Analysis of Cognitive Radio Networks with Imperfect Sensing

Isameldin Mohammed SULIMAN; Janne J. LEHTOMÄKI; Kenta UMEBAYASHI; Marcos KATZ

doi:10.1587/transcom.E96.B.1605

Analysis of Cognitive Radio Networks with Imperfect Sensing

Isameldin Mohammed SULIMAN, Janne J. LEHTOMÄKI, Kenta UMEBAYASHI, Marcos KATZ

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It is well known that cognitive radio (CR) techniques have great potential for supporting future demands on the scarce radio spectrum resources. For example, by enabling the utilization of spectrum bands temporarily not utilized by primary users (PUs) licensed to operate on those bands. Spectrum sensing is a well-known CR technique for detecting those unutilized bands. However, the spectrum sensing outcomes cannot be perfect and there will always be some misdetections and false alarms which will affect the performance thereby degrading the quality of service (QoS) of PUs. Continuous time Markov chain (CTMC) based modeling has been widely used in the literature to evaluate the performance of CR networks (CRNs). A major limitation of the available literature is that all the key factors and realistic elements such as the effect of imperfect sensing and state dependent transition rates are not modeled in a single work. In this paper, we present a CTMC based model for analyzing the performance of CRNs. The proposed model differs from the existing models by accurately incorporating key elements such as full state dependent transition rates, multi-channel support, handoff capability, and imperfect sensing. We derive formulas for primary termination probability, secondary success probability, secondary blocking probability, secondary forced termination probability, and radio resource utilization. The results show that incorporating fully state dependent transition rates in the CTMC can significantly improve analysis accuracy, thus achieving more realistic and accurate analytical model. The results from extensive Monte Carlo simulations confirm the validity of our proposed model.

Publication: IEICE TRANSACTIONS on Communications Vol.E96-B No.6 pp.1605-1615

Publication Date: 2013/06/01

Publicized

Online ISSN: 1745-1345

DOI: 10.1587/transcom.E96.B.1605

Type of Manuscript: PAPER

Category: Terrestrial Wireless Communication/Broadcasting Technologies

Authors

Isameldin Mohammed SULIMAN
  University of Oulu
Janne J. LEHTOMÄKI
  University of Oulu
Kenta UMEBAYASHI
  Tokyo University of Agriculture and Technology
Marcos KATZ
  University of Oulu

Keyword

cognitive radios, performance analysis, continuous time Markov chain, state dependent transition rates, imperfect sensing, multi-channel support

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Isameldin Mohammed SULIMAN, Janne J. LEHTOMÄKI, Kenta UMEBAYASHI, Marcos KATZ, "Analysis of Cognitive Radio Networks with Imperfect Sensing" in IEICE TRANSACTIONS on Communications, vol. E96-B, no. 6, pp. 1605-1615, June 2013, doi: 10.1587/transcom.E96.B.1605.
Abstract: It is well known that cognitive radio (CR) techniques have great potential for supporting future demands on the scarce radio spectrum resources. For example, by enabling the utilization of spectrum bands temporarily not utilized by primary users (PUs) licensed to operate on those bands. Spectrum sensing is a well-known CR technique for detecting those unutilized bands. However, the spectrum sensing outcomes cannot be perfect and there will always be some misdetections and false alarms which will affect the performance thereby degrading the quality of service (QoS) of PUs. Continuous time Markov chain (CTMC) based modeling has been widely used in the literature to evaluate the performance of CR networks (CRNs). A major limitation of the available literature is that all the key factors and realistic elements such as the effect of imperfect sensing and state dependent transition rates are not modeled in a single work. In this paper, we present a CTMC based model for analyzing the performance of CRNs. The proposed model differs from the existing models by accurately incorporating key elements such as full state dependent transition rates, multi-channel support, handoff capability, and imperfect sensing. We derive formulas for primary termination probability, secondary success probability, secondary blocking probability, secondary forced termination probability, and radio resource utilization. The results show that incorporating fully state dependent transition rates in the CTMC can significantly improve analysis accuracy, thus achieving more realistic and accurate analytical model. The results from extensive Monte Carlo simulations confirm the validity of our proposed model.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E96.B.1605/_p

