Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

DeepDyve requires Javascript to function. Please enable Javascript on your browser to continue.

Non-Orthogonal Multiple Access for Massive Connectivity

Part of the SpringerBriefs in Computer Science Book Series

DOI: 10.1007/978-3-030-30975-6

ISBN: 978-3-030-30974-9

Publisher: Springer International Publishing - Springer Journals

Nov 5, 2019

Available

Introduction

Liu, Yuanwei; Qin, Zhijin; Ding, Zhiguo

Pages: 3 –6

[This chapter introduces the background of using non-orthogonal multiple access (NOMA), including overview of NOMA in the fifth generation (5G) communication systems and the beyond as well as the related standards.]
Available

What Is NOMA?

Liu, Yuanwei; Qin, Zhijin; Ding, Zhiguo

Pages: 7 –12

[This chapter provides an overview of the background knowledge of NOMA from an information theoretic perspective.]
Available

Compatibility in NOMA

Liu, Yuanwei; Qin, Zhijin; Ding, Zhiguo

Pages: 15 –44

[In this chapter, the compatibility of NOMA will be introduced by discussing the applications of NOMA to various techniques, such as heterogeneous networks (HetNets), cognitive radio networks (CRNs), and multiple-input multiple-output (MIMO). Particularly, the average performance of NOMA enabled...
Available

Sustainability of NOMA

Liu, Yuanwei; Qin, Zhijin; Ding, Zhiguo

Pages: 45 –65

[In this chapter, the sustainability of NOMA will be discussed by talking about cooperative NOMA and wireless powered NOMA networks. Particularly, the average performance of wireless powered NOMA will be presented in detail as an example.]
Available

Security in NOMA

Liu, Yuanwei; Qin, Zhijin; Ding, Zhiguo

Pages: 67 –87

[In this chapter, the security of NOMA will be demonstrating by introducing average performance of physical layer security (PLS).]
Available

Artificial Intelligence (AI) Enabled NOMA

Liu, Yuanwei; Qin, Zhijin; Ding, Zhiguo

Pages: 89 –94

[Recently, machine learning has been extensively applied in various areas including wireless communications. The more recent work has shown the power of deep learning in physical layer communications (Qin et al., IEEE Wireless Commun 26:93–99, 2019) and resource allocation (Ye et al., IEEE Veh...
Available

Challenges and Conclusions

Liu, Yuanwei; Qin, Zhijin; Ding, Zhiguo

Pages: 97 –98

[There still exist some open research issues in context of the implementation of NOMA enabled UAV networks.]

results per page

Prev
1
Next

Browse All eBooks