Code-division multiple access (CDMA) has emerged as an access protocol well-suited for voice and data transmission. One significant limitation of the conventional CDMA system is the near-far problem where strong signals interfere with the detection of a weak signal. Multiuser detectors assume knowledge of all of the modulation waveforms and channel parameters, and exploit this information to eliminate multiple-access interference (MAI) and to achieve near-far resistance. A major problem in practical application of multiuser detection is the estimation of the signal and channel parameters in a near-far limited system. We consider maximum-likelihood estimation of users delay, amplitude, and phase in a CDMA communication system. We present an approach for decomposing this multiuser estimation problem into a series of single-user problems. In this method the interfering users are treated as colored non-Gaussian noise. The observation vectors are preprocessed to be able to apply a Gaussian model for the MAI. The maximum-likelihood estimate (MLE) of each user's parameters based on the processed observation vectors becomes tractable. The estimator includes a whitening filter derived from the sample covariance matrix which is used to suppress the MAI, thus yielding a near-far resistant estimator.

We consider the estimation of channel parameters for code-division multiple access (CDMA) communication systems operating over channels with either single or multiple propagation paths. The multiuser channel estimation problem is decomposed into a series of single user problems through a subspace-based approach. By exploiting the eigenstructure of the received signal's sample correlation matrix, the observation space can be partitioned into a signal subspace and a noise subspace without prior knowledge of the unknown parameters. The channel estimate is formed by projecting a given user's spreading waveform into the estimated noise subspace and then either minimizing the likelihood or minimizing the Euclidean norm of this projection. Both of these approaches yield algorithms which are near-far resistant and do not require a preamble.