CN106211300A

CN106211300A - Security-aware energy efficiency and power allocation optimization method for heterogeneous cloud wireless access network

Info

Publication number: CN106211300A
Application number: CN201610514726.6A
Authority: CN
Inventors: 徐雷; 周迅钊; 张功萱; 张小飞; 王俊; 钱芳
Original assignee: Nanjing University of Science and Technology
Current assignee: Nanjing University of Science and Technology
Priority date: 2016-06-30
Filing date: 2016-06-30
Publication date: 2016-12-07

Abstract

The invention discloses effect and the power distribution optimization method of a kind of isomery cloud Radio Access Network safe perception energy, step is: Resource Block is divided into two parts；Collect each resource using information；Finally use the distribution of genetic algorithm optimization efficiency: initialize genetic algorithm parameter；Initialize population；Population is intersected and variation, obtain progeny population；Eliminate the individuality not meeting model constraint, and calculate population each ideal adaptation degree；Select preferably individual from filial generation with female generation, from new composition female generation；Iteration, to maximum iteration time, exports optimum individual.Present invention resource block assignments mode based on isomery cloud Radio Access Network, it is provided that a kind of high efficient and reliable efficiency resource allocation methods, for improving the Energy Efficiency Ratio in isomery cloud wireless network access method.

Description

Security-aware energy efficiency and power allocation optimization method for heterogeneous cloud wireless access network

技术领域technical field

本发明属于计算机网络技术领域，特别是一种异构云无线接入网络安全感知能的效及功率分配优化方法。The invention belongs to the technical field of computer networks, in particular to a method for optimizing security perception performance and power distribution of heterogeneous cloud wireless access networks.

背景技术Background technique

利用混合网络(heterogeneous networks，HetNet)以及云无线接入网络(cloudaccess radio access networks，C-RAN)的优势，有研究提出异构云无线接入网络(heterogeneous cloud radio access networks，H-CRAN)来强化频谱效率和功率效率，它使用远程无线头(remote radio heads，RRH)来为用户提供高服务质量(quality ofservice，QoS)要求的高速数据传输率。如，文献1(M.Peng,Y.Li,et.al.,“Heterogeneouscloud radio access networks:a new perspective for enhancing spectral andenergy efficiencies”,IEEE Wireless Commun.,Dec.2014.)所描述。Using the advantages of hybrid networks (heterogeneous networks, HetNet) and cloud radio access networks (cloud access radio access networks, C-RAN), some studies have proposed heterogeneous cloud radio access networks (heterogeneous cloud radio access networks, H-CRAN) to To enhance spectral efficiency and power efficiency, it uses remote radio heads (RRH) to provide users with high-speed data transmission rates required by high quality of service (QoS). For example, document 1 (M.Peng, Y.Li, et.al., "Heterogeneouscloud radio access networks: a new perspective for enhancing spectral and energy efficiencies", IEEE Wireless Commun., Dec.2014.) described.

正交频分多址技术(orthogonal frequency division multiple access，OFDMA)应用在4G，它比起蜂窝网络，能够提供高的数据传输率。为了4G向后兼容，H-CRAN通过给资源块(resource block，RB)分配给不同的用户设备(user equipment，UE)来应用OFDMA。为了提高无线资源分配(resource allocation，RA)的频谱效率(spectral efficiency，SE)，放大中转中继选择问题、渐进资源分配方法等方法已经被提出。Orthogonal frequency division multiple access (OFDMA) technology is applied in 4G, and it can provide a higher data transmission rate than a cellular network. For 4G backward compatibility, H-CRAN applies OFDMA by allocating resource blocks (resource block, RB) to different user equipment (user equipment, UE). In order to improve the spectral efficiency (SE) of radio resource allocation (resource allocation, RA), methods such as amplifying the transit relay selection problem and progressive resource allocation methods have been proposed.

