Sabuj Sarkar

Bio: Sabuj Sarkar is an academic researcher from Khulna University of Engineering & Technology. The author has contributed to research in topics: MIMO & Rayleigh fading. The author has an hindex of 2, co-authored 8 publications receiving 16 citations.

An advanced detection technique in MIMO-PSK wireless communication systems using MMSE-SIC detection over a Rayleigh fading channel

Abstract: Efficient signal detection at the receiving end of multi antenna wireless communication is a major challenge in presence of channel fading, interference & noise phenomena. Combination of multiple input multiple output (MIMO) with phase shift keying (PSK) termed as (MIMO-PSK) modulation achieves significant transmission capacity and link quality through spatial diversity and multiplexing. This paper mainly focuses on detection algorithms and by incorporating well-known minimum mean square error successive interference cancellation (MMSE-SIC), proposes an advanced MMSE-SIC detection technique. Via a series of MATLAB simulations it is demonstrated that the proposed detection technique outperforms the conventional zero forcing, MMSE and even that of maximum likelihood detections by further reducing bit error rate without adding substantial system complexity.

Bit error rate improvement for QPSK modulation technique in a MIMO rayleigh fading channel by maximum likelihood equalization

Abstract: Multiple Input Multiple Output (MIMO) systems have recently emerged as one of the most promising technology in wireless communication systems that give tremendous advancement in achieving high data rates and improved system performance In this paper, we discussed about the receiver structure called Maximum Likelihood (ML) Equalization and simulated this structure in a Rayleigh fading channel with QPSK modulation Depending on bit error rates, we compared the performances of MIMO system for different combinations of transmit-receive structure Simulation results showed that the BER can be improved by ML receiver for 2×4 MIMO Rayleigh fading channel than other lower order combinations

A Novel Method for Optimizing Power Efficiency of a Solar Photovoltaic Device

Abstract: Most recently, photovoltaic energy has made an incredible technological advancement for the forthcoming decades towards mitigating the ever-increasing energy demand worldwide through generating electric power. Present paper proposes a novel solar photovoltaic (SPV) device model that achieves optimal power efficiency from simulation and graphical performance analysis of SPV device characteristics. First of all, power as well as current performances is compared for varying irradiance and temperatures circumstances. Then, output current characteristics of the SPV device for the proposed as well as existing model with variable temperatures is plotted. Later, power versus voltage performances of a SPV device for the proposed model with varying irradiance and temperature criterions is compared. Finally, power–voltage characteristics are plotted graphically for the existing as well as proposed SPV device model that achieves significant amount of output power for the proposed model than the existing model and optimal power efficiency is obtained for the novel SPV device model.

Optimizing channel capacity in a MIMO–PSK wireless rayleigh fading channel

Abstract: Due to the continual advancement of future generation communication systems, channel capacity is considered to be an important parameter for achieving greater signal strength by means of faster data transmission speed. Usually in multiple input multiple output (MIMO) systems, the diversity technique lessens the propagation speed significantly than the channel capacity. Using MIMO in combination with phase shift keying (PSK) modulation jointly known as MIMO–PSK systems, the present paper focuses on a novel distribution method in order to optimize the channel capacity over a Rayleigh fading channel. First, ergodic channel capacity and distribution capacity in terms of outage capacity is simulated for different MIMO–PSK configurations as well as channel conditions. Later, the channel performance of MIMO–PSK for the novel distribution conditions with antenna correlation effect is evaluated and finally the optimal channel capacity is determined.

On achieving optimal channel capacity of MIMO-PSK systems over Rayleigh fading channel

Abstract: Continuous demand of higher bandwidth is an emerging topic in high speed wireless multiple input multiple output (MIMO) systems. Channel capacity is an important paradigm for achieving higher bandwidth as well as higher data rates. This paper mainly focuses on ergodic channel capacity as well as outage capacity of wireless MIMO combined with phase shift keying (PSK) systems over Rayleigh fading channels. First, ergodic channel capacity and distribution capacity in terms of outage channel capacity is analyzed for variable MIMO-PSK systems without knowing the transmitter channel state information (CSI). Finally, joint MIMO-PSK channel capacity in the presence of antenna correlation effect is estimated and consequently the antenna distribution that optimizes the channel capacity is determined.

