Bayes-Optimal Joint Channel-and-Data Estimation for Massive MIMO with Low-Precision ADCs

This paper considers a multiple-input multiple-output (MIMO) receiver with very low-precision analog-to-digital convertors (ADCs) with the goal of developing massive MIMO antenna systems that require minimal cost and power. Previous studies demonstrated that the training duration should be {\em relatively long} to obtain acceptable channel state information. To address this requirement, we adopt a joint channel-and-data (JCD) estimation method based on Bayes-optimal inference. This method yields minimal mean square errors with respect to the channels and payload data. We develop a Bayes-optimal JCD estimator using a recent technique based on approximate message passing. We then present an analytical framework to study the theoretical performance of the estimator in the large-system limit. Simulation results confirm our analytical results, which allow the efficient evaluation of the performance of quantized massive MIMO systems and provide insights into effective system design.Comment: accepted in IEEE Transactions on Signal Processin

Compact, Efficient, and Wideband Near-Field Resonant Parasitic Filtennas

As a hybrid component in RF front-end systems, filtennas possess the distinctive advantages of simultaneously combining filtering and radiating performance characteristics. Consequently, filtennas not only save space and costs but also reduce transmission losses. In this chapter, three sorts of filtennas have been proposed: the first sort is band-pass/band-stop filtennas, which are mainly realized by assembling band-pass/band-stop filters and antennas to achieve the combined functions; the second sort is multi-resonator-cascaded filtennas, which are obtained by altering the coupled-resonators in the last stage of the filters to act as the radiating elements; and the third sort is near-field resonant parasitic, bandwidth-enhanced filtennas, which are accomplished through organically combining radiator and filtering structures. For the second and third sorts, it is worth noting that the design methods witness significant electrical size reduction without degrading the radiation performance of the filtennas in general

Two-pion interferometry for partially coherent sources in relativistic heavy-ion collisions in a multi-phase transport model

We perform two-pion Hanbury Brown-Twiss (HBT) interferometry for the partially coherent pion-emitting sources in relativistic heavy-ion collisions, using a multi-phase transport (AMPT) model. A longitudinal coherent emission length, as well as a transverse coherent emission length, are introduced to the pion generation coordinates in calculating the HBT correlation functions of the partially coherent sources. We compare the model results with and without coherent emission conditions with experimental data in Au-Au collisions at center-of-mass energy $\sqrt{s_{NN}}=$200 GeV, and in Pb-Pb collisions at center-of-mass energy $\sqrt{s_{NN}}=$2.76 TeV, and find that the HBT results of the partially coherent sources are closer to the experimental data than those of chaotic sources.Comment: 20 pages,8 figure

miR-638 is a new biomarker for outcome prediction of non-small cell lung cancer patients receiving chemotherapy.

MicroRNAs (miRNAs), a class of small non-coding RNAs, mediate gene expression by either cleaving target mRNAs or inhibiting their translation. They have key roles in the tumorigenesis of several cancers, including non-small cell lung cancer (NSCLC). The aim of this study was to investigate the clinical significance of miR-638 in the evaluation of NSCLC patient prognosis in response to chemotherapy. First, we detected miR-638 expression levels in vitro in the culture supernatants of the NSCLC cell line SPC-A1 treated with cisplatin, as well as the apoptosis rates of SPC-A1. Second, serum miR-638 expression levels were detected in vivo by using nude mice xenograft models bearing SPC-A1 with and without cisplatin treatment. In the clinic, the serum miR-638 levels of 200 cases of NSCLC patients before and after chemotherapy were determined by quantitative real-time PCR, and the associations of clinicopathological features with miR-638 expression patterns after chemotherapy were analyzed. Our data helped in demonstrating that cisplatin induced apoptosis of the SPC-A1 cells in a dose- and time-dependent manner accompanied by increased miR-638 expression levels in the culture supernatants. In vivo data further revealed that cisplatin induced miR-638 upregulation in the serum derived from mice xenograft models, and in NSCLC patient sera, miR-638 expression patterns after chemotherapy significantly correlated with lymph node metastasis. Moreover, survival analyses revealed that patients who had increased miR-638 levels after chemotherapy showed significantly longer survival time than those who had decreased miR-638 levels. Our findings suggest that serum miR-638 levels are associated with the survival of NSCLC patients and may be considered a potential independent predictor for NSCLC prognosis

Optimal treatment allocation for efficient policy evaluation in sequential decision making

A/B testing is critical for modern technological companies to evaluate the effectiveness of newly developed products against standard baselines. This paper studies optimal designs that aim to maximize the amount of information obtained from online experiments to estimate treatment effects accurately. We propose three optimal allocation strategies in a dynamic setting where treatments are sequentially assigned over time. These strategies are designed to minimize the variance of the treatment effect estimator when data follow a non-Markov decision process or a (time-varying) Markov decision process. We further develop estimation procedures based on existing off-policy evaluation (OPE) methods and conduct extensive experiments in various environments to demonstrate the effectiveness of the proposed methodologies. In theory, we prove the optimality of the proposed treatment allocation design and establish upper bounds for the mean squared errors of the resulting treatment effect estimator