Interference estimation with applications to blind multiple-access communication over fading channels

Abstract

Abstract-We consider the detection of nonorthogonal multipulse signals on multiple-access fading channels. The generalized maximum-likelihood rule is employed to decode users whose complex fading gains are unknown. We develop geometrical interpretations for the resulting detectors and their corresponding asymptotic efficiencies. The generalized maximum-likelihood detection rule is then applied to find a matched subspace detector for the frequency-selective fading channel, under the assumption of a short coherence time (or long coherence time without the computational power to track the fading parameters). We propose blind implementations of these detectors for nonorthogonal multipulse signaling on both frequency-nonselective and frequency-selective multiple-access fading channels. These blind detectors extend the results of Wang and Poor to multipulse modulation and fast frequency selective fading. For comparison, the minimum mean-squared error decision rules for these channels are derived and blind implementations of their corresponding detectors are developed.

Modulation and Coding for Noncoherent Communications

“…We presented an outline of the extension of these ideas to the Rayleigh fading channel. There is also a rich multi-user detection theory of NMM on the noncoherent channel which has been developed in [1,2,[27][28][29][30][31][32][33].…”

“…We develop a theory of detection, signal design, coding, and equalization for noncoherent communications with NMM. Our results on detectors and their performance analysis (Sections 3, 4) are presented primarily as review, as they appear in the journal references [1,2]. The equalization (Section 9) and NMM signal design (Section 5) results have appeared in conference versions of this paper in [3] and [4], respectively.…”

Blind adaptive noncoherent multiuser detection for nonlinear modulation

“…The noise variance could be estimated either once, before the filter updates, or iteratively as in [12], the latter being an computation. The subspace-based adaptation scheme of [5], on the other hand, involves an subspace tracking step, followed by an matrix multiplication step and finally an orthonormal basis decomposition step. Note that the noise variance estimation is a by-product of the subspace-tracking step.…”

“…Three post-decorrelative detectors, namely, the optimum, the asymptotically optimum, and the generalized likelihood ratio test (GLRT) based detectors were obtained in [1]. These detection schemes can be extended to linearly dependent signaling [3]- [5]. Improvement in terms of spectral efficiency and probability of error performance beyond these initial solutions to the multiuser NMM problem can be realized through signal design as in [2] and superior detection strategies.…”

“…As we will see later in this paper, blind NMM noncoherent decorrelative detection has to not only estimate the decorrelator blindly, but also has to implement the post-decorrelative decision algorithm in a blind fashion. A generalization of the subspace tracking approach in [11] can be found in [5], which addresses the issue of blind adaptive noncoherent detection for NMM. The subspace tracking method is computationally more intensive than the simpler stochastic approximation based adaptation strategies obtained in this paper.…”