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This paper addresses the problem of blind identification of multichannel systems. It proposes three different novel algorithms by exploiting the inherent Toeplitz /Sylvester structures impeded in the system model. The first algorithm is the structured signal subspace (SSS) method, which involves direct estimation of the signal from a multiple-input multiple-output (MIMO) system. The second algorithm is the structured channel subspace (SCS) method, whereby the MIMO channel matrix is estimated by employing its embedded Toeplitz structure. The last algorithm deals with the bilinear blind identification by utilizing the information (embedded structure) of both rows and columns subspaces of the received signals. The proposed methods exploit the block Sylvester structure of the signal and the channel matrix to formulate a quadratic cost function whose minimization enables us to estimate the desired system parameters. The simulation results of the proposed structured subspace methods are appealing in different scenarios.
Abdulmajid Lawal; Naveed Iqbal; Azzedine Zerguine; Qadri Mayyala; Karim Abed-Meraim. Toeplitz Structured Subspace for Multi-Channel Blind Identification Methods. Signal Processing 2021, 108152 .
AMA StyleAbdulmajid Lawal, Naveed Iqbal, Azzedine Zerguine, Qadri Mayyala, Karim Abed-Meraim. Toeplitz Structured Subspace for Multi-Channel Blind Identification Methods. Signal Processing. 2021; ():108152.
Chicago/Turabian StyleAbdulmajid Lawal; Naveed Iqbal; Azzedine Zerguine; Qadri Mayyala; Karim Abed-Meraim. 2021. "Toeplitz Structured Subspace for Multi-Channel Blind Identification Methods." Signal Processing , no. : 108152.
Several approaches have been used in the past to predict fatigue crack growth rates in T-joints of the offshore structures, but there are relatively few cases of applying structural health monitoring during the non-destructive testing of jacket platforms. This paper presents an experimental method based on the sensing of the piezoelectric sensors and finite element analysis method for studying the fatigue cracks in the offshore steel jacket structure. Three types of joints are selected in the current research work: T-type plate, T-type tube-plate, and T-type tube joints. The finite element analysis model established in the current study computes and analyzes the high stress and high strain regions in the T-type joints. The fatigue damage in the T-type joints was successfully detected by utilizing both the finite element analysis and experimental methods. The results showed that fatigue cracks of the three types of joints are prone to appear at the weld toe and spread in the welding direction. The fatigue damage location of T-type plate and T-type tube-plate joints is more concentrated in the upper weld toe area, and the fatigue damage location of the T-type tube joint is closer to the lower weld toe area.
Liaqat Ali; Sikandar Khan; Salem Bashmal; Naveed Iqbal; Weishun Dai; Yong Bai. Fatigue Crack Monitoring of T-Type Joints in Steel Offshore Oil and Gas Jacket Platform. Sensors 2021, 21, 3294 .
AMA StyleLiaqat Ali, Sikandar Khan, Salem Bashmal, Naveed Iqbal, Weishun Dai, Yong Bai. Fatigue Crack Monitoring of T-Type Joints in Steel Offshore Oil and Gas Jacket Platform. Sensors. 2021; 21 (9):3294.
Chicago/Turabian StyleLiaqat Ali; Sikandar Khan; Salem Bashmal; Naveed Iqbal; Weishun Dai; Yong Bai. 2021. "Fatigue Crack Monitoring of T-Type Joints in Steel Offshore Oil and Gas Jacket Platform." Sensors 21, no. 9: 3294.
There has been a recent rise in the uses and applications of passive seismic data, such as tomographic imaging, volcanic monitoring, and hydrocarbon exploration. Consequently, the sharp increase in passive seismic applications requires real-time event detection capabilities with high accuracy. Proper analysis of such events depends largely on the signal-to-noise ratio improvement through noise suppression techniques. Recent advances in the fields of signal processing and deep learning coupled with the available computational resources provide a great opportunity to address this challenge. In this work, a workflow is proposed where a residual deep neural network is customized and employed to detect passive seismic events. The automated detection is followed by a denoising step to extract the signal of interest from background noise using an IIR Wiener filter. The proposed method does not require any prior knowledge of the signal/noise, and therefore, it can work with various types of signals/noises. Another benefit of the proposed detection method is that the deep neural network is trained only on synthetic seismic data without the need to use real data in the training process. Nevertheless, it exhibits high accuracy in detecting and denoising events from real passive seismic data sets. In particular, field seismic data is recorded in northern Saudi Arabia and used to test the complete detection and denoising method. The detection method proved its capability of detecting events automatically in large data sets and in real time (due to off-line training).
