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RISS 인기검색어
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- 저자 : Abbas Ali Sharifi (검색결과 6 건)
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Discrete Hartley matrix transform precoding-based OFDM system to reduce the high PAPR
Abbas Ali Sharifi 한국통신학회 2019 ICT Express Vol.5 No.2
The high peak-to-average power ratio (PAPR) is one of the major drawbacks of the orthogonal frequency division multiplexing (OFDM) systems that make non-linear distortion in practical implementation of high power amplifier (HPA). In this study, the performance of discrete Hartley matrix transform (DHMT) in precoding based OFDM system is analyzed to minimize the high PAPR. We demonstrate that the DHMT precoding method eliminates the multicarrier assumption in a special circumstance. The remarkable results are achieved and compared with conventional OFDM, Walsh–Hadamard matrix transform (WHMT), discrete cosine matrix transform (DCMT) and Vandermonde-like matrix transform (VLMT) in precoding based OFDM signals.
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PAPR reduction of optical OFDM signals in visible light communications
Abbas Ali Sharifi 한국통신학회 2019 ICT Express Vol.5 No.3
Orthogonal frequency division multiplexing (OFDM) is extensively used in optical communications to achieve high rate transmission. In this paper, Vandermonde like matrix (VLM) pre-coding approach is offered to reduce the high peak-to-average power ratio (PAPR) of DC-biased optical OFDM (DCO-OFDM) and asymmetrically clipped optical OFDM (ACO-OFDM) signals in visible light communications. The proposed method is compared with Walsh–Hadamard transform (WHT), discrete cosine transform (DCT), and discrete Hartley transform (DHT) pre-coding approaches in terms of PAPR reduction performance. Simulation results indicate that the proposed method effectively reduces the PAPR of an optical signal in both DCO-OFDM and ACO-OFDM techniques.
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Ghanbar Azarnia,Abbas Ali Sharifi,Hojjat Emami 한국통신학회 2020 ICT Express Vol.6 No.4
Orthogonal frequency division multiplexing (OFDM) has been proposed to achieve high data rate transmission in wireless communications. The OFDM system usually suffers from the high envelope fluctuations called peak-to-average power ratio (PAPR). The high PAPR causes a signal clipping distortion, and consequently, the performance is degraded. To mitigate the PAPR, we introduce a new PAPR reduction approach using a compressive sensing approach at the transmitter side and an orthogonal matching pursuit (OMP) reconstruction algorithm at the receiver end. Numerical results show the significant reduction of the PAPR compared with the traditional OFDM system without degrading the bit error rate performance.
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An improved backtracking search optimization algorithm for cubic metric reduction of OFDM signals
Hojjat Emami,Abbas Ali Sharifi 한국통신학회 2020 ICT Express Vol.6 No.3
The large amplitude variations of OFDM signals generate in-band distortion and out-of-band radiation. In recent years, cubic metric (CM) has been verified as a more accurate metric to measure the amplitude variations. In this paper, the PTS technique is used to decrease the CM of OFDM signals. To overcome the search complexity of an exhaustive search based PTS technique, we introduce an improved backtracking search (IBS) optimization algorithm. Simulations are conducted to show the advantages of the proposed IBS based PTS approach compared with the conventional OFDM, and several state-of-the-art methods in terms of search complexity and CM reduction performance.
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PAPR reduction in OFDM systems: An efficient PTS approach based on particle swarm optimization
Mehdi Hosseinzadeh Aghdama,Abbas Ali Sharifi 한국통신학회 2019 ICT Express Vol.5 No.3
Orthogonal frequency division multiplexing (OFDM) is a multicarrier modulation technique for high-speed data transmission in wireless communications. In an OFDM system, a large number of subcarriers are used to transmit the modulated symbols and consequently, the OFDM signals have a high peak-to-average power ratio (PAPR). To reduce the high PAPR, we propose a partial transmit sequence (PTS) method based on the adaptive particle swarm optimization. Also, the proposed method efficiently search the optimal combination of phase rotation factors to decrease the computational complexity. Experimental results show that the proposed method significantly has reduced the PAPR and computational complexity.
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Kazem Khalagi,Mohammad Ali Mansournia,Afarin Rahimi-Movaghar,Keramat Nourijelyani,Masoumeh Amin-Esmaeili,Ahmad Hajebi,Vandad Sharifi,Reza Radgoodarzi,Mitra Hefazi,Abbas Motevalian 한국역학회 2016 Epidemiology and Health Vol.38 No.-
Latent class analysis (LCA) is a method of assessing and correcting measurement error in surveys. The local independence assumption in LCA assumes that indicators are independent from each other condition on the latent variable. Violation of this assumption leads to unreliable results. We explored this issue by using LCA to estimate the prevalence of illicit drug use in the Iranian Mental Health Survey. The following three indicators were included in the LCA models: five or more instances of using any illicit drug in the past 12 months (indicator A), any use of any illicit drug in the past 12 months (indicator B), and the self-perceived need of treatment services or having received treatment for a substance use disorder in the past 12 months (indicator C). Gender was also used in all LCA models as a grouping variable. One LCA model using indicators A and B, as well as 10 different LCA models using indicators A, B, and C, were fitted to the data. The three models that had the best fit to the data included the following correlations between indicators: (AC and AB), (AC), and (AC, BC, and AB). The estimated prevalence of illicit drug use based on these three models was 28.9%, 6.2% and 42.2%, respectively. None of these models completely controlled for violation of the local independence assumption. In order to perform unbiased estimations using the LCA approach, the factors violating the local independence assumption (behaviorally correlated error, bivocality, and latent heterogeneity) should be completely taken into account in all models using well-known methods.
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