而在这种广泛的研究下，无线网络通信的安全要求也越来越被人重视。这之后提出了窃听者的概念。网络中的用户都有可能成为潜在的窃听者，因此确保保密率的方法被提出，也有通过限制延迟达到保密的方法。Under this kind of extensive research, the security requirements of wireless network communication have been paid more and more attention. This was followed by the concept of an eavesdropper. All users in the network may become potential eavesdroppers, so a method to ensure the confidentiality rate is proposed, and there is also a method to achieve confidentiality by limiting the delay.

直观来讲，提升SE和能效(energy efficiency，EE)性能的关键是跨单元或者跨层干扰的缓解。一些HetNet的进一步算法被提出，例如单元联合以及频分复用(FFR)等。Intuitively, the key to improving SE and energy efficiency (EE) performance is the mitigation of cross-unit or cross-layer interference. Some further algorithms of HetNet have been proposed, such as unit union and frequency division multiplexing (FFR).

然而还没有解决H-CRAN能效问题的方法被提出，RRH/HPN(high power node，高功率节点)分配策略应比传统的接收最强功率策略更进一步，并且在H-CRAN不容易进行，因此需要提出一个基于RRH/HPN资源分配以及干扰缓解的资源功率联合优化分配方法。However, no method to solve the energy efficiency problem of H-CRAN has been proposed. The RRH/HPN (high power node, high power node) allocation strategy should go further than the traditional strategy of receiving the strongest power, and it is not easy to carry out in H-CRAN, so It is necessary to propose a resource power joint optimal allocation method based on RRH/HPN resource allocation and interference mitigation.

发明内容Contents of the invention

本发明提出一种基于RRH/HPN资源分配以及干扰缓解的异构云无线接入网络安全感知能的效及功率分配优化方法，用于提升H-CRAN网络的EE性能以及SE性能。The present invention proposes a heterogeneous cloud wireless access network security perception efficiency and power allocation optimization method based on RRH/HPN resource allocation and interference mitigation, which is used to improve the EE performance and SE performance of the H-CRAN network.

实现本发明的技术解决方案为：Realize the technical solution of the present invention is:

一种异构云无线接入网络安全感知能的效及功率分配优化方法，在RRH足够多且远多于HPN情况下，全局性能看做对RRH的优化，通过遗传算法达到优化资源及功率分配，包括以下步骤：An efficiency and power allocation optimization method for security awareness of heterogeneous cloud wireless access networks. When there are enough RRHs and far more than HPNs, the global performance is regarded as the optimization of RRHs, and the genetic algorithm is used to optimize resource and power allocation. , including the following steps:

步骤1：将资源块划分为Ω₁和Ω₂两个部分，其中Ω₁只提供给射频拉远头RRH连接到用户UE服务RUE，用以满足高传输率约束的服务，Ω₂提供给RUE和高功率基站HPN连接到用户UE的服务HUE，用以满足低传输率约束的服务。Step 1: Divide the resource block into two parts, Ω ₁ and Ω ₂ , where Ω ₁ is only provided to the remote radio head RRH to connect to the user UE to serve the RUE to meet the high transmission rate constraints, and Ω ₂ is provided to the RUE The serving HUE connected to the user UE with the high-power base station HPN is used to satisfy the service of the low transmission rate constraint.

步骤2：在通过步骤1划分资源块作用后，收集各个资源块使用的信息，包括信道资源信息、电路资源信息、资源块信息、服务质量约束信息、干扰约束量、最大转换能。并且通过获得的信息进行建模得出全局能效模型，作为适应度函数。Step 2: After dividing the role of resource blocks through step 1, collect the information used by each resource block, including channel resource information, circuit resource information, resource block information, service quality constraint information, interference constraint amount, and maximum conversion energy. And the global energy efficiency model is obtained by modeling the obtained information as a fitness function.

步骤3：使用步骤2获得的适应度函数，通过遗传算法优化RRH链接UE的资源及功率分配，使得全局能效最大化。Step 3: Using the fitness function obtained in step 2, the resource and power allocation of the RRH-linked UE is optimized through a genetic algorithm, so as to maximize the global energy efficiency.

步骤3.1：初始化遗传算法参数。Step 3.1: Initialize genetic algorithm parameters.

