One of the first key self-organizing features introduced in LTE was Automatic Neighbor Relations (ANR), a function for automatically generating relations between radio network entities that is needed for many features such as active-mode mobility, load balancing and dual connectivity. Given the benefits of ANR in LTE, we foresee that procedures for relations establishment will be an important integrated part of 5G systems as well. Moreover, the new emerging technology components of 5G such as massive beamforming expand the scope of relations beyond the neighbor relations of LTE. In this paper, we give an overview of key 5G radio access components, discuss the need for different types of identifiers and relations, and comment on the impact from the ultra-lean design principle of 5G. The paper also discusses some possible procedures for automatic relation establishment in consideration of the specific 5G aspects.

The commercial interest in proximity services is increasing. Application examples include location- based information and advertisements, logistics, social networking, file sharing, etc. In this paper, we consider network-based positioning based on times series of proximity reports from a mobile device, either only a proximity indicator, or a vector of RSS from observed nodes. Such positioning corresponds to a latent and nonlinear observation model. To address these problems, we combine two powerful tools, namely particle filtering and Gaussian process regression (GPR) for radio signal propagation modeling. The latter also provides some insights into the spatial correlation of the radio propagation in the considered area. Radio propagation modeling and positioning performance are evaluated in a typical office area with Bluetooth-Low-Energy (BLE) beacons deployed for proximity detection and reports. Results show that the positioning accuracy can be improved by using GPR.

Heterogeneous network 3GPP LTE deployments are considered as a prime candidate to meet increasing demand for mobile broadband service coverage and capacity. Such deployments also need to support mobility that is as robust as in traditional macro deployments. In this paper, we analyze the handover performance in heterogeneous deployments to better understand potential behavior of automatic handover parameter adjustments. Such a Self-Organizing Network (SON) feature is commonly known as Mobility Robustness Optimization (MRO). The analyses indicate difficult challenges with handovers from low power to high power base stations, and an evolved handover procedure that improves the user experience during such times is presented. These results are key for developing an MRO algorithm for heterogeneous networks.

Cell outage management is a self-healing functionality in future mobile cellular networks, aiming to automatically detect cell or site level outages (cell outage detection) as well as to mitigate as much as possible the caused degradation of coverage, capacity and/or service quality (cell outage compensation). Cell outage compensation has a variety of control parameters (and combination thereof) at its disposal in surrounding cells/sites, including the reference signal power PRS, antenna tilt, scheduling parameters and the uplink target received power level P0. By appropriately tuning these control parameters, the outage-induced performance effects can be minimised, in terms of some operator-specified balance of relevant performance metrics. This paper analyses the effectiveness of selected control parameters in mitigating the effects of cell/site outages, learning that the antenna tilt and P0are most effective in restoring coverage, while P0is most effective in restoring user throughput performance.

Self-organising Networks (SON) as introduced for 3G Long Term Evolution (LTE) will typically involve several different SON functions. These functions are not necessarily aware of each other and may have complex relations and interdependencies, for example, conflicting parameter settings, depending on their design and approach. Coordination of SON functions may become necessary in order to harmonise the actions of SON functions and allow for a stable and reliable behaviour of the SON system. This paper describes different conflict types, harmonisation approaches to avoid and resolve conflicts, and a functional framework with different roles to achieve harmonisation. An exemplary case study is given. The paper concludes that, in case several conflicting SON functions are implemented in a network, a SON Coordinator may be beneficial to prevent from network instabilities and/or to improve the performance.

Cell outage compensation is a self-healing function and as such part of the Self-Organising Networks concept for mobile wireless networks. It aims at mitigating the degradation of coverage, capacity and service quality caused by a cell or site level outage. Upon detection of such an outage, cell outage compensation tunes a variety of control parameters, e.g., electrical antenna tilt and uplink target received power level, in cells surrounding the affected site, in order to minimise the outage-induced performance effects in accordance with the operator policy. In this paper we outline the cell outage management framework, propose concrete compensation algorithms and assess the achieved performance effects in various scenarios. The reported simulation results show that the proposed compensation algorithm is able to recover a significant percentage of the users that would otherwise be dropped, while still providing sufficient service quality in the compensating cells.

Energy consumption in mobile networks, e.g. 3G networks, may be a substantial part of an operator''s expenses. A large part of the total energy of the network is consumed by the radio access network. One promising way to reduce the energy consumption of the radio network is to deactivate radio units during periods with low load. Significant gains can be achieved using this approach since each radio unit, connected to an antenna, has relatively high energy consumption during idle periods. In this paper we evaluate an approach where the number of active radio units needed for handling the traffic is computed based on the traffic load. This approach is evaluated using traffic measurements from a 3G network in a European city during 2009 and the results show that an energy saving around 50% is achievable. This suggests that there is potential for significantly enhancing energy efficiency in mobile networks by adaptive activation of radio units.

