Cost-Effective Cache Deployment in Mobile Heterogeneous Networks

This paper investigates one of the fundamental issues in cache-enabled heterogeneous networks (HetNets): how many cache instances should be deployed at different base stations, in order to provide guaranteed service in a cost-effective manner. Specifically, we consider two-tier HetNets with hierarchical caching, where the most popular files are cached at small cell base stations (SBSs) while the less popular ones are cached at macro base stations (MBSs). For a given network cache deployment budget, the cache sizes for MBSs and SBSs are optimized to maximize network capacity while satisfying the file transmission rate requirements. As cache sizes of MBSs and SBSs affect the traffic load distribution, inter-tier traffic steering is also employed for load balancing. Based on stochastic geometry analysis, the optimal cache sizes for MBSs and SBSs are obtained, which are threshold-based with respect to cache budget in the networks constrained by SBS backhauls. Simulation results are provided to evaluate the proposed schemes and demonstrate the applications in cost-effective network deployment

Network virtualization in next-generation cellular networks: a spectrum pooling approach

The hardship of expanding the cellular network market results from the tremendous high cost of mobile infrastructure, i.e. the capital expenditures (CAPEX) and the operational expenditures (OPEX). Spectrum Sharing is one of the proposed solution for the high-cost of scalability of cellular networks. However, most of the proposed spectrum pooling frameworks in the literature are mostly approached from a technical view besides there are no good cost models based on real datasets for quantifying the circumstances under which sharing the spectrum and network resources would be beneficial to mobile operators. In this thesis, by studying different sharing scenarios in a fiber-based backhaul mobile network, we assess the incentives for service providers (SPs) to share spectrum/infrastructure in different cellular market areas/economic areas (CMA/BEAs) with different population density, allocated bandwidth (BW), spectrum bid values and considering different network topologies. Moreover, we look at the technical problem of sharing the spectrum between two SPs sharing the same basestation (BS), yet they have different traffic demand as well as different QoS constraints. We design a resource allocation scheme to provision real-time (RT), non-real-time (NRT) as well as Ultra-reliable Low Latency Communications (URLLC) traffic in a single shared BS scenario such that SPs achieve isolation, fairness and enforce their QoS constraints. Finally, we exploit spectrum pooling to develop an approach for dynamically re-configuring the base stations that survive a disaster and are powered by a microgrid to form a multi-hop mesh network in order to provide local cellular service

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions