This paper links the direct-sequence code-division multiple access (DS-CDMA
) multiuser separation-equalization-detection problem to the parallel facto
r (PARAFAC) model, which is an analysis tool rooted in psychometrics and ch
emo metrics. Exploiting this link, it derives a deterministic blind PARAFAC
DS-CDMA receiver with performance close to nonblind minimum mean-squared e
rror (MMSE). The proposed PARAFAC receiver capitalizes on code, spatial, an
d temporal diversity-combining, thereby supporting small sample sizes, more
users than sensors, and/or less spreading than users. Interestingly, PARAF
AC does not require knowledge of spreading codes, the specifics of multipat
h (interchip interference), DOA-calibration information, finite alphabet/co
nstant modulus, or statistical independence/whiteness to recover the inform
ation-bearing signals. Instead, PARAFAC relies on a fundamental result rega
rding the uniqueness of low-rank three-wag array decomposition due to Krusk
al (and generalized herein to the complex-valued case) that guarantees iden
tifiability of all relevant signals and propagation parameters. These and o
ther issues ar-e also demonstrated in pertinent simulation experiments.