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signal processing from power amplifier operation control point of view

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MORE MATH 65<br />

In the later chapter on MMSE equalization, we consider addressing cochannel interference<br />

by modeling it as some form of noise.<br />

If the number of receive antennas is equal to the number of transmitters (N r —<br />

N t ), then we can obtain zero-forcing solution by solving the set of equations<br />

N,<br />

y m «][>,s (¿) (m) (3-39)<br />

for the s'*) (m) values. We can write these equations in the form<br />

y = Hs, (3.40)<br />

where we've collected all the symbols into one column vector. The zero-forcing<br />

solution is then<br />

β = Η-ν· (3.41)<br />

Notice that we had just enough equations to solve for the symbols. If we have fewer<br />

equations, zero-forcing is not possible.<br />

If N r > N t , then we have more equations than unknowns (H is no longer a<br />

square matrix). We could simply discard some extra equations, but this would<br />

not be the best strategy. To reduce the number of equations, we introduce spatial<br />

matched filtering for each symbol:<br />

z = H H y. (3.42)<br />

This allows us to collect signal energy and reduce the number of equations to the<br />

number of unknowns. The symbol estimates are then given by<br />

s = (Η'Ή)-^ = (Η'ΉΓ'Ην (3.43)<br />

Is this the only choice when JV r > JV t ? Actually not. Recall that there is a<br />

copy of s m in y m +i- Before we sacrificed this copy to avoid ISI from future symbol<br />

blocks. However, if N r > 2N t , then we have enough equations that we could use<br />

y m+ i as well.<br />

3.4.2 MIMO/cochannel scenario<br />

Recall that in the MIMO/cochannel scenario, the received sample vector corresponding<br />

to a particular PMC can be modeled as<br />

x μ HAs + n, (3.44)<br />

where n is a vector of Gaussian r.v.s with zero-mean and covariance C n and s is the<br />

set of symbols transmitted from different transmitters. We will assume C n — N tt I<br />

and A = E S I. Let's initially assume N r = N t , so that H is a square matrix.<br />

With ZF DFE, we first need to triangularize the problem. Using QR decomposition,<br />

we can write H as QR, where Q is orthonormal (Q _1 = Q H ) and R is upper<br />

triangular. Substituting H = QR into (3.44) and multiplying both sides by Q H<br />

gives<br />

r (= v/ËjRs + e, (3.45)

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