10 results on '"Mohsen Ghadyani"'
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2. Adaptive joint sparse recovery algorithm based on Tabu Search.
- Author
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Mohsen Ghadyani and Ali Shahzadi
- Published
- 2017
- Full Text
- View/download PDF
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3. Multiple random access for massive MIMO framework: A unified Compressive Sensing based approach.
- Author
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Mohsen Ghadyani and Ali Shahzadi
- Published
- 2017
- Full Text
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4. Compressive Sampling Rate Optimization for Multiple Measurement Vector Problems
- Author
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Ali Shahzadi and Mohsen Ghadyani
- Subjects
Signal processing ,Optimization problem ,Computer Networks and Communications ,Computer science ,Monte Carlo method ,Energy Engineering and Power Technology ,020206 networking & telecommunications ,02 engineering and technology ,01 natural sciences ,Signal ,Compressed sensing ,Sampling (signal processing) ,0103 physical sciences ,Signal Processing ,0202 electrical engineering, electronic engineering, information engineering ,Range (statistics) ,Computer Vision and Pattern Recognition ,Electrical and Electronic Engineering ,Joint (audio engineering) ,010301 acoustics ,Algorithm - Abstract
Joint sparse recovery is a recently proposed signal processing approach which has become conventional due to its capability of solving a wide range of optimization problems. However, despite the appropriate performance of existing joint sparse recovery algorithms, all of them suffer from the same drawback: They collect pre-defined number of compressed measurements, which may be too small to precisely reconstruct the row support, or may be unnecessarily large which leads to the wastage of sampling resources. To address this issue and unlike the previous studies, this paper proposes a novel compressed sensing-based method to adaptively adjust the optimal sampling rate of joint sparse recovery problem. A data-driven approach based on Monte Carlo simulation is introduced to obtain the minimum number of samples which is required for precise row support estimation using several well-known joint sparse recovery techniques. Then, a sequential joint sparse recovery framework is developed where the first step predicts the optimal number of measurements and the second step reconstructs signal vectors applying the determined sampling rate. Numerical simulations investigate the effectiveness of suggested method to reduce both the required number of measurements and average algorithm runtime, without losing the recovery performance. more...
- Published
- 2018
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- View/download PDF
5. Compressive sensing power control for interference management in D2D underlaid massive MIMO systems
- Author
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Ali Shahzadi and Mohsen Ghadyani
- Subjects
Computer science ,Distributed computing ,020206 networking & telecommunications ,02 engineering and technology ,Scheduling (computing) ,Compressed sensing ,Computer Science::Networking and Internet Architecture ,0202 electrical engineering, electronic engineering, information engineering ,Cellular network ,020201 artificial intelligence & image processing ,Electrical and Electronic Engineering ,Random access ,5G ,Decoding methods ,Efficient energy use ,Power control - Abstract
This paper introduces a sparsity controlled multiple random access scheme for efficient user scheduling in Device-to-Device (D2D) underlaid massive MIMO systems. In order to both avoid collision and enhance the Energy Efficiency (EE) of two-tier Heterogeneous Cellular Networks (HCNs), a unified Compressed Sensing (CS) based interference management strategy is proposed which guarantees concurrent cellular and D2D multi-user transmissions without collision. Specifically, supposing the natural sparsity in practical fifth generation (5G) scenarios and employing the sparse signal processing techniques, an analytical random access based model is adopted where provides several user scheduling and channel gain constraints to permit user identification, channel estimation and data decoding simultaneously. Furthermore, by developing a tractable tradeoff between the total power consumption and overall throughput of D2D tier, the transmission power is optimized such that the EE of D2D tier is maximized. Numerical simulations demonstrate the effectiveness of suggested approach to improve the collision avoidance capability and EE of D2D underlaid massive MIMO systems, even for crowded scenarios where the sparsity constraint does not meet sufficiently. more...
