherein p isiAnd wiRespectively representing the ith remote antenna unit RAUiTransmit power and beamforming column vectors of; pmaxTo indicate RAUiThe maximum transmit power of; etaEE(pi，wi) Representing the transmission power p of the distributed MISO system at each remote antenna unitiAnd a beamforming vector wiEnergy efficiency of the process;
step 2) for the distributed MISO system, giving an optimal beam forming scheme for maximizing EE, and reconstructing an optimization problem:
the constraint conditions are as follows: p is more than or equal to 0i≤Pmax，i＝1，...，N；
Step 3) converting gammaiIn a descending order, wherein,
is the channel-to-noise ratio, SiIs logarithmic shadow fading;
is RAUiPath loss to the subscriber, where α is the path loss exponent, hiIs a small-scale fading channel vector; constructing a Lagrangian function for the optimization model converted in the step (2), and thus obtaining a general form of an optimal power distribution solution by using a KKT condition;
step 4) constructing a piecewise function according to the form of the optimal power distribution solution given in the step 3; the piecewise function is a convex function, so that only one optimal solution exists;
step 5) solving the optimal solution of the piecewise function in the step 4) by utilizing a Lambert function or a dichotomy, so that the optimal power distribution and the maximum energy efficiency of all RAUs can be obtained;
the method for allocating power to a distributed MISO system based on beamforming with low complexity is characterized in that:
(1) according to the method for calculating the energy efficiency of the distributed MISO system in the step 1), calculating the ratio of the data transmission rate and the total consumed power obtained at the mobile station after the distributed MISO system is added into the beam forming;
(2) determining w according to the optimal beamforming scheme for maximizing energy efficiency in said step 2), i.e. using distributed maximal ratio transmissioni；
(3) According to the form of the optimal power distribution solution in the step 3), the optimal power distribution solution is as follows:
wherein,
when maximum energy efficiency is expressed
Is noted here as N0When the number is equal to 1, the alloy is put into a container,
when N is present0When the content of the organic acid is more than or equal to 2,
(4) constructing a piecewise function according to the form of the optimal power distribution solution in the step 4):
wherein
(5) According to V'j(pj) Is determined by the sign of0When N is present0When 1, it can be determined from a Lambert function
Wherein
When N is present0When the average particle size is more than or equal to 2, the dichotomy is utilized in the interval
Upper solution
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