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@ARTICLE{e96-b_6_1605,
author={Isameldin Mohammed SULIMAN, Janne J. LEHTOMÄKI, Kenta UMEBAYASHI, Marcos KATZ, },
journal={IEICE TRANSACTIONS on Communications},
title={Analysis of Cognitive Radio Networks with Imperfect Sensing},
year={2013},
volume={E96-B},
number={6},
pages={1605-1615},
abstract={It is well known that cognitive radio (CR) techniques have great potential for supporting future demands on the scarce radio spectrum resources. For example, by enabling the utilization of spectrum bands temporarily not utilized by primary users (PUs) licensed to operate on those bands. Spectrum sensing is a well-known CR technique for detecting those unutilized bands. However, the spectrum sensing outcomes cannot be perfect and there will always be some misdetections and false alarms which will affect the performance thereby degrading the quality of service (QoS) of PUs. Continuous time Markov chain (CTMC) based modeling has been widely used in the literature to evaluate the performance of CR networks (CRNs). A major limitation of the available literature is that all the key factors and realistic elements such as the effect of imperfect sensing and state dependent transition rates are not modeled in a single work. In this paper, we present a CTMC based model for analyzing the performance of CRNs. The proposed model differs from the existing models by accurately incorporating key elements such as full state dependent transition rates, multi-channel support, handoff capability, and imperfect sensing. We derive formulas for primary termination probability, secondary success probability, secondary blocking probability, secondary forced termination probability, and radio resource utilization. The results show that incorporating fully state dependent transition rates in the CTMC can significantly improve analysis accuracy, thus achieving more realistic and accurate analytical model. The results from extensive Monte Carlo simulations confirm the validity of our proposed model.},
keywords={},
doi={10.1587/transcom.E96.B.1605},
ISSN={1745-1345},
month={June},}

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TY - JOUR
TI - Analysis of Cognitive Radio Networks with Imperfect Sensing
T2 - IEICE TRANSACTIONS on Communications
SP - 1605
EP - 1615
AU - Isameldin Mohammed SULIMAN
AU - Janne J. LEHTOMÄKI
AU - Kenta UMEBAYASHI
AU - Marcos KATZ
PY - 2013
DO - 10.1587/transcom.E96.B.1605
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E96-B
IS - 6
JA - IEICE TRANSACTIONS on Communications
Y1 - June 2013
AB - It is well known that cognitive radio (CR) techniques have great potential for supporting future demands on the scarce radio spectrum resources. For example, by enabling the utilization of spectrum bands temporarily not utilized by primary users (PUs) licensed to operate on those bands. Spectrum sensing is a well-known CR technique for detecting those unutilized bands. However, the spectrum sensing outcomes cannot be perfect and there will always be some misdetections and false alarms which will affect the performance thereby degrading the quality of service (QoS) of PUs. Continuous time Markov chain (CTMC) based modeling has been widely used in the literature to evaluate the performance of CR networks (CRNs). A major limitation of the available literature is that all the key factors and realistic elements such as the effect of imperfect sensing and state dependent transition rates are not modeled in a single work. In this paper, we present a CTMC based model for analyzing the performance of CRNs. The proposed model differs from the existing models by accurately incorporating key elements such as full state dependent transition rates, multi-channel support, handoff capability, and imperfect sensing. We derive formulas for primary termination probability, secondary success probability, secondary blocking probability, secondary forced termination probability, and radio resource utilization. The results show that incorporating fully state dependent transition rates in the CTMC can significantly improve analysis accuracy, thus achieving more realistic and accurate analytical model. The results from extensive Monte Carlo simulations confirm the validity of our proposed model.
ER -