5G超密网络：模型、分析与应用详解

需积分: 5 68 浏览量更新于2024-06-25 收藏 8.11MB PDF 举报

《超密集网络：5G及未来的研究、分析与应用》是一部由Trung Q. Duong教授（来自英国女王大学贝尔法斯特分校电子、电气工程与计算机科学学院）、Xiaoli Chu教授（来自英国谢菲尔德大学）和Himal A. Suraweera教授（来自斯里兰卡佩拉德尼雅大学）共同编辑的专著。该书于2019年首次出版，由John Wiley & Sons Ltd发行。书中深入探讨了超密集网络在5G时代的理论建模、详尽分析以及实际应用场景。超密集网络（Ultra-Dense Networks, UDNs）是5G和下一代通信技术的关键组成部分，其核心思想是通过大幅增加网络中基站的密度来提升无线网络的容量和覆盖范围，以满足高数据速率、大规模连接和低延迟的需求。这些网络结构对于实现毫米波通信、大规模MIMO（Multiple-Input Multiple-Output）系统以及更高效的频谱利用至关重要。在本书中，作者们首先对超密集网络的基本概念进行阐述，包括网络部署策略、无线资源管理、干扰管理和能效优化等方面。他们讨论了如何通过精确的数学模型来理解和预测UDNs的行为，这些模型通常基于信号处理、射频技术和无线传播理论。然后，书中对UDNs的性能进行了深入的分析，包括吞吐量、容量、覆盖范围、能效等关键指标的提升潜力，以及可能遇到的挑战如网络优化问题、频谱效率降低和网络自组织能力等。此外，书中还涵盖了与超密集网络相关的新型无线接入技术，如小基站（Small Cells）、微型蜂窝（Microcells）和热点区域的部署。针对实际应用，作者们提供了丰富的案例研究和实践经验，展示了如何将超密集网络应用于不同场景，如大规模公共活动、工业4.0环境、无人驾驶汽车通信以及物联网（IoT）设备的连接。同时，书中也讨论了5G及后续网络演进中的超密集网络融合，例如与云计算、边缘计算的协同，以及与物联网、虚拟现实/增强现实等新兴技术的集成。《超密集网络：5G及未来的研究、分析与应用》是一本综合性的参考资料，不仅涵盖了理论基础，还为工程实践者提供了宝贵的洞察和策略，对于理解并推动超密集网络在5G和未来通信网络中的发展具有重要的指导意义。版权方面，所有使用本书内容必须遵守版权法规定，并可通过 Wiley 的官方网站获取许可。

k k

Preface xvii

Chapter 2 presents a general framework for performance analysis of ultra-dense

networks, particularly focusing on the impact of pathloss and multipath fading.

Under a pathloss model that incorporates both LoS and NLoS propagations and a

distance-dependent multi-path Rician fading model with a variant Rician K -factor,

the analytical expressions for both coverage probability and area spectral eﬃciency

are derived. Comparing the performance impact of LoS and NLoS transmissions in

interference-limited ultra-dense networks under Rician fading with that under Rayleigh

fading, the analytical and simulation results show that pathloss dominates the overall

system performance of ultra-dense networks, while the impact of multi-path fading is

quite limited and does not help to mitigate the performance loss caused by the many

LoS interfering links in ultra-dense networks, especially in single-input single-output

systems.

Chapter 3 considers the network densiﬁcations in wireless user devices, radio access

nodes, and cloud edge nodes, and presents a comprehensive introduction to mean ﬁeld

game theory and tools, which is useful in the design and analysis of ultra-dense networks

with spatial-temporal dynamics. Following that, the authors present a survey of the lat-

est applications of mean ﬁeld games in the 5G era in general (including device-to-device

communications and cloud-edge networks) and in 5G ultra-dense networks in partic-

ular, with a focus on resource management problems such as interference mitigation,

energy management, and caching.

Chapters 4 to 8 are grouped into Part II, which exploits the synergy between

ultra-dense networks and other key 5G candidate technologies.

