Josep Sala

Multiantenna spectrum sensing for Cognitive Radio: overcoming noise uncertainty

Spectrum sensing is a key ingredient of the dynamic spectrum access paradigm, but it needs powerful detectors operating at SNRs well below the decodability levels of primary signals. Noise uncertainty poses a significant challenge to the development of such schemes, requiring some degree of diversity (spatial, temporal, or in distribution) for identifiability of the noise level. Multiantenna detectors exploit spatial independence of receiver thermal noise. We review this class of schemes and propose a novel detector trading off performance and complexity. However, most of these methods assume that the noise power, though unknown, is the same at all antennas. As it turns out, calibration errors have a substantial impact on these detectors. Another novel detector is proposed, based on an approximation to the Generalized Likelihood Ratio, outperforming previous schemes for uncalibrated multiantenna receivers.

Average performance analysis of circular and hyperbolic geolocation

A comparative performance analysis of four geolocation methods in terms of their theoretical root mean square positioning errors is provided. Comparison is established in two different ways: strict and average. In the strict type, methods are examined for a particular geometric configuration of base stations (BSs) with respect to mobile position, which determines a given noise profile affecting the respective time-of-arrival (TOA) or time-difference-of-arrival (TDOA) estimates. In the average type, methods are evaluated in terms of the expected covariance matrix of the position error over an ensemble of random geometries, so that comparison is geometry independent. Exact semianalytical equations and associated lower bounds (depending solely on the noise profile) are obtained for the average covariance matrix of the position error in terms of the so-called information matrix specific to each geolocation method. Statistical channel models inferred from field trials are used to define realistic prior probabilities for the random geometries. A final evaluation provides extensive results relating the expected position error to channel model parameters and the number of base stations.

Conditional maximum likelihood timing recovery: estimators and bounds

This paper is concerned with the derivation of new estimators and performance bounds for the problem of timing estimation of (linearly) digitally modulated signals. The conditional maximum likelihood (CML) method is adopted, in contrast to the classical low-SNR unconditional ML (UML) formulation that is systematically applied in the literature for the derivation of non-data-aided (NDA) timing-error-detectors (TEDs). A new CML TED is derived and proved to be self-noise free, in contrast to the conventional low-SNR-UML TED. In addition, the paper provides a derivation of the conditional Cramer-Rao bound (CRB/sub c/), which is higher (less optimistic) than the modified CRB (MCRB) [which is only reached by decision-directed (DD) methods]. It is shown that the CRB, is a lower bound on the asymptotic statistical accuracy of the set of consistent estimators that are quadratic with respect to the received signal. Although the obtained bound is not general, it applies to most NDA synchronizers proposed in the literature. A closed-form expression of the conditional CRB is obtained, and numerical results confirm that the CML TED attains the new bound for moderate to high E/sub s//N/sub o/.

Multiantenna Spectrum Sensing Exploiting Spectral a priori Information

Dynamic Spectrum Access (DSA) is receiving considerable interest as a means to improve spectral usage in licensed bands. In order to avoid interference to licensed users, spectrum sensing has emerged as an enabling technology for DSA. The requirements for spectrum sensors are stringent, as licensed user detection must be performed reliably at low signal-to-noise ratios (SNR). Sensing performance can be improved by exploiting signal features not present in the background noise. These approaches result in tradeoffs among performance and robustness to departures from the signal model. We consider second-order signal features and develop detectors exploiting spatial (by using multiple antennas) as well as temporal signal correlation, taking advantage of the fact that the power spectrum of the primary signal at each antenna can be known up to a complex scalar representing the unknown propagation channel. A low-SNR Generalized Likelihood Ratio approach is adopted in order to overcome this uncertainty, resulting in different tests intimately related to familiar diversity combining techniques. The performance of the proposed detectors is analyzed and tested in different scenarios.

Spectrum Sensing Using Correlated Receiving Multiple Antennas in Cognitive Radios

In this paper, we address the problem of multiantenna spectrum sensing in Cognitive Radios (CRs) by considering the correlation between the received channels at different antennas. First, we derive the optimum genie-aided detector which assumes perfect knowledge of the antenna correlation coefficients, Primary User (PU) signal power and noise variance. This is used as a benchmark for comparing with more practical detectors when some or all of these parameters are unknown to the Secondary User (SU). Two scenarios are considered: 1) the antenna correlation coefficients and PU signal power are unknown to the SU; 2) the antenna correlation coefficients, PU signal power and noise variance are unknown to the SU. To derive sub-optimum detectors for these two scenarios, we apply the Rao test, an asymptotically equivalent test to the Generalized Likelihood Ratio Test (GLRT) that does not require the Maximum Likelihood (ML) estimates of unknown parameters. Additionally, we calculate analytical approximations to the detection and false-alarm probabilities of the proposed detectors and verify them with Monte-Carlo simulations. The simulation results show that these new detectors outperform several recently proposed detectors for CR using multiple antennas.

CDF estimation for pre-distortion of non-linear high power amplifiers

Efficient use of High Power Amplifiers (HP A) in communications systems, where power consumption is of primary concern, can be achieved taking maximum advantage of the whole available dynamic range. This means that it is desirable to operate the HPA as near saturation as possible most of the time. Thus, any efficiency improvement can only be obtained at the cost of high AM/AM and AM/PM distortion due to the non-linearity of the HPA. This becomes more critical when non-constant amplitude modulations, such as OFDM and MQAM, are applied. In this work we present an efficient pre-distortion scheme based on statistical properties between sets of input and output signal samples at the HPA. The estimation of the cumulative distribution functions (CDF) is obtained by means of order statistics and then applied in the pre-distortion design, providing a self-adaptive compensation of the non-linearity with a very low computational cost.

Coarse time delay estimation for pre-correction of high power amplifiers in OFDM communications

Orthogonal frequency division multiplexing (OFDM) modulations used in DAB, DVB-T and future 4G communications standards are highly sensitive to the presence of non-linear distortion and synchronization errors between the transmitted and received signals. Digital precorrection schemes can be applied for the compensation of the AM/AM and AM/PM distortion introduced by high power amplifiers (HPA). However, this linearization process cannot be realized successfully unless the path delay introduced by the analog chains, required for the observation of the HPA output, is previously estimated. A coarse time delay estimation (TDE) algorithm robust to the presence of unknown non-linearities is presented. The proposed technique provides the basis for a fast time alignment of base-band signals within a range of several samples with a discrete resolution. Any residual timing offset can be then estimated and compensated by defining some analysis criteria over the obtained TDE spectrum.

Asymptotic and Finite User PER Analysis of Successive Interference Cancellation for DS-CDMA

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 error correcting 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.

Unconditionally convergent pre-distortion of non-linear high power amplifiers

The use of high power amplifiers (HPA) in digital communications is plagued with non-linear (AM/AM and AM/PM) distortion. This is specially acute for those modulations that are not constant amplitude, such as OFDM or MQAM. We present a pre-distortion algorithm that, in contrast to previously researched architectures, is unconditionally convergent.

Asymptotically Optimum Energy Profile for Successive Interference Cancellation in DS-CDMA under a Power Unbalance Constraint

A new Ordinary Differential Equation (ODE) governing the SNIR evolution of a Successive Interference Canceller (SIC) for DS-CDMA is derived when the number of users tends to infinity and all users share the same channel encoder. Using Variational Calculus, this ODE is applied to obtaining the energy profile that maximizes the average spectral efficiency when a constraint on the power unbalance (maximum power to minimum power ratio) of received users is enforced. The conditions for extremality of the optimum energy profile are established in terms of the common encoders Packet Error Rate (PER) function.