Chapter 10, in contrast to all the previous chapters that focused on the performance of the downlink, analyzes the performance of the uplink of an ultra-dense network. Importantly, this chapter shows that the phenomena presented in – and the conclusions derived from – all the previous chapters also apply to the uplink, despite its different features, e.g. uplink transmit power control, inter-cell interference source distribution. System-level simulations are used in this chapter to conduct the study.

Chapter 9, using the new capacity scaling law presented in the previous chapter, explores three relevant network optimization problems: i) the small cell base station deployment/activation problem, ii) the network-wide user equipment admission/scheduling problem, and iii) the spatial spectrum reuse problem. These problems are formally presented, and exemplary solutions are provided, with the corresponding discussion on the intuition behind the proposed solutions.

Chapter 11 shows the benefits of dynamic time division duplexing with respect to a more static time division duplexing assignment of time resources in an ultra-dense network. As studied in previous chapters, the amount of user equipment per small cell reduces significantly in a denser network. As a result, a dynamic assignment of time resources to the downlink and the uplink according to the load in each small cell can avoid resource waste, and significantly enhance its capacity. The dynamic time division duplexing protocol is modelled and analyzed through system-level simulations in this chapter too, and its performance carefully examined.

Chapter 1 introduces the capacity challenge faced by modern wireless communication systems and presents ultra-dense wireless networks as an appealing solution to address it. Moreover, it provides background on the small cell concept – the fundamental building block of an ultra-dense wireless network – describing its main characteristics, benefits and drawbacks. This chapter also presents the structure of the book and the fundamental concepts required for its systematic understanding.

Chapter 6 brings attention to another important feature of ultra-dense networks, i.e. the surplus of the number of small cell base stations with respect to the amount of user equipment. Building on this fact and looking ahead at next generation small cell base stations, the ability to go into idle mode, transmit no signalling meanwhile, and thus mitigate inter-cell interference is presented in this chapter, as a key tool to enhance ultra-dense network performance and combat the previously presented caveats. Special attention is paid to the upgraded modelling and analysis of the idle mode capability at the small cell base stations.

Chapter 5 studies in detail – and also from a theoretical perspective – yet another and more important caveat towards a satisfactory network performance in the ultra-dense regime, i.e. that of the impact of the antenna height difference between the user equipment and the small cell base stations. Similarly as in the previous chapter, such antenna-related modelling upgrades, the new derivations in a three-dimensional space and the new obtained results are carefully presented and discussed in this book chapter for the better understanding of the readers. Moreover, several small cell deployment and configuration guidelines are provided to improve the network performance.

Chapter 2 introduces the need for wireless network performance analysis tools to drive optimal network deployments and set optimal parameter values and describes the main building blocks and models of any wireless network performance analysis tool. In more details, it focuses on i) the system-level simulation and ii) the theoretical performance analysis concepts used in this book, paying particular attention to stochastic geometry frameworks.

Chapter 7 investigates the impact of ultra-dense networks on multi-user diversity. A denser network reduces the number of user equipment per small cell in a significant manner, and thus can significantly reduce – and potentially neglect – the gains of channel-dependent scheduling techniques. These performance gain degradations are theoretically analyzed in this chapter, and the performance of a proportional fair scheduler is compared to that of a round robin one.

Chapter 8, standing on the shoulders of all previous chapters, presents a new capacity scaling law for ultra-dense networks. Interestingly, the signal and the inter-cell interference powers become bounded in the ultra-dense regime. The former is due to the antenna height difference between the user equipment and the small cell base stations, and the latter is due to the finite user equipment density as well as the idle mode capability at the small cell base stations. This leads to a constant signal-to-interference-plus-noise ratio at the user equipment, and thus to an asymptotic capacity behaviour in such a regime. From this new capacity scaling law, it can be concluded that, for a given user equipment density, the network densification should not be abused indefinitely, and instead, it should be stopped at a certain level. Network densification beyond such a point is a waste of both invested money and energy consumption.

Chapter 4 analyzes in detail – from a theoretical perspective – the first practical caveat towards such linear growth of capacity in the ultra-dense regime, i.e. that of the impact of the transition of a large number of interfering links from non-line-of-sight to line-of-sight. Importantly, this chapter shows that the theoretical tools used until then to analyze traditional sparse or dense small cell networks, such as that presented in the previous chapter, do not directly apply to ultra-dense ones, and neither do their conclusions. In this chapter, we detail the path loss modelling upgrades necessary for a more realistic and accurate modelling of ultra-dense networks, present the subsequent and new theoretical derivations, and analyze the obtained results for the better understanding of the readers.

Ultra-dense cloud radio access network (UDCRAN) architecture, which integrates the capability of cloud computing and edge computing with the massively deployed radio access points, is a promising solution for the fifth-generation and beyond mobile communications. In order to accommodate the anticipated explosive growth of data traffic, fronthauling technology becomes a challenge technical issue in the fifth-generation and beyond UDCRANs. Moreover, the schemes related to interference management and resource management need to be reconsidered. In this chapter, we will provide a comprehensive review of the current research progress on fronthauling technology. Moreover, we will compare the advantages of various candidate fronthaul schemes.

Network densification has become a major contributor to expanding network capacity for the 5th generation and beyond wireless networks. Despite the potential benefits, however, the network over-densification would as well result in unhandlable interference, which is primarily due to the over-use of spectral resources. Therefore, whether the available interference management (IM) techniques are still capable of effectively handling the interference in ultra-dense networks (UDN) becomes doubtful. In this chapter, we first study the new features of the interference in UDN. Then, we make a brief overview of the IM techniques. Performance evaluation is further made, which indicates typical IM techniques fail to effectively mitigate interference in UDN. Considering the new features of the interference, we then discuss how to implement effectively interference management through designing an IM entity for UDN. With the aid of the IM entity, we tailor an effectively IM approach, which is capable of mitigating the severe interference and decorrelating the temporal interference correlation. Results show that the proposed could greatly enhancing network capacity in UDN.