WO2014187126A1 - 基于不同QoS的业务卸载方法 - Google Patents

基于不同QoS的业务卸载方法 Download PDF

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Publication number
WO2014187126A1
WO2014187126A1 PCT/CN2013/090161 CN2013090161W WO2014187126A1 WO 2014187126 A1 WO2014187126 A1 WO 2014187126A1 CN 2013090161 W CN2013090161 W CN 2013090161W WO 2014187126 A1 WO2014187126 A1 WO 2014187126A1
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Prior art keywords
service
cell
user
qos
matching degree
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PCT/CN2013/090161
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English (en)
French (fr)
Inventor
许晓东
冉月恒
张慧鑫
陶小峰
张平
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北京邮电大学
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Publication of WO2014187126A1 publication Critical patent/WO2014187126A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/0005—Control or signalling for completing the hand-off
    • H04W36/0055—Transmission or use of information for re-establishing the radio link
    • H04W36/0061—Transmission or use of information for re-establishing the radio link of neighbour cell information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/16—Performing reselection for specific purposes
    • H04W36/22—Performing reselection for specific purposes for handling the traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management

Definitions

  • the present invention relates to the field of communications, and in particular, to a service offloading method based on different QoS (Quality of Service). Background technique
  • Another approach is to increase the number of service sites, not only macro base stations, but also many new service sites such as micro base stations, pico base stations, femto base stations, remote radio heads, WLANs, and 60 GHz transmission points. These different types of base stations will be deployed together in a heterogeneous hybrid network to serve users together.
  • the existing service offloading method has a multi-root homogeneous wireless environment, that is, the scenario includes only one type of base station.
  • the continuous emergence of new base stations greatly increases the heterogeneity and complexity of the wireless environment. Different characteristics, suitable for different scenarios and services.
  • existing service offloading methods cannot allocate resources reasonably, and wireless in the case of load imbalance or inability to best match Resources cannot be used efficiently, resulting in a problem that the rate of wireless resources is not high. How to perform reasonable service offloading in the case of heterogeneous hybrid networking to achieve efficient use of resources and improve the effectiveness of wireless resources has become an urgent problem to be solved. Summary of the invention
  • the technical problem to be solved by the present invention is to solve the problem that the radio resources in the heterogeneous hybrid networking environment cannot be efficiently obtained due to the load imbalance or the inability to optimally match, and the utilization of the radio resources is improved.
  • the present invention provides a service offloading method based on different QoS, including: SL acquiring a cell that can meet the QoS requirement of the user service;
  • step S1 the method further includes: obtaining a QoS requirement value of the user service;
  • the evasive threshold of the service offload of the current cell of the ⁇ household is calculated by using a T mode - 7' is the evasive threshold of the service offload of the current cell, A is the number of times the next type of service in the current cell appears next time, 6 is a preset weight factor, and M is a service offload The maximum value that can be reached by the evasion threshold, where V is the number of types of services that appear in the current cell.
  • the evasive threshold of the service offload of the current cell of the current household is calculated as follows -
  • r is the evasive threshold of the service offload of the current cell, where % is the number of times the first service type in the current cell appears next time, 3 ⁇ 4 is the preset weighting factor, and M is the service offloading
  • N is the number of traffic types that appear in the current cell
  • ⁇ is a preset constant, and the ⁇ is used to adjust the contribution rate of the number of services to the evasion threshold growth rate.
  • steps include -
  • S10L acquires all cells that the user can access
  • step S2 includes: calculating a power RMS value of the user in each of the acquired cells; and/or
  • the degree of matching between the user service and each of the acquired cells is calculated.
  • the degree of matching between the user service and each of the acquired cells is calculated by: obtaining an attribute matching degree between the user service and the cell;
  • the matching degree between the user service and the cell is the attribute matching degree between the user service and the cell
  • / 2 is the degree of resource matching with the user service cell, is /; corresponding weights, is / 2 corresponding weights.
  • obtaining the attribute matching degree between the service and the cell including:
  • the attribute matching table includes a correspondence between the category and the attribute matching degree
  • the attribute matching degree corresponding to the category in the attribute matching table is used as the attribute matching degree of the user service and the cell.
  • the invention solves the heterogeneous hybrid group by calculating the relevant impact factor of the service offloading of each acquired cell in each acquired cell, and then determining the target cell for service offload according to the relevant impact factor, thereby matching the user service to the most suitable cell.
  • wireless resources cannot be efficiently utilized, and the utilization of wireless resources is improved.
  • FIG. 1 is a flowchart of a service unloading method based on different QoS according to the present invention
  • FIG. 2 is a schematic diagram of a networking scenario provided by the present invention.