- Published
- 2018
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- View/download PDF
6. Adaptive Data-Driven Wideband Compressive Spectrum Sensing for Cognitive Radio Networks
- Author
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Ali Shahzadi and Mohsen Ghadyani
- Subjects
Computer Networks and Communications ,Computer science ,020206 networking & telecommunications ,02 engineering and technology ,Data-driven ,Support vector machine ,Compressed sensing ,Cognitive radio ,Sampling (signal processing) ,0202 electrical engineering, electronic engineering, information engineering ,Electrical and Electronic Engineering ,Wideband ,Throughput (business) ,Algorithm ,Data transmission - Abstract
This paper presents a novel adaptive wideband compressed spectrum sensing scheme for cognitive radio (CR) networks. Compared to the traditional CSSbased CR scenarios, the proposed approach reconstructs neither the received signal nor its spectrum during the compressed sensing procedure. On the contrary, a precise estimation of wide spectrum support is recovered with a fewer number of compressed measurements. Then, the spectrum occupancy is determined directly from the reconstructed support vector. To carry out this process, a data-driven methodology is utilized to obtain the minimum number of necessary samples required for support reconstruction, and a closed-form expression is obtained that optimally estimates the number of desired samples as a function of the sparsity level and number of channels. Following this phase, an adjustable sequential framework is developed where the first step predicts the optimal number of compressed measurements and the second step recovers the sparse support and makes sensing decision. Theoretical analysis and numerical simulations demonstrate the improvement achieved with the proposed algorithm to significantly reduce both sampling costs and average sensing time without any deterioration in detection performance. Furthermore, the remainder of the sensing time can be employed by secondary users for data transmission, thus leading to the enhancement of the total throughput of the CR network. more...
- Published
- 2018
- Full Text
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7. Improvement of the instability of compressible lattice Boltzmann model by shock-detecting sensor
- Author
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Mohsen Ghadyani and Vahid Esfahanian
- Subjects
Mesoscopic physics ,Shock (fluid dynamics) ,Lattice Boltzmann, Numerical hybrid filter, Shock-detecting sensor, Stability, Supersonic ,Mechanical Engineering ,Lattice Boltzmann methods ,Mechanics ,Classification of discontinuities ,Instability ,Discontinuity (linguistics) ,Classical mechanics ,Mechanics of Materials ,Inviscid flow ,Compressibility ,Mathematics - Abstract
Recently, lattice Boltzmann method (LBM) has drawn attention as an alternative and promising numerical technique for simulating fluid flows. The stability of LBM is a challenging problem in the simulation of compressible flows with different types of embedded discontinuities. This study, proposes a complementary scheme for simulating inviscid flows by a compressible lattice Boltzmann model in order to improve the instability using a shock-detecting procedure. The advantages and disadvantages of using a numerical hybrid filter on the primitive or conservative variables, in addition to, macroscopic or mesoscopic variables are investigated. The study demonstrates that the robustness of the utilized LB model is improved for inviscid compressible flows by implementation of the complementary scheme on mesoscopic variables. The validity of the procedure to capture shocks and resolve contact discontinuity and rarefaction waves in well-known benchmark problems is investigated. The numerical results show that the scheme is capable of generating more robust solutions in the simulation of compressible flows and prevents the formation of oscillations. Good agreements are obtained for all test cases. more...
- Published
- 2015
- Full Text
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8. A More Robust Compressible Lattice Boltzmann Model by using the Numerical Filters
- Author
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Vahid Esfahanian and Mohsen Ghadyani
- Subjects
Physics ,HPP model ,Applied Mathematics ,Mechanical Engineering ,Lattice Boltzmann methods ,Classification of discontinuities ,Lattice boltzmann, Stability, Numerical filter, Shock-detecting sensor ,Condensed Matter Physics ,Stability (probability) ,Discontinuity (linguistics) ,Robustness (computer science) ,Inviscid flow ,Applied mathematics ,Statistical physics ,GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries) ,ComputingMilieux_MISCELLANEOUS ,Linear filter - Abstract
The stability of the lattice Boltzmann model (LBM) is a challenging problem in the simulation of compressible flows with different types of embedded discontinuities. This study, proposes a complementary scheme for simulation of inviscid compressible flows by the lattice Boltzmann models using the numerical filters to improve the stability. The advantages and disadvantages of the implementation of numerical filters on the primitive and conservative variables, in addition to, mesoscopic and macroscopic variables are investigated. Moreover, a shock-detecting sensor, which activates a second-order linear filter near the discontinuities and a higher-order linear filter in smooth regions, is described and assessed. This study demonstrates that the proposed complementary scheme is practical. Also the accuracy and robustness of the utilized LB models are improved for inviscid compressible flows by implementation of the numerical filters on primitive variables. The validity of the procedure to capture shocks and to resolve contact discontinuity and rarefaction waves in well-known benchmarks is investigated and good agreements are obtained for all test cases. more...
