MIMO Multiple Input Multiple Output Communications On the

MIMO Multiple Input Multiple Output Communications “On the Capacity of Radio Communication Systems with Diversity in a Rayleigh Fading Environment” IEEE Journal on Selected Areas in Communication SAC-5, NO. 5, JUNE 1987 © Omar Ahmad Prepared for Advanced Wireless Networks, Spring 2006 VOL.

Part 1 • An Intuition of SISO MISO and MIMO • A Look at the Channel Capacity

An Intuition SISO Single Input Single Output Disclaimer: This Intuition is incomplete with respect to how communication signals are actually analyzed Forget about noise for now and the frequency domain transformation. Assume we have an antenna, which transmits a signal x at a frequency f. As the signal propagates through an environment, the signal is faded, which is modeled as a multiplicative coefficient h. The received signal y will be hx. x 1 y 1 = h 1 x 1 fading h 1 transmit receive

An Intuition SIMO Single Input Multiple Output Now assume we have two receiving antennas. There will be two received signals y 1 and y 2 with different fading coefficients h 1 and h 2. The effect upon the signal x for a given path (from a transmit antenna to a receive antenna) is called a channel. The channel capacity has not increased The multiple receive antennas can help us get a stronger signal through diversity x 1 g din h 2 fa fading h 1 transmit y 2 = h 2 x 1 y 1 = h 1 x 1 receive

An Intuition MISO Multiple Input Single Output Assume 2 transmitting antennas and 1 receive antenna. There Time 1 Time 2 x 2 -x 1* will be one received signal y 1 (sum of x 1 h 1 and x 2 h 2). In order to separate x 1 and x 2 we will need to also transmit, at a different time, -x 1* and x 2*. The channel capacity has not really increased because we still have to transmit -x 1* and x 2* at time 2. (Alamouti scheme) fading h 2 Time 1 x 1 y 1 = h 1 x 1+ h 2 x 2 Time 2 x 2 * g fadin transmit h 1 y 2 = h 1 x 2*+ h 2 -x 1* receive

An Intuition MIMO Multiple Input Multiple Output With 2 transmitting antennas and 2 receiving antennas, we actually add a degree of freedom! Its quite simple and intuitive. However, in this simple model, we are assuming that the h coefficients of fading are independent, and uncorrelated. If they are correlated, we will have a hard time finding an approximation for the inverse of H. In practical terms, this means that we cannot recover x 1 and x 2. x 1 y 1 fading h 1 d fa x 2 fa y 1 = h 1 x 1+ h 2 x 2 ng i din g h 3 h y 2 = h 3 x 1+ h 4 x 2 y 2 2 y = Hx + w fading h 4 transmit Finally Assume there is some white Gaussian Noise, and we have a set of linear equations receive All 2 degrees of freedom are being utilized in the MIMO case, giving us Spatial Multiplexing.

A Look at the Channel Capacity x 1 y 1 fading h 1 d fa x 2 fa ng i din g Once again, the time invariant MIMO channel is described by h 3 y = Hx + w h y 2 2 fading h 4 transmit receive H, the channel matrix, is assumed to be constant, and known to both transmitter and receiver. From basic linear algebra, every linear transformation (i. e. , H applied to x) can be decomposed into a rotation, scale, and another rotation (SVD) H=

A Look a the Channel Capacity U and V are unitary (rotation) matrices. Is a diagonal matrix whose elements: are the ordered singular values of the matrix H. The SVD can be rewritten as We then Define And rewrite the channel y = Hx + w as or equivalently

A Look at the Channel Capacity This expression looks VERY similar to something we should know how to calculate the channel capacity of very easily! That is, Parallel Additive Gaussian Channels where the channels are separated by time: By information theory, we know the noise capacity to be for parallel Gaussian Channels to be So for the case of MIMO, the spatial dimension plays the role of time. The capacity is now

A Look at the Channel Capacity So what else does this mean? Each eigenvalue Corresponds to an eigenmode of the channel (also called an eigen-channel) Each non-zero eigen-channel can support a data stream; thus, the capacity of MIMO depends upon the rank of the channel matrix!

Part 2 Multipath Fading

Multipath Fading Each entry in the Channel matrix is actually a sum of different multipaths which interfere with one another to form the fading coefficient. We can easily show this in the time domain: The channel coefficients can be modeled as complex Rayleigh fading coefficients. The analysis proceeds then with the following:

Multipath Fading • There should be a significant number of multipaths for each of the coefficients • The energy should be equally spread out • If there are very few or no paths in some of the directions, then H will be correlated • The antennas should be properly spaced otherwise H will be correlated

Conclusions • MIMO adds a full degree of freedom • Think of it as a dimensionality extension to existing techniques of time and frequency • The more entropy in the fading environment, the more “richly” scattered, and less likely for zero eigenvalues • Rayleigh fading is a reasonable estimate