【通信】两层无线 Femtocell 网络上行链路中的最优功率分配附matlab代码

本文主要是介绍【通信】两层无线 Femtocell 网络上行链路中的最优功率分配附matlab代码,希望对大家解决编程问题提供一定的参考价值,需要的开发者们随着小编来一起学习吧!

1 简介

In this thesis, the problem of efficient power allocation in the uplink of two-tier closed-access femtocell networks is addressed. Specifically, a single CDMA macrocell is assumed, where Ν femtocells reside within the macrocell. Within the proposed framework, which supports multiple services, appropriate utility functions are adopted to reflect users’ degree of satisfaction with respect to their actual throughput requirements and the corresponding power consumption. The overall problem is formulated as a non-cooperative game where users aim selfishly at maximizing their utility-based performance while taking into account the interference caused by both the CDMA macrocell and the neighbouring femtocells.

The existence and uniqueness of a Nash equilibrium point of the proposed Multi Service Two-Tier Power Control Game with Pricing (MTTPG) is proven, at which all users have achieved a targeted SINR threshold value or transmit with their maximum power, leading essentially to an SINR-balanced system. Moreover, a distributed iterative algorithm for reaching MTTPG game’s equilibrium is provided. Finally, the operational effectiveness of the proposed approach is evaluated through modeling and simulation, while its superiority is illustrated via presenting various scenarios of the proposed framework.

The surest way to increase the system capacity of a wireless link is by getting the transmitter and receiver closer to each other, which creates the dual benefits of higher quality links and more spatial reuse. In a network with nomadic users, this inevitably involves deploying more infrastructure, typically in the form of microcells, hotspots, distributed antennas, or relays. A less expensive alternative is the recent concept of femtocells – also called home base-stations (HBS), home node-stations (HNB) or FAPs (Femto Access Points) – which are data access points installed by home users to get better indoor voice and data coverage (Chandrasekhar & Andrews, 2008).

The demand for higher data rates in wireless networks is unrelenting, and has triggered the design and development of new data-minded cellular standards such as WiMAX (802.16e), 3GPP’s High Speed Packet Access (HSPA) and LTE standards, as well as the 3GPP2’s EVDO and UMB standards. Moreover, Wi-Fi networks are nowadays the de facto standard in wireless data communications since they can provide the high transmission rates demanded by the consumers in indoor buildings for Internet services. Although the Wi-Fi networks will not be able to support the same level of mobility and coverage as the cellular standards, to be competitive for home and office use, cellular data systems will need to provide service roughly comparable to that offered by the Wi-Fi networks. Studies on wireless usage show that more than 50 percent of all voice calls and more than 70 percent of data traffic originate indoors and thus, cellular networks must meet the expectations of both kinds of user services (Chandrasekhar & Andrews, 2008).

The growth in wireless capacity is exemplified by this observation from Martin Cooper of Arraycomm: “The wireless capacity has doubled every 30 months over the last 104 years”. This translates into an approximately million-fold capacity increase since 1957. Breaking down these gains shows a 25x improvement from wider spectrum, a 5x improvement by dividing the spectrum into smaller slices, a 5x improvement by designing better modulation schemes, and a whopping 1600x gain through reduced cell sizes and transmit distance. This reduction of the cell boundaries was done gradually, starting from the microcells (with a radius of a few hundred meters), then the relays that functioned as small base stations which provided service for places where the central macrocell could not have satisfactory cover and reaching the femtocells today (Andrews, Claussen, Dohler, Rangan, & Reed, 2012).

Figure 1: A macrocell deployed with 5 femtocells for better network coverage

While the femtocells can be installed easily by the home users, have a low transmission power and can cover a small-radius distance, they also allow the network provider to greatly improve the indoors coverage, especially in places such as apartments, metro stations, company offices, etc. Moreover, by assigning the indoor users to femtocells, the macrocell can distribute its resources to less users and thus improve their service quality. So, not only can femtocells contribute to the improvement of the network’s indoor coverage but also to its overall throughput (see Figure 1). Regarding the communication of the femtocells with the rest of the cellular network, this can be done over an existing broadband connection such as the digital subscriber line (DSL), via the cable modem (IP backhaul) or by a separate radio frequency (RF) backhaul channel (see Figure 2).