  • FIG. 3 is a flowchart of a service unloading method based on different QoS according to the present invention. detailed description The core idea of the present invention is: calculating a relevant impact factor for performing service offloading by each user in each acquired cell, and then determining a target cell for service offload according to the relevant impact factor, thereby matching the user service to the most suitable cell, and improving the radio resource. Utilization.
  • Figure 1 is a method for service offloading based on different QoS according to the present invention, including the following steps - S1. Acquiring a cell capable of satisfying QoS requirements of a service of a user;
  • the present embodiment solves the heterogeneous hybrid by calculating the relevant impact factor of the service offloading of each acquired cell in the cell, and then determining the target cell for service offload according to the relevant impact factor, thereby matching the user service to the most suitable cell.
  • the problem that wireless resources cannot be efficiently utilized in a networked environment improves the utilization of wireless resources.
  • the determining may be performed before step S1 to determine whether to perform service offloading, and the specific steps are as follows: acquiring a QoS requirement value of the user service;
  • the QoS requirements of the user service may include requirements for bandwidth, delay, power, jitter, packet loss rate, bit error rate, and the like. There are several ways to obtain the QoS requirements of the user service.
  • the QoS requirements can be reported directly by the user, or the service type can be identified first, and then the QoS requirements are defined according to the protocol.
  • the identification of business types can be based on the largest «?
  • the service flow identification algorithm, the feature word-based monitoring technology, the backbone data packet size, and the service identification algorithm of the clustering algorithm are performed.
  • the QoS requirement value of the user service can be obtained by the QoS requirement of the user service, for example, it can be bandwidth-oriented.
  • the required value is used as the QoS requirement value of the household service, and the value of the required value of the parameters such as the actual time and the delay may be weighted and averaged as the QoS demand value of the household service.
  • the current cell is a cell currently serving the user, and the load of the next moment of the cell can be predicted by calculating the evasive threshold of the service offload of the current cell, and the load prediction is a change according to the load of the previous cell.
  • Regularity exploring the intrinsic correlation between specific moments and load, and then estimating the amount of load in the future.
  • There are several implementation methods for load prediction such as Kaimsm filtering, Markov chain, neural network, and so on.
  • the invention proposes an improved load prediction method, which takes into account the QoS difference of the service.
  • the core idea is that the more high-speed services appear in the next moment of the cell, the greater the possibility of service unloading.
  • the load pre-Liu object may be the number of times different types of services appear in the cell at the next-time, and the prediction result is used to adjust the evasive threshold of the service offload.
  • the evasive threshold of the service offload of the current cell of the user may be calculated as follows:
  • the ⁇ is the evasive threshold of the service offload of the current cell, where A is the number of times that the first type of service in the current cell appears next time, which is the preset weighting factor of A, and M is the service offload avoidance threshold.
  • the maximum value that can be reached, N is the number of types of services that appear in the current cell.
  • the evasive threshold of the service offload of the current cell of the user may also be calculated as follows:
  • the r is the evasive threshold of the service offload of the current cell, and is the number of times the first service type T in the current cell appears at the moment, which is the preset weighting factor, and M is the service offload avoidance threshold.
  • the maximum value that can be reached, N is the number of types of services that appear in the current cell, and A is a preset constant, and the A is used to adjust the contribution rate of the number of services to the increase of the evasion threshold.
  • the step S1 specifically includes -
  • 5101 Obtain a cell that all the users can access
  • 5102 Perform simulation resource allocation on the cell obtained in step S01.
  • analog resource allocation is performed for each available cell of the current service to determine whether the resources that the service can share can meet its QoS requirements.
  • wireless resources such as water injection algorithm, genetic algorithm, simulated annealing algorithm and so on. If the resources provided by all accessible cells of the current service cannot meet the QoS requirements of the service, the service unloading process is terminated. If there is a resource provided by at least one cell to meet the QoS requirement of the service, the cell that does not satisfy the QoS requirement is removed, and the subsequent service unloading process is performed.
  • step S2 comprises:
  • the relevant impact factor of the user performing service offloading in each of the acquired cells may be obtained by calculating the power effective value of the user in each of the acquired cells, and the calculation method is as follows - power efficiency
  • 3 ⁇ 4wer ice « y is a power effective value of the user in the acquired cell, a bandwidth required by the user, fffW is a signal to interference ratio of the user, and P is a base station of the acquired cell
  • the transmit power is the QoS requirement that the user can obtain in the acquired cell, and is the QoS requirement of the user.
  • the power RMS is used to measure the ability of the system to use a limited resource to obtain higher performance. The higher the power RMS value of the user in the cell, the higher the resource utilization of the user in the cell, and the calculated user is in each After the power RMS value of the acquired cell, the cell with the largest power RMS value may be used as the target cell for the user service offload.
  • the correlation factor of performing service offloading by the user in each of the acquired cells may be obtained by calculating a matching degree between the user service and each of the acquired cells, because different types of base stations have different characteristics. Different types of services also have different QoS requirements. Therefore, a reasonable matching of user services and cells can better benefit system resources and bring a superior user experience.
