ML-Based Blind Frequency Offset Estimation Schemes for OFDM Systems in Non-Gaussian Noise Environments

This paper proposes frequency offset (FO) estimation schemes robust to the non-Gaussian noise for orthogonal frequency division multiplexing (OFDM) systems. A maximum-likelihood (ML) scheme and a low-complexity estimation scheme are proposed by applying the probability density function of the cyclic prefix of OFDM symbols to the ML criterion. From simulation results, it is confirmed that the proposed schemes offer a significant FO estimation performance improvement over the conventional estimation scheme in non-Gaussian noise environments

ML-Based Blind Frequency Offset Estimation Schemes for OFDM Systems in Non-Gaussian Noise Environments

This paper proposes frequency offset (FO) estimation schemes robust to the non-Gaussian noise for orthogonal frequency division multiplexing (OFDM) systems. A maximum-likelihood (ML) scheme and a low-complexity estimation scheme are proposed by applying the probability density function of the cyclic prefix of OFDM symbols to the ML criterion. From simulation results, it is confirmed that the proposed schemes offer a significant FO estimation performance improvement over the conventional estimation scheme in non-Gaussian noise environments

ML-Based Blind Frequency Offset Estimation Schemes for OFDM Systems in Non-Gaussian Noise Environments

This paper proposes frequency offset (FO) estimation schemes robust to the non-Gaussian noise for orthogonal frequency division multiplexing (OFDM) systems. A maximum-likelihood (ML) scheme and a low-complexity estimation scheme are proposed by applying the probability density function of the cyclic prefix of OFDM symbols to the ML criterion. From simulation results, it is confirmed that the proposed schemes offer a significant FO estimation performance improvement over the conventional estimation scheme in non-Gaussian noise environments

ML-Based Blind Frequency Offset Estimation Schemes for OFDM Systems in Non-Gaussian Noise Environments

This paper proposes frequency offset (FO) estimation schemes robust to the non-Gaussian noise for orthogonal frequency division multiplexing (OFDM) systems. A maximum-likelihood (ML) scheme and a low-complexity estimation scheme are proposed by applying the probability density function of the cyclic prefix of OFDM symbols to the ML criterion. From simulation results, it is confirmed that the proposed schemes offer a significant FO estimation performance improvement over the conventional estimation scheme in non-Gaussian noise environments

ML-Based Blind Frequency Offset Estimation Schemes for OFDM Systems in Non-Gaussian Noise Environments

This paper proposes frequency offset (FO) estimation schemes robust to the non-Gaussian noise for orthogonal frequency division multiplexing (OFDM) systems. A maximum-likelihood (ML) scheme and a low-complexity estimation scheme are proposed by applying the probability density function of the cyclic prefix of OFDM symbols to the ML criterion. From simulation results, it is confirmed that the proposed schemes offer a significant FO estimation performance improvement over the conventional estimation scheme in non-Gaussian noise environments

The ice composition in the disk around V883 Ori revealed by its stellar outburst

Complex organic molecules (COMs), which are the seeds of prebiotic material and precursors of amino acids and sugars, form in the icy mantles of circumstellar dust grains but cannot be detected remotely unless they are heated and released to the gas phase. Around solar-mass stars, water and COMs only sublimate in the inner few au of the disk, making them extremely difficult to spatially resolve and study. Sudden increases in the luminosity of the central star will quickly expand the sublimation front (so-called snow line) to larger radii, as seen previously in the FU Ori outburst of the young star V883 Ori. In this paper, we take advantage of the rapid increase in disk temperature of V883 Ori to detect and analyze five different COMs, methanol, acetone, acetonitrile, acetaldehyde, and methyl formate, in spatially-resolved submillimeter observations. The COMs abundances in V883 Ori is in reasonable agreement with cometary values. This result suggests that outbursting young stars can provide a special opportunity to study the ice composition of material directly related to planet formation.Comment: Published in Nature Astronom

Evidence of Accretion Burst: The Viscously Heated Inner Disk of the Embedded Protostar IRAS 16316-1540

Outbursts of young stellar objects occur when the mass accretion rate suddenly increases. However, such outbursts are difficult to detect for deeply embedded protostars due to their thick envelope and the rarity of outbursts. The near-IR spectroscopy is a useful tool to identify ongoing outburst candidates by the characteristic absorption features that indicate a disk origin. However, without high-resolution spectroscopy, the spectra of outburst candidates can be confused with the late-type stars since they have similar spectral features. For the protostar IRAS 16316-1540, the near-IR spectrum has line equivalent widths that are consistent with M-dwarf photospheres. However, our high-resolution IGRINS spectra reveal that the absorption lines have boxy and/or double-peaked profiles, as expected from a disk and not the star. The continuum emission source is likely the hot, optically thick disk, heated by viscous accretion. The projected disk rotation velocity of 41 +/- 5 km s(-1) corresponds to similar to 0.1 au. Based on the result, we suggest IRAS 16316-1540 as an ongoing outburst candidate. Viscous heating of disks is usually interpreted as evidence for ongoing bursts, which may be more common than previously estimated from low-resolution near-IR spectra

ML-Based Blind Frequency Offset Estimation Schemes for OFDM Systems in Non-Gaussian Noise Environments

This paper proposes frequency offset (FO) estimation schemes robust to the non-Gaussian noise for orthogonal frequency division multiplexing (OFDM) systems. A maximum-likelihood (ML) scheme and a low-complexity estimation scheme are proposed by applying the probability density function of the cyclic prefix of OFDM symbols to the ML criterion. From simulation results, it is confirmed that the proposed schemes offer a significant FO estimation performance improvement over the conventional estimation scheme in non-Gaussian noise environments