Henrik Klessig

Requirements and current solutions of wireless communication in industrial automation

The industrial wireless automation sector exhibits a huge market growth in the last years. Today, many applications already use wireless technologies. However, the existing wireless solutions do not yet offer sufficient performance with respect to real-time and reliability requirements, particularly for closed-loop control applications. As a result, low latency wireless communication technologies will bridge the gap and can become a key factor for the wide-spread penetration of wireless in industrial communication systems. It is therefore the main goal of this paper to provide a comprehensive overview on requirements, current solutions, and challenges as well as opportunities for future wireless industrial systems. Thereby, presented requirement figures, analysis results, and performance evaluations are based on numerous practical examples from industry.

Concurrent Load-Aware Adjustment of User Association and Antenna Tilts in Self-Organizing Radio Networks

Network operators expect a coordinated handling of parameter changes submitted to the operating networks configuration management entity by closed-loop self-organizing network (SON) techniques. For this reason, a major research goal for emerging SON technologies is to achieve coordinated results out of a plethora of independently or even concurrently running use-case implementations. In this paper, we extend current frameworks to compute desirable user associations by an interference model that explicitly takes base-station loads into account. With the aid of this model, we are able to make considerably more accurate estimations and predictions of cell loads compared with established methods. Based on the ability to predict cell loads, we derive algorithms that jointly adapt user-association policies and antenna-tilt settings for multiple cells. We demonstrate by detailed numerical evaluations of realistic networks that these algorithms can be applied to capacity and coverage optimization, mobility load balancing, and cell outage compensation use cases. As a result, rather than performing any heading or tailing coordination, the joint technique inherently comprises all three use cases, making their coordination redundant. For all scenarios studied, the joint optimization of tilts and user association improves quality of service in terms of the fifth percentile of user throughput compared with state-of-the-art techniques. The proposed models and techniques can be straightforwardly extended to other physical and soft parameters.

Latency Critical IoT Applications in 5G: Perspective on the Design of Radio Interface and Network Architecture

Next generation mobile networks not only envision enhancing the traditional MBB use case but also aim to meet the requirements of new use cases, such as the IoT. This article focuses on latency critical IoT applications and analyzes their requirements. We discuss the design challenges and propose solutions for the radio interface and network architecture to fulfill these requirements, which mainly benefit from flexibility and service-centric approaches. The article also discusses new business opportunities through IoT connectivity enabled by future networks.

A Framework Enabling Spatial Analysis of Mobile Traffic Hot Spots

An enormous increase in data traffic demanded by mobile users calls for efficient deployment strategies such as multi-layer heterogeneous networks. However, placing small cells at the desired locations to offload as much traffic as possible from overlaying macro cells is a crucial task. In this regard, geo-location and user equipment positioning techniques help obtain spatial distributions of user locations and their respective traffic volumes. In this paper, we provide a tool capable of reducing errors that stem from spatial discretization of traffic data and that can autonomously detect hot spots given a certain threshold. Based on geo-located traffic in a 3G network in a dense urban city, we find that traffic in the area is approximately log-normally distributed and that the size of traffic hot spots are approximately Weibull distributed. Based on our statistical findings, we observe that utilizing 4 small cells per km2 covering 3.2% of the total area and around 34% of the total traffic volume is a very meaningful deployment strategy; however, deploying more small cells in larger hot zones becomes increasingly costly in terms of the ratio of area covered and traffic demand serviced.

Improving coverage and load conditions through joint adaptation of antenna tilts and cell selection rules in mobile networks

One major topic of research into SON technology is the coordination of SON use cases. Network operators expect a coordinated handling of the parameter and configuration changes submitted to the operating network by closed-loop SON use case implementations. Beside a published conceptual framework, SON coordination has already been treated in the literature, especially regarding the mobility load balancing (MLB) and mobility robustness optimization (MRO) use cases. In this paper, we utilize the capacity and coverage optimization (CCO) and MLB use cases. Rather than performing any heading or tailing coordination of separate CCO and MLB algorithms, in our work we concentrate on the optimization considering both use cases in a joint algorithm. Our approach introduces cell-individual loads and the joint treatment of cell selection policies and antenna tilt settings into well-known and previously reported optimization concepts. Using system simulations of a sample LTE real deployment scenario, we verify that our joint optimization of antenna tilts and cell selection rules including the notion of cell-individual loads outperforms the optimization of tilts-only (with and without the notion of cell-individual loads within the algorithm) and of the cell selection rules-only in terms of spectral and energy efficiency.

