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 dc.contributor.author Ali, Konpal S. * dc.contributor.author Haenggi, Martin * dc.contributor.author Elsawy, Hesham * dc.contributor.author Chaaban, Anas * dc.contributor.author Alouini, Mohamed-Slim * dc.date.accessioned 2018-04-04T12:38:14Z dc.date.available 2018-04-04T12:38:14Z dc.date.issued 2018-03-21 en dc.identifier.uri http://hdl.handle.net/10754/627405.1 dc.description.abstract A network model is considered where Poisson distributed base stations transmit to $N$ power-domain non-orthogonal multiple access (NOMA) users (UEs) each that employ successive interference cancellation (SIC) for decoding. We propose three models for the clustering of NOMA UEs and consider two different ordering techniques for the NOMA UEs: mean signal power-based and instantaneous signal-to-intercell-interference-and-noise-ratio-based. For each technique, we present a signal-to-interference-and-noise ratio analysis for the coverage of the typical UE. We plot the rate region for the two-user case and show that neither ordering technique is consistently superior to the other. We propose two efficient algorithms for finding a feasible resource allocation that maximize the cell sum rate $\mathcal{R}_{\rm tot}$, for general $N$, constrained to: 1) a minimum rate $\mathcal{T}$ for each UE, 2) identical rates for all UEs. We show the existence of: 1) an optimum $N$ that maximizes the constrained $\mathcal{R}_{\rm tot}$ given a set of network parameters, 2) a critical SIC level necessary for NOMA to outperform orthogonal multiple access. The results highlight the importance in choosing the network parameters $N$, the constraints, and the ordering technique to balance the $\mathcal{R}_{\rm tot}$ and fairness requirements. We also show that interference-aware UE clustering can significantly improve performance. en dc.publisher arXiv en dc.relation.url http://arxiv.org/abs/1803.07866v1 en dc.relation.url http://arxiv.org/pdf/1803.07866v1 en dc.rights Archived with thanks to arXiv en dc.title Downlink Non-Orthogonal Multiple Access (NOMA) in Poisson Networks en dc.type Preprint en dc.contributor.department Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division * dc.contributor.department Electrical Engineering Program * dc.eprint.version Pre-print en dc.contributor.institution Department of Electrical Engineering, University of Notre Dame, USA. * dc.identifier.arxivid arXiv:1803.07866 en kaust.person Ali, Konpal S. kaust.person Elsawy, Hesham kaust.person Chaaban, Anas kaust.person Alouini, Mohamed-Slim refterms.dateFOA 2018-06-14T03:58:17Z
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