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Machine learning algorithms have recently attracted much interest for effective link adaptation due to their flexibility and ability to capture more environmental effects implicitly than classical adaptation algorithms. However, past applications are limited to rather simple configurations such as identifying channel condition or link adaptation in fixed or slowly varying channels. Recently, more...
MIMO-OFDM wireless systems require adaptive modulation and coding based on channel state information (CSI) to maximize throughput in changing wireless channels. Traditional adaptive modulation and coding attempts to predict the best rate available by estimating the packet error rate for each modulation and coding scheme (MCS) by using CSI, which has shown to be challenging. This paper considers supervised...
We consider two widely used notions in machine learning, namely: sparsity and algorithmic stability. Both notions are deemed desirable in designing algorithms, and are believed to lead to good generalization ability. In this paper, we show that these two notions contradict each other. That is, a sparse algorithm can not be stable and vice versa. Thus, one has to tradeoff sparsity and stability in...
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