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In this paper, we propose a novel Adaptive Modulation and Coding (AMC) scheme enabled by Artificial Neural Network (ANN) aided Signal-to-Noise power Ratio (SNR) estimation. The Power Spectral Density (PSD) values are trained for SNR classification and it is mapped to respective Modulation and Coding Scheme (MCS) sets. Once trained, optimal MCS can be determined in low calculation complexity. The proposed approach is robust especially in high mobility environment since the PSD appearance is hardly influenced by the Doppler shift. Its effectiveness in terms of throughput is presented through computer simulations compared to the existing Error Vector Magnitude (EVM) based link adaptation scheme.INDEX TERMS SNR estimation, artificial neural network, adaptive modulation and coding.

As the next‐generation video coding standard, High Efficiency Video Coding (HEVC) has adopted advanced coding tools despite the increase in computational complexity. In this paper, we propose a selective bi‐prediction method to reduce the encoding complexity of HEVC. The proposed method evaluates the statistical property of the sum of absolute differences in the motion estimation process and determines whether bi‐prediction is performed. A performance comparison of the complexity reduction is provided to show the effectiveness of the proposed method compared to the HEVC test model version 4.0. On average, 50% of the bi‐prediction time can be reduced by the proposed method, while maintaining a negligible bit increment and a minimal loss of image quality.

Orthogonal frequency division multiplexing (OFDM) systems have attracted attention as the fourth generation mobile communication systems. In OFDM systems, the impact of a fading is identified by using the channel estimation (CE). However, a large number of pilot symbols is required to identify an accurate channel state information (CSI) in the conventional OFDM systems. To reduce this problem, a time frequency interferometry (TFI) for OFDM has been proposed. Under the fast fading environment, since the channel is rapidly changed, many errors are occurred. Therefore, we have proposed the fast fading compensation method by using the decision direct and linear prediction methods. However, this method has several problems. To overcome these problems, in this paper, we propose the nonlinear prediction method based on the weighted channel variance.

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