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Journal Article
Large-Scale Network Analysis on NOMA-Aided Broadcast/Unicast Joint Transmission Scenarios Considering Content Popularity
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- Authors
- Sungjun Ahn, Sung-Ik Park, Jae-Young Lee, Namho Hur, Yiyan Wu, Liang Zhang, Wei Li, Jeongchang Kim
- Issue Date
- 2020-12
- Citation
- IEEE Transactions on Broadcasting, v.66, no.4, pp.770-785
- ISSN
- 0018-9316
- Publisher
- IEEE
- Language
- English
- Type
- Journal Article
- Abstract
- This paper analyzes reliability and quality gains that can be achieved by a broadcast-unicast convergent platform operating in non-orthogonal multiple access (NOMA)-based cellular networks. The analysis is addressed in network scope, dealing with randomly distributed users and base stations (BSs) within a stochastic geometry framework. Additionally a well-known Zipf-distribution popularity model on user demands is also taken into account, which effectively reflects a broadcasting gain from reducing redundant resource expense toward the users demanding same contents. Based on the stochastic modeling, closed-form delivery success probabilities and the consequent transmission rates are analytically derived, where the derivations are obtained separately for the networks with different access control strategies. Furthermore, the optimal broadcast content selection strategy maximizing the transmission rates is proposed. By means of numerical calculations, NOMA is shown to achieve substantial gain at broadcast/unicast joint transmission compared to the conventional orthogonal multiple access (OMA) network, particularly for the service configuration with popular contents having lower target service rates than less popular contents as long as the number of broadcast programs is restricted to be equal to the number of unicast services in a single frame.
- KSP Keywords
- Cellular networks, Content Popularity, Content Selection, Control strategy, Joint transmission, Network analysis, Nonorthogonal multiple access(NOMA), Numerical calculation, Selection strategy, Service Configuration, Single frame