Alam Zaib - Academia.edu (original) (raw)
Papers by Alam Zaib
Radioengineering, Sep 1, 2024
This paper proposes a deep neural network (DNN)-based approach for radiation pattern synthesis of... more This paper proposes a deep neural network (DNN)-based approach for radiation pattern synthesis of 8 elements phased array antenna. For this purpose, 181 points of a desired radiation pattern are fed as input to the DNN and phases of array elements are extracted as the outputs. Existing DNN techniques for radiation pattern synthesis are not directly applicable to higher-order arrays as the dataset size grows exponentially with array dimensions. To overcome this bottleneck, we propose novel and efficient methods of generating datasets for DNN. Specifically, by leveraging the constant phase-shift characteristic of the phased array antenna, dataset size is reduced by several orders of magnitude and made independent of the array size. This has considerable advantages in terms of speed and complexity, especially in real-time applications as the DNN can immediately learn and synthesize the desired patterns. The performance of the proposed methods is validated by using an ideal square beam and an optimal array pattern as reference inputs to the DNN. The results generated in MATLAB as well as in CST, demonstrate the effectiveness of the proposed methods in synthesizing the desired radiation patterns.
TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES
In this paper, the impact of initial search radius on the complexity and performance of a sphere ... more In this paper, the impact of initial search radius on the complexity and performance of a sphere decoding algorithm is investigated for different user positions within a distributed antenna system. In a distributed antenna system, users can take up random positions within the cell clusters. The channel matrix can therefore take up infinitely different forms. In the presented work, a distributed antenna system with three different user positions in the cooperating cells is considered by employing different channel matrices. The effect on the complexity and performance of the sphere decoder due to the choice of the initial sphere radius is investigated for these user positions. It is shown that the signal lattice volume changes considerably for different user positions within the cells. A dynamic radius allocation algorithm is proposed in which the behavior is exploited by dynamically adjusting the initial sphere radius based on the knowledge of the channel path gain matrix. The simulation results show that the proposed algorithm results in a considerable reduction in the complexity of the sphere decoder in a distributed antenna system. Additionally, the performance of the sphere decoder in different coupling scenarios within the distributed antenna system has been investigated for a different number of candidates. It is shown that the performance of cell edge users can be considerably enhanced with high channel diversity, which otherwise could severely deteriorate the overall system performance.
Telecommunication Systems
TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES
In this paper a low-cost link level performance prediction technique is proposed for a single inp... more In this paper a low-cost link level performance prediction technique is proposed for a single input and multiple output system. Receiver link level abstraction is used in system level simulations of large networks in order to reduce their complexity. Usually, a single lookup table is employed in link level abstraction to predict a receiver's performance under different channel conditions. In the presented work, the mean frame error rate curve of different diverse channels is proposed as the reference for performance prediction in single input multiple output systems. Its generation involves compression of the received code word into a single quality measure based on the postdetection signal to noise ratio values using nonlinear capacity, exponential, and mutual mapping functions. The overall performance difference between simulated and predicted frame error rates shows that the proposed scheme gives very good performance approximations under different modulation and coding schemes, clearly outperforming the classical line of sight channel lookup table.
The objective of this work is to detect the cell phone and/or camera used by a person in restrict... more The objective of this work is to detect the cell phone and/or camera used by a person in restricted areas. The paper is based on intensive image processing techniques, such as, features extraction and image classification. The dataset of images is generated with cell phone camera including positive (with cell phone) and negative (without cell phone) images. We then extract relevant features by using classical features extraction techniques including Histogram of Oriented Gradients (HOG) and Speeded up Robust Features (SURF).The extracted features are then, passed to classifier for detection. We employ Support Vector Machine (SVM), Nearest Neighbor (K-NN) and Decision tree classifier which are already trained on our dataset of training images of persons using mobile or otherwise. Finally, the detection performance in terms of error rate is compared for various combinations of feature extraction and classification techniques. Our results show that SURF with SVM classifier gives the best accuracy.
