This paper presents a signal processing model for the delay-hopped transmit-reference ultra widehand communications system introduced by Hoctor and Todinson. In that paper, a single-user receiver based on a bank of correlators and a sliding window integrator was proposed. However, the radio propagation channel also introduces correlations, which have a significant effect not taken into account by the Hoctor-Tomlinson receiver. Here we propose an accurate signal processing model for the transmit-reference system, including the effect of the propagation channel, as well as an algorithm to estimate the resulting effective channel coefficients.

Abstract-A communication system based on transmit-reference (TR) ultra-wideband (UWB) is studied and further developed. Introduced by Hoctor and Tomlinson, the aim of the TR-UWB transceiver is to provide a straightforward impulse radio system, feasible to implement with current technology, and to achieve either high data rate transmissions at short distances or low data rate transmissions in typical office or industrial environments. The main contribution in this paper is the derivation of a signal processing model that takes into account the effects of the radio propagation channel, in particular, for the case where the two pulses in a doublet are closely spaced. Several receivers based on the code-division multiple-access-like properties of the proposed model are derived, and the performance of the algorithms is tested in a simulation.Index Terms-Impulse radio, receiver algorithm, signal processing, transmit-reference (TR), ultra-wideband (UWB).

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While collaborative filtering (CF) is the most popular approach for recommendation systems, it only makes use of the ratings given to items by users and neglects side information about user attributes or item features. In this work, a natural language processing (NLP) technique is applied to generate a more consistent version of Tag Genome, a side information which is associated with each movie in the MovieLens 20M dataset. Subsequently, we propose a 3-layer autoencoder to create a more compact representation of these tags which improves the performance of the system both in accuracy and in computational complexity. Finally, the proposed representation and the well-known matrix factorization techniques are combined into a unified framework that outperforms the state-of-the-art models by at least 2.87% and 3.36% in terms of RMSE and MAE, respectively.

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