Abdullah Othman; Naveed Iqbal; Sherif M. Hanafy; Umair Bin Waheed. Automated Event Detection and Denoising Method for Passive Seismic Data Using Residual Deep Convolutional Neural Networks. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -11.
AMA StyleAbdullah Othman, Naveed Iqbal, Sherif M. Hanafy, Umair Bin Waheed. Automated Event Detection and Denoising Method for Passive Seismic Data Using Residual Deep Convolutional Neural Networks. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-11.
Chicago/Turabian StyleAbdullah Othman; Naveed Iqbal; Sherif M. Hanafy; Umair Bin Waheed. 2021. "Automated Event Detection and Denoising Method for Passive Seismic Data Using Residual Deep Convolutional Neural Networks." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-11.
Traditional cable-based geophone network is not an efficient way of seismic data transmission owing to the related cost and weight. To alleviate these drawbacks, in this work, unlike the existing setup, an OFDMA-TDMA based wireless geophone network is proposed to maximize the throughput for timely delivery of the seismic data from geophones to the data center. Furthermore, a TV white space is utilized for transmission in order to have long-distance links between the wireless geophones and the data center, and ultimately evading the use of intermediate relays. This, in turn, reduces the delivery time of the seismic data. More importantly, the seismic data transmission is modeled using a Markov chain and analytical expressions of throughput and transmission time are derived. Finally, our theoretical findings are substantially corroborated by simulation results.
Naveed Iqbal; Azzedine Zerguine; Sikandar Khan. OFDMA-TDMA-Based Seismic Data Transmission Over TV White Space. IEEE Communications Letters 2021, 25, 1720 -1724.
AMA StyleNaveed Iqbal, Azzedine Zerguine, Sikandar Khan. OFDMA-TDMA-Based Seismic Data Transmission Over TV White Space. IEEE Communications Letters. 2021; 25 (5):1720-1724.
Chicago/Turabian StyleNaveed Iqbal; Azzedine Zerguine; Sikandar Khan. 2021. "OFDMA-TDMA-Based Seismic Data Transmission Over TV White Space." IEEE Communications Letters 25, no. 5: 1720-1724.
The release of large quantities of CO2 into the atmosphere is one of the major causes of global warming. The most viable method to control the level of CO2 in the atmosphere is to capture and permanently sequestrate the excess amount of CO2 in subsurface geological reservoirs. The injection of CO2 gives rise to pore pressure buildup. It is crucial to monitor the rising pore pressure in order to prevent the potential failure of the reservoir and the subsequent leakage of the stored CO2 into the overburden layers, and then back to the atmosphere. In this paper, the Minjur sandstone reservoir in eastern Saudi Arabia was considered for establishing a coupled geomechanical model and performing the corresponding stability analysis. During the geomechanical modeling process, the fault passing through the Minjur and Marrat layers was also considered. The injection-induced pore-pressure and ground uplift profiles were calculated for the case of absence of a fault across the reservoir, as well as the case with a fault. The stability analysis was performed using the Mohr–Coulomb failure criterion. In the current study, the excessive increase in pore pressure, in the absence of geological faults, moved the reservoir closer to the failure envelope, but in the presence of geological faults, the reservoir reached to the failure envelope and the faults were activated. The developed geomechanical model provided estimates for the safe injection parameters of CO2 based on the magnitudes of the reservoir pore pressure and stresses in the reservoir.
Sikandar Khan; Yehia Khulief; Abdullatif Al-Shuhail; Salem Bashmal; Naveed Iqbal. The Geomechanical and Fault Activation Modeling during CO2 Injection into Deep Minjur Reservoir, Eastern Saudi Arabia. Sustainability 2020, 12, 9800 .
