Energy Efficiency

This book is one of the most comprehensive and up-to-date books written on Energy Efficiency. The readers will learn about different technologies for energy efficiency policies and programs to reduce the amount of energy. The book provides some studies and specific sets of policies and programs that are implemented in order to maximize the potential for energy efficiency improvement. It contains unique insights from scientists with academic and industrial expertise in the field of energy efficiency collected in this multi-disciplinary forum

A composite approach to self-sustainable transmissions : rethinking OFDM

This paper proposes two novel strategies to extend the battery life of an orthogonal frequency division multiplexing (OFDM) receiver, by exploiting the concept of wireless power transfer (WPT). First a new receiver architecture is devised that does not discard the cyclic prefix (CP), but instead, exploits it to extract power from the received signal, realizing a WPT between the transmitter and the receiver. Subsequently, a flexible composite transmit strategy is designed, in which the OFDM transmitter transmits to the receiver two independent signals coexisting in the same band. It is shown that, by means of this approach, the transmitter can arbitrarily increase the power concentrated within the CP at the OFDM receiver, without increasing the redundancy of the transmission. The feasibility conditions for the self-sustainability of the transmission are derived, in terms of power consumption at the receiver, for both legacy and composite transmission. Numerical findings show that, under reasonable conditions, the amount of power carried in the CP could be made sufficient to decode the information symbols, making the transmission fully self-sustainable. The potential of the proposed approach is confirmed by the encouraging results obtained when the full self-sustainability constraint is relaxed, and partially self-sustainable OFDM transmissions are analyzed

Resource allocation in future green wireless networks : applications and challenges

Over the past few years, green radio communication has been an emerging topic since the footprint from the Information and Communication Technologies (ICT) is predicted to increase 7.3% annually and then exceed 14% of the global footprint by 2040. Moreover, the explosive progress of ICT, e.g., the fifth generation (5G) networks, has resulted in expectations of achieving 10-fold longer device battery lifetime, and 1000-fold higher global mobile data traffic over the fourth generation (4G) networks. Therefore, the demands for increasing the data rate and the lifetime while reducing the footprint in the next-generation wireless networks call for more efficient utilization of energy and other resources. To overcome this challenge, the concepts of small-cell, energy harvesting, and wireless information and power transfer networks can be evaluated as promising solutions for re-greening the world. In this dissertation, the technical contributions in terms of saving economical cost, protecting the environment, and guaranteeing human health are provided. More specifically, novel communication scenarios are proposed to minimize energy consumption and hence save economic costs. Further, energy harvesting (EH) techniques are applied to exploit available green resources in order to reduce carbon footprint and then protect the environment. In locations where implemented user devices might not harvest energy directly from natural resources, base stations could harvest-and-store green energy and then use such energy to power the devices wirelessly. However, wireless power transfer (WPT) techniques should be used in a wise manner to avoid electromagnetic pollution and then guarantee human health. To achieve all these aspects simultaneously, this thesis proposes promising schemes to optimally manage and allocate resources in future networks. Given this direction, in the first part, Chapter 2 mainly studies a transmission power minimization scheme for a two-tier heterogeneous network (HetNet) over frequency selective fading channels. In addition, the HetNet backhaul connection is unable to support a sufficient throughput for signaling an information exchange between two tiers. A novel idea is introduced in which the time reversal (TR) beamforming technique is used at a femtocell while zero-forcing-based beamforming is deployed at a macrocell. Thus, a downlink power minimizationscheme is proposed, and optimal closed-form solutions are provided. In the second part, Chapters 3, 4, and 5 concentrate on EH and wireless information and power transfer (WIPT) using RF signals. More specifically, Chapter 3 presents an overview of the recent progress in green radio communications and discusses potential technologies for some emerging topics on the platforms of EH and WPT. Chapter 4 develops a new integrated information and energy receiver architecture based on the direct use of alternating current (AC) for computation. It is shown that the proposed approach enhances not only the computational ability but also the energy efficiency over the conventional one. Furthermore, Chapter 5 proposes a novel resource allocation scheme in simultaneous wireless information and power transfer (SWIPT) networks where three crucial issues: power-efficient improvement, user-fairness guarantee, and non-ideal channel reciprocity effect mitigation, are jointly addressed. Hence, novel methods to derive optimal and suboptimal solutions are provided. In the third part, Chapters 6, 7, and 8 focus on simultaneous lightwave information and power transfer (SLIPT) for indoor applications, as a complementary technology to RF SWIPT. In this research, Chapter 6 investigates a hybrid RF/visible light communication (VLC) ultrasmall cell network where optical transmitters deliver information and power using the visible light, whereas an RF access point works as a complementary power transfer system. Thus, a novel resource allocation scheme exploiting RF and visible light for power transfer is devised. Chapter 7 proposes the use of lightwave power transfer to enable future sustainable Federated Learning (FL)-based wireless networks. FL is a new data privacy protection technique for training shared machine learning models in a distributed approach. However, the involvement of energy-constrained mobile devices in the construction of the shared learning models may significantly reduce their lifetime. The proposed approach can support the FL-based wireless network to overcome the issue of limited energy at mobile devices. Chapter 8 introduces a novel framework for collaborative RF and lightwave power transfer for wireless communication networks. The constraints on the transmission power set by safety regulations result in significant challenges to enhance the power transfer performance. Thus, the study of technologies complementary to conventional RF SWIPT is essential. To cope with this isue, this chapter proposes a novel collaborative RF and lightwave power transfer technology for next-generation wireless networks