步骤3.2：初始化种群。Step 3.2: Initialize the population.

步骤3.3：对种群进行交叉与变异，得到子代种群。Step 3.3: Perform crossover and mutation on the population to obtain the offspring population.

步骤3.4：淘汰不符合模型约束的个体，并计算种群每个个体适应度。Step 3.4: Eliminate individuals that do not meet the model constraints, and calculate the fitness of each individual in the population.

步骤3.5：从子代与母代中挑选较优的个体，从新组成母代。Step 3.5: Select better individuals from the offspring and the mother generation to form the mother generation again.

步骤3.6：若达到最大优化次数Tm，则取适应度值最大的个体作为优化结果；否则，转到步骤3.3。Step 3.6: If the maximum number of optimization times Tm is reached, take the individual with the largest fitness value as the optimization result; otherwise, go to step 3.3.

本发明与现有技术相比，其显著优点是：(1)异构云无线接入网中，使用遗传算法解决能效资源分配最大化问题。(2)提出增强的软频分复用(S-FFR)，限制重用，来降低干扰，以便更快获得优化分配方案。(3)使用传输率控制的方法达到安全感知的效果(4)为高效利用异构云接入网中的能效资源提供技术支持。Compared with the prior art, the present invention has the following significant advantages: (1) In the heterogeneous cloud wireless access network, the genetic algorithm is used to solve the problem of maximizing energy efficiency resource allocation. (2) An enhanced soft frequency division multiplexing (S-FFR) is proposed to limit reuse to reduce interference so as to obtain an optimal allocation scheme faster. (3) Use the method of transmission rate control to achieve the effect of security awareness (4) Provide technical support for efficient utilization of energy efficiency resources in heterogeneous cloud access networks.

附图说明Description of drawings

图1为本发明异构云无线接入能效优化的流程图。FIG. 1 is a flowchart of energy efficiency optimization for heterogeneous cloud wireless access in the present invention.

图2为本发明资源分配示意图。FIG. 2 is a schematic diagram of resource allocation in the present invention.

图3为本发明遗传算法流程图。Fig. 3 is a flowchart of the genetic algorithm of the present invention.

具体实施方式detailed description

下面结合附图及具体实施实例对本发明作进一步说明。The present invention will be further described below in conjunction with the accompanying drawings and specific implementation examples.

结合图1，本发明是一种异构云无线接入网络安全感知能的效及功率分配优化方法，通过遗传算法达到优化资源及功率分配，包括以下步骤：Referring to Fig. 1, the present invention is an efficiency and power allocation optimization method for heterogeneous cloud wireless access network security perception, which optimizes resource and power allocation through genetic algorithm, including the following steps:

步骤1：将资源块划分为Ω₁和Ω₂两个部分，其中Ω₁只提供给射频拉远头RRH连接到用户UE服务RUE，用以满足高传输率约束的服务，Ω₂提供给RUE和高功率基站HPN连接到用户UE的服务HUE，用以满足低传输率约束的服务。图2为，资源块分配示意图。Step 1: Divide the resource block into two parts, Ω ₁ and Ω ₂ , where Ω ₁ is only provided to the remote radio head RRH to connect to the user UE to serve the RUE to meet the high transmission rate constraints, and Ω ₂ is provided to the RUE The serving HUE connected to the user UE with the high-power base station HPN is used to satisfy the service of the low transmission rate constraint. FIG. 2 is a schematic diagram of allocation of resource blocks.

首先，第k资源块划分给第n个RUE，信道干扰增加噪音比(CINR)可如下计算。First, the kth resource block is assigned to the nth RUE, and the channel interference-increased-noise ratio (CINR) can be calculated as follows.

