Mitigating Inter-Cluster Interference on the Uplink for a 3-Cell Clustered Cooperative Network

In this paper, we propose a practical and scalable solution to mitigating interference on the uplink through static clustering and adaptive fractional frequency reuse (CFFR). The focus is on a three-cell clustered network due to its low complexity. Moreover, we have previously shown that a performance comparable to that of global coordination is achievable using a cluster size of three. In this paper, for a clustered planar Wyner network, we derive analytical capacity equations for zero forcing (ZF) and linear minimum mean squared error (LMMSE)-based receivers. The theoretical results show that inter-cluster interference is the major performance bottleneck and that the smallest interference from the neighbouring clusters is sufficient to significantly lower the system performance. We then switch our study to a more realistic network setting and augment our CFFR technique by adopting an entirely distributed architecture and by implementing a location classification algorithm based on logistic regression. We then show through simulations that CFFR performs significantly better than the widely studied dynamic clustering (DC) technique. Since the inter-cluster interference intensity of CFFR is much lower than DC, the per-cell sum rate performance is 1.5 × better, especially at high loads. We also show that the CFFR algorithm is a lot less complex than DC in terms of running time

Benefits and limits of machine learning for the implicit coordination on SON functions

Bedingt durch die Einführung neuer Netzfunktionen in den Mobilfunknetzen der nächsten Generation, z. B. Slicing oder Mehrantennensysteme, sowie durch die Koexistenz mehrerer Funkzugangstechnologien, werden die Optimierungsaufgaben äußerst komplex und erhöhen die OPEX (OPerational EXpenditures). Um den Nutzern Dienste mit wettbewerbsfähiger Dienstgüte (QoS) zu bieten und gleichzeitig die Betriebskosten niedrig zu halten, wurde von den Standardisierungsgremien das Konzept des selbstorganisierenden Netzes (SON) eingeführt, um das Netzmanagement um eine Automatisierungsebene zu erweitern. Es wurden dafür mehrere SON-Funktionen (SFs) vorgeschlagen, um einen bestimmten Netzbereich, wie Abdeckung oder Kapazität, zu optimieren. Bei dem konventionellen Entwurf der SFs wurde jede Funktion als Regler mit geschlossenem Regelkreis konzipiert, der ein lokales Ziel durch die Einstellung bestimmter Netzwerkparameter optimiert. Die Beziehung zwischen mehreren SFs wurde dabei jedoch bis zu einem gewissen Grad vernachlässigt. Daher treten viele widersprüchliche Szenarien auf, wenn mehrere SFs in einem mobilen Netzwerk instanziiert werden. Solche widersprüchlichen Funktionen in den Netzen verschlechtern die QoS der Benutzer und beeinträchtigen die Signalisierungsressourcen im Netz. Es wird daher erwartet, dass eine existierende Koordinierungsschicht (die auch eine Entität im Netz sein könnte) die Konflikte zwischen SFs lösen kann. Da diese Funktionen jedoch eng miteinander verknüpft sind, ist es schwierig, ihre Interaktionen und Abhängigkeiten in einer abgeschlossenen Form zu modellieren. Daher wird maschinelles Lernen vorgeschlagen, um eine gemeinsame Optimierung eines globalen Leistungsindikators (Key Performance Indicator, KPI) so voranzubringen, dass die komplizierten Beziehungen zwischen den Funktionen verborgen bleiben. Wir nennen diesen Ansatz: implizite Koordination. Im ersten Teil dieser Arbeit schlagen wir eine zentralisierte, implizite und auf maschinellem Lernen basierende Koordination vor und wenden sie auf die Koordination zweier etablierter SFs an: Mobility Robustness Optimization (MRO) und Mobility Load Balancing (MLB). Anschließend gestalten wir die Lösung dateneffizienter (d. h. wir erreichen die gleiche Modellleistung mit weniger Trainingsdaten), indem wir eine geschlossene Modellierung einbetten, um einen Teil des optimalen Parametersatzes zu finden. Wir nennen dies einen "hybriden Ansatz". Mit dem hybriden Ansatz untersuchen wir den Konflikt zwischen MLB und Coverage and Capacity Optimization (CCO) Funktionen. Dann wenden wir ihn auf die Koordinierung zwischen MLB, Inter-Cell Interference Coordination (ICIC) und Energy Savings (ES) Funktionen an. Schließlich stellen wir eine Möglichkeit vor, MRO formal in den hybriden Ansatz einzubeziehen, und zeigen, wie der Rahmen erweitert werden kann, um anspruchsvolle Netzwerkszenarien wie Ultra-Reliable Low Latency Communications (URLLC) abzudecken.Due to the introduction of new network functionalities in next-generation mobile networks, e.g., slicing or multi-antenna systems, as well as the coexistence of multiple radio access technologies, the optimization tasks become extremely complex, increasing the OPEX (OPerational EXpenditures). In order to provide services to the users with competitive Quality of Service (QoS) while keeping low operational costs, the Self-Organizing Network (SON) concept was introduced by the standardization bodies to add an automation layer to the network management. Thus, multiple SON functions (SFs) were proposed to optimize a specific network domain, like coverage or capacity. The conventional design of SFs conceived each function as a closed-loop controller optimizing a local objective by tuning specific network parameters. However, the relationship among multiple SFs was neglected to some extent. Therefore, many conflicting scenarios appear when multiple SFs are instantiated in a mobile network. Having conflicting functions in the networks deteriorates the users’ QoS and affects the signaling resources in the network. Thus, it is expected to have a coordination layer (which could also be an entity in the network), conciliating the conflicts between SFs. Nevertheless, due to interleaved linkage among those functions, it is complex to model their interactions and dependencies in a closed form. Thus, machine learning is proposed to drive a joint optimization of a global Key Performance Indicator (KPI), hiding the intricate relationships between functions. We call this approach: implicit coordination. In the first part of this thesis, we propose a centralized, fully-implicit coordination approach based on machine learning (ML), and apply it to the coordination of two well-established SFs: Mobility Robustness Optimization (MRO) and Mobility Load Balancing (MLB). We find that this approach can be applied as long as the coordination problem is decomposed into three functional planes: controllable, environmental, and utility planes. However, the fully-implicit coordination comes at a high cost: it requires a large amount of data to train the ML models. To improve the data efficiency of our approach (i.e., achieving good model performance with less training data), we propose a hybrid approach, which mixes ML with closed-form models. With the hybrid approach, we study the conflict between MLB and Coverage and Capacity Optimization (CCO) functions. Then, we apply it to the coordination among MLB, Inter-Cell Interference Coordination (ICIC), and Energy Savings (ES) functions. With the hybrid approach, we find in one shot, part of the parameter set in an optimal manner, which makes it suitable for dynamic scenarios in which fast response is expected from a centralized coordinator. Finally, we present a manner to formally include MRO in the hybrid approach and show how the framework can be extended to cover challenging network scenarios like Ultra-Reliable Low Latency Communications (URLLC)

