In this letter, we propose a multi-user detection scheme based on a hidden training sequence for DS-UWB systems. The hidden training sequence, which uses a fraction of the informative sequence's transmitting power as training information, is utilized for the receiver filter adaptation and channel estimation. By using this, the proposed scheme offers increased bandwidth efficiency (no period dedicated for training) and also shows reasonably good performance and near-far resistance in single and multiple-access UWB indoor multipath channel environment.

Ultra-wideband (UWB) technology is an excellent candidate for supporting wireless personal area networks (WPANs) because of its wide bandwidth, low transmission power, low complexity and multipath immunity. We study density-aware exclusive region (ER)-based scheduling for a nonuniform UWB-WPAN. Using a generalized radius for the ER based on statistical topology, we propose a scheduling scheme that uses a radius for the ER that varies according to the density information around the destination in the nonuniform network. Computer simulations show that (i) our approach to the radius of the generalized ER provides better scheduling performance than the radius solution of the conventional work [3] and (ii) scheduling that is based on an adaptive ER radius can always outperform both the fixed ER-based scheme and the TDMA scheme with respect to network throughput.

Spatial Multiplexing with precoding provides an opportunity to enhance the capacity and reliability of multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. However, precoder selection may require knowledeg of all subcarriers, which may cause a large amount of feedback if not properly designed. In addition, if the maximum-likelihood (ML) detector is employed, the conventional precoder selection that maximizes the minimum stream SNR is not optimal in terms of the error probability. In this paper, we propose to reduce the feedback overhead by introducing a ML clustering concept in selecting the optimal precoder for ML detector. Numerical results show that the proposed precoder selection based on the ML clustering provides enhanced performance for ML receiver compared with conventional interpolation and clustering algorithms.