Abstract
We first develop a reduced-rank minimum mean square error (MMSE) detector for direct-sequence (DS) code division multiple access (CDMA) by forcing the linear MMSE detector to lie in a signal subspace of a reduced dimension. While a reduced-rank MMSE detector has lower complexity, it cannot outperform the full-rank MMSE detector. We then concentrate on the blind reduced-rank MMSE detector which is obtained from an estimated covariance matrix. Our analysis and simulation results show that when the desired user's signal is in a low-dimensional subspace, there exists an optimal subspace so that the blind reduced-rank MMSE detector lying in this subspace has the best performance. By properly choosing a subspace, we guarantee that the optimal blind reduced-rank MMSE detector is obtained. An adaptive blind reduced-rank MMSE detector, based on a subspace tracking algorithm, is developed. The adaptive blind reduced-rank MMSE detector exhibits superior steady-state performance and fast convergence speed.
Highlights
The major limitation on the performance and channel capacity of direct-sequence (DS) code division multiple access (CDMA) system is the multiple-access interference (MAI) due to simultaneous transmissions
In this paper, based on the estimated covariance matrix, we study all possible blind minimum mean square error (MMSE) detectors lying in different subspaces [10] and observe that there always exists an optimal subspace, in which the blind reduced-rank lies, achieves the highest signal-to-interference ratio (SIR)
Our reduced-rank MMSE detector uses the cross spectral metric (CSM) approach in [13] to select a subspace, and we focus on the blind MMSE detector which uses an estimated covariance matrix
Summary
The major limitation on the performance and channel capacity of direct-sequence (DS) code division multiple access (CDMA) system is the multiple-access interference (MAI) due to simultaneous transmissions. It was shown that the blind adaptive MMSE detector obtained from the signal space parameters has much better steady-state performance compared with the blind MOE detector [6]. We first investigate the reduced-rank MMSE detector that lies in a subspace of the signal space under the assumption that the detector has all active users’ information. In CDMA systems, EURASIP Journal on Applied Signal Processing active users typically transmit with different powers; the desired user may lie in a subspace that has a lower dimension than the signal space. In this paper, based on the estimated covariance matrix, we study all possible blind MMSE detectors lying in different subspaces [10] and observe that there always exists an optimal subspace, in which the blind reduced-rank lies, achieves the highest SIR.
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