Signal Processing in Cognitive Radio

Ma, Ji; Li, G.Y.; Juang, Biing Hwang

doi:10.1109/jproc.2009.2015707

Cited by 424 publications

(27 citation statements)

References 54 publications

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“…The related paraunitary filter bank design problem is formulated as a convex optimization problem, and the performance of the resulting window is investigated under different channel conditions [10].Jun Ma et al (2009) discussed the practical issues involved to build a CR network from perspective of signal processing. The author identified spectrum sensing and spectrum sculpting as the two fundamental capabilities for any CR network to adapt to its environment and provide resilience under adverse conditions [11].H. Zhang et al(2009), studied channel capacity of cognitive radio (CR) networks using CP-OFDM and FBMC waveforms, taking into consideration the effects of resource allocation algorithms, intercell interference due to timing offsets, and Rayleigh fading.…”

Section: Related Work: Signal Processing Prospectives In Cognitive Radiomentioning

confidence: 99%

Performance Analysis of Filter Bank Multicarrier Cognitive Radio for Physical Layer under Binary Symmetric Radio Fading Channel

Kang¹,

Vig²

2014

IJCA

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The wireless communication has undergone a revolution due to advancements in technology. For each new user or application to be a part of communication network the preliminary requirement is the allocation of frequency spectrum band. This frequency band is a limited resource and it is impossible to expand its boundaries. So the need is to employ intelligent, adaptive and reconfigurable communication systems which can investigate the requirements of the end user and assign the requisite resources in contrast to the traditional communication systems which allocate a fixed amount of resource to the user under adaptive, autonomic and opportunistic cognitive radio environment. Cognitive Radio(CR) Technology has emerged from software defined radios wherein the key parameters of interest are frequency, power and modulation technique adopted. The role of Cognitive Radio is to alter these parameters under ubiquitous situations. The Spectrum Sensing is an important task to determine the availability of the vacant channels to be utilised by the secondary users without posing any harmful interference to the primary users. In Multicarrier Communication using Digital Signal Processing Techniques, Filter Bank Multi Carrier has an edge over other technologies in terms of Bandwidth and Spectral Efficiency. The present paper deals with the Multi Rate FIR Decimation and Interpolation Filter approach for physical layer of Cognitive Radio under Binary Symmetric fading channel environment.

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Section: Related Work: Signal Processing Prospectives In Cognitive Radiomentioning

confidence: 99%

Performance Analysis of Filter Bank Multicarrier Cognitive Radio for Physical Layer under Binary Symmetric Radio Fading Channel

Kang¹,

Vig²

2014

IJCA

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“…To the best of the author's knowledge, neither schemes exist in the literature. Note that a complete modified GramSchmidt orthogonalization of multichannel input data, which avoids matrix inversions and enables scalar only operations, is attained, and this feature is considered critical, as matrix inversion is a major bottleneck in the design of embedded receiver architectures [51]. Additionally, the computational complexity as well as the performances of the proposed lattice filter combination schemes in stationary and nonstationary channel identification scenarios are investigated and compared to those of the multiple combination of least mean square (M-CLMS) and decoupled combination of least mean square (D-CLMS) schemes in [49].…”

Section: Introductionmentioning

confidence: 99%

Sequential convex combinations of multiple adaptive lattice filters in cognitive radio channel identification

Ozden

2018

EURASIP J. Adv. Signal Process.

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Sequential convex combinations of multiple adaptive lattice filters using different exponential weighting factors in cognitive radio (CR) channel identification framework have been considered in this presentation. First, the sequential processing multichannel lattice stages (SPMLSs) are modified so as to be used in filter combination task. Then, two different combination schemes, i.e., regular combination of multiple lattice filters (R-CMLF) and decoupled combination of multiple lattice filters (D-CMLF), that utilize modified SPMLSs as filter structure have been proposed. A modified Gram-Schmidt orthogonalization of multiple channels of data, which is constituted in multiple filter combination task, is accomplished. A highly modular, regular, and reconfigurable filter structure, which is suitable for cognitive radios, is achieved with the combination processing implemented in an order-recursive fashion. The mean square deviation (MSD) performances of the schemes under stationary and nonstationary conditions have been presented and compared with the performances of multiple combination of least mean square (M-CLMS), decoupled combination of least mean square (D-CLMS) schemes, and component filters. It has also been shown that the fault tolerances of the proposed schemes are better than those of the component filters due to the redundancy introduced with combination processing, and that the proposed schemes bring together the desired adaptive filter features such as fast convergence and low steady-state MSD levels, which do not normally coexist.

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“…Spectrum utilization can be increased by opportunistically allowing the unlicensed secondary user (SU) to utilize a licensed band in the absence of the primary user (PU) [1][2][3][4]. The ability of providing awareness about the usage of the frequency spectrum or the detection of the PU in a desired frequency band lets the SU access the radio communication channel without causing harmful interference to the PU [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Outage Performance Analysis of Underlay Cognitive Radio Networks with Decode‐and‐Forward Relaying

Namdar¹,

Başgümüş²

2017

Cognitive Radio

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In this chapter, we evaluate the outage performance of decode-and-forward relaying in cognitive radio networks over Rayleigh fading channels, subject to the relay location for a secondary user. In particular, we obtain the optimal relay location in wireless communications systems for the cognitive radio networks, using differential evolution optimization algorithm. Then, we investigate the optimal transmission rate of the secondary user. We present the numerical results to validate the proposed theoretical analysis and to show the effects of the Rayleigh fading channel parameters for the whole system performance.

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Signal Processing in Cognitive Radio

Cited by 424 publications

References 54 publications

Performance Analysis of Filter Bank Multicarrier Cognitive Radio for Physical Layer under Binary Symmetric Radio Fading Channel

Performance Analysis of Filter Bank Multicarrier Cognitive Radio for Physical Layer under Binary Symmetric Radio Fading Channel

Sequential convex combinations of multiple adaptive lattice filters in cognitive radio channel identification

Outage Performance Analysis of Underlay Cognitive Radio Networks with Decode‐and‐Forward Relaying

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