Naveed Iqbal

Decision Feedback Equalization using Particle Swarm Optimization

It is well-known that the Decision Feedback Equalizer (DFE) outperforms the Linear Equalizer (LE) for highly dispersive channels. For time-varying channels, adaptive equalizers are commonly designed based on the Least Mean Square (LMS) algorithm which, unfortunately, has the limitation of slow convergence specially in channels having large eigenvalue spread. The eigenvalue problem becomes even more pronounced in Multiple-Input Multiple-Output (MIMO) channels. Particle Swarm Optimization (PSO) enjoys fast convergence and, therefore, its application to the DFE merits investigation. In this paper, we show that a PSO-DFE with a variable constriction factor is superior to the LMS/RLS-based DFE (LMS/RLS-DFE) and PSO-based LE (PSO-LE), especially on channels with large eigenvalue spread. We also propose a hybrid PSO-LMS-DFE algorithm, and modify it to deal with complex-valued data. The PSO-LMS-DFE not only outperforms the PSO-DFE in terms of performance but its complexity is also low. To further reduce its complexity, a fast PSO-LMS-DFE algorithm is introduced. HighlightsDevelopment of PSO-LMS algorithm to a DFE.Development of PSO-LMS algorithm for MIMO channels.Development of PSO-LMS algorithm for complex data.Development of a new fast PSO-LMS algorithm.

Adaptive Frequency-Domain RLS DFE for Uplink MIMO SC-FDMA

It is well known that, in the case of highly frequency-selective fading channels, the linear equalizer (LE) can suffer significant performance degradation compared with the decision feedback equalizer (DFE). In this paper, we develop a low-complexity adaptive frequency-domain DFE (AFD-DFE) for single-carrier frequency-division multiple-access (SC-FDMA) systems, where both the feedforward and feedback filters operate in the frequency domain and are adapted using the well-known block recursive least squares (RLS) algorithm. Since this DFE operates entirely in the frequency domain, the complexity of the block RLS algorithm can be substantially reduced when compared with its time-domain counterpart by exploiting a matrix structure in the frequency domain. Furthermore, we extend our formulation to multiple-input-multiple-output (MIMO) SC-FDMA systems, where we show that the AFD-DFE enjoys a significant reduction in computational complexity when compared with the frequency-domain nonadaptive DFE. Finally, extensive simulations are carried out to demonstrate the robustness of our proposed AFD-DFE to high Doppler and carrier frequency offset (CFO).

Automated SVD filtering of time-frequency distribution for enhancing the SNR of microseismic/microquake events

Recently, there has been a growing interest in continuous passive recording of passive microseismic experiments during reservoir fluid-injection monitoring, hydraulic-fracture monitoring and fault-movement monitoring, to name a few. The ability to accurately detect and analyze microseismic events generated by these activities is valuable in monitoring them. However, microseismic events usually have very low signal-to-noise ratio (SNR), especially when monitoring sensors (receivers) are located at the surface where coherent and non-coherent noise sources are overwhelming. Therefore, enhancing the SNR of the microseismic event will improve the localization process over the reservoir. In this study, a new method of enhancing the microseismic event is presented which relies on one trace per receiver record unlike other methods. The proposed method relies on a time-frequency representation and noise eliminating process which uses the singular-value decomposition (SVD) technique. Furthermore, the SVD is applied on the matrix representing the time-frequency decomposition of a trace. More importantly, an automated SVD filtering is proposed, so the SVD filtering becomes observation-driven instead of user-defined. Finally, it is shown that the proposed technique gives promising results with very low SNR, making it suitable to locate passive microseismic events even if the sensors are located on the surface.

Iterative interferometry-based method for picking microseismic events

Abstract Continuous microseismic monitoring of hydraulic fracturing is commonly used in many engineering, environmental, mining, and petroleum applications. Microseismic signals recorded at the surface, suffer from excessive noise that complicates first-break picking and subsequent data processing and analysis. This study presents a new first-break picking algorithm that employs concepts from seismic interferometry and time-frequency (TF) analysis. The algorithm first uses a TF plot to manually pick a reference first-break and then iterates the steps of cross-correlation, alignment, and stacking to enhance the signal-to-noise ratio of the relative first breaks. The reference first-break is subsequently used to calculate final first breaks from the relative ones. Testing on synthetic and real data sets at high levels of additive noise shows that the algorithm enhances the first-break picking considerably. Furthermore, results show that only two iterations are needed to converge to the true first breaks. Indeed, iterating more can have detrimental effects on the algorithm due to increasing correlation of random noise.

