Performance enhancement solutions in wireless communication networks

In this dissertation thesis, we study the new relaying protocols for different wireless network systems. We analyze and evaluate an efficiency of the transmission in terms of the outage probability over Rayleigh fading channels by mathematical analyses. The theoretical analyses are verified by performing Monte Carlo simulations. First, we study the cooperative relaying in the Two-Way Decode-and-Forward (DF) and multi-relay DF scheme for a secondary system to obtain spectrum access along with a primary system. In particular, we proposed the Two-Way DF scheme with Energy Harvesting, and the Two-Way DF Non-orthogonal Multiple Access (NOMA) scheme with digital network coding. Besides, we also investigate the wireless systems with multi-relay; the best relay selection is presented to optimize the effect of the proposed scheme. The transmission protocols of the proposed schemes EHAF (Energy Harvesting Amplify and Forward) and EHDF (Energy Harvesting Decode and Forward) are compared together in the same environment and in term of outage probability. Hence, with the obtained results, we conclude that the proposed schemes improve the performance of the wireless cooperative relaying systems, particularly their throughput. Second, we focus on investigating the NOMA technology and proposing the optimal solutions (protocols) to advance the data rate and to ensure the Quality of Service (QoS) for the users in the next generation of wireless communications. In this thesis, we propose a Two-Way DF NOMA scheme (called a TWNOMA protocol) in which an intermediate relay helps two source nodes to communicate with each other. Simulation and analysis results show that the proposed protocol TWNOMA is improving the data rate when comparing with a conventional Two-Way scheme using digital network coding (DNC) (called a TWDNC protocol), Two-Way scheme without using DNC (called a TWNDNC protocol) and Two-Way scheme in amplify-and-forward(AF) relay systems (called a TWANC protocol). Finally, we considered the combination of the NOMA and physical layer security (PLS) in the Underlay Cooperative Cognitive Network (UCCN). The best relay selection strategy is investigated, which uses the NOMA and considers the PLS to enhance the transmission efficiency and secrecy of the new generation wireless networks.V této dizertační práci je provedena studie nových přenosových protokolů pro různé bezdrátové síťové systémy. S využitím matematické analýzy jsme analyzovali a vyhodnotili efektivitu přenosu z hlediska pravděpodobnosti výpadku přes Rayleighův kanál. Teoretické analýzy jsou ověřeny provedenými simulacemi metodou Monte Carlo. Nejprve došlo ke studii kooperativního přenosu ve dvoucestném dekóduj-a-předej (Two-Way Decode-and-Forward–TWDF) a vícecestném DF schématu s větším počtem přenosových uzlů pro sekundární systém, kdy takto byl získán přístup ke spektru spolu s primárním systémem. Konkrétně jsme navrhli dvoucestné DF schéma se získáváním energie a dvoucestné DF neortogonální schéma s mnohonásobným přístupem (Non-orthogonal Multiple Access–NOMA) s digitálním síťovým kódováním. Kromě toho rovněž zkoumáme bezdrátové systémy s větším počtem přenosových uzlů, kde je přítomen výběr nejlepšího přenosového uzlu pro optimalizaci efektivnosti navrženého schématu. Přenosové protokoly navržených schémat EHAF (Energy Harvesting Amplify and Forward) a EHDF(Energy Harvesting Decode and Forward) jsou společně porovnány v identickém prostředí z pohledu pravděpodobnosti výpadku. Následně, na základě získaných výsledků, jsme dospěli k závěru, že navržená schémata vylepšují výkonnost bezdrátových kooperativních systémů, konkrétně jejich propustnost. Dále jsme se zaměřili na zkoumání NOMA technologie a navrhli optimální řešení (protokoly) pro urychlení datového přenosu a zajištění QoS v další generaci bezdrátových komunikací. V této práci jsme navrhli dvoucestné DF NOMA schéma (nazýváno jako TWNOMA protokol), ve kterém mezilehlý přenosový uzel napomáhá dvěma zdrojovým uzlům komunikovat mezi sebou. Výsledky simulace a analýzy ukazují, že navržený protokol TWNOMA vylepšuje dosaženou přenosovou rychlost v porovnání s konvenčním dvoucestným schématem používajícím DNC (TWDNC protokol), dvoucestným schématem bez použití DNC (TWNDNC protokol) a dvoucestným schématem v zesil-a-předej (amplify-and-forward) přenosových systémech (TWANC protokol). Nakonec jsme zvážili využití kombinace NOMA a zabezpečení fyzické vrstvy (Physical Layer Security–PLS) v podpůrné kooperativní kognitivní síti (Underlay Cooperative Cognitive Network–UCCN). Zde je zde zkoumán výběr nejlepšího přenosového uzlu, který užívá NOMA a bere v úvahu PLS pro efektivnější přenos a zabezpečení nové generace bezdrátových sítí.440 - Katedra telekomunikační technikyvyhově