2019 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), 2019
Channel estimation is an essential and crucial research problem in orthogonal frequency division ... more Channel estimation is an essential and crucial research problem in orthogonal frequency division multiplexing (OFDM) based wireless communication systems. Several techniques have been applied and investigated for wireless channel estimation but each of them has its own limitations. The major drawback in current channel estimation techniques is that a large pilot overhead is needed to yield a satisfactory performance which reduces the spectral efficiency. To overcome this shortcoming, we suggest a method of data aided channel estimation which minimizes the pilot overhead without compromising the quality of channel estimation in OFDM systems. The proposed technique is based on reliable data carrier's selection algorithm in which reliable data carriers are used as virtual pilots, thus eliminating the need for additional pilots. Simulation results show a significant improvement in terms of mean square error (MSE) and bit error rate (BER) of the proposed algorithm.
IEEE Access, 2021
The energy efficient resource allocation scheme based on genetic algorithm (GA) for the downlink ... more The energy efficient resource allocation scheme based on genetic algorithm (GA) for the downlink orthogonal frequency division multiple access (OFDMA) heterogeneous networks (HetNets) is developed in this paper. To maximize the spectrum efficiency for the fifth generation (5G) mobile networks, frequency reuse-1 is employed. Thus, advanced inter-cell interference coordination techniques are required to mitigate the inter-cell interference for 5G HetNets. In this paper, the energy efficient optimization problem based on coordinated scheduling is formulated, which is a mixed-integer nonlinear fractional programming problem and is intractable to solve directly. To tackle this, a two-step GA based scheme is proposed to solve the optimization problem. In the first step, the resource blocks matrix is solved by normal GA in the spectral efficiency aspect with fixed power distribution matrix, and then the power distribution matrix is obtained in the second step by non-dominated sorting genetic algorithm II (NSGA-II) with obtained resource blocks allocation matrix. Finally, the system level numerical evaluation process is provided to illustrate the effectiveness of the developed scheme.
2019 2nd World Symposium on Communication Engineering (WSCE), 2019
This paper presents a low complexity pattern recovery method for a strongly deformed conformal ph... more This paper presents a low complexity pattern recovery method for a strongly deformed conformal phased-array based on Linear Pattern Correction Method (LPCM) by using pre-stored individual antenna radiation patterns. In order to reduce the storage requirements, the radiation patterns of wedge dipole arrays considered in this work, have been taken at certain deformation factor and the rest of the patterns are obtained through interpolation. Comprehensive analysis of pattern recovery using desired and interpolated patterns show good recovery of radiation pattern with the position of the nulls and the main lobe restored. Mean Square Error (MSE) and Nulls depth comparison for pattern recovery using interpolated patterns is also investigated.
IEEE Access, 2021
Linear discriminant analysis (LDA) based classifiers tend to falter in many practical settings wh... more Linear discriminant analysis (LDA) based classifiers tend to falter in many practical settings where the training data size is smaller than, or comparable to, the number of features. As a remedy, different regularized LDA (RLDA) methods have been proposed. These methods may still perform poorly depending on the size and quality of the available training data. In particular, the test data deviation from the training data model, for example, due to noise contamination, can cause severe performance degradation. Moreover, these methods commit further to the Gaussian assumption (upon which LDA is established) to tune their regularization parameters, which may compromise accuracy when dealing with real data. To address these issues, we propose a doubly regularized LDA classifier that we denote as R2LDA. In the proposed R2LDA approach, the RLDA score function is converted into an inner product of two vectors. By substituting the expressions of the regularized estimators of these vectors, we obtain the R2LDA score function that involves two regularization parameters. To set the values of these parameters, we adopt three existing regularization techniques; the constrained perturbation regularization approach (COPRA), the bounded perturbation regularization (BPR) algorithm, and the generalized cross-validation (GCV) method. These methods are used to tune the regularization parameters based on linear estimation models, with the sample covariance matrix's square root being the linear operator. Results obtained from both synthetic and real data demonstrate the consistency and effectiveness of the proposed R2LDA approach, especially in scenarios involving test data contaminated with noise that is not observed during the training phase.