AMA StyleSikandar Khan, Yehia Khulief, Abdullatif Al-Shuhail, Salem Bashmal, Naveed Iqbal. The Geomechanical and Fault Activation Modeling during CO2 Injection into Deep Minjur Reservoir, Eastern Saudi Arabia. Sustainability. 2020; 12 (23):9800.
Chicago/Turabian StyleSikandar Khan; Yehia Khulief; Abdullatif Al-Shuhail; Salem Bashmal; Naveed Iqbal. 2020. "The Geomechanical and Fault Activation Modeling during CO2 Injection into Deep Minjur Reservoir, Eastern Saudi Arabia." Sustainability 12, no. 23: 9800.
Traditional seismic data acquisition systems used for surveying during the exploration of oil and gas rely on cables between geophones and the data collection center. Despite the fact that cable-based systems provide reliable seismic data transfer, their deployment and maintenance costs increase substantially as the survey area increases in scale. Therefore, a three layer wireless network architecture is proposed in this work, which consists of wireless geophones (WG) and a data center with an intermediate wireless gateway node (WGN). This paper investigates the aggregate data throughput, transmission time, and energy consumption from WGs to the WGN in a wireless geophone network architecture based on the IEEE 802.11af standard. This standard is considered in order to have the maximum possible range and low power consumption due to operating in TV bands. Analytical expressions of the aforementioned quantities are derived using Markov chain models. Two Markov models are considered for this purpose: one for modeling the access method that allows multiple WGs to connect to a WGN and the other for representing a buffer in a WG. Since seismic data is recorded at regular intervals, arrivals of data packets in the buffer of the WG is deterministic. On the other hand, departure is random due to the multiple access method. Hence, in this work D/M/1/B queue is used for the first time to model the buffer in a wireless geophone. Furthermore, the physical layer constraints are also taken into account together with proper wireless path-loss channel models. The results obtained are useful for designing such wireless seismic networks without extensive simulations. In particular, the proposed joint medium access control, physical layer, and D/M/1/B model enables us to optimize the required number of WGNs. Finally, sectoring is also introduced in order to minimize the total number of WGNs needed to cover the whole surveying area.
Naveed Iqbal; Suhail Ibrahim Al-Dharrab; Ali H. Muqaibel; Wessam Mesbah; Gordon L. Stuber. Cross-Layer Design and Analysis of Wireless Geophone Networks Utilizing TV White Space. IEEE Access 2020, 8, 118542 -118558.
AMA StyleNaveed Iqbal, Suhail Ibrahim Al-Dharrab, Ali H. Muqaibel, Wessam Mesbah, Gordon L. Stuber. Cross-Layer Design and Analysis of Wireless Geophone Networks Utilizing TV White Space. IEEE Access. 2020; 8 (99):118542-118558.
Chicago/Turabian StyleNaveed Iqbal; Suhail Ibrahim Al-Dharrab; Ali H. Muqaibel; Wessam Mesbah; Gordon L. Stuber. 2020. "Cross-Layer Design and Analysis of Wireless Geophone Networks Utilizing TV White Space." IEEE Access 8, no. 99: 118542-118558.
In this study, a data-driven linear filtering method is proposed to recover microseismic signals from noisy data/observations. The proposed method is based on the statistics of the background noise and the observation, which are directly extracted from the recorded data, obviating prior statistical knowledge of the microseismic source signal. The proposed method does not depend on any specific underlying noise statistics; therefore, it works for any type of noise, e.g. uncorrelated (random/white Gaussian), temporally correlated and spatially correlated noises. Consequently, the proposed method is suitable for microquake data sets that are recorded in contrastive noise environments. A mathematical analysis is presented to interpret the proposed method in two different ways. Furthermore, a number of practical concerns are discussed and their corresponding solutions are introduced. Finally, the proposed scheme is evaluated using both field and synthetic data sets and the experimental results show a reasonable and robust performance.
