Cristiano M. Panazio

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The performance of three downlink beamforming techniques in a TDMA/FDD context is investigated in the present work. Two techniques based on the uplink processing are studied together with a decoupled space-time structure that provides the antenna weights. The third one is a downlink beamforming technique, based on the spatial covariance matrix, which is(More)
In this paper, we make use of a blind adaptive linear predictor for channel shortening in single input multiple output (SIMO) channels. We compare our approach to the so-called MERRY blind channel shortener. We assess through simulations that our proposed approach provides faster convergence rate and it better exploits the spatio-temporal diversity present(More)
This paper shows how performance gain in a mobile radio environment can be achieved by using joint space-time equalization and decoding. We apply different joint procedures with two types of space-time equalization techniques. As a matter of fact, the joint techniques are able to provide a considerable gain with none to small additional computational cost.
This work aims at investigating the performance of a Decoupled Space-Time processing structure based on a Delayed Decision-Feedback Sequence Estimator (D-ST-DDFSE), in the context of the Enhanced Data rates for GSM Evolution (EDGE) system. The main idea in using Decoupled Space-Time (D-ST) processing structures is to separate Co-Channel Interference (CCI)(More)
In this paper, we investigate the performance of a decoupled space-time processing technique in a TDMA cellular system. This structure has, as its main characteristic, the possibility of giving more degrees of freedom to an antenna array, and it can thereby provide better co-channel interference cancellation. We analyze its performance by link-level(More)
Adaptive Equalization is a classical technique for mitigating ISI in unknown or time varying channels. Decisionfeedback equalizer (DFE) is considered to be an efficient approach in many types of channels where linear equalizers fail. Unfortunately, it suffers from error propagation phenomenon. In order to reduce such effect, the present paper deals with the(More)
In this work, we propose an evolutionary-like approach to the problem of blind adaptive spatial filtering that is based on the decision-directed criterion and on the doptaiNet, an artificial immune network conceived to perform multimodal search in dynamic environments. The proposal was tested under static and time-varying undermodeled channel models, and,(More)
In th is work we present a new paradigm for unsupervised nonlinear equalization based on prediction-error fuzzy filters. Tests in different linear channel scenarios are carried out i n order to assess the performance of t h e equalizer. T h e results show that the proposal is solid and may provide a performance close to that of a Bayesian equalizer.