Essays on monetary policy, credit and housing.

The present thesis consists of two independent chapters. The contribution of the thesis lies in the field of monetary policy, particularly in the conjunction of monetary policy with credit and housing. The first chapter contributes to the literature by shedding light on the interaction of monetary policy with Government Sponsored Enterprises (GSEs) in the U.S. and revealing their crucial role in the transmission of monetary policy through financial intermediaries. The analysis suggests that GSEs expand their share in the mortgage market after a monetary policy tightening. We discuss three reasons behind this result and then focus on its implication on the transmission mechanism of monetary policy shocks. We conduct a counterfactual experiment to measure the effects of a monetary tightening on the economy when GSEs’ future market share is constrained not to respond to this shock. We document a sizable difference between the standard and the counterfactual impulse responses. Under the counterfactual, monetary policy is more effective in contracting real activity, prices and increasing credit cost. Thus GSEs’ share expansion after a monetary tightening erodes the effects of the latter on the economy. We link those findings with the bank-lending channel of monetary policy. We argue that GSEs mitigate the increase in the cost of financing for financial intermediaries after a monetary tightening. As the bank-lending channel predicts, a relatively lower cost of liquid funds implies a smaller increase in external finance premium and, therefore, a lower impact of a monetary tightening on the economy. The second chapter constitutes the first body of research to provide estimates of the dynamic effects of monetary policy on regional house prices in the U.K. and reveal heterogeneity in the responses of regional house prices to monetary policy shocks. The existing literature dedicates much attention to differences in local housing supply to interpret the heterogenous response of regional house prices to economic shocks. The chapter contributes to this debate by showing that heterogeneous regional house price developments after a monetary policy shock relate to borrowing constraints and the household balance sheet compositions in the region. To the best of our knowledge, this thesis is the first which adds this dimension to regional house price heterogeneity. After a monetary expansion, in regions with low loan-to-income ratios, households exploit lower mortgage rates and increase regional housing demand via intertemporal substitution. On the contrary, in regions with low housing affordability, a large share of households are constrained to borrowing and cannot increase housing demand. Consequently, house prices appreciate relatively less after a monetary policy expansion

Διαχείριση ετερογενών ευρυζωνικών δικτύων με χρήση μηχανισμών μηχανικής μάθησης

Cellular networks are one of the most impactful technologies of today’s ICT industry. They provide wireless access to internet and services with very high availability and effectiveness. The evolution of this technology comes with the maturity of the 3GPP-based network and their upcoming releases that promise to deliver even higher quality of service, additional capabilities, and solutions to previous drawbacks. To achieve this, vendors of these technologies must analyze the complexity of these networks and their different deployment options and provide intelligent management software. Variations of cellular networks can be found in literature as Heterogeneous Cellular Networks (HetNets) or Ultra-Dense networks which are improved design flavors of the same system with increased complexity and configurations. The added capabilities of these networks must be used as a toolbox to improve various operational aspects of the networks such as energy efficiency, network performance and system fault prevention. The scope of this Doctorate Thesis is to analyze different approaches of optimizing HetNets in order to suggest plausible suggestions for extensions that will optimize all high-level objectives. Static management and configuration will be used in conjunction with knowledge-building to improve the energy efficiency of key simulation scenarios of 3GPP networks. Dynamic Resource allocation schemes will be used as a real time management algorithm to improve quality of service in a micro-scale. Predictive models based on acquired historical data will be used to predict network operational KPIs, evaluate the probability of network congestion and identification of unknown network element groups based on their behavior. These generated insights will help the infrastructure providers to impose countermeasures to prevent quality deterioration and enforce the technological standards. They will also lead to the reduction of the OPEX and the energy footprint of the system making technology investments sustainable and profitable for network operators. The framework for developing and testing these algorithms is a custom-designed software platform for HetNet simulations and algorithm experimentation. This system is designed according to standards and specifications in order to provide realistic results that will establish the suggested algorithms as strong candidates to be included in future 3GPP-based wireless networks.Τα κυψελωτά δίκτυα κινητών επικοινωνιών είναι μία από τις τεχνολογίες με την μεγαλύτερη επίδραση στην σημερινή βιομηχανία των τεχνολογιών επικοινωνιών και πληροφορικής. Παρέχουν ασύρματη πρόσβαση στο διαδίκτυο αλλά και μια πληθώρα άλλων υπηρεσιών με πάρα πολύ υψηλή διαθεσιμότητα και αποτελεσματικότητα. Η εξέλιξη αυτής της τεχνολογίας έρχεται με την ωρίμανση των δικτύων προδιαγραφών 3GPP, η πρόοδος των οποίων υπόσχεται να παρέχει ακόμα υψηλότερη ποιότητα υπηρεσιών, περισσότερες δυνατότητες αλλά και λύσεις σε προβλήματα των παλαιότερων γενεών. Για την επίτευξη αυτών των στόχων, οι κατασκευαστές αυτής της τεχνολογίας πρέπει να αναλύσουν προσεκτικά την πολυπλοκότητα αυτών των δικτύων αλλά και των δυνατοτήτων εγκατάστασης τους και να παρέχουν ευφυές λογισμικό διαχείρισης τους. Διαφοροποιήσεις σε κυψελωτά δίκτυα τύπου 3GPP όπως τα ετερογενή κυψελωτά δίκτυα αλλά και τα «υπερ-πυκνά» δίκτυα είναι εξελίξεις αυτών των δικτύων με αυξημένη πολυπλοκότητα και δυνατότητες που βρίσκεται στη βιβλιογραφία. Μέσω αυτών των δυνατοτήτων μπορούμε να βελτιώσουμε τους διάφορους λειτουργικούς στόχους της υποδομής όπως ενεργειακή αποδοτικότητα, δικτυακές επιδόσεις και αποφυγή σφαλμάτων. Αυτή η διδακτορική διατριβή έχει ως σκοπό να αναλύσει διαφορετικές προσεγγίσεις βελτιστοποίησης ετερογενών δικτύων καταλήγοντας έτσι σε προτάσεις για επέκταση τους επηρεάζοντας όσο το δυνατών περισσότερους στόχους-κλειδιά. Στατική διαχείριση και ρύθμιση σε συνδυασμό με συλλογή γνώσης θα χρησιμοποιηθεί για την βελτίωση ενεργειακή επίδοσης σεναρίων-κλειδιών για την 4η γενιάς κινητής τηλεφωνίας. Αλγόριθμοι δυναμικού διαμοιρασμού πόρων θα χρησιμοποιηθούν σαν μία μέθοδος διαχείρισης πραγματικού χρόνου με σκοπό την βελτίωση ποιότητας υπηρεσιών σε μικρό-κλίμακα. Τέλος, μοντέλα μηχανικής μάθησης θα εκπαιδευτούν σε ιστορικά δεδομένα με σκοπό την πρόβλεψη των λειτουργικών δεικτών του δικτύου, εκτίμηση της πιθανότητας δικτυακής υπερφόρτωσης και αναγνώριση άγνωστων ομάδων δικτυακών στοιχείων βασισμένα στην συμπεριφορά τους. Αυτές οι προβλέψεις θα βοηθήσουν την διαχείριση της υποδομής στην ενεργοποίηση αντίμετρων για την αποφυγή της υποβάθμισης της ποιότητας υπηρεσιών αλλά και την επικράτηση των προδιαγραφών της τεχνολογίας. Αυτές θα οδηγήσουν επίσης στην ελάττωση του OPEX αλλά και του ενεργειακού αποτυπώματος του συστήματος οδηγώντας έτσι σε βιώσιμες και επιτυχημένες επενδύσεις για τους παρόχους. Το πλαίσιο ανάπτυξης για αυτούς τους αλγορίθμους είναι ένα αυτοσχέδιο πρωτότυπο προσομοιωτή ετερογενών δικτύων και πλατφόρμα εκτέλεσης πειραματικών αλγορίθμων. Αυτό το σύστημα σχεδιάστηκε με βάση τις πρότυπες προδιαγραφές προσομοίωσης τέτοιων συστημάτων και τα ρεαλιστικά δεδομένα που θα εξαχθούν από αυτό θα βοηθήσουν στην υπεράσπιση της αποτελεσματικότητας των προτεινόμενων αλγορίθμων για την ένταξη τους στην τεχνολογία κυψελωτών επικοινωνιών 3GPP