Arabian Journal for Science and Engineering, 2021
![Research paper thumbnail of Correction to “Optimal Joint Channel Estimation and Data Detection for Massive SIMO Wireless Systems: A Polynomial Complexity Solution” [Mar 20 1822-1844]](https://attachments.academia-assets.com/118830707/thumbnails/1.jpg)
](IEEE Transactions on Information Theory, 2020
IEEE Transactions on Information Theory, 2019
By exploiting large antenna arrays, massive MIMO (multiple input multiple output) systems can gre... more By exploiting large antenna arrays, massive MIMO (multiple input multiple output) systems can greatly increase spectral and energy efficiency over traditional MIMO systems. However, increasing the number of antennas at the base station (BS) makes the uplink joint channel estimation and data detection (JED) challenging in massive MIMO systems. In this paper, we consider the JED problem for massive SIMO (single input multiple output) wireless systems, which is a special case of wireless systems with large antenna arrays. We propose exact Generalized Likelihood Ratio Test (GLRT) optimal JED algorithms with low expected complexity, for both constantmodulus and nonconstant-modulus constellations. We show that, despite the large number of unknown channel coefficients, the expected computational complexity of these algorithms is polynomial in channel coherence time (T) and the number of receive antennas (N), even when the number of receive antennas grows polynomially in the channel coherence time (N = O(T 11) suffices to guarantee an expected computational complexity cubic in T and linear in N). Simulation results show that the GLRT-optimal JED algorithms achieve significant performance gains (up to 5 dB improvement in energy efficiency) with low computational complexity.
Wireless Personal Communications, 2017
Wireless communication systems utilizing orthogonal frequency division multiplexing (OFDM) transm... more Wireless communication systems utilizing orthogonal frequency division multiplexing (OFDM) transmissions are capable of delivering high data rates over multipath frequency selective channels. This paper deals with joint estimation/interpolation of wireless channel using pilot symbols transmitted concurrently with the data. We propose a low complexity, spectrally efficient minimum mean square error channel estimator which exploits the correlation structure of channel frequency response for reducing the complexity. Specifically, it is shown that if pilots are inserted appropriately across OFDM subcarriers, the proposed algorithm requires no matrix inversion, thereby significantly relieving the computational burden without deteriorating the performance. Moreover, the knowledge of channel correlation is also not required for the proposed estimator. Simulation results validate that the proposed technique outperforms existing low-complexity variants in terms of mean square error and computational complexity.
IEEE Transactions on Communications, 2016
Massive MIMO communication systems, by virtue of utilizing very large number of antennas, have a ... more Massive MIMO communication systems, by virtue of utilizing very large number of antennas, have a potential to yield higher spectral and energy efficiency in comparison with the conventional MIMO systems. In this paper, we consider uplink channel estimation in massive MIMO-OFDM systems with frequency selective channels. With increased number of antennas, the channel estimation problem becomes very challenging as exceptionally large number of channel parameters have to be estimated. We propose an efficient distributed linear minimum mean square error (LMMSE) algorithm that can achieve near optimal channel estimates at very low complexity by exploiting the strong spatial correlations and symmetry of large antenna array elements. The proposed method involves solving a (fixed) reduced dimensional LMMSE problem at each antenna followed by a repetitive sharing of information through collaboration among neighboring antenna elements. To further enhance the channel estimates and/or reduce the number of reserved pilot tones, we propose a data-aided estimation technique that relies on finding a set of most reliable data carriers. We also analyse the effect of pilot contamination on the mean square error (MSE) performance of different channel estimation techniques. Unlike the conventional approaches, we use stochastic geometry to obtain analytical expression for interference variance (or power) across OFDM frequency tones and use it to derive the MSE expressions for different algorithms under both noise and pilot contaminated regimes. Simulation results validate our analysis and the near optimal MSE performance of proposed estimation algorithms.
