required to communicate with a massive number patients’ electronic medical
devices to improve their quality of health. IoH services include real-time
remote diagnosis, remote treatment (telemedicine), and remote surgery for
emergencies. NOMA is a promising candidate capable of simultaneously
transmitting information to multiple patients maintaining coordination among
numerous smart devices. To establish in-home medical networks, Xuewan et al.
proposed a multi-carrier NOMA framework that connects comparatively more
monitoring units and transfers more information bits compared to OMA-based
designs [296]. Based on patients’ medical history, some medical advices as
first aid services can be cached in the health monitoring unit. Cache enabled
NOMA is particularly useful because, often, the medical information is data
heavy (high resolution tomography or video) and local storage of patient data
is useful considering their limited mobility.

####  VI-B6 NOMA-aided Visible Light Communications

For short-range communications, visible light communication (VLC) is a
promising communication scheme operated through an unlicensed spectrum with
high secrecy and low energy. Efficient MA techniques are required to be
implemented for VLC systems to improve spectral efficiency since the
modulation bandwidth is narrow in the VLC. NOMA could be an attractive MA for
the VLC. Each transmitter of practical VLC serves has a limited number of
users which leads to a reduced SIC-based NOMA decoding process keeping control
of the outage probabilities. Furthermore, the slow-varying channel
characteristic of the VLC reduces the overhead and complexity required for
accurate CSI estimation at the NOMA transmitter. These two unique features of
the VLC make the NOMA-based VLC advantageous over the OMA-VLC in terms of the
ergodic sum-rate [297]. MIMO-NOMA-based VLC is also becoming a popular
approach to enhance the sum-rate [298]. Depending on the requirement and
network model, the NOMA-based VLC can be extended to cache-aided NOMA-based
VLC systems.

####  VI-B7 Hybrid Free Space Optical Communication

Like the VLC, free-space optical (FSO) communication is another short-range,
line-of-sight communication system exploiting the optical domain for the next
generation. The FSO communication system fundamentally works on intensity
modulation of laser and direct detection by a photodetector in the near-
infrared range ($750-1600$ nm) and transmits data in Gbps range through high
bandwidth optical channels. The FSO communication uses a laser as a
transmitter with low implementation cost, and the directional communication
nature provides higher security compared to traditional communication systems.
Despite so many advantages, weather conditions, atmospheric turbulence, and
pointing errors (misalignment between the optical transmitter and receiver)
are the inescapable challenges of FSO systems. In [299] the authors have
analyzed the performance of a mixed RF-FSO system and validated the
superiority of the FSO backhauling and high-reliability NOMA systems over the
conventional RF backhauling. Cooperative relay transmission capability can
make NOMA the most suitable technology for hybrid FSO/ radio frequency
communication systems. Cache-aided NOMA systems are particularly attractive
for hybrid RF/FSO systems as cache can help in alleviating issues like
difference of data rates over parallel RF and FSO links.

##  VII Conclusion

This article presented a comprehensive survey and reviewed the state-of-the-
art research contributions of the cache-aided NOMA technique in wireless
communications. First, we explained the fundamental concepts, operating
principles, and major challenges associated with the cache and NOMA
techniques. We then flexibly amalgamated cache with NOMA and discussed the
motivations and goals of cache-aided NOMA-based wireless networks. This
article explicitly presented the primary considerations related to cache-aided
NOMA network design, including cache memory allocation, most popular content
selection, and optimal content placement. This survey paper thoroughly
reviewed the frameworks for achieving primary goals such as the increased
probability of successful decoding, sum-rate maximization, delay reduction,
interference cancellation, and energy consumption minimization. We found
efficient placement of popular contents and suitable power allocation are the
two essential requirements for designing cache-aided NOMA systems. Next, we
categorized the research articles depending on the application scenarios of
cache-aided NOMA systems. We then identified that the cache-aided NOMA
technology is mostly applied in vehicular communications, UAV-based networks,
MEC, and cellular communications. Advantages and challenges related to the
utilization of cache-aided NOMA technology in these scenarios are presented.
We discussed the benefits of using cache-aided NOMA in these scenarios, and
concluded that balancing the performance and energy consumption is the most
challenging tasks. Finally, we highlighted existing open challenges and future
research directions of the cache-aided NOMA technology.

