Sense-and-predict: Opportunistic MAC based on spatial interference correlation for cognitive radio networks

Jeemin Kim; Seung Woo Ko; Han Cha; Seong Lyun Kim

doi:10.1109/DySPAN.2017.7920787

Sense-and-predict: Opportunistic MAC based on spatial interference correlation for cognitive radio networks

Jeemin Kim, Seung Woo Ko, Han Cha, Seong Lyun Kim

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Citations (Scopus)

Abstract

Opportunity detection at secondary transmitters (TXs) is a key technique enabling cognitive radio (CR) networks. Such detection however cannot guarantee reliable communication at secondary receivers (RXs), especially when their association distance is long. To cope with the issue, this paper proposes a novel MAC called sense-and-predict (SaP), where each secondary TX decides whether to access or not based on the prediction of the interference level at RX. Firstly, we provide the spatial interference correlation in a probabilistic form using stochastic geometry, and utilize it to maximize the area spectral efficiency (ASE) for secondary networks while guaranteeing the service quality of primary networks. Through simulations and testbed experiments using USRP, SaP is shown to always achieve ASE improvement compared with the conventional TX based sensing.

Original language	English
Title of host publication	2017 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509028306
DOIs	https://doi.org/10.1109/DySPAN.2017.7920787
Publication status	Published - 2017 May 5
Event	2017 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2017 - Baltimore, United States Duration: 2017 Mar 6 → 2017 Mar 9

Publication series

Name	2017 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2017

Other

Other	2017 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2017
Country	United States
City	Baltimore
Period	17/3/6 → 17/3/9

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

All Science Journal Classification (ASJC) codes

Computer Networks and Communications

Access to Document

10.1109/DySPAN.2017.7920787

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Kim, J., Ko, S. W., Cha, H., & Kim, S. L. (2017). Sense-and-predict: Opportunistic MAC based on spatial interference correlation for cognitive radio networks. In 2017 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2017 [7920787] (2017 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DySPAN.2017.7920787

Kim, Jeemin ; Ko, Seung Woo ; Cha, Han ; Kim, Seong Lyun. / Sense-and-predict : Opportunistic MAC based on spatial interference correlation for cognitive radio networks. 2017 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2017. Institute of Electrical and Electronics Engineers Inc., 2017. (2017 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2017).

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title = "Sense-and-predict: Opportunistic MAC based on spatial interference correlation for cognitive radio networks",

abstract = "Opportunity detection at secondary transmitters (TXs) is a key technique enabling cognitive radio (CR) networks. Such detection however cannot guarantee reliable communication at secondary receivers (RXs), especially when their association distance is long. To cope with the issue, this paper proposes a novel MAC called sense-and-predict (SaP), where each secondary TX decides whether to access or not based on the prediction of the interference level at RX. Firstly, we provide the spatial interference correlation in a probabilistic form using stochastic geometry, and utilize it to maximize the area spectral efficiency (ASE) for secondary networks while guaranteeing the service quality of primary networks. Through simulations and testbed experiments using USRP, SaP is shown to always achieve ASE improvement compared with the conventional TX based sensing.",

author = "Jeemin Kim and Ko, {Seung Woo} and Han Cha and Kim, {Seong Lyun}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2017 ; Conference date: 06-03-2017 Through 09-03-2017",

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doi = "10.1109/DySPAN.2017.7920787",

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Kim, J, Ko, SW, Cha, H & Kim, SL 2017, Sense-and-predict: Opportunistic MAC based on spatial interference correlation for cognitive radio networks. in 2017 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2017., 7920787, 2017 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2017, Institute of Electrical and Electronics Engineers Inc., 2017 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2017, Baltimore, United States, 17/3/6. https://doi.org/10.1109/DySPAN.2017.7920787

Sense-and-predict : Opportunistic MAC based on spatial interference correlation for cognitive radio networks. / Kim, Jeemin; Ko, Seung Woo; Cha, Han; Kim, Seong Lyun.

2017 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 7920787 (2017 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - Opportunity detection at secondary transmitters (TXs) is a key technique enabling cognitive radio (CR) networks. Such detection however cannot guarantee reliable communication at secondary receivers (RXs), especially when their association distance is long. To cope with the issue, this paper proposes a novel MAC called sense-and-predict (SaP), where each secondary TX decides whether to access or not based on the prediction of the interference level at RX. Firstly, we provide the spatial interference correlation in a probabilistic form using stochastic geometry, and utilize it to maximize the area spectral efficiency (ASE) for secondary networks while guaranteeing the service quality of primary networks. Through simulations and testbed experiments using USRP, SaP is shown to always achieve ASE improvement compared with the conventional TX based sensing.

AB - Opportunity detection at secondary transmitters (TXs) is a key technique enabling cognitive radio (CR) networks. Such detection however cannot guarantee reliable communication at secondary receivers (RXs), especially when their association distance is long. To cope with the issue, this paper proposes a novel MAC called sense-and-predict (SaP), where each secondary TX decides whether to access or not based on the prediction of the interference level at RX. Firstly, we provide the spatial interference correlation in a probabilistic form using stochastic geometry, and utilize it to maximize the area spectral efficiency (ASE) for secondary networks while guaranteeing the service quality of primary networks. Through simulations and testbed experiments using USRP, SaP is shown to always achieve ASE improvement compared with the conventional TX based sensing.

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Kim J, Ko SW, Cha H, Kim SL. Sense-and-predict: Opportunistic MAC based on spatial interference correlation for cognitive radio networks. In 2017 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2017. Institute of Electrical and Electronics Engineers Inc. 2017. 7920787. (2017 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2017). https://doi.org/10.1109/DySPAN.2017.7920787

Sense-and-predict: Opportunistic MAC based on spatial interference correlation for cognitive radio networks

Abstract

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