2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE), 2015
Massive MIMO systems have made significant progress in increasing spectral and energy efficiency ... more Massive MIMO systems have made significant progress in increasing spectral and energy efficiency over traditional MIMO systems by exploiting large antenna arrays. In this paper we consider the joint maximum likelihood (ML) channel estimation and data detection problem for massive SIMO (single input multiple output) wireless systems. Despite the large number of unknown channel coefficients for massive SIMO systems, we improve an algorithm to achieve the exact ML non-coherent data detection with a low expected complexity. We show that the expected computational complexity of this algorithm is linear in the number of receive antennas and polynomial in channel coherence time. Simulation results show the performance gain of the optimal non-coherent data detection with a low computational complexity.
2015 23rd European Signal Processing Conference (EUSIPCO), 2015
We consider frequency selective channel estimation in the uplink of massive MIMO-OFDM systems, wh... more We consider frequency selective channel estimation in the uplink of massive MIMO-OFDM systems, where our major concern is complexity. A low complexity distributed LMMSE algorithm is proposed that attains near optimal channel impulse response (CIR) estimates from noisy observations at receive antenna array. In proposed method, every antenna estimates the CIRs of its neighborhood followed by recursive sharing of estimates with immediate neighbors. At each step, every antenna calculates the weighted average of shared estimates which converges to near optimal LMMSE solution. The simulation results validate the near optimal performance of proposed algorithm in terms of mean square error (MSE).
EURASIP Journal on Advances in Signal Processing, 2014
This paper investigates the joint maximum likelihood (ML) data detection and channel estimation p... more This paper investigates the joint maximum likelihood (ML) data detection and channel estimation problem for Alamouti space-time block-coded (STBC) orthogonal frequency-division multiplexing (OFDM) wireless systems. The joint ML estimation and data detection is generally considered a hard combinatorial optimization problem. We propose an efficient low-complexity algorithm based on branch-estimate-bound strategy that renders exact joint ML solution. However, the computational complexity of blind algorithm becomes critical at low signal-to-noise ratio (SNR) as the number of OFDM carriers and constellation size are increased especially in multiple-antenna systems. To overcome this problem, a semi-blind algorithm based on a new framework for reducing the complexity is proposed by relying on subcarrier reordering and decoding the carriers with different levels of confidence using a suitable reliability criterion. In addition, it is shown that by utilizing the inherent structure of Alamouti coding, the estimation performance improvement or the complexity reduction can be achieved. The proposed algorithms can reliably track the wireless Rayleigh fading channel without requiring any channel statistics. Simulation results presented against the perfect coherent detection demonstrate the effectiveness of blind and semi-blind algorithms over frequency-selective channels with different fading characteristics.
International Journal of Communication Systems, 2019
This paper presents link to system (L2S) interfacing technique for multiple input and multiple ou... more This paper presents link to system (L2S) interfacing technique for multiple input and multiple output (MIMO) iterative receivers. In L2S interfacing, usually the post detection signal to noise ratio (SNR)-based frame error rate lookup tables (LUT) are used to predict the link level performance of receivers. While L2S interfacing for linear MIMO receivers can be conveniently implemented, it is more challenging for MIMO iterative receivers due to unavailability of the closed form SNR expressions. In this paper, we propose three methods for post detection SNR estimation for MIMO iterative receivers. The first is based on the QR decomposition of the channel matrix, the second relies on the residual noise calculation based on the soft symbols, and the third exploits the closed form SNR expressions for linear receivers. A link to system interface model for iterative receivers is developed for evaluating the reference curves for different modulation and coding schemes, and results are validated by comparing the simulated and predicted frame error rates. It is shown that linear and residual noise-based SNR approximations result in a very good prediction performance whereas the performance of QR decomposition-based method degrades for higher order modulations and coding schemes. This paper presents link to system interfacing technique for MIMO iterative receivers. A link to system interface model for iterative receivers is developed for evaluating the reference curves for different modulation and coding schemes, and results are validated by comparing the simulated and predicted frame error rates. Three post detection SNR evaluation schemes have been proposed for link to system interfacing all of which give good prediction performance especially at lower order modulation. K E Y W O R D S capacity effective SNR mapping, exponential effective SNR mapping, iterative receiver, minimum mean square error filter, multiple input multiple output, mutual information effective SNR mapping
