WO2018120935A1 - 一种协作蜂窝网络的资源分配和能量管理方法 - Google Patents
一种协作蜂窝网络的资源分配和能量管理方法 Download PDFInfo
- Publication number
- WO2018120935A1 WO2018120935A1 PCT/CN2017/102745 CN2017102745W WO2018120935A1 WO 2018120935 A1 WO2018120935 A1 WO 2018120935A1 CN 2017102745 W CN2017102745 W CN 2017102745W WO 2018120935 A1 WO2018120935 A1 WO 2018120935A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- base station
- energy
- optimal
- user
- subcarrier
- Prior art date
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/04—Wireless resource allocation
- H04W72/044—Wireless resource allocation based on the type of the allocated resource
- H04W72/0453—Resources in frequency domain, e.g. a carrier in FDMA
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/04—Wireless resource allocation
- H04W72/044—Wireless resource allocation based on the type of the allocated resource
- H04W72/0473—Wireless resource allocation based on the type of the allocated resource the resource being transmission power
-
- 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Definitions
- the invention relates to a resource allocation and energy management method for a cooperative cellular network, and belongs to the technical field of wireless communication.
- Multi-carrier technology is highly resistant to interference and has great flexibility in distribution. Therefore, it is widely used in cellular networks. For example, orthogonal frequency division multiplexing technology.
- orthogonal frequency division multiplexing technology In order to cope with the contradiction of a large increase in mobile devices due to spectrum tension, sub-carrier sharing allocation between different cellular systems is also a feasible way to alleviate the spectrum tension problem.
- Chinese patent CN102638891A discloses an energy efficiency based wireless communication resource allocation method and system.
- the system is a network system with relays, which cooperates through relay nodes.
- the system is researching resource allocation, it is to improve the energy efficiency of the system, that is, to transmit as much information as possible within the unit energy, while considering energy.
- the subcarrier allocation method in the resource allocation method in this patent is mainly based on the target rate, instantaneous energy performance and instantaneous data rate of the node; resource allocation only involves subcarrier allocation, and no power allocation and energy are designed. Management; in effect, the system is dedicated to improving system energy efficiency.
- the present invention provides a resource allocation and energy management method for a cooperative cellular network.
- ⁇ i is the Lagrangian factor corresponding to the i-th inequality constraint f i (x) ⁇
- the channel gain of the kth user on the nth subcarrier is denoted as h i,k,n ; throughout the network, the energy required by each base station is derived from the energy shared by renewable energy, the power grid, and other base stations.
- a base station The collected renewable energy is sufficient, and when the renewable energy collected by another base station is insufficient, the base station will share part of the energy to another base station, the process is: the base station first informs another base station that it can The amount of energy shared, then another base station in turn gives its own demand, and then the base station performs a boost operation and injects the energy that needs to be shared into the grid, while another base station performs a buck operation. Retrieve shared energy from the grid to reduce the cost of purchasing energy across the network;
- the base station communicates with the user through subcarriers, and the communication rate sum of each user on all subcarriers to which it is allocated is:
- N 0 represents the power spectral density of the Gaussian white noise
- each base station has three parts: the first part is the circuit consumption P c,i ; the second part is the energy P i required to transmit the signal, and The third part is the energy e i shared by the base station i, then the total energy consumed by the base station i:
- Express Base station M ⁇ i ⁇ indicates that i belongs to the set after the set M removes the element i; Indicates the unit price of renewable energy; Representing the unit price of electrical energy in the grid; R i,k represents the minimum communication rate required by each user; E i represents the amount of renewable energy purchased by base station i; G i represents the energy purchased by base station i from the grid; Stationary base Energy shared to base station i; ⁇ represents energy transfer efficiency; Represents the maximum energy that a renewable energy company can provide; solves the objective function The minimum value is called the original problem;
- the symbol min represents the minimum value symbol
- the symbol Subject to represents the constraint symbol.
- the above expression represents the allocation restriction for each subcarrier, the maximum energy that each base station can purchase from the renewable energy company, and the minimum communication rate for each user. Solving the objective function under constraints of demand and total energy consumed by each base station The minimum value; the minimum value of the solution objective function is called the original problem;
- the optimization problem contains integer variables x i, k, n and continuous variables, so the optimization problem is a mixed binary integer programming problem.
- the integer variable x i,k,n is relaxed to 0 to 1, that is, x i, k, n ⁇ [0, 1], at this time, the original optimization problem from the original
- the mixed binary integer programming problem becomes a convex optimization problem.
- we redefine a variable s i,k,n , and s i,k,n x i,k,n p i,k , n ;
- the optimization problem (4) is a convex problem with a unique global optimal solution.
- Lagrangian duality theory it is possible to establish a minimum problem, that is, the relationship between the original problem and a maximization problem, that is, the dual problem. Because the original problem studied has strong duality, we can get the optimal solution of the original problem by solving the dual problem.
- ⁇ instead of E i , G i and e i ;
- variable s i, k, n and x i, k the optimum value of n; by applying the KKT condition, the variable s i, k, n and x i, k, n to obtain an optimum value necessary and sufficient conditions are:
- ⁇ , ⁇ , ⁇ represent the dual vectors of the first four constraints in equation (4)
- ⁇ i, k , ⁇ i and ⁇ n respectively represent the Lagrangian dual factor corresponding to each of the first four constraints in equation (4)
- ⁇ i, k , ⁇ i and ⁇ n are the dual vector ⁇ , respectively.
- the elements in ⁇ , ⁇ , and the dual problem corresponding to the dual function (7) are as follows:
- the optimal value obtained by the dual problem (8) is the optimal value of the original problem
- Duality factor is limited by constraints So by optimizing the dual factor ⁇ , ,, ⁇ to solve the objective function, that is, the dual function
- the user with the smallest H i,k,n is assigned to the nth subcarrier, namely:
- the symbol Indicates the value of k when the part within [] takes the minimum value
- the base station i does not need to purchase energy from the power grid, namely:
- the base station can be known. No need to share energy, ie:
- the base station Since the price of renewable energy is lower than that of the traditional grid, the base station should give priority to the purchase of renewable energy, then the base station You should purchase all the renewable energy that you can buy;
- base station i For the renewable energy that base station i can purchase, base station i will share its own energy demand, and its excess renewable energy will be shared to the base station. However, base station i is shared to the base station There are two possibilities for energy: the energy shared by base station i can satisfy the base station. Or can not meet the base station Demand, we consider the loss ⁇ of the shared energy during transmission;
- base station i is shared to the base station Energy to satisfy the base station Demand, ie
- the optimal energy shared by the base station i is:
- the renewable energy purchased by base station i is the energy consumed by itself And sharing it with the base station energy of That is, the energy of the renewable energy that the base station i needs to purchase is:
- base station i Since base station i is shared to the base station Energy can satisfy the base station Demand, therefore base station There is no need to purchase energy from the grid, ie:
- base station i is shared to the base station Energy cannot satisfy the base station Demand, ie Then base station i should purchase all renewable resources, namely:
- the base station i should share all the renewable energy remaining beyond the self-energy requirement to the base station. That is, the base station i shares to the base station
- the optimal energy is:
- the base station After receiving the energy shared by the base station i, the base station Still missing energy by base station Buying itself into the grid, ie the base station
- the energy purchased from the grid is:
- Equations (10) and (15) contain Lagrangian dual factors ⁇ i,k and ⁇ i , and when they are optimal, the optimal transmit power And optimal subcarrier allocation And optimal energy management with Also got the optimal value.