AFD-DFE Using Constraint-Based RLS and Phase Noise Compensation for Uplink SC-FDMA

In this paper, we develop a constraint-based block recursive least-squares (CRLS) for an adaptive frequency-domain decision feedback equalizer (AFD-DFE) in uplink single-carrier frequency division multiple access systems. For the AFD-DFE, both the feedforward and feedback filters are implemented in the frequency domain; therefore, the CRLS complexity can be reduced substantially when compared to its time-domain counterpart by exploiting the matrix structure in the frequency domain. The performance of the CRLS algorithm is better than that of the RLS when applied to the AFD-DFE, with no increase in the computational complexity. Our designed AFD-DFE with CRLS not only enjoys a lower computational complexity when compared to the frequency-domain channel-estimate-based minimum mean square error DFE (MMSE DFE), but its performance is also better than that of the MMSE DFE with decision errors (practical case) and is close to the MMSE DFE with correct decisions (ideal case). Moreover, we iteratively compensate the transmitter and receiver phase noise using its properties in the time and frequency domains. Simulation results demonstrate the robustness of our designed AFD-DFE using CRLS.

RLS-based frequency-domain DFE for uplink SC-FDMA

The decision feedback equalizer is well known to outperform a linear equalizer in highly frequency-selective fading channels. In this paper, we develop a low-complexity Adaptive Frequency Domain Decision Feedback Equalizer (AFD-DFE) for Single-Carrier Frequency Division Multiple Access (SC-FDMA) systems. Both the feedforward and feedback filters operate in the frequency-domain and are adapted using the block Recursive Least Squares (RLS) algorithm. Since this DFE design operation is performed entirely in the frequency-domain, the complexity of the block RLS algorithm can be reduced substantially when compared to its time-domain counterpart by exploiting matrix structure in the frequency-domain. We also show that the RLS-based AFD-DFE not only enjoys a significant reduction in computational complexity when compared to the frequency-domain non-adaptive channel-estimate-based MMSE-DFE but its performance is also better than that of the practical MMSE DFE (with decision errors) and close to the ideal MMSE DFE (with correct decisions).

Microseismic events enhancement and detection in sensor arrays using autocorrelation-based filtering

Passive microseismic data are commonly buried in noise, which presents a significant challenge for signal detection and recovery. For recordings from a surface sensor array where each trace contains a time-delayed arrival from the event, we propose an autocorrelation-based stacking method that designs a denoising filter from all the traces, as well as a multi-channel detection scheme. This approach circumvents the issue of time aligning the traces prior to stacking because every trace’s autocorrelation is centred at zero in the lag domain. The effect of white noise is concentrated near zero lag; thus, the filter design requires a predictable adjustment of the zero-lag value. Truncation of the autocorrelation is employed to smooth the impulse response of the denoising filter. In order to extend the applicability of the algorithm, we also propose a noise prewhitening scheme that addresses cases with coloured noise. The simplicity and robustness of this method are validated with synthetic and real seismic traces.

CFO mitigation using adaptive frequency-domain decision feedback equalization for uplink SC-FDMA

To mitigate Inter-Carrier Interference (ICI) due to large Carrier Frequency Offset (CFO) in an uplink Single- Carrier Frequency Division Multiple Access system, a 3-tap Adaptive Frequency Domain Decision Feedback Equalizer (AFD- DFE) is designed in this work by exploiting the banded and sparse structure of the equivalent channel matrix. The block Recursive Least Squares (RLS) algorithm is used for adaptation and both the feedforward and feedback filters are implemented in the frequency-domain. Consequently, the complexity of the block RLS, by exploiting the matrix structure in the frequency-domain, is reduced substantially when compared to its time-domain counterpart. Ultimately, it will be shown that the proposed AFD- DFE exhibits significant excellent performance improvement when compared to a 1-tap AFD-DFE while still enjoying a low computational complexity.

Enhancement of Microseismic Events Using Tensor Decomposition and Time-frequency Representation

Analysis of passive microseismic data is usually a challenging task due to low signal-to-noise ratio environment. This study introduces an approach for enhancing the microseismic events using tensor decomposition and time-frequency representation. The proposed method shows promising results when applied on microseismic data set.