Abstract
This paper deals with synchronous direct-sequence
code-division multiple access (CDMA) transmission using orthogonal
channel codes in frequency selective multipath, motivated by the
forward link in 3G CDMA systems. The chip-level minimum
mean square error (MMSE) estimate of the (multiuser) synchronous
sum signal transmitted by the base, followed by a correlate and
sum, has been shown to perform very well in saturated systems
compared to a Rake receiver. In this paper, we present the
reduced-rank, chip-level MMSE estimation based on the multistage
nested Wiener filter (MSNWF). We show that, for the case of a
known channel, only a small number of stages of the MSNWF is
needed to achieve near full-rank MSE performance over a practical
single-to-noise ratio (SNR) range. This holds true even for an
edge-of-cell scenario, where two base stations are
contributing near equal-power signals, as well as for the single
base station case. We then utilize the code-multiplexed pilot
channel to train the MSNWF coefficients and show that adaptive
MSNWF operating in a very low rank subspace performs slightly
better than full-rank recursive least square (RLS) and
significantly better than least mean square (LMS). An important
advantage of the MSNWF is that it can be implemented in a
lattice structure, which involves significantly
less computation than RLS. We also present structured MMSE
equalizers that exploit the estimate of the multipath arrival
times and the underlying channel structure to project the data
vector onto a much lower dimensional subspace. Specifically, due
to the sparseness of high-speed CDMA multipath channels, the
channel vector lies in the subspace spanned by a small number of
columns of the pulse shaping filter convolution matrix. We
demonstrate that the performance of these structured low-rank
equalizers is much superior to unstructured equalizers in terms
of convergence speed and error rates.