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|  Dipen Bepari received his B-Tech degree in Electronics and Communication
Engineering from Jalpaiguri Government Engineering College, West Bengal,
India, and completed M.Tech. from National Institute of Technology, Durgapur,
India. He has received Ph.D. degree from Department of Electronics
Engineering, IIT (ISM), Dhanbad, India. He received scholarship from the
University Grant Commission (UGC), Government of India for the period
2008–2010 during M.Tech., and from the Ministry of Human Resource and
Development (MHRD), Government of India for the period 2012–2017 during Ph.D.
Presently he is working at National Institute of Technology, Raipur, India.
His research interests include cognitive radio networks, wireless sensor
networks, energy harvesting, and NOMA technique.
---|---
|  Soumen Mondal (S’16-S’20) received his B.Tech degree in Electronics and
Communication Engineering in 2008 from Haldia Institute of Technology, Haldia,
India, M.Tech. degree in Telecommunication Engineering in 2010, and Ph.D.
degree in 2021 from National Institute of Technology, Durgapur, India. Dr.
Mondal has published about 15 research papers in refereed journals. His
research interests include Cognitive Radio Networks, Energy Harvesting,
Intelligent Reflecting Surface, FSO and NOMA.
---|---
|  Aniruddha Chandra (M’08–SM’16) received BE, ME, and PhD degrees from
Jadavpur University, Kolkata, India, in 2003, 2005 and 2011, respectively. He
joined the Electronics and Communication Engineering Department, National
Institute of Technology, Durgapur, India, in 2005. He is currently an
Associate Professor there. In 2011, he was a Visiting Lecturer at the Asian
Institute of Technology, Bangkok. From 2014 to 2016, he worked as a Marie
Curie fellow at Brno University of Technology, Czech Republic. In 2019, he
worked as a Visiting Researcher at the Slovak University of Technology,
Slovakia. In 2022, he was a Guest Researcher at Niigata University, Japan. Dr.
Chandra has published about 130 research papers in refereed journals and peer-
reviewed conferences. He is a co-recipient of the best short paper award at
IEEE VNC 2014, held in Paderborn, Germany, and delivered a keynote lecture at
IEEE MNCApps 2012, held in Bangalore, India. He is currently the secretary of
the IEEE P2982 Standard working group and IEEE ComSoc RCC SIG on Propagation
Channels for 5G and Beyond. His primary area of research is physical layer
issues in wireless communication.
---|---
|  Rajeev Shukla (S’20) received his BE degree in Electronics and
Telecommunication Engineering from Rungta College of Engineering and
Technology in 2011 and ME degree in Communication Engineering from
Chhattrapati Shivaji Institute of Engineering and Technology in 2015. He is
currently pursuing his PhD degree in Wireless Communication from National
Institute of Technology, Durgapur, India. He has worked as an Assistant
Professor from 2012 to 2015 in School of Engineering, MATS University, Raipur
and from 2016 to 2018 in Shekhawati Institute of Engineering and Technology,
Sikar. From 2018 to 2020, he worked as a Junior Research Fellow at Indian
Institute of Technology, Jodhpur, where he worked on a project on SAR image
processing. His research interests include 5G, millimeter waves, channel
modelling, and cognitive radio.
---|---
|  Yuanwei Liu (S’13–M’16–SM’19, http://www.eecs.qmul.ac.uk/ yuanwei) received
the B.S. and M.S. degrees from the Beijing University of Posts and
Telecommunications in 2011 and 2014, respectively, and the PhD degree in
electrical engineering from the Queen Mary University of London, U.K., in
2016. He was with the Department of Informatics, King’s College London, from
2016 to 2017, where he was a Post-Doctoral Research Fellow. He has been a
Senior Lecturer (Associate Professor) with the School of Electronic
Engineering and Computer Science, Queen Mary University of London, since Aug.
2021, where he was a Lecturer (Assistant Professor) from 2017 to 2021. His
research interests include non-orthogonal multiple access, 5G/6G networks,
RIS, integrated sensing and communications, and machine learning. Yuanwei Liu
is a Web of Science Highly Cited Researcher 2021. He is currently a Senior
Editor of IEEE Communications Letters, an Editor of the IEEE Transactions on
Wireless Communications and the IEEE Transactions on Communications. He serves
as the leading Guest Editor for IEEE JSAC special issue on Next Generation
Multiple Access, a Guest Editor for IEEE JSTSP special issue on Signal
Processing Advances for Non-Orthogonal Multiple Access in Next Generation
Wireless Networks. He received IEEE ComSoc Outstanding Young Researcher Award
for EMEA in 2020. He received the 2020 IEEE Signal Processing and Computing
