SummaryThe omnipresence of drones in the civilian air space has led to their malicious usage raising high alert security issues. In this paper, a deep learning approach to detect and identify drones and to determine their flight modes... more
SummaryThe omnipresence of drones in the civilian air space has led to their malicious usage raising high alert security issues. In this paper, a deep learning approach to detect and identify drones and to determine their flight modes from the remotely sensed radio frequency (RF) signatures is presented. This work intends to detect the presence of drones using two‐class classification, the presence along with identification of their make using four‐class classification. And this is further extended to the determination of their flight modes using ten‐class classification. It employs the proposed architectures of prominent deep learning classifiers, namely, autoencoder (AE), long short‐term memory (LSTM), convolutional neural network (CNN), and CNN‐LSTM hybrid model. To procure the relevant information from 227 RF signatures having 100 fragments each, the seven significant temporal statistical features, namely, maxima, minima, mean, variance, skewness, kurtosis, and root mean square, are extracted. In a two‐class classification scenario, all considered classifiers perform near to idle, whereas in a four‐class classification scenario, CNN performs best, followed by AE, CNN‐LSTM, and LSTM, respectively. Moreover, in a ten‐class classification scenario, AE far outperforms CNN, followed by LSTM and CNN‐LSTM, respectively. The best performance in terms of accuracy and classification time confirms the feasibility of the proposed AE classifier for the three considered drone operations.
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Research Interests:
The paper presents an analytical framework for analysing multiple branch receive diversity systems for a Rayleigh fading channel. The performance of receive diversity system depends on the combining technique used to merge the signals... more
The paper presents an analytical framework for analysing multiple branch receive diversity systems for a Rayleigh fading channel. The performance of receive diversity system depends on the combining technique used to merge the signals received by the antenna elements. Among them, the most popular combining schemes are selection, equal gain, and maximal ratio. Some combining techniques outperform others under certain conditions, and order of implementation determines which method is preferred. This paper examines most popular combining techniques used for processing the signals received by the branches of a diversity system. An analytical expression for the probability density function of the signal to noise ratio at the output of a multiple branch selection combining, equal gain combining and maximal ratio combining are derived and analysed in this paper.
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A novel scheme ‘user assisted cooperative relaying in beamspace massive multiple input multiple output (M-MIMO) non-orthogonal multiple access (NOMA) system’ has been proposed to improve coverage area, spectrum and energy efficiency for... more
A novel scheme ‘user assisted cooperative relaying in beamspace massive multiple input multiple output (M-MIMO) non-orthogonal multiple access (NOMA) system’ has been proposed to improve coverage area, spectrum and energy efficiency for millimeter wave (mmWave) communications. A downlink system for M users, where base station (BS) is equipped with beamforming lens antenna structure having NRF radio frequency (RF) chains, has been considered. A dynamic cluster of users is formed within a beam and the intermediate users (in that cluster) between beam source and destination (user) act as relaying stations. By the use of successive interference cancellation (SIC) technique of NOMA within a cluster, the relaying stations relay the symbols with improved power to the destination. For maximizing achievable sum rate, transmit precoding and dynamic power allocation for both intra and inter beam power optimization are implemented. Simulations for performance evaluation are carried out to validate that the proposed system outperforms the conventional beamspace M-MIMO NOMA system for mmWave communications in terms of spectrum and energy efficiency.
Research Interests:
Research Interests:
SummaryThe omnipresence of drones in the civilian air space has led to their malicious usage raising high alert security issues. In this paper, a deep learning approach to detect and identify drones and to determine their flight modes... more
SummaryThe omnipresence of drones in the civilian air space has led to their malicious usage raising high alert security issues. In this paper, a deep learning approach to detect and identify drones and to determine their flight modes from the remotely sensed radio frequency (RF) signatures is presented. This work intends to detect the presence of drones using two‐class classification, the presence along with identification of their make using four‐class classification. And this is further extended to the determination of their flight modes using ten‐class classification. It employs the proposed architectures of prominent deep learning classifiers, namely, autoencoder (AE), long short‐term memory (LSTM), convolutional neural network (CNN), and CNN‐LSTM hybrid model. To procure the relevant information from 227 RF signatures having 100 fragments each, the seven significant temporal statistical features, namely, maxima, minima, mean, variance, skewness, kurtosis, and root mean square,...
