Prediction-Based Adaptation (PRADA) Algorithm for Modulation and Coding
Date Issued
2012
Date
2012
Author(s)
Jiang, Jhe-Syong
Abstract
In wireless environment, adaptive modulation and coding (AMC) is a useful way to achieve better transmission throughput. At the same time, the receiver has to feed back some channel state information (CSI) to the transmitter for the adaptation. There have been plenty of works on AMC, which exploit the bandwidth more efficiently with the CSI feedback to the transmitter. However, frequent CSI feedback is not desired in some systems. In this thesis, a novel AMC algorithm is proposed to reduce the feedback frequency of the CSI. This work uses finite-state Markov chain (FSMC) to model the channel condition and the current modulation and coding. Based on the FSMC, we can reduce the feedback load while maximizing the overall throughput. In addition, the close-form of the frame error rate (FER) is derived based on channel prediction and limited CSI feedback. Instead of switching modulation and coding according to the CSI, we also provide methods to combine both CSI and FER as the switching parameter. Numerical results illustrate that the average throughput of the proposed algorithm has significant performance improvement over fixed modulation and coding while the CSI feedback being largely reduced.
Subjects
AMC
Prediction
Type
thesis
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