${σ σ}_{n no,, k k} = = \{\begin{matrix} \frac{{d d}_{n no}^{R R} {h h}_{n no,, k k}^{R R}}{{B B}_{00} {N N}_{00}},, & k k &Element; &Element; {Ω Ω}_{11} \\ \frac{{d d}_{n no}^{R R} {h h}_{n no,, k k}^{R R}}{(({P P}^{M m} {d d}_{n no}^{M m} {h h}_{n no,, k k}^{M m} + + {B B}_{00} {N N}_{00}))},, & k k &Element; &Element; {Ω Ω}_{22} \end{matrix} - - - - - - ((11))$

和分别表示RUE n连接到RRH和HPN的路径损耗，和分别表示在使用第k资源块时，RUE n连接到RRH和HPN的信道增量。P^M是在每个HPN上资源块允许的功率转换分配值。N₀表示估计能量密度(PSD),B₀表示带宽。 and represent the path loss of RUE n connected to RRH and HPN respectively, and Respectively represent the channel increments when RUE n is connected to RRH and HPN when using the kth resource block. P ^M is the allowed power conversion allocation value of resource blocks on each HPN. N ₀ represents the estimated power density (PSD), and B ₀ represents the bandwidth.

其次，每个RRH数据率总量表示为：Second, the total data rate per RRH is expressed as:

$C C ((a a,, p p)) = = {Σ Σ}_{n no = = 11}^{N N + + M m} {Σ Σ}_{k k = = 11}^{K K} {a a}_{n no,, k k} {B B}_{00} {log log}_{22} ((11 + + {σ σ}_{n no,, k k} {p p}_{n no,, k k}))$

使用安全感知策略，通过限制信噪比来限制最低传输率，从而改写公式：Using a security-aware strategy, the minimum transmission rate is limited by limiting the signal-to-noise ratio, thus rewriting the formula:

$C C ((a a,, p p)) = = {Σ Σ}_{n no = = 11}^{N N + + M m} {Σ Σ}_{k k = = 11}^{K K} {a a}_{n no,, k k} {B B}_{00} {[[{log log}_{22} ((11 + + {σ σ}_{n no,, k k} {p p}_{n no,, k k})) - - {log log}_{22} ((11 + + {λ λ}_{n no,, k k} {p p}_{n no,, k k}))]]}^{+ +} - - - - - - ((22))$

n∈{1，...，N}代表使用Ω₁资源块的RUE，n∈{N+1，...，N+M}表示使用Ω₂资源块的RUE。(N+M)×K的矩阵a＝[a_n，k]_(N+M)×K和p＝[p_n，k]_(N+M)×K分别代表资源块和功率分配策略。λ_n,k代表最高的信噪比，[.]⁺＝max{.,0}，因此确保达到最小保密率。n∈{1,...,N} represents the RUE using Ω ₁ resource blocks, and n∈{N+1,...,N+M} represents the RUE using Ω ₂ resource blocks. (N+M)×K matrices a=[a _{n, k} ] _(N+M)×K and p=[p _n,k ] _(N+M)×K respectively represent resource blocks and power allocation strategies. λ _n,k represents the highest signal-to-noise ratio, [.] ⁺ =max{.,0}, thus ensuring the minimum secrecy rate.

然后，每个RRH能耗表示为：Then, the energy consumption per RRH is expressed as:

和P_bh分别表示能量放大器效率，电路功率以及前传线路的能耗。 and P _bh represent energy amplifier efficiency, circuit power and energy consumption of the fronthaul line, respectively.

最后，总能效比可近似表示为：Finally, the total energy efficiency ratio can be approximated as:

$γ γ = = \frac{C C ((a a,, p p))}{P P ((a a,, p p))} - - - - - - ((44))$

有约束：With constraints:

${Σ Σ}_{n no = = 11}^{N N + + M m} {a a}_{n no,, k k} = = 11,, {a a}_{n no,, k k} &Element; &Element; {{00,, 11}},, &ForAll; &ForAll; k k - - - - - - ((55))$

${Σ Σ}_{k k = = 11}^{K K} {C C}_{n no,, k k} &GreaterEqual; &Greater Equal; {η η}_{R R},, 11 \leq \leq n no \leq \leq N N - - - - - - ((66))$

${Σ Σ}_{k k = = 11}^{K K} {C C}_{n no,, k k} &GreaterEqual; &Greater Equal; {η η}_{E E. R R},, N N + + 11 \leq \leq n no \leq \leq N N - - - - - - ((77))$