A Study about Heterogeneous Network Issues Management based on Enhanced Inter-cell Interference Coordination and Machine Learning Algorithms

Under the circumstance of fast growing demands for mobile data, Heterogeneous Networks (HetNets) has been considered as one of the key technologies to solve 1000 times mobile data challenge in the coming decade. Although the unique multi-tier topology of HetNets has achieved high spectrum efficiency and enhanced Quality of Service (QoS), it also brings a series of critical issues. In this thesis, we present an investigation on understanding the cause of HetNets challenges and provide a research on state of arts techniques to solve three major issues: interference, offloading and handover. The first issue addressed in the thesis is the cross-tier interference of HetNets. We introduce Almost Blank Subframes (ABS) to free small cell UEs from cross-tier interference, which is the key technique of enhanced Inter-Cell Interference Coordination (eICIC). Nash Bargain Solution (NBS) is applied to optimize ABS ratio and UE partition. Furthermore, we propose a power based multi-layer NBS Algorithm to obtain optimal parameters of Further enhanced Inter-cell Interference Coordination (FeICIC), which significantly improve macrocell efficiency compared to eICIC. This algorithm not only introduces dynamic power ratio but also defined opportunity cost for each layer instead of conventional zero-cost partial fairness. Simulation results show the performance of proposed algorithm may achieve up to 31.4% user throughput gain compared to eICIC and fixed power ratio FeICIC. This thesis’ second focusing issue is offloading problem of HetNets. This includes (1) UE offloading from macro cell and (2) small cell backhaul offloading. For first aspect, we have discussed the capability of machine learning algorithms tackling this challenge and propose the User-Based K-means Algorithm (UBKCA). The proposed algorithm establishes a closed loop Self-Organization system on our HetNets scenario to maintain desired offloading factor of 50%, with cell edge user factor 17.5% and CRE bias of 8dB. For second part, we further apply machine learning clustering method to establish cache system, which may achieve up to 70.27% hit-ratio and reduce request latency by 60.21% for Youtube scenario. K-Nearest Neighbouring (KNN) is then applied to predict new users’ content preference and prove our cache system’s suitability. Besides that, we have also proposed a system to predict users’ content preference even if the collected data is not complete. The third part focuses on offloading phase within HetNets. This part detailed discusses CRE’s positive effect on mitigating ping-pong handover during UE offloading, and CRE’s negative effect on increasing cross-tier interference. And then a modified Markov Chain Process is established to map the handover phases for UE to offload from macro cell to small cell and vice versa. The transition probability of MCP has considered both effects of CRE so that the optimal CRE value for HetNets can be achieved, and result for our scenario is 7dB. The combination of CRE and Handover Margin is also discussed

