Energy Efficient Resource Allocation in Machine-to-Machine Communications with Multiple Access and Energy Harvesting for IoT

© 2014 IEEE. This paper studies energy efficient resource allocation for a machine-to-machine enabled cellular network with nonlinear energy harvesting, especially focusing on two different multiple access strategies, namely nonorthogonal multiple access (NOMA) and time division multiple access (TDMA). Our goal is to minimize the total energy consumption of the network via joint power control and time allocation while taking into account circuit power consumption. For both NOMA and TDMA strategies, we show that it is optimal for each machine type communication device (MTCD) to transmit with the minimum throughput, and the energy consumption of each MTCD is a convex function with respect to the allocated transmission time. Based on the derived optimal conditions for the transmission power of MTCDs, we transform the original optimization problem for NOMA to an equivalent problem which can be solved suboptimally via an iterative power control and time allocation algorithm. Through an appropriate variable transformation, we also transform the original optimization problem for TDMA to an equivalent tractable problem, which can be iteratively solved. Numerical results verify the theoretical findings and demonstrate that NOMA consumes less total energy than TDMA at low circuit power regime of MTCDs, while at high circuit power regime of MTCDs TDMA achieves better network energy efficiency than NOMA

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

Efficient energy management for the internet of things in smart cities

The drastic increase in urbanization over the past few years requires sustainable, efficient, and smart solutions for transportation, governance, environment, quality of life, and so on. The Internet of Things offers many sophisticated and ubiquitous applications for smart cities. The energy demand of IoT applications is increased, while IoT devices continue to grow in both numbers and requirements. Therefore, smart city solutions must have the ability to efficiently utilize energy and handle the associated challenges. Energy management is considered as a key paradigm for the realization of complex energy systems in smart cities. In this article, we present a brief overview of energy management and challenges in smart cities. We then provide a unifying framework for energy-efficient optimization and scheduling of IoT-based smart cities. We also discuss the energy harvesting in smart cities, which is a promising solution for extending the lifetime of low-power devices and its related challenges. We detail two case studies. The first one targets energy-efficient scheduling in smart homes, and the second covers wireless power transfer for IoT devices in smart cities. Simulation results for the case studies demonstrate the tremendous impact of energy-efficient scheduling optimization and wireless power transfer on the performance of IoT in smart cities

Optimizing resource allocation in eh-enabled internet of things

Internet of Things (IoT) aims to bridge everyday physical objects via the Internet. Traditional energy-constrained wireless devices are powered by fixed energy sources like batteries, but they may require frequent battery replacements or recharging. Wireless Energy Harvesting (EH), as a promising solution, can potentially eliminate the need of recharging or replacing the batteries. Unlike other types of green energy sources, wireless EH does not depend on nature and is thus a reliable source of energy for charging devices. Meanwhile, the rapid growth of IoT devices and wireless applications is likely to demand for more operating frequency bands. Although the frequency spectrum is currently scarce, owing to inefficient conventional regulatory policies, a considerable amount of the radio spectrum is greatly underutilized. Cognitive radio (CR) can be exploited to mitigate the spectrum scarcity problem of IoT applications by leveraging the spectrum holes. Therefore, transforming the IoT network into a cognitive based IoT network is essential to utilizing the available spectrum opportunistically. To address the two aforementioned issues, a novel model is proposed to leverage wireless EH and CR for IoT. In particular, the sum rate of users is maximized for a CR-based IoT network enabled with wireless EH. Users operate in a time switching fashion, and each time slot is partitioned into three non-overlapping parts devoted for EH, spectrum sensing and data transmission. There is a trade-off among the lengths of these three operations and thus the time slot structure is to be optimized. The general problem of joint resource allocation and EH optimization is formulated as a mixed integer nonlinear programming task which is NP-hard and intractable. Therefore, a sub-channel allocation scheme is first proposed to approximately satisfy users rate requirements and remove the integer constraints. In the second step, the general optimization problem is reduced to a convex optimization task. Another optimization framework is also designed to capture a fundamental tradeoff between energy efficiency (EE) and spectral efficiency for an EH-enabled IoT network. In particular, an EE maximization problem is formulated by taking into consideration of user buffer occupancy, data rate fairness, energy causality constraints and interference constraints. Then, a low complexity heuristic algorithm is proposed to solve the resource allocation and EE optimization problem. The proposed algorithm is shown to be capable of achieving a near optimal solution with polynomial complexity. To support Machine Type Communications (MTC) in next generation mobile networks, NarrowBand-IoT (NB-IoT) has emerged as a promising solution to provide extended coverage and low energy consumption for low cost MTC devices. However, the existing orthogonal multiple access scheme in NB-IoT cannot provide connectivity for a massive number of MTC devices. In parallel with the development of NB-IoT, Non-Orthogonal Multiple Access (NOMA), introduced for the fifth generation wireless networks, is deemed to significantly improve the network capacity by providing massive connectivity through sharing the same spectral resources. To leverage NOMA in the context of NB-IoT, a power domain NOMA scheme is proposed with user clustering for an NB-IoT system. In particular, the MTC devices are assigned to different ranks within the NOMA clusters where they transmit over the same frequency resources. Then, an optimization problem is formulated to maximize the total throughput of the network by optimizing the resource allocation of MTC devices and NOMA clustering while satisfying the transmission power and quality of service requirements. Furthermore, an efficient heuristic algorithm is designed to solve the proposed optimization problem by jointly optimizing NOMA clustering and resource allocation of MTC devices

Energy-efficient non-orthogonal multiple access for wireless communication system

Non-orthogonal multiple access (NOMA) has been recognized as a potential solution for enhancing the throughput of next-generation wireless communications. NOMA is a potential option for 5G networks due to its superiority in providing better spectrum efficiency (SE) compared to orthogonal multiple access (OMA). From the perspective of green communication, energy efficiency (EE) has become a new performance indicator. A systematic literature review is conducted to investigate the available energy efficient approach researchers have employed in NOMA. We identified 19 subcategories related to EE in NOMA out of 108 publications where 92 publications are from the IEEE website. To help the reader comprehend, a summary for each category is explained and elaborated in detail. From the literature review, it had been observed that NOMA can enhance the EE of wireless communication systems. At the end of this survey, future research particularly in machine learning algorithms such as reinforcement learning (RL) and deep reinforcement learning (DRL) for NOMA are also discussed