Chapter 4 studies diﬀerent schemes of wireless self-backhauling in ultra-dense net-

works, with a focus on the recently proposed in-band full-duplex self-backhauling. e

deployment of wireless backhauling, in general, helps to resolve the scalability problems

faced by wired backhaul connections, because wireless backhauling does not require

physical cables. e use of inband wireless self-backhauling is even more intriguing

than traditional wireless backhauling, because it enables the reuse of spectral resources

between radio access links and backhaul links. In this respect, no additional or ded-

icated frequency resources are required for the backhaul, and thus the in-band wire-

less self-backhauling will be commercially beneﬁcial. ree diﬀerent in-band wireless

self-backhauling schemes based on full-duplex or half-duplex operations are analyzed

and compared.

Chapter 5 aims to optimally leverage the dense deployment of small cells and massive

MIMO in future mobile networks. Using analytical tools from stochastic geometry, the

authors derive a tight approximation of the achievable downlink rate for a two-tier het-

erogeneous cellular network and use it to compare the performance between densifying

small cells and expanding base station antenna arrays. e results show that increas-

ing the density of small cells improves the downlink rate much faster than expanding

antenna arrays at base stations. However, when the small cell density exceeds a certain

threshold, the network capacity may start to deteriorate. On the contrary, the network

capacity keeps increasing with the expansion of base station antenna arrays until it

reaches an upper bound, which is caused by pilot contamination. is upper bound

surpasses the maximum network capacity achieved by the dense deployment of small

cells. Moreover, the authors provide practical design insights into the tradeoﬀ between

the dense deployment of small cells and massive MIMO in future high-capacity wireless

networks.

k k

xviii Preface

Chapter 6 introduces a promising 5G technology termed as cell-free massive MIMO,

where radio access points distributed in an area coherently serve mobile users in the area

using the same time/frequency resource. It is called cell-free because there is no bound-

ary among cells, unlike as seen in traditional cellular networks. Cell-free massive MIMO

networks complement ultra-dense networks by connecting closely located access points

together. e connection of access points is feasible because only a limited amount of

necessary information is exchanged and the central processing unit is not required to

have a high computing capability. In particular, the authors study the physical layer secu-

rity in cell-free massive MIMO networks under a pilot spooﬁng attack, where an active

eavesdropper attacks the uplink training, and present a simple counterattack scheme

based on the transmit power control at all the involved radio access points.

Chapter 7 proposes to solve the spectrum crunch in ultra-dense networks by employ-

ing massive MIMO in unlicensed spectrum and by leveraging the spatial interference

suppression capabilities of multi-antenna systems. e authors discuss the motivation

and justiﬁcation of using unlicensed spectrum bands in ultra-dense networks, describe

the technical fundamentals of massive MIMO in an unlicensed spectrum, and iden-

tify the main use cases and the associated challenges. e results show that massive

MIMO in an unlicensed spectrum is capable of signiﬁcantly boosting the performance

of ultra-dense networks in both outdoor and indoor environments.

In Chapter 8, a set of advanced optimization tools is introduced to maximize the

energy eﬃciency for ultra-dense networks, where the total power consumption may

become a critical concern as the number of network nodes and connected devices

increases. e chapter begins with the introduction of optimization techniques that

are useful for maximizing energy eﬃciency, including concave–convex fractional

programming, non-tractable fractional programming, and the alternating direction

method of multipliers for distributed implementation. e chapter then moves on to

demonstrate how these optimization techniques can be applied to maximizing the

energy eﬃciency of spectrum-sharing dense small cell networks.

In Part III, which includes Chapter 9 to Chapter 12, the promising applications of

ultra-dense networks in the 5G era are presented and discussed.

In Chapter 9, the authors provide an insightful discussion on how big data methods

can be used to improve the deployment of and the QoS in ultra-dense networks, cov-

ering both structured and unstructured data analytics. It is demonstrated that having

access to consumer data and being able to analyse it in the wireless network context

would allow eﬀective user-centric deployment and operations of ultra-dense networks.

