CN114337882B - Energy efficiency power distribution method in multi-user DAS under incomplete channel information - Google Patents
Energy efficiency power distribution method in multi-user DAS under incomplete channel information Download PDFInfo
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention provides an energy efficiency power distribution method in a multi-user DAS under incomplete channel information, which is based on research on an incomplete CSI distributed antenna system. The invention considers both large-scale fading and small-scale fading and incomplete channel state information during channel modeling, and estimates the channel, so that the channel model is more perfect, and the analysis result obtained by the method is more fit with reality; by converting the non-convex energy efficiency optimization problem into a new convex function, nearly the same energy efficiency as the particle swarm approach can be obtained. By assigning a specific power to each RAU, the system energy efficiency is improved as much as possible.
Description
Technical Field
The invention belongs to the field of mobile communication, relates to a power distribution method for optimizing energy efficiency of a mobile communication system, and particularly relates to a power distribution method for energy efficiency in a multi-user DAS under incomplete channel information.
Background
In recent years, with the development and popularization of high-rate communication, the demand for transmission rate is increasing, and the energy consumption of the whole network is increasing, so that the demand for low-power network equipment is increasing, and the method has important significance in the research of energy sustainable technology. Distributed Antenna Systems (DAS) are considered a key technology for next generation wireless communication systems because of their advantages of increased capacity, EE, and reliability by expanding system coverage and increasing rate. Energy Efficiency (EE) is required to be better in future green wireless communication systems. Therefore, research on energy efficiency in DAS will be widely focused in practical applications.
Document 1 (x.yu, w.xu, s. -h.leung, q.shi, and j.chu, "Power allocation for energy efficient optimization of distributed MIMO system with beamforming," IEEE trans.veh.technology, vol.68, no.9, pp.8966-8981, sep.2019.) proposes an optimal and suboptimal power allocation scheme with relatively low complexity to achieve EE maximization in a distributed multiple input multiple output system. In literature 2 (y. Huang, m.liu, and y. Liu, "Energy-efficient SWIPT in IoT distributed antenna systems," IEEE Internet Things j., vol.5, no.4, pp.2646-2656, aug.2018.) the EE of distributed IoT was studied, and a corresponding power allocation scheme was proposed to maximize the system EE, which is based on full Channel State Information (CSI).
According to the above analysis, the existing energy-efficient optimization method is based on full Channel State Information (CSI) and does not consider a plurality of users, but in practice, it is difficult to obtain full CSI due to channel estimation errors or feedback delays, while a plurality of users exist. In summary, there is a lack of research on power allocation methods for multi-user distributed antenna systems, especially in the case of incomplete CSI.
Disclosure of Invention
1. The technical problems to be solved are as follows:
existing energy efficient optimization methods are based on full Channel State Information (CSI), which is difficult to obtain due to channel estimation errors or feedback delays, and do not consider multiple users.
2. The technical scheme is as follows:
in order to solve the above problems, an energy efficiency power distribution method in a multi-user DAS under incomplete channel information is provided, and a distributed antenna system is researched based on incomplete CSI.
The invention provides a method for distributing energy efficiency power in a multi-user DAS under incomplete channel information, which comprises the following steps:
step S01: distributed antenna system and channel modeling: establishing a downlink transmission model of a distributed antenna system with K users and N antennas, wherein the channel fading coefficient between the first RAU and the kth user is recorded ash k,l Complex gaussian distributions representing small-scale fading and obeying mutually independent; l (L) k,l ＝S k,l d k,l -v Representing large scale fading, S k,l And d k,l Expressed as logarithmic shadowing fading and distance between the kth user and the l-th RAU, respectively, v representing the path loss coefficient;
step S02: using minimum mean square error estimation, small scale channel coefficient h k,n The relation with the estimation and the estimation error is thatThe received signal at the user terminal is:
wherein x is k,n Is the transmission signal of the nth RAU to the kth user, p k,n For the transmit power thereof to be sufficient,is equivalent noise, where w k Is additive white Gaussian noise with a mean value of 0 and a variance of sigma 2 ；
Step S03: constructing an energy efficiency optimization problem of the distributed Internet of things system based on incomplete channel state information by taking the maximum power of each RAU as a constraint condition;
step S04: converting the optimization function obtained in the step S03 into a reduced form by using a split programming;
step S05: the function obtained in the step S04 is at a first-order Taylor expansion point p 0 Expanding the position to obtain a new convex optimization problem; combining the secondary gradient descent method and the block coordinate descent algorithm to obtain power distribution.
