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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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Superimposed training (ST) is a semiblind channel estimation technique, proposed for orthogonal frequency division multiplexing (OFDM), where training sequences are added to data symbols, avoiding the use of dedicated pilot-subcarriers, and increasing the available bandwidth compared with pilot symbol assisted modulation (PSAM). Filter bank multicarrier offset quadrature amplitude modulation (FBMC-OQAM) is a promising waveform technique considered to replace the OFDM, which takes advantage of well-designed filters to avoid the use of cyclic prefix and reduce the out-band-emissions. In this paper, we provide the expressions of the average channel capacity of the FBMC-OQAM combined with either PSAM or ST schemes, considering imperfect channel estimation and the presence of the pilot sequences. In order to compute the capacity expression of our proposal, ST-FBMC-OQAM, we analyze the channel estimation error and its variance. The average channel capacity is deduced considering the noise, data interference from ST, and the intrinsic self-interference of the FBMC-OQAM. Additionally, to maximize the average channel capacity, the optimal value of data power allocation is also obtained. The simulation results confirm the validity of the capacity analysis and demonstrate the superiority of the ST-FBMC-OQAM over existing proposals.

IntroductionMesenchymal stem cells (MSCs) are a multipotent population of adult stem cells, which may represent a promising therapeutic approach for neurological autoimmune diseases such as multiple sclerosis. The mouse is the most used species for obtaining and studying the characteristics of MSC and their potential as autologous transplants in pre-clinical models. However, conflicting data have been published disclosing intraspecies variations. The choice of the mouse strain and the tissue source appear, among others, as important factors in the experimental application of MSCs.MethodsAdipose tissue-derived MSCs obtained from the SJL/JCrl mouse strain (SJL-AdMSC) have been cultured for a long time (from passage 0 up to 15) under controlled experimental conditions, and their growth rate, morphology, stromal and haematopoietic marker expression profiles and differentiation capacity towards adipocytes, osteocytes and chondrocytes have been determined. Moreover, their preclinical efficacy has been assessed by autologous transplant in relapsing-remitting experimental autoimmune encephalomielitis (RR-EAE)-induced SJL mice (a well established mice model for the study of RR-multiple sclerosis).ResultsWe demonstrate that SJL-AdMSCs show the same fibroblastic shape, growth rate, profile of markers expression and multipotency described for MSCs in every passage evaluated (up to passage 15). Additionally, SJL-AdMSCs ameliorate the RR-EAE course, suggesting that they could modulate disease progression. Moreover, their features studied are fully comparable with the standardized Ad-MSCs obtained from the C57BL/6 mouse strain, which strengthens their use in cell therapy.ConclusionSJL-AdMSCs might be a suitable source of Ad-MSCs for studies related to the properties of MSCs and their application as promising therapeutic tools in autologous transplants in experimental medicine.

Abstract-Maximum Likelihood (ML) joint detection of MultiCarrier Code Division Multiple Access (MC-CDMA) systems can be effi iently implemented with a Sphere Decoding (SD) algorithm. In this paper, we examine the application of complex instead of real SD to detect MC-CDMA, which solves many problems in a more elegant manner and extends SD adaptability to any constellation. We f rst propose a new complex SD algorithm whose effi iency is based on not requiring an estimate of the initial search radius but selecting the Babai Point as the initial sphere radius instead; also, effi ient strategies regarding sorting the list of possible lattice points are applied. Indeed, complex SD allows complex matrix operations which are faster than real counterparts in double dimension. Next, a novel lattice representation for the MC-CDMA system is introduced, which allows optimum multiuser detection directly from the received signal. This avoids noise whitening operation, and also despreading and equalization procedures are not required further at the receiver side.Index Terms-Multi carrier code division multiple access, maximum likelihood decoding, sphere decoding, multiuser detection.