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—In this paper, a novel pilot-aided iterative algorithm is developed for MIMO-OFDM systems operating in fast time-varying environment. An L-path channel model with known path delays is considered to jointly estimate the multi-path Rayleigh channel complex gains and Carrier Frequency Offset (CFO). Each complex gain time-variation within one OFDM symbol is(More)
—This paper deals with the case of a high speed mobile receiver operating in an orthogonal-frequency-division-multiplexing (OFDM) communication system. Assuming the knowledge of delay-related information, we propose an iterative algorithm for joint multi-path Rayleigh channel complex gains and data recovery in fast fading environments. Each complex gain(More)
This paper deals with on-line Bayesian Cramer-Rao (BCRB) lower bound for complex gains dynamic estimation of time-varying multi-path Rayleigh channels. We propose three novel lower bounds for 4-QAM OFDM systems in case of negligible channel variation within one symbol, and assuming both channel delay and Doppler frequency related information. We derive the(More)
In this paper, we consider the Bayesian Cramer-Rao bound (BCRB) for the dynamical estimation of multi-path Rayleigh channel complex gains in data-aided (DA) and non-data-aided (NDA) OFDM systems. This bound is derived in an on-line and off-line scenarios for time-invariant and time-varying complex gains within one OFDM symbol, assuming the availability of(More)
—In this paper, we present an iterative algorithm for channel complex gains estimation with inter-sub-carrier-interference (ICI) reduction in orthogonal-frequency-division-multiplexing downlink mobile communication systems using comb-type pilot. Assuming delays information, the time-variation of the multipath complex gains within one OFDM symbol are(More)
Classification of cancer based on gene expression has provided insight into possible treatment strategies. Thus, developing machine learning methods that can successfully distinguish among cancer subtypes or normal versus cancer samples is important. This work discusses supervised learning techniques that have been employed to classify cancers. Furthermore,(More)
UNLABELLED An important topic in systems biology is the reverse engineering of regulatory mechanisms through reconstruction of context-dependent gene networks. A major challenge is to identify the genes and the regulations specific to a condition or phenotype, given that regulatory processes are highly connected such that a specific response is typically(More)