User mobility prediction and management using machine learning

The next generation mobile networks (NGMNs) are envisioned to overcome current user mobility limitations while improving the network performance. Some of the limitations envisioned for mobility management in the future mobile networks are: addressing the massive traffic growth bottlenecks; providing better quality and experience to end users; supporting ultra high data rates; ensuring ultra low latency, seamless handover (HOs) from one base station (BS) to another, etc. Thus, in order for future networks to manage users mobility through all of the stringent limitations mentioned, artificial intelligence (AI) is deemed to play a key role automating end-to-end process through machine learning (ML). The objectives of this thesis are to explore user mobility predictions and management use-cases using ML. First, background and literature review is presented which covers, current mobile networks overview, and ML-driven applications to enable user’s mobility and management. Followed by the use-cases of mobility prediction in dense mobile networks are analysed and optimised with the use of ML algorithms. The overall framework test accuracy of 91.17% was obtained in comparison to all other mobility prediction algorithms through artificial neural network (ANN). Furthermore, a concept of mobility prediction-based energy consumption is discussed to automate and classify user’s mobility and reduce carbon emissions under smart city transportation achieving 98.82% with k-nearest neighbour (KNN) classifier as an optimal result along with 31.83% energy savings gain. Finally, context-aware handover (HO) skipping scenario is analysed in order to improve over all quality of service (QoS) as a framework of mobility management in next generation networks (NGNs). The framework relies on passenger mobility, trains trajectory, travelling time and frequency, network load and signal ratio data in cardinal directions i.e, North, East, West, and South (NEWS) achieving optimum result of 94.51% through support vector machine (SVM) classifier. These results were fed into HO skipping techniques to analyse, coverage probability, throughput, and HO cost. This work is extended by blockchain-enabled privacy preservation mechanism to provide end-to-end secure platform throughout train passengers mobility

Excellentia Eminentia Effectio

"In these pages you will learn about the fascinating research endeavors that each of our faculty members is undertaking. We have divided their research into the broad categories of health, sustainability, information, and systems. While we recognize the imperfect nature of categorizing research that, by its very nature may be interdisciplinary or transdisciplinary, we nonetheless believe it will be helpful as a way to see the depth and breadth of our research endeavors within each grouping. As you read the profiles on these pages, I know you will begin to appreciate that, taken as a whole, the research spectrum at Columbia Engineering is exceptional and that, as our professors go about their work, they are at the cusp of making breakthroughs that will have a major impact on the way we live our lives today and tomorrow.

Enhancing the energy efficiency of radio base stations

This thesis is concerned with the energy efficiency of cellular networks. It studies the dominant power consumer in future cellular networks, the Long Term Evolution (LTE) radio Base Station (BS), and proposes mechanisms that enhance the BS energy efficiency by reducing its power consumption under target rate constraints. These mechanisms trade spare capacity for power saving. First, the thesis describes how much power individual components of a BS consume and what parameters affect this consumption based on third party experimental data. These individual models are joined into a component power model for an entire BS. The component model is an essential step in analysis but is too complex for many applications. It is therefore abstracted into a much simpler parameterized model to reduce its complexity. The parameterized model is further simplified into an affine model which can be applied in power minimization. Second, Power Control (PC) and Discontinuous Transmission (DTX) are identified as promising power-saving Radio Resource Management (RRM) mechanisms and applied to multi-user downlink transmission. PC reduces the power consumption of the Power Amplifier (PA) and is found to be most effective at high traffic loads. DTX mostly reduces the power consumption of the Baseband (BB) unit while interrupting transmission and is better applied in low traffic loads. Joint optimization of these two techniques is found to enable additional power-saving at medium traffic loads and to be a convex problem which can be solved efficiently. The convex problem is extended to provide a comprehensive power-saving Orthogonal Frequency Division Multiple Access (OFDMA) frame resource scheduler. The proposed scheduler is shown to reduce power consumption by 25-40% in computer simulations, depending on the traffic load. Finally, the thesis investigates the influence of interference on power consumption in a network of multiple power-saving BSs. It discusses three popular alternative distributed uncoordinated methods which align DTX mode between neighbouring BSs. To address drawbacks of these three, a fourth memory-based DTX alignment method is proposed. It decreases power consumption by up to 40% and retransmission probability by around 20%, depending on the traffic load