Nonorthogonal Multiple Access in Large-Scale Underlay Cognitive Radio Networks

By Yuanwei Liu, Zhiguo Ding, Maged Elkashlan, Jinhong Yuan. IEEE Transactions on Vehicular Technology, 2016

321 citations recorded by the source corpus.

In this paper, nonorthogonal multiple access (NOMA) is applied to large-scale underlay cognitive radio (CR) networks with randomly deployed users. To characterize the performance of the considered network, new closed-form expressions of the outage probability are derived using stochastic geometry. More importantly, by carrying out the diversity analysis, new insights are obtained under the two scenarios with different power constraints: 1) fixed transmit power of the primary transmitters (PTs); and 2) transmit power of the PTs being proportional to that of the secondary base station. For the first scenario, a diversity order of m is experienced at the mth-ordered NOMA user. For the second scenario, there is an asymptotic error floor for the outage probability. Simulation results are provided to verify the accuracy of the derived results. A pivotal conclusion is reached that by carefully designing target data rates and power allocation coefficients of users, NOMA can outperform conventional orthogonal multiple access in underlay CR networks.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/cdc50beca509b69bebd45fc479c0a4839d99892b

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Citations in the corpus

Evolution of NOMA Toward Next Generation Multiple Access (NGMA) for 6G (2021, background): “Although the non-orthogonal transmission strategies increase the transmitter and receiver complexity, significant benefits can be achieved, such as supporting massive connectivity [14], [15], achieving high SE and EE [16], [17], and guaranteeing user fairness [18].”
Nonorthogonal Multiple Access for 5G and Beyond (2017, methodology): “t is natural to link them for achieving an improved bandwidth efﬁciency.Liu et al. investigated the application of NOMA in large-scale underlay CR networks by relying on the stochastic geometry model [112]. The diversity order of the NOMA users was characterized analytically in two scenarios. The cl”
Deep Learning for an Effective Nonorthogonal Multiple Access Scheme (2018, unknown): “Digital Object Identifier 10.1109/TVT.2018.2848294 and has drawn significant interest from researchers because of its very promising application in fifth generation (5G) networks [1].”
Cooperative Non-orthogonal Multiple Access With Simultaneous Wireless Information and Power Transfer (2015, methodology): “Additionally, the performance of NOMA in large-scale cognitive radio networks was investigated in [6], where the positions of the primary and secondary users were modeled using stochastic geometry.”
A Survey on Non-Orthogonal Multiple Access for 5G Networks: Research Challenges and Future Trends (2017, methodology): “The benefits of NOMA for heterogeneous networks has been demonstrated in [117], [123]–[125], as more users can be served in a small cell by exploiting the NOMA principle.”
Enhancing the Physical Layer Security of Non-Orthogonal Multiple Access in Large-Scale Networks (2016, methodology): “Chebyshev quadrature relationship [47] is invoked for finding an approximation of (A.”
Modulation and Multiple Access for 5G Networks (2017, background): “In [98], NOMA-based underlay CR networks have been studied and the closed-form expressions of the outage probability have been derived.”
Exploiting Full/Half-Duplex User Relaying in NOMA Systems (2018, methodology): “As the interplay between NOMA and cognitive radio is bidirectional, NOMA was also applied to cognitive radio networks in [14].”