ALPACA: An Asymmetric Loss Prediction Algorithm for Channel Adaptation Based on a Convolutional-Recurrent Neural Network in URLLC Systems

A key feature of 5G systems is the Ultra-Reliable Low-Latency Communication (URLLC), which can be used for remote surgery, smart grids, industrial control, etc. URLLC requires millisecond-level delays and very high reliability, i.e., less than
$10^{-5}$
packet loss probability. The ability to satisfy these very strict quality of service requirements depends on selecting the Modulation and Coding Schemes (MCS) for data transmissions. On the one hand, the selected MCS shall be robust enough to avoid multiple retransmissions within a small delay budget. On the other hand, the MCS shall be high-rate to reduce channel resource consumption and, thus, shall increase the system capacity for URLLC. The MCS selection problem is extremely challenging to capture the quickly varying wireless channel effects, e.g., in highly mobile scenarios, because the decision shall be made long before the actual transmission occurs. The paper proposes a novel MCS selection algorithm called ALPACA (Asymmetric Loss Prediction Algorithm for Channel Adaptation), which relies on a widely used class of convolutional-recurrent neural networks. However, in contrast to existing approaches, ALPACA explicitly considers the asymmetric error cost for channel prediction by utilizing quantile regression loss. Both real-life channel measurements and 3GPP channel models are used to evaluate the performance of ALPACA. Numerical results demonstrate the increase in the reliability and reduction in resource consumption compared with the existing MCS selection algorithms, which results in 40% growth of the network capacity.