Radioengineering, Sep 1, 2024
This paper proposes a deep neural network (DNN)-based approach for radiation pattern synthesis of... more This paper proposes a deep neural network (DNN)-based approach for radiation pattern synthesis of 8 elements phased array antenna. For this purpose, 181 points of a desired radiation pattern are fed as input to the DNN and phases of array elements are extracted as the outputs. Existing DNN techniques for radiation pattern synthesis are not directly applicable to higher-order arrays as the dataset size grows exponentially with array dimensions. To overcome this bottleneck, we propose novel and efficient methods of generating datasets for DNN. Specifically, by leveraging the constant phase-shift characteristic of the phased array antenna, dataset size is reduced by several orders of magnitude and made independent of the array size. This has considerable advantages in terms of speed and complexity, especially in real-time applications as the DNN can immediately learn and synthesize the desired patterns. The performance of the proposed methods is validated by using an ideal square beam and an optimal array pattern as reference inputs to the DNN. The results generated in MATLAB as well as in CST, demonstrate the effectiveness of the proposed methods in synthesizing the desired radiation patterns.
TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES
In this paper, the impact of initial search radius on the complexity and performance of a sphere ... more In this paper, the impact of initial search radius on the complexity and performance of a sphere decoding algorithm is investigated for different user positions within a distributed antenna system. In a distributed antenna system, users can take up random positions within the cell clusters. The channel matrix can therefore take up infinitely different forms. In the presented work, a distributed antenna system with three different user positions in the cooperating cells is considered by employing different channel matrices. The effect on the complexity and performance of the sphere decoder due to the choice of the initial sphere radius is investigated for these user positions. It is shown that the signal lattice volume changes considerably for different user positions within the cells. A dynamic radius allocation algorithm is proposed in which the behavior is exploited by dynamically adjusting the initial sphere radius based on the knowledge of the channel path gain matrix. The simulation results show that the proposed algorithm results in a considerable reduction in the complexity of the sphere decoder in a distributed antenna system. Additionally, the performance of the sphere decoder in different coupling scenarios within the distributed antenna system has been investigated for a different number of candidates. It is shown that the performance of cell edge users can be considerably enhanced with high channel diversity, which otherwise could severely deteriorate the overall system performance.
Telecommunication Systems
TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES
In this paper a low-cost link level performance prediction technique is proposed for a single inp... more In this paper a low-cost link level performance prediction technique is proposed for a single input and multiple output system. Receiver link level abstraction is used in system level simulations of large networks in order to reduce their complexity. Usually, a single lookup table is employed in link level abstraction to predict a receiver's performance under different channel conditions. In the presented work, the mean frame error rate curve of different diverse channels is proposed as the reference for performance prediction in single input multiple output systems. Its generation involves compression of the received code word into a single quality measure based on the postdetection signal to noise ratio values using nonlinear capacity, exponential, and mutual mapping functions. The overall performance difference between simulated and predicted frame error rates shows that the proposed scheme gives very good performance approximations under different modulation and coding schemes, clearly outperforming the classical line of sight channel lookup table.
The objective of this work is to detect the cell phone and/or camera used by a person in restrict... more The objective of this work is to detect the cell phone and/or camera used by a person in restricted areas. The paper is based on intensive image processing techniques, such as, features extraction and image classification. The dataset of images is generated with cell phone camera including positive (with cell phone) and negative (without cell phone) images. We then extract relevant features by using classical features extraction techniques including Histogram of Oriented Gradients (HOG) and Speeded up Robust Features (SURF).The extracted features are then, passed to classifier for detection. We employ Support Vector Machine (SVM), Nearest Neighbor (K-NN) and Decision tree classifier which are already trained on our dataset of training images of persons using mobile or otherwise. Finally, the detection performance in terms of error rate is compared for various combinations of feature extraction and classification techniques. Our results show that SURF with SVM classifier gives the best accuracy.
