CN109451516B - Power distribution method based on user side demand energy efficiency - Google Patents
Power distribution method based on user side demand energy efficiency Download PDFInfo
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- H—ELECTRICITY
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- H—ELECTRICITY
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- H04W—WIRELESS COMMUNICATION NETWORKS
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Abstract
The invention belongs to the technical field of wireless communication resource allocation, and particularly relates to a power allocation method based on energy efficiency required by a user side. The invention relates to theoretical frameworks such as Energy Efficiency (Energy Efficiency), Power Control (Power Control), User Satisfaction (User Satisfailure regression) and the like. Based on energy efficiency, a novel user satisfaction function is constructed in a multi-user OFDM downlink scene, a system optimization objective function comprehensively considering system side requirements (EE) and user side requirements (USD) is provided, and a damping Newton architecture (Damped Newton Method) and a Lagrange multiplier Method are used for solving the power distribution problem under the linear constraint. Compared with the traditional power distribution method, the invention achieves better balance between the system performance requirement and the user performance requirement and is more flexible in power distribution.
Description
Technical Field
The invention belongs to the technical field of wireless communication resource allocation, and particularly relates to a power allocation method based on energy efficiency required by a user side. The invention relates to theoretical frameworks such as Energy Efficiency (Energy Efficiency), Power Control (Power Control), User Satisfaction (User Satisfailure regression) and the like.
Background
An Energy Efficiency (EE) -based signaling scheme is one of the mainstream research directions to cope with the challenges of increased energy consumption and green communication demand of next-generation mobile communication. Compared with the traditional transmission power distribution scheme for maximizing the system throughput or minimizing the system energy consumption and the like, the power distribution scheme based on the energy efficiency improves the system energy utilization rate under the condition of comprehensively considering the channel quality and the system throughput, and is beneficial to solving the problems of excessive resource consumption, sustainable development and the like caused by the increase of the energy consumption of a future communication network. However, if the system optimization goal is only to maximize energy efficiency, it may bring some degradation to the performance such as quality of service (QoS), Spectral Efficiency (SE), and system throughput. In order to avoid the influence of optimizing only energy efficiency on the performance of other systems, in the existing related research, under different scene requirements, additional performance considerations are added on the basis of optimizing energy efficiency, such as joint optimization of EE and SE, joint optimization of EE and system throughput, and EE optimization under maximum delay constraint or minimum rate constraint, so that the resource allocation scheme can reach the balance of the performance of EE and other related systems to a certain extent. However, most of these EE studies start from system requirements, and few or neglect individual requirements of users as system components, or maximize system performance only under the condition of meeting the minimum requirements of users, so that the power allocation method does not conform to the service concept of future communication based on user experience.
The User Satisfaction (USD) can map specific parameters such as user speed, packet loss rate and the like between 0 and 1 through functions, serve as one of indexes for measuring user service quality, and can be added into a system optimization target to improve the influence of user side requirements. However, most of the existing user satisfaction functions are only related to the actual rate of the user, which may cause the system to bias to allocate more power to the user with good channel quality, resulting in the decrease of the satisfaction of other users and affecting the service quality of the user of the overall system. The user satisfaction function mapping relation is provided by combining the actual service rate of the user and the expected rate of the user, and the user satisfaction function mapping relation is used as a weight factor of different user EE to form a system optimization target function based on the energy efficiency of the user demand side. The optimization function can comprehensively consider energy efficiency and user satisfaction, traditional nonlinear user rate constraint is removed through a novel user satisfaction mapping function, a power control distribution optimization model only having linear constraint is formed, and model solving complexity is reduced.
Disclosure of Invention
Based on energy efficiency, a novel user satisfaction function is constructed in a multi-user OFDM downlink scene, a system optimization objective function comprehensively considering system side requirements (EE) and user side requirements (USD) is provided, and a damping Newton architecture (Damped Newton Method) and a Lagrange multiplier Method are used for solving the power distribution problem under the linear constraint. Compared with the traditional power distribution method, the invention achieves better balance between the system performance requirement and the user performance requirement and is more flexible in power distribution.
