On a New Approach to SNR Estimation of BPSK Signals

Moazzeni, Taleb; Amei, Amei; Ma, Jian; Jiang, Yingtao

doi:10.2478/v10177-012-0038-y

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…AWGN with zero mean δ and unity variance σ 2 degrades the arriving time domain signal. The estimating technique is based on the signal's sampled noise component variance extraction [27], [28].…”

Section: Cnn-based Snr Estimationmentioning

confidence: 99%

Deep Learning-based SNR Estimation for Multistage Spectrum Sensing in Cognitive Radio Networks

Jeevangi¹,

Jawaligi²,

Patil³

2022

JTIT

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Vacant frequency bands are used in cognitive radio (CR) by incorporating the spectrum sensing (SS) technique. Spectrum sharing plays a central role in ensuring the effectiveness of CR applications. Therefore, a new multi-stage detector for robust signal and spectrum sensing applications is introduced here. Initially, the sampled signal is subjected to SNR estimation by using a convolutional neural network (CNN). Next, the detection strategy is selected in accordance with the predicted SNR levels of the received signal. Energy detector (ED) and singular value-based detector (SVD) are the solutions utilized in the event of high SNR, whilst refined non-negative matrix factorization (MNMF) is employed in the case of low SNR. CNN weights are chosen via the Levy updated sea lion optimization (LU-SLNO) algorithm inspired by the traditional sea lion optimization (SLNO) approach. Finally, the outcomes of the selected detectors are added, offering a precise decision on spectrum tenancy and existence of the signal.

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Section: Cnn-based Snr Estimationmentioning

confidence: 99%

Deep Learning-based SNR Estimation for Multistage Spectrum Sensing in Cognitive Radio Networks

Jeevangi¹,

Jawaligi²,

Patil³

2022

JTIT

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“…Signal-to-noise ratio (SNR) as a traditional performance metric has been used in evaluation channel quality [ 9 , 10 , 11 ]. Many existing works that approach the adaptive modulation problem are based on DA-SNR.…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive Modulation Based on Nondata-Aided Error Vector Magnitude in Non-Line-Of-Sight Condition of Wireless Sensor Network

Yang

Zeng

Mao

et al. 2018

Sensors

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The high demand for multimedia applications in environmental monitoring, invasion detection, and disaster aid has led to the rise of wireless sensor network (WSN). With the increase of reliability and diversity of information streams, the higher requirements on throughput and quality of service (QoS) have been put forward in data transmission between two sensor nodes. However, lower spectral efficiency becomes a bottleneck in non-line-of-sight (NLOS) transmission of WSN. This paper proposes a novel nondata-aided error vector magnitude based adaptive modulation (NDA-EVM-AM) to solve the problem. NDA-EVM is considered as a new metric to evaluate the quality of NLOS link for adaptive modulation in WSN. By modeling the NLOS scenario as the η−μ fading channel, a closed-form expression for the NDA-EVM of multilevel quadrature amplitude modulation (MQAM) signals over the η−μ fading channel is derived, and the relationship between SER and NDA-EVM is also formulated. Based on these results, NDA-EVM state machine is designed for adaptation strategy. The algorithmic complexity of NDA-EVM-AM is analyzed and the outage capacity of NDA-EVM-AM in an NLOS scenario is also given. The performances of NDA-EVM-AM are compared by simulation, and the results show that NDA-EVM-AM is an effective technique to be used in the NLOS scenarios of WSN. This technique can accurately reflect the channel variations and efficiently adjust modulation order to better match the channel conditions, hence, obtaining better performance in average spectral efficiency.

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