5G technology in manufacturing can enable a more flexible, versatile and efficient usage of production assets, including robots, by shifting the intelligence to the cloud and controlling the devices wirelessly. However, achieving very low cycle times in the range of milliseconds for real-time control of such applications is still a major challenge in currently available 5G networks. In this work, we present a test setup that uses a control-split approach which allows to operate a robotic arm with a variable cycle time by using a piece-wise spline interpolation on the motion trajectory. We verified functionality of this approach over a private 5G network deployed in a factory. Measurement results on the network performance and the robot trajectory accuracy are presented. Our results show that the proposed robot control implementation operates reliably even under high network utilization due to background traffic with only moderate tracking error.

This paper presents first angle-resolved measurement results in the terahertz domain for an industrial environment. The measurement campaign carried out is to be understood as a feasibility study on terahertz communication in industrial environments. Impulse response data accumulated with a correlative time-domain channel sounder that operates at a carrier frequency of 300 GHz with a measurement bandwidth of 2 GHz is analyzed with regards to channel parameters such as path gain, path loss, delay spread and angular spread. Three distinguishing carried out industrial measurement scenarios each report a successful execution. Multipath component analysis is realised based on an exceptionally high dynamic range of about 60 dB. The analysis of the collected data shows that mentioned channel parameters can be obtained for industrial surroundings that are heavily dominated by machinery, metal cladding and concrete.

The integration of wireless communications into industrial production allows processes to be faster, more flexible and more cost effective. Fifth generation (5G) mobile networks are supposed to meet the requirements for ultra-reliable low latency communication, especially in non-public deployments. Already, spectrum around 3.7 GHz is reserved for such deployments and an expansion into millimeter wave range around 28 GHz is foreseeable. This paper presents a channel measurement campaign at both 3.7 and 28 GHz in an industrial production hall in three different relevant scenarios. Path loss, K-factor and RMS delay spread are evaluated.

Mobile networks have to cope with an ever increasing demand for video streaming, an application that imposes high quality requirements for user satisfaction. In this paper, we present an analytical model to calculate two important quality measures for streaming traffic, namely the video startup delay distribution and the buffer starvation probability. The queuing-theoretic model differs from related work by incorporating data flow dynamics of the considered cell, as well as the dynamics of fluctuating interference from neighboring cells, with the goal of accurately representing a multi-cellular environment. In this regard, we propose a finite-volume method to approximate the solution of the involved system of partial differential equations, where other approaches from comparable work were faced with numerical problems. We also evaluate two simplified versions of the model, where only the interference dynamics or all coupling terms are omitted, respectively. The model allows us to study the impact of different parameters on buffered video streaming performance. Additionally, we propose a user-centric metric to measure quality of experience (QoE). To the best of our knowledge, our approach is novel and has not been covered by comparable work. The presented results can help to design future cellular networks with enhanced video streaming experience.

Wireless communication systems have been evolving since the first generation. With the fifth generation of wireless systems, not only the evolutionary aspect of increased data rates is tackled but also the revolutionary aspect. Here, emerging use cases such as massive machine-type communication and ultrareliable low-latency communication will play a crucial role. Within this context, applications with stringent latency and reliability requirements are emerging. Wireless reliability is understood as successfully transmitting the desired amount of data within a given time. Diversity techniques, such as multiconnectivity, are potential solutions to achieve stringent reliability requirements. However, in a multiuser scenario, in which resources are shared, this might not always be possible. In this paper, we discuss the feasibility of various multiconnectivity approaches and propose a matching theory-based algorithm together with a novel scheduler aiming to guarantee the reliability requirements of as many users as possible in a multicellular, multiuser system. System-level simulations demonstrate that the proposed approach achieves 100% reliability for the fifth-percentile users in a highly loaded system. The maximum gain of fifth-percentile user throughput as compared to a static multiconnectivity approach is 150%.

Cellular network performance is predominantly driven by the spatial distribution of the data traffic demand. We investigate the spatio-temporal correlation between the spatial mobile data traffic and spatially resolved information obtained from Twitter. The data stems from the centers of two European cities and is largely independent of the user device type and the wireless technology. We observe a high temporal and a moderate to high spatial correlation. In order to assess the actual suitability of Twitter data as input for self-organizing networks, we make use of a queuing-theoretic network performance evaluation tool and quantify cell utilizations and average flow sojourn times within an example network. We find that both metrics obtained with the linearly scaled Tweet density strongly correlate with the ones obtained with the actual data traffic density for reasonably chosen inter-site distances. The insights presented can help network operators to plan their cellular networks in regions with little information about spatially resolved traffic demand but high social media activity, and to further develop Big Data- enabled self-organizing 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.

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 framework's 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.

The enormous demand for mobile video streaming in the future necessitates an accurate estimation of video quality of experience. In this paper, we give insights on how buffered video streaming performance metrics can be modeled in a multi-cellular context by a joint treatment of data flow dynamics seen by a streaming flow in its own cell and dynamic inter-cell interference dynamics seen in neighboring cells. In particular, we show that dynamic inter-cell interference strongly affects video streaming startup delay distributions, not only for users at the cell edge, but also in the cell center. The model provided can help designing and optimizing cellular networks in the future.

