276 research outputs found

    Low-complexity Location-aware Multi-user Massive MIMO Beamforming for High Speed Train Communications

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    Massive Multiple-input Multiple-output (MIMO) adaption is one of the primary evolving objectives for the next generation high speed train (HST) communication system. In this paper, we consider how to design an efficient low-complexity location-aware beamforming for the multi-user (MU) massive MIMO system in HST scenario. We first put forward a low-complexity beamforming based on location information, where multiple users are considered. Then, without considering inter-beam interference, a closed-form solution to maximize the total service competence of base station (BS) is proposed in this MU HST scenario. Finally, we present a location-aid searching-based suboptimal solution to eliminate the inter-beam interference and maximize the BS service competence. Various simulations are given to exhibit the advantages of our proposed massive MIMO beamforming method.Comment: This paper has been accepted for future publication by VTC2017-Sprin

    High-Resolution Channel Sounding and Parameter Estimation in Multi-Site Cellular Networks

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    Understanding of electromagnetic propagation properties in real environments is necessary for efficient design and deployment of cellular systems. In this paper, we show a method to estimate high-resolution channel parameters with a massive antenna array in real network deployments. An antenna array mounted on a vehicle is used to receive downlink long-term evolution (LTE) reference signals from neighboring base stations (BS) with mutual interference. Delay and angular information of multipath components is estimated with a novel inter-cell interference cancellation algorithm and an extension of the RIMAX algorithm. The estimated high-resolution channel parameters are consistent with the movement pattern of the vehicle and the geometry of the environment and allow for refined channel modeling and precise cellular positioning

    De-noising of Power Quality Disturbance Detection Based on Ensemble Empirical Mode Decomposition Threshold Algorithm

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    Actual power quality signal which is often affected by noise pollution impacts the analysis results of the disturbance signal. In this paper, EEMD (Ensemble Empirical Mode Decomposition)-based threshold de-noising method is proposed for power quality signal with different SNR (Signal-to-Noise Ratio). As a comparison, we use other four thresholds, namely, the heuristic threshold, the self-adaptive threshold, the fixed threshold and the minimax threshold to filter the noises from power quality signal. Through the analysis and comparison of three characteristics of the signal pre-and-post de-noised, including waveforms, SNR and MSE (Mean Square Error), furthermore the instantaneous attribute of corresponding time by HHT (Hilbert Huang Transform). Simulation results show that EEMD threshold de-noising method can make the waveform close to the actual value. The SNR is higher and the MSE is smaller compared with other four thresholds. The instantaneous attribute can reflect the actual disturbance signal more exactly. The optimal threshold EEMD-based algorithm is proposed for power quality disturbance signal de-noising. Meanwhile, EEMD threshold de-noising method with adaptivity is suitable for composite disturbance signal de-noising

    Building Transportation Foundation Model via Generative Graph Transformer

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    Efficient traffic management is crucial for maintaining urban mobility, especially in densely populated areas where congestion, accidents, and delays can lead to frustrating and expensive commutes. However, existing prediction methods face challenges in terms of optimizing a single objective and understanding the complex composition of the transportation system. Moreover, they lack the ability to understand the macroscopic system and cannot efficiently utilize big data. In this paper, we propose a novel approach, Transportation Foundation Model (TFM), which integrates the principles of traffic simulation into traffic prediction. TFM uses graph structures and dynamic graph generation algorithms to capture the participatory behavior and interaction of transportation system actors. This data-driven and model-free simulation method addresses the challenges faced by traditional systems in terms of structural complexity and model accuracy and provides a foundation for solving complex transportation problems with real data. The proposed approach shows promising results in accurately predicting traffic outcomes in an urban transportation setting


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    The purpose of this study was to compare some parameters of breaststroke swimmers in a swimming pool with those for breaststroke swimming in the flume, to search whether there is some difference between two test circumstances of swimming pool and flume in technical parameters. Four male breaststroke swimmers aged between16 and 18 years were studied. Subjects were required to swim in a 25m pool for best or familiar stroke length and tried to decrease stroke rate, and performed at three minute intervals at speeds ranging from 70% to 100% of the best performance of individuals. Subjects were familiarized to flume swimming on the day prior to be tested, then swam at the same speed based upon conversion from pool in swimming flume. According to testing we found that stroke rate, stroke length and efficiency index for pool and swimming flume at corresponding speeds were similar. Of course, there was as expected significant difference in the stroke rate and stroke length used between subjects to swim at the various speeds

    Paraoxon Attenuates Vascular Smooth Muscle Contraction through Inhibiting Ca2+ Influx in the Rabbit Thoracic Aorta

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    We investigated the effect of paraoxon on vascular contractility using organ baths in thoracic aortic rings of rabbits and examined the effect of paraoxon on calcium homeostasis using a whole-cell patch-clamp technique in isolated aortic smooth muscle cells of rabbits. The findings show that administration of paraoxon (30 μM) attenuated thoracic aorta contraction induced by phenylephrine (1 μM) and/or a high K+ environment (80 mM) in both the presence and absence of thoracic aortic endothelium. This inhibitory effect of paraoxon on vasoconstrictor-induced contraction was abolished in the absence of extracellular Ca2+, or in the presence of the Ca2+ channel inhibitor, verapamil. But atropine had little effect on the inhibitory effect of paraoxon on phenylephrine-induced contraction. Paraoxon also attenuated vascular smooth muscle contraction induced by the cumulative addition of CaCl2 and attenuated an increase of intracellular Ca2+ concentration induced by K+ in vascular smooth muscle cells. Moreover, paraoxon (30 μM) inhibited significantly L-type calcium current in isolated aortic smooth muscle cells of rabbits. In conclusion, our results demonstrate that paraoxon attenuates vasoconstrictor-induced contraction through inhibiting Ca2+ influx in the rabbits thoracic aorta