Practical Extensions to the Evaluation and Analysis of Wireless Coexistence in Unlicensed Bands

Sharing spectrum resources in unlicensed bands has proven cost effective and beneficial for providing ubiquitous access to wireless functionality for a broad range of applications. Chipsets designed to implement communication standards in the Industrial, Scientific and Medical (ISM) band have become increasingly inexpensive and widely available, making wireless-enabled medical and non-medical devices attractive to an increased number of users. Consequently, wireless coexistence becomes a concern. In response, the U.S. Food and Drug Administration (FDA) has issued a guidance document to assist medical device manufacturers ensure reasonable safety and effectiveness. Coexistence-testing methods are now being reported in literature, and novel solutions are under consideration for inclusion in the American National Standards Institute (ANSI) C63.27 Standard for Evaluation of Wireless Coexistence. This dissertation addresses practical issues for evaluating and reporting wireless coexistence. During testing, an under-test-system (UTS) is evaluated in the presence of an interfering system (IS). Accordingly, an innovative method is suggested for estimating channel utilization of multiple, concurrent wireless transmitters sharing an unlicensed band in the context of radiated open environment coexistence testing (ROECT). Passively received power measurements were collected, and then a Gaussian mixture model (GMM) was used to build a classifier for labeling observed power samples relative to their source. Overall accuracy was verified at 98.86%. Case studies are presented utilizing IEEE 802.11n as an IS with UTS based on either IEEE 802.11n or ZigBee. Results demonstrated the mutual effect of spectrum sharing on both IS and UTS in terms of per-second channel utilization and frame collision. The process of approximating the probability of a device to coexist in its intended environment is discussed, and a generalized framework for modeling the environment is presented. An 84-day spectrum survey of the 2.4 GHz to 2.48 GHz ISM band in a hospital environment serves as proof of concept. A custom platform was used to monitor power flux spectral density and record received power in both an intensive care unit (ICU) and a post-surgery recovery room (RR). Observations indicated that significant correlation in activity patterns corresponded mainly to IEEE 802.11 channels 1, 6, and 11. Consequently, channel utilization of three non-overlapping channels of 20 MHz bandwidth---relative to IEEE 802.11 channels 1, 6, and 11---were calculated and fitted to a generalized extreme value (GEV) distribution. Low channel utilization ( 50%), was observed in the surveyed environment. Reported findings can be complementary to wireless coexistence testing. Quantifying the probability of UTS coexistence in a given environment is central to the evaluation of coexistence, as evidenced in the draft of the C63.27 standard. Notably, a method for this calculation is not currently provided in the standard. To fill this void, the work presented herein proposes the use of logistic regression (LR) to estimate coexistence probability. ROECT was utilized to test a scenario with an 802.11n IS and ZigBee UTS medical device. Findings demonstrate that fitted LR model achieves 92.72% overall accuracy of classification on a testing dataset that included the outcome of a wide variety of coexistence testing scenarios. Results were incorporated with those reported in [1] using Monte Carlo simulation to estimate UTS probability of coexistence in a hospital environment

