Superior execution time design of optimal (Wiener) time‐frequency filter

Ivanovic, Veselin N.; Jovanovski, Srdjan; Radovic, Nevena

doi:10.1049/el.2016.1565

Cited by 17 publications

(3 citation statements)

References 4 publications

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“…For example, within the hardware development, the trigonometric functions are implemented by using the first two terms of the corresponding Taylor series [17], whereas the MATLAB uses 250 terms. Finally, note that the influence of the finite register lengths is the additional source of difference between the hardware-based and softwarebased results [27][28][29][30][31]. Difference between the hardware-based and software-based results can be reduced, if required, by using more precise approximations of mathematical formulas in hardware realisation.…”

Section: Testing and Verificationmentioning

confidence: 99%

Multi‐core hardware realisation of the quasi maximum likelihood PPS estimator

Brnović

Ivanovic

Djurović

et al. 2020

IET Computers & Digital Techniques

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Multi-core hardware realisation of the quasi maximum likelihood algorithm as the state-of-the-art estimator of polynomial phase signals (PPSs) is proposed in this study. Developed multiple-clock-cycle realisation is suitable for real-time implementation. To prove this, the proposed design is implemented on a field programmable gate array circuit. The hardware realisation is tested and verified on PPSs corrupted with various amounts of the Gaussian noise. Obtained results are compared with software simulations showing excellent match between the proposed system-based and the software-based outputs.

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Section: Testing and Verificationmentioning

confidence: 99%

Multi‐core hardware realisation of the quasi maximum likelihood PPS estimator

Brnović

Ivanovic

Djurović

et al. 2020

IET Computers & Digital Techniques

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show abstract

“…Note that there are special purpose hardware systems with different configurations for the STFT realisation [32][33][34][35][36][37][38][39]. The STFT realisations are commonly used, as an initial stage, in more sophisticated systems dedicated for the realisation of the advanced time-frequency (TF) representations [40][41][42][43][44][45], as well as for realisation of the advanced time-varying and space-varying filtering systems [46][47][48][49]. However, multiple STFTs are evaluated in the QML which is one of the main difficulties in both software and hardware realisations of this algorithm.…”

Section: Introductionmentioning

confidence: 99%

Hardware implementation of the quasi‐maximum likelihood estimator core for polynomial phase signals

Brnović

Djurović

Ivanovic

et al. 2018

IET Circuits, Devices & Systems

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Flexible, multiple-clock-cycle, hardware design for the quasi maximum likelihood (QML) algorithm core realization for the polynomial phase signals (PPSs) estimation is proposed. The QML algorithm significantly outperforms existing PPS estimators in terms of accuracy. However, its practical applications require efficient software and hardware systems. The main challenges in the proposed hardware development with respect to existing systems for time-frequency analysis are realization of time-frequency (TF) representation based instantaneous frequency (IF) estimator, the polynomial regression, and phase extraction. The developed design is tested on a PPS corrupted by a white Gaussian noise and verified by a field programmable gate array (FPGA) circuit design. All implementation and verification details are provided along with the comparison of the results achieved by hardware and software implementations.

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“…Various schemes for time-varying filters have been proposed, including the explicit time-varying filter [ 13 ], the implicit time-varying filter [ 13 ], the variable bandwidth filter [ 14 ], the time-frequency projection filter [ 15 ], and the adaptive filter [ 16 , 17 ]. In an adaptive filter, the received noisy signal is subjected to filtering and the filtered output is compared with a desired signal to compute the error.…”

Section: Introductionmentioning

confidence: 99%

Time-Frequency Energy Sensing of Communication Signals and Its Application in Co-Channel Interference Suppression

Liang

Sha

2018

Sensors

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As the number of mobile users and video traffics grow explosively, the data rate demands increase tremendously. To improve the spectral efficiency, the spectrum are reused inter cell or intra cell, such as the ultra dense network with multi-cell or the cellular network with Device-to-Device communications, where the co-channel interferences are brought and needs to be suppressed. According to the time-frequency energy sensing to the communication signals, the desired signal and the interference signal have different energy concentration areas on the time frequency plane, which provide opportunities to suppress the co-channel interference with time varying filter. This paper analyzes the time-frequency distributions of the Gaussian pulse shaping signals, discusses the effect of the analyzing window length on the time-frequency resolution, exploits the equivalence between the time frequency analysis at the baseband and at the radio front end, and finally reveals the advantages of the proposed masking threshold constrained time varying filter based co-channel interference mitigation method. The pass region for the linear time varying filter is generated according to the time-varying energy characteristics of the I/Q separated 4-QAM pulse shaping signals, where the optimum masking threshold is obtained by the optimum-BER criterion. The proposed co-channel interference suppression method is evaluated in aspect of BER performance, and simulation results show that the proposed method outperforms existing methods with low-pass or band-pass filters.

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Superior execution time design of optimal (Wiener) time‐frequency filter

Cited by 17 publications

References 4 publications

Multi‐core hardware realisation of the quasi maximum likelihood PPS estimator

Multi‐core hardware realisation of the quasi maximum likelihood PPS estimator

Hardware implementation of the quasi‐maximum likelihood estimator core for polynomial phase signals

Time-Frequency Energy Sensing of Communication Signals and Its Application in Co-Channel Interference Suppression

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