Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks

Soaring capacity and coverage demands dictate that future cellular networks need to soon migrate towards ultra-dense networks. However, network densification comes with a host of challenges that include compromised energy efficiency, complex interference management, cumbersome mobility management, burdensome signaling overheads and higher backhaul costs. Interestingly, most of the problems, that beleaguer network densification, stem from legacy networks' one common feature i.e., tight coupling between the control and data planes regardless of their degree of heterogeneity and cell density. Consequently, in wake of 5G, control and data planes separation architecture (SARC) has recently been conceived as a promising paradigm that has potential to address most of aforementioned challenges. In this article, we review various proposals that have been presented in literature so far to enable SARC. More specifically, we analyze how and to what degree various SARC proposals address the four main challenges in network densification namely: energy efficiency, system level capacity maximization, interference management and mobility management. We then focus on two salient features of future cellular networks that have not yet been adapted in legacy networks at wide scale and thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and device-to-device (D2D) communications. After providing necessary background on CoMP and D2D, we analyze how SARC can particularly act as a major enabler for CoMP and D2D in context of 5G. This article thus serves as both a tutorial as well as an up to date survey on SARC, CoMP and D2D. Most importantly, the article provides an extensive outlook of challenges and opportunities that lie at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201

Ultra-Dense Mobile Networks: Optimal Design and Communications Strategies

This thesis conducts an extensive analysis within the mobile telecommunications sub-field of the ultra-dense mobile networks, in which a massive deployment of network’s pieces of equipment is assumed. Future cache-enabled mobile networks are expected to meet most of the generated content demands directly at the edge, where each node has the availability to proactively store a set of contents in a local memory. This thesis makes several important contributions. The research being presented in this thesis proposes new analytical expressions to modeling the performance associated to the network’s edge. Base-stations’ idling technologies are also investigated to temporarily turn off some network nodes, saving energy and, in some circumstances, improving the overall performance by contributing less interference at the network’s edge. On the other hand, making use of fewer base-stations however reduces the amount of available resources at the network’s edge. A trade-off is investigated, which balances among interference saturation and available resources to increase the average user’s quality of experience. In this work, we treat the edge node density as a variable of the problem. This greatly increases the difficulty of obtaining analytical expressions, but also offers a direct access for optimizing the users’ average performance and network’s energy consumptions. An energy-focused performance metric is subsequently proposed, with the intention to highlight an interesting duality within the same network’s tier, which can transition from a better efficient to a more performing state, according to the energy expenses from the operators. Nonetheless, under an ultra-dense scenario, line-of-sight wireless links between the user and the nodes become more likely. The introduction of a main component of the multi-path propagated copies of a signal involves analytical complications. A feasible approximation is proposed and validated through a set of computer simulations. The scalability of the proposed technique allows to generalise existing results in the literature