Capacity allocation in wireless communication networks : models and analyses

This monograph has concentrated on capacity allocation in cellular and Wireless Local Area Networks, primarily with a network operator’s perspective. In the introduc- tory chapter, a reference model has been proposed for the extensive suite of capacity allocation mechanisms that can be applied at different time scales, in order to influ- ence the inherent trade-offs between investment costs, network capacity and service quality. The subsequent chapters presented a number of comprehensive studies with the objective to understand the joint impact of the different control mechanisms on the network operations and service provisioning, as well as the influence of the largely uncontrollable traffic and mobility characteristics on the system- and service-level performance

Seeing the Unobservable: Channel Learning for Wireless Communication Networks

Wireless communication networks rely heavily on channel state information (CSI) to make informed decision for signal processing and network operations. However, the traditional CSI acquisition methods is facing many difficulties: pilot-aided channel training consumes a great deal of channel resources and reduces the opportunities for energy saving, while location-aided channel estimation suffers from inaccurate and insufficient location information. In this paper, we propose a novel channel learning framework, which can tackle these difficulties by inferring unobservable CSI from the observable one. We formulate this framework theoretically and illustrate a special case in which the learnability of the unobservable CSI can be guaranteed. Possible applications of channel learning are then described, including cell selection in multi-tier networks, device discovery for device-to-device (D2D) communications, as well as end-to-end user association for load balancing. We also propose a neuron-network-based algorithm for the cell selection problem in multi-tier networks. The performance of this algorithm is evaluated using geometry-based stochastic channel model (GSCM). In settings with 5 small cells, the average cell-selection accuracy is 73% - only a 3.9% loss compared with a location-aided algorithm which requires genuine location information.Comment: 6 pages, 4 figures, accepted by GlobeCom'1

Analyzing wireless communication network vulnerability with homological invariants

This article explains how sheaves and homology theory can be applied to simplicial complex models of wireless communication networks to study their vulnerability to jamming. It develops two classes of invariants (one local and one global) for studying which nodes and links present more of a liability to the network's performance when under attack.Comment: Submitted to ICASSP 201

6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented

Network coding for wireless communication networks

This special issue includes a collection of 19 outstanding research papers which cover a diversity of topics on the application of network coding in wireless communication networks.published_or_final_versio

Optimized Cell Planning for Network Slicing in Heterogeneous Wireless Communication Networks

We propose a cell planning scheme to maximize the resource efficiency of a wireless communication network while considering quality-of-service requirements imposed by different mobile services. In dense and heterogeneous cellular 5G networks, the available time-frequency resources are orthogonally partitioned among different slices, which are serviced by the cells. The proposed scheme achieves a joint optimization of the resource distribution between network slices, the allocation of cells to operate on different slices, and the allocation of users to cells. Since the original problem formulation is computationally intractable, we propose a convex inner approximation. Simulations show that the proposed approach optimizes the resource efficiency and enables a service-centric network design paradigm.Comment: This article has been accepted for publication in a future issue of the IEEE Communications Letters, https://ieeexplore.ieee.org/document/8368293, (c) 2018 IEE

Wireless Communications and Mobile Computing using Machine learning

This work deals with the use of emerging deep learning techniques in future wireless communication networks. It will be shown that data-driven approaches should not re-place, but rather complement traditional design techniques based on mathematical models. Extensive motivation is given for why deep learning based on artificial neural networks will be an indispensable tool for the design and operation of future wireless communication networks, and our vision of how artificial neural networks should be integrated into the architecture of future wireless communication networks is present-ed. A thorough description of deep learning methodologies is provided, starting with the general machine learning paradigm, followed by a more in-depth discussion about deep learning and artificial neural networks, covering the most widely-used artificial neural network architectures and their training methods. Deep learning will also be connected to other major learning frameworks such as reinforcement learning and transfer learning. A thorough survey of the literature on deep learning for wireless communication networks is provided, followed by a detailed description of several novel case-studies wherein the use of deep learning proves extremely useful for net-work design. For each case-study, it will be shown how the use of (even approximate) mathematical models can significantly reduce the amount of live data that needs to be acquired/measured to implement data-driven approaches

Era of Deep Learning in Wireless Networking

This work deals with the use of emerging deep learning techniques in future wireless communication networks. It will be shown that data-driven approaches should not replace, but rather complement traditional design techniques based on mathematical models. Extensive motivation is given for why deep learning based on artificial neural networks will be an indispensable tool for the design and operation of future wireless communication networks, and our vision of how artificial neural networks should be integrated into the architecture of future wireless communication networks is present-ed. A thorough description of deep learning methodologies is provided, starting with the general machine learning paradigm, followed by a more in-depth discussion about deep learning and artificial neural networks, covering the most widely-used artificial neural network architectures and their training methods. Deep learning will also be connected to other major learning frameworks such as reinforcement learning and transfer learning. A thorough survey of the literature on deep learning for wireless communication networks is provided, followed by a detailed description of several novel case-studies wherein the use of deep learning proves extremely useful for net-work design. For each case-study, it will be shown how the use of (even approximate) mathematical models can significantly reduce the amount of live data that needs to be acquired/measured to implement data-driven approaches