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Papers by Muhammad Omer Bin Saeed
2020 54th Asilomar Conference on Signals, Systems, and Computers, 2020
The Least Mean Square (LMS) algorithm has an inherent trade-off issue between convergence speed a... more The Least Mean Square (LMS) algorithm has an inherent trade-off issue between convergence speed and steady-state error performance. One of the algorithms proposed to tackle this issue is called the Noise Constrained LMS algorithm, which uses the noise variance to iteratively vary the step-size. This work uses the q-derivative to propose an improved Noise Constrained LMS algorithm. Simulation results show that the proposed algorithm shows better performance than the conventional algorithm at the cost of only a minimal increase in complexity. Steady-state analysis for the proposed algorithm has also been carried out.
The least-mean-squares (LMS) algorithm is the most popular algorithm in adaptive filtering. Sever... more The least-mean-squares (LMS) algorithm is the most popular algorithm in adaptive filtering. Several variable step-size strategies have been suggested to improve the performance of the LMS algorithm. These strategies enhance the performance of the algorithm but a major drawback is the complexity in the theoretical analysis of the resultant algorithms. Researchers use several assumptions to find closed-form analytical solutions. This work presents a unified approach for the analysis of variable step-size LMS algorithms. The approach is then applied to several variable step-size strategies and theoretical and simulation results are compared.
ArXiv, 2014
Particle swam optimization (PSO) is a popular stochastic optimization method that has found wide ... more Particle swam optimization (PSO) is a popular stochastic optimization method that has found wide applications in diverse fields. However, PSO suffers from high computational complexity and slow convergence speed. High computational complexity hinders its use in applications that have limited power resources while slow convergence speed makes it unsuitable for time critical applications. In this paper, we propose two techniques to overcome these limitations. The first technique reduces the computational complexity of PSO while the second technique speeds up its convergence. These techniques can be applied, either separately or in conjunction, to any existing PSO variant. The proposed techniques are robust to the number of dimensions of the optimization problem. Simulation results are presented for the proposed techniques applied to the standard PSO as well as to several PSO variants. The results show that the use of both these techniques in conjunction results in a reduction in the n...
2019 5th International Conference on Frontiers of Signal Processing (ICFSP), 2019
This work proposes a distributed estimation algorithm for wireless sensor network, based on the i... more This work proposes a distributed estimation algorithm for wireless sensor network, based on the incremental scheme. The proposed algorithm utilizes the noise variance in order to improve performance. The derivation and mean analysis are shown. The mean analysis of the algorithm is performed which show the range of step size and the stability of the algorithm. Under different scenarios experimental results show the superiority of the proposed algorithm.
ArXiv, 2019
The incremental least mean square (ILMS) algorithm was presented in \cite{Lopes2007}. The article... more The incremental least mean square (ILMS) algorithm was presented in \cite{Lopes2007}. The article included theoretical analysis of the algorithm along with simulation results under different scenarios. However, the transient analysis was left incomplete. This work presents the complete transient analysis, including the learning behavior. The analysis results are verified through several experimental results.
2018 3rd International Conference on Control and Robotics Engineering (ICCRE), 2018
This paper proposes an automated algorithm to correct loss of sub-carriers in Orthogonal Frequenc... more This paper proposes an automated algorithm to correct loss of sub-carriers in Orthogonal Frequency Division Multiplexing (OFDM) system using Low-Density Parity-Check (LDPC) codes. OFDM transmits data on high bit-rate but high-peak-to-average ratio PAPR, subcarriers-loss due to deep fades in multipath causes 2-dimensional errors i.e., in time and frequency-domain, and inter-symbol-interference (ISI) etc. occurs in multipath OFDM system. In order to reduce such OFDM errors, error correcting codes have been used by researchers like Turbo-code, LDPC-code, ReedSolomon-code, Alamouti-code etc. LDPC-coded OFDM system is employed in the paper to correct 2D error and make improvement in OFDM BER curve. LDPC was first introduced by Gallager during his graduation in 1962. The main purpose for LDPC selection amongst all error correcting codes is its performance which is near Shannon limit. Information bits are encoded using LDPC and modulated using binary phase-shift-keying (BPSK). Then, OFDM s...
ArXiv, 2013
The particle swarm approach provides a low complexity solution to the optimization problem among ... more The particle swarm approach provides a low complexity solution to the optimization problem among various existing heuristic algorithms. Recent advances in the algorithm resulted in improved performance at the cost of increased computational complexity, which is undesirable. Literature shows that the particle swarm optimization algorithm based on comprehensive learning provides the best complexity-performance trade-off. We show how to reduce the complexity of this algorithm further, with a slight but acceptable performance loss. This enhancement allows the application of the algorithm in time critical applications, such as, real-time tracking, equalization etc.
