A survey and taxonomy on nonorthogonal multiple-access schemes for 5G networks

Copyright © 2017 John Wiley & Sons, Ltd. The intensity in the requirements of Internet of Things and mobile internet makes the efficiency of fifth-generation (5G) wireless communications very challenging to achieve. Accomplishing the drastically increasing demand of massive connectivity and high spectral efficiency is a strenuous task. Because of the very large number of devices, 5G wireless communication systems are inevitable to satisfy the traffic requirements. Recently, nonorthogonal multiple-access (NOMA) schemes are immensely being explored to address the challenges in 5G, which include effective bandwidth utilization, support for a massive number of devices, and low latency. This paper provides the reader with a holistic view of multiple-access schemes, methods, and strategies for optimization in NOMA. First, we discuss the taxonomy of multiple-access schemes in the literature; then, we provide a detailed discussion of objectives, constraints, problem types, and solution approaches for NOMA. This paper also discusses the decoding methods and key performance indicators used in NOMA. Finally, we outline future research directions

Learning paradigms for communication and computing technologies in IoT systems

© 2020 Elsevier B.V. Wireless communication and computation technologies are becoming increasingly complex and dynamic due to the sophisticated and ubiquitous Internet of things (IoT) applications. Therefore, future wireless networks and computation solutions must be able to handle these challenges and dynamic user requirements for the success of IoT systems. Recently, learning strategies (particularly deep learning and reinforcement learning) are explored immensely to deal with the complexity and dynamic nature of communication and computation technologies for IoT systems, mainly because of their power to predict and efficient data analysis. Learning strategies can significantly enhance the performance of IoT systems at different stages, including at IoT node level, local communication, long-range communication, edge gateway, cloud platform, and corporate data centers. This paper presents a comprehensive overview of learning strategies for IoT systems. We categorize learning paradigms for communication and computing technologies in IoT systems into reinforcement learning, Bayesian algorithms, stochastic learning, and miscellaneous. We then present research in IoT with the integration of learning strategies from the optimization perspective where the optimization objectives are categorized into maximization and minimization along with corresponding applications. Learning strategies are discussed to illustrate how these strategies can enhance the performance of IoT applications. We also identify the key performance indicators (KPIs) used to evaluate the performance of IoT systems and discuss learning algorithms for these KPIs. Lastly, we provide future research directions to further enhance IoT systems using learning strategie

Energy efficient resource allocation for NOMA in cellular IoT with energy harvesting

© 2017 IEEE. The Internet of Things (IoT) offers connectivity of massive low-power devices and sensors through the Internet which requires spectrum and energy efficient solutions. Recently, non-orthogonal multiple access (NOMA) has been investigated to address the challenges associated with spectral efficiency and dense deployment of a large number of devices in 5G cellular networks. Further, energy harvesting can enhance the energy efficiency of IoT devices. In this paper, we propose an energy-efficient resource allocation scheme for NOMA (EERA-NOMA) in cellular IoT with RF energy harvesting to address the above mentioned challenges. We model a framework to optimize user grouping of IoT devices in most appropriate resource blocks, power allocation, and time allocation for information transfer and energy harvesting. The objective is to maximize energy efficiency while satisfying constraints on the minimum data rate requirement of each user and transmit power. We adopted mesh adaptive direct search (MADS) algorithm to solve the formulated problem. Simulation results are presented to show the performance of proposed framework in comparison with existing work

Association of Dietary Practices and Lifestyle Modifications in Gastroesophageal Reflux Disease in Pakistani Women

Background: Gastroesophageal Reflux Disease (GERD) incidence is increasing day by day due to lifestyle changes and living standards that resulted in esophagitis, esophageal adenocarcinoma, Barrett’s esophagus and many other illness worldwide. Patients with GERD live with poor quality life and have low work capacity.  Aims: Main aim of the study is to diagnose GERD in early stages for the reduction in mortality and morbidity at different age groups. Methods: The pre-tested questionnaire was used to collect data from Sir Ganga Ram Hospital Lahore. A total of 230 female patients screened for GERD symptoms were included in this study. The collection of demographic data, dietary intake, lifestyle habits, physiology, and physical analysis were gathered during the 4 months.   Results: Data analysis shows us that GERD is highly significant with age, occupation. Moreover, burping is highly significant in these patients. Fried fatty foods, spicy foods, fizzy drinks, garlic intake were also correlated to GERD symptoms. These subjects also suffer from more skin problems.  Conclusion: From our results, we infer that GERD has a very strong bond with dietary and lifestyle patterns. If these parameters are kept under control, GERD patients will be less agonize from complications and minimize our morbidity and mortality.&nbsp

Internet of Things Platform for Transparency and Traceability of Food Supply Chain

© 2019 IEEE. The Internet of things (IoT) platform can provide product provenance (information that traces a product to its origin) in the food supply chain (FSC). This empowers consumers with the information and opportunity to make the most informed decision. Integrating IoT technology with blockchain can make FSC transparent, making it more productive by providing trustworthy and secure information to both consumers and relevant parties. This paper proposes an architecture of a blockchain- enabled IoT platform to replace the third party present in the FSC, which would authorize the product data. This platform will ensure that the product data collected through sensors from each stage of the supply chain is legitimate and adheres to terms agreed by all parties involved in the system. We present a case study to support the idea of accessible IoT technology being used to create a data network. It details which different technologies that can be used in IoT platform and how it supports the proposed architecture. We show how blockchain can be used in tangent with IoT technologies in order to modernize and optimize the standard FSC

Non-Orthogonal Radio Resource Management for RF Energy Harvested 5G Networks

© 2013 IEEE. Fifth generation (5G) networks are expected to support a large number of devices, provide spectral efficiency and energy efficiency. Non-orthogonal multiple access (NOMA) has been recently investigated to accommodate a large number of devices as well as spectral efficiency. On the other hand, energy efficiency in 5G networks can be addressed using energy harvesting. In this paper, we investigate NOMA in 5G networks with RF energy harvesting to maximize the number of admitted users as well as system throughput. We model a mathematical framework to optimize user grouping, power allocation, and time allocation for information transfer and energy harvesting while satisfying the minimum data rate and transmit power requirements of users. The proposed framework for optimization is a mixed integer non-linear programming problem. The mesh adaptive direct search (MADS) algorithm is adopted to find solution of the proposed framework. The MADS algorithm provides an epsilon optimal solution. The exhaustive search algorithm is used as a bench mark. Finally, the effectiveness of the proposed framework is supported by simulation results