3. The beneficial effects are that:
the invention considers both large-scale fading and small-scale fading and incomplete channel state information during channel modeling, and estimates the channel, so that the channel model is more perfect, and the analysis result obtained by the method is more fit with reality; by converting the non-convex energy efficiency optimization problem into a new convex function, nearly the same energy efficiency as the particle swarm approach can be obtained. By assigning a specific power to each RAU, the system energy efficiency is improved as much as possible.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention.
Fig. 2 is a diagram illustrating a comparison of simulation results and energy efficiency of the internet of things distributed system under different estimation errors according to an embodiment.
Fig. 3 is a graph showing the comparison between simulation results and the energy efficiency of the distributed system under different RAU numbers according to the particle swarm method and the particle swarm algorithm in the embodiment.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings.
The invention relates to a downlink distributed antenna system model with multiple users, comprising a central Base Station (BS), N RAUs supporting K users, and all RAUs being connected to each other by fiber links. Each RAU and user is equipped with a single antenna. The invention relates to a method for distributing energy efficiency power in a multi-user distributed antenna system under incomplete channel information, which is shown in figure 1 and comprises the following steps:
s1: establishing a downlink transmission model of a distributed antenna system with K users and N antennas, wherein the system experiences a composite Rayleigh fading channel with large-scale and small-scale fading; wherein the channel fading coefficient between the first remote antenna unit RAU and the kth user is recorded ash k,l Complex gaussian distributions representing small-scale fading and obeying mutually independent; l (L) k,l ＝S k,l d k,l -v Representing large scale fading, S k,l And d k,l Expressed as logarithmic shadowing fading and distance between the kth user and the l-th RAU, respectively, v representing the path loss coefficient;
s2: considering that small-scale fading varies rapidly and is difficult to estimate, minimum mean square error estimation is used. Small scale channel coefficient h k,n The relation with the estimation and the estimation error is thatThe received signal at the user terminal is:
wherein x is k,n Is the transmission signal of the nth RAU to the kth user, p k,n For the transmit power thereof to be sufficient,is equivalent noise, where w k Is additive white Gaussian noise with a mean value of 0 and a variance of sigma 2 ；
S3: constructing a multi-user distributed antenna system energy efficiency optimization problem based on incomplete channel state information by taking the maximum power of each RAU as a constraint condition;
s4: converting the optimization target obtained in the step S3 into a reduced form by using a fractional programming, and converting the denominator term of the logarithmic function in the objective function into a Taylor expansion point p 0 The position is unfolded, and a new convex optimization problem can be obtained; and then solving the power distribution by combining a secondary gradient descent method and a block coordinate descent algorithm.
Further, S2 comprises the following sub-steps: estimating errors to obey complex gaussian distribution e k,n CN (0, delta), where delta is the estimated error variance, is a constant value between 0 and 1, soThe effective signal-to-noise ratio of the kth user and the sum rate of the system are respectively as follows:
thus, the energy efficiency of the system can be expressed as:
wherein, is the power amplifier efficiency, P c Is a constant that represents the power consumption of the static circuit.
Further, as described in S3, the maximum power of each RAU is used as a constraint condition to construct a distributed internet of things system energy efficiency optimization problem based on incomplete channel state information;
wherein P is max,n Indicating the maximum transmit power of the nth RAU.
Further, S4 comprises the sub-steps of: the objective function is converted into a reduced form using a split programming:
further, the process of solving the power distribution method by using the concave-convex process method comprises the following sub-steps:
(a) The denominator term of the logarithmic function in the optimization target obtained in the step S4 is positioned at the Taylor expansion point p 0 The position is unfolded, and a new convex optimization problem can be obtained by using a concave-convex process method:
wherein,
(b) Solving a power distribution method by combining a secondary gradient descent method and a block coordinate descent method:
the lagrangian equation is listed:
let its derivative equal to 0, the current power optimum value is;
and fixing other power values to obtain target power, and finally updating Lagrangian multipliers by using a secondary gradient descent method:
wherein alpha is (l) Is the step size at the first iteration.
