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학술지 Constellation Design for Bit-Interleaved Coded Modulation (BICM) Systems in Advanced Broadcast Standards
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저자
Jon Barrueco, Jon Montalban, Cristina Regueiro, Mnuel Velez, 김흥묵, 박성익
발행일
201712
출처
IEEE Transactions on Broadcasting, v.63 no.4, pp.603-614
ISSN
0018-9316
출판사
IEEE
DOI
https://dx.doi.org/10.1109/TBC.2017.2677259
협약과제
17HR2100, 초고품질 UHD (UHQ) 전송 기술 개발, 허남호
초록
This paper presents a generic methodology to optimize constellations based on their geometrical shaping for bit-interleaved coded modulation (BICM) systems. While the method can be applicable to any wireless standard design it has been tailored to two delivery scenarios typical of broadcast systems: 1) robust multimedia delivery and 2) UHDTV quality bitrate services. The design process is based on maximizing the BICM channel capacity for a given power constraint. The major contribution of this paper is a low complexity optimization algorithm for the design of optimal constellation schemes. The proposal consists of a set of initial conditions for a particle swarm optimization algorithm, and afterward, a customized post processing procedure for further improving the constellation alphabet. According to the broadcast application cases, the sizes of the constellations proposed range from 16 to 4096 symbols. The BICM channel capacities and performance of the designed constellations are compared to conventional quadrature amplitude modulation constellations for different application scenarios. The results show a significant improvement in terms of system performance and BICM channel capacities under additive white Gaussian noise and Rayleigh independently and identically distributed channel conditions.
키워드
BICM channel capacity, coding and modulation, constellation design, non uniform constellation, QAM, signal shaping processing for transmission
KSP 제안 키워드
Additive white Gaussian noise(AWGN), Application scenarios, Broadcast systems, Channel capacity, Coding and Modulation, Design process, Initial conditions, Multimedia Delivery, Non Uniform Constellation, Particle swarm optimization algorithm, Post-Processing