5G与超密集网络：模型、分析与应用探索

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随着物联网技术的发展和无线应用需求的爆炸性增长， Ultra-Dense Networks (UDNs) 已成为5G及未来网络的关键组成部分。UDN是指在特定区域内部署大量小型基站（如微蜂窝、迷你蜂窝或微微蜂窝），以提供极高的数据速率、低延迟和高可靠性连接。预计到2019年，全球无线连接设备数量将超过115亿，2030年无线数据流量将增长10000倍，这对现有无线基础设施构成了严峻挑战。 5G和未来网络中的UDN主要涉及以下几个方面： 1. **网络模型与设计**： UDN的设计旨在通过密集部署网络节点来提高系统容量，减少覆盖盲区，并优化频谱效率。这些网络模型通常包括密集型宏蜂窝、超密集小细胞网络（HetNets）和多层网络结构，以实现无缝的移动性和广域覆盖。 2. **分析与优化**：对UDN的性能进行深入分析至关重要，包括吞吐量、信号质量、干扰管理和能效。这涉及到数学建模、仿真工具以及无线资源管理算法，如动态调度、波束赋形和网络自适应。研究目标是找到最优网络配置，以平衡网络容量、覆盖和服务质量。 3. **应用与场景**： UDN的应用广泛，包括智慧城市、自动驾驶、远程医疗、虚拟现实和增强现实等高带宽需求场景。例如，在大规模事件通信中，UDN能够提供实时的高清视频流；在工业4.0中，它支持设备间的高速数据交换和自动化控制。 4. **能源效率与可持续性**：面对能源消耗的问题，UDN的研究也关注如何在提供高性能的同时降低基础设施的能耗。这包括绿色无线技术和能效优化措施，如节能的无线模块、智能电源管理系统以及利用能源 harvesting技术从环境中获取能量。 5. **网络运维与管理**：高密度网络带来了新的运维挑战，如网络规划、故障检测与隔离、资源分配和性能监控。通过自动化和人工智能技术，可以提升网络的自我愈合能力和管理效率。 6. **标准化与政策**： UDN的发展受到标准制定者和政策制定者的密切关注。国际电信联盟（ITU）和3GPP等组织正在制定相应的5G和未来网络标准，以确保UDN的互操作性和安全性。 Ultra-Dense Networks for 5G and Beyond这本书籍提供了关于这一领域全面而深入的探讨，涵盖了理论模型、技术分析以及实际应用场景，对于理解和推动无线网络向更高密度、更高效的方向发展具有重要意义。随着5G时代的到来和未来网络的演进，UDN将继续发挥关键作用，解决日益增长的数据流量和网络压力。

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Preface

We are observing an ever-increasing number of connected devices and the rapid

growth of bandwidth-intensive wireless applications. e number of wirelessly con-

nected devices is anticipated to exceed 11.5 billion by 2019, i.e. nearly 1.5 mobile

devices per capita. In addition, it is expected that we will witness a 10 000-fold growth

in wireless data traﬃc by the year 2030. Such unprecedented increases in mobile data

traﬃc and network loads are pushing contemporary wireless network infrastructures

to a breaking point. ese predictions have raised alarm to the wireless industry and

mobile network operators who are faced with the challenges of provisioning high-rate,

low-delay, and highly reliable connectivity anytime and anywhere without signiﬁcantly

increasing energy consumption at the infrastructure, such as base stations, fronthaul

and backhaul networks, and core networks.

e above challenges demand a paradigm shift to the wireless network infrastructure.

In this context, ultra-dense networks, which are characterized by a very high density

of low-power radio access nodes with diﬀerent transmit power levels, radio frequency

coverage areas, and signal/data processing capabilities, such as small cell (e.g. pico- or

femto-cells) access points, remote radio heads, and relay nodes, have attracted a lot of

interest from the wireless industry and research community. Compared to a high-power

macrocell, each small cell has a much smaller coverage radius (from several meters to

several hundred meters) and can assign a larger amount of radio resources to each user

within its coverage area than in conventional cellular networks, thus improving users’

quality-of-service (QoS) and quality-of-experience (QoE). Moreover, the low transmis-

sion power of small cells makes it possible for them to operate in an unlicensed spec-

trum; for example, the large available bandwidth in the millimeter-wave (mm-Wave)

frequency range from 30 GHz to 300 GHz, to mitigate the scarcity of licensed spectrum

in mobile networks.

