A UAV Air-to-Ground Channel Estimation Algorithm Based on Deep Learning

Mai, Zhiyuan; Chen, Yueyun; Zhao, Haoxiang; Du, Liping; Hao, Conghui

doi:10.1007/s11277-021-09459-z

Cited by 9 publications

(2 citation statements)

References 19 publications

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“…Another CNN, inspired by the image superresolution technique was proposed by [22]. In [9], [17], [23]- [29] a channel state information predictor is proposed based on a recurrent neural network and its modifications, such as Long-Short Term Memory (LSTM) [30], [31] network. The simulations and experimental studies confirm that the neural network can pick up and learn the channel evolution patterns in various fading channels, including the Rayleigh channel [24], as well as experimental measurements [32].…”

Section: Related Workmentioning

confidence: 99%

“…Paper [27] proposed an algorithm to estimate Channel State Information (CSI) in a complex MIMO system, named CSINet, which uses an end-to-end approach for CSI representation and reconstruction. In [29], the LSTM network is trained to predict the CSI for the drone-ground data link scenario. Recently, the authors of the paper [33] proposed neural network designs with a socalled attention mechanism, which increases the weight (or importance) of some elements of the sequence and, thus, improves the prediction performance.…”

Section: Related Workmentioning

confidence: 99%

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

Glinskiy,

Kureev,

Khorov

2024

IEEE Access

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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 millisecondlevel 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.

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