Naveed Iqbal; Bo Liu; Mohamed Deriche; Abdullatif Al-Shuhail; Sanlinn Kaka; Azzedine Zerguine. Blind noise estimation and denoising filter for recovery of microquake signals. Exploration Geophysics 2019, 50, 502 -513.
AMA StyleNaveed Iqbal, Bo Liu, Mohamed Deriche, Abdullatif Al-Shuhail, Sanlinn Kaka, Azzedine Zerguine. Blind noise estimation and denoising filter for recovery of microquake signals. Exploration Geophysics. 2019; 50 (5):502-513.
Chicago/Turabian StyleNaveed Iqbal; Bo Liu; Mohamed Deriche; Abdullatif Al-Shuhail; Sanlinn Kaka; Azzedine Zerguine. 2019. "Blind noise estimation and denoising filter for recovery of microquake signals." Exploration Geophysics 50, no. 5: 502-513.
In this work, an efficient numerical scheme is presented for seismic blind deconvolution in a multichannel scenario. The proposed method iterate with two steps: first, wavelet estimation across all channels and second, refinement of the reflectivity estimate simultaneously in all channels using sparse deconvolution. The reflectivity update step is formulated as a basis pursuit denoising problem and a sparse solution is obtained with the spectral projected-gradient algorithm – faithfulness to the recorded traces is constrained by the measured noise level. Wavelet re-estimation has a closed form solution when performed in the frequency domain by finding the minimum energy wavelet common to all channels. Nothing is assumed known about the wavelet apart from its time duration. In tests with both synthetic and real data, the method yields sparse reflectivity series and stable wavelet estimates results compared to existing methods with significantly less computational effort.
Naveed Iqbal; Entao Liu; James H. McClellan; Abdullatif Al-Shuhail. Sparse Multichannel Blind Deconvolution of Seismic Data via Spectral Projected-Gradient. IEEE Access 2019, 7, 23740 -23751.
AMA StyleNaveed Iqbal, Entao Liu, James H. McClellan, Abdullatif Al-Shuhail. Sparse Multichannel Blind Deconvolution of Seismic Data via Spectral Projected-Gradient. IEEE Access. 2019; 7 (99):23740-23751.
Chicago/Turabian StyleNaveed Iqbal; Entao Liu; James H. McClellan; Abdullatif Al-Shuhail. 2019. "Sparse Multichannel Blind Deconvolution of Seismic Data via Spectral Projected-Gradient." IEEE Access 7, no. 99: 23740-23751.
Anupama Govinda Raj; James H. McClellan; Naveed Iqbal; Abdullatif A. Al-Shuhail; SanLinn I. Kaka. Automatic microseismic event detection using constant false alarm rate processing in time-frequency domain. SEG Technical Program Expanded Abstracts 2018 2018, 1 .
AMA StyleAnupama Govinda Raj, James H. McClellan, Naveed Iqbal, Abdullatif A. Al-Shuhail, SanLinn I. Kaka. Automatic microseismic event detection using constant false alarm rate processing in time-frequency domain. SEG Technical Program Expanded Abstracts 2018. 2018; ():1.
Chicago/Turabian StyleAnupama Govinda Raj; James H. McClellan; Naveed Iqbal; Abdullatif A. Al-Shuhail; SanLinn I. Kaka. 2018. "Automatic microseismic event detection using constant false alarm rate processing in time-frequency domain." SEG Technical Program Expanded Abstracts 2018 , no. : 1.
Reliable detection and recovery of a microseismic event in large volume of passive monitoring data is usually a challenging task due to the low signal-to-noise ratio environment. The accuracy of weak microseismic event identification is a very important step in the analysis and interpretation of microseismic data. This study introduces an approach for detecting (presence indication) and denoising (accurate recovery) microseismic events using tensor decomposition by considering the time-frequency representation of multiple traces as a 3D tensor. A tensor is a multiway array having dimension greater than two, and recent signal processing techniques have been developed to manipulate such data by taking advantage of the multidimensional structure. With advances in technology and the availability of cheap memory, it is now possible to store and do mathematical operations, such as higher-order singular-value decomposition or tensor decomposition, on multiway data. In active seismic, tensor decomposition has been used for multidimensional reconstruction via higher order interpolation to obtain missing observations. In this work, we use 3D tensor decomposition to process passive seismic data. Experiments performed on synthetic and field data sets show promising results achieved by these new methods.
