Simplified Temporal Convolutional-Based Channel Estimation for a WiFi Vehicular Communication Channel
This paper proposes a simplified Temporal Convolutional Network-based estimator to improve channel estimation in vehicular communication.
The paper proposes a novel simplified Temporal Convolutional Network-based estimator to improve channel estimation in vehicular communication, which is more efficient than existing methods.
Keywords
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Applications
- →Intelligent transportation systems
- →Vehicular communication
To understand this paper, make sure you know these concepts first:
- Channel estimationfind papers →
- Vehicular communicationfind papers →
Abstract
More Like ThisChannel estimation in vehicular communication is a crucial element in the advancement of intelligent transportation systems. However, the use of pilot signals in the IEEE 802.11p standard is insufficient for accurate channel estimation in high-mobility scenarios. Data pilot-aided (DPA) estimation helps address this, but suffers from demapping errors. We propose a simplified Temporal Convolutional Network-based estimator (DPA-TCN) trained on a mixed signal-to-noise ratio dataset to improve estimation performance and reduce computational complexity. Our DPA-TCN estimator achieves a bit error rate comparable to a state-of-the-art long-short-term memory network with DPA and temporal averaging (LSTM-DPA-TA) while reducing the complexity of the model by approximately 65%.