Blind PARAFAC receivers for DS-CDMA systems

• Published in: IEEE transactions on signal processing Vol. 48; no. 3; pp. 810 - 823
• Main Authors: Sidiropoulos, N.D., Giannakis, G.B., Bro, R.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.03.2000; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Alphabets; Antenna arrays; Antennas and propagation; Applied sciences; Arrays; Blinds; Calibration; Direct-sequence code-division multiple access; Exact sciences and technology; Frequency estimation; Information, signal and communications theory; Interference; Links; Mathematical models; Multiaccess communication; Psychometric testing; Receivers; Sensor arrays; Signal and communications theory; Signal processing; Signal, noise; Spreading; Statistical methods; Telecommunications and information theory; Performance evaluation; Waveform; Monte Carlo method; Data analysis; Identifiability; Bit error rate; Decomposition method; Multipath propagation; Psychometrics; Receiver; Equalization; Modeling; Mean square error; Separation method; Experimental result; Code division multiple access; Arrival angle; Discrete time; Signal processing; Numerical simulation; Chemometrics; Signal detection
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/78.824675

This paper links the direct-sequence code-division multiple access (DS-CDMA) multiuser separation-equalization-detection problem to the parallel factor (PARAFAC) model, which is an analysis tool rooted in psychometrics and chemometrics. Exploiting this link, it derives a deterministic blind PARAFAC DS-CDMA receiver with performance close to non-blind minimum mean-squared error (MMSE). The proposed PARAFAC receiver capitalizes on code, spatial, and temporal diversity-combining, thereby supporting small sample sizes, more users than sensors, and/or less spreading than users. Interestingly, PARAFAC does not require knowledge of spreading codes, the specifics of multipath (interchip interference), DOA-calibration information, finite alphabet/constant modulus, or statistical independence/whiteness to recover the information-bearing signals. Instead, PARAFAC relies on a fundamental result regarding the uniqueness of low-rank three-way array decomposition due to Kruskal (1977, 1988) (and generalized herein to the complex-valued case) that guarantees identifiability of all relevant signals and propagation parameters. These and other issues are also demonstrated in pertinent simulation experiments.