  • the user service and the cell matching degree can be calculated, including two aspects, one is the attribute matching degree between the user service and the cell, and the other is the resource matching degree between the user service and the cell.
  • the attribute matching degree of the cell to the cell can be expressed as a value between -1 and i. The better the matching degree, the closer the value is to the worse the degree of matching, and the value is closer to -1.
  • the available resources of the cell can be represented by the resource matching degree of the cell service and the cell.
  • the existing available resources of the cell also affect the probability that the user service is offloaded to the cell. The more available bandwidth of the cell, the more the transmission rate is. The higher the channel condition is, the greater the probability that the service is offloaded to the cell, and the better the matching of the user service to the cell resource. On the contrary, the degree of matching of user resources to cell resources is worse.
  • the user service is weighted and summed with the cell attribute matching degree and the resource matching degree to obtain the matching degree between the user service and the cell, and the cell with the greatest matching degree with the service is used as the target cell for service unloading.
  • the matching degree between the user service and each of the acquired cells is separately calculated as follows:
  • / 2 is the degree of resource matching with the user traffic cell,; of /; corresponding weights, is / 2 corresponding weights.
  • the obtaining the attribute matching degree between the user service and the cell specifically includes:
  • the attribute matching degree corresponding to the category in the attribute matching table is used as the attribute matching degree of the user service and the cell.
  • the obtaining the resource matching degree between the service and the cell includes: - the current bandwidth availability of the cell (the ratio of the available bandwidth of the cell to the maximum bandwidth of the cell), and the power availability
  • the rate (cell available power and cell maximum power ratio) is weighted and summed to obtain the resource matching degree between the user service and the cell.
  • the power RMS value and the matching degree between the user service and the cell are used as the relevant impact factors of the service offload, and the target cell of the service offload is determined by using the power RMS value that the cell can provide and the matching degree between the cell and the user service.
  • the fuzzy logic algorithm is used to determine the target cell for service offload. For example, the power efficiency can be divided into five levels according to the power RMS (very low, low, medium, high, and very high), according to the matching degree between the user and the cell.
  • the degree of matching between the user and the cell is divided into three levels (low, medium, and high), and then the degree of goodness of the cell as the target cell for service offload is determined according to Table 1, for example, when the power efficiency of the A cell is "low”
  • the matching degree is “medium”, and the goodness of the target area is “low”
  • the power efficiency of the B cell is “high”
  • the matching degree is "medium”
  • the ⁇ B cell is used as the target cell.
  • the degree of good is "medium”
  • the cell with high degree of goodness is selected as the target cell for service unloading. More than one cell with the highest degree is selected, and an optimal cell spoofing is randomly selected as the target cell.
  • the service unloading operation is terminated, and the resource scheduling of the user service unloading can be performed by various methods.
  • Implementation such as polling algorithm, inverse fairness algorithm, MLWDF algorithm, etc.
  • FIG. 2 is a schematic diagram of a networking scenario provided by the present invention, including a macro base station 1, a micro base station 2, a high speed user 3, a low speed user 4, and a service offload controller 5, wherein the macro base station and the micro
  • the available bandwidth between the base stations does not overlap each other, but the bandwidth between the four pico base stations is shared.
  • the user can choose to access all base stations in the scene.
  • the users are randomly distributed in this scenario, and the service offload is completed through interaction with the base station (direct) and the service offload controller (indirect).
  • the prediction of the cell load condition is completed by applying the feedforward neural network model of the backward propagation algorithm, and the selection of the target cell for service unloading is completed by the fuzzy logic algorithm.
  • the service offloading decision is triggered in two cases. One is when the user's service type changes (for example, the low-speed service is converted to high-speed service), and the other is that the user detects that the current serving cell can provide too low power efficiency. Time.
  • the service unloading request is initiated by the user, and the decision is made by the service offloading controller.
  • the base station functions as a connection user and a service offload controller and provides resources.
  • the service unloading process is as shown in FIG. 3, and the service is uninstalled.
  • the steps include: 5301.
  • the service offload controller performs load prediction on the current cell of the user by applying a feedforward neural network of the backward propagation algorithm, and calculates an evasive threshold of the service offload of the current cell of the user.
  • the user measures the wireless environment in the vicinity thereof, and sends a service offload request to the accessible cell.
  • the service offload controller performs analog resource allocation for each cell that the ffi user can access according to a predefined resource allocation rule, on the premise that the user accesses.
  • the service offload controller acquires a small area that can satisfy the QoS requirement of the user service according to the resource allocation result. If no cell satisfies the QoS requirements of the 3 ⁇ 4 households, the service unloading is terminated.