Signal detection of MIMO-OFDM system based on auto encoder and extreme learning machine

Abstract: In this paper, we address the problem of signal detection in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system by using auto-encoder (AE) network and extreme learning machine (ELM). The existing signal detection algorithms, such as zero-forcing successive-interference-cancellation (ZF-SIC), minimum-mean-square-error successive-interference-cancellation (MMSE-SIC), maximum likelihood detection (MLD) and quantum-genetic radial-basis-function (QGA-RBF) etc., haven't considered the characteristics invariance of signals in the process of transmission. Combined AE network with ELM, a novel signal detection scheme for MIMO-OFDM system is proposed. The proposed algorithm can obtain the features of received signals effectively through AE and recognize the corresponding original signals quickly via ELM. Moreover, the channel matrix is not required in the process of signal detection. We have derived a theoretically model and analyze the feasibility of feature extraction in received signals, and simulations are also carried out to evaluate the performance and compare that with some traditional and state-of-the-art algorithms. The simulation results confirm that the performance of the proposed scheme outperforms that of many detection schemes such as zero-forcing (ZF), ZF-SIC, minimum-mean-square-error (MMSE), MMSE-SIC, and reaches the similar bit-error-rate (BER) performance of MLD and QGA-RBF with much lower complexity.

Efficient MIMO Detection with Imperfect Channel Knowledge - A Deep Learning Approach

Abstract: Multiple-input multiple-output (MIMO) system is the key technology for long term evolution (LTE) and 5G. The information detection problem at the receiver side is in general difficult due to the imbalance of decoding complexity and decoding accuracy within conventional methods. Hence, a deep learning based efficient MIMO detection approach is proposed in this paper. In our work, we use a neural network to directly get a mapping function of received signals, channel matrix and transmitted bit streams. Then, we compare the end-to-end approach using deep learning with the conventional methods in possession of perfect channel knowledge and imperfect channel knowledge. Simulation results show that our method presents a better trade-off in the performance for accuracy versus decoding complexity. At the same time, better robustness can be achieved in condition of imperfect channel knowledge compared with conventional algorithms.

Moving Toward Intelligence: Detecting Symbols on 5G Systems Through Deep Echo State Network

Abstract: Due to the nonlinear distortion caused by radio-frequency (RF) components in the transceiver, detecting transmitted symbols for multiple-input and multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems can be challenging and resource consuming. In this work, we introduce a Deep Echo State Network (DESN) to serve as the symbol detector for 5G communication networks. Our DESN employs memristive synapses as the dynamic reservoir layer to accelerate the learning algorithm and computation. By cascading multiple dynamic reservoir layers in a hierarchical processing structure, our DESN processes received signal in both spatial and temporal domains. The resulting hybrid memristor-CMOS co-design provides the nonlinear computation required by the reservoir layer while significantly reduces the power consumption. From the benchmark on nonlinear system prediction, our DESN exhibits 10.31 X reduction on the prediction error compared to state-of-the-art neural network designs. Moreover, our DESN records a bit error rate (BER) of $5.76 \times 10^{-2}$ on the high-speed transmitted symbol detection task for MIMO-OFDM systems, yielding 47.73% more precise than state-of-the-art techniques in the literate for 5G communication networks.

Photovoltaic Review of all Generations: Environmental Impact and Its Market Potential

Abstract: With the technology and innovation rising at its peak, the demand for energy has increased exponentially To cater this, researchers are persistently exploring ways to fulfil this deficit between demand and supply One of the feasible solutions is the use of energy from renewable resources such as Solar Energy due to its abundance availability and easy accessibility Seeing it’s trans formative potential to address growing concerns about environment, pollution and sustainable energy integration, there is an intemperate research going on in developing highly efficient Photovoltaic Cells (PVC) The PVC’s are effectuated to convert solar energy from the sunlight directly to electrical energy Furthermore, the PVC has gone through various generations with the aim to optimise its cost/watt of delivered solar electricity and efficiency of solar cell This paper is an effort to compare all the generations which PVC has undergone and the recent advancements in this area The results of this research study will be fruitful for researchers working in this direction

Assessment of Meta-Heuristic and Classical Methods for GMPPT of PV System

Abstract: Numerous global peak searching mechanisms have been proposed to solve the problem of energy loss due to partial shading of solar photovoltaic (PV) plants but still there is a great need for an efficient and fast global maximum power point tracker (GMPPT). A detailed review of GMPPT based on various meta-heuristic and classical methods along with the basics of partial shading phenomenon, proper positioning of the PV panels, distributed MPPT, Field MPPT etc. will be of great help to the researchers. Till date, it looks like that not a single literature has covered these issues at a single place and thus, this article fills this gap by providing a detailed assessment, tabulated gist of the various GMPPT along with the basics of key issues related to partial shading of PV plants. Various classical and bio-inspired meta-heuristic based GMPPT methods have been compared in this literature. It is expected that this paper will prove to be a valuable asset and a complete reference for the academicians and professionals for further research and proper selection of a GMPPT technique.