2019 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), 2019
Channel estimation is an essential and crucial research problem in orthogonal frequency division ... more Channel estimation is an essential and crucial research problem in orthogonal frequency division multiplexing (OFDM) based wireless communication systems. Several techniques have been applied and investigated for wireless channel estimation but each of them has its own limitations. The major drawback in current channel estimation techniques is that a large pilot overhead is needed to yield a satisfactory performance which reduces the spectral efficiency. To overcome this shortcoming, we suggest a method of data aided channel estimation which minimizes the pilot overhead without compromising the quality of channel estimation in OFDM systems. The proposed technique is based on reliable data carrier's selection algorithm in which reliable data carriers are used as virtual pilots, thus eliminating the need for additional pilots. Simulation results show a significant improvement in terms of mean square error (MSE) and bit error rate (BER) of the proposed algorithm.
IEEE Access, 2021
The energy efficient resource allocation scheme based on genetic algorithm (GA) for the downlink ... more The energy efficient resource allocation scheme based on genetic algorithm (GA) for the downlink orthogonal frequency division multiple access (OFDMA) heterogeneous networks (HetNets) is developed in this paper. To maximize the spectrum efficiency for the fifth generation (5G) mobile networks, frequency reuse-1 is employed. Thus, advanced inter-cell interference coordination techniques are required to mitigate the inter-cell interference for 5G HetNets. In this paper, the energy efficient optimization problem based on coordinated scheduling is formulated, which is a mixed-integer nonlinear fractional programming problem and is intractable to solve directly. To tackle this, a two-step GA based scheme is proposed to solve the optimization problem. In the first step, the resource blocks matrix is solved by normal GA in the spectral efficiency aspect with fixed power distribution matrix, and then the power distribution matrix is obtained in the second step by non-dominated sorting genetic algorithm II (NSGA-II) with obtained resource blocks allocation matrix. Finally, the system level numerical evaluation process is provided to illustrate the effectiveness of the developed scheme.
2019 2nd World Symposium on Communication Engineering (WSCE), 2019
This paper presents a low complexity pattern recovery method for a strongly deformed conformal ph... more This paper presents a low complexity pattern recovery method for a strongly deformed conformal phased-array based on Linear Pattern Correction Method (LPCM) by using pre-stored individual antenna radiation patterns. In order to reduce the storage requirements, the radiation patterns of wedge dipole arrays considered in this work, have been taken at certain deformation factor and the rest of the patterns are obtained through interpolation. Comprehensive analysis of pattern recovery using desired and interpolated patterns show good recovery of radiation pattern with the position of the nulls and the main lobe restored. Mean Square Error (MSE) and Nulls depth comparison for pattern recovery using interpolated patterns is also investigated.
IEEE Access, 2021
Linear discriminant analysis (LDA) based classifiers tend to falter in many practical settings wh... more Linear discriminant analysis (LDA) based classifiers tend to falter in many practical settings where the training data size is smaller than, or comparable to, the number of features. As a remedy, different regularized LDA (RLDA) methods have been proposed. These methods may still perform poorly depending on the size and quality of the available training data. In particular, the test data deviation from the training data model, for example, due to noise contamination, can cause severe performance degradation. Moreover, these methods commit further to the Gaussian assumption (upon which LDA is established) to tune their regularization parameters, which may compromise accuracy when dealing with real data. To address these issues, we propose a doubly regularized LDA classifier that we denote as R2LDA. In the proposed R2LDA approach, the RLDA score function is converted into an inner product of two vectors. By substituting the expressions of the regularized estimators of these vectors, we obtain the R2LDA score function that involves two regularization parameters. To set the values of these parameters, we adopt three existing regularization techniques; the constrained perturbation regularization approach (COPRA), the bounded perturbation regularization (BPR) algorithm, and the generalized cross-validation (GCV) method. These methods are used to tune the regularization parameters based on linear estimation models, with the sample covariance matrix's square root being the linear operator. Results obtained from both synthetic and real data demonstrate the consistency and effectiveness of the proposed R2LDA approach, especially in scenarios involving test data contaminated with noise that is not observed during the training phase.
