Carlos Mosquera

Dynamic Spectrum Leasing: A New Paradigm for Spectrum Sharing in Cognitive Radio Networks

We recently proposed the dynamic spectrum leasing (DSL) paradigm for dynamic spectrum access in cognitive radio networks. In this paper, we formalize this concept by developing a general game-theoretic framework for the DSL and by carefully identifying requirements for the coexistence of primary and secondary systems via spectrum leasing. In contrast to hierarchical spectrum access, spectrum owners in proposed DSL networks, which are denoted as primary users, dynamically adjust the amount of secondary interference that they are willing to tolerate in response to the demand from secondary transmitters. The secondary transmitters, in turn, opportunistically attempt to achieve maximum possible throughput, or another suitably defined reward, while not violating the interference limit that is set by the primary users. The new game-theoretic model, however, allows the secondary users to encourage the spectrum owners to push the interference cap upward based on demand. We have proposed a general structure for the utility functions of primary users and secondary users that allows the primary users to control the price and the demand for spectrum access based on their required quality of service (QoS). We establish that, with these utility functions, the DSL game has a unique Nash equilibrium to which the best response adaptation finally converges. Moreover, it is shown that the proposed coexistence and best response adaptations can be achieved with no significant interaction between the two systems. In fact, it is shown that the only requirement is that the primary system periodically broadcasts two parameter values. We use several examples to illustrate the system behavior at the equilibrium and use the performance at the equilibrium to identify suitable system design parameters.

Non-Linear Interference Mitigation for Broadband Multimedia Satellite Systems

This contribution explores the use of interference mitigation techniques applied to broadband satellite systems with co-channel interference. In particular, our focus is on nonlinear precoding techniques, borrowing ideas from the theory of broadcast MIMO channels. A number of schemes are compared, including several implementations of Tomlinson-Harashima precoding and their linear precoding counterparts. Simulations on realistic scenarios show potential improvements of non-linear precoding with respect to linear interference mitigation and classical countermeasures based on frequency division among beams. Also, we identify several practical issues related to the implementation of Tomlinson-Harashima Precoding in satellite communication systems.

Cramer-Rao lower bound and EM algorithm for envelope-based SNR estimation of nonconstant modulus constellations

Signal-to-noise ratio (SNR) estimation for linearly modulated signals is addressed in this letter, focusing on envelope-based estimators, which are robust to carrier offsets and phase jitter, and on the challenging case of nonconstant modulus constellations. For comparison purposes, the true Cramer-Rao lower bound is numerically evaluated, obtaining an analytical expression in closed form for the asymptotic case of high SNR values, which quantifies the performance loss with respect to coherent estimation. As the maximum-likelihood algorithm is too complex for practical implementation, an expectation-maximization (EM) approach is proposed, achieving a good tradeoff between complexity and performance for medium-to-high SNRs. Finally, a hybrid scheme based on EM and moments-based estimates is suggested, which performs close to the theoretical limit over a wide SNR range.

Non-Data-Aided Symbol Rate Estimation of Linearly Modulated Signals

The estimation of the symbol rate of a linearly modulated signal is addressed, with special focus on low signal-to-noise ratio (SNR) scenarios. This problem finds application in automatic modulation classification and signal monitoring. A maximum-likelihood (ML) approach is adopted to derive practical estimators, exploiting information on the cyclostationarity of the modulated signal as well as knowledge of the received signaling pulse shape. The structure of the ML estimator suggests a two-step estimation procedure, whereby an initial coarse search determines first a neighborhood from which a subsequent fine search yields the final symbol rate estimate. Links between the ML approach and previous results from the literature in symbol rate estimation are established as well. The proposed scheme is applicable even for small excess bandwidths, at the cost of a higher complexity with respect to simpler estimators known to fail under such conditions.

