— Effective Inter-carrier Interference (ICI) mitigation for MIMO-OFDM requires accurate channel estimation which is very challenging due to the large number and fast time-varying nature of the channel parameters to be estimated using scattered pilots. We present a novel SFBC-OFDM scheme for doubly-selective channels and a reduced-complexity channel… (More)
—Direct conversion orthogonal frequency division mul-tiplexing (OFDM) systems suffer from transmit and receive analog processing impairments such as in-phase/quadrature (I/Q) imbalance causing inter-carrier interference (ICI) among sub-carriers. Another source of performance-limiting ICI in OFDM systems is Doppler spread due to mobility. However, the nature… (More)
Dissolution-controlled drug delivery systems are characterized by a phase erosion of the polymer carrier that is associated with fast or slow dissolution of the macromolecular chains. The molecular nature of the dissolution phenomenon was examined by analyzing the water transport process and the subsequent polymer chain disentanglement that is usually… (More)
—Direct-conversion multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) transceivers enjoy high data rates and reliability at practical implementation complexity. However, analog front-end impairments such as I/Q imbalance and high mobility requirements of next-generation broadband wireless standards result in… (More)
— I/Q imbalance and high mobility in OFDM systems result in performance-limiting intercarrier interference (ICI). However, the nature of ICI due to each of them is quite different. Unlike previous works which considered these two impairments separately, we develop a unified mathematical framework to characterize and mitigate ICI when both impairments are… (More)
(MIMO) case, Spatial Multiplexing (SM) in particular.
We consider the problem of estimating an unknown distribution function F in the presence of censoring under the conditions that a parametric model is believed to hold approximately. We use a Bayesian approach, in which the prior on F is a mixture of Dirichlet distributions. A hyperparameter of the prior determines the extent to which this prior concentrates… (More)
In a Bayesian analysis one xes a prior on the unknown parameter, observes the data, and obtains the posterior distribution of the parameter given the data. For a number of problems the posterior cannot be obtained in closed form and one uses instead the Markov chain simulation method, which in eeect produces a sequence of random variables distributed… (More)