${Σ Σ}_{n no = = N N}^{N N + + M m} {a a}_{n no,, k k} {p p}_{n no,, k k} {d d}_{k k}^{R R 22 M m} {h h}_{k k}^{R R 22 M m} \leq \leq {δ δ}_{00},, k k &Element; &Element; {Ω Ω}_{22} - - - - - - ((88))$

${Σ Σ}_{n no = = 11}^{N N + + M m} {Σ Σ}_{k k = = 11}^{K K} {a a}_{n no,, k k} {p p}_{n no,, k k} \leq \leq {P P}_{max max}^{R R},, {p p}_{n no,, k k} &GreaterEqual; &Greater Equal; 00,, &ForAll; &ForAll; k k,, &ForAll; &ForAll; n no - - - - - - ((99))$

约束(5)表示资源块不同时分配给多个RUE。约束(6)(7)分别表示高服务要求传输率约束和低服务要求传输率约束。(8)体现了增强软频分复用的思想，限制重用的层间干扰，和分别表示路径损耗和第k资源块上参考RRH到干扰的HUE的信道增长。(9)中表示最大转换能。Constraint (5) means that resource blocks are not allocated to multiple RUEs at the same time. Constraints (6) and (7) represent high service requirement transmission rate constraints and low service requirement transmission rate constraints respectively. (8) Embodies the idea of enhancing soft frequency division multiplexing, limiting the interlayer interference of reuse, and represent the path loss and the channel growth from the reference RRH to the interfering HUE on the kth resource block, respectively. (9) Indicates the maximum conversion energy.

步骤3：通过遗传算法优化RUE的资源及功率分配，使得全局能效最大化，结合图3，步骤如下：Step 3: Optimize the resource and power allocation of RUE through the genetic algorithm to maximize the global energy efficiency. Combined with Figure 3, the steps are as follows:

步骤3.1，初始化遗传算法参数，具体为：Step 3.1, initialize genetic algorithm parameters, specifically:

种群规模大小N，适应度函数γ，以及迭代次数T_m，交叉率P_c，变异率为P_m,资源块数目k，RUE数目n+m，n为高服务质量约束的RUE，m为低服务质量约束的RUE。Population size N, fitness function γ, and number of iterations T _m , crossover rate P _c , mutation rate P _m , number of resource blocks k, number of RUEs n+m, n is the RUE with high quality of service constraints, and m is low RUE of QoS constraints.

步骤3.2，初始化种群，产生随机的N组向量[α₁，α₂，…,α_k],α_i∈(1，2...，n+m),作为母群体X1。根据权利要求3中公式(5)所示，可用这些向量代表资源块分配矩阵a＝[a_n，k]_(N+M)×K的分配情况,α_i代表基因，设定此种群为X1，t＝1。Step 3.2, initialize the population, and generate N groups of random vectors [α ₁ , α ₂ , ..., α _k ], α _i ∈ (1, 2 ..., n+m), as the parent population X1. Shown in formula (5) according to claim 3, these vectors can be used to represent the allocation situation of the resource block allocation matrix a=[a _{n, k} ] _(N+M)×K , α _i represents the gene, and this population is set as X1 , t=1.

步骤3.3，对种群进行交叉与变异，得到子代种群，具体为：Step 3.3, perform crossover and mutation on the population to obtain the offspring population, specifically:

对种群X1中的基因组依据交叉率P_c和变异率P_m进行交叉与变异，获得子代群体X2.The genome in the population X1 is crossed and mutated according to the crossover rate _Pc and the mutation rate _Pm , and the offspring population X2 is obtained.

步骤3.4，淘汰不符合约束的个体，并计算种群每个个体适应度，具体是，计算每个个体γ_i的值，并在计算时淘汰不符合约束条件的个体。Step 3.4, eliminate the individuals who do not meet the constraints, and calculate the fitness of each individual in the population, specifically, calculate the value of γ _i for each individual, and eliminate individuals who do not meet the constraints during the calculation.

