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

Energy-Efficient NOMA Enabled Heterogeneous Cloud Radio Access Networks

Heterogeneous cloud radio access networks (H-CRANs) are envisioned to be promising in the fifth generation (5G) wireless networks. H-CRANs enable users to enjoy diverse services with high energy efficiency, high spectral efficiency, and low-cost operation, which are achieved by using cloud computing and virtualization techniques. However, H-CRANs face many technical challenges due to massive user connectivity, increasingly severe spectrum scarcity and energy-constrained devices. These challenges may significantly decrease the quality of service of users if not properly tackled. Non-orthogonal multiple access (NOMA) schemes exploit non-orthogonal resources to provide services for multiple users and are receiving increasing attention for their potential of improving spectral and energy efficiency in 5G networks. In this article a framework for energy-efficient NOMA H-CRANs is presented. The enabling technologies for NOMA H-CRANs are surveyed. Challenges to implement these technologies and open issues are discussed. This article also presents the performance evaluation on energy efficiency of H-CRANs with NOMA.Comment: This work has been accepted by IEEE Network. Pages 18, Figure

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

A survey of multi-access edge computing in 5G and beyond : fundamentals, technology integration, and state-of-the-art

Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research