Nokia (Australia)

Diagnosis functionality as a key component for automated Network Management (NM) systems allows rapid, machine-level interpretation of acquired data. In existing work, network diagnosis has focused on building point solutions using configuration and performance management, alarm, and topology information from one network. While the use of automated anomaly detection and diagnosis techniques within a single network improves operational efficiency, the knowledge learned by running these techniques across different networks that are managed by the same operator can be further maximized when that knowledge is shared. This paper presents a novel diagnosis cloud framework that enables the extraction and transfer of knowledge from one network to another. It also presents use cases and requirements. We present the implementation details of the diagnosis cloud framework for two specific types of models: topic models and Markov Logic Networks (MLNs). For each, we describe methods for assessing the quality of the local model, ranking models, adapting models to a new network, and performing detection and diagnosis. We performed experiments for the diagnosis cloud framework using real cellular network datasets. Our experiments demonstrate the feasibility of sharing topic models and MLNs.

Despite the orthogonality of LTE’s uplink transmission, it can happen that signals which are received at the base station with low power are drown out by strong signals from other users. Reason is the limited quantization resolution of the base station’s analog-to-digital converter. Since this effect causes a quality of service degradation for users at the cell edge, the dynamic receive power range of uplink signals arriving at the base station should be upper limited. However, setting an adequate upper limit for the dynamic receive power range is not trivial since many effects, such as quantization noise, fast fading etc., decrease the dynamic range which the analog-to-digital converter can handle in theory. In this paper, we measure the maximal uplink dynamic receive power range, denoted as uplink dynamic range threshold, for our LTE-like measurement setup by monitoring the uplink bit-error-rate of a first user while decreasing its transmit power during the presence of a second adjacently scheduled user with fixed transmit power. As the measured threshold is 31.4±7.6 dB, we can conclude that a link-budget-based estimation of the dynamic receive power range, which has been published previously, is accurate since its result coincides with the measurement.

The arrival of the fifth generation (5G) is expected to come together with three important enablers. First, the densification of access nodes will continue. Second, 5G networks must be highly flexible and adapt to the dynamism of the traffic location and patterns. For this, some of the radio access network (RAN) functionalities will run in large computer centers, able to dynamically assign more or fewer units of computation to the virtual cells distributed in the network. Finally, a complex landscape of spectrum availability and access will emerge where multiple frequency bands, subject to different regulations including various forms of shared spectrum, are expected to be available to wireless communication systems.

This article reviews the application of the recent European Licensed Shared Access (LSA) concept for spectrum sharing between a mobile network operator (MNO) and an incumbent user. LSA, as a new area of application of cognitive technology, provides the MNO an opportunity to access new frequency resources on a shared basis. The article presents critical design criteria of LSA from the MNO point of view in order to allow future cognitive cellular networks to efficiently exploit shared spectrum bands. We describe the role of LSA bands in the context of heterogeneous networking, and identify the Long Term Evolution (LTE) and LTE-Advanced enabling technologies that support the introduction of LSA. Such technologies include traffic steering, carrier aggregation, and self-organizing networking. Additionally, we introduce an LSA management unit controlled by the MNO, to be implemented on top of the existing LTE/LTEAdvanced architecture, and we discuss the functionalities required for the optimization and automation of LSA resource management. We also depict the interrelations of the tasks between the LSA management unit and the supporting LTE/LTE-Advanced technologies. Based on the findings in this article, the ongoing cellular system evolution is shown to form a solid base for the introduction of new shared spectrum bands for cognitive cellular systems.

Over the past decades, the scale of communications networks has been growing rapidly with the emergence of more and more networkbased services. However, network operators all over the world are experiencing a decline in profitability in the last few years. Responding to such a challenge and paradox, the concept of network function virtualization (NFV) has been introduced with the aim of efficiently enabling networkbased services (1) by deploying standardized and programmable hardware systems and (2) by virtualizing network functions with software. Compared to the current approaches to network and service deployment, which are based on a large variety of propriety equipment, NFV opens up many opportunities to the telecommunications industry. By reducing the cost of equipment and increasing the revenue with virtualized services, NFV has the potential to revolutionize the entire telecommunication industry.

Deployment of LTE in unlicensed bands is being considered in Rel-13 of LTE. This feature is called Licensed-Assisted Access (LAA) using LTE. Unlicensed band is attractive due to the large amount of available spectrum. However, in shared spectrum the coexistence between LAA and WiFi systems becomes a primary challenge. This paper presents an analytical framework to investigate the downlink coexistence performance between two systems with a simple listen-before-talk (LBT) mechanism enforced on LAA. Using this framework, theoretical models based on Markov chains are established for both systems and downlink throughput can be calculated. Numerical results from the models show that the simple listen-before-talk scheme is very effective in LAA and LAA coexistence scenario (i.e. two LAA systems sharing the same spectrum). In LAA and WiFi coexistence scenario, it can improve WiFi performance substantially.

Networks beyond 2020 will experience 10000-fold increase in wireless traffic, connect 10-100 times more devices and support the most diverse use cases. Thus, the 5G architecture needs to be flexible and cater for both traffic volumes and diversity of service requirements. Among the set of new use cases, support of delay sensitive mobile applications, such as vehicular communications (V2X, where X stands for either Vehicle or Infrastructure), require architecture enhancements to natively offer low latency and high mobility. In this paper we propose the necessary technology enablers for the architectural solution to support such use cases.

Low power nodes have been a hot topic in research, standardization, and industry communities, which is typically considered under an umbrella term called heterogeneous networking. In this paper we look at the problem of deploying optimally low power nodes in the context of relay networking, when an operator connects low power nodes (or small cells) via the wireless backhaul that uses the same spectrum and the same wireless access technology. We present an analytical model that can calculate optimal coordinates for low power nodes based on the input parameters, such as preferred number of nodes, their transmission power, parameters of the environment etc. The analytical calculations are complemented by extensive dynamic system level simulations, by means of which we analyze overall system performance for the obtained coordinates. We also show that even relatively marginal deviations from optimal coordinates can lead to worse system performance.

A novel built-in self-test (BIST) algorithm has been devised in this work. The concept of the algorithm is that process variation within a die is usually location based; it is not random. Therefore, if a low frequency ring oscillator is placed 40μm away from the transistors in an 80 GHz low noise amplifier (LNA), both the LNA and the ring oscillator undergo similar process variation in device sizes. Device variation in the ring oscillator can be predicted from it's oscillation frequency shift. It is assumed that the amplifier's device also vary in similar fashion. Thus, from a low frequency measurement of the ring oscillator's frequency, the device variation in the high frequency amplifier can be estimated. This reduces the cost of high frequency measurements. The work is demonstrated in Advanced Design System (ADS) simulation.

The management and operation of a Self-Organizing Network (SON)-enabled mobile network still requires considerable human effort. On the one hand, SON Functions need to be configured through low-level parameters in order to control the optimization of the network. On the other hand, an operator wants to steer the system with solely technical objectives, and the underlying network should be adapted accordingly. This opens up a gap in network management that is currently closed manually. This paper presents an approach that overcomes the manual gap between technical objectives and SON Functions by choosing the best values for the SON Functions' configurations automatically. Main advantage of this approach is that it allows to manage a system at a high level of abstraction and, at the same time, reduces manual effort. The approach is explained by applying it in a case study in the field of mobile networks with four SON Functions, namely Mobility Load Balancing (MLB), Coverage and Capacity Optimization (CCO), Energy Savings Management (ESM) and Mobility Robustness Optimization (MRO).