research-article

In-band spectrum sensing in cognitive radio networks: energy detection or feature detection?

Authors:

Kang G. ShinAuthors Info & Claims

MobiCom '08: Proceedings of the 14th ACM international conference on Mobile computing and networking

Pages 14 - 25

https://doi.org/10.1145/1409944.1409948

Published: 14 September 2008 Publication History

Abstract

In a cognitive radio network (CRN), in-band spectrum sensing is essential for the protection of legacy spectrum users, with which the presence of primary users (PUs) can be detected promptly, allowing secondary users (SUs) to vacate the channels immediately. For in-band sensing, it is important to meet the detectability requirements, such as the maximum allowed latency of detection (e.g., 2 seconds in IEEE 802.22) and the probability of mis-detection and false-alarm. In this paper, we propose an effcient periodic in-band sensing algorithm that optimizes sensing-frequency and sensing-time by minimizing sensing overhead while meeting the detectability requirements. The proposed scheme determines the better of energy or feature detection that incurs less sensing overhead at each SNR level, and derives the threshold aRSS_threshold on the average received signal strength (RSS) of a primary signal below which feature detection is preferred. We showed that energy detection under lognormal shadowing could still perform well at the average SNR < SNR_wall [1] when collaborative sensing is used for its location diversity. Two key factors affecting detection performance are also considered: noise uncertainty and inter-CRN interference. aRSS_threshold appears to lie between -114.6 dBm and -109.9 dBm with the noise uncertainty ranging from 0.5 dB to 2 dB, and between -112.9 dBm and -110.5 dBm with 1~6 interfering CRNs.

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Index Terms

In-band spectrum sensing in cognitive radio networks: energy detection or feature detection?
1. Networks
  1. Network types
    1. Mobile networks
    2. Wireless access networks

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cover image ACM Conferences

MobiCom '08: Proceedings of the 14th ACM international conference on Mobile computing and networking

September 2008

374 pages

ISBN:9781605580968

DOI:10.1145/1409944

General Chair:
J. J. Garcia-Luna-Aceves
University of California, Santa Cruz, USA & Palo Alto Research Center, USA
,
Program Chairs:
Raghupathy Sivakumar
Georgia Institute of Technology, USA
,
Peter Steenkiste
Carnegie Mellon University, USA

Copyright © 2008 ACM.

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Published: 14 September 2008

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September 14 - 19, 2008

California, San Francisco, USA

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Overall Acceptance Rate 440 of 2,972 submissions, 15%

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https://doi.org/10.1109/TWC.2024.3450972
Zhang JWu X(2024)An Anti-Jamming Game Between Dynamically-Sensing Jammer and Legitimate User With Faking-Slot TransmissionIEEE Transactions on Vehicular Technology10.1109/TVT.2024.337296973:7(10287-10300)Online publication date: Jul-2024
https://doi.org/10.1109/TVT.2024.3372969
Li KLi CChen JZhang QLiu ZHe S(2023)Boost Spectrum Prediction With Temporal-Frequency Fusion Network via Transfer LearningIEEE Transactions on Mobile Computing10.1109/TMC.2021.313694122:6(3209-3223)Online publication date: 1-Jun-2023
https://doi.org/10.1109/TMC.2021.3136941
Sudha KKumari KVarunika D(2023)A Critical Survey on Security Issues in Cognitive Radio Networks2023 International Conference on Intelligent Systems for Communication, IoT and Security (ICISCoIS)10.1109/ICISCoIS56541.2023.10100508(292-297)Online publication date: 9-Feb-2023
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Lorincz JRamljak IBegušić D(2021)A Survey on the Energy Detection of OFDM Signals with Dynamic Threshold Adaptation: Open Issues and Future ChallengesSensors10.3390/s2109308021:9(3080)Online publication date: 28-Apr-2021
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Dias Santana GCristo RLucas Jaquie Castelo Branco K(2021)Integrating Cognitive Radio with Unmanned Aerial Vehicles: An OverviewSensors10.3390/s2103083021:3(830)Online publication date: 27-Jan-2021
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Hossain MXie J(2020)Hide and Seek: A Markov-Based Defense Strategy Against Off-Sensing Attack in Cognitive Radio NetworksIEEE Transactions on Network Science and Engineering10.1109/TNSE.2020.30117077:4(3028-3041)Online publication date: 1-Oct-2020
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Zhang DDing WLiu CWang HZhang B(2020)Modulated Autocorrelation Convolution Networks for Automatic Modulation Classification Based on Small Sample SetIEEE Access10.1109/ACCESS.2020.29715868(27097-27105)Online publication date: 2020
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Zeng YChandrasekaran VBanerjee SGiustiniano DAgarwal SGreenstein BBalasubramanian AGollakota SZhang X(2019)A Framework for Analyzing Spectrum Characteristics in Large Spatio-temporal ScalesThe 25th Annual International Conference on Mobile Computing and Networking10.1145/3300061.3345450(1-16)Online publication date: 5-Aug-2019
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Liu YZhang QNi L(2019)A General Framework for Spectrum Sensing Using Dedicated Spectrum Sensor NetworksACM Transactions on Sensor Networks10.1145/327524415:1(1-23)Online publication date: 30-Jan-2019
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