- the solution of the Lagrangian dual factor optimal value can be solved by the sub-gradient iterative algorithm;
- s_ ⁇ (t) and s_ ⁇ (t) respectively represent the iteration step size corresponding to the corresponding Lagrangian dual factor, and t represents the number of iterations;
- the user is a single antenna user; the subcarrier is an orthogonal narrowband subcarrier.
- the method of dividing the entire authorized frequency band into N sub-carriers with the same bandwidth is to divide the entire authorized frequency band into N sub-carriers with the same bandwidth by using orthogonal frequency division multiplexing modulation technology.
- the resource allocation and energy management method of the cooperative cellular network of the present invention ensures the communication quality of each user by satisfying the minimum communication rate of each user; not only can the cost of the two cooperative communication networks be minimized, but also Can guarantee the communication rate requirement of each user;
- the use of the subcarriers of the present invention is used by two base stations together, which not only improves the frequency utilization, but also avoids the excess of one base station subcarrier caused by the method of fixing the number of subcarriers used by each base station, and
- the subcarrier shortage phenomenon of a base station, and the subcarrier allocation method is also aimed at minimizing the cost of the entire network, and is an optimal subcarrier allocation scheme in the network system;
- the power distribution according to the present invention aims to minimize the cost of the entire network, reduces the power loss of the entire network as much as possible, and reduces the energy loss from the source, thereby reducing the cost of the entire network, and the power is reduced. Distribution is the optimal allocation scheme in this kind of network system;
- the energy management scheme of the present invention is an optimal energy management scheme in a network established in the present invention, which introduces renewable energy, and preferentially purchases renewable energy that is cheaper in price, and is insufficient in renewable energy.
- this approach not only ensures the stability of the entire network, but also reduces the purchase cost of the network from the source of purchase;
- Energy sharing can be performed between two base stations in the energy management scheme of the present invention.
- one of the base stations can purchase more renewable resources and the other base station has the opposite state, this is The base station will share part of the renewable energy to another base station, which increases the purchase cost of the base station, but further reduces the purchase cost of the entire network;
- the resource allocation and energy management method of the cooperative cellular network of the present invention directly performs energy cooperation between the two base stations, does not contain a relay node; and introduces renewable energy, and the energy supply of the entire system is renewable.
- the energy and the traditional power grid jointly completed, the purchase price and purchase quantity of these two kinds of energy have an important impact on the system and must be considered;
- the resource allocation and energy management method of the cooperative cellular network according to the present invention is to minimize the cost of the entire system, that is, to consume as little energy as possible, and to purchase cheap energy as much as possible, while considering energy consumption. And the issue of buying price and cost.
- Figure 1 is a schematic structural view of the system of the present invention
- the channel gain of the kth user on the nth subcarrier is denoted as h i,k,n ; throughout the network, the energy required by each base station is derived from the energy shared by renewable energy, the power grid, and other base stations.
- a base station The collected renewable energy is sufficient, and when the renewable energy collected by another base station is insufficient, the base station will share part of the energy to another base station, the process is: the base station first informs another base station that it can The amount of energy shared, then another base station in turn gives its own demand, and then the base station performs a boost operation and injects the energy that needs to be shared into the grid, while another base station performs a buck operation. Retrieve shared energy from the grid to reduce the cost of purchasing energy across the network;
- the base station communicates with the user through subcarriers, and the communication rate sum of each user on all subcarriers to which it is allocated is:
- N 0 represents the power spectral density of the Gaussian white noise
- each base station has three parts: the first part is the circuit consumption P c,i ; the second part is the energy P i required to transmit the signal, and The third part is the energy e i shared by the base station i, then the total energy consumed by the base station i:
- Express Base station M ⁇ i ⁇ indicates that i belongs to the set after the set M removes the element i; Indicates the unit price of renewable energy; Representing the unit price of electrical energy in the grid; R i,k represents the minimum communication rate required by each user; E i represents the amount of renewable energy purchased by base station i; G i represents the energy purchased by base station i from the grid; Stationary base Energy shared to base station i; ⁇ represents energy transfer efficiency; Represents the maximum energy that a renewable energy company can provide; solves the objective function The minimum value is called the original problem;
- the symbol min represents the minimum value symbol
- the symbol Subject to represents the constraint symbol.
- the above expression represents the allocation restriction for each subcarrier, the maximum energy that each base station can purchase from the renewable energy company, and the minimum communication rate for each user. Solving the objective function under constraints of demand and total energy consumed by each base station The minimum value; the minimum value of the solution objective function is called the original problem;
- the optimization problem contains integer variables x i, k, n and continuous variables, so the optimization problem is a mixed binary integer programming problem.
- the integer variable x i,k,n is relaxed to 0 to 1, that is, x i, k, n ⁇ [0, 1], at this time, the original optimization problem from the original
- the mixed binary integer programming problem becomes a convex optimization problem.
- we redefine a variable s i,k,n , and s i,k,n x i,k,n p i,k , n ;
- the optimization problem (4) is a convex problem with a unique global optimal solution.
- Lagrangian duality theory it is possible to establish a minimum problem, that is, the relationship between the original problem and a maximization problem, that is, the dual problem. Because the original problem studied has strong duality, we can get the optimal solution of the original problem by solving the dual problem.
- ⁇ instead of E i , G i and e i ;
- variable s i, k, n and x i, k the optimum value of n; by applying the KKT condition, the variable s i, k, n and x i, k, n to obtain an optimum value necessary and sufficient conditions are:
- ⁇ , ⁇ , ⁇ represent the dual vectors of the first four constraints in equation (4)
- ⁇ i, k , ⁇ i and ⁇ n respectively represent the Lagrangian dual factor corresponding to each of the first four constraints in equation (4)
- ⁇ i, k , ⁇ i and ⁇ n are the dual vector ⁇ , respectively.
