1,204 research outputs found
IRS-aided UAV for Future Wireless Communications: A Survey and Research Opportunities
Both unmanned aerial vehicles (UAVs) and intelligent reflecting surfaces
(IRS) are gaining traction as transformative technologies for upcoming wireless
networks. The IRS-aided UAV communication, which introduces IRSs into UAV
communications, has emerged in an effort to improve the system performance
while also overcoming UAV communication constraints and issues. The purpose of
this paper is to provide a comprehensive overview of IRSassisted UAV
communications. First, we provide five examples of how IRSs and UAVs can be
combined to achieve unrivaled potential in difficult situations. The
technological features of the most recent relevant researches on IRS-aided UAV
communications from the perspective of the main performance criteria, i.e.,
energy efficiency, security, spectral efficiency, etc. Additionally, previous
research studies on technology adoption as machine learning algorithms. Lastly,
some promising research directions and open challenges for IRS-aided UAV
communication are presented
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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