User mobility prediction and management using machine learning

The next generation mobile networks (NGMNs) are envisioned to overcome current user mobility limitations while improving the network performance. Some of the limitations envisioned for mobility management in the future mobile networks are: addressing the massive traffic growth bottlenecks; providing better quality and experience to end users; supporting ultra high data rates; ensuring ultra low latency, seamless handover (HOs) from one base station (BS) to another, etc. Thus, in order for future networks to manage users mobility through all of the stringent limitations mentioned, artificial intelligence (AI) is deemed to play a key role automating end-to-end process through machine learning (ML). The objectives of this thesis are to explore user mobility predictions and management use-cases using ML. First, background and literature review is presented which covers, current mobile networks overview, and ML-driven applications to enable user’s mobility and management. Followed by the use-cases of mobility prediction in dense mobile networks are analysed and optimised with the use of ML algorithms. The overall framework test accuracy of 91.17% was obtained in comparison to all other mobility prediction algorithms through artificial neural network (ANN). Furthermore, a concept of mobility prediction-based energy consumption is discussed to automate and classify user’s mobility and reduce carbon emissions under smart city transportation achieving 98.82% with k-nearest neighbour (KNN) classifier as an optimal result along with 31.83% energy savings gain. Finally, context-aware handover (HO) skipping scenario is analysed in order to improve over all quality of service (QoS) as a framework of mobility management in next generation networks (NGNs). The framework relies on passenger mobility, trains trajectory, travelling time and frequency, network load and signal ratio data in cardinal directions i.e, North, East, West, and South (NEWS) achieving optimum result of 94.51% through support vector machine (SVM) classifier. These results were fed into HO skipping techniques to analyse, coverage probability, throughput, and HO cost. This work is extended by blockchain-enabled privacy preservation mechanism to provide end-to-end secure platform throughout train passengers mobility

XIII Jornadas de ingeniería telemática (JITEL 2017)

Las Jornadas de Ingeniería Telemática (JITEL), organizadas por la Asociación de Telemática (ATEL), constituyen un foro propicio de reunión, debate y divulgación para los grupos que imparten docencia e investigan en temas relacionados con las redes y los servicios telemáticos. Con la organización de este evento se pretende fomentar, por un lado el intercambio de experiencias y resultados, además de la comunicación y cooperación entre los grupos de investigación que trabajan en temas relacionados con la telemática. En paralelo a las tradicionales sesiones que caracterizan los congresos científicos, se desea potenciar actividades más abiertas, que estimulen el intercambio de ideas entre los investigadores experimentados y los noveles, así como la creación de vínculos y puntos de encuentro entre los diferentes grupos o equipos de investigación. Para ello, además de invitar a personas relevantes en los campos correspondientes, se van a incluir sesiones de presentación y debate de las líneas y proyectos activos de los mencionados equiposLloret Mauri, J.; Casares Giner, V. (2018). XIII Jornadas de ingeniería telemática (JITEL 2017). Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/97612EDITORIA

Self-organization for 5G and beyond mobile networks using reinforcement learning

The next generations of mobile networks 5G and beyond, must overcome current networks limitations as well as improve network performance. Some of the requirements envisioned for future mobile networks are: addressing the massive growth required in coverage, capacity and traffic; providing better quality of service and experience to end users; supporting ultra high data rates and reliability; ensuring latency as low as one millisecond, among others. Thus, in order for future networks to enable all of these stringent requirements, a promising concept has emerged, self organising networks (SONs). SONs consist of making mobile networks more adaptive and autonomous and are divided in three main branches, depending on their use-cases, namely: self-configuration, self-optimisation, and self-healing. SON is a very promising and broad concept, and in order to enable it, more intelligence needs to be embedded in the mobile network. As such, one possible solution is the utilisation of machine learning (ML) algorithms. ML has many branches, such as supervised, unsupervised and Reinforcement Learning (RL), and all can be used in different SON use-cases. The objectives of this thesis are to explore different RL techniques in the context of SONs, more specifically in self-optimization use-cases. First, the use-case of user-cell association in future heterogeneous networks is analysed and optimised. This scenario considers not only Radio Access Network (RAN) constraints, but also in terms of the backhaul. Based on this, a distributed solution utilizing RL is proposed and compared with other state-of-the-art methods. Results show that the proposed RL algorithm outperforms current ones and is able to achieve better user satisfaction, while minimizing the number of users in outage. Another objective of this thesis is the evaluation of Unmanned Aerial vehicles (UAVs) to optimize cellular networks. It is envisioned that UAVs can be utilized in different SON use-cases and integrated with RL algorithms to determine their optimal 3D positions in space according to network constraints. As such, two different mobile network scenarios are analysed, one emergency and a pop-up network. The emergency scenario considers that a major natural disaster destroyed most of the ground network infrastructure and the goal is to provide coverage to the highest number of users possible using UAVs as access points. The second scenario simulates an event happening in a city and, because of the ground network congestion, network capacity needs to be enhanced by the deployment of aerial base stations. For both scenarios different types of RL algorithms are considered and their complexity and convergence are analysed. In both cases it is shown that UAVs coupled with RL are capable of solving network issues in an efficient and quick manner. Thus, due to its ability to learn from interaction with an environment and from previous experience, without knowing the dynamics of the environment, or relying on previously collected data, RL is considered as a promising solution to enable SON