EURASIP Journal on Advances in Signal Processing
IEEE Transactions on Circuits and Systems II: Express Briefs
ITM Web of Conferences
Orthogonal Frequency Division Multiplexing (OFDM) is a highly regarded technique used in the 4G m... more Orthogonal Frequency Division Multiplexing (OFDM) is a highly regarded technique used in the 4G mobile communication systems to provide reliable communication and high data rates due to the orthogonality between its sub carriers. However, it cannot be used in the next generation cellular system i.e. 5G. Thus, a new technique Generalized Frequency Division Multiplexing (GFDM) has been proposed to meet the demands of the next generation systems, which are higher data rates than 4G, minimum response time, lower power consumption etc. GFDM is a non-orthogonal, multicarrier scheme, which seems to fulfil the requirements of the new wireless communication system. The aim of this paper is to use the pilot symbols and their optimum placements within the data for the channel estimation of the GFDM system. It is shown that the optimum arrangement of the pilot symbols is to place them uniformly on equal intervals within the data and to cluster them in the middle of the data.
Telecommunication Systems
Arabian Journal for Science and Engineering, 2017
2016 IEEE 21st International Workshop on Computer Aided Modelling and Design of Communication Links and Networks (CAMAD), 2016
The particle swarm approach provides a low complexity solution to the optimization problem among ... more The particle swarm approach provides a low complexity solution to the optimization problem among various existing heuristic algorithms. Recent advances in the algorithm resulted in improved performance at the cost of increased computational complexity, which is undesirable. Literature shows that the particle swarm optimization algorithm based on comprehensive learning provides the best complexity-performance trade-off. We show how to reduce the complexity of this algorithm further, with a slight but acceptable performance loss. This enhancement allows the application of the algorithm in time critical applications, such as, real-time tracking, equalization etc.
Particle swam optimization (PSO) is a popular stochastic optimization method that has found wide ... more Particle swam optimization (PSO) is a popular stochastic optimization method that has found wide applications in diverse fields. However, PSO suffers from high computational complexity and slow convergence speed. High computational complexity hinders its use in applications that have limited power resources while slow convergence speed makes it unsuitable for time critical applications. In this paper, we propose two techniques to overcome these limitations. The first technique reduces the computational complexity of PSO while the second technique speeds up its convergence. These techniques can be applied, either separately or in conjunction, to any existing PSO variant. The proposed techniques are robust to the number of dimensions of the optimization problem. Simulation results are presented for the proposed techniques applied to the standard PSO as well as to several PSO variants. The results show that the use of both these techniques in conjunction results in a reduction in the number of computations required as well as faster convergence speed while maintaining an acceptable error performance for time-critical applications.
2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2011
2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2012
2020 54th Asilomar Conference on Signals, Systems, and Computers, 2020
The Least Mean Square (LMS) algorithm has an inherent trade-off issue between convergence speed a... more The Least Mean Square (LMS) algorithm has an inherent trade-off issue between convergence speed and steady-state error performance. One of the algorithms proposed to tackle this issue is called the Noise Constrained LMS algorithm, which uses the noise variance to iteratively vary the step-size. This work uses the q-derivative to propose an improved Noise Constrained LMS algorithm. Simulation results show that the proposed algorithm shows better performance than the conventional algorithm at the cost of only a minimal increase in complexity. Steady-state analysis for the proposed algorithm has also been carried out.
The least-mean-squares (LMS) algorithm is the most popular algorithm in adaptive filtering. Sever... more The least-mean-squares (LMS) algorithm is the most popular algorithm in adaptive filtering. Several variable step-size strategies have been suggested to improve the performance of the LMS algorithm. These strategies enhance the performance of the algorithm but a major drawback is the complexity in the theoretical analysis of the resultant algorithms. Researchers use several assumptions to find closed-form analytical solutions. This work presents a unified approach for the analysis of variable step-size LMS algorithms. The approach is then applied to several variable step-size strategies and theoretical and simulation results are compared.
ArXiv, 2014
Particle swam optimization (PSO) is a popular stochastic optimization method that has found wide ... more Particle swam optimization (PSO) is a popular stochastic optimization method that has found wide applications in diverse fields. However, PSO suffers from high computational complexity and slow convergence speed. High computational complexity hinders its use in applications that have limited power resources while slow convergence speed makes it unsuitable for time critical applications. In this paper, we propose two techniques to overcome these limitations. The first technique reduces the computational complexity of PSO while the second technique speeds up its convergence. These techniques can be applied, either separately or in conjunction, to any existing PSO variant. The proposed techniques are robust to the number of dimensions of the optimization problem. Simulation results are presented for the proposed techniques applied to the standard PSO as well as to several PSO variants. The results show that the use of both these techniques in conjunction results in a reduction in the n...
2019 5th International Conference on Frontiers of Signal Processing (ICFSP), 2019
This work proposes a distributed estimation algorithm for wireless sensor network, based on the i... more This work proposes a distributed estimation algorithm for wireless sensor network, based on the incremental scheme. The proposed algorithm utilizes the noise variance in order to improve performance. The derivation and mean analysis are shown. The mean analysis of the algorithm is performed which show the range of step size and the stability of the algorithm. Under different scenarios experimental results show the superiority of the proposed algorithm.
