Spectrum Sensing Algorithms for Cognitive TV White-Spaces Systems

Riviello, Daniel Gaetano; Benco, Sergio; Crespi, Floriana Loredana; Ghittino, Andrea; Garello, Roberto; Perotti, Alberto

doi:10.1007/978-3-319-01402-9_4

Cited by 3 publications

(3 citation statements)

References 15 publications

(10 reference statements)

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“…The γ value is pre-determined and it is based on targeted false alarm rate P f a . For the known noise level, MED based CSS algorithm delivers the best detection probability P d , specifically in higher SNR regime [6], [7]. On the other side, MME based CSS-algorithm performs optimally at lower SNR and primitively for unknown noise level [6], [7].…”

Section: Preliminaries and Proposed Vlsi Algorithmmentioning

confidence: 99%

“…For the known noise level, MED based CSS algorithm delivers the best detection probability P d , specifically in higher SNR regime [6], [7]. On the other side, MME based CSS-algorithm performs optimally at lower SNR and primitively for unknown noise level [6], [7]. These algorithms first compute the sample covariance matrix (of size m×m) Rx= 1 N •X•X † from the received vector X.…”

Section: Preliminaries and Proposed Vlsi Algorithmmentioning

confidence: 99%

“…Note thatRx is Hermitian and positive-definite matrix whose eigenvalues λ1, λ2, ..., λm must be computed. The maximum among these eigenvalues represents the ΘT for MED CSS algorithm and the ratio of maximum and minimum eigenvalues is the ΘT for MME CSS-algorithm [6], [7]. Further, ΘT is compared with γ to determine the spectrum occupancy by PUs.…”

Section: Preliminaries and Proposed Vlsi Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Hardware-Efficient ASIC Implementation of Eigenvalue Based Spectrum Sensor Reconfigurable-Architecture for Cooperative Cognitive-Radio Network

Chaurasiya

Shrestha

2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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Spectrum sensing is an imperative process that primarily affects the reliability of cognitive radio networks (CRNs). Cooperative spectrum sensing (CSS) is the contemporary process of detecting the occupancy of spectrum by licensed users in CRN. It outperforms the conventional stand-alone spectrum-sensing (SSS) algorithms. However, such CSS algorithms have higher implementation-complexity than SSS algorithms, resulting in higher resource utilization and alleviating the hardware efficiency. Our work focuses on the design of hardware efficient VLSI-architecture for such CSS algorithms. Specifically, this paper proposes reconfigurable VLSI-architecture of cooperative spectrum sensor (CSR) for both maximum-minimum eigenvalue (MME) and maximum eigenvalue (MED) based CSS algorithms for the data-fusion based CRN. This CSR architecture has been designed based on iterative and shift power-methods to compute maximum and minimum eigenvalues in MME and MED CSS-algorithms. The proposed reconfigurable-CSR is fabricated in UMC 130-nm CMOS process and it has a die dimension of h × w = 1.5 mm × 1.5 mm. Furthermore, this work presents the analysis of hardware-complexity, sensing-time and performance with the increasing number of secondary users (or antenna array of CSR) in CRN. Eventually, the fabricated ASIC chip of CSR has been tested and verified in a real-world test environment.

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Section: Preliminaries and Proposed Vlsi Algorithmmentioning

confidence: 99%

Section: Preliminaries and Proposed Vlsi Algorithmmentioning

confidence: 99%

Section: Preliminaries and Proposed Vlsi Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Hardware-Efficient ASIC Implementation of Eigenvalue Based Spectrum Sensor Reconfigurable-Architecture for Cooperative Cognitive-Radio Network

Chaurasiya

Shrestha

2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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A New Hardware-Efficient Spectrum-Sensor VLSI Architecture for Data-Fusion-Based Cooperative Cognitive-Radio Network

Chaurasiya

Shrestha

2021

IEEE Trans. VLSI Syst.

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Performance Analysis of Multi-User MIMO Schemes under Realistic 3GPP 3-D Channel Model for 5G mmWave Cellular Networks

2022

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Novel techniques such as mmWave transmission and massive MIMO have proven to present many attractive features able to support high data demand for 5G NR technologies. Towards the standardization of 5G networks, channel modeling has become an important step in order to test the reliability of theoretical studies. In this paper, we study the performance of a 5G network at mmWave range for the downlink. We consider a single trisectorized base station equipped with planar arrays, and we model users as a spatial Poisson process in a hexagonal grid. We adopt the latest 3GPP channel model described in TR 38.901 and we provide a thorough description and step-by-step tutorial of it along with our customizations and MATLAB scripts for channel generation in the presented scenario. Moreover, we evaluate the performance of Multi-User Multi-Layer MIMO techniques, such as Signal-to-Leakage-plus-Noise Ratio (SLNR) precoding and MMSE combined with different system configurations by means of achievable per-user rate.

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Spectrum Sensing Algorithms for Cognitive TV White-Spaces Systems

Cited by 3 publications

References 15 publications

Hardware-Efficient ASIC Implementation of Eigenvalue Based Spectrum Sensor Reconfigurable-Architecture for Cooperative Cognitive-Radio Network

Hardware-Efficient ASIC Implementation of Eigenvalue Based Spectrum Sensor Reconfigurable-Architecture for Cooperative Cognitive-Radio Network

A New Hardware-Efficient Spectrum-Sensor VLSI Architecture for Data-Fusion-Based Cooperative Cognitive-Radio Network

Performance Analysis of Multi-User MIMO Schemes under Realistic 3GPP 3-D Channel Model for 5G mmWave Cellular Networks

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