Femtocells are compatible with cell phones, personal computers and generally every 3G-enabled device. Due to their short transmit-receive distance, femtocells can greatly lower transmit power, prolong handset battery life, and achieve a higher signal-to-interference-plus-noise ratio (SINR). All these translate into improved reception – the so-called five-bar coverage – and higher capacity. Because of the reduced interference, more users can be packed into a given area in the same region of spectrum, thus increasing the area spectral efficiency, or equivalently, the total number of active users per Hz per unit area. Lastly, the cost benefit of deploying femtocells is huge because it reduces the operating and capital expenditure costs for the network operators. A typical urban macrocell costs upwards of $1000/month in site lease, and additional costs for electricity and backhaul. In the future, the macrocell network will be stressed by the operating expenses, especially when the subscriber growth does not match the increased demand for data traffic. The deployment of femtocells will reduce the need for adding macro-BS towers. Recent studies show that the operating expenses scale from $60,000/year/macrocell to just $200/year/femtocell (Chandrasekhar & Andrews, 2008).

Figure 2: Communication of the FAP device with the ISP via DSL and cable broadband connections

1.1 Spectrum Assignment Policies and Interference in Femtocells

Femtocells have critical effects on the performance of mobile networks. While adding femtocells will produce huge benefits for both the operators and the users, a careful preparation should take place regarding the spectrum allocation because without unique spectrum for the femtocell or very careful spectrum planning in the network, femtocells could suffer from severe interference problems (Abdulrahman & Ahmad, 2012). In a Two-Tier Cellular Network (one that has both macrocells and femtocells deployed) there exist two types of interference:

1.Cross-Tier interference: This type of interference is caused between different tier users – that is between a femto-user and a macro-user.

2.Co-Tier interference: This type of interference is caused between users of the same kind of cell – that is between two macro-users or between neighboring femtocells.

Considering the above categorization, three different approaches – spectrum assignment policies – have been proposed to solve the issue of spectrum allocation in Two-Tier Networks (Mesodiakaki, 2011):

1.Dedicated Spectrum: In this case, different frequencies are assigned to femtocells and macrocell. Thus, the cross-tier interference is completely avoided, since the two tiers operate in different channels. On the other hand, this policy results in smaller spectral efficiency since the cells of a tier can only have access to a subset of the total available bandwidth.

2.Shared Spectrum: In this case, maximum spectrum allocation is achieved since all cells (femto and macro) share the same bandwidth and thus have access to all available network resources. However, in such an implementation, the cross-tier interference could degrade the overall performance of the system if it is not effectively addressed. There exist two sub-policies regarding the channel assignment between femtocells and macrocell when we have a shared spectrum policy:

a.Orthogonal assignment: The frequency channel that a macro-user uses is orthogonal with the one assigned to a femto-user (OFDMA) and thus, while sharing the same spectrum, there is no interference between the two users.

b.Co-channel assignment: Every user can be assigned any frequency channel simultaneously and the separation of the users’ signals is done by code-division (simple CDMA).

Figure 3:  The 3 Spectrum Assignment Policies: Dedicated Spectrum, Partially Shared Spectrum and Shared Spectrum

3.Partially Shared Spectrum: This case is considered a middle ground solution between the previous 2 policies since the macrocell has access to all the spectrum while the femtocells operate only on a subset of it. It is considered the best spectrum assignment policy because:

a.The spectral efficiency achieved is even better than the one from the shared spectrum case and

b.It is possible to reduce the cross-tier interference since the macro-users that suffer (or even cause) such interference can use the exclusively dedicated to the macrocell bandwidth – something which the macro-users in the shared spectrum policy simply cannot do (see Figure 3).​

