Cooperative retransmission protocols in fading channels : issues, solutions and applications

Future wireless systems are expected to extensively rely on cooperation between terminals, mimicking MIMO scenarios when terminal dimensions limit implementation of multiple antenna technology. On this line, cooperative retransmission protocols are considered as particularly promising technology due to their opportunistic and flexible exploitation of both spatial and time diversity. In this dissertation, some of the major issues that hinder the practical implementation of this technology are identified and pertaining solutions are proposed and analyzed. Potentials of cooperative and cooperative retransmission protocols for a practical implementation of dynamic spectrum access paradigm are also recognized and investigated. Detailed contributions follow. While conventionally regarded as energy efficient communications paradigms, both cooperative and retransmission concepts increase circuitry energy and may lead to energy overconsumption as in, e.g., sensor networks. In this context, advantages of cooperative retransmission protocols are reexamined in this dissertation and their limitation for short transmission ranges observed. An optimization effort is provided for extending an energy- efficient applicability of these protocols. Underlying assumption of altruistic relaying has always been a major stumbling block for implementation of cooperative technologies. In this dissertation, provision is made to alleviate this assumption and opportunistic mechanisms are designed that incentivize relaying via a spectrum leasing approach. Mechanisms are provided for both cooperative and cooperative retransmission protocols, obtaining a meaningful upsurge of spectral efficiency for all involved nodes (source-destination link and the relays). It is further recognized in this dissertation that the proposed relaying-incentivizing schemes have an additional and certainly not less important application, that is in dynamic spectrum access for property-rights cognitive-radio implementation. Provided solutions avoid commons-model cognitive-radio strict sensing requirements and regulatory and taxonomy issues of a property-rights model

Optimization and Performance Analysis of High Speed Mobile Access Networks

The end-to-end performance evaluation of high speed broadband mobile access networks is the main focus of this work. Novel transport network adaptive flow control and enhanced congestion control algorithms are proposed, implemented, tested and validated using a comprehensive High speed packet Access (HSPA) system simulator. The simulation analysis confirms that the aforementioned algorithms are able to provide reliable and guaranteed services for both network operators and end users cost-effectively. Further, two novel analytical models one for congestion control and the other for the combined flow control and congestion control which are based on Markov chains are designed and developed to perform the aforementioned analysis efficiently compared to time consuming detailed system simulations. In addition, the effects of the Long Term Evolution (LTE) transport network (S1and X2 interfaces) on the end user performance are investigated and analysed by introducing a novel comprehensive MAC scheduling scheme and a novel transport service differentiation model

Control-Data Separation with Decentralized Edge Control in Fog-Assisted Uplink Communications

Fog-aided network architectures for 5G systems encompass wireless edge nodes, referred to as remote radio systems (RRSs), as well as remote cloud center (RCC) processors, which are connected to the RRSs via a fronthaul access network. RRSs and RCC are operated via Network Functions Virtualization (NFV), enabling a flexible split of network functionalities that adapts to network parameters such as fronthaul latency and capacity. This work focuses on uplink communications and investigates the cloud-edge allocation of two important network functions, namely the control functionality of rate selection and the data-plane function of decoding. Three functional splits are considered: (i) Distributed Radio Access Network (D-RAN), in which both functions are implemented in a decentralized way at the RRSs, (ii) Cloud RAN (C-RAN), in which instead both functions are carried out centrally at the RCC, and (iii) a new functional split, referred to as Fog RAN (F-RAN), with separate decentralized edge control and centralized cloud data processing. The model under study consists of a time-varying uplink channel in which the RCC has global but delayed channel state information (CSI) due to fronthaul latency, while the RRSs have local but more timely CSI. Using the adaptive sum-rate as the performance criterion, it is concluded that the F-RAN architecture can provide significant gains in the presence of user mobility.Comment: 28 pages, 11 figures. This manuscript was presented in part at arXiv:1606.0913

Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions

The ever-increasing number of resource-constrained Machine-Type Communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the unique technical challenge of supporting a huge number of MTC devices, which is the main focus of this paper. The related challenges include QoS provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead and Radio Access Network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy Random Access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT. Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions towards addressing RAN congestion problem, and then identify potential advantages, challenges and use cases for the applications of emerging Machine Learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future publication in IEEE Communications Surveys and Tutorial