A Survey on Applications of Cache-Aided NOMA

Contrary to orthogonal multiple-access (OMA), non-orthogonal multiple-access (NOMA) schemes can serve a pool of users without exploiting the scarce frequency or time domain resources. This is useful in meeting the future network requirements (5G and beyond systems), such as, low latency, massive connectivity, users' fairness, and high spectral efficiency. On the other hand, content caching restricts duplicate data transmission by storing popular contents in advance at the network edge which reduces data traffic. In this survey, we focus on cache-aided NOMA-based wireless networks which can reap the benefits of both cache and NOMA; switching to NOMA from OMA enables cache-aided networks to push additional files to content servers in parallel and improve the cache hit probability. Beginning with fundamentals of the cache-aided NOMA technology, we summarize the performance goals of cache-aided NOMA systems, present the associated design challenges, and categorize the recent related literature based on their application verticals. Concomitant standardization activities and open research challenges are highlighted as well

Intelligent Reflective Surface Deployment in 6G: A Comprehensive Survey

Intelligent reflecting surfaces (IRSs) are considered a promising technology that can smartly reconfigure the wireless environment to enhance the performance of future wireless networks. However, the deployment of IRSs still faces challenges due to highly dynamic and mobile unmanned aerial vehicle (UAV) enabled wireless environments to achieve higher capacity. This paper sheds light on the different deployment strategies for IRSs in future terrestrial and non-terrestrial networks. Specifically, in this paper, we introduce key theoretical concepts underlying the IRS paradigm and discuss the design aspects related to the deployment of IRSs in 6G networks. We also explore optimization-based IRS deployment techniques to improve system performance in terrestrial and aerial IRSs. Furthermore, we survey model-free reinforcement learning (RL) techniques from the deployment aspect to address the challenges of achieving higher capacity in complex and mobile IRS-assisted UAV wireless systems. Finally, we highlight challenges and future research directions from the deployment aspect of IRSs for improving system performance for the future 6G network.Comment: 16 pages, 3 Figures, 7 table

Caching UAV-enabled small-cell networks

Unmanned aerial vehicles (UAVs) can be utilized to provide flexible wireless access in future wireless networks, with larger coverage and higher transmission rate. However, the wireless backhaul for UAVs is usually capacity-limited and congested, and UAVs cannot operate for a long time due to the limited battery life. In this paper, a framework of caching UAV-enabled small-cell networks is proposed, to offload data traffic for the small-cell base stations via caching. In the proposed scheme, the most popular contents are stored at the local caches of UAVs in advance, which can be delivered to mobile users directly from the caches when required. Thus, the congestion of wireless backhaul can be alleviated, the energy consumption can be reduced, and the quality of experience can be improved

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

A Survey on UAV-Aided Maritime Communications: Deployment Considerations, Applications, and Future Challenges

Maritime activities represent a major domain of economic growth with several emerging maritime Internet of Things use cases, such as smart ports, autonomous navigation, and ocean monitoring systems. The major enabler for this exciting ecosystem is the provision of broadband, low-delay, and reliable wireless coverage to the ever-increasing number of vessels, buoys, platforms, sensors, and actuators. Towards this end, the integration of unmanned aerial vehicles (UAVs) in maritime communications introduces an aerial dimension to wireless connectivity going above and beyond current deployments, which are mainly relying on shore-based base stations with limited coverage and satellite links with high latency. Considering the potential of UAV-aided wireless communications, this survey presents the state-of-the-art in UAV-aided maritime communications, which, in general, are based on both conventional optimization and machine-learning-aided approaches. More specifically, relevant UAV-based network architectures are discussed together with the role of their building blocks. Then, physical-layer, resource management, and cloud/edge computing and caching UAV-aided solutions in maritime environments are discussed and grouped based on their performance targets. Moreover, as UAVs are characterized by flexible deployment with high re-positioning capabilities, studies on UAV trajectory optimization for maritime applications are thoroughly discussed. In addition, aiming at shedding light on the current status of real-world deployments, experimental studies on UAV-aided maritime communications are presented and implementation details are given. Finally, several important open issues in the area of UAV-aided maritime communications are given, related to the integration of sixth generation (6G) advancements