Today, the network operators have to perform drive/walk tests when planning and optimizing their networks. These drive/walk tests are costly and time-consuming, cover only a limited part of the network, and capture only a snapshot in time. To overcome these difficulties, the mobile devices in the network can be used as probes by continuously reporting the observed service quality together with their positions. In this paper, we introduce the concept of X-map estimation, which is a function that processes the measurement data into a geographic map with overlay performance information, referred to as an X-map. By continuously processing measurements, the X-map estimation function approximates the spatial characteristics of the network performance and therefore reduces the costs of drive/walk tests. Additionally, X-maps can be used by Self-Organizing Networks (SON), especially in functionalities that address optimization of coverage, capacity, and quality. In this paper, we introduce two approaches for determining X-maps and we analyze the influence of the applied positioning method on the accuracy of the X-maps. We show that the accuracy of the positioning technique can have a significant impact on the accuracy of the X-map.

Information about the radio coverage is essential for network planning and network optimization. In order to obtain the necessary information, planning tools are used which are in some aspects inaccurate due to, e.g., inaccurate input data, simplifications in the applied propagation models, and changes in the environment or traffic distribution. Thus, the planning tools have to be optimized using measurement campaigns. For this, drive/walk tests have to be performed which are not ideal since only a limited part of the network can be covered due to the costs and access restrictions, and only a snapshot of the conditions in the field is captured. Therefore, we introduce the concept of the X-map estimation function which continuously monitors the network and estimates the spatial network performance by processing the UE reports in a geographic map with overlay performance information, referred to as an X-map. Two different approaches for creating X-maps are evaluated in this paper. We analyze the influence of the positioning accuracy, the UE measurement accuracy, and the number of measurements taken on the accuracy of the resulting X-maps. We show that the influence of the different factors on the accuracy of the X-maps depends on the used approach and that the positioning accuracy and the number of measurements taken have the largest impact.

Future radio access networks are expected to show a high degree of self-organization. This paper addresses self- tuning of the random access channel (RACH) in the 3G Long Term Evolution (LTE). The feasibility of self-tuning is investigated by means of simulation, where the coupling between several parameters and the performance of RACH is provided. The conclusion of the simulations is that RACH self-tuning is indeed possible given that UE assisted measurements are available for the self-tuning mechanism.

Wireless network operators today allocate considerable manual effort in managing their networks. A viable solution for lowering the manual effort is to introduce self-organisation functionalities. In this paper we discuss the challenges that are encountered when embedding multiple self-organisation functionalities into an overall self-organisation concept for future wireless networks. We foresee that there will most likely be a need of a rather complex coordination mechanism for handling multiple self-organisation functionalities in future wireless access networks.

Radio network management simplification concerns to some extent the removal, not the simplification, of tasks. In this paper we present an approach for automatic network management in 3G long term evolution (LTE), namely, methods for automatic configuration of locally-unique physical cell identities and neighbor cell relation lists. We show that these issues can be removed from the list of planning tasks and completely replaced by autonomous algorithms. These algorithms make use of mobile measurements to detect local cell identity conflicts, resolve them, and to update the neighbor cell relation lists in the cells. The performance of the approach is determined using simulations of realistically deployed macro networks. The simulations illustrate the ability of the algorithms to resolve local cell identity conflicts. In particular, the algorithms are capable of both accommodating new cells and handling a worst case scenario where all cells are initiated with the same local cell identities and where neighbor cell relation lists are empty. The contributions in this paper are meant to aid operators by allowing them to replace time consuming and costly tasks with automatic mechanisms, thus, reducing operational expenditure.

Today most universities are using course evaluations. However, course evaluations are often discussed and questioned. This paper reports on a survey where we aim at finding out (1) if students have a preconceived notion of a course, (2) if course evaluation scores can be predicted early in a course, (3) if exam throughput impacts course evaluation, and (4) if web-based evaluation reflects the general opinion from students. The results from the study indicate that students do not let preconceived notion impact nor does exam throughput matter to course evaluation. Further, the final web-based results seem to correlate with opinion of students attending lectures. However, the evaluation grades tend to be defined early in the course; hence first impression lasts.

Recently, cheap and large capacity non-volatile memory such as flash memory is rapidly replacing disks in embedded systems. While the access time of flash memory is highly predictable, deadline misses may occur if data objects in flash memory are not properly managed in real-time embedded databases. Buffer cache can be used to mitigate this problem. However, since the workload of a real-time database cannot be precisely predicted, it may not be feasible to provide enough buffer space to satisfy all timing constraints. Several deadline miss ratio management schemes have been proposed, but they do not consider I/O activities. In this paper, we present an I/O-aware deadline miss ratio management scheme in real-time embedded databases whose secondary storage is flash memory. We propose an adaptive I/O deadline assignment scheme, in which I/O deadlines are derived from up-to-date system status. We also present a deadline miss ratio management architecture where a control theory-based feedback control loop prevents resource overload both in I/O and CPU. A simulation study shows that our approach can effectively cope with both I/O and CPU overload to achieve the desired deadline miss ratio.