步骤3.5，从子代与母代中挑选较优的个体，从新组成母代，具体是从X1、X2中挑选γ值大的N个个体作为新的母代，并且t＝t+1。Step 3.5: Select better individuals from the offspring and the mother generation to form a new mother generation. Specifically, select N individuals with a large γ value from X1 and X2 as the new mother generation, and t=t+1.

步骤3.6，判断是否达到迭代次数Tm，如果未达到就重复步骤3.3～3.6，如果达到，输出此时种群中γ值最大的一个个体，作为输出的优化方案。Step 3.6, judge whether the number of iterations Tm is reached, if not, repeat steps 3.3 to 3.6, if it is reached, output the individual with the largest γ value in the population at this time, as the output optimization scheme.

实施例Example

本发明采用遗传算法进行能效资源优化，步骤如下：The present invention adopts genetic algorithm to optimize energy efficiency resources, and the steps are as follows:

步骤1，将RB划分为Ω₁和Ω₂，其中Ω₁只提供给RUE(RRH连接到UE)，用以满足高传输率约束的服务，Ω₂提供给RUE和HUE(HPN连接到UE)，用以满足低传输率约束的服务。这有数量10的RUE处于每个RRH中，有着高速率保持的服务要求，并且通过正交的资源块组Ω₁来分配。M用来表示高低速率保持的服务要求的RUE。Step 1, divide RB into Ω ₁ and Ω ₂ , where Ω ₁ is only provided to RUE (RRH is connected to UE) to meet the service of high transmission rate constraints, Ω ₂ is provided to RUE and HUE (HPN is connected to UE) , to satisfy the service with low transmission rate constraints. There are a number of RUEs of 10 in each RRH, with high rate maintenance service requirements, and allocated by orthogonal resource block groups Ω ₁ . M is used to represent the RUE required by the high and low rate maintenance service.

步骤2，收集各个资源使用的信息，包括信道资源信息、电路资源信息、资源块信息、服务质量约束信息、干扰约束量、最大转换能。并且将全局能效公式化为适应度函数。设定在1≤n≤N的情况下在N+1≤n≤N+M的情况下参考的RRH与重用的第k个RB的HUE的距离是RB总数量K＝25，带宽B₀＝5MHz。HPN的总转换功率是43dBm，分配到全部的RB上。RRH到HUE路径损耗模型表示为31.5+40.0*log₁₀(d)，HPN到RUE和RRH到HUE连接的模型是31.5+35.0*log₁₀(d)，d是以米为计量的发射器与接收器距离。低速率、高速率传输率约束的服务要求假定分别为η_PR＝64kbit/s和η_R＝128kbit/s。设定静态电路能耗放大器能效为对HPN设定前传连接和回传连接的功率消耗设置为P_bh＝0.2W。Step 2, collect the information used by each resource, including channel resource information, circuit resource information, resource block information, service quality constraint information, interference constraint amount, and maximum conversion energy. And the global energy efficiency is formulated as a fitness function. Set in the case of 1≤n≤N In the case of N+1≤n≤N+M The distance between the reference RRH and the HUE of the reused kth RB is The total number of RBs K=25, and the bandwidth B ₀ =5 MHz. The total switching power of HPN is 43dBm, which is distributed to all RBs. The RRH to HUE path loss model is expressed as 31.5+40.0*log ₁₀ (d), the HPN to RUE and RRH to HUE connections are modeled as 31.5+35.0*log ₁₀ (d), and d is the transmitter and receiver measured in meters device distance. The service requirements of the low-rate and high-rate transmission rate constraints are assumed to be η _PR =64 kbit/s and η _R =128 kbit/s respectively. Setting Static Circuit Energy Consumption The efficiency of the amplifier is Setting for HPN The power consumption of the fronthaul connection and the backhaul connection is set as P _bh =0.2W.

