Energy Efficiency Optimization in Green Wireless Communications

The rising energy concern and the ubiquity of energy-consuming wireless applications have sparked a keen interest in the development and deployment of energy-efficient and eco-friendly wireless communication technology. Green Wireless Communications aims to find innovative solutions to improve energy efficiency, and to relieve/reduce the carbon footprint of wireless industry, while maintaining/improving performance metrics. Looking back at the wireless communications of the past decades, the air-interface design and network deployment had mainly focused on the spectral efficiency, instead of energy efficiency. From the cellular network to the personal area network, no matter what size the wireless network is, the milestones along the evolutions of wireless networks had always been higher-and-higher data rates throughout these years. Most of these throughput-oriented optimizations lead to a full-power operation to support a higher throughput or spectral efficiency, which is typically not energy-efficient. To qualify as green wireless communications, we believe that a candidate technology needs to be of high energy efficiency, reduced electromagnetic pollution, and low-complexity. In this dissertation research, towards the evolution of the green wireless communications, we have extended our efforts in two important aspects of the wireless communications system: air-interface and networking. In the first aspect of this work, we study a promising green communications technology, the time reversal system, as a novel air-interface of the future green wireless communications. We propose a concept of time reversal division multiple access (TRDMA) as a novel wireless media access scheme for wireless broadband networks, and investigate its fundamental theoretical limits. Motivated by the great energy-harvesting potential of the TRDMA, we develop an asymmetric architecture for the TRDMA based multiuser networks. The unique asymmetric architecture shifts the most complexity to the BS in both downlink and uplink schemes, facilitating very low-cost terminal users in the networks. To further enhance the system performance, a 2D parallel interference cancellation scheme is presented to explore the inherent structure of the interference signals, and therefore efficiently improve the resulting SINR and system performance. In the second aspect of this work, we explore the energy-saving potential of the cooperative networking for cellular systems. We propose a dynamic base-station switching strategy and incorporate the cooperative base-station operation to improve the energy-efficiency of the cellular networks without sacrificing the quality of service of the users. It is shown that significant energy saving potential can be achieved by the proposed scheme

Evolution Toward 5G Mobile Networks - A Survey on Enabling Technologies

In this paper, an extensive review has been carried out on the trends of existing as well as proposed potential enabling technologies that are expected to shape the fifth generation (5G) mobile wireless networks. Based on the classification of the trends, we develop a 5G network architectural evolution framework that comprises three evolutionary directions, namely, (1) radio access network node and performance enabler, (2) network control programming platform, and (3) backhaul network platform and synchronization. In (1), we discuss node classification including low power nodes in emerging machine-type communications, and network capacity enablers, e.g., millimeter wave communications and massive multiple-input multiple-output. In (2), both logically distributed cell/device-centric platforms, and logically centralized conventional/wireless software defined networking control programming approaches are discussed. In (3), backhaul networks and network synchronization are discussed. A comparative analysis for each direction as well as future evolutionary directions and challenges toward 5G networks are discussed. This survey will be helpful for further research exploitations and network operators for a smooth evolution of their existing networks toward 5G networks

Next Generation Opportunistic Networking in Beyond 5G Networks

Beyond 5G networks are expected to support massive traffic through decentralized solutions and advanced networking mechanisms. This paper aims at contributing towards this vision through the integration of device-centric wireless networks, including Device-to-Device (D2D) communications, and the Next Generation of Opportunistic networking (NGO). This integration offers multiple communication modes such as opportunistic cellular and opportunistic D2D-aided communications. Previous studies have demonstrated the potential and benefits of this integration in terms of energy efficiency, spectral efficiency and traffic offloading. We propose an integration of device-centric wireless networks and NGO that is not driven by a precise knowledge of the presence of the links. The proposed technique utilizes a novel concept of graph to model the evolution of the networking conditions and network connectivity. Uncertainties and future conditions are included in the proposed graph model through anticipatory mobile networking to estimate the transmission energy cost of the different communication modes. Based on these estimates, the devices schedule their transmissions using the most efficient communication mode. These decisions are later revisited in real-time using more precise knowledge about the network state. The conducted evaluation shows that the proposed technique significantly reduces the energy consumption (from 60% to 90% depending on the scenario) compared to traditional single-hop cellular communications and performs closely to an ideal “oracle based” system with full knowledge of present and future events. The transmission and computational overheads of the proposed technique show small impact on such energy gains.This work has been partially funded by the Spanish Ministry of Science, Innovation and Universities, AEI, and FEDER funds (TEC2017-88612-R)the Ministry of Science, Innovation and Universities (IJC2018-036862-I)the UMH (‘Ayudas a la Investigación e Innovación de la Universidad Miguel Hernández de Elche 2018’)and by the European Commission under the H2020 REPLICATE (691735), SoBigData (654024) and AUTOWARE (723909) project