  • the fuzzy logic algorithm is used to calculate the degree of goodness of the cell as the target cell for service offload, and the cell with the highest degree of selectivity is selected as the target cell. If there is more than one cell with the highest degree of goodness, an optimal cell is randomly selected as the target cell for service offload.
  • the selected service offload target cell is the current cell, the service offloading operation is terminated, and the resource scheduling of the user service offloading can be implemented by various methods, such as a polling algorithm, a proportional fairness algorithm, an MLWDF algorithm, and the like.
  • the service offloading method based on different QoS provided by the embodiment of the present invention calculates the relevant impact factor of the service offloading by the user in each acquired cell, and then determines the target cell for service offload according to the relevant influencing factor, fully considering the base station and the 3 ⁇ 4
  • the characteristics of the user service adaptively offloading the most suitable service to the most suitable cell, and dynamically selecting according to the degree of matching between the service and the cell and the power availability of the cell under the premise of satisfying the QoS requirement of the user
  • a suitable cell provides communication services for users, which not only improves the utilization of wireless resources in a heterogeneous hybrid networking environment, but also improves the user experience.

Abstract

本发明提供了一种基于不同QoS的业务卸载方法，该基于不同QoS的业务卸载方法包括：S1. 获取能够满足用户业务的QoS需求的小区；S2. 计算该用户在每一个该获取的小区进行业务卸载的相关影响因子；S3. 根据该相关影响因子在该获取的小区中确定业务卸载的目标小区。本发明提高了在异构混合组网环境下无线资源的利用率。

Description

基于不同 QoS的业务卸载方法
技术领域
本发明涉及通信领域， 具体涉及一种基于不同 QoS (Quality of Service, 服务质量） 的业务卸载方法。 背景技术
随着智能终端数量的不断增长，多媒体技术的广泛应用带来了无线容量需求的迅猛增 长， 同 也加剧了其与有限的无线资源之间的矛盾。 缓解此矛盾一般通过两种途径进行， 一是提高现有资源的利用率，二是通过增加基站的数量来实现迸一步的容量增益。就提高 现有资源利 ffl率而言， 业务卸载是一种很有前景的解决方案。所谓业务卸载， 是指一种在 特定条件下将网络中的部分数据流量卸载到其他网络的技术，它可以在不改变现有的网络 架构的基础上， 为系统提供较大的容量增益。另一种途径则是增加服务站点的数量， 不仅 指宏基站， 还包括微基站， 微微基站， 毫微微基站、 远端射频头、 WLAN和 60GHz发射点 等许多新型服务站点。这些不同类型的基站将通过异构混合组网的方式部署在一起，共同 为用户提供服务。