- the elements in ⁇ , ⁇ , and the dual problem corresponding to the dual function (7) are as follows:
- the optimal value obtained by the dual problem (8) is the optimal value of the original problem
- Duality factor is limited by constraints So by optimizing the dual factor ⁇ , ,, ⁇ to solve the objective function, that is, the dual function
- the user with the smallest H i,k,n is assigned to the nth subcarrier, namely:
- the symbol Indicates the value of k when the part within [] takes the minimum value
- the base station i does not need to purchase energy from the power grid, namely:
- the base station can be known. No need to share energy, ie:
- the base station Since the price of renewable energy is lower than that of the traditional grid, the base station should give priority to the purchase of renewable energy, then the base station You should purchase all the renewable energy that you can buy;
- base station i For the renewable energy that base station i can purchase, base station i will share its own energy demand, and its excess renewable energy will be shared to the base station. However, base station i is shared to the base station There are two possibilities for energy: the energy shared by base station i can satisfy the base station. Or can not meet the base station Demand, we consider the loss ⁇ of the shared energy during transmission;
- base station i is shared to the base station Energy to satisfy the base station Demand, ie
- the optimal energy shared by the base station i is:
- the renewable energy purchased by base station i is the energy consumed by itself And sharing it with the base station energy of That is, the energy of the renewable energy that the base station i needs to purchase is:
- base station i Since base station i is shared to the base station Energy can satisfy the base station Demand, therefore base station There is no need to purchase energy from the grid, ie:
- base station i is shared to the base station Energy cannot satisfy the base station Demand, ie Then base station i should purchase all renewable resources, namely:
- the base station i should share all the renewable energy remaining beyond the self-energy requirement to the base station. That is, the base station i shares to the base station
- the optimal energy is:
- the base station After receiving the energy shared by the base station i, the base station Still missing energy by base station Buying itself into the grid, ie the base station
- the energy purchased from the grid is:
- Equations (10) and (15) contain Lagrangian dual factors ⁇ i,k and ⁇ i , and when they are optimal, the optimal transmit power And optimal subcarrier allocation And optimal energy management with Also got the optimal value.
- the solution of the Lagrangian dual factor optimal value can be solved by the sub-gradient iterative algorithm;
- s_ ⁇ (t) and s_ ⁇ (t) respectively represent the iteration step size corresponding to the corresponding Lagrangian dual factor, and t represents the number of iterations;
- the resource allocation and energy management method of the cooperative cellular network according to Embodiment 1 is different in that the method of dividing the entire authorized frequency band into N subcarriers having the same bandwidth is to adopt orthogonal frequency division multiplexing modulation technology.
- the entire authorized frequency band is equally divided into N subcarriers having the same bandwidth.
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
本发明涉及一种协作蜂窝网络的资源分配和能量管理方法。本发明所述协作蜂窝网络的资源分配和能量管理方法,通过满足每个用户的最小通信速率以保证每个用户的通信质量;不仅能够最小化两个协作通信网络的成本和,同时还能够保证每个用户的通信速率要求。
Description
本发明涉及一种协作蜂窝网络的资源分配和能量管理方法,属于无线通信的技术领域。
无线通信技术快速发展,已经发展到5G技术。技术的快速发展使得通信速率和通信质量越来越好,同时,无线设备的数量也是随之快速增长,进而,整个网络系统的能量消耗也是大幅度增长。为了响应绿色通信的理念,科研学者开始将目光投向可再生能源,以此来取代部分传统的电能,例如,太阳能,风能等可再生能源。这一做法不但符合可持续发展以及绿色通信的理念,而且,可再生能源的价格也低于传统电网的价格,这也使得购买电能的成本大大降低。因此,越来越多的学者开始研究这种具有能量收集功能的网络中的功率分配和能量管理问题。