步骤3：通过遗传算法优化RUE的资源及功率分配，使得全局能效最大化，Step 3: Optimize the resource and power allocation of RUE through genetic algorithm to maximize the global energy efficiency,

图3表示遗传算法优化能效分配的流程：Figure 3 shows the flow of genetic algorithm to optimize energy efficiency allocation:

首先，初始化遗传算法参数：种群规模大小N＝30，适应度函数以及迭代次数T_m＝1000，交叉率P_c＝0.9，变异率为P_m＝0.1,资源块数目K＝25。First, initialize the genetic algorithm parameters: population size N=30, fitness function And the number of iterations T _m =1000, the crossover rate P _c =0.9, the mutation rate P _m =0.1, and the number of resource blocks K=25.

然后，初始化种群，产生随机的N组向量α＝[α₁，α₂，…,α_k],α_i∈(1，2...，n+m),作为母群体X1。根据权利要求3中公式(5)所示，可用这些向量代表资源块分配矩阵a＝[a_n，k]_(N+M)×K的分配情况,α_i代表基因，设定此种群为X1，t＝1。Then, initialize the population to generate random N groups of vectors α=[α ₁ , α ₂ ,...,α _k ], α _i ∈(1, 2...,n+m), as the parent group X1. Shown in formula (5) according to claim 3, these vectors can be used to represent the allocation situation of the resource block allocation matrix a=[a _{n, k} ] _(N+M)×K , α _i represents the gene, and this population is set as X1 , t=1.

其次，对种群X1中的基因组依据交叉率P_c和变异率P_m进行交叉与变异，获得子代群体X2。Secondly, the genome in the population X1 is crossed and mutated according to the crossover rate P _c and the mutation rate P _m to obtain the offspring population X2.

再次，计算每个个体γ_i的值，并在计算时淘汰不符合约束条件的个体。Again, calculate the value of γ _i for each individual, and eliminate individuals who do not meet the constraints during calculation.

之后，从X1、X2中挑选γ值大的N个个体作为新的母代，并且t＝t+1。Afterwards, N individuals with large γ values are selected from X1 and X2 as new parents, and t=t+1.

最后，判断是否达到迭代次数Tm，如果未达到就转到步骤3.3，如果达到，输出此时种群中γ值最大的一个个体，作为输出的优化方案。Finally, judge whether the number of iterations Tm is reached, if not, go to step 3.3, if it is reached, output the individual with the largest γ value in the population at this time, as the output optimization scheme.

综上所述，本发明异构云无线接入网络安全感知能的效及功率分配优化方法，提供了一种高效可靠安全的感知能效资源分配方法，用于提高异构云无线网络接入方法中的能效比。To sum up, the present invention provides an efficient, reliable and safe resource allocation method for energy-aware energy efficiency of heterogeneous cloud wireless access networks, which is used to improve the efficiency of heterogeneous cloud wireless network access methods. The energy efficiency ratio in .

Claims

1. An efficiency and power allocation optimization method for security awareness of a heterogeneous cloud wireless access network, characterized in that it comprises the following steps:

Step 1: Divide the resource block into two parts, Ω ₁ and Ω ₂ , where Ω ₁ is only provided to the service RUE connected to the user UE by the remote radio head RRH to meet the high transmission rate constraints, and Ω ₂ is provided to The RUE and the high-power base station HPN are connected to the serving HUE of the user UE to meet the service of the low transmission rate constraint;

Step 2: Collect the information used by each resource block, including channel resource information, circuit resource information, resource block information, service quality constraint information, interference constraint amount, maximum conversion energy, and model the global energy efficiency model through the obtained information , as a fitness function;

Step 3: Using the fitness function obtained in step 2, the resource and power allocation of the RRH-linked UE is optimized through a genetic algorithm, so as to maximize the global energy efficiency.

