Non-uniform Quantized Data Fusion Rule for Data Rate Saving and Reducing Control Channel Overhead for Cooperative Spectrum Sensing in Cognitive Radio Networks

Chakraborty, Arpita; Banerjee, Jyoti Sekhar; Chattopadhyay, Abir

doi:10.1007/s11277-018-6054-1

Cited by 21 publications

(9 citation statements)

References 27 publications

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“…Similarly to [9] the authors of [10] propose a quantization strategy with better results than the other four competing strategies, with the possibility of maintaining the performance close to that obtained with infinite resolution, but using only 3, 4, 5 and 6 quantization bits. A new nonuniform quantization method is proposed in [11], attaining a performance close to that obtained by uniform quantization. The method applies a variable number of quantization bits, employing higher resolution to specific signal levels.…”

Section: A Related Workmentioning

confidence: 99%

Performance of the Gerschgorin Radii and Centers Ratio Detector for Cooperative Spectrum Sensing under Burst Control Channel Errors

Costa¹,

Guimarães²,

Souza³

2019

JCIS

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In centralized cooperative spectrum sensing (CSS), cognitive radios (CRs) monitor the spectrum and send the related data to a fusion center (FC) via a control channel in order to more efficiently detect possible idle bands for opportunistic occupation. In practice, such data must be quantized prior to transmission, which can lead to loss of the global spectrum sensing performance due to quantization errors. Additionally, wireless control channel impairments, such as the multipath fading, further contributes to performance degradation. In many researches, the control channel is considered error-free. Even when errors are considered, they usually affect the transmitted symbols independently, that is, the control channel is assumed to be memoryless. Error bursts come from the memory effect of the channel, and are typically found in wireless communications. In this paper we consider the effects of burst errors in the control channel as well as the problem of signal distortion caused by three methods of quantization in centralized CSS with sample fusion. The Gerschgorin Radii and Centers Ratio (GRCR) detector is used as the test statistic for spectrum sensing. Our findings show that: i) the GRCR is robust in the scenarios taken into consideration, ii) the uniform quantization may be preferred in some cases when the control channel is considered perfect, iii) the nonuniform quantization attains better performance under errors in the control channel, and iv) the effect of memory in the control channel may produce, in some situations, performance gains with respect to the memoryless channel, when both have the same average bit error rate.

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Section: A Related Workmentioning

confidence: 99%

Performance of the Gerschgorin Radii and Centers Ratio Detector for Cooperative Spectrum Sensing under Burst Control Channel Errors

Costa¹,

Guimarães²,

Souza³

2019

JCIS

View full text Add to dashboard Cite

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“…• Combined metric-based schemes which balance the trade-off between the detection performance and the incurred overhead simultaneously. The balance can be achieved by combining two different fusion rules over the same cluster or CRN as shown in [15][16][17][18][19][20][21].…”

mentioning

confidence: 99%

“…On the other hand, different combination mechanisms have been proposed in [18][19][20][21]. The work in [18] proposed three modules system which are local detection, data reconstruction module and global decision.…”

mentioning

confidence: 99%

“…This work allows a large number of CRs to forward their observations over a control channel with a limited bandwidth. Similarly, the work in [21] proposed a non-uniform quantized data fusion (N-QDF) rule to minimize the reporting overhead for ED-based CSS in CRNs. An algorithm with variable number of QDF bits is proposed in that work to balance between saving data and minimizing the reporting overhead, where employing small QDF bit results in losing the information while employing large QDF bit leads to increase the overhead.…”

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confidence: 99%

“…A compressed QDF bits are transmitted over the control channel. However, the works in [18][19][20][21] add computational complexity, such as quantizer and data compressor, and some of them require a prior knowledge of the statistical distribution of the observation, such as in [18]. Note that designing a quantizer for n bits requires 2 n − 1 thresholds which remarkably incurs extra computational complexity.…”

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confidence: 99%

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Combined Fusion Rules in Cognitive Radio Networks Using Different Threshold Strategies

2019

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Compromising the performance and overhead is a crucial factor in designing cognitive radio networks (CRNs). One way to achieve this goal is to combine different fusion rules for a CRN with multiple clusters of cognitive radios (CRs). This paper proposes a new adaptive combination algorithm to balance between detection performance of a CRN and its reporting overhead through combining different fusion rules over the CRN. Initially, the paper describes how to combine hard decision, i.e., one-bit, and soften-hard decision, i.e., two-bit, fusion rules over a CRN with multiple clusters of CRs using different strategies. Simple combination and modified combination strategies, to consider a trade off between performance improvement and incurred reporting overhead, are considered. The paper adopts different threshold strategies to implement the proposed combinations. Moreover, the proposed algorithms are examined under the Rayleigh fading channel model and simulated to investigate their detection performance and to compare their detection performance with existing works. The simulation results show that the adaptive threshold strategy outperforms the two proposed fixed threshold strategies and conventional fusion schemes.

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Facial Expression Recognition for Human Computer Interaction

Chattopadhyay¹,

Kundu

Chakraborty³

et al. 2020

New Trends in Computational Vision and Bio-Inspired Computing

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Non-uniform Quantized Data Fusion Rule for Data Rate Saving and Reducing Control Channel Overhead for Cooperative Spectrum Sensing in Cognitive Radio Networks

Cited by 21 publications

References 27 publications

Performance of the Gerschgorin Radii and Centers Ratio Detector for Cooperative Spectrum Sensing under Burst Control Channel Errors

Performance of the Gerschgorin Radii and Centers Ratio Detector for Cooperative Spectrum Sensing under Burst Control Channel Errors

Combined Fusion Rules in Cognitive Radio Networks Using Different Threshold Strategies

Facial Expression Recognition for Human Computer Interaction

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