1,216 research outputs found
Recent Advances in Joint Wireless Energy and Information Transfer
In this paper, we provide an overview of the recent advances in
microwave-enabled wireless energy transfer (WET) technologies and their
applications in wireless communications. Specifically, we divide our
discussions into three parts. First, we introduce the state-of-the-art WET
technologies and the signal processing techniques to maximize the energy
transfer efficiency. Then, we discuss an interesting paradigm named
simultaneous wireless information and power transfer (SWIPT), where energy and
information are jointly transmitted using the same radio waveform. At last, we
review the recent progress in wireless powered communication networks (WPCN),
where wireless devices communicate using the power harvested by means of WET.
Extensions and future directions are also discussed in each of these areas.Comment: Conference submission accepted by ITW 201
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
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