Umebayashi, Kenta, Kobayashi, Masanao and Lopez-Benitez, Miguel ORCID: 0000-0003-0526-6687
(2018)
Efficient Time Domain Deterministic-Stochastic Model of Spectrum Usage.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 17 (3).
pp. 1518-1527.
Text
IEEE_TWC_2017.pdf - Author Accepted Manuscript Download (7MB) |
Abstract
For achieving an efficient spectrum sharing in a context of dynamic spectrum access, understanding the spectrum usage by licensed users [primary users (PUs)], is important for secondary users (SUs). Duty cycle (DC) has been used to express the deterministic and stochastic aspects of spectrum usage. Specifically, a deterministic model for the mean of the duty cycle (M-DC) has been proposed in a previous work. The deterministic aspect of M-DC is affected by social behavior, and common habits of users, which can be confirmed in cellular systems. On the other hand, the observed DC (O-DC) during short time duration has randomness and a stochastic model is more suitable, e.g. distribution of O-DC. In this paper, we extend the conventional approach, in which only either the deterministic or stochastic aspect is considered, to a combined deterministic-stochastic (DS) model, which represents both the deterministic and stochastic aspects at once. For the distribution of the O-DC, the beta distribution has been used as stochastic model, but we employ a mixture of beta distributions. The mixture-beta distribution can achieve higher accuracy but requires more capacity for data storage in spectrum usage measurements since it has a higher number of parameters than the beta distribution. For this issue, we employ regression analysis in DS-model since this approach can reduce the number of parameters while retaining the accuracy. We show the validity of DS-model based on exhaustive spectrum measurements in IEEE 802.11-based wireless local area networks and long-term evolution uplink.
Item Type: | Article |
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Uncontrolled Keywords: | Dynamic spectrum access, spectrum measurement, cognitive radio, smart spectrum access, duty cycle, deterministic model |
Depositing User: | Symplectic Admin |
Date Deposited: | 27 Nov 2017 16:57 |
Last Modified: | 15 Mar 2024 06:09 |
DOI: | 10.1109/TWC.2017.2779511 |
Related URLs: | |
URI: | https://livrepository.liverpool.ac.uk/id/eprint/3013003 |