Interference may pose a severe limitation to massive wireless multiple access unless the central receiver is endowed with strong multipacket reception capabilities. In that respect, Interference Cancellation (IC) has been extensively studied [1]–[3]. Such systems have been proposed for satellite communications, combining Direct Sequence (DS) Code Division Multiple Access (CDMA) with a SIC receiver [4]–[6] or using multipacket transmission as in Contention Resolution Diversity Slotted ALOHA (CRDSA) [7] [8]. The use of powerful encoders coupled with power imbalance at reception favours IC and has considerably improved the throughput of such random access protocols [4] [9]. In a context characterized by high user activity and spectrum/orbit congestion [10], it is of interest to develop schemes for space assets that optimize the aggregate spectral efficiency of the user population.Peer ReviewedPostprint (published version

Abstract-An expression is derived for the average Packet Error Rate (PER) of a Successive Interference Canceller (SIC) for DS-CDMA when the number of users asymptotically tends to infinity. The asymptotic probability density function of the interference power is governed by a Fokker-Planck differential equation with drift and (asymptotically vanishing) diffusion depending on the PER function of the adopted forward errorcorrecting code (FEC). In addition to the asymptotic solution for the PER, a particle-based algorithm is also developed for computing efficiently the PER in the finite user case.Index Terms-Successive interference cancellation, FokkerPlanck equation, packet error rate, CDMA, particle.

Signal-to-noise ratio (SNR) estimators of linear modulation schemes usually operate at one sample per symbol at the matched filter output. In this paper we propose a new method for estimating the SNR in the complex additive white Gaussian noise (AWGN) channel that operates directly on the oversampled cyclostationary signal at the matched filter input. Exploiting cyclostationarity proves to be advantageous due to the fact that a signal-free Euclidean noise subspace can be identified such that only second order moments of the received waveform need to be computed. The proposed method is nondata-aided (NDA), as well as constellation and phase independent, and only requires prior timing synchronization to fully exploit the cyclostationarity property. The estimator can also be applied to nonconstant modulus constellations without requiring any tuning, which is a feature not found in existing approaches. Implementation aspects and simpler suboptimal solutions are also provided.

Cyclostationary processes exhibit a form of frequency diversity. Based on that, we show that a digital waveform with symbol period T can be asymptotically represented as a rank-1 frequency-domain vector process which exhibits uncorrelation at different frequencies inside the Nyquist spectral support of 1/T. By resorting to the fast Fourier transform (FFT), this formulation obviates the need of estimating a cumbersome covariance matrix to characterize the likelihood function. We then derive the generalized likelihood ratio test (GLRT) for the detection of a cyclostationary signal in unknown white noise without the need of a assuming a synchronized receiver. This provides a sound theoretical basis for the exploitation of the cyclostationary feature and highlights an explicit link with classical square timing recovery schemes, which appear implicitly in the core of the GLRT. Moreover, to avoid the well-known sensitivity of cyclostationary-based detection schemes to frequency-selective fading channels, a parametric channel model based on a lower bound on the coherence bandwidth is adopted and incorporated into the GLRT. By exploiting the rank-1 structure of small spectral covariance matrices, the obtained detector outperforms the classical spectral correlation magnitude detector.

This work addresses the optimization of the network spectral efficiency (SE) under successive interference cancellation (SIC) at a given blocklength n. We adopt a proof-of-concept satellite scenario where network users can vary their transmission power and select their transmission rate from a set of encoders, for which decoding is characterized by a known packet error rate (PER) function. In the large-system limit, we apply variational calculus (VC) to obtain the user-energy distribution, the assigned per-user rate and the SIC decoding order maximizing the network SE under a sum-power constraint at the SIC input. We analyze two encoder sets: (i) an infinite set of encoders achieving information-theoretic finite blocklength PER results over a continuum of code rates, where the large-n second order expansion of the maximal channel coding rate is used; (ii) a feasible finite set of encoders. Simulations quantify the performance gap between the two schemes.

Abstract-This work provides a general framework for the design of second-order blind estimators without adopting any approximation about the observation statistics or the a priori distribution of the parameters. The proposed solution is obtained minimizing the estimator variance subject to some constraints on the estimator bias. The resulting optimal estimator is found to depend on the observation fourth-order moments that can be calculated analytically from the known signal model. Unfortunately, in most cases, the performance of this estimator is severely limited by the residual bias inherent to nonlinear estimation problems. To overcome this limitation, the second-order minimum variance unbiased estimator is deduced from the general solution by assuming accurate prior information on the vector of parameters. This small-error approximation is adopted to design iterative estimators or trackers. It is shown that the associated variance constitutes the lower bound for the variance of any unbiased estimator based on the sample covariance matrix.The paper formulation is then applied to track the angle-of-arrival (AoA) of multiple digitally-modulated sources by means of a uniform linear array. The optimal second-order tracker is compared with the classical maximum likelihood (ML) blind methods that are shown to be quadratic in the observed data as well. Simulations have confirmed that the discrete nature of the transmitted symbols can be exploited to improve considerably the discrimination of near sources in medium-to-high SNR scenarios.

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Abstract-This paper deals with the goodness of the Gaussian assumption when designing second-order blind estimation methods in the context of digital communications. The low-and high-signal-to-noise ratio (SNR) asymptotic performance of the maximum likelihood estimator-derived assuming Gaussian transmitted symbols-is compared with the performance of the optimal second-order estimator, which exploits the actual distribution of the discrete constellation. The asymptotic study concludes that the Gaussian assumption leads to the optimal second-order solution if the SNR is very low or if the symbols belong to a multilevel constellation such as quadrature-amplitude modulation (QAM) or amplitude-phase-shift keying (APSK). On the other hand, the Gaussian assumption can yield important losses at high SNR if the transmitted symbols are drawn from a constant modulus constellation such as phase-shift keying (PSK) or continuous-phase modulations (CPM). These conclusions are illustrated for the problem of direction-of-arrival (DOA) estimation of multiple digitally-modulated signals.