Flow-level models for capacity planning and management in interference-coupled wireless data networks

In 4G cellular networks, both the adaptation of data rates to current interference conditions due to adaptive modulation and coding as well as a frequency reuse of one mandate precise techniques to estimate cell capacities and cell loads in order to accurately predict the quality of service delivered to end users. Such estimation happens ideally already during the network planning phase and is further required for self-optimization at runtime. Classic flow-level techniques to estimate cell loads, capacities, and related quality of service metrics assume static and worst case interference, which is analytically simple, but may produce considerable errors and lead to disadvantageous planning and optimization results. Appropriate models where individual cells are coupled through interference are rendered analytically intractable. This article first introduces basic flow-level modeling techniques and then reviews recent results in the field of flow-level network models, which allow the actual loads and capacities in interference- coupled wireless networks to be bound and closely approximated. We discuss trade-offs between accuracy and numerical complexity of different techniques and identify a model based on the notion of average interference as the most practically relevant. Simulation results for a large scenario based on a real network illustrate its applicability to practical network planning.

Joint Bandwidth Allocation and Small Cell Switching in Heterogeneous Networks

One major topic of research into self-organizing network technology is the coordination of SON use cases. Network operators expect a coordinated handling of the parameter and configuration changes submitted to the operating network by closed-loop SON use case implementations. There are currently two basic approaches for SON use case coordination discussed in the literature: A so-called heading or tailing use case external coordination and the combination of separate use cases into one joint algorithm. In this paper, we extend a verified framework to combine mobility load balancing and inter-cell interference coordination use cases, especially for a heterogeneous network environment. Our approach results in a coordinated set of cell range expansion offsets, an efficient bandwidth allocation to support the (enhanced) inter-cell interference coordination use case, and an energy-efficient smart cell switching of the small capacity cells in a heterogeneous networks environment for a varying traffic demand during the course of a day, resulting in significant capacity enhancements while saving energy at the same time.

A Performance Evaluation Framework for Interference-Coupled Cellular Data Networks

In regard to the continuing network densification as a part of the solution to the mobile data traffic demand explosion, managing future 5G ultra-dense networks is becoming increasingly complex. Moreover, the problem of (partly) limited capacity in time and space requires the joint treatment of spatio-temporal data traffic and intercell interference dynamics. Concerning this matter, we propose a performance evaluation framework, which is capable of estimating various cell-specific and user-specific key performance metrics considering the complex spatio-temporal interaction of traffic and interference dynamics. We provide methods for obtaining these metrics with low complexity, making the framework attractive to self-organizing network solutions for future (ultra)dense networks. We stress the frameworks broad applicability and demonstrate the effects of internal flow and external interference dynamics on network performance under various conditions. In particular, we highlight the dominance of these dynamics over the impact of the speed of the variation of intercell interference, the scheduler, the file size distribution, and fast fading.

From Immune Cells to Self-Organizing Ultra-Dense Small Cell Networks

In order to cope with the wireless traffic demand explosion within the next decade, operators are underlying their macrocellular networks with low power base stations in a more dense manner. Such networks are typically referred to as heterogeneous or ultra-dense small cell networks, and their deployment entails a number of challenges in terms of backhauling, capacity provision, and dynamics in spatio-temporally fluctuating traffic load. Self-organizing network (SON) solutions have been defined to overcome these challenges. Since self-organization occurs in a plethora of biological systems, we identify the design principles of immune system self-regulation and draw analogies with respect to ultra-dense small cell networks. In particular, we develop a mathematical model of an artificial immune system (AIS) that autonomously activates or deactivates small cells in response to the local traffic demand. The main goal of the proposed AIS-based SON approach is the enhancement of energy efficiency and improvement of cell-edge throughput. As a proof of principle, system level simulations are carried out in which the bio-inspired algorithm is evaluated for various parameter settings, such as the speed of small cell activation and the delay of deactivation. Analysis using spatio-temporally varying traffic exhibiting uncertainty through geo-location demonstrates the robustness of the AIS-based SON approach proposed.

Increasing the Capacity of Large-Scale HetNets through Centralized Dynamic Data Offloading

Typically, mobile users cluster around points of interest in dense urban environments such as city centers forming so-called data traffic hot spots and hot zones. To provide capacity to such users efficiently, mobile operators deploy small cells. However, the deployment of heterogeneous networks, which consist of overlaying macro cells and many co-channel small cells, entails many problems. One typical problem is that, more often than not, hot spot users are not covered by the small cells due to the spatially fluctuating nature of the traffic demand. Data offloading, meaning actively shifting macro cell users to small cells, is a promising approach to address this issue. In this paper, we extend a queuing- theoretic model based on the notion of elastic data flows in order to model data offloading, or more specifically, cell range expansion along with inter-cell interference coordination. The model explicitly considers mutual co-channel interference and enables predicting the performance of networks consisting of hundreds of cells with very low computational effort. Based on this model, we present a heuristic centralized data offloading algorithm, which, for a certain traffic demand, is able to increase the 5th percentile of the data flow throughput by a factor of 4.5 and to halve the probability of service unavailability. Moreover, we show that the network capacity can be increased by about 41.3% if data offloading is performed.