Naveed Iqbal; Entao Liu; James H. McClellan; Abdullatif Al-Shuhail; San Linn I. Kaka; Azzedine Zerguine. Detection and Denoising of Microseismic Events Using Time–Frequency Representation and Tensor Decomposition. IEEE Access 2018, 6, 22993 -23006.
AMA StyleNaveed Iqbal, Entao Liu, James H. McClellan, Abdullatif Al-Shuhail, San Linn I. Kaka, Azzedine Zerguine. Detection and Denoising of Microseismic Events Using Time–Frequency Representation and Tensor Decomposition. IEEE Access. 2018; 6 ():22993-23006.
Chicago/Turabian StyleNaveed Iqbal; Entao Liu; James H. McClellan; Abdullatif Al-Shuhail; San Linn I. Kaka; Azzedine Zerguine. 2018. "Detection and Denoising of Microseismic Events Using Time–Frequency Representation and Tensor Decomposition." IEEE Access 6, no. : 22993-23006.
Reliable analysis of low-energy earthquakes (microseismic) depends on how accurately one can detect and pick the arrival times, which are strongly influenced by the noise content. The study of microseismic events becomes even more challenging when the sensors are located on the surface because of the poor signal-to-noise ratio (SNR). Consequently, efficient and robust techniques for denoising microseismic data are necessary. In this study, we propose a method based on an infinite impulse response (IIR) Wiener filter. The proposed method uses statistics based on signal observations (noisy data) and the underlying noise, both recorded by various sensors. The method presented here precludes the need for statistics or prior knowledge of the signal of interest. The second-order statistics of the noise and the noisy data are extracted from the recorded data only. As an advantage, in deriving the filter’s impulse response, no underlying structure of noise is assumed. Therefore, our method works for various types of noise, e.g., uncorrelated, spatially correlated, temporally correlated, Gaussian and non-Gaussian noise. Hence, the proposed method can be suitable as well for microseismic data recorded in diverse seismic noise environments. The criteria used to optimize the filter impulse response is the minimization of the mean square error. The proposed method is tested on synthetic and field data sets and found to be effective in denoising microseismic data with very low SNR (\(-~12\) dB).
Naveed Iqbal; Azzedine Zerguine; Sanlinn Kaka; Abdullatif Al-Shuhail. Observation-Driven Method Based on IIR Wiener Filter for Microseismic Data Denoising. Pure and Applied Geophysics 2018, 175, 2057 -2075.
AMA StyleNaveed Iqbal, Azzedine Zerguine, Sanlinn Kaka, Abdullatif Al-Shuhail. Observation-Driven Method Based on IIR Wiener Filter for Microseismic Data Denoising. Pure and Applied Geophysics. 2018; 175 (6):2057-2075.
Chicago/Turabian StyleNaveed Iqbal; Azzedine Zerguine; Sanlinn Kaka; Abdullatif Al-Shuhail. 2018. "Observation-Driven Method Based on IIR Wiener Filter for Microseismic Data Denoising." Pure and Applied Geophysics 175, no. 6: 2057-2075.
Naveed Iqbal; Abdullatif A. Al-Shuhail; SanLinn I. Kaka; Entao Liu; Anupama Govinda Raj; James H. McClellan. Iterative interferometry-based method for picking microseismic events. Journal of Applied Geophysics 2017, 140, 52 -61.
AMA StyleNaveed Iqbal, Abdullatif A. Al-Shuhail, SanLinn I. Kaka, Entao Liu, Anupama Govinda Raj, James H. McClellan. Iterative interferometry-based method for picking microseismic events. Journal of Applied Geophysics. 2017; 140 ():52-61.
Chicago/Turabian StyleNaveed Iqbal; Abdullatif A. Al-Shuhail; SanLinn I. Kaka; Entao Liu; Anupama Govinda Raj; James H. McClellan. 2017. "Iterative interferometry-based method for picking microseismic events." Journal of Applied Geophysics 140, no. : 52-61.
