Modeling and analysis of Uplink-Downlink relationship in Heterogeneous Cellular Network

Xu, Hao; Chang, Yanxun; Sun, Tengxiao; Zheng, Daixu

doi:10.1109/pimrc.2014.7136376

Cited by 3 publications

(4 citation statements)

References 9 publications

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“…For analytical tractability, we model the location of these active aggregators as a HPPP with density λ a = (1 − Pr(K 1 = 0))λ a . 6 For an active aggregator with K 1 active channels, its aggregated data can be successfully decoded by its associated BS as long as its SIR meets the following condition [27], [30]…”

Section: B Average Channel Success Probabilitymentioning

confidence: 99%

“…Remark 4: For the special case of m 2 = 1, which corresponds to the case that the typical link experiences Rayleigh fading, we can havep suc2 ≈ exp −2(1 − p void )s degrades. Note that p suc2 (k 1 ) in (16) has been studied in the stochastic geometry literature with different system setups, e.g., [27], [30], [31]. However, in this paper, we are interested inp suc2 in (15).…”

Section: Proof: See Appendix Ementioning

confidence: 99%

“…However, the consideration of such a point process leads to intractable results. Hence, similar to [27], [30], we approximate the the location of interfering aggregators as a HPPP with density (1 − p void )λ B , where p void = 1 + λ a 3.5λ B −3.5…”

Section: Appendix E: Proof Of Propositionmentioning

confidence: 99%

“…If the number of channels N increases, it increases s as well as E K 1 [K 1 ] and hence psuc2 degrades. Note that p suc2 (k 1 ) in ( 16) has been studied in the stochastic geometry literature with different system setups, e.g., [27], [30], [31]. However, in this paper, we are interested in psuc2 in (15).…”

Section: B Average Channel Success Probabilitymentioning

confidence: 99%

See 3 more Smart Citations

Massive Machine Type Communication With Data Aggregation and Resource Scheduling

Guo

Durrani

Zhou

et al. 2017

IEEE Trans. Commun.

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To enable massive machine type communication (mMTC), data aggregation is a promising approach to reduce the congestion caused by a massive number of machine type devices (MTDs). In this work, we consider a two-phase cellular-based mMTC network where MTDs transmit to aggregators (i.e., aggregation phase) and the aggregated data is then relayed to base stations (i.e., relaying phase). Due to the limited resources, the aggregators not only aggregate data, but also schedule resources among MTDs. We consider two scheduling schemes: random resource scheduling (RRS) and channel-aware resource scheduling (CRS). By leveraging the stochastic geometry, we present a tractable analytical framework to investigate the signal-to-interference ratio (SIR) for each phase, thereby computing the MTD success probability, the average number of successful MTDs and probability of successful channel utilization, which are the key metrics characterizing the overall mMTC performance. Our numerical results show that, although the CRS outperforms the RRS in terms of SIR at the aggregation phase, the simpler RRS has almost the same performance as the CRS for most cases with regards to the overall mMTC performance. Furthermore, the provision of more resources at the aggregation phase is not always beneficial to the mMTC performance.

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Section: B Average Channel Success Probabilitymentioning

confidence: 99%