Updating real-time system software is often needed in response to errors and added requirements to the software. Stopping a running application, updating the software, and then restarting the application is not suitable for systems with high availability requirements. On the other hand, dynamically updating a system may increase the execution time of the tasks, thus, degrading the performance of the system. Degradation is not acceptable for performancecritical real-time systems as there are strict requirements on the performance. In this paper we present an approach that enables dynamic reconfiguration of a real-time system, where the performance of the system during a reconfiguration satisfies a given worst-case performance specification. Evaluation shows that the presented method is efficient in guaranteeing the worst-case performance of dynamically reconfigurable firm real-time systems.

Real-time applications such as e-commerce, flight control, chemical and nuclear control, and telecommunication are becoming increasingly sophisticated in their data needs, resulting in greater demands for real-time data services that are provided by real-time databases. Since the workload of real-time databases cannot be precisely predicted, they can become overloaded and thereby cause temporal violations, resulting in damage or even a catastrophe. Imprecise computation techniques address this problem and allow graceful degradation during overloads. In this paper, we present a framework for QoS specification and management consisting of a model for expressing QoS requirements, an architecture based on feedback control scheduling, and a set of algorithms implementing different policies and behaviors. Our approach gives a robust and controlled behavior of real-time databases, even for transient overloads and with inaccurate runtime estimates of the transactions. Further, performance experiments show that the proposed algorithms outperform a set of baseline algorithms that uses feedback control.

In the control of continuous and physical systems, the controlled system is sampled sufficiently fast to capture the system dynamics. In general, this property cannot be applied to the control of computer systems as the measured variables are often computed over a data set, e.g., deadline miss ratio. In this paper we quantize the disturbance present in the measured variable as a function of the sampling period and we propose a measurement disturbance suppressive control structure. The experiments we have carried out show that a controller using the proposed control structure outperforms a traditional control structure with regard to performance reliability and adaptation.

The intricacy of real-time data service management increases mainly due to the emergence of applications operating in open and unpredictable environments, increases in software complexity, and need for performance guarantees. In this paper we propose an approach for managing the quality of service of real-time databases that provide imprecise and differentiated services, and that operate in unpredictable environments. Transactions are classified into service classes according to their level of importance. Transactions within each service class are further classified into subclasses based on their quality of service requirements. In this way transactions are explicitly differentiated according to their importance and quality of service requests. The performance evaluation shows that during overloads the most important transactions are guaranteed to meet their deadlines and that reliable quality of service is provided even in the face of varying load and execution time estimation errors.

Several applications, such as Web services and e-commerce, are operating in open environments where the workload characteristics, such as the load applied on the system and the worst-case execution times, are inaccurate or even not known in advance. This implies that transactions submitted to a real-time database cannot be subject to exact schedulability analysis given the lack of a priori knowledge of the workload. In this paper we propose an approach, based on feedback control, for managing the quality of service of real-time databases that provide imprecise and differentiated services, given inaccurate workload characteristics. For each service class, the database operator specifies the quality of service requirements by explicitly declaring the precision requirements of the data and the results of the transactions. The performance evaluation shows that our approach provides reliable quality of service even in the face of varying load and inaccurate execution time estimates.

Real-time systems is a topic that one cannot overlook in an engineer's education. However, teaching real-time systems in an undergraduate syllabus is a challenging experience due to conflicting constraints placed on such a course. In this paper we present a new setup for laboratories in the real-time systems course that successfully meets the constraints of mass education, stable environment management, short time span for the labs, and still enables deep involvement of students in the central topic of resource allocation with high motivation.

In applications such as Web-applications, e-commerce (electronic commerce), and engine control, the demand for real-time data services has increased. In these applications, requests have to be processed within their deadlines using fresh data. Since the workload of these systems cannot be precisely predicted, they can become overloaded and as a result, deadline and freshness violations may occur. To address this problem we propose a QoS-sensitive (quality of service) approach based on imprecise computation, applied on transactions and data objects. We propose two algorithms FCS-HEF (feedback control scheduling) (highest error first) and FCS-HEDF (feedback control scheduling) (highest error density first) that give a robust and controlled behavior of RTDB (real-time database) in terms of transaction and data preciseness, even for transient overloads and with inaccurate run-time estimates of the transactions. Further, performance experiments show that the propose algorithms out perform a set of baseline algorithms including FCS-EDF (feedback control scheduling) (earliest deadline first), which schedules the transactions using EDF (earliest deadline first).

Real-time applications such as e-commerce, flight control, chemical and nuclear control, and telecommunication are becoming increasingly sophisticated in their data needs, resulting in greater demands for real-time data services. Since the workload of real-time databases (RTDBs), providing real-time data services, cannot be precisely predicted, they can become overloaded and thereby cause temporal violations, resulting in a damage or even a catastrophe. Imprecise computation techniques address this problem and allow graceful degradation during overloads. In this paper, we present a framework consisting of a model for expressing QoS requirements in terms of data and transaction preciseness, an architecture based on feedback control scheduling, and a set of algorithms implementing different policies and behaviors. Our approach gives a robust and controlled behavior of RTDBs, even for transient overloads and with inaccurate run-time estimates of the transactions. Further, performance experiments show that the proposed algorithms outperform a set of baseline algorithms, including FCS-EDF that schedules the transactions using EDF and feedback control.