在通信系统中,频谱是另外一种紧缺的资源。多载波技术具有很强的抗干扰能力,而且在分配时也具有很大的灵活性。因此被广泛地应用在蜂窝网络中。例如正交频分复用技术。为了应对频谱紧张而移动设备大量增加的矛盾问题,不同的蜂窝系统之间进行子载波共享分配也是一个可行的办法,以此来缓解频谱紧张问题。
最近有很多学者来研究联合分配上述两种资源(能量资源和频谱资源),但是并没有考虑在引入可再生能源的模式下,以最小化整个网络的成本为驱动目标,将这两种资源同时进行联合共享分配。例如,中国专利CN102638891A公开了一种基于能量有效性的无线通信资源分配方法和系统。该系统是一种含有中继的网络系统,通过中继节点进行协作;该系统虽然是研究资源分配,但是是以提高系统的能效,即单位能量内能够传输尽可能多的信息,同时考虑能量消耗和信息传输量;该专利中的资源分配方法中子载波分配方法主要依据是节点的目标速率,瞬时能量性能以及瞬时数据速率;资源分配只涉及到子载波分配,没有设计到功率分配和能量管理;在效果上,该系统是致力于提高系统能效。
目前,查阅到的资料中,仍然没有在引入可再生能源的模式下的协作蜂窝网络中在保证移动用户通信速率要求且联合资源共享分配的先例。
发明内容
针对现有技术的不足,本发明提供一种协作蜂窝网络的资源分配和能量管理方法。
术语说明:
KKT条件:Karush-Kuhn-Tucker
优化问题的标准形式为:
minimize f0(x)
subjectto fi(x)≤0,i=1,...,m
hi(x)=0,i=1,...,p
其中,f0(x)是目标函数,fi(x)≤0是不等式约束,hi(x)=0是等式约束,该优化问题的拉格朗日函数定义为:
其中,λi为第i个不等式约束fi(x)≤0对应的拉格朗日因子,νi为第i个等式约束hi(x)=0对应的拉格朗日因子。当优化问题是凸问题的时候,满足KKT条件的点也就是原问题的最优解,我们定义x*,λ*,ν*为x,λ,ν所对应的最优解,则有:
fi(x*)≤0,i=1,...,m
hi(x*)=0,i=1,...,p
本发明的技术方案为:
一种协作蜂窝网络的资源分配和能量管理方法,由以下系统实现:该系统包括两个蜂窝网络,每个蜂窝网络包括一个基站和Ki个用户,其中i为基站编号,i∈M,M={1,2},Ki表示在基站i中有Ki个用户,用户k∈K1∪K2,集合K1={1,2,...,K1}和K2={1,2,...,K2}分别表示两个基站中用户的集合;两个基站共享同一段频谱,并将整个授权的频带等分成N个带宽相同的子载波,每个子载波的带宽为B;令xi,k,n为子载波分配因子,其中,n表示第n个子载波,n∈N,子载波的集合N={1,2,...,N},当子载波n分配给了基站i中的第k个用户时,xi,k,n=1;
否则,xi,k,n=0,且每个子载波仅能分配给一个用户;将基站i中的第k个用户在第n个子载波上的信道增益表示为hi,k,n;在整个网络中,每个基站所需的能量来源于可再生能源、电网以及其他基站所共享的能量,当某个基站所收集到的可再生能源比较充足,而另一个基站所收集到的可再生能源不充足时,则该基站就会向另一个基站共享部分能量,其过程为:该基站首先通知另外一个基站其可以共享的能量的多少,然后另一个基站反过来给出自己的需求量,其次该基站进行升压操作,并将需要共享的能量注入电网之中,与此同时,另一个基站进行降压操作,从电网中获取共享的能量,以此来降低整个网络购买能源的成本;
具体步骤如下:
1)计算每个用户的通信速率和:
基站与用户之间通过子载波通信,每个用户在其所分配到的所有子载波上的通信速率和为:
其中pi,k,n表示基站i中第k个用户在第n子载波上的传输功率,N0表示高斯白噪声的功率谱密度;
2)计算每个基站所消耗的能量
3)确定优化问题
以整个系统的成本为目标函数,每个子载波的分配情况、每个基站从可再生能源公司所能购买的最大能量、每个用户的通信速率和以及每个基站消耗的总能量为约束条件,构造如下优化问题:
其中,表示第基站,M\{i}表示i属于集合M除去元素i之后的集合;表示可再生能源的单价;表示电网中的电能的单价;Ri,k表示每个用户所需的最小通信速率;Ei表示基站i购买的可再生能源的量;Gi表示基站i从电网中购买的能量;表示基站共享给基站i的能量;η表示能量传输效率;表示可再生能源公司所能提供的最大能量;求解目标函数的最小值被称为原问题;
符号min表示最小值符号,符号Subject to表示约束符号,上述式子表示在对每个子载波的分配限制、每个基站所能从可再生能源公司所购买的最大能量、每个用户的最小通信速率需求以及每个基站消耗的总能量的约束条件下,求解目标函数的最小值;求解目标函数的最小值被称为原问题;
4)求解优化问题
所述优化问题中含有整数变量xi,k,n和连续型变量,因此该优化问题是一个混合二进制整数规划问题,为了让这个问题容易求解,我们采用放松整数型变量xi,k,n的方法,将整数型变量xi,k,n从原来的0、1取值放松到0到1,即xi,k,n∈[0,1],此时,原优化问题由原来的混合二进制整数规划问题变成了一个凸优化问题,同时,为了后面表述简单,我们重新定义一个变量si,k,n,且si,k,n=xi,k,npi,k,n;
将整数型变量xi,k,n从原来的0、1取值放松到0到1,即xi,k,n∈[0,1],重新规划原优化问题:
很容易证明优化问题(4)是一个凸问题,具有唯一的全局最优解,利用拉格朗日对偶理论,可以建立最小化问题即原问题与一个最大化问题即对偶问题之间的关系,因为所研究的原问题具有强对偶性,因此可以我们可以通过求解对偶问题来得到原问题的最优解,为了表达方便,我们定义符号Ψ来代替Ei,Gi和ei;
定义符号Ψ代替Ei,Gi和ei,即Ψ={Ei,Gi,ei,i∈M,k∈Ki,n∈N},则原问题的拉格朗日函数为:
原问题的对偶函数为:
其中λ,μ,ν分别表示公式(4)中前四个约束条件的对偶向量,λi,k、μi、νn分别表示公式(4)中前四个约束条件中每一个约束式所对应的拉格朗日对偶因子,λi,k、μi、νn分别是对偶向量λ、μ、ν中的元素,对偶函数(7)对应的对偶问题表述如下:
对偶问题(8)所求得的最优值即为原问题的最优值;
A)最优功率分配求解
将原问题的拉格朗日函数对变量si,k,n求偏导,并且令其偏导等于0,即:
其中符号[]+表示[]中的部分取非负值;
B)最优子载波分配求解
将原问题的拉尔朗日函数对变量xi,k,n求偏导,即:
将式(10)代入式(11),并应用KKT条件得到:
其中,
应用充要条件(6)中的第二个条件可得:
由问题(4)中的第四个约束条件可知,子载波的分配问题被分解为N个独立的问题,对于每一个子载波而言,如果Hi,k,n均不相同,那么将仅有一个用户在使用该子载波的时候,其Hi,k,n将最小,换而言之,Hi,k,n最小的用户将会被分配到该子载波;
Hi,k,n最小的用户被分配到第n个子载波,即:
C)最优能量管理求解
至此已经求出最优的发射功率和最优子载波分配下一步求解最优的
和为了降低整个网络的能量购买成本,我们优先购买可再生能源,因为可再生能源的价格比传统电网的电能价格低,此外,能量共享的原则是:当某一个基站可以购买的可再生能源比其所需求的要多,而另一个基站所能购买的可再生能源不够其需求,此时该基站就会向另一个基站共享部分能量,以此来进一步降低网络的成本,换而言之,当两个基站所能购买的可再生能源均不够其需求或者均能满足其需求时,此时两个基站将不会共享其能量给其他基站,因此我们可以根据两个基站所共享的能量是否为零,将和的求解问题采用分类讨论的思想进行求解:
情形一、最优的共享能量为0,即:
情形二、最优的共享能量不为0:两个基站中有一个基站所能购买的可再生能源比较充足,而另一个基站所能购买的可再生能源不充足,此时我们假设基站i所能购买可再生能源是充足的,而基站所能购买的可再生能源不充足,即:且
对于基站i所能购买到的可再生能源,基站i在满足自身的能量需求外,其过剩的可再生能源将会共享给基站但是,基站i共享给基站的能量有两种可能性,即:基站i共享的能量能够满足基站或者不能够满足基站的需求,我们考虑共享的能量在传输过程中的损耗η;
式(10)和式(15)中含有拉格朗日对偶因子λi,k和μi,当它们取到最优时,最优的发射功率和最优子载波分配以及最优的能量管理和也取到了最优值。拉格朗日对偶因子最优值的求解可以通过子梯度迭代算法求解;
拉格朗日对偶因子最优值的具体求解过程如下:
a)设初始迭代次数t=0,设每个用户的最小通信速率,初始化对偶因子集合初始值λ(0),μ(0)为非负实数;
b)当迭代次数为t时,用λ(t),μ(t)表示当前更新的拉格朗日对偶因子,将对偶因子集合λ(t)、μ(t)代入公式(10)和(15)中得到对应的最优信号传输功率和最优子载波分配然后根据式(16)-(27)计算出最优的能量管理和
