Multiband Spectrum Sensing with Non-exponential Channel Occupancy Times

Cheng, Hanke; Mark, Brian L.; Ephraim, Yariv; Chen, Chun‐Hung

doi:10.1109/icc42927.2021.9500620

Cited by 1 publication

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Authors in [ 20 ] evaluate the reliability of HMM based cooperative spectrum sensing in cognitive radio networks, in the presence of random malfunctioning of secondary user nodes participating in the process. Occupancy prediction schemes based on a discrete hidden Markov model (DHMM) and a continuous hidden Markov model are investigated in [ 21 , 22 , 23 , 24 ], respectively. Authors in [ 21 , 22 ] adopt DHMM to model the spectrum sensing procedure, which do not make full use of the information obtained by the receiver.…”

Section: Related Workmentioning

confidence: 99%

“…Authors in [ 21 , 22 ] adopt DHMM to model the spectrum sensing procedure, which do not make full use of the information obtained by the receiver. Authors in [ 23 , 24 ] use CHMM to model the sensing procedure of CUAVNs. However, they do not consider the dynamicity of UAVs, which is an important characteristic of the UAV networks [ 25 ].…”

Section: Related Workmentioning

confidence: 99%

“…However, they do not consider the dynamicity of UAVs, which is an important characteristic of the UAV networks [ 25 ]. Besides, centralized spectrum sensing methods in [ 23 , 24 ] do not work well in CUAVNs since the global fusion center is usually unreachable to secondary CUAVs. Thus, in this paper, we propose a CHMM-based spectrum sensing method to enhance the spectrum utilization in CUANs.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

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

Feng

Zhang

et al. 2022

Sensors

View full text Add to dashboard Cite

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.

show abstract