34,662 research outputs found
Universal Estimation of Directed Information
Four estimators of the directed information rate between a pair of jointly
stationary ergodic finite-alphabet processes are proposed, based on universal
probability assignments. The first one is a Shannon--McMillan--Breiman type
estimator, similar to those used by Verd\'u (2005) and Cai, Kulkarni, and
Verd\'u (2006) for estimation of other information measures. We show the almost
sure and convergence properties of the estimator for any underlying
universal probability assignment. The other three estimators map universal
probability assignments to different functionals, each exhibiting relative
merits such as smoothness, nonnegativity, and boundedness. We establish the
consistency of these estimators in almost sure and senses, and derive
near-optimal rates of convergence in the minimax sense under mild conditions.
These estimators carry over directly to estimating other information measures
of stationary ergodic finite-alphabet processes, such as entropy rate and
mutual information rate, with near-optimal performance and provide alternatives
to classical approaches in the existing literature. Guided by these theoretical
results, the proposed estimators are implemented using the context-tree
weighting algorithm as the universal probability assignment. Experiments on
synthetic and real data are presented, demonstrating the potential of the
proposed schemes in practice and the utility of directed information estimation
in detecting and measuring causal influence and delay.Comment: 23 pages, 10 figures, to appear in IEEE Transactions on Information
Theor
Performance of adaptive bayesian equalizers in outdoor environments
Outdoor communications are affected by multipath propagation that imposes an upper limit on the system data rate and restricts possible applications. In order to overcome the degrading effect introduced by the channel, conventional equalizers implemented with digital filters have been traditionally used. A new approach based on neural networks is considered. In particular, the behavior of the adaptive Bayesian equalizer implemented by means of radial basis functions applied to the channel equalization of radio outdoor environments has been analyzed. The method used to train the equalizer coefficients is based on a channel response estimation. We compare the results obtained with three channel estimation methods: the least sum of square errors (LSSE) channel estimation algorithm, recursive least square (RLS) algorithm employed only to obtain one channel estimation and, finally, the RLS algorithm used to estimate the channel every decided symbol for the whole frame.Peer ReviewedPostprint (published version
Blind Receiver Design for OFDM Systems Over Doubly Selective Channels
We develop blind data detectors for orthogonal frequency-division multiplexing (OFDM) systems over doubly selective channels by exploiting both frequency-domain and time-domain correlations of the received signal. We thus derive two blind data detectors: a time-domain data detector and a frequency-domain data detector. We also contribute a reduced complexity, suboptimal version of a time-domain data detector that performs robustly when the normalized Doppler rate is less than 3%. Our frequency-domain data detector and suboptimal time-domain data detector both result in integer least-squares (LS) problems. We propose the use of the V-BLAST detector and the sphere decoder. The time-domain data detector is not limited to the Doppler rates less than 3%, but cannot be posed as an integer LS problem. Our solution is to develop an iterative algorithm that starts from the suboptimal time-domain data detector output. We also propose channel estimation and prediction algorithms using a polynomial expansion model, and these estimators work with data detectors (decision-directed mode) to reduce the complexity. The estimators for the channel statistics and the noise variance are derived using the likelihood function for the data. Our blind data detectors are fairly robust against the parameter mismatch
Non-linear minimum variance estimation for discrete-time multi-channel systems
A nonlinear operator approach to estimation in discrete-time systems is described. It involves inferential estimation of a signal which enters a communications channel involving both nonlinearities and transport delays. The measurements are assumed to be corrupted by a colored noise signal which is correlated with the signal to be estimated. The system model may also include a communications channel involving either static or dynamic nonlinearities. The signal channel is represented in a very general nonlinear operator form. The algorithm is relatively simple to derive and to implement
Analysis and optimization of pilot symbol-assisted Rake receivers for DS-CDMA systems
The effect of imperfect channel estimation (CE) on the performance of pilot-symbol-assisted modulation (PSAM) and MRC Rake reception over time- or frequency-selective fading channels with either a uniform power delay profile (UPDP) or a nonuniform power delay profile (NPDP) is investigated. For time-selective channels, a Wiener filter or linear minimum mean square error (LMMSE) filter for CE is considered, and a closed-form asymptotic expression for the mean square error (MSE) when the number of pilots used for CE approaches infinity is derived. In high signal-to-noise ratio (SNR), the MSE becomes independent of the channel Doppler spectrum. A characteristic function method is used to derive new closed-form expressions for the bit error rate (BER) of Rake receivers in UPDP and NPDP channels. The results are extended to two-dimensional (2-D) Rake receivers. The pilot-symbol spacing and pilot-to-data power ratio are optimized by minimizing the BER. For UPDP channels, elegant results are obtained in the asymptotic case. Furthermore, robust spacing design criteria are derived for the maximum Doppler frequency
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