c)采用以下公式分别更新2种拉格朗日对偶因子:
其中,s_λ(t)和s_μ(t)分别表示相应的拉格朗日对偶因子对应的迭代步长,t表示迭代次数;
d)令λ*=λ(t+1),μ*=μ(t+1),若λ*和μ*满足预定义的数据精度,则输出最优对偶因子集合λ*和μ*,否则,令t=t+1,跳转至步骤b),继续迭代,直到满足预定义的数据精度;
5)计算基站与每个用户通信时的最优发射功率,最优子载波分配以及最优能量管理;
将得到的最优拉格朗日因子最优集合λ*和μ*代入式(10)-(27)中,即可得到在满足每个用户的最低通信速率的条件之下的最优资源分配和能量管理。
优选的,所述用户为单天线用户;所述子载波为正交窄带子载波。
优选的,将整个授权的频带等分成N个带宽相同的子载波的方法为,采用正交频分复用调制技术将整个授权的频带等分成N个带宽相同的子载波。
本发明的有益效果为:
1.本发明所述协作蜂窝网络的资源分配和能量管理方法,通过满足每个用户的最小通信速率以保证每个用户的通信质量;不仅能够最小化两个协作通信网络的成本和,同时还能够保证每个用户的通信速率要求;
2.本发明所述子载波使用是两个基站共同使用的,这不仅能够提高频率利用率,避免出现了固定每个基站的子载波使用数量的方法所造成的一个基站子载波过剩,而另一个基站的子载波短缺的现象,同时该子载波分配方法也是以最小化整个网络的成本和为目标,是在该种网络系统中最优的子载波分配方案;
3.本发明所述功率分配是以最小化整个网络的成本为目标,在满足约束条件下尽可能减少整个网络的功率损耗,从源头上降低能量损耗,从而降低整个网络的成本,且该功率分配是在该种网络系统中最优的分配方案;
4.本发明所述能量管理方案是一种本文所建立的网络中最优能量管理方案,该方案引入可再生能源,并且优先购买价格更加便宜的可再生能源,另外,在可再生能源不充足的时候购买传统电网中的电能,这一做法既保证了整个网络的稳定性,同时从购买源上来降低网络的购买成本;
5.本发明所述能量管理方案中的两个基站之间是可以进行能量共享的,当其中某一个基站所能购买的可再生资源较充足而另一个基站的状态却恰好相反时,这是该基站就会向另一个基站共享部分可再生能源,虽然增加了该基站的购买成本,但是却进一步降低了整个网络的购买成本;
6.本发明所述协作蜂窝网络的资源分配和能量管理方法中的两个基站之间是直接进行能量协作,不含有中继节点;且引入了可再生能源,整个系统的能量供应是可再生能源和传统电网共同完成的,这两种能源的购买价格,购买量都对系统形成重要的影响,必须被考虑;
7.本发明所述协作蜂窝网络的资源分配和能量管理方法,研究目标是最小化整个系统的成本,即尽可能少消耗点能量,并且尽可能购买价格便宜的能源,同时考虑的是能量消耗和购买价格和成本的问题。
图1为本发明所述系统的结构示意图;
下面结合实施例和说明书附图对本发明做进一步说明,但不限于此。
实施例1
如图1所示。
一种协作蜂窝网络的资源分配和能量管理方法,由以下系统实现:该系统包括两个蜂窝网络,每个蜂窝网络包括一个基站和Ki个用户,其中i为基站编号,i∈M,M={1,2},Ki表示在基站i中有Ki个用户,用户k∈K1∪K2,集合K1={1,2,...,K1}和K2={1,2,...,K2}分别表示两个基站中用户的集合;两个基站共享同一段频谱,并将整个授权的频带等分成N个带宽相同的子载波,每个子载波的带宽为B;令xi,k,n为子载波分配因子,其中,n表示第n个子载波,n∈N,子载波的集合N={1,2,...,N},当子载波n分配给了基站i中的第k个用户时,xi,k,n=1;否则,xi,k,n=0,且每个子载波仅能分配给一个用户;将基站i中的第k个用户在第n个子载波上的信道增益表示为hi,k,n;在整个网络中,每个基站所需的能量来源于可再生能源、电网以及其他基站所共享的能量,当某个基站所收集到的可再生能源比较充足,而另一个基站所收集到的可再生能源不充足时,则该基站就会向另一个基站共享部分能量,其过程为:该基站首先通知另外一个基站其可以共享的能量的多少,然后另一个基站反过来给出自己的需求量,其次该基站进行升压操作,并将需要共享的能量注入电网之中,与此同时,另一个基站
进行降压操作,从电网中获取共享的能量,以此来降低整个网络购买能源的成本;
具体步骤如下:
1)计算每个用户的通信速率和:
基站与用户之间通过子载波通信,每个用户在其所分配到的所有子载波上的通信速率和为:
其中pi,k,n表示基站i中第k个用户在第n子载波上的传输功率,N0表示高斯白噪声的功率谱密度;
2)计算每个基站所消耗的能量
3)确定优化问题
以整个系统的成本为目标函数,每个子载波的分配情况、每个基站从可再生能源公司所能购买的最大能量、每个用户的通信速率和以及每个基站消耗的总能量为约束条件,构造如下优化问题:
其中,表示第基站,M\{i}表示i属于集合M除去元素i之后的集合;表示可再生能源的单价;表示电网中的电能的单价;Ri,k表示每个用户所需的最小通信速率;Ei表示基站i购买的可再生能源的量;Gi表示基站i从电网中购买的能量;表示基站共享给基站i的能量;η表示能量传输效率;表示可再生能源公司所能提供的最大能量;求解目标函数的最小值被称为原问题;
符号min表示最小值符号,符号Subject to表示约束符号,上述式子表示在对每个子载波的分配限制、每个基站所能从可再生能源公司所购买的最大能量、每个用户的最小通信速率需求以及每个基站消耗的总能量的约束条件下,求解目标函数的最小值;求解目标函数的最小值被称为原问题;
4)求解优化问题
所述优化问题中含有整数变量xi,k,n和连续型变量,因此该优化问题是一个混合二进制整数规划问题,为了让这个问题容易求解,我们采用放松整数型变量xi,k,n的方法,将整数型变量xi,k,n从原来的0、1取值放松到0到1,即xi,k,n∈[0,1],此时,原优化问题由原来的混合二进制整数规划问题变成了一个凸优化问题,同时,为了后面表述简单,我们重新定义一个变量si,k,n,且si,k,n=xi,k,npi,k,n;
将整数型变量xi,k,n从原来的0、1取值放松到0到1,即xi,k,n∈[0,1],重新规划原优化问题:
很容易证明优化问题(4)是一个凸问题,具有唯一的全局最优解,利用拉格朗日对偶理论,可以建立最小化问题即原问题与一个最大化问题即对偶问题之间的关系,因为所研究的原问题具有强对偶性,因此可以我们可以通过求解对偶问题来得到原问题的最优解,为了表达方便,我们定义符号Ψ来代替Ei,Gi和ei;
定义符号Ψ代替Ei,Gi和ei,即Ψ={Ei,Gi,ei,i∈M,k∈Ki,n∈N},则原问题的拉格朗日函数为:
原问题的对偶函数为:
其中λ,μ,ν分别表示公式(4)中前四个约束条件的对偶向量,λi,k、μi、νn分别表示
公式(4)中前四个约束条件中每一个约束式所对应的拉格朗日对偶因子,λi,k、μi、νn分别是对偶向量λ、μ、ν中的元素,对偶函数(7)对应的对偶问题表述如下:
对偶问题(8)所求得的最优值即为原问题的最优值;
A)最优功率分配求解
将原问题的拉格朗日函数对变量si,k,n求偏导,并且令其偏导等于0,即:
其中符号[]+表示[]中的部分取非负值;
B)最优子载波分配求解
将原问题的拉尔朗日函数对变量xi,k,n求偏导,即:
将式(10)代入式(11),并应用KKT条件得到:
其中,
应用充要条件(6)中的第二个条件可得:
由问题(4)中的第四个约束条件可知,子载波的分配问题被分解为N个独立的问题,对于每一个子载波而言,如果Hi,k,n均不相同,那么将仅有一个用户在使用该子载波的时候,其Hi,k,n将最小,换而言之,Hi,k,n最小的用户将会被分配到该子载波;
Hi,k,n最小的用户被分配到第n个子载波,即:
C)最优能量管理求解
至此已经求出最优的发射功率和最优子载波分配下一步求解最优的和为了降低整个网络的能量购买成本,我们优先购买可再生能源,因为可再生能源的价格比传统电网的电能价格低,此外,能量共享的原则是:当某一个基站可以购买的可再生能源比其所需求的要多,而另一个基站所能购买的可再生能源不够其需求,此时该基站就会向另一个基站共享部分能量,以此来进一步降低网络的成本,换而言之,当两个基站所能购买的可再生能源均不够其需求或者均能满足其需求时,此时两个基站将不会共享其能量给其他基站,因此我们可以根据两个基站所共享的能量是否为零,将和的求解问题采用分类讨论的思想进行求解:
情形一、最优的共享能量为0,即:
情形二、最优的共享能量不为0:两个基站中有一个基站所能购买的可再生能源比较充足,而另一个基站所能购买的可再生能源不充足,此时我们假设基站i所能购买可再生能源是充足的,而基站所能购买的可再生能源不充足,即:且
对于基站i所能购买到的可再生能源,基站i在满足自身的能量需求外,其过剩的可再生能源将会共享给基站但是,基站i共享给基站的能量有两种可能性,即:基站i共享的能量能够满足基站或者不能够满足基站的需求,我们考虑共享的能量在传输过程中的损耗η;