for Communications (SPCC) Technical Early Achievement Award, IEEE
Communication Theory Technical Committee (CTTC) 2021 Early Achievement Award.
He received IEEE ComSoc Young Professional Outstanding Nominee Award in 2021.
He has served as the Publicity Co-Chair for VTC 2019-Fall. He is the leading
contributor for “Best Readings for Non-Orthogonal Multiple Access (NOMA)” and
the primary contributor for “Best Readings for Reconfigurable Intelligent
Surfaces (RIS)”. He serves as the chair of Special Interest Group (SIG) in
SPCC Technical Committee on the topic of signal processing Techniques for next
generation multiple access (NGMA), the vice-chair of SIG Wireless
Communications Technical Committee (WTC) on the topic of Reconfigurable
Intelligent Surfaces for Smart Radio Environments (RISE), and the Tutorials
and Invited Presentations Officer for Reconfigurable Intelligent Surfaces
Emerging Technology Initiative.
---|---
|  Mohsen Guizani (M’89–SM’99–F’09) received the BS (with distinction), MS and
PhD degrees in Electrical and Computer engineering from Syracuse University,
Syracuse, NY, USA in 1985, 1987 and 1990, respectively. He is currently a
Professor of Machine Learning and the Associate Provost at Mohamed Bin Zayed
University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE. Previously, he
worked in different institutions in the USA. His research interests include
applied machine learning and artificial intelligence, Internet of Things
(IoT), intelligent autonomous systems, smart city, and cybersecurity. He was
elevated to the IEEE Fellow in 2009 and was listed as a Clarivate Analytics
Highly Cited Researcher in Computer Science in 2019, 2020 and 2021. Dr.
Guizani has won several research awards including the “2015 IEEE
Communications Society Best Survey Paper Award”, the Best ComSoc Journal Paper
Award in 2021 as well five Best Paper Awards from ICC and Globecom
Conferences. He is the author of ten books and more than 800 publications. He
is also the recipient of the 2017 IEEE Communications Society Wireless
Technical Committee (WTC) Recognition Award, the 2018 AdHoc Technical
Committee Recognition Award, and the 2019 IEEE Communications and Information
Security Technical Recognition (CISTC) Award. He served as the Editor-in-Chief
of IEEE Network and is currently serving on the Editorial Boards of many IEEE
Transactions and Magazines. He was the Chair of the IEEE Communications
Society Wireless Technical Committee and the Chair of the TAOS Technical
Committee. He served as the IEEE Computer Society Distinguished Speaker and is
currently the IEEE ComSoc Distinguished Lecturer.
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|  Arumugam Nallanathan (S’97-–M’00-–SM’05-–F’17) has been a Professor of
Wireless Communications and the Head of the Communication Systems Research
Group, School of Electronic Engineering and Computer Science, Queen Mary
University of London, since September 2017. He was with the Department of
Informatics, King’s College London from December 2007 to August 2017, where he
was a Professor of Wireless Communications from April 2013 to August 2017 and
a Visiting Professor from September 2017. He was an Assistant Professor with
the Department of Electrical and Computer Engineering, National University of
Singapore from August 2000 to December 2007. He published nearly 500 technical
papers in scientific journals and international conferences. His research
interests include artificial intelligence for wireless systems, B5G wireless
networks, Internet of Things, and molecular communications. He is a co-
recipient of the Best Paper Awards presented at the IEEE Communications
Society SPCE Outstanding Service Award 2012, the IEEE Communications Society
RCC Outstanding Service Award 2014, the IEEE International Conference on
Communications in 2016, IEEE Global Communications Conference 2017, and the
IEEE Vehicular Technology Conference in 2018. He served as the Chair for the
Signal Processing and Communication Electronics Technical Committee of IEEE
Communications Society and Technical Program Chair and member of Technical
Program Committees in numerous IEEE conferences. He is an Editor-at-Large for
IEEE TRANSACTIONS ON COMMUNICATIONS and a Senior Editor for IEEE WIRELESS
COMMUNICATIONS LETTERS. He was an Editor for IEEE TRANSACTIONS ON WIRELESS
COMMUNICATIONS from 2006 to 2011, IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
from 2006 to 2017, and IEEE SIGNAL PROCESSING LETTERS. He has been selected as
a Web of Science Highly Cited Researcher in 2016 and an AI 2000 Internet of
Things Most Influential Scholar in 2020. He is an IEEE Distinguished Lecturer.
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