2. According to the method for optimizing security awareness and power allocation of heterogeneous cloud wireless access networks described in claim 1, it is characterized in that: the implementation process in the step 2 is

First, the kth resource block is assigned to the nth RUE, and the channel interference increase noise ratio CINR is calculated by the following formula

and represent the path loss of RUE n connected to RRH and HPN respectively, and Respectively represent the channel increment of RUE n connected to RRH and HPN when using the kth resource block; P ^M is the power conversion allocation value allowed on each HPN resource block, N ₀ represents the estimated energy density PSD, B ₀ represents bandwidth;

Second, the total data rate per RRH is expressed as:

Using a security-aware strategy, the minimum transmission rate is limited by limiting the signal-to-noise ratio, thus rewriting the formula:

n∈{1,...,N} represents the RUE using Ω ₁ resource block, n∈{N+1,...,N+M} represents the RUE using Ω ₂ resource block; (N+M)× The matrix a=[a _{n, k} ] _(N+M)×K and p=[p _n,k ] _(N+M)×K of K represent resource blocks and power allocation strategies respectively; λ _n,k represents the highest SNR, [.] ⁺ ＝max{.,0}, thus ensuring the minimum secrecy rate;

Then, the energy consumption per RRH is expressed as:

φ _eff , and P _bh represent energy amplifier efficiency, circuit power and energy consumption of the fronthaul line, respectively;

Finally, the total energy efficiency ratio is approximately expressed as:

With constraints:

Constraint (5) means that resource blocks are not allocated to multiple RUEs at the same time. Constraints (6) and (7) respectively represent high service requirement transmission rate constraints and low service requirement transmission rate constraints. Constraint (8) embodies enhanced soft frequency division multiplexing The idea of limiting reuse between layers of interference, and Denote the path loss and the channel growth from the reference RRH to the interfering HUE on the kth resource block, respectively, in constraint (9) Indicates the maximum conversion energy.

3. According to the heterogeneous cloud wireless access network security perception efficiency and power allocation optimization method described in claim 1, it is characterized in that: the specific process of step 3 is as follows:

Step 3.1: Initialize genetic algorithm parameters;

Step 3.2: Initialize the population;

Step 3.3: Perform crossover and mutation on the population to obtain the offspring population;

Step 3.4: Eliminate individuals that do not meet the model constraints, and calculate the fitness of each individual in the population;

Step 3.5: Select better individuals from the offspring and the mother generation, and re-form the mother generation;

Step 3.6: If the maximum number of optimization times Tm is reached, take the individual with the largest fitness value as the optimization result; otherwise, go to step 3.3.

4. The efficiency and power allocation optimization method of heterogeneous cloud wireless access network security awareness according to claim 3, characterized in that: the initialization genetic algorithm parameters described in step 3.1 are specifically:

Population size N, fitness function γ, and number of iterations T _m , crossover rate P _c , mutation rate P _m , number of resource blocks k, number of RUEs n+m, n is the RUE with high quality of service constraints, and m is low RUE of QoS constraints.

5. The efficiency and power allocation optimization method of heterogeneous cloud wireless access network security awareness according to claim 4, characterized in that: the initialization population described in step 3.2 generates random N groups of vectors [α ₁ , α ₂ ,...,α _k ],α _i ∈(1,2...,n+m), as the parent group X1; according to the formula (5), use these vectors to represent the resource block allocation matrix a=[a _{n , k} ] Distribution of _(N+M)×K , α _i represents gene, set this population as X1, t=1.

6. The efficiency and power allocation optimization method of heterogeneous cloud wireless access network security perception performance according to claim 4, characterized in that: performing crossover and mutation to the population as described in step 3.3 to obtain the offspring population, specifically: :

The genome in the population X1 is crossed and mutated according to the crossover rate P _c and the mutation rate P _m to obtain the offspring population X2.

7. The energy efficiency and power allocation optimization method for security perception of heterogeneous cloud wireless access network according to claim 4, characterized in that: step 3.4 eliminates individuals that do not meet the constraints, and calculates the fitness of each individual in the population , specifically, calculate the value of γ _i for each individual, and eliminate individuals that do not meet the constraints during calculation.

8. The energy efficiency and power allocation optimization method for security perception of heterogeneous cloud wireless access network according to claim 4, characterized in that: in step 3.5, a better individual is selected from the child generation and the mother generation, and the new composition The mother generation, specifically, select N individuals with a large γ value from X1 and X2 as the new mother generation, and t=t+1.