式(10)和式(15)中含有拉格朗日对偶因子λi,k和μi,当它们取到最优时,最优的发射功率和最优子载波分配以及最优的能量管理和也取到了最优值。拉格朗日对偶因子最优值的求解可以通过子梯度迭代算法求解;
拉格朗日对偶因子最优值的具体求解过程如下:
a)设初始迭代次数t=0,设每个用户的最小通信速率,初始化对偶因子集合初始值λ(0),μ(0)为非负实数;
b)当迭代次数为t时,用λ(t),μ(t)表示当前更新的拉格朗日对偶因子,将对偶因子集合λ(t)、μ(t)代入公式(10)和(15)中得到对应的最优信号传输功率和最优子载波分配然后根据式(16)-(27)计算出最优的能量管理和
c)采用以下公式分别更新2种拉格朗日对偶因子:
其中,s_λ(t)和s_μ(t)分别表示相应的拉格朗日对偶因子对应的迭代步长,t表示迭代次数;
d)令λ*=λ(t+1),μ*=μ(t+1),若λ*和μ*满足预定义的数据精度,则输出最优对偶因子集合λ*和μ*,否则,令t=t+1,跳转至步骤b),继续迭代,直到满足预定义的数据精度;
5)计算基站与每个用户通信时的最优发射功率,最优子载波分配以及最优能量管理;
将得到的最优拉格朗日因子最优集合λ*和μ*代入式(10)-(27)中,即可得到在满足每个用户的最低通信速率的条件之下的最优资源分配和能量管理。
实施例2
如实施例1所述的协作蜂窝网络的资源分配和能量管理方法,所不同的是,所述用户为单天线用户;所述子载波为正交窄带子载波。
实施例3
如实施例1所述的协作蜂窝网络的资源分配和能量管理方法,所不同的是,将整个授权的频带等分成N个带宽相同的子载波的方法为,采用正交频分复用调制技术将整个授权的频带等分成N个带宽相同的子载波。
Claims (3)
- 一种协作蜂窝网络的资源分配和能量管理方法,由以下系统实现:该系统包括两个蜂窝网络,每个蜂窝网络包括一个基站和Ki个用户,其中i为基站编号,i∈M,M={1,2},Ki表示在基站i中有Ki个用户,用户k∈K1∪K2,集合K1={1,2,...,K1}和K2={1,2,...,K2}分别表示两个基站中用户的集合;两个基站共享同一段频谱,并将整个授权的频带等分成N个带宽相同的子载波,每个子载波的带宽为B;令xi,k,n为子载波分配因子,其中,n表示第n个子载波,n∈N,子载波的集合N={1,2,...,N},当子载波n分配给了基站i中的第k个用户时,xi,k,n=1;否则,xi,k,n=0,且每个子载波仅能分配给一个用户;将基站i中的第k个用户在第n个子载波上的信道增益表示为hi,k,n;其特征在于,具体步骤如下:1)计算每个用户的通信速率和:基站与用户之间通过子载波通信,每个用户在其所分配到的所有子载波上的通信速率和为:其中pi,k,n表示基站i中第k个用户在第n子载波上的传输功率,N0表示高斯白噪声的功率谱密度;2)计算每个基站所消耗的能量3)确定优化问题以整个系统的成本为目标函数,每个子载波的分配情况、每个基站从可再生能源公司所能购买的最大能量、每个用户的通信速率和以及每个基站消耗的总能量为约束条件,构造如下优化问题:其中,表示第基站,M\{i}表示属于集合M除去元素i之后的集合;表示可再生能源的单价;表示电网中的电能的单价;Ri,k表示每个用户所需的最小通信速率;Ei表示基站i购买的可再生能源的量;Gi表示基站i从电网中购买的能量;表示基站共享给基站i的能量;η表示能量传输效率;表示可再生能源公司所能提供的最大能量;求解目标函数的最小值被称为原问题;4)求解优化问题将整数型变量xi,k,n从原来的0、1取值放松到0到1,即xi,k,n∈[0,1],重新规划原优化问题:定义符号Ψ代替Ei,Gi和ei,即Ψ={Ei,Gi,ei,i∈M,k∈Ki,n∈N},则原问题的拉格朗日函数为:原问题的对偶函数为:其中λ,μ,ν分别表示公式(4)中前四个约束条件的对偶向量,λi,k、μi、νn分别表示公式(4)中前四个约束条件中每一个约束式所对应的拉格朗日对偶因子,λi,k、μi、νn分别是对偶向量λ、μ、ν中的元素,对偶函数(7)对应的对偶问题表述如下:对偶问题(8)所求得的最优值即为原问题的最优值;A)最优功率分配求解将原问题的拉格朗日函数对变量si,k,n求偏导,并且令其偏导等于0,即:其中符号[]+表示[]中的部分取非负值;B)最优子载波分配求解将原问题的拉尔朗日函数对变量xi,k,n求偏导,即:将式(10)代入式(11),并应用KKT条件得到:其中,应用充要条件(6)中的第二个条件可得:Hi,k,n最小的用户被分配到第n个子载波,即:C)最优能量管理求解情形一、最优的共享能量为0,即:情形二、最优的共享能量不为0:拉格朗日对偶因子最优值的具体求解过程如下:a)设初始迭代次数t=0,设每个用户的最小通信速率,初始化对偶因子集合初始值λ(0),μ(0)为非负实数;b)当迭代次数为t时,用λ(t),μ(t)表示当前更新的拉格朗日对偶因子,将对偶因子集合λ(t)、μ(t)代入公式(10)和(15)中得到对应的最优信号传输功率和最优子载波分配然后根据式(16)-(27)计算出最优的能量管理c)采用以下公式分别更新2种拉格朗日对偶因子:其中,s_λ(t)和s_μ(t)分别表示相应的拉格朗日对偶因子对应的迭代步长,t表示迭代次数;d)令λ*=λ(t+1),μ*=μ(t+1),若λ*和μ*满足预定义的数据精度,则输出最优对偶因子集合λ*和μ*,否则,令t=t+1,跳转至步骤b),继续迭代,直到满足预定义的数据精度;5)计算基站与每个用户通信时的最优发射功率,最优子载波分配以及最优能量管理;将得到的最优拉格朗日因子最优集合λ*和μ*代入式(10)-(27)中,即可得到在满足每个用户的最低通信速率的条件之下的最优资源分配和能量管理。
- 根据权利要求1所述的协作蜂窝网络的资源分配和能量管理方法,其特征在于,所述用户为单天线用户;所述子载波为正交窄带子载波。
- 根据权利要求1所述的协作蜂窝网络的资源分配和能量管理方法,其特征在于,将整个授权的频带等分成N个带宽相同的子载波的方法为,采用正交频分复用调制技术将整个授 权的频带等分成N个带宽相同的子载波。
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN2016112683268 | 2016-12-31 | ||
CN201611268326.8A CN106604400A (zh) | 2016-12-31 | 2016-12-31 | 一种协作蜂窝网络的资源分配和能量管理方法 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2018120935A1 true WO2018120935A1 (zh) | 2018-07-05 |
Family
ID=58582155
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/CN2017/102745 WO2018120935A1 (zh) | 2016-12-31 | 2017-09-21 | 一种协作蜂窝网络的资源分配和能量管理方法 |
Country Status (2)
Country | Link |
---|---|
CN (1) | CN106604400A (zh) |
WO (1) | WO2018120935A1 (zh) |
Cited By (14)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109362080A (zh) * | 2018-11-08 | 2019-02-19 | 南京邮电大学 | 一种中断约束下的认知无线供电网络资源分配方法 |
CN109587690A (zh) * | 2018-12-07 | 2019-04-05 | 郑州航空工业管理学院 | 无人机辅助认知卫星地面通信网络多点协作传输方法 |
CN109661034A (zh) * | 2018-12-11 | 2019-04-19 | 南京邮电大学 | 一种无线供能通信网络中的天线选择和资源分配方法 |
CN109743736A (zh) * | 2019-03-22 | 2019-05-10 | 东莞理工学院 | 一种以用户为中心的超密集网络用户接入与资源分配方法 |
CN111314938A (zh) * | 2020-02-24 | 2020-06-19 | 厦门大学 | 一种用于单个蜂窝小区的蜂窝网络时频域资源分配的优化方法 |
CN111465054A (zh) * | 2020-02-27 | 2020-07-28 | 南京邮电大学 | 基于效用公平性的d2d通信资源分配方法 |
CN113207126A (zh) * | 2021-03-22 | 2021-08-03 | 南京邮电大学 | 基于分区的非正交多址异构网络、资源分配方法及系统 |
CN113473499A (zh) * | 2021-06-17 | 2021-10-01 | 清华大学 | 能效公平无蜂窝网络的资源分配及预编码方法与装置 |