While the commercial consensus is fuzzy regarding which technologies will be used

for ﬁfth generation (5G) wireless communications, ultra-dense networks have been

widely considered as a key enabler for 5G and beyond in wireless communication

network implementation. In addition to their potential in provisioning ubiquitous

high-capacity wireless connectivity, ultra-dense networks also oﬀer numerous oppor-

tunities and ﬂexibility to be incorporated with other 5G candidate technologies, such

as mm-Wave communications, massive multiple-input multiple-output (MIMO),

non-orthogonal multiple access, in-band full-duplex operation, simultaneous wireless

information and power transfer (SWIPT), device-to-device (D2D) communications,

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xvi Preface

and distributed caching, to enable the realization of 5G technologies and systems’ full

potential.

Dense coexistence of various neighboring and/or overlapping radio access nodes,

dynamic network topologies, complicated co-channel interference scenarios, backhaul

provisioning, energy consumption, QoS provisioning, and security issues bring new

challenges for ultra-dense network deployment. ese technical challenges need to

be addressed in order to exploit the true potential of ultra-dense networks. Although

some research attempts have been made toward understanding the theoretical and

practical performance limits, a comprehensive mathematical methodology to capture

the dynamic topologies, large-scale heterogeneous interference, and the high level

of randomness in ultra-dense networks essential to the system-level analysis and

design of 5G networks is missing in the existing literature. In light of the ongoing

densiﬁcation of radio access nodes and wireless connected devices toward 5G wireless

networks, there is an urgent need for the research community, industry, and even end

users to better understand the fundamental technical details as well as the achievable

performance gains of ultra-dense networks.

Ultra-dense Networks for 5G and Beyond: Modelling, Analysis, and Applic ations pro-

vides a comprehensive and systematic exposition on the state-of-the-art of ultra-dense

networks and their applications in 5G cellular and other wireless networks, with a

delicate balance between mathematical modeling, theoretical analysis, and practical

design. It contains cutting-edge tutorials on the theoretical and technical foundations

that underpin ultra-dense networks, as well as insightful surveys of ultra-dense

network-related emerging technological trends that will be interesting and informative

to readers of all backgrounds. e book is written by researchers currently leading

the research and development of ultra-dense networks, covering a wide spectrum of

topics, ranging from system modeling and performance analysis, network performance

optimization, radio resource management, wireless self-backhauling, massive MIMO

to unlicensed spectrum, energy eﬃciency, big data analytics, physical layer security,

SWIPT, distributed caching, and cooperative video streaming, etc.

e book is organized into 12 chapters, which are grouped into three parts. In the

following, we provide a brief tour through the book’s parts and chapters to show how

this book addresses the challenges faced by ultra-dense networks from various aspects.

Part I, including Chapters 1 to 3, presents the background information and funda-

mental knowledge necessary for understanding the theoretical and technical founda-

tions of ultra-dense networks from three main disparate aspects.

Chapter 1 explores the fundamental performance limits of ultra-dense networks due

to physical limits of radio wave propagation. Focusing on the impact of network node

density on the network performance, the authors model the spatial distribution of net-

work nodes using Poisson point processes (PPP). As a result of the close proximity

of base stations and mobile devices in ultra-dense networks, the elevated base station

height and dual-slope line-of-sight (LoS) and non-line-of-sight (NLoS) propagations

are considered in the modeling of small-scale and large-scale fading. Under this sys-

tem model, analytical expressions are derived for ultra-dense network downlink per-

formance in terms of coverage probability, throughput, and average transmission rate.

en, under general multi-slope pathloss and channel power distribution models, an

analytical framework is presented to enable the analysis of asymptotic performance lim-

its (i.e. scaling laws) of network densiﬁcation.

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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.

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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.

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