A reconfigurable wavelet packet filter bank transceiver for spectral analysis and dynamic spectrum access

Лакшманан, М.; Ariananda, Dyonisius Dony; Nikookar, H.

doi:10.1109/dyspan.2011.5936247

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…Lakshmanan M.K et al [28] have proposed a reconfigurable wavelet packet transceiver for spectral analysis and dynamic spectrum access. The transceiver consists of a Wavelet Packet Spectral Estimator (WPSE) and a Wavelet Packet Multi-carrier Modulator (WPM) both of which were implemented using filter banks.…”

Section: Related Work: a Brief Reviewmentioning

confidence: 99%

Wavelets in the Analysis of Autoregressive Conditional Heteroskedasticity (ARCH) Models Using Neural Network

Ataulla¹,

Yunus

Alsoufi

2016

AJAM

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In the paper, proposed a new method for the time frequency signal analysis, speech processing and other signal processing applications. Stationary signal components can be analyzed by a powerful tool called as Fourier transform. But it is fizzled for analysing the non-stationary signal whereas wavelet transform allows the components of a non-stationary signal to be analyzed. It is the improved version of Fourier transform. Wavelets allow complex information such as music, speech, images and patterns to be decomposed into elementary forms at different positions and scales and subsequently reconstructed with high precision. Here, for extracting the best features of non-stationary signal we use discrete wavelet transform. This can be decomposed into two components named as high frequency component and low frequency component. The decomposed output component is sent for regression analysis. This is done by passing through ARCH model which can characterize and model observed time series. An ARCH time series is the one in which the variance of the error in a period depends on upon size of the squared error in the previous period i.e. if a large error occurs in one period, the variance of the error in the next period will be even larger. The performance of the ARCH will be improved by predicting its co-efficient or cofactor using an artificial technique. The artificial technique presented in this paper is neural network, which is capable of handling sophisticated computations similar to the human brain. The proposed model algorithm will be implemented in MATLAB and the output performances are estimated.

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Section: Related Work: a Brief Reviewmentioning

confidence: 99%

Wavelets in the Analysis of Autoregressive Conditional Heteroskedasticity (ARCH) Models Using Neural Network

Ataulla¹,

Yunus

Alsoufi

2016

AJAM

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“…All three dimensions of the MU offer possibilities to position the MU-shapes in the medium and to avoid interference, by:  Reducing spectral overlap. This can be achieved by changing frequency, reducing bandwidth, using noncontiguous bandwidth modulation [15,16,17], decreasing PSD (increasing bandwidth), or by omitting channels in the hopping sequence [18].  Reducing overlap in time.…”

Section: B Adaptive Behaviormentioning

confidence: 99%

Medium usage model for the design of dynamic spectrum management in ISM bands

Witvliet

Bentum

Schiphorst

et al. 2012

2012 IEEE International Conference on Communications (ICC)

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This paper presents a new approach for dynamic spectrum management for heterogeneous wireless devices. Local congestion degrades the reliability of wireless applications in the License Exempt bands. This leads to the research questions: (1) how to realize equal spectrum sharing between dissimilar systems, and (2) how to improve the collective spectrum efficiency. A solution is in dynamic distribution of the available spectrum between contesting devices. Politeness mechanisms embedded in the individual devices are considered as building blocks for the creation of a distributed dynamic spectrum management system. Medium Usage defines the medium occupied by each transmitter and receiver. A regulatory view is chosen, favoring technology neutrality and including receiver parameters.

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“…In these methods, the bit error rate (BER) increases when a specific power is dedicated to CCs. As an alternative to OFDM, filter-bank based multicarrier (FBMC) systems are another air interface candidate in physical layer of CRs which include; discrete wavelet packet (DWP) [10], filtered multi-tone (FMT) [11], cosine modulated multi-tone (CMT) [12], and staggered modulated multi-tone (SMT) [12]. In these methods, a well-designed prototype filter is employed to guarantee a desired spectral containment of subcarriers.…”

Section: Introductionmentioning

confidence: 99%

Deep out-of-band radiation reduction by using joint filterbank and cancellation carriers in cognitive radios

Aliasgari

Birjandtalab

Fakhraie

et al. 2012

6th International Symposium on Telecommunications (IST)

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A deep spectrum sculpting is necessary to enable coexistence between a cognitive radio (CR) user and a licensed user which has low dynamic range and is susceptible to interferences. In this paper, we propose to use joint filter-bank based multicarrier system and cancellation carriers to guarantee an almost free interference transmission and keep the quality of services of licensed adjacent bands unchanged. We investigate the performance of the proposed method with different well-known existing methods from viewpoints of the average level of out-of-band power, spectral efficiency, and computational complexity.

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A reconfigurable wavelet packet filter bank transceiver for spectral analysis and dynamic spectrum access

Cited by 6 publications

References 18 publications

Wavelets in the Analysis of Autoregressive Conditional Heteroskedasticity (ARCH) Models Using Neural Network

Wavelets in the Analysis of Autoregressive Conditional Heteroskedasticity (ARCH) Models Using Neural Network

Medium usage model for the design of dynamic spectrum management in ISM bands

Deep out-of-band radiation reduction by using joint filterbank and cancellation carriers in cognitive radios

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