ArXiv, 2019
The incremental least mean square (ILMS) algorithm was presented in \cite{Lopes2007}. The article... more The incremental least mean square (ILMS) algorithm was presented in \cite{Lopes2007}. The article included theoretical analysis of the algorithm along with simulation results under different scenarios. However, the transient analysis was left incomplete. This work presents the complete transient analysis, including the learning behavior. The analysis results are verified through several experimental results.
2018 3rd International Conference on Control and Robotics Engineering (ICCRE), 2018
This paper proposes an automated algorithm to correct loss of sub-carriers in Orthogonal Frequenc... more This paper proposes an automated algorithm to correct loss of sub-carriers in Orthogonal Frequency Division Multiplexing (OFDM) system using Low-Density Parity-Check (LDPC) codes. OFDM transmits data on high bit-rate but high-peak-to-average ratio PAPR, subcarriers-loss due to deep fades in multipath causes 2-dimensional errors i.e., in time and frequency-domain, and inter-symbol-interference (ISI) etc. occurs in multipath OFDM system. In order to reduce such OFDM errors, error correcting codes have been used by researchers like Turbo-code, LDPC-code, ReedSolomon-code, Alamouti-code etc. LDPC-coded OFDM system is employed in the paper to correct 2D error and make improvement in OFDM BER curve. LDPC was first introduced by Gallager during his graduation in 1962. The main purpose for LDPC selection amongst all error correcting codes is its performance which is near Shannon limit. Information bits are encoded using LDPC and modulated using binary phase-shift-keying (BPSK). Then, OFDM s...
ArXiv, 2013
The particle swarm approach provides a low complexity solution to the optimization problem among ... more The particle swarm approach provides a low complexity solution to the optimization problem among various existing heuristic algorithms. Recent advances in the algorithm resulted in improved performance at the cost of increased computational complexity, which is undesirable. Literature shows that the particle swarm optimization algorithm based on comprehensive learning provides the best complexity-performance trade-off. We show how to reduce the complexity of this algorithm further, with a slight but acceptable performance loss. This enhancement allows the application of the algorithm in time critical applications, such as, real-time tracking, equalization etc.
EURASIP Journal on Advances in Signal Processing
IEEE Transactions on Circuits and Systems II: Express Briefs
ITM Web of Conferences
Orthogonal Frequency Division Multiplexing (OFDM) is a highly regarded technique used in the 4G m... more Orthogonal Frequency Division Multiplexing (OFDM) is a highly regarded technique used in the 4G mobile communication systems to provide reliable communication and high data rates due to the orthogonality between its sub carriers. However, it cannot be used in the next generation cellular system i.e. 5G. Thus, a new technique Generalized Frequency Division Multiplexing (GFDM) has been proposed to meet the demands of the next generation systems, which are higher data rates than 4G, minimum response time, lower power consumption etc. GFDM is a non-orthogonal, multicarrier scheme, which seems to fulfil the requirements of the new wireless communication system. The aim of this paper is to use the pilot symbols and their optimum placements within the data for the channel estimation of the GFDM system. It is shown that the optimum arrangement of the pilot symbols is to place them uniformly on equal intervals within the data and to cluster them in the middle of the data.
Telecommunication Systems
Arabian Journal for Science and Engineering, 2017
2016 IEEE 21st International Workshop on Computer Aided Modelling and Design of Communication Links and Networks (CAMAD), 2016
The particle swarm approach provides a low complexity solution to the optimization problem among ... more The particle swarm approach provides a low complexity solution to the optimization problem among various existing heuristic algorithms. Recent advances in the algorithm resulted in improved performance at the cost of increased computational complexity, which is undesirable. Literature shows that the particle swarm optimization algorithm based on comprehensive learning provides the best complexity-performance trade-off. We show how to reduce the complexity of this algorithm further, with a slight but acceptable performance loss. This enhancement allows the application of the algorithm in time critical applications, such as, real-time tracking, equalization etc.
Particle swam optimization (PSO) is a popular stochastic optimization method that has found wide ... more Particle swam optimization (PSO) is a popular stochastic optimization method that has found wide applications in diverse fields. However, PSO suffers from high computational complexity and slow convergence speed. High computational complexity hinders its use in applications that have limited power resources while slow convergence speed makes it unsuitable for time critical applications. In this paper, we propose two techniques to overcome these limitations. The first technique reduces the computational complexity of PSO while the second technique speeds up its convergence. These techniques can be applied, either separately or in conjunction, to any existing PSO variant. The proposed techniques are robust to the number of dimensions of the optimization problem. Simulation results are presented for the proposed techniques applied to the standard PSO as well as to several PSO variants. The results show that the use of both these techniques in conjunction results in a reduction in the number of computations required as well as faster convergence speed while maintaining an acceptable error performance for time-critical applications.
2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2011
2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2012