Reverse Ordering Techniques for Attention-Based Channel Prediction
arXiv:2302.00341 · doi:10.1109/OJSP.2023.3344024
Abstract
This work aims to predict channels in wireless communication systems based on noisy observations, utilizing sequence-to-sequence models with attention (Seq2Seq-attn) and transformer models. Both models are adapted from natural language processing to tackle the complex challenge of channel prediction. Additionally, a new technique called reverse positional encoding is introduced in the transformer model to improve the robustness of the model against varying sequence lengths. Similarly, the encoder outputs of the Seq2Seq-attn model are reversed before applying attention. Simulation results demonstrate that the proposed ordering techniques allow the models to better capture the relationships between the channel snapshots within the sequence, irrespective of the sequence length, as opposed to existing methods.
arXiv commentSubmitted to IEEE for publication
References in corpus (8)
- Massive MIMO for Next Generation Wireless Systems
- Gaussian Error Linear Units (GELUs)
- Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case
- Sionna: An Open-Source Library for Next-Generation Physical Layer Research
- Massive MIMO Channel Prediction Via Meta-Learning and Deep Denoising: Is a Small Dataset Enough?
- Neural Augmentation of Kalman Filter with Hypernetwork for Channel Tracking
- Design of an Efficient CSI Feedback Mechanism in Massive MIMO Systems: A Machine Learning Approach using Empirical Data
- Predicting Multi-Antenna Frequency-Selective Channels via Meta-Learned Linear Filters based on Long-Short Term Channel Decomposition