1
2. REVIEW OF LINEAR ALGEBRA
Lecture 1 Review:
Linear models have the form (in matrix notation)
Y = X + ,
where Y n1 response vector and Xnp is the model matrix (or design matrix) with one row for every subject and one column for every
regression paramet
5. MULTIVARIATE NORMAL DISTRIBUTION (Part II)
1
Lecture 4 Review:
Three denitions of normal random vectors:
1. Normal probability density function (p.d.f.),
2. Moment generating function,
3. Relationship with independent univariate normals.
5.1 Orthogonal
4. MULTIVARIATE NORMAL DISTRIBUTION (Part I)
1
Lecture 3 Review:
Random vectors: vectors of random variables.
The expectation of a random vector is just the vector of expectations.
cov(X, Y) is a matrix with i, j entry cov(Xi , Yj )
cov(AX, BY) = Acov(
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 49, NO. 5, MAY 2003
1073
Diversity and Multiplexing: A Fundamental Tradeoff
in Multiple-Antenna Channels
Lizhong Zheng, Member, IEEE, and David N. C. Tse, Member, IEEE
AbstractMultiple antennas can be used for
1
1
Diversity Multiplexing Tradeo (DMT)
1.1
DMT [1][Chapter 9,2]
Two benets can be achieved by the MIMO system:
Increased data rate (via an increase of the multiplexing gain)
Increased reliability (via an increase of diversity gain)
Data rate:
In the
