Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Iterative Code-Aided ML Phase Estimation and Phase Ambiguity Resolution | EURASIP Journal on Applied Signal Processing 2005 6 981-988 2005 Hindawi Publishing Corporation Iterative Code-Aided ML Phase Estimation and Phase Ambiguity Resolution Henk Wymeersch Digital Communications Research Group Department of Telecommunications and Information Processing Ghent University Sint-Pietersnieuwstraat 41 9000 Ghent Belgium Email hwymeers@ Marc Moeneclaey Digital Communications Research Group Department of Telecommunications and Information Processing Ghent University Sint-Pietersnieuwstraat 41 9000 Ghent Belgium Email mm@ Received 29 September 2003 Revised 25 May 2004 As many coded systems operate at very low signal-to-noise ratios synchronization becomes a very difficult task. In many cases conventional algorithms will either require long training sequences or result in large BER degradations. By exploiting code properties these problems can be avoided. In this contribution we present several iterative maximum-likelihood ML algorithms for joint carrier phase estimation and ambiguity resolution. These algorithms operate on coded signals by accepting soft information from the MAP decoder. Issues of convergence and initialization are addressed in detail. Simulation results are presented for turbo codes and are compared to performance results of conventional algorithms. Performance comparisons are carried out in terms of BER performance and mean square estimation error MSEE . We show that the proposed algorithm reduces the MSEE and more importantly the BER degradation. Additionally phase ambiguity resolution can be performed without resorting to a pilot sequence thus improving the spectral efficiency. Keywords and phrases turbo synchronization phase estimation phase ambiguity resolution EM algorithm. 1. INTRODUCTION In packet-based communications frames arrive at the receiver with an unknown carrier phase. When phase estimation PE is performed by means of a conventional non-data-aided NDA algorithm 1 the resulting .