现有的业务卸载方法多基干同构的无线环境，即场景中只包含一种类型的基站,然而， 新型基站的不断出现大大增加了无线环境的异构性及复杂性， 这些基站各有其不同的特 点， 适 于不同的场景和业务 ·， 在这种异构混合组网的情况下， 现有的业务卸载方法并不 能合理的分配资源，在负载失衡或无法最佳匹配情况下的无线资源无法得到高效利用，导 致了无线资源利 ffl率不高的问题。如何在异构混合组网的情况下进行合理的业务卸载， 以 实现资源的高效利用， 提高无线资源利 ffl的有效性， 成为亟待解决的问题。 发明内容
(一） 要解决的技术 题
本发明要解决的技术问题是:解决异构混合组网环境下由于负载失衡或无法最佳匹配 情况下的无线资源无法得到高效利 ^的问题， 提高无线资源的利用率。
(二） 技术方案 为解决上述技术问题， 本发明提供了一种基于不同 QoS的业务卸载方法， 包括： SL 获取能够满足用户业务的 QoS需求的小区；
52. 计算所述用户在每一个所述获取的小区进行业务卸载的相关影响因子；
53. 根据所述相关影响因子在所述获取的小区中确定业务卸载的目标小区。
进一歩地， 在歩骤 S1之前还包括- 获取所述用户业务的 QoS需求值；
什算所述用户当前小区的业务卸载的规避门限值；
判断所述 QoS需求值是否大于所述规避门限值， 若是， 则执行歩骤 S 〜 S3。
进一步地， 通过如 T方式计算所述 ^户当前小区的业务卸载的规避门限值-
Figure imgf000004_0001
其中， 7'为所述当前小区的业务卸载的规避门限值， A为所述当前小区中第 ^种业务 种类下一时刻出现的次数， 6 为 的预设的权重因子， M为业务卸载规避门限所能达到 的最大的值， V为所述当前小区中出现的业务种类数。
进 ·歩地， 通过如下方式计算所述 ]¾户当前小区的业务卸载的规避门限值-
其中， r为所述当前小区的业务卸载的规避门限值， ％为所述当前小区中第 种业务 种类下一时刻出现的次数， ¾为《,的预设的权重因子， M为业务卸载规避门限所能达到 的最大的值， N为所述当前小区中出现的业务种类数， λ为预先设置的常数， 所述 Α用于 调整业务个数对所述规避门限值增长的贡献率》
进一步地， 步骤 包括-
S10L 获取所有所述用户可接入的小区；
5102. 对步骤 S101中获取的小区进行模拟资源分配；
5103. 根据所述模拟资源分配的结果在步骤 S101获取的小区中获取能够满足所述 ]¾ 户业务的 QoS需求的小区。
进一歩地， 步骤 S2包括- 算所述用户在每一个所述获取的小区的功率有效值； 和 /或
†算所述用户业务与每一个所述获取的小区的匹配度。
进一步地， 通过如下方式分别计算所述 ^户在每一个所述获取的小区的功率有效值； 、v
power efficiency - '
Figure imgf000005_0001
其中， /wwer i^dew y为所述用户在所述获取的小区的功率有效值， 为所述用户 需求的带宽， 5/ 为所述用户的信干比， P为所述获取的小区的基站的发射功率， ^为 所述用户在所述获取的小区能够获取到的 QoS需求， 为所述用户的 g0s需求。
进一歩地， 通过如下方式分别计算所述用户业务与每一个所述获取的小区的匹配度： 获取所述用户业务与小区的属性匹配度；
获取所述用户业务与小区的资源匹配度；
采 ^以下公式 if算用户业务与小区的匹配度：
― C！ /j -卜 j ί
其中， 为所述用户业务与小区的匹配度， 为所述用户业务与小区的属性匹配度，
/2为所述用户业务与小区的资源匹配度， 为 /;对应的权值， 为/2对应的权值。
进一步地， 获取所述 ]¾户业务与小区的属性匹配度， 包括：
获取所述用户业务的种类；
获取所述小区的属性匹配表,所述属性匹配表包括所述种类与所述属性匹配度的对应 关系；
将所述属性匹配表中所述种类对应的属性匹配度作为所述用户业务与小区的属性匹 配度。
(：；£) 有益效果
本发明通过计算 ¾户在每一个获取到的小区进行业务卸载的相关影响因子,然后根据 相关影响因子确定业务卸载的目标小区，从而将用户业务匹配到最合适的小区，解决了异 构混合组网环境下无线资源无法得到高效利 的问题， 提高了无线资源的利用率。 附图说明
图 1为本发明提供的一种基于不同 QoS的业务卸载方法的流程图；
图 2是本发明提供的一种组网场景的示意图；
图 3为本发明提供的一种基于不同 QoS的业务卸载方法的流程图。 具体实施方式 本发明的核心思想为： 计算用户在每一个获取到的小区进行业务卸载的相关影响因 子，然后根据相关影响因子确定业务卸载的目标小区，从而将用户业务匹配到最合适的小 区， 提高无线资源的利用率。
图】为本发明提供的一种基于不同 QoS的业务卸载方法， 包括以下歩骤- S1. 获取能够满足 ]¾户业务的 QoS需求的小区；
52. 计算所述 ¾户在每一个所述获取的小区进行业务卸载的相关影响因子；
