Fronthaul-Constrained Cloud Radio Access Networks: Insights and Challenges

As a promising paradigm for fifth generation (5G) wireless communication systems, cloud radio access networks (C-RANs) have been shown to reduce both capital and operating expenditures, as well as to provide high spectral efficiency (SE) and energy efficiency (EE). The fronthaul in such networks, defined as the transmission link between a baseband unit (BBU) and a remote radio head (RRH), requires high capacity, but is often constrained. This article comprehensively surveys recent advances in fronthaul-constrained C-RANs, including system architectures and key techniques. In particular, key techniques for alleviating the impact of constrained fronthaul on SE/EE and quality of service for users, including compression and quantization, large-scale coordinated processing and clustering, and resource allocation optimization, are discussed. Open issues in terms of software-defined networking, network function virtualization, and partial centralization are also identified.Comment: 5 Figures, accepted by IEEE Wireless Communications. arXiv admin note: text overlap with arXiv:1407.3855 by other author

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

LearnQoS: a learning approach for optimizing QoS over multimedia-based SDNs

As video-based services become an integral part of the end-users’ lives, there is an imminent need for increase in the backhaul capacity and resource management efficiency to enable a highly enhanced multimedia experience to the endusers. The next-generation networking paradigm offers wide advantages over the traditional networks through simplifying the management layer, especially with the adoption of Software Defined Networks (SDN). However, enabling Quality of Service (QoS) provisioning still remains a challenge that needs to be optimized especially for multimedia-based applications. In this paper, we propose LearnQoS, an intelligent QoS management framework for multimedia-based SDNs. LearnQoS employs a policy-based network management (PBNM) to ensure the compliance of QoS requirements and optimizes the operation of PBNM through Reinforcement Learning (RL). The proposed LearnQoS framework is implemented and evaluated under an experimental setup environment and compared with the default SDN operation in terms of PSNR, MOS, throughput and packet loss

Mobile Communications Industry Scenarios and Strategic Implications for Network Equipment Vendors

Mobile infrastructure markets have changed dramatically during the past years. The industry is experiencing a shift from traditional large-scale, hardware-driven system roll-outs to software and services -driven business models. Also, the telecommunications and internet worlds are colliding in both mobile infrastructure and services domains requiring established network equipment vendors and mobile operators to transform and adapt to the new business environment. This paper utilizes Schoemaker's scenario planning process to reveal critical uncertain elements shaping the future of the industry. Four possible scenarios representing different value systems between industry's key stakeholders are created. After this, five strategic options with differing risk and cost factors for established network equipment vendors are discussed in order to aid firm's strategic planning process. --