The improved system optimization objective function balances system EE and user-side needs by multiplying user EE with user EE as a weighting factor. Specifically, consider a cell with a base station and multiple users in the cell, and a downlink existsThe set of users with communication needs is denoted as U ═ U1 u2 ... uNAnd the set of OFDM channels is marked as C ═ C1 c2 … cMFor simplicity of analysis, consider M-N and assume that the channel is fixedly allocated to the user; the user's received signal can be expressed as
Wherein p isiDenotes the transmission power, h, allocated by the base station to the ith useriChannel parameter, x, representing the channel used by the ith useriRepresenting the normalized transmission signal of the ith user, IiIndicating adjacent channel interference due to power leakage, niRepresenting the background noise of the channel used by the ith user with a obedience parameter σ2(ii) a gaussian distribution of; if the Adjacent Channel Interference Ratio (ACIR) of the jth channel to the ith channel is set as aijThen the ith channel suffers from adjacent channel interference Ii|2Can be expressed as
The signal to interference plus noise ratio (SINR) of the channel may be expressed as
Wherein N is0＝B·σ2Representing the background noise power and B the channel bandwidth. For convenience of expression, the numerator and denominator of formula (2) are divided by N0After the symbol is simplified to
Wherein g isi＝|hi|2/N0。
According to the shannon capacity formula, the channel rate can be expressed as (Mbps)
ri(pi)＝Blog(1+γi(pi)) (4)
Defined by energy efficiency, user EE can be expressed as (Mbps/W)
Wherein p iscstIndicating a loop fixed power consumption.
In order to consider the user side requirement, the invention adds the user satisfaction degree to the system optimization objective function, which is different from the existing user satisfaction degree function only considering the actual service rate of the user, the invention combines the actual service rate of the user and the expected rate of the user, newly defines the user satisfaction degree function as follows
Wherein
Indicating the desired rate for the ith user.
From the above, the system optimization objective function for combining the system-side demand (EE) and the user-side demand (USD) can be defined as f (p) ═ ωTS (r), where p is the power allocation column vector for each user, and ω and s (r) are the column vectors formed by EE and USD for each user, respectively. Finally, a power distribution system optimization model based on user-side demand energy efficiency can be expressed as
Wherein p ismaxIs a maximum total power constraint.
The technical scheme of the invention is as follows:
and S1, converting the original optimization problem by a damping Newton framework and a Lagrange multiplier method. (7) Condition of formula KKT
Wherein the content of the first and second substances,
can be expressed as
Wherein
From this, the power allocation iteration of each user can be updated to
Reduced notation
Later, a water-filling-like problem that can be solved with conventional water-filling algorithms as follows can be obtained
And S2, initializing input parameters. Such as the maximum number of iterative updates TmaxInitial power allocation vector p0And the adjacent channel interference ratio matrix a is (a)ij)N×NMaximum total power transmitted pmaxUser desired rate
And the like.
S3, calculating the related parameters A according to the formula (10)i、Bi、Ci、Qi.
And S4, substituting the parameters obtained in the step S3 into the formula (12), and obtaining an updated power distribution vector p by using a traditional water filling algorithm.
S5, if the power vector is stable and converged or the iteration number reaches TmaxThen ending iteration to obtain optimized power distribution vector p*(ii) a Otherwise, the iterative process continues back to S3.
According to the technical scheme, a novel user satisfaction function is established, a system side requirement (EE) and a user side requirement (USD) are combined, the user satisfaction is used as a weight factor to be multiplied by the user energy efficiency to be used as a system optimization objective function, a damping Newton framework and a Lagrange multiplier method are used for solving the optimization problem, and a power distribution scheme based on the user requirement side energy efficiency is obtained. The invention has the beneficial effects that: based on the user side requirement, the energy efficiency of the system is optimized, and meanwhile, the user requirement is well balanced, so that the problem that the conventional power distribution scheme is not considered enough on the user side is solved.
Drawings
FIG. 1 is a graph of energy efficiency performance versus simulation for different power allocation schemes under different maximum total transmit power constraints; wherein sum (ee.s) represents the power allocation scheme of the present invention, sum (EE) represents the power allocation scheme with total user EE as the optimization target under the minimum rate constraint, sum (R)/sum (P) represents the power allocation scheme with system EE as the optimization target under the minimum rate constraint, and sum ((log (R)) -sum (P) represents the power allocation scheme considering the inter-user EE fairness;
fig. 2 is a graph of user satisfaction performance versus simulation for different power allocation schemes under different maximum total transmit power constraints.
