2 research outputs found
Code-timing synchronization in DS-CDMA systems using space-time diversity
The synchronization of a desired user transmitting a known training sequence in a direct-sequence (DS) asynchronous code-division multiple-access (CDMA) sys-tem is addressed. It is assumed that the receiver consists of an arbitrary antenna array and works in a near-far, frequency-nonselective, slowly fading channel. The estimator that we propose is derived by applying the maximum likelihood (ML) principle to a signal model in which the contribution of all the interfering compo-nents (e.g., multiple-access interference, external interference and noise) is modeled as a Gaussian term with an unknown and arbitrary space-time correlation matrix. The main contribution of this paper is the fact that the estimator makes eÆcient use of the structure of the signals in both the space and time domains. Its perfor-mance is compared with the Cramer-Rao Bound, and with the performance of other methods proposed recently that also employ an antenna array but only exploit the structure of the signals in one of the two domains, while using the other simply as a means of path diversity. It is shown that the use of the temporal and spatial structures is necessary to achieve synchronization in heavily loaded systems or in the presence of directional external interference.Peer ReviewedPostprint (published version
ML estimator and hybrid beamformer for multipath and interference mitigation in GNSS receivers
This paper addresses the estimation of the code-phase(pseudorange) and the carrier-phase of the direct signal received from a direct-sequence spread-spectrum satellite transmitter. The
signal is received by an antenna array in a scenario with interference
and multipath propagation. These two effects are generally
the limiting error sources in most high-precision positioning applications.
A new estimator of the code- and carrier-phases is derived
by using a simplified signal model and the maximum likelihood
(ML) principle. The simplified model consists essentially of
gathering all signals, except for the direct one, in a component with
unknown spatial correlation. The estimator exploits the knowledge
of the direction-of-arrival of the direct signal and is much simpler
than other estimators derived under more detailed signal models.
Moreover, we present an iterative algorithm, that is adequate for a
practical implementation and explores an interesting link between
the ML estimator and a hybrid beamformer. The mean squared
error and bias of the new estimator are computed for a number
of scenarios and compared with those of other methods. The presented
estimator and the hybrid beamforming outperform the existing
techniques of comparable complexity and attains, in many
situations, the Cramér–Rao lower bound of the problem at hand.Peer Reviewe