53. 根据所述相关影响因子在所述获取的小区中确定业务卸载的目标小区。
本实施方式通过计算 ^户在每一个获取到的小区进行业务卸载的相关影响因子,然后 根据相关影响因子确定业务卸载的目标小区，从而将用户业务匹配到最合适的小区，解决 了异构混合组网环境下无线资源无法得到高效利用的问题， 提高了无线资源的利用率。
优选地， 可以在步骤 S1之前进行判断， 判断是否要迸行业务卸载， 具体步骤如下： 获取所述用户业务的 QoS需求值；
用户业务的 QoS需求可以包括其对带宽、 时延、 功率、 抖动、 丢包率、 误码率等的要 求。 获取用户业务的 QoS需求有多种方法， 可以直接由用户报告其 QoS需求， 也可以先识 别业务类型， 再根据协议定义其 QoS需求。 业务类型的识别可以通过基于最大 «的？业务 流识别算法、基于特征字的监测技术、基干数据包大小和聚类算法的业务识别算法等进行， 用户业务的 QoS需求值可以由用户业务的 QoS需求得到， 例如， 可以将其对带宽的要求的 值作为 户业务的 QoS需求值， 也可以将其对带竟、 时延等多个参数的要求的值进行加权 平均后作为 户业务的 QoS需求值。
†算所述用户当前小区的业务卸载的规避门限值；
其中， 当前小区为当前为用户提供服务的小区，通过计算当前小区的业务卸载的规避 门限值就可以对该小区的下一刻的负载进行预测，负载预测是一项根据以往小区的负载量 变化规律，探索具体时刻与负载量之间的内在关联，进而对将来时刻的负载量做出估计的 技术。 负载预测有多种实现方法， 例如 Kaimsm滤波、 马尔科夫链、 神经网络等。 本发明 提出了一种改进的负载预测方法， 将业务的 QoS差别纳入考虑， 其核心思想是小区下一 刻出现的高速业务越多， 发生业务卸载的可能性越大。例如， 负载预劉的对象可以为下-一 时刻小区中不同种类的业务出现的次数， 预测结果用于调整业务卸载的规避门限值。
判断所述 QoS需求值是否大于所述规避门限值， 若是， 则执行歩骤 S 〜 S3。
优选地， 可以通过如下方式 if算所述用户当前小区的业务卸载的规避门限值：
Figure imgf000006_0001
其中， Γ为所述当前小区的业务卸载的规避门限值， A为所述当前小区中第 种业务 种类下一时刻出现的次数， 为 A的预设的权重因子， M为业务卸载规避门限所能达到 的最大的值， N为所述当前小区中出现的业务种类数。
此外， 还可以通过如下方式计算所述用户当前小区的业务卸载的规避门限值-
Λ'
Τ ^ Μ -- Υ ω λα'
' '
其中， r为所述当前小区的业务卸载的规避门限值， 为所述当前小区中第 ^种业务 种类 T一时刻出现的次数， 为《的预设的权重因子， M为业务卸载规避门限所能达到 的最大的值， N为所述当前小区中出现的业务种类数， A为预先设置的常数， 所述 A用于 调整业务个数对所述规避 Π限值增长的贡献率。
优选地， 该步骤 S1具体包括-
5101 , 获取所有所述用户可接入的小区；
5102, 对歩骤 S】01中获取的小区进行模拟资源分配；
倒如， 在用户接入的前提下， 对每个当前业务的可接入小区进行模拟资源分配， 以确 定业务可以分得的资源是否能够满足其 QoS需求。 无线资源的分配有多种可实现方式， 例 如注水算法、遗传算法、模拟退火算法等。若当前业务的所有可接入小区提供的资源均无 法满足业务的 QoS需求， 终止业务卸载过程。若存在至少一个小区提供的资源满足业务的 QoS需求， 剔除不满足 QoS需求的小区， 并进行之后的业务卸载过程。
5103, 根据所述模拟资源分配的结果在步骤 S101获取的小区中获取能够满足所述 ^ 户业务的 QoS需求的小区。
优选地， 步骤 S2包括：
†算所述用户在每一个所述获取的小区的功率有效值； 和 /或
计算所述用户业务与每一个所述获取的小区的匹配度。
可以通过^算所述用户在每一个所述获取的小区的功率有效值得到所述用户在每 个所述获取的小区进行业务卸载的相关影响因子， 计算方法如下- power efficiency
Figure imgf000007_0001
其中， ¾wer i c e« y为所述用户在所述获取的小区的功率有效值， 为所述用户 需求的带宽， ，fflW?为所述用户的信干比， P为所述获取的小区的基站的发射功率， 为 所述用户在所述获取的小区能够获取到的 QoS需求， 为所述用户的 QoS需求。 功率有效值用于衡量系统应用有限的资源获取更高性能的能力，用户在小区的功率有 效值越高，就说明该用户在该小区的资源利用率越高，在计算完所述用户在每一个所述获 取的小区的功率有效值之后，可以将功率有效值最大的小区作为该用户业务卸载的目标小 区。
