Deep mobile traffic forecast and complementary base station clustering for C-RAN optimization

The increasingly growing data traffic has posed great challenges for mobile operators to increase their data processing capacity, which incurs a significant energy consumption and deployment cost. With the emergence of the Cloud Radio Access Network (C-RAN) architecture, the data processing units can now be centralized in data centers and shared among base stations. By mapping a cluster of base stations with complementary traffic patterns to a data processing unit, the processing unit can be fully utilized in different periods of time, and the required capacity to be deployed is expected to be smaller than the sum of capacities of single base stations. However, since the traffic patterns of base stations are highly dynamic in different time and locations, it is challenging to foresee and characterize the traffic patterns in advance to make optimal clustering schemes. In this paper, we address these issues by proposing a deep-learning-based C-RAN optimization framework. First, we exploit a Multivariate Long Short-Term Memory (MuLSTM) model to learn the temporal dependency and spatial correlation among base station traffic patterns, and make accurate traffic forecast for a future period of time. Afterwards, we build a weighted graph to model the complementarity of base stations according to their traffic patterns, and propose a Distance-Constrained Complementarity-Aware (DCCA) algorithm to find optimal base station clustering schemes with the objectives of optimizing capacity utility and deployment cost. We evaluate the performance of our framework using data in two months from real-world mobile networks in Milan and Trentino, Italy. Results show that our method effectively increases the average capacity utility to 83.4% and 76.7%, and reduces the overall deployment cost to 48.4% and 51.7% of the traditional RAN architecture in the two datasets, respectively, which consistently outperforms the state-of-the-art baseline methods

mTreeIllustrator: A Mixed-Initiative Framework for Visual Exploratory Analysis of Multidimensional Hierarchical Data

Multidimensional hierarchical (mTree) data are very common in daily life and scientific research. However, mTree data exploration is a laborious and time-consuming process due to its structural complexity and large dimension combination space. To address this problem, we present mTreeIllustrator, a mixed-initiative framework for exploratory analysis of multidimensional hierarchical data with faceted visualizations. First, we propose a recommendation pipeline for the automatic selection and visual representation of important subspaces of mTree data. Furthermore, we design a visual framework and an interaction schema to couple automatic recommendations with human specifications to facilitate progressive exploratory analysis. Comparative experiments and user studies demonstrate the usability and effectiveness of our framework

Reconfigurable multi-carrier transmitters and their application in next generation optical networks

With the advent of new series of Internet services and applications, future networks will have to go beyond basic Internet connectivity and encompass diverse services including connected sensors, smart devices, vehicles, and homes. Today’s telecommunication systems are static, with pre-provisioned links requiring an expensive and time-consuming reconfiguration process. Hence, future networks need to be flexible and programmable, allowing for resources to be directed, where the demand exists, thus improving network efficiency. A cost-effective solution is to utilise the legacy fibre infrastructure more efficiently, by reducing the size of the guard bands and allowing closer optical carrier spacing, thereby increasing the overall spectral efficiency. However, such a scheme imposes stringent transmitter requirements such as frequency stability, which would not be met with the incumbent laser-array based transmitters. An attractive alternative would be to employ an optical frequency comb (OFC), which generates multiple phase correlated carriers with precise frequency separation. The reconfigurability of such a multi-carrier transmitter would enable tuning of channel spacing, number of carriers and emission wavelengths, according to the dynamic network demands. This research thesis presents the work carried out, in the physical layer, towards realising reconfigurability of an optical multi-carrier transmitter system. The work focuses on an externally injected gain-switched laser-based OFC (EI-GSL), which is a particular type of multi-carrier source. Apart from the detailed characterisation of GSL OFCs, advances to the state of the art are achieved via comb expansion, investigating new demultiplexing methods and system implementations. Firstly, two novel broadband GS-OFC generation techniques are proposed and experimentally demonstrated. Subsequently, two flexible and compact demultiplexing solutions, based on micro-ring resonators and laser based active demultiplexers are investigated. Finally, the application of a reconfigurable multi-carrier transmitter, employed in access and data centre networks, as well as analog-radio over fibre (A-RoF) distribution systems, is experimentally demonstrated