Efficient Traffic Management Algorithms for the Core Network using Device-to-Device Communication and Edge Caching

Exponentially growing number of communicating devices and the need for faster, more reliable and secure communication are becoming major challenges for current mobile communication architecture. More number of connected devices means more bandwidth and a need for higher Quality of Service (QoS) requirements, which bring new challenges in terms of resource and traffic management. Traffic offload to the edge has been introduced to tackle this demand-explosion that let the core network offload some of the contents to the edge to reduce the traffic congestion. Device-to-Device (D2D) communication and edge caching, has been proposed as promising solutions for offloading data. D2D communication refers to the communication infrastructure where the users in proximity communicate with each other directly. D2D communication improves overall spectral efficiency, however, it introduces additional interference in the system. To enable D2D communication, efficient resource allocation must be introduced in order to minimize the interference in the system and this benefits the system in terms of bandwidth efficiency. In the first part of this thesis, low complexity resource allocation algorithm using stable matching is proposed to optimally assign appropriate uplink resources to the devices in order to minimize interference among D2D and cellular users. Edge caching has recently been introduced as a modification of the caching scheme in the core network, which enables a cellular Base Station (BS) to keep copies of the contents in order to better serve users and enhance Quality of Experience (QoE). However, enabling BSs to cache data on the edge of the network brings new challenges especially on deciding on which and how the contents should be cached. Since users in the same cell may share similar content-needs, we can exploit this temporal-spatial correlation in the favor of caching system which is referred to local content popularity. Content popularity is the most important factor in the caching scheme which helps the BSs to cache appropriate data in order to serve the users more efficiently. In the edge caching scheme, the BS does not know the users request-pattern in advance. To overcome this bottleneck, a content popularity prediction using Markov Decision Process (MDP) is proposed in the second part of this thesis to let the BS know which data should be cached in each time-slot. By using the proposed scheme, core network access request can be significantly reduced and it works better than caching based on historical data in both stable and unstable content popularity

Digital Currencies and 5G

Στη παρούσα διπλωματική εργασία με τίτλο “Digital Currencies and 5G” μελετάται η τεχνολογία blockchain, ο τρόπος με τον οποίο γίνεται η τιμολόγηση της υπηρεσίας που εξαρτάται από τον τύπο, το πλήθος, το είδος της υπηρεσίας που επιθυμεί ο εκάστοτε χρήστης αλλά και πως επηρεάζεται το QoE από την υφιστάμενη πολιτική τιμολόγησης.In this work entitled "Digital Currencies and 5G" the blockchain technology is studied, the way in which the service is priced depending on the type, number, type of service desired by each user and how the QoE from the existing pricing policy

Online algorithms for content caching: an economic perspective

Content Caching at intermediate nodes, such that future requests can be served without going back to the origin of the content, is an effective way to optimize the operations of computer networks. Therefore, content caching reduces the delivery delay and improves the users’ Quality of Experience (QoE). The current literature either proposes offline algorithms that have complete knowledge of the request profile a priori, or proposes heuristics without provable performance. In this dissertation, online algorithms are presented for content caching in three different network settings: the current Internet Network, collaborative multi-cell coordinated network, and future Content Centric Networks (CCN). Due to the difficulty of obtaining a prior knowledge of contents’ popularities in real scenarios, an algorithm has to make a decision whether to cache a content or not when a request for the content is made, and without the knowledge of any future requests. The performance of the online algorithms is measured through a competitive ratio analysis, comparing the performance of the online algorithm to that of an omniscient optimal offline algorithm. Through theoretical analyses, it is shown that the proposed online algorithms achieve either the optimal or close to the optimal competitive ratio. Moreover, the algorithms have low complexity and can be implemented in a distributed way. The theoretical analyses are complemented with simulation-based experiments, and it is shown that the online algorithms have better performance compared to the state of the art caching schemes

Cache Content Placement Using Triangular Network Coding

Abstract—Video is one of the main causes of the dramatic increase in data traffic over cellular networks. Caching is an effective mechanism that decreases the download rate from base stations and, as a result, the load on the base station, by storing the most popular files or videos on the caches and providing them to the users. The problem of efficient content placement on the caches is known as an NP-complete problem. In this paper, we study the role of network coding by increasing the amount of available data to the users through the cache nodes. We propose a network coding-based content placement method, and we compare it to the best uncoded content placement and the best triangular network coding strategies. Our method not only increases the amount of available data to the users, but also results in a fair distribution of data. Index Terms—Linear network coding, triangular network coding, caching, content placement, wireless networks. I