A Survey of Deep Learning for Data Caching in Edge Network

The concept of edge caching provision in emerging 5G and beyond mobile networks is a promising method to deal both with the traffic congestion problem in the core network as well as reducing latency to access popular content. In that respect end user demand for popular content can be satisfied by proactively caching it at the network edge, i.e, at close proximity to the users. In addition to model based caching schemes learning-based edge caching optimizations has recently attracted significant attention and the aim hereafter is to capture these recent advances for both model based and data driven techniques in the area of proactive caching. This paper summarizes the utilization of deep learning for data caching in edge network. We first outline the typical research topics in content caching and formulate a taxonomy based on network hierarchical structure. Then, a number of key types of deep learning algorithms are presented, ranging from supervised learning to unsupervised learning as well as reinforcement learning. Furthermore, a comparison of state-of-the-art literature is provided from the aspects of caching topics and deep learning methods. Finally, we discuss research challenges and future directions of applying deep learning for cachin

Identification of key research topics in 5G using co-word analysis

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe aim of this research is to better understand the field of 5G by analyzing the more than 10000 publications found in the Web of Science database. To achieve this, a co-word analysis was performed to identify research topics based on the author keywords and a strategic diagram was used to measure their level of maturity and relevance to the field. In total this analysis identified that all the articles can be grouped into seven topics, from which, two are mature but peripheral, one is both well developed and central to the field, and the rest are central, but underdeveloped. The value of this research, was the usage of a well-established technique that has been used in many fields, but never in the field of 5G which is growing in relevance

Mobile Ad-Hoc Networks

Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: vehicular ad-hoc networks, security and caching, TCP in ad-hoc networks and emerging applications. It is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks

Advanced Technologies for Device-to-device Communications Underlaying Cellular Networks

The past few years have seen a major change in cellular networks, as explosive growth in data demands requires more and more network capacity and backhaul capability. New wireless technologies have been proposed to tackle these challenges. One of the emerging technologies is device-to-device (D2D) communications. It enables two cellular user equip- ment (UEs) in proximity to communicate with each other directly reusing cellular radio resources. In this case, D2D is able to of oad data traf c from central base stations (BSs) and signi cantly improve the spectrum ef ciency of a cellular network, and thus is one of the key technologies for the next generation cellular systems. Radio resource management (RRM) for D2D communications and how to effectively exploit the potential bene ts of D2D are two paramount challenges to D2D communications underlaying cellular networks. In this thesis, we focus on four problems related to these two challenges. In Chapter 2, we utilise the mixed integer non-linear programming (MINLP) to model and solve the RRM optimisation problems for D2D communications. Firstly we consider the RRM optimisation problem for D2D communications underlaying the single carrier frequency division multiple access (SC-FDMA) system and devise a heuristic sub- optimal solution to it. Then we propose an optimised RRM mechanism for multi-hop D2D communications with network coding (NC). NC has been proven as an ef cient technique to improve the throughput of ad-hoc networks and thus we apply it to multi-hop D2D communications. We devise an optimal solution to the RRM optimisation problem for multi-hop D2D communications with NC. In Chapter 3, we investigate how the location of the D2D transmitter in a cell may affect the RRM mechanism and the performance of D2D communications. We propose two optimised location-based RRM mechanisms for D2D, which maximise the throughput and the energy ef ciency of D2D, respectively. We show that, by considering the location information of the D2D transmitter, the MINLP problem of RRM for D2D communications can be transformed into a convex optimisation problem, which can be ef ciently solved by the method of Lagrangian multipliers. In Chapter 4, we propose a D2D-based P2P le sharing system, which is called Iunius. The Iunius system features: 1) a wireless P2P protocol based on Bittorrent protocol in the application layer; 2) a simple centralised routing mechanism for multi-hop D2D communications; 3) an interference cancellation technique for conventional cellular (CC) uplink communications; and 4) a radio resource management scheme to mitigate the interference between CC and D2D communications that share the cellular uplink radio resources while maximising the throughput of D2D communications. We show that with the properly designed application layer protocol and the optimised RRM for D2D communications, Iunius can signi cantly improve the quality of experience (QoE) of users and of oad local traf c from the base station. In Chapter 5, we combine LTE-unlicensed with D2D communications. We utilise LTE-unlicensed to enable the operation of D2D in unlicensed bands. We show that not only can this improve the throughput of D2D communications, but also allow D2D to work in the cell central area, which normally regarded as a “forbidden area” for D2D in existing works. We achieve these results mainly through numerical optimisation and simulations. We utilise a wide range of numerical optimisation theories in our works. Instead of utilising the general numerical optimisation algorithms to solve the optimisation problems, we modify them to be suitable for the speci c problems, thereby reducing the computational complexity. Finally, we evaluate our proposed algorithms and systems through sophisticated numer- ical simulations. We have developed a complete system-level simulation framework for D2D communications and we open-source it in Github: https://github.com/mathwuyue/py- wireless-sys-sim

Optimization for Networks and Object Recognition

The present thesis explores two different application areas of combinatorial optimization, the work presented, indeed, is two fold, since it deals with two distinct problems, one related to data transfer in networks and the other to object recognition. Caching is an essential technique to improve throughput and latency in a vast variety of applications. The core idea is to duplicate content in memories distributed across the network, which can then be exploited to deliver requested content with less congestion and delay. In particular, it has been shown that the use of caching together with smart offloading strategies in a RAN composed of evolved NodeBs (eNBs), AP (e.g., WiFi), and UEs, can significantly reduce the backhaul traffic and service latency. The traditional role of cache memories is to deliver the maximal amount of requested content locally rather than from a remote server. While this approach is optimal for single-cache systems, it has recently been shown to be, in general, significantly suboptimal for systems with multiple caches (i.e., cache networks) since it allows only additive caching gain, while instead, cache memories should be used to enable a multiplicative caching gain. Recent studies have shown that storing different portions of the content across the wireless network caches and capitalizing on the spatial reuse of device-to-device (D2D) communications, or exploiting globally cached information in order to multicast coded messages simultaneously useful to a large number of users, enables a global caching gain. We focus on the case of a single server (e.g., a base station) and multiple users, each of which caches segments of files in a finite library. Each user requests one (whole) file in the library and the server sends a common coded multicast message to satisfy all users at once. The problem consists of finding the smallest possible codeword length to satisfy such requests. To solve this problem we present two achievable caching and coded delivery scheme, and one correlation-aware caching scheme, each of them is based on a heuristic polynomial-time coloring algorithm. Automatic object recognition has become, over the last decades, a central toping the in the artificial intelligence research, with a a significant burt over the last new year with the advent of the deep learning paradigm. In this context, the objective of the work discussed in the last two chapter of this thesis is an attempt at improving the performance of a natural images classifier introducing in the loop knowledge coming from the real world, expressed in terms of probability of a set of spatial relations between the objects in the images. In different words, the framework presented in this work aims at integrating the output of standard classifiers on different image parts with some domain knowledge, encoded in a probabilistic ontology