CN114337882A - Multi-user DAS (distributed optical system) energy-efficient power distribution method under incomplete channel information - Google Patents
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Abstract
The invention provides a multi-user DAS (distributed antenna System) energy-efficient power distribution method under incomplete channel information, which is used for researching a distributed antenna system based on incomplete CSI (channel State information). According to the method, large-scale fading and small-scale fading are considered simultaneously during channel modeling, incomplete channel state information is considered, and the channel is estimated, so that a channel model is more perfect, and an obtained analysis result is more practical; by converting the non-convex energy efficiency optimization problem into a new convex function, the energy efficiency which is nearly the same as that of the particle swarm optimization can be obtained. By allocating 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 an energy efficiency power distribution method in 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-consumption network equipment is increasing, and the energy sustainable technology research is of great significance. Distributed Antenna Systems (DAS) are considered as a key technology of the next generation wireless communication system due to their advantages of increasing capacity, EE and reliability by expanding system coverage and increasing rate. Better Energy Efficiency (EE) is required in future green wireless communication systems. Therefore, the research on the energy efficiency in DAS will be receiving a great deal of attention in practical applications.
Document 1(x.yu, w.xu, s. — h.leung, q.shi, and j.chu, "Power allocation for energy efficiency optimization of distributed MIMO system with beamforming," IEEE trans.veh.technol., 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. Distributed IoT EEs are studied in document 2(y.huang, m.liu, and y.liu, "Energy-efficiency SWIPT in IoT distributed antenna systems," IEEE Internet Things j., vol.5, No.4, pp.2646-2656, aug.2018.), and corresponding power allocation schemes are proposed to maximize system EE, which is based on full Channel State Information (CSI).
From the above analysis, the existing energy-efficient optimization method is based on full Channel State Information (CSI) and does not consider multiple users, but in practice, it is difficult to obtain full CSI due to channel estimation errors or feedback delay, and multiple users exist simultaneously. In summary, the related research on the power allocation method of the multi-user distributed antenna system is lacked in the existing research, especially in the case of incomplete CSI.
Disclosure of Invention
1. The technical problem to be solved is as follows:
the existing energy-efficient optimization method is based on complete Channel State Information (CSI), and due to channel estimation errors or feedback delay, complete CSI is difficult to obtain and multiple users are not considered.
2. The technical scheme is as follows:
in order to solve the problems, an energy-efficient power distribution method in multi-user DAS under incomplete channel information is provided, a distributed antenna system is researched based on incomplete CSI, the method considers the maximum transmitting power requirement of a Remote Antenna Unit (RAU), a concave-convex process method is applied, a non-convex energy efficiency optimization problem is converted into a new convex problem, and the energy-efficient power distribution method is provided by combining a sub-gradient descent method and a block coordinate descent method.
The invention provides a multi-user DAS (distributed optical system) energy-efficient power distribution method under incomplete channel information, which comprises the following steps of:
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 a channel fading coefficient between the ith RAU and the kth user is recorded as
hk,lRepresenting small scale fading and obeying mutually independent complex gaussian distributions; l isk,l＝Sk,ldk,l -vRepresenting large scale fading, Sk,lAnd dk,lExpressed as logarithms between the kth user and the l-th RAU, respectivelyShadow fading and distance, v represents the path loss coefficient;
step S02: using minimum mean square error estimation, small scale channel coefficients hk,nIn relation to its estimation and estimation errors are
The received signal of the ue is:
wherein x isk,nIs the transmitted signal of the nth RAU to the kth user, pk,nFor the purpose of its transmission power,
is equivalent noise, where wkIs additive white Gaussian noise with mean of 0 and variance of σ2；
Step S03: constructing a distributed Internet of things system energy efficiency optimization problem 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 subtractive form by using a fractional programming;
step S05: the function obtained in step S04 is expanded to a first order Taylor expansion point p0Expanding to obtain a new convex optimization problem; and combining a sub-gradient descent method and a block coordinate descent algorithm to obtain power distribution.