- Published
- 2014
- Full Text
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9. Bandwidth extension of narrowband speech in log spectra domain using neural network
- Author
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Sara Pourmohammadi, Mohsen Ghadyani, and Mansour Vali
- Subjects
Voice activity detection ,Telephone network ,General Computer Science ,Artificial neural network ,Computer science ,Speech recognition ,Bandwidth (signal processing) ,Bandwidth extension ,Bandwidth extension,log spectra domain,narrowband speech,neural network,wideband speech ,Filter bank ,Wideband audio ,Computer Science::Sound ,Mel-frequency cepstrum ,Electrical and Electronic Engineering - Abstract
In recent years, there have been significant advances in communication technology, but speech signals still suffer from low perceived quality caused by bandwidth limitations of telephone networks. The bandwidth extension (BWE) approach adds high-frequency components of the speech signal to band-limited telephone speech and increases speech perception significantly. In this work, we develop a new method for representation of vocal tract filter coefficients using log of filter bank energy (LFBE) parameters as an alternative for mel-frequency cepstral coefficients (MFCCs). This approach is based on a strong correlation between the spectral components of low- and high-band spectrums. Furthermore, the performances of Gaussian mixture model and multilayer perceptron neural network methods for estimation of the high-frequency envelope are evaluated. Objective evaluations of the obtained results indicate that the LFBE feature vectors have better performance than the MFCCs. In addition, findings of the objective evaluations showed that using a neural network, which is not common in BWE, achieves a better performance as compared to the Gaussian mixture model. more...
- Published
- 2015
10. The use of shock-detecting sensor to improve the stability of Lattice Boltzmann Model for high Mach number flows
- Author
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Vahid Esfahanian, Mohammad Taeibi-Rahni, and Mohsen Ghadyani
- Subjects
Mathematical optimization ,Shock (fluid dynamics) ,Lattice Boltzmann methods ,General Physics and Astronomy ,Statistical and Nonlinear Physics ,Mechanics ,Computer Science Applications ,symbols.namesake ,Discontinuity (linguistics) ,Riemann problem ,Computational Theory and Mathematics ,Mach number ,Inviscid flow ,Mesh generation ,symbols ,Choked flow ,Mathematical Physics ,Mathematics - Abstract
Attempts to simulate compressible flows with moderate Mach number to relatively high ones using Lattice Boltzmann Method (LBM) have been made by numerous researchers in the recent decade. The stability of the LBM is a challenging problem in the simulation of compressible flows with different types of embedded discontinuities. The present study proposes an approach for simulation of inviscid flows by a compressible LB model in order to enhance the robustness using a combination of Essentially NonOscillatory (ENO) scheme and Shock-Detecting Sensor (SDS) procedure. A sensor is introduced with adjustable parameters which is active near the discontinuities and affects less on smooth regions. The validity of the improved model to capture shocks and to resolve contact discontinuity and rarefaction waves in the well-known benchmarks such as, Riemann problem, and shock reflection is investigated. In addition, the problem of supersonic flow in a channel with ramp is simulated using a skewed rectangular grid generated by an algebraic grid generation method. The numerical results are compared with analytical ones and those obtained by solving the original model. The numerical results show that the presented scheme is capable of generating more robust solutions in the simulation of compressible flows and is almost free of oscillations for high Mach numbers. Good agreements are obtained for all problems. more...
- Published
- 2015
- Full Text
- View/download PDF
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