Arabian Journal for Science and Engineering, 2021
IEEE Transactions on Information Theory, 2020
IEEE Transactions on Information Theory, 2019
By exploiting large antenna arrays, massive MIMO (multiple input multiple output) systems can gre... more By exploiting large antenna arrays, massive MIMO (multiple input multiple output) systems can greatly increase spectral and energy efficiency over traditional MIMO systems. However, increasing the number of antennas at the base station (BS) makes the uplink joint channel estimation and data detection (JED) challenging in massive MIMO systems. In this paper, we consider the JED problem for massive SIMO (single input multiple output) wireless systems, which is a special case of wireless systems with large antenna arrays. We propose exact Generalized Likelihood Ratio Test (GLRT) optimal JED algorithms with low expected complexity, for both constantmodulus and nonconstant-modulus constellations. We show that, despite the large number of unknown channel coefficients, the expected computational complexity of these algorithms is polynomial in channel coherence time (T) and the number of receive antennas (N), even when the number of receive antennas grows polynomially in the channel coherence time (N = O(T 11) suffices to guarantee an expected computational complexity cubic in T and linear in N). Simulation results show that the GLRT-optimal JED algorithms achieve significant performance gains (up to 5 dB improvement in energy efficiency) with low computational complexity.
Wireless Personal Communications, 2017
Wireless communication systems utilizing orthogonal frequency division multiplexing (OFDM) transm... more Wireless communication systems utilizing orthogonal frequency division multiplexing (OFDM) transmissions are capable of delivering high data rates over multipath frequency selective channels. This paper deals with joint estimation/interpolation of wireless channel using pilot symbols transmitted concurrently with the data. We propose a low complexity, spectrally efficient minimum mean square error channel estimator which exploits the correlation structure of channel frequency response for reducing the complexity. Specifically, it is shown that if pilots are inserted appropriately across OFDM subcarriers, the proposed algorithm requires no matrix inversion, thereby significantly relieving the computational burden without deteriorating the performance. Moreover, the knowledge of channel correlation is also not required for the proposed estimator. Simulation results validate that the proposed technique outperforms existing low-complexity variants in terms of mean square error and computational complexity.
IEEE Transactions on Communications, 2016
Massive MIMO communication systems, by virtue of utilizing very large number of antennas, have a ... more Massive MIMO communication systems, by virtue of utilizing very large number of antennas, have a potential to yield higher spectral and energy efficiency in comparison with the conventional MIMO systems. In this paper, we consider uplink channel estimation in massive MIMO-OFDM systems with frequency selective channels. With increased number of antennas, the channel estimation problem becomes very challenging as exceptionally large number of channel parameters have to be estimated. We propose an efficient distributed linear minimum mean square error (LMMSE) algorithm that can achieve near optimal channel estimates at very low complexity by exploiting the strong spatial correlations and symmetry of large antenna array elements. The proposed method involves solving a (fixed) reduced dimensional LMMSE problem at each antenna followed by a repetitive sharing of information through collaboration among neighboring antenna elements. To further enhance the channel estimates and/or reduce the number of reserved pilot tones, we propose a data-aided estimation technique that relies on finding a set of most reliable data carriers. We also analyse the effect of pilot contamination on the mean square error (MSE) performance of different channel estimation techniques. Unlike the conventional approaches, we use stochastic geometry to obtain analytical expression for interference variance (or power) across OFDM frequency tones and use it to derive the MSE expressions for different algorithms under both noise and pilot contaminated regimes. Simulation results validate our analysis and the near optimal MSE performance of proposed estimation algorithms.