In particular, the chapter outlines two big data approaches for improving ultra-dense

network deployment: (1) identify the spatial-temporal traﬃc and service patterns to aid

targeted deployment of dense networks; and (2) identify social community patterns to

assist ultra-dense peer-to-peer and D2D networking.

In Chapter 10, the authors look into physical layer security opportunities and chal-

lenges for ultra-dense networks and show how physical layer security technologies can

be applied to mitigate issues caused by unreliable wireless backhaul links in ultra-dense

networks. Given that the wireless backhaul links are not reliable at all times, secrecy

outage probability for a spectrum sharing ultra-dense network under the impact of unre-

liable backhaul links is evaluated.

k k

Fundamental Limits of Ultra-dense Networks

Marios Kountouris and Van Minh Nguyen

Mathematical and Algorithmic Sciences Lab, Paris Research Center, Huawei Technologies Co. Ltd., France

1.1 Introduction

Mobile traﬃc has signiﬁcantly increased over the last decade, mainly due to the stunning

expansion of smart wireless devices and bandwidth-demanding applications. is trend

is forecast to be maintained, especially with the deployment of ﬁfth generation (5G)

and beyond networks and machine-type communications. A major part of the mobile

throughput growth during the past few years has been enabled by the so-called net-

work densiﬁcation, i.e. adding more base stations (BSs) and access points and exploiting

spatial reuse of the spectrum. Emerging 5G cellular network deployments are envis-

aged to be heterogeneous and dense, primarily through the provisioning of small cells

such as picocells and femtocells. Ultra-dense networks (UDNs) will remain among the

most promising solutions to boost capacity and to enhance coverage with low-cost and

power-eﬃcient infrastructure in 5G networks. e underlying foundation of this expec-

tation is the presumed linear capacity scaling with the number of small cells deployed

in the network. In other words, doubling the number of BSs doubles the capacity the

network supports in a given area and this can be done indeﬁnitely. Nevertheless, in

this context, several important questions arise: how close are we to fundamental limits

of network densiﬁcation? Can UDNs indeﬁnitely bring higher overall data throughput

gains in the network by just adding more infrastructure? If the capacity growth arrives

to a plateau, what will cause this saturation and how the network should be optimized

to push this saturation point further? ese are the questions explored in this chapter.

e performance of wireless networks relies critically on their spatial conﬁguration

upon which inter-node distances, fading characteristics, received signal power, and

interference are dependent. Cellular networks have been traditionally modeled by plac-

ing the base stations on a regular grid (usually on a hexagonal lattice), with mobile users

either randomly scattered or placed deterministically. Tractable analysis can sometimes

be achieved for a ﬁxed user location with a small number of interfering BSs and Monte

Carlo simulations are usually performed for accurate performance evaluation. As cel-

lular networks have become denser, they have also become increasingly irregular. is

is particularly true for small cells, which are deployed opportunistically and in hotspots

and dense heterogeneous networks (HetNets). As a result, the widely used deterministic

grid model has started showing its limitations and cannot be used for general and

Ultra-dense Networks for 5G and Beyond: Modelling, Analysis, and Applications,

First Edition. Edited by Trung Q. Duong, Xiaoli Chu and Himal A. Suraweera.

剩余301页未读，继续阅读

digit123

粉丝: 1
资源: 32

5G超密网络：模型、分析与应用详解

5G超密集组网网络架构及实现

Ultra-Dense Networks for 5G and Beyond：Modelling, Analysis, and App 2019.pdf

基于5G网络的应急通信超密集组网技术研究

An Adaptive Clustering Approach for Small Cell in Ultra-Dense Networks

A MDP-Based Network Selection Scheme in 5G Ultra-Dense Network

Coherent ONU based on 850 μm-long cavity-RSOA for next-generation ultra-dense access network

HighRes-MVSNet-A-Fast-Multi-View-Stereo-Network-for-Dense-3D-Rec

Localization-Distillation-for-Dense-Object-Detection

Residual-Dense-Net-for-Super-Resolution

ORB-SLAM2-RGBD-DENSE-MAP-data.tar

最新资源