Motion Sensor-based Small Cell Sleep Scheduling for 5G Networks

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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

Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G

The next wave of wireless technologies is proliferating in connecting things among themselves as well as to humans. In the era of the Internet of things (IoT), billions of sensors, machines, vehicles, drones, and robots will be connected, making the world around us smarter. The IoT will encompass devices that must wirelessly communicate a diverse set of data gathered from the environment for myriad new applications. The ultimate goal is to extract insights from this data and develop solutions that improve quality of life and generate new revenue. Providing large-scale, long-lasting, reliable, and near real-time connectivity is the major challenge in enabling a smart connected world. This paper provides a comprehensive survey on existing and emerging communication solutions for serving IoT applications in the context of cellular, wide-area, as well as non-terrestrial networks. Specifically, wireless technology enhancements for providing IoT access in fifth-generation (5G) and beyond cellular networks, and communication networks over the unlicensed spectrum are presented. Aligned with the main key performance indicators of 5G and beyond 5G networks, we investigate solutions and standards that enable energy efficiency, reliability, low latency, and scalability (connection density) of current and future IoT networks. The solutions include grant-free access and channel coding for short-packet communications, non-orthogonal multiple access, and on-device intelligence. Further, a vision of new paradigm shifts in communication networks in the 2030s is provided, and the integration of the associated new technologies like artificial intelligence, non-terrestrial networks, and new spectra is elaborated. Finally, future research directions toward beyond 5G IoT networks are pointed out.Comment: Submitted for review to IEEE CS&

Joint delay and energy aware dragonfly optimization-based uplink resource allocation scheme for LTE-A networks in a cross-layer environment

The exponential growth in data traffic from smart devices has led to a need for highly capable wireless networks with faster data transmission rates and improved spectral efficiency. Allocating resources efficiently in a 5G communication system with a huge number of machine type communication (MTC) devices is essential to ensure optimal performance and meet the diverse requirements of different applications. The LTE-A network offers high-speed mobile data services and caters to MTC devices and has relatively low data service requirements compared to human-to-human (H2H) communications. LTE-A networks require advanced scheduling schemes to manage the limited spectrum and ensure efficient transmissions. This necessitates effective resource allocation schemes to minimize interference between cells in future networks. To address this issue, a joint delay and energy aware Levy flight Brownian movement-based dragonfly optimization (DELFBDO)-based uplink resource allocation scheme for LTE-A Networks is proposed in this work to optimize energy efficiency, maximize the throughput and reduce the latency. The DELFDO algorithm efficiently organizes packets in both time and frequency domains for H2H and MTC devices, resulting in improved quality of service while minimizing energy consumption. The Simulation results demonstrate that the proposed method increases the energy efficiency by producing the appropriate channel and power assignment for UEs and MTC devices.© 2024 The Authors. The Journal of Engineering published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.fi=vertaisarvioitu|en=peerReviewed