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This paper investigates the effect of antenna array configurations on propagation environment in massive-multiple-input multiple-output (M-MIMO) cellular system with line of sight (LoS) channels. In this study, uniform linear array (ULA)... more
This paper investigates the effect of antenna array configurations on propagation environment in massive-multiple-input multiple-output (M-MIMO) cellular system with line of sight (LoS) channels. In this study, uniform linear array (ULA) for two-dimensional (2D) and uniform planar array (UPA) for three-dimensional (3D) antenna configurations using multi-cell minimum mean squared error (M-MMSE) detection technique are considered for the uplink scenario. For serving user equipments (UEs) particularly in the azimuth domain, the simulation results shows that the system deploying horizontal uniform linear array (HULA) outperforms in terms of spectrum efficiency (SE) among considered array configurations with the same number of antennas. It is observed that ULA favors the propagation to UEs distributed in the respective domain of 2D plane, and UPA performs better for 3D plane to capture the entire propagation environment than linear ones due to its capability of receiving incident waves from both elevation and azimuth domains.
Research Interests:
Research Interests:
The paper presents an analytical framework for analysing multiple branch receive diversity systems for a Rayleigh fading channel. The performance of receive diversity system depends on the combining technique used to merge the signals... more
The paper presents an analytical framework for analysing multiple branch receive diversity systems for a Rayleigh fading channel. The performance of receive diversity system depends on the combining technique used to merge the signals received by the antenna elements. Among them, the most popular combining schemes are selection, equal gain, and maximal ratio. Some combining techniques outperform others under certain conditions, and order of implementation determines which method is preferred. This paper examines most popular combining techniques used for processing the signals received by the branches of a diversity system. An analytical expression for the probability density function of the signal to noise ratio at the output of a multiple branch selection combining, equal gain combining and maximal ratio combining are derived and analysed in this paper.
Research Interests:
A novel scheme ‘user assisted cooperative relaying in beamspace massive multiple input multiple output (M-MIMO) non-orthogonal multiple access (NOMA) system’ has been proposed to improve coverage area, spectrum and energy efficiency for... more
A novel scheme ‘user assisted cooperative relaying in beamspace massive multiple input multiple output (M-MIMO) non-orthogonal multiple access (NOMA) system’ has been proposed to improve coverage area, spectrum and energy efficiency for millimeter wave (mmWave) communications. A downlink system for M users, where base station (BS) is equipped with beamforming lens antenna structure having NRF radio frequency (RF) chains, has been considered. A dynamic cluster of users is formed within a beam and the intermediate users (in that cluster) between beam source and destination (user) act as relaying stations. By the use of successive interference cancellation (SIC) technique of NOMA within a cluster, the relaying stations relay the symbols with improved power to the destination. For maximizing achievable sum rate, transmit precoding and dynamic power allocation for both intra and inter beam power optimization are implemented. Simulations for performance evaluation are carried out to validate that the proposed system outperforms the conventional beamspace M-MIMO NOMA system for mmWave communications in terms of spectrum and energy efficiency.
Research Interests:
Research Interests:
Research Interests:
In this paper, we combine the benefits of the diversity transmission and forward error correction coding that provides considerable bit error rate performance gain. Alamouti Space Time Block code for 2 transmit antennas with 1 receive... more
In this paper, we combine the benefits of the diversity transmission and forward error correction coding that provides considerable bit error rate performance gain. Alamouti Space Time Block code for 2 transmit antennas with 1 receive antenna and 2 transmit antennas with 2 receive antennas are explored by using BPSK and 16-QAM modulation schemes with Convolution coding. This paper investigates the Bit Error Rate performance comparison between MIMO and Alamouti using 2 transmit antennas and 2 receive antennas over Rayleigh fading channel by using Zero Forcing (ZF) and Minimum Mean Squared Error (MMSE) receivers.