Typically, wireless network performance decreases in high traffic regimes, e. g., at traffic hot spots and peak hours, since a large number of active users have to share limited radio resources. From a network perspective, even congestion may occur in some cells, which may lead to drastic deterioration of user throughputs. As a consequence, network operators employ admission control in their base stations in order to prevent congestion and enhance quality of experience. In this paper, we develop an effective network planning and optimization tool set, which considers the dynamic behavior of mobile traffic. The tool set allows for a more accurate prediction of data request blocking probabilities and data throughputs under admission control, since it explicitly considers the inter-cell interference coupled nature of frequency reuse one networks. This enables more reliable planning and (self-) optimization of wireless networks. We prove utility by applying the tool set to a traffic-adaptive admission control scheme and compare the resulting network performance with that of static admission control schemes under demands of high mobile data traffic. We find that the adaptive algorithm is able to exploit the trade-offs between blocking of requests, reduced interference, and guaranteed resources for individual data transmissions in each of the cells. Under high traffic conditions, it yields better performance compared to any other static scheme investigated.

In recent years, research on flexible algorithms for self-organizing networks has been a significant part of the development of wireless network technologies, such as Long Term Evolution. In this paper, we focus on modeling specific error types related to input data, i. e., to mobile data traffic maps, and investigate their impact on self- organizing network algorithm and network performance. We develop traffic map error patterns, which are able to reproduce erroneous geolocation of mobile users, e. g., by methods like (Enhanced) Cell-ID, Observed Time Difference of Arrival, or Global Positioning System. Furthermore, we model errors that occur when call traces and performance measurement counters are used to determine user locations. We find that self-organizing network performance may be robust to systematic distortions of user and traffic distributions, provided that 1) the overall traffic in the network is estimated accurately, i. e., regions without traffic information are avoided, 2) performance measurement counters in the base stations provide additional information on imperfect geolocation data, and 3) a certain degree of correlation between original heterogeneous user distributions and distorted maps is maintained to identify traffic hot spots.

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 5thpercentile 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.

Mobile traffic demand varies significantly in time and space. Hence, wireless radio resources in hotspot areas and at peak traffic hours may be scarce. Consequently, special attention has to be paid to effects induced by admission control, i. e., blocking of data requests by base stations in case of high utilization or overload. Moreover, rising traffic demand requires denser deployments and frequency reuse one. Due to the resulting inter-cell interference, the base stations' utilizations have to be considered mutually dependent, which affects the admission control performance. In this paper, we extend a flow level model for elastic traffic, which explicitly takes into account the dynamic mutual inter-cell interference among base stations, by admission control. The model presented allows computing exact values for the average base station resource utilization, flow throughputs, and blocking probabilities. To analyze large networks containing many cells, we extend two approximation techniques, a state aggregation and an average interference approach, and compare them with the exact solution. Both techniques require far less computational effort and show remarkable accuracy. We believe that the extended flow level model is a positive step towards a more accurate, flexible, and holistic framework for network analysis and planning, and self-organizing network techniques.

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.

Self-organizing networks are considered to be the next generation technology for network management and, therefore, also treated as one possible way to tackle economical and ecological challenges in the future. In this paper, we extend our framework for integrated self-organizing networks by the energy saving management self-organizing network use case. In contrast to coordination among multiple use cases, this approach targets at an integrated solution, where the coordination is inherent in the optimization enabling managing multiple use cases concurrently. Moreover, it considers dynamic traffic and interference situations, and predicts cell loads for various network configurations based on measurements of receiving and traffic conditions. Using the cell load as a proxy for evaluating network performance and base station energy consumption, we show that a joint optimization of cell individual offsets and antenna downtilts is superior to the adjustment of cell individual offsets only. Throughout a day, we observe remarkable improvements in user throughputs and reduction in network energy consumption further enhanced by switching off base stations.

With the explosion of wireless communications in number of users and data rates, the reduction of network power consumption becomes more and more critical. This is especially true for base stations which represent a dominant share of the total power in cellular networks. In order to study power reduction techniques, a convenient power model is required, providing estimates of the power consumption in different scenarios. This paper proposes such a model, accurate but simple to use. It evaluates the base station power consumption for different types of cells supporting the 3GPP LTE standard. It is flexible enough to enable comparisons between state-of-the-art and advanced configurations, and an easy adaptation to various scenarios. The model is based on a combination of base station components and sub-components as well as power scaling rules as functions of the main system parameters.

With the rollout of LTE networks the energy consumption of wireless networks will further increase. We study the impact of power amplifier improvements in combination with scheduling strategies for traffic profiles expected around 2015. We compare a scheduling policy with adapted bandwidth (capacity) using a power amplifier with adaptive operation point vs. a micro DTX scheduler using a power amplifier with a deactivation mode. In times of low traffic up to 30% of energy saving can be achieved, over a day 13% to 24% are achieved.