Spectrum Sensing for Cognitive Radio Using Blind Source Separation and Hidden Markov Model

Mukherjee, Amrit; Maiti, Satyabrata; Datta, Amlan

doi:10.1109/acct.2014.63

Cited by 18 publications

(9 citation statements)

References 11 publications

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“…Ivrigh and Mohammad-Sajad [20] refined the work of [17] by use of Markov model for predicting the status of PU for the purpose of achieving dynamic scenario. To strengthen the conventional BSS-based spectrum sensing algorithms, Mukherjee et al [22] was devoted to combine BSS with prediction using hidden Markov model (HMM) for enhancing spectrum sensing performance. Ferreira et al [21] applied ICA to identify signal sources in a broad band of frequencies and to use this information for spectrum sensing.…”

Section: Reviews Of Cognitive Radio Assisted By Blind Source Sepamentioning

confidence: 99%

Full-Duplex Cognitive Radio Using Guided Independent Component Analysis and Cumulant Criterion

Luo

Zhu

2019

IEEE Access

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Due to the ever-increasing wireless service requirements, the issues of a limited frequency resource and underutilized frequency allocation scheme as well as frequency crowd have come to become increasingly severe. It gives rise to being difficult to effectively eliminate the spectrum interference and fulfill an efficient spectrum access mechanism using the congested frequency band. For this purpose, this paper conceives a simultaneous transmission and sensing scheme based on the guided independent component analysis combined with the cumulant-based non-Gaussian criterion for fulfilling full-duplex cognitive radio (FD-CR). The philosophy of this proposed scheme is that the ICA based blind source separation principle is firstly utilized for blind separation of the observation mixtures, and then the separated signals are recognized through correlation processing and non-Gaussian criterion based on cumulant. In particular, the known secondary user signal is used as a signature signal for assisting ICA framework construction for blind extraction of signals. As a key component of machine learning, the ICA theory can contribute significantly to conquering the static sensing problem and residual self-interference influence in existing conventional cognitive radio systems. The simulation experiments and analysis corroborate the effectiveness of the proposed method and system design. In addition, the low complexity and performance enhancement of the proposed method are also validated by comparing with that of the two practical FD-CR schemes. INDEX TERMS Full-duplex, cognitive radio, blind source separation, independent component analysis, cumulant.

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Section: Reviews Of Cognitive Radio Assisted By Blind Source Sepamentioning

confidence: 99%

Full-Duplex Cognitive Radio Using Guided Independent Component Analysis and Cumulant Criterion

Luo

Zhu

2019

IEEE Access

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“…Applying the source identified with respect to the majority frequency components in above equation of continuous autocorrelation [6,7], we get:…”

Section: Simulation and Analysismentioning

confidence: 99%

Vector Quantization based Power Allocation for Non - Ergodic Cognitive Radio Systems

Mukherjee¹,

Datta²

2016

JESTR

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This Letter addresses the problem of dynamic power allocation based on spectrum sensing outputs for non-ergodic systems. On such type of systems, the power allocation for each corresponding nodes to be sensed by cognitive transmitter may not require all the time. The dynamic power allocation in nodes of each channel in cognitive radio (CR) network using vector quantization based on autocorrelation error is proposed.

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“…where U denotes the detection requirement of Probability of Detection (P d ) and Probability of False Alarm (P f ) and ε1 and ε2denotes the minimum and data values for finding P f in primary and secondary users [7,8]. The relation between observation window N and SNR is…”

Section: Effect Of Noise Uncertaintymentioning

confidence: 99%

“…Applying the Welch's PSD [2], the signal is splitted into 2048 overlapping segments of length 3999 samples. These overlapping segments are then windowed in the time domain [8,9]. The Periodogram equation after windowing the data segments can be written as:…”

Section: Analysis Of Ber Based On Cwps With Hamming Windowmentioning

confidence: 99%

Bit Error Rate analysis using Converged Welch’s Method for Energy Detection Spectrum Sensing in Cognitive Radio

Mukherjee¹,

Datta²

2016

JESTR

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In this Letter, a novel approach is used for improving the Bit Error Rate (BER) for non-stationary environment based spectrum sensing. Here, we have considered Converged Welch's Power Spectral Density (PSD) for analyzing of power. The signal of interest is thus further studied in terms of BER keeping the antenna and channel parameters constant. The proposed method yields an improvement of 34.6% as compared with the conventional method.

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Spectrum Sensing for Cognitive Radio Using Blind Source Separation and Hidden Markov Model

Cited by 18 publications

References 11 publications

Full-Duplex Cognitive Radio Using Guided Independent Component Analysis and Cumulant Criterion

Full-Duplex Cognitive Radio Using Guided Independent Component Analysis and Cumulant Criterion

Vector Quantization based Power Allocation for Non - Ergodic Cognitive Radio Systems

Bit Error Rate analysis using Converged Welch’s Method for Energy Detection Spectrum Sensing in Cognitive Radio

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