Abstract-We propose a blind multiuser detection technique for array processing and code division multiple access (CDMA) systems that does not require knowledge of the array geometry or transmitter signature sequences. The technique has two key elements: an adaptive algorithm for separating the signal subspace from the noise subspace and an adaptive whitener based on linear prediction. The proposed algorithm offers low complexity, fast convergence, compatibility with shaped signal constellations, near-Wiener steady-state performance, and optimal near-far resistance.

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Recent research has shown that second-order statistics (SOS) are sufficient to blindly identify or equalize a broad class of channels. We relax the assumption of perfect carrier recovery and determine if SOS are sufficient for channel identification, equalization, and carrier recovery in the presence of frequency offset. We show that while equalization may still be possible, channel identification and carrier recovery require use of higher-order statistics. We show that SOS-based channel identification is possible only with knowledge of the carrier frequency offset, and conversely, that SOS-based carrier-offset identification is possible only with knowledge of the channel. We describe algorithms and present simulation results to demonstrate these claims.

We propose a blind implementation of the finite-tap linear MMSE detector for asynchronous directsequence CDMA. Unlike partially blind detectors that require knowledge of the signature sequence of the desired user in lieu of a training sequence, the proposed detector requires neither training nor kpowledge of any of the signature sequences. Moreover, the detector need not know the number of interfering users, the size of their QAM alphabets, nor the amount of memory in the channel. The detector first transforms the channel into an equivalent higher-dimensional channel without memory by stacking a sufficient number of receiver observations. The three factors in a singular-value decomposition of the MMSE detector are then implemented one by one, with each factor being adapted blindly and independently. Numerical results demonstrate that, unlike many subspace-based detectors, the proposed detector is robust to inaccuracies in its estimate of the signal subspace dimension.