2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE), 2015
Massive MIMO systems have made significant progress in increasing spectral and energy efficiency ... more Massive MIMO systems have made significant progress in increasing spectral and energy efficiency over traditional MIMO systems by exploiting large antenna arrays. In this paper we consider the joint maximum likelihood (ML) channel estimation and data detection problem for massive SIMO (single input multiple output) wireless systems. Despite the large number of unknown channel coefficients for massive SIMO systems, we improve an algorithm to achieve the exact ML non-coherent data detection with a low expected complexity. We show that the expected computational complexity of this algorithm is linear in the number of receive antennas and polynomial in channel coherence time. Simulation results show the performance gain of the optimal non-coherent data detection with a low computational complexity.
2015 23rd European Signal Processing Conference (EUSIPCO), 2015
We consider frequency selective channel estimation in the uplink of massive MIMO-OFDM systems, wh... more We consider frequency selective channel estimation in the uplink of massive MIMO-OFDM systems, where our major concern is complexity. A low complexity distributed LMMSE algorithm is proposed that attains near optimal channel impulse response (CIR) estimates from noisy observations at receive antenna array. In proposed method, every antenna estimates the CIRs of its neighborhood followed by recursive sharing of estimates with immediate neighbors. At each step, every antenna calculates the weighted average of shared estimates which converges to near optimal LMMSE solution. The simulation results validate the near optimal performance of proposed algorithm in terms of mean square error (MSE).
EURASIP Journal on Advances in Signal Processing, 2014
This paper investigates the joint maximum likelihood (ML) data detection and channel estimation p... more This paper investigates the joint maximum likelihood (ML) data detection and channel estimation problem for Alamouti space-time block-coded (STBC) orthogonal frequency-division multiplexing (OFDM) wireless systems. The joint ML estimation and data detection is generally considered a hard combinatorial optimization problem. We propose an efficient low-complexity algorithm based on branch-estimate-bound strategy that renders exact joint ML solution. However, the computational complexity of blind algorithm becomes critical at low signal-to-noise ratio (SNR) as the number of OFDM carriers and constellation size are increased especially in multiple-antenna systems. To overcome this problem, a semi-blind algorithm based on a new framework for reducing the complexity is proposed by relying on subcarrier reordering and decoding the carriers with different levels of confidence using a suitable reliability criterion. In addition, it is shown that by utilizing the inherent structure of Alamouti coding, the estimation performance improvement or the complexity reduction can be achieved. The proposed algorithms can reliably track the wireless Rayleigh fading channel without requiring any channel statistics. Simulation results presented against the perfect coherent detection demonstrate the effectiveness of blind and semi-blind algorithms over frequency-selective channels with different fading characteristics.
International Journal of Communication Systems, 2019
This paper presents link to system (L2S) interfacing technique for multiple input and multiple ou... more This paper presents link to system (L2S) interfacing technique for multiple input and multiple output (MIMO) iterative receivers. In L2S interfacing, usually the post detection signal to noise ratio (SNR)-based frame error rate lookup tables (LUT) are used to predict the link level performance of receivers. While L2S interfacing for linear MIMO receivers can be conveniently implemented, it is more challenging for MIMO iterative receivers due to unavailability of the closed form SNR expressions. In this paper, we propose three methods for post detection SNR estimation for MIMO iterative receivers. The first is based on the QR decomposition of the channel matrix, the second relies on the residual noise calculation based on the soft symbols, and the third exploits the closed form SNR expressions for linear receivers. A link to system interface model for iterative receivers is developed for evaluating the reference curves for different modulation and coding schemes, and results are validated by comparing the simulated and predicted frame error rates. It is shown that linear and residual noise-based SNR approximations result in a very good prediction performance whereas the performance of QR decomposition-based method degrades for higher order modulations and coding schemes. This paper presents link to system interfacing technique for MIMO iterative receivers. A link to system interface model for iterative receivers is developed for evaluating the reference curves for different modulation and coding schemes, and results are validated by comparing the simulated and predicted frame error rates. Three post detection SNR evaluation schemes have been proposed for link to system interfacing all of which give good prediction performance especially at lower order modulation. K E Y W O R D S capacity effective SNR mapping, exponential effective SNR mapping, iterative receiver, minimum mean square error filter, multiple input multiple output, mutual information effective SNR mapping