Detailed Description
The technical scheme of the invention is described in detail in the following with reference to the attached drawings and examples:
in this example, the number N of users is set to 5; the channel bandwidth B is 10M; maximum total transmitted power pmaxLess than or equal to 5W; channel parameter G ═ 302015105](ii) a Desired rate Rthr＝[10 15 20 30 50]Mbps; the pilot interference ratio matrix is
Fixed power consumption p of linkcst＝0.2W。
The method comprises the following specific steps:
step 1: inputting initial parameter setting, and converting the original problem according to a damping Newton framework and a Lagrange multiplier method;
step 2: respectively calculating relevant parameters according to a formula (10) to obtain a water injection problem-like mathematical expression;
and step 3: according to the formula (12), the power distribution vector p update value is obtained by the water filling algorithm
And 4, step 4: if the power vector is stable and converged or the iteration number reaches TmaxThen ending iteration to obtain optimized power distribution vector p*(ii) a Otherwise, go back to step 2 to continue the iterative process.
As can be seen from the simulation diagram, under the constraint conditions of different maximum total transmission powers, the Energy Efficiency (EE) of the present invention is improved compared with other conventional power distribution schemes, and is the same as the EE of the power distribution scheme which only takes the system EE as the optimization target under higher power; meanwhile, the power distribution scheme of the invention improves the user satisfaction to a certain extent. In general, the power allocation method based on the energy efficiency of the user side demand can achieve better balance on the system side demand (EE) and the User Side Demand (USD).
Claims (1)
1. The method is used for a cell, and a base station and a plurality of users are set in the cell, and a set of users with communication demands in a downlink is defined as U ═ U ═1 u2 ... uNAnd the set of OFDM channels is marked as C ═ C1 c2 ... cMN, and a channel is fixedly allocated to a user; the user receives the signal as
Wherein p isiDenotes the transmission power, h, allocated by the base station to the ith useriIndicating the channel used by the ith userOf the channel parameter xiRepresenting the normalized transmission signal of the ith user, IiIndicating adjacent channel interference due to power leakage, niRepresenting the background noise of the channel used by the ith user with a obedience parameter σ2(ii) a gaussian distribution of; the power distribution method is characterized by comprising the following steps:
s1, establishing an optimization model, which specifically comprises the following steps:
defining the system side requirement EE as:
wherein r isiFor channel rate, pcstRepresents loop fixed power consumption;
defining the user side requirement USD as:
wherein r isi thrIndicating the desired rate of the ith user;
combining EE and USD, defining an objective function of f (p) ═ ωTS (r), then the power distribution system optimization model based on the user-side energy demand efficiency is:
where p assigns a column vector, ω ands (r) is a column vector consisting of EE and USD for each user, pmaxIs a maximum total power constraint;
s2, converting the optimization model established in the step S1 by a damping Newton framework and a Lagrange multiplier method:
wherein u, λiFor lagrange multipliers corresponding to different constraints,
is shown as
Wherein
Wherein, aijFor the adjacent channel interference ratio of the jth channel to the ith channel, gi＝|hi|2/N0，N0＝B·σ2Representing background noise power, B being the channel bandwidth, gammajThe signal to interference plus noise ratio of the jth channel;
obtain the power allocation iteration update of each user
Reduced notation
Then, a similar waterflood problem that can be solved with a waterflood algorithm is obtained as follows:
s3, solving the model established in the step S2 to obtain an optimized power distribution vector p*The method specifically comprises the following steps:
s3-1, setting initial parameters including maximum iteration updating times TmaxInitial power allocation vector p0And the adjacent channel interference ratio matrix a is (a)ij)N×NMaximum total power transmitted pmaxUser desired rate rthr＝(ri thr)N×1；
S3-2, calculating related parameter Ai、Bi、Ci、Qi；
S3-2, substituting the relevant parameters into a similar water injection problem, and obtaining an updated power distribution vector p by using a water injection algorithm;
s3-3, if the power vector is stable and converged or the iteration number reaches TmaxThen ending iteration to obtain optimized power distribution vector p*(ii) a Otherwise, the iterative process continues back to S3-2.
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