此外，还可以通过计算所述用户业务与每一个所述获取的小区的匹配度得到所述用户 在每一个所述获取的小区进行业务卸载的相关影响因子，由于不同类型的基站具有不同的 特性， 不同类型的业务也有不同的 QoS要求， 因此， 合理匹配用户业务与小区能够更好的 利 系统资源， 带来更优越的用户体验。可以对用户业务与小区匹配度进行计算， 其中包 括两个方面， 一是用户业务与小区的属性匹配度， 二是用户业务与小区的资源匹配度。例 如， 可以将 ]¾户业务对小区的属性匹配程度表示为- 1到 i之间的某个值， 匹配程度越好， 值越趋近于 配程度越差， 值越趋近于 -1 , 小区的现有可用资源可以通过 ^户业务与 小区的资源匹配度来表示，小区的现有可用资源的多少也影响了用户业务卸载到该小区的 概率， 小区的可用带宽越多、传输速率越高、 信道状况越好， 业务卸载到此小区的概率越 大， 用户业务对小区资源的匹配程度越好。 反之， 用户业务对小区资源的匹配程度越差。 将用户业务对小区属性匹配度和资源匹配度进行加权求和，可以得到用户业务与小区的匹 配度， 将与 ]¾户业务匹配度最大的小区作为业务卸载的目标小区。具体地， 通过如下方式 分别 算所述用户业务与每一个所述获取的小区的匹配度：
获取所述用户业务与小区的属性匹配度；
获取所述用户业务与小区的资源匹配度；
采 ]¾以下公式计算用户业务与小区的匹配度：
L— c j ~ ~ c 其中， z为所述用户业务与小区的匹配度， /t为所述用户业务与小区的属性匹配度，
/2为所述用户业务与小区的资源匹配度， ;为/;对应的权值， 为/2对应的权值。
其中， 获取所述用户业务与小区的属性匹配度具体包括；
获取所述用户业务的种类- 获取所述小区的属性匹配表,所述属性匹配表包括所述种类与所述属性匹配度的对应 关系；
将所述属性匹配表中所述种类对应的属性匹配度作为所述用户业务与小区的属性匹 配度。
其中， 获取所述 ¾户业务与小区的资源匹配度具体包括- 将小区当前的带宽可利用率（小区可利用带宽与小区最大带宽的比值）、功率可利 率（小区可利用功率与小区最大功率比值）等进行加权求和得到用户业务与小区的资源匹 配度。
优选地,可以将功率有效值以及用户业务与小区的匹配度共同作为业务卸载的相关影 响因子,利用小区所能提供的功率有效值以及小区与用户业务的匹配度决定业务卸载的目 标小区， 通过模糊逻辑算法来确定业务卸载的目标小区， 飼如， 可以根据功率有效值将功 率有效性分为五个层次 （非常低、 低、 中等、 高以及非常高） ， 根据用户与小区的匹配度 将 ^户与小区的匹配程度分为三个层次 （低、 中等以及高） ， 然后根据表 1确定小区作为 业务卸载的目标小区的优良程度， 例如， 当 A小区对应的功率有效性为 "低"， 匹配程度 为 "中等" , 贝 i A小区作为目标小区的优良程度为 "低" ， 当 B小区对应的功率有效性为 "高" ， 匹配程度为 "中等" , 剣 B小区作为目标小区的优良程度为 "中等" ， 然后将优 良程度高的小区选择为业务卸载的目标小区，若优良程度最高的小区不止一个， 随机选取 一个最优小区诈为目标小区》此夕卜， 当选择的业务卸载目标小区为当前小区时， 终止业务 卸载操作， 用户业务卸载的资源调度可以通过多种方法实现， ^如轮询算法、 比倒公平算 法、 MLWDF算法等。
表 1
Figure imgf000009_0001
参见图 2, 图 2是本发明提供的一种组网场景的示意图， 包括宏基站 1 , 徵微基站 2, 高 速 ^户 3, 低速用户 4以及业务卸载控制器 5， 其中， 宏基站和微徵基站之间的可用带宽互 不重叠， 但 4个微微基站之间的带宽共用。 用户可以选择接入所述场景中的所有基站。 户随机分布在此场景中，通过与基站 (直接)和业务卸载控制器 (间接)的交互完成业务卸载。 小区负载状况的预测通过应用后向传播算法的前馈神经网络模型完成，业务卸载的目标小 区的选择通过模糊逻辑算法完成。
业务卸载的决策在两种情况下被触发， 一是当用户的业务类型发生变化时 (如 ώ低速 业务转化为高速业务)， 二是用户检测到当前服务小区所能提供的功率有效性过低时。 业 务卸载的请求由用户端发起，决策由业务卸载控制器做出，基站起到了连接用户与业务卸 载控制器和提供资源的作 ]¾， 其业务卸载流程如图 3所示， 业务卸载的歩骤包括： 5301.业务卸载控制器通过应用后向传播算法的前馈神经网络， 对用户的当前小区进 行负载预测， 计算用户当前小区的业务卸载的规避门限值。
5302. 判断用户业务的 QoS需求值是否大于所述规避门限值， 若是， 則执行开始执行 以下业务卸载过程， 若否， 终止业务卸载。
S303.用户测量其周边的无线环境， 向其中可接入小区发送业务卸载请求。
5304.业务卸载控制器根据預定义的资源分配规则， 在假设用户接入的前提下， 分别 对 ffi户可接入的各小区进行模拟资源分配。
5305.业务卸载控制器根据资源的分配结果获取能够满足用户业务的 QoS需求的小 区。 若没有一个小区满足 )¾户的 QoS需求， 终止业务卸载。
S306. †算用户在每一个能够满足用户业务的 QoS需求的小区迸行业务卸载的相关 影响因子，业务卸载控制器对剩下的小区计算业务与小区的匹配程度以及小区所能提供的 功率有效性。
