Energy Aware Channel Allocation with Spectrum Sensing in Pilot Contamination Analysis for Cognitive Radio Networks

Cognitive radio (CR) is an innovative and contemporary technology that has been making an effort to overcome the problems of bandwidth reduction by rising the usage of mobile cellular bandwidth connections. The reallocation and distribution of channels is a fundamental characteristic of cellular mobile networks (CMN) to exploit the consumption of CMS. Meanwhile, throughput maximization might lead to higher power utilization, the spectrum sensing system must tackle the energy throughput tradeoff. The spectrum sensing time should be defined by the residual battery energy of secondary user (SU). In that context, energy effective algorithm for spectrum sensing should be developed for meeting the energy constraint of CRN. This study designs a new quantum particle swarm optimization-based energy aware spectrum sensing scheme (QPSO-EASSS) for CRNs. Here, the presented QPSO-EASSS technique dynamically estimates the sensing time depending upon the battery energy level of SUs and the transmission power can be computed based on the battery energy level and PU signal of the SUs. In addition, in this work, the QPSO-EASSS technique applies the QPSO algorithm for throughput maximization with energy constraints in the CRN. The detailed set of experimentations take place and reported the improvements of the QPSO-EASSS technique compared to existing models

Deep Reinforcement Learning for Power Control in Next-Generation WiFi Network Systems

This paper presents a deep reinforcement learning (DRL) solution for power control in wireless communications, describes its embedded implementation with WiFi transceivers for a WiFi network system, and evaluates the performance with high-fidelity emulation tests. In a multi-hop wireless network, each mobile node measures its link quality and signal strength, and controls its transmit power. As a model-free solution, reinforcement learning allows nodes to adapt their actions by observing the states and maximize their cumulative rewards over time. For each node, the state consists of transmit power, link quality and signal strength; the action adjusts the transmit power; and the reward combines energy efficiency (throughput normalized by energy consumption) and penalty of changing the transmit power. As the state space is large, Q-learning is hard to implement on embedded platforms with limited memory and processing power. By approximating the Q-values with a DQN, DRL is implemented for the embedded platform of each node combining an ARM processor and a WiFi transceiver for 802.11n. Controllable and repeatable emulation tests are performed by inducing realistic channel effects on RF signals. Performance comparison with benchmark schemes of fixed and myopic power allocations shows that power control with DRL provides major improvements to energy efficiency and throughput in WiFi network systems.Comment: 5 pages, 6 figures, 1 tabl

On-Site and External Energy Harvesting in Underground Wireless

Energy efficiency is vital for uninterrupted long-term operation of wireless underground communication nodes in the field of decision agriculture. In this paper, energy harvesting and wireless power transfer techniques are discussed with applications in underground wireless communications (UWC). Various external wireless power transfer techniques are explored. Moreover, key energy harvesting technologies are presented that utilize available energy sources in the field such as vibration, solar, and wind. In this regard, the Electromagnetic(EM)- and Magnetic Induction(MI)-based approaches are explained. Furthermore, the vibration-based energy harvesting models are reviewed as well. These energy harvesting approaches lead to design of an efficient wireless underground communication system to power underground nodes for prolonged field operation in decision agriculture

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

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

Enabling Hardware Green Internet of Things: A review of Substantial Issues

Between now and the near future, the Internet of Things (IoT) will redesign the socio-ecological morphology of the human terrain. The IoT ecosystem deploys diverse sensor platforms connecting millions of heterogeneous objects through the Internet. Irrespective of sensor functionality, most sensors are low energy consumption devices and are designed to transmit sporadically or continuously. However, when we consider the millions of connected sensors powering various user applications, their energy efficiency (EE) becomes a critical issue. Therefore, the importance of EE in IoT technology, as well as the development of EE solutions for sustainable IoT technology, cannot be overemphasised. Propelled by this need, EE proposals are expected to address the EE issues in the IoT context. Consequently, many developments continue to emerge, and the need to highlight them to provide clear insights to researchers on eco-sustainable and green IoT technologies becomes a crucial task. To pursue a clear vision of green IoT, this study aims to present the current state-of-the art insights into energy saving practices and strategies on green IoT. The major contribution of this study includes reviews and discussions of substantial issues in the enabling of hardware green IoT, such as green machine to machine, green wireless sensor networks, green radio frequency identification, green microcontroller units, integrated circuits and processors. This review will contribute significantly towards the future implementation of green and eco-sustainable IoT