Trade-off between power and bandwidth consumption in a reconfigurable xhaul network architecture

The increasing number of wireless devices, the high required traffic bandwidth, and power consumption will lead to a revolution of mobile access networks, which is not a simple evolution of traditional ones. Cloud radio access network technologies are seen as promising solution in order to deal with the heavy requirements defined for 5G mobile networks. The introduction of the common public radio interface (CPRI) technology allows for a centralization in BaseBand unit (BBU) of some access functions with advantages in terms of power consumption saving when switching off algorithms are implemented. Unfortunately, the advantages of the CPRI technology are to be paid with an increase in required bandwidth to carry the traffic between the BBU and the radio remote unit (RRU), in which only the radio functions are implemented. For this reason, a tradeoff solution between power and bandwidth consumption is proposed and evaluated. The proposed solution consists of: 1) handling the traffic generated by the users through both RRU and traditional radio base stations (RBS) and 2) carrying the traffic generated by the RRU and RBS (CPRI and Ethernet flows) with a reconfigurable network. The proposed solution is investigated under the lognormal spatial traffic distribution assumption. After proposing resource dimensioning analytical models validated by simulation, we show how the sum of the bandwidth and power consumption may be minimized with the deployment of a given percentage of RRU. For instance we show how in 5G traffic scenarios this percentage can vary from 30% to 50% according to total traffic amount handled by a switching node of the reconfigurable network

Optical communications and networking solutions for the support of C-RAN in a 5G environment

The widespread availability of mobile devices such as tablets and smartphones has led to fast-increasing mobile data traffic in the last few years [...

Dynamic Resource Management in Clouds: A Probabilistic Approach

Dynamic resource management has become an active area of research in the Cloud Computing paradigm. Cost of resources varies significantly depending on configuration for using them. Hence efficient management of resources is of prime interest to both Cloud Providers and Cloud Users. In this work we suggest a probabilistic resource provisioning approach that can be exploited as the input of a dynamic resource management scheme. Using a Video on Demand use case to justify our claims, we propose an analytical model inspired from standard models developed for epidemiology spreading, to represent sudden and intense workload variations. We show that the resulting model verifies a Large Deviation Principle that statistically characterizes extreme rare events, such as the ones produced by "buzz/flash crowd effects" that may cause workload overflow in the VoD context. This analysis provides valuable insight on expectable abnormal behaviors of systems. We exploit the information obtained using the Large Deviation Principle for the proposed Video on Demand use-case for defining policies (Service Level Agreements). We believe these policies for elastic resource provisioning and usage may be of some interest to all stakeholders in the emerging context of cloud networkingComment: IEICE Transactions on Communications (2012). arXiv admin note: substantial text overlap with arXiv:1209.515

A probabilistic threshold-based bandwidth sharing policy for wireless multirate loss networks

We propose a probabilistic bandwidth sharing policy, based on the threshold (TH) policy, for a single cell of fixed capacity in a homogeneous wireless cellular network. The cell accommodates random input-traffic originated from K service-classes. We distinguish call requests to new and handover, and therefore, the cell supports 2K types of arrivals. If the number of in-service calls (new or handover) of a service-class exceeds a threshold (different for new and handover calls of a service-class), a new or handover arriving call of the same service-class is not always blocked, as it happens in the TH policy, but it is accepted in the system with a predefined state-dependent probability. The cell is analyzed as a multirate loss system, via a reversible continuous-time Markov chain, which leads to a product form solution (PFS) for the steady state distribution. Thanks to the PFS, the calculation of performance measures is accurate, but complex. To reduce the computational complexity, we determine performance measures via a convolution algorithm

A Survey of Deep Learning for Data Caching in Edge Network

The concept of edge caching provision in emerging 5G and beyond mobile networks is a promising method to deal both with the traffic congestion problem in the core network as well as reducing latency to access popular content. In that respect end user demand for popular content can be satisfied by proactively caching it at the network edge, i.e, at close proximity to the users. In addition to model based caching schemes learning-based edge caching optimizations has recently attracted significant attention and the aim hereafter is to capture these recent advances for both model based and data driven techniques in the area of proactive caching. This paper summarizes the utilization of deep learning for data caching in edge network. We first outline the typical research topics in content caching and formulate a taxonomy based on network hierarchical structure. Then, a number of key types of deep learning algorithms are presented, ranging from supervised learning to unsupervised learning as well as reinforcement learning. Furthermore, a comparison of state-of-the-art literature is provided from the aspects of caching topics and deep learning methods. Finally, we discuss research challenges and future directions of applying deep learning for cachin

Enhancing LTE with Cloud-RAN and Load-Controlled Parasitic Antenna Arrays

Cloud radio access network systems, consisting of remote radio heads densely distributed in a coverage area and connected by optical fibers to a cloud infrastructure with large computational capabilities, have the potential to meet the ambitious objectives of next generation mobile networks. Actual implementations of C-RANs tackle fundamental technical and economic challenges. In this article, we present an end-to-end solution for practically implementable C-RANs by providing innovative solutions to key issues such as the design of cost-effective hardware and power-effective signals for RRHs, efficient design and distribution of data and control traffic for coordinated communications, and conception of a flexible and elastic architecture supporting dynamic allocation of both the densely distributed RRHs and the centralized processing resources in the cloud to create virtual base stations. More specifically, we propose a novel antenna array architecture called load-controlled parasitic antenna array (LCPAA) where multiple antennas are fed by a single RF chain. Energy- and spectral-efficient modulation as well as signaling schemes that are easy to implement are also provided. Additionally, the design presented for the fronthaul enables flexibility and elasticity in resource allocation to support BS virtualization. A layered design of information control for the proposed end-to-end solution is presented. The feasibility and effectiveness of such an LCPAA-enabled C-RAN system setup has been validated through an over-the-air demonstration