W. W. CAO ET AL. 185

Figure 4. Sum capacity of K = 4, N = 12, Mk = 2.

is small but the simplified power allocation scheme ob-

tain the performance gain with no additional computation

and it not only makes up the performance loss in [11],

but also obtains the performance gain about 0.5dB in

BER and 0.3bps/HZ in capacity compared with the con-

ventional algorithm.

The results show that the proposed scheme has the best

performance from both BER and system capacity. Using

perturbation theory to obtain the updated SLNR and the

updated precoding vector rather than decomposing the

matrix to get the generalized eigenvalues and eigenvec-

tors is an excellent way to achieve balance between algo-

rithm complexity and system performance. What’s more,

using the updated SLNR value to do a power allocation

can further improve the system performance. From (2),

the SLNR precoding design depends on the amount of

power allocated to each user so that allocating more

power to the user having good channel quality can in-

crease the system performance. This strategy to compen-

sate the performance loss in [12] is feasible.

5. Conclusions

In this paper, we have investigated the power allocation

scheme using the updated SLNR value base on perturba-

tion theory. As the time-varying channel is taken into

consideration, we avoid doing the eigen-decomposition

in two consecutive time step. It leads to relatively less

amount of calculation compared to the conventional

SLNR algorithm and better system performance com-

pared to the scheme only updating precoding vector.

Then the proposed power allocation scheme using up-

dated SLNR value which is more accuracy as an indica-

tor to the channel quality compensates the performance

loss caused by the approximate calculation.

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SNR (dB)

Sum capacity (bps/HZ)

Sum capacity vs SNR

SLNR-PERTUBATED

USLNR-PA

MMSE

CSLNR

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