Efficient Dynamic Spectrum Sharing in Cognitive Radio Networks: Centralized Dynamic Spectrum Leasing (C-DSL)

In this paper, we propose the concept of centralized dynamic spectrum leasing (C-DSL), in which multiple primary users belonging to the same primary system participate in the spectrum leasing process with secondary users (potential bidder for spectrum) under centralized control. We develop a new game-theoretic user interaction model suitable for C-DSL in a cognitive radio network. Dynamic Spectrum Leasing (DSL), proposed in allows active participation of both primary and secondary users in the spectrum sharing process. Motivated by network spectrum utilization considerations, we propose generalizations to the primary system utility function defined in and a new utility function for the secondary users. We also generalize the proposed non-cooperative C-DSL game to allow for linear multiuser detectors at the secondary base stations. We formulate the conditions on the primary system and the secondary user utility functions so that the proposed C-DSL game has desired equilibrium properties. We prove that the proposed C-DSL game has unique Nash equilibria (NE) under both matched filter (MF) and linear minimum mean-squared error (LMMSE) receivers. Equilibrium performance of the system and robustness of the proposed game theoretic adaptive implementation are investigated through simulations.

Primary User Enters the Game: Performance of Dynamic Spectrum Leasing in Cognitive Radio Networks

Dynamic spectrum leasing (DSL) is one of the schemes proposed for dynamic spectrum sharing (DSS) in cognitive radio networks. In DSL, spectrum owners, denoted as primary users, dynamically adjust the amount of secondary interference they are willing to tolerate in response to the demand from secondary transmitters. In this correspondence we investigate how much can be gained by primary users if this limited interaction with secondary system is allowed, compared to a scheme in which the interference cap allowed by primary users is fixed {a priori} by a regulatory authority. To that end, we define performance metrics for both primary and secondary systems based on the theoretically achievable multiuser sum-rate of the secondary system and analyze both schemes behavior with respect to different system parameters. This analysis shows that (i) in dynamic environments DSL based schemes may present an important advantage over other schemes with fixed interference constraints, and (ii) DSL schemes are robust against inaccurate a priori information that may degrade system performance.

ML and EM algorithm for non-data-aided SNR estimation of linearly modulated signals

The recently published Cramer-Rao lower bound for non-data-aided (NDA) estimation of the signal-to-noise ratio (SNR) reveals a considerable gap, when compared to the jitter performance of NDA algorithms available from the open literature. The maximum-likelihood (ML) solution derived in this paper closes this gap. However, the latter provides a set of two nonlinear vector equations, which might be simplified only for modulation schemes with constant envelope like M-ary PSK. For signals with nonconstant envelope, like 16-QAM as most prominent example in this respect, a much less complex approach based on the expectation-maximization (EM) principle is developed in this paper. In the medium SNR range, this bridges part of the performance gap mentioned previously. Over the full SNR range, we propose a hybrid algorithm, where the EM estimate is replaced by a moment-based method as soon as the true SNR drops below a predefined threshold.

Distributed Sequential Estimation With Noisy, Correlated Observations

The problem of distributed sequential estimation of a nonrandom parameter over noisy communication links is considered, with observations that are correlated spatially across the sensor field. A recursive algorithm for updating the sequential estimator is derived assuming wide-sense stationary observations. It is shown that the performance of the sequential estimator is a trade-off between the quality of node observations and communication channels. A sufficient condition for the convergence of the estimator variance is derived, and asymptotic expressions for variance when this condition is met are obtained for both iid and correlated observations.

Joint NDA Estimation of Carrier Frequency/Phase and SNR for Linearly Modulated Signals

We consider the problem of joint non-data-aided (NDA) estimation of carrier frequency and phase offsets, together with that of signal and noise powers, obtained from the baud-rate samples of a linearly modulated signal distorted by Gaussian noise. The Crame¿r-Rao lower bound (CRLB) is derived, for quadrature-symmetric constellations showing that the carrier parameters are decoupled from signal and noise powers. Furthermore, a joint NDA maximum-likelihood estimator is developed, based on the application of the expectation-maximization algorithm, which exhibits a jitter performance close to the CRLB for a wide SNR range.

Stepsize Sequence Design for Distributed Average Consensus

Starting from local observations, iterative consensus algorithms attempt to drive a sensor network to a common estimate in a decentralized, incremental manner. When additive noise perturbs the sensor exchanges, a decreasing stepsize guarantees convergence under certain conditions, although the design of such stepsize sequence for fastest convergence is an unsettled issue. We present a greedy approach to stepsize sequence design, which minimizes the mean squared error at each iteration. This design requires knowledge of the network topology; in order to overcome this drawback, a modified design based only on average descriptors of the network is also developed.