Nonorthogonal Multiple Access in Large-Scale Underlay Cognitive Radio Networks

Yuanwei Liu, Zhiguo Ding, Maged Elkashlan, Jinhong Yuan

Research output: Contribution to journalArticlepeer-review

327 Scopus citations


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.

Original languageBritish English
Article number7398134
Pages (from-to)10152-10157
Number of pages6
JournalIEEE Transactions on Vehicular Technology
Issue number12
StatePublished - Dec 2016


  • Cognitive radio (CR)
  • large-scale network
  • nonorthogonal multiple access (NOMA)
  • stochastic geometry


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