We introduce endpoint projection (EPP), a translation of choreographies into implementations given in terms of compositions of processes. Endpoint projection is proven to guarantee choreography compliance by establishing a correspondence between the semantics of source choreographies and their corresponding implementations returned by EPP for them. Choreographies can be seen as descriptions of desired emergent behaviour for communicating processes. Endpoint projection is a way to obtain processes that will collectively implement the desired emergent behaviour. Building on the correctness of EPP and the properties of choreographies, we prove that the implementations generated by EPP are always communication-safe and starvation-free.

We extend our choreographic and process languages with conditionals (if-then-else). The introduction of conditionals has deep ramifications for the theory of EPP since it makes it possible to write choreographies where processes do not have enough information to know what they are supposed to do. This is known as knowledge of choice. We study principles for the transmission of knowledge and extend EPP to address knowledge of choice.

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 3 summarizes the modelling, derivations and main findings of probably one of the most important works on small cell theoretical performance analysis, which concluded that the fears of an inter-cell interference overload in small cell networks were not well-grounded, and that the network capacity – or in more technical words, the area spectral efficiency – linearly grows with the number of deployed small cells. This research was the cornerstone of much of the research that followed on small cells performance analysis.

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.