CN113473625A (zh) * | 2021-07-02 | 2021-10-01 | 电子科技大学长三角研究院(湖州) | 一种数能一体化网络中的频分复用资源分配方法 |
CN113542124A (zh) * | 2021-06-25 | 2021-10-22 | 西安交通大学 | 一种d2d缓存网络中信用驱动的协作传输方法 |
CN113905442A (zh) * | 2021-09-30 | 2022-01-07 | 哈尔滨工业大学 | 一种智能电网按优先度分配子载波的传感器资源分配方法 |
CN115001975A (zh) * | 2021-04-25 | 2022-09-02 | 国网安徽省电力有限公司 | 一种5g切片接入的配电网保护优化方法及系统 |
CN115276768A (zh) * | 2022-07-22 | 2022-11-01 | 南京邮电大学 | 融合干扰缓解与资源分配的无人机时延最小化方法 |
CN116029506A (zh) * | 2022-12-28 | 2023-04-28 | 西安交通大学 | 一种基于需求侧响应的能源系统优化调度方法 |
Families Citing this family (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106604400A (zh) * | 2016-12-31 | 2017-04-26 | 山东大学 | 一种协作蜂窝网络的资源分配和能量管理方法 |
CN107590000B (zh) * | 2017-09-06 | 2020-11-10 | 中国科学院上海高等研究院 | 二次随机资源管理方法/系统、计算机存储介质及设备 |
CN108601087B (zh) * | 2018-04-27 | 2022-07-08 | 哈尔滨工业大学深圳研究生院 | 一种基于网络切片的无线通信资源分配方法 |
CN109039660B (zh) * | 2018-08-05 | 2020-11-20 | 广西师范大学 | Eh分布式基站系统的能量共享和功率分配方法 |
CN113991744B (zh) * | 2021-11-29 | 2024-04-16 | 山东瑞吉德自动化股份有限公司 | 一种智慧能源供应系统 |
CN114548956B (zh) * | 2022-04-26 | 2022-09-16 | 国网天津市电力公司电力科学研究院 | 一种基于蜂窝拓扑的综合能源配电系统及其运行方法 |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101192850A (zh) * | 2006-11-27 | 2008-06-04 | 华为技术有限公司 | 一种动态功率频谱管理方法及频谱优化系统及客户端设备 |
US20080219364A1 (en) * | 2007-03-09 | 2008-09-11 | The Hong Kong University Of Science And Technology | Delay-sensitive cross layer scheduler for multi-user wireless communication systems |
CN106255194A (zh) * | 2016-08-08 | 2016-12-21 | 山东大学 | 混合供电模式下协作蜂窝网络中的联合资源协作分配方法 |
CN106604400A (zh) * | 2016-12-31 | 2017-04-26 | 山东大学 | 一种协作蜂窝网络的资源分配和能量管理方法 |
Family Cites Families (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102740303B (zh) * | 2012-05-28 | 2015-01-21 | 上海交通大学 | 改进型中继系统的联合功率分配和子载波匹配方法 |
CN104348774B (zh) * | 2013-07-31 | 2017-11-28 | 华为技术有限公司 | 接入信道的方法和设备 |
CN103957597B (zh) * | 2014-03-18 | 2017-09-22 | 南京邮电大学 | 基于选择性子载波配对的中继选择与功率联合优化方法 |
CN104640217B (zh) * | 2015-01-27 | 2017-12-15 | 浙江大学 | 基于网络编码的ofdma网络上下行资源联合分配方法 |
CN105007631B (zh) * | 2015-08-05 | 2018-06-26 | 山东大学 | 一种协作认知网络中保证QoS要求的联合资源分配方法 |
CN105050191B (zh) * | 2015-08-27 | 2018-06-26 | 山东大学 | 基于时延QoS与公平的多用户安全认知网络资源分配方法 |
CN105721127B (zh) * | 2016-02-01 | 2023-05-26 | 国网新疆电力公司电力科学研究院 | 基于用户最小速率需求的电力线信道通信资源分配方法 |
-
2016
- 2016-12-31 CN CN201611268326.8A patent/CN106604400A/zh active Pending
-
2017
- 2017-09-21 WO PCT/CN2017/102745 patent/WO2018120935A1/zh active Application Filing
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101192850A (zh) * | 2006-11-27 | 2008-06-04 | 华为技术有限公司 | 一种动态功率频谱管理方法及频谱优化系统及客户端设备 |
US20080219364A1 (en) * | 2007-03-09 | 2008-09-11 | The Hong Kong University Of Science And Technology | Delay-sensitive cross layer scheduler for multi-user wireless communication systems |
CN106255194A (zh) * | 2016-08-08 | 2016-12-21 | 山东大学 | 混合供电模式下协作蜂窝网络中的联合资源协作分配方法 |
CN106604400A (zh) * | 2016-12-31 | 2017-04-26 | 山东大学 | 一种协作蜂窝网络的资源分配和能量管理方法 |
Non-Patent Citations (2)
Title |
---|
HU , SHOUDU ET AL.: "Downlink Spectrum Allocation Strategy for Cognitive Radio System with Fairness Consideration", COMPUTER ENGINEERING AND APPLICATIONS, vol. 48, no. 9, 25 July 2011 (2011-07-25), ISSN: 1002-8331 * |
LU , QIANXI ET AL.: "Subcarrier and Power Allocation Algorithm in Uplink of Cognitive Radio Networks", JOURNAL OF JILIN UNIVERSITY ENGINEERING AND TECHNOLOGY EDITION, vol. 40, no. 4, 31 July 2010 (2010-07-31), ISSN: 1671-5497 * |
Cited By (21)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109362080A (zh) * | 2018-11-08 | 2019-02-19 | 南京邮电大学 | 一种中断约束下的认知无线供电网络资源分配方法 |
CN109362080B (zh) * | 2018-11-08 | 2022-08-02 | 南京邮电大学 | 一种中断约束下的认知无线供电网络资源分配方法 |
CN109587690A (zh) * | 2018-12-07 | 2019-04-05 | 郑州航空工业管理学院 | 无人机辅助认知卫星地面通信网络多点协作传输方法 |
CN109587690B (zh) * | 2018-12-07 | 2023-04-28 | 郑州航空工业管理学院 | 无人机辅助认知卫星地面通信网络多点协作传输方法 |
