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Superimposed Training (ST) is a recently addressed technique used for channel estimation where known training sequences are arithmetically added to data symbols, avoiding the use of dedicated pilot subcarriers, and thus, increasing the available bandwidth compared with traditional Pilot Symbol Assisted Modulation (PSAM) schemes. However, the system handles data interference over channel estimation as a result of the ST process; also, data detection is degraded by pilot interference. Recent ST methods have analyzed the data interference and presented schemes that deal with it. We propose a novel superimposed model over a precoded data scheme, named Partial-Data Superimposed Training (PDST), where an interference control factor assigns the adequate information level to be added to the training sequence in Orthogonal Frequency Division Multiplexing (OFDM) systems. Also, a data detection method is introduced to improve the Symbol Error Rate (SER) performance. Moreover, a capacity analysis of the system has been derived. Finally, simulation results confirm that performance of PDST is superior to previous proposals.

Nonparametric belief propagation (NBP) is one of the best-known methods for cooperative localization in sensor networks. It is capable of providing information about location estimation with appropriate uncertainty and to accommodate non-Gaussian distance measurement errors. However, the accuracy of NBP is questionable in loopy networks. Therefore, in this paper, we propose a novel approach, NBP based on spanning trees (NBP-ST) created by breadth first search (BFS) method. In addition, we propose a reliable indoor model based on obtained measurements in our lab. According to our simulation results, NBP-ST performs better than NBP in terms of accuracy and communication cost in the networks with high connectivity (i.e., highly loopy networks). Furthermore, the computational and communication costs are nearly constant with respect to the transmission radius. However, the drawbacks of proposed method are a little bit higher computational cost and poor performance in low-connected networks.

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