Challenges And R&D Opportunities In Small Cells/Hetnets
Regardless of the physical layer waveform and spectrum band adapted for fifth generation (5G) cellular systems, it is clear that the majority of the 1000x target capacity gain must come from network densification. Realizing the massive potential of network densification by small cells, industry pundits have been forecasting an explosive growth of small cells for the past few years. However, to date, mass deployments of indoor small cells remain elusive mainly due to the fact that the ultra-dense deployment of small cells comes with their own set of peculiar challenges. In this back drop this report:
1) Pinpoint the key challenges that are acting as Achilles’ heel for small cells and HetNets;
2) Identify specific R&D directions to address these challenges in timely fashion;
Part 1 consists of chapter 1. This part provides a very basic background in HetNets to facilitate the more technical discussion in the rest of report and make the report a self-sufficient document.
Part 2 of the deliverable consists of chapter 2 and 3. This part is aimed to identify and summarize both academic and industrial perspective of challenges, opportunities and prospects in small cell deployment in heterogeneous network scenario. More specifically, this part aims to answer following four questions:
What are the challenges for small cell/HetNet deployment and cost effective optimization?
What existing methods will work or will not work anymore in HetNet scenarios including planned, semi planned and unplanned deployments.
What new solutions and improvements in existing solutions are needed to make small cells work in ultra-dense heterogeneous environments of different types.
In the back drop of identified challenges, what are the possible innovation and research ideas that are worth investigating keeping in mind time to market constraints.
To answer this questions, as directly as possible, each subsection in the chapter 2 and 3, addresses one particular challenge. Based on extensive literature search, a total of 15 challenges are identified in the two chapters, each with a dedicated section. Specific R&D opportunities identified within each challenge are then discussed in a subsection titled “R&D opportunities” within the section dedicated to a challenge. Both challenges and R&D opportunities are then summarized in tabular format at the end of each section.
For more information about this report, contact Dr. Ali Imran (email: email@example.com)
Cellular system optimisation, a cornerstone of cellular systems paradigm, requires new focus shift because of the emergence of plethora of new features shaping the cellular landscape. These features include self-organising networks with added flavours of heterogeneity of cell sizes and base station types, adaptive antenna radiation patterns, energy efficiency, spatial homogeneity of service levels and focus shift from coverage to capacity. Moreover, to effectively tackle spatiotemporal dynamics of network conditions, a generic low-complexity framework to quantify the key facets of performance, that is, capacity, quality of service and energy efficiency of the various network topology configurations (NTC), is needed for enabling self-organising networks empowered cellular system optimisation on the fly. In this paper, we address this problem and present a performance characterisation framework that quantifies the multiple performance aspects of a given heterogeneous NTC through a unified set of metrics that are derived as function of key optimisation parameters and also present a cross comparison of a wide range of potential NTCs. Moreover, we propose a low-complexity heuristic approach for holistic optimisation of future heterogeneous cellular systems for joint optimality in the multiple desired performance indicators. The performance characterisation framework also provides quantitative insights into the new tradeoffs involved in optimisation of emerging heterogeneous networks and can pave the way for much needed further research in this area.
SON Attack! Exploring new dimensions in SON enabled Cellular Networks
Building on the pillars of Moore’s law backed computing power and novel Machine Learning algorithms, the recently emerged Self Organizing Network (SON) paradigm is now being considered vital for provision of seamless and limitless connectivity for wide range of emerging applications and services that will extend capabilities of 5G networks far beyond previous generation of cellular networks. However, one peculiar feature of SON Network can be exploited to devise SON attack giving a new dimension to security aspect in cellular networks. Unfortunately, existing literature on SON lacks this important perspective. In this upcoming article, we first discuss how this particular feature can be exploited to launch SON attack. We then highlight the repercussions of this exploitation on the performance of the network. Finally we propose a comprehensive framework to detect and heal this SON attack.
It is anticipated that the future cellular networks will consist of an ultra-dense deployment of complex heterogeneous Base Stations (BSs). Consequently, Self-Organizing Networks (SON) features are considered to be inevitable for efficient and reliable management of such a complex network. Given their unfathomable complexity, cellular networks are inherently prone to partial or complete cell outages due to hardware and/or software failures and parameter misconfiguration caused by human error, multivendor incompatibility or operational drift. Forthcoming cellular networks, vis-a-vis 5G are susceptible to even higher cell outage rates due to their higher parametric complexity and also due to potential conflicts among multiple SON functions. These realities pose a major challenge for reliable operation of future ultra-dense cellular networks in cost effective manner. In this paper, we present a stochastic analytical model to analyze the effects of arrival of faults in a cellular network. We exploit Continuous Time Markov Chain (CTMC) with exponential distribution for failures and recovery times to model the reliability behavior of a BS. We leverage the developed model and subsequent analysis to propose an adaptive fault predictive framework. The proposed fault prediction framework can adapt the CTMC model by dynamically learning from past database of failures, and hence can reduce network recovery time thereby improving its reliability. Numerical results from three case studies, representing different types of network, are evaluated to demonstrate the applicability of the proposed analytical model.