An optimal generalized theory of signal representation
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 03
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 05
Structured estimation of sparse channels in quasi-synchronous DS-CDMA
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 05
Reduced-rank adaptive filtering
IEEE Transactions on Signal Processing
Blind multiuser detection: a subspace approach
IEEE Transactions on Information Theory
A multistage representation of the Wiener filter based on orthogonal projections
IEEE Transactions on Information Theory
Adaptive maximum SINR RAKE filtering for DS-CDMA multipath fading channels
IEEE Journal on Selected Areas in Communications
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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.