7,816 research outputs found
A QoS-Aware Scheduling Algorithm for High-Speed Railway Communication System
With the rapid development of high-speed railway (HSR), how to provide the
passengers with multimedia services has attracted increasing attention. A key
issue is to develop an effective scheduling algorithm for multiple services
with different quality of service (QoS) requirements. In this paper, we
investigate the downlink service scheduling problem in HSR network taking
account of end-to-end deadline constraints and successfully packet delivery
ratio requirements. Firstly, by exploiting the deterministic high-speed train
trajectory, we present a time-distance mapping in order to obtain the highly
dynamic link capacity effectively. Next, a novel service model is developed for
deadline constrained services with delivery ratio requirements, which enables
us to turn the delivery ratio requirement into a single queue stability
problem. Based on the Lyapunov drift, the optimal scheduling problem is
formulated and the corresponding scheduling service algorithm is proposed by
stochastic network optimization approach. Simulation results show that the
proposed algorithm outperforms the conventional schemes in terms of QoS
requirements.Comment: 6 pages, 3 figures, accepted by IEEE ICC 2014 conferenc
MIMO Channel Information Feedback Using Deep Recurrent Network
In a multiple-input multiple-output (MIMO) system, the availability of
channel state information (CSI) at the transmitter is essential for performance
improvement. Recent convolutional neural network (NN) based techniques show
competitive ability in realizing CSI compression and feedback. By introducing a
new NN architecture, we enhance the accuracy of quantized CSI feedback in MIMO
communications. The proposed NN architecture invokes a module named long
short-term memory (LSTM) which admits the NN to benefit from exploiting
temporal and frequency correlations of wireless channels. Compromising
performance with complexity, we further modify the NN architecture with a
significantly reduced number of parameters to be trained. Finally, experiments
show that the proposed NN architectures achieve better performance in terms of
both CSI compression and recovery accuracy
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