Analysis and Design of Channel Estimation in Multicell Multiuser MIMO OFDM Systems

This paper investigates the uplink transmission in multicell multiuser multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. The system model considers imperfect channel estimation, pilot contamination (PC), and multicarrier and multipath channels. Analytical expressions are first presented on the mean square error (MSE) of two classical channel estimation algorithms [i.e., least squares (LS) and minimum mean square error (MMSE)] in the presence of PC. Then, a simple H-infinity (H-inf) channel estimation approach is proposed to have good suppression to PC. This approach exploits the space-alternating generalized expectation–maximization (SAGE) iterative process to decompose the multicell multiuser MIMO (MU-MIMO) problem into a series of single-cell single-user single-input single-output (SISO) problems, which reduces the complexity significantly. According to the analytic results given herein, increasing the number of pilot subcarriers cannot mitigate PC, and a clue for suppressing PC is obtained. It is shown from the results that the H-inf has better suppression capability to PC than classical estimation algorithms. Its performance is close to that of the optimal MMSE as the length of channel impulse response (CIR) is increased. By using the SAGE process, the performance of the H-inf does not degrade when the number of antennas is large at the base station (BS)

Ayuda, Inc. v. Thornburgh: Did Congress Give the Executive Branch Free Rein to Define the Scope of Legislation

The Note argues that the Ayuda decision is inconsistent with the congressional intent behind IRCA and prior case law. The Note further argues that the purposes underlying IRCA will best be served by prompt judicial resolution of policy disputes about legalization

First detection of GeV emission from an ultraluminous infrared galaxy: Arp 220 as seen with the Fermi Large Area Telescope

Cosmic rays (CRs) in starburst galaxies produce high energy gamma-rays by colliding with the dense interstellar medium (ISM). Arp 220 is the nearest ultra luminous infrared galaxy (ULIRG) that has star-formation at extreme levels, so it has long been predicted to emit high-energy gamma-rays. However, no evidence of gamma-ray emission was found despite intense efforts of search. Here we report the discovery of high-energy gamma-ray emission above 200 MeV from Arp 220 at a confidence level of $\sim 6.3 \sigma$ using 7.5 years of \textsl {Fermi} Large Area Telescope observations. The gamma-ray emission shows no significant variability over the observation period and it is consistent with the quasi-linear scaling relation between the gamma-ray luminosity and total infrared luminosity for star-forming galaxies, suggesting that these gamma-rays arise from CR interactions. As the high density medium of Arp 220 makes it an ideal CR calorimeter, the gamma-ray luminosity can be used to measure the efficiency of powering CRs by supernova (SN) remnants given a known supernova rate in Arp 220. We find that this efficiency is about $4.2\pm2.6\%$ for CRs above 1 GeV.Comment: Accepted by ApJL, 6 pages, 3 figure

Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme

Joint extraction of entities and relations is an important task in information extraction. To tackle this problem, we firstly propose a novel tagging scheme that can convert the joint extraction task to a tagging problem. Then, based on our tagging scheme, we study different end-to-end models to extract entities and their relations directly, without identifying entities and relations separately. We conduct experiments on a public dataset produced by distant supervision method and the experimental results show that the tagging based methods are better than most of the existing pipelined and joint learning methods. What's more, the end-to-end model proposed in this paper, achieves the best results on the public dataset