(c) When the Lagrangian multiplier and the RAU power are converged, ending the iterative process and returning to the optimal power distribution p * 。
The effectiveness of the power distribution method for optimizing the energy efficiency of the distributed antenna system based on incomplete channel state information is verified through simulation of a Matlab platform. There are N RAUs in the system, we assume P for convenience max,n ＝P max 。
Fig. 2 shows the simulation results with different estimation errors versus the energy efficiency of the particle swarm method (PSO, particle Swarm Optimization). It can be seen that the proposed method provides nearly the same energy efficiency performance as the particle swarm method, indicating the effectiveness of the method. And, the larger the error variance, the lower the optimized system energy efficiency due to the negative impact of the estimation error.
Fig. 3 shows a graph of simulation results with different RAU numbers N under incomplete channel state information versus energy efficiency of the particle swarm algorithm. It can be seen that the EE of the distributed antenna system increases with increasing N, since increasing N brings higher spatial diversity gain and thus increases performance. Furthermore, for n=3 and 5, the present method has EE which is almost the same as the benchmark provided by the PSO method.
In summary, the power allocation method for optimizing energy efficiency in the multi-user distributed antenna system based on incomplete channel state information can maximize the energy efficiency of the system under the maximum power constraint of each RAU, which fully explains the effectiveness of the method.
Claims (3)
1. A method for distributing energy efficiency power in multi-user DAS under incomplete channel information comprises the following steps:
step S01: distributed antenna system and channel modeling: establishing a downlink transmission model of a distributed antenna system with K users and N antennas, wherein the channel fading coefficient between the first RAU and the kth user is recorded ash k,l Complex gaussian distributions representing small-scale fading and obeying mutually independent; l (L) k,l ＝S k,l d k,l -v Representing large scale fading, S k,l And d k,l Expressed as logarithmic shadowing fading and distance between the kth user and the l-th RAU, respectively, v representing the path loss coefficient;
step S02: using minimum mean square error estimation, small scale channel coefficient h k,n The relation with the estimation and the estimation error is thatThe received signal at the user terminal is:
wherein x is k,n Is the transmission signal of the nth RAU to the kth user, p k,n For the transmit power thereof to be sufficient,is equivalent noise, where w k Is additive white Gaussian noise with a mean value of 0 and a variance of sigma 2 ；
Step S03: constructing an energy efficiency optimization problem of the distributed Internet of things system based on incomplete channel state information by taking the maximum power of each RAU as a constraint condition; constructing an energy efficiency optimization problem of the distributed Internet of things system based on incomplete channel state information by taking the maximum power of each RAU as a constraint condition;
wherein P is max,n Represents the maximum transmit power of the nth RAU, θ=1/ζ, ζ is power amplifier efficiency, P c Is a constant representing the power consumption of the static circuit;
step S04: converting the optimization function obtained in the step S03 into a reduced form by using a split programming; the objective function of the split programming converted into the subtractive form is:
step S05: the function obtained in the step S04 is at a first-order Taylor expansion point p 0 Expanding the position to obtain a new convex optimization problem; combining a secondary gradient descent method and a block coordinate descent algorithm to obtain power distribution, wherein the specific method comprises the following steps of: (a) The denominator term of the logarithmic function in the optimization target obtained in the step S4 is set at an initial value p 0 The Taylor series expansion is carried out at the position, and a new convex optimization problem can be obtained by utilizing a concave-convex process method:
wherein,
(b) Solving a power distribution method by using a secondary gradient descent method and a block coordinate descent method: considering the nth RAU, fixing other RAU power, and updating p; updating Lagrangian multipliers by using a secondary gradient descent method;
(c) When the Lagrangian multiplier and the RAU power are converged, ending the iterative process and returning to the optimal power distribution method p * 。
2. The method of claim 1, wherein: in step S02, the error is estimated to obey a complex Gaussian distribution, i.e. e k,n CN (0, delta), where delta is the estimated error variance, is a constant value between 0 and 1, soThe effective signal-to-noise ratio and the system and rate of the kth user are respectively as follows:
i.
accordingly, the energy efficiency of the system is:
ii.
wherein, is the power amplifier efficiency, P c Is a constant that represents the power consumption of the static circuit.
3. The method of claim 1, wherein: the method for solving the power distribution by using the secondary gradient descent method and the block coordinate descent method comprises the following steps: the lagrangian equation is listed:
let its derivative equal to 0, the current power optimum value is;
and fixing other power values to obtain target power, and finally updating Lagrangian multipliers by using a secondary gradient descent method:
wherein a is (l) Is the step size at the first iteration.
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