S307.根据歩骤 S306中†算出的相关影响因子的值， 通过模糊逻辑算法计算小区作为 业务卸载的目标小区的优良程度，将优良程度最高的小区选择为目标小区。若优良程度最 高的小区不止一个， 随机选择一个最优小区作为业务卸载的目标小区。此夕卜， 当选择的业 务卸载目标小区为当前小区时，终止业务卸载操作，用户业务卸载的资源调度可以通过多 种方法实现， 例如轮询算法、 比例公平算法、 MLWDF算法等。
本发明实施方式提供的基于不同 QoS的业务卸载方法，通过计算用户在每一个获取到 的小区进行业务卸载的相关影响因子， 然后根据相关影响因子确定业务卸载的目标小区， 充分考虑基站和 ]¾户业务的特性， 自适应地将最合适的业务卸载到最合适的小区，在满足 用户 QoS需求的前提下， 根据 ^户业务与小区的匹配程度、 小区所能提供的功率有效性， 动态选择合适的小区为用户提供通信服务，不仅提高了在异构混合组网环境下无线资源的 利用率， 而且还提高了用户体验。

Claims

权利要求书
1、 一种基于不同 QoS的业务卸载方法， 其特征在于， 包括；
S1 . 获取能够满足用户业务的 QoS需求的小区；
S2. 计算所述用户在每一个所述获取的小区进行业务卸载的相关影响因子；
S3. 根据所述相关影响因子在所述获取的小区中确定业务卸载的目标小区。
2、根据权利要求 i所述的基于不同 QoS的业务卸载方法， 其特征在于， 在歩骤 S1之前 还包括：
获取所述用户业务的 QoS需求值；
计算所述用户当前小区的业务卸载的规避门限值；
判断所述 QoS需求值是否大于所述规避门限值， 若是， 则执行步骤 S1~S3。
3、 根据权利要求 2所述的基于不同 QoS的业务卸载方法， 其特征在于， 通过如下方式 †算所述用户当前小区的业务卸载的规避门限值-
其中， Γ为所述当前小区的业务卸载的规避门限值， 为所述当前小区中第 ^种业务 种类 T—时刻出现的次数， ^¾为 的预设的权重因子， -M为业务卸载规避门限所能达到 的最大的值， W为所述当前小区中出现的业务种类数。
4、 根据权利要求 2所述的基于不同 QoS的业务卸载方法， 其特征在于， 通过如下方式 †算所述用户当前小区的业务卸载的规避门限值： r = M— 其中， Γ为所述当前小区的业务卸载的规避门限值， 为所述当前小区中第〖种业务 种类下一时刻出现的次数， ω,为《,的预设的权重因子， Μ为业务卸载规避门限所能达到 的最大的值， N为所述当前小区中出现的业务种类数， 为预先设置的常数， 所述 用于 调整业务个数对所述规避 Π限值增长的贡献率。
5、 根据权利要求 I所述的基于不同 QoS的业务卸载方法， 其特征在于， 步骤 S1包括：
5101. 获取所有所述用户可接入的小区；
5102. 对步骤 S101中获取的小区迸行模拟资源分配；
5103. 根据所述模拟资源分配的结果在步骤 S101获取的小区中获取能够满足所述 ]¾ 户业务的 QoS需求的小区。 6、 根据权利要求 所述的基于不同 QoS的业务卸载方法， 其特征在于， 步骤 S2包括； 计算所述用户在每一个所述获取的小区的功率有效值： 和 /或
†算所述用户业务与每一个所述获取的小区的匹配度。
7、 根据权利要求 6所述的基于不同 QoS的业务卸载方法， 其特征在于， 通过如下方式 分别^算所述用户在每一个所述获取的小区的功率有效值- 、
,Λ γ
power—efficiency
Figure imgf000012_0001
其中， ower— ^c ew y为所述用户在所述获取的小区的功率有效值， 为所述用户 需求的带宽， 为所述用户的信干比， P为所述获取的小区的基站的发射功率， 为 所述用户在所述获取的小区能够获取到的 QoS需求， 为所述 ¾户的 QoS需求。
8、 根据权利要求 6所述的基于不同 QoS的业务卸载方法， 其特征在于， 通过如下方式 分别†算所述用户业务与每一个所述获取的小区的匹配度：
获取所述用户业务与小区的属性匹配度 - 获取所述用户业务与小区的资源匹配度；
采用以下公式计算用户业务与小区的匹配度-
Figure imgf000012_0002
其中， 为所述用户业务与小区的匹配度， 为所述用户业务与小区的属性匹配度， 2为所述用户业务与小区的资源匹配度， ^为^对应的权值， 为 2对应的权值。
9、 根据权利要求 8所述的基于不同 QoS的业务卸载方法， 其特征在于， 获取所述用户 业务与小区的属性匹配度， 包括：
获取所述用户业务的种类；
获取所述小区的属性匹配表，所述属性匹配表包括所述种类与所述属性匹配度的对应 关系；
将所述属性匹配表中所述种类对应的属性匹配度作为所述用户业务与小区的属性匹 配度。
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CN113766514B (zh) * 2020-06-02 2023-08-01 中国移动通信集团河南有限公司 组网资源分流方法、装置及电子设备、存储介质

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