A Code-Aided and Moment-Based Joint SNR Estimation for M-APSK over AWGN Channels

He, Rui; Yang, Dongwen; Wang, Hua; Kuang, Jingming; Xiao, Wen

doi:10.1109/vtcspring.2017.8108433

Cited by 1 publication

(1 citation statement)

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“…Conventionally, SNR estimators require knowledge of the signal or the channel, such as the maximum likelihood (ML) SNR estimator [ 26 ] and particle swarm optimization (PSO) SNR estimator, based on parameters of hardwares or channels [ 27 ]. In addition, there exist estimators designed for specific signals or specific feature spectrum sensing methods, such as the SNR estimator [ 28 ] for M-ary amplitude phase shift keying (M-APSK) modulated signals, the SNR estimator for signal with Polar code [ 29 ], and the estimator for eigenvalue-based spectrum detectors [ 30 ]. Lacking the prior knowledge of the signal/channel and design for specific signals/sensing methods make it hard to generally adopt the above estimators to various CUAVNs.…”

Section: Related Workmentioning

confidence: 99%

Continuous Hidden Markov Model Based Spectrum Sensing with Estimated SNR for Cognitive UAV Networks

Feng

Zhang

et al. 2022

Sensors

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In this paper, to enhance the spectrum utilization in cognitive unmanned aerial vehicle networks (CUAVNs), we propose a cooperative spectrum sensing scheme based on a continuous hidden Markov model (CHMM) with a novel signal-to-noise ratio (SNR) estimation method. First, to exploit the Markov property in the spectrum state, we model the spectrum states and the corresponding fusion values as a hidden Markov model. A spectrum prediction is obtained by combining the parameters of CHMM and a preliminary sensing result (obtained from a clustered heterogeneous two-stage-fusion scheme), and this prediction can further guide the sensing detection procedure. Then, we analyze the detection performance of the proposed scheme by deriving its closed-formed expressions. Furthermore, considering imperfect SNR estimation in practical applications, we design a novel SNR estimation scheme which is inspired by the reconstruction of the signal on graphs to enhance the proposed CHMM-based sensing scheme with practical SNR estimation. Simulation results demonstrate the proposed CHMM-based cooperative spectrum sensing scheme outperforms the ones without CHMM, and the CHMM-based sensing scheme with the proposed SNR estimator can outperform the existing algorithm considerably.

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