3. Has the advantages that:
according to the method, large-scale fading and small-scale fading are considered simultaneously during channel modeling, incomplete channel state information is considered, and the channel is estimated, so that a channel model is more perfect, and an obtained analysis result is more practical; by converting the non-convex energy efficiency optimization problem into a new convex function, the energy efficiency which is nearly the same as that of the particle swarm optimization can be obtained. By allocating 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 an energy efficiency comparison graph of the simulation result of the embodiment and a particle swarm optimization of the internet of things distributed system under different estimation errors.
Fig. 3 is a graph comparing the simulation result of the embodiment with the energy efficiency of the distributed system under different RAU numbers by the particle swarm optimization and the particle swarm optimization.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings.
The invention relates to a downlink distributed antenna system model with a plurality of users, which comprises a central Base Station (BS), N RAUs (random access units) and K users, wherein all the RAUs are connected with each other through optical fiber links. Each RAU and user is equipped with a single antenna. The invention discloses an energy efficiency power distribution method in a multi-user distributed antenna system under incomplete channel information, which comprises the following steps as shown in figure 1:
s1: establishing a downlink transmission model of a distributed antenna system with K users and N antennas, wherein the system is subjected to a composite Rayleigh fading channel with large-scale fading and small-scale fading; wherein the channel fading coefficient between the l-th remote antenna unit RAU and the k-th user is recorded as
hk,lRepresenting small scale fading and obeying mutually independent complex gaussian distributions; l isk,l＝Sk,ldk,l -vRepresenting large scale fading, Sk,lAnd dk,lRespectively expressed as logarithmic shadow fading and distance between the kth user and the l-th RAU, and v represents a path loss coefficient;
s2: considering that small-scale fading is fast and difficult to estimate, minimum mean square error estimation is used. Small scale channel coefficient hk,nIn relation to its estimation and estimation errors are
The received signal of the ue is:
wherein x isk,nIs the transmitted signal of the nth RAU to the kth user, pk,nFor the purpose of its transmission power,
is equivalent noise, where wkIs additive white Gaussian noise with mean of 0 and variance of σ2；
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 enabling the denominator term of the logarithmic function in the target function to be at the Taylor expansion point p0A new convex optimization problem can be obtained by unfolding; and then, combining a sub-gradient descent method and a block coordinate descent algorithm to solve the power distribution.
Further, S2 includes the following sub-steps: the estimation errors obey a complex Gaussian distribution, i.e. ek,nCN (0, delta), where delta is the estimated error variance, is a constant value between 0 and 1, so
The effective snr and the system sum rate of the kth user can be obtained as follows:
thus, the energy efficiency of the system can be expressed as:
wherein the content of the first and second substances,
is the power amplifier efficiency, PcIs 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, and a distributed internet of things system energy efficiency optimization problem based on incomplete channel state information is constructed;
wherein, Pmax,nRepresents the maximum transmit power of the nth RAU.
Further, S4 includes the following sub-steps: transforming the objective function into a subtractive form using a fractional programming:
further, the process of solving the power distribution method by using the concave-convex process method includes the following sub-steps:
(a) the denominator term of the logarithmic function in the optimization objective obtained in the step S4 is at the Taylor expansion point p0And a new convex optimization problem can be obtained by unfolding and using a concave-convex process method:
wherein the content of the first and second substances,
(b) and (3) solving a power distribution method by combining a sub-gradient descent method and a block coordinate descent method:
the lagrange equation is listed:
the derivative is equal to 0, and the current optimal power value is obtained;
fixing other power values to obtain the target power, and finally updating the Lagrange multiplier by using a sub-gradient descent method:
wherein alpha is(l)Is the step size at the l-th iteration.