2 部分代码

<span style="color:#333333"><span style="background-color:rgba(0, 0, 0, 0.03)"><code><span style="color:#afafaf">%</span> <span style="color:#dd1144">NTUA Thesis code</span></code><code><span style="color:#afafaf">%</span> <span style="color:#dd1144">John Zobolas, May 2013</span></code><code>​</code><code>function <span style="color:#dd1144">femtopower</span></code><code>    format <span style="color:#dd1144">long;</span></code><code>    global <span style="color:#dd1144">results;</span></code><code>    results = <span style="color:#dd1144">cell(1,10);</span></code><code>    </code><code>    <span style="color:#afafaf">%</span> <span style="color:#dd1144">the "standard" coordinates of the FAPs</span></code><code>    x = <span style="color:#dd1144">[200 400 400 700 600 600 300 850 800 200 600 800 500 200 100 400 700 900];</span></code><code>    y = <span style="color:#dd1144">[500 300 600 600 800 200 800 400 800 200 400 200 900 700 350 100 300 550];</span></code><code>    <span style="color:#afafaf">%</span> <span style="color:#dd1144">Uncomment the below to get a random placement of the FAPs inside the Macrocell</span></code><code>    <span style="color:#afafaf">%x</span> = <span style="color:#dd1144">randi([150 850],1,30);</span></code><code>    <span style="color:#afafaf">%y</span> = <span style="color:#dd1144">randi([150 850],1,30);</span></code><code>    <span style="color:#afafaf">%length(x)</span> = <span style="color:#dd1144">18;</span></code><code>    xf = <span style="color:#dd1144">zeros(1,36);</span></code><code>    yf = <span style="color:#dd1144">zeros(1,36);</span></code><code>    for <span style="color:#dd1144">i=1:18</span></code><code>       while <span style="color:#dd1144">true</span></code><code>           random = <span style="color:#dd1144">2*randi([-18 18],1,4);</span></code><code>            if <span style="color:#dd1144">all(random) % no zeros</span></code><code>                break;</code><code>            end</code><code>       end</code><code>       <span style="color:#afafaf">xf(2*i-1)</span> = <span style="color:#dd1144">x(i)+random(1);% NRT users</span></code><code>       <span style="color:#afafaf">yf(2*i-1)</span> = <span style="color:#dd1144">y(i)+random(2);</span></code><code>       <span style="color:#afafaf">xf(2*i)</span> = <span style="color:#dd1144">x(i)+random(3);% RT users</span></code><code>       <span style="color:#afafaf">yf(2*i)</span> = <span style="color:#dd1144">y(i)+random(4);</span></code><code>    end</code><code>    </code><code>​</code><code>end</code><code>​</code><code>function <span style="color:#dd1144">circle(x,y,r,color)</span></code><code>   th = <span style="color:#dd1144">0:pi/500:2*pi;</span></code><code>   xunit = <span style="color:#dd1144">r * cos(th) + x;</span></code><code>   yunit = <span style="color:#dd1144">r * sin(th) + y;</span></code><code>   <span style="color:#afafaf">plot(xunit,</span> <span style="color:#dd1144">yunit, color);</span></code><code>end</code><code>​</code></span></span>

3 仿真结果

4 参考文献

[1]郑成锵. Femtocell双层网络中上行链路功率控制方法的研究[D]. 南京邮电大学.

博主简介:擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真,相关matlab代码问题可私信交流。

部分理论引用网络文献,若有侵权联系博主删除。

这篇关于【通信】两层无线 Femtocell 网络上行链路中的最优功率分配附matlab代码的文章就介绍到这儿,希望我们推荐的文章对编程师们有所帮助!



http://www.chinasem.cn/article/349943

相关文章

Linux中压缩、网络传输与系统监控工具的使用完整指南

《Linux中压缩、网络传输与系统监控工具的使用完整指南》在Linux系统管理中,压缩与传输工具是数据备份和远程协作的桥梁,而系统监控工具则是保障服务器稳定运行的眼睛,下面小编就来和大家详细介绍一下它... 目录引言一、压缩与解压:数据存储与传输的优化核心1. zip/unzip:通用压缩格式的便捷操作2.

