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Author

Arif Azad

Bio: Arif Azad is an academic researcher. The author has contributed to research in topics: Communication channel & Multipath propagation. The author has an hindex of 2, co-authored 2 publications receiving 4 citations.

Papers
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Journal Article
TL;DR: In this paper, the authors used pilot sequences to estimate the time-varying channel frequency response for the OFDM symbols in a mobile wireless channel with low complexity, which is a useful channel estimation technique for accurate estimation of the transmitted information.
Abstract: In modern world error free transmission is one of the main aims in wireless communications. As the increase in multimedia application, bulky data is being transmitted over wireless communications. Due to the effect of channel fading and the Doppler shifts caused by user mobility, a common problem in the wireless systems, additional technologies are needed to be combat multipath propagation fading and Doppler shifts. The time-variant channel estimation is one of such a crucial technique used to improve the performance of the modern wireless systems with Doppler spread and the multipath spreading. Channel estimation is usually done by estimating the time-varying channel frequency response for the OFDM symbols. . In order to reduce complexity of MIMO system, various detection algorithm such as Zero Forcing (ZF), Minimum Mean Square Error (MMSE), Maximum Likelihood (ML) and LMMSE is studied that reduce Bit Error Rate (BER) by using spatial multiplexing. In time-variant channel estimations using the pilot Sequences technique is a useful channel estimation techniques in mobile wireless communication for accurately estimation of the transmitted information. The main advantage of pilot sequences is to allowing the more accurate representation of the high mobility mobile wireless channels with low complexity. The main goal is to test the most recent proposed method, time-variant estimation of the channel using pilot sequences.

2 citations

Journal Article
TL;DR: In this paper antenna 2x2 configuration is used and a Novel algorithm namely ALMMSE is proposed that reduce Bit Error Rate (BER) by using spatial multiplexing and Signal to Noise Ratio (SNR) curve of equalizer exceeds that of ZF, ZFSIC, ZF-SIC-OO, MMSE , MMSE-S IC, MMSA, ML, LS and AlMMSE equalizer.
Abstract: Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) systems have emerged as wide application technology in wireless communication systems for increasing data rate and the system performance. The effect of fading & interference can be reduced to increase the capacity of the link. MIMO systems uses multiple (input) Transmit and multiple (output) Receive antennas which exploit the multipath propagation in the rich scattering environment. The matrix channel plays very pivotal role in the throughput of a MIMO link since the modulation, data rate, power allocation & the antenna weights are the various dependent parameters on the channel gain. When data rate is been transmitted at high bit rate, the channel impulse response can be extended over many symbol periods which leads to Inter-Symbol Interference (ISI). ISI always causing an issue for signal recovery in wireless communication. In order to reduce complexity of MIMO system, various detection algorithm such as Zero Forcing (Z

2 citations


Cited by
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Proceedings ArticleDOI
22 Mar 2017
TL;DR: Estimating the channel parameters for conventional MIMO and massive MIMo based on training-based and blind channel estimation techniques wherein the performance of both is compared is compared.
Abstract: Multiple-input multiple-output (MIMO) technology is becoming mature in wireless communication systems. It has led to third and fourth generation wireless systems, which has been providing good range, reliability and higher data rates. For the increased demand of much higher data rates, coverage, spectral efficiency, capacity and reduced latency, the evolution of the next generation i.e., the fifth generation technology is necessary. Massive MIMO technology is one of the most promising solution for the above-mentioned challenge. In massive MIMO, the base station is incorporated with hundreds to thousands of antenna array wherein the degrees of freedom can be exploited and the energy can be efficiently used due to the fact that the extra antennas at the base station helps focus the energy into the smaller regions of space. To reap the benefits provided by the extra antennas, the channel information is necessary which makes it possible to have a reliable communication. Therefore, to acquire the channel knowledge, channel state information is required at the base station and estimating the channel parameters plays an important role. In this paper, we concentrate on estimating the channel parameters for conventional MIMO and massive MIMO based on training-based and blind channel estimation techniques wherein the performance of both is compared.

19 citations

Journal Article
TL;DR: In this paper antenna configuration is used and QPSK modulation is treated here for simulation purpose, and signal-noise ratio (SNR) curve of Constant modulus algorithm(CMA) equalizer exceeds that of ZF, MMSE and ML equalizer.
Abstract: Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing(OFDM) systems have recently emerged as key technology in wireless communication systems for increasing data rate and system performance. The effect of fading and interference can be combated to increase the capacity of the link. MIMO systems uses Multiple Transmit and Multiple Receive antennas which exploit the multipath propagation in rich scattering environment. The matrix channel plays a pivotal role in the throughput of a MIMO link since the modulation, data rate, power allocation and antenna weights are dependent on the channel gain. When data rate is transmitted at high bit rate, the channel impulse response can extend over many symbol periods which leads to Inter-Symbol Interference(ISI). ISI always caused an issue for signal recovery in wireless communication. In order to reduce the complexity of MIMO system, various detection algorithm such as Zero forcing(ZF), Minimum Mean Square Error(MMSE), Maximum Likelihood(ML) and a novel algorithm namely Constant Modulus Algorithm(CMA) are proposed that reduce bit error rate(BER) via spatial multiplexing. QPSK modulation is treated here for simulation purpose.Simulations are done by MatLab that shows BER vs. signal-noise ratio (SNR) curve of Constant modulus algorithm(CMA) equalizer exceeds that of ZF, MMSE and ML equalizer. In this paper antenna configuration is used.

2 citations

Proceedings ArticleDOI
12 Apr 2019
TL;DR: This work proposes and study a new method based on μ-Law companding type and Polar Codes to reduce PAPR values in a MIMO-OFDM system that operates with less complexity and outperforms the most well-known methods.
Abstract: MIMO-OFDM is a method adopted by new highspeed communications technologies such as IEEE802.11, IEEE 802.16 and 4G. Among the advantages of MIMO-OFDM, its ability to increase the transmission rate, and its adaptation to multipath channel with fading. Unfortunately the signals modulated by the OFDM technique generate high values of Peak-to-Average Power Ratio (PAPR). To solve this major drawback, several techniques have been proposed in literature. Among others, we find those that combine coding and companding. In this context we propose and study a new method based on μ-Law companding type and Polar Codes to reduce PAPR values in a MIMO-OFDM system. In order to evaluate the performances of our method in terms of PAPR and BER, several simulations are performed taking into consideration parameters related to coding and companding rates, and the number of sub carriers. The plotted curves show that the results are very significant; indeed, firstly our method achieves a gain of 8 dB in terms of PAPR by ensuring a good compromise in term of BER thanks to the use of the Polar Codes. Secondly our method operates with less complexity and outperforms the most well-known methods.

1 citations

Book ChapterDOI
01 Jan 2022
TL;DR: In this article, the BER performance and outage capacity of MIMO and massive mIMO on Rayleigh, Rician and Nakagami channels are compared. And the performance analysis between MIMI and Massive MIMOs is analyzed with different channels corresponding to the output and result obtained.
Abstract: Multiple Input Multiple Output (MIMO) provides an environment in wireless communication that offers simple antennas and resource allocation is simplified utilizing Time Division multiplexing. MIMO overcomes bandwidth restrictions with its channel state information and multiple antennas. Massive MIMO uses large-scale antenna systems and are capable of improving the channel capability. Extra antennas improve the throughput and energy efficiency. The performance analysis between MIMO and Massive MIMO is analyzed with different channels corresponding to the output and result obtained. In this paper, the BER performance and outage capacity of MIMO and Massive MIMO on Rayleigh, Rician and Nakagami channels are compared.

1 citations