文件名称：MIMO Channels and Space-Time Coding lectures
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更新时间：2013-09-09 00:52:08
MIMO， Space-Time Coding， lectures
Lecture 1: Channel Capacity In this lecture we review the Shannon capacity of an additive white Gaussian noise channel, the capacity theorem and spectral efficiency and power efficiencies possible. • Lecture 2: MIMO Channel Capacity We then extend the capacity to parallel channels and study the optimal power distribution, which leads to the waterfilling theorem. Next the multiple-input multiple-ouput (MIMO) channel is considered and related to the parallel Gaussian channel via the singular value decomposition (SVD). Its capacity and equal power (symmetric) capacity are discussed. Finally the capacity fromula is extended to fading MIMO channels. • Lecture 3: Real-World MIMO channels We study different types of MIMO channels, their rank and capacity gain. Examples are studied by emulating the propagation environment of MIMO channels. It is argued and demonstrated that in the low signal-to-noise ratio regime, MIMO channels offers no capacity advantage over single point-to-point channels. • Lecture 4: Linear Multiantenna Processing We discuss linear processing for multiple-antenna channels and beam-forming. The multiple signal classification (MUSIC) algorithm is introduced as the predominant sub-space method for parameter identification. Projection beam-formers and minimum-mean square error (MMSE) beam-formers are then discussed as well as the minimum-variance distortionless beam-former. • Lecture