Java内存分配与JVM参数详解(推荐)

《Java内存分配与JVM参数详解(推荐)》本文详解JVM内存结构与参数调整,涵盖堆分代、元空间、GC选择及优化策略,帮助开发者提升性能、避免内存泄漏,本文给大家介绍Java内存分配与JVM参数详解,... 目录引言JVM内存结构JVM参数概述堆内存分配年轻代与老年代调整堆内存大小调整年轻代与老年代比例元空

Java中调用数据库存储过程的示例代码

《Java中调用数据库存储过程的示例代码》本文介绍Java通过JDBC调用数据库存储过程的方法,涵盖参数类型、执行步骤及数据库差异,需注意异常处理与资源管理,以优化性能并实现复杂业务逻辑,感兴趣的朋友... 目录一、存储过程概述二、Java调用存储过程的基本javascript步骤三、Java调用存储过程示

Visual Studio 2022 编译C++20代码的图文步骤

《VisualStudio2022编译C++20代码的图文步骤》在VisualStudio中启用C++20import功能,需设置语言标准为ISOC++20,开启扫描源查找模块依赖及实验性标... 默认创建Visual Studio桌面控制台项目代码包含C++20的import方法。右键项目的属性:

SQLite3 在嵌入式C环境中存储音频/视频文件的最优方案

《SQLite3在嵌入式C环境中存储音频/视频文件的最优方案》本文探讨了SQLite3在嵌入式C环境中存储音视频文件的优化方案,推荐采用文件路径存储结合元数据管理,兼顾效率与资源限制,小文件可使用B... 目录SQLite3 在嵌入式C环境中存储音频/视频文件的专业方案一、存储策略选择1. 直接存储 vs

MySQL数据库的内嵌函数和联合查询实例代码

《MySQL数据库的内嵌函数和联合查询实例代码》联合查询是一种将多个查询结果组合在一起的方法,通常使用UNION、UNIONALL、INTERSECT和EXCEPT关键字,下面:本文主要介绍MyS... 目录一.数据库的内嵌函数1.1聚合函数COUNT([DISTINCT] expr)SUM([DISTIN

Java实现自定义table宽高的示例代码

《Java实现自定义table宽高的示例代码》在桌面应用、管理系统乃至报表工具中,表格(JTable)作为最常用的数据展示组件,不仅承载对数据的增删改查,还需要配合布局与视觉需求,而JavaSwing... 目录一、项目背景详细介绍二、项目需求详细介绍三、相关技术详细介绍四、实现思路详细介绍五、完整实现代码

Go语言代码格式化的技巧分享

《Go语言代码格式化的技巧分享》在Go语言的开发过程中,代码格式化是一个看似细微却至关重要的环节,良好的代码格式化不仅能提升代码的可读性,还能促进团队协作,减少因代码风格差异引发的问题,Go在代码格式... 目录一、Go 语言代码格式化的重要性二、Go 语言代码格式化工具:gofmt 与 go fmt(一)

HTML5实现的移动端购物车自动结算功能示例代码

《HTML5实现的移动端购物车自动结算功能示例代码》本文介绍HTML5实现移动端购物车自动结算,通过WebStorage、事件监听、DOM操作等技术,确保实时更新与数据同步,优化性能及无障碍性,提升用... 目录1. 移动端购物车自动结算概述2. 数据存储与状态保存机制2.1 浏览器端的数据存储方式2.1.

基于 HTML5 Canvas 实现图片旋转与下载功能(完整代码展示)

《基于HTML5Canvas实现图片旋转与下载功能(完整代码展示)》本文将深入剖析一段基于HTML5Canvas的代码,该代码实现了图片的旋转(90度和180度)以及旋转后图片的下载... 目录一、引言二、html 结构分析三、css 样式分析四、JavaScript 功能实现一、引言在 Web 开发中,