Ever since Marconi succeeded in his first demonstration on the possibility to communicate over the air overseas about a century ago, wireless communications have experienced dramatic improvements. Today's world sees the penetration of wireless communications into human life almost everywhere, from a simple remote control for TV to a cellular phone. With a better understanding of the adverse nature of the wireless propagation channels, engineers have been able to invent various clever techniques, i.e. Multiple Input Multiple Output (MIMO) technology, spread spectrum communications, Orthogonal Frequency Division Multiplexing (OFDM) to name a few, to achieve fast and reliable communications over each point-to-point link. Communications between multiple parties create networks. Limited Radio Frequency (RF) resources, e.g. transmit power, channel bandwidth, signaling time slots, etc., call for an optimal distribution of these resources among the users in the network. In this dissertation, two types of communication networks are of particular interest: cellular networks and mobile-relay-aided networks. For a symmetric cellular network, where a fixed communication infrastructure is assumed and each user has similar average Signal-to-Noise Ratio (SNR), we study the performance of a Maximum SNR (Max-SNR) scheduler, which schedules the strongest user for service, with the effects of channel estimation error, the Modulation and Coding Scheme (MCS), channel feedback delay, and Doppler shift all taken into account. The degradation of the throughput of a Max-SNR scheduler due to outdated channel knowledge for a system with large Doppler shift and asymmetric users is analyzed and mathematical derivations of the capacity of the system based upon an Auto-Regressive (AR) channel model are presented in the dissertation as well. Unlike the schedulers proposed in the literature, which instantaneously keep track of the strongest user, an optimal scheduler that operates on the properties of Doppler and the average SNR of each user is proposed. The high flexibility and easy deployment characteristics that Unmanned Aerial Vehicles (UAVs) possess endow them with the possibility to act as mobile relays to create secure and reliable communication links in severe environments. Unlike cellular communications, where the base stations are stationary, the mobility in a UAV-assisted network can be exploited to improve the quality of the communications. Herein, the deployment and optimal motion control problem for a mobile-relay-aided network is considered. A network protocol which achieves optimal throughput and maintains a certain Quality of Service (QoS) requirement is proposed from a cross-layer perspective. The handoff problem of the Access Point (AP) between various relays is studied and the effect of the mobility on the handoff algorithm is addressed.

No description available.

Yes / As 5th Generation research reaches the twilight, the research community must go beyond 5G and look towards the 2030 connectivity landscape, namely 6G. In this context, this work takes a step towards the 6G vision by proposing a next generation communication platform, which aims to extend the rigid coverage area of fixed deployment networks by considering virtual mobile small cells (MSC) that are created on demand. Relying on emerging computing paradigms such as NFV (Network Function Virtualization) and SDN (Software Defined Networking), these cells can harness radio and networking capability locally reducing protocol signalling latency and overhead. These MSCs constitute an intelligent pool of networking resources that can collaborate to form a wireless network of MSCs providing a communication platform for localized, ubiquitous and reliable connectivity. The technology enablers for implementing the MSC concept are also addressed in terms of virtualization, lightweight wireless security, and energy efficient RF. The benefits of the MSC architecture towards reliable and efficient cell-offloading are demonstrated as a use-case. / This project has received funding from the European Union's H2020 research and innovation program under grant agreement H2020-MCSAITN- 2016-SECRET 722424 [2].