Spectrum Sensing of OFDM Signals in the Presence of Carrier Frequency Offset

Xu, Weiyang; Wang, Xiang; Elkashlan, Maged; Mehrpouyan, Hani

doi:10.1109/tvt.2015.2478517

Cited by 17 publications

(18 citation statements)

References 14 publications

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“…Reference [403] proposes a spectrum sensing algorithm robust to the CFO for OFDM-based cognitive radio network. The covariance matrix of the DFT of the detector's input vector is utilized for developing the algorithm.…”

Section: ) Cognitive Radio Systemsmentioning

confidence: 99%

RF Impairments in Wireless Transceivers: Phase Noise, CFO, and IQ Imbalance – A Survey

Mohammadian

Tellambura

2021

IEEE Access

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Wireless transceivers for mass-market applications must be cost effective. We may achieve this goal by deploying non-ideal low-cost radio frequency (RF) analog components. However, their imperfections may result in RF impairments, including phase noise (PN), carrier frequency offset (CFO), and in-phase (I) and quadrature-phase (Q) imbalance. These impairments introduce in-band and out-of-band interference terms and degrade the performance of wireless systems. In this survey, we present RF-impairment signal models and discuss their impacts. Moreover, we review RF-impairment estimation and compensation in singlecarrier (SC) and multicarrier systems, especially orthogonal frequency division multiplexing (OFDM). Furthermore, we discuss the effects of the RF impairments in already-established wireless technologies, e.g., multiple-input multiple-output (MIMO), massive MIMO, full-duplex, and millimeter-wave communications and review existing estimation and compensation algorithms. Finally, future research directions investigate the RF impairments in emerging technologies, including cell-free massive MIMO communications, nonorthogonal multicarrier systems, non-orthogonal multiple access (NOMA), ambient backscatter communications, and intelligent reflecting surface (IRS)-assisted communications. Furthermore, we discuss artificial intelligence (AI) approaches for developing estimation and compensation algorithms for RF impairments.

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Section: ) Cognitive Radio Systemsmentioning

confidence: 99%

RF Impairments in Wireless Transceivers: Phase Noise, CFO, and IQ Imbalance – A Survey

Mohammadian

Tellambura

2021

IEEE Access

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“…Next, we describe how the various parameters in (7), ( 8) and ( 9) stem from the imperfections associated with the RF front-end.…”

Section: B Non-ideal Rf Front-endmentioning

confidence: 99%

“…Moreover, based on (9), the joint effects of PHN and IQI, described by the terms parameter ξ described in (7).…”

Section: B Non-ideal Rf Front-endmentioning

confidence: 99%

Spectrum Sensing Under Hardware Constraints

Boulogeorgos¹,

Chatzidiamantis²,

Karagiannidis³

2015

Preprint

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Direct-conversion radio (DCR) receivers can offer highly integrated low-cost hardware solutions for spectrum sensing in cognitive radio systems. However, DCR receivers are susceptible to radio frequency (RF) impairments, such as in-phase and quadrature-phase imbalance, low-noise amplifier nonlinearities and phase noise, which limit the spectrum sensing capabilities. In this paper, we investigate the joint effects of RF impairments on energy detection based spectrum sensing for cognitive radio (CR) systems in multi-channel environments. In particular, we provide closed-form expressions for the evaluation of the detection and false alarm probabilities, assuming Rayleigh fading. Furthermore, we extend the analysis to the case of CR networks with cooperative sensing, where the secondary users suffer from different levels of RF imperfections, considering both scenarios of error free and imperfect reporting channel.Numerical and simulation results demonstrate the accuracy of the analysis as well as the detrimental effects of RF imperfections on the spectrum sensing performance, which bring significant losses in the spectrum utilization.

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“…The spectrum sensing and sharing algorithms are widely used in a variety of wireless technologies, for example, DeepWiFi [2] for WiFi spectrum sensing, OutSense [3] for ZigBee spectrum sensing, and RealSense [4] for TV spectrum sensing. Spectrum sensing and sharing methods are also used in cross-technology coexisted environments, for example, literature [5][6][7][8][9][10][11] conducts the spectrum sensing and management between WiFi and LTE-U technologies, literature [12][13][14][15] introduces the spectrum sensing between ZigBee and WiFi technologies, [16][17][18][19][20][21], uses spectrum sensing method for OFDM-based heterogeneous wireless devices.…”

Section: Introductionmentioning

confidence: 99%

A Novel Subcarrier-Level Spectrum Sensing Method by Utilizing Fine-Grained Channel State Information in Wireless Networks

Wang

Sun

et al. 2021

Wireless Communications and Mobile Computing

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Traditionally, the channelization structures of wireless technologies (802.11/ZigBee/BLE) have been fixed. Each node content for the spectrum is assigned one channel with a specific bandwidth. However, classical channel-based spectrum sensing and sharing algorithms have great limitations to further optimize spectrum utilization when multiple IoT with different wireless technologies coexisting in the same environment. Therefore, exploring the fine-grained spectrum sensing algorithm becomes an essential work to further improve the spectrum utilization efficiency, especially in the Industrial Scientific Medical (ISM) band. This paper proposes Subcarrier-Sniffer, a novel subcarrier-level spectrum sensing and sharing method, which utilizes channel state information (CSI) to sense the fine-grained status of each subcarrier of the traditional channel. To evaluate the performance of Subcarrier-Sniffer, we implemented Subcarrier-Sniffer by USRP B200min, and the experimental results show that the accuracy of subcarrier-level spectrum sensing could achieve 100% in our settings that the distance between Subcarrier-Sniffer and the monitor is not greater than 7 m. Subcarrier-Sniffer could be applied in WiFi and ZigBee, WiFi and BLE, and WiFi and LTE-U coexisted environments for better spectrum utilization.

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Spectrum Sensing of OFDM Signals in the Presence of Carrier Frequency Offset

Cited by 17 publications

References 14 publications

RF Impairments in Wireless Transceivers: Phase Noise, CFO, and IQ Imbalance – A Survey

RF Impairments in Wireless Transceivers: Phase Noise, CFO, and IQ Imbalance – A Survey

Spectrum Sensing Under Hardware Constraints

A Novel Subcarrier-Level Spectrum Sensing Method by Utilizing Fine-Grained Channel State Information in Wireless Networks

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