On the outage performance of network NOMA (N-NOMA) modeled by poisson line cox point process

Yanshi Sun, Zhiguo Ding, Xuchu Dai

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


This paper studies the application of network non-orthogonal multiple access (N-NOMA) to vehicular communication networks, modeled by a Poisson line Cox point process (PLC). Particularly, the road infrastructure is modeled by a Poisson line point process (PLP), whereas base stations (BSs) and users are located according to 1D homogeneous Poisson point processes (HPPPs) on each road. In the considered N-NOMA scheme, there are two types of users, i.e., coordinated multi-point (CoMP) users which are far from their BSs and NOMA users which are close to their BSs, where the CoMP users and the NOMA users are served simultaneously by applying the principle of NOMA. Specifically, the BSs collaborate with each other to provide reliable transmission for the CoMP user by applying Almouti coding, while each BS employs superposition coding to opportunistically serve an additional NOMA user. Compared to a conventional OMA based scheme, where only a CoMP user is served, the proposed N-NOMA scheme significantly improves the performance in terms of outage sum rates and user connectivity, since more users are served in the same resource block. A comprehensive analytical framework is developed to characterize the system level performance of the proposed N-NOMA scheme by applying PLC. Closed form expressions for the outage probabilities achieved by the CoMP and NOMA users are obtained. Computer simulation results are provided to show the superior performance of the proposed scheme and also validate the accuracy of the developed analytical results.

Original languageBritish English
Article number9477118
Pages (from-to)7936-7950
Number of pages15
JournalIEEE Transactions on Vehicular Technology
Issue number8
StatePublished - Aug 2021


  • cox process
  • Network non-orthogonal multiple access (N-NOMA)
  • poisson line point process (PLP)
  • stochastic geometry
  • superposition coding (SC)


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