CAJ | 학술논문

超宽带技术已逐渐成为无线通信领域研究、开发的一个热点,并被视为下一代无线通信的关键技术之一.UWB无线通信可以分成两种:无载波UWB、载波调制UWB.载波调制UWB有多种实现方案,主要有DS-CDMA UWB和MB-OFDM UWB.MB-OFDM作为UWB的一个重要候选方案,在频谱利用率、与其他窄带系统共存、抗干扰等方面有着出色的表现,而信道估计是无线通信领域的关键技术之一,是进行相关检测、解调、均衡的基础.文中在介绍MB-OFDM系统的构成、MB-OFDM系统信号结构以及超宽带信道模型的基础上,提出了一种基于前导训练系列的信道估计算法,对UWB信道条件下采用前导训练序列的LS估计和MMSE估计的性能进行了深入的分析和比较,最后给出了相关仿真结果.实验结果表明,利用前导训练系列的信道估计可以获得良好的性能.
초관대기술이축점성위무선통신영역연구、개발적일개열점,병피시위하일대무선통신적관건기술지일.UWB무선통신가이분성량충:무재파UWB、재파조제UWB.재파조제UWB유다충실현방안,주요유DS-CDMA UWB화MB-OFDM UWB.MB-OFDM작위UWB적일개중요후선방안,재빈보이용솔、여기타착대계통공존、항간우등방면유착출색적표현,이신도고계시무선통신영역적관건기술지일,시진행상관검측、해조、균형적기출.문중재개소MB-OFDM계통적구성、MB-OFDM계통신호결구이급초관대신도모형적기출상,제출료일충기우전도훈련계렬적신도고계산법,대UWB신도조건하채용전도훈련서렬적LS고계화MMSE고계적성능진행료심입적분석화비교,최후급출료상관방진결과.실험결과표명,이용전도훈련계렬적신도고계가이획득량호적성능.
Ultra wideband (UWB) is becoming a hotspot in wireless communication field research and development gradually. Moreover, it has been regarded as one of the key technologies in wire-less communication of the next generation. UWB wireless communication can be divided into two types: one is based on impulse radio technique, the other is based on carrier modulation. The latter has many various realization schemes, DS-CDMA and MB-OFDM being the mostly schemes. As an important candidate of schemes, MB-OFDM has excellent performance in many aspects, such as uti-lization rate of spectrum, coexistence with other narrowband system, interference rejection etc. Channel estimation is one of the key technologies in wireless communication field, which is the base of correlation detection, demodulation and equalization. After the analysis of architecture, signal and channel model of MB-OFDM UWB wireless systems, channel estimation based on preamble training symbols is introduced in this paper. The performances of channel estimation approaches based on preamble training sequences according to the criterions of LS and MMSE respectively are investigated and compared in detail. At last, some simulation results are given. It has been proved by some simulation results that the performance of channel estimation using preamble training sym-bols is very good.