CN109661034A (zh) * | 2018-12-11 | 2019-04-19 | 南京邮电大学 | 一种无线供能通信网络中的天线选择和资源分配方法 |
CN109743736A (zh) * | 2019-03-22 | 2019-05-10 | 东莞理工学院 | 一种以用户为中心的超密集网络用户接入与资源分配方法 |
CN111314938A (zh) * | 2020-02-24 | 2020-06-19 | 厦门大学 | 一种用于单个蜂窝小区的蜂窝网络时频域资源分配的优化方法 |
CN111314938B (zh) * | 2020-02-24 | 2021-08-20 | 厦门大学 | 一种用于单个蜂窝小区的蜂窝网络时频域资源分配的优化方法 |
CN111465054A (zh) * | 2020-02-27 | 2020-07-28 | 南京邮电大学 | 基于效用公平性的d2d通信资源分配方法 |
CN113207126A (zh) * | 2021-03-22 | 2021-08-03 | 南京邮电大学 | 基于分区的非正交多址异构网络、资源分配方法及系统 |
CN115001975B (zh) * | 2021-04-25 | 2023-11-03 | 国网安徽省电力有限公司 | 一种5g切片接入的配电网保护优化方法及系统 |
CN115001975A (zh) * | 2021-04-25 | 2022-09-02 | 国网安徽省电力有限公司 | 一种5g切片接入的配电网保护优化方法及系统 |
CN113473499A (zh) * | 2021-06-17 | 2021-10-01 | 清华大学 | 能效公平无蜂窝网络的资源分配及预编码方法与装置 |
CN113542124A (zh) * | 2021-06-25 | 2021-10-22 | 西安交通大学 | 一种d2d缓存网络中信用驱动的协作传输方法 |
CN113542124B (zh) * | 2021-06-25 | 2022-12-09 | 西安交通大学 | 一种d2d缓存网络中信用驱动的协作传输方法 |
CN113473625B (zh) * | 2021-07-02 | 2023-05-09 | 电子科技大学长三角研究院(湖州) | 一种数能一体化网络中的频分复用资源分配方法 |
CN113473625A (zh) * | 2021-07-02 | 2021-10-01 | 电子科技大学长三角研究院(湖州) | 一种数能一体化网络中的频分复用资源分配方法 |
CN113905442A (zh) * | 2021-09-30 | 2022-01-07 | 哈尔滨工业大学 | 一种智能电网按优先度分配子载波的传感器资源分配方法 |
CN115276768A (zh) * | 2022-07-22 | 2022-11-01 | 南京邮电大学 | 融合干扰缓解与资源分配的无人机时延最小化方法 |
CN115276768B (zh) * | 2022-07-22 | 2023-07-28 | 南京邮电大学 | 融合干扰缓解与资源分配的无人机时延最小化方法 |
CN116029506A (zh) * | 2022-12-28 | 2023-04-28 | 西安交通大学 | 一种基于需求侧响应的能源系统优化调度方法 |
Also Published As
Publication number | Publication date |
---|---|
CN106604400A (zh) | 2017-04-26 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
WO2018120935A1 (zh) | 一种协作蜂窝网络的资源分配和能量管理方法 | |
Zhang et al. | Energy-efficient resource allocation in NOMA heterogeneous networks | |
Han et al. | Backhaul-aware user association and resource allocation for energy-constrained HetNets | |
CN108650689B (zh) | 基于noma下行链路的无线携能通信系统能效优化方法 | |
CN105898851B (zh) | 超密集网络中考虑能量收集的高能效功率控制方法 | |
CN106255194B (zh) | 混合供电模式下协作蜂窝网络中的联合资源协作分配方法 | |
CN105744629B (zh) | 一种基于中继选择的能量采集中继系统时间优化分配方法 | |
CN108811069A (zh) | 一种全双工非正交多址接入系统基于能效的功率控制方法 | |
CN104159310A (zh) | Lte系统中基于非合作博弈的资源分配和干扰抑制方法 | |
CN109088686B (zh) | 一种基于5g高低频段的同时无线信息与能量传输方法 | |
CN103619024A (zh) | 同小区中蜂窝用户与d2d用户间频谱资源分配的方法 | |
CN104640185B (zh) | 一种基于基站协作的小区休眠节能方法 | |
CN110493854A (zh) | 一种基于优化理论的wpt-mec网络上下行资源分配与功率控制机制 | |
CN109768851B (zh) | 一种scma下行系统中基于能效的资源分配方法 | |
Piovesan et al. | Joint load control and energy sharing for autonomous operation of 5G mobile networks in micro-grids | |
CN110418360A (zh) | 无线携能网络多用户子载波比特联合分配方法 | |
Li et al. | An energy-effective network deployment scheme for 5G Cloud Radio Access Networks | |
CN108521672B (zh) | 一种分布式无线能量和信息传输系统的资源分配方法 | |
Li et al. | Resource sharing for cellular-assisted D2D communications with imperfect CSI: a many-to-many strategy | |
Wang et al. | A novel resource allocation method in ultra-dense network based on noncooperation game theory | |
Yuan et al. | A resource allocation method based on energy efficiency in SCMA systems | |
CN109219069B (zh) | 基于多运营商合作的绿色回程网络的能源分配和定价方法 | |
Gao et al. | Resource allocation optimization based on energy efficiency in green cloud radio access network | |
Tang et al. | Energy efficiency optimization for heterogeneous cellular networks | |
Bai et al. | Cognitive radio optimal price power control algorithm based on stackelberg game |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 17888930 Country of ref document: EP Kind code of ref document: A1 |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
32PN | Ep: public notification in the ep bulletin as address of the adressee cannot be established |
Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC ( EPO FORM 1205A DATED 18/10/2019 ) |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 17888930 Country of ref document: EP Kind code of ref document: A1 |