(c) When the Lagrange multiplier and the RAU power are converged, the iteration process is ended, and the optimal power distribution p is returned*。
The effectiveness of the power allocation method for optimizing the energy efficiency of the distributed antenna system based on incomplete channel state information, which is provided by the invention, is verified through simulation of a Matlab platform. There are N RAUs in the system, and for convenience we assume Pmax,n＝Pmax。
Fig. 2 shows a comparison of simulation results with different estimation errors and energy efficiency of Particle Swarm Optimization (PSO). It can be seen that the method proposed by the present invention can provide almost the same energy efficiency performance as the particle swarm optimization, which indicates the effectiveness of the method. And, the larger the error variance, the less energy efficient the optimized system is due to the negative effects of the estimation error.
Fig. 3 shows a comparison graph of simulation results with different RAU numbers N and energy efficiency of the particle swarm optimization under incomplete channel state information. It can be seen that the EE of the distributed antenna system increases with the increase of N, because the increase of N brings higher spatial diversity gain, thereby improving performance. Furthermore, for N-3 and 5, the method has almost the same EE as the benchmark provided by the PSO method.
In summary, the power allocation method for energy efficiency optimization in a multi-user distributed antenna system based on incomplete channel state information can maximize the system energy efficiency under the maximum power constraint of each RAU, which fully explains the effectiveness of the method.
Claims (6)
1. A multi-user DAS energy-efficient power distribution method 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 a channel fading coefficient between the ith RAU and the kth user is recorded as
hk,lRepresenting small scale fading and obeying mutually independent complex gaussian distributions; l isk,l＝Sk,ldk,l -vRepresenting large scale fading, Sk,lAnd dk,lRespectively expressed as logarithmic shadow fading and distance between the kth user and the l-th RAU, and v represents a path loss coefficient;
step S02: using minimum mean square error estimation, small scale channel coefficients hk,nIn relation to its estimation and estimation errors are
The received signal of the ue is:
wherein x isk,nIs the transmitted signal of the nth RAU to the kth user, pk,nFor the purpose of its transmission power,
is equivalent noise, where wkIs additive white Gaussian noise with mean of 0 and variance of σ2；
Step S03: constructing a distributed Internet of things system energy efficiency optimization problem 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 subtractive form by using a fractional programming;
step S05: the function obtained in step S04 is expanded to a first order Taylor expansion point p0Expanding to obtain a new convex optimization problem; and combining a sub-gradient descent method and a block coordinate descent algorithm to obtain power distribution.
2. The method of claim 1, wherein: in step S02, the estimation error follows a complex Gaussian distribution, i.e., ek,nCN (0, delta), where delta is the estimated error variance, is a constant value between 0 and 1, so
The obtained effective snr and system and rate for the kth user are respectively:
i.
and
accordingly, the energy efficiency of the system is:
ii.
wherein the content of the first and second substances,
is the power amplifier efficiency, PcIs a constant that represents the power consumption of the static circuit.
3. The method of claim 1, wherein: in step S03, constructing a distributed internet of things system energy efficiency optimization problem based on incomplete channel state information, with the maximum power of each RAU as a constraint condition;
wherein, Pmax,nRepresents the maximum transmit power of the nth RAU.
4. The method of claim 3, wherein: in step S04, the objective function of the conversion of the fractional program into a subtractive form is:
5. the method of any one of claims 1 to 4, wherein: in step S05, the specific method is:
(a) setting the denominator term of the logarithmic function in the optimization objective obtained in the step S4 at the initial value p0And performing Taylor series expansion, and obtaining a new convex optimization problem by using a concave-convex process method:
wherein the content of the first and second substances,
(b) solving a power distribution method by using a sub-gradient descent method and a block coordinate descent method: considering the nth RAU, fixing the power of other RAUs, and updating p; updating a Lagrange multiplier by using a sub-gradient descent method;
(c) when the Lagrange multiplier and the RAU power are converged, ending the iteration process and returning to the optimal power distribution method p*。
6. The method of claim 5, wherein: the method for solving the power distribution by using the sub-gradient descent method and the block coordinate descent method specifically comprises the following steps: the lagrange equation is listed:
the derivative is equal to 0, and the current optimal power value is obtained;
fixing other power values to obtain the target power, and finally updating the Lagrange multiplier by using a sub-gradient descent method:
wherein alpha is(l)Is the step size at the l-th iteration.
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