Hybrid Network–Spatial Clustering for Optimizing 5G Mobile Networks

5G is the new generation of 3GPP-based cellular communications that provides remarkable connectivity capabilities and extreme network performance to mobile network operators and cellular users worldwide. The rollout process of a new capacity layer (cell) on top of the existing previous cellular technologies is a complex process that requires time and manual effort from radio planning-engineering teams and parameter optimization teams. When it comes to optimum configuration of the 5G gNB cell parameters, the maximization of achieved coverage (RSRP) and quality (SINR) of the served mobile terminals are of high importance for achieving the very high data transmission rates expected in 5G. This process strongly relies on network measurements that can be even more insightful when mobile terminal localization information is present. This information can be generated by modern algorithmic techniques that act on the cellular network signaling measurements. Configuration algorithms can then use these measurements combined with location information to optimize various cell deployment parameters such as cell azimuth. Furthermore, data-driven approaches are shown in the literature to outperform traditional, model-based algorithms as they can automate the optimization of parameters while specializing in the characteristics of each individual geographical zone. In the context of the above, in this paper, we tested the automated network reconfiguration schemes based on unsupervised learning and applied statistics for cell azimuth steering. We compared network metric clustering and geospatial clustering to be used as our baseline algorithms that are based on K-means with the proposed scheme—hybrid network and spatial clustering based on hierarchical DBSCAN. Each of these algorithms used data generated by an initial scenario to produce cell re-configuration actions and their performance was then evaluated on a validated simulation platform to capture the impact of each set of gNB reconfiguration actions. Our performance evaluation methodology was based on statistical distribution analysis for RSRP and SINR metrics for the reference scenario as well as for each reconfiguration scheme. It is shown that while both baseline algorithms improved the overall performance of the network, the proposed hybrid network–spatial scheme greatly outperformed them in all statistical criteria that were evaluated, making it a better candidate for the optimization of 5G capacity layers in modern urban environments

Context-aware radio resource management below 6 GHz for enabling dynamic channel assignment in the 5G era

Abstract Heterogeneous networks constitute a promising solution to the emerging challenges of 5G networks. According to the specific network architecture, a macro-cell base station (MBS) shares the same spectral resources with a number of small cell base stations (SBSs), resulting in increased co-channel interference (CCI). The efficient management of CCI has been studied extensively in the literature and various dynamic channel assignment (DCA) schemes have been proposed. However, the majority of these schemes consider a uniform approach for the users without taking into account the different quality requirements of each application. In this work, we propose an algorithm for enabling dynamic channel assignment in the 5G era that receives information about the interference and QoS levels and dynamically assigns the best channel. This algorithm is compared to state-of-the-art channel assignment algorithm. Results show an increase of performance, e.g., in terms of throughput and air interface latency. Finally, potential challenges and way forward are also discussed