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.
Naveed Iqbal; Azzedine Zerguine; Sanlinn Kaka; Abdullatif Al-Shuhail; Sanlinn Kaka. Automated SVD filtering of time-frequency distribution for enhancing the SNR of microseismic/microquake events. Journal of Geophysics and Engineering 2016, 13, 964 -973.
AMA StyleNaveed Iqbal, Azzedine Zerguine, Sanlinn Kaka, Abdullatif Al-Shuhail, Sanlinn Kaka. Automated SVD filtering of time-frequency distribution for enhancing the SNR of microseismic/microquake events. Journal of Geophysics and Engineering. 2016; 13 (6):964-973.
Chicago/Turabian StyleNaveed Iqbal; Azzedine Zerguine; Sanlinn Kaka; Abdullatif Al-Shuhail; Sanlinn Kaka. 2016. "Automated SVD filtering of time-frequency distribution for enhancing the SNR of microseismic/microquake events." Journal of Geophysics and Engineering 13, no. 6: 964-973.
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).
Naveed Iqbal; Naofal Al-Dhahir; Azzedine Zerguine; Abdelmalek Zidouri. Adaptive Frequency-Domain RLS DFE for Uplink MIMO SC-FDMA. IEEE Transactions on Vehicular Technology 2014, 64, 1 -1.
AMA StyleNaveed Iqbal, Naofal Al-Dhahir, Azzedine Zerguine, Abdelmalek Zidouri. Adaptive Frequency-Domain RLS DFE for Uplink MIMO SC-FDMA. IEEE Transactions on Vehicular Technology. 2014; 64 (7):1-1.
Chicago/Turabian StyleNaveed Iqbal; Naofal Al-Dhahir; Azzedine Zerguine; Abdelmalek Zidouri. 2014. "Adaptive Frequency-Domain RLS DFE for Uplink MIMO SC-FDMA." IEEE Transactions on Vehicular Technology 64, no. 7: 1-1.
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.
Naveed Iqbal; Azzedine Zerguine; Naofal Al-Dhahir. Decision Feedback Equalization using Particle Swarm Optimization. Signal Processing 2014, 108, 1 -12.
AMA StyleNaveed Iqbal, Azzedine Zerguine, Naofal Al-Dhahir. Decision Feedback Equalization using Particle Swarm Optimization. Signal Processing. 2014; 108 ():1-12.
Chicago/Turabian StyleNaveed Iqbal; Azzedine Zerguine; Naofal Al-Dhahir. 2014. "Decision Feedback Equalization using Particle Swarm Optimization." Signal Processing 108, no. : 1-12.
An adaptive frequency-domain equaliser for the single carrier frequency division multiple access (SC-FDMA) system using the particle swarm optimisation (PSO) technique is proposed. Unlike the stochastic gradient and recursive least squares algorithms, the PSO is known to have fast convergence which does not depend on the underlying structure. The cost function used in a PSO is formulated based on the respective structure of the equaliser, whether it is a linear equaliser or a decision feedback equaliser. The robustness of the proposed PSO algorithm is demonstrated on a high Doppler scenario. Furthermore, it is shown that the performance improves more when using re-randomisation. Finally, it is shown that the PSO-based frequency-domain equaliser is more computationally efficient than its time-domain counterpart.
N. Iqbal; A. Zerguine; N. Al‐Dhahir. Adaptive equalisation using particle swarm optimisation for uplink SC‐FDMA. Electronics Letters 2014, 50, 469 -471.
AMA StyleN. Iqbal, A. Zerguine, N. Al‐Dhahir. Adaptive equalisation using particle swarm optimisation for uplink SC‐FDMA. Electronics Letters. 2014; 50 (6):469-471.
Chicago/Turabian StyleN. Iqbal; A. Zerguine; N. Al‐Dhahir. 2014. "Adaptive equalisation using particle swarm optimisation for uplink SC‐FDMA." Electronics Letters 50, no. 6: 469-471.