Key result
SE-LSTM improves V2V channel prediction accuracy by ~53% over baseline algorithms.
Why the study?
Accurate and generalizable vehicle-to-vehicle channel prediction is critical for intelligent vehicle control, accident reduction, and energy efficiency, but remains challenging due to the complexity of different environments.
The proposed SE-LSTM model significantly improves V2V channel prediction accuracy across diverse geographic layouts compared to existing algorithms.
Vehicle-to-vehicle (V2V) communications are crucial for intelligent vehicle control, reducing accidents, and increasing energy efficiency. This paper focuses on accurate and generalizable V2V channel prediction, which is a critical component of V2V communications systems, but which is challenging due to the complexity of different V2V environments. Addressing this, we leverage recent machine learning (ML) advancements for time series modeling, applying innovative model-agnostic ML algorithms to this complex issue. Our key innovation is the creation of a unique Squeeze and Excitation long-short-term memory (SE-LSTM) recurrent neural network. This model combines a squeeze and excitation block, global information embedding, and stacked memories within the LSTM framework. It excels in data modeling across diverse domains (Time, Doppler, Delay, and Angular) and effectively captures both intra- and inter- sequence dependencies in Channel State Information (CSI) data. We also utilize meta Pseudo-Label learning for network training, enhancing generalization across different geographic layouts. Extensive evaluations using data from our measurement campaign show that our method surpasses both baseline and state-of-the-art predictive algorithms, with over 53.4%/4.9% improvement in same-geometry tests and 25.4%/9.5% in cross-geometry tests.
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Chu et al. (2024) studied Vehicle-to-vehicle (V2V) communications. Squeeze and Excitation long-short-term memory (SE-LSTM) recurrent neural network vs. Baseline and state-of-the-art predictive algorithms was evaluated on Channel prediction accuracy (53.4%/4.9% and 25.4%/9.5% improvement). The SE-LSTM recurrent neural network surpassed baseline algorithms for V2V channel prediction, improving same-geometry tests by over 53.4% and cross-geometry tests by over 25.4%.
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